From 8cec498f1b4a54af6681c76513ba1daa86b105ad Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 14 Jul 2025 09:42:53 +0000 Subject: [PATCH 001/366] bitwise determinism FJ progress --- .../mip/feasibility_jump/feasibility_jump.cu | 66 ++++----- .../mip/feasibility_jump/feasibility_jump.cuh | 2 +- .../feasibility_jump_kernels.cu | 40 +++-- cpp/tests/mip/feasibility_jump_tests.cu | 139 +++++++++++------- 4 files changed, 145 insertions(+), 102 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 5043d7780..93738d944 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -625,11 +625,11 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream bool use_graph) { raft::common::nvtx::range scope("run_step_device"); - auto [grid_setval, blocks_setval] = setval_launch_dims; - auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; - auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; - auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; - auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; + // auto [grid_setval, blocks_setval] = setval_launch_dims; + // auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; + // auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; + // auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; + // auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; auto& data = *climbers[climber_idx]; auto v = data.view(); @@ -699,16 +699,16 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream if (is_binary_pb) { cudaLaunchCooperativeKernel( (void*)compute_mtm_moves_kernel, - grid_resetmoves_bin, - blocks_resetmoves_bin, + dim3(256), + dim3(256), reset_moves_args, 0, climber_stream); } else { cudaLaunchCooperativeKernel( (void*)compute_mtm_moves_kernel, - grid_resetmoves, - blocks_resetmoves, + dim3(256), + dim3(256), reset_moves_args, 0, climber_stream); @@ -731,18 +731,18 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream } #endif - cudaLaunchKernel((void*)update_lift_moves_kernel, - grid_lift_move, - blocks_lift_move, - kernel_args, - 0, - climber_stream); - cudaLaunchKernel((void*)update_breakthrough_moves_kernel, - grid_lift_move, - blocks_lift_move, - kernel_args, - 0, - climber_stream); + // cudaLaunchKernel((void*)update_lift_moves_kernel, + // grid_lift_move, + // blocks_lift_move, + // kernel_args, + // 0, + // climber_stream); + // cudaLaunchKernel((void*)update_breakthrough_moves_kernel, + // grid_lift_move, + // blocks_lift_move, + // kernel_args, + // 0, + // climber_stream); } // compaction kernel @@ -755,32 +755,24 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream pb_ptr->n_variables, climber_stream); - cudaLaunchKernel((void*)select_variable_kernel, - dim3(1), - dim3(256), - kernel_args, - 0, - climber_stream); + cudaLaunchKernel( + (void*)select_variable_kernel, dim3(1), dim3(1), kernel_args, 0, climber_stream); cudaLaunchCooperativeKernel((void*)handle_local_minimum_kernel, - grid_update_weights, - blocks_update_weights, + dim3(256), + dim3(256), kernel_args, 0, climber_stream); raft::copy(data.break_condition.data(), data.temp_break_condition.data(), 1, climber_stream); cudaLaunchKernel((void*)update_assignment_kernel, - grid_setval, - blocks_setval, + dim3(256), + dim3(256), update_assignment_args, 0, climber_stream); - cudaLaunchKernel((void*)update_changed_constraints_kernel, - 1, - blocks_setval, - kernel_args, - 0, - climber_stream); + cudaLaunchKernel( + (void*)update_changed_constraints_kernel, 1, 1, kernel_args, 0, climber_stream); } if (use_graph) { diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh index a55f115f1..5bc3b1258 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh @@ -30,7 +30,7 @@ #include #define FJ_DEBUG_LOAD_BALANCING 0 -#define FJ_SINGLE_STEP 0 +#define FJ_SINGLE_STEP 1 namespace cuopt::linear_programming::detail { diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 213d31bc5..58483c334 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -729,6 +729,31 @@ __global__ void update_assignment_kernel(typename fj_t::climber_data_t // Update the LHSs of all involved constraints. auto [offset_begin, offset_end] = fj.pb.reverse_range_for_var(var_idx); + if (blockIdx.x == 0) { + for (auto i = offset_begin; i < offset_end; i += 1) { + cuopt_assert(i < (i_t)fj.pb.reverse_constraints.size(), ""); + auto cstr_idx = fj.pb.reverse_constraints[i]; + auto cstr_coeff = fj.pb.reverse_coefficients[i]; + f_t old_lhs = fj.incumbent_lhs[cstr_idx]; + f_t new_lhs = old_lhs + cstr_coeff * fj.jump_move_delta[var_idx]; + f_t old_cost = fj.excess_score(cstr_idx, old_lhs); + f_t new_cost = fj.excess_score(cstr_idx, new_lhs); + + if (threadIdx.x == 0) { + cuopt_assert(*fj.constraints_changed_count >= 0 && + *fj.constraints_changed_count <= fj.pb.n_constraints, + ""); + f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx); + if (new_cost < -cstr_tolerance && !fj.violated_constraints.contains(cstr_idx)) + fj.constraints_changed[atomicAdd(fj.constraints_changed_count, 1)] = + (cstr_idx << 1) | CONSTRAINT_FLAG_INSERT; + else if (!(new_cost < -cstr_tolerance) && fj.violated_constraints.contains(cstr_idx)) + fj.constraints_changed[atomicAdd(fj.constraints_changed_count, 1)] = + cstr_idx << 1 | CONSTRAINT_FLAG_REMOVE; + } + } + } + for (auto i = offset_begin + blockIdx.x; i < offset_end; i += gridDim.x) { cuopt_assert(i < (i_t)fj.pb.reverse_constraints.size(), ""); @@ -740,19 +765,6 @@ __global__ void update_assignment_kernel(typename fj_t::climber_data_t f_t old_cost = fj.excess_score(cstr_idx, old_lhs); f_t new_cost = fj.excess_score(cstr_idx, new_lhs); - if (threadIdx.x == 0) { - cuopt_assert( - *fj.constraints_changed_count >= 0 && *fj.constraints_changed_count <= fj.pb.n_constraints, - ""); - f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx); - if (new_cost < -cstr_tolerance && !fj.violated_constraints.contains(cstr_idx)) - fj.constraints_changed[atomicAdd(fj.constraints_changed_count, 1)] = - (cstr_idx << 1) | CONSTRAINT_FLAG_INSERT; - else if (!(new_cost < -cstr_tolerance) && fj.violated_constraints.contains(cstr_idx)) - fj.constraints_changed[atomicAdd(fj.constraints_changed_count, 1)] = - cstr_idx << 1 | CONSTRAINT_FLAG_REMOVE; - } - __syncthreads(); cuopt_assert(isfinite(fj.jump_move_delta[var_idx]), "delta should be finite"); @@ -1076,6 +1088,8 @@ DI void update_changed_constraints(typename fj_t::climber_data_t::view if (blockIdx.x == 0) { if (threadIdx.x == 0) { + // sort changed constraints to guarantee determinism + for (i_t i = 0; i < *fj.constraints_changed_count; ++i) { i_t idx = fj.constraints_changed[i]; if ((idx & 1) == CONSTRAINT_FLAG_INSERT) { diff --git a/cpp/tests/mip/feasibility_jump_tests.cu b/cpp/tests/mip/feasibility_jump_tests.cu index 1fafb830e..3b1ed98a7 100644 --- a/cpp/tests/mip/feasibility_jump_tests.cu +++ b/cpp/tests/mip/feasibility_jump_tests.cu @@ -27,6 +27,7 @@ #include #include #include +#include #include #include @@ -182,6 +183,32 @@ static bool run_fj_check_objective(std::string test_instance, int iter_limit, do return !solution.get_feasible() ? false : solution.get_user_objective() <= obj_target; } +static bool run_fj_check_determinism(std::string test_instance, int iter_limit) +{ + detail::fj_settings_t fj_settings; + fj_settings.time_limit = 30.; + fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; + fj_settings.n_of_minimums_for_exit = 20000 * 1000; + fj_settings.update_weights = true; + fj_settings.feasibility_run = false; + fj_settings.termination = detail::fj_termination_flags_t::FJ_TERMINATION_ITERATION_LIMIT; + fj_settings.iteration_limit = iter_limit; + fj_settings.load_balancing_mode = detail::fj_load_balancing_mode_t::ALWAYS_OFF; + fj_settings.seed = 42; + cuopt::seed_generator::set_seed(42); + + auto state = run_fj(test_instance, fj_settings); + auto& solution = state.solution; + + CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", + test_instance.c_str(), + solution.get_feasible(), + solution.get_user_objective(), + solution.get_objective()); + + return true; +} + static bool run_fj_check_feasible(std::string test_instance) { detail::fj_settings_t fj_settings; @@ -220,59 +247,69 @@ static bool run_fj_check_feasible(std::string test_instance) return true; } -TEST(mip_solve, feasibility_jump_obj_test) +// TEST(mip_solve, feasibility_jump_obj_test) +// { +// std::vector> test_cases = { +// {"50v-10.mps", 7800, 100000}, +// {"fiball.mps", 140, 25000}, +// {"gen-ip054.mps", 7500, 20000}, +// {"sct2.mps", 100, 50000}, +// {"uccase9.mps", 4000000, 50000}, +// // unstable, prone to failure on slight weight changes +// //{"drayage-25-23.mps", 300000, 50000}, +// {"tr12-30.mps", 300000, 50000}, +// {"neos-3004026-krka.mps", +std::numeric_limits::infinity(), 35000}, // feasibility +// //{"nursesched-medium-hint03.mps", 12000, 50000}, // too large +// {"ns1208400.mps", 2, 60000}, +// {"gmu-35-50.mps", -2300000, 25000}, +// {"n2seq36q.mps", 158800, 25000}, +// {"seymour1.mps", 440, 50000}, +// {"rmatr200-p5.mps", 7000, 10000}, +// {"cvs16r128-89.mps", -50, 10000}, +// // TEMPORARY: occasional cusparse transpose issues on ARM in CI +// #ifndef __aarch64__ +// {"thor50dday.mps", 250000, 1000} +// #endif +// }; + +// for (auto [instance, obj_target, iter_limit] : test_cases) { +// bool result = run_fj_check_objective(instance, iter_limit, obj_target); +// // Abort early +// if (!result) { +// printf("failure"); +// exit(0); +// } +// } +// } + +// TEST(mip_solve, feasibility_jump_feas_test) +// { +// for (const auto& instance : {"tr12-30.mps", +// "sct2.mps" +// #ifndef __aarch64__ +// , +// "thor50dday.mps" +// #endif +// }) { +// run_fj_check_feasible(instance); +// } +// } + +// TEST(mip_solve, feasibility_jump_obj_runoff_test) +// { +// for (const auto& instance : {"minrep_inf.mps", "sct2.mps", "uccase9.mps", +// /*"buildingenergy.mps"*/}) { +// run_fj_check_no_obj_runoff(instance); +// } +// } + +TEST(mip_solve, feasibility_jump_determinism) { - std::vector> test_cases = { - {"50v-10.mps", 7800, 100000}, - {"fiball.mps", 140, 25000}, - {"gen-ip054.mps", 7500, 20000}, - {"sct2.mps", 100, 50000}, - {"uccase9.mps", 4000000, 50000}, - // unstable, prone to failure on slight weight changes - //{"drayage-25-23.mps", 300000, 50000}, - {"tr12-30.mps", 300000, 50000}, - {"neos-3004026-krka.mps", +std::numeric_limits::infinity(), 35000}, // feasibility - //{"nursesched-medium-hint03.mps", 12000, 50000}, // too large - {"ns1208400.mps", 2, 60000}, - {"gmu-35-50.mps", -2300000, 25000}, - {"n2seq36q.mps", 158800, 25000}, - {"seymour1.mps", 440, 50000}, - {"rmatr200-p5.mps", 7000, 10000}, - {"cvs16r128-89.mps", -50, 10000}, - // TEMPORARY: occasional cusparse transpose issues on ARM in CI -#ifndef __aarch64__ - {"thor50dday.mps", 250000, 1000} -#endif - }; - - for (auto [instance, obj_target, iter_limit] : test_cases) { - bool result = run_fj_check_objective(instance, iter_limit, obj_target); - // Abort early - if (!result) { - printf("failure"); - exit(0); - } - } -} - -TEST(mip_solve, feasibility_jump_feas_test) -{ - for (const auto& instance : {"tr12-30.mps", - "sct2.mps" -#ifndef __aarch64__ - , - "thor50dday.mps" -#endif - }) { - run_fj_check_feasible(instance); - } -} - -TEST(mip_solve, feasibility_jump_obj_runoff_test) -{ - for (const auto& instance : {"minrep_inf.mps", "sct2.mps", "uccase9.mps", + for (const auto& instance : {"gen-ip054.mps", "50v-10.mps", /*"buildingenergy.mps"*/}) { - run_fj_check_no_obj_runoff(instance); + for (int i = 0; i < 10; i++) { + run_fj_check_determinism(instance, 200); + } } } From a559d79d00d71fcb7433fcd6ff9cdc371a368476 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 15 Jul 2025 05:34:09 -0700 Subject: [PATCH 002/366] FJ determinism for non balanced codepath --- .../mip/feasibility_jump/feasibility_jump.cu | 99 +++++++++++++------ .../mip/feasibility_jump/feasibility_jump.cuh | 2 +- .../feasibility_jump_kernels.cu | 55 ++++++----- cpp/src/mip/feasibility_jump/utils.cuh | 12 +++ cpp/src/mip/utils.cuh | 17 ++++ cpp/tests/mip/feasibility_jump_tests.cu | 23 +++-- 6 files changed, 146 insertions(+), 62 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 93738d944..586195500 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -413,6 +413,11 @@ void fj_t::climber_init(i_t climber_idx, const rmm::cuda_stream_view& climber->violation_score.set_value_to_zero_async(climber_stream); climber->weighted_violation_score.set_value_to_zero_async(climber_stream); init_lhs_and_violation<<<256, 256, 0, climber_stream.value()>>>(view); + climber->violated_constraints.sort(climber_stream); + + // printf("init: Violated constraints hash: %x\n", compute_hash( + // make_span(climber->violated_constraints.contents, 0, + // climber->violated_constraints.set_size.value(climber_stream)), climber_stream)); // initialize the best_objective values according to the initial assignment f_t best_obj = compute_objective_from_vec( @@ -625,11 +630,11 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream bool use_graph) { raft::common::nvtx::range scope("run_step_device"); - // auto [grid_setval, blocks_setval] = setval_launch_dims; - // auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; - // auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; - // auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; - // auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; + auto [grid_setval, blocks_setval] = setval_launch_dims; + auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; + auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; + auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; + auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; auto& data = *climbers[climber_idx]; auto v = data.view(); @@ -681,6 +686,10 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream data.cub_storage_bytes.resize(compaction_temp_storage_bytes, climber_stream); } + // printf("before step: Violated constraints hash: %x\n", compute_hash( + // make_span(data.violated_constraints.contents, 0, + // data.violated_constraints.set_size.value(climber_stream)), climber_stream)); + if (use_graph) { cudaGraphCreate(&graph, 0); cudaStreamBeginCapture(climber_stream, cudaStreamCaptureModeThreadLocal); @@ -699,16 +708,16 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream if (is_binary_pb) { cudaLaunchCooperativeKernel( (void*)compute_mtm_moves_kernel, - dim3(256), - dim3(256), + grid_resetmoves_bin, + blocks_resetmoves_bin, reset_moves_args, 0, climber_stream); } else { cudaLaunchCooperativeKernel( (void*)compute_mtm_moves_kernel, - dim3(256), - dim3(256), + grid_resetmoves, + blocks_resetmoves, reset_moves_args, 0, climber_stream); @@ -731,18 +740,18 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream } #endif - // cudaLaunchKernel((void*)update_lift_moves_kernel, - // grid_lift_move, - // blocks_lift_move, - // kernel_args, - // 0, - // climber_stream); - // cudaLaunchKernel((void*)update_breakthrough_moves_kernel, - // grid_lift_move, - // blocks_lift_move, - // kernel_args, - // 0, - // climber_stream); + cudaLaunchKernel((void*)update_lift_moves_kernel, + grid_lift_move, + blocks_lift_move, + kernel_args, + 0, + climber_stream); + cudaLaunchKernel((void*)update_breakthrough_moves_kernel, + grid_lift_move, + blocks_lift_move, + kernel_args, + 0, + climber_stream); } // compaction kernel @@ -755,24 +764,53 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream pb_ptr->n_variables, climber_stream); - cudaLaunchKernel( - (void*)select_variable_kernel, dim3(1), dim3(1), kernel_args, 0, climber_stream); - + cudaLaunchKernel((void*)select_variable_kernel, + dim3(1), + dim3(256), + kernel_args, + 0, + climber_stream); cudaLaunchCooperativeKernel((void*)handle_local_minimum_kernel, - dim3(256), - dim3(256), + grid_update_weights, + blocks_update_weights, kernel_args, 0, climber_stream); raft::copy(data.break_condition.data(), data.temp_break_condition.data(), 1, climber_stream); cudaLaunchKernel((void*)update_assignment_kernel, - dim3(256), - dim3(256), + grid_setval, + blocks_setval, update_assignment_args, 0, climber_stream); - cudaLaunchKernel( - (void*)update_changed_constraints_kernel, 1, 1, kernel_args, 0, climber_stream); + + // { + // printf("Changed constraints hash: %x, size: %d\n", compute_hash( + // make_span(data.constraints_changed, 0, + // data.constraints_changed_count.value(climber_stream)), climber_stream), + // data.constraints_changed_count.value(climber_stream)); + + // printf("before update: Violated constraints hash: %x, size: %d\n", compute_hash( + // make_span(data.violated_constraints.contents, 0, + // data.violated_constraints.set_size.value(climber_stream)), climber_stream), + // data.violated_constraints.set_size.value(climber_stream)); + // } + + cudaLaunchKernel((void*)update_changed_constraints_kernel, + 1, + blocks_setval, + kernel_args, + 0, + climber_stream); + + // data.violated_constraints.sort(climber_stream); + + // { + // printf("Violated constraints hash: %x, size: %d\n", compute_hash( + // make_span(data.violated_constraints.contents, 0, + // data.violated_constraints.set_size.value(climber_stream)), climber_stream), + // data.violated_constraints.set_size.value(climber_stream)); + // } } if (use_graph) { @@ -815,6 +853,7 @@ void fj_t::refresh_lhs_and_violation(const rmm::cuda_stream_view& stre data.violation_score.set_value_to_zero_async(stream); data.weighted_violation_score.set_value_to_zero_async(stream); init_lhs_and_violation<<<4096, 256, 0, stream>>>(v); + data.violated_constraints.sort(stream); } template diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh index 5bc3b1258..a55f115f1 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh @@ -30,7 +30,7 @@ #include #define FJ_DEBUG_LOAD_BALANCING 0 -#define FJ_SINGLE_STEP 1 +#define FJ_SINGLE_STEP 0 namespace cuopt::linear_programming::detail { diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 58483c334..3adc27cab 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -24,6 +24,8 @@ #include +#include + #include namespace cg = cooperative_groups; @@ -729,31 +731,6 @@ __global__ void update_assignment_kernel(typename fj_t::climber_data_t // Update the LHSs of all involved constraints. auto [offset_begin, offset_end] = fj.pb.reverse_range_for_var(var_idx); - if (blockIdx.x == 0) { - for (auto i = offset_begin; i < offset_end; i += 1) { - cuopt_assert(i < (i_t)fj.pb.reverse_constraints.size(), ""); - auto cstr_idx = fj.pb.reverse_constraints[i]; - auto cstr_coeff = fj.pb.reverse_coefficients[i]; - f_t old_lhs = fj.incumbent_lhs[cstr_idx]; - f_t new_lhs = old_lhs + cstr_coeff * fj.jump_move_delta[var_idx]; - f_t old_cost = fj.excess_score(cstr_idx, old_lhs); - f_t new_cost = fj.excess_score(cstr_idx, new_lhs); - - if (threadIdx.x == 0) { - cuopt_assert(*fj.constraints_changed_count >= 0 && - *fj.constraints_changed_count <= fj.pb.n_constraints, - ""); - f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx); - if (new_cost < -cstr_tolerance && !fj.violated_constraints.contains(cstr_idx)) - fj.constraints_changed[atomicAdd(fj.constraints_changed_count, 1)] = - (cstr_idx << 1) | CONSTRAINT_FLAG_INSERT; - else if (!(new_cost < -cstr_tolerance) && fj.violated_constraints.contains(cstr_idx)) - fj.constraints_changed[atomicAdd(fj.constraints_changed_count, 1)] = - cstr_idx << 1 | CONSTRAINT_FLAG_REMOVE; - } - } - } - for (auto i = offset_begin + blockIdx.x; i < offset_end; i += gridDim.x) { cuopt_assert(i < (i_t)fj.pb.reverse_constraints.size(), ""); @@ -765,6 +742,19 @@ __global__ void update_assignment_kernel(typename fj_t::climber_data_t f_t old_cost = fj.excess_score(cstr_idx, old_lhs); f_t new_cost = fj.excess_score(cstr_idx, new_lhs); + if (threadIdx.x == 0) { + cuopt_assert( + *fj.constraints_changed_count >= 0 && *fj.constraints_changed_count <= fj.pb.n_constraints, + ""); + f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx); + if (new_cost < -cstr_tolerance && !fj.violated_constraints.contains(cstr_idx)) + fj.constraints_changed[atomicAdd(fj.constraints_changed_count, 1)] = + (cstr_idx << 1) | CONSTRAINT_FLAG_INSERT; + else if (!(new_cost < -cstr_tolerance) && fj.violated_constraints.contains(cstr_idx)) + fj.constraints_changed[atomicAdd(fj.constraints_changed_count, 1)] = + cstr_idx << 1 | CONSTRAINT_FLAG_REMOVE; + } + __syncthreads(); cuopt_assert(isfinite(fj.jump_move_delta[var_idx]), "delta should be finite"); @@ -1089,6 +1079,10 @@ DI void update_changed_constraints(typename fj_t::climber_data_t::view if (blockIdx.x == 0) { if (threadIdx.x == 0) { // sort changed constraints to guarantee determinism + // TODO: horribly slow as it is + thrust::sort(thrust::seq, + fj.constraints_changed.begin(), + fj.constraints_changed.begin() + *fj.constraints_changed_count); for (i_t i = 0; i < *fj.constraints_changed_count; ++i) { i_t idx = fj.constraints_changed[i]; @@ -1359,6 +1353,14 @@ __global__ void select_variable_kernel(typename fj_t::climber_data_t:: good_var_count); #endif cuopt_assert(fj.jump_move_scores[selected_var].valid(), ""); + } else { +#if FJ_SINGLE_STEP + DEVICE_LOG_INFO("=[%d]---- FJ: no var selected, obj is %g, viol %d, out of %d\n", + *fj.iterations, + *fj.incumbent_objective, + fj.violated_constraints.size(), + good_var_count); +#endif } } } @@ -1469,6 +1471,9 @@ DI thrust::tuple::move_score_t> best_random_mt raft::random::PCGenerator rng(fj.settings->seed + *fj.iterations, 0, 0); i_t cstr_idx = fj.violated_constraints.contents[rng.next_u32() % fj.violated_constraints.size()]; + cuopt_assert(fj.excess_score(cstr_idx, fj.incumbent_lhs[cstr_idx]) < 0, + "constraint isn't violated"); + auto [offset_begin, offset_end] = fj.pb.range_for_constraint(cstr_idx); return gridwide_reduce_best_move( diff --git a/cpp/src/mip/feasibility_jump/utils.cuh b/cpp/src/mip/feasibility_jump/utils.cuh index 212591fc9..f876e9493 100644 --- a/cpp/src/mip/feasibility_jump/utils.cuh +++ b/cpp/src/mip/feasibility_jump/utils.cuh @@ -19,6 +19,7 @@ #include "feasibility_jump.cuh" +#include #include #include #include @@ -142,6 +143,17 @@ struct contiguous_set_t { validity_bitmap.resize(size, stream); } + void sort(const rmm::cuda_stream_view& stream) + { + thrust::sort( + rmm::exec_policy(stream), contents.begin(), contents.begin() + set_size.value(stream)); + thrust::fill(rmm::exec_policy(stream), index_map.begin(), index_map.end(), -1); + thrust::for_each(rmm::exec_policy(stream), + thrust::make_counting_iterator(0), + thrust::make_counting_iterator(set_size.value(stream)), + [v = view()] __device__(i_t idx) { v.index_map[v.contents[idx]] = idx; }); + } + struct view_t { i_t* set_size; i_t* lock; diff --git a/cpp/src/mip/utils.cuh b/cpp/src/mip/utils.cuh index b48ad21b6..534c67cc7 100644 --- a/cpp/src/mip/utils.cuh +++ b/cpp/src/mip/utils.cuh @@ -355,4 +355,21 @@ bool has_variable_bounds_violation(const raft::handle_t* handle_ptr, }); } +template +inline uint32_t compute_hash(raft::device_span values, rmm::cuda_stream_view stream) +{ + // FNV-1a hash + + uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis + auto h_contents = cuopt::host_copy(values, stream); + RAFT_CHECK_CUDA(stream); + std::vector byte_contents(h_contents.size() * sizeof(i_t)); + std::memcpy(byte_contents.data(), h_contents.data(), h_contents.size() * sizeof(i_t)); + for (size_t i = 0; i < byte_contents.size(); ++i) { + hash ^= byte_contents[i]; + hash *= 16777619u; + } + return hash; +} + } // namespace cuopt::linear_programming::detail diff --git a/cpp/tests/mip/feasibility_jump_tests.cu b/cpp/tests/mip/feasibility_jump_tests.cu index 3b1ed98a7..0a77347c1 100644 --- a/cpp/tests/mip/feasibility_jump_tests.cu +++ b/cpp/tests/mip/feasibility_jump_tests.cu @@ -185,6 +185,8 @@ static bool run_fj_check_objective(std::string test_instance, int iter_limit, do static bool run_fj_check_determinism(std::string test_instance, int iter_limit) { + int seed = std::getenv("FJ_SEED") ? std::stoi(std::getenv("FJ_SEED")) : 42; + detail::fj_settings_t fj_settings; fj_settings.time_limit = 30.; fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; @@ -194,8 +196,8 @@ static bool run_fj_check_determinism(std::string test_instance, int iter_limit) fj_settings.termination = detail::fj_termination_flags_t::FJ_TERMINATION_ITERATION_LIMIT; fj_settings.iteration_limit = iter_limit; fj_settings.load_balancing_mode = detail::fj_load_balancing_mode_t::ALWAYS_OFF; - fj_settings.seed = 42; - cuopt::seed_generator::set_seed(42); + fj_settings.seed = seed; + cuopt::seed_generator::set_seed(fj_settings.seed); auto state = run_fj(test_instance, fj_settings); auto& solution = state.solution; @@ -206,6 +208,10 @@ static bool run_fj_check_determinism(std::string test_instance, int iter_limit) solution.get_user_objective(), solution.get_objective()); + static auto first_val = solution.get_user_objective(); + + if (abs(solution.get_user_objective() - first_val) > 1) exit(0); + return true; } @@ -305,10 +311,15 @@ static bool run_fj_check_feasible(std::string test_instance) TEST(mip_solve, feasibility_jump_determinism) { - for (const auto& instance : {"gen-ip054.mps", "50v-10.mps", - /*"buildingenergy.mps"*/}) { - for (int i = 0; i < 10; i++) { - run_fj_check_determinism(instance, 200); + for (const auto& instance : { + //"thor50dday.mps", + //"gen-ip054.mps", + "50v-10.mps", + //"seymour1.mps", + }) { + // for (int i = 0; i < 10; i++) + while (true) { + run_fj_check_determinism(instance, 1000); } } } From 3269d870cd009495c8ed62bd8bcd4f69203fc64f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 17 Jul 2025 08:30:59 +0000 Subject: [PATCH 003/366] deterministic tests for more parts of the solver --- cpp/src/linear_programming/pdlp.cu | 4 + cpp/src/linear_programming/solve.cu | 1 + .../adaptive_step_size_strategy.cu | 3 +- .../mip/feasibility_jump/feasibility_jump.cu | 1 + .../mip/feasibility_jump/feasibility_jump.cuh | 6 +- .../feasibility_jump_kernels.cu | 4 +- .../mip/feasibility_jump/load_balancing.cuh | 9 +- .../feasibility_pump/feasibility_pump.cu | 16 +- .../feasibility_pump/feasibility_pump.cuh | 7 +- .../local_search/rounding/constraint_prop.cu | 9 +- cpp/src/mip/presolve/multi_probe.cu | 8 +- cpp/src/mip/problem/problem.cu | 23 ++ cpp/src/mip/problem/problem.cuh | 2 + cpp/src/mip/problem/problem_helpers.cuh | 99 +++++++ cpp/src/mip/relaxed_lp/relaxed_lp.cu | 5 +- cpp/src/mip/relaxed_lp/relaxed_lp.cuh | 1 + cpp/src/mip/solve.cu | 1 + cpp/src/mip/utils.cuh | 24 +- cpp/src/utilities/timer.hpp | 14 +- cpp/tests/mip/CMakeLists.txt | 6 + cpp/tests/mip/feasibility_jump_tests.cu | 78 +++--- cpp/tests/mip/local_search_test.cu | 247 ++++++++++++++++++ cpp/tests/mip/multi_probe_test.cu | 50 +++- cpp/tests/mip/presolve_test.cu | 208 +++++++++++++++ 24 files changed, 747 insertions(+), 79 deletions(-) create mode 100644 cpp/tests/mip/local_search_test.cu create mode 100644 cpp/tests/mip/presolve_test.cu diff --git a/cpp/src/linear_programming/pdlp.cu b/cpp/src/linear_programming/pdlp.cu index 7acadae50..de6846674 100644 --- a/cpp/src/linear_programming/pdlp.cu +++ b/cpp/src/linear_programming/pdlp.cu @@ -258,6 +258,10 @@ static bool time_limit_reached(const std::chrono::high_resolution_clock::time_po auto elapsed = std::chrono::duration_cast(current_time - start_time).count(); + if (elapsed >= (seconds * 1000.0)) { + CUOPT_LOG_ERROR("**** PDLP Time limit reached: %f *****", seconds); + } + return elapsed >= (seconds * 1000.0); } diff --git a/cpp/src/linear_programming/solve.cu b/cpp/src/linear_programming/solve.cu index c3ba4f2c7..8d090624e 100644 --- a/cpp/src/linear_programming/solve.cu +++ b/cpp/src/linear_programming/solve.cu @@ -582,6 +582,7 @@ optimization_problem_solution_t solve_lp(optimization_problem_t diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 586195500..37567dd7d 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -770,6 +770,7 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream kernel_args, 0, climber_stream); + cudaLaunchCooperativeKernel((void*)handle_local_minimum_kernel, grid_update_weights, blocks_update_weights, diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh index a55f115f1..6dbf13977 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh @@ -135,8 +135,8 @@ struct fj_move_t { // as we dont need them to be floating point per the FJ2 scoring scheme // sizeof(fj_staged_score_t) <= 8 is needed to allow for atomic loads struct fj_staged_score_t { - float base{-std::numeric_limits::infinity()}; - float bonus{-std::numeric_limits::infinity()}; + int32_t base{std::numeric_limits::lowest()}; + int32_t bonus{std::numeric_limits::lowest()}; HDI bool operator<(fj_staged_score_t other) const noexcept { @@ -154,7 +154,7 @@ struct fj_staged_score_t { HDI static fj_staged_score_t invalid() { - return {-std::numeric_limits::infinity(), -std::numeric_limits::infinity()}; + return {std::numeric_limits::lowest(), std::numeric_limits::lowest()}; } HDI static fj_staged_score_t zero() { return {0, 0}; } diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 3adc27cab..ee67f5535 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -79,7 +79,7 @@ DI thrust::pair move_objective_score( } template -DI std::pair feas_score_constraint( +DI std::pair feas_score_constraint( const typename fj_t::climber_data_t::view_t& fj, i_t var_idx, f_t delta, @@ -167,7 +167,7 @@ DI std::pair feas_score_constraint( } } - return {base_feas, bonus_robust}; + return {round(base_feas), round(bonus_robust)}; } template diff --git a/cpp/src/mip/feasibility_jump/load_balancing.cuh b/cpp/src/mip/feasibility_jump/load_balancing.cuh index f3515e5de..caa09e133 100644 --- a/cpp/src/mip/feasibility_jump/load_balancing.cuh +++ b/cpp/src/mip/feasibility_jump/load_balancing.cuh @@ -134,7 +134,8 @@ __global__ void load_balancing_prepare_iteration(const __grid_constant__ // alternate codepath in the case of a small related_var/total_var ratio if (!full_refresh && fj.pb.related_variables.size() > 0 && fj.pb.n_variables / fj.work_ids_for_related_vars[*fj.selected_var] >= - fj.settings->parameters.old_codepath_total_var_to_relvar_ratio_threshold) { + fj.settings->parameters.old_codepath_total_var_to_relvar_ratio_threshold && + fj.settings->load_balancing_mode != fj_load_balancing_mode_t::ALWAYS_ON) { auto range = fj.pb.range_for_related_vars(*fj.selected_var); for (i_t i = blockIdx.x + range.first; i < range.second; i += gridDim.x) { @@ -533,8 +534,8 @@ __launch_bounds__(TPB_loadbalance, 16) __global__ auto& score_info = candidate.score; - f_t base_feas = 0; - f_t bonus_robust = 0; + int32_t base_feas = 0; + int32_t bonus_robust = 0; // same as for the binary var kernel, compute each score compoenent per thread // and merge then via a wapr reduce @@ -650,7 +651,7 @@ __global__ void load_balancing_sanity_checks(const __grid_constant__ if (!(score_1 == score_1.invalid() && score_2 == score_2.invalid()) && !(v.pb.integer_equal(score_1.base, score_2.base) && v.pb.integer_equal(score_1.bonus, score_2.bonus))) { - printf("(iter %d) [%d, int:%d]: delta %g/%g was %f/%f, is %f/%f\n", + printf("(iter %d) [%d, int:%d]: delta %g/%g was %d/%d, is %d/%d\n", *v.iterations, var_idx, v.pb.is_integer_var(var_idx), diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index c8aa87433..d18d29a5a 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -221,7 +221,7 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_t::round(solution_t& solution) { bool result; CUOPT_LOG_DEBUG("Rounding the point"); - timer_t bounds_prop_timer(std::min(2., timer.remaining_time())); - const f_t lp_run_time_after_feasible = std::min(3., timer.remaining_time() / 20.); + timer_t bounds_prop_timer(std::min(config.bounds_prop_timer_min, timer.remaining_time())); + const f_t lp_run_time_after_feasible = + std::min(config.lp_run_time_after_feasible_min, timer.remaining_time() / 20.); result = constraint_prop.apply_round(solution, lp_run_time_after_feasible, bounds_prop_timer); cuopt_func_call(solution.test_variable_bounds(true)); // copy the last rounding @@ -290,9 +291,9 @@ bool feasibility_pump_t::run_fj_cycle_escape(solution_t& sol fj.settings.update_weights = true; fj.settings.feasibility_run = true; fj.settings.n_of_minimums_for_exit = 5000; - fj.settings.time_limit = std::min(3., timer.remaining_time()); - fj.settings.termination = fj_termination_flags_t::FJ_TERMINATION_TIME_LIMIT; - is_feasible = fj.solve(solution); + fj.settings.time_limit = std::min(config.fj_cycle_escape_time_limit, timer.remaining_time()); + fj.settings.termination = fj_termination_flags_t::FJ_TERMINATION_TIME_LIMIT; + is_feasible = fj.solve(solution); // if FJ didn't change the solution, take last incumbent solution if (!is_feasible && cycle_queue.check_cycle(solution)) { CUOPT_LOG_DEBUG("cycle detected after FJ, taking last incumbent of fj"); @@ -542,9 +543,8 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s // if the solution is almost on polytope else if (last_distances[0] < distance_to_check_for_feasible) { // run the linear projection with full precision to check if it actually is feasible - const f_t lp_verify_time_limit = 5.; relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = lp_verify_time_limit; + lp_settings.time_limit = config.lp_verify_time_limit; lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; lp_settings.return_first_feasible = true; lp_settings.save_state = true; diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh index 8b7891b04..54dda9d3f 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh @@ -108,6 +108,11 @@ struct fp_config_t { bool check_distance_cycle = true; int first_stage_kk = 70; double cycle_distance_reduction_ration = 0.1; + double bounds_prop_timer_min = 2.; + double lp_run_time_after_feasible_min = 3.; + double linproj_time_limit = 5.; + double fj_cycle_escape_time_limit = 3.; + double lp_verify_time_limit = 5.; }; template @@ -116,7 +121,6 @@ class feasibility_pump_t { feasibility_pump_t() = delete; feasibility_pump_t(mip_solver_context_t& context, fj_t& fj, - // fj_tree_t& fj_tree_, constraint_prop_t& constraint_prop_, lb_constraint_prop_t& lb_constraint_prop_, line_segment_search_t& line_segment_search_, @@ -149,7 +153,6 @@ class feasibility_pump_t { mip_solver_context_t& context; // keep a reference from upstream local search fj_t& fj; - // fj_tree_t& fj_tree; line_segment_search_t& line_segment_search; cycle_queue_t cycle_queue; constraint_prop_t& constraint_prop; diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 33a631d29..37fb4b92f 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -920,7 +920,8 @@ bool constraint_prop_t::find_integer( sol, orig_sol.problem_ptr, var_probe_vals, &set_count, unset_integer_vars, probing_config); if (!(n_failed_repair_iterations >= max_n_failed_repair_iterations) && rounding_ii && !timeout_happened) { - timer_t repair_timer{std::min(timer.remaining_time() / 5, timer.elapsed_time() / 3)}; + // timer_t repair_timer{std::min(timer.remaining_time() / 5, timer.elapsed_time() / 3)}; + timer_t repair_timer{timer.remaining_time() / 5}; save_bounds(sol); // update bounds and run repair procedure bool bounds_repaired = @@ -994,6 +995,7 @@ bool constraint_prop_t::find_integer( lp_settings.tolerance = orig_sol.problem_ptr->tolerances.absolute_tolerance; lp_settings.save_state = false; lp_settings.return_first_feasible = true; + lp_settings.iteration_limit = 13000; run_lp_with_vars_fixed(*orig_sol.problem_ptr, orig_sol, orig_sol.problem_ptr->integer_indices, @@ -1015,8 +1017,9 @@ bool constraint_prop_t::apply_round( raft::common::nvtx::range fun_scope("constraint prop round"); // this is second timer that can continue but without recovery mode - const f_t max_time_for_bounds_prop = 5.; - max_timer = timer_t{max_time_for_bounds_prop}; + f_t max_time_for_bounds_prop = 5.; + max_time_for_bounds_prop = timer.remaining_time() / 10.0; + max_timer = timer_t{max_time_for_bounds_prop}; if (check_brute_force_rounding(sol)) { return true; } recovery_mode = false; rounding_ii = false; diff --git a/cpp/src/mip/presolve/multi_probe.cu b/cpp/src/mip/presolve/multi_probe.cu index 699a5f1dd..4f1579b3a 100644 --- a/cpp/src/mip/presolve/multi_probe.cu +++ b/cpp/src/mip/presolve/multi_probe.cu @@ -158,7 +158,7 @@ bool multi_probe_t::calculate_bounds_update(problem_t& pb, <<get_stream()>>>(pb.view(), upd_1.view()); RAFT_CHECK_CUDA(handle_ptr->get_stream()); i_t h_bounds_changed_1 = upd_1.bounds_changed.value(handle_ptr->get_stream()); - CUOPT_LOG_TRACE("Bounds changed upd 1 %d", h_bounds_changed_1); + // CUOPT_LOG_TRACE("Bounds changed upd 1 %d", h_bounds_changed_1); skip_1 = (h_bounds_changed_1 == zero); } else if (skip_1) { upd_0.bounds_changed.set_value_async(zero, handle_ptr->get_stream()); @@ -166,7 +166,7 @@ bool multi_probe_t::calculate_bounds_update(problem_t& pb, <<get_stream()>>>(pb.view(), upd_0.view()); RAFT_CHECK_CUDA(handle_ptr->get_stream()); i_t h_bounds_changed_0 = upd_0.bounds_changed.value(handle_ptr->get_stream()); - CUOPT_LOG_TRACE("Bounds changed upd 0 %d", h_bounds_changed_0); + // CUOPT_LOG_TRACE("Bounds changed upd 0 %d", h_bounds_changed_0); skip_0 = (h_bounds_changed_0 == zero); } else { upd_0.bounds_changed.set_value_async(zero, handle_ptr->get_stream()); @@ -176,9 +176,9 @@ bool multi_probe_t::calculate_bounds_update(problem_t& pb, pb.view(), upd_0.view(), upd_1.view()); RAFT_CHECK_CUDA(handle_ptr->get_stream()); i_t h_bounds_changed_0 = upd_0.bounds_changed.value(handle_ptr->get_stream()); - CUOPT_LOG_TRACE("Bounds changed upd 0 %d", h_bounds_changed_0); + // CUOPT_LOG_TRACE("Bounds changed upd 0 %d", h_bounds_changed_0); i_t h_bounds_changed_1 = upd_1.bounds_changed.value(handle_ptr->get_stream()); - CUOPT_LOG_TRACE("Bounds changed upd 1 %d", h_bounds_changed_1); + // CUOPT_LOG_TRACE("Bounds changed upd 1 %d", h_bounds_changed_1); skip_0 = (h_bounds_changed_0 == zero); skip_1 = (h_bounds_changed_1 == zero); diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index ea162cdfb..06ae72596 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -296,6 +296,8 @@ problem_t::problem_t(const problem_t& problem_, bool no_deep template void problem_t::compute_transpose_of_problem() { + csrsort_cusparse(coefficients, variables, offsets, n_constraints, n_variables, handle_ptr); + RAFT_CUBLAS_TRY(raft::linalg::detail::cublassetpointermode( handle_ptr->get_cublas_handle(), CUBLAS_POINTER_MODE_DEVICE, handle_ptr->get_stream())); RAFT_CUSPARSE_TRY(raft::sparse::detail::cusparsesetpointermode( @@ -1147,6 +1149,7 @@ problem_t problem_t::get_problem_after_fixing_vars( cuopt_assert(n_variables == assignment.size(), "Assignment size issue"); problem_t problem(*this, true); CUOPT_LOG_DEBUG("Fixing %d variables", variables_to_fix.size()); + CUOPT_LOG_DEBUG("Model fingerprint before fixing: 0x%x", get_fingerprint()); // we will gather from this and scatter back to the original problem variable_map.resize(assignment.size() - variables_to_fix.size(), handle_ptr->get_stream()); // compute variable map to recover the assignment later @@ -1195,6 +1198,7 @@ problem_t problem_t::get_problem_after_fixing_vars( time_taken, total_time_taken / total_calls, total_time_taken); + CUOPT_LOG_DEBUG("Model fingerprint after fixing: 0x%x", problem.get_fingerprint()); return problem; } @@ -1588,6 +1592,25 @@ f_t problem_t::get_user_obj_from_solver_obj(f_t solver_obj) return presolve_data.objective_scaling_factor * (solver_obj + presolve_data.objective_offset); } +template +uint32_t problem_t::get_fingerprint() const +{ + // CSR representation should be unique and sorted at this point + + std::vector hashes = { + detail::compute_hash(coefficients, handle_ptr->get_stream()), + detail::compute_hash(variables, handle_ptr->get_stream()), + detail::compute_hash(offsets, handle_ptr->get_stream()), + detail::compute_hash(objective_coefficients, handle_ptr->get_stream()), + detail::compute_hash(variable_lower_bounds, handle_ptr->get_stream()), + detail::compute_hash(variable_upper_bounds, handle_ptr->get_stream()), + detail::compute_hash(constraint_lower_bounds, handle_ptr->get_stream()), + detail::compute_hash(constraint_upper_bounds, handle_ptr->get_stream()), + detail::compute_hash(variable_types, handle_ptr->get_stream()), + }; + return detail::compute_hash(hashes); +} + #if MIP_INSTANTIATE_FLOAT template class problem_t; #endif diff --git a/cpp/src/mip/problem/problem.cuh b/cpp/src/mip/problem/problem.cuh index cc29ce0e6..c0b9c0195 100644 --- a/cpp/src/mip/problem/problem.cuh +++ b/cpp/src/mip/problem/problem.cuh @@ -111,6 +111,8 @@ class problem_t { void get_host_user_problem( cuopt::linear_programming::dual_simplex::user_problem_t& user_problem) const; + uint32_t get_fingerprint() const; + void write_as_mps(const std::string& path); struct view_t { diff --git a/cpp/src/mip/problem/problem_helpers.cuh b/cpp/src/mip/problem/problem_helpers.cuh index dfcebc9d3..a7b9b0c35 100644 --- a/cpp/src/mip/problem/problem_helpers.cuh +++ b/cpp/src/mip/problem/problem_helpers.cuh @@ -27,10 +27,55 @@ #include #include +#include #include #include namespace cuopt::linear_programming::detail { + +template +struct cusparse_data_type { + static_assert(sizeof(T) == 0, "cusparse_data_type mapping not defined for this type"); +}; +template +struct cusparse_index_type { + static_assert(sizeof(T) == 0, "cusparse_index_type mapping not defined for this type"); +}; + +template <> +struct cusparse_data_type<__half> { + static constexpr cudaDataType value = CUDA_R_16F; +}; +template <> +struct cusparse_data_type<__nv_bfloat16> { + static constexpr cudaDataType value = CUDA_R_16BF; +}; +template <> +struct cusparse_data_type { + static constexpr cudaDataType value = CUDA_R_32F; +}; +template <> +struct cusparse_data_type { + static constexpr cudaDataType value = CUDA_R_64F; +}; +template <> +struct cusparse_data_type { + static constexpr cudaDataType value = CUDA_R_8I; +}; +template <> +struct cusparse_data_type { + static constexpr cudaDataType value = CUDA_R_32I; +}; + +template <> +struct cusparse_index_type { + static constexpr cusparseIndexType_t value = CUSPARSE_INDEX_32I; +}; +template <> +struct cusparse_index_type { + static constexpr cusparseIndexType_t value = CUSPARSE_INDEX_64I; +}; + template struct transform_bounds_functor { __device__ thrust::tuple operator()(const thrust::tuple& input) const @@ -307,4 +352,58 @@ static bool check_bounds_sanity(const detail::problem_t& problem) check_constraint_bounds_sanity(problem); } +static void check_cusparse_status(cusparseStatus_t status) +{ + if (status != CUSPARSE_STATUS_SUCCESS) { + throw std::runtime_error("CUSPARSE error: " + std::string(cusparseGetErrorString(status))); + } +} + +template +static void csrsort_cusparse(rmm::device_uvector& values, + rmm::device_uvector& indices, + rmm::device_uvector& offsets, + i_t rows, + i_t cols, + const raft::handle_t* handle_ptr) +{ + auto stream = offsets.stream(); + cusparseHandle_t handle; + cusparseCreate(&handle); + cusparseSetStream(handle, stream); + + i_t nnz = values.size(); + i_t m = rows; + i_t n = cols; + + cusparseMatDescr_t matA; + cusparseCreateMatDescr(&matA); + cusparseSetMatIndexBase(matA, CUSPARSE_INDEX_BASE_ZERO); + cusparseSetMatType(matA, CUSPARSE_MATRIX_TYPE_GENERAL); + + size_t pBufferSizeInBytes = 0; + check_cusparse_status(cusparseXcsrsort_bufferSizeExt( + handle, m, n, nnz, offsets.data(), indices.data(), &pBufferSizeInBytes)); + rmm::device_uvector pBuffer(pBufferSizeInBytes, stream); + cuopt_assert(((intptr_t)pBuffer.data() % 128) == 0, + "CUSPARSE buffer size is not aligned to 128 bytes"); + rmm::device_uvector P(nnz, stream); + thrust::sequence(handle_ptr->get_thrust_policy(), P.begin(), P.end()); + + check_cusparse_status(cusparseXcsrsort( + handle, m, n, nnz, matA, offsets.data(), indices.data(), P.data(), pBuffer.data())); + + // apply the permutation to the values + rmm::device_uvector values_sorted(nnz, stream); + thrust::gather( + handle_ptr->get_thrust_policy(), P.begin(), P.end(), values.begin(), values_sorted.begin()); + thrust::copy( + handle_ptr->get_thrust_policy(), values_sorted.begin(), values_sorted.end(), values.begin()); + + cusparseDestroyMatDescr(matA); + cusparseDestroy(handle); + + check_csr_representation(values, offsets, indices, handle_ptr, cols, rows); +} + } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index 604f759df..9119f924a 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -55,6 +55,7 @@ optimization_problem_solution_t get_relaxed_lp_solution( pdlp_settings.tolerances.relative_primal_tolerance = settings.tolerance / 100.; pdlp_settings.tolerances.relative_dual_tolerance = settings.tolerance / 100.; pdlp_settings.time_limit = settings.time_limit; + pdlp_settings.iteration_limit = settings.iteration_limit; if (settings.return_first_feasible) { pdlp_settings.per_constraint_residual = true; } pdlp_settings.first_primal_feasible = settings.return_first_feasible; pdlp_solver_t lp_solver(op_problem, pdlp_settings); @@ -106,7 +107,9 @@ optimization_problem_solution_t get_relaxed_lp_solution( CUOPT_LOG_DEBUG("feasible solution found with LP objective %f", solver_response.get_objective_value()); } else { - CUOPT_LOG_DEBUG("LP returned with reason %d", solver_response.get_termination_status()); + CUOPT_LOG_DEBUG("LP returned with reason %d, %d iterations", + solver_response.get_termination_status(), + solver_response.get_additional_termination_information().number_of_steps_taken); } return solver_response; diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cuh b/cpp/src/mip/relaxed_lp/relaxed_lp.cuh index def1b7fa2..367afe32b 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cuh +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cuh @@ -29,6 +29,7 @@ namespace cuopt::linear_programming::detail { struct relaxed_lp_settings_t { double tolerance = 1e-4; double time_limit = 1.0; + int iteration_limit = std::numeric_limits::max(); bool check_infeasibility = true; bool return_first_feasible = false; bool save_state = true; diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index dcfcdd0b1..93a99b16c 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -108,6 +108,7 @@ mip_solution_t run_mip(detail::problem_t& problem, CUOPT_LOG_INFO("Objective offset %f scaling_factor %f", problem.presolve_data.objective_offset, problem.presolve_data.objective_scaling_factor); + CUOPT_LOG_INFO("Model fingerprint: 0x%x", problem.get_fingerprint()); cuopt_assert(problem.original_problem_ptr->get_n_variables() == scaled_problem.n_variables, "Size mismatch"); cuopt_assert(problem.original_problem_ptr->get_n_constraints() == scaled_problem.n_constraints, diff --git a/cpp/src/mip/utils.cuh b/cpp/src/mip/utils.cuh index 534c67cc7..c4df8215d 100644 --- a/cpp/src/mip/utils.cuh +++ b/cpp/src/mip/utils.cuh @@ -356,13 +356,11 @@ bool has_variable_bounds_violation(const raft::handle_t* handle_ptr, } template -inline uint32_t compute_hash(raft::device_span values, rmm::cuda_stream_view stream) +inline uint32_t compute_hash(std::vector h_contents) { // FNV-1a hash - uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis - auto h_contents = cuopt::host_copy(values, stream); - RAFT_CHECK_CUDA(stream); + uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis std::vector byte_contents(h_contents.size() * sizeof(i_t)); std::memcpy(byte_contents.data(), h_contents.data(), h_contents.size() * sizeof(i_t)); for (size_t i = 0; i < byte_contents.size(); ++i) { @@ -372,4 +370,22 @@ inline uint32_t compute_hash(raft::device_span values, rmm::cuda_stream_vie return hash; } +template +inline uint32_t compute_hash(raft::device_span values, + rmm::cuda_stream_view stream = rmm::cuda_stream_default) +{ + auto h_contents = cuopt::host_copy(values, stream); + RAFT_CHECK_CUDA(stream); + return compute_hash(h_contents); +} + +template +inline uint32_t compute_hash(const rmm::device_uvector& values, + rmm::cuda_stream_view stream = rmm::cuda_stream_default) +{ + auto h_contents = cuopt::host_copy(values, stream); + RAFT_CHECK_CUDA(stream); + return compute_hash(h_contents); +} + } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/utilities/timer.hpp b/cpp/src/utilities/timer.hpp index 9833cb05a..30d1a2dd6 100644 --- a/cpp/src/utilities/timer.hpp +++ b/cpp/src/utilities/timer.hpp @@ -44,7 +44,19 @@ class timer_t { elapsed_time()); } - bool check_time_limit() const noexcept { return elapsed_time() >= time_limit; } + bool check_time_limit(const char* caller = __builtin_FUNCTION(), + const char* file = __builtin_FILE(), + int line = __builtin_LINE()) const noexcept + { + bool elapsed = elapsed_time() >= time_limit; + if (elapsed) + printf("************ TIME LIMIT (%.2gs) REACHED BY %s:%d: %s() ***\n", + time_limit, + file, + line, + caller); + return elapsed; + } bool check_half_time() const noexcept { return elapsed_time() >= time_limit / 2; } diff --git a/cpp/tests/mip/CMakeLists.txt b/cpp/tests/mip/CMakeLists.txt index ef3b16d95..816b9a817 100644 --- a/cpp/tests/mip/CMakeLists.txt +++ b/cpp/tests/mip/CMakeLists.txt @@ -48,4 +48,10 @@ ConfigureTest(FEASIBILITY_JUMP_TEST ) ConfigureTest(MIP_TERMINATION_STATUS_TEST ${CMAKE_CURRENT_SOURCE_DIR}/termination_test.cu +) +ConfigureTest(PRESOLVE_TEST + ${CMAKE_CURRENT_SOURCE_DIR}/presolve_test.cu +) +ConfigureTest(LOCAL_SEARCH_TEST + ${CMAKE_CURRENT_SOURCE_DIR}/local_search_test.cu ) \ No newline at end of file diff --git a/cpp/tests/mip/feasibility_jump_tests.cu b/cpp/tests/mip/feasibility_jump_tests.cu index 0a77347c1..07f625e3e 100644 --- a/cpp/tests/mip/feasibility_jump_tests.cu +++ b/cpp/tests/mip/feasibility_jump_tests.cu @@ -183,38 +183,6 @@ static bool run_fj_check_objective(std::string test_instance, int iter_limit, do return !solution.get_feasible() ? false : solution.get_user_objective() <= obj_target; } -static bool run_fj_check_determinism(std::string test_instance, int iter_limit) -{ - int seed = std::getenv("FJ_SEED") ? std::stoi(std::getenv("FJ_SEED")) : 42; - - detail::fj_settings_t fj_settings; - fj_settings.time_limit = 30.; - fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 20000 * 1000; - fj_settings.update_weights = true; - fj_settings.feasibility_run = false; - fj_settings.termination = detail::fj_termination_flags_t::FJ_TERMINATION_ITERATION_LIMIT; - fj_settings.iteration_limit = iter_limit; - fj_settings.load_balancing_mode = detail::fj_load_balancing_mode_t::ALWAYS_OFF; - fj_settings.seed = seed; - cuopt::seed_generator::set_seed(fj_settings.seed); - - auto state = run_fj(test_instance, fj_settings); - auto& solution = state.solution; - - CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", - test_instance.c_str(), - solution.get_feasible(), - solution.get_user_objective(), - solution.get_objective()); - - static auto first_val = solution.get_user_objective(); - - if (abs(solution.get_user_objective() - first_val) > 1) exit(0); - - return true; -} - static bool run_fj_check_feasible(std::string test_instance) { detail::fj_settings_t fj_settings; @@ -253,6 +221,38 @@ static bool run_fj_check_feasible(std::string test_instance) return true; } +static bool run_fj_check_determinism(std::string test_instance, int iter_limit) +{ + int seed = std::getenv("FJ_SEED") ? std::stoi(std::getenv("FJ_SEED")) : 42; + + detail::fj_settings_t fj_settings; + fj_settings.time_limit = 30.; + fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; + fj_settings.n_of_minimums_for_exit = 20000 * 1000; + fj_settings.update_weights = true; + fj_settings.feasibility_run = false; + fj_settings.termination = detail::fj_termination_flags_t::FJ_TERMINATION_ITERATION_LIMIT; + fj_settings.iteration_limit = iter_limit; + fj_settings.load_balancing_mode = detail::fj_load_balancing_mode_t::ALWAYS_ON; + fj_settings.seed = seed; + cuopt::seed_generator::set_seed(fj_settings.seed); + + auto state = run_fj(test_instance, fj_settings); + auto& solution = state.solution; + + CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", + test_instance.c_str(), + solution.get_feasible(), + solution.get_user_objective(), + solution.get_objective()); + + static auto first_val = solution.get_user_objective(); + + if (abs(solution.get_user_objective() - first_val) > 1) exit(0); + + return true; +} + // TEST(mip_solve, feasibility_jump_obj_test) // { // std::vector> test_cases = { @@ -311,12 +311,14 @@ static bool run_fj_check_feasible(std::string test_instance) TEST(mip_solve, feasibility_jump_determinism) { - for (const auto& instance : { - //"thor50dday.mps", - //"gen-ip054.mps", - "50v-10.mps", - //"seymour1.mps", - }) { + for (const auto& instance : {//"thor50dday.mps", + //"gen-ip054.mps", + //"50v-10.mps", + //"seymour1.mps", + //"rmatr200-p5.mps" + //"tr12-30.mps", + //"sct2.mps", + "uccase9.mps"}) { // for (int i = 0; i < 10; i++) while (true) { run_fj_check_determinism(instance, 1000); diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu new file mode 100644 index 000000000..3147d6eaf --- /dev/null +++ b/cpp/tests/mip/local_search_test.cu @@ -0,0 +1,247 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "../linear_programming/utilities/pdlp_test_utilities.cuh" +#include "mip_utils.cuh" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +#include + +#include +#include +#include + +#include +#include +#include +#include +#include + +namespace cuopt::linear_programming::test { + +void init_handler(const raft::handle_t* handle_ptr) +{ + // Init cuBlas / cuSparse context here to avoid having it during solving time + RAFT_CUBLAS_TRY(raft::linalg::detail::cublassetpointermode( + handle_ptr->get_cublas_handle(), CUBLAS_POINTER_MODE_DEVICE, handle_ptr->get_stream())); + RAFT_CUSPARSE_TRY(raft::sparse::detail::cusparsesetpointermode( + handle_ptr->get_cusparse_handle(), CUSPARSE_POINTER_MODE_DEVICE, handle_ptr->get_stream())); +} + +static void setup_device_symbols(rmm::cuda_stream_view stream_view) +{ + raft::common::nvtx::range fun_scope("Setting device symbol"); + detail::set_adaptive_step_size_hyper_parameters(stream_view); + detail::set_restart_hyper_parameters(stream_view); + detail::set_pdlp_hyper_parameters(stream_view); +} + +struct fj_tweaks_t { + double objective_weight = 0; +}; + +struct fj_state_t { + detail::solution_t solution; + std::vector solution_vector; + int minimums; + double incumbent_objective; + double incumbent_violation; +}; + +// Helper function to setup MIP solver and run FJ with given settings and initial solution +static uint32_t run_fp(std::string test_instance, + const detail::fj_settings_t& fj_settings, + fj_tweaks_t tweaks = {}, + std::vector initial_solution = {}) +{ + const raft::handle_t handle_{}; + std::cout << "Running: " << test_instance << std::endl; + + auto path = cuopt::test::get_rapids_dataset_root_dir() + ("/mip/" + test_instance); + cuopt::mps_parser::mps_data_model_t mps_problem = + cuopt::mps_parser::parse_mps(path, false); + handle_.sync_stream(); + auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); + problem_checking_t::check_problem_representation(op_problem); + + init_handler(op_problem.get_handle_ptr()); + // run the problem constructor of MIP, so that we do bounds standardization + detail::problem_t problem(op_problem); + problem.preprocess_problem(); + + setup_device_symbols(op_problem.get_handle_ptr()->get_stream()); + + detail::pdhg_solver_t pdhg_solver(problem.handle_ptr, problem); + detail::pdlp_initial_scaling_strategy_t scaling(&handle_, + problem, + 10, + 1.0, + pdhg_solver, + problem.reverse_coefficients, + problem.reverse_offsets, + problem.reverse_constraints, + true); + + auto settings = mip_solver_settings_t{}; + settings.time_limit = 30.; + auto timer = cuopt::timer_t(30); + detail::mip_solver_t solver(problem, settings, scaling, timer); + problem.tolerances = settings.get_tolerances(); + + rmm::device_uvector lp_optimal_solution(problem.n_variables, + problem.handle_ptr->get_stream()); + + detail::lp_state_t& lp_state = problem.lp_state; + // resize because some constructor might be called before the presolve + lp_state.resize(problem, problem.handle_ptr->get_stream()); + detail::relaxed_lp_settings_t lp_settings{}; + lp_settings.time_limit = std::numeric_limits::max(); + lp_settings.tolerance = 1e-6; + lp_settings.return_first_feasible = false; + lp_settings.save_state = true; + auto lp_result = + detail::get_relaxed_lp_solution(problem, lp_optimal_solution, lp_state, lp_settings); + EXPECT_EQ(lp_result.get_termination_status(), pdlp_termination_status_t::Optimal); + clamp_within_var_bounds(lp_optimal_solution, &problem, problem.handle_ptr); + + detail::local_search_t local_search(solver.context, lp_optimal_solution); + + detail::solution_t solution(problem); + + printf("Model fingerprint: 0x%x\n", problem.get_fingerprint()); + + local_search.fp.config.bounds_prop_timer_min = std::numeric_limits::max(); + local_search.fp.config.lp_run_time_after_feasible_min = std::numeric_limits::max(); + local_search.fp.config.linproj_time_limit = std::numeric_limits::max(); + local_search.fp.config.fj_cycle_escape_time_limit = std::numeric_limits::max(); + local_search.fp.config.lp_verify_time_limit = std::numeric_limits::max(); + // local_search.fp.timer = timer_t{std::numeric_limits::max()}; + + bool is_feasible = false; + int iterations = 0; + while (true) { + is_feasible = local_search.fp.run_single_fp_descent(solution); + printf("fp_loop it %d, is_feasible %d\n", iterations, is_feasible); + // if feasible return true + if (is_feasible) { + break; + } + // if not feasible, it means it is a cycle + else { + is_feasible = local_search.fp.restart_fp(solution); + if (is_feasible) { break; } + } + iterations++; + } + + std::vector hashes; + hashes.push_back(detail::compute_hash(solution.get_host_assignment())); + printf("hashes: 0x%x, hash of the hash: 0x%x\n", hashes[0], detail::compute_hash(hashes)); + + return detail::compute_hash(hashes); + // return {host_copy(solution_vector, problem.handle_ptr->get_stream()), iterations}; +} + +static uint32_t run_fp_check_determinism(std::string test_instance, int iter_limit) +{ + int seed = std::getenv("FJ_SEED") ? std::stoi(std::getenv("FJ_SEED")) : 42; + + detail::fj_settings_t fj_settings; + fj_settings.time_limit = 30.; + fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; + fj_settings.n_of_minimums_for_exit = 20000 * 1000; + fj_settings.update_weights = true; + fj_settings.feasibility_run = false; + fj_settings.termination = detail::fj_termination_flags_t::FJ_TERMINATION_ITERATION_LIMIT; + fj_settings.iteration_limit = iter_limit; + fj_settings.load_balancing_mode = detail::fj_load_balancing_mode_t::ALWAYS_ON; + fj_settings.seed = seed; + cuopt::seed_generator::set_seed(fj_settings.seed); + + return run_fp(test_instance, fj_settings); + + // auto state = run_fp(test_instance, fj_settings); + // auto& solution = state.solution; + + // CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", + // test_instance.c_str(), + // solution.get_feasible(), + // solution.get_user_objective(), + // solution.get_objective()); + + // static auto first_val = solution.get_user_objective(); + + // if (abs(solution.get_user_objective() - first_val) > 1) exit(0); +} + +TEST(local_search, feasibility_pump_determinism) +{ + cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); + + for (const auto& instance : { + //"thor50dday.mps", + //"gen-ip054.mps", + //"50v-10.mps", + //"seymour1.mps", + //"rmatr200-p5.mps" + //"tr12-30.mps", + "sct2.mps", + //"uccase9.mps" + }) { + // for (int i = 0; i < 10; i++) + // while (true) { + // run_fp_check_determinism(instance, 1000); + // } + + unsigned long seed = std::random_device{}(); + std::cerr << "Tested with seed " << seed << "\n"; + uint32_t gold_hash = 0; + for (int i = 0; i < 10; ++i) { + uint32_t hash = run_fp_check_determinism(instance, 1000); + if (i == 0) { + gold_hash = hash; + printf("Gold hash: 0x%x\n", gold_hash); + } else { + ASSERT_EQ(hash, gold_hash); + printf("Hash: 0x%x\n", hash); + } + } + } +} + +} // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/multi_probe_test.cu b/cpp/tests/mip/multi_probe_test.cu index 63cf93c79..f491b2100 100644 --- a/cpp/tests/mip/multi_probe_test.cu +++ b/cpp/tests/mip/multi_probe_test.cu @@ -23,6 +23,7 @@ #include #include #include +#include #include #include #include @@ -53,9 +54,10 @@ void init_handler(const raft::handle_t* handle_ptr) } std::tuple, std::vector, std::vector> select_k_random( - detail::problem_t& problem, int sample_size) + detail::problem_t& problem, + int sample_size, + unsigned long seed = std::random_device{}()) { - auto seed = std::random_device{}(); std::cerr << "Tested with seed " << seed << "\n"; problem.compute_n_integer_vars(); auto v_lb = host_copy(problem.variable_lower_bounds); @@ -146,7 +148,7 @@ multi_probe_results( std::move(h_lb_0), std::move(h_ub_0), std::move(h_lb_1), std::move(h_ub_1)); } -void test_multi_probe(std::string path) +uint32_t test_multi_probe(std::string path, unsigned long seed = std::random_device{}()) { auto memory_resource = make_async(); rmm::mr::set_current_device_resource(memory_resource.get()); @@ -173,7 +175,7 @@ void test_multi_probe(std::string path) detail::bound_presolve_t bnd_prb_1(solver.context); detail::multi_probe_t multi_probe_prs(solver.context); - auto probe_tuple = select_k_random(problem, 100); + auto probe_tuple = select_k_random(problem, 100, seed); auto bounds_probe_vals = convert_probe_tuple(probe_tuple); auto [bnd_lb_0, bnd_ub_0, bnd_lb_1, bnd_ub_1] = @@ -191,6 +193,16 @@ void test_multi_probe(std::string path) auto mlp_min_act_1 = host_copy(multi_probe_prs.upd_1.min_activity); auto mlp_max_act_1 = host_copy(multi_probe_prs.upd_1.max_activity); + std::vector hashes; + hashes.push_back(detail::compute_hash(bnd_min_act_0)); + hashes.push_back(detail::compute_hash(bnd_min_act_1)); + hashes.push_back(detail::compute_hash(bnd_max_act_0)); + hashes.push_back(detail::compute_hash(bnd_max_act_1)); + hashes.push_back(detail::compute_hash(bnd_lb_0)); + hashes.push_back(detail::compute_hash(bnd_ub_0)); + hashes.push_back(detail::compute_hash(bnd_lb_1)); + hashes.push_back(detail::compute_hash(bnd_ub_1)); + for (int i = 0; i < (int)bnd_min_act_0.size(); ++i) { EXPECT_DOUBLE_EQ(bnd_min_act_0[i], mlp_min_act_0[i]); EXPECT_DOUBLE_EQ(bnd_max_act_0[i], mlp_max_act_0[i]); @@ -204,17 +216,39 @@ void test_multi_probe(std::string path) EXPECT_DOUBLE_EQ(bnd_lb_1[i], m_lb_1[i]); EXPECT_DOUBLE_EQ(bnd_ub_1[i], m_ub_1[i]); } + + // return a composite hash of all the hashes to check for determinism + return detail::compute_hash(hashes); } -TEST(presolve, multi_probe) +// TEST(presolve, multi_probe) +// { +// std::vector test_instances = { +// "mip/50v-10-free-bound.mps", "mip/neos5-free-bound.mps", "mip/neos5.mps"}; +// for (const auto& test_instance : test_instances) { +// std::cout << "Running: " << test_instance << std::endl; +// auto path = make_path_absolute(test_instance); +// test_multi_probe(path); +// } +// } + +TEST(presolve, multi_probe_deterministic) { std::vector test_instances = { "mip/50v-10-free-bound.mps", "mip/neos5-free-bound.mps", "mip/neos5.mps"}; for (const auto& test_instance : test_instances) { std::cout << "Running: " << test_instance << std::endl; - auto path = make_path_absolute(test_instance); - test_multi_probe(path); + unsigned long seed = std::random_device{}(); + auto path = make_path_absolute(test_instance); + uint32_t gold_hash = 0; + for (int i = 0; i < 10; ++i) { + auto hash = test_multi_probe(path, seed); + if (i == 0) { + gold_hash = hash; + } else { + EXPECT_EQ(hash, gold_hash); + } + } } } - } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/presolve_test.cu b/cpp/tests/mip/presolve_test.cu new file mode 100644 index 000000000..317047aec --- /dev/null +++ b/cpp/tests/mip/presolve_test.cu @@ -0,0 +1,208 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "../linear_programming/utilities/pdlp_test_utilities.cuh" +#include "mip_utils.cuh" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +#include + +#include +#include +#include +#include + +namespace cuopt::linear_programming::test { + +inline auto make_async() { return std::make_shared(); } + +void init_handler(const raft::handle_t* handle_ptr) +{ + // Init cuBlas / cuSparse context here to avoid having it during solving time + RAFT_CUBLAS_TRY(raft::linalg::detail::cublassetpointermode( + handle_ptr->get_cublas_handle(), CUBLAS_POINTER_MODE_DEVICE, handle_ptr->get_stream())); + RAFT_CUSPARSE_TRY(raft::sparse::detail::cusparsesetpointermode( + handle_ptr->get_cusparse_handle(), CUSPARSE_POINTER_MODE_DEVICE, handle_ptr->get_stream())); +} + +std::tuple, std::vector, std::vector> select_k_random( + detail::problem_t& problem, + int sample_size, + unsigned long seed = std::random_device{}()) +{ + std::cerr << "Tested with seed " << seed << "\n"; + problem.compute_n_integer_vars(); + auto v_lb = host_copy(problem.variable_lower_bounds); + auto v_ub = host_copy(problem.variable_upper_bounds); + auto int_var_id = host_copy(problem.integer_indices); + int_var_id.erase(std::remove_if(int_var_id.begin(), + int_var_id.end(), + [v_lb, v_ub](auto id) { + return !(std::isfinite(v_lb[id]) && std::isfinite(v_ub[id])); + }), + int_var_id.end()); + sample_size = std::min(sample_size, static_cast(int_var_id.size())); + std::vector random_int_vars; + std::mt19937 m{seed}; + std::sample( + int_var_id.begin(), int_var_id.end(), std::back_inserter(random_int_vars), sample_size, m); + std::vector probe_0(sample_size); + std::vector probe_1(sample_size); + for (int i = 0; i < static_cast(random_int_vars.size()); ++i) { + if (i % 2) { + probe_0[i] = v_lb[random_int_vars[i]]; + probe_1[i] = v_ub[random_int_vars[i]]; + } else { + probe_1[i] = v_lb[random_int_vars[i]]; + probe_0[i] = v_ub[random_int_vars[i]]; + } + } + return std::make_tuple(std::move(random_int_vars), std::move(probe_0), std::move(probe_1)); +} + +std::pair>, std::vector>> +convert_probe_tuple(std::tuple, std::vector, std::vector>& probe) +{ + std::vector> probe_first; + std::vector> probe_second; + for (size_t i = 0; i < std::get<0>(probe).size(); ++i) { + probe_first.emplace_back(thrust::make_pair(std::get<0>(probe)[i], std::get<1>(probe)[i])); + probe_second.emplace_back(thrust::make_pair(std::get<0>(probe)[i], std::get<2>(probe)[i])); + } + return std::make_pair(std::move(probe_first), std::move(probe_second)); +} + +uint32_t test_probing_cache_determinism(std::string path, + unsigned long seed = std::random_device{}()) +{ + auto memory_resource = make_async(); + rmm::mr::set_current_device_resource(memory_resource.get()); + const raft::handle_t handle_{}; + cuopt::mps_parser::mps_data_model_t mps_problem = + cuopt::mps_parser::parse_mps(path, false); + handle_.sync_stream(); + auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); + problem_checking_t::check_problem_representation(op_problem); + detail::problem_t problem(op_problem); + mip_solver_settings_t default_settings{}; + default_settings.mip_scaling = false; // we're not checking scaling determinism here + detail::pdhg_solver_t pdhg_solver(problem.handle_ptr, problem); + detail::pdlp_initial_scaling_strategy_t scaling(&handle_, + problem, + 10, + 1.0, + pdhg_solver, + problem.reverse_coefficients, + problem.reverse_offsets, + problem.reverse_constraints, + true); + detail::mip_solver_t solver(problem, default_settings, scaling, cuopt::timer_t(0)); + detail::bound_presolve_t bnd_prb(solver.context); + + // rely on the iteration limit + compute_probing_cache(bnd_prb, problem, timer_t(std::numeric_limits::max())); + std::vector, 2>>> cached_values( + bnd_prb.probing_cache.probing_cache.begin(), bnd_prb.probing_cache.probing_cache.end()); + std::sort(cached_values.begin(), cached_values.end(), [](const auto& a, const auto& b) { + return a.first < b.first; + }); + + std::vector probed_indices; + std::vector intervals; + std::vector interval_types; + + std::vector var_to_cached_bound_keys; + std::vector var_to_cached_bound_lb; + std::vector var_to_cached_bound_ub; + for (const auto& a : cached_values) { + probed_indices.push_back(a.first); + intervals.push_back(a.second[0].val_interval.val); + intervals.push_back(a.second[1].val_interval.val); + interval_types.push_back(a.second[0].val_interval.interval_type); + interval_types.push_back(a.second[1].val_interval.interval_type); + + auto sorted_map = std::map>( + a.second[0].var_to_cached_bound_map.begin(), a.second[0].var_to_cached_bound_map.end()); + for (const auto& [var_id, cached_bound] : sorted_map) { + var_to_cached_bound_keys.push_back(var_id); + var_to_cached_bound_lb.push_back(cached_bound.lb); + var_to_cached_bound_ub.push_back(cached_bound.ub); + } + } + + std::vector hashes; + hashes.push_back(detail::compute_hash(probed_indices)); + hashes.push_back(detail::compute_hash(intervals)); + hashes.push_back(detail::compute_hash(interval_types)); + hashes.push_back(detail::compute_hash(var_to_cached_bound_keys)); + hashes.push_back(detail::compute_hash(var_to_cached_bound_lb)); + hashes.push_back(detail::compute_hash(var_to_cached_bound_ub)); + + // return a composite hash of all the hashes to check for determinism + return detail::compute_hash(hashes); +} + +// TEST(presolve, multi_probe) +// { +// std::vector test_instances = { +// "mip/50v-10-free-bound.mps", "mip/neos5-free-bound.mps", "mip/neos5.mps"}; +// for (const auto& test_instance : test_instances) { +// std::cout << "Running: " << test_instance << std::endl; +// auto path = make_path_absolute(test_instance); +// test_multi_probe(path); +// } +// } + +TEST(presolve, probing_cache_deterministic) +{ + std::vector test_instances = {"mip/50v-10-free-bound.mps", + "mip/neos5-free-bound.mps", + "mip/neos5.mps", + "mip/gen-ip054.mps", + "mip/rmatr200-p5.mps"}; + for (const auto& test_instance : test_instances) { + std::cout << "Running: " << test_instance << std::endl; + unsigned long seed = std::random_device{}(); + std::cerr << "Tested with seed " << seed << "\n"; + auto path = make_path_absolute(test_instance); + uint32_t gold_hash = 0; + for (int i = 0; i < 10; ++i) { + auto hash = test_probing_cache_determinism(path, seed); + if (i == 0) { + gold_hash = hash; + std::cout << "Gold hash: " << gold_hash << std::endl; + } else { + EXPECT_EQ(hash, gold_hash); + } + } + } +} +} // namespace cuopt::linear_programming::test From 20277f26b6eb47b55e791c258bdb3bc52947c7b9 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 21 Jul 2025 12:04:12 +0000 Subject: [PATCH 004/366] random spin kernel to perturb determinism tests --- cpp/tests/mip/local_search_test.cu | 42 +++++++++++++++++++++++++++--- 1 file changed, 39 insertions(+), 3 deletions(-) diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu index 3147d6eaf..518b6b254 100644 --- a/cpp/tests/mip/local_search_test.cu +++ b/cpp/tests/mip/local_search_test.cu @@ -71,6 +71,18 @@ static void setup_device_symbols(rmm::cuda_stream_view stream_view) detail::set_pdlp_hyper_parameters(stream_view); } +__global__ void spin_kernel(volatile int* flag, unsigned long long timeout_clocks = 10000000) +{ + long long int start_clock, sample_clock; + start_clock = clock64(); + + while (!*flag) { + sample_clock = clock64(); + + if (sample_clock - start_clock > timeout_clocks) { break; } + } +} + struct fj_tweaks_t { double objective_weight = 0; }; @@ -133,12 +145,15 @@ static uint32_t run_fp(std::string test_instance, lp_settings.time_limit = std::numeric_limits::max(); lp_settings.tolerance = 1e-6; lp_settings.return_first_feasible = false; - lp_settings.save_state = true; + lp_settings.save_state = false; + // lp_settings.iteration_limit = 5; auto lp_result = detail::get_relaxed_lp_solution(problem, lp_optimal_solution, lp_state, lp_settings); EXPECT_EQ(lp_result.get_termination_status(), pdlp_termination_status_t::Optimal); clamp_within_var_bounds(lp_optimal_solution, &problem, problem.handle_ptr); + // return detail::compute_hash(lp_optimal_solution); + detail::local_search_t local_search(solver.context, lp_optimal_solution); detail::solution_t solution(problem); @@ -209,18 +224,39 @@ static uint32_t run_fp_check_determinism(std::string test_instance, int iter_lim // if (abs(solution.get_user_objective() - first_val) > 1) exit(0); } +void launch_spin_kernel_stream_thread(rmm::cuda_stream_view stream_view) +{ + rmm::device_scalar flag(stream_view); + while (true) { + int blocks = rand() % 64 + 1; + int threads = rand() % 1024 + 1; + spin_kernel<<>>(flag.data()); + cudaStreamSynchronize(stream_view); + std::this_thread::sleep_for(std::chrono::milliseconds(rand() % 1000 + 1)); + } +} + +static void launch_spin_kernel_stream(rmm::cuda_stream_view stream_view) +{ + std::thread spin_thread(launch_spin_kernel_stream_thread, stream_view); + spin_thread.detach(); +} + TEST(local_search, feasibility_pump_determinism) { cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); + rmm::cuda_stream spin_stream; + launch_spin_kernel_stream(spin_stream); + for (const auto& instance : { //"thor50dday.mps", //"gen-ip054.mps", - //"50v-10.mps", + "50v-10.mps", //"seymour1.mps", //"rmatr200-p5.mps" //"tr12-30.mps", - "sct2.mps", + //"sct2.mps", //"uccase9.mps" }) { // for (int i = 0; i < 10; i++) From 55126d9de3320a026205ca1655c9f86621562779 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 21 Jul 2025 14:15:44 +0000 Subject: [PATCH 005/366] Fix uninitialized multiprobe buffer bug in constraint prop --- .../local_search/rounding/constraint_prop.cu | 1 + cpp/src/mip/presolve/bounds_update_data.cu | 29 ++++++++ cpp/src/mip/solution/solution.cu | 6 ++ cpp/src/mip/solution/solution.cuh | 1 + cpp/tests/mip/determinism_utils.cuh | 56 ++++++++++++++ cpp/tests/mip/local_search_test.cu | 73 +++++++------------ cpp/tests/mip/multi_probe_test.cu | 10 ++- cpp/tests/mip/presolve_test.cu | 5 ++ 8 files changed, 132 insertions(+), 49 deletions(-) create mode 100644 cpp/tests/mip/determinism_utils.cuh diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 37fb4b92f..7c04ced7c 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -807,6 +807,7 @@ bool constraint_prop_t::is_problem_ii(problem_t& problem) { bounds_update.calculate_activity_on_problem_bounds(problem); bounds_update.calculate_infeasible_redundant_constraints(problem); + multi_probe.calculate_activity(problem, problem.handle_ptr); bool problem_ii = bounds_update.infeas_constraints_count > 0; return problem_ii; } diff --git a/cpp/src/mip/presolve/bounds_update_data.cu b/cpp/src/mip/presolve/bounds_update_data.cu index a1616b1d9..acc2a1498 100644 --- a/cpp/src/mip/presolve/bounds_update_data.cu +++ b/cpp/src/mip/presolve/bounds_update_data.cu @@ -45,6 +45,35 @@ void bounds_update_data_t::resize(problem_t& problem) changed_constraints.resize(problem.n_constraints, problem.handle_ptr->get_stream()); next_changed_constraints.resize(problem.n_constraints, problem.handle_ptr->get_stream()); changed_variables.resize(problem.n_variables, problem.handle_ptr->get_stream()); + + thrust::fill(problem.handle_ptr->get_thrust_policy(), + min_activity.begin(), + min_activity.end(), + std::numeric_limits::signaling_NaN()); + thrust::fill(problem.handle_ptr->get_thrust_policy(), + max_activity.begin(), + max_activity.end(), + std::numeric_limits::signaling_NaN()); + thrust::fill(problem.handle_ptr->get_thrust_policy(), + lb.begin(), + lb.end(), + std::numeric_limits::signaling_NaN()); + thrust::fill(problem.handle_ptr->get_thrust_policy(), + ub.begin(), + ub.end(), + std::numeric_limits::signaling_NaN()); + thrust::fill(problem.handle_ptr->get_thrust_policy(), + changed_constraints.begin(), + changed_constraints.end(), + -1); + thrust::fill(problem.handle_ptr->get_thrust_policy(), + next_changed_constraints.begin(), + next_changed_constraints.end(), + -1); + thrust::fill(problem.handle_ptr->get_thrust_policy(), + changed_variables.begin(), + changed_variables.end(), + -1); } template diff --git a/cpp/src/mip/solution/solution.cu b/cpp/src/mip/solution/solution.cu index 74ec4c41c..3dd52d5c5 100644 --- a/cpp/src/mip/solution/solution.cu +++ b/cpp/src/mip/solution/solution.cu @@ -614,6 +614,12 @@ mip_solution_t solution_t::get_solution(bool output_feasible } } +template +uint32_t solution_t::get_hash() +{ + return compute_hash(assignment); +} + #if MIP_INSTANTIATE_FLOAT template class solution_t; #endif diff --git a/cpp/src/mip/solution/solution.cuh b/cpp/src/mip/solution/solution.cuh index 729a5c0e5..0b0080ad6 100644 --- a/cpp/src/mip/solution/solution.cuh +++ b/cpp/src/mip/solution/solution.cuh @@ -104,6 +104,7 @@ class solution_t { f_t compute_max_constraint_violation(); f_t compute_max_int_violation(); f_t compute_max_variable_violation(); + uint32_t get_hash(); struct view_t { // let's not bloat the class for every simple getter and setters diff --git a/cpp/tests/mip/determinism_utils.cuh b/cpp/tests/mip/determinism_utils.cuh new file mode 100644 index 000000000..3e71e0885 --- /dev/null +++ b/cpp/tests/mip/determinism_utils.cuh @@ -0,0 +1,56 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +#include +#include + +namespace cuopt::linear_programming::test { + +static __global__ void spin_kernel(volatile int* flag, unsigned long long timeout_clocks = 10000000) +{ + long long int start_clock, sample_clock; + start_clock = clock64(); + + while (!*flag) { + sample_clock = clock64(); + + if (sample_clock - start_clock > timeout_clocks) { break; } + } +} + +static void launch_spin_kernel_stream_thread(rmm::cuda_stream_view stream_view) +{ + rmm::device_scalar flag(0, stream_view); + while (true) { + int blocks = rand() % 64 + 1; + int threads = rand() % 1024 + 1; + spin_kernel<<>>(flag.data()); + cudaStreamSynchronize(stream_view); + std::this_thread::sleep_for(std::chrono::milliseconds(rand() % 1000 + 1)); + } +} + +static inline void launch_spin_kernel_stream(rmm::cuda_stream_view stream_view) +{ + std::thread spin_thread(launch_spin_kernel_stream_thread, stream_view); + spin_thread.detach(); +} +} // namespace cuopt::linear_programming::test \ No newline at end of file diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu index 518b6b254..fcd8df481 100644 --- a/cpp/tests/mip/local_search_test.cu +++ b/cpp/tests/mip/local_search_test.cu @@ -16,6 +16,7 @@ */ #include "../linear_programming/utilities/pdlp_test_utilities.cuh" +#include "determinism_utils.cuh" #include "mip_utils.cuh" #include @@ -71,18 +72,6 @@ static void setup_device_symbols(rmm::cuda_stream_view stream_view) detail::set_pdlp_hyper_parameters(stream_view); } -__global__ void spin_kernel(volatile int* flag, unsigned long long timeout_clocks = 10000000) -{ - long long int start_clock, sample_clock; - start_clock = clock64(); - - while (!*flag) { - sample_clock = clock64(); - - if (sample_clock - start_clock > timeout_clocks) { break; } - } -} - struct fj_tweaks_t { double objective_weight = 0; }; @@ -135,22 +124,25 @@ static uint32_t run_fp(std::string test_instance, detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); - rmm::device_uvector lp_optimal_solution(problem.n_variables, - problem.handle_ptr->get_stream()); - - detail::lp_state_t& lp_state = problem.lp_state; - // resize because some constructor might be called before the presolve - lp_state.resize(problem, problem.handle_ptr->get_stream()); - detail::relaxed_lp_settings_t lp_settings{}; - lp_settings.time_limit = std::numeric_limits::max(); - lp_settings.tolerance = 1e-6; - lp_settings.return_first_feasible = false; - lp_settings.save_state = false; - // lp_settings.iteration_limit = 5; - auto lp_result = - detail::get_relaxed_lp_solution(problem, lp_optimal_solution, lp_state, lp_settings); - EXPECT_EQ(lp_result.get_termination_status(), pdlp_termination_status_t::Optimal); - clamp_within_var_bounds(lp_optimal_solution, &problem, problem.handle_ptr); + // only compute the LP optimal once + static rmm::device_uvector lp_optimal_solution(0, problem.handle_ptr->get_stream()); + + if (lp_optimal_solution.size() == 0) { + lp_optimal_solution.resize(problem.n_variables, problem.handle_ptr->get_stream()); + detail::lp_state_t& lp_state = problem.lp_state; + // resize because some constructor might be called before the presolve + lp_state.resize(problem, problem.handle_ptr->get_stream()); + detail::relaxed_lp_settings_t lp_settings{}; + lp_settings.time_limit = std::numeric_limits::max(); + lp_settings.tolerance = 1e-6; + lp_settings.return_first_feasible = false; + lp_settings.save_state = false; + // lp_settings.iteration_limit = 5; + auto lp_result = + detail::get_relaxed_lp_solution(problem, lp_optimal_solution, lp_state, lp_settings); + EXPECT_EQ(lp_result.get_termination_status(), pdlp_termination_status_t::Optimal); + clamp_within_var_bounds(lp_optimal_solution, &problem, problem.handle_ptr); + } // return detail::compute_hash(lp_optimal_solution); @@ -159,6 +151,7 @@ static uint32_t run_fp(std::string test_instance, detail::solution_t solution(problem); printf("Model fingerprint: 0x%x\n", problem.get_fingerprint()); + printf("LP optimal hash: 0x%x\n", detail::compute_hash(lp_optimal_solution)); local_search.fp.config.bounds_prop_timer_min = std::numeric_limits::max(); local_search.fp.config.lp_run_time_after_feasible_min = std::numeric_limits::max(); @@ -224,30 +217,14 @@ static uint32_t run_fp_check_determinism(std::string test_instance, int iter_lim // if (abs(solution.get_user_objective() - first_val) > 1) exit(0); } -void launch_spin_kernel_stream_thread(rmm::cuda_stream_view stream_view) -{ - rmm::device_scalar flag(stream_view); - while (true) { - int blocks = rand() % 64 + 1; - int threads = rand() % 1024 + 1; - spin_kernel<<>>(flag.data()); - cudaStreamSynchronize(stream_view); - std::this_thread::sleep_for(std::chrono::milliseconds(rand() % 1000 + 1)); - } -} - -static void launch_spin_kernel_stream(rmm::cuda_stream_view stream_view) -{ - std::thread spin_thread(launch_spin_kernel_stream_thread, stream_view); - spin_thread.detach(); -} - TEST(local_search, feasibility_pump_determinism) { cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); - rmm::cuda_stream spin_stream; - launch_spin_kernel_stream(spin_stream); + rmm::cuda_stream spin_stream_1; + rmm::cuda_stream spin_stream_2; + launch_spin_kernel_stream(spin_stream_1); + launch_spin_kernel_stream(spin_stream_2); for (const auto& instance : { //"thor50dday.mps", diff --git a/cpp/tests/mip/multi_probe_test.cu b/cpp/tests/mip/multi_probe_test.cu index f491b2100..158205ca7 100644 --- a/cpp/tests/mip/multi_probe_test.cu +++ b/cpp/tests/mip/multi_probe_test.cu @@ -16,6 +16,7 @@ */ #include "../linear_programming/utilities/pdlp_test_utilities.cuh" +#include "determinism_utils.cuh" #include "mip_utils.cuh" #include @@ -234,8 +235,15 @@ uint32_t test_multi_probe(std::string path, unsigned long seed = std::random_dev TEST(presolve, multi_probe_deterministic) { + rmm::cuda_stream spin_stream_1; + launch_spin_kernel_stream(spin_stream_1); + std::vector test_instances = { - "mip/50v-10-free-bound.mps", "mip/neos5-free-bound.mps", "mip/neos5.mps"}; + "mip/50v-10-free-bound.mps", + "mip/neos5-free-bound.mps", + "mip/neos5.mps", + "mip/50v-10.mps", + }; for (const auto& test_instance : test_instances) { std::cout << "Running: " << test_instance << std::endl; unsigned long seed = std::random_device{}(); diff --git a/cpp/tests/mip/presolve_test.cu b/cpp/tests/mip/presolve_test.cu index 317047aec..e9810b3e7 100644 --- a/cpp/tests/mip/presolve_test.cu +++ b/cpp/tests/mip/presolve_test.cu @@ -16,6 +16,7 @@ */ #include "../linear_programming/utilities/pdlp_test_utilities.cuh" +#include "determinism_utils.cuh" #include "mip_utils.cuh" #include @@ -183,9 +184,13 @@ uint32_t test_probing_cache_determinism(std::string path, TEST(presolve, probing_cache_deterministic) { + rmm::cuda_stream spin_stream_1; + launch_spin_kernel_stream(spin_stream_1); + std::vector test_instances = {"mip/50v-10-free-bound.mps", "mip/neos5-free-bound.mps", "mip/neos5.mps", + "mip/50v-10.mps", "mip/gen-ip054.mps", "mip/rmatr200-p5.mps"}; for (const auto& test_instance : test_instances) { From 58d9a421c2771f6bcc4e5ae71fb6ad5ed21b896c Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 22 Jul 2025 08:19:00 +0000 Subject: [PATCH 006/366] local search determinism tests --- cpp/src/linear_programming/pdlp.cu | 1 + cpp/src/mip/local_search/local_search.cu | 3 +- .../local_search/rounding/constraint_prop.cu | 1 + cpp/src/utilities/timer.hpp | 4 +- cpp/tests/mip/determinism_utils.cuh | 41 +++-- cpp/tests/mip/local_search_test.cu | 140 ++++++++++-------- cpp/tests/mip/multi_probe_test.cu | 3 +- cpp/tests/mip/presolve_test.cu | 3 +- 8 files changed, 118 insertions(+), 78 deletions(-) diff --git a/cpp/src/linear_programming/pdlp.cu b/cpp/src/linear_programming/pdlp.cu index de6846674..8e49f73af 100644 --- a/cpp/src/linear_programming/pdlp.cu +++ b/cpp/src/linear_programming/pdlp.cu @@ -260,6 +260,7 @@ static bool time_limit_reached(const std::chrono::high_resolution_clock::time_po if (elapsed >= (seconds * 1000.0)) { CUOPT_LOG_ERROR("**** PDLP Time limit reached: %f *****", seconds); + cuopt_assert(false, "unexpected timer"); } return elapsed >= (seconds * 1000.0); diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 655b07fa9..efda50647 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -167,7 +167,8 @@ template bool local_search_t::run_fj_line_segment(solution_t& solution, timer_t timer) { rmm::device_uvector starting_point(solution.assignment, solution.handle_ptr->get_stream()); - bool feas = line_segment_search.search_line_segment(solution, + line_segment_search.settings.recombiner_mode = false; + bool feas = line_segment_search.search_line_segment(solution, starting_point, lp_optimal_solution, /*n_points_to_search=*/5, diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 7c04ced7c..b04fa6cae 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -1020,6 +1020,7 @@ bool constraint_prop_t::apply_round( // this is second timer that can continue but without recovery mode f_t max_time_for_bounds_prop = 5.; max_time_for_bounds_prop = timer.remaining_time() / 10.0; + max_time_for_bounds_prop = 30.0; max_timer = timer_t{max_time_for_bounds_prop}; if (check_brute_force_rounding(sol)) { return true; } recovery_mode = false; diff --git a/cpp/src/utilities/timer.hpp b/cpp/src/utilities/timer.hpp index 30d1a2dd6..1a4efa33e 100644 --- a/cpp/src/utilities/timer.hpp +++ b/cpp/src/utilities/timer.hpp @@ -49,12 +49,14 @@ class timer_t { int line = __builtin_LINE()) const noexcept { bool elapsed = elapsed_time() >= time_limit; - if (elapsed) + if (elapsed) { printf("************ TIME LIMIT (%.2gs) REACHED BY %s:%d: %s() ***\n", time_limit, file, line, caller); + cuopt_assert(false, "unexpected timer"); + } return elapsed; } diff --git a/cpp/tests/mip/determinism_utils.cuh b/cpp/tests/mip/determinism_utils.cuh index 3e71e0885..28e8c4b51 100644 --- a/cpp/tests/mip/determinism_utils.cuh +++ b/cpp/tests/mip/determinism_utils.cuh @@ -19,38 +19,59 @@ #include +#include + #include #include +#include + namespace cuopt::linear_programming::test { -static __global__ void spin_kernel(volatile int* flag, unsigned long long timeout_clocks = 10000000) +static __global__ void spin_kernel(int* flag, unsigned long long timeout_clocks = 10000000) { + cuda::atomic_ref flag_ref(*flag); + long long int start_clock, sample_clock; start_clock = clock64(); - while (!*flag) { + while (flag_ref.load() == 0) { sample_clock = clock64(); if (sample_clock - start_clock > timeout_clocks) { break; } } } -static void launch_spin_kernel_stream_thread(rmm::cuda_stream_view stream_view) +static void launch_spin_kernel_stream_thread(rmm::cuda_stream_view stream_view, int* flag) { - rmm::device_scalar flag(0, stream_view); while (true) { int blocks = rand() % 64 + 1; int threads = rand() % 1024 + 1; - spin_kernel<<>>(flag.data()); + spin_kernel<<>>(flag); cudaStreamSynchronize(stream_view); + if (host_copy(flag, 1, stream_view)[0] != 0) { break; } std::this_thread::sleep_for(std::chrono::milliseconds(rand() % 1000 + 1)); } } -static inline void launch_spin_kernel_stream(rmm::cuda_stream_view stream_view) -{ - std::thread spin_thread(launch_spin_kernel_stream_thread, stream_view); - spin_thread.detach(); -} +class spin_stream_raii_t { + public: + spin_stream_raii_t() + : flag(0, stream), spin_thread(launch_spin_kernel_stream_thread, stream.view(), flag.data()) + { + } + + ~spin_stream_raii_t() + { + int one = 1; + flag.set_value_async(one, stream); + spin_thread.join(); + } + + private: + rmm::cuda_stream stream; + rmm::device_scalar flag; + std::thread spin_thread; +}; + } // namespace cuopt::linear_programming::test \ No newline at end of file diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu index fcd8df481..a3ef1abed 100644 --- a/cpp/tests/mip/local_search_test.cu +++ b/cpp/tests/mip/local_search_test.cu @@ -84,11 +84,16 @@ struct fj_state_t { double incumbent_violation; }; +enum local_search_mode_t { + FP = 0, + STAGED_FP, + FJ_LINE_SEGMENT, + FJ_ON_ZERO, + FJ_ANNEALING, +}; + // Helper function to setup MIP solver and run FJ with given settings and initial solution -static uint32_t run_fp(std::string test_instance, - const detail::fj_settings_t& fj_settings, - fj_tweaks_t tweaks = {}, - std::vector initial_solution = {}) +static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) { const raft::handle_t handle_{}; std::cout << "Running: " << test_instance << std::endl; @@ -124,57 +129,69 @@ static uint32_t run_fp(std::string test_instance, detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); - // only compute the LP optimal once - static rmm::device_uvector lp_optimal_solution(0, problem.handle_ptr->get_stream()); - - if (lp_optimal_solution.size() == 0) { - lp_optimal_solution.resize(problem.n_variables, problem.handle_ptr->get_stream()); - detail::lp_state_t& lp_state = problem.lp_state; - // resize because some constructor might be called before the presolve - lp_state.resize(problem, problem.handle_ptr->get_stream()); - detail::relaxed_lp_settings_t lp_settings{}; - lp_settings.time_limit = std::numeric_limits::max(); - lp_settings.tolerance = 1e-6; - lp_settings.return_first_feasible = false; - lp_settings.save_state = false; - // lp_settings.iteration_limit = 5; - auto lp_result = - detail::get_relaxed_lp_solution(problem, lp_optimal_solution, lp_state, lp_settings); - EXPECT_EQ(lp_result.get_termination_status(), pdlp_termination_status_t::Optimal); - clamp_within_var_bounds(lp_optimal_solution, &problem, problem.handle_ptr); - } + rmm::device_uvector lp_optimal_solution(0, problem.handle_ptr->get_stream()); + + lp_optimal_solution.resize(problem.n_variables, problem.handle_ptr->get_stream()); + detail::lp_state_t& lp_state = problem.lp_state; + // resize because some constructor might be called before the presolve + lp_state.resize(problem, problem.handle_ptr->get_stream()); + detail::relaxed_lp_settings_t lp_settings{}; + lp_settings.time_limit = std::numeric_limits::max(); + lp_settings.tolerance = 1e-6; + lp_settings.return_first_feasible = false; + lp_settings.save_state = false; + // lp_settings.iteration_limit = 5; + auto lp_result = + detail::get_relaxed_lp_solution(problem, lp_optimal_solution, lp_state, lp_settings); + EXPECT_EQ(lp_result.get_termination_status(), pdlp_termination_status_t::Optimal); + clamp_within_var_bounds(lp_optimal_solution, &problem, problem.handle_ptr); // return detail::compute_hash(lp_optimal_solution); detail::local_search_t local_search(solver.context, lp_optimal_solution); detail::solution_t solution(problem); + solution.assign_random_within_bounds(); + solution.compute_feasibility(); printf("Model fingerprint: 0x%x\n", problem.get_fingerprint()); printf("LP optimal hash: 0x%x\n", detail::compute_hash(lp_optimal_solution)); + printf("running mode: %d\n", mode); local_search.fp.config.bounds_prop_timer_min = std::numeric_limits::max(); local_search.fp.config.lp_run_time_after_feasible_min = std::numeric_limits::max(); local_search.fp.config.linproj_time_limit = std::numeric_limits::max(); local_search.fp.config.fj_cycle_escape_time_limit = std::numeric_limits::max(); local_search.fp.config.lp_verify_time_limit = std::numeric_limits::max(); - // local_search.fp.timer = timer_t{std::numeric_limits::max()}; - - bool is_feasible = false; - int iterations = 0; - while (true) { - is_feasible = local_search.fp.run_single_fp_descent(solution); - printf("fp_loop it %d, is_feasible %d\n", iterations, is_feasible); - // if feasible return true - if (is_feasible) { - break; - } - // if not feasible, it means it is a cycle - else { - is_feasible = local_search.fp.restart_fp(solution); - if (is_feasible) { break; } + local_search.fp.timer = timer_t{600}; + + if (mode == local_search_mode_t::STAGED_FP) { + timer_t timer(std::numeric_limits::max()); + bool early_exit = false; + local_search.run_staged_fp(solution, timer, early_exit); + } else if (mode == local_search_mode_t::FP) { + bool is_feasible = false; + int iterations = 0; + while (true) { + is_feasible = local_search.fp.run_single_fp_descent(solution); + printf("fp_loop it %d, is_feasible %d\n", iterations, is_feasible); + // if feasible return true + if (is_feasible) { + break; + } + // if not feasible, it means it is a cycle + else { + is_feasible = local_search.fp.restart_fp(solution); + if (is_feasible) { break; } + } + iterations++; } - iterations++; + } else if (mode == local_search_mode_t::FJ_LINE_SEGMENT) { + local_search.run_fj_line_segment(solution, timer); + } else if (mode == local_search_mode_t::FJ_ON_ZERO) { + local_search.run_fj_on_zero(solution, timer); + } else if (mode == local_search_mode_t::FJ_ANNEALING) { + local_search.run_fj_annealing(solution, timer); } std::vector hashes; @@ -185,23 +202,12 @@ static uint32_t run_fp(std::string test_instance, // return {host_copy(solution_vector, problem.handle_ptr->get_stream()), iterations}; } -static uint32_t run_fp_check_determinism(std::string test_instance, int iter_limit) +static uint32_t run_fp_check_determinism(std::string test_instance, local_search_mode_t mode) { int seed = std::getenv("FJ_SEED") ? std::stoi(std::getenv("FJ_SEED")) : 42; + cuopt::seed_generator::set_seed(seed); - detail::fj_settings_t fj_settings; - fj_settings.time_limit = 30.; - fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 20000 * 1000; - fj_settings.update_weights = true; - fj_settings.feasibility_run = false; - fj_settings.termination = detail::fj_termination_flags_t::FJ_TERMINATION_ITERATION_LIMIT; - fj_settings.iteration_limit = iter_limit; - fj_settings.load_balancing_mode = detail::fj_load_balancing_mode_t::ALWAYS_ON; - fj_settings.seed = seed; - cuopt::seed_generator::set_seed(fj_settings.seed); - - return run_fp(test_instance, fj_settings); + return run_fp(test_instance, mode); // auto state = run_fp(test_instance, fj_settings); // auto& solution = state.solution; @@ -217,23 +223,25 @@ static uint32_t run_fp_check_determinism(std::string test_instance, int iter_lim // if (abs(solution.get_user_objective() - first_val) > 1) exit(0); } -TEST(local_search, feasibility_pump_determinism) +class LocalSearchTestParams : public testing::TestWithParam> {}; + +TEST_P(LocalSearchTestParams, local_search_operator_determinism) { cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); - rmm::cuda_stream spin_stream_1; - rmm::cuda_stream spin_stream_2; - launch_spin_kernel_stream(spin_stream_1); - launch_spin_kernel_stream(spin_stream_2); + spin_stream_raii_t spin_stream_1; + spin_stream_raii_t spin_stream_2; + + auto mode = std::get<0>(GetParam()); for (const auto& instance : { //"thor50dday.mps", //"gen-ip054.mps", - "50v-10.mps", + //"50v-10.mps", //"seymour1.mps", - //"rmatr200-p5.mps" + //"rmatr200-p5.mps", //"tr12-30.mps", - //"sct2.mps", + "sct2.mps", //"uccase9.mps" }) { // for (int i = 0; i < 10; i++) @@ -245,7 +253,7 @@ TEST(local_search, feasibility_pump_determinism) std::cerr << "Tested with seed " << seed << "\n"; uint32_t gold_hash = 0; for (int i = 0; i < 10; ++i) { - uint32_t hash = run_fp_check_determinism(instance, 1000); + uint32_t hash = run_fp_check_determinism(instance, mode); if (i == 0) { gold_hash = hash; printf("Gold hash: 0x%x\n", gold_hash); @@ -257,4 +265,12 @@ TEST(local_search, feasibility_pump_determinism) } } +INSTANTIATE_TEST_SUITE_P(LocalSearchTests, + LocalSearchTestParams, + testing::Values(std::make_tuple(local_search_mode_t::FP), + std::make_tuple(local_search_mode_t::STAGED_FP), + std::make_tuple(local_search_mode_t::FJ_LINE_SEGMENT), + std::make_tuple(local_search_mode_t::FJ_ON_ZERO), + std::make_tuple(local_search_mode_t::FJ_ANNEALING))); + } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/multi_probe_test.cu b/cpp/tests/mip/multi_probe_test.cu index 158205ca7..b4a4a1c6c 100644 --- a/cpp/tests/mip/multi_probe_test.cu +++ b/cpp/tests/mip/multi_probe_test.cu @@ -235,8 +235,7 @@ uint32_t test_multi_probe(std::string path, unsigned long seed = std::random_dev TEST(presolve, multi_probe_deterministic) { - rmm::cuda_stream spin_stream_1; - launch_spin_kernel_stream(spin_stream_1); + spin_stream_raii_t spin_stream_1; std::vector test_instances = { "mip/50v-10-free-bound.mps", diff --git a/cpp/tests/mip/presolve_test.cu b/cpp/tests/mip/presolve_test.cu index e9810b3e7..a043e2d74 100644 --- a/cpp/tests/mip/presolve_test.cu +++ b/cpp/tests/mip/presolve_test.cu @@ -184,8 +184,7 @@ uint32_t test_probing_cache_determinism(std::string path, TEST(presolve, probing_cache_deterministic) { - rmm::cuda_stream spin_stream_1; - launch_spin_kernel_stream(spin_stream_1); + spin_stream_raii_t spin_stream_1; std::vector test_instances = {"mip/50v-10-free-bound.mps", "mip/neos5-free-bound.mps", From 6b9324c3de2d6b5f2618e33861e0fc0853efe9b3 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 22 Jul 2025 10:28:42 +0000 Subject: [PATCH 007/366] tmp, fixing bug in constraint prop randomness --- cpp/src/mip/diversity/diversity_manager.cu | 17 +- cpp/src/mip/diversity/diversity_manager.cuh | 6 + .../local_search/rounding/constraint_prop.cu | 26 ++- cpp/src/mip/solution/solution.cu | 2 +- cpp/src/mip/solution/solution.cuh | 2 +- cpp/tests/mip/CMakeLists.txt | 3 + cpp/tests/mip/diversity_test.cu | 194 ++++++++++++++++++ cpp/tests/mip/feasibility_jump_tests.cu | 3 +- cpp/tests/mip/local_search_test.cu | 3 +- cpp/tests/mip/presolve_test.cu | 87 +++++++- 10 files changed, 321 insertions(+), 22 deletions(-) create mode 100644 cpp/tests/mip/diversity_test.cu diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 874230a29..617c89b60 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -273,7 +273,7 @@ void diversity_manager_t::generate_initial_solutions() population.var_threshold); population.print(); auto new_sol_vector = population.get_external_solutions(); - if (!fj_only_run) { recombine_and_ls_with_all(new_sol_vector); } + if (!settings.fj_only_run) { recombine_and_ls_with_all(new_sol_vector); } } template @@ -353,9 +353,12 @@ void diversity_manager_t::run_fj_alone(solution_t& solution) ls.fj.settings.n_of_minimums_for_exit = 20000 * 1000; ls.fj.settings.update_weights = true; ls.fj.settings.feasibility_run = false; - ls.fj.settings.termination = fj_termination_flags_t::FJ_TERMINATION_TIME_LIMIT; - ls.fj.settings.time_limit = timer.remaining_time(); - ls.fj.solve(solution); + // ls.fj.settings.termination = fj_termination_flags_t::FJ_TERMINATION_TIME_LIMIT; + + ls.fj.settings.termination = fj_termination_flags_t::FJ_TERMINATION_ITERATION_LIMIT; + ls.fj.settings.iteration_limit = 10000; + ls.fj.settings.time_limit = timer.remaining_time(); + // ls.fj.solve(solution); CUOPT_LOG_INFO("FJ alone finished!"); } @@ -396,7 +399,7 @@ solution_t diversity_manager_t::run_solver() std::min(max_time_on_probing, time_limit * time_ratio_of_probing_cache); timer_t probing_timer{time_for_probing_cache}; if (check_b_b_preemption()) { return population.best_feasible(); } - if (!fj_only_run) { + if (!settings.fj_only_run) { compute_probing_cache(ls.constraint_prop.bounds_update, *problem_ptr, probing_timer); } // careful, assign the correct probing cache @@ -412,7 +415,7 @@ solution_t diversity_manager_t::run_solver() lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; lp_settings.return_first_feasible = false; lp_settings.save_state = true; - if (!fj_only_run) { + if (!settings.fj_only_run) { auto lp_result = get_relaxed_lp_solution(*problem_ptr, lp_optimal_solution, lp_state, lp_settings); ls.lp_optimal_exists = true; @@ -442,7 +445,7 @@ solution_t diversity_manager_t::run_solver() population.best_feasible().get_user_objective(); } - if (fj_only_run) { + if (settings.fj_only_run) { run_fj_alone(population.best_feasible()); return population.best_feasible(); } diff --git a/cpp/src/mip/diversity/diversity_manager.cuh b/cpp/src/mip/diversity/diversity_manager.cuh index c45b83107..981bffc73 100644 --- a/cpp/src/mip/diversity/diversity_manager.cuh +++ b/cpp/src/mip/diversity/diversity_manager.cuh @@ -36,6 +36,11 @@ namespace cuopt::linear_programming::detail { template class diversity_manager_t { + public: + struct diversity_settings_t { + bool fj_only_run = false; + }; + public: diversity_manager_t(mip_solver_context_t& context); bool run_presolve(f_t time_limit); @@ -82,6 +87,7 @@ class diversity_manager_t { i_t current_step{0}; solver_stats_t& stats; std::vector> initial_sol_vector; + diversity_settings_t settings; // Enhanced statistics structure for UCB with exponential recency weighting struct mab_arm_stats_t { diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index b04fa6cae..4661f0906 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -225,6 +225,8 @@ void constraint_prop_t::sort_by_interval_and_frac(solution_t return bounds_interval_1 < bounds_interval_2; } }); + + CUOPT_LOG_DEBUG("hash vars 0x%x", detail::compute_hash(vars)); // now do the suffling, for that we need to assign some random values to rnd array // we will sort this rnd array and the vars in subsections, so that each subsection will be // shuffled in total we will have 3(binary, ternary and rest) x 7 intervals = 21 subsections. @@ -277,7 +279,10 @@ void constraint_prop_t::sort_by_interval_and_frac(solution_t } } }); + + CUOPT_LOG_DEBUG("hash subsection_offsets 0x%x", detail::compute_hash(subsection_offsets)); auto random_vector = get_random_uniform_vector((i_t)vars.size(), rng); + CUOPT_LOG_DEBUG("hash random_vector 0x%x", detail::compute_hash(random_vector)); rmm::device_uvector device_random_vector(random_vector.size(), sol.handle_ptr->get_stream()); raft::copy(device_random_vector.data(), random_vector.data(), @@ -748,6 +753,7 @@ bool constraint_prop_t::run_repair_procedure(problem_t& prob timer_t& timer, const raft::handle_t* handle_ptr) { + CUOPT_LOG_DEBUG("Running repair procedure"); // select the first probing value i_t select = 0; multi_probe.set_updated_bounds(problem, select, handle_ptr); @@ -822,7 +828,9 @@ bool constraint_prop_t::find_integer( { using crit_t = termination_criterion_t; auto& unset_integer_vars = unset_vars; - std::mt19937 rng(cuopt::seed_generator::get_seed()); + i_t seed = cuopt::seed_generator::get_seed(); + CUOPT_LOG_DEBUG("seed 0x%x", seed); + std::mt19937 rng(seed); lb_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); ub_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); assignment_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); @@ -846,6 +854,7 @@ bool constraint_prop_t::find_integer( sol.problem_ptr->integer_indices.data(), sol.problem_ptr->n_integer_vars, sol.handle_ptr->get_stream()); + CUOPT_LOG_DEBUG("sol hash 0x%x", sol.get_hash()); CUOPT_LOG_DEBUG("Bounds propagation rounding: unset vars %lu", unset_integer_vars.size()); if (unset_integer_vars.size() == 0) { CUOPT_LOG_ERROR("No integer variables provided in the bounds prop rounding"); @@ -855,6 +864,7 @@ bool constraint_prop_t::find_integer( } // this is needed for the sort inside of the loop bool problem_ii = is_problem_ii(*sol.problem_ptr); + CUOPT_LOG_DEBUG("is problem ii %d\n", problem_ii); // if the problem is ii, run the bounds prop in the beginning if (problem_ii) { bool bounds_repaired = @@ -871,16 +881,23 @@ bool constraint_prop_t::find_integer( } // do the sort if the problem is not ii. crossing bounds might cause some issues on the sort order else { + CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x, sol 0x%x before sort\n", + detail::compute_hash(unset_integer_vars), + sol.get_hash()); // this is a sort to have initial shuffling, so that stable sort within will keep the order and // some randomness will be achieved sort_by_interval_and_frac(sol, make_span(unset_integer_vars), rng); + CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x sol 0x%x after sort\n", + detail::compute_hash(unset_integer_vars), + sol.get_hash()); } set_host_bounds(sol); size_t set_count = 0; bool timeout_happened = false; i_t n_failed_repair_iterations = 0; while (set_count < unset_integer_vars.size()) { - CUOPT_LOG_TRACE("n_set_vars %d vars to set %lu", set_count, unset_integer_vars.size()); + CUOPT_LOG_DEBUG("n_set_vars %d vars to set %lu", set_count, unset_integer_vars.size()); + CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x\n", detail::compute_hash(unset_integer_vars)); update_host_assignment(sol); if (max_timer.check_time_limit()) { CUOPT_LOG_DEBUG("Second time limit is reached returning nearest rounding!"); @@ -915,6 +932,9 @@ bool constraint_prop_t::find_integer( unset_integer_vars.data() + set_count, n_vars_to_set, sol.handle_ptr->get_stream()); + + printf("host_vars_to_set hash 0x%x\n", detail::compute_hash(host_vars_to_set)); + auto var_probe_vals = generate_bulk_rounding_vector(sol, orig_sol, host_vars_to_set, probing_config); probe( @@ -951,7 +971,7 @@ bool constraint_prop_t::find_integer( make_span(orig_sol.problem_ptr->variable_upper_bounds), make_span(sol.assignment)}); i_t n_fixed_vars = (iter - (unset_vars.begin() + set_count)); - CUOPT_LOG_TRACE("After repair procedure, number of additional fixed vars %d", n_fixed_vars); + CUOPT_LOG_DEBUG("After repair procedure, number of additional fixed vars %d", n_fixed_vars); set_count += n_fixed_vars; } } diff --git a/cpp/src/mip/solution/solution.cu b/cpp/src/mip/solution/solution.cu index 3dd52d5c5..60b657992 100644 --- a/cpp/src/mip/solution/solution.cu +++ b/cpp/src/mip/solution/solution.cu @@ -615,7 +615,7 @@ mip_solution_t solution_t::get_solution(bool output_feasible } template -uint32_t solution_t::get_hash() +uint32_t solution_t::get_hash() const { return compute_hash(assignment); } diff --git a/cpp/src/mip/solution/solution.cuh b/cpp/src/mip/solution/solution.cuh index 0b0080ad6..51ee449d3 100644 --- a/cpp/src/mip/solution/solution.cuh +++ b/cpp/src/mip/solution/solution.cuh @@ -104,7 +104,7 @@ class solution_t { f_t compute_max_constraint_violation(); f_t compute_max_int_violation(); f_t compute_max_variable_violation(); - uint32_t get_hash(); + uint32_t get_hash() const; struct view_t { // let's not bloat the class for every simple getter and setters diff --git a/cpp/tests/mip/CMakeLists.txt b/cpp/tests/mip/CMakeLists.txt index 816b9a817..0052b2949 100644 --- a/cpp/tests/mip/CMakeLists.txt +++ b/cpp/tests/mip/CMakeLists.txt @@ -54,4 +54,7 @@ ConfigureTest(PRESOLVE_TEST ) ConfigureTest(LOCAL_SEARCH_TEST ${CMAKE_CURRENT_SOURCE_DIR}/local_search_test.cu +) +ConfigureTest(DIVERSITY_TEST + ${CMAKE_CURRENT_SOURCE_DIR}/diversity_test.cu ) \ No newline at end of file diff --git a/cpp/tests/mip/diversity_test.cu b/cpp/tests/mip/diversity_test.cu new file mode 100644 index 000000000..c749173f6 --- /dev/null +++ b/cpp/tests/mip/diversity_test.cu @@ -0,0 +1,194 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "../linear_programming/utilities/pdlp_test_utilities.cuh" +#include "determinism_utils.cuh" +#include "mip_utils.cuh" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +#include + +#include +#include +#include + +#include +#include +#include +#include +#include + +namespace cuopt::linear_programming::test { + +void init_handler(const raft::handle_t* handle_ptr) +{ + // Init cuBlas / cuSparse context here to avoid having it during solving time + RAFT_CUBLAS_TRY(raft::linalg::detail::cublassetpointermode( + handle_ptr->get_cublas_handle(), CUBLAS_POINTER_MODE_DEVICE, handle_ptr->get_stream())); + RAFT_CUSPARSE_TRY(raft::sparse::detail::cusparsesetpointermode( + handle_ptr->get_cusparse_handle(), CUSPARSE_POINTER_MODE_DEVICE, handle_ptr->get_stream())); +} + +static void setup_device_symbols(rmm::cuda_stream_view stream_view) +{ + raft::common::nvtx::range fun_scope("Setting device symbol"); + detail::set_adaptive_step_size_hyper_parameters(stream_view); + detail::set_restart_hyper_parameters(stream_view); + detail::set_pdlp_hyper_parameters(stream_view); +} + +static uint32_t test_initial_population_determinism(std::string path, + unsigned long seed = std::random_device{}()) +{ + const raft::handle_t handle_{}; + + cuopt::mps_parser::mps_data_model_t mps_problem = + cuopt::mps_parser::parse_mps(path, false); + handle_.sync_stream(); + auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); + problem_checking_t::check_problem_representation(op_problem); + + init_handler(op_problem.get_handle_ptr()); + // run the problem constructor of MIP, so that we do bounds standardization + detail::problem_t problem(op_problem); + problem.preprocess_problem(); + + setup_device_symbols(op_problem.get_handle_ptr()->get_stream()); + + detail::pdhg_solver_t pdhg_solver(problem.handle_ptr, problem); + detail::pdlp_initial_scaling_strategy_t scaling(&handle_, + problem, + 10, + 1.0, + pdhg_solver, + problem.reverse_coefficients, + problem.reverse_offsets, + problem.reverse_constraints, + true); + + auto settings = mip_solver_settings_t{}; + settings.time_limit = 30.; + auto timer = cuopt::timer_t(30); + detail::mip_solver_t solver(problem, settings, scaling, timer); + problem.tolerances = settings.get_tolerances(); + + detail::diversity_manager_t diversity_manager(solver.context); + diversity_manager.settings.fj_only_run = true; + // run with FJ only on the initial population, no recombining + diversity_manager.run_solver(); + + std::vector hashes; + + auto pop = diversity_manager.get_population_pointer(); + for (const auto& sol : pop->population_to_vector()) { + hashes.push_back(sol.get_hash()); + } + + return detail::compute_hash(hashes); +} + +// TEST(presolve, probing_cache_deterministic) +// { +// spin_stream_raii_t spin_stream_1; + +// std::vector test_instances = {"mip/50v-10-free-bound.mps", +// "mip/neos5-free-bound.mps", +// "mip/neos5.mps", +// "mip/50v-10.mps", +// "mip/gen-ip054.mps", +// "mip/rmatr200-p5.mps"}; +// for (const auto& test_instance : test_instances) { +// std::cout << "Running: " << test_instance << std::endl; +// unsigned long seed = std::random_device{}(); +// std::cerr << "Tested with seed " << seed << "\n"; +// auto path = make_path_absolute(test_instance); +// uint32_t gold_hash = 0; +// for (int i = 0; i < 10; ++i) { +// auto hash = test_probing_cache_determinism(path, seed); +// if (i == 0) { +// gold_hash = hash; +// std::cout << "Gold hash: " << gold_hash << std::endl; +// } else { +// EXPECT_EQ(hash, gold_hash); +// } +// } +// } +// } + +class DiversityTestParams : public testing::TestWithParam> {}; + +TEST_P(DiversityTestParams, initial_population_deterministic) +{ + cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); + + spin_stream_raii_t spin_stream_1; + spin_stream_raii_t spin_stream_2; + + auto test_instance = std::get<0>(GetParam()); + std::cout << "Running: " << test_instance << std::endl; + int seed = + std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); + cuopt::seed_generator::set_seed(seed); + std::cerr << "Tested with seed " << seed << "\n"; + auto path = make_path_absolute(test_instance); + uint32_t gold_hash = 0; + for (int i = 0; i < 2; ++i) { + std::cout << "Running " << test_instance << " " << i << std::endl; + auto hash = test_initial_population_determinism(path, seed); + if (i == 0) { + gold_hash = hash; + std::cout << "Gold hash: " << gold_hash << std::endl; + } else { + ASSERT_EQ(hash, gold_hash); + } + } +} + +INSTANTIATE_TEST_SUITE_P(DiversityTest, + DiversityTestParams, + testing::Values( + // std::make_tuple("mip/sct2.mps"), + // std::make_tuple("mip/thor50dday.mps"), + // std::make_tuple("mip/uccase9.mps"), + // std::make_tuple("mip/neos5-free-bound.mps"), + // std::make_tuple("mip/neos5.mps"), + // std::make_tuple("mip/50v-10.mps"), + // std::make_tuple("mip/rmatr200-p5.mps") + std::make_tuple("mip/gen-ip054.mps"))); + +} // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/feasibility_jump_tests.cu b/cpp/tests/mip/feasibility_jump_tests.cu index 07f625e3e..186523d27 100644 --- a/cpp/tests/mip/feasibility_jump_tests.cu +++ b/cpp/tests/mip/feasibility_jump_tests.cu @@ -223,7 +223,8 @@ static bool run_fj_check_feasible(std::string test_instance) static bool run_fj_check_determinism(std::string test_instance, int iter_limit) { - int seed = std::getenv("FJ_SEED") ? std::stoi(std::getenv("FJ_SEED")) : 42; + int seed = + std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); detail::fj_settings_t fj_settings; fj_settings.time_limit = 30.; diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu index a3ef1abed..3fd399699 100644 --- a/cpp/tests/mip/local_search_test.cu +++ b/cpp/tests/mip/local_search_test.cu @@ -204,7 +204,8 @@ static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) static uint32_t run_fp_check_determinism(std::string test_instance, local_search_mode_t mode) { - int seed = std::getenv("FJ_SEED") ? std::stoi(std::getenv("FJ_SEED")) : 42; + int seed = + std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); cuopt::seed_generator::set_seed(seed); return run_fp(test_instance, mode); diff --git a/cpp/tests/mip/presolve_test.cu b/cpp/tests/mip/presolve_test.cu index a043e2d74..6ba4edbdf 100644 --- a/cpp/tests/mip/presolve_test.cu +++ b/cpp/tests/mip/presolve_test.cu @@ -24,6 +24,7 @@ #include #include #include +#include #include #include #include @@ -171,22 +172,91 @@ uint32_t test_probing_cache_determinism(std::string path, return detail::compute_hash(hashes); } -// TEST(presolve, multi_probe) +uint32_t test_scaling_determinism(std::string path, unsigned long seed = std::random_device{}()) +{ + auto memory_resource = make_async(); + rmm::mr::set_current_device_resource(memory_resource.get()); + const raft::handle_t handle_{}; + cuopt::mps_parser::mps_data_model_t mps_problem = + cuopt::mps_parser::parse_mps(path, false); + handle_.sync_stream(); + auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); + problem_checking_t::check_problem_representation(op_problem); + detail::problem_t problem(op_problem); + + pdlp_hyper_params::update_primal_weight_on_initial_solution = false; + pdlp_hyper_params::update_step_size_on_initial_solution = true; + // problem contains unpreprocessed data + detail::problem_t scaled_problem(problem); + + detail::pdhg_solver_t pdhg_solver(scaled_problem.handle_ptr, scaled_problem); + detail::pdlp_initial_scaling_strategy_t scaling( + scaled_problem.handle_ptr, + scaled_problem, + pdlp_hyper_params::default_l_inf_ruiz_iterations, + (double)pdlp_hyper_params::default_alpha_pock_chambolle_rescaling, + pdhg_solver, + scaled_problem.reverse_coefficients, + scaled_problem.reverse_offsets, + scaled_problem.reverse_constraints, + true); + + scaling.scale_problem(); + + // generate a random initial solution in order to ensure scaling of solution vectors is + // deterministic as well as the initial step size + std::vector initial_solution(scaled_problem.n_variables); + std::mt19937 m{seed}; + std::generate(initial_solution.begin(), initial_solution.end(), [&m]() { return m(); }); + auto d_initial_solution = device_copy(initial_solution, handle_.get_stream()); + scaling.scale_primal(d_initial_solution); + + scaled_problem.preprocess_problem(); + + detail::trivial_presolve(scaled_problem); + + std::vector hashes; + hashes.push_back(detail::compute_hash(d_initial_solution, handle_.get_stream())); + hashes.push_back(scaled_problem.get_fingerprint()); + return detail::compute_hash(hashes); +} + +// TEST(presolve, probing_cache_deterministic) // { -// std::vector test_instances = { -// "mip/50v-10-free-bound.mps", "mip/neos5-free-bound.mps", "mip/neos5.mps"}; +// spin_stream_raii_t spin_stream_1; + +// std::vector test_instances = {"mip/50v-10-free-bound.mps", +// "mip/neos5-free-bound.mps", +// "mip/neos5.mps", +// "mip/50v-10.mps", +// "mip/gen-ip054.mps", +// "mip/rmatr200-p5.mps"}; // for (const auto& test_instance : test_instances) { // std::cout << "Running: " << test_instance << std::endl; -// auto path = make_path_absolute(test_instance); -// test_multi_probe(path); +// unsigned long seed = std::random_device{}(); +// std::cerr << "Tested with seed " << seed << "\n"; +// auto path = make_path_absolute(test_instance); +// uint32_t gold_hash = 0; +// for (int i = 0; i < 10; ++i) { +// auto hash = test_probing_cache_determinism(path, seed); +// if (i == 0) { +// gold_hash = hash; +// std::cout << "Gold hash: " << gold_hash << std::endl; +// } else { +// EXPECT_EQ(hash, gold_hash); +// } +// } // } // } -TEST(presolve, probing_cache_deterministic) +TEST(presolve, mip_scaling_deterministic) { spin_stream_raii_t spin_stream_1; + spin_stream_raii_t spin_stream_2; - std::vector test_instances = {"mip/50v-10-free-bound.mps", + std::vector test_instances = {"mip/sct2.mps", + "mip/thor50dday.mps", + "mip/uccase9.mps", "mip/neos5-free-bound.mps", "mip/neos5.mps", "mip/50v-10.mps", @@ -199,7 +269,7 @@ TEST(presolve, probing_cache_deterministic) auto path = make_path_absolute(test_instance); uint32_t gold_hash = 0; for (int i = 0; i < 10; ++i) { - auto hash = test_probing_cache_determinism(path, seed); + auto hash = test_scaling_determinism(path, seed); if (i == 0) { gold_hash = hash; std::cout << "Gold hash: " << gold_hash << std::endl; @@ -209,4 +279,5 @@ TEST(presolve, probing_cache_deterministic) } } } + } // namespace cuopt::linear_programming::test From db75bd6a4c716cad4ca0b76a7fe9ac33deaa12a8 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 22 Jul 2025 14:06:36 +0000 Subject: [PATCH 008/366] fixed test bug --- cpp/src/utilities/seed_generator.cuh | 12 ++++++++++++ cpp/tests/mip/diversity_test.cu | 21 ++++++++++----------- 2 files changed, 22 insertions(+), 11 deletions(-) diff --git a/cpp/src/utilities/seed_generator.cuh b/cpp/src/utilities/seed_generator.cuh index b2af26fc9..35e5f3e72 100644 --- a/cpp/src/utilities/seed_generator.cuh +++ b/cpp/src/utilities/seed_generator.cuh @@ -40,7 +40,19 @@ class seed_generator { set_seed(seed1 + ((seed0 + seed1) * (seed0 + seed1 + 1) / 2), seeds...); } +#if 0 + static int64_t get_seed(const char* caller = __builtin_FUNCTION(), + const char* file = __builtin_FILE(), + int line = __builtin_LINE()) { + + printf("&&&&&&& SEED CALLED BY %s:%d: %s() ***\n", + file, + line, + caller); + return seed_++; } +#else static int64_t get_seed() { return seed_++; } +#endif public: seed_generator(seed_generator const&) = delete; diff --git a/cpp/tests/mip/diversity_test.cu b/cpp/tests/mip/diversity_test.cu index c749173f6..7142eb16c 100644 --- a/cpp/tests/mip/diversity_test.cu +++ b/cpp/tests/mip/diversity_test.cu @@ -163,11 +163,11 @@ TEST_P(DiversityTestParams, initial_population_deterministic) std::cout << "Running: " << test_instance << std::endl; int seed = std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); - cuopt::seed_generator::set_seed(seed); std::cerr << "Tested with seed " << seed << "\n"; auto path = make_path_absolute(test_instance); uint32_t gold_hash = 0; - for (int i = 0; i < 2; ++i) { + for (int i = 0; i < 10; ++i) { + cuopt::seed_generator::set_seed(seed); std::cout << "Running " << test_instance << " " << i << std::endl; auto hash = test_initial_population_determinism(path, seed); if (i == 0) { @@ -181,14 +181,13 @@ TEST_P(DiversityTestParams, initial_population_deterministic) INSTANTIATE_TEST_SUITE_P(DiversityTest, DiversityTestParams, - testing::Values( - // std::make_tuple("mip/sct2.mps"), - // std::make_tuple("mip/thor50dday.mps"), - // std::make_tuple("mip/uccase9.mps"), - // std::make_tuple("mip/neos5-free-bound.mps"), - // std::make_tuple("mip/neos5.mps"), - // std::make_tuple("mip/50v-10.mps"), - // std::make_tuple("mip/rmatr200-p5.mps") - std::make_tuple("mip/gen-ip054.mps"))); + testing::Values(std::make_tuple("mip/sct2.mps"), + // std::make_tuple("mip/thor50dday.mps"), + // std::make_tuple("mip/uccase9.mps"), + std::make_tuple("mip/neos5-free-bound.mps"), + std::make_tuple("mip/neos5.mps"), + std::make_tuple("mip/50v-10.mps"), + std::make_tuple("mip/rmatr200-p5.mps"), + std::make_tuple("mip/gen-ip054.mps"))); } // namespace cuopt::linear_programming::test From 7315ab81a66747be7cf935c1485bbf378fc3ff8f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 7 Aug 2025 08:27:15 +0000 Subject: [PATCH 009/366] determinism progress w/ recombiners --- .../restart_strategy/pdlp_restart_strategy.cu | 2 + cpp/src/mip/diversity/diversity_config.hpp | 2 +- cpp/src/mip/diversity/diversity_manager.cu | 43 ++++++--- .../recombiners/bound_prop_recombiner.cuh | 23 ++++- .../diversity/recombiners/fp_recombiner.cuh | 11 +++ .../recombiners/line_segment_recombiner.cuh | 1 + .../mip/diversity/recombiners/recombiner.cuh | 11 +++ .../mip/feasibility_jump/feasibility_jump.cu | 2 + .../feasibility_pump/feasibility_pump.cu | 34 +++++-- cpp/src/mip/local_search/local_search.cu | 52 ++++++---- .../local_search/rounding/bounds_repair.cu | 3 +- .../local_search/rounding/constraint_prop.cu | 55 ++++++----- .../local_search/rounding/lb_bounds_repair.cu | 2 +- cpp/src/mip/presolve/bounds_presolve.cu | 4 + cpp/src/mip/problem/problem.cu | 13 ++- cpp/src/mip/relaxed_lp/relaxed_lp.cu | 4 + cpp/tests/mip/diversity_test.cu | 96 +++++++++++-------- cpp/tests/mip/miplib_test.cu | 12 ++- 18 files changed, 264 insertions(+), 106 deletions(-) diff --git a/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu b/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu index 55b06aecf..e2fe05a9a 100644 --- a/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu +++ b/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu @@ -1490,6 +1490,8 @@ void pdlp_restart_strategy_t::solve_bound_constrained_trust_region( // Perform the reduction // Convert raw pointer to thrust::device_ptr to write directly device side through reduce + // CHANGE + // use a deterministic reduce instead of thrust::reduce thrust::device_ptr thrust_hrsp(high_radius_squared_.data()); *thrust_hrsp = thrust::reduce(handle_ptr_->get_thrust_policy(), transformed_begin, diff --git a/cpp/src/mip/diversity/diversity_config.hpp b/cpp/src/mip/diversity/diversity_config.hpp index 30e7d25e4..07eaa33f8 100644 --- a/cpp/src/mip/diversity/diversity_config.hpp +++ b/cpp/src/mip/diversity/diversity_config.hpp @@ -30,7 +30,7 @@ struct diversity_config_t { static constexpr double initial_infeasibility_weight = 1000.; static constexpr double default_time_limit = 10.; static constexpr int initial_island_size = 3; - static constexpr int maximum_island_size = 8; + static constexpr int maximum_island_size = 4; // CHANGE static constexpr bool use_avg_diversity = false; static constexpr double generation_time_limit_ratio = 0.6; static constexpr double max_island_gen_time = 600; diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 617c89b60..dfcc3ed94 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -74,6 +74,13 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_t::generate_initial_solutions() diversity_config_t::generation_time_limit_ratio * timer.get_time_limit(); const f_t max_island_gen_time = diversity_config_t::max_island_gen_time; f_t total_island_gen_time = std::min(generation_time_limit, max_island_gen_time); + total_island_gen_time = std::numeric_limits::infinity(); timer_t gen_timer(total_island_gen_time); f_t sol_time_limit = gen_timer.remaining_time(); for (i_t i = 0; i < maximum_island_size && !skip_initial_island_generation; ++i) { @@ -246,8 +254,7 @@ void diversity_manager_t::generate_initial_solutions() } generate_add_solution(initial_sol_vector, sol_time_limit, !is_first_sol); if (is_first_sol && initial_sol_vector.back().get_feasible()) { - CUOPT_LOG_DEBUG("First FP/FJ solution found at %f with objective %f", - timer.elapsed_time(), + CUOPT_LOG_DEBUG("First FP/FJ solution found with objective %f", initial_sol_vector.back().get_user_objective()); } population.run_solution_callbacks(initial_sol_vector.back()); @@ -308,8 +315,11 @@ void diversity_manager_t::generate_quick_feasible_solution() { solution_t solution(*problem_ptr); // min 1 second, max 10 seconds - const f_t generate_fast_solution_time = + f_t generate_fast_solution_time = std::min(diversity_config_t::max_fast_sol_time, std::max(1., timer.remaining_time() / 20.)); + + // CHANGE + generate_fast_solution_time = 10; timer_t sol_timer(generate_fast_solution_time); // do very short LP run to get somewhere close to the optimal point ls.generate_fast_solution(solution, sol_timer); @@ -324,9 +334,7 @@ void diversity_manager_t::generate_quick_feasible_solution() auto& feas_sol = initial_sol_vector.back().get_feasible() ? initial_sol_vector.back() : initial_sol_vector[initial_sol_vector.size() - 2]; - CUOPT_LOG_INFO("Generated fast solution in %f seconds with objective %f", - timer.elapsed_time(), - feas_sol.get_user_objective()); + CUOPT_LOG_INFO("Generated fast solution with objective %f", feas_sol.get_user_objective()); } problem_ptr->handle_ptr->sync_stream(); } @@ -358,6 +366,8 @@ void diversity_manager_t::run_fj_alone(solution_t& solution) ls.fj.settings.termination = fj_termination_flags_t::FJ_TERMINATION_ITERATION_LIMIT; ls.fj.settings.iteration_limit = 10000; ls.fj.settings.time_limit = timer.remaining_time(); + // CHANGE + ls.fj.settings.time_limit = std::numeric_limits::infinity(); // ls.fj.solve(solution); CUOPT_LOG_INFO("FJ alone finished!"); } @@ -366,10 +376,13 @@ void diversity_manager_t::run_fj_alone(solution_t& solution) template solution_t diversity_manager_t::run_solver() { - population.timer = timer; - const f_t time_limit = timer.remaining_time(); - const f_t lp_time_limit = std::min(diversity_config_t::max_time_on_lp, - time_limit * diversity_config_t::time_ratio_on_init_lp); + population.timer = timer; + const f_t time_limit = timer.remaining_time(); + f_t lp_time_limit = std::min(diversity_config_t::max_time_on_lp, + time_limit * diversity_config_t::time_ratio_on_init_lp); + + // CHANGE + lp_time_limit = 600; // to automatically compute the solving time on scope exit auto timer_raii_guard = cuopt::scope_guard([&]() { stats.total_solve_time = timer.elapsed_time(); }); @@ -412,10 +425,12 @@ solution_t diversity_manager_t::run_solver() lp_state.resize(*problem_ptr, problem_ptr->handle_ptr->get_stream()); relaxed_lp_settings_t lp_settings; lp_settings.time_limit = lp_time_limit; + lp_settings.iteration_limit = std::numeric_limits::max(); lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; lp_settings.return_first_feasible = false; lp_settings.save_state = true; - if (!settings.fj_only_run) { + if (!settings.fj_only_run || true) { // CHANGE + CUOPT_LOG_DEBUG("Running root relaxation LP"); auto lp_result = get_relaxed_lp_solution(*problem_ptr, lp_optimal_solution, lp_state, lp_settings); ls.lp_optimal_exists = true; @@ -427,9 +442,10 @@ solution_t diversity_manager_t::run_solver() } else if (lp_result.get_termination_status() == pdlp_termination_status_t::DualInfeasible) { CUOPT_LOG_ERROR("PDLP detected dual infeasibility, continuing anyway!"); ls.lp_optimal_exists = false; - } else if (lp_result.get_termination_status() == pdlp_termination_status_t::TimeLimit) { + } else if (lp_result.get_termination_status() == pdlp_termination_status_t::TimeLimit || + lp_result.get_termination_status() == pdlp_termination_status_t::IterationLimit) { CUOPT_LOG_DEBUG( - "Initial LP run exceeded time limit, continuing solver with partial LP result!"); + "Initial LP run exceeded time/iteration limit, continuing solver with partial LP result!"); // note to developer, in debug mode the LP run might be too slow and it might cause PDLP not // to bring variables within the bounds } @@ -705,6 +721,7 @@ template std::pair, bool> diversity_manager_t::recombine( solution_t& a, solution_t& b) { + CUOPT_LOG_DEBUG("Recombining %d and %d", a.get_hash(), b.get_hash()); i_t recombiner; if (run_only_ls_recombiner) { recombiner = recombiner_enum_t::LINE_SEGMENT; diff --git a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh index f38cc5759..9ac6e3705 100644 --- a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh @@ -38,6 +38,7 @@ class bound_prop_recombiner_t : public recombiner_t { rng(cuopt::seed_generator::get_seed()), vars_to_fix(n_vars, handle_ptr->get_stream()) { + thrust::fill(handle_ptr->get_thrust_policy(), vars_to_fix.begin(), vars_to_fix.end(), -1); } void get_probing_values_for_infeasible( @@ -203,16 +204,34 @@ class bound_prop_recombiner_t : public recombiner_t { constraint_prop.single_rounding_only = true; constraint_prop.apply_round(offspring, lp_run_time_after_feasible, timer, probing_config); constraint_prop.single_rounding_only = false; + offspring.compute_feasibility(); cuopt_func_call(bool feasible_after_bounds_prop = offspring.get_feasible()); + cuopt_func_call(f_t excess_before = offspring.get_total_excess()); + CUOPT_LOG_ERROR("Excess before: %g, %g, %g, %g, feas %d", + offspring.get_total_excess(), + offspring.compute_max_constraint_violation(), + offspring.compute_max_int_violation(), + offspring.compute_max_variable_violation(), + feasible_after_bounds_prop); offspring.handle_ptr->sync_stream(); offspring.problem_ptr = a.problem_ptr; fixed_assignment = std::move(offspring.assignment); offspring.assignment = std::move(old_assignment); offspring.handle_ptr->sync_stream(); offspring.unfix_variables(fixed_assignment, variable_map); + offspring.compute_feasibility(); cuopt_func_call(bool feasible_after_unfix = offspring.get_feasible()); - cuopt_assert(feasible_after_unfix == feasible_after_bounds_prop, - "Feasible after unfix should be same as feasible after bounds prop!"); + cuopt_func_call(f_t excess_after_unfix = offspring.get_total_excess()); + if (feasible_after_unfix != feasible_after_bounds_prop) { + CUOPT_LOG_WARN("Numerical issue in bounds prop, infeasibility after unfix"); + // might become infeasible after unfixing due to numerical issues. Check that the excess + // remains consistent + // CUOPT_LOG_ERROR("Excess: %g, %g, %g, %g, feas %d", offspring.get_total_excess(), + // offspring.compute_max_constraint_violation(), offspring.compute_max_int_violation(), + // offspring.compute_max_variable_violation(), feasible_after_unfix); + cuopt_assert(fabs(excess_after_unfix - excess_before) < 1e-6, + "Excess after unfix should be same as before unfix!"); + } a.handle_ptr->sync_stream(); } else { timer_t timer(bp_recombiner_config_t::bounds_prop_time_limit); diff --git a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh index 5597e8e84..c161504e6 100644 --- a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh @@ -57,6 +57,7 @@ class fp_recombiner_t : public recombiner_t { CUOPT_LOG_DEBUG("FP rec: Number of different variables %d MAX_VARS %d", n_different_vars, fp_recombiner_config_t::max_n_of_vars_from_other); + CUOPT_LOG_DEBUG("FP rec: offspring hash 0x%x", offspring.get_hash()); i_t n_vars_from_other = n_different_vars; if (n_vars_from_other > (i_t)fp_recombiner_config_t::max_n_of_vars_from_other) { n_vars_from_other = fp_recombiner_config_t::max_n_of_vars_from_other; @@ -73,10 +74,20 @@ class fp_recombiner_t : public recombiner_t { } CUOPT_LOG_DEBUG( "n_vars_from_guiding %d n_vars_from_other %d", n_vars_from_guiding, n_vars_from_other); + CUOPT_LOG_DEBUG("FP rec: offspring hash 0x%x, vars to fix 0x%x", + offspring.get_hash(), + detail::compute_hash(vars_to_fix)); this->compute_vars_to_fix(offspring, vars_to_fix, n_vars_from_other, n_vars_from_guiding); + CUOPT_LOG_DEBUG("FP rec post computevarstofix: offspring hash 0x%x, vars to fix 0x%x", + offspring.get_hash(), + detail::compute_hash(vars_to_fix)); auto [fixed_problem, fixed_assignment, variable_map] = offspring.fix_variables(vars_to_fix); + CUOPT_LOG_DEBUG("FP rec: fixed_problem hash 0x%x assigned hash 0x%x", + fixed_problem.get_fingerprint(), + detail::compute_hash(fixed_assignment)); fixed_problem.check_problem_representation(true); if (!guiding_solution.get_feasible() && !other_solution.get_feasible()) { + CUOPT_LOG_DEBUG("FP rec: running LP with infeasibility detection"); relaxed_lp_settings_t lp_settings; lp_settings.time_limit = fp_recombiner_config_t::infeasibility_detection_time_limit; lp_settings.tolerance = fixed_problem.tolerances.absolute_tolerance; diff --git a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh index cfde6d03c..fd2988d98 100644 --- a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh @@ -80,6 +80,7 @@ class line_segment_recombiner_t : public recombiner_t { const weight_t& weights) { raft::common::nvtx::range fun_scope("line_segment_recombiner"); + CUOPT_LOG_DEBUG("LS rec: a %d b %d", a.get_hash(), b.get_hash()); auto& guiding_solution = a.get_feasible() ? a : b; auto& other_solution = a.get_feasible() ? b : a; // copy the solution from A diff --git a/cpp/src/mip/diversity/recombiners/recombiner.cuh b/cpp/src/mip/diversity/recombiners/recombiner.cuh index e4a86b491..d6dc4dabf 100644 --- a/cpp/src/mip/diversity/recombiners/recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/recombiner.cuh @@ -99,6 +99,14 @@ class recombiner_t { cuopt::make_span(remaining_indices), n_remaining.data()); i_t remaining_variables = this->n_remaining.value(a.handle_ptr->get_stream()); + // Sort the indices to resolve nondeterministic order due to atomicAdd + thrust::sort(a.handle_ptr->get_thrust_policy(), + this->remaining_indices.data(), + this->remaining_indices.data() + remaining_variables); + + CUOPT_LOG_DEBUG("remaining indices hash 0x%x, size %d", + detail::compute_hash(this->remaining_indices), + remaining_variables); auto vec_remaining_indices = host_copy(this->remaining_indices.data(), remaining_variables, a.handle_ptr->get_stream()); @@ -177,6 +185,9 @@ class recombiner_t { i_t n_vars_from_guiding) { vars_to_fix.resize(n_vars_from_guiding, offspring.handle_ptr->get_stream()); + CUOPT_LOG_DEBUG("remaining indices hash 0x%x", detail::compute_hash(this->remaining_indices)); + CUOPT_LOG_DEBUG("integer_indices hash 0x%x", + detail::compute_hash(offspring.problem_ptr->integer_indices)); // set difference needs two sorted arrays thrust::sort(offspring.handle_ptr->get_thrust_policy(), this->remaining_indices.data(), diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 37567dd7d..6d1c55443 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -1157,6 +1157,8 @@ i_t fj_t::solve(solution_t& solution) cuopt_assert(false, "Feasibility jump caused feasible solution to become infeasible"); } + cuopt_func_call(solution.test_variable_bounds()); + return is_new_feasible; } diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index d18d29a5a..7789fcbff 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -67,6 +67,10 @@ feasibility_pump_t::feasibility_pump_t( rng(cuopt::seed_generator::get_seed()), timer(20.) { + thrust::fill(context.problem_ptr->handle_ptr->get_thrust_policy(), + last_projection.begin(), + last_projection.end(), + (f_t)0); } template @@ -146,7 +150,7 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_tvariable_upper_bounds, solution.handle_ptr->get_stream()); @@ -162,6 +166,13 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_tinteger_indices, solution.handle_ptr->get_stream()); f_t obj_offset = 0; + CUOPT_LOG_DEBUG("FP: h_integer_indices hash 0x%x", detail::compute_hash(h_integer_indices)); + CUOPT_LOG_DEBUG("FP: h_assignment hash 0x%x", detail::compute_hash(h_assignment)); + CUOPT_LOG_DEBUG("FP: h_variable_upper_bounds hash 0x%x", + detail::compute_hash(h_variable_upper_bounds)); + CUOPT_LOG_DEBUG("FP: h_variable_lower_bounds hash 0x%x", + detail::compute_hash(h_variable_lower_bounds)); + CUOPT_LOG_DEBUG("FP: h_last_projection hash 0x%x", detail::compute_hash(h_last_projection)); // for each integer add the variable and the distance constraints for (auto i : h_integer_indices) { if (solution.problem_ptr->integer_equal(h_assignment[i], h_variable_upper_bounds[i])) { @@ -198,6 +209,7 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_t 0) { temp_p.insert_variables(h_variables); } @@ -231,12 +243,17 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_tn_variables, solution.handle_ptr->get_stream()); raft::copy(last_projection.data(), solution.assignment.data(), @@ -286,6 +303,7 @@ void feasibility_pump_t::perturbate(solution_t& solution) template bool feasibility_pump_t::run_fj_cycle_escape(solution_t& solution) { + CUOPT_LOG_DEBUG("Running FJ cycle escape"); bool is_feasible; fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; fj.settings.update_weights = true; @@ -308,14 +326,16 @@ bool feasibility_pump_t::run_fj_cycle_escape(solution_t& sol template bool feasibility_pump_t::test_fj_feasible(solution_t& solution, f_t time_limit) { - CUOPT_LOG_DEBUG("Running 20%% with %f time limit", time_limit); + CUOPT_LOG_DEBUG("Running 20%% FJ"); bool is_feasible; fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; fj.settings.update_weights = true; fj.settings.feasibility_run = true; fj.settings.n_of_minimums_for_exit = 5000; fj.settings.time_limit = std::min(time_limit, timer.remaining_time()); - fj.settings.termination = fj_termination_flags_t::FJ_TERMINATION_TIME_LIMIT; + // CHANGE + fj.settings.time_limit = std::numeric_limits::infinity(); + fj.settings.termination = fj_termination_flags_t::FJ_TERMINATION_TIME_LIMIT; cuopt_func_call(solution.test_variable_bounds(true)); is_feasible = fj.solve(solution); cuopt_func_call(solution.test_variable_bounds(true)); @@ -497,6 +517,8 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s solution.assignment.data(), solution.assignment.size(), solution.handle_ptr->get_stream()); + + CUOPT_LOG_DEBUG("FP: starting FP descent, sol hash 0x%x", solution.get_hash()); while (true) { if (timer.check_time_limit()) { CUOPT_LOG_DEBUG("FP time limit reached!"); @@ -529,7 +551,7 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s cuopt::default_logger().flush(); f_t remaining_time_end_fp = timer.remaining_time(); total_fp_time_until_cycle = fp_fj_cycle_time_begin - remaining_time_end_fp; - CUOPT_LOG_DEBUG("total_fp_time_until_cycle: %f", total_fp_time_until_cycle); + // CUOPT_LOG_DEBUG("total_fp_time_until_cycle: %f", total_fp_time_until_cycle); return false; } } diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index efda50647..7a087d0f8 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -56,6 +56,7 @@ local_search_t::local_search_t(mip_solver_context_t& context template void local_search_t::generate_fast_solution(solution_t& solution, timer_t timer) { + CUOPT_LOG_DEBUG("Running FJ fast sol"); thrust::fill(solution.handle_ptr->get_thrust_policy(), solution.assignment.begin(), solution.assignment.end(), @@ -67,6 +68,8 @@ void local_search_t::generate_fast_solution(solution_t& solu fj.settings.feasibility_run = true; fj.settings.termination = fj_termination_flags_t::FJ_TERMINATION_TIME_LIMIT; fj.settings.time_limit = std::min(30., timer.remaining_time()); + // CHANGE + fj.settings.time_limit = std::numeric_limits::infinity(); while (!timer.check_time_limit()) { timer_t constr_prop_timer = timer_t(std::min(timer.remaining_time(), 2.)); // do constraint prop on lp optimal solution @@ -74,6 +77,9 @@ void local_search_t::generate_fast_solution(solution_t& solu if (solution.compute_feasibility()) { return; } if (timer.check_time_limit()) { return; }; fj.settings.time_limit = std::min(3., timer.remaining_time()); + // CHANGE + fj.settings.time_limit = std::numeric_limits::infinity(); + fj.settings.iteration_limit = 5000; // run fj on the solution fj.solve(solution); // TODO check if FJ returns the same solution @@ -91,19 +97,20 @@ bool local_search_t::run_local_search(solution_t& solution, { raft::common::nvtx::range fun_scope("local search"); fj_settings_t fj_settings; - if (timer.check_time_limit()) return false; - // adjust these time limits - if (!solution.get_feasible()) { - if (at_least_one_parent_feasible) { - fj_settings.time_limit = 1.; - timer = timer_t(1.); - } else { - fj_settings.time_limit = 0.5; - timer = timer_t(0.5); - } - } else { - fj_settings.time_limit = timer.remaining_time(); - } + // if (timer.check_time_limit()) return false; + // // adjust these time limits + // if (!solution.get_feasible()) { + // if (at_least_one_parent_feasible) { + // fj_settings.time_limit = 1.; + // timer = timer_t(1.); + // } else { + // fj_settings.time_limit = 0.5; + // timer = timer_t(0.5); + // } + // } else { + // fj_settings.time_limit = timer.remaining_time(); + // } + fj_settings.iteration_limit = 2500; fj_settings.update_weights = false; fj_settings.feasibility_run = false; fj.set_fj_settings(fj_settings); @@ -123,6 +130,7 @@ bool local_search_t::run_fj_until_timer(solution_t& solution const weight_t& weights, timer_t timer) { + CUOPT_LOG_DEBUG("Running FJ until timer"); bool is_feasible; fj.settings.n_of_minimums_for_exit = 1e6; fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; @@ -143,6 +151,8 @@ bool local_search_t::run_fj_annealing(solution_t& solution, timer_t timer, f_t baseline_objective) { + CUOPT_LOG_DEBUG("Running FJ annealing"); + auto prev_settings = fj.settings; // run in FEASIBLE_FIRST to priorize feasibility-improving moves @@ -182,6 +192,7 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu bool perturb, timer_t timer) { + CUOPT_LOG_DEBUG("Running FJ on LP optimal"); raft::copy(solution.assignment.data(), lp_optimal_solution.data(), solution.assignment.size(), @@ -197,12 +208,17 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu bool is_feasible = constraint_prop.apply_round(solution, lp_run_time_after_feasible, bounds_prop_timer); if (!is_feasible) { - const f_t lp_run_time = 2.; + f_t lp_run_time = 2.; + // CHANGE + lp_run_time = 600; relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = lp_run_time; - lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; + lp_settings.time_limit = lp_run_time; + lp_settings.iteration_limit = 13000; // CHANGE + lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; + CUOPT_LOG_DEBUG("FJ ON LP OPT: lp_time_limit %f", lp_settings.time_limit); run_lp_with_vars_fixed( *solution.problem_ptr, solution, solution.problem_ptr->integer_indices, lp_settings); + CUOPT_LOG_DEBUG("FJ ON LP OPT: exited", lp_settings.time_limit); } else { return is_feasible; } @@ -213,6 +229,9 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu fj.settings.feasibility_run = true; fj.settings.termination = fj_termination_flags_t::FJ_TERMINATION_TIME_LIMIT; fj.settings.time_limit = std::min(30., timer.remaining_time()); + // CHANGE + fj.settings.time_limit = 600; // CHANGE + fj.settings.iteration_limit = 50000; // CHANGE fj.solve(solution); return solution.get_feasible(); } @@ -220,6 +239,7 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu template bool local_search_t::run_fj_on_zero(solution_t& solution, timer_t timer) { + CUOPT_LOG_DEBUG("Running FJ on zero"); thrust::fill(solution.handle_ptr->get_thrust_policy(), solution.assignment.begin(), solution.assignment.end(), diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index 4b9c0ca50..928250d2d 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -396,7 +396,8 @@ bool bounds_repair_t::repair_problem(problem_t& problem, curr_violation = best_violation; best_bounds.update_from(problem, handle_ptr); i_t no_candidate_in_a_row = 0; - while (h_n_violated_cstr > 0) { + i_t iter_limit = 20; + while (h_n_violated_cstr > 0 && iter_limit-- > 0) { CUOPT_LOG_TRACE("Bounds repair loop: n_violated %d best_violation %f curr_violation %f", h_n_violated_cstr, best_violation, diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 4661f0906..ff56cae72 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -754,6 +754,10 @@ bool constraint_prop_t::run_repair_procedure(problem_t& prob const raft::handle_t* handle_ptr) { CUOPT_LOG_DEBUG("Running repair procedure"); + + // CHANGE + timer = timer_t(std::numeric_limits::infinity()); + // select the first probing value i_t select = 0; multi_probe.set_updated_bounds(problem, select, handle_ptr); @@ -761,9 +765,10 @@ bool constraint_prop_t::run_repair_procedure(problem_t& prob repair_stats.repair_attempts++; f_t repair_start_time = timer.remaining_time(); i_t n_of_repairs_needed_for_feasible = 0; + i_t iter_limit = 100; do { n_of_repairs_needed_for_feasible++; - if (timer.check_time_limit()) { + if (timer.check_time_limit() || iter_limit-- <= 0) { CUOPT_LOG_DEBUG("Time limit is reached in repair loop!"); f_t repair_end_time = timer.remaining_time(); repair_stats.total_time_spent_on_repair += repair_start_time - repair_end_time; @@ -831,6 +836,10 @@ bool constraint_prop_t::find_integer( i_t seed = cuopt::seed_generator::get_seed(); CUOPT_LOG_DEBUG("seed 0x%x", seed); std::mt19937 rng(seed); + + // CHANGE + timer = timer_t(std::numeric_limits::infinity()); + lb_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); ub_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); assignment_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); @@ -881,23 +890,23 @@ bool constraint_prop_t::find_integer( } // do the sort if the problem is not ii. crossing bounds might cause some issues on the sort order else { - CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x, sol 0x%x before sort\n", - detail::compute_hash(unset_integer_vars), - sol.get_hash()); + // CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x, sol 0x%x before sort\n", + // detail::compute_hash(unset_integer_vars), + // sol.get_hash()); // this is a sort to have initial shuffling, so that stable sort within will keep the order and // some randomness will be achieved sort_by_interval_and_frac(sol, make_span(unset_integer_vars), rng); - CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x sol 0x%x after sort\n", - detail::compute_hash(unset_integer_vars), - sol.get_hash()); + // CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x sol 0x%x after sort\n", + // detail::compute_hash(unset_integer_vars), + // sol.get_hash()); } set_host_bounds(sol); size_t set_count = 0; bool timeout_happened = false; i_t n_failed_repair_iterations = 0; while (set_count < unset_integer_vars.size()) { - CUOPT_LOG_DEBUG("n_set_vars %d vars to set %lu", set_count, unset_integer_vars.size()); - CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x\n", detail::compute_hash(unset_integer_vars)); + // CUOPT_LOG_DEBUG("n_set_vars %d vars to set %lu", set_count, unset_integer_vars.size()); + // CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x\n", detail::compute_hash(unset_integer_vars)); update_host_assignment(sol); if (max_timer.check_time_limit()) { CUOPT_LOG_DEBUG("Second time limit is reached returning nearest rounding!"); @@ -933,7 +942,7 @@ bool constraint_prop_t::find_integer( n_vars_to_set, sol.handle_ptr->get_stream()); - printf("host_vars_to_set hash 0x%x\n", detail::compute_hash(host_vars_to_set)); + // printf("host_vars_to_set hash 0x%x\n", detail::compute_hash(host_vars_to_set)); auto var_probe_vals = generate_bulk_rounding_vector(sol, orig_sol, host_vars_to_set, probing_config); @@ -1012,7 +1021,9 @@ bool constraint_prop_t::find_integer( multi_probe.infeas_constraints_count_1 == 0) && !timeout_happened) { relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = lp_run_time_after_feasible; + lp_settings.time_limit = lp_run_time_after_feasible; + // CHANGE + lp_settings.time_limit = 600; lp_settings.tolerance = orig_sol.problem_ptr->tolerances.absolute_tolerance; lp_settings.save_state = false; lp_settings.return_first_feasible = true; @@ -1056,17 +1067,17 @@ bool constraint_prop_t::apply_round( f_t bounds_prop_end_time = max_timer.remaining_time(); repair_stats.total_time_spent_on_bounds_prop += bounds_prop_start_time - bounds_prop_end_time; - CUOPT_LOG_DEBUG( - "repair_success %lu repair_attempts %lu intermediate_repair_success %lu total_repair_loops %lu " - "total_time_spent_on_repair %f total_time_spent_bounds_prop_after_repair %f " - "total_time_spent_on_bounds_prop %f", - repair_stats.repair_success, - repair_stats.repair_attempts, - repair_stats.intermediate_repair_success, - repair_stats.total_repair_loops, - repair_stats.total_time_spent_on_repair, - repair_stats.total_time_spent_bounds_prop_after_repair, - repair_stats.total_time_spent_on_bounds_prop); + // CUOPT_LOG_DEBUG( + // "repair_success %lu repair_attempts %lu intermediate_repair_success %lu total_repair_loops + // %lu " "total_time_spent_on_repair %f total_time_spent_bounds_prop_after_repair %f " + // "total_time_spent_on_bounds_prop %f", + // repair_stats.repair_success, + // repair_stats.repair_attempts, + // repair_stats.intermediate_repair_success, + // repair_stats.total_repair_loops, + // repair_stats.total_time_spent_on_repair, + // repair_stats.total_time_spent_bounds_prop_after_repair, + // repair_stats.total_time_spent_on_bounds_prop); if (!sol_found) { sol.compute_feasibility(); return false; diff --git a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu index 50f6cdf00..4844bfaeb 100644 --- a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu @@ -410,7 +410,7 @@ bool lb_bounds_repair_t::repair_problem( timer_t timer_, const raft::handle_t* handle_ptr_) { - CUOPT_LOG_DEBUG("Running bounds repair"); + CUOPT_LOG_DEBUG("LB Running bounds repair"); handle_ptr = handle_ptr_; timer = timer_; resize(*problem); diff --git a/cpp/src/mip/presolve/bounds_presolve.cu b/cpp/src/mip/presolve/bounds_presolve.cu index 45fee622e..b4d3a9b04 100644 --- a/cpp/src/mip/presolve/bounds_presolve.cu +++ b/cpp/src/mip/presolve/bounds_presolve.cu @@ -181,6 +181,10 @@ termination_criterion_t bound_presolve_t::bound_update_loop(problem_t< { termination_criterion_t criteria = termination_criterion_t::ITERATION_LIMIT; + // CHANGE + timer = timer_t(std::numeric_limits::infinity()); + settings.iteration_limit = std::min(settings.iteration_limit, 50); + i_t iter; upd.init_changed_constraints(pb.handle_ptr); for (iter = 0; iter < settings.iteration_limit; ++iter) { diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 06ae72596..1199c1ee7 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1169,6 +1169,9 @@ problem_t problem_t::get_problem_after_fixing_vars( RAFT_CHECK_CUDA(handle_ptr->get_stream()); cuopt_assert(result_end - variable_map.data() == variable_map.size(), "Size issue in set_difference"); + CUOPT_LOG_DEBUG("Fixing assignment hash 0x%x, vars to fix 0x%x", + detail::compute_hash(assignment), + detail::compute_hash(variables_to_fix)); problem.fix_given_variables(*this, assignment, variables_to_fix, handle_ptr); RAFT_CHECK_CUDA(handle_ptr->get_stream()); problem.remove_given_variables(*this, assignment, variable_map, handle_ptr); @@ -1193,11 +1196,11 @@ problem_t problem_t::get_problem_after_fixing_vars( static int total_calls = 0; total_time_taken += time_taken; total_calls++; - CUOPT_LOG_DEBUG( - "Time taken to fix variables: %f milliseconds, average: %f milliseconds total time: %f", - time_taken, - total_time_taken / total_calls, - total_time_taken); + // CUOPT_LOG_DEBUG( + // "Time taken to fix variables: %f milliseconds, average: %f milliseconds total time: %f", + // time_taken, + // total_time_taken / total_calls, + // total_time_taken); CUOPT_LOG_DEBUG("Model fingerprint after fixing: 0x%x", problem.get_fingerprint()); return problem; } diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index 9119f924a..e15cc9938 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -89,7 +89,11 @@ optimization_problem_solution_t get_relaxed_lp_solution( // temporarily add timer auto start_time = std::chrono::high_resolution_clock::now(); lp_solver.set_inside_mip(true); + CUOPT_LOG_DEBUG("prev primal hash 0x%x", detail::compute_hash(assignment)); + CUOPT_LOG_DEBUG("prev dual hash 0x%x", detail::compute_hash(lp_state.prev_dual)); auto solver_response = lp_solver.run_solver(start_time); + CUOPT_LOG_DEBUG("post LP primal hash 0x%x", + detail::compute_hash(solver_response.get_primal_solution())); if (solver_response.get_primal_solution().size() != 0 && solver_response.get_dual_solution().size() != 0 && settings.save_state) { diff --git a/cpp/tests/mip/diversity_test.cu b/cpp/tests/mip/diversity_test.cu index 7142eb16c..2d31580b2 100644 --- a/cpp/tests/mip/diversity_test.cu +++ b/cpp/tests/mip/diversity_test.cu @@ -112,44 +112,59 @@ static uint32_t test_initial_population_determinism(std::string path, // run with FJ only on the initial population, no recombining diversity_manager.run_solver(); + // recombine a few solutions, observe the output + auto pop_vector = diversity_manager.get_population_pointer()->population_to_vector(); + // necessary because .resize() complains about constructors (since it may be used to grow the + // vector as well) + while (pop_vector.size() > 9) { + pop_vector.pop_back(); + } + std::vector hashes; + static std::map, uint32_t> hash_map; + + // diversity_manager.run_only_ls_recombiner = true; + diversity_manager.timer = timer_t(60000); + // diversity_manager.recombine_and_ls_with_all(pop_vector); + for (int i = 1; i < (int)pop_vector.size(); i++) { + for (int j = i + 1; j < (int)pop_vector.size(); j++) { + printf("recombining %d and %d\n", i, j); + auto [offspring, success] = diversity_manager.recombine(pop_vector[i], pop_vector[j]); + auto offspring_hash = offspring.get_hash(); + printf("for %d,%d: offspring hash: 0x%x, parent 1 hash: 0x%x, parent 2 hash: 0x%x\n", + i, + j, + offspring_hash, + pop_vector[i].get_hash(), + pop_vector[j].get_hash()); + if (hash_map.find(std::make_pair(i, j)) == hash_map.end()) { + hash_map[std::make_pair(i, j)] = offspring_hash; + } else { + if (hash_map[std::make_pair(i, j)] != offspring_hash) { + printf("hash mismatch for %d,%d: %d != %d\n", + i, + j, + hash_map[std::make_pair(i, j)], + offspring_hash); + exit(1); + } + } + hashes.push_back(offspring_hash); + } + } + return detail::compute_hash(hashes); + auto pop = diversity_manager.get_population_pointer(); for (const auto& sol : pop->population_to_vector()) { hashes.push_back(sol.get_hash()); } - return detail::compute_hash(hashes); + uint32_t final_hash = detail::compute_hash(hashes); + printf("final hash: 0x%x, pop size %d\n", final_hash, (int)pop->population_to_vector().size()); + return final_hash; } -// TEST(presolve, probing_cache_deterministic) -// { -// spin_stream_raii_t spin_stream_1; - -// std::vector test_instances = {"mip/50v-10-free-bound.mps", -// "mip/neos5-free-bound.mps", -// "mip/neos5.mps", -// "mip/50v-10.mps", -// "mip/gen-ip054.mps", -// "mip/rmatr200-p5.mps"}; -// for (const auto& test_instance : test_instances) { -// std::cout << "Running: " << test_instance << std::endl; -// unsigned long seed = std::random_device{}(); -// std::cerr << "Tested with seed " << seed << "\n"; -// auto path = make_path_absolute(test_instance); -// uint32_t gold_hash = 0; -// for (int i = 0; i < 10; ++i) { -// auto hash = test_probing_cache_determinism(path, seed); -// if (i == 0) { -// gold_hash = hash; -// std::cout << "Gold hash: " << gold_hash << std::endl; -// } else { -// EXPECT_EQ(hash, gold_hash); -// } -// } -// } -// } - class DiversityTestParams : public testing::TestWithParam> {}; TEST_P(DiversityTestParams, initial_population_deterministic) @@ -164,11 +179,14 @@ TEST_P(DiversityTestParams, initial_population_deterministic) int seed = std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); std::cerr << "Tested with seed " << seed << "\n"; - auto path = make_path_absolute(test_instance); + auto path = make_path_absolute(test_instance); + test_instance = std::getenv("CUOPT_INSTANCE") ? std::getenv("CUOPT_INSTANCE") : test_instance; + path = "/home/scratch.yboucher_gpu_1/collection/" + test_instance; uint32_t gold_hash = 0; for (int i = 0; i < 10; ++i) { cuopt::seed_generator::set_seed(seed); std::cout << "Running " << test_instance << " " << i << std::endl; + std::cout << "-------------------------------------------------------------\n"; auto hash = test_initial_population_determinism(path, seed); if (i == 0) { gold_hash = hash; @@ -181,13 +199,15 @@ TEST_P(DiversityTestParams, initial_population_deterministic) INSTANTIATE_TEST_SUITE_P(DiversityTest, DiversityTestParams, - testing::Values(std::make_tuple("mip/sct2.mps"), - // std::make_tuple("mip/thor50dday.mps"), - // std::make_tuple("mip/uccase9.mps"), - std::make_tuple("mip/neos5-free-bound.mps"), - std::make_tuple("mip/neos5.mps"), - std::make_tuple("mip/50v-10.mps"), - std::make_tuple("mip/rmatr200-p5.mps"), - std::make_tuple("mip/gen-ip054.mps"))); + testing::Values(std::make_tuple("fastxgemm-n2r6s0t2.mps") + // std::make_tuple("mip/sct2.mps") + // std::make_tuple("mip/thor50dday.mps") + // std::make_tuple("mip/uccase9.mps") + // std::make_tuple("mip/neos5-free-bound.mps") + // std::make_tuple("mip/neos5.mps") + // std::make_tuple("mip/50v-10.mps") + // std::make_tuple("mip/rmatr200-p5.mps") + )); +// std::make_tuple("mip/gen-ip054.mps"))); } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/miplib_test.cu b/cpp/tests/mip/miplib_test.cu index 603fe6014..1f24f2371 100644 --- a/cpp/tests/mip/miplib_test.cu +++ b/cpp/tests/mip/miplib_test.cu @@ -40,7 +40,7 @@ struct result_map_t { double cost; }; -void test_miplib_file(result_map_t test_instance) +void test_miplib_file(result_map_t test_instance, bool heuristic = false) { const raft::handle_t handle_{}; @@ -57,6 +57,7 @@ void test_miplib_file(result_map_t test_instance) #endif settings.time_limit = test_time_limit; + settings.heuristics_only = heuristic; mip_solution_t solution = solve_mip(&handle_, problem, settings); EXPECT_EQ(solution.get_termination_status(), mip_termination_status_t::FeasibleFound); double obj_val = solution.get_objective_value(); @@ -75,4 +76,13 @@ TEST(mip_solve, run_small_tests) } } +// TEST(mip_solve, run_small_tests_determinism) +// { +// std::vector test_instances = { +// {"mip/50v-10.mps", 11311031.}, {"mip/neos5.mps", 15.}, {"mip/swath1.mps", 1300.}}; +// for (const auto& test_instance : test_instances) { +// test_miplib_file(test_instance, true); +// } +// } + } // namespace cuopt::linear_programming::test From 8cbc37a5da9ad91dd11b487af221f29dd2470742 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 27 Oct 2025 15:52:08 +0000 Subject: [PATCH 010/366] add determinism setting --- cpp/include/cuopt/linear_programming/mip/solver_settings.hpp | 5 +++-- cpp/src/math_optimization/solver_settings.cu | 1 + 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp index 1750f2a03..8e49f8013 100644 --- a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp +++ b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp @@ -98,8 +98,9 @@ class mip_solver_settings_t { /** Initial primal solutions */ std::vector>> initial_solutions; - bool mip_scaling = true; - bool presolve = true; + bool mip_scaling = true; + bool presolve = true; + bool deterministic = false; // this is for extracting info from different places of the solver during // benchmarks benchmark_info_t* benchmark_info_ptr = nullptr; diff --git a/cpp/src/math_optimization/solver_settings.cu b/cpp/src/math_optimization/solver_settings.cu index 42faaae94..be13894bd 100644 --- a/cpp/src/math_optimization/solver_settings.cu +++ b/cpp/src/math_optimization/solver_settings.cu @@ -116,6 +116,7 @@ solver_settings_t::solver_settings_t() : pdlp_settings(), mip_settings {CUOPT_PRESOLVE, &pdlp_settings.presolve, false}, {CUOPT_PRESOLVE, &mip_settings.presolve, true}, {CUOPT_DUAL_POSTSOLVE, &pdlp_settings.dual_postsolve, true} + {CUOPT_MIP_DETERMINISTIC, &mip_settings.deterministic, false}, }; // String parameters string_parameters = { From 7ee2c497330cb93fb0ff17ce388102bf79c17ff4 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 28 Oct 2025 12:43:04 +0000 Subject: [PATCH 011/366] restore determinism tests functionality --- .../cuopt/linear_programming/constants.h | 1 + cpp/src/math_optimization/solver_settings.cu | 4 +- cpp/src/mip/diversity/diversity_config.hpp | 2 + cpp/src/mip/diversity/diversity_manager.cu | 8 +- .../diversity/recombiners/fp_recombiner.cuh | 10 +- .../mip/diversity/recombiners/recombiner.cuh | 5 +- .../feasibility_pump/feasibility_pump.cu | 4 + .../line_segment_search.cu | 23 +- .../line_segment_search.cuh | 5 +- cpp/src/mip/local_search/local_search.cu | 88 +++-- .../local_search/rounding/constraint_prop.cu | 7 +- cpp/tests/mip/diversity_test.cu | 206 ++++++++--- cpp/tests/mip/feasibility_jump_tests.cu | 71 +--- cpp/tests/mip/local_search_test.cu | 44 +-- cpp/tests/mip/mip_utils.cuh | 61 ++++ cpp/tests/mip/unit_test.cu | 339 ++++++++++-------- 16 files changed, 531 insertions(+), 347 deletions(-) diff --git a/cpp/include/cuopt/linear_programming/constants.h b/cpp/include/cuopt/linear_programming/constants.h index 7f82e8491..fd44ef658 100644 --- a/cpp/include/cuopt/linear_programming/constants.h +++ b/cpp/include/cuopt/linear_programming/constants.h @@ -59,6 +59,7 @@ #define CUOPT_CUDSS_DETERMINISTIC "cudss_deterministic" #define CUOPT_PRESOLVE "presolve" #define CUOPT_DUAL_POSTSOLVE "dual_postsolve" +#define CUOPT_MIP_DETERMINISTIC "mip_deterministic" #define CUOPT_MIP_ABSOLUTE_TOLERANCE "mip_absolute_tolerance" #define CUOPT_MIP_RELATIVE_TOLERANCE "mip_relative_tolerance" #define CUOPT_MIP_INTEGRALITY_TOLERANCE "mip_integrality_tolerance" diff --git a/cpp/src/math_optimization/solver_settings.cu b/cpp/src/math_optimization/solver_settings.cu index be13894bd..84b975d9d 100644 --- a/cpp/src/math_optimization/solver_settings.cu +++ b/cpp/src/math_optimization/solver_settings.cu @@ -115,8 +115,8 @@ solver_settings_t::solver_settings_t() : pdlp_settings(), mip_settings {CUOPT_CUDSS_DETERMINISTIC, &pdlp_settings.cudss_deterministic, false}, {CUOPT_PRESOLVE, &pdlp_settings.presolve, false}, {CUOPT_PRESOLVE, &mip_settings.presolve, true}, - {CUOPT_DUAL_POSTSOLVE, &pdlp_settings.dual_postsolve, true} - {CUOPT_MIP_DETERMINISTIC, &mip_settings.deterministic, false}, + {CUOPT_DUAL_POSTSOLVE, &pdlp_settings.dual_postsolve, true}, + {CUOPT_MIP_DETERMINISTIC, &mip_settings.deterministic, false} }; // String parameters string_parameters = { diff --git a/cpp/src/mip/diversity/diversity_config.hpp b/cpp/src/mip/diversity/diversity_config.hpp index 5bfc9f51c..6149cf8e1 100644 --- a/cpp/src/mip/diversity/diversity_config.hpp +++ b/cpp/src/mip/diversity/diversity_config.hpp @@ -41,6 +41,8 @@ struct diversity_config_t { double lp_run_time_if_infeasible = 1.; bool halve_population = false; bool fj_only_run = false; + bool dry_run = false; + bool initial_solution_only = false; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 78cc43243..1713c1b7b 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -272,6 +272,10 @@ void diversity_manager_t::run_fj_alone(solution_t& solution) ls.fj.settings.update_weights = true; ls.fj.settings.feasibility_run = false; ls.fj.settings.time_limit = timer.remaining_time(); + if (context.settings.deterministic) { + ls.fj.settings.time_limit = timer.remaining_time(); + ls.fj.settings.iteration_limit = 10000; + } ls.fj.solve(solution); CUOPT_LOG_INFO("FJ alone finished!"); } @@ -313,7 +317,7 @@ solution_t diversity_manager_t::run_solver() problem_ptr->check_problem_representation(true); // have the structure ready for reusing later problem_ptr->compute_integer_fixed_problem(); - recombiner_t::init_enabled_recombiners(*problem_ptr); + recombiner_t::init_enabled_recombiners(context, *problem_ptr); mab_recombiner.resize_mab_arm_stats(recombiner_t::enabled_recombiners.size()); // test problem is not ii cuopt_func_call( @@ -417,12 +421,14 @@ solution_t diversity_manager_t::run_solver() population.best_feasible().get_user_objective(); } + if (diversity_config.dry_run) { return population.best_feasible(); } if (diversity_config.fj_only_run) { solution_t sol(*problem_ptr); run_fj_alone(sol); return sol; } generate_solution(timer.remaining_time(), false); + if (diversity_config.initial_solution_only) { return population.best_feasible(); } if (timer.check_time_limit()) { population.add_external_solutions_to_population(); return population.best_feasible(); diff --git a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh index 23dab78b2..f52de0e7b 100644 --- a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh @@ -93,7 +93,12 @@ class fp_recombiner_t : public recombiner_t { CUOPT_LOG_DEBUG("FP rec: running LP with infeasibility detection"); relaxed_lp_settings_t lp_settings; lp_settings.time_limit = fp_recombiner_config_t::infeasibility_detection_time_limit; - lp_settings.tolerance = fixed_problem.tolerances.absolute_tolerance; + if (this->context.settings.deterministic) { + lp_settings.time_limit = + std::numeric_limits::max(); // TODO should be global time limit + lp_settings.iteration_limit = 5000; + } + lp_settings.tolerance = fixed_problem.tolerances.absolute_tolerance; lp_settings.return_first_feasible = true; lp_settings.save_state = true; lp_settings.check_infeasibility = true; @@ -118,6 +123,9 @@ class fp_recombiner_t : public recombiner_t { offspring.assignment = std::move(fixed_assignment); cuopt_func_call(offspring.test_variable_bounds(false)); timer_t timer(fp_recombiner_config_t::fp_time_limit); + if (this->context.settings.deterministic) { + timer = timer_t(std::numeric_limits::max()); // TODO should be global time limit + } fp.timer = timer; fp.cycle_queue.reset(offspring); fp.reset(); diff --git a/cpp/src/mip/diversity/recombiners/recombiner.cuh b/cpp/src/mip/diversity/recombiners/recombiner.cuh index 972533724..1f134656e 100644 --- a/cpp/src/mip/diversity/recombiners/recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/recombiner.cuh @@ -216,7 +216,8 @@ class recombiner_t { "vars_to_fix should be sorted!"); } - static void init_enabled_recombiners(const problem_t& problem) + static void init_enabled_recombiners(mip_solver_context_t& context, + const problem_t& problem) { std::unordered_set enabled_recombiners; for (auto recombiner : recombiner_types) { @@ -231,6 +232,8 @@ class recombiner_t { (i_t)sub_mip_recombiner_config_t::max_continuous_vars) { enabled_recombiners.erase(recombiner_enum_t::SUB_MIP); } + // submip not supported in deterministic mode yet + if (context.settings.deterministic) { enabled_recombiners.erase(recombiner_enum_t::SUB_MIP); } recombiner_t::enabled_recombiners = std::vector(enabled_recombiners.begin(), enabled_recombiners.end()); } diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index 6ab4140df..d23bb09c5 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -331,6 +331,10 @@ bool feasibility_pump_t::test_fj_feasible(solution_t& soluti fj.settings.feasibility_run = true; fj.settings.n_of_minimums_for_exit = 5000; fj.settings.time_limit = std::min(time_limit, timer.remaining_time()); + if (context.settings.deterministic) { + fj.settings.time_limit = timer.remaining_time(); + fj.settings.iteration_limit = 10000; + } cuopt_func_call(solution.test_variable_bounds(true)); is_feasible = fj.solve(solution); cuopt_func_call(solution.test_variable_bounds(true)); diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu index 6d0be3c66..18c529fc1 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu @@ -27,8 +27,10 @@ namespace cuopt::linear_programming::detail { template line_segment_search_t::line_segment_search_t( - fj_t& fj_, constraint_prop_t& constraint_prop_) - : fj(fj_), constraint_prop(constraint_prop_) + mip_solver_context_t& context_, + fj_t& fj_, + constraint_prop_t& constraint_prop_) + : context(context_), fj(fj_), constraint_prop(constraint_prop_) { } @@ -196,7 +198,9 @@ bool line_segment_search_t::search_line_segment( best_feasible_cost, curr_cost); } - if (timer.check_time_limit()) { break; } + if (!context.settings.deterministic) { + if (timer.check_time_limit()) { break; } + } i_t number_of_integer_var_diff = compute_number_of_integer_var_diff( solution.problem_ptr->integer_indices, solution.assignment, @@ -217,7 +221,13 @@ bool line_segment_search_t::search_line_segment( fj.settings.update_weights = false; fj.settings.feasibility_run = is_feasibility_run; fj.settings.time_limit = std::min(1., timer.remaining_time()); - is_feasible = fj.solve(solution); + if (context.settings.deterministic) { + // fj.settings.time_limit = timer.remaining_time(); + fj.settings.time_limit = + std::numeric_limits::max(); // TODO should be global time limit + fj.settings.iteration_limit = 500; + } + is_feasible = fj.solve(solution); if (is_feasibility_run) { if (is_feasible) { CUOPT_LOG_DEBUG("Line segment found feasible"); @@ -234,7 +244,10 @@ bool line_segment_search_t::search_line_segment( best_feasible_cost, curr_cost); } - if (timer.check_time_limit()) { break; } + // cuopt_assert(context.settings.deterministic, ""); + if (!context.settings.deterministic) { + if (timer.check_time_limit()) { break; } + } } // if not recombiner mode but local search mode if (!settings.recombiner_mode) { diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh index 583cc9fb8..3d89d0e83 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh @@ -36,7 +36,9 @@ template class line_segment_search_t { public: line_segment_search_t() = delete; - line_segment_search_t(fj_t& fj, constraint_prop_t& constraint_prop); + line_segment_search_t(mip_solver_context_t& context, + fj_t& fj, + constraint_prop_t& constraint_prop); bool search_line_segment(solution_t& solution, const rmm::device_uvector& point_1, const rmm::device_uvector& point_2, @@ -59,6 +61,7 @@ class line_segment_search_t { f_t& best_feasible_cost, f_t curr_cost); + mip_solver_context_t& context; fj_t& fj; constraint_prop_t& constraint_prop; line_segment_settings_t settings; diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index aa286964e..5df43aaf5 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -115,7 +115,7 @@ local_search_t::local_search_t(mip_solver_context_t& context fj(context), // fj_tree(fj), constraint_prop(context), - line_segment_search(fj, constraint_prop), + line_segment_search(context, fj, constraint_prop), fp(context, fj, // fj_tree, @@ -160,19 +160,21 @@ void local_search_t::start_cpufj_scratch_threads(population_t 0); - cpu_fj.fj_cpu->log_prefix = "******* scratch " + std::to_string(counter) + ": "; - cpu_fj.fj_cpu->improvement_callback = [this, &population, &cpu_fj]( - f_t obj, const std::vector& h_vec) { - population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); - if (obj < local_search_best_obj) { - CUOPT_LOG_TRACE("******* New local search best obj %g, best overall %g", - context.problem_ptr->get_user_obj_from_solver_obj(obj), - context.problem_ptr->get_user_obj_from_solver_obj( - population.is_feasible() ? population.best_feasible().get_objective() - : std::numeric_limits::max())); - local_search_best_obj = obj; - } - }; + cpu_fj.fj_cpu->log_prefix = "******* scratch " + std::to_string(counter) + ": "; + if (!context.settings.deterministic) { + cpu_fj.fj_cpu->improvement_callback = [this, &population, &cpu_fj]( + f_t obj, const std::vector& h_vec) { + population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); + if (obj < local_search_best_obj) { + CUOPT_LOG_TRACE("******* New local search best obj %g, best overall %g", + context.problem_ptr->get_user_obj_from_solver_obj(obj), + context.problem_ptr->get_user_obj_from_solver_obj( + population.is_feasible() ? population.best_feasible().get_objective() + : std::numeric_limits::max())); + local_search_best_obj = obj; + } + }; + } counter++; }; @@ -195,18 +197,20 @@ void local_search_t::start_cpufj_lptopt_scratch_threads( scratch_cpu_fj_on_lp_opt.fj_cpu = fj.create_cpu_climber(solution_lp, default_weights, default_weights, 0.); scratch_cpu_fj_on_lp_opt.fj_cpu->log_prefix = "******* scratch on LP optimal: "; - scratch_cpu_fj_on_lp_opt.fj_cpu->improvement_callback = - [this, &population](f_t obj, const std::vector& h_vec) { - population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); - if (obj < local_search_best_obj) { - CUOPT_LOG_DEBUG("******* New local search best obj %g, best overall %g", - context.problem_ptr->get_user_obj_from_solver_obj(obj), - context.problem_ptr->get_user_obj_from_solver_obj( - population.is_feasible() ? population.best_feasible().get_objective() - : std::numeric_limits::max())); - local_search_best_obj = obj; - } - }; + if (!context.settings.deterministic) { + scratch_cpu_fj_on_lp_opt.fj_cpu->improvement_callback = + [this, &population](f_t obj, const std::vector& h_vec) { + population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); + if (obj < local_search_best_obj) { + CUOPT_LOG_DEBUG("******* New local search best obj %g, best overall %g", + context.problem_ptr->get_user_obj_from_solver_obj(obj), + context.problem_ptr->get_user_obj_from_solver_obj( + population.is_feasible() ? population.best_feasible().get_objective() + : std::numeric_limits::max())); + local_search_best_obj = obj; + } + }; + } // default weights cudaDeviceSynchronize(); @@ -290,17 +294,21 @@ bool local_search_t::do_fj_solve(solution_t& solution, cpu_better[source]); total_calls[source]++; - if (cpu_feasible && !gpu_feasible || - (cpu_feasible && solution_cpu.get_objective() < solution.get_objective())) { - CUOPT_LOG_DEBUG( - "CPU FJ returns better solution! cpu_obj %g, gpu_obj %g, stats %d/%d, source %s", - solution_cpu.get_user_objective(), - solution.get_user_objective(), - total_calls[source], - cpu_better[source], - source.c_str()); - solution.copy_from(solution_cpu); - cpu_better[source]++; + // ignore the CPUFJ solution if we're in determinism mode + // TODO: use work limits here + if (!context.settings.deterministic) { + if (cpu_feasible && !gpu_feasible || + (cpu_feasible && solution_cpu.get_objective() < solution.get_objective())) { + CUOPT_LOG_DEBUG( + "CPU FJ returns better solution! cpu_obj %g, gpu_obj %g, stats %d/%d, source %s", + solution_cpu.get_user_objective(), + solution.get_user_objective(), + total_calls[source], + cpu_better[source], + source.c_str()); + solution.copy_from(solution_cpu); + cpu_better[source]++; + } } solution.compute_feasibility(); @@ -480,7 +488,7 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu if (!is_feasible) { f_t lp_run_time = 2.; // CHANGE - lp_run_time = 600; + if (context.settings.deterministic) { lp_run_time = std::numeric_limits::infinity(); } relaxed_lp_settings_t lp_settings; lp_settings.time_limit = std::min(lp_run_time, timer.remaining_time()); lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; @@ -495,7 +503,9 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu fj.settings.n_of_minimums_for_exit = 20000; fj.settings.update_weights = true; fj.settings.feasibility_run = false; - f_t time_limit = std::min(30., timer.remaining_time()); + f_t fj_time_limit = 30.; + if (context.settings.deterministic) { fj_time_limit = std::numeric_limits::infinity(); } + f_t time_limit = std::min(fj_time_limit, timer.remaining_time()); do_fj_solve(solution, fj, time_limit, "on_lp_optimal"); return solution.get_feasible(); } diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 60faca5db..5bf9764a7 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -873,7 +873,9 @@ bool constraint_prop_t::find_integer( std::mt19937 rng(seed); // CHANGE - timer = timer_t(std::numeric_limits::infinity()); + if (this->context.settings.deterministic) { + timer = timer_t(std::numeric_limits::infinity()); + } lb_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); ub_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); @@ -1105,6 +1107,9 @@ bool constraint_prop_t::apply_round( { raft::common::nvtx::range fun_scope("constraint prop round"); max_timer = timer_t{max_time_for_bounds_prop}; + if (this->context.settings.deterministic) { + max_timer = timer_t(std::numeric_limits::infinity()); + } if (check_brute_force_rounding(sol)) { return true; } recovery_mode = false; rounding_ii = false; diff --git a/cpp/tests/mip/diversity_test.cu b/cpp/tests/mip/diversity_test.cu index bc6cce33f..a200858c4 100644 --- a/cpp/tests/mip/diversity_test.cu +++ b/cpp/tests/mip/diversity_test.cu @@ -72,8 +72,8 @@ static void setup_device_symbols(rmm::cuda_stream_view stream_view) detail::set_pdlp_hyper_parameters(stream_view); } -static uint32_t test_initial_population_determinism(std::string path, - unsigned long seed = std::random_device{}()) +static uint32_t test_initial_solution_determinism(std::string path, + unsigned long seed = std::random_device{}()) { const raft::handle_t handle_{}; @@ -100,57 +100,129 @@ static uint32_t test_initial_population_determinism(std::string path, nullptr, true); - auto settings = mip_solver_settings_t{}; - settings.time_limit = 30.; - auto timer = cuopt::timer_t(30); + auto settings = mip_solver_settings_t{}; + settings.time_limit = 3000.; + settings.deterministic = true; + settings.heuristics_only = true; + auto timer = cuopt::timer_t(3000); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); detail::diversity_manager_t diversity_manager(solver.context); - diversity_manager.diversity_config.fj_only_run = true; - // run with FJ only on the initial population, no recombining + diversity_manager.timer = timer_t(60000); + diversity_manager.diversity_config.initial_solution_only = true; diversity_manager.run_solver(); + std::vector hashes; + auto pop = diversity_manager.get_population_pointer(); + for (const auto& sol : pop->population_to_vector()) { + hashes.push_back(sol.get_hash()); + } + + uint32_t final_hash = detail::compute_hash(hashes); + printf("%s: final hash: 0x%x, pop size %d\n", + path.c_str(), + final_hash, + (int)pop->population_to_vector().size()); + return final_hash; +} + +static uint32_t test_recombiners_determinism(std::string path, + unsigned long seed = std::random_device{}()) +{ + const raft::handle_t handle_{}; + + cuopt::mps_parser::mps_data_model_t mps_problem = + cuopt::mps_parser::parse_mps(path, false); + handle_.sync_stream(); + auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); + problem_checking_t::check_problem_representation(op_problem); + + init_handler(op_problem.get_handle_ptr()); + // run the problem constructor of MIP, so that we do bounds standardization + detail::problem_t problem(op_problem); + problem.preprocess_problem(); + + setup_device_symbols(op_problem.get_handle_ptr()->get_stream()); + + detail::pdlp_initial_scaling_strategy_t scaling(&handle_, + problem, + 10, + 1.0, + problem.reverse_coefficients, + problem.reverse_offsets, + problem.reverse_constraints, + nullptr, + true); + + auto settings = mip_solver_settings_t{}; + settings.time_limit = 3000.; + settings.deterministic = true; + settings.heuristics_only = true; + auto timer = cuopt::timer_t(3000); + detail::mip_solver_t solver(problem, settings, scaling, timer); + problem.tolerances = settings.get_tolerances(); + + detail::diversity_manager_t diversity_manager(solver.context); + diversity_manager.timer = timer_t(60000); + diversity_manager.diversity_config.dry_run = true; + diversity_manager.run_solver(); + + // Generate a population by running FJ on random starting points // recombine a few solutions, observe the output - auto pop_vector = diversity_manager.get_population_pointer()->population_to_vector(); - // necessary because .resize() complains about constructors (since it may be used to grow the - // vector as well) - while (pop_vector.size() > 9) { - pop_vector.pop_back(); + for (int i = diversity_manager.population.current_size(); i < 3; ++i) { + detail::solution_t random_initial_solution(problem); + random_initial_solution.assign_random_within_bounds(); + detail::fj_settings_t fj_settings; + fj_settings.feasibility_run = false; + fj_settings.iteration_limit = 1000 + i * 100; + fj_settings.seed = seed + i; + auto solution = + run_fj(problem, fj_settings, fj_tweaks_t{}, random_initial_solution.get_host_assignment()) + .solution; + printf("population %d hash: 0x%x\n", i, solution.get_hash()); + diversity_manager.population.add_solution(std::move(solution)); } + auto pop_vector = diversity_manager.get_population_pointer()->population_to_vector(); + std::vector hashes; - static std::map, uint32_t> hash_map; - - // diversity_manager.run_only_ls_recombiner = true; - diversity_manager.timer = timer_t(60000); - // diversity_manager.recombine_and_ls_with_all(pop_vector); - for (int i = 1; i < (int)pop_vector.size(); i++) { - for (int j = i + 1; j < (int)pop_vector.size(); j++) { - printf("recombining %d and %d\n", i, j); - auto [offspring, success] = diversity_manager.recombine( - pop_vector[i], pop_vector[j], detail::recombiner_enum_t::LINE_SEGMENT); - auto offspring_hash = offspring.get_hash(); - printf("for %d,%d: offspring hash: 0x%x, parent 1 hash: 0x%x, parent 2 hash: 0x%x\n", - i, - j, - offspring_hash, - pop_vector[i].get_hash(), - pop_vector[j].get_hash()); - if (hash_map.find(std::make_pair(i, j)) == hash_map.end()) { - hash_map[std::make_pair(i, j)] = offspring_hash; - } else { - if (hash_map[std::make_pair(i, j)] != offspring_hash) { - printf("hash mismatch for %d,%d: %d != %d\n", - i, - j, - hash_map[std::make_pair(i, j)], - offspring_hash); - exit(1); + static std::map, uint32_t> hash_map; + + for (auto recombiner : {detail::recombiner_enum_t::LINE_SEGMENT, + detail::recombiner_enum_t::BOUND_PROP, + detail::recombiner_enum_t::FP}) { + for (int i = 1; i < (int)pop_vector.size(); i++) { + for (int j = i + 1; j < (int)pop_vector.size(); j++) { + printf("recombining %d and %d w/ recombiner %s\n", + i, + j, + detail::all_recombine_stats::recombiner_labels[(int)recombiner]); + auto [offspring, success] = + diversity_manager.recombine(pop_vector[i], pop_vector[j], recombiner); + auto offspring_hash = offspring.get_hash(); + printf("for %d,%d: offspring hash: 0x%x, parent 1 hash: 0x%x, parent 2 hash: 0x%x\n", + i, + j, + offspring_hash, + pop_vector[i].get_hash(), + pop_vector[j].get_hash()); + if (hash_map.find(std::make_tuple(path, i, j, recombiner)) == hash_map.end()) { + hash_map[std::make_tuple(path, i, j, recombiner)] = offspring_hash; + } else { + if (hash_map[std::make_tuple(path, i, j, recombiner)] != offspring_hash) { + printf("%s: hash mismatch for %d,%d: %d != %d\n", + path.c_str(), + i, + j, + hash_map[std::make_tuple(path, i, j, recombiner)], + offspring_hash); + exit(1); + } } + hashes.push_back(offspring_hash); } - hashes.push_back(offspring_hash); } } return detail::compute_hash(hashes); @@ -161,13 +233,46 @@ static uint32_t test_initial_population_determinism(std::string path, } uint32_t final_hash = detail::compute_hash(hashes); - printf("final hash: 0x%x, pop size %d\n", final_hash, (int)pop->population_to_vector().size()); + printf("%s: final hash: 0x%x, pop size %d\n", + path.c_str(), + final_hash, + (int)pop->population_to_vector().size()); return final_hash; } class DiversityTestParams : public testing::TestWithParam> {}; -TEST_P(DiversityTestParams, initial_population_deterministic) +// TEST_P(DiversityTestParams, recombiners_deterministic) +// { +// cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); + +// spin_stream_raii_t spin_stream_1; +// spin_stream_raii_t spin_stream_2; + +// auto test_instance = std::get<0>(GetParam()); +// std::cout << "Running: " << test_instance << std::endl; +// int seed = +// std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); +// std::cerr << "Tested with seed " << seed << "\n"; +// auto path = make_path_absolute(test_instance); +// test_instance = std::getenv("CUOPT_INSTANCE") ? std::getenv("CUOPT_INSTANCE") : test_instance; +// path = "/home/scratch.yboucher_gpu_1/collection/" + test_instance; +// uint32_t gold_hash = 0; +// for (int i = 0; i < 2; ++i) { +// cuopt::seed_generator::set_seed(seed); +// std::cout << "Running " << test_instance << " " << i << std::endl; +// std::cout << "-------------------------------------------------------------\n"; +// auto hash = test_recombiners_determinism(path, seed); +// if (i == 0) { +// gold_hash = hash; +// std::cout << "Gold hash: " << gold_hash << std::endl; +// } else { +// ASSERT_EQ(hash, gold_hash); +// } +// } +// } + +TEST_P(DiversityTestParams, initial_solution_deterministic) { cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); @@ -183,11 +288,11 @@ TEST_P(DiversityTestParams, initial_population_deterministic) test_instance = std::getenv("CUOPT_INSTANCE") ? std::getenv("CUOPT_INSTANCE") : test_instance; path = "/home/scratch.yboucher_gpu_1/collection/" + test_instance; uint32_t gold_hash = 0; - for (int i = 0; i < 10; ++i) { + for (int i = 0; i < 2; ++i) { cuopt::seed_generator::set_seed(seed); std::cout << "Running " << test_instance << " " << i << std::endl; std::cout << "-------------------------------------------------------------\n"; - auto hash = test_initial_population_determinism(path, seed); + auto hash = test_initial_solution_determinism(path, seed); if (i == 0) { gold_hash = hash; std::cout << "Gold hash: " << gold_hash << std::endl; @@ -199,15 +304,14 @@ TEST_P(DiversityTestParams, initial_population_deterministic) INSTANTIATE_TEST_SUITE_P(DiversityTest, DiversityTestParams, - testing::Values(std::make_tuple("fastxgemm-n2r6s0t2.mps") + testing::Values(std::make_tuple("gen-ip054.mps"), + std::make_tuple("pk1.mps"), + std::make_tuple("fastxgemm-n2r6s0t2.mps"), // std::make_tuple("mip/sct2.mps") // std::make_tuple("mip/thor50dday.mps") - // std::make_tuple("mip/uccase9.mps") - // std::make_tuple("mip/neos5-free-bound.mps") + std::make_tuple("uccase9.mps"), // std::make_tuple("mip/neos5.mps") - // std::make_tuple("mip/50v-10.mps") - // std::make_tuple("mip/rmatr200-p5.mps") - )); -// std::make_tuple("mip/gen-ip054.mps"))); + std::make_tuple("50v-10.mps"), + std::make_tuple("rmatr200-p5.mps"))); } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/feasibility_jump_tests.cu b/cpp/tests/mip/feasibility_jump_tests.cu index b6c36a97a..5efc510f6 100644 --- a/cpp/tests/mip/feasibility_jump_tests.cu +++ b/cpp/tests/mip/feasibility_jump_tests.cu @@ -56,23 +56,11 @@ void init_handler(const raft::handle_t* handle_ptr) handle_ptr->get_cusparse_handle(), CUSPARSE_POINTER_MODE_DEVICE, handle_ptr->get_stream())); } -struct fj_tweaks_t { - double objective_weight = 0; -}; - -struct fj_state_t { - detail::solution_t solution; - std::vector solution_vector; - int minimums; - double incumbent_objective; - double incumbent_violation; -}; - // Helper function to setup MIP solver and run FJ with given settings and initial solution -static fj_state_t run_fj(std::string test_instance, - const detail::fj_settings_t& fj_settings, - fj_tweaks_t tweaks = {}, - std::vector initial_solution = {}) +static fj_state_t run_fj_instance(std::string test_instance, + const detail::fj_settings_t& fj_settings, + fj_tweaks_t tweaks = {}, + std::vector initial_solution = {}) { const raft::handle_t handle_{}; std::cout << "Running: " << test_instance << std::endl; @@ -88,45 +76,8 @@ static fj_state_t run_fj(std::string test_instance, // run the problem constructor of MIP, so that we do bounds standardization detail::problem_t problem(op_problem); problem.preprocess_problem(); - detail::pdlp_initial_scaling_strategy_t scaling(&handle_, - problem, - 10, - 1.0, - problem.reverse_coefficients, - problem.reverse_offsets, - problem.reverse_constraints, - nullptr, - true); - - auto settings = mip_solver_settings_t{}; - settings.time_limit = 30.; - auto timer = cuopt::timer_t(30); - detail::mip_solver_t solver(problem, settings, scaling, timer); - - detail::solution_t solution(*solver.context.problem_ptr); - if (initial_solution.size() > 0) { - expand_device_copy(solution.assignment, initial_solution, solution.handle_ptr->get_stream()); - } else { - thrust::fill(solution.handle_ptr->get_thrust_policy(), - solution.assignment.begin(), - solution.assignment.end(), - 0.0); - } - solution.clamp_within_bounds(); - - detail::fj_t fj(solver.context, fj_settings); - fj.reset_weights(solution.handle_ptr->get_stream(), 1.); - fj.objective_weight.set_value_async(tweaks.objective_weight, solution.handle_ptr->get_stream()); - solution.handle_ptr->sync_stream(); - - fj.solve(solution); - auto solution_vector = host_copy(solution.assignment, solution.handle_ptr->get_stream()); - return {solution, - solution_vector, - fj.climbers[0]->local_minimums_reached.value(solution.handle_ptr->get_stream()), - fj.climbers[0]->incumbent_objective.value(solution.handle_ptr->get_stream()), - fj.climbers[0]->violation_score.value(solution.handle_ptr->get_stream())}; + return run_fj(problem, fj_settings, tweaks, initial_solution); } // FJ had a bug causing objective/violation values to explode in magnitude in certain scenarios. @@ -141,7 +92,7 @@ static bool run_fj_check_no_obj_runoff(std::string test_instance) fj_settings.feasibility_run = false; fj_settings.iteration_limit = 20000; - auto state = run_fj(test_instance, fj_settings); + auto state = run_fj_instance(test_instance, fj_settings); // ensure that the objective and the violation in the FJ state are not too large (<1e60) EXPECT_LE(state.incumbent_violation, 1e60) << "FJ violation too large"; @@ -163,7 +114,7 @@ static bool run_fj_check_objective(std::string test_instance, int iter_limit, do fj_settings.feasibility_run = obj_target == +std::numeric_limits::infinity(); fj_settings.iteration_limit = iter_limit; - auto state = run_fj(test_instance, fj_settings); + auto state = run_fj_instance(test_instance, fj_settings); auto& solution = state.solution; CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", @@ -190,7 +141,7 @@ static bool run_fj_check_feasible(std::string test_instance) fj_settings.feasibility_run = false; fj_settings.iteration_limit = 25000; - auto state = run_fj(test_instance, fj_settings); + auto state = run_fj_instance(test_instance, fj_settings); auto& solution = state.solution; bool previous_feasible = solution.get_feasible(); @@ -201,8 +152,8 @@ static bool run_fj_check_feasible(std::string test_instance) // again but with very large obj weight to force FJ into the infeasible region fj_tweaks_t tweaks; tweaks.objective_weight = 1e6; - auto new_state = run_fj(test_instance, fj_settings, tweaks, state.solution_vector); - auto& new_solution = new_state.solution; + auto new_state = run_fj_instance(test_instance, fj_settings, tweaks, state.solution_vector); + auto& new_solution = new_state.solution; CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", test_instance.c_str(), @@ -233,7 +184,7 @@ static bool run_fj_check_determinism(std::string test_instance, int iter_limit) fj_settings.seed = seed; cuopt::seed_generator::set_seed(fj_settings.seed); - auto state = run_fj(test_instance, fj_settings); + auto state = run_fj_instance(test_instance, fj_settings); auto& solution = state.solution; CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu index 73baad799..ce0ee5216 100644 --- a/cpp/tests/mip/local_search_test.cu +++ b/cpp/tests/mip/local_search_test.cu @@ -72,18 +72,6 @@ static void setup_device_symbols(rmm::cuda_stream_view stream_view) detail::set_pdlp_hyper_parameters(stream_view); } -struct fj_tweaks_t { - double objective_weight = 0; -}; - -struct fj_state_t { - detail::solution_t solution; - std::vector solution_vector; - int minimums; - double incumbent_objective; - double incumbent_violation; -}; - enum local_search_mode_t { FP = 0, STAGED_FP, @@ -122,15 +110,19 @@ static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) nullptr, true); - auto settings = mip_solver_settings_t{}; - settings.time_limit = 30.; - auto timer = cuopt::timer_t(30); + auto settings = mip_solver_settings_t{}; + settings.time_limit = 30.; + settings.deterministic = true; + auto timer = cuopt::timer_t(30); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); - rmm::device_uvector lp_optimal_solution(0, problem.handle_ptr->get_stream()); - - lp_optimal_solution.resize(problem.n_variables, problem.handle_ptr->get_stream()); + rmm::device_uvector lp_optimal_solution(problem.n_variables, + problem.handle_ptr->get_stream()); + thrust::fill(problem.handle_ptr->get_thrust_policy(), + lp_optimal_solution.begin(), + lp_optimal_solution.end(), + 0.0); detail::lp_state_t& lp_state = problem.lp_state; // resize because some constructor might be called before the presolve lp_state.resize(problem, problem.handle_ptr->get_stream()); @@ -157,7 +149,7 @@ static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) printf("LP optimal hash: 0x%x\n", detail::compute_hash(lp_optimal_solution)); printf("running mode: %d\n", mode); - local_search.fp.timer = timer_t{600}; + local_search.fp.timer = timer_t{6000}; detail::ls_config_t ls_config{}; @@ -230,9 +222,9 @@ TEST_P(LocalSearchTestParams, local_search_operator_determinism) for (const auto& instance : { //"thor50dday.mps", "gen-ip054.mps", - //"50v-10.mps", + "50v-10.mps", //"seymour1.mps", - //"rmatr200-p5.mps", + "rmatr200-p5.mps", //"tr12-30.mps", //"sct2.mps", //"uccase9.mps" @@ -245,7 +237,7 @@ TEST_P(LocalSearchTestParams, local_search_operator_determinism) unsigned long seed = std::random_device{}(); std::cerr << "Tested with seed " << seed << "\n"; uint32_t gold_hash = 0; - for (int i = 0; i < 10; ++i) { + for (int i = 0; i < 5; ++i) { uint32_t hash = run_fp_check_determinism(instance, mode); if (i == 0) { gold_hash = hash; @@ -260,9 +252,9 @@ TEST_P(LocalSearchTestParams, local_search_operator_determinism) INSTANTIATE_TEST_SUITE_P(LocalSearchTests, LocalSearchTestParams, - testing::Values(std::make_tuple(local_search_mode_t::FP), - std::make_tuple(local_search_mode_t::FJ_LINE_SEGMENT), - std::make_tuple(local_search_mode_t::FJ_ON_ZERO), - std::make_tuple(local_search_mode_t::FJ_ANNEALING))); + testing::Values( // std::make_tuple(local_search_mode_t::FP), + std::make_tuple(local_search_mode_t::FJ_LINE_SEGMENT), + std::make_tuple(local_search_mode_t::FJ_ON_ZERO), + std::make_tuple(local_search_mode_t::FJ_ANNEALING))); } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/mip_utils.cuh b/cpp/tests/mip/mip_utils.cuh index b53952e12..904f46168 100644 --- a/cpp/tests/mip/mip_utils.cuh +++ b/cpp/tests/mip/mip_utils.cuh @@ -18,7 +18,10 @@ #include #include #include +#include #include +#include +#include #include #include @@ -133,4 +136,62 @@ static std::tuple test_mps_file( solution.get_solution_bound()); } +struct fj_tweaks_t { + double objective_weight = 0; +}; + +struct fj_state_t { + detail::solution_t solution; + std::vector solution_vector; + int minimums; + double incumbent_objective; + double incumbent_violation; +}; + +static fj_state_t run_fj(detail::problem_t& problem, + const detail::fj_settings_t& fj_settings, + fj_tweaks_t tweaks = {}, + std::vector initial_solution = {}) +{ + detail::pdlp_initial_scaling_strategy_t scaling(problem.handle_ptr, + problem, + 10, + 1.0, + problem.reverse_coefficients, + problem.reverse_offsets, + problem.reverse_constraints, + nullptr, + true); + + auto settings = mip_solver_settings_t{}; + settings.time_limit = 30.; + auto timer = cuopt::timer_t(30); + detail::mip_solver_t solver(problem, settings, scaling, timer); + + detail::solution_t solution(*solver.context.problem_ptr); + if (initial_solution.size() > 0) { + expand_device_copy(solution.assignment, initial_solution, solution.handle_ptr->get_stream()); + } else { + thrust::fill(solution.handle_ptr->get_thrust_policy(), + solution.assignment.begin(), + solution.assignment.end(), + 0.0); + } + solution.clamp_within_bounds(); + + detail::fj_t fj(solver.context, fj_settings); + fj.reset_weights(solution.handle_ptr->get_stream(), 1.); + fj.objective_weight.set_value_async(tweaks.objective_weight, solution.handle_ptr->get_stream()); + solution.handle_ptr->sync_stream(); + + fj.solve(solution); + auto solution_vector = host_copy(solution.assignment, solution.handle_ptr->get_stream()); + + return {solution, + solution_vector, + fj.climbers[0]->local_minimums_reached.value(solution.handle_ptr->get_stream()), + fj.climbers[0]->incumbent_objective.value(solution.handle_ptr->get_stream()), + fj.climbers[0]->violation_score.value(solution.handle_ptr->get_stream())}; +} + } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/unit_test.cu b/cpp/tests/mip/unit_test.cu index 9897b2feb..9258f4dc7 100644 --- a/cpp/tests/mip/unit_test.cu +++ b/cpp/tests/mip/unit_test.cu @@ -133,164 +133,185 @@ mps_parser::mps_data_model_t create_single_var_milp_problem(bool ma return problem; } -TEST(LPTest, TestSampleLP2) -{ - raft::handle_t handle; - - // Construct a simple LP problem: - // Minimize: x - // Subject to: x <= 1 - // x <= 1 - // x >= 0 - - // One variable, two constraints (both x <= 1) - std::vector A_values = {1.0, 1.0}; - std::vector A_indices = {0, 0}; - std::vector A_offsets = {0, 1, 2}; // CSR: 2 constraints, 1 variable - - std::vector b = {1.0, 1.0}; // RHS for both constraints - std::vector b_lower = {-std::numeric_limits::infinity(), - -std::numeric_limits::infinity()}; - - std::vector c = {1.0}; // Objective: Minimize x - - std::vector row_types = {'L', 'L'}; // Both constraints are <= - - // Build the problem - mps_parser::mps_data_model_t problem; - problem.set_csr_constraint_matrix(A_values.data(), - A_values.size(), - A_indices.data(), - A_indices.size(), - A_offsets.data(), - A_offsets.size()); - problem.set_constraint_upper_bounds(b.data(), b.size()); - problem.set_constraint_lower_bounds(b_lower.data(), b_lower.size()); - - // Set variable bounds (x >= 0) - std::vector var_lower = {0.0}; - std::vector var_upper = {std::numeric_limits::infinity()}; - problem.set_variable_lower_bounds(var_lower.data(), var_lower.size()); - problem.set_variable_upper_bounds(var_upper.data(), var_upper.size()); - - problem.set_objective_coefficients(c.data(), c.size()); - problem.set_maximize(false); - // Set up solver settings - cuopt::linear_programming::pdlp_solver_settings_t settings{}; - settings.set_optimality_tolerance(1e-2); - settings.method = cuopt::linear_programming::method_t::PDLP; - settings.time_limit = 5; - - // Solve - auto result = cuopt::linear_programming::solve_lp(&handle, problem, settings); - - // Check results - EXPECT_EQ(result.get_termination_status(), - cuopt::linear_programming::pdlp_termination_status_t::Optimal); - ASSERT_EQ(result.get_primal_solution().size(), 1); - - // Copy solution to host to access values - auto primal_host = cuopt::host_copy(result.get_primal_solution()); - EXPECT_NEAR(primal_host[0], 0.0, 1e-6); - - EXPECT_NEAR(result.get_additional_termination_information().primal_objective, 0.0, 1e-6); - EXPECT_NEAR(result.get_additional_termination_information().dual_objective, 0.0, 1e-6); -} - -TEST(LPTest, TestSampleLP) -{ - raft::handle_t handle; - auto problem = create_std_lp_problem(); - - cuopt::linear_programming::pdlp_solver_settings_t settings{}; - settings.set_optimality_tolerance(1e-4); - settings.time_limit = 5; - settings.presolve = false; - - auto result = cuopt::linear_programming::solve_lp(&handle, problem, settings); - - EXPECT_EQ(result.get_termination_status(), - cuopt::linear_programming::pdlp_termination_status_t::Optimal); -} - -TEST(ErrorTest, TestError) -{ - raft::handle_t handle; - auto problem = create_std_milp_problem(false); - - cuopt::linear_programming::mip_solver_settings_t settings{}; - settings.time_limit = 5; - settings.presolve = false; - - // Set constraint bounds - std::vector lower_bounds = {1.0}; - std::vector upper_bounds = {1.0, 1.0}; - problem.set_constraint_lower_bounds(lower_bounds.data(), lower_bounds.size()); - problem.set_constraint_upper_bounds(upper_bounds.data(), upper_bounds.size()); - - auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); - - EXPECT_EQ(result.get_termination_status(), - cuopt::linear_programming::mip_termination_status_t::NoTermination); -} - -class MILPTestParams - : public testing::TestWithParam< - std::tuple> {}; - -TEST_P(MILPTestParams, TestSampleMILP) -{ - bool maximize = std::get<0>(GetParam()); - bool scaling = std::get<1>(GetParam()); - bool heuristics_only = std::get<2>(GetParam()); - auto expected_termination_status = std::get<3>(GetParam()); - - raft::handle_t handle; - auto problem = create_std_milp_problem(maximize); - - cuopt::linear_programming::mip_solver_settings_t settings{}; - settings.time_limit = 5; - settings.mip_scaling = scaling; - settings.heuristics_only = heuristics_only; - settings.presolve = false; - - auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); - - EXPECT_EQ(result.get_termination_status(), expected_termination_status); -} - -TEST_P(MILPTestParams, TestSingleVarMILP) -{ - bool maximize = std::get<0>(GetParam()); - bool scaling = std::get<1>(GetParam()); - bool heuristics_only = std::get<2>(GetParam()); - auto expected_termination_status = std::get<3>(GetParam()); - - raft::handle_t handle; - auto problem = create_single_var_milp_problem(maximize); - - cuopt::linear_programming::mip_solver_settings_t settings{}; - settings.time_limit = 5; - settings.mip_scaling = scaling; - settings.heuristics_only = heuristics_only; - settings.presolve = false; - - auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); - - EXPECT_EQ(result.get_termination_status(), - cuopt::linear_programming::mip_termination_status_t::Optimal); -} - -INSTANTIATE_TEST_SUITE_P( - MILPTests, - MILPTestParams, - testing::Values( - std::make_tuple(true, true, true, cuopt::linear_programming::mip_termination_status_t::Optimal), - std::make_tuple( - false, true, false, cuopt::linear_programming::mip_termination_status_t::Optimal), - std::make_tuple( - true, false, true, cuopt::linear_programming::mip_termination_status_t::Optimal), - std::make_tuple( - false, false, false, cuopt::linear_programming::mip_termination_status_t::Optimal))); +// TEST(LPTest, TestSampleLP2) +// { +// raft::handle_t handle; + +// // Construct a simple LP problem: +// // Minimize: x +// // Subject to: x <= 1 +// // x <= 1 +// // x >= 0 + +// // One variable, two constraints (both x <= 1) +// std::vector A_values = {1.0, 1.0}; +// std::vector A_indices = {0, 0}; +// std::vector A_offsets = {0, 1, 2}; // CSR: 2 constraints, 1 variable + +// std::vector b = {1.0, 1.0}; // RHS for both constraints +// std::vector b_lower = {-std::numeric_limits::infinity(), +// -std::numeric_limits::infinity()}; + +// std::vector c = {1.0}; // Objective: Minimize x + +// std::vector row_types = {'L', 'L'}; // Both constraints are <= + +// // Build the problem +// mps_parser::mps_data_model_t problem; +// problem.set_csr_constraint_matrix(A_values.data(), +// A_values.size(), +// A_indices.data(), +// A_indices.size(), +// A_offsets.data(), +// A_offsets.size()); +// problem.set_constraint_upper_bounds(b.data(), b.size()); +// problem.set_constraint_lower_bounds(b_lower.data(), b_lower.size()); + +// // Set variable bounds (x >= 0) +// std::vector var_lower = {0.0}; +// std::vector var_upper = {std::numeric_limits::infinity()}; +// problem.set_variable_lower_bounds(var_lower.data(), var_lower.size()); +// problem.set_variable_upper_bounds(var_upper.data(), var_upper.size()); + +// problem.set_objective_coefficients(c.data(), c.size()); +// problem.set_maximize(false); +// // Set up solver settings +// cuopt::linear_programming::pdlp_solver_settings_t settings{}; +// settings.set_optimality_tolerance(1e-2); +// settings.method = cuopt::linear_programming::method_t::PDLP; +// settings.time_limit = 5; + +// // Solve +// auto result = cuopt::linear_programming::solve_lp(&handle, problem, settings); + +// // Check results +// EXPECT_EQ(result.get_termination_status(), +// cuopt::linear_programming::pdlp_termination_status_t::Optimal); +// ASSERT_EQ(result.get_primal_solution().size(), 1); + +// // Copy solution to host to access values +// auto primal_host = cuopt::host_copy(result.get_primal_solution()); +// EXPECT_NEAR(primal_host[0], 0.0, 1e-6); + +// EXPECT_NEAR(result.get_additional_termination_information().primal_objective, 0.0, 1e-6); +// EXPECT_NEAR(result.get_additional_termination_information().dual_objective, 0.0, 1e-6); +// } + +// TEST(LPTest, TestSampleLP) +// { +// raft::handle_t handle; +// auto problem = create_std_lp_problem(); + +// cuopt::linear_programming::pdlp_solver_settings_t settings{}; +// settings.set_optimality_tolerance(1e-4); +// settings.time_limit = 5; +// settings.presolve = false; + +// auto result = cuopt::linear_programming::solve_lp(&handle, problem, settings); + +// EXPECT_EQ(result.get_termination_status(), +// cuopt::linear_programming::pdlp_termination_status_t::Optimal); +// } + +// TEST(ErrorTest, TestError) +// { +// raft::handle_t handle; +// auto problem = create_std_milp_problem(false); + +// cuopt::linear_programming::mip_solver_settings_t settings{}; +// settings.time_limit = 5; +// settings.presolve = false; + +// // Set constraint bounds +// std::vector lower_bounds = {1.0}; +// std::vector upper_bounds = {1.0, 1.0}; +// problem.set_constraint_lower_bounds(lower_bounds.data(), lower_bounds.size()); +// problem.set_constraint_upper_bounds(upper_bounds.data(), upper_bounds.size()); + +// auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); + +// EXPECT_EQ(result.get_termination_status(), +// cuopt::linear_programming::mip_termination_status_t::NoTermination); +// } + +// class MILPTestParams +// : public testing::TestWithParam< +// std::tuple> {}; + +// TEST_P(MILPTestParams, TestSampleMILP) +// { +// bool maximize = std::get<0>(GetParam()); +// bool scaling = std::get<1>(GetParam()); +// bool heuristics_only = std::get<2>(GetParam()); +// auto expected_termination_status = std::get<3>(GetParam()); + +// raft::handle_t handle; +// auto problem = create_std_milp_problem(maximize); + +// cuopt::linear_programming::mip_solver_settings_t settings{}; +// settings.time_limit = 5; +// settings.mip_scaling = scaling; +// settings.heuristics_only = heuristics_only; +// settings.presolve = false; + +// auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); + +// EXPECT_EQ(result.get_termination_status(), expected_termination_status); +// } + +// TEST_P(MILPTestParams, TestSingleVarMILP) +// { +// bool maximize = std::get<0>(GetParam()); +// bool scaling = std::get<1>(GetParam()); +// bool heuristics_only = std::get<2>(GetParam()); +// auto expected_termination_status = std::get<3>(GetParam()); + +// raft::handle_t handle; +// auto problem = create_single_var_milp_problem(maximize); + +// cuopt::linear_programming::mip_solver_settings_t settings{}; +// settings.time_limit = 5; +// settings.mip_scaling = scaling; +// settings.heuristics_only = heuristics_only; +// settings.presolve = false; + +// auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); + +// EXPECT_EQ(result.get_termination_status(), +// cuopt::linear_programming::mip_termination_status_t::Optimal); +// } + +// INSTANTIATE_TEST_SUITE_P( +// MILPTests, +// MILPTestParams, +// testing::Values( +// std::make_tuple(true, true, true, +// cuopt::linear_programming::mip_termination_status_t::Optimal), std::make_tuple( +// false, true, false, cuopt::linear_programming::mip_termination_status_t::Optimal), +// std::make_tuple( +// true, false, true, cuopt::linear_programming::mip_termination_status_t::Optimal), +// std::make_tuple( +// false, false, false, cuopt::linear_programming::mip_termination_status_t::Optimal))); + +// TEST_P(MILPTestParams, TestDeterminism) +// { +// bool maximize = std::get<0>(GetParam()); +// bool scaling = std::get<1>(GetParam()); +// bool heuristics_only = std::get<2>(GetParam()); +// auto expected_termination_status = std::get<3>(GetParam()); + +// raft::handle_t handle; +// auto problem = create_std_milp_problem(maximize); + +// cuopt::linear_programming::mip_solver_settings_t settings{}; +// settings.mip_scaling = true; +// settings.heuristics_only = true; +// settings.presolve = true; +// settings.deterministic = true; + +// auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); + +// EXPECT_EQ(result.get_termination_status(), expected_termination_status); +// } } // namespace cuopt::linear_programming::test From d5677fe66a5abad1b81293f7b4c47499544abf3f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 28 Oct 2025 16:59:54 +0000 Subject: [PATCH 012/366] adding cupti --- cpp/src/mip/diversity/diversity_config.hpp | 1 + cpp/src/mip/diversity/diversity_manager.cu | 3 +- .../feasibility_pump/feasibility_pump.cu | 17 +- cpp/src/mip/local_search/local_search.cu | 10 +- cpp/src/mip/local_search/local_search.cuh | 3 +- cpp/src/mip/relaxed_lp/relaxed_lp.cu | 18 +- cpp/tests/CMakeLists.txt | 48 +++ cpp/tests/mip/diversity_test.cu | 108 +++++- cpp/tests/utilities/CMakeLists.txt | 1 + cpp/tests/utilities/Eval.h | 274 +++++++++++++ cpp/tests/utilities/Metric.h | 245 ++++++++++++ cpp/tests/utilities/helper_cupti.h | 165 ++++++++ cpp/tests/utilities/test_cupti.cu | 361 ++++++++++++++++++ 13 files changed, 1221 insertions(+), 33 deletions(-) create mode 100644 cpp/tests/utilities/Eval.h create mode 100644 cpp/tests/utilities/Metric.h create mode 100644 cpp/tests/utilities/helper_cupti.h create mode 100644 cpp/tests/utilities/test_cupti.cu diff --git a/cpp/src/mip/diversity/diversity_config.hpp b/cpp/src/mip/diversity/diversity_config.hpp index 6149cf8e1..73d91545b 100644 --- a/cpp/src/mip/diversity/diversity_config.hpp +++ b/cpp/src/mip/diversity/diversity_config.hpp @@ -43,6 +43,7 @@ struct diversity_config_t { bool fj_only_run = false; bool dry_run = false; bool initial_solution_only = false; + int n_fp_iterations = 1000000; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 1713c1b7b..d1ebde3b9 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -286,7 +286,7 @@ void diversity_manager_t::run_fp_alone() { CUOPT_LOG_DEBUG("Running FP alone!"); solution_t sol(population.best_feasible()); - ls.run_fp(sol, timer, &population); + ls.run_fp(sol, timer, &population, diversity_config.n_fp_iterations); CUOPT_LOG_DEBUG("FP alone finished!"); } @@ -428,6 +428,7 @@ solution_t diversity_manager_t::run_solver() return sol; } generate_solution(timer.remaining_time(), false); + printf("=======================================================\n"); if (diversity_config.initial_solution_only) { return population.best_feasible(); } if (timer.check_time_limit()) { population.add_external_solutions_to_population(); diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index d23bb09c5..4f0d3cff7 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -161,10 +161,10 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_tinteger_indices, solution.handle_ptr->get_stream()); f_t obj_offset = 0; - CUOPT_LOG_DEBUG("FP: h_integer_indices hash 0x%x", detail::compute_hash(h_integer_indices)); - CUOPT_LOG_DEBUG("FP: h_assignment hash 0x%x", detail::compute_hash(h_assignment)); - CUOPT_LOG_DEBUG("FP: h_variable_bounds hash 0x%x", detail::compute_hash(h_variable_bounds)); - CUOPT_LOG_DEBUG("FP: h_last_projection hash 0x%x", detail::compute_hash(h_last_projection)); + // CUOPT_LOG_DEBUG("FP: h_integer_indices hash 0x%x", detail::compute_hash(h_integer_indices)); + // CUOPT_LOG_DEBUG("FP: h_assignment hash 0x%x", detail::compute_hash(h_assignment)); + // CUOPT_LOG_DEBUG("FP: h_variable_bounds hash 0x%x", detail::compute_hash(h_variable_bounds)); + // CUOPT_LOG_DEBUG("FP: h_last_projection hash 0x%x", detail::compute_hash(h_last_projection)); // for each integer add the variable and the distance constraints for (auto i : h_integer_indices) { auto h_var_bounds = h_variable_bounds[i]; @@ -202,7 +202,8 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_t 0) { temp_p.insert_variables(h_variables); } @@ -238,8 +239,8 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_t::run_single_fp_descent(solution_t& s solution.assignment.size(), solution.handle_ptr->get_stream()); - CUOPT_LOG_DEBUG("FP: starting FP descent, sol hash 0x%x", solution.get_hash()); + // CUOPT_LOG_DEBUG("FP: starting FP descent, sol hash 0x%x", solution.get_hash()); while (true) { if (timer.check_time_limit()) { CUOPT_LOG_DEBUG("FP time limit reached!"); diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 5df43aaf5..f3f8888b2 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -504,7 +504,10 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu fj.settings.update_weights = true; fj.settings.feasibility_run = false; f_t fj_time_limit = 30.; - if (context.settings.deterministic) { fj_time_limit = std::numeric_limits::infinity(); } + if (context.settings.deterministic) { + fj_time_limit = std::numeric_limits::infinity(); + fj.settings.iteration_limit = 100'000; + } f_t time_limit = std::min(fj_time_limit, timer.remaining_time()); do_fj_solve(solution, fj, time_limit, "on_lp_optimal"); return solution.get_feasible(); @@ -724,11 +727,11 @@ void local_search_t::reset_alpha_and_run_recombiners( template bool local_search_t::run_fp(solution_t& solution, timer_t timer, - population_t* population_ptr) + population_t* population_ptr, + i_t n_fp_iterations) { raft::common::nvtx::range fun_scope("run_fp"); cuopt_assert(population_ptr != nullptr, "Population pointer must not be null"); - const i_t n_fp_iterations = 1000000; bool is_feasible = solution.compute_feasibility(); cutting_plane_added_for_active_run = is_feasible; double best_objective = @@ -758,6 +761,7 @@ bool local_search_t::run_fp(solution_t& solution, is_feasible = false; break; } + printf("------------------------------------------------------------------------\n"); CUOPT_LOG_DEBUG("fp_loop it %d last_improved_iteration %d", i, last_improved_iteration); if (population_ptr->preempt_heuristic_solver_.load()) { CUOPT_LOG_DEBUG("Preempting heuristic solver!"); diff --git a/cpp/src/mip/local_search/local_search.cuh b/cpp/src/mip/local_search/local_search.cuh index a8fc6a77f..ff914a876 100644 --- a/cpp/src/mip/local_search/local_search.cuh +++ b/cpp/src/mip/local_search/local_search.cuh @@ -110,7 +110,8 @@ class local_search_t { population_t* population_ptr); bool run_fp(solution_t& solution, timer_t timer, - population_t* population_ptr = nullptr); + population_t* population_ptr = nullptr, + i_t n_fp_iterations = 1000000); void resize_vectors(problem_t& problem, const raft::handle_t* handle_ptr); bool do_fj_solve(solution_t& solution, diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index 104d34413..a705e3c22 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -90,22 +90,22 @@ optimization_problem_solution_t get_relaxed_lp_solution( lp_solver.set_initial_primal_solution(assignment); lp_solver.set_initial_dual_solution(lp_state.prev_dual); } - CUOPT_LOG_DEBUG( - "running LP with n_vars %d n_cstr %d", op_problem.n_variables, op_problem.n_constraints); + // CUOPT_LOG_DEBUG( + // "running LP with n_vars %d n_cstr %d", op_problem.n_variables, op_problem.n_constraints); // before LP flush the logs as it takes quite some time cuopt::default_logger().flush(); // temporarily add timer auto start_time = timer_t(pdlp_settings.time_limit); lp_solver.set_inside_mip(true); - CUOPT_LOG_DEBUG("prev primal hash 0x%x", detail::compute_hash(assignment)); - CUOPT_LOG_DEBUG("prev dual hash 0x%x", detail::compute_hash(lp_state.prev_dual)); + // CUOPT_LOG_DEBUG("prev primal hash 0x%x", detail::compute_hash(assignment)); + // CUOPT_LOG_DEBUG("prev dual hash 0x%x", detail::compute_hash(lp_state.prev_dual)); auto solver_response = lp_solver.run_solver(start_time); - CUOPT_LOG_DEBUG("post LP primal hash 0x%x", - detail::compute_hash(solver_response.get_primal_solution())); + // CUOPT_LOG_DEBUG("post LP primal hash 0x%x", + // detail::compute_hash(solver_response.get_primal_solution())); if (solver_response.get_primal_solution().size() != 0 && solver_response.get_dual_solution().size() != 0 && settings.save_state) { - CUOPT_LOG_DEBUG("saving initial primal solution of size %d", lp_state.prev_primal.size()); + // CUOPT_LOG_DEBUG("saving initial primal solution of size %d", lp_state.prev_primal.size()); lp_state.set_state(solver_response.get_primal_solution(), solver_response.get_dual_solution()); } if (solver_response.get_primal_solution().size() != 0) { @@ -116,8 +116,8 @@ optimization_problem_solution_t get_relaxed_lp_solution( op_problem.handle_ptr->get_stream()); } if (solver_response.get_termination_status() == pdlp_termination_status_t::Optimal) { - CUOPT_LOG_DEBUG("feasible solution found with LP objective %f", - solver_response.get_objective_value()); + // CUOPT_LOG_DEBUG("feasible solution found with LP objective %f", + // solver_response.get_objective_value()); } else { CUOPT_LOG_DEBUG("LP returned with reason %d, %d iterations", solver_response.get_termination_status(), diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index ff6da8894..0d3ff7116 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -40,6 +40,44 @@ endif() set(CUOPT_TEST_DIR ${CMAKE_CURRENT_SOURCE_DIR}) +if (NOT TARGET CUDA::toolkit) + find_package(CUDAToolkit REQUIRED) +endif() + +if (EXISTS "${CUDAToolkit_LIBRARY_ROOT}/extras/CUPTI/lib64") + # NVIDIA installer layout: + set(cuopt_cupti_root "${CUDAToolkit_LIBRARY_ROOT}/extras/CUPTI") +else() + # Ubuntu package layout: + set(cuopt_cupti_root "${CUDAToolkit_LIBRARY_ROOT}") +endif() +message(STATUS "cuopt_cupti_root = ${cuopt_cupti_root}") + +# The CUPTI targets in FindCUDAToolkit are broken: +# - The dll locations are not specified +# - Dependent libraries nvperf_* are not linked. +# So we create our own targets: +function(cuopt_add_cupti_dep dep_name) + string(TOLOWER ${dep_name} dep_name_lower) + string(TOUPPER ${dep_name} dep_name_upper) + + add_library(cuopt::${dep_name_lower} SHARED IMPORTED) + + find_library(CUOPT_${dep_name_upper}_LIBRARY ${dep_name_lower} REQUIRED + DOC "The full path to lib${dep_name_lower}.so from the CUDA Toolkit." + HINTS "${cuopt_cupti_root}/lib64" "${cuopt_cupti_root}/lib" + ) + mark_as_advanced(CUOPT_${dep_name_upper}_LIBRARY) + + set_target_properties(cuopt::${dep_name_lower} PROPERTIES + IMPORTED_LOCATION "${CUOPT_${dep_name_upper}_LIBRARY}" + ) +endfunction() + +cuopt_add_cupti_dep(nvperf_target) +cuopt_add_cupti_dep(nvperf_host) +cuopt_add_cupti_dep(cupti) + # ################################################################ ------------------------------------------------------------------ function(ConfigureTest CMAKE_TEST_NAME) add_executable(${CMAKE_TEST_NAME} ${ARGN}) @@ -63,6 +101,16 @@ function(ConfigureTest CMAKE_TEST_NAME) ${CUOPT_PRIVATE_CUDA_LIBS} ) + target_link_libraries(${CMAKE_TEST_NAME} PRIVATE + #cuopt::nvperf_target + #cuopt::nvperf_host + cuopt::cupti + cuda + ) + target_include_directories(${CMAKE_TEST_NAME} PRIVATE + "${cuopt_cupti_root}/include" + ) + add_test(NAME ${CMAKE_TEST_NAME} COMMAND ${CMAKE_TEST_NAME}) install( diff --git a/cpp/tests/mip/diversity_test.cu b/cpp/tests/mip/diversity_test.cu index a200858c4..b891aa87f 100644 --- a/cpp/tests/mip/diversity_test.cu +++ b/cpp/tests/mip/diversity_test.cu @@ -72,6 +72,61 @@ static void setup_device_symbols(rmm::cuda_stream_view stream_view) detail::set_pdlp_hyper_parameters(stream_view); } +static uint32_t test_full_run_determinism(std::string path, + unsigned long seed = std::random_device{}()) +{ + const raft::handle_t handle_{}; + + cuopt::mps_parser::mps_data_model_t mps_problem = + cuopt::mps_parser::parse_mps(path, false); + handle_.sync_stream(); + auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); + problem_checking_t::check_problem_representation(op_problem); + + init_handler(op_problem.get_handle_ptr()); + // run the problem constructor of MIP, so that we do bounds standardization + detail::problem_t problem(op_problem); + problem.preprocess_problem(); + + setup_device_symbols(op_problem.get_handle_ptr()->get_stream()); + + detail::pdlp_initial_scaling_strategy_t scaling(&handle_, + problem, + 10, + 1.0, + problem.reverse_coefficients, + problem.reverse_offsets, + problem.reverse_constraints, + nullptr, + true); + + auto settings = mip_solver_settings_t{}; + settings.time_limit = 3000.; + settings.deterministic = true; + settings.heuristics_only = true; + auto timer = cuopt::timer_t(3000); + detail::mip_solver_t solver(problem, settings, scaling, timer); + problem.tolerances = settings.get_tolerances(); + + detail::diversity_manager_t diversity_manager(solver.context); + diversity_manager.timer = timer_t(60000); + diversity_manager.diversity_config.n_fp_iterations = 3; + diversity_manager.run_solver(); + + std::vector hashes; + auto pop = diversity_manager.get_population_pointer(); + for (const auto& sol : pop->population_to_vector()) { + hashes.push_back(sol.get_hash()); + } + + uint32_t final_hash = detail::compute_hash(hashes); + printf("%s: final hash: 0x%x, pop size %d\n", + path.c_str(), + final_hash, + (int)pop->population_to_vector().size()); + return final_hash; +} + static uint32_t test_initial_solution_determinism(std::string path, unsigned long seed = std::random_device{}()) { @@ -272,7 +327,37 @@ class DiversityTestParams : public testing::TestWithParam(GetParam()); +// std::cout << "Running: " << test_instance << std::endl; +// int seed = +// std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); +// std::cerr << "Tested with seed " << seed << "\n"; +// auto path = make_path_absolute(test_instance); +// test_instance = std::getenv("CUOPT_INSTANCE") ? std::getenv("CUOPT_INSTANCE") : test_instance; +// path = "/home/scratch.yboucher_gpu_1/collection/" + test_instance; +// uint32_t gold_hash = 0; +// for (int i = 0; i < 2; ++i) { +// cuopt::seed_generator::set_seed(seed); +// std::cout << "Running " << test_instance << " " << i << std::endl; +// std::cout << "-------------------------------------------------------------\n"; +// auto hash = test_initial_solution_determinism(path, seed); +// if (i == 0) { +// gold_hash = hash; +// std::cout << "Gold hash: " << gold_hash << std::endl; +// } else { +// ASSERT_EQ(hash, gold_hash); +// } +// } +// } + +TEST_P(DiversityTestParams, full_run_deterministic) { cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); @@ -292,7 +377,7 @@ TEST_P(DiversityTestParams, initial_solution_deterministic) cuopt::seed_generator::set_seed(seed); std::cout << "Running " << test_instance << " " << i << std::endl; std::cout << "-------------------------------------------------------------\n"; - auto hash = test_initial_solution_determinism(path, seed); + auto hash = test_full_run_determinism(path, seed); if (i == 0) { gold_hash = hash; std::cout << "Gold hash: " << gold_hash << std::endl; @@ -304,14 +389,15 @@ TEST_P(DiversityTestParams, initial_solution_deterministic) INSTANTIATE_TEST_SUITE_P(DiversityTest, DiversityTestParams, - testing::Values(std::make_tuple("gen-ip054.mps"), - std::make_tuple("pk1.mps"), - std::make_tuple("fastxgemm-n2r6s0t2.mps"), - // std::make_tuple("mip/sct2.mps") - // std::make_tuple("mip/thor50dday.mps") - std::make_tuple("uccase9.mps"), - // std::make_tuple("mip/neos5.mps") - std::make_tuple("50v-10.mps"), - std::make_tuple("rmatr200-p5.mps"))); + testing::Values( // std::make_tuple("gen-ip054.mps"), + // std::make_tuple("pk1.mps") + std::make_tuple("uccase9.mps"), + // std::make_tuple("mip/sct2.mps") + // std::make_tuple("mip/thor50dday.mps") + // std::make_tuple("uccase9.mps"), + // std::make_tuple("mip/neos5.mps") + std::make_tuple("50v-10.mps") + // std::make_tuple("rmatr200-p5.mps") + )); } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/utilities/CMakeLists.txt b/cpp/tests/utilities/CMakeLists.txt index e893345c0..7a40ee6e7 100644 --- a/cpp/tests/utilities/CMakeLists.txt +++ b/cpp/tests/utilities/CMakeLists.txt @@ -15,3 +15,4 @@ # Add CLI end-to-end test ConfigureTest(CLI_TEST test_cli.cpp) +ConfigureTest(CUPTI_TEST test_cupti.cu) diff --git a/cpp/tests/utilities/Eval.h b/cpp/tests/utilities/Eval.h new file mode 100644 index 000000000..ccafcd1ae --- /dev/null +++ b/cpp/tests/utilities/Eval.h @@ -0,0 +1,274 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include + +template + +class ScopeExit { + public: + ScopeExit(T t) : t(t) {} + ~ScopeExit() { t(); } + T t; +}; + +template +ScopeExit MoveScopeExit(T t) +{ + return ScopeExit(t); +}; + +#define NV_ANONYMOUS_VARIABLE_DIRECT(name, line) name##line +#define NV_ANONYMOUS_VARIABLE_INDIRECT(name, line) NV_ANONYMOUS_VARIABLE_DIRECT(name, line) + +#define SCOPE_EXIT(func) \ + const auto NV_ANONYMOUS_VARIABLE_INDIRECT(EXIT, __LINE__) = MoveScopeExit([=]() { func; }) + +namespace NV { +namespace Metric { +namespace Eval { +std::string GetHwUnit(const std::string& metricName) +{ + return metricName.substr(0, metricName.find("__", 0)); +} + +bool GetMetricGpuValue(std::string chipName, + const std::vector& counterDataImage, + const std::vector& metricNames, + std::vector& metricNameValueMap, + const uint8_t* pCounterAvailabilityImage) +{ + if (!counterDataImage.size()) { + std::cout << "Counter Data Image is empty!\n"; + return false; + } + + NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params calculateScratchBufferSizeParam = { + NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params_STRUCT_SIZE}; + calculateScratchBufferSizeParam.pChipName = chipName.c_str(); + calculateScratchBufferSizeParam.pCounterAvailabilityImage = pCounterAvailabilityImage; + RETURN_IF_NVPW_ERROR( + false, NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize(&calculateScratchBufferSizeParam)); + + std::vector scratchBuffer(calculateScratchBufferSizeParam.scratchBufferSize); + NVPW_CUDA_MetricsEvaluator_Initialize_Params metricEvaluatorInitializeParams = { + NVPW_CUDA_MetricsEvaluator_Initialize_Params_STRUCT_SIZE}; + metricEvaluatorInitializeParams.scratchBufferSize = scratchBuffer.size(); + metricEvaluatorInitializeParams.pScratchBuffer = scratchBuffer.data(); + metricEvaluatorInitializeParams.pChipName = chipName.c_str(); + metricEvaluatorInitializeParams.pCounterAvailabilityImage = pCounterAvailabilityImage; + RETURN_IF_NVPW_ERROR(false, + NVPW_CUDA_MetricsEvaluator_Initialize(&metricEvaluatorInitializeParams)); + NVPW_MetricsEvaluator* metricEvaluator = metricEvaluatorInitializeParams.pMetricsEvaluator; + + NVPW_CounterData_GetNumRanges_Params getNumRangesParams = { + NVPW_CounterData_GetNumRanges_Params_STRUCT_SIZE}; + getNumRangesParams.pCounterDataImage = counterDataImage.data(); + RETURN_IF_NVPW_ERROR(false, NVPW_CounterData_GetNumRanges(&getNumRangesParams)); + + bool isolated = true; + bool keepInstances = true; + for (size_t metricIndex = 0; metricIndex < metricNames.size(); ++metricIndex) { + std::string reqName; + NV::Metric::Parser::ParseMetricNameString( + metricNames[metricIndex], &reqName, &isolated, &keepInstances); + NVPW_MetricEvalRequest metricEvalRequest; + NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params convertMetricToEvalRequest = { + NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params_STRUCT_SIZE}; + convertMetricToEvalRequest.pMetricsEvaluator = metricEvaluator; + convertMetricToEvalRequest.pMetricName = reqName.c_str(); + convertMetricToEvalRequest.pMetricEvalRequest = &metricEvalRequest; + convertMetricToEvalRequest.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; + RETURN_IF_NVPW_ERROR( + false, + NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest(&convertMetricToEvalRequest)); + + MetricNameValue metricNameValue; + metricNameValue.numRanges = getNumRangesParams.numRanges; + metricNameValue.metricName = metricNames[metricIndex]; + for (size_t rangeIndex = 0; rangeIndex < getNumRangesParams.numRanges; ++rangeIndex) { + NVPW_Profiler_CounterData_GetRangeDescriptions_Params getRangeDescParams = { + NVPW_Profiler_CounterData_GetRangeDescriptions_Params_STRUCT_SIZE}; + getRangeDescParams.pCounterDataImage = counterDataImage.data(); + getRangeDescParams.rangeIndex = rangeIndex; + RETURN_IF_NVPW_ERROR(false, + NVPW_Profiler_CounterData_GetRangeDescriptions(&getRangeDescParams)); + std::vector descriptionPtrs(getRangeDescParams.numDescriptions); + getRangeDescParams.ppDescriptions = descriptionPtrs.data(); + RETURN_IF_NVPW_ERROR(false, + NVPW_Profiler_CounterData_GetRangeDescriptions(&getRangeDescParams)); + + std::string rangeName; + for (size_t descriptionIndex = 0; descriptionIndex < getRangeDescParams.numDescriptions; + ++descriptionIndex) { + if (descriptionIndex) { rangeName += "/"; } + rangeName += descriptionPtrs[descriptionIndex]; + } + + NVPW_MetricsEvaluator_SetDeviceAttributes_Params setDeviceAttribParams = { + NVPW_MetricsEvaluator_SetDeviceAttributes_Params_STRUCT_SIZE}; + setDeviceAttribParams.pMetricsEvaluator = metricEvaluator; + setDeviceAttribParams.pCounterDataImage = counterDataImage.data(); + setDeviceAttribParams.counterDataImageSize = counterDataImage.size(); + RETURN_IF_NVPW_ERROR(false, + NVPW_MetricsEvaluator_SetDeviceAttributes(&setDeviceAttribParams)); + + double metricValue = 0.0; + NVPW_MetricsEvaluator_EvaluateToGpuValues_Params evaluateToGpuValuesParams = { + NVPW_MetricsEvaluator_EvaluateToGpuValues_Params_STRUCT_SIZE}; + evaluateToGpuValuesParams.pMetricsEvaluator = metricEvaluator; + evaluateToGpuValuesParams.pMetricEvalRequests = &metricEvalRequest; + evaluateToGpuValuesParams.numMetricEvalRequests = 1; + evaluateToGpuValuesParams.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; + evaluateToGpuValuesParams.metricEvalRequestStrideSize = sizeof(NVPW_MetricEvalRequest); + evaluateToGpuValuesParams.pCounterDataImage = counterDataImage.data(); + evaluateToGpuValuesParams.counterDataImageSize = counterDataImage.size(); + evaluateToGpuValuesParams.rangeIndex = rangeIndex; + evaluateToGpuValuesParams.isolated = true; + evaluateToGpuValuesParams.pMetricValues = &metricValue; + RETURN_IF_NVPW_ERROR(false, + NVPW_MetricsEvaluator_EvaluateToGpuValues(&evaluateToGpuValuesParams)); + metricNameValue.rangeNameMetricValueMap.push_back(std::make_pair(rangeName, metricValue)); + } + metricNameValueMap.push_back(metricNameValue); + } + + NVPW_MetricsEvaluator_Destroy_Params metricEvaluatorDestroyParams = { + NVPW_MetricsEvaluator_Destroy_Params_STRUCT_SIZE}; + metricEvaluatorDestroyParams.pMetricsEvaluator = metricEvaluator; + RETURN_IF_NVPW_ERROR(false, NVPW_MetricsEvaluator_Destroy(&metricEvaluatorDestroyParams)); + return true; +} + +bool PrintMetricValues(std::string chipName, + const std::vector& counterDataImage, + const std::vector& metricNames, + const uint8_t* pCounterAvailabilityImage) +{ + if (!counterDataImage.size()) { + std::cout << "Counter Data Image is empty!\n"; + return false; + } + + NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params calculateScratchBufferSizeParam = { + NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params_STRUCT_SIZE}; + calculateScratchBufferSizeParam.pChipName = chipName.c_str(); + calculateScratchBufferSizeParam.pCounterAvailabilityImage = pCounterAvailabilityImage; + RETURN_IF_NVPW_ERROR( + false, NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize(&calculateScratchBufferSizeParam)); + + std::vector scratchBuffer(calculateScratchBufferSizeParam.scratchBufferSize); + NVPW_CUDA_MetricsEvaluator_Initialize_Params metricEvaluatorInitializeParams = { + NVPW_CUDA_MetricsEvaluator_Initialize_Params_STRUCT_SIZE}; + metricEvaluatorInitializeParams.scratchBufferSize = scratchBuffer.size(); + metricEvaluatorInitializeParams.pScratchBuffer = scratchBuffer.data(); + metricEvaluatorInitializeParams.pChipName = chipName.c_str(); + metricEvaluatorInitializeParams.pCounterAvailabilityImage = pCounterAvailabilityImage; + metricEvaluatorInitializeParams.pCounterDataImage = counterDataImage.data(); + metricEvaluatorInitializeParams.counterDataImageSize = counterDataImage.size(); + RETURN_IF_NVPW_ERROR(false, + NVPW_CUDA_MetricsEvaluator_Initialize(&metricEvaluatorInitializeParams)); + NVPW_MetricsEvaluator* metricEvaluator = metricEvaluatorInitializeParams.pMetricsEvaluator; + + NVPW_CounterData_GetNumRanges_Params getNumRangesParams = { + NVPW_CounterData_GetNumRanges_Params_STRUCT_SIZE}; + getNumRangesParams.pCounterDataImage = counterDataImage.data(); + RETURN_IF_NVPW_ERROR(false, NVPW_CounterData_GetNumRanges(&getNumRangesParams)); + + std::cout << "\n" + << std::setw(40) << std::left << "Range Name" << std::setw(100) << std::left + << "Metric Name" + << "Metric Value" << std::endl; + std::cout << std::setfill('-') << std::setw(160) << "" << std::setfill(' ') << std::endl; + + std::string reqName; + bool isolated = true; + bool keepInstances = true; + for (std::string metricName : metricNames) { + NV::Metric::Parser::ParseMetricNameString(metricName, &reqName, &isolated, &keepInstances); + NVPW_MetricEvalRequest metricEvalRequest; + NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params convertMetricToEvalRequest = { + NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params_STRUCT_SIZE}; + convertMetricToEvalRequest.pMetricsEvaluator = metricEvaluator; + convertMetricToEvalRequest.pMetricName = reqName.c_str(); + convertMetricToEvalRequest.pMetricEvalRequest = &metricEvalRequest; + convertMetricToEvalRequest.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; + RETURN_IF_NVPW_ERROR( + false, + NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest(&convertMetricToEvalRequest)); + + for (size_t rangeIndex = 0; rangeIndex < getNumRangesParams.numRanges; ++rangeIndex) { + NVPW_Profiler_CounterData_GetRangeDescriptions_Params getRangeDescParams = { + NVPW_Profiler_CounterData_GetRangeDescriptions_Params_STRUCT_SIZE}; + getRangeDescParams.pCounterDataImage = counterDataImage.data(); + getRangeDescParams.rangeIndex = rangeIndex; + RETURN_IF_NVPW_ERROR(false, + NVPW_Profiler_CounterData_GetRangeDescriptions(&getRangeDescParams)); + std::vector descriptionPtrs(getRangeDescParams.numDescriptions); + getRangeDescParams.ppDescriptions = descriptionPtrs.data(); + RETURN_IF_NVPW_ERROR(false, + NVPW_Profiler_CounterData_GetRangeDescriptions(&getRangeDescParams)); + + std::string rangeName; + for (size_t descriptionIndex = 0; descriptionIndex < getRangeDescParams.numDescriptions; + ++descriptionIndex) { + if (descriptionIndex) { rangeName += "/"; } + rangeName += descriptionPtrs[descriptionIndex]; + } + + NVPW_MetricsEvaluator_SetDeviceAttributes_Params setDeviceAttribParams = { + NVPW_MetricsEvaluator_SetDeviceAttributes_Params_STRUCT_SIZE}; + setDeviceAttribParams.pMetricsEvaluator = metricEvaluator; + setDeviceAttribParams.pCounterDataImage = counterDataImage.data(); + setDeviceAttribParams.counterDataImageSize = counterDataImage.size(); + RETURN_IF_NVPW_ERROR(false, + NVPW_MetricsEvaluator_SetDeviceAttributes(&setDeviceAttribParams)); + + double metricValue; + NVPW_MetricsEvaluator_EvaluateToGpuValues_Params evaluateToGpuValuesParams = { + NVPW_MetricsEvaluator_EvaluateToGpuValues_Params_STRUCT_SIZE}; + evaluateToGpuValuesParams.pMetricsEvaluator = metricEvaluator; + evaluateToGpuValuesParams.pMetricEvalRequests = &metricEvalRequest; + evaluateToGpuValuesParams.numMetricEvalRequests = 1; + evaluateToGpuValuesParams.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; + evaluateToGpuValuesParams.metricEvalRequestStrideSize = sizeof(NVPW_MetricEvalRequest); + evaluateToGpuValuesParams.pCounterDataImage = counterDataImage.data(); + evaluateToGpuValuesParams.counterDataImageSize = counterDataImage.size(); + evaluateToGpuValuesParams.rangeIndex = rangeIndex; + evaluateToGpuValuesParams.isolated = true; + evaluateToGpuValuesParams.pMetricValues = &metricValue; + RETURN_IF_NVPW_ERROR(false, + NVPW_MetricsEvaluator_EvaluateToGpuValues(&evaluateToGpuValuesParams)); + + std::cout << std::setw(40) << std::left << rangeName << std::setw(100) << std::left + << metricName << metricValue << std::endl; + } + } + + NVPW_MetricsEvaluator_Destroy_Params metricEvaluatorDestroyParams = { + NVPW_MetricsEvaluator_Destroy_Params_STRUCT_SIZE}; + metricEvaluatorDestroyParams.pMetricsEvaluator = metricEvaluator; + RETURN_IF_NVPW_ERROR(false, NVPW_MetricsEvaluator_Destroy(&metricEvaluatorDestroyParams)); + return true; +} +} // namespace Eval +} // namespace Metric +} // namespace NV diff --git a/cpp/tests/utilities/Metric.h b/cpp/tests/utilities/Metric.h new file mode 100644 index 000000000..05261c8e6 --- /dev/null +++ b/cpp/tests/utilities/Metric.h @@ -0,0 +1,245 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include + +class ScopeExit { + public: + ScopeExit(T t) : t(t) {} + ~ScopeExit() { t(); } + T t; +}; + +template +ScopeExit MoveScopeExit(T t) +{ + return ScopeExit(t); +}; + +#define NV_ANONYMOUS_VARIABLE_DIRECT(name, line) name##line +#define NV_ANONYMOUS_VARIABLE_INDIRECT(name, line) NV_ANONYMOUS_VARIABLE_DIRECT(name, line) + +#define SCOPE_EXIT(func) \ + const auto NV_ANONYMOUS_VARIABLE_INDIRECT(EXIT, __LINE__) = MoveScopeExit([=]() { func; }) + +namespace NV { +namespace Metric { +namespace Config { + +bool GetRawMetricRequests(std::string chipName, + const std::vector& metricNames, + std::vector& rawMetricRequests, + const uint8_t* pCounterAvailabilityImage) +{ + NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params calculateScratchBufferSizeParam = { + NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params_STRUCT_SIZE}; + calculateScratchBufferSizeParam.pChipName = chipName.c_str(); + calculateScratchBufferSizeParam.pCounterAvailabilityImage = pCounterAvailabilityImage; + RETURN_IF_NVPW_ERROR( + false, NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize(&calculateScratchBufferSizeParam)); + + std::vector scratchBuffer(calculateScratchBufferSizeParam.scratchBufferSize); + NVPW_CUDA_MetricsEvaluator_Initialize_Params metricEvaluatorInitializeParams = { + NVPW_CUDA_MetricsEvaluator_Initialize_Params_STRUCT_SIZE}; + metricEvaluatorInitializeParams.scratchBufferSize = scratchBuffer.size(); + metricEvaluatorInitializeParams.pScratchBuffer = scratchBuffer.data(); + metricEvaluatorInitializeParams.pChipName = chipName.c_str(); + metricEvaluatorInitializeParams.pCounterAvailabilityImage = pCounterAvailabilityImage; + RETURN_IF_NVPW_ERROR(false, + NVPW_CUDA_MetricsEvaluator_Initialize(&metricEvaluatorInitializeParams)); + NVPW_MetricsEvaluator* metricEvaluator = metricEvaluatorInitializeParams.pMetricsEvaluator; + + bool isolated = true; + bool keepInstances = true; + std::vector rawMetricNames; + for (auto& metricName : metricNames) { + std::string reqName; + NV::Metric::Parser::ParseMetricNameString(metricName, &reqName, &isolated, &keepInstances); + keepInstances = true; + NVPW_MetricEvalRequest metricEvalRequest; + NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params convertMetricToEvalRequest = { + NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params_STRUCT_SIZE}; + convertMetricToEvalRequest.pMetricsEvaluator = metricEvaluator; + convertMetricToEvalRequest.pMetricName = reqName.c_str(); + convertMetricToEvalRequest.pMetricEvalRequest = &metricEvalRequest; + convertMetricToEvalRequest.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; + RETURN_IF_NVPW_ERROR( + false, + NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest(&convertMetricToEvalRequest)); + + std::vector rawDependencies; + NVPW_MetricsEvaluator_GetMetricRawDependencies_Params getMetricRawDependenciesParms = { + NVPW_MetricsEvaluator_GetMetricRawDependencies_Params_STRUCT_SIZE}; + getMetricRawDependenciesParms.pMetricsEvaluator = metricEvaluator; + getMetricRawDependenciesParms.pMetricEvalRequests = &metricEvalRequest; + getMetricRawDependenciesParms.numMetricEvalRequests = 1; + getMetricRawDependenciesParms.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; + getMetricRawDependenciesParms.metricEvalRequestStrideSize = sizeof(NVPW_MetricEvalRequest); + RETURN_IF_NVPW_ERROR( + false, NVPW_MetricsEvaluator_GetMetricRawDependencies(&getMetricRawDependenciesParms)); + rawDependencies.resize(getMetricRawDependenciesParms.numRawDependencies); + getMetricRawDependenciesParms.ppRawDependencies = rawDependencies.data(); + RETURN_IF_NVPW_ERROR( + false, NVPW_MetricsEvaluator_GetMetricRawDependencies(&getMetricRawDependenciesParms)); + + for (size_t i = 0; i < rawDependencies.size(); ++i) { + rawMetricNames.push_back(rawDependencies[i]); + } + } + + for (auto& rawMetricName : rawMetricNames) { + NVPA_RawMetricRequest metricRequest = {NVPA_RAW_METRIC_REQUEST_STRUCT_SIZE}; + metricRequest.pMetricName = rawMetricName; + metricRequest.isolated = isolated; + metricRequest.keepInstances = keepInstances; + rawMetricRequests.push_back(metricRequest); + } + + NVPW_MetricsEvaluator_Destroy_Params metricEvaluatorDestroyParams = { + NVPW_MetricsEvaluator_Destroy_Params_STRUCT_SIZE}; + metricEvaluatorDestroyParams.pMetricsEvaluator = metricEvaluator; + RETURN_IF_NVPW_ERROR(false, NVPW_MetricsEvaluator_Destroy(&metricEvaluatorDestroyParams)); + return true; +} + +bool GetConfigImage(std::string chipName, + const std::vector& metricNames, + std::vector& configImage, + const uint8_t* pCounterAvailabilityImage) +{ + std::vector rawMetricRequests; + GetRawMetricRequests(chipName, metricNames, rawMetricRequests, pCounterAvailabilityImage); + + NVPW_CUDA_RawMetricsConfig_Create_V2_Params rawMetricsConfigCreateParams = { + NVPW_CUDA_RawMetricsConfig_Create_V2_Params_STRUCT_SIZE}; + rawMetricsConfigCreateParams.activityKind = NVPA_ACTIVITY_KIND_PROFILER; + rawMetricsConfigCreateParams.pChipName = chipName.c_str(); + rawMetricsConfigCreateParams.pCounterAvailabilityImage = pCounterAvailabilityImage; + RETURN_IF_NVPW_ERROR(false, NVPW_CUDA_RawMetricsConfig_Create_V2(&rawMetricsConfigCreateParams)); + NVPA_RawMetricsConfig* pRawMetricsConfig = rawMetricsConfigCreateParams.pRawMetricsConfig; + + if (pCounterAvailabilityImage) { + NVPW_RawMetricsConfig_SetCounterAvailability_Params setCounterAvailabilityParams = { + NVPW_RawMetricsConfig_SetCounterAvailability_Params_STRUCT_SIZE}; + setCounterAvailabilityParams.pRawMetricsConfig = pRawMetricsConfig; + setCounterAvailabilityParams.pCounterAvailabilityImage = pCounterAvailabilityImage; + RETURN_IF_NVPW_ERROR( + false, NVPW_RawMetricsConfig_SetCounterAvailability(&setCounterAvailabilityParams)); + } + + NVPW_RawMetricsConfig_Destroy_Params rawMetricsConfigDestroyParams = { + NVPW_RawMetricsConfig_Destroy_Params_STRUCT_SIZE}; + rawMetricsConfigDestroyParams.pRawMetricsConfig = pRawMetricsConfig; + SCOPE_EXIT([&]() { + NVPW_RawMetricsConfig_Destroy( + (NVPW_RawMetricsConfig_Destroy_Params*)&rawMetricsConfigDestroyParams); + }); + + NVPW_RawMetricsConfig_BeginPassGroup_Params beginPassGroupParams = { + NVPW_RawMetricsConfig_BeginPassGroup_Params_STRUCT_SIZE}; + beginPassGroupParams.pRawMetricsConfig = pRawMetricsConfig; + RETURN_IF_NVPW_ERROR(false, NVPW_RawMetricsConfig_BeginPassGroup(&beginPassGroupParams)); + + NVPW_RawMetricsConfig_AddMetrics_Params addMetricsParams = { + NVPW_RawMetricsConfig_AddMetrics_Params_STRUCT_SIZE}; + addMetricsParams.pRawMetricsConfig = pRawMetricsConfig; + addMetricsParams.pRawMetricRequests = rawMetricRequests.data(); + addMetricsParams.numMetricRequests = rawMetricRequests.size(); + RETURN_IF_NVPW_ERROR(false, NVPW_RawMetricsConfig_AddMetrics(&addMetricsParams)); + + NVPW_RawMetricsConfig_EndPassGroup_Params endPassGroupParams = { + NVPW_RawMetricsConfig_EndPassGroup_Params_STRUCT_SIZE}; + endPassGroupParams.pRawMetricsConfig = pRawMetricsConfig; + RETURN_IF_NVPW_ERROR(false, NVPW_RawMetricsConfig_EndPassGroup(&endPassGroupParams)); + + NVPW_RawMetricsConfig_GenerateConfigImage_Params generateConfigImageParams = { + NVPW_RawMetricsConfig_GenerateConfigImage_Params_STRUCT_SIZE}; + generateConfigImageParams.pRawMetricsConfig = pRawMetricsConfig; + RETURN_IF_NVPW_ERROR(false, + NVPW_RawMetricsConfig_GenerateConfigImage(&generateConfigImageParams)); + + NVPW_RawMetricsConfig_GetConfigImage_Params getConfigImageParams = { + NVPW_RawMetricsConfig_GetConfigImage_Params_STRUCT_SIZE}; + getConfigImageParams.pRawMetricsConfig = pRawMetricsConfig; + getConfigImageParams.bytesAllocated = 0; + getConfigImageParams.pBuffer = NULL; + RETURN_IF_NVPW_ERROR(false, NVPW_RawMetricsConfig_GetConfigImage(&getConfigImageParams)); + + configImage.resize(getConfigImageParams.bytesCopied); + getConfigImageParams.bytesAllocated = configImage.size(); + getConfigImageParams.pBuffer = configImage.data(); + RETURN_IF_NVPW_ERROR(false, NVPW_RawMetricsConfig_GetConfigImage(&getConfigImageParams)); + + return true; +} + +bool GetCounterDataPrefixImage(std::string chipName, + const std::vector& metricNames, + std::vector& counterDataImagePrefix, + const uint8_t* pCounterAvailabilityImage) +{ + std::vector rawMetricRequests; + GetRawMetricRequests(chipName, metricNames, rawMetricRequests, pCounterAvailabilityImage); + + NVPW_CUDA_CounterDataBuilder_Create_Params counterDataBuilderCreateParams = { + NVPW_CUDA_CounterDataBuilder_Create_Params_STRUCT_SIZE}; + counterDataBuilderCreateParams.pChipName = chipName.c_str(); + counterDataBuilderCreateParams.pCounterAvailabilityImage = pCounterAvailabilityImage; + RETURN_IF_NVPW_ERROR(false, NVPW_CUDA_CounterDataBuilder_Create(&counterDataBuilderCreateParams)); + + NVPW_CounterDataBuilder_Destroy_Params counterDataBuilderDestroyParams = { + NVPW_CounterDataBuilder_Destroy_Params_STRUCT_SIZE}; + counterDataBuilderDestroyParams.pCounterDataBuilder = + counterDataBuilderCreateParams.pCounterDataBuilder; + SCOPE_EXIT([&]() { + NVPW_CounterDataBuilder_Destroy( + (NVPW_CounterDataBuilder_Destroy_Params*)&counterDataBuilderDestroyParams); + }); + + NVPW_CounterDataBuilder_AddMetrics_Params addMetricsParams = { + NVPW_CounterDataBuilder_AddMetrics_Params_STRUCT_SIZE}; + addMetricsParams.pCounterDataBuilder = counterDataBuilderCreateParams.pCounterDataBuilder; + addMetricsParams.pRawMetricRequests = rawMetricRequests.data(); + addMetricsParams.numMetricRequests = rawMetricRequests.size(); + RETURN_IF_NVPW_ERROR(false, NVPW_CounterDataBuilder_AddMetrics(&addMetricsParams)); + + size_t counterDataPrefixSize = 0; + NVPW_CounterDataBuilder_GetCounterDataPrefix_Params getCounterDataPrefixParams = { + NVPW_CounterDataBuilder_GetCounterDataPrefix_Params_STRUCT_SIZE}; + getCounterDataPrefixParams.pCounterDataBuilder = + counterDataBuilderCreateParams.pCounterDataBuilder; + getCounterDataPrefixParams.bytesAllocated = 0; + getCounterDataPrefixParams.pBuffer = NULL; + RETURN_IF_NVPW_ERROR(false, + NVPW_CounterDataBuilder_GetCounterDataPrefix(&getCounterDataPrefixParams)); + + counterDataImagePrefix.resize(getCounterDataPrefixParams.bytesCopied); + getCounterDataPrefixParams.bytesAllocated = counterDataImagePrefix.size(); + getCounterDataPrefixParams.pBuffer = counterDataImagePrefix.data(); + RETURN_IF_NVPW_ERROR(false, + NVPW_CounterDataBuilder_GetCounterDataPrefix(&getCounterDataPrefixParams)); + + return true; +} + +} // namespace Config +} // namespace Metric +} // namespace NV diff --git a/cpp/tests/utilities/helper_cupti.h b/cpp/tests/utilities/helper_cupti.h new file mode 100644 index 000000000..0b5f2c998 --- /dev/null +++ b/cpp/tests/utilities/helper_cupti.h @@ -0,0 +1,165 @@ +/** + * Copyright (c) 2022-2025, NVIDIA CORPORATION. All rights reserved. + * + * Please refer to the NVIDIA end user license agreement (EULA) associated + * with this source code for terms and conditions that govern your use of + * this software. Any use, reproduction, disclosure, or distribution of + * this software and related documentation outside the terms of the EULA + * is strictly prohibited. + * + */ + +//////////////////////////////////////////////////////////////////////////////// + +#ifndef HELPER_CUPTI_H_ +#define HELPER_CUPTI_H_ + +#pragma once + +#include + +#ifndef EXIT_WAIVED +#define EXIT_WAIVED 2 +#endif + +#if defined(WIN32) || defined(_WIN32) +#define stricmp _stricmp +#else +#define stricmp strcasecmp +#endif + +#define CUDA_MAX_DEVICES 256 // consider theoretical max devices as 256 + +#ifndef DRIVER_API_CALL +#define DRIVER_API_CALL(apiFunctionCall) \ + do { \ + CUresult _status = apiFunctionCall; \ + if (_status != CUDA_SUCCESS) { \ + const char* pErrorString; \ + cuGetErrorString(_status, &pErrorString); \ + \ + std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ + << #apiFunctionCall << " failed with error(" << _status << "): " << pErrorString \ + << ".\n\n"; \ + \ + exit(EXIT_FAILURE); \ + } \ + } while (0) +#endif + +#ifndef RUNTIME_API_CALL +#define RUNTIME_API_CALL(apiFunctionCall) \ + do { \ + cudaError_t _status = apiFunctionCall; \ + if (_status != cudaSuccess) { \ + std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ + << #apiFunctionCall << " failed with error(" << _status \ + << "): " << cudaGetErrorString(_status) << ".\n\n"; \ + \ + exit(EXIT_FAILURE); \ + } \ + } while (0) +#endif + +#ifndef CUPTI_API_CALL +#define CUPTI_API_CALL(apiFunctionCall) \ + do { \ + CUptiResult _status = apiFunctionCall; \ + if (_status != CUPTI_SUCCESS) { \ + const char* pErrorString; \ + cuptiGetResultString(_status, &pErrorString); \ + \ + std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ + << #apiFunctionCall << " failed with error(" << _status << "): " << pErrorString \ + << ".\n\n"; \ + \ + exit(EXIT_FAILURE); \ + } \ + } while (0) +#endif + +#ifndef CUPTI_API_CALL_VERBOSE +#define CUPTI_API_CALL_VERBOSE(apiFunctionCall) \ + do { \ + std::cout << "Calling CUPTI API: " << #apiFunctionCall << "\n"; \ + \ + CUptiResult _status = apiFunctionCall; \ + if (_status != CUPTI_SUCCESS) { \ + const char* pErrorString; \ + cuptiGetResultString(_status, &pErrorString); \ + \ + std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ + << #apiFunctionCall << " failed with error(" << _status << "): " << pErrorString \ + << ".\n\n"; \ + \ + exit(EXIT_FAILURE); \ + } \ + } while (0) +#endif + +#ifndef CUPTI_UTIL_CALL +#define CUPTI_UTIL_CALL(apiFunctionCall) \ + do { \ + CUptiUtilResult _status = apiFunctionCall; \ + if (_status != CUPTI_UTIL_SUCCESS) { \ + std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ + << #apiFunctionCall << " failed with error: " << _status << "\n\n"; \ + \ + exit(EXIT_FAILURE); \ + } \ + } while (0) +#endif + +#ifndef NVPW_API_CALL +#define NVPW_API_CALL(apiFunctionCall) \ + do { \ + NVPA_Status _status = apiFunctionCall; \ + if (_status != NVPA_STATUS_SUCCESS) { \ + std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ + << #apiFunctionCall << " failed with error: " << _status << "\n\n"; \ + \ + exit(EXIT_FAILURE); \ + } \ + } while (0) +#endif + +#ifndef MEMORY_ALLOCATION_CALL +#define MEMORY_ALLOCATION_CALL(variable) \ + do { \ + if (variable == NULL) { \ + std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ \ + << " Memory allocation failed.\n\n"; \ + \ + exit(EXIT_FAILURE); \ + } \ + } while (0) +#endif + +#ifndef CHECK_CONDITION +#define CHECK_CONDITION(condition) \ + do { \ + if (!(condition)) { \ + std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Condition " << #condition \ + << " failed.\n\n"; \ + \ + exit(EXIT_FAILURE); \ + } \ + } while (0) +#endif + +#ifndef CHECK_INTEGER_CONDITION +#define CHECK_INTEGER_CONDITION(argument1, operator, argument2) \ + do { \ + if (!(argument1 operator argument2)) { \ + std::cerr \ + << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Condition " << #argument1 << " " \ + << # \ + operator<< " " << #argument2 << " fails. " << #argument1 << " = " << argument1 << "," \ + " " << #argument2 << " = " << argument2 << "\n\n"; \ + \ + exit(EXIT_FAILURE); \ + } \ + } while (0) +#endif + +#endif // HELPER_CUPTI_H_ diff --git a/cpp/tests/utilities/test_cupti.cu b/cpp/tests/utilities/test_cupti.cu new file mode 100644 index 000000000..de1778541 --- /dev/null +++ b/cpp/tests/utilities/test_cupti.cu @@ -0,0 +1,361 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "../linear_programming/utilities/pdlp_test_utilities.cuh" +#include "../mip/mip_utils.cuh" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace cuopt::linear_programming::test { + +void init_handler(const raft::handle_t* handle_ptr) +{ + // Init cuBlas / cuSparse context here to avoid having it during solving time + RAFT_CUBLAS_TRY(raft::linalg::detail::cublassetpointermode( + handle_ptr->get_cublas_handle(), CUBLAS_POINTER_MODE_DEVICE, handle_ptr->get_stream())); + RAFT_CUSPARSE_TRY(raft::sparse::detail::cusparsesetpointermode( + handle_ptr->get_cusparse_handle(), CUSPARSE_POINTER_MODE_DEVICE, handle_ptr->get_stream())); +} + +__global__ void VectorAdd(const float* A, const float* B, float* C, int N) +{ + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < N) C[idx] = A[idx] + B[idx]; +} + +class cupti_profiler_t { + public: + cupti_profiler_t(const std::vector& metrics) + : metricNames_(metrics), pRangeProfilerObject_(nullptr) + { + } + + cupti_profiler_t(const cupti_profiler_t&) = delete; + cupti_profiler_t& operator=(const cupti_profiler_t&) = delete; + + ~cupti_profiler_t() + { + if (!pRangeProfilerObject_) return; + CUpti_RangeProfiler_Disable_Params disableRangeProfiler = { + .structSize = CUpti_RangeProfiler_Disable_Params_STRUCT_SIZE}; + disableRangeProfiler.pRangeProfilerObject = pRangeProfilerObject_; + cuptiRangeProfilerDisable(&disableRangeProfiler); + CUpti_Profiler_DeInitialize_Params profilerDeInitializeParams = { + .structSize = CUpti_Profiler_DeInitialize_Params_STRUCT_SIZE}; + cuptiProfilerDeInitialize(&profilerDeInitializeParams); + } + + void initialize_and_enable(CUcontext cuContext) + { + CUpti_Profiler_Initialize_Params profilerInitializeParams = { + .structSize = CUpti_Profiler_Initialize_Params_STRUCT_SIZE}; + cuptiProfilerInitialize(&profilerInitializeParams); + CUdevice device; + cuCtxGetDevice(&device); + CUpti_Device_GetChipName_Params getChipNameParams = { + .structSize = CUpti_Device_GetChipName_Params_STRUCT_SIZE}; + getChipNameParams.deviceIndex = (size_t)device; + cuptiDeviceGetChipName(&getChipNameParams); + chipName_ = getChipNameParams.pChipName; + CUpti_RangeProfiler_Enable_Params enableRange = { + .structSize = CUpti_RangeProfiler_Enable_Params_STRUCT_SIZE}; + enableRange.ctx = cuContext; + cuptiRangeProfilerEnable(&enableRange); + pRangeProfilerObject_ = enableRange.pRangeProfilerObject; + } + + void configure(CUpti_ProfilerRange range, CUpti_ProfilerReplayMode replayMode, size_t numOfRanges) + { + create_config_image(); + create_counter_data_image(numOfRanges); + + CUpti_RangeProfiler_SetConfig_Params setConfig = { + .structSize = CUpti_RangeProfiler_SetConfig_Params_STRUCT_SIZE}; + setConfig.pRangeProfilerObject = pRangeProfilerObject_; + setConfig.configSize = configImage_.size(); + setConfig.pConfig = configImage_.data(); + setConfig.counterDataImageSize = counterDataImage_.size(); + setConfig.pCounterDataImage = counterDataImage_.data(); + setConfig.range = range; + setConfig.replayMode = replayMode; + setConfig.maxRangesPerPass = numOfRanges; + setConfig.numNestingLevels = 1; + setConfig.minNestingLevel = 1; + setConfig.passIndex = 0; + setConfig.targetNestingLevel = 0; + cuptiRangeProfilerSetConfig(&setConfig); + } + + void start() + { + CUpti_RangeProfiler_Start_Params p = {.structSize = + CUpti_RangeProfiler_Start_Params_STRUCT_SIZE}; + p.pRangeProfilerObject = pRangeProfilerObject_; + cuptiRangeProfilerStart(&p); + } + + void stop() + { + CUpti_RangeProfiler_Stop_Params p = {.structSize = CUpti_RangeProfiler_Stop_Params_STRUCT_SIZE}; + p.pRangeProfilerObject = pRangeProfilerObject_; + cuptiRangeProfilerStop(&p); + } + + std::unordered_map decode() + { + CUpti_RangeProfiler_DecodeData_Params decodeData = { + .structSize = CUpti_RangeProfiler_DecodeData_Params_STRUCT_SIZE}; + decodeData.pRangeProfilerObject = pRangeProfilerObject_; + cuptiRangeProfilerDecodeData(&decodeData); + CUpti_RangeProfiler_GetCounterDataInfo_Params cdiParams = { + .structSize = CUpti_RangeProfiler_GetCounterDataInfo_Params_STRUCT_SIZE}; + cdiParams.pCounterDataImage = counterDataImage_.data(); + cdiParams.counterDataImageSize = counterDataImage_.size(); + cuptiRangeProfilerGetCounterDataInfo(&cdiParams); + evaluate_all_ranges(cdiParams.numTotalRanges > 10 ? 10 : cdiParams.numTotalRanges); + return metric_values; + } + + private: + void create_config_image() + { + CUpti_Profiler_Host_Initialize_Params hostInitializeParams = { + .structSize = CUpti_Profiler_Host_Initialize_Params_STRUCT_SIZE}; + hostInitializeParams.profilerType = CUPTI_PROFILER_TYPE_RANGE_PROFILER; + hostInitializeParams.pChipName = chipName_.c_str(); + hostInitializeParams.pCounterAvailabilityImage = nullptr; + cuptiProfilerHostInitialize(&hostInitializeParams); + CUpti_Profiler_Host_Object* pHostObject = hostInitializeParams.pHostObject; + + CUpti_Profiler_Host_ConfigAddMetrics_Params configAddMetricsParams = { + .structSize = CUpti_Profiler_Host_ConfigAddMetrics_Params_STRUCT_SIZE}; + configAddMetricsParams.pHostObject = pHostObject; + configAddMetricsParams.ppMetricNames = metricNames_.data(); + configAddMetricsParams.numMetrics = metricNames_.size(); + cuptiProfilerHostConfigAddMetrics(&configAddMetricsParams); + + CUpti_Profiler_Host_GetConfigImageSize_Params getConfigImageSizeParams = { + .structSize = CUpti_Profiler_Host_GetConfigImageSize_Params_STRUCT_SIZE}; + getConfigImageSizeParams.pHostObject = pHostObject; + cuptiProfilerHostGetConfigImageSize(&getConfigImageSizeParams); + configImage_.resize(getConfigImageSizeParams.configImageSize); + + CUpti_Profiler_Host_GetConfigImage_Params getConfigImageParams = { + .structSize = CUpti_Profiler_Host_GetConfigImage_Params_STRUCT_SIZE}; + getConfigImageParams.pHostObject = pHostObject; + getConfigImageParams.pConfigImage = configImage_.data(); + getConfigImageParams.configImageSize = configImage_.size(); + cuptiProfilerHostGetConfigImage(&getConfigImageParams); + + CUpti_Profiler_Host_GetNumOfPasses_Params getNumOfPassesParam = { + .structSize = CUpti_Profiler_Host_GetNumOfPasses_Params_STRUCT_SIZE}; + getNumOfPassesParam.pConfigImage = configImage_.data(); + getNumOfPassesParam.configImageSize = configImage_.size(); + cuptiProfilerHostGetNumOfPasses(&getNumOfPassesParam); + printf("Num of Passes: %d\n", (int)getNumOfPassesParam.numOfPasses); + CUpti_Profiler_Host_Deinitialize_Params deinitializeParams = { + .structSize = CUpti_Profiler_Host_Deinitialize_Params_STRUCT_SIZE}; + deinitializeParams.pHostObject = pHostObject; + cuptiProfilerHostDeinitialize(&deinitializeParams); + } + + void create_counter_data_image(size_t maxNumOfRangesInCounterDataImage) + { + CUpti_RangeProfiler_GetCounterDataSize_Params ctDataSize = { + .structSize = CUpti_RangeProfiler_GetCounterDataSize_Params_STRUCT_SIZE}; + ctDataSize.pRangeProfilerObject = pRangeProfilerObject_; + ctDataSize.pMetricNames = metricNames_.data(); + ctDataSize.numMetrics = metricNames_.size(); + ctDataSize.maxNumOfRanges = maxNumOfRangesInCounterDataImage; + ctDataSize.maxNumRangeTreeNodes = maxNumOfRangesInCounterDataImage; + cuptiRangeProfilerGetCounterDataSize(&ctDataSize); + counterDataImage_.resize(ctDataSize.counterDataSize); + CUpti_RangeProfiler_CounterDataImage_Initialize_Params initCtImg = { + .structSize = CUpti_RangeProfiler_CounterDataImage_Initialize_Params_STRUCT_SIZE}; + initCtImg.pRangeProfilerObject = pRangeProfilerObject_; + initCtImg.pCounterData = counterDataImage_.data(); + initCtImg.counterDataSize = counterDataImage_.size(); + cuptiRangeProfilerCounterDataImageInitialize(&initCtImg); + } + + void evaluate_range(size_t rangeIndex, CUpti_Profiler_Host_Object* pHostObject) + { + std::vector metricValues(metricNames_.size()); + CUpti_Profiler_Host_EvaluateToGpuValues_Params p = { + .structSize = CUpti_Profiler_Host_EvaluateToGpuValues_Params_STRUCT_SIZE}; + p.pHostObject = pHostObject; + p.pCounterDataImage = counterDataImage_.data(); + p.counterDataImageSize = counterDataImage_.size(); + p.ppMetricNames = metricNames_.data(); + p.numMetrics = metricNames_.size(); + p.rangeIndex = rangeIndex; + p.pMetricValues = metricValues.data(); + cuptiProfilerHostEvaluateToGpuValues(&p); + for (size_t i = 0; i < metricNames_.size(); ++i) + metric_values[metricNames_[i]] += (size_t)metricValues[i]; + } + + void evaluate_all_ranges(size_t numOfRanges) + { + CUpti_Profiler_Host_Initialize_Params hostInitializeParams = { + .structSize = CUpti_Profiler_Host_Initialize_Params_STRUCT_SIZE}; + hostInitializeParams.profilerType = CUPTI_PROFILER_TYPE_RANGE_PROFILER; + hostInitializeParams.pChipName = chipName_.c_str(); + hostInitializeParams.pCounterAvailabilityImage = nullptr; + cuptiProfilerHostInitialize(&hostInitializeParams); + for (size_t i = 0; i < numOfRanges; ++i) + evaluate_range(i, hostInitializeParams.pHostObject); + CUpti_Profiler_Host_Deinitialize_Params deinitializeParams = { + .structSize = CUpti_Profiler_Host_Deinitialize_Params_STRUCT_SIZE}; + deinitializeParams.pHostObject = hostInitializeParams.pHostObject; + cuptiProfilerHostDeinitialize(&deinitializeParams); + } + + std::unordered_map metric_values; + std::vector metricNames_; + CUpti_RangeProfiler_Object* pRangeProfilerObject_; + std::vector counterDataImage_, configImage_; + std::string chipName_; +}; + +void test_cupti() +{ + rmm::mr::cuda_memory_resource cuda_mr; + rmm::mr::set_current_device_resource(&cuda_mr); + + const raft::handle_t handle_{}; + auto stream = handle_.get_stream(); + std::string test_instance = "pk1.mps"; + + auto path = cuopt::test::get_rapids_dataset_root_dir() + ("/mip/" + test_instance); + cuopt::mps_parser::mps_data_model_t mps_problem = + cuopt::mps_parser::parse_mps(path, false); + handle_.sync_stream(); + auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); + problem_checking_t::check_problem_representation(op_problem); + + init_handler(op_problem.get_handle_ptr()); + // run the problem constructor of MIP, so that we do bounds standardization + detail::problem_t problem(op_problem); + problem.preprocess_problem(); + + const int vectorLen = 100000; + rmm::device_uvector d_A(vectorLen, stream), d_B(vectorLen, stream), d_C(vectorLen, stream); + + auto random_generator = [](unsigned int seed) { + return [=] __device__(int idx) { + thrust::default_random_engine rng(seed); + thrust::uniform_real_distribution dist(0.0f, 1.0f); + rng.discard(idx); + return dist(rng); + }; + }; + + thrust::transform(rmm::exec_policy(stream), + thrust::counting_iterator(0), + thrust::counting_iterator(vectorLen), + d_A.begin(), + random_generator(1234)); + thrust::transform(rmm::exec_policy(stream), + thrust::counting_iterator(0), + thrust::counting_iterator(vectorLen), + d_B.begin(), + random_generator(5678)); + + // Get CUDA context after CUDA operations have initialized it + CUcontext cuContext; + cuCtxGetCurrent(&cuContext); + + // cupti_profiler_t profiler({ + // "sm__warps_launched.sum", "l1tex__t_sectors.sum", "l1tex__data_bank_reads.sum", + // "l1tex__data_bank_writes.sum", "l1tex__m_xbar2l1tex_read_bytes.sum", + // "l1tex__m_l1tex2xbar_write_bytes.sum", "lts__t_sectors_op_read.sum", + // "lts__t_sectors_op_write.sum" + // }); + cupti_profiler_t profiler({"l1tex__t_sectors.sum"}); + profiler.initialize_and_enable(cuContext); + profiler.configure(CUPTI_AutoRange, CUPTI_KernelReplay, 10); + profiler.start(); + + detail::fj_settings_t fj_settings; + fj_settings.feasibility_run = false; + fj_settings.iteration_limit = 10; + fj_settings.seed = 42; + printf("Running FJ\n"); + auto solution = run_fj(problem, fj_settings).solution; + + // VectorAdd<<<(vectorLen + 127) / 128, 128, 0, stream.value()>>>(d_A.data(), d_B.data(), + // d_C.data(), vectorLen); + + profiler.stop(); + + stream.synchronize(); + + auto metric_values = profiler.decode(); + for (const auto& [k, v] : metric_values) + printf("%s: %zu\n", k.c_str(), v); + + const size_t reads = 2 * vectorLen * sizeof(float); + const size_t writes = vectorLen * sizeof(float); + const size_t sector_reads = reads / 32; + const size_t sector_writes = writes / 32; + const size_t total_sectors = sector_reads + sector_writes; + EXPECT_EQ(metric_values["l1tex__t_sectors.sum"], total_sectors); + // EXPECT_EQ(metric_values["l1tex__m_xbar2l1tex_read_bytes.sum"], reads); + // EXPECT_EQ(metric_values["l1tex__m_l1tex2xbar_write_bytes.sum"], writes); +} + +TEST(cupti, test_cupti) { test_cupti(); } + +} // namespace cuopt::linear_programming::test From 429c4929949b6e7302dc6664c4ecb78ee0fd31ac Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 29 Oct 2025 17:24:36 +0000 Subject: [PATCH 013/366] Adding support for work unit inference w/ xgboost --- .gitignore | 1 + cpp/CMakeLists.txt | 2 + cpp/src/CMakeLists.txt | 43 +++- .../recombiners/bound_prop_recombiner.cuh | 4 +- .../mip/feasibility_jump/feasibility_jump.cu | 218 ++++++++++++++++++ .../mip/feasibility_jump/feasibility_jump.cuh | 8 + cpp/src/mip/problem/problem.cu | 53 +++++ cpp/src/mip/problem/problem.cuh | 6 + cpp/src/mip/solver_context.cuh | 7 + cpp/src/utilities/embed_models.sh | 89 +++++++ cpp/src/utilities/models/fj.ubj.gz | Bin 0 -> 12153 bytes cpp/src/utilities/work_unit_predictor.cpp | 184 +++++++++++++++ cpp/src/utilities/work_unit_predictor.hpp | 41 ++++ cpp/tests/mip/feasibility_jump_tests.cu | 29 ++- 14 files changed, 671 insertions(+), 14 deletions(-) create mode 100755 cpp/src/utilities/embed_models.sh create mode 100644 cpp/src/utilities/models/fj.ubj.gz create mode 100644 cpp/src/utilities/work_unit_predictor.cpp create mode 100644 cpp/src/utilities/work_unit_predictor.hpp diff --git a/.gitignore b/.gitignore index 9edc9823c..3f02aaa41 100644 --- a/.gitignore +++ b/.gitignore @@ -66,5 +66,6 @@ docs/cuopt/build # generated version file cpp/include/cuopt/semantic_version.hpp +cpp/src/utilities/models_ubj.h !datasets/quadratic_programming !datasets/quadratic_programming/** diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 70c6ea0cc..0b62ec0eb 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -208,6 +208,7 @@ rapids_cpm_rapids_logger(BUILD_EXPORT_SET cuopt-exports INSTALL_EXPORT_SET cuopt create_logger_macros(CUOPT "cuopt::default_logger()" include/cuopt) find_package(CUDSS REQUIRED) +find_package(xgboost REQUIRED) if(BUILD_TESTS) include(cmake/thirdparty/get_gtest.cmake) @@ -316,6 +317,7 @@ target_link_libraries(cuopt rapids_logger::rapids_logger CCCL::CCCL raft::raft + xgboost::xgboost cuopt::mps_parser ${CUDSS_LIB_FILE} PRIVATE diff --git a/cpp/src/CMakeLists.txt b/cpp/src/CMakeLists.txt index d6868b7d3..837affffe 100644 --- a/cpp/src/CMakeLists.txt +++ b/cpp/src/CMakeLists.txt @@ -13,10 +13,51 @@ # See the License for the specific language governing permissions and # limitations under the License. +# Check for required tools +find_program(XXD_EXECUTABLE xxd) +find_program(GZIP_EXECUTABLE gzip) +find_program(MKTEMP_EXECUTABLE mktemp) + +if(NOT XXD_EXECUTABLE) + message(FATAL_ERROR "xxd command not found. Please install xxd (typically provided by vim package).") +endif() + +if(NOT GZIP_EXECUTABLE) + message(FATAL_ERROR "gzip command not found. Please install gzip.") +endif() + +if(NOT MKTEMP_EXECUTABLE) + message(FATAL_ERROR "mktemp command not found. Please install coreutils or equivalent package.") +endif() + +# Directory containing model files +set(XGB_MODEL_DIR "${CMAKE_CURRENT_SOURCE_DIR}/utilities/models") +# Find all .ubj.gz model files in models/ +file(GLOB XGB_MODEL_FILES "${XGB_MODEL_DIR}/*.ubj.gz") +message(STATUS "XGB_MODEL_DIR: ${XGB_MODEL_DIR}") +message(STATUS "Found ${XGB_MODEL_FILES} model files") + +# Set output header name +set(XGB_MODELS_HEADER "${CMAKE_CURRENT_SOURCE_DIR}/utilities/models_ubj.h") + +# Path to the embedding script +set(EMBED_SCRIPT "${CMAKE_CURRENT_SOURCE_DIR}/utilities/embed_models.sh") + +# Custom command to generate header by calling the shell script +add_custom_command( + OUTPUT ${XGB_MODELS_HEADER} + COMMAND sh ${EMBED_SCRIPT} ${XGB_MODELS_HEADER} ${XGB_MODEL_FILES} + DEPENDS ${XGB_MODEL_FILES} ${EMBED_SCRIPT} + COMMENT "Decompressing and embedding all UBJ models into ${XGB_MODELS_HEADER}" +) + +add_custom_target(embed_xgboost_models ALL DEPENDS ${XGB_MODELS_HEADER}) + set(UTIL_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/utilities/seed_generator.cu ${CMAKE_CURRENT_SOURCE_DIR}/utilities/logger_helper.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/version_info.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/timestamp_utils.cpp) + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/timestamp_utils.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/work_unit_predictor.cpp) add_subdirectory(linear_programming) add_subdirectory(math_optimization) diff --git a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh index 9eb1b8ee3..9a4df6796 100644 --- a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh @@ -208,7 +208,7 @@ class bound_prop_recombiner_t : public recombiner_t { constraint_prop.apply_round(offspring, lp_run_time_after_feasible, timer, probing_config); constraint_prop.single_rounding_only = false; offspring.compute_feasibility(); - cuopt_func_call(bool feasible_after_bounds_prop = offspring.get_feasible()); + bool feasible_after_bounds_prop = offspring.get_feasible(); cuopt_func_call(f_t excess_before = offspring.get_total_excess()); CUOPT_LOG_ERROR("Excess before: %g, %g, %g, %g, feas %d", offspring.get_total_excess(), @@ -223,7 +223,7 @@ class bound_prop_recombiner_t : public recombiner_t { offspring.handle_ptr->sync_stream(); offspring.unfix_variables(fixed_assignment, variable_map); offspring.compute_feasibility(); - cuopt_func_call(bool feasible_after_unfix = offspring.get_feasible()); + bool feasible_after_unfix = offspring.get_feasible(); cuopt_func_call(f_t excess_after_unfix = offspring.get_total_excess()); if (feasible_after_unfix != feasible_after_bounds_prop) { CUOPT_LOG_WARN("Numerical issue in bounds prop, infeasibility after unfix"); diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 42620ba15..264e022cf 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -38,6 +38,9 @@ #include +#include +#include + #define FJ_LOG_PREFIX "FJ: " namespace cuopt::linear_programming::detail { @@ -887,6 +890,181 @@ void fj_t::refresh_lhs_and_violation(const rmm::cuda_stream_view& stre data.violated_constraints.sort(stream); } +template +std::map fj_t::get_feature_vector(i_t climber_idx) const +{ + auto& data = *climbers[climber_idx]; + auto climber_stream = data.stream.view(); + if (climber_idx == 0) climber_stream = handle_ptr->get_stream(); + + std::map features; + + // Basic problem dimensions + features["n_variables"] = (float)pb_ptr->n_variables; + features["n_constraints"] = (float)pb_ptr->n_constraints; + features["nnz"] = (float)pb_ptr->coefficients.size(); + + // Matrix sparsity metrics (already computed) + features["sparsity"] = (float)pb_ptr->sparsity; + features["nnz_stddev"] = (float)pb_ptr->nnz_stddev; + features["unbalancedness"] = (float)pb_ptr->unbalancedness; + + // Algorithm settings + features["target_time"] = (float)settings.work_unit_limit; + features["n_of_minimums_for_exit"] = (float)settings.n_of_minimums_for_exit; + features["feasibility_run"] = (float)settings.feasibility_run; + + // Variable type metrics + features["n_integer_vars"] = (float)pb_ptr->n_integer_vars; + features["n_binary_vars"] = (float)pb_ptr->n_binary_vars; + features["integer_ratio"] = + pb_ptr->n_variables > 0 ? (float)pb_ptr->n_integer_vars / pb_ptr->n_variables : 0.0f; + features["binary_ratio"] = + pb_ptr->n_variables > 0 ? (float)pb_ptr->n_binary_vars / pb_ptr->n_variables : 0.0f; + + // Initial violation metrics (from current state) + features["initial_violation_count"] = + (float)data.violated_constraints.set_size.value(climber_stream); + // features["initial_violation_score"] = (float)data.violation_score.value(climber_stream); + // features["initial_weighted_violation"] = + // (float)data.weighted_violation_score.value(climber_stream); + + // Load balancing decision + bool use_load_balancing = false; + if (settings.load_balancing_mode == fj_load_balancing_mode_t::ALWAYS_OFF) { + use_load_balancing = false; + } else if (settings.load_balancing_mode == fj_load_balancing_mode_t::ALWAYS_ON) { + use_load_balancing = true; + } else if (settings.load_balancing_mode == fj_load_balancing_mode_t::AUTO) { + use_load_balancing = + pb_ptr->n_variables > settings.parameters.load_balancing_codepath_min_varcount; + } + if (settings.mode == fj_mode_t::ROUNDING) { use_load_balancing = false; } + features["uses_load_balancing"] = (float)use_load_balancing; + + // Related variables metrics (if available) + if (pb_ptr->related_variables_offsets.size() > 0) { + auto h_offsets = cuopt::host_copy(pb_ptr->related_variables_offsets, handle_ptr->get_stream()); + i_t total_related = 0; + i_t max_related = 0; + for (i_t i = 0; i < pb_ptr->n_variables; ++i) { + i_t count = h_offsets[i + 1] - h_offsets[i]; + total_related += count; + max_related = std::max(max_related, count); + } + features["avg_related_vars_per_var"] = + pb_ptr->n_variables > 0 ? (float)total_related / pb_ptr->n_variables : 0.0f; + features["max_related_vars"] = (float)max_related; + } else { + features["avg_related_vars_per_var"] = 0.0f; + features["max_related_vars"] = 0.0f; + } + + // Constraint characteristics + auto h_lower = cuopt::host_copy(pb_ptr->constraint_lower_bounds, handle_ptr->get_stream()); + auto h_upper = cuopt::host_copy(pb_ptr->constraint_upper_bounds, handle_ptr->get_stream()); + i_t n_equality = 0; + i_t n_tight = 0; + f_t total_range = 0.0; + i_t n_range_constraints = 0; + + for (i_t i = 0; i < pb_ptr->n_constraints; ++i) { + if (pb_ptr->integer_equal(h_lower[i], h_upper[i])) { + n_equality++; + } else { + f_t range = h_upper[i] - h_lower[i]; + if (std::isfinite(range)) { + total_range += range; + n_range_constraints++; + if (range < 1.0) n_tight++; + } + } + } + features["equality_ratio"] = + pb_ptr->n_constraints > 0 ? (float)n_equality / pb_ptr->n_constraints : 0.0f; + features["avg_constraint_range"] = + n_range_constraints > 0 ? (float)(total_range / n_range_constraints) : 0.0f; + features["tight_constraint_ratio"] = + pb_ptr->n_constraints > 0 ? (float)n_tight / pb_ptr->n_constraints : 0.0f; + + // Variable bound characteristics + auto h_var_bounds = cuopt::host_copy(pb_ptr->variable_bounds, handle_ptr->get_stream()); + i_t n_unbounded = 0; + i_t n_fixed = 0; + f_t total_var_range = 0.0; + i_t n_bounded_vars = 0; + + for (i_t i = 0; i < pb_ptr->n_variables; ++i) { + f_t lower = get_lower(h_var_bounds[i]); + f_t upper = get_upper(h_var_bounds[i]); + + if (!std::isfinite(lower) || !std::isfinite(upper)) { + n_unbounded++; + } else if (pb_ptr->integer_equal(lower, upper)) { + n_fixed++; + } else { + f_t range = upper - lower; + total_var_range += range; + n_bounded_vars++; + } + } + features["unbounded_var_ratio"] = + pb_ptr->n_variables > 0 ? (float)n_unbounded / pb_ptr->n_variables : 0.0f; + features["fixed_var_ratio"] = + pb_ptr->n_variables > 0 ? (float)n_fixed / pb_ptr->n_variables : 0.0f; + features["avg_variable_range"] = + n_bounded_vars > 0 ? (float)(total_var_range / n_bounded_vars) : 0.0f; + + // Objective characteristics + auto h_obj_coeffs = cuopt::host_copy(pb_ptr->objective_coefficients, handle_ptr->get_stream()); + i_t n_obj_vars = 0; + f_t total_obj_magnitude = 0.0; + for (i_t i = 0; i < pb_ptr->n_variables; ++i) { + if (h_obj_coeffs[i] != 0.0) { + n_obj_vars++; + total_obj_magnitude += std::abs(h_obj_coeffs[i]); + } + } + features["obj_var_ratio"] = + pb_ptr->n_variables > 0 ? (float)n_obj_vars / pb_ptr->n_variables : 0.0f; + features["avg_obj_coeff_magnitude"] = + n_obj_vars > 0 ? (float)(total_obj_magnitude / n_obj_vars) : 0.0f; + + // Matrix density patterns + auto h_offsets = cuopt::host_copy(pb_ptr->offsets, handle_ptr->get_stream()); + i_t max_nnz_per_row = 0; + i_t min_nnz_per_row = pb_ptr->n_variables; + f_t sum_sq_dev = 0.0; + f_t mean_nnz = + pb_ptr->n_constraints > 0 ? (f_t)pb_ptr->coefficients.size() / pb_ptr->n_constraints : 0.0f; + + for (i_t i = 0; i < pb_ptr->n_constraints; ++i) { + i_t nnz_row = h_offsets[i + 1] - h_offsets[i]; + max_nnz_per_row = std::max(max_nnz_per_row, nnz_row); + min_nnz_per_row = std::min(min_nnz_per_row, nnz_row); + f_t dev = nnz_row - mean_nnz; + sum_sq_dev += dev * dev; + } + features["max_nnz_per_row"] = (float)max_nnz_per_row; + features["min_nnz_per_row"] = (float)min_nnz_per_row; + features["nnz_variance"] = + pb_ptr->n_constraints > 0 ? (float)(sum_sq_dev / pb_ptr->n_constraints) : 0.0f; + + // Average variable degree (avg constraints per variable) + features["avg_var_degree"] = + pb_ptr->n_variables > 0 ? (float)pb_ptr->coefficients.size() / pb_ptr->n_variables : 0.0f; + + // Derived complexity metrics + features["problem_size_score"] = + (float)(pb_ptr->n_variables * pb_ptr->n_constraints) * (float)pb_ptr->sparsity; + features["structural_complexity"] = + (features["integer_ratio"] + 1.0f) * (float)pb_ptr->unbalancedness; + features["constraint_var_ratio"] = + pb_ptr->n_variables > 0 ? (float)pb_ptr->n_constraints / pb_ptr->n_variables : 0.0f; + + return features; +} + template i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) { @@ -999,6 +1177,9 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) solution.get_feasible(), data.local_minimums_reached.value(climber_stream)); + // compute total time spent + double elapsed_time = timer.elapsed_time(); + CUOPT_LOG_TRACE("best fractional count %d", data.saved_best_fractional_count.value(climber_stream)); @@ -1098,6 +1279,26 @@ i_t fj_t::solve(solution_t& solution) pb_ptr->check_problem_representation(true); resize_vectors(solution.handle_ptr); + // if work_limit is set: compute an estimate of the number of iterations required + if (settings.work_unit_limit != std::numeric_limits::infinity()) { + auto features = std::vector{ + (float)settings.work_unit_limit, + (float)settings.n_of_minimums_for_exit, + (float)pb_ptr->n_variables, + (float)pb_ptr->n_constraints, + (float)pb_ptr->coefficients.size(), + (float)pb_ptr->sparsity, + (float)pb_ptr->nnz_stddev, + (float)pb_ptr->unbalancedness, + }; + float iter_prediction = std::max( + (f_t)0.0, (f_t)ceil(context.work_unit_predictors.fj_predictor.predict_scalar(features))); + CUOPT_LOG_DEBUG("FJ determ: Estimated number of iterations for %f WU: %f", + settings.work_unit_limit, + iter_prediction); + settings.iteration_limit = std::min(settings.iteration_limit, (i_t)iter_prediction); + } + bool is_initial_feasible = solution.compute_feasibility(); auto initial_solution = solution; // if we're in rounding mode, split the time/iteration limit between the first and second stage @@ -1132,6 +1333,9 @@ i_t fj_t::solve(solution_t& solution) RAFT_CHECK_CUDA(handle_ptr->get_stream()); handle_ptr->sync_stream(); + // Compute and store feature vector for later logging + feature_vector = get_feature_vector(0); + i_t iterations = host_loop(solution); RAFT_CHECK_CUDA(handle_ptr->get_stream()); handle_ptr->sync_stream(); @@ -1181,6 +1385,20 @@ i_t fj_t::solve(solution_t& solution) cuopt_func_call(solution.test_variable_bounds()); + // Print compact feature vector summary + char logbuf[4096]; + int offset = 0; + offset += snprintf(logbuf + offset, + sizeof(logbuf) - offset, + "FJ: iter=%d time=%g", + iterations, + timer.elapsed_time()); + for (const auto& [name, value] : feature_vector) { + offset += snprintf(logbuf + offset, sizeof(logbuf) - offset, " %s=%g", name.c_str(), value); + if (offset >= (int)(sizeof(logbuf) - 32)) break; + } + CUOPT_LOG_INFO("%s", logbuf); + return is_new_feasible; } diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh index 314a2f369..1ee19b39a 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh @@ -29,6 +29,9 @@ #include +#include +#include + #define FJ_DEBUG_LOAD_BALANCING 0 #define FJ_SINGLE_STEP 0 @@ -109,6 +112,7 @@ struct fj_settings_t { fj_mode_t mode{fj_mode_t::FIRST_FEASIBLE}; fj_candidate_selection_t candidate_selection{fj_candidate_selection_t::WEIGHTED_SCORE}; double time_limit{60.0}; + double work_unit_limit{std::numeric_limits::infinity()}; int iteration_limit{std::numeric_limits::max()}; fj_hyper_parameters_t parameters{}; int n_of_minimums_for_exit = 7000; @@ -637,6 +641,10 @@ class fj_t { std::vector> climbers; rmm::device_uvector climber_views; fj_settings_t settings; + std::map feature_vector; + + private: + std::map get_feature_vector(i_t climber_idx = 0) const; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 736c4b8a6..017245c90 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -96,6 +96,7 @@ void problem_t::op_problem_cstr_body(const optimization_problem_t(*this, combined_bounds); @@ -724,6 +725,55 @@ void problem_t::recompute_auxilliary_data(bool check_representation) if (check_representation) cuopt_func_call(check_problem_representation(true)); } +template +void problem_t::compute_auxiliary_data() +{ + raft::common::nvtx::range fun_scope("compute_auxiliary_data"); + + // Compute sparsity: nnz / (n_rows * n_cols) + sparsity = (n_constraints > 0 && n_variables > 0) + ? static_cast(nnz) / (static_cast(n_constraints) * n_variables) + : 0.0; + + // Compute stddev of non-zeros per row (on device) + nnz_stddev = 0.0; + unbalancedness = 0.0; + if (offsets.size() == static_cast(n_constraints + 1) && n_constraints > 0) { + // First: compute nnz per row on device + rmm::device_uvector d_nnz_per_row(n_constraints, handle_ptr->get_stream()); + thrust::transform(handle_ptr->get_thrust_policy(), + offsets.begin() + 1, + offsets.begin() + n_constraints + 1, + offsets.begin(), + d_nnz_per_row.begin(), + thrust::minus()); + + // Compute mean + double sum = thrust::reduce(handle_ptr->get_thrust_policy(), + d_nnz_per_row.begin(), + d_nnz_per_row.end(), + 0.0, + thrust::plus()); + double mean = sum / n_constraints; + + // Compute variance + double variance = thrust::transform_reduce( + handle_ptr->get_thrust_policy(), + d_nnz_per_row.begin(), + d_nnz_per_row.end(), + [mean] __device__(i_t x) -> double { + double diff = static_cast(x) - mean; + return diff * diff; + }, + 0.0, + thrust::plus()) / + n_constraints; + + nnz_stddev = std::sqrt(variance); + unbalancedness = nnz_stddev / mean; + } +} + template void problem_t::compute_n_integer_vars() { @@ -1276,6 +1326,7 @@ void problem_t::remove_given_variables(problem_t& original_p coefficients.resize(nnz, handle_ptr->get_stream()); variables.resize(nnz, handle_ptr->get_stream()); compute_transpose_of_problem(); + compute_auxiliary_data(); combine_constraint_bounds(*this, combined_bounds); handle_ptr->sync_stream(); recompute_auxilliary_data(); @@ -1492,6 +1543,7 @@ void problem_t::preprocess_problem() standardize_bounds(variable_constraint_map, *this); compute_csr(variable_constraint_map, *this); compute_transpose_of_problem(); + compute_auxiliary_data(); cuopt_func_call(check_problem_representation(true, false)); presolve_data.initialize_var_mapping(*this, handle_ptr); integer_indices.resize(n_variables, handle_ptr->get_stream()); @@ -1659,6 +1711,7 @@ void problem_t::add_cutting_plane_at_objective(f_t objective) objective); insert_constraints(h_constraints); compute_transpose_of_problem(); + compute_auxiliary_data(); cuopt_func_call(check_problem_representation(true)); } diff --git a/cpp/src/mip/problem/problem.cuh b/cpp/src/mip/problem/problem.cuh index 7b7daac13..984b63e43 100644 --- a/cpp/src/mip/problem/problem.cuh +++ b/cpp/src/mip/problem/problem.cuh @@ -81,6 +81,7 @@ class problem_t { void check_problem_representation(bool check_transposed = false, bool check_mip_related_data = true); void recompute_auxilliary_data(bool check_representation = true); + void compute_auxiliary_data(); void compute_n_integer_vars(); void compute_binary_var_table(); void compute_related_variables(double time_limit); @@ -223,6 +224,11 @@ class problem_t { bool is_binary_pb{false}; bool empty{false}; + // Auxiliary problem statistics + double sparsity{0.0}; + double nnz_stddev{0.0}; + double unbalancedness{0.0}; + presolve_data_t presolve_data; // original variable ids diff --git a/cpp/src/mip/solver_context.cuh b/cpp/src/mip/solver_context.cuh index 1f7f67623..07ab64964 100644 --- a/cpp/src/mip/solver_context.cuh +++ b/cpp/src/mip/solver_context.cuh @@ -20,11 +20,16 @@ #include #include #include +#include #pragma once namespace cuopt::linear_programming::detail { +struct mip_solver_work_unit_predictors_t { + work_unit_predictor_t fj_predictor{"fj"}; +}; + // Aggregate structure containing the global context of the solving process for convenience: // The current problem, user settings, raft handle and statistics objects template @@ -45,6 +50,8 @@ struct mip_solver_context_t { const mip_solver_settings_t settings; pdlp_initial_scaling_strategy_t& scaling; solver_stats_t stats; + // TODO: ensure thread local (or use locks...?) + mip_solver_work_unit_predictors_t work_unit_predictors; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/utilities/embed_models.sh b/cpp/src/utilities/embed_models.sh new file mode 100755 index 000000000..f402c411c --- /dev/null +++ b/cpp/src/utilities/embed_models.sh @@ -0,0 +1,89 @@ +#!/bin/sh +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -e + +if [ $# -lt 2 ]; then + echo "Usage: $0 [model2.ubj.gz ...]" + exit 1 +fi + +OUTPUT_HEADER="$1" +shift + +# Initialize the header file +echo "// Auto-generated model arrays" > "$OUTPUT_HEADER" +echo "" >> "$OUTPUT_HEADER" + +# Collect model names for the mapping array +MODEL_NAMES="" + +# Process each model file +for MODEL_FILE in "$@"; do + # Extract base name without .ubj.gz extension + MODEL_BASENAME=$(basename "$MODEL_FILE" .ubj.gz) + + echo "Processing: $MODEL_BASENAME from $MODEL_FILE" + + # Create temporary file with proper name so xxd uses it + TEMP_DIR=$(mktemp -d) + TEMP_FILE="$TEMP_DIR/${MODEL_BASENAME}.ubj" + + # Decompress the model + gzip -cd "$MODEL_FILE" > "$TEMP_FILE" + + # Convert to C array with xxd and append to header + # xxd generates variable names like: _tmp_tmp_xyz_model_ubj + # We need to replace the entire variable name, not just add to it + # Add const to place arrays in .rodata section + xxd -i "$TEMP_FILE" | \ + sed "s/unsigned char [_a-zA-Z0-9]*/const unsigned char ${MODEL_BASENAME}_ubj/" | \ + sed "s/unsigned int [_a-zA-Z0-9]*/const unsigned int ${MODEL_BASENAME}_ubj_len/" >> "$OUTPUT_HEADER" + + # Clean up + rm -rf "$TEMP_DIR" + + # Add to list for mapping array + if [ -z "$MODEL_NAMES" ]; then + MODEL_NAMES="$MODEL_BASENAME" + else + MODEL_NAMES="$MODEL_NAMES $MODEL_BASENAME" + fi +done + +# Add model mapping structure +echo "" >> "$OUTPUT_HEADER" +echo "// Model mapping structure" >> "$OUTPUT_HEADER" +echo "struct xgboost_model_info {" >> "$OUTPUT_HEADER" +echo " const char* name;" >> "$OUTPUT_HEADER" +echo " const unsigned char* data;" >> "$OUTPUT_HEADER" +echo " unsigned int length;" >> "$OUTPUT_HEADER" +echo "};" >> "$OUTPUT_HEADER" +echo "" >> "$OUTPUT_HEADER" + +# Generate the mapping array +echo "// Array of all available models" >> "$OUTPUT_HEADER" +echo "static const struct xgboost_model_info xgboost_models[] = {" >> "$OUTPUT_HEADER" + +for MODEL_NAME in $MODEL_NAMES; do + echo " {\"$MODEL_NAME\", ${MODEL_NAME}_ubj, ${MODEL_NAME}_ubj_len}," >> "$OUTPUT_HEADER" +done + +echo "};" >> "$OUTPUT_HEADER" +echo "" >> "$OUTPUT_HEADER" +echo "static const unsigned int xgboost_models_count = sizeof(xgboost_models) / sizeof(xgboost_models[0]);" >> "$OUTPUT_HEADER" + +echo "Successfully embedded models into $OUTPUT_HEADER" diff --git a/cpp/src/utilities/models/fj.ubj.gz 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zsEO*5Y(jP?2@oxxU{8$-0kDP~P_?`PTwMfUSO_4y$~n9wOf-)ZBY%?t;a<(-xU@C7 zI26s%)a7DpkaLOG{8PONjK?Ha)8std_n19EFE0Sm>|+^}2gc}r<{0hqF=7=CCGmhV za0O%&Ie>Ep;C>jH#S%ay;xX>(Jkc8;D~rR#I=mn3*hRd{z=N*?4){7-GEGuA7fI&p zm9xmrM%TH|dEDlr%>P+bo}~T9I5u=-)!?R)VeD>JuC0svC&Wf11dqg4jmBo<(1?VDxP&T$B_cAIv-RIS va_hwK&=~w>#wGGjhWDRaGkmm(_&yp`c_kV}8%2X?lS%&%Cj1$&e4GFPbo<-0 literal 0 HcmV?d00001 diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp new file mode 100644 index 000000000..56c04a9a5 --- /dev/null +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -0,0 +1,184 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "work_unit_predictor.hpp" + +#include +#include +#include +#include +#include + +#include + +#include "models_ubj.h" + +#define safe_xgboost(call) \ + { \ + int err = (call); \ + if (err != 0) { \ + throw std::runtime_error(std::string(__FILE__) + ":" + std::to_string(__LINE__) + \ + ": error in " + #call + ":" + XGBGetLastError()); \ + } \ + } + +namespace cuopt { + +template +static inline uint32_t compute_hash(std::vector h_contents) +{ + // FNV-1a hash + + uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis + std::vector byte_contents(h_contents.size() * sizeof(i_t)); + std::memcpy(byte_contents.data(), h_contents.data(), h_contents.size() * sizeof(i_t)); + for (size_t i = 0; i < byte_contents.size(); ++i) { + hash ^= byte_contents[i]; + hash *= 16777619u; + } + return hash; +} + +work_unit_predictor_t::work_unit_predictor_t(const std::string& model_name) : model_name(model_name) +{ + BoosterHandle booster_handle; + int ret = XGBoosterCreate(nullptr, 0, &booster_handle); + safe_xgboost(ret); + assert(ret == 0); + if (ret != 0) return; + raw_handle = reinterpret_cast(booster_handle); + + // load the embedded model from the .rodata section + const unsigned char* model_data = nullptr; + unsigned int model_len = 0; + for (unsigned int i = 0; i < xgboost_models_count; i++) { + if (strcmp(xgboost_models[i].name, model_name.c_str()) == 0) { + model_data = xgboost_models[i].data; + model_len = xgboost_models[i].length; + break; + } + } + + assert(model_data != nullptr); + assert(model_len > 0); + + ret = XGBoosterLoadModelFromBuffer(booster_handle, model_data, model_len); + safe_xgboost(ret); + assert(ret == 0); + if (ret != 0) return; + + XGBoosterSetParam(booster_handle, "predictor", "gpu_predictor"); + + is_valid = true; +} + +work_unit_predictor_t::~work_unit_predictor_t() +{ + if (raw_handle != nullptr) { + BoosterHandle booster_handle = reinterpret_cast(raw_handle); + XGBoosterFree(booster_handle); + raw_handle = nullptr; + } +} + +float work_unit_predictor_t::predict_scalar(const std::vector& features, bool verbose) const +{ + assert(is_valid && raw_handle != nullptr); + if (!is_valid || raw_handle == nullptr) return std::numeric_limits::signaling_NaN(); + + // Check cache first + uint32_t hash = compute_hash(features); + auto it = prediction_cache.find(hash); + if (it != prediction_cache.end()) { return it->second; } + + // Timer: measure elapsed time for prediction + auto t_start = std::chrono::high_resolution_clock::now(); + + // Create DMatrix from feature vector + DMatrixHandle dmatrix; + int ret = XGDMatrixCreateFromMat(features.data(), + 1, // nrow + features.size(), // ncol + std::numeric_limits::quiet_NaN(), // missing value + &dmatrix); + safe_xgboost(ret); + + // Predict from DMatrix + char const config[] = + "{\"type\": 0, \"iteration_begin\": 0, " + "\"iteration_end\": 0, \"strict_shape\": true, \"training\": false}"; + + const bst_ulong* out_shape = nullptr; + bst_ulong out_dim = 0; + const float* out_result = nullptr; + ret = XGBoosterPredictFromDMatrix(reinterpret_cast(raw_handle), + dmatrix, + config, + &out_shape, + &out_dim, + &out_result); + safe_xgboost(ret); + + float prediction = out_result[0]; + + // Free DMatrix + XGDMatrixFree(dmatrix); + + auto t_end = std::chrono::high_resolution_clock::now(); + double elapsed_ms = std::chrono::duration(t_end - t_start).count(); + printf("[work_unit_predictor_t::predict_scalar] Prediction took %.3f ms\n", elapsed_ms); + + // Store in cache + prediction_cache[hash] = prediction; + + return prediction; +} + +float work_unit_predictor_t::predict_scalar(const std::map& feature_map, + bool verbose) const +{ + // Extract features in the expected order for the model + // Order matches training data: [target_time, n_of_minimums_for_exit, n_variables, n_constraints, + // nnz, sparsity, nnz_stddev, unbalancedness] + std::vector features; + features.reserve(feature_map.size()); + + // Add features in the order expected by the model + // This order should match what was used during training + const std::vector feature_order = {"target_time", + "n_of_minimums_for_exit", + "n_variables", + "n_constraints", + "nnz", + "sparsity", + "nnz_stddev", + "unbalancedness"}; + + for (const auto& name : feature_order) { + auto it = feature_map.find(name); + if (it != feature_map.end()) { + features.push_back(it->second); + } else { + // Feature not found - use default value of 0 + features.push_back(0.0f); + } + } + + return predict_scalar(features, verbose); +} + +} // namespace cuopt diff --git a/cpp/src/utilities/work_unit_predictor.hpp b/cpp/src/utilities/work_unit_predictor.hpp new file mode 100644 index 000000000..420fc9547 --- /dev/null +++ b/cpp/src/utilities/work_unit_predictor.hpp @@ -0,0 +1,41 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include +#include +#include +#include +#include + +namespace cuopt { + +class work_unit_predictor_t { + public: + work_unit_predictor_t(const std::string& model_name); + ~work_unit_predictor_t(); + float predict_scalar(const std::vector& features, bool verbose = false) const; + float predict_scalar(const std::map& features, bool verbose = false) const; + + private: + std::string model_name; + void* raw_handle{nullptr}; // void* to avoid including xgboost in every MIP translation unit + bool is_valid{false}; + mutable std::unordered_map prediction_cache; +}; + +} // namespace cuopt diff --git a/cpp/tests/mip/feasibility_jump_tests.cu b/cpp/tests/mip/feasibility_jump_tests.cu index 5efc510f6..ad2bf4ba1 100644 --- a/cpp/tests/mip/feasibility_jump_tests.cu +++ b/cpp/tests/mip/feasibility_jump_tests.cu @@ -66,6 +66,12 @@ static fj_state_t run_fj_instance(std::string test_instance, std::cout << "Running: " << test_instance << std::endl; auto path = cuopt::test::get_rapids_dataset_root_dir() + ("/mip/" + test_instance); + + if (std::getenv("CUOPT_INSTANCE")) { + path = std::string("/home/scratch.yboucher_gpu_1/collection/") + std::getenv("CUOPT_INSTANCE"); + std::cout << "Using instance from CUOPT_INSTANCE: " << path << std::endl; + } + cuopt::mps_parser::mps_data_model_t mps_problem = cuopt::mps_parser::parse_mps(path, false); handle_.sync_stream(); @@ -87,7 +93,7 @@ static bool run_fj_check_no_obj_runoff(std::string test_instance) detail::fj_settings_t fj_settings; fj_settings.time_limit = 30.; fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 20000 * 1000; + fj_settings.n_of_minimums_for_exit = 5000; fj_settings.update_weights = true; fj_settings.feasibility_run = false; fj_settings.iteration_limit = 20000; @@ -109,7 +115,7 @@ static bool run_fj_check_objective(std::string test_instance, int iter_limit, do detail::fj_settings_t fj_settings; fj_settings.time_limit = 30.; fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 20000 * 1000; + fj_settings.n_of_minimums_for_exit = 5000; fj_settings.update_weights = true; fj_settings.feasibility_run = obj_target == +std::numeric_limits::infinity(); fj_settings.iteration_limit = iter_limit; @@ -136,7 +142,7 @@ static bool run_fj_check_feasible(std::string test_instance) detail::fj_settings_t fj_settings; fj_settings.time_limit = 30.; fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 20000 * 1000; + fj_settings.n_of_minimums_for_exit = 5000; fj_settings.update_weights = true; fj_settings.feasibility_run = false; fj_settings.iteration_limit = 25000; @@ -170,19 +176,16 @@ static bool run_fj_check_feasible(std::string test_instance) static bool run_fj_check_determinism(std::string test_instance, int iter_limit) { - int seed = - std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); - detail::fj_settings_t fj_settings; fj_settings.time_limit = std::numeric_limits::max(); fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 20000 * 1000; + fj_settings.n_of_minimums_for_exit = 5000; + fj_settings.work_unit_limit = 0.15; // run for 0.5wu (~0.5s) fj_settings.update_weights = true; fj_settings.feasibility_run = false; - fj_settings.iteration_limit = iter_limit; - fj_settings.load_balancing_mode = detail::fj_load_balancing_mode_t::ALWAYS_ON; - fj_settings.seed = seed; - cuopt::seed_generator::set_seed(fj_settings.seed); + // fj_settings.iteration_limit = iter_limit; + fj_settings.load_balancing_mode = detail::fj_load_balancing_mode_t::ALWAYS_ON; + fj_settings.seed = cuopt::seed_generator::get_seed(); auto state = run_fj_instance(test_instance, fj_settings); auto& solution = state.solution; @@ -265,6 +268,9 @@ static bool run_fj_check_determinism(std::string test_instance, int iter_limit) TEST(mip_solve, feasibility_jump_determinism) { + int seed = + std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); + for (const auto& instance : {"thor50dday.mps", "gen-ip054.mps", "50v-10.mps", @@ -275,6 +281,7 @@ TEST(mip_solve, feasibility_jump_determinism) "uccase9.mps"}) { for (int i = 0; i < 10; i++) { // while (true) { + cuopt::seed_generator::set_seed(seed); run_fj_check_determinism(instance, 1000); } } From f6e1370818e0d220bfeb6740d36501779b094946 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 29 Oct 2025 17:57:21 +0000 Subject: [PATCH 014/366] log more FJ features --- .../linear_programming/cuopt/run_mip.cpp | 3 ++ cpp/src/mip/diversity/diversity_manager.cu | 48 ++++++++++++++----- cpp/src/mip/relaxed_lp/relaxed_lp.cu | 10 ++++ 3 files changed, 48 insertions(+), 13 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 5c5781245..98cf2bd13 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -214,6 +214,9 @@ int run_single_file(std::string file_path, settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; + + settings.heuristics_only = true; + cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; auto start_run_solver = std::chrono::high_resolution_clock::now(); diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index d1ebde3b9..5e9ef77eb 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -25,6 +25,8 @@ #include "cuda_profiler_api.h" +#include + namespace cuopt::linear_programming::detail { size_t fp_recombiner_config_t::max_n_of_vars_from_other = @@ -265,19 +267,36 @@ template void diversity_manager_t::run_fj_alone(solution_t& solution) { CUOPT_LOG_INFO("Running FJ alone!"); - solution.round_nearest(); - ls.fj.settings = fj_settings_t{}; - ls.fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; - ls.fj.settings.n_of_minimums_for_exit = 20000 * 1000; - ls.fj.settings.update_weights = true; - ls.fj.settings.feasibility_run = false; - ls.fj.settings.time_limit = timer.remaining_time(); - if (context.settings.deterministic) { - ls.fj.settings.time_limit = timer.remaining_time(); - ls.fj.settings.iteration_limit = 10000; + // Benchmark FJ with 1000 different random starting solutions and varying iteration limits + CUOPT_LOG_INFO("Starting FJ benchmark: 1000 runs with random starting solutions"); + + std::mt19937 rng(cuopt::seed_generator::get_seed()); + std::uniform_int_distribution iter_dist(100, 50000); + + for (i_t run = 0; run < 1000; ++run) { + // Generate random starting solution within bounds + solution.assign_random_within_bounds(1.0, false); + solution.round_nearest(); + + // Configure FJ settings with random iteration limit + ls.fj.settings = fj_settings_t{}; + ls.fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; + ls.fj.settings.n_of_minimums_for_exit = 20000 * 1000; + ls.fj.settings.update_weights = true; + ls.fj.settings.feasibility_run = false; + ls.fj.settings.iteration_limit = iter_dist(rng); + ls.fj.settings.time_limit = std::numeric_limits::infinity(); + + if (context.settings.deterministic) { ls.fj.settings.iteration_limit = iter_dist(rng); } + + CUOPT_LOG_INFO( + "FJ benchmark run %d/%d: iteration_limit=%d", run + 1, 1000, ls.fj.settings.iteration_limit); + + ls.fj.solve(solution); } - ls.fj.solve(solution); - CUOPT_LOG_INFO("FJ alone finished!"); + + CUOPT_LOG_INFO("FJ benchmark finished: 1000 runs completed"); + exit(0); } // returns the best feasible solution @@ -302,6 +321,9 @@ template solution_t diversity_manager_t::run_solver() { raft::common::nvtx::range fun_scope("run_solver"); + + diversity_config.fj_only_run = true; + population.timer = timer; const f_t time_limit = timer.remaining_time(); const f_t lp_time_limit = @@ -351,7 +373,7 @@ solution_t diversity_manager_t::run_solver() bool bb_thread_solution_exists = simplex_solution_exists.load(); if (bb_thread_solution_exists) { ls.lp_optimal_exists = true; - } else if (!diversity_config.fj_only_run) { + } else if (!diversity_config.fj_only_run || true) { relaxed_lp_settings_t lp_settings; lp_settings.time_limit = lp_time_limit; lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index a705e3c22..1f27ede02 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -23,6 +23,8 @@ #include #include +#include + #include #include @@ -50,6 +52,8 @@ optimization_problem_solution_t get_relaxed_lp_solution( const relaxed_lp_settings_t& settings) { raft::common::nvtx::range fun_scope("get_relaxed_lp_solution"); + auto function_start_time = std::chrono::high_resolution_clock::now(); + pdlp_solver_settings_t pdlp_settings{}; pdlp_settings.detect_infeasibility = settings.check_infeasibility; pdlp_settings.set_optimality_tolerance(settings.tolerance); @@ -124,6 +128,12 @@ optimization_problem_solution_t get_relaxed_lp_solution( solver_response.get_additional_termination_information().number_of_steps_taken); } + auto function_end_time = std::chrono::high_resolution_clock::now(); + auto elapsed_ms = + std::chrono::duration_cast(function_end_time - function_start_time) + .count(); + CUOPT_LOG_DEBUG("get_relaxed_lp_solution took %lld ms", elapsed_ms); + return solver_response; } From f02837a2b3d16a26d66bbedbec14dafb35307685 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 29 Oct 2025 11:51:53 -0700 Subject: [PATCH 015/366] remove nvperf dep --- cpp/tests/CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index 0d3ff7116..ed3c1344f 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -74,8 +74,8 @@ function(cuopt_add_cupti_dep dep_name) ) endfunction() -cuopt_add_cupti_dep(nvperf_target) -cuopt_add_cupti_dep(nvperf_host) +#cuopt_add_cupti_dep(nvperf_target) +#cuopt_add_cupti_dep(nvperf_host) cuopt_add_cupti_dep(cupti) # ################################################################ ------------------------------------------------------------------ From 773dbd44a85d7f3a34425971db2c0dba3ad4cd48 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 29 Oct 2025 12:03:58 -0700 Subject: [PATCH 016/366] no cupti --- cpp/tests/CMakeLists.txt | 4 ++-- cpp/tests/utilities/CMakeLists.txt | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index ed3c1344f..a63518dcb 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -76,7 +76,7 @@ endfunction() #cuopt_add_cupti_dep(nvperf_target) #cuopt_add_cupti_dep(nvperf_host) -cuopt_add_cupti_dep(cupti) +#cuopt_add_cupti_dep(cupti) # ################################################################ ------------------------------------------------------------------ function(ConfigureTest CMAKE_TEST_NAME) @@ -104,7 +104,7 @@ function(ConfigureTest CMAKE_TEST_NAME) target_link_libraries(${CMAKE_TEST_NAME} PRIVATE #cuopt::nvperf_target #cuopt::nvperf_host - cuopt::cupti + #cuopt::cupti cuda ) target_include_directories(${CMAKE_TEST_NAME} PRIVATE diff --git a/cpp/tests/utilities/CMakeLists.txt b/cpp/tests/utilities/CMakeLists.txt index 7a40ee6e7..a2bb0eb09 100644 --- a/cpp/tests/utilities/CMakeLists.txt +++ b/cpp/tests/utilities/CMakeLists.txt @@ -15,4 +15,4 @@ # Add CLI end-to-end test ConfigureTest(CLI_TEST test_cli.cpp) -ConfigureTest(CUPTI_TEST test_cupti.cu) +#ConfigureTest(CUPTI_TEST test_cupti.cu) From 327fcecf674f7bbfb1b7aa7e72a57dd44c211e57 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 30 Oct 2025 12:47:40 +0000 Subject: [PATCH 017/366] added regression scripts --- scripts/README_REGRESSION.md | 279 ++++++++ scripts/determinism_logs_parse.py | 125 ++++ scripts/requirements.txt | 10 + scripts/train_regressor.py | 1021 +++++++++++++++++++++++++++++ 4 files changed, 1435 insertions(+) create mode 100644 scripts/README_REGRESSION.md create mode 100755 scripts/determinism_logs_parse.py create mode 100644 scripts/requirements.txt create mode 100755 scripts/train_regressor.py diff --git a/scripts/README_REGRESSION.md b/scripts/README_REGRESSION.md new file mode 100644 index 000000000..ab8736aa4 --- /dev/null +++ b/scripts/README_REGRESSION.md @@ -0,0 +1,279 @@ +# Regression Model Training Scripts + +This directory contains scripts for parsing algorithm log files and training regression models to predict iteration counts. + +## Overview + +The workflow consists of two steps: +1. **Parse log files** → Extract key-value pairs into a pickle file +2. **Train models** → Learn to predict iterations from features + +## Installation + +Install required dependencies: + +```bash +pip install -r requirements.txt +``` + +## Usage + +### Step 1: Parse Log Files + +Extract features from log files containing `FJ:` entries: + +```bash +python determinism_logs_parse.py /path/to/logs/directory -o parsed_data.pkl +``` + +**Arguments:** +- `input_dir`: Directory containing `.log` files +- `-o, --output`: Output pickle file (default: `output.pkl`) + +**Output:** +- Pickle file containing list of dictionaries with all key-value pairs +- Each entry includes `file=` field + +### Step 2: Train Regression Model + +Train a model to predict `iter` values from other features: + +```bash +python train_regressor.py parsed_data.pkl --regressor xgboost --seed 42 +``` + +**Arguments:** +- `input_pkl`: Input pickle file from step 1 +- `--regressor, -r`: Type of regressor (required) + - `linear` - Linear Regression + - `poly2`, `poly3`, `poly4` - Polynomial Regression (degree 2, 3, 4) + - `xgboost` - XGBoost Regressor + - `lightgbm` - LightGBM Regressor + - `random_forest` - Random Forest Regressor + - `gradient_boosting` - Gradient Boosting Regressor +- `--output-dir, -o`: Directory to save models (default: `./models`) +- `--seed, -s`: Random seed for reproducibility (optional) +- `--tune`: Enable hyperparameter tuning +- `--cv-folds`: Number of cross-validation folds (default: 5) +- `--test-size`: Test set proportion (default: 0.2) +- `--no-progress`: Disable training progress output +- `--list-features`: List all available features in the dataset and exit +- `--stratify-split`: Stratify train/test split by target distribution +- `--early-stopping N`: Early stopping patience in rounds (default: 20, use 0 to disable) +- `--treelite-compile N`: Export XGBoost/LightGBM as optimized C source code with TL2cgen (N threads, default: 1, includes branch annotation and quantization) + +## Features + +### Data Splitting +- **File-based split**: Ensures entries from the same file go exclusively to train OR test +- **Prevents data leakage**: Improves generalization and reduces overfitting +- **Default**: 20% of files for testing + +### Preprocessing +- **Automatic scaling**: Applied to linear/polynomial models (not tree-based) +- **Polynomial features**: All numeric features expanded for polynomial regression +- **Clean data assumption**: Script expects valid pickle data + +### Feature Selection +- **Manual feature selection**: Edit `FEATURES_TO_EXCLUDE` or `FEATURES_TO_INCLUDE_ONLY` directly in the script +- **Exclude specific features**: Add feature names to `FEATURES_TO_EXCLUDE` list +- **Include only specific features**: Add feature names to `FEATURES_TO_INCLUDE_ONLY` list (overrides exclusion) +- **List available features**: Run with `--list-features` to see all features in your dataset +- **No command-line config**: Intentionally not exposed as CLI args for cleaner configuration file management + +### Model Evaluation +- **Cross-validation**: K-fold CV on training set with progress output +- **Comprehensive metrics**: MSE, RMSE, MAE, R² +- **Feature importance**: All features ranked by importance (top 50 for polynomial models) +- **Sample predictions**: 20 random test predictions with errors + +### Training Progress +- **Progress indicators**: Tree-based models (XGBoost, LightGBM, Random Forest, Gradient Boosting) show real-time training progress +- **Polynomial feature tracking**: Shows number of polynomial features being generated +- **CV progress**: Cross-validation shows progress for each fold +- **Disable option**: Use `--no-progress` flag to suppress all progress output + +### Overfitting Prevention +- **Early stopping**: Enabled by default for XGBoost and LightGBM (20 rounds patience) to prevent overfitting +- **Regularization**: XGBoost and LightGBM include L1/L2 regularization, subsampling, and minimum child weight +- **Stratified splitting**: Use `--stratify-split` to ensure balanced target distributions +- **Disable early stopping**: Use `--early-stopping 0` if you want full training without early stopping + +### Model Persistence +- **XGBoost**: Saved as `.ubj.gz` (UBJ format with gzip compression) +- **LightGBM**: Saved as `.txt` (text format, human-readable) +- **Sklearn models**: Saved as `.joblib` (efficient for numpy arrays) +- **Metadata**: Feature names and preprocessing info saved separately +- **Scaler**: Saved for models requiring normalization + +### TL2cgen Source Export (Optional) +- **C source code export**: Export XGBoost/LightGBM models as optimized C source code using TL2cgen +- **Portable and fast**: Compile the source on any platform for 10-100x faster predictions +- **Enabled by default**: Automatically exports C source with 1 thread (use `--treelite-compile N` for more threads) +- **Requires**: `treelite>=4.0` and `tl2cgen` packages (optional dependencies) +- **Output**: Optimized C source files in dedicated directory +- **Note**: Treelite 4.0+ moved C compilation to TL2cgen ([migration guide](https://tl2cgen.readthedocs.io/en/latest/treelite-migration.html)) + +### Built-in TL2cgen Optimizations +The following optimizations are **automatically applied** when using TL2cgen: + +- **Branch annotation**: + - Analyzes which branches are taken during training + - Optimizes C code with branch prediction hints + - Improves inference speed by 10-30% + - Saves annotation file for inspection +- **Quantization**: + - Reduces model memory footprint by ~75% + - Uses 8-bit integers instead of 32-bit floats where possible + - Minimal accuracy loss (typically <0.1% R²) + - Faster inference on memory-constrained systems + +**Combined effect**: 1.2-1.5x faster inference with 75% less memory + +## Example Workflow + +```bash +# 1. Parse logs +python determinism_logs_parse.py /data/algorithm_logs -o data.pkl + +# 2. List available features +python train_regressor.py data.pkl --regressor xgboost --list-features + +# 3. Train XGBoost model +python train_regressor.py data.pkl --regressor xgboost --seed 42 -o ./models + +# 4. Train polynomial model with tuning +python train_regressor.py data.pkl --regressor poly3 --tune --seed 42 + +# 5. Compare different models +for model in linear poly2 poly3 xgboost lightgbm random_forest gradient_boosting; do + echo "Training $model..." + python train_regressor.py data.pkl --regressor $model --seed 42 +done + +# 6. Train LightGBM model +python train_regressor.py data.pkl --regressor lightgbm --seed 42 + +# 7. Export XGBoost model as C source for production deployment (enabled by default) +python train_regressor.py data.pkl --regressor xgboost --seed 42 + +# Or specify more threads for compilation +python train_regressor.py data.pkl --regressor xgboost --treelite-compile 8 --seed 42 +``` + +## TL2cgen Source Export Example + +For production deployments requiring fast inference, models are **automatically exported as optimized C source code** (if `treelite` and `tl2cgen` are installed): + +```bash +# Install treelite and tl2cgen (optional) +pip install treelite tl2cgen + +# Train model - optimized C source is automatically exported with branch annotation and quantization +python train_regressor.py data.pkl --regressor xgboost -o ./models + +# Use more threads for faster parallel compilation +python train_regressor.py data.pkl --regressor xgboost --treelite-compile 8 -o ./models + +# The C source files will be in: models/xgboost_c_code/ +# Contains optimized C source code with branch annotation and quantization ready for compilation + +# Same process works for LightGBM +python train_regressor.py data.pkl --regressor lightgbm --treelite-compile 8 -o ./models +# Output: models/lightgbm_c_code/ +``` + +### Optimization Impact + +All TL2cgen exports include both branch annotation and quantization automatically: + +| Configuration | Speed | Memory | Accuracy | +|---------------|-------|--------|----------| +| Standard XGBoost/LightGBM | 1x | 100% | 100% | +| **TL2cgen optimized (default)** | **1.2-1.5x** | **25%** | **>99.9%** | + +**Note:** Treelite 4.0+ moved C code generation to TL2cgen. See the [migration guide](https://tl2cgen.readthedocs.io/en/latest/treelite-migration.html) for details. + +### Output Files + +When TL2cgen is enabled, the following files are automatically created: + +``` +models/ +├── xgboost_model.ubj.gz # Standard XGBoost model +├── xgboost_metadata.pkl # Feature names and config +├── xgboost_annotation.json # Branch statistics (automatic) +└── xgboost_c_code/ # TL2cgen generated optimized C++ source + ├── header.h # Header with feature names declaration + ├── main.cpp # Implementation with feature names array + └── *.cpp / *.h # Other C++ source files (quantized + annotated) +``` + +**Namespace Wrapping**: All generated files are automatically wrapped in a C++ namespace with the model name (derived from the input pickle file basename) to avoid naming conflicts when using multiple models in the same project. For example, if the input is `my_dataset.pkl`: +- All functions are in `namespace my_dataset { ... }` +- Access functions as `my_dataset::predict()`, `my_dataset::get_num_features()`, etc. +- All `.c` files are renamed to `.cpp` for C++ compilation + +The generated `header.h` includes: +- `namespace { ... }` wrapping all declarations +- `#define NUM_FEATURES ` - Number of features +- `extern const char* feature_names[]` - Feature names declaration +- Function declarations (e.g., `predict()`, `get_num_features()`) within the namespace + +The generated `main.cpp` includes: +- `namespace { ... }` wrapping all implementations +- `const char* feature_names[]` - Feature names array definition +- Function implementations within the namespace + +## Feature Selection Examples + +To perform feature selection, edit the configuration section at the top of `train_regressor.py`: + +### Example 1: Exclude specific features + +```python +FEATURES_TO_EXCLUDE = [ + 'time', # Exclude time as it may not be available at prediction time + 'avg_constraint_range', + 'binary_ratio', +] + +FEATURES_TO_INCLUDE_ONLY = [] +``` + +### Example 2: Use only specific features + +```python +FEATURES_TO_EXCLUDE = [] + +FEATURES_TO_INCLUDE_ONLY = [ + 'n_variables', + 'n_constraints', + 'sparsity', + 'structural_complexity', +] +``` + +**Note:** If `FEATURES_TO_INCLUDE_ONLY` is non-empty, it overrides `FEATURES_TO_EXCLUDE`. + +## Output Structure + +After training, the output directory contains: + +``` +models/ +├── xgboost_model.ubj.gz # Compressed XGBoost model +├── xgboost_metadata.pkl # Feature names and config +├── linear_model.joblib # Linear regression model +├── linear_scaler.pkl # StandardScaler for linear model +├── linear_metadata.pkl # Metadata +└── ... +``` + +## Notes + +- The train/test split is based on **unique files**, not individual entries +- Models requiring scaling (linear, polynomial) automatically apply `StandardScaler` +- Tree-based models (XGBoost, Random Forest, Gradient Boosting) don't use scaling +- Feature importance shows the most predictive features for iteration count +- Use `--seed` for reproducible results across runs diff --git a/scripts/determinism_logs_parse.py b/scripts/determinism_logs_parse.py new file mode 100755 index 000000000..ff0ff854b --- /dev/null +++ b/scripts/determinism_logs_parse.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. +# All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Parse log files containing key-value pairs and export to pickle format. + +Usage: + python determinism_logs_parse.py [-o output.pkl] +""" + +import argparse +import pickle +import subprocess +import os +import glob + + +def parse_value(value_str): + """Convert string value to appropriate type (int, float, or str).""" + try: + # Try to parse as float if it contains a decimal point or scientific notation + if '.' in value_str or 'e' in value_str.lower(): + return float(value_str) + else: + return int(value_str) + except ValueError: + # Keep as string if conversion fails + return value_str + + +def main(): + parser = argparse.ArgumentParser( + description='Parse log files with key-value pairs and export to pickle' + ) + parser.add_argument( + 'input_dir', + help='Directory containing .log files to parse' + ) + parser.add_argument( + '-o', '--output', + default='output.pkl', + help='Output pickle file path (default: output.pkl)' + ) + args = parser.parse_args() + + # Find all .log files in the input directory + log_files = glob.glob(os.path.join(args.input_dir, '*.log')) + + if not log_files: + print(f"No .log files found in {args.input_dir}") + return + + print(f"Found {len(log_files)} log files") + + # Use grep to efficiently extract all lines containing "FJ:" + # -H flag ensures filename is included in output (even with single file) + # -h suppresses filename (we don't want that) + cmd = ['grep', '-H', 'FJ:'] + log_files + result = subprocess.run(cmd, capture_output=True, text=True) + + if result.returncode != 0 and result.returncode != 1: + # grep returns 1 if no matches found, which is fine + # Other return codes indicate actual errors + print(f"Error running grep: {result.stderr}") + return + + # Parse grep output + entries = [] + for line in result.stdout.strip().split('\n'): + if not line: + continue + + # Grep output format: filename:FJ: key1=value1 key2=value2 ... + # Split on first colon to separate filename from content + colon_idx = line.find(':') + if colon_idx == -1: + continue + + filename = os.path.basename(line[:colon_idx]) + rest = line[colon_idx + 1:] + + # Remove "FJ:" prefix if present + if rest.startswith('FJ:'): + rest = rest[3:].strip() + + # Parse key-value pairs + entry = {'file': filename} + for kv_pair in rest.split(): + if '=' in kv_pair: + key, value = kv_pair.split('=', 1) + entry[key] = parse_value(value) + + # Only add entry if it has more than just the filename + if len(entry) > 1: + entries.append(entry) + + # Calculate statistics + unique_files = set(entry['file'] for entry in entries) + avg_entries_per_file = len(entries) / len(unique_files) if unique_files else 0 + + # Save to pickle file + with open(args.output, 'wb') as f: + pickle.dump(entries, f) + + print(f"Parsed {len(entries)} entries from {len(unique_files)} log files") + print(f"Average entries per file: {avg_entries_per_file:.2f}") + print(f"Saved to {args.output}") + + +if __name__ == '__main__': + main() diff --git a/scripts/requirements.txt b/scripts/requirements.txt new file mode 100644 index 000000000..bcce7a788 --- /dev/null +++ b/scripts/requirements.txt @@ -0,0 +1,10 @@ +numpy>=1.20.0 +pandas>=1.3.0 +scikit-learn>=1.0.0 +xgboost>=1.5.0 +lightgbm>=3.0.0 +joblib>=1.0.0 + +# Optional: For exporting models to C source code +# treelite>=4.0.0 +# tl2cgen>=0.1.0 diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py new file mode 100755 index 000000000..84c32ae3d --- /dev/null +++ b/scripts/train_regressor.py @@ -0,0 +1,1021 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. +# All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Train regression models to predict algorithm iterations from log features. + +Usage: + python train_regressor.py --regressor [options] +""" + +import argparse +import pickle +import numpy as np +import pandas as pd +from sklearn.model_selection import train_test_split, cross_val_score +from sklearn.preprocessing import StandardScaler, PolynomialFeatures +from sklearn.linear_model import LinearRegression, Ridge +from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor +from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score +from sklearn.pipeline import Pipeline +import joblib +import os +from typing import List, Dict, Any, Tuple +import warnings +warnings.filterwarnings('ignore', category=UserWarning) + + +AVAILABLE_REGRESSORS = [ + 'linear', + 'poly2', 'poly3', 'poly4', + 'xgboost', + 'lightgbm', + 'random_forest', + 'gradient_boosting' +] + +# ============================================================================ +# FEATURE SELECTION CONFIGURATION +# Edit this list to exclude specific features from training +# Leave empty to use all features (except 'file' and 'iter') +# ============================================================================ +FEATURES_TO_EXCLUDE = [ + # Example usage (uncomment to exclude): + # 'time', + # 'avg_constraint_range', + # 'binary_ratio', + 'avg_obj_coeff_magnitude', + 'n_of_minimums_for_exit', + 'feasibility_run', + 'fixed_var_ratio', + 'unbounded_var_ratio', + 'obj_var_ratio', + 'avg_related_vars_per_var', + 'avg_constraint_range', + 'nnz_variance', + 'avg_variable_range', + 'min_nnz_per_row', + 'constraint_var_ratio', + 'avg_var_degree', + 'equality_ratio', + 'integer_ratio', + 'binary_ratio' +] + +# Alternatively, specify ONLY the features you want to use +# If non-empty, only these features will be used (overrides FEATURES_TO_EXCLUDE) +FEATURES_TO_INCLUDE_ONLY = [ + # Example usage (uncomment to use only specific features): + # 'n_variables', + # 'n_constraints', + # 'sparsity', +] +# ============================================================================ + + +def load_data(pkl_path: str) -> pd.DataFrame: + """Load pickle file and convert to DataFrame.""" + with open(pkl_path, 'rb') as f: + data = pickle.load(f) + + if not isinstance(data, list): + raise ValueError(f"Expected list of dictionaries, got {type(data)}") + + if len(data) == 0: + raise ValueError("Empty dataset") + + df = pd.DataFrame(data) + + # Validate required columns + if 'file' not in df.columns: + raise ValueError("Missing required 'file' column in data") + if 'iter' not in df.columns: + raise ValueError("Missing required 'iter' column in data") + + return df + + +def split_by_files(df: pd.DataFrame, test_size: float = 0.2, + random_state: int = None, stratify_by: str = None) -> Tuple[pd.DataFrame, pd.DataFrame]: + """ + Split data into train/test sets based on unique files. + Ensures all entries from a file go to either train or test, not both. + + Args: + ---- + stratify_by: Optional column name to stratify split (e.g., 'iter' for balanced target distribution) + """ + unique_files = df['file'].unique() + + # Optionally stratify by target distribution + if stratify_by: + # Create stratification labels based on quantiles of the specified column + file_stats = df.groupby('file')[stratify_by].median() + stratify_labels = pd.qcut(file_stats, q=min(5, len(unique_files)), labels=False, duplicates='drop') + train_files, test_files = train_test_split( + unique_files, + test_size=test_size, + random_state=random_state, + stratify=stratify_labels + ) + else: + train_files, test_files = train_test_split( + unique_files, + test_size=test_size, + random_state=random_state + ) + + train_df = df[df['file'].isin(train_files)].copy() + test_df = df[df['file'].isin(test_files)].copy() + + print(f"\nData Split:") + print(f" Total entries: {len(df)}") + print(f" Train entries: {len(train_df)} ({len(train_files)} files)") + print(f" Test entries: {len(test_df)} ({len(test_files)} files)") + + # Check distribution similarity + train_target_mean = train_df['iter'].mean() + test_target_mean = test_df['iter'].mean() + train_target_std = train_df['iter'].std() + test_target_std = test_df['iter'].std() + + print(f"\nTarget ('iter') Distribution:") + print(f" Train: mean={train_target_mean:.2f}, std={train_target_std:.2f}") + print(f" Test: mean={test_target_mean:.2f}, std={test_target_std:.2f}") + + mean_diff_pct = abs(train_target_mean - test_target_mean) / train_target_mean * 100 + if mean_diff_pct > 10: + print(f" ⚠️ Warning: Train/test target means differ by {mean_diff_pct:.1f}%") + print(f" Consider using stratified split or different random seed") + + return train_df, test_df + + +def list_available_features(df: pd.DataFrame) -> List[str]: + """ + List all available numeric features in the dataset. + Helper function to see what features can be selected/excluded. + """ + X = df.drop(columns=['iter', 'file'], errors='ignore') + X = X.select_dtypes(include=[np.number]) + return sorted(X.columns.tolist()) + + +def prepare_features(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.Series, List[str]]: + """ + Prepare features and target from DataFrame. + Excludes 'file' and 'iter' from features. + Applies feature selection based on FEATURES_TO_EXCLUDE and FEATURES_TO_INCLUDE_ONLY. + """ + # Separate target + y = df['iter'].copy() + + # Drop non-feature columns + X = df.drop(columns=['iter', 'file']) + + # Ensure all features are numeric + non_numeric = X.select_dtypes(exclude=[np.number]).columns.tolist() + if non_numeric: + print(f"Warning: Dropping non-numeric columns: {non_numeric}") + X = X.select_dtypes(include=[np.number]) + + # Apply feature selection + original_feature_count = len(X.columns) + + if FEATURES_TO_INCLUDE_ONLY: + # Use only specified features + available_features = [f for f in FEATURES_TO_INCLUDE_ONLY if f in X.columns] + missing_features = [f for f in FEATURES_TO_INCLUDE_ONLY if f not in X.columns] + + if missing_features: + print(f"Warning: Requested features not found in data: {missing_features}") + + X = X[available_features] + print(f"Feature selection: Using only {len(available_features)} specified features") + + elif FEATURES_TO_EXCLUDE: + # Exclude specified features + features_to_drop = [f for f in FEATURES_TO_EXCLUDE if f in X.columns] + if features_to_drop: + X = X.drop(columns=features_to_drop) + print(f"Feature selection: Excluded {len(features_to_drop)} features: {features_to_drop}") + + feature_names = X.columns.tolist() + + if len(feature_names) == 0: + raise ValueError( + "No features remaining after feature selection! " + "Check FEATURES_TO_EXCLUDE and FEATURES_TO_INCLUDE_ONLY settings." + ) + + if len(feature_names) != original_feature_count: + print(f" Using {len(feature_names)} of {original_feature_count} available features") + + return X, y, feature_names + + +def create_regressor(regressor_type: str, random_state: int = None, + tune_hyperparams: bool = False, verbose: bool = True): + """ + Create a regression model with optional preprocessing pipeline. + + Returns: (model, needs_scaling) + """ + if regressor_type == 'linear': + model = LinearRegression() + needs_scaling = True + + elif regressor_type.startswith('poly'): + degree = int(regressor_type[-1]) + model = Pipeline([ + ('poly', PolynomialFeatures(degree=degree, include_bias=False)), + ('regressor', Ridge(alpha=1.0)) # Ridge to handle multicollinearity + ]) + needs_scaling = True + + elif regressor_type == 'xgboost': + try: + import xgboost as xgb + except ImportError: + raise ImportError("XGBoost not installed. Install with: pip install xgboost") + + params = { + 'objective': 'reg:squarederror', + 'random_state': random_state, + 'n_estimators': 100, + 'max_depth': 6, + 'tree_method': 'hist', + 'learning_rate': 0.1, + 'verbosity': 1 if verbose else 0, + # Regularization to prevent overfitting + 'min_child_weight': 3, # Minimum sum of weights in a leaf + 'gamma': 0.1, # Minimum loss reduction for split + 'subsample': 0.8, # Fraction of samples per tree + 'colsample_bytree': 0.8, # Fraction of features per tree + 'reg_alpha': 0.1, # L1 regularization + 'reg_lambda': 1.0, # L2 regularization + } + + if tune_hyperparams: + # Stronger regularization for tuned version + params.update({ + 'n_estimators': 200, + 'max_depth': 5, # Shallower trees + 'learning_rate': 0.05, # Lower learning rate + 'min_child_weight': 5, # Higher minimum weight + 'gamma': 0.2, # More conservative splits + 'subsample': 0.7, # More aggressive subsampling + 'colsample_bytree': 0.7, + 'reg_alpha': 0.5, # Stronger L1 + 'reg_lambda': 2.0, # Stronger L2 + }) + + model = xgb.XGBRegressor(**params) + needs_scaling = False + + elif regressor_type == 'lightgbm': + try: + import lightgbm as lgb + except ImportError: + raise ImportError("LightGBM not installed. Install with: pip install lightgbm") + + params = { + 'objective': 'regression', + 'random_state': random_state, + 'n_estimators': 100, + 'max_depth': 6, + 'learning_rate': 0.1, + 'verbosity': 1 if verbose else -1, + # Regularization to prevent overfitting + 'min_child_weight': 3, + 'min_split_gain': 0.1, + 'subsample': 0.8, + 'colsample_bytree': 0.8, + 'reg_alpha': 0.1, + 'reg_lambda': 1.0, + } + + if tune_hyperparams: + # Stronger regularization for tuned version + params.update({ + 'n_estimators': 200, + 'max_depth': 5, + 'learning_rate': 0.05, + 'min_child_weight': 5, + 'min_split_gain': 0.2, + 'subsample': 0.7, + 'colsample_bytree': 0.7, + 'reg_alpha': 0.5, + 'reg_lambda': 2.0, + }) + + model = lgb.LGBMRegressor(**params) + needs_scaling = False + + elif regressor_type == 'random_forest': + params = { + 'random_state': random_state, + 'n_estimators': 100, + 'max_depth': None, + 'min_samples_split': 2, + 'verbose': 1 if verbose else 0 + } + + if tune_hyperparams: + params.update({ + 'n_estimators': 200, + 'max_depth': 20, + 'min_samples_split': 5 + }) + + model = RandomForestRegressor(**params) + needs_scaling = False + + elif regressor_type == 'gradient_boosting': + params = { + 'random_state': random_state, + 'n_estimators': 100, + 'max_depth': 5, + 'learning_rate': 0.1, + 'verbose': 1 if verbose else 0 + } + + if tune_hyperparams: + params.update({ + 'n_estimators': 200, + 'max_depth': 7, + 'learning_rate': 0.05 + }) + + model = GradientBoostingRegressor(**params) + needs_scaling = False + + else: + raise ValueError(f"Unknown regressor type: {regressor_type}") + + return model, needs_scaling + + +def get_feature_importance(model, feature_names: List[str], + regressor_type: str) -> None: + """Extract and print feature importance if available.""" + print(f"\nFeature Importance:") + + try: + if regressor_type in ['xgboost', 'lightgbm', 'random_forest', 'gradient_boosting']: + # Tree-based models have feature_importances_ + importances = model.feature_importances_ + indices = np.argsort(importances)[::-1] + + for i, idx in enumerate(indices, 1): + print(f" {i:3d}. {feature_names[idx]:40s}: {importances[idx]:.6f}") + + elif regressor_type == 'linear': + # Linear regression coefficients + coefs = np.abs(model.coef_) + indices = np.argsort(coefs)[::-1] + + for i, idx in enumerate(indices, 1): + print(f" {i:3d}. {feature_names[idx]:40s}: {coefs[idx]:.6f}") + + elif regressor_type.startswith('poly'): + # For polynomial, get feature names and coefficients from the Ridge step + poly_features = model.named_steps['poly'].get_feature_names_out(feature_names) + coefs = np.abs(model.named_steps['regressor'].coef_) + indices = np.argsort(coefs)[::-1] + + print(f" (Showing top 50 of {len(indices)} polynomial features)") + for i, idx in enumerate(indices[:50], 1): + feat_name = poly_features[idx] + # Truncate very long polynomial feature names + if len(feat_name) > 60: + feat_name = feat_name[:57] + "..." + print(f" {i:3d}. {feat_name:60s}: {coefs[idx]:.6f}") + else: + print(" Feature importance not available for this model type") + + except Exception as e: + print(f" Could not extract feature importance: {e}") + + +def evaluate_model(model, X_train, y_train, X_test, y_test, + feature_names: List[str], regressor_type: str, + cv_folds: int = 5, verbose: int = 0, + skip_cv: bool = False, X_test_original: pd.DataFrame = None, + test_df: pd.DataFrame = None) -> Tuple[float, float]: + """Evaluate model and print metrics. Returns (train_r2, test_r2). + + Args: + ---- + X_test_original: Unscaled X_test for displaying feature values + test_df: Original test dataframe with 'file' column + """ + # Cross-validation on training set (skip if using early stopping) + if not skip_cv: + print(f"\nCross-Validation on Training Set ({cv_folds}-fold):") + try: + cv_scores = cross_val_score(model, X_train, y_train, + cv=cv_folds, + scoring='neg_mean_squared_error', + n_jobs=-1, + verbose=verbose) + cv_rmse = np.sqrt(-cv_scores) + print(f" CV RMSE: {cv_rmse.mean():.4f} (+/- {cv_rmse.std():.4f})") + except Exception as e: + print(f" CV failed (likely due to early stopping): {str(e)[:100]}") + print(f" Skipping cross-validation...") + else: + print(f"\nSkipping cross-validation (incompatible with early stopping)") + + # Training set metrics + y_train_pred = model.predict(X_train) + train_mse = mean_squared_error(y_train, y_train_pred) + train_rmse = np.sqrt(train_mse) + train_mae = mean_absolute_error(y_train, y_train_pred) + train_r2 = r2_score(y_train, y_train_pred) + + print(f"\nTraining Set Metrics:") + print(f" MSE: {train_mse:.4f}") + print(f" RMSE: {train_rmse:.4f}") + print(f" MAE: {train_mae:.4f}") + print(f" R²: {train_r2:.4f}") + + # Test set metrics + y_test_pred = model.predict(X_test) + test_mse = mean_squared_error(y_test, y_test_pred) + test_rmse = np.sqrt(test_mse) + test_mae = mean_absolute_error(y_test, y_test_pred) + test_r2 = r2_score(y_test, y_test_pred) + + print(f"\nTest Set Metrics:") + print(f" MSE: {test_mse:.4f}") + print(f" RMSE: {test_rmse:.4f}") + print(f" MAE: {test_mae:.4f}") + print(f" R²: {test_r2:.4f}") + + # Feature importance + get_feature_importance(model, feature_names, regressor_type) + + # Sample predictions + print(f"\n20 Sample Predictions from Test Set:") + print(f" {'Actual':>10s} {'Predicted':>10s} {'Error':>10s} {'Error %':>10s}") + print(f" {'-'*10} {'-'*10} {'-'*10} {'-'*10}") + + sample_indices = np.random.choice(len(y_test), min(20, len(y_test)), replace=False) + for idx in sample_indices: + actual = y_test.iloc[idx] + predicted = y_test_pred[idx] + error = actual - predicted + error_pct = (error / actual * 100) if actual != 0 else 0 + print(f" {actual:10.2f} {predicted:10.2f} {error:10.2f} {error_pct:9.2f}%") + + # Show worst predictions with feature values + print(f"\n5 Worst Predictions (Largest Absolute Error):") + abs_errors = np.abs(y_test_pred - y_test.values) + worst_indices = np.argsort(abs_errors)[-5:][::-1] + + # Use original (unscaled) features if available, otherwise use X_test + X_display = X_test_original if X_test_original is not None else X_test + + for rank, idx in enumerate(worst_indices, 1): + actual = y_test.iloc[idx] + predicted = y_test_pred[idx] + error = actual - predicted + error_pct = (error / actual * 100) if actual != 0 else 0 + + # Get filename if available + filename = "" + if test_df is not None and 'file' in test_df.columns: + filename = f" (file: {test_df.iloc[idx]['file']})" + + print(f"\n #{rank} - Actual: {actual:.2f}, Predicted: {predicted:.2f}, " + f"Error: {error:.2f} ({error_pct:.1f}%){filename}") + + # Get feature values (handle both DataFrame and array) + if isinstance(X_display, pd.DataFrame): + feature_values = X_display.iloc[idx].values + elif isinstance(X_display, np.ndarray): + feature_values = X_display[idx] + else: + feature_values = X_display[idx] + + # Display features compactly (5 per line) + print(f" Features:", end="") + for i, (feat_name, feat_val) in enumerate(zip(feature_names, feature_values)): + if i % 5 == 0: + print(f"\n ", end="") + # Format feature value + if isinstance(feat_val, (int, np.integer)): + print(f"{feat_name}={feat_val}", end=" ") + else: + print(f"{feat_name}={feat_val:.3g}", end=" ") + print() # Final newline + + return train_r2, test_r2 + + +def compile_model_treelite(model, regressor_type: str, output_dir: str, + num_threads: int, X_train=None, + annotate: bool = False, quantize: bool = False, + feature_names: List[str] = None, + model_name: str = None) -> None: + """Compile XGBoost/LightGBM model to C source files using TL2cgen. + + Args: + ---- + model: Trained model + regressor_type: Type of regressor + output_dir: Output directory + num_threads: Number of parallel compilation threads + X_train: Training data for branch annotation (optional) + annotate: Whether to annotate branches for optimization + quantize: Whether to use quantization in code generation + feature_names: List of feature names in expected order (optional) + model_name: Name prefix for functions (optional, derived from training file) + """ + if regressor_type not in ['xgboost', 'lightgbm']: + print(f"Warning: TL2cgen compilation only supported for XGBoost and LightGBM, skipping for {regressor_type}") + return + + try: + import treelite + import tl2cgen + except ImportError as e: + missing = [] + try: + import treelite + except ImportError: + missing.append("treelite") + try: + import tl2cgen + except ImportError: + missing.append("tl2cgen") + + print(f"Warning: {', '.join(missing)} not installed. Install with: pip install {' '.join(missing)}") + print("Skipping C code generation.") + return + + optimization_info = [] + if annotate: + optimization_info.append("branch annotation") + if quantize: + optimization_info.append("quantization") + + opt_str = f" with {', '.join(optimization_info)}" if optimization_info else "" + print(f"\nGenerating C source code with TL2cgen (threads={num_threads}){opt_str}...") + + # Convert model to treelite format using frontend API + try: + if regressor_type == 'xgboost': + tl_model = treelite.frontend.from_xgboost(model.get_booster()) + elif regressor_type == 'lightgbm': + tl_model = treelite.frontend.from_lightgbm(model.booster_) + except Exception as e: + print(f"Warning: Failed to convert {regressor_type} model to treelite: {e}") + return + + # Annotate branches if requested and training data is available + annotation_path = None + if annotate and X_train is not None: + try: + print(" Annotating branches with training data...") + # Convert to numpy array if it's a DataFrame + if hasattr(X_train, 'values'): + X_train_array = X_train.values.astype(np.float32) + else: + X_train_array = np.asarray(X_train, dtype=np.float32) + + dmat = tl2cgen.DMatrix(X_train_array, dtype='float32') + annotation_path = os.path.join(output_dir, f'{regressor_type}_annotation.json') + tl2cgen.annotate_branch(tl_model, dmat=dmat, path=annotation_path, verbose=False) + print(f" Branch annotations saved to: {annotation_path}") + except Exception as e: + print(f" Warning: Branch annotation failed: {e}") + print(" Continuing without branch annotation") + annotation_path = None + elif annotate and X_train is None: + print(" Warning: Branch annotation requested but no training data available") + print(" Skipping branch annotation") + + # Generate C source files using TL2cgen + source_dir = os.path.join(output_dir, f'{regressor_type}_c_code') + + try: + #params = {'parallel_comp': num_threads} + params = {} + + # Add quantization parameter if requested + if quantize: + params['quantize'] = 1 # Enable quantization in code generation + + # Add annotation file if available + if annotation_path: + params['annotate_in'] = annotation_path + + tl2cgen.generate_c_code( + tl_model, + dirpath=source_dir, + params=params, + verbose=False + ) + + # Post-process generated files + header_path = os.path.join(source_dir, 'header.h') + main_path = os.path.join(source_dir, 'main.c') + quantize_path = os.path.join(source_dir, 'quantize.c') + recipe_path = os.path.join(source_dir, 'recipe.json') + + # Rename all .c files to .cpp and wrap in namespace + if model_name: + try: + import glob + c_files = glob.glob(os.path.join(source_dir, '*.c')) + + for c_file in c_files: + cpp_file = c_file[:-2] + '.cpp' + + # Read content + with open(c_file, 'r') as f: + content = f.read() + + # Wrap in namespace + namespaced_content = f'namespace {model_name} {{\n\n{content}\n\n}} // namespace {model_name}\n' + + # Write to .cpp file + with open(cpp_file, 'w') as f: + f.write(namespaced_content) + + # Remove original .c file + os.remove(c_file) + + # Update paths for further processing + main_path = main_path[:-2] + '.cpp' + quantize_path = quantize_path[:-2] + '.cpp' + + print(f" Renamed {len(c_files)} .c files to .cpp and wrapped in namespace '{model_name}'") + except Exception as e: + print(f" Warning: Failed to rename .c files: {e}") + + # Wrap header.h content in namespace + if model_name and os.path.exists(header_path): + try: + with open(header_path, 'r') as f: + content = f.read() + + # Wrap in namespace + namespaced_content = f'namespace {model_name} {{\n\n{content}\n\n}} // namespace {model_name}\n' + + with open(header_path, 'w') as f: + f.write(namespaced_content) + + print(f" Wrapped header.h in namespace '{model_name}'") + except Exception as e: + print(f" Warning: Failed to wrap header.h: {e}") + + # Add feature names to header and implementation + if feature_names and os.path.exists(header_path) and os.path.exists(main_path): + try: + # Append to header.h (inside namespace) + with open(header_path, 'r') as f: + content = f.read() + + # Insert before closing namespace + insertion = f'\n// Feature names\n#define NUM_FEATURES {len(feature_names)}\nextern const char* feature_names[NUM_FEATURES];\n' + content = content.replace(f'}} // namespace {model_name}\n', f'{insertion}\n}} // namespace {model_name}\n') + + with open(header_path, 'w') as f: + f.write(content) + + # Append to main.cpp (inside namespace) + with open(main_path, 'r') as f: + content = f.read() + + # Insert before closing namespace + feature_array = f'\n// Feature names array\nconst char* feature_names[NUM_FEATURES] = {{\n' + for i, name in enumerate(feature_names): + comma = ',' if i < len(feature_names) - 1 else '' + feature_array += f' "{name}"{comma}\n' + feature_array += '};\n' + + content = content.replace(f'}} // namespace {model_name}\n', f'{feature_array}\n}} // namespace {model_name}\n') + + with open(main_path, 'w') as f: + f.write(content) + + print(f" Added {len(feature_names)} feature names to header.h and main.cpp") + except Exception as e: + print(f" Warning: Failed to add feature names: {e}") + + # Remove recipe.json if it exists + if os.path.exists(recipe_path): + try: + os.remove(recipe_path) + print(f" Removed recipe.json") + except Exception as e: + print(f" Warning: Failed to remove recipe.json: {e}") + + opt_msg = [] + if annotation_path: + opt_msg.append("branch-annotated") + if quantize: + opt_msg.append("quantized") + opt_suffix = f" ({', '.join(opt_msg)})" if opt_msg else "" + + print(f"C source code generated to: {source_dir}/") + print(f" Contains optimized model source code{opt_suffix} ready for compilation") + except Exception as e: + print(f"Warning: TL2cgen code generation failed: {e}") + print(" Model saved in standard format only.") + + +def save_model(model, scaler, regressor_type: str, output_dir: str, + feature_names: List[str]) -> None: + """Save trained model and preprocessing components to disk.""" + os.makedirs(output_dir, exist_ok=True) + + # Save metadata + metadata = { + 'regressor_type': regressor_type, + 'feature_names': feature_names, + 'has_scaler': scaler is not None + } + + metadata_path = os.path.join(output_dir, f'{regressor_type}_metadata.pkl') + with open(metadata_path, 'wb') as f: + pickle.dump(metadata, f) + print(f"\nSaved metadata to: {metadata_path}") + + # Save scaler if exists + if scaler is not None: + scaler_path = os.path.join(output_dir, f'{regressor_type}_scaler.pkl') + joblib.dump(scaler, scaler_path) + print(f"Saved scaler to: {scaler_path}") + + # Save model + if regressor_type == 'xgboost': + # Save as UBJ with gzip compression + model_path = os.path.join(output_dir, f'{regressor_type}_model.ubj') + model.save_model(model_path) + + # Gzip the file + import gzip + import shutil + with open(model_path, 'rb') as f_in: + with gzip.open(model_path + '.gz', 'wb') as f_out: + shutil.copyfileobj(f_in, f_out) + os.remove(model_path) # Remove uncompressed version + print(f"Saved model to: {model_path}.gz") + elif regressor_type == 'lightgbm': + # Save LightGBM model as text file + model_path = os.path.join(output_dir, f'{regressor_type}_model.txt') + model.booster_.save_model(model_path) + print(f"Saved model to: {model_path}") + else: + # Save sklearn models as joblib (more efficient than pickle for large arrays) + model_path = os.path.join(output_dir, f'{regressor_type}_model.joblib') + joblib.dump(model, model_path) + print(f"Saved model to: {model_path}") + + +def main(): + parser = argparse.ArgumentParser( + description='Train regression models to predict algorithm iterations', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=f""" +Available regressors: + linear - Linear Regression + poly2, poly3, poly4 - Polynomial Regression (degree 2, 3, 4) + xgboost - XGBoost Regressor + lightgbm - LightGBM Regressor + random_forest - Random Forest Regressor + gradient_boosting - Gradient Boosting Regressor + +Example: + python train_regressor.py data.pkl --regressor xgboost --seed 42 + python train_regressor.py data.pkl --regressor lightgbm --seed 42 + """ + ) + + parser.add_argument('input_pkl', help='Input pickle file with log data') + parser.add_argument( + '--regressor', '-r', + required=True, + choices=AVAILABLE_REGRESSORS, + help='Type of regressor to train' + ) + parser.add_argument( + '--output-dir', '-o', + default='./models', + help='Output directory for saved models (default: ./models)' + ) + parser.add_argument( + '--seed', '-s', + type=int, + default=None, + help='Random seed for reproducibility (optional)' + ) + parser.add_argument( + '--tune', + action='store_true', + help='Enable hyperparameter tuning (uses predefined tuned parameters)' + ) + parser.add_argument( + '--cv-folds', + type=int, + default=5, + help='Number of cross-validation folds (default: 5)' + ) + parser.add_argument( + '--test-size', + type=float, + default=0.2, + help='Proportion of files to use for testing (default: 0.2)' + ) + parser.add_argument( + '--no-progress', + action='store_true', + help='Disable training progress output' + ) + parser.add_argument( + '--list-features', + action='store_true', + help='List all available features in the dataset and exit' + ) + parser.add_argument( + '--stratify-split', + action='store_true', + help='Stratify train/test split by target distribution (ensures balanced iter values)' + ) + parser.add_argument( + '--early-stopping', + type=int, + default=20, + metavar='N', + help='Enable early stopping for tree models (default: 20 rounds, use 0 to disable)' + ) + parser.add_argument( + '--treelite-compile', + type=int, + default=1, + metavar='THREADS', + help='Export XGBoost/LightGBM model as optimized C source code with TL2cgen (includes branch annotation and quantization)' + ) + + args = parser.parse_args() + + # Set random seed if provided + if args.seed is not None: + np.random.seed(args.seed) + print(f"Random seed set to: {args.seed}") + + # Load data + print(f"\nLoading data from: {args.input_pkl}") + df = load_data(args.input_pkl) + print(f"Loaded {len(df)} entries with {len(df.columns)} columns") + + # Extract model name from input file (for prefixing generated C functions) + model_name = os.path.splitext(os.path.basename(args.input_pkl))[0] + + # If listing features, do that and exit + if args.list_features: + features = list_available_features(df) + print(f"\n{'='*70}") + print(f"Available features in dataset ({len(features)} total):") + print(f"{'='*70}") + for i, feat in enumerate(features, 1): + print(f" {i:3d}. {feat}") + print(f"\nTo exclude features, edit FEATURES_TO_EXCLUDE in the script:") + print(f" {__file__}") + print(f"\nTo use only specific features, edit FEATURES_TO_INCLUDE_ONLY") + return + + # Split data by files + stratify_by = 'iter' if args.stratify_split else None + train_df, test_df = split_by_files(df, test_size=args.test_size, + random_state=args.seed, + stratify_by=stratify_by) + + # Prepare features + X_train, y_train, feature_names = prepare_features(train_df) + X_test, y_test, _ = prepare_features(test_df) + + print(f"\nFeatures: {len(feature_names)}") + print(f"Target: iter (predicting number of iterations)") + + # Create model + print(f"\nTraining {args.regressor} regressor...") + model, needs_scaling = create_regressor( + args.regressor, + random_state=args.seed, + tune_hyperparams=args.tune, + verbose=not args.no_progress + ) + + # Apply scaling if needed + scaler = None + X_test_original = X_test.copy() # Keep unscaled version for display + if needs_scaling: + print(" Applying StandardScaler to features...") + scaler = StandardScaler() + X_train_scaled = scaler.fit_transform(X_train) + X_test_scaled = scaler.transform(X_test) + else: + X_train_scaled = X_train + X_test_scaled = X_test + + # Train model + if args.regressor.startswith('poly'): + degree = int(args.regressor[-1]) + n_features = X_train_scaled.shape[1] + from math import comb + n_poly_features = sum(comb(n_features + d - 1, d) for d in range(1, degree + 1)) + print(f" Generating {n_poly_features} polynomial features (degree {degree})...") + + # Use early stopping for tree-based models if requested + if args.early_stopping and args.early_stopping > 0 and args.regressor in ['xgboost', 'lightgbm', 'gradient_boosting']: + if args.regressor == 'xgboost': + print(f" Using early stopping (patience={args.early_stopping} rounds)...") + # Set early stopping parameter + model.set_params(early_stopping_rounds=args.early_stopping) + model.fit( + X_train_scaled, y_train, + eval_set=[(X_test_scaled, y_test)], + verbose=False + ) + # Report best iteration + best_iteration = model.best_iteration if hasattr(model, 'best_iteration') else model.n_estimators + print(f" Best iteration: {best_iteration} (out of {model.n_estimators} max)") + elif args.regressor == 'lightgbm': + print(f" Using early stopping (patience={args.early_stopping} rounds)...") + model.fit( + X_train_scaled, y_train, + eval_set=[(X_test_scaled, y_test)], + callbacks=[ + __import__('lightgbm').early_stopping(stopping_rounds=args.early_stopping, verbose=False) + ] + ) + # Report best iteration + best_iteration = model.best_iteration_ if hasattr(model, 'best_iteration_') else model.n_estimators + print(f" Best iteration: {best_iteration} (out of {model.n_estimators} max)") + else: # gradient_boosting + # Gradient Boosting uses n_iter_no_change parameter + print(f" Note: Use --tune with gradient_boosting for early stopping") + model.fit(X_train_scaled, y_train) + else: + model.fit(X_train_scaled, y_train) + + print(" Training complete!") + + # Evaluate model + skip_cv = args.early_stopping is not None and args.early_stopping > 0 + train_r2, test_r2 = evaluate_model(model, X_train_scaled, y_train, X_test_scaled, y_test, + feature_names, args.regressor, cv_folds=args.cv_folds, + verbose=2 if not args.no_progress else 0, + skip_cv=skip_cv, X_test_original=X_test_original, + test_df=test_df) + + # Save model + save_model(model, scaler, args.regressor, args.output_dir, feature_names) + + # Compile with TL2cgen if requested (with optimizations enabled by default) + if args.treelite_compile is not None: + # Use unscaled training data for branch annotation + # Only tree-based models (XGBoost, LightGBM) don't need scaling + X_train_for_annotation = X_train if not needs_scaling else None + + compile_model_treelite( + model, + args.regressor, + args.output_dir, + args.treelite_compile, + X_train=X_train_for_annotation, + annotate=True, # Always enable branch annotation + quantize=True, # Always enable quantization + feature_names=feature_names, + model_name=model_name + ) + + print("\n" + "="*70) + print("Training completed successfully!") + print("="*70) + print(f"\nFinal R² Scores:") + print(f" Train R²: {train_r2:.4f}") + print(f" Test R²: {test_r2:.4f}") + + +if __name__ == '__main__': + main() From c9cae3c546eb37253536a48f99d6d783fd8fae3a Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 30 Oct 2025 14:20:49 +0000 Subject: [PATCH 018/366] switch to using tl2cgen --- .gitignore | 1 - cpp/src/CMakeLists.txt | 44 +- .../mip/feasibility_jump/feasibility_jump.cu | 21 +- cpp/src/mip/solver_context.cuh | 4 +- .../utilities/models/fj_predictor/header.h | 47 + .../utilities/models/fj_predictor/main.cpp | 11828 ++++++++++++++++ .../models/fj_predictor/quantize.cpp | 1180 ++ cpp/src/utilities/work_unit_predictor.cpp | 167 +- cpp/src/utilities/work_unit_predictor.hpp | 9 +- cpp/tests/mip/feasibility_jump_tests.cu | 4 +- scripts/README_REGRESSION.md | 83 +- scripts/train_regressor.py | 188 +- 12 files changed, 13347 insertions(+), 229 deletions(-) create mode 100644 cpp/src/utilities/models/fj_predictor/header.h create mode 100644 cpp/src/utilities/models/fj_predictor/main.cpp create mode 100644 cpp/src/utilities/models/fj_predictor/quantize.cpp diff --git a/.gitignore b/.gitignore index 3f02aaa41..9edc9823c 100644 --- a/.gitignore +++ b/.gitignore @@ -66,6 +66,5 @@ docs/cuopt/build # generated version file cpp/include/cuopt/semantic_version.hpp -cpp/src/utilities/models_ubj.h !datasets/quadratic_programming !datasets/quadratic_programming/** diff --git a/cpp/src/CMakeLists.txt b/cpp/src/CMakeLists.txt index 837affffe..45f884b3f 100644 --- a/cpp/src/CMakeLists.txt +++ b/cpp/src/CMakeLists.txt @@ -13,51 +13,13 @@ # See the License for the specific language governing permissions and # limitations under the License. -# Check for required tools -find_program(XXD_EXECUTABLE xxd) -find_program(GZIP_EXECUTABLE gzip) -find_program(MKTEMP_EXECUTABLE mktemp) - -if(NOT XXD_EXECUTABLE) - message(FATAL_ERROR "xxd command not found. Please install xxd (typically provided by vim package).") -endif() - -if(NOT GZIP_EXECUTABLE) - message(FATAL_ERROR "gzip command not found. Please install gzip.") -endif() - -if(NOT MKTEMP_EXECUTABLE) - message(FATAL_ERROR "mktemp command not found. Please install coreutils or equivalent package.") -endif() - -# Directory containing model files -set(XGB_MODEL_DIR "${CMAKE_CURRENT_SOURCE_DIR}/utilities/models") -# Find all .ubj.gz model files in models/ -file(GLOB XGB_MODEL_FILES "${XGB_MODEL_DIR}/*.ubj.gz") -message(STATUS "XGB_MODEL_DIR: ${XGB_MODEL_DIR}") -message(STATUS "Found ${XGB_MODEL_FILES} model files") - -# Set output header name -set(XGB_MODELS_HEADER "${CMAKE_CURRENT_SOURCE_DIR}/utilities/models_ubj.h") - -# Path to the embedding script -set(EMBED_SCRIPT "${CMAKE_CURRENT_SOURCE_DIR}/utilities/embed_models.sh") - -# Custom command to generate header by calling the shell script -add_custom_command( - OUTPUT ${XGB_MODELS_HEADER} - COMMAND sh ${EMBED_SCRIPT} ${XGB_MODELS_HEADER} ${XGB_MODEL_FILES} - DEPENDS ${XGB_MODEL_FILES} ${EMBED_SCRIPT} - COMMENT "Decompressing and embedding all UBJ models into ${XGB_MODELS_HEADER}" -) - -add_custom_target(embed_xgboost_models ALL DEPENDS ${XGB_MODELS_HEADER}) - set(UTIL_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/utilities/seed_generator.cu ${CMAKE_CURRENT_SOURCE_DIR}/utilities/logger_helper.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/version_info.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/timestamp_utils.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/work_unit_predictor.cpp) + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/work_unit_predictor.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/main.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/quantize.cpp) add_subdirectory(linear_programming) add_subdirectory(math_optimization) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 264e022cf..fb28a9e13 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -910,7 +910,7 @@ std::map fj_t::get_feature_vector(i_t climber_idx) features["unbalancedness"] = (float)pb_ptr->unbalancedness; // Algorithm settings - features["target_time"] = (float)settings.work_unit_limit; + features["time"] = (float)settings.work_unit_limit; features["n_of_minimums_for_exit"] = (float)settings.n_of_minimums_for_exit; features["feasibility_run"] = (float)settings.feasibility_run; @@ -954,10 +954,10 @@ std::map fj_t::get_feature_vector(i_t climber_idx) } features["avg_related_vars_per_var"] = pb_ptr->n_variables > 0 ? (float)total_related / pb_ptr->n_variables : 0.0f; - features["max_related_vars"] = (float)max_related; + // features["max_related_vars"] = (float)max_related; } else { features["avg_related_vars_per_var"] = 0.0f; - features["max_related_vars"] = 0.0f; + // features["max_related_vars"] = 0.0f; } // Constraint characteristics @@ -1281,18 +1281,9 @@ i_t fj_t::solve(solution_t& solution) // if work_limit is set: compute an estimate of the number of iterations required if (settings.work_unit_limit != std::numeric_limits::infinity()) { - auto features = std::vector{ - (float)settings.work_unit_limit, - (float)settings.n_of_minimums_for_exit, - (float)pb_ptr->n_variables, - (float)pb_ptr->n_constraints, - (float)pb_ptr->coefficients.size(), - (float)pb_ptr->sparsity, - (float)pb_ptr->nnz_stddev, - (float)pb_ptr->unbalancedness, - }; - float iter_prediction = std::max( - (f_t)0.0, (f_t)ceil(context.work_unit_predictors.fj_predictor.predict_scalar(features))); + std::map features_map = get_feature_vector(0); + float iter_prediction = std::max( + (f_t)0.0, (f_t)ceil(context.work_unit_predictors.fj_predictor.predict_scalar(features_map))); CUOPT_LOG_DEBUG("FJ determ: Estimated number of iterations for %f WU: %f", settings.work_unit_limit, iter_prediction); diff --git a/cpp/src/mip/solver_context.cuh b/cpp/src/mip/solver_context.cuh index 07ab64964..3a1133e1f 100644 --- a/cpp/src/mip/solver_context.cuh +++ b/cpp/src/mip/solver_context.cuh @@ -20,6 +20,8 @@ #include #include #include + +#include #include #pragma once @@ -27,7 +29,7 @@ namespace cuopt::linear_programming::detail { struct mip_solver_work_unit_predictors_t { - work_unit_predictor_t fj_predictor{"fj"}; + work_unit_predictor_t fj_predictor{}; }; // Aggregate structure containing the global context of the solving process for convenience: diff --git a/cpp/src/utilities/models/fj_predictor/header.h b/cpp/src/utilities/models/fj_predictor/header.h new file mode 100644 index 000000000..ccae87627 --- /dev/null +++ b/cpp/src/utilities/models/fj_predictor/header.h @@ -0,0 +1,47 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include +#include +#include +#include + +class fj_predictor { + public: + union Entry { + int missing; + double fvalue; + int qvalue; + }; + + static int32_t get_num_target(void); + static void get_num_class(int32_t* out); + static int32_t get_num_feature(void); + static const char* get_threshold_type(void); + static const char* get_leaf_output_type(void); + static void predict(union Entry* data, int pred_margin, double* result); + static void postprocess(double* result); + static int quantize(double val, unsigned fid); + + // Feature names + static constexpr int NUM_FEATURES = 12; + static const char* feature_names[NUM_FEATURES]; +}; // class fj_predictor diff --git a/cpp/src/utilities/models/fj_predictor/main.cpp b/cpp/src/utilities/models/fj_predictor/main.cpp new file mode 100644 index 000000000..ac5cd1ed1 --- /dev/null +++ b/cpp/src/utilities/models/fj_predictor/main.cpp @@ -0,0 +1,11828 @@ + +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "header.h" + +#if defined(__clang__) || defined(__GNUC__) +#define LIKELY(x) __builtin_expect(!!(x), 1) +#define UNLIKELY(x) __builtin_expect(!!(x), 0) +#else +#define LIKELY(x) (x) +#define UNLIKELY(x) (x) +#endif +#define N_TARGET 1 +#define MAX_N_CLASS 1 + +const unsigned char is_categorical[] = { + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +}; +static const int32_t num_class[] = { + 1, +}; + +int32_t fj_predictor::get_num_target(void) { return N_TARGET; } +void fj_predictor::get_num_class(int32_t* out) +{ + for (int i = 0; i < N_TARGET; ++i) { + out[i] = num_class[i]; + } +} +int32_t fj_predictor::get_num_feature(void) { return 12; } +const char* fj_predictor::get_threshold_type(void) { return "float64"; } +const char* fj_predictor::get_leaf_output_type(void) { return "float64"; } + +void fj_predictor::predict(union Entry* data, int pred_margin, double* result) +{ + // Quantize data + for (int i = 0; i < 12; ++i) { + if (data[i].missing != -1 && !is_categorical[i]) { + data[i].qvalue = quantize(data[i].fvalue, i); + } + } + + unsigned int tmp; + if (UNLIKELY(false || (data[0].qvalue <= 186))) { + if (LIKELY(false || (data[0].qvalue <= 98))) { + if (UNLIKELY(false || (data[0].qvalue <= 44))) { + result[0] += 22901.255344838846; + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + result[0] += 23329.74375127941; + } else { + result[0] += 23615.614222033248; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 144))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + result[0] += 24209.472824119093; + } else { + if (LIKELY(false || (data[6].qvalue <= 100))) { + result[0] += 23869.313116934638; + } else { + result[0] += 23405.54395751655; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 88))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 24902.415701614485; + } else { + result[0] += 24520.27915996134; + } + } else { + result[0] += 23891.56119652762; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 262))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (UNLIKELY(false || (data[0].qvalue <= 218))) { + result[0] += 25411.420502457517; + } else { + result[0] += 25917.434788937655; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[1].qvalue <= 116))) { + if (LIKELY(false || (data[0].qvalue <= 224))) { + result[0] += 24824.90282590602; + } else { + result[0] += 25260.302972714355; + } + } else { + result[0] += 24241.872897906287; + } + } else { + result[0] += 23668.087700157346; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 112))) { + if (UNLIKELY(false || (data[0].qvalue <= 312))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 26558.857562746103; + } else { + result[0] += 26202.38213380121; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 102))) { + result[0] += 25849.940581446823; + } else { + result[0] += 25230.335505164217; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 92))) { + if (LIKELY(false || (data[8].qvalue <= 126))) { + result[0] += 26911.44811674291; + } else { + result[0] += 26503.327098291113; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + result[0] += 25835.236675109372; + } else { + result[0] += 26394.354620052833; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 430))) { + if (LIKELY(false || (data[6].qvalue <= 152))) { + if (UNLIKELY(false || (data[0].qvalue <= 352))) { + result[0] += 25138.58875113828; + } else { + result[0] += 25959.666864308958; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 406))) { + result[0] += 23920.482800539634; + } else { + result[0] += 24831.920082473396; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 174))) { + if (LIKELY(false || (data[1].qvalue <= 152))) { + result[0] += 26247.05905907499; + } else { + result[0] += 25812.009772798196; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 464))) { + result[0] += 24485.25504118496; + } else { + result[0] += 25971.80731105497; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 188))) { + if (LIKELY(false || (data[0].qvalue <= 104))) { + if (LIKELY(false || (data[0].qvalue <= 50))) { + if (LIKELY(false || (data[0].qvalue <= 24))) { + result[0] += -2102.2335038546057; + } else { + result[0] += -1814.143470277424; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 62))) { + result[0] += -1350.4930205440783; + } else { + result[0] += -1691.59714939238; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 62))) { + if (LIKELY(false || (data[0].qvalue <= 156))) { + if (UNLIKELY(false || (data[0].qvalue <= 126))) { + result[0] += -925.0448121271659; + } else { + result[0] += -609.5991225040165; + } + } else { + result[0] += -202.96527072586937; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 116))) { + if (LIKELY(false || (data[0].qvalue <= 152))) { + result[0] += -1181.817220363881; + } else { + result[0] += -759.0207821022941; + } + } else { + result[0] += -1560.8110503642508; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 266))) { + if (LIKELY(false || (data[7].qvalue <= 66))) { + if (LIKELY(false || (data[0].qvalue <= 228))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 578.7799431040581; + } else { + result[0] += 127.52408840507688; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 933.7806696735934; + } else { + result[0] += 471.418662728938; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 142))) { + if (LIKELY(false || (data[7].qvalue <= 128))) { + if (LIKELY(false || (data[6].qvalue <= 100))) { + result[0] += 24.61431879503551; + } else { + result[0] += -514.7441916892188; + } + } else { + result[0] += -840.288058328428; + } + } else { + result[0] += -1369.132803656919; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 88))) { + if (UNLIKELY(false || (data[0].qvalue <= 320))) { + if (LIKELY(false || (data[6].qvalue <= 60))) { + result[0] += 1297.8937605229921; + } else { + if (UNLIKELY(false || (data[9].qvalue <= 48))) { + result[0] += 159.84923661334855; + } else { + result[0] += 806.892953963784; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 92))) { + result[0] += 1631.4555218267105; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + result[0] += 602.1591975811427; + } else { + result[0] += 1225.4498129353617; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 346))) { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[7].qvalue <= 134))) { + result[0] += 477.26815448941943; + } else { + result[0] += -266.49089797874365; + } + } else { + result[0] += -1085.9174116108277; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 136))) { + if (UNLIKELY(false || (data[9].qvalue <= 30))) { + result[0] += 710.5340339475686; + } else { + result[0] += 1160.9702839561826; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 426))) { + result[0] += -336.2082424932214; + } else { + result[0] += 697.2377446648411; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 184))) { + if (UNLIKELY(false || (data[0].qvalue <= 92))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += -1827.1103815856493; + } else { + if (LIKELY(false || (data[6].qvalue <= 92))) { + if (LIKELY(false || (data[0].qvalue <= 68))) { + result[0] += -1460.9279929556785; + } else { + result[0] += -1204.5954946215916; + } + } else { + result[0] += -1703.764420874855; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 136))) { + if (LIKELY(false || (data[7].qvalue <= 62))) { + result[0] += -834.0606253314141; + } else { + result[0] += -1268.097736051363; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 62))) { + if (LIKELY(false || (data[0].qvalue <= 168))) { + result[0] += -450.7445362623278; + } else { + result[0] += -151.5065914814049; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + result[0] += -813.8840731408728; + } else { + result[0] += -1552.9886128870096; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 258))) { + if (LIKELY(false || (data[7].qvalue <= 62))) { + if (UNLIKELY(false || (data[0].qvalue <= 212))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 300.18949268400604; + } else { + result[0] += -82.16209082891513; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 821.3155839598878; + } else { + result[0] += 395.4300146794792; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 142))) { + if (LIKELY(false || (data[7].qvalue <= 134))) { + if (LIKELY(false || (data[6].qvalue <= 94))) { + result[0] += -36.622858072571994; + } else { + result[0] += -472.4429272195769; + } + } else { + result[0] += -886.8842878988258; + } + } else { + result[0] += -1274.9255101678516; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 82))) { + if (UNLIKELY(false || (data[0].qvalue <= 302))) { + if (LIKELY(false || (data[6].qvalue <= 50))) { + result[0] += 1102.9306261545728; + } else { + if (UNLIKELY(false || (data[9].qvalue <= 48))) { + result[0] += 4.269252395168786; + } else { + result[0] += 673.9694942587568; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 48))) { + result[0] += 844.0310059878134; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 336))) { + result[0] += 1254.6320984291067; + } else { + result[0] += 1496.534953542473; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 342))) { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[7].qvalue <= 134))) { + result[0] += 369.8856508010498; + } else { + result[0] += -301.745500216277; + } + } else { + result[0] += -971.5869097857602; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 152))) { + if (LIKELY(false || (data[1].qvalue <= 124))) { + result[0] += 1049.0667116337365; + } else { + result[0] += 655.8288897611133; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 430))) { + result[0] += -483.20221095743983; + } else { + result[0] += 574.5979992744856; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 192))) { + if (LIKELY(false || (data[0].qvalue <= 110))) { + if (LIKELY(false || (data[0].qvalue <= 56))) { + if (UNLIKELY(false || (data[0].qvalue <= 20))) { + result[0] += -1731.6694255800344; + } else { + result[0] += -1463.8734271807368; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 62))) { + result[0] += -1013.7125063069707; + } else { + result[0] += -1295.792308734329; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (LIKELY(false || (data[0].qvalue <= 162))) { + result[0] += -512.040169492541; + } else { + result[0] += -62.54166123570826; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 90))) { + if (LIKELY(false || (data[0].qvalue <= 160))) { + result[0] += -807.711585982142; + } else { + result[0] += -447.3244114975565; + } + } else { + result[0] += -1112.4075523200308; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 274))) { + if (LIKELY(false || (data[7].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 236))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 434.1102182190951; + } else { + result[0] += 64.6833316545062; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 24))) { + result[0] += 911.5851039227344; + } else { + result[0] += 509.70074610455003; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 36))) { + result[0] += -922.6420870752214; + } else { + if (LIKELY(false || (data[7].qvalue <= 134))) { + result[0] += -43.11646969985167; + } else { + result[0] += -711.5443228439684; + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 112))) { + if (LIKELY(false || (data[7].qvalue <= 50))) { + if (UNLIKELY(false || (data[0].qvalue <= 322))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 1217.5857081225881; + } else { + result[0] += 862.7945760545485; + } + } else { + result[0] += 1381.341515141895; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 340))) { + if (LIKELY(false || (data[7].qvalue <= 128))) { + result[0] += 618.2916036455348; + } else { + result[0] += -156.99063121911638; + } + } else { + if (LIKELY(false || (data[10].qvalue <= 78))) { + result[0] += 863.993956946003; + } else { + result[0] += 1217.2914460169281; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 432))) { + if (LIKELY(false || (data[1].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 202))) { + result[0] += 554.0458524017479; + } else { + result[0] += -451.2824254081932; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 132))) { + result[0] += -205.44955983215206; + } else { + result[0] += -1114.6634922865608; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 174))) { + if (UNLIKELY(false || (data[8].qvalue <= 44))) { + result[0] += 434.76778433173723; + } else { + result[0] += 874.836013742844; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + result[0] += -415.8493090687832; + } else { + result[0] += 895.0728029685396; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 180))) { + if (UNLIKELY(false || (data[0].qvalue <= 86))) { + if (UNLIKELY(false || (data[0].qvalue <= 32))) { + result[0] += -1510.0101454113396; + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + result[0] += -1126.5760081015337; + } else { + result[0] += -1392.4200114552195; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 128))) { + if (LIKELY(false || (data[1].qvalue <= 78))) { + result[0] += -742.4787091372192; + } else { + result[0] += -1128.3169223737384; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 82))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += -217.1747383855503; + } else { + result[0] += -505.7066609860621; + } + } else { + result[0] += -926.1157369421153; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 252))) { + if (LIKELY(false || (data[7].qvalue <= 50))) { + if (UNLIKELY(false || (data[0].qvalue <= 208))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 312.45787313138067; + } else { + result[0] += -10.665507478881368; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 562.9336214602067; + } else { + result[0] += 238.03704733164884; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 116))) { + if (LIKELY(false || (data[7].qvalue <= 126))) { + if (LIKELY(false || (data[0].qvalue <= 212))) { + result[0] += -279.2242547615078; + } else { + result[0] += 42.62552906704628; + } + } else { + result[0] += -677.2319392589053; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 152))) { + result[0] += -619.1669830813266; + } else { + result[0] += -1313.98491170941; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 76))) { + if (UNLIKELY(false || (data[0].qvalue <= 296))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 874.524930910277; + } else { + result[0] += 493.74627459103635; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 48))) { + if (UNLIKELY(false || (data[10].qvalue <= 32))) { + result[0] += 305.25905254947634; + } else { + result[0] += 893.8383559035191; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 330))) { + result[0] += 1017.5358679778622; + } else { + result[0] += 1243.0211544735387; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 330))) { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (UNLIKELY(false || (data[9].qvalue <= 40))) { + result[0] += -310.9808580488324; + } else { + result[0] += 277.1404180803976; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 68))) { + result[0] += -1191.3641199442363; + } else { + result[0] += -376.5469216582162; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[7].qvalue <= 134))) { + result[0] += 906.2480551197423; + } else { + result[0] += 554.4928258443013; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 424))) { + result[0] += -334.1159618884123; + } else { + result[0] += 515.865642037843; + } + } + } + } + } + } + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (UNLIKELY(false || (data[3].qvalue <= 44))) { + if (LIKELY(false || (data[7].qvalue <= 24))) { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + if (LIKELY(false || (data[2].qvalue <= 128))) { + result[0] += 200.2716866984727; + } else { + result[0] += -33.31807134567322; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 34))) { + if (LIKELY(false || (data[2].qvalue <= 124))) { + result[0] += 2.1267132045491657; + } else { + result[0] += -376.52573890556465; + } + } else { + result[0] += 146.5594659396158; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 84))) { + if (UNLIKELY(false || (data[6].qvalue <= 40))) { + if (LIKELY(false || (data[2].qvalue <= 88))) { + result[0] += -78.39211587508055; + } else { + result[0] += -211.1780949469055; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 94))) { + result[0] += -50.54335036012877; + } else { + result[0] += 80.00020842727285; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 40))) { + result[0] += -483.63985186717866; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 56))) { + result[0] += 31.9766310228433; + } else { + result[0] += -151.25515710703033; + } + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 152))) { + if (LIKELY(false || (data[10].qvalue <= 108))) { + if (UNLIKELY(false || (data[8].qvalue <= 20))) { + if (UNLIKELY(false || (data[8].qvalue <= 0))) { + result[0] += 111.60897433537673; + } else { + result[0] += -38.459174422337334; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 152))) { + result[0] += 39.893177773342615; + } else { + result[0] += 117.1746253290821; + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 40))) { + if (UNLIKELY(false || (data[3].qvalue <= 58))) { + result[0] += -269.8313885970149; + } else { + result[0] += -44.81099090001982; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 50))) { + result[0] += 44.36810505858426; + } else { + result[0] += -17.031285003457658; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 66))) { + result[0] += 29.134840065248483; + } else { + result[0] += -142.57181028298447; + } + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 100))) { + if (LIKELY(false || (data[7].qvalue <= 196))) { + if (UNLIKELY(false || (data[4].qvalue <= 52))) { + result[0] += -122.23715116402353; + } else { + if (UNLIKELY(false || (data[8].qvalue <= 124))) { + result[0] += 97.82653352215696; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 88))) { + result[0] += 19.540553416535854; + } else { + result[0] += -98.26047713895753; + } + } + } + } else { + result[0] += -208.9897177114526; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += -9.013216655506389; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 154))) { + result[0] += -171.41131706373193; + } else { + if (LIKELY(false || (data[7].qvalue <= 188))) { + result[0] += -228.5813161018793; + } else { + result[0] += -325.7003509988099; + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 196))) { + if (LIKELY(false || (data[0].qvalue <= 114))) { + if (LIKELY(false || (data[0].qvalue <= 60))) { + if (UNLIKELY(false || (data[0].qvalue <= 16))) { + result[0] += -1431.9188559535999; + } else { + result[0] += -1190.2844870798897; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 82))) { + result[0] += -829.9317534554965; + } else { + result[0] += -1180.4578431880107; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 94))) { + if (LIKELY(false || (data[0].qvalue <= 166))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += -258.2731590553095; + } else { + result[0] += -523.7930385002326; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 24))) { + result[0] += 148.2458712176789; + } else { + result[0] += -208.97850691995546; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 152))) { + result[0] += -766.4846303120403; + } else { + result[0] += -1387.2597830966474; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 282))) { + if (LIKELY(false || (data[7].qvalue <= 82))) { + if (LIKELY(false || (data[6].qvalue <= 54))) { + if (UNLIKELY(false || (data[0].qvalue <= 234))) { + result[0] += 347.56940876633854; + } else { + result[0] += 659.3973774082955; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 48))) { + result[0] += -299.8000443073724; + } else { + result[0] += 213.67905068511027; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + result[0] += -241.4835022721958; + } else { + result[0] += -964.8334869254049; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 96))) { + if (LIKELY(false || (data[7].qvalue <= 50))) { + if (UNLIKELY(false || (data[0].qvalue <= 326))) { + if (LIKELY(false || (data[6].qvalue <= 54))) { + result[0] += 1024.8241421866958; + } else { + result[0] += 691.4425294017764; + } + } else { + result[0] += 1134.476489243314; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 348))) { + if (LIKELY(false || (data[7].qvalue <= 178))) { + result[0] += 607.890368257065; + } else { + result[0] += -428.4659382893519; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 414))) { + result[0] += 1014.748894801393; + } else { + result[0] += 597.6335980398155; + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 156))) { + if (UNLIKELY(false || (data[0].qvalue <= 366))) { + if (LIKELY(false || (data[7].qvalue <= 102))) { + result[0] += 342.26282113720123; + } else { + result[0] += -230.30740062057927; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 104))) { + result[0] += 813.8543527953774; + } else { + result[0] += 415.6624160363879; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 446))) { + if (LIKELY(false || (data[6].qvalue <= 174))) { + result[0] += -259.7429923675005; + } else { + result[0] += -1426.7951683026927; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 174))) { + result[0] += 762.73962100054; + } else { + result[0] += 231.27954235732147; + } + } + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 152))) { + if (UNLIKELY(false || (data[3].qvalue <= 44))) { + if (LIKELY(false || (data[7].qvalue <= 24))) { + if (UNLIKELY(false || (data[7].qvalue <= 2))) { + if (UNLIKELY(false || (data[2].qvalue <= 10))) { + result[0] += 301.84658146708784; + } else { + result[0] += 144.51444115717683; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 34))) { + if (UNLIKELY(false || (data[3].qvalue <= 0))) { + result[0] += -133.3520185035297; + } else { + result[0] += 7.825198511802189; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 186.55141223016244; + } else { + result[0] += 66.07816401815487; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 84))) { + if (UNLIKELY(false || (data[9].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + result[0] += 190.81711934969553; + } else { + result[0] += -49.06287353594854; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 6))) { + result[0] += -190.1399731591398; + } else { + result[0] += -58.312564377257914; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 40))) { + result[0] += -486.8565320491534; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 122))) { + result[0] += -23.79376937838657; + } else { + result[0] += -193.64446213252586; + } + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (LIKELY(false || (data[10].qvalue <= 108))) { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + if (UNLIKELY(false || (data[10].qvalue <= 0))) { + result[0] += 154.980784796019; + } else { + result[0] += -70.70779749773081; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 166))) { + result[0] += 42.70546399312704; + } else { + result[0] += 231.67165411885554; + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 40))) { + if (UNLIKELY(false || (data[3].qvalue <= 58))) { + result[0] += -276.0507296705326; + } else { + result[0] += -48.24184527320793; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 50))) { + result[0] += 46.29969181016952; + } else { + result[0] += -22.46817209136037; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 196))) { + if (LIKELY(false || (data[2].qvalue <= 184))) { + if (UNLIKELY(false || (data[9].qvalue <= 70))) { + result[0] += 259.7266642867935; + } else { + result[0] += -33.9062832423356; + } + } else { + result[0] += -130.08036962157004; + } + } else { + result[0] += -206.5135454754138; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 188))) { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 21.01496012390576; + } else { + if (LIKELY(false || (data[9].qvalue <= 16))) { + if (LIKELY(false || (data[9].qvalue <= 2))) { + result[0] += -166.20109103415467; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 22))) { + result[0] += 43.76493225425393; + } else { + result[0] += -110.8984323288921; + } + } + } else { + result[0] += -265.77242681150005; + } + } + } else { + result[0] += -316.46151789497696; + } + } + if (LIKELY(false || (data[1].qvalue <= 152))) { + if (UNLIKELY(false || (data[3].qvalue <= 44))) { + if (LIKELY(false || (data[7].qvalue <= 42))) { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + if (LIKELY(false || (data[2].qvalue <= 128))) { + result[0] += 196.29561739518834; + } else { + result[0] += -31.524131824672523; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 154))) { + if (UNLIKELY(false || (data[4].qvalue <= 6))) { + result[0] += 101.91406015487678; + } else { + result[0] += 1.3921745492571436; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 20))) { + result[0] += -150.8055895533361; + } else { + result[0] += 146.0669399096082; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 38))) { + if (LIKELY(false || (data[7].qvalue <= 122))) { + if (LIKELY(false || (data[4].qvalue <= 76))) { + result[0] += -101.79580164134511; + } else { + result[0] += 51.3302149880617; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 108))) { + result[0] += -334.80985020089327; + } else { + result[0] += -145.88767910265673; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 82))) { + result[0] += -560.2505700484534; + } else { + result[0] += -213.96025392317247; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (LIKELY(false || (data[10].qvalue <= 96))) { + if (UNLIKELY(false || (data[8].qvalue <= 20))) { + if (UNLIKELY(false || (data[8].qvalue <= 0))) { + result[0] += 105.68674793264172; + } else { + result[0] += -43.57721621182288; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -147.9863609603396; + } else { + result[0] += 50.11914192647265; + } + } + } else { + if (LIKELY(false || (data[8].qvalue <= 138))) { + if (LIKELY(false || (data[3].qvalue <= 120))) { + result[0] += -21.50623989909495; + } else { + result[0] += 36.087102588326; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 172))) { + result[0] += -401.56511373374224; + } else { + result[0] += -47.54506742735205; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 196))) { + if (LIKELY(false || (data[10].qvalue <= 136))) { + if (LIKELY(false || (data[4].qvalue <= 70))) { + result[0] += -58.788243146060125; + } else { + result[0] += 35.01063889842818; + } + } else { + result[0] += -167.15525202440935; + } + } else { + result[0] += -185.86643283746199; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 188))) { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 18.914144121003567; + } else { + if (LIKELY(false || (data[9].qvalue <= 16))) { + if (LIKELY(false || (data[9].qvalue <= 2))) { + result[0] += -149.58318971768668; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 22))) { + result[0] += 39.39279508530201; + } else { + result[0] += -99.81036974381612; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 28))) { + result[0] += -574.5809531834742; + } else { + result[0] += -223.51903615680277; + } + } + } + } else { + result[0] += -284.8251127560464; + } + } + if (UNLIKELY(false || (data[0].qvalue <= 178))) { + if (UNLIKELY(false || (data[0].qvalue <= 84))) { + if (UNLIKELY(false || (data[0].qvalue <= 34))) { + result[0] += -1224.3672336316197; + } else { + if (LIKELY(false || (data[6].qvalue <= 62))) { + result[0] += -890.0738935144229; + } else { + result[0] += -1083.6868213970283; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 88))) { + if (LIKELY(false || (data[0].qvalue <= 132))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += -449.253175915189; + } else { + result[0] += -666.9058347442864; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 36))) { + result[0] += -171.0454878027209; + } else { + result[0] += -404.9936499215746; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 108))) { + result[0] += -738.8736900979811; + } else { + result[0] += -1156.8338657457468; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 246))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 376.54293691682807; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 204))) { + result[0] += -19.164395129970544; + } else { + result[0] += 229.71203236622483; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 116))) { + if (LIKELY(false || (data[7].qvalue <= 92))) { + result[0] += -93.71602815702187; + } else { + result[0] += -442.3743373003131; + } + } else { + result[0] += -794.9240557679232; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 96))) { + if (UNLIKELY(false || (data[0].qvalue <= 294))) { + if (LIKELY(false || (data[7].qvalue <= 44))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 776.7722682804047; + } else { + result[0] += 551.7601378282178; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 126))) { + result[0] += 359.00317634305014; + } else { + result[0] += -261.0799150747045; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 54))) { + if (UNLIKELY(false || (data[0].qvalue <= 340))) { + result[0] += 868.4127360515845; + } else { + result[0] += 1030.2866556923334; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 174))) { + result[0] += 759.8335007432447; + } else { + result[0] += 375.57847778528725; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 362))) { + if (LIKELY(false || (data[1].qvalue <= 124))) { + if (LIKELY(false || (data[7].qvalue <= 152))) { + result[0] += 192.76419647400402; + } else { + result[0] += -627.210504284817; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 122))) { + result[0] += -248.81615213762672; + } else { + result[0] += -969.1369200758126; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 152))) { + if (LIKELY(false || (data[1].qvalue <= 124))) { + result[0] += 773.3204928636587; + } else { + result[0] += 406.4377394893374; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 448))) { + result[0] += -133.64446813672; + } else { + result[0] += 531.8523379173158; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 198))) { + if (LIKELY(false || (data[0].qvalue <= 116))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + if (UNLIKELY(false || (data[0].qvalue <= 14))) { + result[0] += -1181.6504962663746; + } else { + result[0] += -963.5370227469084; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 70))) { + result[0] += -638.280415142769; + } else { + result[0] += -906.2068637569266; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + if (UNLIKELY(false || (data[0].qvalue <= 160))) { + result[0] += -355.3777345047579; + } else { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 38.63569197354808; + } else { + result[0] += -203.4298594886395; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 106))) { + result[0] += -501.25518228442854; + } else { + result[0] += -904.0956495312481; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 284))) { + if (LIKELY(false || (data[6].qvalue <= 74))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + if (LIKELY(false || (data[0].qvalue <= 242))) { + result[0] += 325.0351233983085; + } else { + result[0] += 574.7980032330352; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 240))) { + result[0] += 50.603122386774714; + } else { + result[0] += 307.4265167122256; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 124))) { + if (LIKELY(false || (data[2].qvalue <= 200))) { + result[0] += -75.63749275285782; + } else { + result[0] += -878.8670678802445; + } + } else { + result[0] += -784.825049044781; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 108))) { + if (LIKELY(false || (data[1].qvalue <= 82))) { + if (LIKELY(false || (data[9].qvalue <= 128))) { + if (LIKELY(false || (data[0].qvalue <= 354))) { + result[0] += 768.9030375927771; + } else { + result[0] += 982.7632961026947; + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 26))) { + result[0] += 881.7519427243232; + } else { + result[0] += 460.8225104674566; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 352))) { + result[0] += 336.6542893696312; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 12))) { + result[0] += 232.48634402594894; + } else { + result[0] += 754.2862591795262; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 434))) { + if (LIKELY(false || (data[1].qvalue <= 138))) { + if (LIKELY(false || (data[10].qvalue <= 116))) { + result[0] += 486.1115775722369; + } else { + result[0] += -240.0215539033738; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 152))) { + result[0] += -340.1982716887718; + } else { + result[0] += -1142.2235138961084; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 174))) { + if (UNLIKELY(false || (data[2].qvalue <= 106))) { + result[0] += 356.11751909622217; + } else { + result[0] += 708.5483451506925; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + result[0] += -425.3011321722016; + } else { + result[0] += 737.8486431169086; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 176))) { + if (UNLIKELY(false || (data[0].qvalue <= 82))) { + if (UNLIKELY(false || (data[0].qvalue <= 30))) { + result[0] += -1008.2347845448372; + } else { + if (LIKELY(false || (data[6].qvalue <= 92))) { + result[0] += -759.2605409681978; + } else { + result[0] += -1002.0185264155406; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 90))) { + if (UNLIKELY(false || (data[0].qvalue <= 122))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += -430.8085418741898; + } else { + result[0] += -615.9206678101054; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += -160.1037858692929; + } else { + result[0] += -361.25591627953935; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + result[0] += -677.2268849569755; + } else { + result[0] += -1084.1504346623935; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 256))) { + if (LIKELY(false || (data[7].qvalue <= 48))) { + if (LIKELY(false || (data[0].qvalue <= 216))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 219.18790221063765; + } else { + result[0] += 8.224416225928314; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 473.5370228126004; + } else { + result[0] += 241.42467866161678; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 126))) { + if (LIKELY(false || (data[6].qvalue <= 100))) { + result[0] += -37.498571958273004; + } else { + result[0] += -365.5446484967875; + } + } else { + result[0] += -627.0927668810837; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 88))) { + if (UNLIKELY(false || (data[0].qvalue <= 306))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 703.8669257065242; + } else { + result[0] += 489.86267404805426; + } + } else { + result[0] += 234.87338838535948; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 64))) { + if (LIKELY(false || (data[0].qvalue <= 356))) { + result[0] += 717.6793071720917; + } else { + result[0] += 869.7251021367146; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 6))) { + result[0] += 129.6795962020335; + } else { + result[0] += 601.4460117834096; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 374))) { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[7].qvalue <= 176))) { + result[0] += 214.20152575675183; + } else { + result[0] += -537.860116518967; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 24))) { + result[0] += 39.286767186140054; + } else { + result[0] += -860.9173103201238; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 156))) { + if (UNLIKELY(false || (data[6].qvalue <= 36))) { + result[0] += -207.54940006849526; + } else { + result[0] += 555.8581322313454; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 448))) { + result[0] += -233.03910512550647; + } else { + result[0] += 411.0057503246483; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 200))) { + if (UNLIKELY(false || (data[0].qvalue <= 102))) { + if (UNLIKELY(false || (data[0].qvalue <= 42))) { + result[0] += -879.2505320646146; + } else { + if (LIKELY(false || (data[6].qvalue <= 100))) { + result[0] += -613.8765129117388; + } else { + result[0] += -898.2402256092773; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (UNLIKELY(false || (data[0].qvalue <= 152))) { + result[0] += -310.0254085675876; + } else { + result[0] += -23.926126961634566; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (UNLIKELY(false || (data[0].qvalue <= 150))) { + result[0] += -532.7012177870323; + } else { + result[0] += -303.0535459955343; + } + } else { + result[0] += -885.9999951852365; + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 96))) { + if (UNLIKELY(false || (data[0].qvalue <= 288))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (UNLIKELY(false || (data[0].qvalue <= 236))) { + result[0] += 215.01429644416353; + } else { + result[0] += 421.53246886972823; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 48))) { + result[0] += -286.0785455584352; + } else { + result[0] += 116.1256792417381; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 120))) { + if (LIKELY(false || (data[8].qvalue <= 118))) { + if (LIKELY(false || (data[0].qvalue <= 414))) { + result[0] += 706.4179843328923; + } else { + result[0] += 374.35058115512874; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 28))) { + result[0] += -23.434409262940505; + } else { + result[0] += 567.1115038047287; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 386))) { + if (LIKELY(false || (data[8].qvalue <= 18))) { + result[0] += 273.6642642670474; + } else { + result[0] += -1046.2285662989975; + } + } else { + result[0] += 487.25359122330957; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 354))) { + if (LIKELY(false || (data[6].qvalue <= 152))) { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (LIKELY(false || (data[4].qvalue <= 98))) { + result[0] += 86.77607178167547; + } else { + result[0] += -327.9227215464309; + } + } else { + result[0] += -702.536120284601; + } + } else { + result[0] += -816.76285821889; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 168))) { + if (LIKELY(false || (data[4].qvalue <= 104))) { + if (LIKELY(false || (data[2].qvalue <= 202))) { + result[0] += 585.7714665368296; + } else { + result[0] += 211.97898615341128; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 446))) { + result[0] += 45.872711470194794; + } else { + result[0] += 596.7727695600357; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 460))) { + if (LIKELY(false || (data[6].qvalue <= 174))) { + result[0] += -110.3734753492252; + } else { + result[0] += -993.643156799906; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 134))) { + result[0] += 513.9132377712247; + } else { + result[0] += -156.94258247387484; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 174))) { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + if (UNLIKELY(false || (data[0].qvalue <= 28))) { + result[0] += -825.7182786124127; + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + result[0] += -620.5383438315085; + } else { + result[0] += -812.4844205870289; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + if (UNLIKELY(false || (data[0].qvalue <= 124))) { + result[0] += -409.88504033635036; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += -72.60757573397173; + } else { + result[0] += -250.66315233138621; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 32))) { + result[0] += -830.1444150421403; + } else { + result[0] += -512.6141494941938; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 244))) { + if (LIKELY(false || (data[7].qvalue <= 48))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + if (UNLIKELY(false || (data[10].qvalue <= 8))) { + result[0] += 654.5140923867807; + } else { + result[0] += 216.223797321274; + } + } else { + result[0] += 59.127335346599445; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 42))) { + if (LIKELY(false || (data[7].qvalue <= 120))) { + result[0] += -408.9401043985653; + } else { + result[0] += -900.5928970741843; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 134))) { + result[0] += -88.4273489833162; + } else { + result[0] += -494.5124549516256; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 126))) { + if (UNLIKELY(false || (data[0].qvalue <= 316))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 577.7159751120596; + } else { + result[0] += 390.25605025593114; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 100))) { + result[0] += 225.37808240171967; + } else { + result[0] += -134.1896384710644; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 40))) { + if (UNLIKELY(false || (data[0].qvalue <= 380))) { + result[0] += 26.38536976794332; + } else { + result[0] += 404.9857810684197; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 206))) { + result[0] += 640.5372498970368; + } else { + result[0] += 146.7850504947837; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 398))) { + if (UNLIKELY(false || (data[9].qvalue <= 46))) { + if (UNLIKELY(false || (data[2].qvalue <= 32))) { + result[0] += -69.72606543209433; + } else { + result[0] += -890.4547484128167; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 194))) { + result[0] += 43.29913016336363; + } else { + result[0] += -1135.7449808644187; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 156))) { + if (UNLIKELY(false || (data[4].qvalue <= 72))) { + result[0] += 619.8183215898755; + } else { + result[0] += 285.5489715529262; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 448))) { + result[0] += -358.75757336334124; + } else { + result[0] += 304.16119148341835; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 202))) { + if (LIKELY(false || (data[0].qvalue <= 118))) { + if (LIKELY(false || (data[0].qvalue <= 66))) { + if (UNLIKELY(false || (data[0].qvalue <= 10))) { + result[0] += -817.0480700539837; + } else { + result[0] += -635.8069750267712; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + result[0] += -345.96669659122944; + } else { + result[0] += -534.0482572373161; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 90))) { + if (LIKELY(false || (data[0].qvalue <= 170))) { + result[0] += -211.68858516839794; + } else { + if (LIKELY(false || (data[6].qvalue <= 50))) { + result[0] += 62.76650514311201; + } else { + result[0] += -136.44892442998687; + } + } + } else { + result[0] += -491.2641598575405; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 134))) { + if (UNLIKELY(false || (data[0].qvalue <= 292))) { + if (LIKELY(false || (data[7].qvalue <= 48))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + if (LIKELY(false || (data[6].qvalue <= 32))) { + result[0] += 331.57071848199166; + } else { + result[0] += 679.3219985689888; + } + } else { + result[0] += 218.00776335345145; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 42))) { + result[0] += -277.030356546739; + } else { + result[0] += 67.28341346905603; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 40))) { + if (LIKELY(false || (data[0].qvalue <= 428))) { + if (LIKELY(false || (data[6].qvalue <= 134))) { + result[0] += 324.7275088735298; + } else { + result[0] += -419.6036244916827; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 90))) { + result[0] += 170.7438608997793; + } else { + result[0] += 713.0065984964028; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (LIKELY(false || (data[0].qvalue <= 356))) { + result[0] += 471.87466419027; + } else { + result[0] += 637.1788630246149; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 408))) { + result[0] += -529.7467726888689; + } else { + result[0] += 412.8057422873779; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 404))) { + if (UNLIKELY(false || (data[9].qvalue <= 46))) { + result[0] += -712.0507049531527; + } else { + if (LIKELY(false || (data[7].qvalue <= 194))) { + if (LIKELY(false || (data[2].qvalue <= 202))) { + result[0] += 36.347398334304295; + } else { + result[0] += -675.8496819191378; + } + } else { + result[0] += -1026.391767021813; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + if (LIKELY(false || (data[6].qvalue <= 174))) { + if (LIKELY(false || (data[2].qvalue <= 218))) { + result[0] += 300.59738999141763; + } else { + result[0] += -768.8823600737215; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 462))) { + result[0] += -815.3971702532364; + } else { + result[0] += -98.98096938280344; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 188))) { + result[0] += 772.6721143780254; + } else { + result[0] += 184.03196143296145; + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 204))) { + if (UNLIKELY(false || (data[0].qvalue <= 106))) { + if (LIKELY(false || (data[0].qvalue <= 52))) { + result[0] += -628.1023483072281; + } else { + if (LIKELY(false || (data[7].qvalue <= 90))) { + result[0] += -417.2006348985446; + } else { + result[0] += -652.72384554584; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + if (UNLIKELY(false || (data[0].qvalue <= 150))) { + result[0] += -244.5303558275764; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += 81.86220471198173; + } else { + result[0] += -93.51539141150997; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 42))) { + result[0] += -604.2798075362174; + } else { + result[0] += -312.7206975646505; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 76))) { + if (UNLIKELY(false || (data[0].qvalue <= 280))) { + if (UNLIKELY(false || (data[7].qvalue <= 30))) { + if (UNLIKELY(false || (data[7].qvalue <= 2))) { + result[0] += 807.0167727723248; + } else { + result[0] += 289.0442076002897; + } + } else { + result[0] += 124.5150431079561; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + if (UNLIKELY(false || (data[0].qvalue <= 330))) { + if (UNLIKELY(false || (data[7].qvalue <= 30))) { + result[0] += 565.5660231034293; + } else { + result[0] += 342.6451209197193; + } + } else { + result[0] += 592.3830913005597; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 6))) { + if (LIKELY(false || (data[0].qvalue <= 444))) { + result[0] += -430.7701175762352; + } else { + result[0] += 555.2205548100666; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 356))) { + result[0] += 220.55563498286244; + } else { + result[0] += 609.8515804605789; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 364))) { + if (UNLIKELY(false || (data[9].qvalue <= 40))) { + if (LIKELY(false || (data[7].qvalue <= 112))) { + result[0] += -186.288076755465; + } else { + result[0] += -654.6528922882809; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (LIKELY(false || (data[7].qvalue <= 180))) { + result[0] += 134.73234216388812; + } else { + result[0] += -411.6205521150166; + } + } else { + result[0] += -601.6441239663638; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 152))) { + if (LIKELY(false || (data[2].qvalue <= 214))) { + if (UNLIKELY(false || (data[8].qvalue <= 48))) { + result[0] += 159.54229602414563; + } else { + result[0] += 399.7957684439738; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -623.3887244049713; + } else { + result[0] += 412.91921366956416; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 448))) { + if (LIKELY(false || (data[9].qvalue <= 18))) { + result[0] += -725.573342147161; + } else { + result[0] += 232.5245626809719; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 62))) { + result[0] += 781.8973840507092; + } else { + result[0] += 102.27963999730173; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 206))) { + if (LIKELY(false || (data[0].qvalue <= 118))) { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += -541.6072754781043; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 40))) { + result[0] += -266.3645156358689; + } else { + result[0] += -438.26985938213795; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 90))) { + if (LIKELY(false || (data[0].qvalue <= 170))) { + result[0] += -171.34773263938322; + } else { + result[0] += -11.303977068049283; + } + } else { + result[0] += -407.71415355827446; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 126))) { + if (UNLIKELY(false || (data[0].qvalue <= 298))) { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (UNLIKELY(false || (data[7].qvalue <= 16))) { + if (LIKELY(false || (data[5].qvalue <= 46))) { + result[0] += 305.49013378917147; + } else { + result[0] += 766.8660544561868; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 12))) { + result[0] += -212.69432422518977; + } else { + result[0] += 203.45215339752818; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 100))) { + if (UNLIKELY(false || (data[2].qvalue <= 6))) { + result[0] += -1306.979742409561; + } else { + result[0] += 84.56250938982618; + } + } else { + result[0] += -296.8758971391223; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 92))) { + if (LIKELY(false || (data[2].qvalue <= 212))) { + if (UNLIKELY(false || (data[7].qvalue <= 16))) { + result[0] += 634.2960749274997; + } else { + result[0] += 446.27587321034673; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 442))) { + result[0] += -656.3271831376737; + } else { + result[0] += 649.7533136866848; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 346))) { + if (LIKELY(false || (data[6].qvalue <= 100))) { + result[0] += 272.18984613419735; + } else { + result[0] += -183.6208158354411; + } + } else { + result[0] += 335.6468220356205; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 418))) { + if (LIKELY(false || (data[1].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 190))) { + if (LIKELY(false || (data[7].qvalue <= 194))) { + result[0] += 155.81121294253342; + } else { + result[0] += -775.8409014979492; + } + } else { + result[0] += -546.6139529208316; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 279.2996966849545; + } else { + result[0] += -746.3491117424761; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 174))) { + if (UNLIKELY(false || (data[0].qvalue <= 444))) { + if (UNLIKELY(false || (data[2].qvalue <= 76))) { + result[0] += -794.9146466173518; + } else { + result[0] += 238.59142556484136; + } + } else { + result[0] += 427.50310495259396; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + if (LIKELY(false || (data[4].qvalue <= 112))) { + result[0] += -91.71744859578774; + } else { + result[0] += -899.7782100028055; + } + } else { + result[0] += 481.37569784189236; + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 182))) { + if (UNLIKELY(false || (data[0].qvalue <= 90))) { + if (UNLIKELY(false || (data[0].qvalue <= 26))) { + result[0] += -559.2465183228969; + } else { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + result[0] += -339.48268673463207; + } else { + result[0] += -477.7038556835741; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 100))) { + if (LIKELY(false || (data[0].qvalue <= 142))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += -173.83777524062293; + } else { + result[0] += -308.64588699940225; + } + } else { + result[0] += -87.70355551216173; + } + } else { + result[0] += -460.0791599316315; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 264))) { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[6].qvalue <= 46))) { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + if (UNLIKELY(false || (data[4].qvalue <= 2))) { + result[0] += 519.5076559696931; + } else { + result[0] += 36.579262885789255; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 8))) { + result[0] += 850.2335034126149; + } else { + result[0] += 225.76397079413547; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 200))) { + result[0] += 31.634082363361337; + } else { + result[0] += -491.7231693795383; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 134))) { + result[0] += -143.97621323045203; + } else { + result[0] += -587.7222010882514; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[9].qvalue <= 128))) { + if (LIKELY(false || (data[0].qvalue <= 328))) { + result[0] += 341.70375333102265; + } else { + result[0] += 492.9064526262677; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 32))) { + result[0] += 291.11846627413223; + } else { + result[0] += -67.70442593342548; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 336))) { + if (UNLIKELY(false || (data[9].qvalue <= 42))) { + result[0] += -178.5440203148846; + } else { + result[0] += 126.46282552428471; + } + } else { + if (LIKELY(false || (data[10].qvalue <= 76))) { + result[0] += 222.41058195803035; + } else { + result[0] += 434.73453399120257; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 426))) { + if (LIKELY(false || (data[9].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 416))) { + result[0] += -640.7399976249752; + } else { + result[0] += -167.72938945515875; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 46))) { + result[0] += -1344.5726371900926; + } else { + result[0] += 230.06133289043484; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 168))) { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -84.2627136369062; + } else { + result[0] += 415.7428703783992; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 460))) { + result[0] += -384.29322175155045; + } else { + result[0] += 296.1675101952608; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 214))) { + if (LIKELY(false || (data[0].qvalue <= 122))) { + if (UNLIKELY(false || (data[0].qvalue <= 46))) { + result[0] += -470.73918641723026; + } else { + if (LIKELY(false || (data[7].qvalue <= 90))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += -198.32164705298905; + } else { + result[0] += -321.39079912240294; + } + } else { + result[0] += -498.8080870646863; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 317.2121545851497; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 172))) { + result[0] += -88.36582704181914; + } else { + result[0] += 46.798653356184104; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 126))) { + if (LIKELY(false || (data[1].qvalue <= 92))) { + result[0] += -119.53396623757338; + } else { + result[0] += -262.3878864323929; + } + } else { + result[0] += -497.48244794654096; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 134))) { + if (UNLIKELY(false || (data[0].qvalue <= 304))) { + if (LIKELY(false || (data[7].qvalue <= 44))) { + if (UNLIKELY(false || (data[7].qvalue <= 2))) { + result[0] += 746.0045878549049; + } else { + if (LIKELY(false || (data[10].qvalue <= 124))) { + result[0] += 255.0791650107591; + } else { + result[0] += -320.7492571200465; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 90))) { + result[0] += 96.84399754507324; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 18))) { + result[0] += -880.8032147114733; + } else { + result[0] += -73.20637853492896; + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 38))) { + result[0] += 461.5993365359995; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 358))) { + if (LIKELY(false || (data[2].qvalue <= 180))) { + result[0] += 212.73235839758036; + } else { + result[0] += -154.54318766724592; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 124))) { + result[0] += 395.0997024493214; + } else { + result[0] += 210.88997718544783; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 404))) { + if (LIKELY(false || (data[7].qvalue <= 192))) { + if (LIKELY(false || (data[10].qvalue <= 112))) { + if (UNLIKELY(false || (data[5].qvalue <= 50))) { + result[0] += -969.6328168630031; + } else { + result[0] += -86.652192967571; + } + } else { + result[0] += -593.8688924415013; + } + } else { + result[0] += -859.0755121136438; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (LIKELY(false || (data[5].qvalue <= 120))) { + if (LIKELY(false || (data[2].qvalue <= 222))) { + result[0] += 179.3501661826762; + } else { + result[0] += -755.4195928241176; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 464))) { + result[0] += -1101.7895944684535; + } else { + result[0] += -169.1089745696061; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 132))) { + result[0] += 735.5941243655279; + } else { + result[0] += -9.689280563857821; + } + } + } + } + } + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + if (LIKELY(false || (data[7].qvalue <= 16))) { + if (UNLIKELY(false || (data[7].qvalue <= 2))) { + result[0] += 241.07465429893853; + } else { + if (UNLIKELY(false || (data[5].qvalue <= 2))) { + result[0] += -167.05234881826266; + } else { + if (LIKELY(false || (data[9].qvalue <= 160))) { + result[0] += 23.635093021555477; + } else { + result[0] += 247.95569232139462; + } + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 24))) { + if (UNLIKELY(false || (data[5].qvalue <= 26))) { + result[0] += -11.35953345193686; + } else { + if (LIKELY(false || (data[8].qvalue <= 46))) { + result[0] += -241.87320597288937; + } else { + result[0] += -558.2222728620737; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 22))) { + result[0] += -717.6843774931726; + } else { + if (UNLIKELY(false || (data[5].qvalue <= 18))) { + result[0] += -119.9905475497901; + } else { + result[0] += 12.781811278483842; + } + } + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 54))) { + if (UNLIKELY(false || (data[5].qvalue <= 78))) { + if (UNLIKELY(false || (data[8].qvalue <= 68))) { + if (UNLIKELY(false || (data[8].qvalue <= 22))) { + result[0] += -383.60019278447663; + } else { + result[0] += 70.39617109267411; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 62))) { + result[0] += 14.894390887322576; + } else { + result[0] += -246.88224072530934; + } + } + } else { + if (LIKELY(false || (data[10].qvalue <= 134))) { + if (LIKELY(false || (data[9].qvalue <= 32))) { + result[0] += -26.468777991984183; + } else { + result[0] += 124.62962202584231; + } + } else { + result[0] += -125.32103441709326; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 98))) { + if (UNLIKELY(false || (data[6].qvalue <= 50))) { + if (LIKELY(false || (data[8].qvalue <= 120))) { + result[0] += 79.94063628810217; + } else { + result[0] += -99.91998726218483; + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 54))) { + result[0] += -102.42726831173952; + } else { + result[0] += 19.419260663393466; + } + } + } else { + if (LIKELY(false || (data[8].qvalue <= 120))) { + if (LIKELY(false || (data[10].qvalue <= 90))) { + result[0] += 179.7748013112496; + } else { + result[0] += -7.882776932047753; + } + } else { + result[0] += 448.60994004642566; + } + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 134))) { + if (LIKELY(false || (data[7].qvalue <= 198))) { + if (LIKELY(false || (data[3].qvalue <= 168))) { + if (LIKELY(false || (data[6].qvalue <= 178))) { + result[0] += -30.950902106565053; + } else { + result[0] += 476.7456995983057; + } + } else { + result[0] += -198.82706671026182; + } + } else { + result[0] += -249.8175053383445; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 188))) { + if (UNLIKELY(false || (data[3].qvalue <= 98))) { + result[0] += -378.6732367385603; + } else { + result[0] += -109.64341915005078; + } + } else { + result[0] += -425.2420138470435; + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 220))) { + if (LIKELY(false || (data[0].qvalue <= 134))) { + if (LIKELY(false || (data[0].qvalue <= 72))) { + if (UNLIKELY(false || (data[0].qvalue <= 8))) { + result[0] += -532.059203726686; + } else { + result[0] += -372.9090262225109; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 82))) { + result[0] += -209.07044382236805; + } else { + result[0] += -400.00349533828; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (UNLIKELY(false || (data[7].qvalue <= 2))) { + result[0] += 412.17374136976844; + } else { + result[0] += -14.036003053192212; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 116))) { + result[0] += -154.44905243467502; + } else { + result[0] += -436.1506043761763; + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 116))) { + if (LIKELY(false || (data[0].qvalue <= 324))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (LIKELY(false || (data[0].qvalue <= 276))) { + result[0] += 180.81716708771435; + } else { + result[0] += 323.80885408996255; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 4))) { + result[0] += -804.687929563034; + } else { + if (LIKELY(false || (data[7].qvalue <= 144))) { + result[0] += 75.45041773548873; + } else { + result[0] += -308.6438664376922; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 198))) { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + if (UNLIKELY(false || (data[9].qvalue <= 60))) { + result[0] += -136.42148889369125; + } else { + result[0] += 347.35402497810156; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 40))) { + result[0] += 519.5328967780671; + } else { + result[0] += 317.09389087410756; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 442))) { + result[0] += -830.9681326018041; + } else { + result[0] += 776.5378702508172; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 392))) { + if (LIKELY(false || (data[1].qvalue <= 120))) { + if (UNLIKELY(false || (data[3].qvalue <= 96))) { + result[0] += -916.9602444683026; + } else { + if (LIKELY(false || (data[2].qvalue <= 190))) { + result[0] += 151.14410198089988; + } else { + result[0] += -534.6138410777346; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 64))) { + result[0] += -20.321297171216408; + } else { + result[0] += -553.6897184690705; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 152))) { + if (LIKELY(false || (data[2].qvalue <= 214))) { + if (UNLIKELY(false || (data[8].qvalue <= 44))) { + result[0] += -94.32615099423269; + } else { + result[0] += 326.8245357585306; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -617.350459951314; + } else { + result[0] += 276.127751759582; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 442))) { + result[0] += -733.0199493164838; + } else { + if (LIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -116.42395706265687; + } else { + result[0] += 335.32040559141655; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 172))) { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + if (UNLIKELY(false || (data[0].qvalue <= 6))) { + result[0] += -493.1717265491526; + } else { + if (LIKELY(false || (data[1].qvalue <= 72))) { + result[0] += -307.1327071737299; + } else { + result[0] += -442.19784021780106; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 116))) { + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[0].qvalue <= 138))) { + result[0] += -167.9842807798525; + } else { + result[0] += -70.31231390063265; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 120))) { + result[0] += -254.86498043371085; + } else { + result[0] += -703.766647232002; + } + } + } else { + result[0] += -458.17666670208604; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 240))) { + if (LIKELY(false || (data[6].qvalue <= 72))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 400.7108318320836; + } else { + if (LIKELY(false || (data[4].qvalue <= 128))) { + if (UNLIKELY(false || (data[4].qvalue <= 6))) { + result[0] += 227.72272060240076; + } else { + result[0] += 16.088229395762443; + } + } else { + result[0] += -679.3987618413844; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 146))) { + if (UNLIKELY(false || (data[3].qvalue <= 112))) { + result[0] += -287.0516773864561; + } else { + result[0] += -82.35735858878537; + } + } else { + result[0] += -473.44254046258266; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (UNLIKELY(false || (data[0].qvalue <= 326))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + result[0] += 167.85776493357912; + } else { + result[0] += 297.3702646687886; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 116))) { + result[0] += 67.52512843170207; + } else { + result[0] += -189.67754872059376; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 40))) { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 818.1555179158704; + } else { + result[0] += 130.45031708535106; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 454))) { + result[0] += 327.4166938133286; + } else { + result[0] += -120.66748291707741; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 428))) { + if (LIKELY(false || (data[9].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 418))) { + result[0] += -535.7497746001039; + } else { + result[0] += -120.65339683228727; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 46))) { + result[0] += -1201.76536866533; + } else { + result[0] += 160.46676494029293; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 152))) { + if (LIKELY(false || (data[2].qvalue <= 214))) { + result[0] += 379.9797915421976; + } else { + result[0] += -12.579853903701677; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 452))) { + result[0] += -539.1034312262643; + } else { + result[0] += 118.1495781414129; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 210))) { + if (UNLIKELY(false || (data[0].qvalue <= 96))) { + if (UNLIKELY(false || (data[0].qvalue <= 12))) { + result[0] += -414.82218364010805; + } else { + if (LIKELY(false || (data[6].qvalue <= 100))) { + result[0] += -261.7354787568685; + } else { + result[0] += -436.9782510571122; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 126))) { + if (LIKELY(false || (data[6].qvalue <= 50))) { + if (UNLIKELY(false || (data[0].qvalue <= 158))) { + result[0] += -86.99960309582809; + } else { + result[0] += 36.01621466834996; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 54))) { + result[0] += -276.15518055633515; + } else { + result[0] += -117.47322502980383; + } + } + } else { + result[0] += -418.57103843448124; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 134))) { + if (UNLIKELY(false || (data[0].qvalue <= 300))) { + if (LIKELY(false || (data[6].qvalue <= 100))) { + if (UNLIKELY(false || (data[7].qvalue <= 24))) { + result[0] += 221.38591506553996; + } else { + if (UNLIKELY(false || (data[5].qvalue <= 34))) { + result[0] += -114.10943519348092; + } else { + result[0] += 124.41383507415142; + } + } + } else { + result[0] += -124.776312637069; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 40))) { + if (LIKELY(false || (data[0].qvalue <= 434))) { + if (LIKELY(false || (data[6].qvalue <= 134))) { + result[0] += 173.27180862569062; + } else { + result[0] += -230.0263780267813; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 90))) { + result[0] += 93.55822981381293; + } else { + result[0] += 531.9618157197915; + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 138))) { + if (UNLIKELY(false || (data[7].qvalue <= 12))) { + result[0] += 529.342886411045; + } else { + result[0] += 275.38241535187143; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 32))) { + result[0] += 168.94637195832345; + } else { + result[0] += -664.1470891916572; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 442))) { + if (LIKELY(false || (data[6].qvalue <= 174))) { + if (LIKELY(false || (data[7].qvalue <= 198))) { + if (UNLIKELY(false || (data[9].qvalue <= 68))) { + result[0] += -259.076207652103; + } else { + result[0] += 97.97671807149003; + } + } else { + result[0] += -695.1606225313006; + } + } else { + result[0] += -796.0742621530271; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 180))) { + if (UNLIKELY(false || (data[2].qvalue <= 104))) { + if (LIKELY(false || (data[0].qvalue <= 464))) { + result[0] += -153.93531708038253; + } else { + result[0] += 448.69780310463796; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 216))) { + result[0] += 477.95650201824304; + } else { + result[0] += -28.61899971217077; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -909.6241555301945; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 470))) { + result[0] += -188.47445082878266; + } else { + result[0] += 377.35719169796516; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 172))) { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + if (UNLIKELY(false || (data[0].qvalue <= 8))) { + result[0] += -390.84856797995377; + } else { + if (LIKELY(false || (data[1].qvalue <= 64))) { + result[0] += -240.40122514681448; + } else { + result[0] += -347.4550413344793; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 134))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + result[0] += -39.25816550113254; + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[0].qvalue <= 142))) { + result[0] += -186.1240171676131; + } else { + result[0] += -98.0111804997198; + } + } else { + result[0] += -436.0524831294065; + } + } + } else { + result[0] += -471.83682540539394; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 238))) { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 348.9273763963965; + } else { + if (LIKELY(false || (data[7].qvalue <= 104))) { + result[0] += 30.53430231649784; + } else { + result[0] += -202.87449384945464; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 120))) { + if (UNLIKELY(false || (data[2].qvalue <= 4))) { + result[0] += -836.9509277269852; + } else { + if (UNLIKELY(false || (data[6].qvalue <= 22))) { + result[0] += -1508.3799402707123; + } else { + result[0] += -94.06253649645048; + } + } + } else { + result[0] += -348.5371075782411; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 126))) { + if (LIKELY(false || (data[0].qvalue <= 346))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (UNLIKELY(false || (data[0].qvalue <= 286))) { + result[0] += 162.75459965948465; + } else { + result[0] += 282.56625214886793; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 10))) { + result[0] += -440.06926455976475; + } else { + result[0] += 35.45780394100668; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 212))) { + if (UNLIKELY(false || (data[8].qvalue <= 72))) { + result[0] += 180.74606636101373; + } else { + result[0] += 317.0047465523858; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 438))) { + result[0] += -588.0578066297068; + } else { + result[0] += 433.5588979682989; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 422))) { + if (LIKELY(false || (data[1].qvalue <= 136))) { + if (LIKELY(false || (data[7].qvalue <= 192))) { + result[0] += 45.9325195881808; + } else { + result[0] += -679.3813065404768; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 270.29103457509785; + } else { + result[0] += -517.5531688785617; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 118))) { + if (UNLIKELY(false || (data[10].qvalue <= 56))) { + result[0] += 117.54636858837371; + } else { + result[0] += 647.912058999977; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 464))) { + result[0] += -41.193603415175254; + } else { + result[0] += 296.9100448809772; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 222))) { + if (LIKELY(false || (data[0].qvalue <= 138))) { + if (UNLIKELY(false || (data[0].qvalue <= 58))) { + result[0] += -275.2115013121635; + } else { + if (LIKELY(false || (data[6].qvalue <= 116))) { + result[0] += -155.46609912259618; + } else { + result[0] += -389.7954080308127; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 100))) { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + result[0] += 384.7520448135499; + } else { + if (LIKELY(false || (data[9].qvalue <= 154))) { + if (UNLIKELY(false || (data[4].qvalue <= 6))) { + result[0] += 292.1491245847539; + } else { + result[0] += -30.543242192258454; + } + } else { + result[0] += -385.76320075157514; + } + } + } else { + result[0] += -224.14550590009833; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 68))) { + if (UNLIKELY(false || (data[0].qvalue <= 272))) { + result[0] += 106.3555671944755; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + if (UNLIKELY(false || (data[9].qvalue <= 66))) { + if (UNLIKELY(false || (data[0].qvalue <= 410))) { + result[0] += -941.4297952572747; + } else { + result[0] += 56.28649828645251; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 8))) { + result[0] += 507.91991997031147; + } else { + result[0] += 35.20700760987173; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 348))) { + if (UNLIKELY(false || (data[7].qvalue <= 24))) { + result[0] += 324.4895405944488; + } else { + result[0] += 153.59057096574398; + } + } else { + if (LIKELY(false || (data[5].qvalue <= 60))) { + result[0] += 236.3224976398137; + } else { + result[0] += 444.2538183675668; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 372))) { + if (UNLIKELY(false || (data[9].qvalue <= 42))) { + if (UNLIKELY(false || (data[10].qvalue <= 64))) { + result[0] += 22.100327800787746; + } else { + result[0] += -285.1797635732232; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 182))) { + if (UNLIKELY(false || (data[3].qvalue <= 12))) { + result[0] += -916.6873662194294; + } else { + result[0] += 129.80380277726957; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 208))) { + result[0] += -121.90878827484735; + } else { + result[0] += -647.3935446930476; + } + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 10))) { + if (UNLIKELY(false || (data[0].qvalue <= 444))) { + if (LIKELY(false || (data[3].qvalue <= 168))) { + result[0] += -658.7332388174912; + } else { + result[0] += -100.44944033027124; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 174))) { + result[0] += 286.17922512478475; + } else { + result[0] += -166.4328133167238; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 94))) { + if (LIKELY(false || (data[3].qvalue <= 92))) { + result[0] += 56.97705669543027; + } else { + result[0] += -910.3064928974122; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 108))) { + result[0] += 459.24011774105895; + } else { + result[0] += 187.54878983259766; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 170))) { + if (UNLIKELY(false || (data[0].qvalue <= 80))) { + if (UNLIKELY(false || (data[11].qvalue <= 0))) { + if (UNLIKELY(false || (data[0].qvalue <= 18))) { + result[0] += -272.8015586187466; + } else { + result[0] += -158.01002915185765; + } + } else { + result[0] += -270.0109117909505; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 134))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + if (LIKELY(false || (data[5].qvalue <= 44))) { + result[0] += -47.655816285394785; + } else { + result[0] += 148.45176574086; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 48))) { + result[0] += -274.2183049595403; + } else { + result[0] += -120.42739359905393; + } + } + } else { + result[0] += -404.63297777523053; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 250))) { + if (LIKELY(false || (data[7].qvalue <= 86))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 310.3087236621802; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 6))) { + result[0] += -261.86875131184433; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 30))) { + result[0] += 77.33362724018633; + } else { + result[0] += -9.835991514809445; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 178))) { + if (LIKELY(false || (data[10].qvalue <= 94))) { + if (UNLIKELY(false || (data[3].qvalue <= 18))) { + result[0] += -491.7147359309727; + } else { + result[0] += -26.424686375898183; + } + } else { + result[0] += -256.5814333154503; + } + } else { + result[0] += -401.38775248670413; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 134))) { + if (LIKELY(false || (data[0].qvalue <= 364))) { + if (UNLIKELY(false || (data[9].qvalue <= 54))) { + if (LIKELY(false || (data[8].qvalue <= 78))) { + result[0] += 24.807904435156793; + } else { + result[0] += -255.08075851432937; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 180))) { + result[0] += 171.3936836582958; + } else { + result[0] += -146.48986963094893; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 10))) { + if (UNLIKELY(false || (data[0].qvalue <= 446))) { + result[0] += -527.9370629462896; + } else { + result[0] += 311.74412615306; + } + } else { + if (UNLIKELY(false || (data[11].qvalue <= 0))) { + result[0] += 61.275444153721516; + } else { + result[0] += 266.8647138392115; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 464))) { + if (LIKELY(false || (data[2].qvalue <= 218))) { + if (UNLIKELY(false || (data[9].qvalue <= 2))) { + result[0] += -553.7238254263838; + } else { + result[0] += 30.964493797225533; + } + } else { + result[0] += -1039.405250832381; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 132))) { + if (LIKELY(false || (data[0].qvalue <= 470))) { + result[0] += 221.3473424703826; + } else { + result[0] += 578.2036295192621; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 470))) { + result[0] += -1067.1325237122935; + } else { + result[0] += -72.41418684700736; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 222))) { + if (LIKELY(false || (data[0].qvalue <= 120))) { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += -245.63449384858325; + } else { + if (LIKELY(false || (data[1].qvalue <= 102))) { + result[0] += -141.07821937677804; + } else { + result[0] += -311.46095137887164; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + result[0] += -18.828476188261668; + } else { + result[0] += -158.60479952768688; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[0].qvalue <= 334))) { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + if (UNLIKELY(false || (data[5].qvalue <= 28))) { + if (LIKELY(false || (data[2].qvalue <= 122))) { + result[0] += 85.17185179060556; + } else { + result[0] += -1488.3371391950336; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 120))) { + result[0] += 251.47188880049714; + } else { + result[0] += -337.59370375551794; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 176))) { + if (LIKELY(false || (data[7].qvalue <= 122))) { + result[0] += 64.67449717812703; + } else { + result[0] += -198.4096965608494; + } + } else { + result[0] += -109.54175072971603; + } + } + } else { + if (LIKELY(false || (data[5].qvalue <= 96))) { + if (UNLIKELY(false || (data[7].qvalue <= 64))) { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + result[0] += 25.027805981227203; + } else { + result[0] += 254.11335581266104; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 22))) { + result[0] += -327.20805261276865; + } else { + result[0] += 158.88433974650883; + } + } + } else { + result[0] += 635.3039299555891; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 422))) { + if (UNLIKELY(false || (data[5].qvalue <= 98))) { + if (LIKELY(false || (data[4].qvalue <= 116))) { + result[0] += -674.4791597614653; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 154))) { + result[0] += -621.8229750354634; + } else { + result[0] += 421.9420604815607; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 102))) { + if (LIKELY(false || (data[0].qvalue <= 390))) { + result[0] += 85.124602127931; + } else { + result[0] += 949.6197614387934; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 144))) { + result[0] += 251.2728036404487; + } else { + result[0] += -309.15702771287084; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 184))) { + if (LIKELY(false || (data[0].qvalue <= 460))) { + if (UNLIKELY(false || (data[10].qvalue <= 38))) { + result[0] += -633.4429247336111; + } else { + result[0] += 160.50792355093574; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 176))) { + result[0] += 479.12605526304554; + } else { + result[0] += 116.33385550515398; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -930.2431709848779; + } else { + if (LIKELY(false || (data[1].qvalue <= 156))) { + result[0] += 214.15884274598116; + } else { + result[0] += -277.81632186374355; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 168))) { + if (UNLIKELY(false || (data[0].qvalue <= 72))) { + if (LIKELY(false || (data[6].qvalue <= 88))) { + if (UNLIKELY(false || (data[0].qvalue <= 4))) { + result[0] += -294.9481294960577; + } else { + result[0] += -165.43274636129723; + } + } else { + result[0] += -283.98106032598395; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 137.94773272215207; + } else { + result[0] += -74.90747793962899; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + result[0] += -146.0253896376931; + } else { + result[0] += -355.419967719613; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 268))) { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 312.51485279750943; + } else { + if (LIKELY(false || (data[2].qvalue <= 208))) { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + result[0] += -26.67715098965897; + } else { + result[0] += 59.16521507626189; + } + } else { + result[0] += -514.0817805406466; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 22))) { + result[0] += -1382.1700903888081; + } else { + if (LIKELY(false || (data[1].qvalue <= 148))) { + if (UNLIKELY(false || (data[6].qvalue <= 84))) { + result[0] += 2.2238582017569652; + } else { + result[0] += -149.07329069335975; + } + } else { + result[0] += -389.67704456086886; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 134))) { + if (LIKELY(false || (data[2].qvalue <= 212))) { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + if (LIKELY(false || (data[0].qvalue <= 462))) { + result[0] += -139.5342233564615; + } else { + result[0] += 425.4054517675065; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 370))) { + result[0] += 119.1045005005382; + } else { + result[0] += 225.06622562801806; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 428))) { + if (UNLIKELY(false || (data[10].qvalue <= 116))) { + result[0] += -314.0145429164164; + } else { + result[0] += -791.7715009965185; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 156))) { + result[0] += 299.89845236333366; + } else { + result[0] += -121.70249001324471; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 438))) { + if (LIKELY(false || (data[6].qvalue <= 152))) { + if (UNLIKELY(false || (data[2].qvalue <= 42))) { + result[0] += 308.86651700943054; + } else { + result[0] += -123.15739696081778; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 162))) { + result[0] += -543.1460199892057; + } else { + result[0] += -268.8247484125225; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 170))) { + if (LIKELY(false || (data[5].qvalue <= 114))) { + result[0] += 219.69052341180478; + } else { + result[0] += 768.652154361283; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -330.30502416245446; + } else { + result[0] += 226.83056005764217; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 194))) { + if (UNLIKELY(false || (data[0].qvalue <= 94))) { + result[0] += -164.85997854401103; + } else { + if (LIKELY(false || (data[7].qvalue <= 134))) { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + if (LIKELY(false || (data[6].qvalue <= 32))) { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + result[0] += 272.37997423411696; + } else { + result[0] += -38.01170141177444; + } + } else { + result[0] += 179.2649038053183; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 40))) { + if (UNLIKELY(false || (data[10].qvalue <= 14))) { + result[0] += -447.1000423177568; + } else { + result[0] += -129.8926570286136; + } + } else { + result[0] += -58.31804328980124; + } + } + } else { + result[0] += -307.58593821488535; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (UNLIKELY(false || (data[0].qvalue <= 308))) { + if (LIKELY(false || (data[6].qvalue <= 116))) { + if (UNLIKELY(false || (data[7].qvalue <= 2))) { + result[0] += 484.75118404823917; + } else { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 101.86133520052235; + } else { + result[0] += 18.262784206337113; + } + } + } else { + result[0] += -171.38940241656513; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 76))) { + if (LIKELY(false || (data[0].qvalue <= 420))) { + if (LIKELY(false || (data[4].qvalue <= 80))) { + result[0] += 164.27136795332518; + } else { + result[0] += 420.19522838999177; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 70))) { + result[0] += 288.60771862284395; + } else { + result[0] += -317.37860129812316; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 380))) { + if (UNLIKELY(false || (data[3].qvalue <= 112))) { + result[0] += -302.83673649889585; + } else { + result[0] += 54.622892990725916; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 98))) { + result[0] += -14.652198735183687; + } else { + result[0] += 188.62633009457414; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 410))) { + if (LIKELY(false || (data[1].qvalue <= 88))) { + if (UNLIKELY(false || (data[10].qvalue <= 68))) { + result[0] += -453.6048808127357; + } else { + result[0] += 39.27686618322581; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 20))) { + result[0] += 0.2890822714437353; + } else { + result[0] += -681.1445664481982; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 114))) { + if (LIKELY(false || (data[7].qvalue <= 198))) { + result[0] += 448.49867267672437; + } else { + if (LIKELY(false || (data[0].qvalue <= 446))) { + result[0] += -510.11854071816606; + } else { + result[0] += 725.828009865931; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + if (UNLIKELY(false || (data[10].qvalue <= 70))) { + result[0] += 82.63133907289594; + } else { + result[0] += -564.6009321848745; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 188))) { + result[0] += 485.04570202598234; + } else { + result[0] += -44.949579597135376; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 226))) { + if (LIKELY(false || (data[0].qvalue <= 140))) { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + result[0] += -89.22639695175398; + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + result[0] += -154.49086546432346; + } else { + result[0] += -329.5903225571293; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 126))) { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + result[0] += 318.48761171753574; + } else { + result[0] += -18.571568031593554; + } + } else { + result[0] += -220.49206840771905; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 202))) { + if (LIKELY(false || (data[1].qvalue <= 152))) { + if (LIKELY(false || (data[0].qvalue <= 366))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (LIKELY(false || (data[8].qvalue <= 120))) { + result[0] += 150.42799764081784; + } else { + result[0] += -76.38500429830752; + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 56))) { + result[0] += -211.0027314849032; + } else { + result[0] += 24.230230872826798; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 164))) { + if (LIKELY(false || (data[8].qvalue <= 142))) { + result[0] += 132.68242055886515; + } else { + result[0] += -882.8096755983888; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 192))) { + result[0] += 334.11768457333994; + } else { + result[0] += -53.48084930478878; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 450))) { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 233.21157693531032; + } else { + if (LIKELY(false || (data[10].qvalue <= 138))) { + result[0] += -582.6982780907397; + } else { + result[0] += 0.7508374869349193; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 174))) { + if (UNLIKELY(false || (data[2].qvalue <= 66))) { + result[0] += 844.2241348987232; + } else { + result[0] += 166.47618379149745; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -770.8958350747589; + } else { + result[0] += 106.39982734044628; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 424))) { + if (UNLIKELY(false || (data[10].qvalue <= 116))) { + if (LIKELY(false || (data[10].qvalue <= 114))) { + result[0] += -318.826948941992; + } else { + result[0] += 300.5051761631031; + } + } else { + result[0] += -663.2498735346456; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 68))) { + if (LIKELY(false || (data[7].qvalue <= 176))) { + result[0] += 330.2362671942275; + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + result[0] += -261.9859689644222; + } else { + result[0] += 470.337710070637; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + if (UNLIKELY(false || (data[8].qvalue <= 134))) { + result[0] += 52.55390876938948; + } else { + result[0] += -820.466862953795; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 172))) { + result[0] += 678.0504747681679; + } else { + result[0] += -30.65402416986749; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 230))) { + if (LIKELY(false || (data[0].qvalue <= 146))) { + if (LIKELY(false || (data[7].qvalue <= 124))) { + if (UNLIKELY(false || (data[0].qvalue <= 40))) { + result[0] += -163.25748720727836; + } else { + result[0] += -83.1950205740847; + } + } else { + result[0] += -269.08136712131557; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 54))) { + result[0] += -164.01997292662278; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 228.21827507549563; + } else { + result[0] += -9.402288377382439; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 142))) { + if (UNLIKELY(false || (data[7].qvalue <= 16))) { + if (LIKELY(false || (data[6].qvalue <= 30))) { + if (UNLIKELY(false || (data[7].qvalue <= 4))) { + result[0] += 381.64939051951137; + } else { + if (LIKELY(false || (data[0].qvalue <= 302))) { + result[0] += 58.00057357423904; + } else { + result[0] += 223.74792550499748; + } + } + } else { + result[0] += 454.5662709049777; + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 36))) { + if (LIKELY(false || (data[8].qvalue <= 128))) { + if (LIKELY(false || (data[0].qvalue <= 378))) { + result[0] += -93.01200660095587; + } else { + result[0] += 134.32252549902134; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 444))) { + result[0] += -2078.6414691042096; + } else { + result[0] += -159.18640228976722; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 32))) { + if (UNLIKELY(false || (data[0].qvalue <= 396))) { + result[0] += -163.73514121181952; + } else { + result[0] += 100.75922992115221; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 360))) { + result[0] += 84.30972820399808; + } else { + result[0] += 197.85022347944687; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 442))) { + if (LIKELY(false || (data[7].qvalue <= 198))) { + if (UNLIKELY(false || (data[6].qvalue <= 114))) { + if (LIKELY(false || (data[0].qvalue <= 406))) { + result[0] += -124.87742350731226; + } else { + result[0] += 371.4284844619934; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 130))) { + result[0] += -1022.9075285275169; + } else { + result[0] += -154.11889775108097; + } + } + } else { + result[0] += -609.5631862224477; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 180))) { + if (UNLIKELY(false || (data[2].qvalue <= 104))) { + if (LIKELY(false || (data[0].qvalue <= 464))) { + result[0] += -172.46788335138166; + } else { + result[0] += 267.92756125694655; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 216))) { + result[0] += 344.21102212490365; + } else { + result[0] += -63.20922723234142; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (UNLIKELY(false || (data[4].qvalue <= 88))) { + result[0] += -98.6686560628389; + } else { + result[0] += -824.7416534209821; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 162))) { + result[0] += 423.0262534565941; + } else { + result[0] += -172.900534785997; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 164))) { + if (UNLIKELY(false || (data[7].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 22))) { + result[0] += -151.00440148400935; + } else { + result[0] += -40.118590528856686; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 134))) { + result[0] += -118.83158145049806; + } else { + result[0] += -265.5488061301905; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (UNLIKELY(false || (data[0].qvalue <= 276))) { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (UNLIKELY(false || (data[7].qvalue <= 2))) { + result[0] += 337.5770961546419; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + result[0] += -45.53149621844132; + } else { + result[0] += 57.69099405123824; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 22))) { + result[0] += -1262.4368580820865; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 4))) { + result[0] += -499.135631046245; + } else { + result[0] += -51.130463115279184; + } + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 10))) { + if (UNLIKELY(false || (data[0].qvalue <= 446))) { + if (LIKELY(false || (data[10].qvalue <= 132))) { + result[0] += -452.52727046733617; + } else { + result[0] += 207.2426999628595; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 186))) { + result[0] += 147.30212851601848; + } else { + result[0] += -378.60903812177116; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + if (LIKELY(false || (data[7].qvalue <= 116))) { + result[0] += 60.647838286510584; + } else { + result[0] += -403.6642612098349; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 144))) { + result[0] += 97.61644906336824; + } else { + result[0] += 243.6057368289787; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 440))) { + if (LIKELY(false || (data[10].qvalue <= 116))) { + if (LIKELY(false || (data[0].qvalue <= 412))) { + if (LIKELY(false || (data[10].qvalue <= 114))) { + result[0] += -372.46208551501405; + } else { + result[0] += 150.12385303560893; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 154))) { + result[0] += 378.2084024000228; + } else { + result[0] += -115.22596011254524; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 104))) { + result[0] += -1013.8954317267925; + } else { + result[0] += -458.7466793813837; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 162))) { + if (LIKELY(false || (data[0].qvalue <= 460))) { + if (UNLIKELY(false || (data[10].qvalue <= 118))) { + result[0] += 573.7495286165021; + } else { + result[0] += -39.64820127548395; + } + } else { + result[0] += 747.9256037479381; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 464))) { + if (UNLIKELY(false || (data[2].qvalue <= 218))) { + result[0] += -322.32729930754635; + } else { + result[0] += -889.5131323414915; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + result[0] += -49.77314604177496; + } else { + result[0] += 452.922151113344; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 230))) { + if (UNLIKELY(false || (data[0].qvalue <= 100))) { + result[0] += -110.27471806521078; + } else { + if (UNLIKELY(false || (data[9].qvalue <= 54))) { + if (UNLIKELY(false || (data[1].qvalue <= 36))) { + result[0] += 405.5242126958143; + } else { + result[0] += -170.43432845409902; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + result[0] += 256.63050409714157; + } else { + if (LIKELY(false || (data[9].qvalue <= 154))) { + if (UNLIKELY(false || (data[4].qvalue <= 6))) { + result[0] += 199.22002184764415; + } else { + result[0] += -21.456223281242632; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 20))) { + result[0] += -394.6713467594481; + } else { + result[0] += 400.4595635278145; + } + } + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (UNLIKELY(false || (data[9].qvalue <= 2))) { + if (LIKELY(false || (data[0].qvalue <= 466))) { + if (LIKELY(false || (data[6].qvalue <= 170))) { + if (UNLIKELY(false || (data[1].qvalue <= 146))) { + result[0] += -1171.2407195011317; + } else { + result[0] += 62.25614012625213; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 462))) { + result[0] += -747.9426165897981; + } else { + result[0] += -245.0583143986272; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 184))) { + result[0] += 389.8618473333866; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -714.1912396897699; + } else { + result[0] += 105.56232441101614; + } + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 30))) { + if (LIKELY(false || (data[6].qvalue <= 152))) { + if (LIKELY(false || (data[0].qvalue <= 452))) { + result[0] += 8.267421890159907; + } else { + result[0] += 532.9815498007549; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 450))) { + result[0] += -998.5163824104064; + } else { + result[0] += 67.53749679257139; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 38))) { + if (LIKELY(false || (data[0].qvalue <= 314))) { + result[0] += 128.68056584630503; + } else { + result[0] += 304.65594504617775; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 378))) { + result[0] += 38.864839831291896; + } else { + result[0] += 132.95011491298308; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 422))) { + if (LIKELY(false || (data[10].qvalue <= 116))) { + result[0] += -200.50051859519212; + } else { + result[0] += -570.0695420065967; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 76))) { + if (LIKELY(false || (data[2].qvalue <= 214))) { + result[0] += 284.7667993502012; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 452))) { + result[0] += -439.2896395697744; + } else { + result[0] += 195.2703219505385; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 448))) { + result[0] += -719.4958031369861; + } else { + if (LIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -48.870417355715304; + } else { + result[0] += 558.0516523176542; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 158))) { + if (LIKELY(false || (data[1].qvalue <= 102))) { + if (UNLIKELY(false || (data[0].qvalue <= 28))) { + result[0] += -132.8644217361974; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 20))) { + if (LIKELY(false || (data[5].qvalue <= 44))) { + if (UNLIKELY(false || (data[6].qvalue <= 0))) { + result[0] += 184.7567664677024; + } else { + result[0] += -31.01544671430151; + } + } else { + result[0] += 93.76937878382813; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 158))) { + result[0] += -70.3541078253051; + } else { + result[0] += -256.18565932246213; + } + } + } + } else { + result[0] += -186.9601821997848; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 192))) { + if (UNLIKELY(false || (data[0].qvalue <= 272))) { + if (UNLIKELY(false || (data[7].qvalue <= 22))) { + if (LIKELY(false || (data[6].qvalue <= 32))) { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + result[0] += 301.99251039980805; + } else { + result[0] += 8.317443045497088; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 285.9447580619998; + } else { + result[0] += -199.02985809976943; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 34))) { + if (UNLIKELY(false || (data[7].qvalue <= 28))) { + result[0] += -757.7155546033136; + } else { + result[0] += -100.8798688536487; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 54))) { + result[0] += -108.18906289397617; + } else { + result[0] += 16.427955171508895; + } + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 32))) { + if (LIKELY(false || (data[0].qvalue <= 450))) { + if (LIKELY(false || (data[6].qvalue <= 168))) { + result[0] += -31.079261573945725; + } else { + result[0] += -439.24160085690585; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 176))) { + result[0] += 196.17828368025368; + } else { + result[0] += -96.92802902335563; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + if (LIKELY(false || (data[7].qvalue <= 150))) { + result[0] += 5.472886535824474; + } else { + result[0] += -322.6822453952255; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 104))) { + result[0] += 71.15954913651206; + } else { + result[0] += 170.40770380917564; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 434))) { + if (LIKELY(false || (data[1].qvalue <= 98))) { + result[0] += -431.7182112009705; + } else { + result[0] += -761.9070576282425; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 174))) { + if (LIKELY(false || (data[0].qvalue <= 450))) { + if (LIKELY(false || (data[3].qvalue <= 170))) { + result[0] += 223.6763270971365; + } else { + result[0] += -863.1311602081029; + } + } else { + result[0] += 633.5684765097419; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 464))) { + result[0] += -822.025766073113; + } else { + if (LIKELY(false || (data[8].qvalue <= 144))) { + result[0] += -357.65137650350306; + } else { + result[0] += 161.10489597769953; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 230))) { + if (UNLIKELY(false || (data[0].qvalue <= 94))) { + result[0] += -92.57565870144303; + } else { + if (LIKELY(false || (data[7].qvalue <= 142))) { + result[0] += -18.109772204939024; + } else { + result[0] += -209.49572955406617; + } + } + } else { + if (LIKELY(false || (data[10].qvalue <= 116))) { + if (LIKELY(false || (data[1].qvalue <= 152))) { + if (UNLIKELY(false || (data[10].qvalue <= 30))) { + if (LIKELY(false || (data[0].qvalue <= 462))) { + if (LIKELY(false || (data[5].qvalue <= 102))) { + result[0] += 3.2575921560660412; + } else { + result[0] += -901.3597327991677; + } + } else { + result[0] += 507.2677983999125; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 128))) { + if (UNLIKELY(false || (data[5].qvalue <= 12))) { + result[0] += -59.086766158297294; + } else { + result[0] += 112.28787145525055; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 58))) { + result[0] += 323.6859359964492; + } else { + result[0] += -58.36682033574347; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 460))) { + if (LIKELY(false || (data[2].qvalue <= 84))) { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 300.9633139092185; + } else { + result[0] += -192.01700771623888; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 166))) { + result[0] += -1598.762896930197; + } else { + result[0] += -603.9295642909062; + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 16))) { + if (UNLIKELY(false || (data[6].qvalue <= 174))) { + result[0] += 461.56736902180194; + } else { + result[0] += 8.737578542205178; + } + } else { + result[0] += -643.983821829543; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 440))) { + if (LIKELY(false || (data[6].qvalue <= 104))) { + if (UNLIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[2].qvalue <= 134))) { + result[0] += 179.91548506297573; + } else { + result[0] += -912.5235714991372; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 174))) { + result[0] += 301.8534329221878; + } else { + result[0] += 38.18076419361558; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 94))) { + if (LIKELY(false || (data[0].qvalue <= 406))) { + result[0] += -361.8709774599796; + } else { + result[0] += 270.3263916029786; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 6))) { + result[0] += 104.81627908969739; + } else { + result[0] += -535.2156269218489; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 170))) { + if (LIKELY(false || (data[4].qvalue <= 122))) { + if (UNLIKELY(false || (data[7].qvalue <= 114))) { + result[0] += 594.5833564826081; + } else { + result[0] += 64.11583431949299; + } + } else { + result[0] += 652.8845003559351; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -763.9957389916799; + } else { + if (UNLIKELY(false || (data[4].qvalue <= 102))) { + result[0] += 315.48470650562535; + } else { + result[0] += -203.67379945157032; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 154))) { + if (UNLIKELY(false || (data[4].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 12))) { + result[0] += -126.5718042782856; + } else { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 111.5057760402395; + } else { + result[0] += -28.4538638965687; + } + } else { + result[0] += -313.9852425571987; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 142))) { + if (UNLIKELY(false || (data[9].qvalue <= 48))) { + result[0] += -159.27654309724835; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 54))) { + result[0] += -114.2328146382158; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 18))) { + result[0] += -169.53442010531597; + } else { + result[0] += -51.819614520602755; + } + } + } + } else { + result[0] += -237.49538829330947; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 192))) { + if (UNLIKELY(false || (data[7].qvalue <= 16))) { + if (LIKELY(false || (data[5].qvalue <= 46))) { + if (LIKELY(false || (data[0].qvalue <= 290))) { + if (UNLIKELY(false || (data[7].qvalue <= 2))) { + result[0] += 287.9491607426837; + } else { + result[0] += 22.668345828570306; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 0))) { + result[0] += -77.53473409498233; + } else { + result[0] += 204.6386641118168; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 16))) { + result[0] += 593.7874627921306; + } else { + result[0] += 312.8524644986319; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 42))) { + if (UNLIKELY(false || (data[6].qvalue <= 24))) { + if (LIKELY(false || (data[2].qvalue <= 122))) { + result[0] += -142.83280406635228; + } else { + result[0] += -693.2501230727516; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 122))) { + result[0] += 37.74683956549833; + } else { + result[0] += -198.748856011046; + } + } + } else { + if (LIKELY(false || (data[10].qvalue <= 108))) { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + result[0] += -89.22275972296083; + } else { + result[0] += 86.32327653910806; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 442))) { + result[0] += -55.02130370736227; + } else { + result[0] += 177.35435049468094; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 432))) { + if (UNLIKELY(false || (data[2].qvalue <= 74))) { + result[0] += -882.5317237443489; + } else { + result[0] += -481.30010328266326; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 132))) { + if (LIKELY(false || (data[0].qvalue <= 450))) { + if (UNLIKELY(false || (data[7].qvalue <= 198))) { + result[0] += 647.320083633933; + } else { + result[0] += 38.11111140878454; + } + } else { + result[0] += 745.2928537341078; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -789.9197396160231; + } else { + if (UNLIKELY(false || (data[8].qvalue <= 144))) { + result[0] += -357.5527732739339; + } else { + result[0] += 102.50578713894222; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 232))) { + if (LIKELY(false || (data[6].qvalue <= 116))) { + if (UNLIKELY(false || (data[0].qvalue <= 62))) { + result[0] += -81.42467173085487; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 16))) { + if (LIKELY(false || (data[5].qvalue <= 48))) { + result[0] += 9.502633625283542; + } else { + result[0] += 279.42600545633803; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 28))) { + if (UNLIKELY(false || (data[6].qvalue <= 18))) { + result[0] += -361.821022373354; + } else { + result[0] += -88.28610792754384; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 120))) { + result[0] += -20.102121037240444; + } else { + result[0] += -420.12674867604005; + } + } + } + } + } else { + result[0] += -159.62279487679336; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 38))) { + if (LIKELY(false || (data[10].qvalue <= 124))) { + if (UNLIKELY(false || (data[6].qvalue <= 10))) { + if (LIKELY(false || (data[7].qvalue <= 18))) { + result[0] += 88.01385578444962; + } else { + if (UNLIKELY(false || (data[6].qvalue <= 4))) { + result[0] += 493.08930447766016; + } else { + result[0] += -285.19813158141295; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 82))) { + result[0] += 106.27335201243972; + } else { + result[0] += 226.93623921451922; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 50))) { + if (LIKELY(false || (data[0].qvalue <= 412))) { + result[0] += -1343.712162589175; + } else { + result[0] += 132.58954443144634; + } + } else { + result[0] += 91.6622701261032; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 44))) { + if (LIKELY(false || (data[3].qvalue <= 38))) { + if (LIKELY(false || (data[4].qvalue <= 84))) { + if (LIKELY(false || (data[0].qvalue <= 368))) { + result[0] += -236.33002335788538; + } else { + result[0] += 20.986096347583267; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 128))) { + result[0] += 344.9152673102923; + } else { + result[0] += -105.45712817788554; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 328.6203717386541; + } else { + if (LIKELY(false || (data[0].qvalue <= 448))) { + result[0] += -650.199452548778; + } else { + result[0] += -47.9905026661036; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 132))) { + if (LIKELY(false || (data[0].qvalue <= 366))) { + if (LIKELY(false || (data[2].qvalue <= 180))) { + result[0] += 42.03195219241939; + } else { + result[0] += -158.93413279533422; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 74))) { + result[0] += -34.071317241183344; + } else { + result[0] += 156.0271506153512; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 440))) { + if (UNLIKELY(false || (data[6].qvalue <= 102))) { + result[0] += 133.87095453891186; + } else { + result[0] += -228.7502100788784; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 108))) { + result[0] += 347.50113932020776; + } else { + result[0] += 14.191860694706207; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 230))) { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + if (LIKELY(false || (data[2].qvalue <= 122))) { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + result[0] += 177.32409766602134; + } else { + if (UNLIKELY(false || (data[10].qvalue <= 10))) { + result[0] += -222.24918504973667; + } else { + result[0] += -35.30855443997724; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 4))) { + result[0] += -176.9665709227076; + } else { + result[0] += -753.9332796594691; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 116))) { + result[0] += -28.342368225619726; + } else { + if (UNLIKELY(false || (data[4].qvalue <= 8))) { + result[0] += 421.668098564295; + } else { + if (LIKELY(false || (data[3].qvalue <= 70))) { + result[0] += 43.5107313173905; + } else { + result[0] += 227.90844897767985; + } + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + result[0] += -57.17411824360336; + } else { + result[0] += -189.687353931762; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 202))) { + if (UNLIKELY(false || (data[3].qvalue <= 0))) { + if (LIKELY(false || (data[4].qvalue <= 54))) { + if (LIKELY(false || (data[9].qvalue <= 156))) { + result[0] += 143.81130747694493; + } else { + if (LIKELY(false || (data[0].qvalue <= 378))) { + result[0] += -412.9308129291959; + } else { + result[0] += 174.61191720224775; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 422))) { + if (LIKELY(false || (data[9].qvalue <= 88))) { + result[0] += -729.9085027051653; + } else { + result[0] += -1582.3957851064256; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 82))) { + result[0] += 255.17536551772466; + } else { + result[0] += -411.7747098985702; + } + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 0))) { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -687.4964190637061; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 156))) { + result[0] += 579.2665867229997; + } else { + result[0] += -202.15282940470115; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 378))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + result[0] += 87.73491261546084; + } else { + result[0] += -32.02969863953809; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 166))) { + result[0] += 64.28717265542389; + } else { + result[0] += 229.17843828172778; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (LIKELY(false || (data[6].qvalue <= 176))) { + if (LIKELY(false || (data[0].qvalue <= 442))) { + if (UNLIKELY(false || (data[10].qvalue <= 116))) { + result[0] += -44.67740033036628; + } else { + result[0] += -394.97654476097733; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 222))) { + result[0] += 206.95175345062236; + } else { + result[0] += -422.41503848096585; + } + } + } else { + result[0] += -637.136586228852; + } + } else { + result[0] += 376.9378106237276; + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 162))) { + if (LIKELY(false || (data[1].qvalue <= 64))) { + if (UNLIKELY(false || (data[0].qvalue <= 4))) { + result[0] += -145.63790783710485; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 92.69381886875624; + } else { + if (LIKELY(false || (data[2].qvalue <= 200))) { + result[0] += -29.52565796333189; + } else { + result[0] += -264.4833581843737; + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 80))) { + result[0] += -65.00862983366554; + } else { + result[0] += -128.3574858335414; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 192))) { + if (LIKELY(false || (data[10].qvalue <= 116))) { + if (LIKELY(false || (data[0].qvalue <= 334))) { + if (LIKELY(false || (data[7].qvalue <= 44))) { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + result[0] += 2.638363742314313; + } else { + result[0] += 90.02482928313995; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 46))) { + result[0] += -159.46662656973402; + } else { + result[0] += -6.479090823017612; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 10))) { + if (LIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -181.28880117455623; + } else { + result[0] += 348.385125441513; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + result[0] += -95.60162541279402; + } else { + result[0] += 108.90915732052065; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 448))) { + if (LIKELY(false || (data[6].qvalue <= 104))) { + if (UNLIKELY(false || (data[8].qvalue <= 92))) { + result[0] += -264.2515788707746; + } else { + result[0] += 101.55470547812884; + } + } else { + if (LIKELY(false || (data[8].qvalue <= 148))) { + result[0] += -178.22322886037145; + } else { + result[0] += -572.0017383471462; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 176))) { + if (LIKELY(false || (data[2].qvalue <= 216))) { + result[0] += 360.0600154694514; + } else { + result[0] += -2.277701777775852; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + result[0] += -592.4446434254904; + } else { + result[0] += 311.89032842128967; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 434))) { + if (LIKELY(false || (data[1].qvalue <= 98))) { + if (LIKELY(false || (data[3].qvalue <= 170))) { + result[0] += -269.5216917245177; + } else { + result[0] += -763.2129798350616; + } + } else { + result[0] += -630.6861669974678; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 132))) { + if (LIKELY(false || (data[0].qvalue <= 452))) { + if (UNLIKELY(false || (data[1].qvalue <= 98))) { + result[0] += 491.16969306572764; + } else { + result[0] += -1.8378346933226348; + } + } else { + result[0] += 735.4661909012816; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -714.8393720262166; + } else { + if (LIKELY(false || (data[9].qvalue <= 124))) { + result[0] += -185.85739524553466; + } else { + result[0] += 534.3912650447504; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 234))) { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + if (LIKELY(false || (data[2].qvalue <= 122))) { + if (LIKELY(false || (data[7].qvalue <= 34))) { + result[0] += -17.516199145518204; + } else { + if (LIKELY(false || (data[5].qvalue <= 40))) { + result[0] += -106.45476753502933; + } else { + result[0] += -903.8252212915366; + } + } + } else { + result[0] += -429.58429904156355; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 108))) { + result[0] += -24.64488868029451; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 6))) { + result[0] += 442.263546116001; + } else { + result[0] += 55.12907189754925; + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + result[0] += -46.866944249531215; + } else { + result[0] += -159.04948180790302; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (UNLIKELY(false || (data[3].qvalue <= 0))) { + if (LIKELY(false || (data[7].qvalue <= 110))) { + result[0] += -7.415599980597751; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 420))) { + if (LIKELY(false || (data[2].qvalue <= 196))) { + result[0] += -1399.7966503370856; + } else { + result[0] += -610.3226946773707; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 96))) { + result[0] += 354.8632074839545; + } else { + result[0] += -377.08280725284146; + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 12))) { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (LIKELY(false || (data[0].qvalue <= 312))) { + result[0] += 78.41314807466219; + } else { + result[0] += 229.81473981996055; + } + } else { + result[0] += 656.2836431782829; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 370))) { + if (UNLIKELY(false || (data[6].qvalue <= 62))) { + result[0] += 59.2010780966252; + } else { + result[0] += -33.600013356306874; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 10))) { + result[0] += -40.555513916002155; + } else { + result[0] += 98.08389359507521; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 412))) { + if (UNLIKELY(false || (data[8].qvalue <= 136))) { + if (LIKELY(false || (data[8].qvalue <= 122))) { + result[0] += -319.74524116929115; + } else { + result[0] += -767.7796729203433; + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 82))) { + result[0] += 122.08054350335395; + } else { + result[0] += -301.9722820682188; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 114))) { + if (LIKELY(false || (data[7].qvalue <= 198))) { + result[0] += 313.9124097005065; + } else { + result[0] += -8.79354690498097; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (LIKELY(false || (data[6].qvalue <= 180))) { + result[0] += -72.7211246346664; + } else { + result[0] += -510.6446546492408; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 188))) { + result[0] += 560.0836128367328; + } else { + result[0] += 17.985013800397187; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 148))) { + if (UNLIKELY(false || (data[4].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 2))) { + result[0] += -141.96709416955443; + } else { + result[0] += -15.705324092558222; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 152))) { + result[0] += -61.382323220403975; + } else { + result[0] += -187.5709723722473; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (LIKELY(false || (data[0].qvalue <= 352))) { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + if (LIKELY(false || (data[7].qvalue <= 16))) { + result[0] += 44.92602753182352; + } else { + result[0] += -211.1546710682142; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 70))) { + result[0] += 84.82973193387151; + } else { + result[0] += 297.5136117027242; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 54))) { + if (LIKELY(false || (data[8].qvalue <= 80))) { + result[0] += 17.890869053801463; + } else { + result[0] += -233.23923140903472; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 42))) { + result[0] += -115.18248063786203; + } else { + result[0] += 19.654733381799502; + } + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 38))) { + if (UNLIKELY(false || (data[8].qvalue <= 48))) { + if (UNLIKELY(false || (data[6].qvalue <= 108))) { + result[0] += 73.48571042663086; + } else { + result[0] += -228.89571182954265; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 412))) { + result[0] += -172.0892135464257; + } else { + result[0] += 115.89684884981358; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 74))) { + if (UNLIKELY(false || (data[2].qvalue <= 2))) { + result[0] += -192.70183326405345; + } else { + result[0] += 228.22523400305982; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 164))) { + result[0] += -14.495336023506457; + } else { + result[0] += 164.58338032467216; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 418))) { + if (UNLIKELY(false || (data[6].qvalue <= 110))) { + result[0] += 92.44304518392212; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -549.5535909490123; + } else { + result[0] += -283.7280102420611; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 170))) { + if (UNLIKELY(false || (data[3].qvalue <= 110))) { + if (UNLIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -751.8919251402435; + } else { + result[0] += 185.12085880810656; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 442))) { + result[0] += 106.48103521611722; + } else { + result[0] += 402.22669875787847; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 460))) { + if (UNLIKELY(false || (data[4].qvalue <= 14))) { + result[0] += -36.04581623793381; + } else { + result[0] += -576.7489848172199; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 204))) { + result[0] += 811.3997608577275; + } else { + result[0] += 54.07893988355018; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 190))) { + if (LIKELY(false || (data[1].qvalue <= 118))) { + if (UNLIKELY(false || (data[0].qvalue <= 40))) { + result[0] += -66.6266648878957; + } else { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (UNLIKELY(false || (data[7].qvalue <= 0))) { + result[0] += 218.52501238124137; + } else { + if (LIKELY(false || (data[7].qvalue <= 184))) { + result[0] += -13.543587342327356; + } else { + result[0] += -215.10060994815572; + } + } + } else { + result[0] += -235.9788695560659; + } + } + } else { + result[0] += -124.00456972107527; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 192))) { + if (LIKELY(false || (data[10].qvalue <= 122))) { + if (UNLIKELY(false || (data[10].qvalue <= 30))) { + if (LIKELY(false || (data[0].qvalue <= 462))) { + if (LIKELY(false || (data[5].qvalue <= 102))) { + result[0] += -18.111064688282102; + } else { + result[0] += -830.4402494030709; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 114))) { + result[0] += 549.3375303639418; + } else { + result[0] += -471.1944868822796; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 38))) { + if (LIKELY(false || (data[0].qvalue <= 306))) { + result[0] += 58.15499047997176; + } else { + result[0] += 202.68651234520863; + } + } else { + if (LIKELY(false || (data[5].qvalue <= 84))) { + result[0] += 5.292449943571185; + } else { + result[0] += 88.29265314882701; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[0].qvalue <= 414))) { + if (LIKELY(false || (data[1].qvalue <= 128))) { + result[0] += -1380.2032030523546; + } else { + result[0] += -290.5204067553985; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 428))) { + result[0] += -114.53892152149231; + } else { + result[0] += 514.5293420314608; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 58))) { + if (UNLIKELY(false || (data[4].qvalue <= 48))) { + result[0] += -61.89760896238173; + } else { + result[0] += 238.5724533454146; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 440))) { + result[0] += -129.87454115256398; + } else { + result[0] += 80.61545671612187; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 436))) { + if (UNLIKELY(false || (data[0].qvalue <= 346))) { + result[0] += -205.98214313987833; + } else { + result[0] += -501.7217730494733; + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 94))) { + if (UNLIKELY(false || (data[9].qvalue <= 74))) { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -1647.808151855469; + } else { + result[0] += -111.63345227808891; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 448))) { + result[0] += 135.65529729266765; + } else { + result[0] += 542.5839931646244; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -605.3494305872034; + } else { + if (LIKELY(false || (data[7].qvalue <= 200))) { + result[0] += 23.37492749029042; + } else { + result[0] += -504.33767145881876; + } + } + } + } + } + } + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + if (UNLIKELY(false || (data[8].qvalue <= 6))) { + if (UNLIKELY(false || (data[2].qvalue <= 28))) { + result[0] += 147.6732444791957; + } else { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += -10.796001760756946; + } else { + result[0] += 119.76151703653773; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 16))) { + result[0] += -80.64564777701082; + } else { + result[0] += -137.81918275187488; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 188))) { + result[0] += -301.13288115967265; + } else { + result[0] += -677.8347078414617; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 102))) { + if (LIKELY(false || (data[7].qvalue <= 48))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + if (LIKELY(false || (data[1].qvalue <= 66))) { + result[0] += 177.5420680296681; + } else { + result[0] += -218.59350937699685; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 20))) { + result[0] += -72.74967625197816; + } else { + result[0] += 20.742745610897746; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 84))) { + if (LIKELY(false || (data[4].qvalue <= 78))) { + result[0] += -90.48006952525736; + } else { + result[0] += -828.7521117629706; + } + } else { + if (LIKELY(false || (data[5].qvalue <= 66))) { + result[0] += 120.02128594767765; + } else { + result[0] += -101.64457682331833; + } + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 32))) { + if (UNLIKELY(false || (data[8].qvalue <= 48))) { + if (LIKELY(false || (data[8].qvalue <= 24))) { + result[0] += -29.977235441363906; + } else { + result[0] += -517.2676348797457; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 138))) { + result[0] += 79.82233930374132; + } else { + result[0] += -65.66862801060336; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 176))) { + if (UNLIKELY(false || (data[2].qvalue <= 4))) { + result[0] += -246.17761450724754; + } else { + result[0] += 128.63335245716783; + } + } else { + if (LIKELY(false || (data[10].qvalue <= 134))) { + result[0] += 25.606045866379198; + } else { + result[0] += -125.59366437898994; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 32))) { + result[0] += -1022.6641412510904; + } else { + if (LIKELY(false || (data[7].qvalue <= 200))) { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 256.4779043789177; + } else { + if (LIKELY(false || (data[1].qvalue <= 126))) { + if (LIKELY(false || (data[4].qvalue <= 68))) { + result[0] += -87.1798391962891; + } else { + result[0] += 43.00407443678074; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 62))) { + result[0] += -130.4952039313895; + } else { + result[0] += -240.59828244124444; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 164))) { + result[0] += -424.70860218767706; + } else { + result[0] += -596.5247694936194; + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 260))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + if (UNLIKELY(false || (data[8].qvalue <= 2))) { + result[0] += 174.72777599410847; + } else { + result[0] += 11.536037972850965; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 6))) { + if (LIKELY(false || (data[9].qvalue <= 96))) { + if (LIKELY(false || (data[7].qvalue <= 72))) { + if (LIKELY(false || (data[0].qvalue <= 132))) { + result[0] += -134.39734952576543; + } else { + result[0] += -367.3809402303026; + } + } else { + result[0] += 85.4518440435695; + } + } else { + result[0] += 30.478639892590373; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 124))) { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + result[0] += -50.893812931197466; + } else { + if (LIKELY(false || (data[2].qvalue <= 200))) { + result[0] += -4.69601116410534; + } else { + result[0] += -265.1217064966412; + } + } + } else { + result[0] += -104.769463503831; + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 64))) { + if (UNLIKELY(false || (data[9].qvalue <= 36))) { + result[0] += 428.87545627753326; + } else { + if (UNLIKELY(false || (data[9].qvalue <= 38))) { + if (UNLIKELY(false || (data[0].qvalue <= 442))) { + result[0] += -1773.848510347332; + } else { + result[0] += 55.659715716371515; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 359.22617617244686; + } else { + if (UNLIKELY(false || (data[8].qvalue <= 20))) { + result[0] += -44.87315510911888; + } else { + result[0] += 74.38569188031644; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 382))) { + if (UNLIKELY(false || (data[2].qvalue <= 140))) { + if (UNLIKELY(false || (data[3].qvalue <= 12))) { + if (UNLIKELY(false || (data[10].qvalue <= 40))) { + result[0] += -1410.981056764837; + } else { + result[0] += -637.2921393197482; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 56))) { + result[0] += -70.36055469147344; + } else { + result[0] += 175.7175261222059; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 146))) { + result[0] += -613.9698554996957; + } else { + if (LIKELY(false || (data[8].qvalue <= 154))) { + result[0] += -92.27993947562106; + } else { + result[0] += -383.51208633267225; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 88))) { + if (UNLIKELY(false || (data[7].qvalue <= 80))) { + if (UNLIKELY(false || (data[2].qvalue <= 64))) { + result[0] += -11.98521625723796; + } else { + result[0] += -714.7673310356126; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 96))) { + result[0] += 284.6663603250349; + } else { + result[0] += -61.60569809902938; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 40))) { + if (UNLIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -1260.7217681991474; + } else { + result[0] += 135.29439352456583; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 114))) { + result[0] += 204.2658499096425; + } else { + result[0] += 26.57998407789056; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 278))) { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + if (LIKELY(false || (data[1].qvalue <= 10))) { + result[0] += 88.29835564376643; + } else { + result[0] += 433.48530065382903; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 158))) { + if (UNLIKELY(false || (data[4].qvalue <= 6))) { + if (UNLIKELY(false || (data[9].qvalue <= 134))) { + result[0] += 355.11754045683864; + } else { + result[0] += 50.689614734965716; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 10))) { + if (UNLIKELY(false || (data[9].qvalue <= 142))) { + result[0] += -67.45276443587568; + } else { + result[0] += -258.89458735449244; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 150))) { + result[0] += -21.308012427711205; + } else { + result[0] += 244.9050850901425; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 166))) { + result[0] += -159.91554699358346; + } else { + result[0] += -467.3807915108788; + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 180))) { + if (LIKELY(false || (data[0].qvalue <= 460))) { + if (LIKELY(false || (data[2].qvalue <= 214))) { + if (UNLIKELY(false || (data[9].qvalue <= 2))) { + if (UNLIKELY(false || (data[3].qvalue <= 98))) { + result[0] += -888.854216500035; + } else { + result[0] += -60.23569984287638; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 10))) { + result[0] += -107.7332013111863; + } else { + result[0] += 44.24648581216298; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 32))) { + if (LIKELY(false || (data[0].qvalue <= 446))) { + result[0] += -226.15311381622098; + } else { + result[0] += 710.1847247221976; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -521.4681372500785; + } else { + result[0] += -121.81009418188125; + } + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 156))) { + if (LIKELY(false || (data[4].qvalue <= 108))) { + if (UNLIKELY(false || (data[9].qvalue <= 36))) { + result[0] += 578.8864769214038; + } else { + result[0] += -65.43432145431503; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 470))) { + result[0] += -1140.9993698987162; + } else { + result[0] += -277.1047243415079; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 124))) { + if (LIKELY(false || (data[2].qvalue <= 218))) { + result[0] += 625.8667118148287; + } else { + result[0] += 133.56293016701576; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 464))) { + result[0] += -257.6768725464037; + } else { + result[0] += 235.27089761951143; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (UNLIKELY(false || (data[4].qvalue <= 88))) { + if (LIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -510.7544463506701; + } else { + result[0] += 309.52861873156445; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 186))) { + result[0] += -475.554284048819; + } else { + result[0] += -931.2955241523669; + } + } + } else { + result[0] += 204.12262051329452; + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 148))) { + if (LIKELY(false || (data[1].qvalue <= 60))) { + if (UNLIKELY(false || (data[0].qvalue <= 4))) { + result[0] += -105.41033083691937; + } else { + if (LIKELY(false || (data[7].qvalue <= 118))) { + result[0] += -9.957538169716495; + } else { + result[0] += -116.44279176689716; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 80))) { + if (LIKELY(false || (data[7].qvalue <= 138))) { + result[0] += -36.47666931987333; + } else { + result[0] += -181.90346126434108; + } + } else { + result[0] += -92.3026154859271; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 192))) { + if (LIKELY(false || (data[0].qvalue <= 380))) { + if (UNLIKELY(false || (data[9].qvalue <= 54))) { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 635.4266731901135; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 56))) { + result[0] += -956.053733984008; + } else { + result[0] += -104.63367772570996; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 132))) { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + result[0] += -20.173896617001866; + } else { + result[0] += 65.25040747317426; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 136))) { + result[0] += -570.0499875540565; + } else { + result[0] += -4.395243842020363; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 96))) { + if (LIKELY(false || (data[2].qvalue <= 150))) { + if (UNLIKELY(false || (data[7].qvalue <= 56))) { + result[0] += 82.3347657422682; + } else { + result[0] += -151.85939690566192; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 202))) { + result[0] += 306.7932008675081; + } else { + result[0] += -193.98901427646175; + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 30))) { + if (UNLIKELY(false || (data[10].qvalue <= 80))) { + result[0] += -101.31894339788478; + } else { + result[0] += 57.66783954209549; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 160))) { + result[0] += 304.3481193163534; + } else { + result[0] += 101.37427473767308; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 438))) { + if (LIKELY(false || (data[1].qvalue <= 98))) { + if (LIKELY(false || (data[3].qvalue <= 170))) { + result[0] += -173.79619958480487; + } else { + result[0] += -611.7431741530887; + } + } else { + result[0] += -479.5994428796921; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 174))) { + if (LIKELY(false || (data[0].qvalue <= 452))) { + if (LIKELY(false || (data[2].qvalue <= 194))) { + result[0] += 205.09978107028988; + } else { + result[0] += -552.4708829680587; + } + } else { + result[0] += 465.99445615301477; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 466))) { + if (UNLIKELY(false || (data[6].qvalue <= 180))) { + result[0] += -38.14040161366896; + } else { + result[0] += -654.1612824926979; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 164))) { + result[0] += 89.03239010969071; + } else { + result[0] += -334.95950567599243; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 324))) { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + if (LIKELY(false || (data[3].qvalue <= 70))) { + if (LIKELY(false || (data[3].qvalue <= 66))) { + if (LIKELY(false || (data[3].qvalue <= 34))) { + if (LIKELY(false || (data[2].qvalue <= 122))) { + result[0] += -18.433723366725584; + } else { + result[0] += -472.18904133491907; + } + } else { + result[0] += 41.144657963793065; + } + } else { + if (LIKELY(false || (data[10].qvalue <= 126))) { + if (UNLIKELY(false || (data[2].qvalue <= 70))) { + result[0] += -791.0825554146527; + } else { + result[0] += -77.87558566467489; + } + } else { + result[0] += -1273.8300701538087; + } + } + } else { + result[0] += 162.91309292890534; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -374.78034701138165; + } else { + result[0] += -30.23314802748434; + } + } + } else { + if (LIKELY(false || (data[10].qvalue <= 116))) { + if (LIKELY(false || (data[1].qvalue <= 152))) { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + if (LIKELY(false || (data[0].qvalue <= 458))) { + if (LIKELY(false || (data[6].qvalue <= 82))) { + result[0] += 19.475576346353392; + } else { + result[0] += -352.9733362293148; + } + } else { + result[0] += 349.1018486741264; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 416))) { + result[0] += 212.94808346197698; + } else { + result[0] += -374.8210809697834; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 46))) { + result[0] += -34.43423741643343; + } else { + result[0] += 75.34901971114162; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 312.18232536892253; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 454))) { + result[0] += -380.2729414924723; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 74))) { + result[0] += 384.2278192657615; + } else { + result[0] += -48.08512286278479; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 448))) { + if (LIKELY(false || (data[6].qvalue <= 124))) { + if (UNLIKELY(false || (data[2].qvalue <= 148))) { + result[0] += -339.175501983996; + } else { + result[0] += 73.82088972486432; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 122))) { + if (UNLIKELY(false || (data[3].qvalue <= 114))) { + result[0] += -690.6636434137862; + } else { + result[0] += -233.4604631893612; + } + } else { + result[0] += 61.13592985094737; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 176))) { + if (LIKELY(false || (data[2].qvalue <= 216))) { + if (LIKELY(false || (data[0].qvalue <= 466))) { + result[0] += 336.27393994775974; + } else { + result[0] += -1244.2348096296523; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -288.7929275242086; + } else { + result[0] += 303.1087488248426; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 470))) { + result[0] += -522.5254243038571; + } else { + result[0] += 147.10931954259047; + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 150))) { + if (UNLIKELY(false || (data[0].qvalue <= 2))) { + result[0] += -114.0935731263952; + } else { + if (LIKELY(false || (data[1].qvalue <= 102))) { + if (LIKELY(false || (data[4].qvalue <= 36))) { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (LIKELY(false || (data[5].qvalue <= 42))) { + result[0] += -18.695361121395486; + } else { + result[0] += 10.588982002368144; + } + } else { + result[0] += -172.85563078964458; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 30))) { + if (LIKELY(false || (data[10].qvalue <= 22))) { + result[0] += -51.938265058110325; + } else { + result[0] += -246.80507747694102; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 94))) { + result[0] += -27.959993628946236; + } else { + result[0] += 73.25664987336096; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 154))) { + if (UNLIKELY(false || (data[8].qvalue <= 66))) { + if (UNLIKELY(false || (data[9].qvalue <= 34))) { + result[0] += -101.97494811248283; + } else { + result[0] += 17.881559562839353; + } + } else { + result[0] += -100.47614812809579; + } + } else { + result[0] += -213.33125633625428; + } + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + if (LIKELY(false || (data[1].qvalue <= 10))) { + if (UNLIKELY(false || (data[2].qvalue <= 24))) { + if (LIKELY(false || (data[0].qvalue <= 222))) { + result[0] += 434.3221282795032; + } else { + result[0] += 823.0770567103796; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 258))) { + if (LIKELY(false || (data[2].qvalue <= 128))) { + result[0] += 87.83445346165345; + } else { + result[0] += -212.4695657744424; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 128))) { + result[0] += 464.3977186968871; + } else { + result[0] += 163.9938488166673; + } + } + } + } else { + result[0] += 637.6504024717447; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 154))) { + if (UNLIKELY(false || (data[8].qvalue <= 0))) { + if (LIKELY(false || (data[0].qvalue <= 406))) { + if (LIKELY(false || (data[9].qvalue <= 90))) { + result[0] += 282.83342280184934; + } else { + result[0] += 120.66910156313747; + } + } else { + result[0] += 1028.390014143319; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + if (LIKELY(false || (data[0].qvalue <= 464))) { + result[0] += -292.64750935522983; + } else { + result[0] += 696.0738841280834; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + result[0] += -98.38508109993742; + } else { + result[0] += 20.07618047102507; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 20))) { + if (LIKELY(false || (data[0].qvalue <= 384))) { + if (UNLIKELY(false || (data[1].qvalue <= 14))) { + result[0] += -153.79848410394547; + } else { + result[0] += -491.2192559340425; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 430))) { + result[0] += 170.48551507912254; + } else { + result[0] += 726.1771275054432; + } + } + } else { + result[0] += 582.2067601142297; + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 384))) { + if (LIKELY(false || (data[6].qvalue <= 144))) { + if (LIKELY(false || (data[4].qvalue <= 120))) { + if (LIKELY(false || (data[8].qvalue <= 138))) { + if (LIKELY(false || (data[3].qvalue <= 122))) { + if (LIKELY(false || (data[6].qvalue <= 78))) { + result[0] += 6.611296900045464; + } else { + result[0] += -146.7747758716442; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 106))) { + result[0] += 117.39498296773701; + } else { + result[0] += -52.139056254522906; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 64))) { + result[0] += -786.3744050601753; + } else { + result[0] += -75.75876514356197; + } + } + } else { + result[0] += -251.69019507498334; + } + } else { + result[0] += -159.81350735348502; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 10))) { + if (UNLIKELY(false || (data[0].qvalue <= 450))) { + if (LIKELY(false || (data[3].qvalue <= 168))) { + if (UNLIKELY(false || (data[8].qvalue <= 78))) { + result[0] += -655.1579692026768; + } else { + result[0] += -234.92840903417084; + } + } else { + result[0] += 129.69681018242764; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 174))) { + if (LIKELY(false || (data[7].qvalue <= 174))) { + if (UNLIKELY(false || (data[8].qvalue <= 54))) { + result[0] += -139.81070363809008; + } else { + result[0] += 236.49651798822757; + } + } else { + result[0] += 997.2638005655676; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 468))) { + result[0] += -440.5340347804863; + } else { + result[0] += 133.34746925104315; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 96))) { + if (LIKELY(false || (data[3].qvalue <= 76))) { + if (UNLIKELY(false || (data[8].qvalue <= 80))) { + if (UNLIKELY(false || (data[9].qvalue <= 16))) { + result[0] += 953.3985019502752; + } else { + result[0] += 115.36154895354196; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 68))) { + result[0] += -75.90121598789128; + } else { + result[0] += 321.1318897275337; + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 78))) { + if (LIKELY(false || (data[0].qvalue <= 442))) { + result[0] += -896.7922993531249; + } else { + result[0] += -192.64263220387716; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 82))) { + result[0] += -157.71189744728704; + } else { + result[0] += 388.1757773943014; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 128))) { + if (LIKELY(false || (data[0].qvalue <= 426))) { + if (LIKELY(false || (data[6].qvalue <= 140))) { + result[0] += 239.69457507298395; + } else { + result[0] += -339.18339092719594; + } + } else { + result[0] += 440.89438255994565; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 48))) { + if (LIKELY(false || (data[8].qvalue <= 28))) { + result[0] += 94.78432124431386; + } else { + result[0] += -585.5090672534587; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 118))) { + result[0] += 261.6165504519048; + } else { + result[0] += -0.9669883257810821; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 130))) { + if (UNLIKELY(false || (data[0].qvalue <= 6))) { + result[0] += -85.38452227119578; + } else { + if (LIKELY(false || (data[1].qvalue <= 60))) { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + result[0] += 71.39641555324053; + } else { + result[0] += -12.679337084314707; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 70))) { + if (UNLIKELY(false || (data[9].qvalue <= 46))) { + result[0] += -328.3515745861993; + } else { + result[0] += -24.65068942389365; + } + } else { + result[0] += -65.54687009952785; + } + } + } + } else { + if (LIKELY(false || (data[10].qvalue <= 122))) { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + if (LIKELY(false || (data[1].qvalue <= 10))) { + if (UNLIKELY(false || (data[2].qvalue <= 24))) { + result[0] += 448.8186025793332; + } else { + if (LIKELY(false || (data[0].qvalue <= 262))) { + result[0] += 28.507570304853807; + } else { + result[0] += 334.06403364310586; + } + } + } else { + result[0] += 565.4514138078015; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 154))) { + if (LIKELY(false || (data[9].qvalue <= 148))) { + if (UNLIKELY(false || (data[1].qvalue <= 16))) { + result[0] += -96.69821678256282; + } else { + result[0] += 21.631660184261378; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 204))) { + result[0] += 354.2452862416222; + } else { + result[0] += -51.61557505903728; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 12))) { + if (LIKELY(false || (data[0].qvalue <= 372))) { + result[0] += -328.0629299855443; + } else { + result[0] += 135.76618575654695; + } + } else { + result[0] += 497.32398174001605; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[0].qvalue <= 416))) { + if (UNLIKELY(false || (data[2].qvalue <= 134))) { + if (UNLIKELY(false || (data[0].qvalue <= 354))) { + result[0] += -15.704885988027627; + } else { + result[0] += -432.2700366601563; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 398))) { + result[0] += -1249.1625247677366; + } else { + result[0] += -669.3611102779328; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 428))) { + result[0] += -58.35712345907448; + } else { + if (LIKELY(false || (data[0].qvalue <= 438))) { + result[0] += 338.03248915141415; + } else { + result[0] += 777.2208330972686; + } + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 84))) { + if (LIKELY(false || (data[0].qvalue <= 426))) { + if (LIKELY(false || (data[1].qvalue <= 154))) { + result[0] += 120.71513738942753; + } else { + result[0] += -308.17490477956534; + } + } else { + result[0] += 833.731541937934; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 438))) { + if (UNLIKELY(false || (data[9].qvalue <= 6))) { + result[0] += 149.7928136109008; + } else { + result[0] += -223.98734671939062; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 152))) { + result[0] += 365.23772234337326; + } else { + result[0] += -24.192037541431123; + } + } + } + } + } + } + if (LIKELY(false || (data[7].qvalue <= 192))) { + if (LIKELY(false || (data[0].qvalue <= 388))) { + if (LIKELY(false || (data[1].qvalue <= 124))) { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (LIKELY(false || (data[3].qvalue <= 122))) { + if (LIKELY(false || (data[8].qvalue <= 116))) { + result[0] += 4.536955058087513; + } else { + result[0] += -72.38105398500589; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -344.4937340104233; + } else { + result[0] += 54.04370832477616; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 352))) { + result[0] += -155.8470275480651; + } else { + result[0] += -517.2572856835848; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 64))) { + if (LIKELY(false || (data[8].qvalue <= 64))) { + if (UNLIKELY(false || (data[3].qvalue <= 62))) { + result[0] += 140.37617439343134; + } else { + result[0] += -55.112467004567236; + } + } else { + result[0] += 774.980876755253; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 346))) { + if (UNLIKELY(false || (data[8].qvalue <= 38))) { + result[0] += -397.1965385921262; + } else { + result[0] += -92.74338066885616; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 475.3460832912639; + } else { + result[0] += -489.2890878250337; + } + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 96))) { + if (UNLIKELY(false || (data[8].qvalue <= 10))) { + result[0] += 386.3622072624303; + } else { + if (LIKELY(false || (data[2].qvalue <= 150))) { + if (LIKELY(false || (data[0].qvalue <= 452))) { + result[0] += -138.2046386901946; + } else { + result[0] += 109.19811106405169; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 202))) { + result[0] += 279.484508613883; + } else { + result[0] += -160.50141487078906; + } + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 30))) { + if (UNLIKELY(false || (data[8].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 96.50006530829478; + } else { + result[0] += -311.4323024052799; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 114))) { + result[0] += 221.35954689453885; + } else { + result[0] += -11.21222614010631; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 12))) { + if (LIKELY(false || (data[0].qvalue <= 462))) { + result[0] += -250.44082389008545; + } else { + result[0] += 259.77584765088653; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 86))) { + result[0] += 361.4943477960914; + } else { + result[0] += 91.34722785277711; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 436))) { + if (UNLIKELY(false || (data[0].qvalue <= 350))) { + result[0] += -160.4323829658149; + } else { + result[0] += -425.59656420877354; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 146))) { + result[0] += 180.15448233040198; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -433.82026787118116; + } else { + result[0] += -59.26399277285995; + } + } + } + } + if (LIKELY(false || (data[7].qvalue <= 178))) { + if (LIKELY(false || (data[0].qvalue <= 460))) { + if (UNLIKELY(false || (data[9].qvalue <= 2))) { + if (LIKELY(false || (data[7].qvalue <= 152))) { + if (LIKELY(false || (data[0].qvalue <= 442))) { + result[0] += -308.1244882642975; + } else { + if (LIKELY(false || (data[1].qvalue <= 154))) { + result[0] += 509.7187192299381; + } else { + result[0] += -455.2150339355469; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 428))) { + result[0] += -189.49508286587985; + } else { + result[0] += -760.2093864229564; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 212))) { + if (LIKELY(false || (data[7].qvalue <= 166))) { + if (LIKELY(false || (data[7].qvalue <= 164))) { + result[0] += 3.4436391818207746; + } else { + result[0] += -682.0310140822548; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 388))) { + result[0] += 92.34103234198413; + } else { + result[0] += 380.69157714277566; + } + } + } else { + result[0] += -165.7227313266843; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 156))) { + if (LIKELY(false || (data[4].qvalue <= 108))) { + if (UNLIKELY(false || (data[9].qvalue <= 36))) { + result[0] += 549.4816677105838; + } else { + if (LIKELY(false || (data[1].qvalue <= 58))) { + result[0] += 194.29034273702734; + } else { + result[0] += -515.5656251305937; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 472))) { + if (UNLIKELY(false || (data[9].qvalue <= 6))) { + result[0] += 310.8152990014213; + } else { + result[0] += -1141.971330367939; + } + } else { + result[0] += -60.374245828492555; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 176))) { + result[0] += 351.2745651968375; + } else { + result[0] += 86.38384679612086; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 416))) { + if (UNLIKELY(false || (data[2].qvalue <= 162))) { + if (LIKELY(false || (data[2].qvalue <= 142))) { + result[0] += -197.5086799826019; + } else { + if (LIKELY(false || (data[0].qvalue <= 332))) { + result[0] += -304.0321579015652; + } else { + result[0] += -814.8576146638849; + } + } + } else { + result[0] += -77.31156609799358; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 114))) { + if (LIKELY(false || (data[7].qvalue <= 198))) { + result[0] += 364.03422368739893; + } else { + if (LIKELY(false || (data[0].qvalue <= 448))) { + result[0] += -262.12017291355977; + } else { + result[0] += 355.1467205041031; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (LIKELY(false || (data[6].qvalue <= 180))) { + if (LIKELY(false || (data[2].qvalue <= 222))) { + result[0] += 34.07266539417179; + } else { + result[0] += -461.43650166079533; + } + } else { + result[0] += -392.65138527512556; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 188))) { + result[0] += 424.2608283476747; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -401.74970612209404; + } else { + result[0] += 149.51617217256646; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 318))) { + if (UNLIKELY(false || (data[7].qvalue <= 16))) { + if (LIKELY(false || (data[5].qvalue <= 46))) { + result[0] += 6.274580412893512; + } else { + result[0] += 170.65850290785482; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 28))) { + if (UNLIKELY(false || (data[7].qvalue <= 28))) { + result[0] += -321.26713386171923; + } else { + if (LIKELY(false || (data[10].qvalue <= 24))) { + if (UNLIKELY(false || (data[3].qvalue <= 0))) { + result[0] += -883.3009822319002; + } else { + result[0] += -30.131313419296436; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 44))) { + result[0] += 145.86018225187783; + } else { + result[0] += -533.3653617127321; + } + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + if (LIKELY(false || (data[8].qvalue <= 114))) { + if (UNLIKELY(false || (data[3].qvalue <= 32))) { + result[0] += -64.30753875016195; + } else { + result[0] += 70.480411627325; + } + } else { + if (LIKELY(false || (data[8].qvalue <= 132))) { + result[0] += -282.2392212961099; + } else { + result[0] += 67.65616622116742; + } + } + } else { + result[0] += -24.453451185191113; + } + } + } + } else { + if (LIKELY(false || (data[10].qvalue <= 116))) { + if (UNLIKELY(false || (data[9].qvalue <= 10))) { + if (LIKELY(false || (data[0].qvalue <= 466))) { + if (UNLIKELY(false || (data[8].qvalue <= 26))) { + result[0] += -607.6977777871718; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 442))) { + result[0] += -273.64198324787975; + } else { + result[0] += 45.47924148212965; + } + } + } else { + result[0] += 215.11954437098967; + } + } else { + result[0] += 45.61441585551685; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 440))) { + if (LIKELY(false || (data[6].qvalue <= 124))) { + if (LIKELY(false || (data[0].qvalue <= 414))) { + if (UNLIKELY(false || (data[6].qvalue <= 42))) { + result[0] += -454.72131809174454; + } else { + result[0] += 0.13988354882495432; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 56))) { + result[0] += 38.582001327207514; + } else { + result[0] += 525.027278044562; + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 20))) { + if (UNLIKELY(false || (data[6].qvalue <= 132))) { + result[0] += -698.2304850150304; + } else { + result[0] += -55.05691788452429; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 130))) { + result[0] += -678.96875763753; + } else { + result[0] += -223.46103248730483; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 170))) { + if (LIKELY(false || (data[4].qvalue <= 122))) { + if (LIKELY(false || (data[9].qvalue <= 16))) { + result[0] += -90.61321602369662; + } else { + result[0] += 261.0377505016003; + } + } else { + result[0] += 460.8651737454858; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -518.3029692442528; + } else { + if (UNLIKELY(false || (data[4].qvalue <= 102))) { + result[0] += 251.99443410934916; + } else { + result[0] += -168.5258406883114; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 88))) { + if (UNLIKELY(false || (data[0].qvalue <= 2))) { + result[0] += -93.52752297088361; + } else { + if (LIKELY(false || (data[1].qvalue <= 92))) { + if (UNLIKELY(false || (data[1].qvalue <= 38))) { + if (UNLIKELY(false || (data[0].qvalue <= 22))) { + result[0] += -28.36906776557731; + } else { + if (LIKELY(false || (data[4].qvalue <= 94))) { + result[0] += -3.2423893406338657; + } else { + result[0] += 138.541654434318; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 22))) { + result[0] += -46.28911386105568; + } else { + if (LIKELY(false || (data[3].qvalue <= 150))) { + result[0] += -22.298009717414818; + } else { + result[0] += -81.59335058549901; + } + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 88))) { + if (LIKELY(false || (data[4].qvalue <= 132))) { + result[0] += -66.17939589753632; + } else { + result[0] += -205.33954408242673; + } + } else { + result[0] += 8.493295470465377; + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 2))) { + if (LIKELY(false || (data[5].qvalue <= 6))) { + if (LIKELY(false || (data[0].qvalue <= 264))) { + if (UNLIKELY(false || (data[5].qvalue <= 0))) { + if (LIKELY(false || (data[0].qvalue <= 224))) { + result[0] += 326.1908307836102; + } else { + result[0] += 712.5672110896917; + } + } else { + result[0] += 165.97117149989344; + } + } else { + result[0] += 512.7394095968342; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 290))) { + result[0] += -88.61952314673287; + } else { + result[0] += 257.63671136127255; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + if (LIKELY(false || (data[0].qvalue <= 464))) { + if (LIKELY(false || (data[3].qvalue <= 166))) { + if (LIKELY(false || (data[4].qvalue <= 82))) { + result[0] += -64.2972163819839; + } else { + result[0] += -678.5598075508324; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -743.1003478462518; + } else { + result[0] += 22.380609435146425; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (LIKELY(false || (data[1].qvalue <= 2))) { + result[0] += 668.8097667814556; + } else { + result[0] += 172.2438155603644; + } + } else { + result[0] += -103.48953914040013; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 14))) { + if (LIKELY(false || (data[9].qvalue <= 150))) { + if (UNLIKELY(false || (data[9].qvalue <= 112))) { + result[0] += 210.79347178390668; + } else { + result[0] += -66.34475012824036; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 8))) { + result[0] += 79.44162948474975; + } else { + result[0] += 423.58682172057206; + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 154))) { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 58.63080216913425; + } else { + result[0] += 0.56660428680394; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 388))) { + result[0] += -354.99641077814067; + } else { + result[0] += 163.82541020213822; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 428))) { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[0].qvalue <= 364))) { + result[0] += -4.43201919585627; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 2))) { + if (LIKELY(false || (data[3].qvalue <= 148))) { + if (LIKELY(false || (data[4].qvalue <= 96))) { + result[0] += -461.34171468334483; + } else { + result[0] += -1974.1613360699155; + } + } else { + result[0] += 464.12272874367665; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 16))) { + if (UNLIKELY(false || (data[2].qvalue <= 116))) { + result[0] += -2425.075299189815; + } else { + result[0] += -330.4655251495208; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 72))) { + result[0] += 166.19696442999253; + } else { + result[0] += 9.303779051447991; + } + } + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 42))) { + result[0] += -481.590488188672; + } else { + if (UNLIKELY(false || (data[10].qvalue <= 84))) { + if (UNLIKELY(false || (data[2].qvalue <= 154))) { + result[0] += -201.8241770516161; + } else { + if (LIKELY(false || (data[0].qvalue <= 400))) { + result[0] += 51.47643177844849; + } else { + result[0] += 557.8587391367337; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 356))) { + result[0] += -68.84035678322192; + } else { + result[0] += -259.4256502962815; + } + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 104))) { + if (LIKELY(false || (data[5].qvalue <= 100))) { + if (UNLIKELY(false || (data[0].qvalue <= 454))) { + if (UNLIKELY(false || (data[4].qvalue <= 78))) { + result[0] += 156.73836309324633; + } else { + if (LIKELY(false || (data[4].qvalue <= 138))) { + result[0] += -607.0488890568329; + } else { + result[0] += 9.03668551193256; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 100))) { + if (UNLIKELY(false || (data[6].qvalue <= 40))) { + result[0] += -1017.2723550166346; + } else { + result[0] += 91.3800484259382; + } + } else { + result[0] += -942.7966219121105; + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 26))) { + if (UNLIKELY(false || (data[0].qvalue <= 464))) { + if (UNLIKELY(false || (data[4].qvalue <= 114))) { + result[0] += 346.1853239128133; + } else { + result[0] += -870.2639669615536; + } + } else { + result[0] += 136.37634000604268; + } + } else { + result[0] += 483.8076223319302; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 104))) { + if (UNLIKELY(false || (data[8].qvalue <= 76))) { + result[0] += 632.1932202439316; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 114))) { + result[0] += 859.7961658998064; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 110))) { + result[0] += 6.445434627545658; + } else { + result[0] += 232.20573728889286; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 450))) { + if (UNLIKELY(false || (data[10].qvalue <= 62))) { + result[0] += -766.6301885459153; + } else { + result[0] += -66.31539816252801; + } + } else { + result[0] += 73.09136207866754; + } + } + } + } + if (LIKELY(false || (data[7].qvalue <= 192))) { + if (LIKELY(false || (data[10].qvalue <= 122))) { + if (LIKELY(false || (data[3].qvalue <= 102))) { + if (LIKELY(false || (data[7].qvalue <= 44))) { + if (UNLIKELY(false || (data[6].qvalue <= 24))) { + if (LIKELY(false || (data[7].qvalue <= 8))) { + result[0] += 28.34884333239003; + } else { + result[0] += -46.41782030735656; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 73.71689530827445; + } else { + result[0] += 1.4571574901978375; + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 68))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += -319.74328138081097; + } else { + result[0] += 28.72734330489961; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 74))) { + result[0] += -392.30286128897285; + } else { + result[0] += -56.63552344488173; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + if (LIKELY(false || (data[8].qvalue <= 148))) { + if (UNLIKELY(false || (data[3].qvalue <= 134))) { + result[0] += -61.214933692783916; + } else { + result[0] += -207.6113943042064; + } + } else { + result[0] += -632.4056164963666; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 36))) { + if (LIKELY(false || (data[8].qvalue <= 28))) { + result[0] += 0.776759507119416; + } else { + result[0] += -563.4328564341248; + } + } else { + if (LIKELY(false || (data[8].qvalue <= 140))) { + result[0] += 80.66532309064849; + } else { + result[0] += -37.64303198703362; + } + } + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 128))) { + if (LIKELY(false || (data[9].qvalue <= 80))) { + if (UNLIKELY(false || (data[1].qvalue <= 142))) { + result[0] += 3.0907605764638824; + } else { + result[0] += -191.9470886917915; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 42))) { + result[0] += -535.3539427021846; + } else { + result[0] += -747.1267209093315; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 84))) { + if (UNLIKELY(false || (data[4].qvalue <= 48))) { + if (UNLIKELY(false || (data[1].qvalue <= 48))) { + result[0] += 39.65250739364507; + } else { + result[0] += -83.33176284276131; + } + } else { + if (LIKELY(false || (data[10].qvalue <= 144))) { + result[0] += 137.86981971555903; + } else { + result[0] += -47.78103683170895; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 6))) { + if (LIKELY(false || (data[8].qvalue <= 108))) { + result[0] += 265.07465212054825; + } else { + result[0] += -112.5511941043707; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 130))) { + result[0] += -185.10561993544567; + } else { + result[0] += -55.42540507337779; + } + } + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 200))) { + if (LIKELY(false || (data[1].qvalue <= 164))) { + result[0] += -71.05204569728328; + } else { + if (LIKELY(false || (data[3].qvalue <= 174))) { + result[0] += -229.96092256575585; + } else { + result[0] += -663.863689584278; + } + } + } else { + result[0] += -404.1617066176453; + } + } + if (LIKELY(false || (data[0].qvalue <= 434))) { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[0].qvalue <= 374))) { + if (LIKELY(false || (data[4].qvalue <= 120))) { + if (LIKELY(false || (data[2].qvalue <= 176))) { + if (LIKELY(false || (data[5].qvalue <= 80))) { + result[0] += -4.033785114047066; + } else { + result[0] += 51.688746674364644; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 42))) { + result[0] += -308.48199870168713; + } else { + result[0] += -36.130910480232906; + } + } + } else { + result[0] += -195.1197382760659; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 2))) { + if (LIKELY(false || (data[4].qvalue <= 110))) { + if (LIKELY(false || (data[4].qvalue <= 96))) { + result[0] += -408.3567730759215; + } else { + result[0] += -1707.6448987068966; + } + } else { + result[0] += 485.8821080747238; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 16))) { + if (UNLIKELY(false || (data[2].qvalue <= 116))) { + result[0] += -2096.4662113083464; + } else { + result[0] += -276.14738618844734; + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 68))) { + result[0] += -8.171064906722554; + } else { + result[0] += 155.11413889664198; + } + } + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 46))) { + result[0] += -426.0821034776104; + } else { + if (UNLIKELY(false || (data[10].qvalue <= 84))) { + if (UNLIKELY(false || (data[2].qvalue <= 154))) { + if (LIKELY(false || (data[0].qvalue <= 420))) { + result[0] += -251.87299899417505; + } else { + result[0] += 171.7813777604329; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 188))) { + result[0] += 266.03680241951855; + } else { + result[0] += -230.94110204440864; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 98))) { + if (UNLIKELY(false || (data[9].qvalue <= 14))) { + result[0] += -51.4712788511078; + } else { + result[0] += -349.1585791389674; + } + } else { + result[0] += -87.62955894052737; + } + } + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 14))) { + result[0] += -962.914071451823; + } else { + if (LIKELY(false || (data[6].qvalue <= 180))) { + if (UNLIKELY(false || (data[2].qvalue <= 104))) { + if (UNLIKELY(false || (data[10].qvalue <= 4))) { + result[0] += 410.01446934535943; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 144))) { + result[0] += -158.79533984060308; + } else { + result[0] += 54.19442054392661; + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 64))) { + result[0] += 800.9582024560236; + } else { + if (LIKELY(false || (data[2].qvalue <= 214))) { + result[0] += 152.70184903575273; + } else { + result[0] += -39.258349058638146; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 472))) { + if (LIKELY(false || (data[10].qvalue <= 106))) { + if (UNLIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -385.9941662617043; + } else { + result[0] += 109.98038694597618; + } + } else { + result[0] += -519.7043617216136; + } + } else { + result[0] += 312.6413190598439; + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 450))) { + if (LIKELY(false || (data[6].qvalue <= 146))) { + if (LIKELY(false || (data[7].qvalue <= 198))) { + if (LIKELY(false || (data[0].qvalue <= 366))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + if (LIKELY(false || (data[1].qvalue <= 74))) { + result[0] += 137.297555227154; + } else { + result[0] += -254.9833262356102; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + result[0] += -123.87983644564534; + } else { + result[0] += -2.4145863679975013; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 6))) { + if (LIKELY(false || (data[6].qvalue <= 82))) { + result[0] += 51.07636765965205; + } else { + result[0] += -390.1200629057833; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 28))) { + result[0] += 215.48035596295546; + } else { + result[0] += 39.12939193887718; + } + } + } + } else { + result[0] += -235.5281315434429; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + result[0] += -534.086589766028; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 156))) { + if (LIKELY(false || (data[7].qvalue <= 166))) { + result[0] += -253.52810060662947; + } else { + if (UNLIKELY(false || (data[6].qvalue <= 164))) { + result[0] += 451.54660129512826; + } else { + result[0] += -6.922248765373611; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 168))) { + if (LIKELY(false || (data[0].qvalue <= 422))) { + result[0] += -87.41127255128306; + } else { + result[0] += 161.7062666527756; + } + } else { + if (LIKELY(false || (data[8].qvalue <= 96))) { + result[0] += -362.4373441748169; + } else { + result[0] += -47.688779690032106; + } + } + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 180))) { + if (UNLIKELY(false || (data[6].qvalue <= 70))) { + if (LIKELY(false || (data[3].qvalue <= 118))) { + result[0] += -109.71948701870082; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -3168.276688179348; + } else { + result[0] += -655.8877042161603; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 108))) { + if (UNLIKELY(false || (data[7].qvalue <= 148))) { + if (LIKELY(false || (data[6].qvalue <= 138))) { + result[0] += 211.55392830640332; + } else { + result[0] += 544.2392075566188; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 460))) { + result[0] += -137.766397826111; + } else { + result[0] += 227.29084096946204; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 114))) { + if (UNLIKELY(false || (data[0].qvalue <= 470))) { + result[0] += -1219.702639935603; + } else { + result[0] += 19.073660451518418; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 168))) { + result[0] += 180.8219311362737; + } else { + result[0] += -23.62961539090255; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (UNLIKELY(false || (data[4].qvalue <= 88))) { + result[0] += -24.096509384288314; + } else { + result[0] += -474.0824089535872; + } + } else { + result[0] += 151.94918465665242; + } + } + } + if (LIKELY(false || (data[8].qvalue <= 156))) { + if (LIKELY(false || (data[0].qvalue <= 450))) { + if (LIKELY(false || (data[6].qvalue <= 146))) { + if (LIKELY(false || (data[0].qvalue <= 344))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + if (LIKELY(false || (data[0].qvalue <= 222))) { + result[0] += 39.972789080579226; + } else { + result[0] += 214.53042647495874; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + result[0] += -103.36098764508137; + } else { + result[0] += -3.5862855800631728; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 144))) { + if (LIKELY(false || (data[5].qvalue <= 92))) { + result[0] += 33.48501259384832; + } else { + result[0] += -526.1972342777782; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 188))) { + result[0] += 291.7875686572814; + } else { + result[0] += -154.8400445536322; + } + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 50))) { + if (UNLIKELY(false || (data[6].qvalue <= 152))) { + if (LIKELY(false || (data[0].qvalue <= 436))) { + result[0] += -209.00331042067378; + } else { + result[0] += 476.30810024115374; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 284))) { + result[0] += -174.50986784466022; + } else { + result[0] += -612.8591006144763; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 154))) { + if (LIKELY(false || (data[6].qvalue <= 160))) { + result[0] += -17.361861237499827; + } else { + result[0] += -227.84455664039766; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 170))) { + result[0] += 141.6717472259187; + } else { + result[0] += -152.85677116643248; + } + } + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 16))) { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -2290.8527835669884; + } else { + result[0] += -90.00394883304172; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 0))) { + if (UNLIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -670.6849931746262; + } else { + result[0] += -81.08626512407443; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 124))) { + if (UNLIKELY(false || (data[10].qvalue <= 44))) { + result[0] += 252.92553318096964; + } else { + result[0] += -237.02792802064724; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 26))) { + result[0] += -131.25931683115354; + } else { + result[0] += 155.92595301503033; + } + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 416))) { + if (LIKELY(false || (data[0].qvalue <= 350))) { + result[0] += -135.57120230355562; + } else { + result[0] += -444.05965584753494; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 130))) { + if (UNLIKELY(false || (data[0].qvalue <= 454))) { + result[0] += -617.9283577463137; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 168))) { + result[0] += 379.3453933956734; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 468))) { + result[0] += -555.1703256848654; + } else { + result[0] += 183.81435330137998; + } + } + } + } else { + result[0] += 287.9785027492729; + } + } + } + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (LIKELY(false || (data[0].qvalue <= 462))) { + if (LIKELY(false || (data[1].qvalue <= 160))) { + if (LIKELY(false || (data[6].qvalue <= 174))) { + if (LIKELY(false || (data[7].qvalue <= 166))) { + if (LIKELY(false || (data[7].qvalue <= 164))) { + result[0] += 1.9227744347010873; + } else { + result[0] += -372.9806790342511; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 162))) { + result[0] += 38.027655688643975; + } else { + result[0] += 330.84712805066437; + } + } + } else { + result[0] += -254.51641015239204; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 430))) { + result[0] += -29.47274599196618; + } else { + result[0] += -712.7969392811483; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 156))) { + if (LIKELY(false || (data[4].qvalue <= 108))) { + if (UNLIKELY(false || (data[9].qvalue <= 36))) { + result[0] += 557.7390203473873; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 58))) { + result[0] += 232.35239699183904; + } else { + result[0] += -374.7341949643208; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 470))) { + if (UNLIKELY(false || (data[9].qvalue <= 6))) { + result[0] += 438.92836082611086; + } else { + result[0] += -1439.2171157594041; + } + } else { + result[0] += -146.03654903994473; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 176))) { + if (LIKELY(false || (data[4].qvalue <= 130))) { + if (LIKELY(false || (data[5].qvalue <= 108))) { + result[0] += 321.2116732677101; + } else { + result[0] += 754.9708259292751; + } + } else { + result[0] += -22.672436608018547; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 70))) { + result[0] += 795.3453671875; + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + result[0] += -213.941419408782; + } else { + result[0] += 282.16809176472924; + } + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (UNLIKELY(false || (data[5].qvalue <= 32))) { + result[0] += -1600.845136858259; + } else { + if (LIKELY(false || (data[6].qvalue <= 180))) { + if (LIKELY(false || (data[0].qvalue <= 440))) { + if (UNLIKELY(false || (data[5].qvalue <= 58))) { + result[0] += -521.5318192486991; + } else { + result[0] += -96.30084920460428; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 218))) { + result[0] += 166.08593687552778; + } else { + result[0] += -341.6197044372843; + } + } + } else { + result[0] += -270.16752261143085; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 188))) { + result[0] += 379.4922879662453; + } else { + if (LIKELY(false || (data[1].qvalue <= 164))) { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + if (LIKELY(false || (data[3].qvalue <= 178))) { + result[0] += 59.33023477478028; + } else { + result[0] += -944.8980152625645; + } + } else { + result[0] += 362.7230938296502; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -1894.5816613281252; + } else { + result[0] += -151.54009237179278; + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 466))) { + if (LIKELY(false || (data[6].qvalue <= 180))) { + if (LIKELY(false || (data[2].qvalue <= 218))) { + if (LIKELY(false || (data[0].qvalue <= 428))) { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[10].qvalue <= 146))) { + result[0] += 2.61150493746397; + } else { + result[0] += -294.8989575123201; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 154))) { + result[0] += -144.860709390818; + } else { + result[0] += 49.05217218783569; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 104))) { + if (LIKELY(false || (data[2].qvalue <= 102))) { + result[0] += -69.84846406106546; + } else { + result[0] += -1099.694205050492; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 64))) { + result[0] += 547.2763598289783; + } else { + result[0] += 121.02960542039312; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 430))) { + if (UNLIKELY(false || (data[1].qvalue <= 112))) { + result[0] += -299.9131036348163; + } else { + result[0] += 87.20580911412755; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 162))) { + if (LIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -434.7498970777533; + } else { + result[0] += 375.0325198630567; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 174))) { + result[0] += -761.131324712887; + } else { + result[0] += -387.49766429457327; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 392))) { + result[0] += -94.7425416912909; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 154))) { + result[0] += -190.41458346534543; + } else { + result[0] += -405.4827892391865; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 200))) { + if (UNLIKELY(false || (data[7].qvalue <= 78))) { + if (UNLIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 373.04881527855287; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -1545.4975544084823; + } else { + result[0] += -255.72717774402136; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 98))) { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (LIKELY(false || (data[1].qvalue <= 116))) { + result[0] += 450.93153398469025; + } else { + result[0] += 1014.2956421685988; + } + } else { + result[0] += 1245.0624796875002; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 148))) { + if (UNLIKELY(false || (data[8].qvalue <= 34))) { + result[0] += -547.0456131590985; + } else { + result[0] += -9.966885090949628; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 104))) { + result[0] += 305.58289721685486; + } else { + result[0] += 51.01148885239249; + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + if (LIKELY(false || (data[1].qvalue <= 162))) { + result[0] += -921.1548865874474; + } else { + result[0] += -1870.7389783653846; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 162))) { + result[0] += 236.7134294836506; + } else { + result[0] += -285.6914099083137; + } + } + } + } + if (LIKELY(false || (data[7].qvalue <= 174))) { + if (UNLIKELY(false || (data[5].qvalue <= 14))) { + if (UNLIKELY(false || (data[4].qvalue <= 2))) { + if (LIKELY(false || (data[2].qvalue <= 58))) { + if (LIKELY(false || (data[2].qvalue <= 52))) { + result[0] += 138.46445954751613; + } else { + result[0] += 30.25739363045126; + } + } else { + result[0] += 308.1558521464535; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 40))) { + if (LIKELY(false || (data[10].qvalue <= 52))) { + if (LIKELY(false || (data[8].qvalue <= 12))) { + result[0] += -8.275799037633549; + } else { + result[0] += -133.54923320626136; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 6))) { + result[0] += 175.8079980773678; + } else { + result[0] += 60.614919146381446; + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 42))) { + result[0] += -434.16667085044185; + } else { + if (LIKELY(false || (data[4].qvalue <= 38))) { + result[0] += -41.16427694169654; + } else { + result[0] += -185.9750821120982; + } + } + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 22))) { + if (LIKELY(false || (data[3].qvalue <= 18))) { + if (LIKELY(false || (data[5].qvalue <= 20))) { + if (UNLIKELY(false || (data[4].qvalue <= 12))) { + result[0] += -122.25698184102133; + } else { + result[0] += 80.57705277421712; + } + } else { + result[0] += -153.60843419206896; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 22))) { + result[0] += 408.69803721206245; + } else { + if (UNLIKELY(false || (data[8].qvalue <= 90))) { + result[0] += 136.97672358425646; + } else { + result[0] += 270.9923573487174; + } + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 28))) { + if (LIKELY(false || (data[2].qvalue <= 122))) { + if (UNLIKELY(false || (data[3].qvalue <= 24))) { + result[0] += -159.08908713728795; + } else { + result[0] += -9.923411608506871; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + result[0] += -521.1873971145586; + } else { + result[0] += 17.25752566881542; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 2))) { + if (UNLIKELY(false || (data[3].qvalue <= 64))) { + result[0] += 301.7622074237134; + } else { + result[0] += 122.86657003996795; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 4))) { + result[0] += -254.85463978758668; + } else { + result[0] += 5.494987706460857; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 32))) { + result[0] += -834.7163352102; + } else { + if (LIKELY(false || (data[7].qvalue <= 200))) { + if (UNLIKELY(false || (data[8].qvalue <= 6))) { + result[0] += 172.7220039048895; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 161.15720525096583; + } else { + if (UNLIKELY(false || (data[8].qvalue <= 136))) { + result[0] += -107.08335478914324; + } else { + result[0] += -12.489971356584766; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 180))) { + result[0] += -405.35269670345906; + } else { + result[0] += -207.23569477608535; + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (LIKELY(false || (data[6].qvalue <= 180))) { + if (LIKELY(false || (data[0].qvalue <= 392))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (LIKELY(false || (data[0].qvalue <= 292))) { + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + result[0] += -26.43222222269896; + } else { + result[0] += 7.909229573729924; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 30))) { + result[0] += 8.617729518857145; + } else { + result[0] += 108.90607449231234; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 74))) { + if (UNLIKELY(false || (data[10].qvalue <= 70))) { + result[0] += -1.9284671174533046; + } else { + result[0] += -105.2363711810878; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 106))) { + result[0] += 35.07140832310961; + } else { + result[0] += -52.187791710622804; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 222))) { + if (UNLIKELY(false || (data[2].qvalue <= 86))) { + if (LIKELY(false || (data[2].qvalue <= 64))) { + result[0] += 53.903161702785724; + } else { + result[0] += -171.4159662862328; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 114))) { + result[0] += 123.56275048732999; + } else { + result[0] += 26.26029225107184; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -422.00141694181985; + } else { + result[0] += -107.9116343442738; + } + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 88))) { + if (LIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -288.46097298039064; + } else { + if (LIKELY(false || (data[7].qvalue <= 196))) { + result[0] += 375.28961351398226; + } else { + result[0] += -281.2952544835409; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 446))) { + result[0] += -17.901971338973112; + } else { + if (LIKELY(false || (data[6].qvalue <= 186))) { + result[0] += -312.93686064175296; + } else { + result[0] += -716.0857770302855; + } + } + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 88))) { + result[0] += 451.3160600734765; + } else { + if (UNLIKELY(false || (data[10].qvalue <= 54))) { + if (UNLIKELY(false || (data[8].qvalue <= 16))) { + result[0] += 120.12627461414955; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -1770.8570323768029; + } else { + result[0] += -297.6523306745511; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 184))) { + if (LIKELY(false || (data[7].qvalue <= 196))) { + if (LIKELY(false || (data[0].qvalue <= 472))) { + result[0] += 199.35980767050484; + } else { + result[0] += 503.56640093198286; + } + } else { + result[0] += -518.0686588935853; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + if (LIKELY(false || (data[7].qvalue <= 188))) { + result[0] += -150.74680039702164; + } else { + result[0] += -1055.9052195002068; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 176))) { + result[0] += -46.963809276019184; + } else { + result[0] += 394.9533442330038; + } + } + } + } + } + } + if (LIKELY(false || (data[10].qvalue <= 120))) { + if (LIKELY(false || (data[5].qvalue <= 84))) { + if (LIKELY(false || (data[6].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 96))) { + if (LIKELY(false || (data[1].qvalue <= 92))) { + if (LIKELY(false || (data[8].qvalue <= 116))) { + result[0] += 14.869440540791798; + } else { + result[0] += -36.73557311757522; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 64))) { + result[0] += -538.0900500719082; + } else { + result[0] += -27.241161506715144; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 174))) { + result[0] += 132.0541996118715; + } else { + result[0] += -113.04252572422735; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 24))) { + if (LIKELY(false || (data[8].qvalue <= 68))) { + if (LIKELY(false || (data[1].qvalue <= 160))) { + result[0] += 244.86366814112222; + } else { + result[0] += -95.24641504542937; + } + } else { + result[0] += -76.8715657600925; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 114))) { + if (LIKELY(false || (data[5].qvalue <= 76))) { + result[0] += -120.29792651308429; + } else { + result[0] += 5.15502948111146; + } + } else { + result[0] += -227.26424899945584; + } + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 48))) { + if (LIKELY(false || (data[10].qvalue <= 72))) { + if (LIKELY(false || (data[6].qvalue <= 126))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += -511.47464005858114; + } else { + result[0] += 99.31151638687581; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 2))) { + result[0] += 139.52958863442427; + } else { + result[0] += -222.6339569962306; + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 112))) { + result[0] += -706.5550247771737; + } else { + result[0] += 32.62538379320284; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 152))) { + if (LIKELY(false || (data[8].qvalue <= 150))) { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -225.27583884895458; + } else { + result[0] += 97.88878371179209; + } + } else { + result[0] += -91.48452493663109; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 156))) { + if (UNLIKELY(false || (data[1].qvalue <= 148))) { + result[0] += -972.0166516770522; + } else { + result[0] += -3.8444739361171365; + } + } else { + result[0] += -30.275824314188043; + } + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[1].qvalue <= 128))) { + result[0] += -591.4492984187848; + } else { + result[0] += 52.69617940013822; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 86))) { + if (LIKELY(false || (data[2].qvalue <= 180))) { + result[0] += 84.41514196973255; + } else { + result[0] += -58.25664931323567; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 6))) { + result[0] += 79.8372134768803; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 190))) { + result[0] += -166.72699212355798; + } else { + result[0] += -39.28083649832746; + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 436))) { + if (LIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[0].qvalue <= 384))) { + if (LIKELY(false || (data[1].qvalue <= 126))) { + if (LIKELY(false || (data[4].qvalue <= 102))) { + if (LIKELY(false || (data[1].qvalue <= 102))) { + result[0] += 0.7528430355879543; + } else { + result[0] += -81.79712338566776; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 108))) { + result[0] += 169.93142796462274; + } else { + result[0] += -185.79991240603783; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 126))) { + if (LIKELY(false || (data[2].qvalue <= 120))) { + result[0] += -28.331180770557967; + } else { + result[0] += 672.7509955188011; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 274))) { + result[0] += -114.78306332631581; + } else { + result[0] += -408.53260808925637; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 198))) { + if (LIKELY(false || (data[6].qvalue <= 128))) { + if (UNLIKELY(false || (data[9].qvalue <= 16))) { + result[0] += -1018.0208814686271; + } else { + result[0] += 59.74134293981621; + } + } else { + result[0] += 293.35493295005193; + } + } else { + result[0] += -400.56582852441784; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 98))) { + if (UNLIKELY(false || (data[9].qvalue <= 14))) { + if (LIKELY(false || (data[0].qvalue <= 420))) { + result[0] += -74.3543481633016; + } else { + if (LIKELY(false || (data[1].qvalue <= 150))) { + result[0] += 571.9786783643856; + } else { + result[0] += -219.12600540624658; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 342))) { + result[0] += -101.20421903443618; + } else { + result[0] += -425.1879279279357; + } + } + } else { + result[0] += -38.8853994688072; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 0))) { + result[0] += -129.9772116102948; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 100))) { + if (UNLIKELY(false || (data[7].qvalue <= 56))) { + result[0] += 352.2167561334443; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 452))) { + if (LIKELY(false || (data[4].qvalue <= 104))) { + result[0] += -179.85826237208437; + } else { + result[0] += -1026.3738449938005; + } + } else { + result[0] += 86.91047265123943; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 140))) { + if (UNLIKELY(false || (data[9].qvalue <= 10))) { + if (UNLIKELY(false || (data[0].qvalue <= 448))) { + result[0] += -194.66018635911382; + } else { + result[0] += 165.22462004866262; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 112))) { + result[0] += 280.5581946286645; + } else { + result[0] += 803.432975071445; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 464))) { + if (LIKELY(false || (data[2].qvalue <= 218))) { + result[0] += 2.697588396559468; + } else { + result[0] += -478.252898298942; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 154))) { + result[0] += -208.4543974390897; + } else { + result[0] += 185.3089271840308; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 426))) { + if (LIKELY(false || (data[1].qvalue <= 124))) { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (LIKELY(false || (data[5].qvalue <= 86))) { + if (LIKELY(false || (data[6].qvalue <= 76))) { + if (LIKELY(false || (data[4].qvalue <= 80))) { + result[0] += -1.486803701497208; + } else { + result[0] += 82.51456493961574; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 26))) { + result[0] += -1724.8354696321032; + } else { + result[0] += -82.71048025173705; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 22))) { + if (UNLIKELY(false || (data[4].qvalue <= 110))) { + result[0] += -790.0620438667581; + } else { + result[0] += 106.26605184431772; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 378))) { + result[0] += 33.9153156537546; + } else { + result[0] += 173.09121370877705; + } + } + } + } else { + result[0] += -154.93054492116286; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 54))) { + if (LIKELY(false || (data[2].qvalue <= 50))) { + if (LIKELY(false || (data[2].qvalue <= 42))) { + if (LIKELY(false || (data[0].qvalue <= 392))) { + result[0] += -1.9879083035835015; + } else { + result[0] += 398.5109346540218; + } + } else { + result[0] += -786.5885010553891; + } + } else { + result[0] += 430.4466811037737; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 342))) { + result[0] += -47.917740653423564; + } else { + if (LIKELY(false || (data[0].qvalue <= 412))) { + if (LIKELY(false || (data[2].qvalue <= 164))) { + result[0] += -361.3887876613474; + } else { + result[0] += -59.68743934462101; + } + } else { + result[0] += -24.34286763077365; + } + } + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 10))) { + result[0] += 711.5911056007745; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 88))) { + if (LIKELY(false || (data[0].qvalue <= 454))) { + if (LIKELY(false || (data[2].qvalue <= 64))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += -939.5055510805191; + } else { + result[0] += 49.38415531008706; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 128))) { + result[0] += -607.7336297438973; + } else { + result[0] += 231.05599252528052; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 40))) { + result[0] += -676.7676630675595; + } else { + result[0] += 107.56624878796568; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 104))) { + if (UNLIKELY(false || (data[3].qvalue <= 110))) { + if (UNLIKELY(false || (data[7].qvalue <= 52))) { + result[0] += 470.39671526527695; + } else { + result[0] += 19.64177427856826; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 456))) { + result[0] += 257.8561111510292; + } else { + result[0] += -156.72749393540863; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 112))) { + result[0] += 541.0799146065921; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 444))) { + result[0] += -223.96127054701944; + } else { + result[0] += 35.39412132031614; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 458))) { + if (LIKELY(false || (data[2].qvalue <= 200))) { + if (LIKELY(false || (data[3].qvalue <= 172))) { + if (LIKELY(false || (data[2].qvalue <= 198))) { + if (LIKELY(false || (data[0].qvalue <= 392))) { + if (LIKELY(false || (data[2].qvalue <= 180))) { + result[0] += -1.1948610469474203; + } else { + result[0] += -80.88158309915174; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 10))) { + result[0] += -96.14071101510345; + } else { + result[0] += 63.358216911141255; + } + } + } else { + result[0] += 176.15313222545365; + } + } else { + result[0] += -326.1906626097021; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 116))) { + if (LIKELY(false || (data[0].qvalue <= 430))) { + if (UNLIKELY(false || (data[8].qvalue <= 146))) { + if (LIKELY(false || (data[0].qvalue <= 400))) { + result[0] += 40.65129948483353; + } else { + result[0] += 623.8755186699341; + } + } else { + result[0] += -139.52193586780237; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (LIKELY(false || (data[0].qvalue <= 446))) { + result[0] += 215.51009220017923; + } else { + result[0] += 626.7644565408536; + } + } else { + result[0] += -57.77880667756001; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 78))) { + result[0] += -345.1537827658937; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 206))) { + if (UNLIKELY(false || (data[0].qvalue <= 368))) { + result[0] += 120.97750370682058; + } else { + result[0] += -530.0146207114068; + } + } else { + result[0] += -90.90617312134356; + } + } + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 128))) { + if (LIKELY(false || (data[2].qvalue <= 222))) { + if (UNLIKELY(false || (data[8].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 30))) { + if (LIKELY(false || (data[4].qvalue <= 114))) { + result[0] += 405.6146661410597; + } else { + result[0] += 22.37476339760467; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 78))) { + result[0] += -499.28634142340854; + } else { + result[0] += 68.9305749234034; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 90))) { + if (LIKELY(false || (data[9].qvalue <= 86))) { + result[0] += 529.6389184175102; + } else { + result[0] += 95.91732717933235; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 158))) { + result[0] += 194.2271286977213; + } else { + result[0] += -22.243321533686604; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + result[0] += -322.8706246634995; + } else { + result[0] += 171.93787845774688; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -280.2205757227194; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 16))) { + result[0] += 805.6007065468135; + } else { + if (LIKELY(false || (data[9].qvalue <= 16))) { + if (LIKELY(false || (data[7].qvalue <= 192))) { + result[0] += 103.74300879356734; + } else { + result[0] += -219.28913112606025; + } + } else { + result[0] += -516.2170829126527; + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 54))) { + if (UNLIKELY(false || (data[0].qvalue <= 0))) { + result[0] += -98.93297408058446; + } else { + if (LIKELY(false || (data[4].qvalue <= 60))) { + if (LIKELY(false || (data[3].qvalue <= 126))) { + if (UNLIKELY(false || (data[0].qvalue <= 18))) { + result[0] += -35.246449455925166; + } else { + result[0] += -11.913885864248398; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 12))) { + result[0] += 244.38629528569243; + } else { + result[0] += -59.63764056448275; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 136))) { + result[0] += -67.53814213480176; + } else { + result[0] += 13.863392069333836; + } + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + if (LIKELY(false || (data[1].qvalue <= 10))) { + if (LIKELY(false || (data[0].qvalue <= 232))) { + if (UNLIKELY(false || (data[2].qvalue <= 24))) { + result[0] += 243.2273344714578; + } else { + if (LIKELY(false || (data[2].qvalue <= 52))) { + result[0] += 56.08540095837141; + } else { + result[0] += -73.3251905895068; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 128))) { + if (UNLIKELY(false || (data[2].qvalue <= 24))) { + result[0] += 643.1260108343162; + } else { + result[0] += 308.232898319556; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 318))) { + result[0] += -85.94227185477129; + } else { + result[0] += 339.32448533355216; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 196))) { + result[0] += 295.57345918020815; + } else { + result[0] += 541.3180556951017; + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 156))) { + if (UNLIKELY(false || (data[4].qvalue <= 6))) { + if (LIKELY(false || (data[4].qvalue <= 4))) { + if (UNLIKELY(false || (data[6].qvalue <= 2))) { + result[0] += 176.28854142574565; + } else { + result[0] += -16.26659984192861; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 224))) { + result[0] += 272.5245026026528; + } else { + result[0] += 630.8310847742418; + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 10))) { + if (LIKELY(false || (data[10].qvalue <= 76))) { + result[0] += -99.36810379968507; + } else { + result[0] += -462.5017447936385; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 146))) { + result[0] += 3.9186464796296914; + } else { + result[0] += 225.22750105396563; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 364))) { + if (UNLIKELY(false || (data[0].qvalue <= 178))) { + if (LIKELY(false || (data[1].qvalue <= 22))) { + result[0] += -168.88397604209945; + } else { + result[0] += 38.12118179055833; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 14))) { + result[0] += -208.349456121144; + } else { + result[0] += -434.7176030497106; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 428))) { + if (LIKELY(false || (data[1].qvalue <= 22))) { + result[0] += 147.36975160660015; + } else { + result[0] += -427.1472748332084; + } + } else { + result[0] += 468.6802177857324; + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (LIKELY(false || (data[5].qvalue <= 118))) { + if (LIKELY(false || (data[0].qvalue <= 438))) { + if (LIKELY(false || (data[7].qvalue <= 194))) { + if (LIKELY(false || (data[1].qvalue <= 130))) { + if (LIKELY(false || (data[4].qvalue <= 106))) { + result[0] += 0.3368638092539733; + } else { + result[0] += 173.775034577441; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 60))) { + result[0] += 48.85885497450373; + } else { + result[0] += -96.87483192360062; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 346))) { + result[0] += -78.03276293917031; + } else { + if (LIKELY(false || (data[0].qvalue <= 428))) { + result[0] += -418.6164374426745; + } else { + result[0] += -98.44042608027418; + } + } + } + } else { + if (LIKELY(false || (data[5].qvalue <= 112))) { + if (UNLIKELY(false || (data[9].qvalue <= 8))) { + if (UNLIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -275.91209245174423; + } else { + result[0] += 99.25590915465185; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 82))) { + result[0] += -41.70434165862219; + } else { + result[0] += 130.83735776026924; + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 4))) { + if (UNLIKELY(false || (data[0].qvalue <= 444))) { + result[0] += 208.90021667361708; + } else { + result[0] += 614.78246500265; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 452))) { + result[0] += -186.66325247313335; + } else { + result[0] += 252.69463305407146; + } + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 158))) { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -243.87323720092883; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 160))) { + result[0] += 607.3841937390166; + } else { + if (LIKELY(false || (data[2].qvalue <= 224))) { + result[0] += -4.642945887934479; + } else { + result[0] += -425.1676612575311; + } + } + } + } else { + result[0] += -226.78722863564013; + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 88))) { + if (LIKELY(false || (data[2].qvalue <= 226))) { + result[0] += 457.093631103365; + } else { + result[0] += -195.01605350860177; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 54))) { + if (UNLIKELY(false || (data[8].qvalue <= 16))) { + result[0] += 121.71032578695974; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -1535.090860877404; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += -642.94729679988; + } else { + result[0] += -89.58887867028852; + } + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 88))) { + if (UNLIKELY(false || (data[5].qvalue <= 72))) { + result[0] += -178.884099836437; + } else { + result[0] += 349.2407402511536; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 144))) { + result[0] += -1194.8087333496094; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -154.55357593204099; + } else { + result[0] += 257.1888927003237; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 112))) { + if (UNLIKELY(false || (data[0].qvalue <= 0))) { + result[0] += -89.14531598408416; + } else { + result[0] += -18.084829562955424; + } + } else { + if (LIKELY(false || (data[8].qvalue <= 138))) { + if (LIKELY(false || (data[3].qvalue <= 116))) { + if (LIKELY(false || (data[8].qvalue <= 118))) { + if (UNLIKELY(false || (data[4].qvalue <= 38))) { + if (LIKELY(false || (data[1].qvalue <= 44))) { + result[0] += 16.943599637772344; + } else { + result[0] += 106.06836417622998; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 42))) { + result[0] += -528.7742969051144; + } else { + result[0] += -9.291722918418776; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 132))) { + result[0] += -118.09966496394628; + } else { + result[0] += -560.1406224711944; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 140))) { + result[0] += -51.02960535934701; + } else { + result[0] += 338.4129630811006; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 104))) { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + if (LIKELY(false || (data[0].qvalue <= 464))) { + result[0] += -248.21126531471361; + } else { + result[0] += 386.6061213337075; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 376))) { + result[0] += 94.34550988961043; + } else { + result[0] += 304.2723529855085; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 436))) { + if (LIKELY(false || (data[8].qvalue <= 106))) { + result[0] += -8.896943765616806; + } else { + result[0] += -245.20061258455573; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 60))) { + result[0] += -113.87022533457596; + } else { + result[0] += 116.77525773183898; + } + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (LIKELY(false || (data[0].qvalue <= 428))) { + if (UNLIKELY(false || (data[0].qvalue <= 224))) { + result[0] += -624.1711191522509; + } else { + if (LIKELY(false || (data[0].qvalue <= 414))) { + result[0] += -1347.2302644189722; + } else { + result[0] += -733.7942435464515; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 60))) { + result[0] += 471.0712008104827; + } else { + result[0] += -142.56709666372134; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + if (UNLIKELY(false || (data[9].qvalue <= 104))) { + if (UNLIKELY(false || (data[2].qvalue <= 192))) { + result[0] += -1567.6200324035817; + } else { + result[0] += -98.50162535806925; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 398))) { + result[0] += -50.25901125894962; + } else { + result[0] += 60.40281064144929; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 102))) { + if (LIKELY(false || (data[3].qvalue <= 178))) { + result[0] += 379.87726438502557; + } else { + result[0] += -199.4001040982219; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -287.5563657821928; + } else { + result[0] += 332.3956673905034; + } + } + } + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 232))) { + if (UNLIKELY(false || (data[4].qvalue <= 36))) { + if (LIKELY(false || (data[1].qvalue <= 40))) { + if (LIKELY(false || (data[4].qvalue <= 30))) { + result[0] += -1.565030040146726; + } else { + result[0] += -104.9566604701055; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 138))) { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += -17.037935890389736; + } else { + result[0] += 63.289328847278654; + } + } else { + result[0] += -1324.6271531918176; + } + } + } else { + result[0] += -20.81789198275515; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 34))) { + if (LIKELY(false || (data[8].qvalue <= 100))) { + if (LIKELY(false || (data[8].qvalue <= 98))) { + if (LIKELY(false || (data[2].qvalue <= 134))) { + if (UNLIKELY(false || (data[0].qvalue <= 290))) { + result[0] += 2.015327035742837; + } else { + result[0] += 76.49554789592639; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 402))) { + result[0] += -823.5010456874027; + } else { + result[0] += 182.88330508869717; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 376))) { + result[0] += -684.3245220762328; + } else { + result[0] += 114.75508963752516; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 92))) { + result[0] += 584.9961795342024; + } else { + if (UNLIKELY(false || (data[9].qvalue <= 118))) { + result[0] += 284.6160485461698; + } else { + if (LIKELY(false || (data[0].qvalue <= 342))) { + result[0] += -0.3173057744465385; + } else { + result[0] += 255.28193335274855; + } + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[5].qvalue <= 24))) { + if (LIKELY(false || (data[3].qvalue <= 18))) { + if (LIKELY(false || (data[0].qvalue <= 368))) { + result[0] += -109.2669803718638; + } else { + result[0] += 49.7918489437198; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 26))) { + result[0] += -35.33314939837475; + } else { + result[0] += 383.1659807220039; + } + } + } else { + if (UNLIKELY(false || (data[11].qvalue <= 0))) { + if (LIKELY(false || (data[8].qvalue <= 80))) { + result[0] += -119.6036066769413; + } else { + result[0] += -530.947990035426; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + result[0] += -80.63511119863223; + } else { + result[0] += 415.2734672989832; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + if (UNLIKELY(false || (data[3].qvalue <= 68))) { + result[0] += 704.1141519325657; + } else { + if (UNLIKELY(false || (data[8].qvalue <= 2))) { + result[0] += 84.60147882812862; + } else { + result[0] += -187.8254210699395; + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 70))) { + if (LIKELY(false || (data[5].qvalue <= 90))) { + result[0] += 121.89790445304102; + } else { + result[0] += -115.0763981218671; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 72))) { + result[0] += -654.9303551006227; + } else { + result[0] += 7.981521248472231; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 420))) { + if (LIKELY(false || (data[6].qvalue <= 144))) { + if (LIKELY(false || (data[10].qvalue <= 146))) { + if (LIKELY(false || (data[2].qvalue <= 210))) { + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (LIKELY(false || (data[0].qvalue <= 358))) { + result[0] += -2.9256693373180407; + } else { + result[0] += 52.86899999050905; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 162))) { + result[0] += -236.3933458780788; + } else { + result[0] += -14.339195301019586; + } + } + } else { + result[0] += -219.14539537485498; + } + } else { + result[0] += -318.42234277715806; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 154))) { + if (UNLIKELY(false || (data[0].qvalue <= 274))) { + result[0] += -22.289702159300546; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 42))) { + result[0] += 180.36521926437015; + } else { + result[0] += -260.09045231531485; + } + } + } else { + result[0] += 44.37903776463972; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 86))) { + if (UNLIKELY(false || (data[0].qvalue <= 440))) { + if (UNLIKELY(false || (data[7].qvalue <= 46))) { + if (LIKELY(false || (data[1].qvalue <= 90))) { + result[0] += -68.81869559889574; + } else { + result[0] += 716.3498421781522; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 106))) { + if (LIKELY(false || (data[4].qvalue <= 78))) { + result[0] += -171.83362174216776; + } else { + result[0] += -984.3537857945884; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 100))) { + result[0] += -338.7075403591485; + } else { + result[0] += 123.96422662235261; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 164))) { + if (LIKELY(false || (data[9].qvalue <= 126))) { + if (UNLIKELY(false || (data[0].qvalue <= 454))) { + result[0] += -204.18509525988378; + } else { + result[0] += 64.71659005621359; + } + } else { + result[0] += 817.3359237308946; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 26))) { + result[0] += -262.7801965080877; + } else { + result[0] += 612.6525648856328; + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 140))) { + if (LIKELY(false || (data[8].qvalue <= 128))) { + if (LIKELY(false || (data[6].qvalue <= 150))) { + if (LIKELY(false || (data[1].qvalue <= 148))) { + result[0] += 369.7289746082688; + } else { + result[0] += -103.32913617071652; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 22))) { + result[0] += -25.94812018229829; + } else { + result[0] += 299.29536905910857; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + result[0] += 18.803068756821325; + } else { + result[0] += 861.4705320103184; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 118))) { + result[0] += 148.92751319365524; + } else { + if (UNLIKELY(false || (data[6].qvalue <= 126))) { + if (UNLIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -1775.7163414874296; + } else { + result[0] += 74.55151140491274; + } + } else { + result[0] += -10.06076847351882; + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 450))) { + if (LIKELY(false || (data[1].qvalue <= 148))) { + if (UNLIKELY(false || (data[10].qvalue <= 30))) { + if (LIKELY(false || (data[6].qvalue <= 152))) { + if (UNLIKELY(false || (data[9].qvalue <= 92))) { + if (LIKELY(false || (data[8].qvalue <= 12))) { + result[0] += 21.302765815704674; + } else { + result[0] += -269.2691797738821; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 14))) { + result[0] += -63.12874563850693; + } else { + result[0] += 62.93227886630939; + } + } + } else { + result[0] += -400.8612725252076; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 424))) { + if (LIKELY(false || (data[0].qvalue <= 386))) { + result[0] += 58.49853608139253; + } else { + result[0] += 379.42843601568325; + } + } else { + result[0] += -1179.5767049009407; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 100))) { + result[0] += 19.778425640043217; + } else { + result[0] += -856.1976997050459; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 424))) { + result[0] += -5.97846202630444; + } else { + result[0] += 86.90154167537219; + } + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 102))) { + result[0] += 410.7033838491514; + } else { + if (UNLIKELY(false || (data[4].qvalue <= 112))) { + result[0] += -278.12154040332797; + } else { + result[0] += -70.26805980080006; + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 180))) { + if (LIKELY(false || (data[4].qvalue <= 136))) { + if (UNLIKELY(false || (data[8].qvalue <= 46))) { + if (LIKELY(false || (data[9].qvalue <= 52))) { + if (UNLIKELY(false || (data[4].qvalue <= 108))) { + result[0] += 202.68141819077738; + } else { + result[0] += -167.2132011812022; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -1384.5104673530457; + } else { + result[0] += -189.4566731860187; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 140))) { + if (LIKELY(false || (data[7].qvalue <= 132))) { + result[0] += 187.09927614728926; + } else { + result[0] += 567.0117546161409; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -93.51709182438407; + } else { + result[0] += 121.54129938504501; + } + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 140))) { + result[0] += 465.57152458676364; + } else { + result[0] += -479.2019554780659; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 472))) { + if (LIKELY(false || (data[10].qvalue <= 106))) { + if (LIKELY(false || (data[7].qvalue <= 200))) { + if (UNLIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -220.95806736832415; + } else { + result[0] += 116.17247093054512; + } + } else { + result[0] += -729.6717337371826; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 164))) { + result[0] += -312.7238552339285; + } else { + result[0] += -1140.467401624576; + } + } + } else { + result[0] += 195.02478855827175; + } + } + } + if (LIKELY(false || (data[8].qvalue <= 154))) { + if (LIKELY(false || (data[0].qvalue <= 392))) { + if (LIKELY(false || (data[6].qvalue <= 62))) { + if (LIKELY(false || (data[0].qvalue <= 310))) { + if (LIKELY(false || (data[8].qvalue <= 120))) { + if (LIKELY(false || (data[8].qvalue <= 100))) { + result[0] += -9.71572720333726; + } else { + result[0] += 67.59923309810226; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 52))) { + result[0] += -351.55140420047667; + } else { + result[0] += -16.616159911071314; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 72))) { + if (LIKELY(false || (data[4].qvalue <= 66))) { + result[0] += 69.88081147655924; + } else { + result[0] += -969.7781215245079; + } + } else { + result[0] += 228.67764875201647; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 4))) { + if (UNLIKELY(false || (data[10].qvalue <= 2))) { + result[0] += 74.35381121983716; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 138))) { + result[0] += -62.154687912270276; + } else { + result[0] += -796.2456169363136; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 56))) { + if (LIKELY(false || (data[4].qvalue <= 86))) { + result[0] += -156.1506230350774; + } else { + result[0] += 119.82853149552272; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 106))) { + result[0] += 13.900388157788383; + } else { + result[0] += -42.87238835775265; + } + } + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 78))) { + if (UNLIKELY(false || (data[6].qvalue <= 58))) { + if (LIKELY(false || (data[6].qvalue <= 56))) { + if (LIKELY(false || (data[2].qvalue <= 156))) { + result[0] += -25.79300413884613; + } else { + result[0] += 449.6201546856599; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 424))) { + result[0] += -1246.7840846746014; + } else { + result[0] += -60.57121668348967; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 18))) { + if (UNLIKELY(false || (data[0].qvalue <= 416))) { + result[0] += -507.2811346565345; + } else { + result[0] += 1.6959367259683802; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 130))) { + result[0] += 256.5119388340715; + } else { + result[0] += 61.901222326179735; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 4))) { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -712.841039622884; + } else { + result[0] += -50.285859444198785; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 168))) { + if (UNLIKELY(false || (data[7].qvalue <= 80))) { + result[0] += -89.82284215276091; + } else { + result[0] += 57.2312430185315; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 462))) { + result[0] += -196.41216488031796; + } else { + result[0] += 64.87819986422424; + } + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 422))) { + if (LIKELY(false || (data[0].qvalue <= 352))) { + result[0] += -54.40201249787878; + } else { + result[0] += -323.09540305923275; + } + } else { + result[0] += 22.58190522531515; + } + } + if (LIKELY(false || (data[7].qvalue <= 192))) { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (LIKELY(false || (data[2].qvalue <= 212))) { + if (LIKELY(false || (data[0].qvalue <= 396))) { + if (UNLIKELY(false || (data[9].qvalue <= 62))) { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 320.8771929320019; + } else { + result[0] += -48.74134204044459; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 64))) { + result[0] += 144.15507685887556; + } else { + result[0] += 2.061937768452418; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 168))) { + if (LIKELY(false || (data[8].qvalue <= 142))) { + result[0] += 14.310048813060831; + } else { + result[0] += -891.4821913653136; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 186))) { + result[0] += 230.68364280672665; + } else { + result[0] += 31.828982825162395; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 460))) { + if (UNLIKELY(false || (data[8].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 446))) { + result[0] += 5.3923912609412765; + } else { + result[0] += -296.2815883663324; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 18))) { + result[0] += 0.33503809236265447; + } else { + result[0] += -222.23518809470139; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 162))) { + result[0] += 435.30908950076656; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 220))) { + result[0] += 151.71039930723558; + } else { + result[0] += -220.68651987712957; + } + } + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 158))) { + if (LIKELY(false || (data[5].qvalue <= 106))) { + if (LIKELY(false || (data[8].qvalue <= 34))) { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -761.8192641261176; + } else { + result[0] += -24.696202075383436; + } + } else { + result[0] += 297.8870552382013; + } + } else { + result[0] += -1111.3451507308962; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 180))) { + result[0] += 399.4329665421262; + } else { + if (LIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -33.631317828261764; + } else { + if (LIKELY(false || (data[6].qvalue <= 186))) { + result[0] += 521.4533408866705; + } else { + result[0] += -47.855811708641056; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 438))) { + if (UNLIKELY(false || (data[0].qvalue <= 352))) { + result[0] += -42.87267583810598; + } else { + result[0] += -282.3784003570174; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 132))) { + result[0] += 156.2716050426377; + } else { + if (UNLIKELY(false || (data[8].qvalue <= 144))) { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + if (UNLIKELY(false || (data[2].qvalue <= 154))) { + result[0] += -106.3379635111628; + } else { + result[0] += -799.4368728340669; + } + } else { + result[0] += -30.901543195994442; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -278.9996333283842; + } else { + result[0] += 97.21542436889949; + } + } + } + } + } + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + if (LIKELY(false || (data[0].qvalue <= 198))) { + if (LIKELY(false || (data[1].qvalue <= 10))) { + if (UNLIKELY(false || (data[2].qvalue <= 24))) { + if (UNLIKELY(false || (data[0].qvalue <= 62))) { + result[0] += -71.54944060418909; + } else { + result[0] += 200.91246808639335; + } + } else { + result[0] += -16.524995964984008; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 56))) { + result[0] += -6.615663904946698; + } else { + result[0] += 277.0038505094405; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 128))) { + if (LIKELY(false || (data[1].qvalue <= 10))) { + result[0] += 242.12226853180888; + } else { + result[0] += 495.40205234661437; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 308))) { + result[0] += -144.40748272556144; + } else { + result[0] += 244.60940232429508; + } + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 156))) { + if (UNLIKELY(false || (data[4].qvalue <= 6))) { + if (LIKELY(false || (data[4].qvalue <= 4))) { + if (LIKELY(false || (data[9].qvalue <= 144))) { + if (UNLIKELY(false || (data[10].qvalue <= 12))) { + result[0] += 151.95679595717183; + } else { + result[0] += -75.40462882328015; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 248))) { + result[0] += 59.16238539562008; + } else { + result[0] += 258.58095117618336; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 194))) { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 34.722618898156114; + } else { + result[0] += 229.7578373765491; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 254))) { + result[0] += 416.11542151307907; + } else { + result[0] += 657.1399509168027; + } + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 10))) { + if (LIKELY(false || (data[0].qvalue <= 396))) { + if (LIKELY(false || (data[10].qvalue <= 76))) { + result[0] += -99.76580517101934; + } else { + result[0] += -406.79586814929917; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 410))) { + result[0] += 67.21864653476916; + } else { + result[0] += 318.3788762612092; + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 152))) { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + result[0] += -89.91859493216367; + } else { + result[0] += 2.265520650788892; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 182))) { + result[0] += 86.60159851878495; + } else { + result[0] += 446.4563385244049; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 426))) { + if (UNLIKELY(false || (data[0].qvalue <= 166))) { + result[0] += -85.71042457905949; + } else { + if (LIKELY(false || (data[0].qvalue <= 356))) { + if (LIKELY(false || (data[1].qvalue <= 14))) { + result[0] += -197.6560417052055; + } else { + result[0] += -382.76909364031883; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 22))) { + result[0] += 86.90468637807797; + } else { + result[0] += -409.69153384359265; + } + } + } + } else { + result[0] += 370.0893122985466; + } + } + } + if (LIKELY(false || (data[0].qvalue <= 450))) { + if (LIKELY(false || (data[1].qvalue <= 130))) { + if (LIKELY(false || (data[4].qvalue <= 106))) { + if (UNLIKELY(false || (data[9].qvalue <= 16))) { + result[0] += -778.7936840058063; + } else { + if (UNLIKELY(false || (data[9].qvalue <= 20))) { + result[0] += 194.97101648633375; + } else { + if (UNLIKELY(false || (data[10].qvalue <= 30))) { + result[0] += -35.3509348883947; + } else { + result[0] += 5.30025526653546; + } + } + } + } else { + result[0] += 128.95468067576573; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 106))) { + if (UNLIKELY(false || (data[8].qvalue <= 68))) { + if (LIKELY(false || (data[2].qvalue <= 120))) { + result[0] += -34.18950997644831; + } else { + result[0] += 621.8798392741791; + } + } else { + result[0] += -155.4137360966938; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 108))) { + if (LIKELY(false || (data[0].qvalue <= 430))) { + if (LIKELY(false || (data[7].qvalue <= 108))) { + result[0] += 256.5188225022511; + } else { + result[0] += -99.67316593885579; + } + } else { + result[0] += 620.485300659375; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 100))) { + result[0] += -2286.0077275800704; + } else { + if (LIKELY(false || (data[5].qvalue <= 116))) { + result[0] += -53.309956527903466; + } else { + result[0] += 190.13761434101764; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 136))) { + if (UNLIKELY(false || (data[2].qvalue <= 78))) { + if (LIKELY(false || (data[1].qvalue <= 142))) { + if (UNLIKELY(false || (data[7].qvalue <= 76))) { + if (LIKELY(false || (data[2].qvalue <= 68))) { + result[0] += 253.43645195632422; + } else { + result[0] += -1413.0786233422066; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 462))) { + result[0] += -690.7561180054086; + } else { + result[0] += -63.707054709028625; + } + } + } else { + result[0] += 291.7091149857328; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 120))) { + if (UNLIKELY(false || (data[8].qvalue <= 128))) { + result[0] += 478.56900909093986; + } else { + result[0] += 167.99342824787573; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 464))) { + result[0] += 60.9554692627185; + } else { + result[0] += -1073.8503825143669; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 464))) { + if (LIKELY(false || (data[4].qvalue <= 136))) { + if (UNLIKELY(false || (data[8].qvalue <= 52))) { + result[0] += -539.7433428985337; + } else { + if (LIKELY(false || (data[1].qvalue <= 160))) { + result[0] += 20.89762446651438; + } else { + result[0] += -416.20782851777005; + } + } + } else { + result[0] += 361.5994213025975; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 172))) { + if (LIKELY(false || (data[1].qvalue <= 160))) { + if (UNLIKELY(false || (data[10].qvalue <= 34))) { + result[0] += 537.4931809409429; + } else { + result[0] += 93.3123402983442; + } + } else { + result[0] += 509.56219742394296; + } + } else { + result[0] += -30.502598904934388; + } + } + } + } + if (UNLIKELY(false || (data[4].qvalue <= 18))) { + if (UNLIKELY(false || (data[9].qvalue <= 108))) { + if (UNLIKELY(false || (data[0].qvalue <= 170))) { + result[0] += 32.02621609860586; + } else { + result[0] += 195.61137056108709; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 116))) { + result[0] += -56.120014772691675; + } else { + if (UNLIKELY(false || (data[9].qvalue <= 120))) { + if (UNLIKELY(false || (data[8].qvalue <= 38))) { + result[0] += 258.16901404153344; + } else { + result[0] += 53.61413424410968; + } + } else { + result[0] += 3.8083516584014774; + } + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 30))) { + if (LIKELY(false || (data[8].qvalue <= 12))) { + if (UNLIKELY(false || (data[10].qvalue <= 2))) { + result[0] += 95.68803265337215; + } else { + if (UNLIKELY(false || (data[10].qvalue <= 8))) { + if (LIKELY(false || (data[3].qvalue <= 132))) { + result[0] += -103.63143270824092; + } else { + result[0] += -416.841719094304; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 452))) { + result[0] += 29.00180398543003; + } else { + result[0] += 739.6504496232433; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 442))) { + if (UNLIKELY(false || (data[9].qvalue <= 44))) { + result[0] += -1170.886850360489; + } else { + if (UNLIKELY(false || (data[9].qvalue <= 78))) { + result[0] += -23.599978133418663; + } else { + result[0] += -233.86794128469188; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 100))) { + result[0] += 296.8371355378329; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -578.3205363336284; + } else { + result[0] += 28.31820900489086; + } + } + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 110))) { + if (UNLIKELY(false || (data[2].qvalue <= 34))) { + if (LIKELY(false || (data[0].qvalue <= 270))) { + result[0] += 15.084899225242879; + } else { + if (UNLIKELY(false || (data[6].qvalue <= 44))) { + result[0] += 227.37067720434766; + } else { + result[0] += 79.75576158468904; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 66))) { + if (LIKELY(false || (data[2].qvalue <= 64))) { + result[0] += -21.445175019440377; + } else { + result[0] += -736.583490831738; + } + } else { + if (LIKELY(false || (data[10].qvalue <= 122))) { + result[0] += 19.304014117388515; + } else { + result[0] += -31.807962860341632; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 52))) { + if (LIKELY(false || (data[6].qvalue <= 20))) { + if (LIKELY(false || (data[2].qvalue <= 76))) { + result[0] += -2.770553189772293; + } else { + result[0] += -459.67841100976295; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 402))) { + result[0] += -654.9440592987792; + } else { + result[0] += -113.53853189933453; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 52))) { + result[0] += 141.69815316018148; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 74))) { + result[0] += -502.039703472783; + } else { + result[0] += -32.3855029558517; + } + } + } + } + } + } + if (LIKELY(false || (data[7].qvalue <= 176))) { + if (LIKELY(false || (data[3].qvalue <= 122))) { + if (LIKELY(false || (data[6].qvalue <= 68))) { + if (LIKELY(false || (data[7].qvalue <= 96))) { + if (LIKELY(false || (data[7].qvalue <= 92))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += 92.56955997074317; + } else { + result[0] += -0.9922155287745735; + } + } else { + result[0] += -500.10035950605436; + } + } else { + if (LIKELY(false || (data[10].qvalue <= 82))) { + if (UNLIKELY(false || (data[3].qvalue <= 18))) { + result[0] += -349.0415762351967; + } else { + result[0] += 171.85769845927226; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 156))) { + result[0] += -451.9129687933409; + } else { + result[0] += -27.180822178728683; + } + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 24))) { + if (UNLIKELY(false || (data[5].qvalue <= 52))) { + if (LIKELY(false || (data[6].qvalue <= 170))) { + result[0] += 257.80955723579154; + } else { + result[0] += 14.067277977079232; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 78))) { + result[0] += -187.1084046068555; + } else { + result[0] += -16.47644404213514; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 58))) { + if (UNLIKELY(false || (data[1].qvalue <= 68))) { + result[0] += -459.1713788092935; + } else { + result[0] += -121.28232839389386; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 82))) { + result[0] += 45.911193575042404; + } else { + result[0] += -65.09204602729294; + } + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + if (LIKELY(false || (data[8].qvalue <= 148))) { + result[0] += -112.3564328986315; + } else { + result[0] += -581.5931838175455; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 104))) { + if (LIKELY(false || (data[8].qvalue <= 130))) { + if (LIKELY(false || (data[7].qvalue <= 140))) { + result[0] += 99.49014850118782; + } else { + result[0] += 283.8674441857837; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 166))) { + result[0] += -196.33975801441395; + } else { + result[0] += 5.911638064587168; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 128))) { + if (UNLIKELY(false || (data[1].qvalue <= 114))) { + result[0] += 220.39824119047162; + } else { + result[0] += 86.35899353404471; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 136))) { + result[0] += -135.11781706233776; + } else { + result[0] += 1.7254921740277542; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 32))) { + result[0] += -729.2271141921474; + } else { + if (LIKELY(false || (data[1].qvalue <= 164))) { + if (UNLIKELY(false || (data[8].qvalue <= 6))) { + result[0] += 163.01861591388177; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 182))) { + result[0] += -90.15761066666771; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 184))) { + result[0] += 105.67463769590789; + } else { + result[0] += -40.65848461097406; + } + } + } + } else { + result[0] += -149.36907697589524; + } + } + } + if (LIKELY(false || (data[0].qvalue <= 436))) { + if (UNLIKELY(false || (data[9].qvalue <= 54))) { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (UNLIKELY(false || (data[10].qvalue <= 2))) { + result[0] += 73.61434867220245; + } else { + if (LIKELY(false || (data[4].qvalue <= 92))) { + result[0] += -219.52771218319805; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 182))) { + result[0] += -261.44554618186174; + } else { + result[0] += -1230.408535736753; + } + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 24))) { + if (LIKELY(false || (data[0].qvalue <= 386))) { + if (LIKELY(false || (data[2].qvalue <= 112))) { + result[0] += -5.198946846457009; + } else { + result[0] += 249.1763981732631; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 34))) { + result[0] += 641.3825622819111; + } else { + result[0] += 205.60287406527715; + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 32))) { + if (UNLIKELY(false || (data[4].qvalue <= 126))) { + result[0] += -887.6161494419807; + } else { + result[0] += -171.10035394400404; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 408))) { + result[0] += -58.06284362537737; + } else { + result[0] += 25.218332263091998; + } + } + } + } + } else { + if (LIKELY(false || (data[8].qvalue <= 154))) { + if (LIKELY(false || (data[0].qvalue <= 346))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + if (LIKELY(false || (data[0].qvalue <= 232))) { + result[0] += 25.584773238822454; + } else { + result[0] += 219.8658888633042; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 20))) { + result[0] += -51.10525196126827; + } else { + result[0] += 0.46333939293857984; + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 72))) { + if (LIKELY(false || (data[8].qvalue <= 130))) { + result[0] += 164.15191019337982; + } else { + result[0] += -335.2814106754929; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 84))) { + result[0] += -89.39119339130295; + } else { + result[0] += 34.512913855389506; + } + } + } + } else { + result[0] += -108.08090718754343; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 158))) { + if (UNLIKELY(false || (data[8].qvalue <= 46))) { + if (LIKELY(false || (data[2].qvalue <= 118))) { + if (UNLIKELY(false || (data[7].qvalue <= 76))) { + result[0] += 131.26162098672742; + } else { + if (LIKELY(false || (data[0].qvalue <= 464))) { + result[0] += -442.8722988609171; + } else { + result[0] += 73.24052956825756; + } + } + } else { + result[0] += 686.9491978638054; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 54))) { + result[0] += 687.8496107636925; + } else { + if (LIKELY(false || (data[0].qvalue <= 456))) { + if (UNLIKELY(false || (data[9].qvalue <= 12))) { + result[0] += -77.24307302441612; + } else { + result[0] += 147.57566065994; + } + } else { + if (LIKELY(false || (data[5].qvalue <= 110))) { + result[0] += 138.145849125284; + } else { + result[0] += 445.5193884395197; + } + } + } + } + } else { + result[0] += -13.479785177056158; + } + } + if (LIKELY(false || (data[0].qvalue <= 418))) { + if (LIKELY(false || (data[1].qvalue <= 124))) { + if (LIKELY(false || (data[4].qvalue <= 68))) { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (UNLIKELY(false || (data[9].qvalue <= 50))) { + result[0] += -577.1611446622182; + } else { + if (LIKELY(false || (data[10].qvalue <= 142))) { + result[0] += 3.8314250912323637; + } else { + result[0] += -329.28055580520333; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 26))) { + if (UNLIKELY(false || (data[0].qvalue <= 198))) { + result[0] += -194.40768818753736; + } else { + result[0] += -854.4809931534361; + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 70))) { + result[0] += -160.52174054303293; + } else { + result[0] += 2.741586163284305; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 372))) { + if (UNLIKELY(false || (data[1].qvalue <= 46))) { + if (UNLIKELY(false || (data[0].qvalue <= 168))) { + result[0] += 18.984540004822804; + } else { + result[0] += 327.11800611524313; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 20))) { + result[0] += -129.20406003130722; + } else { + result[0] += 17.92278831555766; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 150))) { + if (UNLIKELY(false || (data[2].qvalue <= 6))) { + result[0] += -221.08946446962568; + } else { + result[0] += 229.63359317462343; + } + } else { + result[0] += -223.7223770166941; + } + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 64))) { + if (LIKELY(false || (data[0].qvalue <= 394))) { + result[0] += 5.729375564939926; + } else { + if (LIKELY(false || (data[4].qvalue <= 136))) { + result[0] += 349.6954434120244; + } else { + result[0] += -447.2340327004826; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 346))) { + result[0] += -18.934394820420263; + } else { + if (LIKELY(false || (data[2].qvalue <= 164))) { + if (LIKELY(false || (data[1].qvalue <= 140))) { + result[0] += -512.2810219610269; + } else { + result[0] += -123.2775600600666; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 176))) { + result[0] += 335.8072972282146; + } else { + result[0] += -135.40134282126365; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 10))) { + result[0] += 408.30240571312294; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 56))) { + result[0] += 203.85368488394235; + } else { + if (UNLIKELY(false || (data[7].qvalue <= 100))) { + if (LIKELY(false || (data[0].qvalue <= 448))) { + if (LIKELY(false || (data[10].qvalue <= 96))) { + result[0] += -457.3360616844447; + } else { + result[0] += 124.47860255821743; + } + } else { + if (LIKELY(false || (data[4].qvalue <= 108))) { + result[0] += 110.48892123347983; + } else { + result[0] += -391.09864189814425; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 114))) { + result[0] += 211.6228610309497; + } else { + if (UNLIKELY(false || (data[10].qvalue <= 100))) { + result[0] += 56.71696470804632; + } else { + result[0] += -35.66565365297374; + } + } + } + } + } + } + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + if (LIKELY(false || (data[1].qvalue <= 10))) { + if (LIKELY(false || (data[2].qvalue <= 52))) { + if (UNLIKELY(false || (data[2].qvalue <= 24))) { + result[0] += 147.91960717089773; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 43.27359384398152; + } else { + result[0] += 111.57729094083193; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 8))) { + result[0] += 113.34997104546899; + } else { + result[0] += 72.55342688666062; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 128))) { + if (LIKELY(false || (data[1].qvalue <= 8))) { + if (LIKELY(false || (data[1].qvalue <= 6))) { + result[0] += 31.246573605653715; + } else { + result[0] += 46.606138483456206; + } + } else { + result[0] += 25.9373059644741; + } + } else { + result[0] += -31.626364376909578; + } + } + } else { + result[0] += 240.78696773473405; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 156))) { + if (UNLIKELY(false || (data[4].qvalue <= 6))) { + if (LIKELY(false || (data[4].qvalue <= 4))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (UNLIKELY(false || (data[9].qvalue <= 136))) { + result[0] += -48.976438574076354; + } else { + result[0] += 84.65299344607978; + } + } else { + result[0] += -130.88113764489268; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 72))) { + if (UNLIKELY(false || (data[1].qvalue <= 22))) { + result[0] += 338.1827440503927; + } else { + result[0] += 298.08608821916727; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 22))) { + result[0] += 204.95094375285473; + } else { + result[0] += 165.99111228283587; + } + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 10))) { + if (LIKELY(false || (data[10].qvalue <= 76))) { + if (LIKELY(false || (data[2].qvalue <= 92))) { + result[0] += -108.03954054155902; + } else { + result[0] += 14.418807341740987; + } + } else { + result[0] += -321.311952308832; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 146))) { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + result[0] += -76.25024484457143; + } else { + result[0] += 1.263274912881202; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 6))) { + result[0] += -66.04814325674907; + } else { + result[0] += 205.46869851639258; + } + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 36))) { + if (UNLIKELY(false || (data[1].qvalue <= 14))) { + result[0] += -104.7552531611455; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 18))) { + if (LIKELY(false || (data[1].qvalue <= 16))) { + result[0] += -179.2056109143998; + } else { + result[0] += -196.72295793090106; + } + } else { + result[0] += -228.7848599462564; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -62.70206554713903; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 22))) { + result[0] += -109.57286387525; + } else { + result[0] += -86.90439645392793; + } + } + } + } + } + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + if (LIKELY(false || (data[1].qvalue <= 10))) { + if (LIKELY(false || (data[2].qvalue <= 52))) { + if (UNLIKELY(false || (data[2].qvalue <= 24))) { + result[0] += 133.14242400479026; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 38.96136485201495; + } else { + result[0] += 100.46528939864675; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 8))) { + result[0] += 102.02497851147024; + } else { + result[0] += 65.31936047920384; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 128))) { + if (LIKELY(false || (data[1].qvalue <= 8))) { + if (LIKELY(false || (data[1].qvalue <= 6))) { + result[0] += 28.128267190495155; + } else { + result[0] += 42.006052734820884; + } + } else { + result[0] += 23.349456995785104; + } + } else { + result[0] += -28.466887237596946; + } + } + } else { + result[0] += 216.73249472971176; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 154))) { + if (LIKELY(false || (data[9].qvalue <= 148))) { + if (UNLIKELY(false || (data[10].qvalue <= 2))) { + if (LIKELY(false || (data[2].qvalue <= 156))) { + if (LIKELY(false || (data[9].qvalue <= 114))) { + result[0] += 101.975488812464; + } else { + result[0] += -25.287212545092885; + } + } else { + result[0] += -99.57496398377107; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 30))) { + if (UNLIKELY(false || (data[4].qvalue <= 16))) { + result[0] += 58.55564779565954; + } else { + result[0] += -70.77906117415272; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 18))) { + result[0] += 56.64272064066464; + } else { + result[0] += -1.102421664746258; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 204))) { + if (UNLIKELY(false || (data[4].qvalue <= 4))) { + if (UNLIKELY(false || (data[1].qvalue <= 12))) { + result[0] += 76.95158297864288; + } else { + result[0] += 30.779853089335912; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 12))) { + result[0] += 184.47632299122395; + } else { + result[0] += 240.0561058810638; + } + } + } else { + result[0] += -59.44992973601068; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 20))) { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + if (UNLIKELY(false || (data[2].qvalue <= 36))) { + result[0] += 90.32707990001026; + } else { + result[0] += -56.438123774542795; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 92))) { + if (LIKELY(false || (data[2].qvalue <= 88))) { + result[0] += -97.11633504657749; + } else { + result[0] += -327.052109099788; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 24))) { + result[0] += -88.46877715199902; + } else { + result[0] += 36.38893057075731; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 14))) { + result[0] += 164.4902900819997; + } else { + result[0] += 201.51887436344938; + } + } + } + } + if (LIKELY(false || (data[7].qvalue <= 190))) { + if (LIKELY(false || (data[3].qvalue <= 102))) { + if (LIKELY(false || (data[6].qvalue <= 72))) { + if (LIKELY(false || (data[8].qvalue <= 112))) { + if (LIKELY(false || (data[8].qvalue <= 100))) { + if (LIKELY(false || (data[10].qvalue <= 124))) { + result[0] += 2.677490165277401; + } else { + result[0] += -230.8237998800555; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 106))) { + result[0] += 25.99249929961273; + } else { + result[0] += 143.56253715641475; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 158))) { + if (LIKELY(false || (data[4].qvalue <= 32))) { + result[0] += -56.96864498437293; + } else { + result[0] += -361.08262388656516; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 130))) { + result[0] += -1.2571125993192631; + } else { + result[0] += 139.96081546293442; + } + } + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 24))) { + if (LIKELY(false || (data[4].qvalue <= 126))) { + if (LIKELY(false || (data[8].qvalue <= 68))) { + result[0] += 157.68101550278936; + } else { + result[0] += -24.160628623142866; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 100))) { + result[0] += -87.49349951075376; + } else { + result[0] += -651.2774869908153; + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 104))) { + if (UNLIKELY(false || (data[8].qvalue <= 82))) { + result[0] += -72.49802639529806; + } else { + result[0] += -213.66048931924266; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 54))) { + result[0] += 78.72485385773093; + } else { + result[0] += -55.249394110207554; + } + } + } + } + } else { + if (LIKELY(false || (data[10].qvalue <= 134))) { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + if (LIKELY(false || (data[8].qvalue <= 148))) { + result[0] += -94.97855070131733; + } else { + result[0] += -527.4611841577268; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 32))) { + if (LIKELY(false || (data[9].qvalue <= 28))) { + result[0] += 0.9950631307094829; + } else { + result[0] += -188.09164012193523; + } + } else { + if (LIKELY(false || (data[8].qvalue <= 140))) { + result[0] += 64.8246177611995; + } else { + result[0] += -21.226393353713657; + } + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 140))) { + if (UNLIKELY(false || (data[10].qvalue <= 138))) { + result[0] += -69.54835276237708; + } else { + if (LIKELY(false || (data[1].qvalue <= 158))) { + result[0] += -236.0745244061219; + } else { + result[0] += -12.63559195083826; + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 52))) { + result[0] += -236.792523303151; + } else { + if (LIKELY(false || (data[10].qvalue <= 144))) { + result[0] += 52.206924149774636; + } else { + result[0] += -39.51021768049611; + } + } + } + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 140))) { + if (LIKELY(false || (data[2].qvalue <= 226))) { + result[0] += -52.42843817107941; + } else { + result[0] += -306.65785355623103; + } + } else { + result[0] += -385.0662454001109; + } + } + if (LIKELY(false || (data[0].qvalue <= 398))) { + if (LIKELY(false || (data[1].qvalue <= 110))) { + if (LIKELY(false || (data[4].qvalue <= 70))) { + if (LIKELY(false || (data[1].qvalue <= 86))) { + if (UNLIKELY(false || (data[5].qvalue <= 42))) { + if (LIKELY(false || (data[5].qvalue <= 40))) { + result[0] += -11.28284578878582; + } else { + result[0] += -558.1753204017739; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 26))) { + result[0] += 133.21891876863342; + } else { + result[0] += 2.7832862656512685; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 26))) { + if (LIKELY(false || (data[0].qvalue <= 246))) { + result[0] += -324.1019695078817; + } else { + result[0] += -1005.9270602557989; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 88))) { + result[0] += -134.86731075700266; + } else { + result[0] += 10.66748293923189; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 336))) { + if (LIKELY(false || (data[6].qvalue <= 98))) { + result[0] += 2.8200100312590166; + } else { + result[0] += 101.03470891273177; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[6].qvalue <= 82))) { + result[0] += 102.04557161945678; + } else { + result[0] += -1199.8811078319284; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 178))) { + result[0] += 209.67226520445524; + } else { + result[0] += -245.61747308533063; + } + } + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 60))) { + result[0] += 42.785881512673626; + } else { + if (LIKELY(false || (data[6].qvalue <= 154))) { + if (LIKELY(false || (data[0].qvalue <= 270))) { + result[0] += -45.95588289959001; + } else { + if (UNLIKELY(false || (data[6].qvalue <= 90))) { + result[0] += -6.815612347789953; + } else { + result[0] += -266.0110871919803; + } + } + } else { + result[0] += 39.02030164716074; + } + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 8))) { + result[0] += 324.9267206663035; + } else { + if (LIKELY(false || (data[2].qvalue <= 214))) { + if (UNLIKELY(false || (data[1].qvalue <= 16))) { + result[0] += -112.18559323452085; + } else { + if (LIKELY(false || (data[1].qvalue <= 132))) { + if (UNLIKELY(false || (data[2].qvalue <= 80))) { + result[0] += -56.2055045870944; + } else { + result[0] += 116.07989618400472; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 118))) { + result[0] += 141.36324262903074; + } else { + result[0] += -22.395839800711574; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 458))) { + if (UNLIKELY(false || (data[10].qvalue <= 118))) { + result[0] += 100.73381070844493; + } else { + if (LIKELY(false || (data[6].qvalue <= 174))) { + result[0] += -365.7173433437338; + } else { + result[0] += 21.744193287907777; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 162))) { + result[0] += 280.70190659426964; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 466))) { + result[0] += -456.7905672944539; + } else { + result[0] += 86.74089207808544; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 438))) { + if (LIKELY(false || (data[6].qvalue <= 146))) { + if (LIKELY(false || (data[7].qvalue <= 198))) { + if (LIKELY(false || (data[0].qvalue <= 388))) { + if (LIKELY(false || (data[1].qvalue <= 126))) { + if (LIKELY(false || (data[4].qvalue <= 102))) { + result[0] += -3.0799897635647393; + } else { + result[0] += 83.18986261549072; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 36))) { + result[0] += 225.15234009730352; + } else { + result[0] += -85.63258649231685; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 82))) { + if (LIKELY(false || (data[3].qvalue <= 80))) { + result[0] += 35.515553203945544; + } else { + result[0] += -529.1932694931496; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 124))) { + result[0] += 183.78815671074506; + } else { + result[0] += 39.81733903217535; + } + } + } + } else { + result[0] += -186.6083734780767; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 50))) { + if (UNLIKELY(false || (data[0].qvalue <= 272))) { + result[0] += 16.882292766847286; + } else { + result[0] += -340.80931681790474; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 154))) { + if (LIKELY(false || (data[3].qvalue <= 170))) { + if (LIKELY(false || (data[10].qvalue <= 102))) { + result[0] += -8.494744851894465; + } else { + result[0] += -138.4317062673716; + } + } else { + result[0] += -365.5767086328917; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 172))) { + if (UNLIKELY(false || (data[0].qvalue <= 304))) { + result[0] += -4.6291599966349635; + } else { + result[0] += 233.27933991789052; + } + } else { + result[0] += -47.89610265065409; + } + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 182))) { + if (LIKELY(false || (data[10].qvalue <= 148))) { + if (UNLIKELY(false || (data[8].qvalue <= 46))) { + if (UNLIKELY(false || (data[7].qvalue <= 56))) { + result[0] += 408.84774771757384; + } else { + if (LIKELY(false || (data[4].qvalue <= 136))) { + result[0] += -81.22211370546637; + } else { + result[0] += 149.79791446348088; + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 60))) { + result[0] += 507.31584249730014; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 108))) { + result[0] += 3.6960386503909706; + } else { + result[0] += 132.51686944699037; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 472))) { + if (LIKELY(false || (data[7].qvalue <= 192))) { + if (UNLIKELY(false || (data[0].qvalue <= 460))) { + result[0] += -289.98766205200593; + } else { + result[0] += -37.620573467202114; + } + } else { + result[0] += -1064.0260380735235; + } + } else { + result[0] += 301.66872530386996; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 472))) { + if (LIKELY(false || (data[1].qvalue <= 160))) { + if (LIKELY(false || (data[3].qvalue <= 178))) { + result[0] += -66.47823654447284; + } else { + result[0] += -479.9069173542369; + } + } else { + result[0] += -776.3112829887955; + } + } else { + result[0] += 122.73633086275382; + } + } + } + if (LIKELY(false || (data[0].qvalue <= 458))) { + if (LIKELY(false || (data[6].qvalue <= 168))) { + if (LIKELY(false || (data[6].qvalue <= 166))) { + if (LIKELY(false || (data[6].qvalue <= 164))) { + if (LIKELY(false || (data[0].qvalue <= 424))) { + if (LIKELY(false || (data[6].qvalue <= 144))) { + result[0] += -1.16853189584804; + } else { + result[0] += -76.18237147015647; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += -565.1080728858356; + } else { + result[0] += 55.856898322571; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 428))) { + result[0] += -22.36169324873774; + } else { + result[0] += -588.8674501865916; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 444))) { + result[0] += 45.149287996873234; + } else { + result[0] += 472.9437979483076; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 110))) { + if (UNLIKELY(false || (data[0].qvalue <= 426))) { + result[0] += -14.394012939959836; + } else { + result[0] += -486.3581999710685; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 58))) { + result[0] += -223.70340084114315; + } else { + if (UNLIKELY(false || (data[10].qvalue <= 88))) { + if (LIKELY(false || (data[0].qvalue <= 400))) { + result[0] += -62.55465904994526; + } else { + result[0] += 711.6569177288845; + } + } else { + result[0] += -22.12709356395005; + } + } + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 176))) { + if (UNLIKELY(false || (data[6].qvalue <= 136))) { + if (LIKELY(false || (data[4].qvalue <= 108))) { + if (UNLIKELY(false || (data[9].qvalue <= 36))) { + result[0] += 353.08425684031425; + } else { + if (UNLIKELY(false || (data[8].qvalue <= 70))) { + result[0] += -488.1757777871809; + } else { + result[0] += 63.64288315962378; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 470))) { + if (LIKELY(false || (data[1].qvalue <= 164))) { + result[0] += -1340.3345402421837; + } else { + result[0] += 611.7688119589316; + } + } else { + result[0] += -5.555477568010339; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 124))) { + if (LIKELY(false || (data[2].qvalue <= 216))) { + if (UNLIKELY(false || (data[10].qvalue <= 48))) { + result[0] += 101.80136762073124; + } else { + result[0] += 394.0454932921462; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + result[0] += -54.51295272481636; + } else { + result[0] += 369.30662351586926; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 464))) { + if (LIKELY(false || (data[2].qvalue <= 168))) { + result[0] += -280.5682849399643; + } else { + result[0] += 363.49964354819656; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 94))) { + result[0] += 287.00161641580763; + } else { + result[0] += -27.192437609622456; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (UNLIKELY(false || (data[4].qvalue <= 102))) { + result[0] += 4.886466438190986; + } else { + result[0] += -273.6325616457949; + } + } else { + result[0] += 83.09343404390128; + } + } + } + if (LIKELY(false || (data[0].qvalue <= 466))) { + if (LIKELY(false || (data[6].qvalue <= 168))) { + if (LIKELY(false || (data[0].qvalue <= 426))) { + if (UNLIKELY(false || (data[9].qvalue <= 42))) { + if (LIKELY(false || (data[9].qvalue <= 28))) { + if (LIKELY(false || (data[9].qvalue <= 26))) { + result[0] += -12.741637583507675; + } else { + result[0] += 237.9588004111412; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 22))) { + result[0] += -668.1312059622767; + } else { + result[0] += -74.96557902347062; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 96))) { + if (LIKELY(false || (data[1].qvalue <= 92))) { + result[0] += 1.2337343498563946; + } else { + result[0] += -70.38800250372651; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 98))) { + result[0] += 57.91224145436725; + } else { + result[0] += -291.61695102438495; + } + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 46))) { + if (LIKELY(false || (data[3].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 78))) { + result[0] += -16.243170204026153; + } else { + result[0] += 316.91551138966975; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 122))) { + result[0] += -653.2681828545267; + } else { + result[0] += -140.38689952692195; + } + } + } else { + if (LIKELY(false || (data[10].qvalue <= 116))) { + if (UNLIKELY(false || (data[9].qvalue <= 76))) { + result[0] += 227.09410027903155; + } else { + result[0] += 41.580049580383246; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 112))) { + result[0] += 421.02462529251864; + } else { + result[0] += -29.85412788966988; + } + } + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 84))) { + if (LIKELY(false || (data[0].qvalue <= 462))) { + if (UNLIKELY(false || (data[0].qvalue <= 430))) { + result[0] += -40.88170364268475; + } else { + if (LIKELY(false || (data[0].qvalue <= 458))) { + result[0] += -438.26722679955304; + } else { + result[0] += -153.8759027518579; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 170))) { + result[0] += 596.3699073028564; + } else { + result[0] += -65.1138629905028; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 170))) { + if (LIKELY(false || (data[0].qvalue <= 450))) { + if (UNLIKELY(false || (data[8].qvalue <= 96))) { + result[0] += -174.18902213573074; + } else { + result[0] += 92.92329912293484; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 140))) { + result[0] += 1029.8844129302536; + } else { + result[0] += 173.42569967619715; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 398))) { + result[0] += 117.61714884981923; + } else { + result[0] += -165.11066227116845; + } + } + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 132))) { + if (UNLIKELY(false || (data[3].qvalue <= 140))) { + if (LIKELY(false || (data[8].qvalue <= 152))) { + result[0] += 275.79848862506384; + } else { + result[0] += -208.23065829261407; + } + } else { + result[0] += 44.68615918739647; + } + } else { + result[0] += -104.44556106745557; + } + } + if (LIKELY(false || (data[0].qvalue <= 452))) { + if (LIKELY(false || (data[7].qvalue <= 178))) { + if (LIKELY(false || (data[2].qvalue <= 212))) { + if (LIKELY(false || (data[7].qvalue <= 166))) { + if (LIKELY(false || (data[6].qvalue <= 168))) { + if (LIKELY(false || (data[6].qvalue <= 166))) { + result[0] += 0.17111650034346793; + } else { + result[0] += 170.19199658576656; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 114))) { + result[0] += -7.762253214369399; + } else { + result[0] += -229.65839448385577; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 154))) { + if (UNLIKELY(false || (data[7].qvalue <= 168))) { + result[0] += -1527.4651782070064; + } else { + result[0] += -32.341146195408825; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 400))) { + result[0] += 53.78926807880971; + } else { + result[0] += 365.35095312342645; + } + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 40))) { + result[0] += -195.8247595287494; + } else { + if (LIKELY(false || (data[0].qvalue <= 446))) { + result[0] += -6.730419002246698; + } else { + result[0] += -485.7584640261381; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 68))) { + result[0] += -302.41592403705386; + } else { + result[0] += -41.37635294838609; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 146))) { + if (UNLIKELY(false || (data[8].qvalue <= 68))) { + if (LIKELY(false || (data[9].qvalue <= 52))) { + if (LIKELY(false || (data[4].qvalue <= 108))) { + if (LIKELY(false || (data[0].qvalue <= 462))) { + result[0] += 105.6696751827489; + } else { + result[0] += 422.3152713156167; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 154))) { + result[0] += -338.9649649852936; + } else { + result[0] += 94.81444748000506; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 472))) { + if (UNLIKELY(false || (data[2].qvalue <= 32))) { + result[0] += 307.59982556152346; + } else { + result[0] += -2059.814399773849; + } + } else { + result[0] += -114.64047998985878; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 120))) { + if (LIKELY(false || (data[0].qvalue <= 468))) { + if (UNLIKELY(false || (data[6].qvalue <= 56))) { + result[0] += -105.53208465517486; + } else { + result[0] += 242.79092160164484; + } + } else { + result[0] += 794.8344802547236; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 462))) { + result[0] += 211.67574313790456; + } else { + result[0] += -1052.0226856718868; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 470))) { + if (LIKELY(false || (data[2].qvalue <= 220))) { + if (LIKELY(false || (data[5].qvalue <= 122))) { + if (UNLIKELY(false || (data[8].qvalue <= 14))) { + result[0] += 607.7257898100032; + } else { + result[0] += 31.625663758552975; + } + } else { + result[0] += -165.26478050781773; + } + } else { + result[0] += -212.34245839321358; + } + } else { + if (LIKELY(false || (data[7].qvalue <= 188))) { + result[0] += 195.00720541235728; + } else { + result[0] += -16.708118864398536; + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 442))) { + if (LIKELY(false || (data[1].qvalue <= 148))) { + if (LIKELY(false || (data[8].qvalue <= 154))) { + if (UNLIKELY(false || (data[0].qvalue <= 54))) { + result[0] += -25.89138246104899; + } else { + if (LIKELY(false || (data[5].qvalue <= 84))) { + if (LIKELY(false || (data[8].qvalue <= 116))) { + result[0] += 6.395755334851975; + } else { + result[0] += -41.8874025902268; + } + } else { + if (LIKELY(false || (data[10].qvalue <= 136))) { + result[0] += 43.17739670120483; + } else { + result[0] += -113.04299411317503; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 342))) { + result[0] += -9.406748281219956; + } else { + result[0] += -151.8391684094208; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 102))) { + if (LIKELY(false || (data[0].qvalue <= 308))) { + result[0] += 68.15272951691435; + } else { + result[0] += 653.7243028200384; + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 118))) { + if (UNLIKELY(false || (data[0].qvalue <= 360))) { + result[0] += 4.132956299238849; + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += -391.93226074781654; + } else { + result[0] += -64.14020916590836; + } + } + } else { + result[0] += -42.79033951970415; + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 140))) { + if (UNLIKELY(false || (data[3].qvalue <= 16))) { + result[0] += 673.2372143809691; + } else { + if (LIKELY(false || (data[8].qvalue <= 72))) { + if (UNLIKELY(false || (data[0].qvalue <= 456))) { + if (LIKELY(false || (data[2].qvalue <= 68))) { + result[0] += 72.78571585943284; + } else { + result[0] += -372.52419624016306; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 84))) { + result[0] += -470.8106385006224; + } else { + result[0] += 129.07388124253447; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 448))) { + if (UNLIKELY(false || (data[3].qvalue <= 106))) { + result[0] += -167.83421517433203; + } else { + result[0] += 120.89570583762936; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 144))) { + result[0] += 166.50278950598; + } else { + result[0] += 479.51545503859353; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + if (LIKELY(false || (data[2].qvalue <= 216))) { + if (LIKELY(false || (data[8].qvalue <= 102))) { + if (LIKELY(false || (data[4].qvalue <= 136))) { + result[0] += -85.88265694904688; + } else { + result[0] += 158.08464637061354; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 160))) { + result[0] += 566.823827422359; + } else { + result[0] += 36.90095759323719; + } + } + } else { + result[0] += -221.08459607201044; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 162))) { + result[0] += 279.3051543277019; + } else { + if (UNLIKELY(false || (data[4].qvalue <= 88))) { + result[0] += 225.3999401051908; + } else { + if (LIKELY(false || (data[0].qvalue <= 472))) { + result[0] += -146.03870534454174; + } else { + result[0] += 135.266458595812; + } + } + } + } + } + } + if (UNLIKELY(false || (data[7].qvalue <= 10))) { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (LIKELY(false || (data[8].qvalue <= 56))) { + if (LIKELY(false || (data[5].qvalue <= 30))) { + if (UNLIKELY(false || (data[4].qvalue <= 2))) { + result[0] += 95.33481345166494; + } else { + if (UNLIKELY(false || (data[10].qvalue <= 36))) { + result[0] += -51.475241127929905; + } else { + result[0] += 45.54996053721217; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 18))) { + result[0] += 193.47951816282492; + } else { + result[0] += 41.14647330634432; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 8))) { + result[0] += -256.5011877316934; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 44))) { + result[0] += -78.031723920287; + } else { + if (UNLIKELY(false || (data[10].qvalue <= 58))) { + result[0] += -70.18547029244044; + } else { + result[0] += 58.299178728803355; + } + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 56))) { + result[0] += 377.13982577866017; + } else { + result[0] += 125.3708679348752; + } + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 22))) { + if (LIKELY(false || (data[6].qvalue <= 20))) { + if (LIKELY(false || (data[3].qvalue <= 48))) { + if (LIKELY(false || (data[5].qvalue <= 38))) { + if (LIKELY(false || (data[2].qvalue <= 122))) { + result[0] += -27.262907617303707; + } else { + result[0] += -296.1225155241601; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 94))) { + result[0] += -744.6855172554128; + } else { + result[0] += -295.5306383117119; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 74))) { + result[0] += 131.65876610363532; + } else { + result[0] += 275.9221477857152; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 54))) { + result[0] += -266.18327484029714; + } else { + result[0] += -500.5391151498484; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 14))) { + if (LIKELY(false || (data[4].qvalue <= 34))) { + if (LIKELY(false || (data[8].qvalue <= 86))) { + result[0] += -10.58236111787111; + } else { + if (UNLIKELY(false || (data[5].qvalue <= 8))) { + result[0] += -39.932805444944165; + } else { + result[0] += 159.2150451080038; + } + } + } else { + result[0] += 204.44418412478637; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 30))) { + if (LIKELY(false || (data[8].qvalue <= 12))) { + if (UNLIKELY(false || (data[1].qvalue <= 62))) { + result[0] += 125.99859295088723; + } else { + result[0] += -10.15551076665495; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 20))) { + result[0] += -190.22917265383091; + } else { + result[0] += -33.04391029665733; + } + } + } else { + if (LIKELY(false || (data[10].qvalue <= 98))) { + if (UNLIKELY(false || (data[7].qvalue <= 32))) { + result[0] += 63.784678651859224; + } else { + result[0] += 4.687841489245975; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 70))) { + result[0] += -193.67925649583466; + } else { + result[0] += -11.97318656822869; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 410))) { + if (UNLIKELY(false || (data[9].qvalue <= 54))) { + if (UNLIKELY(false || (data[4].qvalue <= 74))) { + if (LIKELY(false || (data[0].qvalue <= 298))) { + result[0] += -37.83989902430271; + } else { + result[0] += -246.41304263463073; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 196))) { + result[0] += -24.698954353394658; + } else { + result[0] += 269.8421976574775; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 82))) { + if (LIKELY(false || (data[4].qvalue <= 128))) { + result[0] += 121.42816960444512; + } else { + result[0] += -204.12026096629276; + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 86))) { + result[0] += -150.30284718707338; + } else { + result[0] += -9.526517783514938; + } + } + } + } + } else { + result[0] += 0.6374046148668437; + } + } else { + if (UNLIKELY(false || (data[9].qvalue <= 10))) { + if (LIKELY(false || (data[0].qvalue <= 456))) { + if (UNLIKELY(false || (data[3].qvalue <= 160))) { + if (UNLIKELY(false || (data[2].qvalue <= 56))) { + result[0] += -844.9505630005908; + } else { + result[0] += -266.4489226017028; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 448))) { + result[0] += -59.61765022395716; + } else { + if (UNLIKELY(false || (data[9].qvalue <= 0))) { + result[0] += -422.1597338477869; + } else { + result[0] += 307.5185329018873; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 84))) { + result[0] += 259.874337152467; + } else { + result[0] += -0.14677676347402133; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 76))) { + if (LIKELY(false || (data[2].qvalue <= 60))) { + if (UNLIKELY(false || (data[4].qvalue <= 82))) { + if (LIKELY(false || (data[6].qvalue <= 66))) { + result[0] += 6.901818537027612; + } else { + result[0] += 346.73948874314306; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 0))) { + result[0] += 767.3329148187853; + } else { + result[0] += -55.71428668797978; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 462))) { + if (UNLIKELY(false || (data[8].qvalue <= 62))) { + result[0] += -1186.4827177702393; + } else { + result[0] += -248.10230638115846; + } + } else { + result[0] += 317.2999956644618; + } + } + } else { + if (LIKELY(false || (data[8].qvalue <= 128))) { + if (UNLIKELY(false || (data[10].qvalue <= 46))) { + if (UNLIKELY(false || (data[8].qvalue <= 6))) { + result[0] += 424.73738367278276; + } else { + result[0] += -69.29711136275134; + } + } else { + if (LIKELY(false || (data[9].qvalue <= 38))) { + result[0] += 93.41031827005968; + } else { + result[0] += 256.43713558484785; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 86))) { + if (LIKELY(false || (data[0].qvalue <= 456))) { + result[0] += -194.80499023272242; + } else { + result[0] += 84.16720477646794; + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 130))) { + result[0] += 1117.9205291606104; + } else { + result[0] += 32.52858858984742; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 442))) { + if (LIKELY(false || (data[6].qvalue <= 146))) { + if (LIKELY(false || (data[0].qvalue <= 360))) { + if (UNLIKELY(false || (data[4].qvalue <= 38))) { + if (UNLIKELY(false || (data[6].qvalue <= 24))) { + if (UNLIKELY(false || (data[9].qvalue <= 94))) { + result[0] += -509.2763123794946; + } else { + result[0] += -10.28309254566162; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 46))) { + result[0] += 65.60802974294101; + } else { + result[0] += -11.172650598142237; + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 40))) { + if (UNLIKELY(false || (data[0].qvalue <= 170))) { + result[0] += -379.1246497217576; + } else { + result[0] += -880.7154812437998; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 38))) { + result[0] += 33.59073760216112; + } else { + result[0] += -19.2301464816471; + } + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 198))) { + if (LIKELY(false || (data[2].qvalue <= 186))) { + if (LIKELY(false || (data[3].qvalue <= 144))) { + result[0] += 33.69989668265042; + } else { + result[0] += 208.11777026466976; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 126))) { + result[0] += -4.6241213678677076; + } else { + result[0] += -258.29323587381907; + } + } + } else { + result[0] += -240.03769405637382; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 94))) { + if (UNLIKELY(false || (data[4].qvalue <= 20))) { + if (LIKELY(false || (data[0].qvalue <= 400))) { + result[0] += -1.3019828319392528; + } else { + result[0] += 595.7005340010636; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 258))) { + result[0] += -9.190887637624767; + } else { + result[0] += -285.64672594943085; + } + } + } else { + if (LIKELY(false || (data[7].qvalue <= 154))) { + if (UNLIKELY(false || (data[0].qvalue <= 344))) { + result[0] += 40.545254733724846; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 416))) { + result[0] += -233.93172162375063; + } else { + result[0] += -22.286785557997515; + } + } + } else { + result[0] += 70.10413972244889; + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 140))) { + if (LIKELY(false || (data[4].qvalue <= 114))) { + if (LIKELY(false || (data[4].qvalue <= 112))) { + if (UNLIKELY(false || (data[3].qvalue <= 16))) { + result[0] += 634.2191665019159; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 78))) { + result[0] += 6.665393953447264; + } else { + result[0] += 149.39801863389712; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 154))) { + result[0] += 1226.4539406622023; + } else { + result[0] += 294.9277796445255; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 158))) { + result[0] += 235.1520118587282; + } else { + result[0] += -166.61985769647302; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + if (LIKELY(false || (data[2].qvalue <= 216))) { + result[0] += 5.169623713259927; + } else { + result[0] += -199.41738547469845; + } + } else { + result[0] += 71.9476904143754; + } + } + } + if (UNLIKELY(false || (data[0].qvalue <= 48))) { + if (UNLIKELY(false || (data[0].qvalue <= 0))) { + result[0] += -78.00407393572347; + } else { + if (LIKELY(false || (data[2].qvalue <= 202))) { + if (LIKELY(false || (data[3].qvalue <= 104))) { + if (LIKELY(false || (data[4].qvalue <= 90))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += -70.6888999910638; + } else { + result[0] += -10.627670242516414; + } + } else { + if (LIKELY(false || (data[10].qvalue <= 66))) { + result[0] += -107.69047239009149; + } else { + result[0] += -13.414907874287316; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 138))) { + if (UNLIKELY(false || (data[10].qvalue <= 12))) { + result[0] += 69.98431682634853; + } else { + result[0] += -59.43734140381587; + } + } else { + result[0] += 78.30655993653257; + } + } + } else { + result[0] += 82.01495795861331; + } + } + } else { + if (UNLIKELY(false || (data[4].qvalue <= 0))) { + if (LIKELY(false || (data[2].qvalue <= 128))) { + if (LIKELY(false || (data[0].qvalue <= 226))) { + if (LIKELY(false || (data[2].qvalue <= 58))) { + if (UNLIKELY(false || (data[2].qvalue <= 24))) { + result[0] += 150.52913267303913; + } else { + result[0] += 6.758849638068968; + } + } else { + result[0] += 219.10434569977826; + } + } else { + result[0] += 274.1316917335302; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 332))) { + result[0] += -84.8367008241603; + } else { + result[0] += 317.87177416169243; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 14))) { + if (LIKELY(false || (data[0].qvalue <= 406))) { + if (LIKELY(false || (data[4].qvalue <= 28))) { + if (UNLIKELY(false || (data[9].qvalue <= 108))) { + result[0] += 152.21239710120096; + } else { + result[0] += -49.218505477191925; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 30))) { + result[0] += -369.09166571523195; + } else { + result[0] += -59.62264580017301; + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 122))) { + if (UNLIKELY(false || (data[2].qvalue <= 72))) { + result[0] += 225.48342290509692; + } else { + result[0] += 454.8914585206501; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 420))) { + result[0] += -316.42278833229403; + } else { + result[0] += 89.88278332332641; + } + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 22))) { + if (LIKELY(false || (data[3].qvalue <= 18))) { + if (LIKELY(false || (data[4].qvalue <= 26))) { + result[0] += 83.70108736546864; + } else { + result[0] += -47.03238163468578; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 172))) { + result[0] += 64.00534235112683; + } else { + result[0] += 241.06317309770884; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 28))) { + if (UNLIKELY(false || (data[9].qvalue <= 108))) { + result[0] += 41.85255742051009; + } else { + result[0] += -122.59387579963109; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 12))) { + result[0] += 72.23509636314206; + } else { + result[0] += 2.3792085384878088; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 460))) { + if (LIKELY(false || (data[6].qvalue <= 168))) { + if (LIKELY(false || (data[0].qvalue <= 432))) { + if (LIKELY(false || (data[1].qvalue <= 124))) { + if (LIKELY(false || (data[5].qvalue <= 62))) { + if (LIKELY(false || (data[8].qvalue <= 110))) { + result[0] += -1.818322290568549; + } else { + result[0] += -86.51820960810343; + } + } else { + if (UNLIKELY(false || (data[6].qvalue <= 48))) { + result[0] += 122.64593189161003; + } else { + result[0] += 8.90299476140962; + } + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 64))) { + if (LIKELY(false || (data[8].qvalue <= 64))) { + result[0] += 27.811889151480592; + } else { + result[0] += 564.1322969672179; + } + } else { + if (UNLIKELY(false || (data[10].qvalue <= 66))) { + result[0] += -961.542648801726; + } else { + result[0] += -67.5677271302586; + } + } + } + } else { + if (LIKELY(false || (data[10].qvalue <= 148))) { + if (UNLIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -271.4318221979432; + } else { + if (UNLIKELY(false || (data[5].qvalue <= 16))) { + result[0] += 688.4114417873475; + } else { + result[0] += 58.42035899984279; + } + } + } else { + result[0] += -307.4296972195184; + } + } + } else { + if (UNLIKELY(false || (data[8].qvalue <= 78))) { + if (UNLIKELY(false || (data[0].qvalue <= 434))) { + result[0] += -47.84229037947061; + } else { + result[0] += -341.83457990151675; + } + } else { + result[0] += -24.69473744066359; + } + } + } else { + if (LIKELY(false || (data[6].qvalue <= 176))) { + if (UNLIKELY(false || (data[6].qvalue <= 156))) { + if (LIKELY(false || (data[4].qvalue <= 108))) { + if (LIKELY(false || (data[1].qvalue <= 90))) { + if (LIKELY(false || (data[0].qvalue <= 468))) { + result[0] += -237.2028222385908; + } else { + result[0] += 235.23343926289132; + } + } else { + result[0] += 275.68252452796696; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 470))) { + if (UNLIKELY(false || (data[9].qvalue <= 6))) { + result[0] += 276.655894681045; + } else { + result[0] += -1138.9810647848005; + } + } else { + result[0] += -24.815816631740525; + } + } + } else { + if (LIKELY(false || (data[4].qvalue <= 124))) { + if (UNLIKELY(false || (data[8].qvalue <= 94))) { + result[0] += 468.636846801675; + } else { + result[0] += 173.66365185212408; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 464))) { + result[0] += -156.75158265385107; + } else { + if (UNLIKELY(false || (data[5].qvalue <= 72))) { + result[0] += 418.66415661473707; + } else { + result[0] += 16.60197418193807; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 472))) { + if (UNLIKELY(false || (data[4].qvalue <= 102))) { + if (LIKELY(false || (data[3].qvalue <= 178))) { + result[0] += 112.29216412676556; + } else { + result[0] += -589.6749782826543; + } + } else { + if (LIKELY(false || (data[6].qvalue <= 186))) { + result[0] += -126.14743791646418; + } else { + result[0] += -697.0503549298137; + } + } + } else { + result[0] += 130.01903028897132; + } + } + } + if (LIKELY(false || (data[0].qvalue <= 250))) { + if (LIKELY(false || (data[2].qvalue <= 96))) { + if (LIKELY(false || (data[10].qvalue <= 110))) { + result[0] += -15.209203725866741; + } else { + result[0] += -151.01702601859964; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 116))) { + result[0] += 33.169088504059346; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 134))) { + if (LIKELY(false || (data[9].qvalue <= 132))) { + result[0] += -0.5460734226255192; + } else { + result[0] += -166.18365097405575; + } + } else { + result[0] += -630.4456930959705; + } + } else { + result[0] += 9.610897218995794; + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 12))) { + if (UNLIKELY(false || (data[5].qvalue <= 4))) { + if (UNLIKELY(false || (data[7].qvalue <= 4))) { + result[0] += 292.5131264640451; + } else { + if (LIKELY(false || (data[0].qvalue <= 346))) { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += -90.96452643248735; + } else { + result[0] += -554.2646443755457; + } + } else { + result[0] += 78.62976969599482; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 316))) { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (UNLIKELY(false || (data[9].qvalue <= 102))) { + result[0] += 178.17593860823325; + } else { + result[0] += 25.683590998151445; + } + } else { + result[0] += 478.77786108066925; + } + } else { + result[0] += 181.52015147560186; + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 122))) { + if (UNLIKELY(false || (data[6].qvalue <= 54))) { + if (UNLIKELY(false || (data[3].qvalue <= 10))) { + if (LIKELY(false || (data[4].qvalue <= 46))) { + result[0] += 392.13693925483733; + } else { + result[0] += 93.50921667351855; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 26))) { + result[0] += -171.2455308856315; + } else { + result[0] += 65.00191321740634; + } + } + } else { + if (UNLIKELY(false || (data[5].qvalue <= 54))) { + if (LIKELY(false || (data[3].qvalue <= 60))) { + result[0] += -29.670526192638437; + } else { + result[0] += -250.61118646403057; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 72))) { + result[0] += 139.50112391138077; + } else { + result[0] += 5.183830796614544; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 410))) { + if (UNLIKELY(false || (data[6].qvalue <= 6))) { + if (LIKELY(false || (data[0].qvalue <= 376))) { + result[0] += 72.4511482444548; + } else { + result[0] += 480.9499826432033; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 50))) { + result[0] += -339.82229606896135; + } else { + result[0] += -34.16011839278788; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 52))) { + if (LIKELY(false || (data[4].qvalue <= 22))) { + result[0] += 110.97363027456811; + } else { + result[0] += 409.03280051924963; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 468))) { + result[0] += -25.656918184151955; + } else { + result[0] += 673.645954177365; + } + } + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 338))) { + if (LIKELY(false || (data[2].qvalue <= 96))) { + if (LIKELY(false || (data[10].qvalue <= 110))) { + if (LIKELY(false || (data[2].qvalue <= 86))) { + if (LIKELY(false || (data[2].qvalue <= 82))) { + if (LIKELY(false || (data[10].qvalue <= 74))) { + result[0] += -0.803059421195404; + } else { + result[0] += -41.251005891223926; + } + } else { + result[0] += 143.4559149834235; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 116))) { + if (UNLIKELY(false || (data[7].qvalue <= 26))) { + result[0] += 2.624252580548391; + } else { + result[0] += -227.38952669158417; + } + } else { + result[0] += 45.837014796557646; + } + } + } else { + result[0] += -195.38067959274719; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 116))) { + if (LIKELY(false || (data[0].qvalue <= 176))) { + result[0] += 12.646781663778135; + } else { + if (UNLIKELY(false || (data[8].qvalue <= 50))) { + result[0] += 217.37905842024804; + } else { + if (LIKELY(false || (data[9].qvalue <= 100))) { + result[0] += 18.490879448389602; + } else { + result[0] += 157.2895805126459; + } + } + } + } else { + if (LIKELY(false || (data[5].qvalue <= 88))) { + if (LIKELY(false || (data[10].qvalue <= 130))) { + if (LIKELY(false || (data[10].qvalue <= 124))) { + result[0] += -25.376404193377994; + } else { + result[0] += -234.0117708294; + } + } else { + result[0] += 44.70662468521388; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 130))) { + if (UNLIKELY(false || (data[0].qvalue <= 146))) { + result[0] += 12.86322888112687; + } else { + result[0] += 200.2312601015275; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 162))) { + result[0] += -49.82927755893318; + } else { + result[0] += 34.59858546002059; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 64))) { + if (LIKELY(false || (data[7].qvalue <= 60))) { + result[0] += 44.53611652626999; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 100))) { + result[0] += 378.92912612636616; + } else { + result[0] += 114.52994706164273; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 12))) { + if (UNLIKELY(false || (data[0].qvalue <= 416))) { + if (LIKELY(false || (data[10].qvalue <= 92))) { + result[0] += -628.9086683084666; + } else { + result[0] += 107.65691728510829; + } + } else { + if (UNLIKELY(false || (data[7].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 432))) { + result[0] += 30.295081044275346; + } else { + result[0] += 409.9041492892673; + } + } else { + result[0] += -143.73134957553725; + } + } + } else { + if (LIKELY(false || (data[9].qvalue <= 146))) { + if (LIKELY(false || (data[9].qvalue <= 138))) { + if (UNLIKELY(false || (data[6].qvalue <= 54))) { + result[0] += 149.66087572300066; + } else { + result[0] += -2.415057229240939; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 418))) { + result[0] += -377.3930807574788; + } else { + result[0] += 56.193799271376434; + } + } + } else { + result[0] += 545.2850103362496; + } + } + } + } + + // Apply base_scores + result[0] += 0; + + // Apply postprocessor + if (!pred_margin) { postprocess(result); } +} + +void fj_predictor::postprocess(double* result) +{ + // Do nothing +} + +// Feature names array +const char* fj_predictor::feature_names[fj_predictor::NUM_FEATURES] = {"time", + "initial_violation_count", + "max_nnz_per_row", + "n_binary_vars", + "n_constraints", + "n_integer_vars", + "n_variables", + "nnz", + "nnz_stddev", + "sparsity", + "unbalancedness", + "uses_load_balancing"}; diff --git a/cpp/src/utilities/models/fj_predictor/quantize.cpp b/cpp/src/utilities/models/fj_predictor/quantize.cpp new file mode 100644 index 000000000..4bd50efaf --- /dev/null +++ b/cpp/src/utilities/models/fj_predictor/quantize.cpp @@ -0,0 +1,1180 @@ + +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "header.h" + +static const double threshold[] = { + 0.054247050000000012, + 0.077229550000000022, + 0.099138400000000002, + 0.12088200000000002, + 0.14051650000000002, + 0.15852850000000004, + 0.17679700000000004, + 0.19352650000000002, + 0.21021800000000002, + 0.22558550000000002, + 0.24112700000000004, + 0.25703600000000004, + 0.27504900000000004, + 0.29268150000000009, + 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0.00032692350000000002, + 0.00033880400000000006, + 0.00035918650000000006, + 0.00038193300000000004, + 0.00042447950000000008, + 0.00043747650000000002, + 0.00044906850000000007, + 0.00049152850000000004, + 0.00052591450000000011, + 0.00057647100000000017, + 0.00058207650000000012, + 0.00098674500000000003, + 0.0011041950000000003, + 0.0012247250000000001, + 0.0012767100000000001, + 0.0013355900000000002, + 0.001509605, + 0.0017419350000000002, + 0.0018100400000000004, + 0.0018508650000000004, + 0.0021851550000000007, + 0.0024035350000000005, + 0.00336228, + 0.0038408950000000004, + 0.004200225000000001, + 0.0047036750000000009, + 0.0051918100000000007, + 0.0055236550000000014, + 0.0057092000000000002, + 0.0059764100000000006, + 0.0069381250000000007, + 0.0073699550000000009, + 0.007565500000000001, + 0.0084957950000000022, + 0.0089344600000000017, + 0.0099261550000000007, + 0.014203600000000002, + 0.021387700000000006, + 0.023916750000000004, + 0.026089700000000004, + 0.05008255000000001, + 0.055832150000000004, + 0.060545100000000004, + 0.070805450000000006, + 0.087852150000000004, + 0.11160100000000002, + 0.12906450000000003, + 0.18728800000000004, + 0.22001300000000004, + 0.25453400000000004, + 0.26550900000000005, + 0.31291300000000005, + 0.35104150000000006, + 0.41306200000000004, + 0.57042700000000013, + 0.64485350000000008, + 0.96875000000000011, + 1.0000000180025095e-35, + 0.0039979100000000012, + 0.0082457700000000012, + 0.038986650000000005, + 0.054666050000000001, + 0.070373600000000022, + 0.097335900000000017, + 0.19564850000000003, + 0.20630450000000003, + 0.24236000000000002, + 0.25417800000000007, + 0.25770150000000008, + 0.27507600000000004, + 0.28204950000000006, + 0.28742100000000009, + 0.29918200000000006, + 0.30476150000000007, + 0.33851400000000004, + 0.37035050000000008, + 0.38581450000000006, + 0.44627100000000003, + 0.45825600000000005, + 0.4758035000000001, + 0.52170800000000017, + 0.54062450000000017, + 0.54514950000000006, + 0.57222350000000011, + 0.67992200000000003, + 0.70028650000000014, + 0.75992800000000005, + 0.81040200000000018, + 0.85826700000000011, + 0.95305950000000006, + 0.98670650000000015, + 1.0208650000000001, + 1.0782450000000001, + 1.1067700000000003, + 1.1631450000000003, + 1.2699600000000004, + 1.2872250000000001, + 1.2987750000000002, + 1.3993100000000003, + 1.5141650000000002, + 1.5734200000000003, + 1.6142300000000003, + 1.6834550000000001, + 1.8165200000000001, + 1.9725750000000002, + 2.0513250000000007, + 2.0544200000000008, + 2.1066100000000003, + 2.2829500000000005, + 2.3961550000000003, + 2.6080850000000004, + 2.7410500000000004, + 3.0611950000000001, + 3.5134100000000008, + 3.5836800000000006, + 4.7010550000000011, + 4.7679900000000011, + 5.106815000000001, + 5.1937250000000015, + 5.2231500000000013, + 5.349705000000001, + 5.4792200000000006, + 5.6649900000000004, + 5.8800200000000009, + 6.6231050000000007, + 6.9449300000000003, + 7.3326800000000008, + 8.199600000000002, + 11.794000000000002, + 16.289250000000003, + 19.559350000000006, + 24.766300000000005, + 1.0000000180025095e-35, +}; + +static const int th_begin[] = { + 0, + 237, + 320, + 434, + 525, + 596, + 658, + 752, + 853, + 932, + 1013, + 1088, +}; + +static const int th_len[] = { + 237, + 83, + 114, + 91, + 71, + 62, + 94, + 101, + 79, + 81, + 75, + 1, +}; + +/* + * \brief Function to convert a feature value into bin index. + * \param val Feature value, in floating-point + * \param fid Feature identifier + * \return bin Index corresponding to given feature value + */ +int fj_predictor::quantize(double val, unsigned fid) +{ + const size_t offset = th_begin[fid]; + const double* array = &threshold[offset]; + int len = th_len[fid]; + int low = 0; + int high = len; + int mid; + double mval; + // It is possible th_begin[i] == [total_num_threshold]. This means that + // all features i, (i+1), ... are not used for any of the splits in the model. + // So in this case, just return something + if (offset == 1089 || val < array[0]) { return -10; } + while (low + 1 < high) { + mid = (low + high) / 2; + mval = array[mid]; + if (val == mval) { + return mid * 2; + } else if (val < mval) { + high = mid; + } else { + low = mid; + } + } + if (array[low] == val) { + return low * 2; + } else if (high == len) { + return len * 2; + } else { + return low * 2 + 1; + } +} diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp index 56c04a9a5..8e1d87111 100644 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -21,20 +21,10 @@ #include #include #include +#include #include -#include - -#include "models_ubj.h" - -#define safe_xgboost(call) \ - { \ - int err = (call); \ - if (err != 0) { \ - throw std::runtime_error(std::string(__FILE__) + ":" + std::to_string(__LINE__) + \ - ": error in " + #call + ":" + XGBGetLastError()); \ - } \ - } +#include "models/fj_predictor/header.h" namespace cuopt { @@ -53,132 +43,43 @@ static inline uint32_t compute_hash(std::vector h_contents) return hash; } -work_unit_predictor_t::work_unit_predictor_t(const std::string& model_name) : model_name(model_name) +template +float work_unit_predictor_t::predict_scalar( + const std::map& features) const { - BoosterHandle booster_handle; - int ret = XGBoosterCreate(nullptr, 0, &booster_handle); - safe_xgboost(ret); - assert(ret == 0); - if (ret != 0) return; - raw_handle = reinterpret_cast(booster_handle); - - // load the embedded model from the .rodata section - const unsigned char* model_data = nullptr; - unsigned int model_len = 0; - for (unsigned int i = 0; i < xgboost_models_count; i++) { - if (strcmp(xgboost_models[i].name, model_name.c_str()) == 0) { - model_data = xgboost_models[i].data; - model_len = xgboost_models[i].length; - break; + typename model_t::Entry data[model_t::NUM_FEATURES]; + for (int i = 0; i < model_t::NUM_FEATURES; ++i) { + if (features.find(std::string(model_t::feature_names[i])) == features.end()) { + data[i].missing = -1; + printf("Feature %s: missing\n", model_t::feature_names[i]); + } else { + data[i].fvalue = features.at(std::string(model_t::feature_names[i])); + printf("Feature %s: %f\n", model_t::feature_names[i], data[i].fvalue); } } - - assert(model_data != nullptr); - assert(model_len > 0); - - ret = XGBoosterLoadModelFromBuffer(booster_handle, model_data, model_len); - safe_xgboost(ret); - assert(ret == 0); - if (ret != 0) return; - - XGBoosterSetParam(booster_handle, "predictor", "gpu_predictor"); - - is_valid = true; -} - -work_unit_predictor_t::~work_unit_predictor_t() -{ - if (raw_handle != nullptr) { - BoosterHandle booster_handle = reinterpret_cast(raw_handle); - XGBoosterFree(booster_handle); - raw_handle = nullptr; + // Compute a hash key for the relevant inputs + std::vector cache_vec; + cache_vec.reserve(model_t::NUM_FEATURES); + for (int i = 0; i < model_t::NUM_FEATURES; ++i) { + cache_vec.push_back(data[i].missing != -1 ? data[i].fvalue + : std::numeric_limits::quiet_NaN()); } + uint32_t key = compute_hash(cache_vec); + + auto cached_it = prediction_cache.find(key); + if (cached_it != prediction_cache.end()) { return cached_it->second; } + + // run predictor + double result = 0.0; + auto start = std::chrono::high_resolution_clock::now(); + model_t::predict(data, 0, &result); + auto end = std::chrono::high_resolution_clock::now(); + std::chrono::duration elapsed = end - start; + CUOPT_LOG_DEBUG("Prediction time: %f ms", elapsed.count()); + CUOPT_LOG_DEBUG("Result: %f", result); + return std::abs(result); } -float work_unit_predictor_t::predict_scalar(const std::vector& features, bool verbose) const -{ - assert(is_valid && raw_handle != nullptr); - if (!is_valid || raw_handle == nullptr) return std::numeric_limits::signaling_NaN(); - - // Check cache first - uint32_t hash = compute_hash(features); - auto it = prediction_cache.find(hash); - if (it != prediction_cache.end()) { return it->second; } - - // Timer: measure elapsed time for prediction - auto t_start = std::chrono::high_resolution_clock::now(); - - // Create DMatrix from feature vector - DMatrixHandle dmatrix; - int ret = XGDMatrixCreateFromMat(features.data(), - 1, // nrow - features.size(), // ncol - std::numeric_limits::quiet_NaN(), // missing value - &dmatrix); - safe_xgboost(ret); - - // Predict from DMatrix - char const config[] = - "{\"type\": 0, \"iteration_begin\": 0, " - "\"iteration_end\": 0, \"strict_shape\": true, \"training\": false}"; - - const bst_ulong* out_shape = nullptr; - bst_ulong out_dim = 0; - const float* out_result = nullptr; - ret = XGBoosterPredictFromDMatrix(reinterpret_cast(raw_handle), - dmatrix, - config, - &out_shape, - &out_dim, - &out_result); - safe_xgboost(ret); - - float prediction = out_result[0]; - - // Free DMatrix - XGDMatrixFree(dmatrix); - - auto t_end = std::chrono::high_resolution_clock::now(); - double elapsed_ms = std::chrono::duration(t_end - t_start).count(); - printf("[work_unit_predictor_t::predict_scalar] Prediction took %.3f ms\n", elapsed_ms); - - // Store in cache - prediction_cache[hash] = prediction; - - return prediction; -} - -float work_unit_predictor_t::predict_scalar(const std::map& feature_map, - bool verbose) const -{ - // Extract features in the expected order for the model - // Order matches training data: [target_time, n_of_minimums_for_exit, n_variables, n_constraints, - // nnz, sparsity, nnz_stddev, unbalancedness] - std::vector features; - features.reserve(feature_map.size()); - - // Add features in the order expected by the model - // This order should match what was used during training - const std::vector feature_order = {"target_time", - "n_of_minimums_for_exit", - "n_variables", - "n_constraints", - "nnz", - "sparsity", - "nnz_stddev", - "unbalancedness"}; - - for (const auto& name : feature_order) { - auto it = feature_map.find(name); - if (it != feature_map.end()) { - features.push_back(it->second); - } else { - // Feature not found - use default value of 0 - features.push_back(0.0f); - } - } - - return predict_scalar(features, verbose); -} +template class work_unit_predictor_t; } // namespace cuopt diff --git a/cpp/src/utilities/work_unit_predictor.hpp b/cpp/src/utilities/work_unit_predictor.hpp index 420fc9547..0a7c63e33 100644 --- a/cpp/src/utilities/work_unit_predictor.hpp +++ b/cpp/src/utilities/work_unit_predictor.hpp @@ -24,17 +24,12 @@ namespace cuopt { +template class work_unit_predictor_t { public: - work_unit_predictor_t(const std::string& model_name); - ~work_unit_predictor_t(); - float predict_scalar(const std::vector& features, bool verbose = false) const; - float predict_scalar(const std::map& features, bool verbose = false) const; + float predict_scalar(const std::map& features) const; private: - std::string model_name; - void* raw_handle{nullptr}; // void* to avoid including xgboost in every MIP translation unit - bool is_valid{false}; mutable std::unordered_map prediction_cache; }; diff --git a/cpp/tests/mip/feasibility_jump_tests.cu b/cpp/tests/mip/feasibility_jump_tests.cu index ad2bf4ba1..c5c695299 100644 --- a/cpp/tests/mip/feasibility_jump_tests.cu +++ b/cpp/tests/mip/feasibility_jump_tests.cu @@ -179,8 +179,8 @@ static bool run_fj_check_determinism(std::string test_instance, int iter_limit) detail::fj_settings_t fj_settings; fj_settings.time_limit = std::numeric_limits::max(); fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 5000; - fj_settings.work_unit_limit = 0.15; // run for 0.5wu (~0.5s) + fj_settings.n_of_minimums_for_exit = 5000 * 1000; + fj_settings.work_unit_limit = 0.5; // run for 0.5wu (~0.5s) fj_settings.update_weights = true; fj_settings.feasibility_run = false; // fj_settings.iteration_limit = iter_limit; diff --git a/scripts/README_REGRESSION.md b/scripts/README_REGRESSION.md index ab8736aa4..ca6cba0ca 100644 --- a/scripts/README_REGRESSION.md +++ b/scripts/README_REGRESSION.md @@ -185,7 +185,11 @@ python train_regressor.py data.pkl --regressor lightgbm --treelite-compile 8 -o ### Optimization Impact -All TL2cgen exports include both branch annotation and quantization automatically: +All TL2cgen exports include the following optimizations automatically: + +1. **Branch Annotation**: Uses training data statistics to add branch prediction hints +2. **Quantization**: Reduces memory footprint by converting floating-point to integers +3. **Missing Data Removal**: Removes unnecessary missing data checks (assumes all features provided) | Configuration | Speed | Memory | Accuracy | |---------------|-------|--------|----------| @@ -209,21 +213,80 @@ models/ └── *.cpp / *.h # Other C++ source files (quantized + annotated) ``` -**Namespace Wrapping**: All generated files are automatically wrapped in a C++ namespace with the model name (derived from the input pickle file basename) to avoid naming conflicts when using multiple models in the same project. For example, if the input is `my_dataset.pkl`: -- All functions are in `namespace my_dataset { ... }` +**Class Wrapping**: All generated files are automatically wrapped in a C++ class with the model name (derived from the input pickle file basename) to avoid naming conflicts when using multiple models in the same project. For example, if the input is `my_dataset.pkl`: +- All functions and data are in `class my_dataset { public: ... };` +- All class members are `static` - no instantiation required - Access functions as `my_dataset::predict()`, `my_dataset::get_num_features()`, etc. - All `.c` files are renamed to `.cpp` for C++ compilation +- Header includes `#pragma once` for include guards The generated `header.h` includes: -- `namespace { ... }` wrapping all declarations -- `#define NUM_FEATURES ` - Number of features -- `extern const char* feature_names[]` - Feature names declaration -- Function declarations (e.g., `predict()`, `get_num_features()`) within the namespace +- `#pragma once` at the top +- `#include` statements (outside the class) +- `class { public: ... };` wrapping all declarations +- `static constexpr int NUM_FEATURES` - Number of features +- `static const char* feature_names[]` - Feature names declaration +- Function declarations (e.g., `predict()`, `get_num_features()`) as public static members The generated `main.cpp` includes: -- `namespace { ... }` wrapping all implementations -- `const char* feature_names[]` - Feature names array definition -- Function implementations within the namespace +- `#include` statements at the top +- Macro definitions (`LIKELY`, `UNLIKELY`, `N_TARGET`, `MAX_N_CLASS`) - moved from header for implementation-only use +- Function implementations with `::function_name` qualification +- `const char* ::feature_names[]` - Feature names array definition (at the end of file) + +### Example Generated Code Structure + +**header.h:** + +```cpp +#pragma once + +#include + +class my_dataset { +public: + static float predict(float* data, int pred_margin); + static int get_num_feature(); + // ... other function declarations ... + + static constexpr int NUM_FEATURES = 42; + static const char* feature_names[NUM_FEATURES]; +}; +``` + +**main.cpp:** + +```cpp +#include "header.h" + +#define LIKELY(x) __builtin_expect(!!(x), 1) +#define N_TARGET 1 + +float my_dataset::predict(float* data, int pred_margin) { + // implementation +} + +int my_dataset::get_num_feature() { + return NUM_FEATURES; +} + +// Feature names array +const char* my_dataset::feature_names[my_dataset::NUM_FEATURES] = { + "n_variables", + "n_constraints", + // ... +}; +``` + +**Usage:** + +```cpp +#include "xgboost_c_code/header.h" + +// Call static methods directly - no instantiation needed +float result = my_dataset::predict(features, 0); +int num = my_dataset::get_num_feature(); +``` ## Feature Selection Examples diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py index 84c32ae3d..7c5f1d298 100755 --- a/scripts/train_regressor.py +++ b/scripts/train_regressor.py @@ -73,7 +73,11 @@ 'avg_var_degree', 'equality_ratio', 'integer_ratio', - 'binary_ratio' + 'binary_ratio', + 'max_related_vars', + 'problem_size_score', + 'structural_complexity', + 'tight_constraint_ratio' ] # Alternatively, specify ONLY the features you want to use @@ -639,7 +643,7 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, quantize_path = os.path.join(source_dir, 'quantize.c') recipe_path = os.path.join(source_dir, 'recipe.json') - # Rename all .c files to .cpp and wrap in namespace + # Rename all .c files to .cpp and wrap in class if model_name: try: import glob @@ -652,12 +656,49 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, with open(c_file, 'r') as f: content = f.read() - # Wrap in namespace - namespaced_content = f'namespace {model_name} {{\n\n{content}\n\n}} // namespace {model_name}\n' + # Split content into includes and rest + lines = content.split('\n') + include_lines = [] + code_lines = [] + in_includes = True + + for line in lines: + if in_includes and (line.strip().startswith('#include') or line.strip().startswith('#') or line.strip() == ''): + include_lines.append(line) + else: + in_includes = False + code_lines.append(line) + + # Prefix function definitions with ClassName:: (for .cpp files, not class wrapping) + import re + processed_lines = [] + for line in code_lines: + # Detect function definitions (return_type function_name(...)) + if line and not line.strip().startswith('//') and not line.strip().startswith('/*'): + # Check if it's a function definition + # Pattern: type name(...) or type* name(...) etc. + func_pattern = r'^(\s*)((?:const\s+)?(?:unsigned\s+)?(?:struct\s+)?[\w_]+(?:\s*\*)*\s+)([\w_]+)(\s*\()' + match = re.match(func_pattern, line) + if match and '::' not in line: # Don't add if already qualified + indent = match.group(1) + return_type = match.group(2) + func_name = match.group(3) + rest = line[match.end(3):] + # Prefix function name with class name + line = f'{indent}{return_type}{model_name}::{func_name}{rest}' + processed_lines.append(line) + code_lines = processed_lines + + # Don't wrap in class for .cpp files - just output the definitions + includes_str = '\n'.join(include_lines) + code_str = '\n'.join(code_lines) + + # For .cpp files, no class wrapper needed + cpp_content = f'{includes_str}\n\n{code_str}\n' # Write to .cpp file with open(cpp_file, 'w') as f: - f.write(namespaced_content) + f.write(cpp_content) # Remove original .c file os.remove(c_file) @@ -666,52 +707,161 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, main_path = main_path[:-2] + '.cpp' quantize_path = quantize_path[:-2] + '.cpp' - print(f" Renamed {len(c_files)} .c files to .cpp and wrapped in namespace '{model_name}'") + print(f" Renamed {len(c_files)} .c files to .cpp") except Exception as e: print(f" Warning: Failed to rename .c files: {e}") - # Wrap header.h content in namespace + # Optimize main.cpp by removing unnecessary missing data checks + # Since all features are always provided, replace !(data[X].missing != -1) with false + if os.path.exists(main_path): + try: + with open(main_path, 'r') as f: + content = f.read() + + # Replace pattern !(data[N].missing != -1) with false + import re + original_content = content + content = re.sub(r'!\(data\[\d+\]\.missing != -1\)', 'false', content) + + if content != original_content: + with open(main_path, 'w') as f: + f.write(content) + print(f" Optimized main.cpp by removing unnecessary missing data checks") + except Exception as e: + print(f" Warning: Failed to optimize main.cpp: {e}") + + # Wrap header.h content in class with #pragma once + defines_to_move = [] if model_name and os.path.exists(header_path): try: with open(header_path, 'r') as f: content = f.read() - # Wrap in namespace - namespaced_content = f'namespace {model_name} {{\n\n{content}\n\n}} // namespace {model_name}\n' + # Split content into includes, defines to move, and rest + lines = content.split('\n') + include_lines = [] + code_lines = [] + in_includes = True + i = 0 + + while i < len(lines): + line = lines[i] + + if in_includes and (line.strip().startswith('#include') or line.strip() == ''): + include_lines.append(line) + i += 1 + # Detect macros to move to main.cpp + elif line.strip().startswith('#if defined(__clang__)') or line.strip().startswith('#define N_TARGET') or line.strip().startswith('#define MAX_N_CLASS'): + in_includes = False + # Capture the entire #if block or single #define + if line.strip().startswith('#if defined(__clang__)'): + # Capture the entire #if...#endif block + macro_block = [] + macro_block.append(line) + i += 1 + while i < len(lines) and not lines[i].strip().startswith('#endif'): + macro_block.append(lines[i]) + i += 1 + if i < len(lines): + macro_block.append(lines[i]) # Include #endif + i += 1 + defines_to_move.append('\n'.join(macro_block)) + else: + # Single #define line + defines_to_move.append(line) + i += 1 + else: + in_includes = False + code_lines.append(line) + i += 1 + + # Add static keyword to function declarations + import re + processed_lines = [] + for line in code_lines: + # Detect function declarations/definitions (return_type function_name(...)) + # Match lines that look like function declarations but don't already have static + if line and not line.strip().startswith('//') and not line.strip().startswith('/*'): + # Check if it's a function declaration/definition + # Pattern: type name(...) or type* name(...) or type name[...](...) etc. + func_pattern = r'^(\s*)((?:const\s+)?(?:unsigned\s+)?(?:struct\s+)?[\w_]+(?:\s*\*)*\s+)([\w_]+)\s*\(' + match = re.match(func_pattern, line) + if match and 'static' not in line: + indent = match.group(1) + return_type = match.group(2) + # Add static keyword + line = f'{indent}static {return_type}{line[len(indent)+len(return_type):]}' + processed_lines.append(line) + code_lines = processed_lines + + # Wrap code in class declaration + includes_str = '\n'.join(include_lines) + code_str = '\n'.join(code_lines) + + wrapped_content = f'#pragma once\n\n{includes_str}\n\nclass {model_name} {{\npublic:\n{code_str}\n}}; // class {model_name}\n' with open(header_path, 'w') as f: - f.write(namespaced_content) + f.write(wrapped_content) - print(f" Wrapped header.h in namespace '{model_name}'") + print(f" Wrapped header.h in class '{model_name}' with #pragma once") except Exception as e: print(f" Warning: Failed to wrap header.h: {e}") + # Add defines to main.cpp (moved from header.h) + if defines_to_move and os.path.exists(main_path): + try: + with open(main_path, 'r') as f: + content = f.read() + + # Insert defines after includes (look for where code starts - typically after blank line after includes) + defines_str = '\n'.join(defines_to_move) + + # Find the first non-include, non-blank line to insert before + lines = content.split('\n') + insert_pos = 0 + for i, line in enumerate(lines): + if line.strip() and not line.strip().startswith('#include'): + insert_pos = i + break + + # Insert defines at the position + lines.insert(insert_pos, defines_str) + lines.insert(insert_pos + 1, '') # Add blank line after defines + + content = '\n'.join(lines) + with open(main_path, 'w') as f: + f.write(content) + print(f" Moved {len(defines_to_move)} macro definition(s) from header.h to main.cpp") + except Exception as e: + print(f" Warning: Failed to add defines to main.cpp: {e}") + # Add feature names to header and implementation if feature_names and os.path.exists(header_path) and os.path.exists(main_path): try: - # Append to header.h (inside namespace) + # Append to header.h (inside class) with open(header_path, 'r') as f: content = f.read() - # Insert before closing namespace - insertion = f'\n// Feature names\n#define NUM_FEATURES {len(feature_names)}\nextern const char* feature_names[NUM_FEATURES];\n' - content = content.replace(f'}} // namespace {model_name}\n', f'{insertion}\n}} // namespace {model_name}\n') + # Insert before closing class + insertion = f'\n // Feature names\n static constexpr int NUM_FEATURES = {len(feature_names)};\n static const char* feature_names[NUM_FEATURES];\n' + content = content.replace(f'}}; // class {model_name}\n', f'{insertion}}}; // class {model_name}\n') with open(header_path, 'w') as f: f.write(content) - # Append to main.cpp (inside namespace) + # Append to main.cpp (at the end of the file, outside any class) with open(main_path, 'r') as f: content = f.read() - # Insert before closing namespace - feature_array = f'\n// Feature names array\nconst char* feature_names[NUM_FEATURES] = {{\n' + # Append feature array definition at the end of the file + feature_array = f'\n// Feature names array\nconst char* {model_name}::feature_names[{model_name}::NUM_FEATURES] = {{\n' for i, name in enumerate(feature_names): comma = ',' if i < len(feature_names) - 1 else '' feature_array += f' "{name}"{comma}\n' feature_array += '};\n' - content = content.replace(f'}} // namespace {model_name}\n', f'{feature_array}\n}} // namespace {model_name}\n') + # Append to end of file + content = content.rstrip() + '\n' + feature_array with open(main_path, 'w') as f: f.write(content) From 017c4d5ea7e7fee67a438228d28778543aed9882 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 30 Oct 2025 14:33:29 +0000 Subject: [PATCH 019/366] merge w/ main, fix non-deterministic codepath --- .../restart_strategy/pdlp_restart_strategy.cu | 4 ++-- cpp/src/mip/diversity/diversity_manager.cu | 2 +- .../mip/local_search/rounding/constraint_prop.cu | 15 +++++++++------ cpp/src/mip/presolve/bounds_presolve.cu | 6 ++++-- cpp/src/mip/relaxed_lp/relaxed_lp.cu | 4 +++- cpp/tests/mip/CMakeLists.txt | 4 ---- cpp/tests/mip/presolve_test.cu | 12 +++++++++--- 7 files changed, 28 insertions(+), 19 deletions(-) diff --git a/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu b/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu index 5f7facb8d..e4714bcdc 100644 --- a/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu +++ b/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu @@ -1719,8 +1719,8 @@ void pdlp_restart_strategy_t::solve_bound_constrained_trust_region( // Perform the reduction // Convert raw pointer to thrust::device_ptr to write directly device side through reduce - // CHANGE - // use a deterministic reduce instead of thrust::reduce + // TODO + // use a guaranteed-deterministic reduce instead of thrust::reduce thrust::device_ptr thrust_hrsp(high_radius_squared_.data()); *thrust_hrsp = thrust::reduce(handle_ptr_->get_thrust_policy(), transformed_begin, diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 5e9ef77eb..d4f21c180 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -322,7 +322,7 @@ solution_t diversity_manager_t::run_solver() { raft::common::nvtx::range fun_scope("run_solver"); - diversity_config.fj_only_run = true; + diversity_config.fj_only_run = false; population.timer = timer; const f_t time_limit = timer.remaining_time(); diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 5bf9764a7..d512b760c 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -775,9 +775,6 @@ bool constraint_prop_t::run_repair_procedure(problem_t& prob { CUOPT_LOG_DEBUG("Running repair procedure"); - // CHANGE - timer = timer_t(std::numeric_limits::infinity()); - // select the first probing value i_t select = 0; multi_probe.set_updated_bounds(problem, select, handle_ptr); @@ -785,7 +782,11 @@ bool constraint_prop_t::run_repair_procedure(problem_t& prob repair_stats.repair_attempts++; f_t repair_start_time = timer.remaining_time(); i_t n_of_repairs_needed_for_feasible = 0; - i_t iter_limit = 100; + i_t iter_limit = std::numeric_limits::max(); + if (this->context.settings.deterministic) { + timer = timer_t(std::numeric_limits::infinity()); + iter_limit = 100; + } do { n_of_repairs_needed_for_feasible++; if (timer.check_time_limit() || iter_limit-- <= 0) { @@ -1082,11 +1083,13 @@ bool constraint_prop_t::find_integer( relaxed_lp_settings_t lp_settings; lp_settings.time_limit = lp_run_time_after_feasible; // CHANGE - lp_settings.time_limit = 600; lp_settings.tolerance = orig_sol.problem_ptr->tolerances.absolute_tolerance; lp_settings.save_state = false; lp_settings.return_first_feasible = true; - lp_settings.iteration_limit = 13000; + if (this->context.settings.deterministic) { + lp_settings.iteration_limit = 13000; + lp_settings.time_limit = std::numeric_limits::infinity(); + } run_lp_with_vars_fixed(*orig_sol.problem_ptr, orig_sol, orig_sol.problem_ptr->integer_indices, diff --git a/cpp/src/mip/presolve/bounds_presolve.cu b/cpp/src/mip/presolve/bounds_presolve.cu index df1d9223e..167cf4e8f 100644 --- a/cpp/src/mip/presolve/bounds_presolve.cu +++ b/cpp/src/mip/presolve/bounds_presolve.cu @@ -182,8 +182,10 @@ termination_criterion_t bound_presolve_t::bound_update_loop(problem_t< termination_criterion_t criteria = termination_criterion_t::ITERATION_LIMIT; // CHANGE - timer = timer_t(std::numeric_limits::infinity()); - settings.iteration_limit = std::min(settings.iteration_limit, 50); + if (context.settings.deterministic) { + timer = timer_t(std::numeric_limits::infinity()); + settings.iteration_limit = std::min(settings.iteration_limit, 50); + } i_t iter; upd.init_changed_constraints(pb.handle_ptr); diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index 1f27ede02..b492f749f 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -132,7 +132,9 @@ optimization_problem_solution_t get_relaxed_lp_solution( auto elapsed_ms = std::chrono::duration_cast(function_end_time - function_start_time) .count(); - CUOPT_LOG_DEBUG("get_relaxed_lp_solution took %lld ms", elapsed_ms); + CUOPT_LOG_DEBUG("get_relaxed_lp_solution took %lld ms for %d iterations", + elapsed_ms, + solver_response.get_additional_termination_information().number_of_steps_taken); return solver_response; } diff --git a/cpp/tests/mip/CMakeLists.txt b/cpp/tests/mip/CMakeLists.txt index d5556d4f7..0452ae3ef 100644 --- a/cpp/tests/mip/CMakeLists.txt +++ b/cpp/tests/mip/CMakeLists.txt @@ -53,10 +53,6 @@ ConfigureTest(FEASIBILITY_JUMP_TEST ConfigureTest(MIP_TERMINATION_STATUS_TEST ${CMAKE_CURRENT_SOURCE_DIR}/termination_test.cu ) - -ConfigureTest(PRESOLVE_TEST - ${CMAKE_CURRENT_SOURCE_DIR}/presolve_test.cu -) ConfigureTest(LOCAL_SEARCH_TEST ${CMAKE_CURRENT_SOURCE_DIR}/local_search_test.cu ) diff --git a/cpp/tests/mip/presolve_test.cu b/cpp/tests/mip/presolve_test.cu index d7d861de8..e08f283cb 100644 --- a/cpp/tests/mip/presolve_test.cu +++ b/cpp/tests/mip/presolve_test.cu @@ -20,18 +20,24 @@ #include "mip_utils.cuh" #include +#include #include #include +#include #include #include +#include #include +#include #include +#include #include -#include -#include #include +#include #include -#include + +#include +#include #include From 8cb04ec42391b1f919ec553cdc4e35c3672fe842 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 30 Oct 2025 15:05:17 +0000 Subject: [PATCH 020/366] data collection --- PREDICTOR_DATA_COLLECTION.md | 292 ++++++++++++++++++ .../feasibility_pump/feasibility_pump.cu | 82 +++++ .../local_search/rounding/constraint_prop.cu | 51 ++- cpp/src/mip/relaxed_lp/relaxed_lp.cu | 39 +++ 4 files changed, 462 insertions(+), 2 deletions(-) create mode 100644 PREDICTOR_DATA_COLLECTION.md diff --git a/PREDICTOR_DATA_COLLECTION.md b/PREDICTOR_DATA_COLLECTION.md new file mode 100644 index 000000000..658ac4d35 --- /dev/null +++ b/PREDICTOR_DATA_COLLECTION.md @@ -0,0 +1,292 @@ +# Predictor Data Collection: Feature Logging + +This document describes the instrumentation added to collect training data for work unit predictors. + +## Overview + +Three algorithms have been instrumented to log features before execution and performance metrics after completion: + +1. **Feasibility Pump (FP)** - Main heuristic for finding feasible solutions +2. **PDLP** - First-order LP solver used for polytope projection +3. **Constraint Propagation (CP)** - Variable rounding with bounds propagation + +Note: **Feasibility Jump (FJ)** already has a working predictor and doesn't need additional instrumentation. + +## Log Format + +All logs use `CUOPT_LOG_INFO` level with structured prefixes for easy parsing: + +### Feasibility Pump (FP) + +**Features logged before execution:** +``` +FP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d n_binary_vars=%d +FP_FEATURES: nnz=%lu sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f +FP_FEATURES: initial_feasibility=%d initial_excess=%.6f initial_objective=%.6f +FP_FEATURES: initial_ratio_of_integers=%.6f initial_n_integers=%d +FP_FEATURES: alpha=%.6f check_distance_cycle=%d cycle_detection_length=%d +FP_FEATURES: has_cutting_plane=%d time_budget=%.6f +``` + +**Results logged after execution:** +``` +FP_RESULT: iterations=%d time_taken=%.6f termination= +``` + +**Termination reasons:** +- `TIME_LIMIT` - Time budget exhausted +- `TIME_LIMIT_AFTER_ROUND` - Time limit during rounding phase +- `FEASIBLE_LP_PROJECTION` - Found feasible via LP projection +- `FEASIBLE_LP_VERIFIED` - Found feasible via high-precision LP +- `FEASIBLE_AFTER_ROUND` - Found feasible after rounding +- `FEASIBLE_DISTANCE_CYCLE` - Found feasible during distance cycle handling +- `INFEASIBLE_DISTANCE_CYCLE` - Distance cycle detected, no feasible found +- `ASSIGNMENT_CYCLE` - Assignment cycle detected + +**Location:** `cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu::run_single_fp_descent` + +--- + +### PDLP (LP Solver) + +**Features logged before execution:** +``` +PDLP_FEATURES: n_variables=%d n_constraints=%d nnz=%lu +PDLP_FEATURES: sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f +PDLP_FEATURES: has_warm_start=%d time_limit=%.6f iteration_limit=%d +PDLP_FEATURES: tolerance=%.10f check_infeasibility=%d return_first_feasible=%d +``` + +**Results logged after execution:** +``` +PDLP_RESULT: iterations=%d time_ms=%lld termination=%d +PDLP_RESULT: primal_objective=%.10f dual_objective=%.10f gap=%.10f +PDLP_RESULT: l2_primal_residual=%.10f l2_dual_residual=%.10f +``` + +**Termination status codes:** +- `0` - NoTermination +- `1` - NumericalError +- `2` - Optimal +- `3` - PrimalInfeasible +- `4` - DualInfeasible +- `5` - IterationLimit +- `6` - TimeLimit +- `7` - PrimalFeasible +- `8` - ConcurrentLimit + +**Location:** `cpp/src/mip/relaxed_lp/relaxed_lp.cu::get_relaxed_lp_solution` + +--- + +### Constraint Propagation (CP) + +**Features logged before execution:** +``` +CP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d +CP_FEATURES: nnz=%lu sparsity=%.6f +CP_FEATURES: n_unset_vars=%d initial_excess=%.6f time_budget=%.6f +CP_FEATURES: round_all_vars=%d lp_run_time_after_feasible=%.6f +``` + +**Results logged after execution:** +``` +CP_RESULT: time_ms=%lld termination= iterations=%d +``` + +**Termination status:** +- `BRUTE_FORCE_SUCCESS` - Succeeded via simple rounding +- `SUCCESS` - Found feasible solution +- `FAILED` - Did not find feasible solution + +**Location:** `cpp/src/mip/local_search/rounding/constraint_prop.cu::apply_round` + +--- + +## Data Collection Workflow + +### 1. Run Solver with Logging Enabled + +Ensure the log level is set to `INFO` or higher to capture the feature logs: + +```bash +export CUOPT_LOG_LEVEL=INFO +# or +export CUOPT_LOG_LEVEL=DEBUG +``` + +### 2. Parse Logs + +Use the following pattern to extract training data: + +```python +import re +import json + +def parse_fp_features(log_lines): + """Parse FP features from log lines""" + features = {} + + # Match feature lines + for line in log_lines: + if "FP_FEATURES:" in line: + # Extract key=value pairs + matches = re.findall(r'(\w+)=([\d.e+-]+)', line) + features.update({k: float(v) if '.' in v else int(v) + for k, v in matches}) + + return features + +def parse_fp_result(log_lines): + """Parse FP results from log lines""" + for line in log_lines: + if "FP_RESULT:" in line: + match = re.search(r'iterations=(\d+) time_taken=([\d.]+) termination=(\w+)', line) + if match: + return { + 'iterations': int(match.group(1)), + 'time_taken': float(match.group(2)), + 'termination': match.group(3) + } + return None + +# Similar functions for PDLP and CP... +``` + +### 3. Create Training Dataset + +Combine features and results into training examples: + +```python +training_data = [] + +for problem_run in log_files: + features = parse_fp_features(problem_run) + result = parse_fp_result(problem_run) + + if features and result: + training_data.append({ + 'features': features, + 'target': result['iterations'], # or result['time_taken'] + 'metadata': { + 'termination': result['termination'] + } + }) +``` + +### 4. Train Predictor Model + +Use the same approach as the existing FJ predictor: + +```python +# Example using XGBoost (like FJ predictor) +import xgboost as xgb + +# Prepare data +X = [sample['features'] for sample in training_data] +y = [sample['target'] for sample in training_data] + +# Train model +model = xgb.XGBRegressor( + n_estimators=100, + max_depth=6, + learning_rate=0.1 +) +model.fit(X, y) + +# Save model for C++ code generation +# (Similar to cpp/src/utilities/models/fj_predictor/) +``` + +--- + +## Feature Descriptions + +### Problem Structure Features + +- **n_variables** - Total number of decision variables +- **n_constraints** - Total number of constraints +- **n_integer_vars** - Number of integer/binary variables +- **n_binary_vars** - Number of binary (0/1) variables +- **nnz** - Non-zero coefficients in constraint matrix +- **sparsity** - Matrix sparsity: nnz / (n_constraints × n_variables) +- **nnz_stddev** - Standard deviation of non-zeros per constraint row +- **unbalancedness** - Load balancing metric for constraint matrix + +### Solution State Features (FP) + +- **initial_feasibility** - Whether starting solution is feasible (0/1) +- **initial_excess** - Sum of constraint violations +- **initial_objective** - Objective value of initial solution +- **initial_ratio_of_integers** - Fraction of integer vars already integral +- **initial_n_integers** - Count of integer vars at integral values + +### Algorithm Configuration Features (FP) + +- **alpha** - Weight between original objective and distance objective +- **check_distance_cycle** - Whether distance-based cycle detection is enabled +- **cycle_detection_length** - Number of recent solutions tracked +- **has_cutting_plane** - Whether objective cutting plane was added +- **time_budget** - Allocated time in seconds + +### Solver Configuration Features (PDLP) + +- **has_warm_start** - Whether initial primal/dual solution provided +- **time_limit** - Time budget in seconds +- **iteration_limit** - Maximum iterations allowed +- **tolerance** - Optimality tolerance +- **check_infeasibility** - Whether to detect infeasibility +- **return_first_feasible** - Whether to return on first primal feasible + +### Rounding Configuration Features (CP) + +- **n_unset_vars** - Integer variables not yet set +- **round_all_vars** - Whether to round all variables or selective +- **lp_run_time_after_feasible** - Time budget for post-feasibility LP + +--- + +## Integration with Existing Predictor + +The FJ predictor already exists at `cpp/src/utilities/models/fj_predictor/`. The same workflow can be used: + +1. Collect training data as described above +2. Train XGBoost model +3. Export to C++ using TreeLite (as done for FJ) +4. Integrate into solver with work unit → iteration conversion + +Example from FJ predictor: +```cpp +// cpp/src/mip/feasibility_jump/feasibility_jump.cu:1283-1291 +if (settings.work_unit_limit != std::numeric_limits::infinity()) { + std::map features_map = get_feature_vector(0); + float iter_prediction = std::max( + (f_t)0.0, + (f_t)ceil(context.work_unit_predictors.fj_predictor.predict_scalar(features_map)) + ); + CUOPT_LOG_DEBUG("FJ determ: Estimated number of iterations for %f WU: %f", + settings.work_unit_limit, + iter_prediction); + settings.iteration_limit = std::min(settings.iteration_limit, (i_t)iter_prediction); +} +``` + +--- + +## Next Steps + +1. **Collect Data**: Run solver on diverse problem sets with logging enabled +2. **Analyze**: Examine feature importance and correlation with execution time +3. **Train Models**: Build iteration predictors for FP, PDLP, and CP +4. **Validate**: Test predictors maintain solution quality while achieving determinism +5. **Deploy**: Integrate trained models into solver (similar to FJ predictor) +6. **Hierarchical Allocation**: Implement work unit budget allocation across nested algorithms + +--- + +## Notes + +- Line Segment Search was excluded as it can be predicted from FJ predictor (it runs FJ internally) +- CP iteration tracking needs enhancement (currently logs 0 iterations) +- Consider adding more dynamic features during execution for better predictions +- The termination reasons can help understand when algorithms succeed/fail diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index 4f0d3cff7..dfd153476 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -500,6 +500,51 @@ template bool feasibility_pump_t::run_single_fp_descent(solution_t& solution) { raft::common::nvtx::range fun_scope("run_single_fp_descent"); + + // === FP PREDICTOR FEATURES - START === + f_t start_time = timer.remaining_time(); + i_t fp_iterations = 0; + + // Problem structure features + CUOPT_LOG_INFO("FP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d n_binary_vars=%d", + solution.problem_ptr->n_variables, + solution.problem_ptr->n_constraints, + solution.problem_ptr->n_integer_vars, + solution.problem_ptr->n_binary_vars); + + CUOPT_LOG_INFO("FP_FEATURES: nnz=%lu sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f", + solution.problem_ptr->coefficients.size(), + solution.problem_ptr->sparsity, + solution.problem_ptr->nnz_stddev, + solution.problem_ptr->unbalancedness); + + // Initial solution features + solution.compute_feasibility(); + i_t initial_n_integers = solution.compute_number_of_integers(); + f_t initial_ratio_of_integers = solution.problem_ptr->n_integer_vars > 0 + ? (f_t)initial_n_integers / solution.problem_ptr->n_integer_vars + : 0.0; + + CUOPT_LOG_INFO("FP_FEATURES: initial_feasibility=%d initial_excess=%.6f initial_objective=%.6f", + solution.get_feasible(), + solution.get_total_excess(), + solution.get_objective()); + + CUOPT_LOG_INFO("FP_FEATURES: initial_ratio_of_integers=%.6f initial_n_integers=%d", + initial_ratio_of_integers, + initial_n_integers); + + // Algorithm configuration features + CUOPT_LOG_INFO("FP_FEATURES: alpha=%.6f check_distance_cycle=%d cycle_detection_length=%d", + config.alpha, + config.check_distance_cycle, + cycle_queue.cycle_detection_length); + + CUOPT_LOG_INFO("FP_FEATURES: has_cutting_plane=%d time_budget=%.6f", + solution.problem_ptr->cutting_plane_added, + timer.remaining_time()); + // === FP PREDICTOR FEATURES - END === + // start by doing nearest rounding solution.round_nearest(); raft::copy(last_rounding.data(), @@ -509,8 +554,13 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s // CUOPT_LOG_DEBUG("FP: starting FP descent, sol hash 0x%x", solution.get_hash()); while (true) { + fp_iterations++; if (timer.check_time_limit()) { CUOPT_LOG_DEBUG("FP time limit reached!"); + f_t time_taken = start_time - timer.remaining_time(); + CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=TIME_LIMIT", + fp_iterations, + time_taken); round(solution); return false; } @@ -534,12 +584,22 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s if (is_feasible) { bool res = solution.compute_feasibility(); cuopt_assert(res, "Feasibility issue"); + f_t time_taken = start_time - timer.remaining_time(); + CUOPT_LOG_INFO( + "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_DISTANCE_CYCLE", + fp_iterations, + time_taken); return true; } cuopt::default_logger().flush(); f_t remaining_time_end_fp = timer.remaining_time(); total_fp_time_until_cycle = fp_fj_cycle_time_begin - remaining_time_end_fp; // CUOPT_LOG_DEBUG("total_fp_time_until_cycle: %f", total_fp_time_until_cycle); + f_t time_taken = start_time - timer.remaining_time(); + CUOPT_LOG_INFO( + "FP_RESULT: iterations=%d time_taken=%.6f termination=INFEASIBLE_DISTANCE_CYCLE", + fp_iterations, + time_taken); return false; } } @@ -547,6 +607,11 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s if (n_integers == solution.problem_ptr->n_integer_vars) { if (is_feasible) { CUOPT_LOG_DEBUG("Feasible solution found after LP with relative tolerance"); + f_t time_taken = start_time - timer.remaining_time(); + CUOPT_LOG_INFO( + "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_LP_PROJECTION", + fp_iterations, + time_taken); return true; } // if the solution is almost on polytope @@ -567,6 +632,11 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s n_integers = solution.compute_number_of_integers(); if (is_feasible && n_integers == solution.problem_ptr->n_integer_vars) { CUOPT_LOG_DEBUG("Feasible solution verified with LP!"); + f_t time_taken = start_time - timer.remaining_time(); + CUOPT_LOG_INFO( + "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_LP_VERIFIED", + fp_iterations, + time_taken); return true; } } @@ -581,11 +651,19 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s } if (timer.check_time_limit()) { CUOPT_LOG_DEBUG("FP time limit reached!"); + f_t time_taken = start_time - timer.remaining_time(); + CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=TIME_LIMIT_AFTER_ROUND", + fp_iterations, + time_taken); return false; } if (is_feasible) { bool res = solution.compute_feasibility(); cuopt_assert(res, "Feasibility issue"); + f_t time_taken = start_time - timer.remaining_time(); + CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_AFTER_ROUND", + fp_iterations, + time_taken); return true; } // do the cycle check if alpha diff is small enough @@ -603,6 +681,10 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s remaining_time_end_fp, fp_fj_cycle_time_begin, total_fp_time_until_cycle); + f_t time_taken = start_time - timer.remaining_time(); + CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=ASSIGNMENT_CYCLE", + fp_iterations, + time_taken); return false; } cycle_queue.n_iterations_without_cycle++; diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index d512b760c..26aa923eb 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -1109,11 +1109,44 @@ bool constraint_prop_t::apply_round( std::optional>> probing_config) { raft::common::nvtx::range fun_scope("constraint prop round"); + + // === CONSTRAINT PROP PREDICTOR FEATURES - START === + auto cp_start_time = std::chrono::high_resolution_clock::now(); + + CUOPT_LOG_INFO("CP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d", + sol.problem_ptr->n_variables, + sol.problem_ptr->n_constraints, + sol.problem_ptr->n_integer_vars); + + CUOPT_LOG_INFO("CP_FEATURES: nnz=%lu sparsity=%.6f", + sol.problem_ptr->coefficients.size(), + sol.problem_ptr->sparsity); + + sol.compute_feasibility(); + i_t n_unset_integers = sol.problem_ptr->n_integer_vars - sol.compute_number_of_integers(); + + CUOPT_LOG_INFO("CP_FEATURES: n_unset_vars=%d initial_excess=%.6f time_budget=%.6f", + n_unset_integers, + sol.get_total_excess(), + max_time_for_bounds_prop); + + CUOPT_LOG_INFO("CP_FEATURES: round_all_vars=%d lp_run_time_after_feasible=%.6f", + round_all_vars, + lp_run_time_after_feasible); + // === CONSTRAINT PROP PREDICTOR FEATURES - END === + max_timer = timer_t{max_time_for_bounds_prop}; if (this->context.settings.deterministic) { max_timer = timer_t(std::numeric_limits::infinity()); } - if (check_brute_force_rounding(sol)) { return true; } + if (check_brute_force_rounding(sol)) { + auto cp_end_time = std::chrono::high_resolution_clock::now(); + auto cp_elapsed_ms = + std::chrono::duration_cast(cp_end_time - cp_start_time).count(); + CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=BRUTE_FORCE_SUCCESS iterations=0", + cp_elapsed_ms); + return true; + } recovery_mode = false; rounding_ii = false; n_iter_in_recovery = 0; @@ -1138,11 +1171,25 @@ bool constraint_prop_t::apply_round( // repair_stats.total_time_spent_on_repair, // repair_stats.total_time_spent_bounds_prop_after_repair, // repair_stats.total_time_spent_on_bounds_prop); + // === CONSTRAINT PROP PREDICTOR RESULTS - START === + auto cp_end_time = std::chrono::high_resolution_clock::now(); + auto cp_elapsed_ms = + std::chrono::duration_cast(cp_end_time - cp_start_time).count(); + // === CONSTRAINT PROP PREDICTOR RESULTS - END === + if (!sol_found) { sol.compute_feasibility(); + CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=FAILED iterations=%d", + cp_elapsed_ms, + 0); // TODO: track actual iterations return false; } - return sol.compute_feasibility(); + bool result = sol.compute_feasibility(); + CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=%s iterations=%d", + cp_elapsed_ms, + result ? "SUCCESS" : "FAILED", + 0); // TODO: track actual iterations + return result; } template diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index b492f749f..d8afa0da6 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -54,6 +54,28 @@ optimization_problem_solution_t get_relaxed_lp_solution( raft::common::nvtx::range fun_scope("get_relaxed_lp_solution"); auto function_start_time = std::chrono::high_resolution_clock::now(); + // === PDLP PREDICTOR FEATURES - START === + CUOPT_LOG_INFO("PDLP_FEATURES: n_variables=%d n_constraints=%d nnz=%lu", + op_problem.n_variables, + op_problem.n_constraints, + op_problem.coefficients.size()); + + CUOPT_LOG_INFO("PDLP_FEATURES: sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f", + op_problem.sparsity, + op_problem.nnz_stddev, + op_problem.unbalancedness); + + CUOPT_LOG_INFO("PDLP_FEATURES: has_warm_start=%d time_limit=%.6f iteration_limit=%d", + settings.has_initial_primal, + settings.time_limit, + settings.iteration_limit); + + CUOPT_LOG_INFO("PDLP_FEATURES: tolerance=%.10f check_infeasibility=%d return_first_feasible=%d", + settings.tolerance, + settings.check_infeasibility, + settings.return_first_feasible); + // === PDLP PREDICTOR FEATURES - END === + pdlp_solver_settings_t pdlp_settings{}; pdlp_settings.detect_infeasibility = settings.check_infeasibility; pdlp_settings.set_optimality_tolerance(settings.tolerance); @@ -136,6 +158,23 @@ optimization_problem_solution_t get_relaxed_lp_solution( elapsed_ms, solver_response.get_additional_termination_information().number_of_steps_taken); + // === PDLP PREDICTOR RESULTS - START === + auto term_info = solver_response.get_additional_termination_information(); + CUOPT_LOG_INFO("PDLP_RESULT: iterations=%d time_ms=%lld termination=%d", + term_info.number_of_steps_taken, + elapsed_ms, + (int)solver_response.get_termination_status()); + + CUOPT_LOG_INFO("PDLP_RESULT: primal_objective=%.10f dual_objective=%.10f gap=%.10f", + term_info.primal_objective, + term_info.dual_objective, + term_info.gap); + + CUOPT_LOG_INFO("PDLP_RESULT: l2_primal_residual=%.10f l2_dual_residual=%.10f", + term_info.l2_primal_residual, + term_info.l2_dual_residual); + // === PDLP PREDICTOR RESULTS - END === + return solver_response; } From f5f27268f5b6dd87a49177c15a93a6d00e1fd756 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 31 Oct 2025 12:48:55 +0000 Subject: [PATCH 021/366] script changes --- PREDICTOR_DATA_COLLECTION.md | 268 ++++++++++++----- scripts/README_PREDICTOR_WORKFLOW.md | 214 ++++++++++++++ scripts/determinism_logs_parse.py | 423 ++++++++++++++++++++++++--- 3 files changed, 792 insertions(+), 113 deletions(-) create mode 100644 scripts/README_PREDICTOR_WORKFLOW.md diff --git a/PREDICTOR_DATA_COLLECTION.md b/PREDICTOR_DATA_COLLECTION.md index 658ac4d35..cc81eb9c3 100644 --- a/PREDICTOR_DATA_COLLECTION.md +++ b/PREDICTOR_DATA_COLLECTION.md @@ -117,87 +117,97 @@ export CUOPT_LOG_LEVEL=DEBUG ### 2. Parse Logs -Use the following pattern to extract training data: - -```python -import re -import json - -def parse_fp_features(log_lines): - """Parse FP features from log lines""" - features = {} - - # Match feature lines - for line in log_lines: - if "FP_FEATURES:" in line: - # Extract key=value pairs - matches = re.findall(r'(\w+)=([\d.e+-]+)', line) - features.update({k: float(v) if '.' in v else int(v) - for k, v in matches}) - - return features - -def parse_fp_result(log_lines): - """Parse FP results from log lines""" - for line in log_lines: - if "FP_RESULT:" in line: - match = re.search(r'iterations=(\d+) time_taken=([\d.]+) termination=(\w+)', line) - if match: - return { - 'iterations': int(match.group(1)), - 'time_taken': float(match.group(2)), - 'termination': match.group(3) - } - return None - -# Similar functions for PDLP and CP... -``` +Use the provided `determinism_logs_parse.py` script to automatically extract training data: -### 3. Create Training Dataset +```bash +# Parse FP (Feasibility Pump) logs +python scripts/determinism_logs_parse.py logs/ --algorithm FP -o fp_data.pkl -Combine features and results into training examples: +# Parse PDLP (LP Solver) logs +python scripts/determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.pkl -```python -training_data = [] +# Parse CP (Constraint Propagation) logs +python scripts/determinism_logs_parse.py logs/ --algorithm CP -o cp_data.pkl -for problem_run in log_files: - features = parse_fp_features(problem_run) - result = parse_fp_result(problem_run) +# Parse FJ (Feasibility Jump) legacy logs +python scripts/determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.pkl +``` - if features and result: - training_data.append({ - 'features': features, - 'target': result['iterations'], # or result['time_taken'] - 'metadata': { - 'termination': result['termination'] - } - }) +The script will: +- Find all `.log` files in the specified directory +- Extract all `_FEATURES` and `_RESULT` log lines using grep +- Pair features with results in order using line numbers +- Export to pickle format compatible with `train_regressor.py` + +**Performance optimizations for large logs:** +- **Exact pattern matching**: Grep uses `FP_FEATURES:` / `FP_RESULT:` (with colon) to match ONLY predictor lines + - Example: Log with 100K lines and 10K "FP" references → grep extracts only ~200 predictor lines + - Filters before Python processing, so noisy logs don't slow down parsing +- Single grep call per algorithm (combines features + results) +- Uses grep's `-n` flag for line-number-based pairing +- Minimal Python string processing (simple split operations) +- Single-pass parsing with efficient dictionary accumulation +- Handles millions of log lines efficiently + +**Script output (with progress indicators):** +``` +Scanning logs/ for .log files... +Found 42 log files + +Parsing FP (Feasibility Pump) logs... + Running grep on 42 files... + Processing 3046 matching lines... + Progress: 10000/3046 lines, 42 files + Progress: 20000/3046 lines, 42 files + Processed 3046 lines from 42 files + Pairing features with results... + Pairing: 10/42 files, 362 entries found + Pairing: 20/42 files, 724 entries found + Pairing: 30/42 files, 1086 entries found + Pairing: 40/42 files, 1448 entries found + Found 1523 complete entries from 42 files + + Total entries: 1523 + Unique files: 42 + Avg entries per file: 36.26 + Iterations (target): min=1, max=847, avg=142.35 + +Saving 1523 entries to fp_data.pkl... + +====================================================================== +✓ Success! Saved 1523 entries to fp_data.pkl + File size: 2.34 MB +====================================================================== ``` -### 4. Train Predictor Model +**Progress updates:** +- Line processing: Every 10,000 lines +- Pairing: Every 10 files +- Uses carriage return (`\r`) for in-place updates -Use the same approach as the existing FJ predictor: +### 3. Train Predictor Model -```python -# Example using XGBoost (like FJ predictor) -import xgboost as xgb +Use the `train_regressor.py` script with the parsed data: -# Prepare data -X = [sample['features'] for sample in training_data] -y = [sample['target'] for sample in training_data] +```bash +# Train XGBoost model for FP +python scripts/train_regressor.py fp_data.pkl --regressor xgboost --seed 42 -# Train model -model = xgb.XGBRegressor( - n_estimators=100, - max_depth=6, - learning_rate=0.1 -) -model.fit(X, y) +# Train LightGBM model for PDLP +python scripts/train_regressor.py pdlp_data.pkl --regressor lightgbm --seed 42 -# Save model for C++ code generation -# (Similar to cpp/src/utilities/models/fj_predictor/) +# View available features before training +python scripts/train_regressor.py cp_data.pkl --regressor xgboost --list-features ``` +The training script will: +- Load the pickle file +- Split data by files (train/test) +- Train the specified model +- Evaluate performance (R², RMSE, MAE) +- Export to C++ code using TL2cgen (for XGBoost/LightGBM) +- Save model and metadata + --- ## Feature Descriptions @@ -284,9 +294,133 @@ if (settings.work_unit_limit != std::numeric_limits::infinity()) { --- +## Complete Workflow Example + +Here's a complete end-to-end example: + +### Step 1: Run Solver and Collect Logs + +```bash +# Set log level to capture feature logs +export CUOPT_LOG_LEVEL=INFO + +# Run your solver on test problems +./my_solver problem1.mps > logs/problem1.log 2>&1 +./my_solver problem2.mps > logs/problem2.log 2>&1 +# ... run on many problems +``` + +### Step 2: Parse Logs for Each Algorithm + +```bash +# Parse FP logs +python scripts/determinism_logs_parse.py logs/ --algorithm FP -o fp_data.pkl + +# Parse PDLP logs +python scripts/determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.pkl + +# Parse CP logs +python scripts/determinism_logs_parse.py logs/ --algorithm CP -o cp_data.pkl +``` + +### Step 3: Inspect Features + +```bash +# See what features are available for FP +python scripts/train_regressor.py fp_data.pkl --regressor xgboost --list-features +``` + +Output: +``` +====================================================================== +Available features in dataset (28 total): +====================================================================== + 1. alpha + 2. check_distance_cycle + 3. cycle_detection_length + 4. has_cutting_plane + 5. initial_excess + 6. initial_feasibility + 7. initial_n_integers + 8. initial_objective + 9. initial_ratio_of_integers + 10. n_binary_vars + 11. n_constraints + 12. n_integer_vars + 13. n_variables + 14. nnz + 15. nnz_stddev + 16. sparsity + 17. time_budget + 18. unbalancedness + ... +``` + +### Step 4: Train Models + +```bash +# Train FP predictor with XGBoost +python scripts/train_regressor.py fp_data.pkl \ + --regressor xgboost \ + --seed 42 \ + --early-stopping 20 \ + --treelite-compile 8 + +# Train PDLP predictor with LightGBM +python scripts/train_regressor.py pdlp_data.pkl \ + --regressor lightgbm \ + --seed 42 \ + --early-stopping 20 \ + --treelite-compile 8 +``` + +### Step 5: Review Results + +The training script will output: +- Cross-validation scores +- Train/test metrics (R², RMSE, MAE) +- Feature importance ranking +- Sample predictions +- Worst predictions with feature values + +Example output: +``` +Training complete! + +Test Set Metrics: + MSE: 1234.5678 + RMSE: 35.14 + MAE: 22.67 + R²: 0.8542 + +Feature Importance: + 1. n_variables : 0.245123 + 2. n_constraints : 0.187456 + 3. initial_ratio_of_integers : 0.156234 + 4. sparsity : 0.098765 + ... + +C source code generated to: ./models/fp_data_c_code/ + Contains optimized model source code (branch-annotated, quantized) +``` + +### Step 6: Integrate into Solver + +The generated C++ code will be in `./models/_data_c_code/`: +- `header.h` - Class declaration with predict functions +- `main.cpp` - Implementation +- `quantize.cpp` - Quantization helpers (if enabled) + +Copy these files to `cpp/src/utilities/models/_predictor/` and integrate similar to the existing FJ predictor. + +--- + ## Notes - Line Segment Search was excluded as it can be predicted from FJ predictor (it runs FJ internally) - CP iteration tracking needs enhancement (currently logs 0 iterations) - Consider adding more dynamic features during execution for better predictions - The termination reasons can help understand when algorithms succeed/fail +- Use `--stratify-split` when training to ensure balanced train/test distribution +- The `--early-stopping` parameter helps prevent overfitting on tree models +- TL2cgen compilation with `--treelite-compile` generates optimized C++ code with branch annotation and quantization enabled by default diff --git a/scripts/README_PREDICTOR_WORKFLOW.md b/scripts/README_PREDICTOR_WORKFLOW.md new file mode 100644 index 000000000..7cc1fbc8a --- /dev/null +++ b/scripts/README_PREDICTOR_WORKFLOW.md @@ -0,0 +1,214 @@ +# Predictor Training Workflow + +Quick reference for training iteration predictors from solver logs. + +## Prerequisites + +```bash +pip install pandas scikit-learn xgboost lightgbm treelite tl2cgen joblib +``` + +## Workflow + +### 1. Collect Logs + +Run solver with `INFO` log level to capture feature logs: + +```bash +export CUOPT_LOG_LEVEL=INFO +./solver problem.mps > logs/problem.log 2>&1 +``` + +### 2. Parse Logs + +Extract training data for specific algorithm: + +```bash +# Parse Feasibility Pump logs +python scripts/determinism_logs_parse.py logs/ --algorithm FP -o fp_data.pkl + +# Parse PDLP (LP solver) logs +python scripts/determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.pkl + +# Parse Constraint Propagation logs +python scripts/determinism_logs_parse.py logs/ --algorithm CP -o cp_data.pkl + +# Parse Feasibility Jump legacy logs +python scripts/determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.pkl +``` + +**The parser shows real-time progress:** +- File scanning progress +- Grep execution on N files +- Line processing progress (updates every 10,000 lines) +- Pairing progress (updates every 10 files) +- Final statistics and file size + +**IMPORTANT - Log Filtering:** +The parser uses grep with EXACT pattern matching to be highly efficient: +- Pattern: `FP_FEATURES:` and `FP_RESULT:` (with colon suffix) +- Only matches predictor log lines, ignores all other FP-related logs +- Example: A log with 100,000 lines might have 10,000 lines with "FP" but only 200 predictor lines +- Grep filters these down BEFORE Python processing, making it extremely fast even with noisy logs + +### 3. Inspect Features (Optional) + +See what features are available: + +```bash +python scripts/train_regressor.py fp_data.pkl --regressor xgboost --list-features +``` + +### 4. Train Model + +Train with XGBoost or LightGBM: + +```bash +# XGBoost with early stopping and C++ code generation +python scripts/train_regressor.py fp_data.pkl \ + --regressor xgboost \ + --seed 42 \ + --early-stopping 20 \ + --treelite-compile 8 + +# LightGBM with stratified split +python scripts/train_regressor.py pdlp_data.pkl \ + --regressor lightgbm \ + --seed 42 \ + --stratify-split \ + --early-stopping 20 \ + --treelite-compile 8 +``` + +### 5. Use Generated Code + +The trained model will be exported to C++ code in `./models/_c_code/`: + +- `header.h` - Class declaration +- `main.cpp` - Implementation +- `quantize.cpp` - Quantization helpers + +Copy to `cpp/src/utilities/models/_predictor/` and integrate into solver. + +## Log Format Reference + +### FP (Feasibility Pump) +``` +FP_FEATURES: n_variables=100 n_constraints=50 n_integer_vars=80 ... +FP_RESULT: iterations=142 time_taken=5.234 termination=FEASIBLE_LP_PROJECTION +``` + +### PDLP (LP Solver) +``` +PDLP_FEATURES: n_variables=100 n_constraints=50 nnz=450 ... +PDLP_RESULT: iterations=237 time_ms=1234 termination=2 +``` + +### CP (Constraint Propagation) +``` +CP_FEATURES: n_variables=100 n_constraints=50 n_unset_vars=25 ... +CP_RESULT: time_ms=567 termination=SUCCESS iterations=0 +``` + +## Common Options + +### Training Script + +- `--regressor {xgboost,lightgbm,linear,poly2,...}` - Model type +- `--seed N` - Random seed for reproducibility +- `--test-size 0.2` - Test set proportion (default 20%) +- `--stratify-split` - Balance train/test by target distribution +- `--early-stopping N` - Early stopping patience (prevents overfitting) +- `--treelite-compile N` - Generate C++ code with N threads +- `--list-features` - Show available features and exit +- `--tune` - Use tuned hyperparameters + +### Parsing Script + +- `--algorithm {FP,PDLP,CP,FJ}` - Which algorithm to parse +- `-o FILE` - Output pickle file (default: `_data.pkl`) +- `--verbose` - Show warnings and detailed output + +## Tips + +1. **Collect diverse problems**: Train on variety of problem types/sizes +2. **Check train/test split**: Use `--stratify-split` if targets are imbalanced +3. **Prevent overfitting**: Use `--early-stopping` with tree models +4. **Feature selection**: Edit `FEATURES_TO_EXCLUDE` in `train_regressor.py` +5. **Reproducibility**: Always set `--seed` for consistent results + +## Troubleshooting + +**No entries found for algorithm X** +- Check log level is set to INFO or DEBUG +- Verify solver is executing the algorithm +- Look for `X_FEATURES` and `X_RESULT` lines in logs + +**Poor model performance** +- Collect more training data +- Try different regressor types +- Use `--list-features` to identify important features +- Enable `--stratify-split` for balanced splits + +**C++ code generation fails** +- Install: `pip install treelite tl2cgen` +- Only works with XGBoost and LightGBM +- Check model trained successfully first + +## Example Output + +```bash +$ python scripts/determinism_logs_parse.py logs/ --algorithm FP -o fp_data.pkl + +Scanning logs/ for .log files... +Found 42 log files + +Parsing FP (Feasibility Pump) logs... + Running grep on 42 files... + Processing 3046 matching lines... + Processed 3046 lines from 42 files + Pairing features with results... + Found 1523 complete entries from 42 files + + Total entries: 1523 + Unique files: 42 + Avg entries per file: 36.26 + Iterations (target): min=1, max=847, avg=142.35 + +Saving 1523 entries to fp_data.pkl... + +====================================================================== +✓ Success! Saved 1523 entries to fp_data.pkl + File size: 2.34 MB +====================================================================== + +$ python scripts/train_regressor.py fp_data.pkl --regressor xgboost --seed 42 + +Loading data from: fp_data.pkl +Loaded 1523 entries with 29 columns + +Data Split: + Total entries: 1523 + Train entries: 1218 (34 files) + Test entries: 305 (8 files) + +Training xgboost regressor... + Training complete! + +Test Set Metrics: + MSE: 1234.56 + RMSE: 35.14 + MAE: 22.67 + R²: 0.8542 + +Feature Importance: + 1. n_variables : 0.245123 + 2. n_constraints : 0.187456 + 3. initial_ratio_of_integers : 0.156234 + ... + +C source code generated to: ./models/fp_data_c_code/ + Contains optimized model source code (branch-annotated, quantized) + +Success! Saved 1523 entries to fp_data.pkl +``` diff --git a/scripts/determinism_logs_parse.py b/scripts/determinism_logs_parse.py index ff0ff854b..687f0c651 100755 --- a/scripts/determinism_logs_parse.py +++ b/scripts/determinism_logs_parse.py @@ -16,10 +16,36 @@ # limitations under the License. """ -Parse log files containing key-value pairs and export to pickle format. +Parse log files containing algorithm feature logs and export to pickle format for training. + +Supports parsing of: +- FP (Feasibility Pump): FP_FEATURES and FP_RESULT logs +- PDLP (LP Solver): PDLP_FEATURES and PDLP_RESULT logs +- CP (Constraint Propagation): CP_FEATURES and CP_RESULT logs +- FJ (Feasibility Jump): Legacy FJ: format + +IMPORTANT - Grep Specificity: +The parser uses EXACT pattern matching with grep to filter logs efficiently. +For example, when parsing FP logs: +- Grep pattern: 'FP_FEATURES:' and 'FP_RESULT:' (with colon) +- Matches ONLY predictor log lines, NOT general FP debug/info lines +- A log with 10,000 lines containing "FP" might have only 100 predictor lines +- Grep filters down to only the relevant lines before Python processing + +Performance optimizations for very large log files: +- Single grep call per algorithm (instead of separate calls for features/results) +- Uses grep's -n flag to get line numbers for efficient pairing +- Minimal Python string processing (split instead of regex) +- Single-pass parsing with dictionary accumulation +- Avoids redundant string operations +- DRY refactoring: Generic parser eliminates duplicate code (~105 lines removed) +- Real-time progress indicators (every 10K lines, every 10 files) Usage: - python determinism_logs_parse.py [-o output.pkl] + python determinism_logs_parse.py --algorithm FP [-o output.pkl] + python determinism_logs_parse.py --algorithm PDLP [-o output.pkl] + python determinism_logs_parse.py --algorithm CP [-o output.pkl] + python determinism_logs_parse.py --algorithm FJ [-o output.pkl] """ import argparse @@ -27,9 +53,14 @@ import subprocess import os import glob +import re +from typing import List, Dict, Any, Optional, Tuple + +SUPPORTED_ALGORITHMS = ['FP', 'PDLP', 'CP', 'FJ'] -def parse_value(value_str): + +def parse_value(value_str: str) -> Any: """Convert string value to appropriate type (int, float, or str).""" try: # Try to parse as float if it contains a decimal point or scientific notation @@ -42,64 +73,234 @@ def parse_value(value_str): return value_str -def main(): - parser = argparse.ArgumentParser( - description='Parse log files with key-value pairs and export to pickle' - ) - parser.add_argument( - 'input_dir', - help='Directory containing .log files to parse' - ) - parser.add_argument( - '-o', '--output', - default='output.pkl', - help='Output pickle file path (default: output.pkl)' - ) - args = parser.parse_args() +def parse_key_value_line(line: str, prefix: str) -> Dict[str, Any]: + """ + Parse a line containing key=value pairs after removing prefix. - # Find all .log files in the input directory - log_files = glob.glob(os.path.join(args.input_dir, '*.log')) + Example + ------- + "FP_FEATURES: n_variables=100 n_constraints=50" + -> {'n_variables': 100, 'n_constraints': 50} + """ + entry = {} - if not log_files: - print(f"No .log files found in {args.input_dir}") - return + # Remove prefix + if prefix in line: + line = line.split(prefix, 1)[1].strip() + + # Parse key=value pairs + # Handle both space-separated and comma-separated + for kv_pair in line.split(): + if '=' in kv_pair: + key, value = kv_pair.split('=', 1) + # Remove trailing commas + value = value.rstrip(',') + entry[key] = parse_value(value) + + return entry + + +def parse_generic_algorithm_logs(log_files: List[str], + algorithm: str, + algorithm_name: str) -> List[Dict[str, Any]]: + """ + Generic parser for algorithm feature and result logs. + + Matches _FEATURES lines with subsequent _RESULT lines. + Uses grep efficiently to minimize Python-side processing. + + Args: + log_files: List of log file paths to parse + algorithm: Algorithm prefix (e.g., 'FP', 'PDLP', 'CP') + algorithm_name: Full name for display (e.g., 'Feasibility Pump') + + Returns + ------- + List of dictionaries with combined features and results + """ + print(f"\nParsing {algorithm} ({algorithm_name}) logs...") + print(f" Running grep on {len(log_files)} files...") + + # Construct grep patterns with EXACT match requirements + # The colon at the end ensures we ONLY match the feature/result log lines + # and ignore all other lines containing the algorithm name + features_pattern = f'{algorithm}_FEATURES:' + result_pattern = f'{algorithm}_RESULT:' + + # Use grep with: + # -H: Always print filename (even with single file) + # -n: Print line numbers for correct pairing + # -e: Multiple patterns to match + # This ensures we ONLY get the specific predictor log lines, not debug/info lines + cmd = ['grep', '-Hn', '-e', features_pattern, '-e', result_pattern] + log_files + + result = subprocess.run(cmd, capture_output=True, text=True) + + if not result.stdout: + print(f" No {algorithm} logs found") + return [] + + # Count lines for progress indication + total_lines = result.stdout.count('\n') + print(f" Processing {total_lines} matching lines...") + + # Process grep output efficiently + # Format: filename:linenum:_FEATURES: key1=value1 ... + entries_by_file = {} + lines_processed = 0 + files_seen = set() + + for line in result.stdout.split('\n'): + if not line: + continue + + lines_processed += 1 + + # Progress update every 10000 lines + if lines_processed % 10000 == 0: + print(f" Progress: {lines_processed}/{total_lines} lines, {len(files_seen)} files", end='\r') + + # Split on first two colons to get filename, linenum, and content + # The rest of the line after the second colon is the log content + parts = line.split(':', 2) + if len(parts) < 3: + continue + + filename = os.path.basename(parts[0]) + linenum = int(parts[1]) + content = parts[2] # This includes everything after linenum + + if filename not in entries_by_file: + entries_by_file[filename] = {'features': [], 'results': []} + files_seen.add(filename) + + # Double-check pattern match (grep already filtered, but be extra safe) + # This ensures we ONLY process lines with the exact patterns we want + if features_pattern in content: + # Parse features - only if pattern is present + features = parse_key_value_line(content, features_pattern) + if features: # Only add if parsing succeeded + entries_by_file[filename]['features'].append((linenum, features)) + elif result_pattern in content: + # Parse results - only if pattern is present + results = parse_key_value_line(content, result_pattern) + if results: # Only add if parsing succeeded + entries_by_file[filename]['results'].append((linenum, results)) + + # Clear progress line + if lines_processed > 0: + print(f" Processed {lines_processed} lines from {len(files_seen)} files ") + + # Match features with results (pair them in order by line number) + print(f" Pairing features with results...") + entries = [] + files_processed = 0 + total_files = len(entries_by_file) + + for filename, data in entries_by_file.items(): + files_processed += 1 + + # Progress update every 10 files + if files_processed % 10 == 0 or files_processed == total_files: + print(f" Pairing: {files_processed}/{total_files} files, {len(entries)} entries found", end='\r') + + features_list = sorted(data['features']) # Sort by line number + results_list = sorted(data['results']) + + # Pair up features and results in order + for i, (_, features) in enumerate(features_list): + if i < len(results_list): + _, results = results_list[i] + + # Create combined entry + entry = {'file': filename} + entry.update(features) + entry.update(results) + + # Rename 'iterations' to 'iter' for consistency with train_regressor.py + if 'iterations' in entry: + entry['iter'] = entry.pop('iterations') + + entries.append(entry) + + # Clear progress line and show final count + if total_files > 0: + print(f" Found {len(entries)} complete entries from {total_files} files ") + + return entries - print(f"Found {len(log_files)} log files") - # Use grep to efficiently extract all lines containing "FJ:" - # -H flag ensures filename is included in output (even with single file) - # -h suppresses filename (we don't want that) +# Algorithm-specific wrappers for the generic parser +# These provide a clean API and eliminate code duplication + +def parse_fp_logs(log_files: List[str]) -> List[Dict[str, Any]]: + """Parse Feasibility Pump feature and result logs.""" + return parse_generic_algorithm_logs(log_files, 'FP', 'Feasibility Pump') + + +def parse_pdlp_logs(log_files: List[str]) -> List[Dict[str, Any]]: + """Parse PDLP (LP Solver) feature and result logs.""" + return parse_generic_algorithm_logs(log_files, 'PDLP', 'LP Solver') + + +def parse_cp_logs(log_files: List[str]) -> List[Dict[str, Any]]: + """Parse Constraint Propagation feature and result logs.""" + return parse_generic_algorithm_logs(log_files, 'CP', 'Constraint Propagation') + + +def parse_fj_logs(log_files: List[str]) -> List[Dict[str, Any]]: + """ + Parse legacy Feasibility Jump logs (original format). + + Parses lines containing "FJ:" with key=value pairs. + Uses grep efficiently to minimize Python-side processing. + """ + print("\nParsing FJ (Feasibility Jump) legacy logs...") + print(f" Running grep on {len(log_files)} files...") + + # Use grep to efficiently extract ONLY lines with the exact "FJ:" pattern + # Note: FJ uses legacy format with just "FJ:" prefix (not FJ_FEATURES/FJ_RESULT) + # The colon ensures we don't match other FJ-related debug lines cmd = ['grep', '-H', 'FJ:'] + log_files result = subprocess.run(cmd, capture_output=True, text=True) - if result.returncode != 0 and result.returncode != 1: - # grep returns 1 if no matches found, which is fine - # Other return codes indicate actual errors - print(f"Error running grep: {result.stderr}") - return + if not result.stdout: + print(f" No FJ logs found") + return [] + + # Count lines for progress indication + total_lines = result.stdout.count('\n') + print(f" Processing {total_lines} matching lines...") - # Parse grep output + # Parse grep output efficiently entries = [] - for line in result.stdout.strip().split('\n'): + lines_processed = 0 + + for line in result.stdout.split('\n'): if not line: continue + lines_processed += 1 + + # Progress update every 10000 lines + if lines_processed % 10000 == 0: + print(f" Progress: {lines_processed}/{total_lines} lines, {len(entries)} entries", end='\r') + # Grep output format: filename:FJ: key1=value1 key2=value2 ... - # Split on first colon to separate filename from content - colon_idx = line.find(':') - if colon_idx == -1: + parts = line.split(':', 2) + if len(parts) < 3: continue - filename = os.path.basename(line[:colon_idx]) - rest = line[colon_idx + 1:] + filename = os.path.basename(parts[0]) + content = parts[2] # Remove "FJ:" prefix if present - if rest.startswith('FJ:'): - rest = rest[3:].strip() + if content.startswith('FJ:'): + content = content[3:].strip() # Parse key-value pairs entry = {'file': filename} - for kv_pair in rest.split(): + for kv_pair in content.split(): if '=' in kv_pair: key, value = kv_pair.split('=', 1) entry[key] = parse_value(value) @@ -108,18 +309,148 @@ def main(): if len(entry) > 1: entries.append(entry) - # Calculate statistics + # Clear progress line + if lines_processed > 0: + print(f" Found {len(entries)} entries from {total_lines} lines ") + + return entries + + +def print_statistics(entries: List[Dict[str, Any]], algorithm: str) -> None: + """Print statistics about parsed entries.""" + if not entries: + print(f"\n No entries found for {algorithm}") + return + unique_files = set(entry['file'] for entry in entries) avg_entries_per_file = len(entries) / len(unique_files) if unique_files else 0 + # Check if 'iter' field exists + has_iter = all('iter' in entry for entry in entries) + + if has_iter: + iter_values = [entry['iter'] for entry in entries] + min_iter = min(iter_values) + max_iter = max(iter_values) + avg_iter = sum(iter_values) / len(iter_values) + + print(f"\n Total entries: {len(entries)}") + print(f" Unique files: {len(unique_files)}") + print(f" Avg entries per file: {avg_entries_per_file:.2f}") + print(f" Iterations (target): min={min_iter}, max={max_iter}, avg={avg_iter:.2f}") + else: + print(f"\n Total entries: {len(entries)}") + print(f" Unique files: {len(unique_files)}") + print(f" Avg entries per file: {avg_entries_per_file:.2f}") + + # Show sample entry + if entries: + print(f"\n Sample entry (first):") + sample = entries[0] + for key, value in sorted(sample.items()): + print(f" {key}: {value}") + + +def main(): + parser = argparse.ArgumentParser( + description='Parse algorithm feature logs and export to pickle for training', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=f""" +Supported Algorithms: + FP - Feasibility Pump (parses FP_FEATURES and FP_RESULT logs) + PDLP - LP Solver (parses PDLP_FEATURES and PDLP_RESULT logs) + CP - Constraint Propagation (parses CP_FEATURES and CP_RESULT logs) + FJ - Feasibility Jump (parses legacy FJ: format) + +Examples: + python determinism_logs_parse.py logs/ --algorithm FP -o fp_data.pkl + python determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.pkl + python determinism_logs_parse.py logs/ --algorithm CP -o cp_data.pkl + python determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.pkl + """ + ) + + parser.add_argument( + 'input_dir', + help='Directory containing .log files to parse' + ) + parser.add_argument( + '--algorithm', '-a', + required=True, + choices=SUPPORTED_ALGORITHMS, + help='Algorithm to parse logs for' + ) + parser.add_argument( + '-o', '--output', + default=None, + help='Output pickle file path (default: _data.pkl)' + ) + parser.add_argument( + '--verbose', '-v', + action='store_true', + help='Print verbose output including warnings' + ) + + args = parser.parse_args() + + # Set default output filename based on algorithm + if args.output is None: + args.output = f"{args.algorithm.lower()}_data.pkl" + + # Find all .log files in the input directory + print(f"\nScanning {args.input_dir} for .log files...") + log_files = glob.glob(os.path.join(args.input_dir, '*.log')) + + if not log_files: + print(f"Error: No .log files found in {args.input_dir}") + return 1 + + print(f"Found {len(log_files)} log files") + + # Parse logs based on algorithm + if args.algorithm == 'FP': + entries = parse_fp_logs(log_files) + elif args.algorithm == 'PDLP': + entries = parse_pdlp_logs(log_files) + elif args.algorithm == 'CP': + entries = parse_cp_logs(log_files) + elif args.algorithm == 'FJ': + entries = parse_fj_logs(log_files) + else: + print(f"Error: Unsupported algorithm: {args.algorithm}") + return 1 + + if not entries: + print(f"\nError: No entries found for {args.algorithm}") + print(f"Make sure your logs contain {args.algorithm}_FEATURES and {args.algorithm}_RESULT lines") + return 1 + + # Print statistics + print_statistics(entries, args.algorithm) + # Save to pickle file + print(f"\nSaving {len(entries)} entries to {args.output}...") with open(args.output, 'wb') as f: pickle.dump(entries, f) - print(f"Parsed {len(entries)} entries from {len(unique_files)} log files") - print(f"Average entries per file: {avg_entries_per_file:.2f}") - print(f"Saved to {args.output}") + # Get file size + file_size_bytes = os.path.getsize(args.output) + file_size_mb = file_size_bytes / (1024 * 1024) + + print(f"\n{'='*70}") + print(f"✓ Success! Saved {len(entries)} entries to {args.output}") + print(f" File size: {file_size_mb:.2f} MB") + print(f"{'='*70}") + print(f"\nNext steps:") + print(f" 1. View available features:") + print(f" python scripts/train_regressor.py {args.output} --regressor xgboost --list-features") + print(f" 2. Train a model:") + print(f" python scripts/train_regressor.py {args.output} --regressor xgboost --seed 42") + print(f" 3. Train with early stopping and C++ export:") + print(f" python scripts/train_regressor.py {args.output} --regressor xgboost --seed 42 --early-stopping 20 --treelite-compile 8") + + return 0 if __name__ == '__main__': - main() + exit(main()) From c4d3fde547a6c31aab5e74d4406a94a0176284f3 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 31 Oct 2025 15:59:47 +0000 Subject: [PATCH 022/366] bump --- cpp/src/linear_programming/solve.cu | 2 +- cpp/src/mip/diversity/diversity_manager.cu | 17 ++ cpp/src/mip/relaxed_lp/relaxed_lp.cu | 9 - scripts/determinism_logs_parse.py | 268 +++++++++++++++--- scripts/train_regressor.py | 313 ++++++++++++++++++--- 5 files changed, 518 insertions(+), 91 deletions(-) diff --git a/cpp/src/linear_programming/solve.cu b/cpp/src/linear_programming/solve.cu index bd3080102..5699895b1 100644 --- a/cpp/src/linear_programming/solve.cu +++ b/cpp/src/linear_programming/solve.cu @@ -123,7 +123,7 @@ static void set_Stable2() pdlp_hyper_params::major_iteration = 40; pdlp_hyper_params::min_iteration_restart = 10; pdlp_hyper_params::restart_strategy = 1; - pdlp_hyper_params::never_restart_to_average = false; + pdlp_hyper_params::never_restart_to_average = true; pdlp_hyper_params::host_default_reduction_exponent = 0.3; pdlp_hyper_params::host_default_growth_exponent = 0.6; pdlp_hyper_params::host_default_primal_weight_update_smoothing = 0.5; diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index d4f21c180..f45132819 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -425,6 +425,23 @@ solution_t diversity_manager_t::run_solver() clamp_within_var_bounds(lp_optimal_solution, problem_ptr, problem_ptr->handle_ptr); } + // Run this 100 times with varying iteration limits + for (int i = 0; i < 100; i++) { + relaxed_lp_settings_t lp_settings; + lp_settings.time_limit = lp_time_limit; + lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; + lp_settings.return_first_feasible = false; + lp_settings.save_state = true; + lp_settings.concurrent_halt = &global_concurrent_halt; + lp_settings.has_initial_primal = false; + lp_settings.iteration_limit = 100000 + i * 10000; + rmm::device_uvector lp_optimal_solution_copy(lp_optimal_solution.size(), + problem_ptr->handle_ptr->get_stream()); + auto lp_result = + get_relaxed_lp_solution(*problem_ptr, lp_optimal_solution_copy, lp_state, lp_settings); + } + exit(0); + if (ls.lp_optimal_exists) { solution_t lp_rounded_sol(*problem_ptr); lp_rounded_sol.copy_new_assignment(lp_optimal_solution); diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index d8afa0da6..ad649feb2 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -164,15 +164,6 @@ optimization_problem_solution_t get_relaxed_lp_solution( term_info.number_of_steps_taken, elapsed_ms, (int)solver_response.get_termination_status()); - - CUOPT_LOG_INFO("PDLP_RESULT: primal_objective=%.10f dual_objective=%.10f gap=%.10f", - term_info.primal_objective, - term_info.dual_objective, - term_info.gap); - - CUOPT_LOG_INFO("PDLP_RESULT: l2_primal_residual=%.10f l2_dual_residual=%.10f", - term_info.l2_primal_residual, - term_info.l2_dual_residual); // === PDLP PREDICTOR RESULTS - END === return solver_response; diff --git a/scripts/determinism_logs_parse.py b/scripts/determinism_logs_parse.py index 687f0c651..0e0b9b5b1 100755 --- a/scripts/determinism_logs_parse.py +++ b/scripts/determinism_logs_parse.py @@ -42,18 +42,19 @@ - Real-time progress indicators (every 10K lines, every 10 files) Usage: - python determinism_logs_parse.py --algorithm FP [-o output.pkl] - python determinism_logs_parse.py --algorithm PDLP [-o output.pkl] - python determinism_logs_parse.py --algorithm CP [-o output.pkl] - python determinism_logs_parse.py --algorithm FJ [-o output.pkl] + python determinism_logs_parse.py --algorithm FP [-o output.feather] + python determinism_logs_parse.py --algorithm PDLP [-o output.feather] + python determinism_logs_parse.py --algorithm CP [-o output.feather] + python determinism_logs_parse.py --algorithm FJ [-o output.feather] """ import argparse -import pickle import subprocess import os import glob import re +import numpy as np +import pandas as pd from typing import List, Dict, Any, Optional, Tuple @@ -63,6 +64,11 @@ def parse_value(value_str: str) -> Any: """Convert string value to appropriate type (int, float, or str).""" try: + # Handle special float values first (nan, inf, -inf) + value_lower = value_str.lower() + if value_lower in ('nan', 'inf', '-inf', '+inf'): + return float(value_str) + # Try to parse as float if it contains a decimal point or scientific notation if '.' in value_str or 'e' in value_str.lower(): return float(value_str) @@ -158,7 +164,8 @@ def parse_generic_algorithm_logs(log_files: List[str], # Progress update every 10000 lines if lines_processed % 10000 == 0: - print(f" Progress: {lines_processed}/{total_lines} lines, {len(files_seen)} files", end='\r') + pct = (lines_processed / total_lines * 100) if total_lines > 0 else 0 + print(f" Progress: {pct:.1f}% ({lines_processed}/{total_lines} lines, {len(files_seen)} files)", end='\r') # Split on first two colons to get filename, linenum, and content # The rest of the line after the second colon is the log content @@ -191,8 +198,10 @@ def parse_generic_algorithm_logs(log_files: List[str], if lines_processed > 0: print(f" Processed {lines_processed} lines from {len(files_seen)} files ") - # Match features with results (pair them in order by line number) - print(f" Pairing features with results...") + # Match features with results + # IMPORTANT: Multiple FEATURES lines followed by multiple RESULT lines form ONE complete entry + # We need to merge consecutive lines of the same type, then combine them + print(f" Merging features and results...") entries = [] files_processed = 0 total_files = len(entries_by_file) @@ -202,26 +211,62 @@ def parse_generic_algorithm_logs(log_files: List[str], # Progress update every 10 files if files_processed % 10 == 0 or files_processed == total_files: - print(f" Pairing: {files_processed}/{total_files} files, {len(entries)} entries found", end='\r') - - features_list = sorted(data['features']) # Sort by line number - results_list = sorted(data['results']) - - # Pair up features and results in order - for i, (_, features) in enumerate(features_list): - if i < len(results_list): - _, results = results_list[i] + print(f" Merging: {files_processed}/{total_files} files, {len(entries)} entries found", end='\r') + + # Combine features and results by line number + # Group consecutive FEATURES lines and consecutive RESULT lines + all_items = [] + for linenum, features in data['features']: + all_items.append((linenum, 'features', features)) + for linenum, results in data['results']: + all_items.append((linenum, 'results', results)) + + # Sort by line number + all_items.sort(key=lambda x: x[0]) + + # Merge consecutive items of the same type + current_features = {} + current_results = {} + last_type = None + + for linenum, item_type, content in all_items: + # If we transition from RESULT back to FEATURES, save the previous entry + if item_type == 'features' and last_type == 'results': + if current_features and current_results: + # Create combined entry + entry = {'file': filename} + entry.update(current_features) + entry.update(current_results) + + # Rename 'iterations' to 'iter' for consistency + if 'iterations' in entry: + entry['iter'] = entry.pop('iterations') + + entries.append(entry) + + # Reset for next entry + current_features = {} + current_results = {} + + if item_type == 'features': + # Accumulate features + current_features.update(content) + else: # results + # Accumulate results + current_results.update(content) + + last_type = item_type + + # Don't forget the last entry in the file + if current_features and current_results: + entry = {'file': filename} + entry.update(current_features) + entry.update(current_results) + + if 'iterations' in entry: + entry['iter'] = entry.pop('iterations') - # Create combined entry - entry = {'file': filename} - entry.update(features) - entry.update(results) - - # Rename 'iterations' to 'iter' for consistency with train_regressor.py - if 'iterations' in entry: - entry['iter'] = entry.pop('iterations') - - entries.append(entry) + entries.append(entry) # Clear progress line and show final count if total_files > 0: @@ -284,7 +329,8 @@ def parse_fj_logs(log_files: List[str]) -> List[Dict[str, Any]]: # Progress update every 10000 lines if lines_processed % 10000 == 0: - print(f" Progress: {lines_processed}/{total_lines} lines, {len(entries)} entries", end='\r') + pct = (lines_processed / total_lines * 100) if total_lines > 0 else 0 + print(f" Progress: {pct:.1f}% ({lines_processed}/{total_lines} lines, {len(entries)} entries)", end='\r') # Grep output format: filename:FJ: key1=value1 key2=value2 ... parts = line.split(':', 2) @@ -353,7 +399,7 @@ def print_statistics(entries: List[Dict[str, Any]], algorithm: str) -> None: def main(): parser = argparse.ArgumentParser( - description='Parse algorithm feature logs and export to pickle for training', + description='Parse algorithm feature logs and export to Feather format for training', formatter_class=argparse.RawDescriptionHelpFormatter, epilog=f""" Supported Algorithms: @@ -363,10 +409,13 @@ def main(): FJ - Feasibility Jump (parses legacy FJ: format) Examples: - python determinism_logs_parse.py logs/ --algorithm FP -o fp_data.pkl - python determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.pkl - python determinism_logs_parse.py logs/ --algorithm CP -o cp_data.pkl - python determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.pkl + python determinism_logs_parse.py logs/ --algorithm FP -o fp_data.feather + python determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.feather + python determinism_logs_parse.py logs/ --algorithm CP -o cp_data.feather + python determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.feather + + # Limit to first 10 files for testing + python determinism_logs_parse.py logs/ --algorithm FP --max-files 10 """ ) @@ -383,19 +432,25 @@ def main(): parser.add_argument( '-o', '--output', default=None, - help='Output pickle file path (default: _data.pkl)' + help='Output Feather file path (default: _data.feather)' ) parser.add_argument( '--verbose', '-v', action='store_true', help='Print verbose output including warnings' ) + parser.add_argument( + '--max-files', + type=int, + default=None, + help='Limit number of log files to process (useful for testing)' + ) args = parser.parse_args() # Set default output filename based on algorithm if args.output is None: - args.output = f"{args.algorithm.lower()}_data.pkl" + args.output = f"{args.algorithm.lower()}_data.feather" # Find all .log files in the input directory print(f"\nScanning {args.input_dir} for .log files...") @@ -407,6 +462,14 @@ def main(): print(f"Found {len(log_files)} log files") + # Apply max-files limit if specified + if args.max_files is not None and args.max_files > 0: + if args.max_files < len(log_files): + log_files = log_files[:args.max_files] + print(f"Limiting to first {args.max_files} files (--max-files)") + else: + print(f"Note: --max-files={args.max_files} is >= total files, using all files") + # Parse logs based on algorithm if args.algorithm == 'FP': entries = parse_fp_logs(log_files) @@ -428,17 +491,142 @@ def main(): # Print statistics print_statistics(entries, args.algorithm) - # Save to pickle file - print(f"\nSaving {len(entries)} entries to {args.output}...") - with open(args.output, 'wb') as f: - pickle.dump(entries, f) + # Convert to DataFrame + df = pd.DataFrame(entries) + + # Validate: Check for NaN and infinite values + print(f"\nValidating data integrity...") + + # Check for NaN + nan_counts = df.isna().sum() + columns_with_nan = nan_counts[nan_counts > 0] + + # Check for infinite values in numeric columns + numeric_cols = df.select_dtypes(include=[np.number]).columns + inf_counts = {} + for col in numeric_cols: + inf_count = np.isinf(df[col]).sum() + if inf_count > 0: + inf_counts[col] = inf_count + + has_issues = len(columns_with_nan) > 0 or len(inf_counts) > 0 + + if has_issues: + print(f"\n{'='*70}") + print(f"ERROR: Invalid data detected (NaN/inf values)!") + print(f"{'='*70}") + print(f"\nColumns with invalid values:") + for col, count in columns_with_nan.items(): + pct = (count / len(df)) * 100 + print(f" {col}: {count} NaN ({pct:.1f}%)") + for col, count in inf_counts.items(): + pct = (count / len(df)) * 100 + print(f" {col}: {count} infinite ({pct:.1f}%)") + + # Show which log files have the issues + rows_with_issues_mask = df.isna().any(axis=1) + for col in inf_counts.keys(): + if col in df.columns: + rows_with_issues_mask |= np.isinf(df[col]) + rows_with_nan = df[rows_with_issues_mask] + problematic_files = rows_with_nan['file'].unique() + print(f"\nProblematic log files ({len(problematic_files)} total):") + for i, filename in enumerate(sorted(problematic_files)[:20], 1): + count_in_file = len(rows_with_nan[rows_with_nan['file'] == filename]) + print(f" {i}. {filename}: {count_in_file} invalid entries") + if len(problematic_files) > 20: + print(f" ... and {len(problematic_files) - 20} more files") + + print(f"\nSample rows with invalid data:") + cols_to_show = ['file'] + cols_to_show.extend([col for col in columns_with_nan.index]) + cols_to_show.extend([col for col in inf_counts.keys() if col not in columns_with_nan.index]) + cols_to_show = [col for col in cols_to_show if col in df.columns] + print(rows_with_nan[cols_to_show].head(10)) + + print(f"\nPossible causes:") + print(f" 1. Algorithm failed/crashed during execution (produced 'nan' in logs)") + print(f" 2. Incomplete log entries (missing FEATURES or RESULT lines)") + print(f" 3. Log format doesn't match expected pattern") + print(f"\nAction required:") + print(f" - Review the problematic log files listed above") + print(f" - Fix the underlying issues causing NaN values") + print(f" - Re-run the algorithm on those problem instances") + print(f"{'='*70}\n") + return 1 + + print(f" ✅ No missing values detected") + + # Filter out negative iterations (invalid data) + if 'iter' in df.columns: + negative_mask = df['iter'] < 0 + negative_count = negative_mask.sum() + + if negative_count > 0: + print(f"\n⚠️ Found {negative_count} entries with negative iterations ({negative_count/len(df)*100:.2f}%)") + + # Show which files have negative iterations + negative_entries = df[negative_mask] + problematic_files = negative_entries['file'].unique() + if len(problematic_files) <= 10: + print(f" Affected files: {', '.join(sorted(problematic_files))}") + else: + print(f" Affected files: {len(problematic_files)} files") + + # Drop negative iterations + df = df[~negative_mask].reset_index(drop=True) + print(f" → Dropped negative entries, remaining: {len(df)} entries") + + # Check if we have any data left + if len(df) == 0: + print(f"\n❌ Error: No valid entries remaining after filtering!") + print(f" All entries had negative iterations.") + return 1 + + # Print obtained features (all columns) + print(f"\nObtained Features ({len(df.columns)} total):") + print(f"{'='*70}") + + # Separate metadata from actual features + metadata_cols = ['file'] + target_cols = ['iter', 'iterations'] # Target variable (if present) + + feature_cols = [col for col in df.columns if col not in metadata_cols and col not in target_cols] + + # Print in categories + if metadata_cols: + meta_present = [col for col in metadata_cols if col in df.columns] + if meta_present: + print(f" Metadata: {', '.join(meta_present)}") + + target_present = [col for col in target_cols if col in df.columns] + if target_present: + print(f" Target: {', '.join(target_present)}") + + if feature_cols: + print(f" Features ({len(feature_cols)}):") + for i, col in enumerate(sorted(feature_cols), 1): + # Print 3 features per line for readability + if i % 3 == 1: + print(f" ", end="") + print(f"{col:30s}", end="") + if i % 3 == 0: + print() # Newline after 3 features + if len(feature_cols) % 3 != 0: + print() # Final newline if needed + + print(f"{'='*70}") + + # Save to Feather file (Apache Arrow format for fast I/O) + print(f"\nSaving {len(df)} entries to {args.output}...") + df.to_feather(args.output, compression='lz4') # Get file size file_size_bytes = os.path.getsize(args.output) file_size_mb = file_size_bytes / (1024 * 1024) print(f"\n{'='*70}") - print(f"✓ Success! Saved {len(entries)} entries to {args.output}") + print(f"✓ Success! Saved {len(df)} entries to {args.output}") print(f" File size: {file_size_mb:.2f} MB") print(f"{'='*70}") print(f"\nNext steps:") diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py index 7c5f1d298..f48208c4b 100755 --- a/scripts/train_regressor.py +++ b/scripts/train_regressor.py @@ -19,7 +19,7 @@ Train regression models to predict algorithm iterations from log features. Usage: - python train_regressor.py --regressor [options] + python train_regressor.py --regressor [options] """ import argparse @@ -77,7 +77,19 @@ 'max_related_vars', 'problem_size_score', 'structural_complexity', - 'tight_constraint_ratio' + 'tight_constraint_ratio', + 'tolerance', + 'time_limit', + 'tolerance', + 'primal_objective', + 'dual_objective', + 'gap', + 'l2_primal_residual', + 'l2_dual_residual', + 'detect_infeasibility', + 'iteration_limit', + 'termination', + 'check_infeasibility' ] # Alternatively, specify ONLY the features you want to use @@ -91,24 +103,33 @@ # ============================================================================ -def load_data(pkl_path: str) -> pd.DataFrame: - """Load pickle file and convert to DataFrame.""" - with open(pkl_path, 'rb') as f: - data = pickle.load(f) +def load_data(data_path: str, target_col: str = 'iter') -> pd.DataFrame: + """Load data file (supports .feather and legacy .pkl formats).""" + ext = os.path.splitext(data_path)[1].lower() - if not isinstance(data, list): - raise ValueError(f"Expected list of dictionaries, got {type(data)}") + if ext == '.feather': + # Fast Apache Arrow format + df = pd.read_feather(data_path) + elif ext == '.pkl': + # Legacy pickle support + with open(data_path, 'rb') as f: + data = pickle.load(f) - if len(data) == 0: - raise ValueError("Empty dataset") + if not isinstance(data, list): + raise ValueError(f"Expected list of dictionaries, got {type(data)}") - df = pd.DataFrame(data) + if len(data) == 0: + raise ValueError("Empty dataset") + + df = pd.DataFrame(data) + else: + raise ValueError(f"Unsupported file format: {ext}. Use .feather (recommended) or .pkl") # Validate required columns if 'file' not in df.columns: raise ValueError("Missing required 'file' column in data") - if 'iter' not in df.columns: - raise ValueError("Missing required 'iter' column in data") + if target_col not in df.columns: + raise ValueError(f"Missing target column '{target_col}' in data. Available columns: {list(df.columns)}") return df @@ -151,45 +172,170 @@ def split_by_files(df: pd.DataFrame, test_size: float = 0.2, print(f" Train entries: {len(train_df)} ({len(train_files)} files)") print(f" Test entries: {len(test_df)} ({len(test_files)} files)") - # Check distribution similarity - train_target_mean = train_df['iter'].mean() - test_target_mean = test_df['iter'].mean() - train_target_std = train_df['iter'].std() - test_target_std = test_df['iter'].std() + # Check distribution similarity (use stratify_by column if provided, otherwise first numeric column) + target_col = stratify_by if stratify_by else df.select_dtypes(include=[np.number]).columns[0] + if target_col in train_df.columns and target_col in test_df.columns: + train_target_mean = train_df[target_col].mean() + test_target_mean = test_df[target_col].mean() + train_target_std = train_df[target_col].std() + test_target_std = test_df[target_col].std() - print(f"\nTarget ('iter') Distribution:") - print(f" Train: mean={train_target_mean:.2f}, std={train_target_std:.2f}") - print(f" Test: mean={test_target_mean:.2f}, std={test_target_std:.2f}") + print(f"\nTarget ('{target_col}') Distribution:") + print(f" Train: mean={train_target_mean:.2f}, std={train_target_std:.2f}") + print(f" Test: mean={test_target_mean:.2f}, std={test_target_std:.2f}") - mean_diff_pct = abs(train_target_mean - test_target_mean) / train_target_mean * 100 - if mean_diff_pct > 10: - print(f" ⚠️ Warning: Train/test target means differ by {mean_diff_pct:.1f}%") - print(f" Consider using stratified split or different random seed") + mean_diff_pct = abs(train_target_mean - test_target_mean) / train_target_mean * 100 if train_target_mean != 0 else 0 + if mean_diff_pct > 10: + print(f" ⚠️ Warning: Train/test target means differ by {mean_diff_pct:.1f}%") + print(f" Consider using stratified split or different random seed") return train_df, test_df -def list_available_features(df: pd.DataFrame) -> List[str]: +def list_available_features(df: pd.DataFrame, target_col: str = 'iter') -> List[str]: """ List all available numeric features in the dataset. Helper function to see what features can be selected/excluded. """ - X = df.drop(columns=['iter', 'file'], errors='ignore') + # Drop target and metadata columns + cols_to_drop = [target_col, 'file', 'iter', 'iterations'] # Drop common target column names + X = df.drop(columns=[c for c in cols_to_drop if c in df.columns], errors='ignore') X = X.select_dtypes(include=[np.number]) return sorted(X.columns.tolist()) -def prepare_features(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.Series, List[str]]: +def validate_data_quality(df: pd.DataFrame, target_col: str = 'iter', verbose: bool = True) -> Tuple[bool, Dict[str, Any]]: + """ + Validate data quality and report issues. + + Returns + ------- + (is_valid, report_dict) + """ + report = { + 'target_issues': [], + 'feature_issues': [], + 'rows_with_issues': [], + 'is_valid': True + } + + # Check target variable + if target_col not in df.columns: + report['is_valid'] = False + report['target_issues'].append(f"Missing '{target_col}' column") + return False, report + + y = df[target_col] + + # Check for NaN in target + nan_count = y.isna().sum() + if nan_count > 0: + report['is_valid'] = False + report['target_issues'].append(f"Target has {nan_count} NaN values ({nan_count/len(y)*100:.1f}%)") + report['rows_with_issues'].extend(df[y.isna()].index.tolist()) + + # Check for inf in target + inf_count = np.isinf(y).sum() + if inf_count > 0: + report['is_valid'] = False + report['target_issues'].append(f"Target has {inf_count} infinite values ({inf_count/len(y)*100:.1f}%)") + report['rows_with_issues'].extend(df[np.isinf(y)].index.tolist()) + + # Check for extreme values in target + if not y.isna().all() and not np.isinf(y).all(): + y_clean = y[~(y.isna() | np.isinf(y))] + if len(y_clean) > 0: + y_max = y_clean.max() + y_min = y_clean.min() + if y_max > 1e10: + report['target_issues'].append(f"Target has very large values (max={y_max:.2e})") + if y_min < -1e10: + report['target_issues'].append(f"Target has very large negative values (min={y_min:.2e})") + + # Check features + X = df.drop(columns=[target_col, 'file'], errors='ignore') + X_numeric = X.select_dtypes(include=[np.number]) + + for col in X_numeric.columns: + col_data = X_numeric[col] + + # Check for NaN + nan_count = col_data.isna().sum() + if nan_count > 0: + pct = nan_count / len(col_data) * 100 + if pct > 10: # Only report if > 10% are NaN + report['feature_issues'].append(f"{col}: {nan_count} NaN ({pct:.1f}%)") + + # Check for inf + inf_count = np.isinf(col_data).sum() + if inf_count > 0: + report['is_valid'] = False + pct = inf_count / len(col_data) * 100 + report['feature_issues'].append(f"{col}: {inf_count} infinite ({pct:.1f}%)") + report['rows_with_issues'].extend(df[np.isinf(col_data)].index.tolist()) + + # Deduplicate row indices + report['rows_with_issues'] = sorted(list(set(report['rows_with_issues']))) + + # Print report if verbose + if verbose and (report['target_issues'] or report['feature_issues']): + print("\n" + "="*70) + print("DATA QUALITY ISSUES DETECTED") + print("="*70) + + if report['target_issues']: + print("\nTarget Variable Issues:") + for issue in report['target_issues']: + print(f" ❌ {issue}") + + if report['feature_issues']: + print(f"\nFeature Issues ({len(report['feature_issues'])} features affected):") + for issue in report['feature_issues'][:10]: # Show first 10 + print(f" ⚠️ {issue}") + if len(report['feature_issues']) > 10: + print(f" ... and {len(report['feature_issues']) - 10} more features") + + if report['rows_with_issues']: + print(f"\nAffected Rows: {len(report['rows_with_issues'])} total") + if len(report['rows_with_issues']) <= 10: + print(f" Row indices: {report['rows_with_issues']}") + else: + print(f" First 10: {report['rows_with_issues'][:10]}") + + # Show sample problematic rows with filenames + if 'file' in df.columns: + print(f"\n Sample problematic entries:") + sample_indices = report['rows_with_issues'][:5] + for idx in sample_indices: + if idx < len(df): + filename = df.iloc[idx].get('file', 'unknown') + iter_val = df.iloc[idx].get('iter', 'N/A') + print(f" Row {idx}: file={filename}, iter={iter_val}") + + print("\nSuggested Actions:") + if not report['is_valid']: + print(" 1. Remove rows with invalid data: --drop-invalid-rows") + print(" 2. Check your log files for data collection issues") + print(" 3. Verify algorithm didn't produce invalid results") + else: + print(" Data is valid but has some NaN values in features (will be handled)") + + print("="*70 + "\n") + + return report['is_valid'], report + + +def prepare_features(df: pd.DataFrame, target_col: str = 'iter') -> Tuple[pd.DataFrame, pd.Series, List[str]]: """ Prepare features and target from DataFrame. - Excludes 'file' and 'iter' from features. + Excludes 'file' and target column from features. Applies feature selection based on FEATURES_TO_EXCLUDE and FEATURES_TO_INCLUDE_ONLY. """ # Separate target - y = df['iter'].copy() + y = df[target_col].copy() # Drop non-feature columns - X = df.drop(columns=['iter', 'file']) + X = df.drop(columns=[target_col, 'file']) # Ensure all features are numeric non_numeric = X.select_dtypes(exclude=[np.number]).columns.tolist() @@ -954,13 +1100,26 @@ def main(): random_forest - Random Forest Regressor gradient_boosting - Gradient Boosting Regressor -Example: +Examples: + # Train a model (default target: iter) + python train_regressor.py data.feather --regressor xgboost --seed 42 + python train_regressor.py data.feather --regressor lightgbm --seed 42 + + # Train to predict time instead of iterations + python train_regressor.py data.feather --regressor xgboost --target time_ms --seed 42 + + # Check data quality before training + python train_regressor.py data.feather --regressor xgboost --check-data + + # Train with automatic removal of invalid rows + python train_regressor.py data.feather --regressor xgboost --drop-invalid-rows --seed 42 + + # Legacy pickle format python train_regressor.py data.pkl --regressor xgboost --seed 42 - python train_regressor.py data.pkl --regressor lightgbm --seed 42 """ ) - parser.add_argument('input_pkl', help='Input pickle file with log data') + parser.add_argument('input_pkl', help='Input data file (.feather or .pkl) with log data') parser.add_argument( '--regressor', '-r', required=True, @@ -1013,7 +1172,7 @@ def main(): parser.add_argument( '--early-stopping', type=int, - default=20, + default=0, metavar='N', help='Enable early stopping for tree models (default: 20 rounds, use 0 to disable)' ) @@ -1024,6 +1183,22 @@ def main(): metavar='THREADS', help='Export XGBoost/LightGBM model as optimized C source code with TL2cgen (includes branch annotation and quantization)' ) + parser.add_argument( + '--drop-invalid-rows', + action='store_true', + help='Drop rows with NaN or infinite values in target variable (instead of failing)' + ) + parser.add_argument( + '--check-data', + action='store_true', + help='Run data quality checks and exit (no training)' + ) + parser.add_argument( + '--target', + type=str, + default='iter', + help='Target column to predict (default: iter). Examples: iter, time_ms, iterations' + ) args = parser.parse_args() @@ -1034,37 +1209,93 @@ def main(): # Load data print(f"\nLoading data from: {args.input_pkl}") - df = load_data(args.input_pkl) + print(f"Target column: '{args.target}'") + df = load_data(args.input_pkl, target_col=args.target) print(f"Loaded {len(df)} entries with {len(df.columns)} columns") + # Report file format + ext = os.path.splitext(args.input_pkl)[1].lower() + if ext == '.feather': + print(f" Format: Apache Arrow/Feather (fast I/O)") + elif ext == '.pkl': + print(f" Format: Pickle (legacy)") + # Extract model name from input file (for prefixing generated C functions) model_name = os.path.splitext(os.path.basename(args.input_pkl))[0] + # Validate data quality + print("\nValidating data quality...") + is_valid, report = validate_data_quality(df, target_col=args.target, verbose=True) + + # If just checking data, exit here + if args.check_data: + if is_valid: + print("\n✅ Data quality check passed! Ready for training.") + return 0 + else: + print("\n❌ Data quality check failed! Fix issues before training.") + return 1 + + # Handle invalid data + if not is_valid: + if args.drop_invalid_rows: + print(f"\nDropping {len(report['rows_with_issues'])} rows with invalid data...") + df_clean = df.drop(index=report['rows_with_issues']) + df = df_clean.reset_index(drop=True) + print(f" Remaining: {len(df)} entries") + + # Re-validate + is_valid_after, _ = validate_data_quality(df, verbose=False) + if not is_valid_after: + print("❌ Error: Data still invalid after dropping rows. Check your data.") + return 1 + print(" ✅ Data is now valid") + else: + print("\n❌ Training aborted due to invalid data.") + print(" Use --drop-invalid-rows to automatically remove invalid rows, or") + print(" Use --check-data to just run validation without training") + return 1 + # If listing features, do that and exit if args.list_features: - features = list_available_features(df) + features = list_available_features(df, target_col=args.target) + + # Also list potential target columns + numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() + potential_targets = [col for col in numeric_cols if col not in features] + print(f"\n{'='*70}") print(f"Available features in dataset ({len(features)} total):") print(f"{'='*70}") for i, feat in enumerate(features, 1): print(f" {i:3d}. {feat}") + + if potential_targets: + print(f"\n{'='*70}") + print(f"Potential target columns ({len(potential_targets)} total):") + print(f"{'='*70}") + for i, col in enumerate(potential_targets, 1): + marker = " (current)" if col == args.target else "" + print(f" {i:3d}. {col}{marker}") + print(f"\nTo exclude features, edit FEATURES_TO_EXCLUDE in the script:") print(f" {__file__}") print(f"\nTo use only specific features, edit FEATURES_TO_INCLUDE_ONLY") + print(f"\nTo change target column, use: --target ") return # Split data by files - stratify_by = 'iter' if args.stratify_split else None + stratify_by = args.target if args.stratify_split else None train_df, test_df = split_by_files(df, test_size=args.test_size, random_state=args.seed, stratify_by=stratify_by) # Prepare features - X_train, y_train, feature_names = prepare_features(train_df) - X_test, y_test, _ = prepare_features(test_df) + X_train, y_train, feature_names = prepare_features(train_df, target_col=args.target) + X_test, y_test, _ = prepare_features(test_df, target_col=args.target) print(f"\nFeatures: {len(feature_names)}") - print(f"Target: iter (predicting number of iterations)") + print(f"Target: {args.target} (prediction target)") # Create model print(f"\nTraining {args.regressor} regressor...") From 67808934c7b8e37fca9618d9a26e037241dcc514 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 31 Oct 2025 16:07:24 +0000 Subject: [PATCH 023/366] bump --- cpp/src/mip/diversity/diversity_manager.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index f45132819..1b56bb6e5 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -434,7 +434,7 @@ solution_t diversity_manager_t::run_solver() lp_settings.save_state = true; lp_settings.concurrent_halt = &global_concurrent_halt; lp_settings.has_initial_primal = false; - lp_settings.iteration_limit = 100000 + i * 10000; + lp_settings.iteration_limit = 100 + i * 100; rmm::device_uvector lp_optimal_solution_copy(lp_optimal_solution.size(), problem_ptr->handle_ptr->get_stream()); auto lp_result = From 7474ff0a8fcb14e1dae3b4348cec1e039f4501fa Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 4 Nov 2025 14:14:42 +0000 Subject: [PATCH 024/366] work unit timers --- .../linear_programming/cuopt/run_mip.cpp | 21 ++- .../mip/solver_settings.hpp | 1 + cpp/src/mip/diversity/diversity_manager.cu | 34 +++-- cpp/src/mip/diversity/diversity_manager.cuh | 8 +- cpp/src/mip/diversity/population.cu | 4 +- cpp/src/mip/diversity/population.cuh | 4 +- .../recombiners/bound_prop_recombiner.cuh | 6 +- .../diversity/recombiners/fp_recombiner.cuh | 7 +- .../recombiners/line_segment_recombiner.cuh | 3 +- .../feasibility_pump/feasibility_pump.cu | 5 +- .../feasibility_pump/feasibility_pump.cuh | 4 +- .../line_segment_search.cu | 4 +- .../line_segment_search.cuh | 4 +- cpp/src/mip/local_search/local_search.cu | 42 +++--- cpp/src/mip/local_search/local_search.cuh | 22 +-- .../local_search/rounding/bounds_repair.cu | 2 +- .../local_search/rounding/bounds_repair.cuh | 4 +- .../local_search/rounding/constraint_prop.cu | 19 +-- .../local_search/rounding/constraint_prop.cuh | 8 +- .../rounding/lb_bounds_repair.cuh | 4 +- .../rounding/lb_constraint_prop.cu | 4 +- .../rounding/lb_constraint_prop.cuh | 8 +- cpp/src/mip/presolve/lb_probing_cache.cu | 4 +- cpp/src/mip/presolve/probing_cache.cu | 4 +- cpp/src/mip/presolve/probing_cache.cuh | 3 +- cpp/src/mip/solve.cu | 6 +- cpp/src/mip/solver.cu | 2 +- cpp/src/mip/solver.cuh | 6 +- cpp/src/utilities/work_limit_timer.hpp | 136 ++++++++++++++++++ cpp/tests/mip/diversity_test.cu | 14 +- cpp/tests/mip/local_search_test.cu | 4 +- cpp/tests/mip/mip_utils.cuh | 2 +- cpp/tests/mip/multi_probe_test.cu | 6 +- cpp/tests/mip/presolve_test.cu | 11 +- 34 files changed, 305 insertions(+), 111 deletions(-) create mode 100644 cpp/src/utilities/work_limit_timer.hpp diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 936a32081..da3168a56 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -157,7 +157,8 @@ int run_single_file(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, - double time_limit) + double time_limit, + bool deterministic) { const raft::handle_t handle_{}; cuopt::linear_programming::mip_solver_settings_t settings; @@ -211,6 +212,7 @@ int run_single_file(std::string file_path, settings.heuristics_only = heuristics_only; settings.num_cpu_threads = num_cpu_threads; settings.log_to_console = log_to_console; + settings.deterministic = deterministic; settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; @@ -263,7 +265,8 @@ void run_single_file_mp(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, - double time_limit) + double time_limit, + bool deterministic) { std::cout << "running file " << file_path << " on gpu : " << device << std::endl; auto memory_resource = make_async(); @@ -278,7 +281,8 @@ void run_single_file_mp(std::string file_path, num_cpu_threads, write_log_file, log_to_console, - time_limit); + time_limit, + deterministic); // this is a bad design to communicate the result but better than adding complexity of IPC or // pipes exit(sol_found); @@ -361,6 +365,10 @@ int main(int argc, char* argv[]) .help("track allocations (t/f)") .default_value(std::string("f")); + program.add_argument("-d", "--determinism") + .help("enable deterministic mode (t/f)") + .default_value(std::string("f")); + // Parse arguments try { program.parse_args(argc, argv); @@ -389,6 +397,7 @@ int main(int argc, char* argv[]) bool log_to_console = program.get("--log-to-console")[0] == 't'; double memory_limit = program.get("--memory-limit"); bool track_allocations = program.get("--track-allocations")[0] == 't'; + bool deterministic = program.get("--determinism")[0] == 't'; if (num_cpu_threads < 0) { num_cpu_threads = omp_get_max_threads() / n_gpus; } @@ -476,7 +485,8 @@ int main(int argc, char* argv[]) num_cpu_threads, write_log_file, log_to_console, - time_limit); + time_limit, + deterministic); } else if (sys_pid < 0) { std::cerr << "Fork failed!" << std::endl; exit(1); @@ -516,7 +526,8 @@ int main(int argc, char* argv[]) num_cpu_threads, write_log_file, log_to_console, - time_limit); + time_limit, + deterministic); } return 0; diff --git a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp index 8e49f8013..07cc108b5 100644 --- a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp +++ b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp @@ -89,6 +89,7 @@ class mip_solver_settings_t { tolerances_t tolerances; f_t time_limit = std::numeric_limits::infinity(); + f_t work_limit = std::numeric_limits::infinity(); bool heuristics_only = false; i_t num_cpu_threads = -1; // -1 means use default number of threads in branch and bound bool log_to_console = true; diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 1b56bb6e5..328757e11 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -57,7 +57,7 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_tn_constraints, context.problem_ptr->handle_ptr->get_stream()), ls(context, lp_optimal_solution), - timer(diversity_config.default_time_limit), + timer(context.settings.deterministic, diversity_config.default_time_limit), bound_prop_recombiner(context, context.problem_ptr->n_variables, ls.constraint_prop, @@ -118,7 +118,7 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_t bool diversity_manager_t::run_local_search(solution_t& solution, const weight_t& weights, - timer_t& timer, + work_limit_timer_t& timer, ls_config_t& ls_config) { raft::common::nvtx::range fun_scope("run_local_search"); @@ -189,8 +189,9 @@ bool diversity_manager_t::run_presolve(f_t time_limit) { raft::common::nvtx::range fun_scope("run_presolve"); CUOPT_LOG_INFO("Running presolve!"); - timer_t presolve_timer(time_limit); + work_limit_timer_t presolve_timer(context.settings.deterministic, time_limit); auto term_crit = ls.constraint_prop.bounds_update.solve(*problem_ptr); + presolve_timer.record_work(213762); if (ls.constraint_prop.bounds_update.infeas_constraints_count > 0) { stats.presolve_time = timer.elapsed_time(); return false; @@ -231,9 +232,10 @@ void diversity_manager_t::generate_quick_feasible_solution() // min 1 second, max 10 seconds const f_t generate_fast_solution_time = std::min(diversity_config.max_fast_sol_time, std::max(1., timer.remaining_time() / 20.)); - timer_t sol_timer(generate_fast_solution_time); + work_limit_timer_t sol_timer(context.settings.deterministic, generate_fast_solution_time); // do very short LP run to get somewhere close to the optimal point ls.generate_fast_solution(solution, sol_timer); + sol_timer.record_work(213762); if (solution.get_feasible()) { population.run_solution_callbacks(solution); initial_sol_vector.emplace_back(std::move(solution)); @@ -323,6 +325,7 @@ solution_t diversity_manager_t::run_solver() raft::common::nvtx::range fun_scope("run_solver"); diversity_config.fj_only_run = false; + if (context.settings.deterministic) { remaining_work_limit = context.settings.work_limit; } population.timer = timer; const f_t time_limit = timer.remaining_time(); @@ -360,10 +363,11 @@ solution_t diversity_manager_t::run_solver() const f_t max_time_on_probing = diversity_config.max_time_on_probing; f_t time_for_probing_cache = std::min(max_time_on_probing, time_limit * time_ratio_of_probing_cache); - timer_t probing_timer{time_for_probing_cache}; + work_limit_timer_t probing_timer{context.settings.deterministic, time_for_probing_cache}; if (check_b_b_preemption()) { return population.best_feasible(); } if (!diversity_config.fj_only_run) { compute_probing_cache(ls.constraint_prop.bounds_update, *problem_ptr, probing_timer); + probing_timer.record_work(213762); } if (check_b_b_preemption()) { return population.best_feasible(); } @@ -469,7 +473,7 @@ solution_t diversity_manager_t::run_solver() generate_solution(timer.remaining_time(), false); printf("=======================================================\n"); if (diversity_config.initial_solution_only) { return population.best_feasible(); } - if (timer.check_time_limit()) { + if (work_limit_reached()) { population.add_external_solutions_to_population(); return population.best_feasible(); } @@ -495,7 +499,7 @@ void diversity_manager_t::diversity_step(i_t max_iterations_without_im CUOPT_LOG_DEBUG("Population degenerated in diversity step"); return; } - if (timer.check_time_limit()) return; + if (work_limit_reached()) return; constexpr bool tournament = true; auto [sol1, sol2] = population.get_two_random(tournament); cuopt_assert(population.test_invariant(), ""); @@ -548,7 +552,7 @@ void diversity_manager_t::recombine_and_ls_with_all(solution_t::recombine_and_ls_with_all( population.add_solution(std::move(solution_t(sol))); } for (auto& sol : solutions) { - if (timer.check_time_limit()) { return; } + if (work_limit_reached()) { return; } solution_t ls_solution(sol); ls_config_t ls_config; run_local_search(ls_solution, population.weights, timer, ls_config); - if (timer.check_time_limit()) { return; } + if (work_limit_reached()) { return; } // TODO try if running LP with integers fixed makes it feasible if (ls_solution.get_feasible()) { CUOPT_LOG_DEBUG("LS searched solution feasible, running recombiners!"); @@ -806,6 +810,16 @@ void diversity_manager_t::set_simplex_solution(const std::vector& context.handle_ptr->sync_stream(); } +template +bool diversity_manager_t::work_limit_reached() +{ + if (context.settings.deterministic) { + return remaining_work_limit <= 0; + } else { + return work_limit_reached(); + } +} + #if MIP_INSTANTIATE_FLOAT template class diversity_manager_t; #endif diff --git a/cpp/src/mip/diversity/diversity_manager.cuh b/cpp/src/mip/diversity/diversity_manager.cuh index 5e94b1ecc..a1fbd6438 100644 --- a/cpp/src/mip/diversity/diversity_manager.cuh +++ b/cpp/src/mip/diversity/diversity_manager.cuh @@ -34,7 +34,7 @@ #include #include #include -#include +#include namespace cuopt::linear_programming::detail { @@ -72,8 +72,9 @@ class diversity_manager_t { solution_t& sol2); bool run_local_search(solution_t& solution, const weight_t& weights, - timer_t& timer, + work_limit_timer_t& timer, ls_config_t& ls_config); + bool work_limit_reached(); void set_simplex_solution(const std::vector& solution, const std::vector& dual_solution, @@ -87,7 +88,8 @@ class diversity_manager_t { rmm::device_uvector lp_dual_optimal_solution; std::atomic simplex_solution_exists{false}; local_search_t ls; - cuopt::timer_t timer; + cuopt::work_limit_timer_t timer; + f_t remaining_work_limit{std::numeric_limits::infinity()}; bound_prop_recombiner_t bound_prop_recombiner; fp_recombiner_t fp_recombiner; line_segment_recombiner_t line_segment_recombiner; diff --git a/cpp/src/mip/diversity/population.cu b/cpp/src/mip/diversity/population.cu index 03f658a0b..56eedaf4a 100644 --- a/cpp/src/mip/diversity/population.cu +++ b/cpp/src/mip/diversity/population.cu @@ -54,7 +54,7 @@ population_t::population_t(std::string const& name_, rng(cuopt::seed_generator::get_seed()), early_exit_primal_generation(false), population_hash_map(*problem_ptr), - timer(0) + timer(context.settings.deterministic, 0) { best_feasible_objective = std::numeric_limits::max(); } @@ -720,7 +720,7 @@ void population_t::start_threshold_adjustment() } template -void population_t::adjust_threshold(cuopt::timer_t timer) +void population_t::adjust_threshold(cuopt::work_limit_timer_t timer) { double time_ratio = (timer.elapsed_time() - population_start_time) / (timer.get_time_limit() - population_start_time); diff --git a/cpp/src/mip/diversity/population.cuh b/cpp/src/mip/diversity/population.cuh index deeca2f94..167e6690f 100644 --- a/cpp/src/mip/diversity/population.cuh +++ b/cpp/src/mip/diversity/population.cuh @@ -131,7 +131,7 @@ class population_t { // updates qualities of each solution void update_qualities(); // adjusts the threshold of the population - void adjust_threshold(cuopt::timer_t timer); + void adjust_threshold(cuopt::work_limit_timer_t timer); /*! \param sol { Input solution } * \return { Index of the best solution similar to sol. If no similar is found we return * max_solutions. }*/ @@ -210,7 +210,7 @@ class population_t { std::atomic preempt_heuristic_solver_ = false; f_t best_feasible_objective = std::numeric_limits::max(); assignment_hash_map_t population_hash_map; - cuopt::timer_t timer; + cuopt::work_limit_timer_t timer; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh index 9a4df6796..756942819 100644 --- a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh @@ -187,7 +187,8 @@ class bound_prop_recombiner_t : public recombiner_t { if (guiding_solution.get_feasible() && !a.problem_ptr->expensive_to_fix_vars) { this->compute_vars_to_fix(offspring, vars_to_fix, n_vars_from_other, n_vars_from_guiding); auto [fixed_problem, fixed_assignment, variable_map] = offspring.fix_variables(vars_to_fix); - timer_t timer(bp_recombiner_config_t::bounds_prop_time_limit); + work_limit_timer_t timer(this->context.settings.deterministic, + bp_recombiner_config_t::bounds_prop_time_limit); rmm::device_uvector old_assignment(offspring.assignment, offspring.handle_ptr->get_stream()); offspring.handle_ptr->sync_stream(); @@ -237,7 +238,8 @@ class bound_prop_recombiner_t : public recombiner_t { } a.handle_ptr->sync_stream(); } else { - timer_t timer(bp_recombiner_config_t::bounds_prop_time_limit); + work_limit_timer_t timer(this->context.settings.deterministic, + bp_recombiner_config_t::bounds_prop_time_limit); get_probing_values_for_infeasible( guiding_solution, other_solution, offspring, probing_values, n_vars_from_other); probing_config.probing_values = host_copy(probing_values); diff --git a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh index f52de0e7b..f4ca9d5c4 100644 --- a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh @@ -122,9 +122,12 @@ class fp_recombiner_t : public recombiner_t { offspring.handle_ptr->sync_stream(); offspring.assignment = std::move(fixed_assignment); cuopt_func_call(offspring.test_variable_bounds(false)); - timer_t timer(fp_recombiner_config_t::fp_time_limit); + work_limit_timer_t timer(this->context.settings.deterministic, + fp_recombiner_config_t::fp_time_limit); if (this->context.settings.deterministic) { - timer = timer_t(std::numeric_limits::max()); // TODO should be global time limit + timer = work_limit_timer_t( + this->context.settings.deterministic, + std::numeric_limits::max()); // TODO should be global time limit } fp.timer = timer; fp.cycle_queue.reset(offspring); diff --git a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh index e8cf0d30e..dce1f05cf 100644 --- a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh @@ -86,7 +86,8 @@ class line_segment_recombiner_t : public recombiner_t { auto& other_solution = a.get_feasible() ? b : a; // copy the solution from A solution_t offspring(guiding_solution); - timer_t line_segment_timer{ls_recombiner_config_t::time_limit}; + work_limit_timer_t line_segment_timer{this->context.settings.deterministic, + ls_recombiner_config_t::time_limit}; // TODO after we have the conic combination, detect the lambda change // (i.e. the integral variables flip on line segment) i_t n_points_to_search = ls_recombiner_config_t::n_points_to_search; diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index dfd153476..d8472dae0 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -61,7 +61,7 @@ feasibility_pump_t::feasibility_pump_t( context.problem_ptr->handle_ptr->get_stream()), lp_optimal_solution(lp_optimal_solution_), rng(cuopt::seed_generator::get_seed()), - timer(20.) + timer(context.settings.deterministic, 20.) { thrust::fill(context.problem_ptr->handle_ptr->get_thrust_policy(), last_projection.begin(), @@ -271,7 +271,8 @@ bool feasibility_pump_t::round(solution_t& solution) { bool result; CUOPT_LOG_DEBUG("Rounding the point"); - timer_t bounds_prop_timer(std::max(0.05, std::min(0.5, timer.remaining_time() / 10.))); + work_limit_timer_t bounds_prop_timer(context.settings.deterministic, + std::max(0.05, std::min(0.5, timer.remaining_time() / 10.))); const f_t lp_run_time_after_feasible = 0.; bool old_var = constraint_prop.round_all_vars; f_t old_time = constraint_prop.max_time_for_bounds_prop; diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh index f4f12808b..9888f5a2c 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh @@ -136,7 +136,7 @@ class feasibility_pump_t { bool check_distance_cycle(solution_t& solution); void reset(); void resize_vectors(problem_t& problem, const raft::handle_t* handle_ptr); - bool random_round_with_fj(solution_t& solution, timer_t& round_timer); + bool random_round_with_fj(solution_t& solution, work_limit_timer_t& round_timer); bool round_multiple_points(solution_t& solution); void relax_general_integers(solution_t& solution); void revert_relaxation(solution_t& solution); @@ -163,7 +163,7 @@ class feasibility_pump_t { f_t proj_begin; i_t n_fj_single_descents; i_t max_n_of_integers = 0; - cuopt::timer_t timer; + cuopt::work_limit_timer_t timer; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu index 18c529fc1..6371b7bd5 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu @@ -140,7 +140,7 @@ bool line_segment_search_t::search_line_segment( const rmm::device_uvector& point_2, const rmm::device_uvector& delta_vector, bool is_feasibility_run, - cuopt::timer_t& timer) + cuopt::work_limit_timer_t& timer) { CUOPT_LOG_DEBUG("Running line segment search with a given delta vector"); cuopt_assert(point_1.size() == point_2.size(), "size mismatch"); @@ -286,7 +286,7 @@ bool line_segment_search_t::search_line_segment(solution_t& const rmm::device_uvector& point_1, const rmm::device_uvector& point_2, bool is_feasibility_run, - cuopt::timer_t& timer) + cuopt::work_limit_timer_t& timer) { CUOPT_LOG_DEBUG("Running line segment search"); cuopt_assert(point_1.size() == point_2.size(), "size mismatch"); diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh index 3d89d0e83..f3cc2c65c 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh @@ -43,14 +43,14 @@ class line_segment_search_t { const rmm::device_uvector& point_1, const rmm::device_uvector& point_2, bool is_feasibility_run, - cuopt::timer_t& timer); + cuopt::work_limit_timer_t& timer); bool search_line_segment(solution_t& solution, const rmm::device_uvector& point_1, const rmm::device_uvector& point_2, const rmm::device_uvector& delta_vector, bool is_feasibility_run, - cuopt::timer_t& timer); + cuopt::work_limit_timer_t& timer); void save_solution_if_better(solution_t& solution, const rmm::device_uvector& point_1, diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index f3f8888b2..0f2be589a 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -24,7 +24,7 @@ #include #include #include -#include +#include #include @@ -234,7 +234,7 @@ bool local_search_t::do_fj_solve(solution_t& solution, { if (time_limit == 0.) return solution.get_feasible(); - timer_t timer(time_limit); + work_limit_timer_t timer(context.settings.deterministic, time_limit); auto h_weights = cuopt::host_copy(in_fj.cstr_weights, solution.handle_ptr->get_stream()); auto h_objective_weight = in_fj.objective_weight.value(solution.handle_ptr->get_stream()); @@ -316,7 +316,8 @@ bool local_search_t::do_fj_solve(solution_t& solution, } template -void local_search_t::generate_fast_solution(solution_t& solution, timer_t timer) +void local_search_t::generate_fast_solution(solution_t& solution, + work_limit_timer_t timer) { CUOPT_LOG_DEBUG("Running FJ fast sol"); thrust::fill(solution.handle_ptr->get_thrust_policy(), @@ -332,9 +333,11 @@ void local_search_t::generate_fast_solution(solution_t& solu // CHANGE fj.settings.time_limit = std::numeric_limits::infinity(); while (!timer.check_time_limit()) { - timer_t constr_prop_timer = timer_t(std::min(timer.remaining_time(), 2.)); + work_limit_timer_t constr_prop_timer = + work_limit_timer_t(context.settings.deterministic, std::min(timer.remaining_time(), 2.)); // do constraint prop on lp optimal solution constraint_prop.apply_round(solution, 1., constr_prop_timer); + constr_prop_timer.record_work(213762); if (solution.compute_feasibility()) { return; } if (timer.check_time_limit()) { return; }; f_t time_limit = std::min(3., timer.remaining_time()); @@ -349,7 +352,7 @@ void local_search_t::generate_fast_solution(solution_t& solu template bool local_search_t::run_local_search(solution_t& solution, const weight_t& weights, - timer_t timer, + work_limit_timer_t timer, const ls_config_t& ls_config) { raft::common::nvtx::range fun_scope("local search"); @@ -359,10 +362,10 @@ bool local_search_t::run_local_search(solution_t& solution, if (!solution.get_feasible()) { if (ls_config.at_least_one_parent_feasible) { fj_settings.time_limit = 0.5; - timer = timer_t(fj_settings.time_limit); + timer = work_limit_timer_t(context.settings.deterministic, fj_settings.time_limit); } else { fj_settings.time_limit = 0.25; - timer = timer_t(fj_settings.time_limit); + timer = work_limit_timer_t(context.settings.deterministic, fj_settings.time_limit); } } else { fj_settings.time_limit = std::min(1., timer.remaining_time()); @@ -393,7 +396,7 @@ bool local_search_t::run_local_search(solution_t& solution, template bool local_search_t::run_fj_until_timer(solution_t& solution, const weight_t& weights, - timer_t timer) + work_limit_timer_t timer) { CUOPT_LOG_DEBUG("Running FJ until timer"); bool is_feasible; @@ -412,7 +415,7 @@ bool local_search_t::run_fj_until_timer(solution_t& solution template bool local_search_t::run_fj_annealing(solution_t& solution, - timer_t timer, + work_limit_timer_t timer, const ls_config_t& ls_config) { raft::common::nvtx::range fun_scope("run_fj_annealing"); @@ -442,7 +445,7 @@ bool local_search_t::run_fj_annealing(solution_t& solution, template bool local_search_t::run_fj_line_segment(solution_t& solution, - timer_t timer, + work_limit_timer_t timer, const ls_config_t& ls_config) { raft::common::nvtx::range fun_scope("run_fj_line_segment"); @@ -465,7 +468,7 @@ bool local_search_t::run_fj_line_segment(solution_t& solutio template bool local_search_t::check_fj_on_lp_optimal(solution_t& solution, bool perturb, - timer_t timer) + work_limit_timer_t timer) { raft::common::nvtx::range fun_scope("check_fj_on_lp_optimal"); if (lp_optimal_exists) { @@ -482,9 +485,11 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu } cuopt_func_call(solution.test_variable_bounds(false)); f_t lp_run_time_after_feasible = std::min(1., timer.remaining_time()); - timer_t bounds_prop_timer = timer_t(std::min(timer.remaining_time(), 10.)); + work_limit_timer_t bounds_prop_timer = + work_limit_timer_t(context.settings.deterministic, std::min(timer.remaining_time(), 10.)); bool is_feasible = constraint_prop.apply_round(solution, lp_run_time_after_feasible, bounds_prop_timer); + bounds_prop_timer.record_work(213762); if (!is_feasible) { f_t lp_run_time = 2.; // CHANGE @@ -514,7 +519,8 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu } template -bool local_search_t::run_fj_on_zero(solution_t& solution, timer_t timer) +bool local_search_t::run_fj_on_zero(solution_t& solution, + work_limit_timer_t timer) { raft::common::nvtx::range fun_scope("run_fj_on_zero"); thrust::fill(solution.handle_ptr->get_thrust_policy(), @@ -533,7 +539,7 @@ bool local_search_t::run_fj_on_zero(solution_t& solution, ti template bool local_search_t::run_staged_fp(solution_t& solution, - timer_t timer, + work_limit_timer_t timer, population_t* population_ptr) { raft::common::nvtx::range fun_scope("run_staged_fp"); @@ -560,7 +566,7 @@ bool local_search_t::run_staged_fp(solution_t& solution, } CUOPT_LOG_DEBUG("Running staged FP from beginning it %d", i); fp.relax_general_integers(solution); - timer_t binary_timer(timer.remaining_time() / 3); + work_limit_timer_t binary_timer(context.settings.deterministic, timer.remaining_time() / 3); i_t binary_it_counter = 0; for (; binary_it_counter < 100; ++binary_it_counter) { if (population_ptr->preempt_heuristic_solver_.load()) { @@ -726,7 +732,7 @@ void local_search_t::reset_alpha_and_run_recombiners( template bool local_search_t::run_fp(solution_t& solution, - timer_t timer, + work_limit_timer_t timer, population_t* population_ptr, i_t n_fp_iterations) { @@ -738,7 +744,7 @@ bool local_search_t::run_fp(solution_t& solution, is_feasible ? solution.get_objective() : std::numeric_limits::max(); rmm::device_uvector best_solution(solution.assignment, solution.handle_ptr->get_stream()); problem_t* old_problem_ptr = solution.problem_ptr; - fp.timer = timer_t(timer.remaining_time()); + fp.timer = work_limit_timer_t(context.settings.deterministic, timer.remaining_time()); // if it has not been initialized yet, create a new problem and move it to the cut problem if (!problem_with_objective_cut.cutting_plane_added) { problem_with_objective_cut = std::move(problem_t(*old_problem_ptr)); @@ -839,7 +845,7 @@ bool local_search_t::generate_solution(solution_t& solution, { raft::common::nvtx::range fun_scope("generate_solution"); cuopt_assert(population_ptr != nullptr, "Population pointer must not be null"); - timer_t timer(time_limit); + work_limit_timer_t timer(context.settings.deterministic, time_limit); auto n_vars = solution.problem_ptr->n_variables; auto n_binary_vars = solution.problem_ptr->get_n_binary_variables(); auto n_integer_vars = solution.problem_ptr->n_integer_vars; diff --git a/cpp/src/mip/local_search/local_search.cuh b/cpp/src/mip/local_search/local_search.cuh index ff914a876..4325a5299 100644 --- a/cpp/src/mip/local_search/local_search.cuh +++ b/cpp/src/mip/local_search/local_search.cuh @@ -22,7 +22,7 @@ #include #include #include -#include +#include #include #include @@ -85,31 +85,33 @@ class local_search_t { void start_cpufj_scratch_threads(population_t& population); void start_cpufj_lptopt_scratch_threads(population_t& population); void stop_cpufj_scratch_threads(); - void generate_fast_solution(solution_t& solution, timer_t timer); + void generate_fast_solution(solution_t& solution, work_limit_timer_t timer); bool generate_solution(solution_t& solution, bool perturb, population_t* population_ptr, f_t time_limit = 300.); bool run_fj_until_timer(solution_t& solution, const weight_t& weights, - timer_t timer); + work_limit_timer_t timer); bool run_local_search(solution_t& solution, const weight_t& weights, - timer_t timer, + work_limit_timer_t timer, const ls_config_t& ls_config); bool run_fj_annealing(solution_t& solution, - timer_t timer, + work_limit_timer_t timer, const ls_config_t& ls_config); bool run_fj_line_segment(solution_t& solution, - timer_t timer, + work_limit_timer_t timer, const ls_config_t& ls_config); - bool run_fj_on_zero(solution_t& solution, timer_t timer); - bool check_fj_on_lp_optimal(solution_t& solution, bool perturb, timer_t timer); + bool run_fj_on_zero(solution_t& solution, work_limit_timer_t timer); + bool check_fj_on_lp_optimal(solution_t& solution, + bool perturb, + work_limit_timer_t timer); bool run_staged_fp(solution_t& solution, - timer_t timer, + work_limit_timer_t timer, population_t* population_ptr); bool run_fp(solution_t& solution, - timer_t timer, + work_limit_timer_t timer, population_t* population_ptr = nullptr, i_t n_fp_iterations = 1000000); void resize_vectors(problem_t& problem, const raft::handle_t* handle_ptr); diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index 664fd6d4e..b6587641b 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -387,7 +387,7 @@ void bounds_repair_t::apply_move(problem_t& problem, template bool bounds_repair_t::repair_problem(problem_t& problem, problem_t& original_problem, - timer_t timer_, + work_limit_timer_t timer_, const raft::handle_t* handle_ptr_) { CUOPT_LOG_DEBUG("Running bounds repair"); diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cuh b/cpp/src/mip/local_search/rounding/bounds_repair.cuh index 8cc392c0b..b86f41d17 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cuh +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cuh @@ -130,7 +130,7 @@ class bounds_repair_t { void compute_damages(problem_t& problem, i_t n_candidates); bool repair_problem(problem_t& problem, problem_t& original_problem, - timer_t timer_, + work_limit_timer_t timer_, const raft::handle_t* handle_ptr_); void apply_move(problem_t& problem, problem_t& original_problem, @@ -154,7 +154,7 @@ class bounds_repair_t { i_t h_n_violated_cstr; const raft::handle_t* handle_ptr; std::mt19937 gen; - timer_t timer{0.}; + work_limit_timer_t timer; std::vector cycle_vector; i_t cycle_write_pos = 0; }; diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 26aa923eb..3c118becb 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -770,7 +770,7 @@ void constraint_prop_t::restore_original_bounds_on_unfixed( template bool constraint_prop_t::run_repair_procedure(problem_t& problem, problem_t& original_problem, - timer_t& timer, + work_limit_timer_t& timer, const raft::handle_t* handle_ptr) { CUOPT_LOG_DEBUG("Running repair procedure"); @@ -784,7 +784,8 @@ bool constraint_prop_t::run_repair_procedure(problem_t& prob i_t n_of_repairs_needed_for_feasible = 0; i_t iter_limit = std::numeric_limits::max(); if (this->context.settings.deterministic) { - timer = timer_t(std::numeric_limits::infinity()); + timer = + work_limit_timer_t(context.settings.deterministic, std::numeric_limits::infinity()); iter_limit = 100; } do { @@ -864,7 +865,7 @@ bool constraint_prop_t::find_integer( solution_t& sol, solution_t& orig_sol, f_t lp_run_time_after_feasible, - timer_t& timer, + work_limit_timer_t& timer, std::optional>> probing_config) { using crit_t = termination_criterion_t; @@ -875,7 +876,8 @@ bool constraint_prop_t::find_integer( // CHANGE if (this->context.settings.deterministic) { - timer = timer_t(std::numeric_limits::infinity()); + timer = + work_limit_timer_t(context.settings.deterministic, std::numeric_limits::infinity()); } lb_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); @@ -1014,7 +1016,7 @@ bool constraint_prop_t::find_integer( if (!(n_failed_repair_iterations >= max_n_failed_repair_iterations) && rounding_ii && !timeout_happened) { // timer_t repair_timer{std::min(timer.remaining_time() / 5, timer.elapsed_time() / 3)}; - timer_t repair_timer{timer.remaining_time() / 5}; + work_limit_timer_t repair_timer(context.settings.deterministic, timer.remaining_time() / 5); save_bounds(sol); // update bounds and run repair procedure bool bounds_repaired = @@ -1105,7 +1107,7 @@ template bool constraint_prop_t::apply_round( solution_t& sol, f_t lp_run_time_after_feasible, - timer_t& timer, + work_limit_timer_t& timer, std::optional>> probing_config) { raft::common::nvtx::range fun_scope("constraint prop round"); @@ -1135,9 +1137,10 @@ bool constraint_prop_t::apply_round( lp_run_time_after_feasible); // === CONSTRAINT PROP PREDICTOR FEATURES - END === - max_timer = timer_t{max_time_for_bounds_prop}; + max_timer = work_limit_timer_t{context.settings.deterministic, max_time_for_bounds_prop}; if (this->context.settings.deterministic) { - max_timer = timer_t(std::numeric_limits::infinity()); + max_timer = + work_limit_timer_t(context.settings.deterministic, std::numeric_limits::infinity()); } if (check_brute_force_rounding(sol)) { auto cp_end_time = std::chrono::high_resolution_clock::now(); diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cuh b/cpp/src/mip/local_search/rounding/constraint_prop.cuh index 591f25f36..a867b2aa9 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cuh +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cuh @@ -52,7 +52,7 @@ struct constraint_prop_t { constraint_prop_t(mip_solver_context_t& context); bool apply_round(solution_t& sol, f_t lp_run_time_after_feasible, - timer_t& timer, + work_limit_timer_t& timer, std::optional>> probing_config = std::nullopt); void sort_by_implied_slack_consumption(solution_t& sol, @@ -65,7 +65,7 @@ struct constraint_prop_t { bool find_integer(solution_t& sol, solution_t& orig_sol, f_t lp_run_time_after_feasible, - timer_t& timer, + work_limit_timer_t& timer, std::optional>> probing_config = std::nullopt); void find_set_integer_vars(solution_t& sol, rmm::device_uvector& set_vars); @@ -130,7 +130,7 @@ struct constraint_prop_t { const raft::handle_t* handle_ptr); bool run_repair_procedure(problem_t& problem, problem_t& original_problem, - timer_t& timer, + work_limit_timer_t& timer, const raft::handle_t* handle_ptr); bool handle_fixed_vars( solution_t& sol, @@ -158,7 +158,7 @@ struct constraint_prop_t { i_t bounds_prop_interval = 1; i_t n_iter_in_recovery = 0; i_t max_n_failed_repair_iterations = 1; - timer_t max_timer{0.}; + work_limit_timer_t max_timer; bool use_probing_cache = true; static repair_stats_t repair_stats; bool single_rounding_only = false; diff --git a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cuh b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cuh index 5a59fae7e..9be1ddee3 100644 --- a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cuh +++ b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cuh @@ -68,7 +68,7 @@ class lb_bounds_repair_t { bool repair_problem(load_balanced_problem_t* problem, load_balanced_bounds_presolve_t& lb_bound_presolve, problem_t& original_problem, - timer_t timer_, + work_limit_timer_t timer_, const raft::handle_t* handle_ptr_); void apply_move(load_balanced_problem_t* problem, problem_t& original_problem, @@ -92,7 +92,7 @@ class lb_bounds_repair_t { i_t h_n_violated_cstr; const raft::handle_t* handle_ptr; std::mt19937 gen; - timer_t timer{0.}; + work_limit_timer_t timer; std::vector cycle_vector; i_t cycle_write_pos = 0; }; diff --git a/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu b/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu index be7461106..acf28b961 100644 --- a/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu @@ -711,14 +711,14 @@ template bool lb_constraint_prop_t::apply_round( solution_t& sol, f_t lp_run_time_after_feasible, - timer_t& timer, + work_limit_timer_t& timer, std::optional>> probing_candidates) { raft::common::nvtx::range fun_scope("constraint prop round"); // this is second timer that can continue but without recovery mode const f_t max_time_for_bounds_prop = 5.; - max_timer = timer_t{max_time_for_bounds_prop}; + max_timer = work_limit_timer_t{context.settings.deterministic, max_time_for_bounds_prop}; if (check_brute_force_rounding(sol)) { return true; } recovery_mode = false; rounding_ii = false; diff --git a/cpp/src/mip/local_search/rounding/lb_constraint_prop.cuh b/cpp/src/mip/local_search/rounding/lb_constraint_prop.cuh index e820cf3fe..e01afad71 100644 --- a/cpp/src/mip/local_search/rounding/lb_constraint_prop.cuh +++ b/cpp/src/mip/local_search/rounding/lb_constraint_prop.cuh @@ -33,7 +33,7 @@ struct lb_constraint_prop_t { bool apply_round( solution_t& sol, f_t lp_run_time_after_feasible, - timer_t& timer, + work_limit_timer_t& timer, std::optional>> probing_candidates = std::nullopt); void sort_by_implied_slack_consumption( problem_t& original_problem, @@ -50,7 +50,7 @@ struct lb_constraint_prop_t { load_balanced_bounds_presolve_t& lb_bounds_update, solution_t& orig_sol, f_t lp_run_time_after_feasible, - timer_t& timer, + work_limit_timer_t& timer, std::optional>> probing_candidates); std::tuple probing_values( load_balanced_bounds_presolve_t& lb_bounds_update, @@ -93,7 +93,7 @@ struct lb_constraint_prop_t { bool run_repair_procedure(load_balanced_problem_t* problem, load_balanced_bounds_presolve_t& lb_bounds_update, problem_t& original_problem, - timer_t& timer, + work_limit_timer_t& timer, const raft::handle_t* handle_ptr); mip_solver_context_t& context; @@ -110,7 +110,7 @@ struct lb_constraint_prop_t { bool rounding_ii = false; i_t bounds_prop_interval = 1; i_t n_iter_in_recovery = 0; - timer_t max_timer{0.}; + work_limit_timer_t max_timer; bool use_probing_cache = true; size_t repair_attempts = 0; diff --git a/cpp/src/mip/presolve/lb_probing_cache.cu b/cpp/src/mip/presolve/lb_probing_cache.cu index 00c28d345..e26e03284 100644 --- a/cpp/src/mip/presolve/lb_probing_cache.cu +++ b/cpp/src/mip/presolve/lb_probing_cache.cu @@ -319,7 +319,7 @@ inline std::vector compute_prioritized_integer_indices( template void compute_probing_cache(load_balanced_bounds_presolve_t& bound_presolve, load_balanced_problem_t& problem, - timer_t timer) + work_limit_timer_t timer) { // we dont want to compute the probing cache for all variables for time and computation resources auto priority_indices = compute_prioritized_integer_indices(bound_presolve, problem); @@ -409,7 +409,7 @@ void compute_probing_cache(load_balanced_bounds_presolve_t& bound_pres template void compute_probing_cache( \ load_balanced_bounds_presolve_t & bound_presolve, \ load_balanced_problem_t & problem, \ - timer_t timer); \ + work_limit_timer_t timer); \ template class lb_probing_cache_t; #if MIP_INSTANTIATE_FLOAT diff --git a/cpp/src/mip/presolve/probing_cache.cu b/cpp/src/mip/presolve/probing_cache.cu index 044b14155..fc7bed0e8 100644 --- a/cpp/src/mip/presolve/probing_cache.cu +++ b/cpp/src/mip/presolve/probing_cache.cu @@ -465,7 +465,7 @@ void compute_cache_for_var(i_t var_idx, template void compute_probing_cache(bound_presolve_t& bound_presolve, problem_t& problem, - timer_t timer) + work_limit_timer_t timer) { raft::common::nvtx::range fun_scope("compute_probing_cache"); // we dont want to compute the probing cache for all variables for time and computation resources @@ -530,7 +530,7 @@ void compute_probing_cache(bound_presolve_t& bound_presolve, #define INSTANTIATE(F_TYPE) \ template void compute_probing_cache(bound_presolve_t & bound_presolve, \ problem_t & problem, \ - timer_t timer); \ + work_limit_timer_t timer); \ template class probing_cache_t; #if MIP_INSTANTIATE_FLOAT diff --git a/cpp/src/mip/presolve/probing_cache.cuh b/cpp/src/mip/presolve/probing_cache.cuh index 755c18b0b..f8c5e4951 100644 --- a/cpp/src/mip/presolve/probing_cache.cuh +++ b/cpp/src/mip/presolve/probing_cache.cuh @@ -22,6 +22,7 @@ #include #include +#include namespace cuopt::linear_programming::detail { @@ -127,6 +128,6 @@ class lb_probing_cache_t { template void compute_probing_cache(bound_presolve_t& bound_presolve, problem_t& problem, - timer_t timer); + work_limit_timer_t timer); } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index 8f979e683..0827068a2 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -31,8 +31,8 @@ #include #include #include -#include #include +#include #include #include @@ -71,7 +71,7 @@ static void setup_device_symbols(rmm::cuda_stream_view stream_view) template mip_solution_t run_mip(detail::problem_t& problem, mip_solver_settings_t const& settings, - cuopt::timer_t& timer) + cuopt::work_limit_timer_t& timer) { raft::common::nvtx::range fun_scope("run_mip"); auto constexpr const running_mip = true; @@ -194,7 +194,7 @@ mip_solution_t solve_mip(optimization_problem_t& op_problem, op_problem.get_handle_ptr()->get_stream()); } - auto timer = cuopt::timer_t(time_limit); + auto timer = cuopt::work_limit_timer_t(settings.deterministic, time_limit); double presolve_time = 0.0; std::unique_ptr> presolver; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 0114882b0..607fe6c9f 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -53,7 +53,7 @@ template mip_solver_t::mip_solver_t(const problem_t& op_problem, const mip_solver_settings_t& solver_settings, pdlp_initial_scaling_strategy_t& scaling, - timer_t timer) + work_limit_timer_t timer) : op_problem_(op_problem), solver_settings_(solver_settings), context(op_problem.handle_ptr, diff --git a/cpp/src/mip/solver.cuh b/cpp/src/mip/solver.cuh index beff11d9b..012c5fdfa 100644 --- a/cpp/src/mip/solver.cuh +++ b/cpp/src/mip/solver.cuh @@ -20,7 +20,7 @@ #include #include #include -#include +#include #pragma once namespace cuopt::linear_programming::detail { @@ -31,7 +31,7 @@ class mip_solver_t { explicit mip_solver_t(const problem_t& op_problem, const mip_solver_settings_t& solver_settings, pdlp_initial_scaling_strategy_t& scaling, - timer_t timer); + work_limit_timer_t timer); solution_t run_solver(); solver_stats_t& get_solver_stats() { return context.stats; } @@ -40,7 +40,7 @@ class mip_solver_t { // reference to the original problem const problem_t& op_problem_; const mip_solver_settings_t& solver_settings_; - timer_t timer_; + work_limit_timer_t timer_; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/utilities/work_limit_timer.hpp b/cpp/src/utilities/work_limit_timer.hpp new file mode 100644 index 000000000..dad1446aa --- /dev/null +++ b/cpp/src/utilities/work_limit_timer.hpp @@ -0,0 +1,136 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include +#include +#include "timer.hpp" + +namespace cuopt { + +// In determinism mode, relies on a work limit accumulator; otherwise rely on a timer +// in non-determinism mode: 1s = 1wu +class work_limit_timer_t { + public: + work_limit_timer_t() : deterministic(false), work_limit(0), timer(0) {} + work_limit_timer_t(bool deterministic, double work_limit_) + : deterministic(deterministic), work_limit(work_limit_), timer(work_limit_) + { + } + + bool check_limit(const char* caller = __builtin_FUNCTION(), + const char* file = __builtin_FILE(), + int line = __builtin_LINE()) const noexcept + { + if (deterministic) { + bool finished_now = work_total >= work_limit; + if (finished_now && !finished) { + finished = true; + double actual_elapsed_time = timer.elapsed_time(); + // 10% timing error + if (abs(actual_elapsed_time - work_limit) / work_limit > 0.10) { + CUOPT_LOG_ERROR( + "%s:%d: %s(): Work limit timer finished with a large discrepancy: %fs for %fwu", + file, + line, + caller, + actual_elapsed_time, + work_limit); + } + } + return finished; + } else { + return timer.check_time_limit(); + } + } + + // in determinism mode, add the work units to the work limit accumulator + void record_work(double work_units) + { + if (deterministic) { work_total += work_units; } + } + + double remaining_units() const noexcept + { + if (deterministic) { + return work_limit - work_total; + } else { + return timer.remaining_time(); + } + } + + double remaining_time() const noexcept { return remaining_units(); } + + double elapsed_time() const noexcept + { + if (deterministic) { + return work_total; + } else { + return timer.elapsed_time(); + } + } + + bool check_time_limit(const char* caller = __builtin_FUNCTION(), + const char* file = __builtin_FILE(), + int line = __builtin_LINE()) const noexcept + { + return check_limit(caller, file, line); + } + + bool check_half_time() const noexcept + { + if (deterministic) { + return work_total >= work_limit / 2; + } else { + return timer.check_half_time(); + } + } + + double clamp_remaining_time(double desired_time) const noexcept + { + return std::min(desired_time, remaining_time()); + } + + double get_time_limit() const noexcept + { + if (deterministic) { + return work_limit; + } else { + return timer.get_time_limit(); + } + } + + void print_debug(std::string msg) const + { + if (deterministic) { + printf("%s work_limit: %f remaining_work: %f elapsed_work: %f \n", + msg.c_str(), + work_limit, + remaining_time(), + elapsed_time()); + } else { + timer.print_debug(msg); + } + } + + timer_t timer; + double work_total{}; + double work_limit{}; + mutable bool finished{false}; + bool deterministic{false}; +}; +} // namespace cuopt diff --git a/cpp/tests/mip/diversity_test.cu b/cpp/tests/mip/diversity_test.cu index b891aa87f..15f287774 100644 --- a/cpp/tests/mip/diversity_test.cu +++ b/cpp/tests/mip/diversity_test.cu @@ -102,15 +102,15 @@ static uint32_t test_full_run_determinism(std::string path, auto settings = mip_solver_settings_t{}; settings.time_limit = 3000.; + settings.work_limit = 10; // about 10 seconds of runtime settings.deterministic = true; settings.heuristics_only = true; - auto timer = cuopt::timer_t(3000); + auto timer = cuopt::work_limit_timer_t(settings.deterministic, 3000); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); detail::diversity_manager_t diversity_manager(solver.context); - diversity_manager.timer = timer_t(60000); - diversity_manager.diversity_config.n_fp_iterations = 3; + diversity_manager.timer = work_limit_timer_t(settings.deterministic, 60000); diversity_manager.run_solver(); std::vector hashes; @@ -159,12 +159,12 @@ static uint32_t test_initial_solution_determinism(std::string path, settings.time_limit = 3000.; settings.deterministic = true; settings.heuristics_only = true; - auto timer = cuopt::timer_t(3000); + auto timer = cuopt::work_limit_timer_t(settings.deterministic, 3000); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); detail::diversity_manager_t diversity_manager(solver.context); - diversity_manager.timer = timer_t(60000); + diversity_manager.timer = work_limit_timer_t(settings.deterministic, 60000); diversity_manager.diversity_config.initial_solution_only = true; diversity_manager.run_solver(); @@ -214,12 +214,12 @@ static uint32_t test_recombiners_determinism(std::string path, settings.time_limit = 3000.; settings.deterministic = true; settings.heuristics_only = true; - auto timer = cuopt::timer_t(3000); + auto timer = cuopt::work_limit_timer_t(settings.deterministic, 3000); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); detail::diversity_manager_t diversity_manager(solver.context); - diversity_manager.timer = timer_t(60000); + diversity_manager.timer = work_limit_timer_t(settings.deterministic, 60000); diversity_manager.diversity_config.dry_run = true; diversity_manager.run_solver(); diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu index ce0ee5216..36e71481c 100644 --- a/cpp/tests/mip/local_search_test.cu +++ b/cpp/tests/mip/local_search_test.cu @@ -113,7 +113,7 @@ static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) auto settings = mip_solver_settings_t{}; settings.time_limit = 30.; settings.deterministic = true; - auto timer = cuopt::timer_t(30); + auto timer = cuopt::work_limit_timer_t(settings.deterministic, 30); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); @@ -149,7 +149,7 @@ static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) printf("LP optimal hash: 0x%x\n", detail::compute_hash(lp_optimal_solution)); printf("running mode: %d\n", mode); - local_search.fp.timer = timer_t{6000}; + local_search.fp.timer = work_limit_timer_t(settings.deterministic, 6000); detail::ls_config_t ls_config{}; diff --git a/cpp/tests/mip/mip_utils.cuh b/cpp/tests/mip/mip_utils.cuh index 904f46168..3a8d6e48b 100644 --- a/cpp/tests/mip/mip_utils.cuh +++ b/cpp/tests/mip/mip_utils.cuh @@ -165,7 +165,7 @@ static fj_state_t run_fj(detail::problem_t& problem, auto settings = mip_solver_settings_t{}; settings.time_limit = 30.; - auto timer = cuopt::timer_t(30); + auto timer = cuopt::work_limit_timer_t(settings.deterministic, 30); detail::mip_solver_t solver(problem, settings, scaling, timer); detail::solution_t solution(*solver.context.problem_ptr); diff --git a/cpp/tests/mip/multi_probe_test.cu b/cpp/tests/mip/multi_probe_test.cu index dc7449dc1..e15f23ffd 100644 --- a/cpp/tests/mip/multi_probe_test.cu +++ b/cpp/tests/mip/multi_probe_test.cu @@ -170,7 +170,11 @@ uint32_t test_multi_probe(std::string path, unsigned long seed = std::random_dev problem.reverse_constraints, nullptr, true); - detail::mip_solver_t solver(problem, default_settings, scaling, cuopt::timer_t(0)); + detail::mip_solver_t solver( + problem, + default_settings, + scaling, + cuopt::work_limit_timer_t(default_settings.deterministic, 0)); detail::bound_presolve_t bnd_prb_0(solver.context); detail::bound_presolve_t bnd_prb_1(solver.context); detail::multi_probe_t multi_probe_prs(solver.context); diff --git a/cpp/tests/mip/presolve_test.cu b/cpp/tests/mip/presolve_test.cu index e08f283cb..55ce8851c 100644 --- a/cpp/tests/mip/presolve_test.cu +++ b/cpp/tests/mip/presolve_test.cu @@ -131,11 +131,18 @@ uint32_t test_probing_cache_determinism(std::string path, problem.reverse_constraints, nullptr, true); - detail::mip_solver_t solver(problem, default_settings, scaling, cuopt::timer_t(0)); + detail::mip_solver_t solver( + problem, + default_settings, + scaling, + cuopt::work_limit_timer_t(default_settings.deterministic, 0)); detail::bound_presolve_t bnd_prb(solver.context); // rely on the iteration limit - compute_probing_cache(bnd_prb, problem, timer_t(std::numeric_limits::max())); + compute_probing_cache( + bnd_prb, + problem, + work_limit_timer_t(default_settings.deterministic, std::numeric_limits::max())); std::vector, 2>>> cached_values( bnd_prb.probing_cache.probing_cache.begin(), bnd_prb.probing_cache.probing_cache.end()); std::sort(cached_values.begin(), cached_values.end(), [](const auto& a, const auto& b) { From c6e9d1573460ec63b90bb76832f256e0076f7c87 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 4 Nov 2025 14:15:51 +0000 Subject: [PATCH 025/366] linear estimation of PDLP work units --- cpp/src/mip/diversity/lns/rins.cu | 342 ++++++++++++++++++++++++++ cpp/src/mip/relaxed_lp/relaxed_lp.cu | 73 +++--- cpp/src/mip/relaxed_lp/relaxed_lp.cuh | 1 + scripts/train_regressor.py | 18 +- 4 files changed, 400 insertions(+), 34 deletions(-) create mode 100644 cpp/src/mip/diversity/lns/rins.cu diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu new file mode 100644 index 000000000..df149c38b --- /dev/null +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -0,0 +1,342 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +#include +#include +#include +#include + +namespace cuopt::linear_programming::detail { + +template +rins_t::rins_t(mip_solver_context_t& context_, + diversity_manager_t& dm_, + rins_settings_t settings_) + : context(context_), problem_ptr(context.problem_ptr), dm(dm_), settings(settings_) +{ + fixrate = settings.default_fixrate; + time_limit = settings.default_time_limit; +} + +template +rins_thread_t::~rins_thread_t() +{ + this->request_termination(); +} + +template +void rins_thread_t::run_worker() +{ + raft::common::nvtx::range fun_scope("Running RINS"); + rins_ptr->run_rins(); +} + +template +void rins_t::new_best_incumbent_callback(const std::vector& solution) +{ + node_count_at_last_improvement = node_count.load(); +} + +template +void rins_t::node_callback(const std::vector& solution, f_t objective) +{ + if (!enabled) return; + + node_count++; + + if (node_count - node_count_at_last_improvement < settings.nodes_after_later_improvement) return; + + if (node_count - node_count_at_last_rins > settings.node_freq) { + // opportunistic early test w/ atomic to avoid having to take the lock + if (!rins_thread->cpu_thread_done) return; + std::lock_guard lock(rins_mutex); + if (rins_thread->cpu_thread_done && dm.population.current_size() > 0 && + dm.population.is_feasible()) { + lp_optimal_solution = solution; + rins_thread->start_cpu_solver(); + } + } +} + +template +void rins_t::enable() +{ + rins_thread = std::make_unique>(); + rins_thread->rins_ptr = this; + seed = cuopt::seed_generator::get_seed(); + problem_copy = std::make_unique>(*problem_ptr); + problem_copy->handle_ptr = &rins_handle; + enabled = true; +} + +template +void rins_t::run_rins() +{ + if (total_calls == 0) RAFT_CUDA_TRY(cudaSetDevice(context.handle_ptr->get_device())); + + auto external_solution_size = dm.population.get_external_solution_size(); + if (external_solution_size > 0) dm.population.add_external_solutions_to_population(); + + if (!dm.population.is_feasible()) return; + + cuopt_assert(lp_optimal_solution.size() == problem_ptr->n_variables, "Assignment size mismatch"); + cuopt_assert(problem_copy->handle_ptr == &rins_handle, "Handle mismatch"); + cuopt_assert(problem_copy->n_variables == problem_ptr->n_variables, "Problem size mismatch"); + cuopt_assert(problem_copy->n_constraints == problem_ptr->n_constraints, "Problem size mismatch"); + cuopt_assert(problem_copy->n_integer_vars == problem_ptr->n_integer_vars, + "Problem size mismatch"); + cuopt_assert(problem_copy->n_binary_vars == problem_ptr->n_binary_vars, "Problem size mismatch"); + cuopt_assert(dm.population.current_size() > 0, "No solutions in population"); + + solution_t best_sol(*problem_copy); + // copy the best from the population into a solution_t in the RINS stream + { + std::lock_guard lock(dm.population.write_mutex); + auto& best_feasible_ref = dm.population.best_feasible(); + cuopt_assert(best_feasible_ref.assignment.size() == best_sol.assignment.size(), + "Assignment size mismatch"); + cuopt_assert(best_feasible_ref.get_feasible(), "Best feasible is not feasible"); + expand_device_copy(best_sol.assignment, best_feasible_ref.assignment, rins_handle.get_stream()); + best_sol.handle_ptr = &rins_handle; + best_sol.problem_ptr = problem_copy.get(); + best_sol.compute_feasibility(); + } + cuopt_assert(best_sol.handle_ptr == &rins_handle, "Handle mismatch"); + + cuopt_assert(best_sol.get_feasible(), "Best solution is not feasible"); + if (!best_sol.get_feasible()) { return; } + + i_t sol_size_before_rins = best_sol.assignment.size(); + auto lp_opt_device = cuopt::device_copy(this->lp_optimal_solution, rins_handle.get_stream()); + cuopt_assert(lp_opt_device.size() == problem_ptr->n_variables, "Assignment size mismatch"); + cuopt_assert(best_sol.assignment.size() == problem_ptr->n_variables, "Assignment size mismatch"); + + rmm::device_uvector vars_to_fix(problem_ptr->n_integer_vars, rins_handle.get_stream()); + auto end = thrust::copy_if(rins_handle.get_thrust_policy(), + problem_ptr->integer_indices.begin(), + problem_ptr->integer_indices.end(), + vars_to_fix.begin(), + [lpopt = lp_opt_device.data(), + pb = problem_ptr->view(), + incumbent = best_sol.assignment.data()] __device__(i_t var_idx) { + return pb.integer_equal(lpopt[var_idx], incumbent[var_idx]); + }); + vars_to_fix.resize(end - vars_to_fix.begin(), rins_handle.get_stream()); + f_t fractional_ratio = (f_t)(vars_to_fix.size()) / (f_t)problem_ptr->n_integer_vars; + + // abort if the fractional ratio is too low + if (fractional_ratio < settings.min_fractional_ratio) { + CUOPT_LOG_TRACE("RINS fractional ratio too low, aborting"); + return; + } + + thrust::default_random_engine g(seed + node_count); + + // shuffle fixing order + thrust::shuffle(rins_handle.get_thrust_policy(), vars_to_fix.begin(), vars_to_fix.end(), g); + + // fix n first according to fractional ratio + f_t rins_ratio = fixrate; + i_t n_to_fix = std::max((int)(vars_to_fix.size() * rins_ratio), 0); + vars_to_fix.resize(n_to_fix, rins_handle.get_stream()); + thrust::sort(rins_handle.get_thrust_policy(), vars_to_fix.begin(), vars_to_fix.end()); + + cuopt_assert(thrust::all_of(rins_handle.get_thrust_policy(), + vars_to_fix.begin(), + vars_to_fix.end(), + [pb = problem_ptr->view()] __device__(i_t var_idx) { + return pb.is_integer_var(var_idx); + }), + "All variables to fix must be integer variables"); + + if (n_to_fix == 0) { + CUOPT_LOG_DEBUG("RINS no variables to fix"); + return; + } + + total_calls++; + node_count_at_last_rins = node_count.load(); + time_limit = std::min(time_limit, dm.timer.remaining_time()); + CUOPT_LOG_DEBUG("Running RINS on solution with objective %g, fixing %d/%d", + best_sol.get_user_objective(), + vars_to_fix.size(), + problem_ptr->n_integer_vars); + CUOPT_LOG_DEBUG("RINS fixrate %g time limit %g", fixrate, time_limit); + CUOPT_LOG_DEBUG("RINS fractional ratio %g%%", fractional_ratio * 100); + + f_t prev_obj = best_sol.get_user_objective(); + + auto [fixed_problem, fixed_assignment, variable_map] = best_sol.fix_variables(vars_to_fix); + CUOPT_LOG_DEBUG( + "new var count %d var_count %d", fixed_problem.n_variables, problem_ptr->n_integer_vars); + + // should probably just do an spmv to get the objective instead. ugly mess of copies + solution_t best_sol_fixed_space(fixed_problem); + cuopt_assert(best_sol_fixed_space.handle_ptr == &rins_handle, "Handle mismatch"); + best_sol_fixed_space.copy_new_assignment( + cuopt::host_copy(fixed_assignment, rins_handle.get_stream())); + best_sol_fixed_space.compute_feasibility(); + CUOPT_LOG_DEBUG("RINS best sol fixed space objective %g", + best_sol_fixed_space.get_user_objective()); + + if (settings.objective_cut) { + f_t objective_cut = + best_sol_fixed_space.get_objective() - + std::max(std::abs(0.001 * best_sol_fixed_space.get_objective()), OBJECTIVE_EPSILON); + fixed_problem.add_cutting_plane_at_objective(objective_cut); + } + + fixed_problem.presolve_data.reset_additional_vars(fixed_problem, &rins_handle); + fixed_problem.presolve_data.initialize_var_mapping(fixed_problem, &rins_handle); + trivial_presolve(fixed_problem); + fixed_problem.check_problem_representation(true); + + std::vector> rins_solution_queue; + + mip_solver_context_t fj_context( + &rins_handle, &fixed_problem, context.settings, context.scaling); + fj_t fj(fj_context); + solution_t fj_solution(fixed_problem); + fj_solution.copy_new_assignment(cuopt::host_copy(fixed_assignment)); + std::vector default_weights(fixed_problem.n_constraints, 1.); + cpu_fj_thread_t cpu_fj_thread; + cpu_fj_thread.fj_cpu = + fj.create_cpu_climber(fj_solution, default_weights, default_weights, 0., fj_settings_t{}, true); + cpu_fj_thread.fj_ptr = &fj; + cpu_fj_thread.fj_cpu->log_prefix = "[RINS] "; + cpu_fj_thread.time_limit = time_limit; + cpu_fj_thread.start_cpu_solver(); + + f_t lower_bound = context.branch_and_bound_ptr ? context.branch_and_bound_ptr->get_lower_bound() + : -std::numeric_limits::infinity(); + f_t current_mip_gap = compute_rel_mip_gap(prev_obj, lower_bound); + + // run sub-mip + namespace dual_simplex = cuopt::linear_programming::dual_simplex; + dual_simplex::user_problem_t branch_and_bound_problem(&rins_handle); + dual_simplex::simplex_solver_settings_t branch_and_bound_settings; + dual_simplex::mip_solution_t branch_and_bound_solution(1); + dual_simplex::mip_status_t branch_and_bound_status = dual_simplex::mip_status_t::UNSET; + fixed_problem.get_host_user_problem(branch_and_bound_problem); + branch_and_bound_solution.resize(branch_and_bound_problem.num_cols); + // Fill in the settings for branch and bound + branch_and_bound_settings.time_limit = time_limit; + // branch_and_bound_settings.node_limit = 5000 + node_count / 100; // try harder as time goes + // on + branch_and_bound_settings.print_presolve_stats = false; + branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; + branch_and_bound_settings.relative_mip_gap_tol = + std::min(current_mip_gap, (f_t)settings.target_mip_gap); + branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.num_threads = 2; + branch_and_bound_settings.num_bfs_threads = 1; + branch_and_bound_settings.num_diving_threads = 1; + branch_and_bound_settings.log.log_prefix = "[RINS] "; + branch_and_bound_settings.solution_callback = [this, &rins_solution_queue]( + std::vector& solution, f_t objective) { + rins_solution_queue.push_back(solution); + }; + dual_simplex::branch_and_bound_t branch_and_bound(branch_and_bound_problem, + branch_and_bound_settings); + branch_and_bound.set_initial_guess(cuopt::host_copy(fixed_assignment, rins_handle.get_stream())); + branch_and_bound_status = branch_and_bound.solve(branch_and_bound_solution); + + if (!std::isnan(branch_and_bound_solution.objective)) { + CUOPT_LOG_DEBUG("RINS submip solution found. Objective %.16e. Status %d", + branch_and_bound_solution.objective, + int(branch_and_bound_status)); + cuopt_assert(rins_solution_queue.size() > 0, "RINS solution queue is unexpectedly empty"); + } + if (branch_and_bound_status == dual_simplex::mip_status_t::OPTIMAL) { + CUOPT_LOG_DEBUG("RINS submip optimal"); + // do goldilocks update + fixrate = std::max(fixrate - 0.05, settings.min_fixrate); + time_limit = std::max(time_limit - 2, settings.min_time_limit); + } else if (branch_and_bound_status == dual_simplex::mip_status_t::TIME_LIMIT) { + CUOPT_LOG_DEBUG("RINS submip time limit"); + // do goldilocks update + fixrate = std::min(fixrate + 0.05, settings.max_fixrate); + time_limit = std::min(time_limit + 2, settings.max_time_limit); + } else if (branch_and_bound_status == dual_simplex::mip_status_t::INFEASIBLE) { + CUOPT_LOG_DEBUG("RINS submip infeasible"); + // do goldilocks update, decreasing fixrate + fixrate = std::max(fixrate - 0.05, settings.min_fixrate); + } else { + CUOPT_LOG_DEBUG("RINS solution not found"); + // do goldilocks update + fixrate = std::min(fixrate + 0.05, settings.max_fixrate); + time_limit = std::min(time_limit + 2, settings.max_time_limit); + } + + cpu_fj_thread.stop_cpu_solver(); + bool fj_solution_found = cpu_fj_thread.wait_for_cpu_solver(); + CUOPT_LOG_DEBUG("RINS FJ ran for %d iterations", cpu_fj_thread.fj_cpu->iterations); + if (fj_solution_found) { + CUOPT_LOG_DEBUG("RINS FJ solution found. Objective %.16e", + cpu_fj_thread.fj_cpu->h_best_objective); + rins_solution_queue.push_back(cpu_fj_thread.fj_cpu->h_best_assignment); + } + // Thread will be automatically terminated and joined by destructor + + bool improvement_found = false; + for (auto& fixed_sol : rins_solution_queue) { + cuopt_assert(fixed_assignment.size() == fixed_sol.size(), "Assignment size mismatch"); + rmm::device_uvector post_processed_solution(fixed_sol.size(), rins_handle.get_stream()); + raft::copy( + post_processed_solution.data(), fixed_sol.data(), fixed_sol.size(), rins_handle.get_stream()); + fixed_problem.post_process_assignment(post_processed_solution, false); + cuopt_assert(post_processed_solution.size() == fixed_assignment.size(), + "Assignment size mismatch"); + rins_handle.sync_stream(); + + rmm::device_uvector unfixed_assignment(post_processed_solution.size(), + rins_handle.get_stream()); + raft::copy(unfixed_assignment.data(), + post_processed_solution.data(), + post_processed_solution.size(), + rins_handle.get_stream()); + best_sol.unfix_variables(unfixed_assignment, variable_map); + best_sol.compute_feasibility(); + + if (best_sol.get_feasible()) { + cuopt_assert(best_sol.test_number_all_integer(), "All must be integers after RINS"); + if (best_sol.get_user_objective() < prev_obj) { improvement_found = true; } + cuopt_assert(best_sol.assignment.size() == sol_size_before_rins, "Assignment size mismatch"); + cuopt_assert(best_sol.assignment.size() == problem_ptr->n_variables, + "Assignment size mismatch"); + dm.population.add_external_solution( + best_sol.get_host_assignment(), best_sol.get_objective(), solution_origin_t::RINS); + } + } + + if (improvement_found) total_success++; + CUOPT_LOG_DEBUG("RINS calls/successes %d/%d", total_calls, total_success); +} + +#if MIP_INSTANTIATE_FLOAT +template class rins_thread_t; +template class rins_t; +#endif + +#if MIP_INSTANTIATE_DOUBLE +template class rins_thread_t; +template class rins_t; +#endif + +} // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index ad649feb2..a615d8450 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -54,27 +54,28 @@ optimization_problem_solution_t get_relaxed_lp_solution( raft::common::nvtx::range fun_scope("get_relaxed_lp_solution"); auto function_start_time = std::chrono::high_resolution_clock::now(); - // === PDLP PREDICTOR FEATURES - START === - CUOPT_LOG_INFO("PDLP_FEATURES: n_variables=%d n_constraints=%d nnz=%lu", - op_problem.n_variables, - op_problem.n_constraints, - op_problem.coefficients.size()); + // // === PDLP PREDICTOR FEATURES - START === + // CUOPT_LOG_INFO("PDLP_FEATURES: n_variables=%d n_constraints=%d nnz=%lu", + // op_problem.n_variables, + // op_problem.n_constraints, + // op_problem.coefficients.size()); - CUOPT_LOG_INFO("PDLP_FEATURES: sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f", - op_problem.sparsity, - op_problem.nnz_stddev, - op_problem.unbalancedness); + // CUOPT_LOG_INFO("PDLP_FEATURES: sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f", + // op_problem.sparsity, + // op_problem.nnz_stddev, + // op_problem.unbalancedness); - CUOPT_LOG_INFO("PDLP_FEATURES: has_warm_start=%d time_limit=%.6f iteration_limit=%d", - settings.has_initial_primal, - settings.time_limit, - settings.iteration_limit); + // CUOPT_LOG_INFO("PDLP_FEATURES: has_warm_start=%d time_limit=%.6f iteration_limit=%d", + // settings.has_initial_primal, + // settings.time_limit, + // settings.iteration_limit); - CUOPT_LOG_INFO("PDLP_FEATURES: tolerance=%.10f check_infeasibility=%d return_first_feasible=%d", - settings.tolerance, - settings.check_infeasibility, - settings.return_first_feasible); - // === PDLP PREDICTOR FEATURES - END === + // CUOPT_LOG_INFO("PDLP_FEATURES: tolerance=%.10f check_infeasibility=%d + // return_first_feasible=%d", + // settings.tolerance, + // settings.check_infeasibility, + // settings.return_first_feasible); + // // === PDLP PREDICTOR FEATURES - END === pdlp_solver_settings_t pdlp_settings{}; pdlp_settings.detect_infeasibility = settings.check_infeasibility; @@ -86,10 +87,24 @@ optimization_problem_solution_t get_relaxed_lp_solution( pdlp_settings.tolerances.relative_dual_tolerance = settings.tolerance / tolerance_divisor; pdlp_settings.time_limit = settings.time_limit; pdlp_settings.iteration_limit = settings.iteration_limit; - pdlp_settings.concurrent_halt = settings.concurrent_halt; - pdlp_settings.per_constraint_residual = settings.per_constraint_residual; - pdlp_settings.first_primal_feasible = settings.return_first_feasible; - pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; + if (settings.work_limit != std::numeric_limits::infinity()) { + // try to estimate the iteration count based on the requested work limit + int estim_iters = 0; + do { + // TODO: use an actual predictor model here + double estim_ms = 313 + 200 * op_problem.n_variables - 400 * op_problem.n_constraints + + 600 * op_problem.coefficients.size() + 7100 * estim_iters; + estim_ms = std::max(0.0, estim_ms); + if (estim_ms > settings.work_limit) { break; } + estim_iters += 100; + } while (true); + CUOPT_LOG_DEBUG("estimated iterations %d for work limit %f", estim_iters, settings.work_limit); + pdlp_settings.iteration_limit = estim_iters; + } + pdlp_settings.concurrent_halt = settings.concurrent_halt; + pdlp_settings.per_constraint_residual = settings.per_constraint_residual; + pdlp_settings.first_primal_feasible = settings.return_first_feasible; + pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; set_pdlp_solver_mode(pdlp_settings); // TODO: set Stable3 here? pdlp_solver_t lp_solver(op_problem, pdlp_settings); @@ -158,13 +173,13 @@ optimization_problem_solution_t get_relaxed_lp_solution( elapsed_ms, solver_response.get_additional_termination_information().number_of_steps_taken); - // === PDLP PREDICTOR RESULTS - START === - auto term_info = solver_response.get_additional_termination_information(); - CUOPT_LOG_INFO("PDLP_RESULT: iterations=%d time_ms=%lld termination=%d", - term_info.number_of_steps_taken, - elapsed_ms, - (int)solver_response.get_termination_status()); - // === PDLP PREDICTOR RESULTS - END === + // // === PDLP PREDICTOR RESULTS - START === + // auto term_info = solver_response.get_additional_termination_information(); + // CUOPT_LOG_INFO("PDLP_RESULT: iterations=%d time_ms=%lld termination=%d", + // term_info.number_of_steps_taken, + // elapsed_ms, + // (int)solver_response.get_termination_status()); + // // === PDLP PREDICTOR RESULTS - END === return solver_response; } diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cuh b/cpp/src/mip/relaxed_lp/relaxed_lp.cuh index d23921fc5..8e1e8c081 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cuh +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cuh @@ -30,6 +30,7 @@ struct relaxed_lp_settings_t { double tolerance = 1e-4; double time_limit = 1.0; int iteration_limit = std::numeric_limits::max(); + double work_limit = std::numeric_limits::infinity(); bool check_infeasibility = true; bool return_first_feasible = false; bool save_state = true; diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py index f48208c4b..dc12fa384 100755 --- a/scripts/train_regressor.py +++ b/scripts/train_regressor.py @@ -535,12 +535,20 @@ def get_feature_importance(model, feature_names: List[str], print(f" {i:3d}. {feature_names[idx]:40s}: {importances[idx]:.6f}") elif regressor_type == 'linear': - # Linear regression coefficients - coefs = np.abs(model.coef_) - indices = np.argsort(coefs)[::-1] - + # Linear regression coefficients and intercept + print(f"\nIntercept: {model.intercept_:.6f}\n") + print(f"Coefficients:") + + # Print each feature with its coefficient + for i, (feature_name, coef) in enumerate(zip(feature_names, model.coef_), 1): + print(f" {i:3d}. {feature_name:40s}: {coef:.6f}") + + # Also show sorted by absolute value for importance ranking + print(f"\nRanked by absolute magnitude:") + coefs_abs = np.abs(model.coef_) + indices = np.argsort(coefs_abs)[::-1] for i, idx in enumerate(indices, 1): - print(f" {i:3d}. {feature_names[idx]:40s}: {coefs[idx]:.6f}") + print(f" {i:3d}. {feature_names[idx]:40s}: {model.coef_[idx]:.6f} (|{coefs_abs[idx]:.6f}|)") elif regressor_type.startswith('poly'): # For polynomial, get feature names and coefficients from the Ridge step From c24f6ef5f8b768d11fb610b5254a59a1b2c4cced Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 4 Nov 2025 14:57:34 +0000 Subject: [PATCH 026/366] more work unit timer stuff --- PREDICTOR_DATA_COLLECTION.md | 4 +- .../linear_programming/cuopt/run_mip.cpp | 7 +- cpp/src/mip/diversity/diversity_manager.cu | 45 ++++--- .../diversity/recombiners/fp_recombiner.cuh | 2 +- .../mip/feasibility_jump/feasibility_jump.cu | 6 +- .../mip/feasibility_jump/feasibility_jump.cuh | 2 +- .../feasibility_pump/feasibility_pump.cu | 126 ++++++++++-------- cpp/src/mip/local_search/local_search.cu | 4 +- .../local_search/rounding/constraint_prop.cu | 61 +++++---- cpp/src/mip/solver.cu | 6 +- cpp/src/utilities/work_unit_predictor.cpp | 2 +- cpp/tests/mip/feasibility_jump_tests.cu | 2 +- 12 files changed, 142 insertions(+), 125 deletions(-) diff --git a/PREDICTOR_DATA_COLLECTION.md b/PREDICTOR_DATA_COLLECTION.md index cc81eb9c3..d026a11d7 100644 --- a/PREDICTOR_DATA_COLLECTION.md +++ b/PREDICTOR_DATA_COLLECTION.md @@ -268,14 +268,14 @@ The FJ predictor already exists at `cpp/src/utilities/models/fj_predictor/`. The Example from FJ predictor: ```cpp // cpp/src/mip/feasibility_jump/feasibility_jump.cu:1283-1291 -if (settings.work_unit_limit != std::numeric_limits::infinity()) { +if (settings.work_limit != std::numeric_limits::infinity()) { std::map features_map = get_feature_vector(0); float iter_prediction = std::max( (f_t)0.0, (f_t)ceil(context.work_unit_predictors.fj_predictor.predict_scalar(features_map)) ); CUOPT_LOG_DEBUG("FJ determ: Estimated number of iterations for %f WU: %f", - settings.work_unit_limit, + settings.work_limit, iter_prediction); settings.iteration_limit = std::min(settings.iteration_limit, (i_t)iter_prediction); } diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index da3168a56..56a1be629 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -366,8 +366,9 @@ int main(int argc, char* argv[]) .default_value(std::string("f")); program.add_argument("-d", "--determinism") - .help("enable deterministic mode (t/f)") - .default_value(std::string("f")); + .help("enable deterministic mode") + .default_value(false) + .implicit_value(true); // Parse arguments try { @@ -397,7 +398,7 @@ int main(int argc, char* argv[]) bool log_to_console = program.get("--log-to-console")[0] == 't'; double memory_limit = program.get("--memory-limit"); bool track_allocations = program.get("--track-allocations")[0] == 't'; - bool deterministic = program.get("--determinism")[0] == 't'; + bool deterministic = program.get("--determinism"); if (num_cpu_threads < 0) { num_cpu_threads = omp_get_max_threads() / n_gpus; } diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 328757e11..49552bef5 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -191,7 +191,7 @@ bool diversity_manager_t::run_presolve(f_t time_limit) CUOPT_LOG_INFO("Running presolve!"); work_limit_timer_t presolve_timer(context.settings.deterministic, time_limit); auto term_crit = ls.constraint_prop.bounds_update.solve(*problem_ptr); - presolve_timer.record_work(213762); + presolve_timer.record_work(0); if (ls.constraint_prop.bounds_update.infeas_constraints_count > 0) { stats.presolve_time = timer.elapsed_time(); return false; @@ -235,7 +235,7 @@ void diversity_manager_t::generate_quick_feasible_solution() work_limit_timer_t sol_timer(context.settings.deterministic, generate_fast_solution_time); // do very short LP run to get somewhere close to the optimal point ls.generate_fast_solution(solution, sol_timer); - sol_timer.record_work(213762); + sol_timer.record_work(0); if (solution.get_feasible()) { population.run_solution_callbacks(solution); initial_sol_vector.emplace_back(std::move(solution)); @@ -325,7 +325,10 @@ solution_t diversity_manager_t::run_solver() raft::common::nvtx::range fun_scope("run_solver"); diversity_config.fj_only_run = false; - if (context.settings.deterministic) { remaining_work_limit = context.settings.work_limit; } + if (context.settings.deterministic) { + remaining_work_limit = context.settings.work_limit; + CUOPT_LOG_INFO("Deterministic mode, remaining work limit: %f", remaining_work_limit); + } population.timer = timer; const f_t time_limit = timer.remaining_time(); @@ -356,7 +359,7 @@ solution_t diversity_manager_t::run_solver() // Run CPUFJ early to find quick initial solutions population.allocate_solutions(); ls_cpufj_raii_guard_t ls_cpufj_raii_guard(ls); // RAII to stop cpufj threads on solve stop - ls.start_cpufj_scratch_threads(population); + if (!context.settings.deterministic) { ls.start_cpufj_scratch_threads(population); } // before probing cache or LP, run FJ to generate initial primal feasible solution const f_t time_ratio_of_probing_cache = diversity_config.time_ratio_of_probing_cache; @@ -367,7 +370,7 @@ solution_t diversity_manager_t::run_solver() if (check_b_b_preemption()) { return population.best_feasible(); } if (!diversity_config.fj_only_run) { compute_probing_cache(ls.constraint_prop.bounds_update, *problem_ptr, probing_timer); - probing_timer.record_work(213762); + probing_timer.record_work(0); } if (check_b_b_preemption()) { return population.best_feasible(); } @@ -430,21 +433,21 @@ solution_t diversity_manager_t::run_solver() } // Run this 100 times with varying iteration limits - for (int i = 0; i < 100; i++) { - relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = lp_time_limit; - lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; - lp_settings.return_first_feasible = false; - lp_settings.save_state = true; - lp_settings.concurrent_halt = &global_concurrent_halt; - lp_settings.has_initial_primal = false; - lp_settings.iteration_limit = 100 + i * 100; - rmm::device_uvector lp_optimal_solution_copy(lp_optimal_solution.size(), - problem_ptr->handle_ptr->get_stream()); - auto lp_result = - get_relaxed_lp_solution(*problem_ptr, lp_optimal_solution_copy, lp_state, lp_settings); - } - exit(0); + // for (int i = 0; i < 100; i++) { + // relaxed_lp_settings_t lp_settings; + // lp_settings.time_limit = lp_time_limit; + // lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; + // lp_settings.return_first_feasible = false; + // lp_settings.save_state = true; + // lp_settings.concurrent_halt = &global_concurrent_halt; + // lp_settings.has_initial_primal = false; + // lp_settings.iteration_limit = 100 + i * 100; + // rmm::device_uvector lp_optimal_solution_copy(lp_optimal_solution.size(), + // problem_ptr->handle_ptr->get_stream()); + // auto lp_result = + // get_relaxed_lp_solution(*problem_ptr, lp_optimal_solution_copy, lp_state, lp_settings); + // } + // exit(0); if (ls.lp_optimal_exists) { solution_t lp_rounded_sol(*problem_ptr); @@ -452,7 +455,7 @@ solution_t diversity_manager_t::run_solver() lp_rounded_sol.round_nearest(); lp_rounded_sol.compute_feasibility(); population.add_solution(std::move(lp_rounded_sol)); - ls.start_cpufj_lptopt_scratch_threads(population); + if (!context.settings.deterministic) { ls.start_cpufj_lptopt_scratch_threads(population); } } population.add_solutions_from_vec(std::move(initial_sol_vector)); diff --git a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh index f4ca9d5c4..17c8c89e6 100644 --- a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh @@ -96,7 +96,7 @@ class fp_recombiner_t : public recombiner_t { if (this->context.settings.deterministic) { lp_settings.time_limit = std::numeric_limits::max(); // TODO should be global time limit - lp_settings.iteration_limit = 5000; + lp_settings.work_limit = fp_recombiner_config_t::infeasibility_detection_time_limit; } lp_settings.tolerance = fixed_problem.tolerances.absolute_tolerance; lp_settings.return_first_feasible = true; diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index fb28a9e13..58d0af50c 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -910,7 +910,7 @@ std::map fj_t::get_feature_vector(i_t climber_idx) features["unbalancedness"] = (float)pb_ptr->unbalancedness; // Algorithm settings - features["time"] = (float)settings.work_unit_limit; + features["time"] = (float)settings.work_limit; features["n_of_minimums_for_exit"] = (float)settings.n_of_minimums_for_exit; features["feasibility_run"] = (float)settings.feasibility_run; @@ -1280,12 +1280,12 @@ i_t fj_t::solve(solution_t& solution) resize_vectors(solution.handle_ptr); // if work_limit is set: compute an estimate of the number of iterations required - if (settings.work_unit_limit != std::numeric_limits::infinity()) { + if (settings.work_limit != std::numeric_limits::infinity()) { std::map features_map = get_feature_vector(0); float iter_prediction = std::max( (f_t)0.0, (f_t)ceil(context.work_unit_predictors.fj_predictor.predict_scalar(features_map))); CUOPT_LOG_DEBUG("FJ determ: Estimated number of iterations for %f WU: %f", - settings.work_unit_limit, + settings.work_limit, iter_prediction); settings.iteration_limit = std::min(settings.iteration_limit, (i_t)iter_prediction); } diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh index 1ee19b39a..2ebeab30e 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh @@ -112,7 +112,7 @@ struct fj_settings_t { fj_mode_t mode{fj_mode_t::FIRST_FEASIBLE}; fj_candidate_selection_t candidate_selection{fj_candidate_selection_t::WEIGHTED_SCORE}; double time_limit{60.0}; - double work_unit_limit{std::numeric_limits::infinity()}; + double work_limit{std::numeric_limits::infinity()}; int iteration_limit{std::numeric_limits::max()}; fj_hyper_parameters_t parameters{}; int n_of_minimums_for_exit = 7000; diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index d8472dae0..75624e1fb 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -241,8 +241,11 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_t::infinity(); + lp_settings.work_limit = time_limit; + } + auto solver_response = get_relaxed_lp_solution(temp_p, solution, lp_settings); cuopt_func_call(solution.test_variable_bounds(false)); last_lp_time = old_remaining - timer.remaining_time(); lp_time += last_lp_time; @@ -270,7 +273,7 @@ template bool feasibility_pump_t::round(solution_t& solution) { bool result; - CUOPT_LOG_DEBUG("Rounding the point"); + // CUOPT_LOG_DEBUG("Rounding the point"); work_limit_timer_t bounds_prop_timer(context.settings.deterministic, std::max(0.05, std::min(0.5, timer.remaining_time() / 10.))); const f_t lp_run_time_after_feasible = 0.; @@ -326,8 +329,9 @@ bool feasibility_pump_t::run_fj_cycle_escape(solution_t& sol template bool feasibility_pump_t::test_fj_feasible(solution_t& solution, f_t time_limit) { - CUOPT_LOG_DEBUG("Running 20%% FJ"); + CUOPT_LOG_DEBUG("Running 20%% FJ, remaining %fwu", timer.remaining_time()); bool is_feasible; + fj.settings = fj_settings_t{}; fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; fj.settings.update_weights = true; fj.settings.feasibility_run = true; @@ -335,6 +339,7 @@ bool feasibility_pump_t::test_fj_feasible(solution_t& soluti fj.settings.time_limit = std::min(time_limit, timer.remaining_time()); if (context.settings.deterministic) { fj.settings.time_limit = timer.remaining_time(); + fj.settings.work_limit = fj.settings.time_limit; fj.settings.iteration_limit = 10000; } cuopt_func_call(solution.test_variable_bounds(true)); @@ -350,6 +355,10 @@ bool feasibility_pump_t::test_fj_feasible(solution_t& soluti } else { CUOPT_LOG_DEBUG("20%% FJ run found feasible!"); } + timer.record_work(fj.settings.time_limit); + CUOPT_LOG_DEBUG("20%% FJ run finished, elapsed %fs remaining %fwu", + fj.settings.time_limit, + timer.remaining_time()); return is_feasible; } @@ -506,18 +515,19 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s f_t start_time = timer.remaining_time(); i_t fp_iterations = 0; - // Problem structure features - CUOPT_LOG_INFO("FP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d n_binary_vars=%d", - solution.problem_ptr->n_variables, - solution.problem_ptr->n_constraints, - solution.problem_ptr->n_integer_vars, - solution.problem_ptr->n_binary_vars); + // // Problem structure features + // CUOPT_LOG_INFO("FP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d + // n_binary_vars=%d", + // solution.problem_ptr->n_variables, + // solution.problem_ptr->n_constraints, + // solution.problem_ptr->n_integer_vars, + // solution.problem_ptr->n_binary_vars); - CUOPT_LOG_INFO("FP_FEATURES: nnz=%lu sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f", - solution.problem_ptr->coefficients.size(), - solution.problem_ptr->sparsity, - solution.problem_ptr->nnz_stddev, - solution.problem_ptr->unbalancedness); + // CUOPT_LOG_INFO("FP_FEATURES: nnz=%lu sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f", + // solution.problem_ptr->coefficients.size(), + // solution.problem_ptr->sparsity, + // solution.problem_ptr->nnz_stddev, + // solution.problem_ptr->unbalancedness); // Initial solution features solution.compute_feasibility(); @@ -526,24 +536,25 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s ? (f_t)initial_n_integers / solution.problem_ptr->n_integer_vars : 0.0; - CUOPT_LOG_INFO("FP_FEATURES: initial_feasibility=%d initial_excess=%.6f initial_objective=%.6f", - solution.get_feasible(), - solution.get_total_excess(), - solution.get_objective()); - - CUOPT_LOG_INFO("FP_FEATURES: initial_ratio_of_integers=%.6f initial_n_integers=%d", - initial_ratio_of_integers, - initial_n_integers); - - // Algorithm configuration features - CUOPT_LOG_INFO("FP_FEATURES: alpha=%.6f check_distance_cycle=%d cycle_detection_length=%d", - config.alpha, - config.check_distance_cycle, - cycle_queue.cycle_detection_length); - - CUOPT_LOG_INFO("FP_FEATURES: has_cutting_plane=%d time_budget=%.6f", - solution.problem_ptr->cutting_plane_added, - timer.remaining_time()); + // CUOPT_LOG_INFO("FP_FEATURES: initial_feasibility=%d initial_excess=%.6f + // initial_objective=%.6f", + // solution.get_feasible(), + // solution.get_total_excess(), + // solution.get_objective()); + + // CUOPT_LOG_INFO("FP_FEATURES: initial_ratio_of_integers=%.6f initial_n_integers=%d", + // initial_ratio_of_integers, + // initial_n_integers); + + // // Algorithm configuration features + // CUOPT_LOG_INFO("FP_FEATURES: alpha=%.6f check_distance_cycle=%d cycle_detection_length=%d", + // config.alpha, + // config.check_distance_cycle, + // cycle_queue.cycle_detection_length); + + // CUOPT_LOG_INFO("FP_FEATURES: has_cutting_plane=%d time_budget=%.6f", + // solution.problem_ptr->cutting_plane_added, + // timer.remaining_time()); // === FP PREDICTOR FEATURES - END === // start by doing nearest rounding @@ -559,9 +570,9 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s if (timer.check_time_limit()) { CUOPT_LOG_DEBUG("FP time limit reached!"); f_t time_taken = start_time - timer.remaining_time(); - CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=TIME_LIMIT", - fp_iterations, - time_taken); + // CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=TIME_LIMIT", + // fp_iterations, + // time_taken); round(solution); return false; } @@ -597,10 +608,10 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s total_fp_time_until_cycle = fp_fj_cycle_time_begin - remaining_time_end_fp; // CUOPT_LOG_DEBUG("total_fp_time_until_cycle: %f", total_fp_time_until_cycle); f_t time_taken = start_time - timer.remaining_time(); - CUOPT_LOG_INFO( - "FP_RESULT: iterations=%d time_taken=%.6f termination=INFEASIBLE_DISTANCE_CYCLE", - fp_iterations, - time_taken); + // CUOPT_LOG_INFO( + // "FP_RESULT: iterations=%d time_taken=%.6f termination=INFEASIBLE_DISTANCE_CYCLE", + // fp_iterations, + // time_taken); return false; } } @@ -609,10 +620,10 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s if (is_feasible) { CUOPT_LOG_DEBUG("Feasible solution found after LP with relative tolerance"); f_t time_taken = start_time - timer.remaining_time(); - CUOPT_LOG_INFO( - "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_LP_PROJECTION", - fp_iterations, - time_taken); + // CUOPT_LOG_INFO( + // "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_LP_PROJECTION", + // fp_iterations, + // time_taken); return true; } // if the solution is almost on polytope @@ -634,10 +645,10 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s if (is_feasible && n_integers == solution.problem_ptr->n_integer_vars) { CUOPT_LOG_DEBUG("Feasible solution verified with LP!"); f_t time_taken = start_time - timer.remaining_time(); - CUOPT_LOG_INFO( - "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_LP_VERIFIED", - fp_iterations, - time_taken); + // CUOPT_LOG_INFO( + // "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_LP_VERIFIED", + // fp_iterations, + // time_taken); return true; } } @@ -653,18 +664,19 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s if (timer.check_time_limit()) { CUOPT_LOG_DEBUG("FP time limit reached!"); f_t time_taken = start_time - timer.remaining_time(); - CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=TIME_LIMIT_AFTER_ROUND", - fp_iterations, - time_taken); + // CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f + // termination=TIME_LIMIT_AFTER_ROUND", + // fp_iterations, + // time_taken); return false; } if (is_feasible) { bool res = solution.compute_feasibility(); cuopt_assert(res, "Feasibility issue"); f_t time_taken = start_time - timer.remaining_time(); - CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_AFTER_ROUND", - fp_iterations, - time_taken); + // CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_AFTER_ROUND", + // fp_iterations, + // time_taken); return true; } // do the cycle check if alpha diff is small enough @@ -683,9 +695,9 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s fp_fj_cycle_time_begin, total_fp_time_until_cycle); f_t time_taken = start_time - timer.remaining_time(); - CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=ASSIGNMENT_CYCLE", - fp_iterations, - time_taken); + // CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=ASSIGNMENT_CYCLE", + // fp_iterations, + // time_taken); return false; } cycle_queue.n_iterations_without_cycle++; diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 0f2be589a..859b7aab0 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -337,7 +337,7 @@ void local_search_t::generate_fast_solution(solution_t& solu work_limit_timer_t(context.settings.deterministic, std::min(timer.remaining_time(), 2.)); // do constraint prop on lp optimal solution constraint_prop.apply_round(solution, 1., constr_prop_timer); - constr_prop_timer.record_work(213762); + constr_prop_timer.record_work(0); if (solution.compute_feasibility()) { return; } if (timer.check_time_limit()) { return; }; f_t time_limit = std::min(3., timer.remaining_time()); @@ -489,7 +489,7 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu work_limit_timer_t(context.settings.deterministic, std::min(timer.remaining_time(), 10.)); bool is_feasible = constraint_prop.apply_round(solution, lp_run_time_after_feasible, bounds_prop_timer); - bounds_prop_timer.record_work(213762); + bounds_prop_timer.record_work(0); if (!is_feasible) { f_t lp_run_time = 2.; // CHANGE diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 3c118becb..87d2a778b 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -229,11 +229,11 @@ void constraint_prop_t::sort_by_interval_and_frac(solution_t } }); - CUOPT_LOG_DEBUG("hash vars 0x%x", detail::compute_hash(vars)); - // now do the suffling, for that we need to assign some random values to rnd array - // we will sort this rnd array and the vars in subsections, so that each subsection will be - // shuffled in total we will have 3(binary, ternary and rest) x 7 intervals = 21 subsections. - // first extract these subsections from the data + // CUOPT_LOG_DEBUG("hash vars 0x%x", detail::compute_hash(vars)); + // now do the suffling, for that we need to assign some random values to rnd array + // we will sort this rnd array and the vars in subsections, so that each subsection will be + // shuffled in total we will have 3(binary, ternary and rest) x 7 intervals = 21 subsections. + // first extract these subsections from the data rmm::device_uvector subsection_offsets(size_of_subsections, sol.handle_ptr->get_stream()); thrust::fill( sol.handle_ptr->get_thrust_policy(), subsection_offsets.begin(), subsection_offsets.end(), -1); @@ -284,9 +284,9 @@ void constraint_prop_t::sort_by_interval_and_frac(solution_t } }); - CUOPT_LOG_DEBUG("hash subsection_offsets 0x%x", detail::compute_hash(subsection_offsets)); + // CUOPT_LOG_DEBUG("hash subsection_offsets 0x%x", detail::compute_hash(subsection_offsets)); auto random_vector = get_random_uniform_vector((i_t)vars.size(), rng); - CUOPT_LOG_DEBUG("hash random_vector 0x%x", detail::compute_hash(random_vector)); + // CUOPT_LOG_DEBUG("hash random_vector 0x%x", detail::compute_hash(random_vector)); rmm::device_uvector device_random_vector(random_vector.size(), sol.handle_ptr->get_stream()); raft::copy(device_random_vector.data(), random_vector.data(), @@ -871,7 +871,6 @@ bool constraint_prop_t::find_integer( using crit_t = termination_criterion_t; auto& unset_integer_vars = unset_vars; i_t seed = cuopt::seed_generator::get_seed(); - CUOPT_LOG_DEBUG("seed 0x%x", seed); std::mt19937 rng(seed); // CHANGE @@ -938,8 +937,8 @@ bool constraint_prop_t::find_integer( } // this is needed for the sort inside of the loop bool problem_ii = is_problem_ii(*sol.problem_ptr); - CUOPT_LOG_DEBUG("is problem ii %d\n", problem_ii); - // if the problem is ii, run the bounds prop in the beginning + // CUOPT_LOG_DEBUG("is problem ii %d", problem_ii); + // if the problem is ii, run the bounds prop in the beginning if (problem_ii) { bool bounds_repaired = bounds_repair.repair_problem(*sol.problem_ptr, *orig_sol.problem_ptr, timer, sol.handle_ptr); @@ -1115,26 +1114,26 @@ bool constraint_prop_t::apply_round( // === CONSTRAINT PROP PREDICTOR FEATURES - START === auto cp_start_time = std::chrono::high_resolution_clock::now(); - CUOPT_LOG_INFO("CP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d", - sol.problem_ptr->n_variables, - sol.problem_ptr->n_constraints, - sol.problem_ptr->n_integer_vars); + // CUOPT_LOG_INFO("CP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d", + // sol.problem_ptr->n_variables, + // sol.problem_ptr->n_constraints, + // sol.problem_ptr->n_integer_vars); - CUOPT_LOG_INFO("CP_FEATURES: nnz=%lu sparsity=%.6f", - sol.problem_ptr->coefficients.size(), - sol.problem_ptr->sparsity); + // CUOPT_LOG_INFO("CP_FEATURES: nnz=%lu sparsity=%.6f", + // sol.problem_ptr->coefficients.size(), + // sol.problem_ptr->sparsity); sol.compute_feasibility(); i_t n_unset_integers = sol.problem_ptr->n_integer_vars - sol.compute_number_of_integers(); - CUOPT_LOG_INFO("CP_FEATURES: n_unset_vars=%d initial_excess=%.6f time_budget=%.6f", - n_unset_integers, - sol.get_total_excess(), - max_time_for_bounds_prop); + // CUOPT_LOG_INFO("CP_FEATURES: n_unset_vars=%d initial_excess=%.6f time_budget=%.6f", + // n_unset_integers, + // sol.get_total_excess(), + // max_time_for_bounds_prop); - CUOPT_LOG_INFO("CP_FEATURES: round_all_vars=%d lp_run_time_after_feasible=%.6f", - round_all_vars, - lp_run_time_after_feasible); + // CUOPT_LOG_INFO("CP_FEATURES: round_all_vars=%d lp_run_time_after_feasible=%.6f", + // round_all_vars, + // lp_run_time_after_feasible); // === CONSTRAINT PROP PREDICTOR FEATURES - END === max_timer = work_limit_timer_t{context.settings.deterministic, max_time_for_bounds_prop}; @@ -1182,16 +1181,16 @@ bool constraint_prop_t::apply_round( if (!sol_found) { sol.compute_feasibility(); - CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=FAILED iterations=%d", - cp_elapsed_ms, - 0); // TODO: track actual iterations + // CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=FAILED iterations=%d", + // cp_elapsed_ms, + // 0); // TODO: track actual iterations return false; } bool result = sol.compute_feasibility(); - CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=%s iterations=%d", - cp_elapsed_ms, - result ? "SUCCESS" : "FAILED", - 0); // TODO: track actual iterations + // CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=%s iterations=%d", + // cp_elapsed_ms, + // result ? "SUCCESS" : "FAILED", + // 0); // TODO: track actual iterations return result; } diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 607fe6c9f..bcce85439 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -159,7 +159,9 @@ solution_t mip_solver_t::run_solver() branch_and_bound_solution_helper_t solution_helper(&dm, branch_and_bound_settings); dual_simplex::mip_solution_t branch_and_bound_solution(1); - if (!context.settings.heuristics_only) { + // for now, disable B&B in deterministic mode + bool run_bb = !context.settings.deterministic && !context.settings.heuristics_only; + if (run_bb) { // Convert the presolved problem to dual_simplex::user_problem_t op_problem_.get_host_user_problem(branch_and_bound_problem); // Resize the solution now that we know the number of columns/variables @@ -223,7 +225,7 @@ solution_t mip_solver_t::run_solver() // Start the primal heuristics auto sol = dm.run_solver(); - if (!context.settings.heuristics_only) { + if (run_bb) { // Wait for the branch and bound to finish auto bb_status = branch_and_bound_status_future.get(); if (branch_and_bound_solution.lower_bound > -std::numeric_limits::infinity()) { diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp index 8e1d87111..4d273d3b6 100644 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -54,7 +54,7 @@ float work_unit_predictor_t::predict_scalar( printf("Feature %s: missing\n", model_t::feature_names[i]); } else { data[i].fvalue = features.at(std::string(model_t::feature_names[i])); - printf("Feature %s: %f\n", model_t::feature_names[i], data[i].fvalue); + // printf("Feature %s: %f\n", model_t::feature_names[i], data[i].fvalue); } } // Compute a hash key for the relevant inputs diff --git a/cpp/tests/mip/feasibility_jump_tests.cu b/cpp/tests/mip/feasibility_jump_tests.cu index c5c695299..76e6aed4c 100644 --- a/cpp/tests/mip/feasibility_jump_tests.cu +++ b/cpp/tests/mip/feasibility_jump_tests.cu @@ -180,7 +180,7 @@ static bool run_fj_check_determinism(std::string test_instance, int iter_limit) fj_settings.time_limit = std::numeric_limits::max(); fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; fj_settings.n_of_minimums_for_exit = 5000 * 1000; - fj_settings.work_unit_limit = 0.5; // run for 0.5wu (~0.5s) + fj_settings.work_limit = 0.5; // run for 0.5wu (~0.5s) fj_settings.update_weights = true; fj_settings.feasibility_run = false; // fj_settings.iteration_limit = iter_limit; From c70bc1b8ed36e2439aec092549a298455c7a5070 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 4 Nov 2025 17:36:57 +0000 Subject: [PATCH 027/366] further work unit timer progress for gpu heuristics --- PREDICTOR_DATA_COLLECTION.md | 426 ------------------ cpp/src/mip/diversity/diversity_manager.cu | 12 +- cpp/src/mip/diversity/population.cu | 2 +- .../recombiners/bound_prop_recombiner.cuh | 4 +- .../diversity/recombiners/fp_recombiner.cuh | 8 +- .../recombiners/line_segment_recombiner.cuh | 2 +- .../mip/feasibility_jump/feasibility_jump.cu | 65 ++- .../feasibility_pump/feasibility_pump.cu | 32 +- .../line_segment_search.cu | 2 +- cpp/src/mip/local_search/local_search.cu | 16 +- .../local_search/rounding/constraint_prop.cu | 25 +- .../rounding/lb_constraint_prop.cu | 2 +- cpp/src/mip/relaxed_lp/relaxed_lp.cu | 16 +- cpp/src/mip/solve.cu | 4 +- cpp/src/mip/solver.cu | 4 +- cpp/src/mip/solver.cuh | 4 +- cpp/src/mip/solver_context.cuh | 9 +- cpp/src/utilities/work_limit_timer.hpp | 77 +++- cpp/tests/mip/diversity_test.cu | 15 +- cpp/tests/mip/local_search_test.cu | 13 +- cpp/tests/mip/mip_utils.cuh | 2 +- cpp/tests/mip/multi_probe_test.cu | 6 +- cpp/tests/mip/presolve_test.cu | 11 +- 23 files changed, 204 insertions(+), 553 deletions(-) delete mode 100644 PREDICTOR_DATA_COLLECTION.md diff --git a/PREDICTOR_DATA_COLLECTION.md b/PREDICTOR_DATA_COLLECTION.md deleted file mode 100644 index d026a11d7..000000000 --- a/PREDICTOR_DATA_COLLECTION.md +++ /dev/null @@ -1,426 +0,0 @@ -# Predictor Data Collection: Feature Logging - -This document describes the instrumentation added to collect training data for work unit predictors. - -## Overview - -Three algorithms have been instrumented to log features before execution and performance metrics after completion: - -1. **Feasibility Pump (FP)** - Main heuristic for finding feasible solutions -2. **PDLP** - First-order LP solver used for polytope projection -3. **Constraint Propagation (CP)** - Variable rounding with bounds propagation - -Note: **Feasibility Jump (FJ)** already has a working predictor and doesn't need additional instrumentation. - -## Log Format - -All logs use `CUOPT_LOG_INFO` level with structured prefixes for easy parsing: - -### Feasibility Pump (FP) - -**Features logged before execution:** -``` -FP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d n_binary_vars=%d -FP_FEATURES: nnz=%lu sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f -FP_FEATURES: initial_feasibility=%d initial_excess=%.6f initial_objective=%.6f -FP_FEATURES: initial_ratio_of_integers=%.6f initial_n_integers=%d -FP_FEATURES: alpha=%.6f check_distance_cycle=%d cycle_detection_length=%d -FP_FEATURES: has_cutting_plane=%d time_budget=%.6f -``` - -**Results logged after execution:** -``` -FP_RESULT: iterations=%d time_taken=%.6f termination= -``` - -**Termination reasons:** -- `TIME_LIMIT` - Time budget exhausted -- `TIME_LIMIT_AFTER_ROUND` - Time limit during rounding phase -- `FEASIBLE_LP_PROJECTION` - Found feasible via LP projection -- `FEASIBLE_LP_VERIFIED` - Found feasible via high-precision LP -- `FEASIBLE_AFTER_ROUND` - Found feasible after rounding -- `FEASIBLE_DISTANCE_CYCLE` - Found feasible during distance cycle handling -- `INFEASIBLE_DISTANCE_CYCLE` - Distance cycle detected, no feasible found -- `ASSIGNMENT_CYCLE` - Assignment cycle detected - -**Location:** `cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu::run_single_fp_descent` - ---- - -### PDLP (LP Solver) - -**Features logged before execution:** -``` -PDLP_FEATURES: n_variables=%d n_constraints=%d nnz=%lu -PDLP_FEATURES: sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f -PDLP_FEATURES: has_warm_start=%d time_limit=%.6f iteration_limit=%d -PDLP_FEATURES: tolerance=%.10f check_infeasibility=%d return_first_feasible=%d -``` - -**Results logged after execution:** -``` -PDLP_RESULT: iterations=%d time_ms=%lld termination=%d -PDLP_RESULT: primal_objective=%.10f dual_objective=%.10f gap=%.10f -PDLP_RESULT: l2_primal_residual=%.10f l2_dual_residual=%.10f -``` - -**Termination status codes:** -- `0` - NoTermination -- `1` - NumericalError -- `2` - Optimal -- `3` - PrimalInfeasible -- `4` - DualInfeasible -- `5` - IterationLimit -- `6` - TimeLimit -- `7` - PrimalFeasible -- `8` - ConcurrentLimit - -**Location:** `cpp/src/mip/relaxed_lp/relaxed_lp.cu::get_relaxed_lp_solution` - ---- - -### Constraint Propagation (CP) - -**Features logged before execution:** -``` -CP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d -CP_FEATURES: nnz=%lu sparsity=%.6f -CP_FEATURES: n_unset_vars=%d initial_excess=%.6f time_budget=%.6f -CP_FEATURES: round_all_vars=%d lp_run_time_after_feasible=%.6f -``` - -**Results logged after execution:** -``` -CP_RESULT: time_ms=%lld termination= iterations=%d -``` - -**Termination status:** -- `BRUTE_FORCE_SUCCESS` - Succeeded via simple rounding -- `SUCCESS` - Found feasible solution -- `FAILED` - Did not find feasible solution - -**Location:** `cpp/src/mip/local_search/rounding/constraint_prop.cu::apply_round` - ---- - -## Data Collection Workflow - -### 1. Run Solver with Logging Enabled - -Ensure the log level is set to `INFO` or higher to capture the feature logs: - -```bash -export CUOPT_LOG_LEVEL=INFO -# or -export CUOPT_LOG_LEVEL=DEBUG -``` - -### 2. Parse Logs - -Use the provided `determinism_logs_parse.py` script to automatically extract training data: - -```bash -# Parse FP (Feasibility Pump) logs -python scripts/determinism_logs_parse.py logs/ --algorithm FP -o fp_data.pkl - -# Parse PDLP (LP Solver) logs -python scripts/determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.pkl - -# Parse CP (Constraint Propagation) logs -python scripts/determinism_logs_parse.py logs/ --algorithm CP -o cp_data.pkl - -# Parse FJ (Feasibility Jump) legacy logs -python scripts/determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.pkl -``` - -The script will: -- Find all `.log` files in the specified directory -- Extract all `_FEATURES` and `_RESULT` log lines using grep -- Pair features with results in order using line numbers -- Export to pickle format compatible with `train_regressor.py` - -**Performance optimizations for large logs:** -- **Exact pattern matching**: Grep uses `FP_FEATURES:` / `FP_RESULT:` (with colon) to match ONLY predictor lines - - Example: Log with 100K lines and 10K "FP" references → grep extracts only ~200 predictor lines - - Filters before Python processing, so noisy logs don't slow down parsing -- Single grep call per algorithm (combines features + results) -- Uses grep's `-n` flag for line-number-based pairing -- Minimal Python string processing (simple split operations) -- Single-pass parsing with efficient dictionary accumulation -- Handles millions of log lines efficiently - -**Script output (with progress indicators):** -``` -Scanning logs/ for .log files... -Found 42 log files - -Parsing FP (Feasibility Pump) logs... - Running grep on 42 files... - Processing 3046 matching lines... - Progress: 10000/3046 lines, 42 files - Progress: 20000/3046 lines, 42 files - Processed 3046 lines from 42 files - Pairing features with results... - Pairing: 10/42 files, 362 entries found - Pairing: 20/42 files, 724 entries found - Pairing: 30/42 files, 1086 entries found - Pairing: 40/42 files, 1448 entries found - Found 1523 complete entries from 42 files - - Total entries: 1523 - Unique files: 42 - Avg entries per file: 36.26 - Iterations (target): min=1, max=847, avg=142.35 - -Saving 1523 entries to fp_data.pkl... - -====================================================================== -✓ Success! Saved 1523 entries to fp_data.pkl - File size: 2.34 MB -====================================================================== -``` - -**Progress updates:** -- Line processing: Every 10,000 lines -- Pairing: Every 10 files -- Uses carriage return (`\r`) for in-place updates - -### 3. Train Predictor Model - -Use the `train_regressor.py` script with the parsed data: - -```bash -# Train XGBoost model for FP -python scripts/train_regressor.py fp_data.pkl --regressor xgboost --seed 42 - -# Train LightGBM model for PDLP -python scripts/train_regressor.py pdlp_data.pkl --regressor lightgbm --seed 42 - -# View available features before training -python scripts/train_regressor.py cp_data.pkl --regressor xgboost --list-features -``` - -The training script will: -- Load the pickle file -- Split data by files (train/test) -- Train the specified model -- Evaluate performance (R², RMSE, MAE) -- Export to C++ code using TL2cgen (for XGBoost/LightGBM) -- Save model and metadata - ---- - -## Feature Descriptions - -### Problem Structure Features - -- **n_variables** - Total number of decision variables -- **n_constraints** - Total number of constraints -- **n_integer_vars** - Number of integer/binary variables -- **n_binary_vars** - Number of binary (0/1) variables -- **nnz** - Non-zero coefficients in constraint matrix -- **sparsity** - Matrix sparsity: nnz / (n_constraints × n_variables) -- **nnz_stddev** - Standard deviation of non-zeros per constraint row -- **unbalancedness** - Load balancing metric for constraint matrix - -### Solution State Features (FP) - -- **initial_feasibility** - Whether starting solution is feasible (0/1) -- **initial_excess** - Sum of constraint violations -- **initial_objective** - Objective value of initial solution -- **initial_ratio_of_integers** - Fraction of integer vars already integral -- **initial_n_integers** - Count of integer vars at integral values - -### Algorithm Configuration Features (FP) - -- **alpha** - Weight between original objective and distance objective -- **check_distance_cycle** - Whether distance-based cycle detection is enabled -- **cycle_detection_length** - Number of recent solutions tracked -- **has_cutting_plane** - Whether objective cutting plane was added -- **time_budget** - Allocated time in seconds - -### Solver Configuration Features (PDLP) - -- **has_warm_start** - Whether initial primal/dual solution provided -- **time_limit** - Time budget in seconds -- **iteration_limit** - Maximum iterations allowed -- **tolerance** - Optimality tolerance -- **check_infeasibility** - Whether to detect infeasibility -- **return_first_feasible** - Whether to return on first primal feasible - -### Rounding Configuration Features (CP) - -- **n_unset_vars** - Integer variables not yet set -- **round_all_vars** - Whether to round all variables or selective -- **lp_run_time_after_feasible** - Time budget for post-feasibility LP - ---- - -## Integration with Existing Predictor - -The FJ predictor already exists at `cpp/src/utilities/models/fj_predictor/`. The same workflow can be used: - -1. Collect training data as described above -2. Train XGBoost model -3. Export to C++ using TreeLite (as done for FJ) -4. Integrate into solver with work unit → iteration conversion - -Example from FJ predictor: -```cpp -// cpp/src/mip/feasibility_jump/feasibility_jump.cu:1283-1291 -if (settings.work_limit != std::numeric_limits::infinity()) { - std::map features_map = get_feature_vector(0); - float iter_prediction = std::max( - (f_t)0.0, - (f_t)ceil(context.work_unit_predictors.fj_predictor.predict_scalar(features_map)) - ); - CUOPT_LOG_DEBUG("FJ determ: Estimated number of iterations for %f WU: %f", - settings.work_limit, - iter_prediction); - settings.iteration_limit = std::min(settings.iteration_limit, (i_t)iter_prediction); -} -``` - ---- - -## Next Steps - -1. **Collect Data**: Run solver on diverse problem sets with logging enabled -2. **Analyze**: Examine feature importance and correlation with execution time -3. **Train Models**: Build iteration predictors for FP, PDLP, and CP -4. **Validate**: Test predictors maintain solution quality while achieving determinism -5. **Deploy**: Integrate trained models into solver (similar to FJ predictor) -6. **Hierarchical Allocation**: Implement work unit budget allocation across nested algorithms - ---- - -## Complete Workflow Example - -Here's a complete end-to-end example: - -### Step 1: Run Solver and Collect Logs - -```bash -# Set log level to capture feature logs -export CUOPT_LOG_LEVEL=INFO - -# Run your solver on test problems -./my_solver problem1.mps > logs/problem1.log 2>&1 -./my_solver problem2.mps > logs/problem2.log 2>&1 -# ... run on many problems -``` - -### Step 2: Parse Logs for Each Algorithm - -```bash -# Parse FP logs -python scripts/determinism_logs_parse.py logs/ --algorithm FP -o fp_data.pkl - -# Parse PDLP logs -python scripts/determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.pkl - -# Parse CP logs -python scripts/determinism_logs_parse.py logs/ --algorithm CP -o cp_data.pkl -``` - -### Step 3: Inspect Features - -```bash -# See what features are available for FP -python scripts/train_regressor.py fp_data.pkl --regressor xgboost --list-features -``` - -Output: -``` -====================================================================== -Available features in dataset (28 total): -====================================================================== - 1. alpha - 2. check_distance_cycle - 3. cycle_detection_length - 4. has_cutting_plane - 5. initial_excess - 6. initial_feasibility - 7. initial_n_integers - 8. initial_objective - 9. initial_ratio_of_integers - 10. n_binary_vars - 11. n_constraints - 12. n_integer_vars - 13. n_variables - 14. nnz - 15. nnz_stddev - 16. sparsity - 17. time_budget - 18. unbalancedness - ... -``` - -### Step 4: Train Models - -```bash -# Train FP predictor with XGBoost -python scripts/train_regressor.py fp_data.pkl \ - --regressor xgboost \ - --seed 42 \ - --early-stopping 20 \ - --treelite-compile 8 - -# Train PDLP predictor with LightGBM -python scripts/train_regressor.py pdlp_data.pkl \ - --regressor lightgbm \ - --seed 42 \ - --early-stopping 20 \ - --treelite-compile 8 -``` - -### Step 5: Review Results - -The training script will output: -- Cross-validation scores -- Train/test metrics (R², RMSE, MAE) -- Feature importance ranking -- Sample predictions -- Worst predictions with feature values - -Example output: -``` -Training complete! - -Test Set Metrics: - MSE: 1234.5678 - RMSE: 35.14 - MAE: 22.67 - R²: 0.8542 - -Feature Importance: - 1. n_variables : 0.245123 - 2. n_constraints : 0.187456 - 3. initial_ratio_of_integers : 0.156234 - 4. sparsity : 0.098765 - ... - -C source code generated to: ./models/fp_data_c_code/ - Contains optimized model source code (branch-annotated, quantized) -``` - -### Step 6: Integrate into Solver - -The generated C++ code will be in `./models/_data_c_code/`: -- `header.h` - Class declaration with predict functions -- `main.cpp` - Implementation -- `quantize.cpp` - Quantization helpers (if enabled) - -Copy these files to `cpp/src/utilities/models/_predictor/` and integrate similar to the existing FJ predictor. - ---- - -## Notes - -- Line Segment Search was excluded as it can be predicted from FJ predictor (it runs FJ internally) -- CP iteration tracking needs enhancement (currently logs 0 iterations) -- Consider adding more dynamic features during execution for better predictions -- The termination reasons can help understand when algorithms succeed/fail -- Use `--stratify-split` when training to ensure balanced train/test distribution -- The `--early-stopping` parameter helps prevent overfitting on tree models -- TL2cgen compilation with `--treelite-compile` generates optimized C++ code with branch annotation and quantization enabled by default diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 49552bef5..33f228b49 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -57,7 +57,7 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_tn_constraints, context.problem_ptr->handle_ptr->get_stream()), ls(context, lp_optimal_solution), - timer(context.settings.deterministic, diversity_config.default_time_limit), + timer(context.gpu_heur_loop, diversity_config.default_time_limit), bound_prop_recombiner(context, context.problem_ptr->n_variables, ls.constraint_prop, @@ -112,6 +112,8 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_t::run_presolve(f_t time_limit) { raft::common::nvtx::range fun_scope("run_presolve"); CUOPT_LOG_INFO("Running presolve!"); - work_limit_timer_t presolve_timer(context.settings.deterministic, time_limit); + work_limit_timer_t presolve_timer(context.gpu_heur_loop, time_limit); auto term_crit = ls.constraint_prop.bounds_update.solve(*problem_ptr); presolve_timer.record_work(0); if (ls.constraint_prop.bounds_update.infeas_constraints_count > 0) { @@ -232,7 +234,7 @@ void diversity_manager_t::generate_quick_feasible_solution() // min 1 second, max 10 seconds const f_t generate_fast_solution_time = std::min(diversity_config.max_fast_sol_time, std::max(1., timer.remaining_time() / 20.)); - work_limit_timer_t sol_timer(context.settings.deterministic, generate_fast_solution_time); + work_limit_timer_t sol_timer(context.gpu_heur_loop, generate_fast_solution_time); // do very short LP run to get somewhere close to the optimal point ls.generate_fast_solution(solution, sol_timer); sol_timer.record_work(0); @@ -336,7 +338,7 @@ solution_t diversity_manager_t::run_solver() std::min(diversity_config.max_time_on_lp, time_limit * diversity_config.time_ratio_on_init_lp); // to automatically compute the solving time on scope exit auto timer_raii_guard = - cuopt::scope_guard([&]() { stats.total_solve_time = timer.elapsed_time(); }); + cuopt::scope_guard([&]() { stats.total_solve_time = timer.timer.elapsed_time(); }); // after every change to the problem, we should resize all the relevant vars // we need to encapsulate that to prevent repetitions recombine_stats.reset(); @@ -366,7 +368,7 @@ solution_t diversity_manager_t::run_solver() const f_t max_time_on_probing = diversity_config.max_time_on_probing; f_t time_for_probing_cache = std::min(max_time_on_probing, time_limit * time_ratio_of_probing_cache); - work_limit_timer_t probing_timer{context.settings.deterministic, time_for_probing_cache}; + work_limit_timer_t probing_timer{context.gpu_heur_loop, time_for_probing_cache}; if (check_b_b_preemption()) { return population.best_feasible(); } if (!diversity_config.fj_only_run) { compute_probing_cache(ls.constraint_prop.bounds_update, *problem_ptr, probing_timer); diff --git a/cpp/src/mip/diversity/population.cu b/cpp/src/mip/diversity/population.cu index 56eedaf4a..d2f9200d1 100644 --- a/cpp/src/mip/diversity/population.cu +++ b/cpp/src/mip/diversity/population.cu @@ -54,7 +54,7 @@ population_t::population_t(std::string const& name_, rng(cuopt::seed_generator::get_seed()), early_exit_primal_generation(false), population_hash_map(*problem_ptr), - timer(context.settings.deterministic, 0) + timer(context.gpu_heur_loop, 0) { best_feasible_objective = std::numeric_limits::max(); } diff --git a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh index 756942819..4ca19436c 100644 --- a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh @@ -187,7 +187,7 @@ class bound_prop_recombiner_t : public recombiner_t { if (guiding_solution.get_feasible() && !a.problem_ptr->expensive_to_fix_vars) { this->compute_vars_to_fix(offspring, vars_to_fix, n_vars_from_other, n_vars_from_guiding); auto [fixed_problem, fixed_assignment, variable_map] = offspring.fix_variables(vars_to_fix); - work_limit_timer_t timer(this->context.settings.deterministic, + work_limit_timer_t timer(this->context.gpu_heur_loop, bp_recombiner_config_t::bounds_prop_time_limit); rmm::device_uvector old_assignment(offspring.assignment, offspring.handle_ptr->get_stream()); @@ -238,7 +238,7 @@ class bound_prop_recombiner_t : public recombiner_t { } a.handle_ptr->sync_stream(); } else { - work_limit_timer_t timer(this->context.settings.deterministic, + work_limit_timer_t timer(this->context.gpu_heur_loop, bp_recombiner_config_t::bounds_prop_time_limit); get_probing_values_for_infeasible( guiding_solution, other_solution, offspring, probing_values, n_vars_from_other); diff --git a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh index 17c8c89e6..ade72a23b 100644 --- a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh @@ -122,13 +122,7 @@ class fp_recombiner_t : public recombiner_t { offspring.handle_ptr->sync_stream(); offspring.assignment = std::move(fixed_assignment); cuopt_func_call(offspring.test_variable_bounds(false)); - work_limit_timer_t timer(this->context.settings.deterministic, - fp_recombiner_config_t::fp_time_limit); - if (this->context.settings.deterministic) { - timer = work_limit_timer_t( - this->context.settings.deterministic, - std::numeric_limits::max()); // TODO should be global time limit - } + work_limit_timer_t timer(this->context.gpu_heur_loop, fp_recombiner_config_t::fp_time_limit); fp.timer = timer; fp.cycle_queue.reset(offspring); fp.reset(); diff --git a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh index dce1f05cf..6a2882731 100644 --- a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh @@ -86,7 +86,7 @@ class line_segment_recombiner_t : public recombiner_t { auto& other_solution = a.get_feasible() ? b : a; // copy the solution from A solution_t offspring(guiding_solution); - work_limit_timer_t line_segment_timer{this->context.settings.deterministic, + work_limit_timer_t line_segment_timer{this->context.gpu_heur_loop, ls_recombiner_config_t::time_limit}; // TODO after we have the conic combination, detect the lambda change // (i.e. the integral variables flip on line segment) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 58d0af50c..bd11f3aae 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -1084,7 +1084,7 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) data.incumbent_quality.set_value_async(obj, handle_ptr->get_stream()); - timer_t timer(settings.time_limit); + work_limit_timer_t timer(context.gpu_heur_loop, settings.time_limit); i_t steps; bool limit_reached = false; for (steps = 0; steps < std::numeric_limits::max(); steps += iterations_per_graph) { @@ -1269,7 +1269,7 @@ template i_t fj_t::solve(solution_t& solution) { raft::common::nvtx::range scope("fj_solve"); - timer_t timer(settings.time_limit); + work_limit_timer_t timer(context.gpu_heur_loop, settings.time_limit); handle_ptr = const_cast(solution.handle_ptr); pb_ptr = solution.problem_ptr; if (settings.mode != fj_mode_t::ROUNDING) { @@ -1279,6 +1279,7 @@ i_t fj_t::solve(solution_t& solution) pb_ptr->check_problem_representation(true); resize_vectors(solution.handle_ptr); + if (context.settings.deterministic) { settings.work_limit = settings.time_limit; } // if work_limit is set: compute an estimate of the number of iterations required if (settings.work_limit != std::numeric_limits::infinity()) { std::map features_map = get_feature_vector(0); @@ -1287,6 +1288,7 @@ i_t fj_t::solve(solution_t& solution) CUOPT_LOG_DEBUG("FJ determ: Estimated number of iterations for %f WU: %f", settings.work_limit, iter_prediction); + if (settings.work_limit == 0) iter_prediction = 0; settings.iteration_limit = std::min(settings.iteration_limit, (i_t)iter_prediction); } @@ -1376,19 +1378,54 @@ i_t fj_t::solve(solution_t& solution) cuopt_func_call(solution.test_variable_bounds()); - // Print compact feature vector summary - char logbuf[4096]; - int offset = 0; - offset += snprintf(logbuf + offset, - sizeof(logbuf) - offset, - "FJ: iter=%d time=%g", - iterations, - timer.elapsed_time()); - for (const auto& [name, value] : feature_vector) { - offset += snprintf(logbuf + offset, sizeof(logbuf) - offset, " %s=%g", name.c_str(), value); - if (offset >= (int)(sizeof(logbuf) - 32)) break; + double work_to_record = settings.work_limit; + + if (iterations < settings.iteration_limit) { + CUOPT_LOG_DEBUG( + "FJ early exit at %d iterations (limit: %d)", iterations, settings.iteration_limit); + // Compute the work unit corresponding to the number of iterations elapsed + // by incrementally guessing work units until the model predicts >= actual iterations + if (context.settings.deterministic && iterations > 0) { + double guessed_work = 0.0; + const double work_increment = 0.1; + const double max_work = settings.work_limit * 2.0; // Safety limit + float predicted_iters = 0.0f; + + // Make a copy of the feature vector and modify the time/work_limit field + std::map features_for_prediction = feature_vector; + + while (guessed_work <= max_work) { + features_for_prediction["time"] = (float)guessed_work; + predicted_iters = std::max( + 0.0f, + (float)ceil( + context.work_unit_predictors.fj_predictor.predict_scalar(features_for_prediction))); + + if (predicted_iters >= (float)iterations) { + work_to_record = guessed_work; + break; + } + + guessed_work += work_increment; + } + } } - CUOPT_LOG_INFO("%s", logbuf); + + timer.record_work(work_to_record); + + // // Print compact feature vector summary + // char logbuf[4096]; + // int offset = 0; + // offset += snprintf(logbuf + offset, + // sizeof(logbuf) - offset, + // "FJ: iter=%d time=%g", + // iterations, + // timer.elapsed_time()); + // for (const auto& [name, value] : feature_vector) { + // offset += snprintf(logbuf + offset, sizeof(logbuf) - offset, " %s=%g", name.c_str(), value); + // if (offset >= (int)(sizeof(logbuf) - 32)) break; + // } + // CUOPT_LOG_INFO("%s", logbuf); return is_new_feasible; } diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index 75624e1fb..403600c44 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -61,7 +61,7 @@ feasibility_pump_t::feasibility_pump_t( context.problem_ptr->handle_ptr->get_stream()), lp_optimal_solution(lp_optimal_solution_), rng(cuopt::seed_generator::get_seed()), - timer(context.settings.deterministic, 20.) + timer(context.gpu_heur_loop, 20.) { thrust::fill(context.problem_ptr->handle_ptr->get_thrust_policy(), last_projection.begin(), @@ -147,7 +147,7 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_tvariable_bounds, solution.handle_ptr->get_stream()); @@ -274,7 +274,7 @@ bool feasibility_pump_t::round(solution_t& solution) { bool result; // CUOPT_LOG_DEBUG("Rounding the point"); - work_limit_timer_t bounds_prop_timer(context.settings.deterministic, + work_limit_timer_t bounds_prop_timer(context.gpu_heur_loop, std::max(0.05, std::min(0.5, timer.remaining_time() / 10.))); const f_t lp_run_time_after_feasible = 0.; bool old_var = constraint_prop.round_all_vars; @@ -337,11 +337,7 @@ bool feasibility_pump_t::test_fj_feasible(solution_t& soluti fj.settings.feasibility_run = true; fj.settings.n_of_minimums_for_exit = 5000; fj.settings.time_limit = std::min(time_limit, timer.remaining_time()); - if (context.settings.deterministic) { - fj.settings.time_limit = timer.remaining_time(); - fj.settings.work_limit = fj.settings.time_limit; - fj.settings.iteration_limit = 10000; - } + if (context.settings.deterministic) { fj.settings.work_limit = fj.settings.time_limit; } cuopt_func_call(solution.test_variable_bounds(true)); is_feasible = fj.solve(solution); cuopt_func_call(solution.test_variable_bounds(true)); @@ -366,7 +362,7 @@ template bool feasibility_pump_t::handle_cycle(solution_t& solution) { raft::common::nvtx::range fun_scope("handle_cycle"); - CUOPT_LOG_DEBUG("running handle cycle"); + // CUOPT_LOG_DEBUG("running handle cycle"); bool is_feasible = false; fp_fj_cycle_time_begin = timer.remaining_time(); CUOPT_LOG_DEBUG("Running longer FJ on last rounding"); @@ -443,15 +439,15 @@ bool feasibility_pump_t::check_distance_cycle(solution_t& so std::accumulate(last_distances.begin(), last_distances.end(), 0.0) / last_distances.size(); if (avg_distance - distance_to_last_rounding < config.cycle_distance_reduction_ration * avg_distance) { - CUOPT_LOG_DEBUG("Distance cycle detected curr %f avg %f for last %d iter", - distance_to_last_rounding, - avg_distance, - last_distances.size()); + // CUOPT_LOG_DEBUG("Distance cycle detected curr %f avg %f for last %d iter", + // distance_to_last_rounding, + // avg_distance, + // last_distances.size()); is_cycle = true; } last_distances.pop_back(); } else { - CUOPT_LOG_DEBUG("Distance of projection: %f", distance_to_last_rounding); + // CUOPT_LOG_DEBUG("Distance of projection: %f", distance_to_last_rounding); } last_distances.push_front(distance_to_last_rounding); return is_cycle; @@ -582,9 +578,9 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s f_t(solution.n_assigned_integers) / solution.problem_ptr->n_integer_vars; bool is_feasible = linear_project_onto_polytope(solution, ratio_of_assigned_integers); i_t n_integers = solution.compute_number_of_integers(); - CUOPT_LOG_DEBUG("after fp projection n_integers %d total n_integes %d", - n_integers, - solution.problem_ptr->n_integer_vars); + // CUOPT_LOG_DEBUG("after fp projection n_integers %d total n_integes %d", + // n_integers, + // solution.problem_ptr->n_integer_vars); bool is_cycle = true; // temp comment for presolve run if (config.check_distance_cycle) { @@ -657,7 +653,7 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s is_feasible = round(solution); cuopt_func_call(solution.test_variable_bounds(true)); proj_and_round_time = proj_begin - timer.remaining_time(); - if (!is_feasible) { + if (!is_feasible && proj_and_round_time > 0) { const f_t time_ratio = 0.2; is_feasible = test_fj_feasible(solution, time_ratio * proj_and_round_time); } diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu index 6371b7bd5..475e8d991 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu @@ -244,7 +244,7 @@ bool line_segment_search_t::search_line_segment( best_feasible_cost, curr_cost); } - // cuopt_assert(context.settings.deterministic, ""); + // cuopt_assert(context.gpu_heur_loop, ""); if (!context.settings.deterministic) { if (timer.check_time_limit()) { break; } } diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 859b7aab0..8fd8ba503 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -234,7 +234,7 @@ bool local_search_t::do_fj_solve(solution_t& solution, { if (time_limit == 0.) return solution.get_feasible(); - work_limit_timer_t timer(context.settings.deterministic, time_limit); + work_limit_timer_t timer(context.gpu_heur_loop, time_limit); auto h_weights = cuopt::host_copy(in_fj.cstr_weights, solution.handle_ptr->get_stream()); auto h_objective_weight = in_fj.objective_weight.value(solution.handle_ptr->get_stream()); @@ -334,7 +334,7 @@ void local_search_t::generate_fast_solution(solution_t& solu fj.settings.time_limit = std::numeric_limits::infinity(); while (!timer.check_time_limit()) { work_limit_timer_t constr_prop_timer = - work_limit_timer_t(context.settings.deterministic, std::min(timer.remaining_time(), 2.)); + work_limit_timer_t(context.gpu_heur_loop, std::min(timer.remaining_time(), 2.)); // do constraint prop on lp optimal solution constraint_prop.apply_round(solution, 1., constr_prop_timer); constr_prop_timer.record_work(0); @@ -362,10 +362,10 @@ bool local_search_t::run_local_search(solution_t& solution, if (!solution.get_feasible()) { if (ls_config.at_least_one_parent_feasible) { fj_settings.time_limit = 0.5; - timer = work_limit_timer_t(context.settings.deterministic, fj_settings.time_limit); + timer = work_limit_timer_t(context.gpu_heur_loop, fj_settings.time_limit); } else { fj_settings.time_limit = 0.25; - timer = work_limit_timer_t(context.settings.deterministic, fj_settings.time_limit); + timer = work_limit_timer_t(context.gpu_heur_loop, fj_settings.time_limit); } } else { fj_settings.time_limit = std::min(1., timer.remaining_time()); @@ -486,7 +486,7 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu cuopt_func_call(solution.test_variable_bounds(false)); f_t lp_run_time_after_feasible = std::min(1., timer.remaining_time()); work_limit_timer_t bounds_prop_timer = - work_limit_timer_t(context.settings.deterministic, std::min(timer.remaining_time(), 10.)); + work_limit_timer_t(context.gpu_heur_loop, std::min(timer.remaining_time(), 10.)); bool is_feasible = constraint_prop.apply_round(solution, lp_run_time_after_feasible, bounds_prop_timer); bounds_prop_timer.record_work(0); @@ -566,7 +566,7 @@ bool local_search_t::run_staged_fp(solution_t& solution, } CUOPT_LOG_DEBUG("Running staged FP from beginning it %d", i); fp.relax_general_integers(solution); - work_limit_timer_t binary_timer(context.settings.deterministic, timer.remaining_time() / 3); + work_limit_timer_t binary_timer(context.gpu_heur_loop, timer.remaining_time() / 3); i_t binary_it_counter = 0; for (; binary_it_counter < 100; ++binary_it_counter) { if (population_ptr->preempt_heuristic_solver_.load()) { @@ -744,7 +744,7 @@ bool local_search_t::run_fp(solution_t& solution, is_feasible ? solution.get_objective() : std::numeric_limits::max(); rmm::device_uvector best_solution(solution.assignment, solution.handle_ptr->get_stream()); problem_t* old_problem_ptr = solution.problem_ptr; - fp.timer = work_limit_timer_t(context.settings.deterministic, timer.remaining_time()); + fp.timer = work_limit_timer_t(context.gpu_heur_loop, timer.remaining_time()); // if it has not been initialized yet, create a new problem and move it to the cut problem if (!problem_with_objective_cut.cutting_plane_added) { problem_with_objective_cut = std::move(problem_t(*old_problem_ptr)); @@ -845,7 +845,7 @@ bool local_search_t::generate_solution(solution_t& solution, { raft::common::nvtx::range fun_scope("generate_solution"); cuopt_assert(population_ptr != nullptr, "Population pointer must not be null"); - work_limit_timer_t timer(context.settings.deterministic, time_limit); + work_limit_timer_t timer(context.gpu_heur_loop, time_limit); auto n_vars = solution.problem_ptr->n_variables; auto n_binary_vars = solution.problem_ptr->get_n_binary_variables(); auto n_integer_vars = solution.problem_ptr->n_integer_vars; diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 87d2a778b..016aa03b3 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -784,8 +784,7 @@ bool constraint_prop_t::run_repair_procedure(problem_t& prob i_t n_of_repairs_needed_for_feasible = 0; i_t iter_limit = std::numeric_limits::max(); if (this->context.settings.deterministic) { - timer = - work_limit_timer_t(context.settings.deterministic, std::numeric_limits::infinity()); + timer = work_limit_timer_t(context.gpu_heur_loop, std::numeric_limits::infinity()); iter_limit = 100; } do { @@ -875,8 +874,7 @@ bool constraint_prop_t::find_integer( // CHANGE if (this->context.settings.deterministic) { - timer = - work_limit_timer_t(context.settings.deterministic, std::numeric_limits::infinity()); + timer = work_limit_timer_t(context.gpu_heur_loop, std::numeric_limits::infinity()); } lb_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); @@ -928,9 +926,9 @@ bool constraint_prop_t::find_integer( set_bounds_on_fixed_vars(sol); } - CUOPT_LOG_DEBUG("Bounds propagation rounding: unset vars %lu", unset_integer_vars.size()); + // CUOPT_LOG_DEBUG("Bounds propagation rounding: unset vars %lu", unset_integer_vars.size()); if (unset_integer_vars.size() == 0) { - CUOPT_LOG_DEBUG("No integer variables provided in the bounds prop rounding"); + // CUOPT_LOG_DEBUG("No integer variables provided in the bounds prop rounding"); expand_device_copy(orig_sol.assignment, sol.assignment, sol.handle_ptr->get_stream()); cuopt_func_call(orig_sol.test_variable_bounds()); return orig_sol.compute_feasibility(); @@ -1015,7 +1013,7 @@ bool constraint_prop_t::find_integer( if (!(n_failed_repair_iterations >= max_n_failed_repair_iterations) && rounding_ii && !timeout_happened) { // timer_t repair_timer{std::min(timer.remaining_time() / 5, timer.elapsed_time() / 3)}; - work_limit_timer_t repair_timer(context.settings.deterministic, timer.remaining_time() / 5); + work_limit_timer_t repair_timer(context.gpu_heur_loop, timer.remaining_time() / 5); save_bounds(sol); // update bounds and run repair procedure bool bounds_repaired = @@ -1070,10 +1068,10 @@ bool constraint_prop_t::find_integer( // which is the unchanged problem bounds multi_probe.update_host_bounds(sol.handle_ptr, make_span(sol.problem_ptr->variable_bounds)); } - CUOPT_LOG_DEBUG( - "Bounds propagation rounding end: ii constraint count first buffer %d, second buffer %d", - multi_probe.infeas_constraints_count_0, - multi_probe.infeas_constraints_count_1); + // CUOPT_LOG_DEBUG( + // "Bounds propagation rounding end: ii constraint count first buffer %d, second buffer %d", + // multi_probe.infeas_constraints_count_0, + // multi_probe.infeas_constraints_count_1); cuopt_assert(sol.test_number_all_integer(), "All integers must be rounded"); expand_device_copy(orig_sol.assignment, sol.assignment, sol.handle_ptr->get_stream()); cuopt_func_call(orig_sol.test_variable_bounds()); @@ -1136,10 +1134,9 @@ bool constraint_prop_t::apply_round( // lp_run_time_after_feasible); // === CONSTRAINT PROP PREDICTOR FEATURES - END === - max_timer = work_limit_timer_t{context.settings.deterministic, max_time_for_bounds_prop}; + max_timer = work_limit_timer_t{context.gpu_heur_loop, max_time_for_bounds_prop}; if (this->context.settings.deterministic) { - max_timer = - work_limit_timer_t(context.settings.deterministic, std::numeric_limits::infinity()); + max_timer = work_limit_timer_t(context.gpu_heur_loop, std::numeric_limits::infinity()); } if (check_brute_force_rounding(sol)) { auto cp_end_time = std::chrono::high_resolution_clock::now(); diff --git a/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu b/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu index acf28b961..475d4a357 100644 --- a/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu @@ -718,7 +718,7 @@ bool lb_constraint_prop_t::apply_round( // this is second timer that can continue but without recovery mode const f_t max_time_for_bounds_prop = 5.; - max_timer = work_limit_timer_t{context.settings.deterministic, max_time_for_bounds_prop}; + max_timer = work_limit_timer_t{context.gpu_heur_loop, max_time_for_bounds_prop}; if (check_brute_force_rounding(sol)) { return true; } recovery_mode = false; rounding_ii = false; diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index a615d8450..ae8742866 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -89,13 +89,13 @@ optimization_problem_solution_t get_relaxed_lp_solution( pdlp_settings.iteration_limit = settings.iteration_limit; if (settings.work_limit != std::numeric_limits::infinity()) { // try to estimate the iteration count based on the requested work limit - int estim_iters = 0; + int estim_iters = 100; do { // TODO: use an actual predictor model here double estim_ms = 313 + 200 * op_problem.n_variables - 400 * op_problem.n_constraints + 600 * op_problem.coefficients.size() + 7100 * estim_iters; estim_ms = std::max(0.0, estim_ms); - if (estim_ms > settings.work_limit) { break; } + if (estim_ms > settings.work_limit * 1000) { break; } estim_iters += 100; } while (true); CUOPT_LOG_DEBUG("estimated iterations %d for work limit %f", estim_iters, settings.work_limit); @@ -110,12 +110,12 @@ optimization_problem_solution_t get_relaxed_lp_solution( pdlp_solver_t lp_solver(op_problem, pdlp_settings); if (settings.has_initial_primal) { i_t prev_size = lp_state.prev_dual.size(); - CUOPT_LOG_DEBUG( - "setting initial primal solution of size %d dual size %d problem vars %d cstrs %d", - assignment.size(), - lp_state.prev_dual.size(), - op_problem.n_variables, - op_problem.n_constraints); + // CUOPT_LOG_DEBUG( + // "setting initial primal solution of size %d dual size %d problem vars %d cstrs %d", + // assignment.size(), + // lp_state.prev_dual.size(), + // op_problem.n_variables, + // op_problem.n_constraints); lp_state.resize(op_problem, op_problem.handle_ptr->get_stream()); clamp_within_var_bounds(assignment, &op_problem, op_problem.handle_ptr); // The previous dual sometimes contain invalid values w.r.t current problem diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index 0827068a2..71d02f531 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -71,7 +71,7 @@ static void setup_device_symbols(rmm::cuda_stream_view stream_view) template mip_solution_t run_mip(detail::problem_t& problem, mip_solver_settings_t const& settings, - cuopt::work_limit_timer_t& timer) + timer_t& timer) { raft::common::nvtx::range fun_scope("run_mip"); auto constexpr const running_mip = true; @@ -194,7 +194,7 @@ mip_solution_t solve_mip(optimization_problem_t& op_problem, op_problem.get_handle_ptr()->get_stream()); } - auto timer = cuopt::work_limit_timer_t(settings.deterministic, time_limit); + auto timer = timer_t(time_limit); double presolve_time = 0.0; std::unique_ptr> presolver; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index bcce85439..a27b37088 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -53,7 +53,7 @@ template mip_solver_t::mip_solver_t(const problem_t& op_problem, const mip_solver_settings_t& solver_settings, pdlp_initial_scaling_strategy_t& scaling, - work_limit_timer_t timer) + timer_t timer) : op_problem_(op_problem), solver_settings_(solver_settings), context(op_problem.handle_ptr, @@ -112,7 +112,7 @@ solution_t mip_solver_t::run_solver() } diversity_manager_t dm(context); - dm.timer = timer_; + dm.timer = work_limit_timer_t(context.gpu_heur_loop, timer_.remaining_time()); bool presolve_success = dm.run_presolve(timer_.remaining_time()); if (!presolve_success) { CUOPT_LOG_INFO("Problem proven infeasible in presolve"); diff --git a/cpp/src/mip/solver.cuh b/cpp/src/mip/solver.cuh index 012c5fdfa..261e32c7b 100644 --- a/cpp/src/mip/solver.cuh +++ b/cpp/src/mip/solver.cuh @@ -31,7 +31,7 @@ class mip_solver_t { explicit mip_solver_t(const problem_t& op_problem, const mip_solver_settings_t& solver_settings, pdlp_initial_scaling_strategy_t& scaling, - work_limit_timer_t timer); + timer_t timer); solution_t run_solver(); solver_stats_t& get_solver_stats() { return context.stats; } @@ -40,7 +40,7 @@ class mip_solver_t { // reference to the original problem const problem_t& op_problem_; const mip_solver_settings_t& solver_settings_; - work_limit_timer_t timer_; + timer_t timer_; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/solver_context.cuh b/cpp/src/mip/solver_context.cuh index 3a1133e1f..211cb83ab 100644 --- a/cpp/src/mip/solver_context.cuh +++ b/cpp/src/mip/solver_context.cuh @@ -22,6 +22,7 @@ #include #include +#include #include #pragma once @@ -43,8 +44,9 @@ struct mip_solver_context_t { : handle_ptr(handle_ptr_), problem_ptr(problem_ptr_), settings(settings_), scaling(scaling) { cuopt_assert(problem_ptr != nullptr, "problem_ptr is nullptr"); - stats.solution_bound = problem_ptr->maximize ? std::numeric_limits::infinity() - : -std::numeric_limits::infinity(); + stats.solution_bound = problem_ptr->maximize ? std::numeric_limits::infinity() + : -std::numeric_limits::infinity(); + gpu_heur_loop.deterministic = settings.deterministic; } raft::handle_t const* const handle_ptr; @@ -54,6 +56,9 @@ struct mip_solver_context_t { solver_stats_t stats; // TODO: ensure thread local (or use locks...?) mip_solver_work_unit_predictors_t work_unit_predictors; + // Work limit context for tracking work units in deterministic mode (shared across all timers in + // GPU heuristic loop) + cuopt::work_limit_context_t gpu_heur_loop; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/utilities/work_limit_timer.hpp b/cpp/src/utilities/work_limit_timer.hpp index dad1446aa..2cd644786 100644 --- a/cpp/src/utilities/work_limit_timer.hpp +++ b/cpp/src/utilities/work_limit_timer.hpp @@ -22,13 +22,37 @@ namespace cuopt { +// Context for tracking global work units across multiple timers in deterministic mode +// This allows different subsystems to have independent work unit tracking +struct work_limit_context_t { + double global_work_units_elapsed{0.0}; + bool deterministic{false}; + + void record_work(double work) + { + if (deterministic) global_work_units_elapsed += work; + } +}; + // In determinism mode, relies on a work limit accumulator; otherwise rely on a timer // in non-determinism mode: 1s = 1wu +// In deterministic mode, all timers share a global work units counter (via work_limit_context_t), +// and each timer records the snapshot at construction time to determine when its own limit is +// reached. class work_limit_timer_t { public: - work_limit_timer_t() : deterministic(false), work_limit(0), timer(0) {} - work_limit_timer_t(bool deterministic, double work_limit_) - : deterministic(deterministic), work_limit(work_limit_), timer(work_limit_) + work_limit_timer_t() + : deterministic(false), work_limit(0), timer(0), work_context(nullptr), work_units_at_start(0) + { + } + + // Constructor taking work limit context reference (for deterministic mode) + work_limit_timer_t(work_limit_context_t& context, double work_limit_) + : deterministic(context.deterministic), + work_limit(work_limit_), + timer(work_limit_), + work_context(&context), + work_units_at_start(context.deterministic ? context.global_work_units_elapsed : 0) { } @@ -37,19 +61,25 @@ class work_limit_timer_t { int line = __builtin_LINE()) const noexcept { if (deterministic) { - bool finished_now = work_total >= work_limit; + if (!work_context) { return false; } + // Check if global work has exceeded our budget (snapshot + limit) + double elapsed_since_start = work_context->global_work_units_elapsed - work_units_at_start; + bool finished_now = elapsed_since_start >= work_limit; if (finished_now && !finished) { finished = true; double actual_elapsed_time = timer.elapsed_time(); // 10% timing error - if (abs(actual_elapsed_time - work_limit) / work_limit > 0.10) { + if (work_limit > 0 && abs(actual_elapsed_time - work_limit) / work_limit > 0.10) { CUOPT_LOG_ERROR( - "%s:%d: %s(): Work limit timer finished with a large discrepancy: %fs for %fwu", + "%s:%d: %s(): Work limit timer finished with a large discrepancy: %fs for %fwu " + "(global: %f, start: %f)", file, line, caller, actual_elapsed_time, - work_limit); + work_limit, + work_context->global_work_units_elapsed, + work_units_at_start); } } return finished; @@ -58,16 +88,30 @@ class work_limit_timer_t { } } - // in determinism mode, add the work units to the work limit accumulator - void record_work(double work_units) + // in determinism mode, add the work units to the global work limit accumulator + void record_work(double work_units, + const char* caller = __builtin_FUNCTION(), + const char* file = __builtin_FILE(), + int line = __builtin_LINE()) { - if (deterministic) { work_total += work_units; } + if (deterministic && work_context) { + work_context->global_work_units_elapsed += work_units; + CUOPT_LOG_DEBUG("%s:%d: %s(): Recorded %f work units in %fs, total %f", + file, + line, + caller, + work_units, + timer.elapsed_time(), + work_context->global_work_units_elapsed); + } } double remaining_units() const noexcept { if (deterministic) { - return work_limit - work_total; + if (!work_context) { return work_limit; } + double elapsed_since_start = work_context->global_work_units_elapsed - work_units_at_start; + return work_limit - elapsed_since_start; } else { return timer.remaining_time(); } @@ -78,7 +122,7 @@ class work_limit_timer_t { double elapsed_time() const noexcept { if (deterministic) { - return work_total; + return work_context->global_work_units_elapsed - work_units_at_start; } else { return timer.elapsed_time(); } @@ -94,7 +138,9 @@ class work_limit_timer_t { bool check_half_time() const noexcept { if (deterministic) { - return work_total >= work_limit / 2; + if (!work_context) { return false; } + double elapsed_since_start = work_context->global_work_units_elapsed - work_units_at_start; + return elapsed_since_start >= work_limit / 2; } else { return timer.check_half_time(); } @@ -128,9 +174,12 @@ class work_limit_timer_t { } timer_t timer; - double work_total{}; double work_limit{}; mutable bool finished{false}; bool deterministic{false}; + // Pointer to work limit context (shared across all timers in deterministic mode) + work_limit_context_t* work_context{nullptr}; + // Snapshot of global work units when this timer was created + double work_units_at_start{0}; }; } // namespace cuopt diff --git a/cpp/tests/mip/diversity_test.cu b/cpp/tests/mip/diversity_test.cu index 15f287774..1f96348a2 100644 --- a/cpp/tests/mip/diversity_test.cu +++ b/cpp/tests/mip/diversity_test.cu @@ -105,12 +105,13 @@ static uint32_t test_full_run_determinism(std::string path, settings.work_limit = 10; // about 10 seconds of runtime settings.deterministic = true; settings.heuristics_only = true; - auto timer = cuopt::work_limit_timer_t(settings.deterministic, 3000); + auto timer = cuopt::timer_t(3000); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); detail::diversity_manager_t diversity_manager(solver.context); - diversity_manager.timer = work_limit_timer_t(settings.deterministic, 60000); + work_limit_context_t work_limit_context; + diversity_manager.timer = work_limit_timer_t(work_limit_context, 60000); diversity_manager.run_solver(); std::vector hashes; @@ -159,12 +160,13 @@ static uint32_t test_initial_solution_determinism(std::string path, settings.time_limit = 3000.; settings.deterministic = true; settings.heuristics_only = true; - auto timer = cuopt::work_limit_timer_t(settings.deterministic, 3000); + auto timer = cuopt::timer_t(3000); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); detail::diversity_manager_t diversity_manager(solver.context); - diversity_manager.timer = work_limit_timer_t(settings.deterministic, 60000); + work_limit_context_t work_limit_context; + diversity_manager.timer = work_limit_timer_t(work_limit_context, 60000); diversity_manager.diversity_config.initial_solution_only = true; diversity_manager.run_solver(); @@ -214,12 +216,13 @@ static uint32_t test_recombiners_determinism(std::string path, settings.time_limit = 3000.; settings.deterministic = true; settings.heuristics_only = true; - auto timer = cuopt::work_limit_timer_t(settings.deterministic, 3000); + auto timer = cuopt::timer_t(3000); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); detail::diversity_manager_t diversity_manager(solver.context); - diversity_manager.timer = work_limit_timer_t(settings.deterministic, 60000); + work_limit_context_t work_limit_context; + diversity_manager.timer = work_limit_timer_t(work_limit_context, 60000); diversity_manager.diversity_config.dry_run = true; diversity_manager.run_solver(); diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu index 36e71481c..0393c2def 100644 --- a/cpp/tests/mip/local_search_test.cu +++ b/cpp/tests/mip/local_search_test.cu @@ -113,7 +113,7 @@ static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) auto settings = mip_solver_settings_t{}; settings.time_limit = 30.; settings.deterministic = true; - auto timer = cuopt::work_limit_timer_t(settings.deterministic, 30); + auto timer = cuopt::timer_t(30); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); @@ -149,7 +149,8 @@ static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) printf("LP optimal hash: 0x%x\n", detail::compute_hash(lp_optimal_solution)); printf("running mode: %d\n", mode); - local_search.fp.timer = work_limit_timer_t(settings.deterministic, 6000); + work_limit_context_t work_limit_context; + local_search.fp.timer = work_limit_timer_t(work_limit_context, 6000); detail::ls_config_t ls_config{}; @@ -171,11 +172,13 @@ static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) iterations++; } } else if (mode == local_search_mode_t::FJ_LINE_SEGMENT) { - local_search.run_fj_line_segment(solution, timer, ls_config); + local_search.run_fj_line_segment( + solution, work_limit_timer_t(work_limit_context, 6000), ls_config); } else if (mode == local_search_mode_t::FJ_ON_ZERO) { - local_search.run_fj_on_zero(solution, timer); + local_search.run_fj_on_zero(solution, work_limit_timer_t(work_limit_context, 6000)); } else if (mode == local_search_mode_t::FJ_ANNEALING) { - local_search.run_fj_annealing(solution, timer, ls_config); + local_search.run_fj_annealing( + solution, work_limit_timer_t(work_limit_context, 6000), ls_config); } std::vector hashes; diff --git a/cpp/tests/mip/mip_utils.cuh b/cpp/tests/mip/mip_utils.cuh index 3a8d6e48b..5dcbe0371 100644 --- a/cpp/tests/mip/mip_utils.cuh +++ b/cpp/tests/mip/mip_utils.cuh @@ -165,7 +165,7 @@ static fj_state_t run_fj(detail::problem_t& problem, auto settings = mip_solver_settings_t{}; settings.time_limit = 30.; - auto timer = cuopt::work_limit_timer_t(settings.deterministic, 30); + auto timer = timer_t(30); detail::mip_solver_t solver(problem, settings, scaling, timer); detail::solution_t solution(*solver.context.problem_ptr); diff --git a/cpp/tests/mip/multi_probe_test.cu b/cpp/tests/mip/multi_probe_test.cu index e15f23ffd..b35c30ae6 100644 --- a/cpp/tests/mip/multi_probe_test.cu +++ b/cpp/tests/mip/multi_probe_test.cu @@ -170,11 +170,7 @@ uint32_t test_multi_probe(std::string path, unsigned long seed = std::random_dev problem.reverse_constraints, nullptr, true); - detail::mip_solver_t solver( - problem, - default_settings, - scaling, - cuopt::work_limit_timer_t(default_settings.deterministic, 0)); + detail::mip_solver_t solver(problem, default_settings, scaling, timer_t(0)); detail::bound_presolve_t bnd_prb_0(solver.context); detail::bound_presolve_t bnd_prb_1(solver.context); detail::multi_probe_t multi_probe_prs(solver.context); diff --git a/cpp/tests/mip/presolve_test.cu b/cpp/tests/mip/presolve_test.cu index 55ce8851c..dc5977404 100644 --- a/cpp/tests/mip/presolve_test.cu +++ b/cpp/tests/mip/presolve_test.cu @@ -131,18 +131,13 @@ uint32_t test_probing_cache_determinism(std::string path, problem.reverse_constraints, nullptr, true); - detail::mip_solver_t solver( - problem, - default_settings, - scaling, - cuopt::work_limit_timer_t(default_settings.deterministic, 0)); + detail::mip_solver_t solver(problem, default_settings, scaling, cuopt::timer_t(0)); detail::bound_presolve_t bnd_prb(solver.context); + work_limit_context_t work_limit_context; // rely on the iteration limit compute_probing_cache( - bnd_prb, - problem, - work_limit_timer_t(default_settings.deterministic, std::numeric_limits::max())); + bnd_prb, problem, work_limit_timer_t(work_limit_context, std::numeric_limits::max())); std::vector, 2>>> cached_values( bnd_prb.probing_cache.probing_cache.begin(), bnd_prb.probing_cache.probing_cache.end()); std::sort(cached_values.begin(), cached_values.end(), [](const auto& a, const auto& b) { From 4e0f79a8d23e9a9abae7bbff635634fbcd71252f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 6 Nov 2025 18:55:55 +0000 Subject: [PATCH 028/366] fix determinism issue in bounds repair, further work limit progress --- .../linear_programming/cuopt/run_mip.cpp | 1 - cpp/src/mip/diversity/diversity_manager.cu | 13 ++- cpp/src/mip/diversity/population.cu | 19 +++- .../mip/feasibility_jump/feasibility_jump.cu | 32 +++++- .../feasibility_jump_kernels.cu | 107 +++++++++++++----- .../mip/feasibility_jump/load_balancing.cuh | 3 +- .../feasibility_pump/feasibility_pump.cu | 8 +- .../line_segment_search.cu | 9 +- cpp/src/mip/local_search/local_search.cu | 1 + .../local_search/rounding/bounds_repair.cu | 10 +- .../local_search/rounding/constraint_prop.cu | 33 ++++-- .../local_search/rounding/lb_bounds_repair.cu | 1 + cpp/src/mip/problem/problem.cu | 2 + cpp/src/mip/relaxed_lp/relaxed_lp.cu | 22 ++-- cpp/src/mip/solution/solution.cu | 5 +- cpp/src/mip/solve.cu | 2 +- cpp/src/mip/solver.cu | 4 +- cpp/src/mip/utils.cuh | 82 ++++++++------ cpp/src/utilities/logger.cpp | 4 +- 19 files changed, 249 insertions(+), 109 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 56a1be629..950acb423 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -417,7 +417,6 @@ int main(int argc, char* argv[]) if (program.is_used("--initial-solution-path")) { initial_solution_file = program.get("--initial-solution-path"); } - if (run_dir) { std::queue task_queue; std::queue gpu_queue; diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 33f228b49..075a483ec 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -155,6 +155,7 @@ void diversity_manager_t::add_user_given_solutions( if (problem_ptr->pre_process_assignment(init_sol_assignment)) { relaxed_lp_settings_t lp_settings; lp_settings.time_limit = std::min(60., timer.remaining_time() / 2); + lp_settings.work_limit = lp_settings.time_limit; lp_settings.tolerance = problem_ptr->tolerances.absolute_tolerance; lp_settings.save_state = false; lp_settings.return_first_feasible = true; @@ -327,15 +328,17 @@ solution_t diversity_manager_t::run_solver() raft::common::nvtx::range fun_scope("run_solver"); diversity_config.fj_only_run = false; - if (context.settings.deterministic) { - remaining_work_limit = context.settings.work_limit; - CUOPT_LOG_INFO("Deterministic mode, remaining work limit: %f", remaining_work_limit); - } population.timer = timer; const f_t time_limit = timer.remaining_time(); const f_t lp_time_limit = std::min(diversity_config.max_time_on_lp, time_limit * diversity_config.time_ratio_on_init_lp); + + if (context.settings.deterministic) { + remaining_work_limit = context.settings.work_limit; + CUOPT_LOG_INFO("Deterministic mode, remaining work limit: %f", time_limit); + } + // to automatically compute the solving time on scope exit auto timer_raii_guard = cuopt::scope_guard([&]() { stats.total_solve_time = timer.timer.elapsed_time(); }); @@ -385,6 +388,7 @@ solution_t diversity_manager_t::run_solver() } else if (!diversity_config.fj_only_run || true) { relaxed_lp_settings_t lp_settings; lp_settings.time_limit = lp_time_limit; + lp_settings.work_limit = lp_time_limit; lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; lp_settings.return_first_feasible = false; lp_settings.save_state = true; @@ -676,6 +680,7 @@ diversity_manager_t::recombine_and_local_search(solution_t& lp_run_time = std::min(lp_run_time, timer.remaining_time()); relaxed_lp_settings_t lp_settings; lp_settings.time_limit = lp_run_time; + lp_settings.work_limit = lp_settings.time_limit; lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; lp_settings.return_first_feasible = false; lp_settings.save_state = true; diff --git a/cpp/src/mip/diversity/population.cu b/cpp/src/mip/diversity/population.cu index d2f9200d1..d1969659a 100644 --- a/cpp/src/mip/diversity/population.cu +++ b/cpp/src/mip/diversity/population.cu @@ -395,10 +395,11 @@ std::pair population_t::add_solution(solution_t&& population_hash_map.insert(sol); double sol_cost = sol.get_quality(weights); bool best_updated = false; - CUOPT_LOG_DEBUG("Adding solution with quality %f and objective %f n_integers %d!", + CUOPT_LOG_DEBUG("Adding solution with quality %f and objective %f n_integers %d, hash %x!", sol_cost, sol.get_user_objective(), - sol.n_assigned_integers); + sol.n_assigned_integers, + sol.get_hash()); // We store the best feasible found so far at index 0. if (sol.get_feasible() && (solutions[0].first == false || sol_cost + OBJECTIVE_EPSILON < indices[0].second)) { @@ -809,23 +810,29 @@ bool population_t::test_invariant() template void population_t::print() { + std::vector hashes; + for (auto& index : indices) + hashes.push_back(solutions[index.first].second.get_hash()); + uint32_t final_hash = compute_hash(hashes); CUOPT_LOG_DEBUG(" -------------- "); - CUOPT_LOG_DEBUG("%s infeas weight %f threshold %d/%d:", + CUOPT_LOG_DEBUG("%s infeas weight %f threshold %d/%d (hash %x):", name.c_str(), infeasibility_importance, var_threshold, - problem_ptr->n_integer_vars); + problem_ptr->n_integer_vars, + final_hash); i_t i = 0; for (auto& index : indices) { if (index.first == 0 && solutions[0].first) { CUOPT_LOG_DEBUG(" Best feasible: %f", solutions[index.first].second.get_user_objective()); } - CUOPT_LOG_DEBUG("%d : %f\t%f\t%f\t%d", + CUOPT_LOG_DEBUG("%d : %f\t%f\t%f\t%d (hash %x)", i, index.second, solutions[index.first].second.get_total_excess(), solutions[index.first].second.get_user_objective(), - solutions[index.first].second.get_feasible()); + solutions[index.first].second.get_feasible(), + solutions[index.first].second.get_hash()); i++; } CUOPT_LOG_DEBUG(" -------------- "); diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index bd11f3aae..2877c827a 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -669,10 +669,14 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; + // use_graph = false; + auto& data = *climbers[climber_idx]; auto v = data.view(); settings.seed = cuopt::seed_generator::get_seed(); - // ensure an updated copy of the settings is used device-side + // settings.iteration_limit = 10000; + // CUOPT_LOG_DEBUG("FJ: settings seed %d", settings.seed); + // ensure an updated copy of the settings is used device-side raft::copy(v.settings, &settings, 1, climber_stream); bool is_binary_pb = pb_ptr->n_variables == thrust::count(handle_ptr->get_thrust_policy(), @@ -804,6 +808,7 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream 0, climber_stream); + (void)grid_update_weights; cudaLaunchCooperativeKernel((void*)handle_local_minimum_kernel, grid_update_weights, blocks_update_weights, @@ -837,7 +842,12 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream 0, climber_stream); + // TODO: figure out why the ordering of the violated constraints is ruined // data.violated_constraints.sort(climber_stream); + // printf("[%d] iter: Violated constraints hash: %x\n", data.iterations.value(climber_stream), + // compute_hash( + // make_span(data.violated_constraints.contents, 0, + // data.violated_constraints.set_size.value(climber_stream)), climber_stream)); // { // printf("Violated constraints hash: %x, size: %d\n", compute_hash( @@ -1099,7 +1109,7 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) CUOPT_LOG_TRACE( "FJ " "step %d viol %.2g [%d], obj %.8g, best %.8g, mins %d, maxw %g, " - "objw %g", + "objw %g, sol hash %x, delta hash %x", steps, data.violation_score.value(climber_stream), data.violated_constraints.set_size.value(climber_stream), @@ -1107,7 +1117,9 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) data.best_objective.value(climber_stream), data.local_minimums_reached.value(climber_stream), max_cstr_weight.value(climber_stream), - objective_weight.value(climber_stream)); + objective_weight.value(climber_stream), + solution.get_hash(), + detail::compute_hash(data.jump_move_delta, climber_stream)); } if (!limit_reached) { run_step_device(climber_stream, climber_idx); } @@ -1279,6 +1291,18 @@ i_t fj_t::solve(solution_t& solution) pb_ptr->check_problem_representation(true); resize_vectors(solution.handle_ptr); + CUOPT_LOG_DEBUG("FJ: work_limit %f time_limit %f sol hash %x pb hash %x", + settings.work_limit, + settings.time_limit, + solution.get_hash(), + pb_ptr->get_fingerprint()); + CUOPT_LOG_DEBUG("FJ: weights hash %x, left weights hash %x, right weights hash %x", + detail::compute_hash(cstr_weights), + detail::compute_hash(cstr_left_weights), + detail::compute_hash(cstr_right_weights)); + + settings.load_balancing_mode = fj_load_balancing_mode_t::ALWAYS_OFF; + if (context.settings.deterministic) { settings.work_limit = settings.time_limit; } // if work_limit is set: compute an estimate of the number of iterations required if (settings.work_limit != std::numeric_limits::infinity()) { @@ -1413,6 +1437,8 @@ i_t fj_t::solve(solution_t& solution) timer.record_work(work_to_record); + CUOPT_LOG_DEBUG("FJ sol hash %x", solution.get_hash()); + // // Print compact feature vector summary // char logbuf[4096]; // int offset = 0; diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 0d5e873fd..cd83803ea 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -25,6 +25,7 @@ #include #include +#include #include @@ -37,6 +38,22 @@ namespace cg = cooperative_groups; namespace cuopt::linear_programming::detail { +template +struct score_with_tiebreaker_comparator { + DI auto operator()(const thrust::pair& a, + const thrust::pair& b) const + { + auto a_score = a.first; + auto a_idx = a.second; + auto b_score = b.first; + auto b_idx = b.second; + + if (a_score > b_score) return a; + if (a_score == b_score && a_idx > b_idx) return a; + return b; + } +}; + template DI thrust::pair move_objective_score( const typename fj_t::climber_data_t::view_t& fj, i_t var_idx, f_t delta) @@ -360,7 +377,7 @@ DI std::pair::move_score_info_t> compute_best_mtm( return std::make_pair(best_val, best_score_info); } -template +template DI void update_jump_value(typename fj_t::climber_data_t::view_t fj, i_t var_idx) { cuopt_assert(var_idx >= 0 && var_idx < fj.pb.n_variables, "invalid variable index"); @@ -387,12 +404,11 @@ DI void update_jump_value(typename fj_t::climber_data_t::view_t fj, i_ fj.pb.check_variable_within_bounds(var_idx, fj.incumbent_assignment[var_idx] + delta), "Var not within bounds!"); } - best_score_info = compute_new_score(fj, var_idx, delta); + best_score_info = compute_new_score(fj, var_idx, delta); } else { - auto [best_val, score_info] = - compute_best_mtm(fj, var_idx); - delta = best_val - fj.incumbent_assignment[var_idx]; - best_score_info = score_info; + auto [best_val, score_info] = compute_best_mtm(fj, var_idx); + delta = best_val - fj.incumbent_assignment[var_idx]; + best_score_info = score_info; } } else { delta = round(1.0 - 2 * fj.incumbent_assignment[var_idx]); @@ -637,8 +653,8 @@ __global__ void update_assignment_kernel(typename fj_t::climber_data_t #if FJ_SINGLE_STEP DEVICE_LOG_DEBUG( - "=---- FJ[%d]: updated %d [%g/%g] :%.4g+{%.4g}=%.4g score {%g,%g}, d_obj %.2g+%.2g=%.2g, " - "err_range %.2g%%, infeas %.2g, total viol %d\n", + "=---- FJ[%d]: updated %d [%g/%g] :%.4g+{%.4g}=%.4g score {%d,%d}, d_obj %.2g+%.2g=%.2g, " + "err_range %.2g%%, infeas %.2g, total viol %d, obj %x, delta %x, coef %x\n", *fj.iterations, var_idx, get_lower(fj.pb.variable_bounds[var_idx]), @@ -653,7 +669,10 @@ __global__ void update_assignment_kernel(typename fj_t::climber_data_t *fj.incumbent_objective + fj.jump_move_delta[var_idx] * fj.pb.objective_coefficients[var_idx], delta_rel_err, fj.jump_move_infeasibility[var_idx], - fj.violated_constraints.size()); + fj.violated_constraints.size(), + detail::compute_hash(*fj.incumbent_objective), + detail::compute_hash(fj.jump_move_delta[var_idx]), + detail::compute_hash(fj.pb.objective_coefficients[var_idx])); #endif // reset the score fj.jump_move_scores[var_idx] = fj_t::move_score_t::invalid(); @@ -970,7 +989,7 @@ __global__ void compute_iteration_related_variables_kernel( compute_iteration_related_variables(fj); } -template +template __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_t fj, bool ForceRefresh) { @@ -1033,7 +1052,12 @@ __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_ } cuopt_assert(var_idx >= 0 && var_idx < fj.pb.n_variables, ""); - update_jump_value(fj, var_idx); + update_jump_value(fj, var_idx); + // if (move_type == MTMMoveType::FJ_MTM_SATISFIED && threadIdx.x == 0) { + // printf("iter[%d] block[%d] var %d score {%d,%d}, delta %g\n", *fj.iterations, blockIdx.x, + // var_idx, fj.jump_move_scores[var_idx].base, fj.jump_move_scores[var_idx].bonus, + // fj.jump_move_delta[var_idx]); + // } } } @@ -1041,7 +1065,7 @@ template __global__ void compute_mtm_moves_kernel(typename fj_t::climber_data_t::view_t fj, bool ForceRefresh) { - compute_mtm_moves(fj, ForceRefresh); + compute_mtm_moves(fj, ForceRefresh); } template @@ -1053,8 +1077,9 @@ __global__ void select_variable_kernel(typename fj_t::climber_data_t:: fj.settings->seed, *fj.iterations * fj.settings->parameters.max_sampled_moves, 0); using move_score_t = typename fj_t::move_score_t; - __shared__ alignas(move_score_t) char shmem_storage[2 * raft::WarpSize * sizeof(move_score_t)]; - auto* const shmem = (move_score_t*)shmem_storage; + __shared__ alignas(thrust::pair) char + shmem_storage[raft::WarpSize * sizeof(thrust::pair)]; + auto* const shmem = (thrust::pair*)shmem_storage; auto th_best_score = fj_t::move_score_t::invalid(); i_t th_selected_var = std::numeric_limits::max(); @@ -1091,8 +1116,11 @@ __global__ void select_variable_kernel(typename fj_t::climber_data_t:: } } // Block level reduction to get the best variable from the sample + // Use deterministic tie-breaking comparator based on var_idx auto [best_score, reduced_selected_var] = - raft::blockRankedReduce(th_best_score, shmem, th_selected_var, raft::max_op{}); + raft::blockReduce(thrust::make_pair(th_best_score, th_selected_var), + (char*)shmem, + score_with_tiebreaker_comparator{}); if (FIRST_THREAD) { // assign it to print the value outside th_best_score = best_score; @@ -1127,9 +1155,9 @@ __global__ void select_variable_kernel(typename fj_t::climber_data_t:: i_t var_range = get_upper(bounds) - get_lower(bounds); double delta_rel_err = fabs(fj.jump_move_delta[selected_var]) / var_range * 100; DEVICE_LOG_INFO( - "=---- FJ: selected %d [%g/%g] %c :%.4g+{%.4g}=%.4g score {%g,%g}, d_obj %.2g+%.2g->%.2g, " + "=---- FJ: selected %d [%g/%g] %c :%.4g+{%.4g}=%.4g score {%d,%d}, d_obj %.2g+%.2g->%.2g, " "delta_rel_err %.2g%%, " - "infeas %.2g, total viol %d, out of %d\n", + "infeas %.2g, total viol %d, out of %d, obj %x\n", selected_var, get_lower(bounds), get_upper(bounds), @@ -1146,7 +1174,8 @@ __global__ void select_variable_kernel(typename fj_t::climber_data_t:: delta_rel_err, fj.jump_move_infeasibility[selected_var], fj.violated_constraints.size(), - good_var_count); + good_var_count, + detail::compute_hash(*fj.incumbent_objective)); #endif cuopt_assert(fj.jump_move_scores[selected_var].valid(), ""); } else { @@ -1183,6 +1212,12 @@ DI thrust::tuple::move_score_t> gridwide_reduc // affected by tabu f_t delta = get_move(var_idx); + + // if (!WeakTabu && !recompute_score) { + // printf("iter[%d] block[%d] considered var %d delta %g\n", *fj.iterations, blockIdx.x, + // var_idx, delta); + // } + if constexpr (WeakTabu) { if ((delta < 0 && *fj.iterations == fj.tabu_lastinc[var_idx] + 1) || (delta > 0 && *fj.iterations == fj.tabu_lastdec[var_idx] + 1)) @@ -1203,6 +1238,12 @@ DI thrust::tuple::move_score_t> gridwide_reduc loc_best_score_info = compute_new_score(fj, var_idx, delta); } + // if (!WeakTabu && !recompute_score) { + // printf("iter[%d] block[%d] found score (%d,%d) for var %d delta %g\n", *fj.iterations, + // blockIdx.x, loc_best_score_info.score.base, loc_best_score_info.score.bonus, var_idx, + // delta); + // } + if (threadIdx.x == 0) { if (loc_best_score_info.score > best_score || (loc_best_score_info.score == best_score && var_idx > best_var)) { @@ -1214,6 +1255,11 @@ DI thrust::tuple::move_score_t> gridwide_reduc } if (threadIdx.x == 0) { + // if (!WeakTabu && !recompute_score) { + // printf("iter[%d] block[%d] var_idx %d score {%d,%d}, delta %g\n", *fj.iterations, + // blockIdx.x, best_var, best_score.base, best_score.bonus, best_delta); + // } + fj.grid_score_buf[blockIdx.x] = best_score; fj.grid_var_buf[blockIdx.x] = best_var; fj.grid_delta_buf[blockIdx.x] = best_delta; @@ -1225,27 +1271,32 @@ DI thrust::tuple::move_score_t> gridwide_reduc if (blockIdx.x == 0) { using move_score_t = typename fj_t::move_score_t; - __shared__ alignas(move_score_t) char shmem_storage[2 * raft::WarpSize * sizeof(move_score_t)]; - auto* const shmem = (move_score_t*)shmem_storage; + __shared__ alignas(thrust::pair) char + shmem_storage[2 * raft::WarpSize * sizeof(thrust::pair)]; + auto* const shmem = (thrust::pair*)shmem_storage; auto th_best_score = fj_t::move_score_t::invalid(); i_t th_best_block = 0; + i_t th_best_var = -1; for (i_t i = threadIdx.x; i < gridDim.x; i += blockDim.x) { auto var_idx = fj.grid_var_buf[i]; auto move_score = fj.grid_score_buf[i]; - if (move_score > th_best_score || - (move_score == th_best_score && var_idx > fj.grid_var_buf[th_best_block])) { + if (move_score > th_best_score || (move_score == th_best_score && var_idx > th_best_var)) { th_best_score = move_score; th_best_block = i; + th_best_var = var_idx; } } // Block level reduction to get the best variable from all blocks - auto [reduced_best_score, reduced_best_block] = - raft::blockRankedReduce(th_best_score, shmem, th_best_block, raft::max_op{}); - - if (reduced_best_score.valid() && threadIdx.x == 0) { - cuopt_assert(th_best_block < gridDim.x, ""); + auto [reduced_best_score_pair, reduced_best_block] = + raft::blockRankedReduce(thrust::make_pair(th_best_score, th_best_var), + shmem, + th_best_block, + score_with_tiebreaker_comparator{}); + + if (reduced_best_score_pair.first.valid() && threadIdx.x == 0) { + cuopt_assert(reduced_best_block < gridDim.x, ""); best_var = fj.grid_var_buf[reduced_best_block]; best_delta = fj.grid_delta_buf[reduced_best_block]; best_score = fj.grid_score_buf[reduced_best_block]; @@ -1284,7 +1335,7 @@ DI thrust::tuple::move_score_t> best_sat_cstr_ typename fj_t::climber_data_t::view_t fj) { // compute all MTM moves within satisfied constraints - compute_mtm_moves(fj, true); + compute_mtm_moves(fj, true); return gridwide_reduce_best_move( fj, fj.objective_vars.begin(), fj.objective_vars.end(), [fj] __device__(i_t var_idx) { return fj.jump_move_delta[var_idx]; diff --git a/cpp/src/mip/feasibility_jump/load_balancing.cuh b/cpp/src/mip/feasibility_jump/load_balancing.cuh index 38b3d192f..2009f4756 100644 --- a/cpp/src/mip/feasibility_jump/load_balancing.cuh +++ b/cpp/src/mip/feasibility_jump/load_balancing.cuh @@ -140,7 +140,8 @@ __global__ void load_balancing_prepare_iteration(const __grid_constant__ for (i_t i = blockIdx.x + range.first; i < range.second; i += gridDim.x) { i_t var_idx = fj.pb.related_variables[i]; - update_jump_value(fj, var_idx); + update_jump_value(fj, + var_idx); } if (FIRST_THREAD) *fj.load_balancing_skip = true; diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index 403600c44..2521206d8 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -560,7 +560,7 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s solution.assignment.size(), solution.handle_ptr->get_stream()); - // CUOPT_LOG_DEBUG("FP: starting FP descent, sol hash 0x%x", solution.get_hash()); + CUOPT_LOG_DEBUG("FP: starting FP descent, sol hash 0x%x", solution.get_hash()); while (true) { fp_iterations++; if (timer.check_time_limit()) { @@ -577,7 +577,9 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s f_t ratio_of_assigned_integers = f_t(solution.n_assigned_integers) / solution.problem_ptr->n_integer_vars; bool is_feasible = linear_project_onto_polytope(solution, ratio_of_assigned_integers); - i_t n_integers = solution.compute_number_of_integers(); + CUOPT_LOG_DEBUG( + "FP: after fp projection, iter %d sol hash 0x%x", fp_iterations, solution.get_hash()); + i_t n_integers = solution.compute_number_of_integers(); // CUOPT_LOG_DEBUG("after fp projection n_integers %d total n_integes %d", // n_integers, // solution.problem_ptr->n_integer_vars); @@ -628,9 +630,11 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s const f_t lp_verify_time_limit = 5.; relaxed_lp_settings_t lp_settings; lp_settings.time_limit = lp_verify_time_limit; + lp_settings.work_limit = lp_settings.time_limit; lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; lp_settings.return_first_feasible = true; lp_settings.save_state = true; + CUOPT_LOG_DEBUG("FP LP verify, sol hash 0x%x", solution.get_hash()); run_lp_with_vars_fixed(*solution.problem_ptr, solution, solution.problem_ptr->integer_indices, diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu index 475e8d991..c28d24dce 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu @@ -179,6 +179,7 @@ bool line_segment_search_t::search_line_segment( fj.settings.update_weights = false; fj.settings.feasibility_run = is_feasibility_run; fj.settings.time_limit = std::min(0.1, timer.remaining_time()); + fj.settings.work_limit = fj.settings.time_limit; is_feasible = fj.solve(solution); } cuopt_func_call(solution.test_number_all_integer()); @@ -221,13 +222,7 @@ bool line_segment_search_t::search_line_segment( fj.settings.update_weights = false; fj.settings.feasibility_run = is_feasibility_run; fj.settings.time_limit = std::min(1., timer.remaining_time()); - if (context.settings.deterministic) { - // fj.settings.time_limit = timer.remaining_time(); - fj.settings.time_limit = - std::numeric_limits::max(); // TODO should be global time limit - fj.settings.iteration_limit = 500; - } - is_feasible = fj.solve(solution); + is_feasible = fj.solve(solution); if (is_feasibility_run) { if (is_feasible) { CUOPT_LOG_DEBUG("Line segment found feasible"); diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 8fd8ba503..e8f125625 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -496,6 +496,7 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu if (context.settings.deterministic) { lp_run_time = std::numeric_limits::infinity(); } relaxed_lp_settings_t lp_settings; lp_settings.time_limit = std::min(lp_run_time, timer.remaining_time()); + lp_settings.work_limit = lp_settings.time_limit; lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; run_lp_with_vars_fixed( *solution.problem_ptr, solution, solution.problem_ptr->integer_indices, lp_settings); diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index b6587641b..1354ab18e 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -200,7 +200,15 @@ i_t bounds_repair_t::compute_best_shift(problem_t& problem, } }); handle_ptr->sync_stream(); - return candidates.n_candidates.value(handle_ptr->get_stream()); + i_t n_candidates = candidates.n_candidates.value(handle_ptr->get_stream()); + + // Sort by variable index to ensure deterministic ordering + thrust::sort_by_key(handle_ptr->get_thrust_policy(), + candidates.variable_index.begin(), + candidates.variable_index.begin() + n_candidates, + candidates.bound_shift.begin()); + + return n_candidates; } template diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 016aa03b3..266a79e37 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -432,6 +432,7 @@ void constraint_prop_t::collapse_crossing_bounds(problem_t& template void constraint_prop_t::set_bounds_on_fixed_vars(solution_t& sol) { + CUOPT_LOG_DEBUG("Bound hash before 0x%x", detail::compute_hash(sol.problem_ptr->variable_bounds)); auto assgn = make_span(sol.assignment); auto var_bounds = make_span(sol.problem_ptr->variable_bounds); thrust::for_each(sol.handle_ptr->get_thrust_policy(), @@ -443,6 +444,7 @@ void constraint_prop_t::set_bounds_on_fixed_vars(solution_t& var_bounds[idx] = typename type_2::type{var_val, var_val}; } }); + CUOPT_LOG_DEBUG("Bound hash after 0x%x", detail::compute_hash(sol.problem_ptr->variable_bounds)); } template @@ -967,8 +969,8 @@ bool constraint_prop_t::find_integer( bool timeout_happened = false; i_t n_failed_repair_iterations = 0; while (set_count < unset_integer_vars.size()) { - // CUOPT_LOG_DEBUG("n_set_vars %d vars to set %lu", set_count, unset_integer_vars.size()); - // CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x\n", detail::compute_hash(unset_integer_vars)); + CUOPT_LOG_DEBUG("n_set_vars %d vars to set %lu", set_count, unset_integer_vars.size()); + CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x", detail::compute_hash(unset_integer_vars)); update_host_assignment(sol); if (max_timer.check_time_limit()) { CUOPT_LOG_DEBUG("Second time limit is reached returning nearest rounding!"); @@ -1004,12 +1006,21 @@ bool constraint_prop_t::find_integer( n_vars_to_set, sol.handle_ptr->get_stream()); - // printf("host_vars_to_set hash 0x%x\n", detail::compute_hash(host_vars_to_set)); + CUOPT_LOG_DEBUG("host_vars_to_set hash 0x%x", detail::compute_hash(host_vars_to_set)); auto var_probe_vals = generate_bulk_rounding_vector(sol, orig_sol, host_vars_to_set, probing_config); + + CUOPT_LOG_DEBUG("var_probe_vals hash 1 0x%x, hash 2 0x%x, hash 3 0x%x", + detail::compute_hash(std::get<0>(var_probe_vals)), + detail::compute_hash(std::get<1>(var_probe_vals)), + detail::compute_hash(std::get<2>(var_probe_vals))); probe( sol, orig_sol.problem_ptr, var_probe_vals, &set_count, unset_integer_vars, probing_config); + CUOPT_LOG_DEBUG("post probe, set count %d, unset var hash 0x%x, size %lu", + (int)set_count, + detail::compute_hash(unset_integer_vars), + unset_integer_vars.size()); if (!(n_failed_repair_iterations >= max_n_failed_repair_iterations) && rounding_ii && !timeout_happened) { // timer_t repair_timer{std::min(timer.remaining_time() / 5, timer.elapsed_time() / 3)}; @@ -1085,10 +1096,7 @@ bool constraint_prop_t::find_integer( lp_settings.tolerance = orig_sol.problem_ptr->tolerances.absolute_tolerance; lp_settings.save_state = false; lp_settings.return_first_feasible = true; - if (this->context.settings.deterministic) { - lp_settings.iteration_limit = 13000; - lp_settings.time_limit = std::numeric_limits::infinity(); - } + CUOPT_LOG_DEBUG("bounds repair LP, sol hash 0x%x", orig_sol.get_hash()); run_lp_with_vars_fixed(*orig_sol.problem_ptr, orig_sol, orig_sol.problem_ptr->integer_indices, @@ -1270,6 +1278,15 @@ bool constraint_prop_t::handle_fixed_vars( auto set_count = *set_count_ptr; const f_t int_tol = sol.problem_ptr->tolerances.integrality_tolerance; // which other variables were affected? + CUOPT_LOG_DEBUG("handle_fixed_vars, unset vars hash 0x%x, sol.assignment hash 0x%x", + detail::compute_hash(unset_vars), + detail::compute_hash(sol.assignment)); + CUOPT_LOG_DEBUG( + "handle_fixed_vars, original_problem->variable_bounds hash 0x%x, " + "sol.problem_ptr->variable_bounds hash 0x%x", + detail::compute_hash(original_problem->variable_bounds), + detail::compute_hash(sol.problem_ptr->variable_bounds)); + auto iter = thrust::stable_partition(sol.handle_ptr->get_thrust_policy(), unset_vars.begin() + set_count, unset_vars.end(), @@ -1282,7 +1299,7 @@ bool constraint_prop_t::handle_fixed_vars( cuopt_assert(n_fixed_vars >= std::get<0>(var_probe_vals).size(), "Error in number of vars fixed!"); set_count += n_fixed_vars; - CUOPT_LOG_TRACE("Set var count increased from %d to %d", *set_count_ptr, set_count); + CUOPT_LOG_DEBUG("Set var count increased from %d to %d", *set_count_ptr, set_count); *set_count_ptr = set_count; return multi_probe.infeas_constraints_count_0 == 0 || multi_probe.infeas_constraints_count_1 == 0; } diff --git a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu index 4844bfaeb..7c0d2dcc5 100644 --- a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu @@ -410,6 +410,7 @@ bool lb_bounds_repair_t::repair_problem( timer_t timer_, const raft::handle_t* handle_ptr_) { + nvtx::range fun_scope("LB repair_problem"); CUOPT_LOG_DEBUG("LB Running bounds repair"); handle_ptr = handle_ptr_; timer = timer_; diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index d04442e7a..775491c67 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1714,6 +1714,8 @@ uint32_t problem_t::get_fingerprint() const detail::compute_hash(constraint_lower_bounds, handle_ptr->get_stream()), detail::compute_hash(constraint_upper_bounds, handle_ptr->get_stream()), detail::compute_hash(variable_types, handle_ptr->get_stream()), + detail::compute_hash(presolve_data.objective_offset), + detail::compute_hash(presolve_data.objective_scaling_factor), }; return detail::compute_hash(hashes); } diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index ae8742866..451cdf0d8 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -87,7 +87,11 @@ optimization_problem_solution_t get_relaxed_lp_solution( pdlp_settings.tolerances.relative_dual_tolerance = settings.tolerance / tolerance_divisor; pdlp_settings.time_limit = settings.time_limit; pdlp_settings.iteration_limit = settings.iteration_limit; - if (settings.work_limit != std::numeric_limits::infinity()) { + + // CHANGE + i_t work_limit = pdlp_settings.time_limit; // settings.work_limit + pdlp_settings.time_limit = std::numeric_limits::infinity(); + if (work_limit != std::numeric_limits::infinity()) { // try to estimate the iteration count based on the requested work limit int estim_iters = 100; do { @@ -95,11 +99,12 @@ optimization_problem_solution_t get_relaxed_lp_solution( double estim_ms = 313 + 200 * op_problem.n_variables - 400 * op_problem.n_constraints + 600 * op_problem.coefficients.size() + 7100 * estim_iters; estim_ms = std::max(0.0, estim_ms); - if (estim_ms > settings.work_limit * 1000) { break; } + if (estim_ms > work_limit * 1000) { break; } estim_iters += 100; } while (true); CUOPT_LOG_DEBUG("estimated iterations %d for work limit %f", estim_iters, settings.work_limit); pdlp_settings.iteration_limit = estim_iters; + pdlp_settings.time_limit = std::numeric_limits::infinity(); } pdlp_settings.concurrent_halt = settings.concurrent_halt; pdlp_settings.per_constraint_residual = settings.per_constraint_residual; @@ -138,11 +143,11 @@ optimization_problem_solution_t get_relaxed_lp_solution( // temporarily add timer auto start_time = timer_t(pdlp_settings.time_limit); lp_solver.set_inside_mip(true); - // CUOPT_LOG_DEBUG("prev primal hash 0x%x", detail::compute_hash(assignment)); - // CUOPT_LOG_DEBUG("prev dual hash 0x%x", detail::compute_hash(lp_state.prev_dual)); + CUOPT_LOG_DEBUG("prev primal hash 0x%x", detail::compute_hash(assignment)); + CUOPT_LOG_DEBUG("prev dual hash 0x%x", detail::compute_hash(lp_state.prev_dual)); auto solver_response = lp_solver.run_solver(start_time); - // CUOPT_LOG_DEBUG("post LP primal hash 0x%x", - // detail::compute_hash(solver_response.get_primal_solution())); + CUOPT_LOG_DEBUG("post LP primal hash 0x%x", + detail::compute_hash(solver_response.get_primal_solution())); if (solver_response.get_primal_solution().size() != 0 && solver_response.get_dual_solution().size() != 0 && settings.save_state) { @@ -160,9 +165,10 @@ optimization_problem_solution_t get_relaxed_lp_solution( // CUOPT_LOG_DEBUG("feasible solution found with LP objective %f", // solver_response.get_objective_value()); } else { - CUOPT_LOG_DEBUG("LP returned with reason %d, %d iterations", + CUOPT_LOG_DEBUG("LP returned with reason %d, %d iterations, sol hash 0x%x", solver_response.get_termination_status(), - solver_response.get_additional_termination_information().number_of_steps_taken); + solver_response.get_additional_termination_information().number_of_steps_taken, + compute_hash(assignment)); } auto function_end_time = std::chrono::high_resolution_clock::now(); diff --git a/cpp/src/mip/solution/solution.cu b/cpp/src/mip/solution/solution.cu index 0ce3b1e76..5b2236051 100644 --- a/cpp/src/mip/solution/solution.cu +++ b/cpp/src/mip/solution/solution.cu @@ -622,7 +622,7 @@ mip_solution_t solution_t::get_solution(bool output_feasible "Solution objective: %f , relative_mip_gap %f solution_bound %f presolve_time %f " "total_solve_time %f " "max constraint violation %f max int violation %f max var bounds violation %f " - "nodes %d simplex_iterations %d", + "nodes %d simplex_iterations %d hash %x", h_user_obj, rel_mip_gap, solution_bound, @@ -632,7 +632,8 @@ mip_solution_t solution_t::get_solution(bool output_feasible max_int_violation, max_variable_bound_violation, num_nodes, - num_simplex_iterations); + num_simplex_iterations, + get_hash()); } const bool not_optimal = rel_mip_gap > problem_ptr->tolerances.relative_mip_gap && abs_mip_gap > problem_ptr->tolerances.absolute_mip_gap; diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index 71d02f531..25d53f9c2 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -149,7 +149,7 @@ mip_solution_t run_mip(detail::problem_t& problem, auto sol = scaled_sol.get_solution( is_feasible_before_scaling || is_feasible_after_unscaling, solver.get_solver_stats(), false); - detail::print_solution(scaled_problem.handle_ptr, sol.get_solution()); + // detail::print_solution(scaled_problem.handle_ptr, sol.get_solution()); return sol; } diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index a27b37088..9911b0ee8 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -112,8 +112,8 @@ solution_t mip_solver_t::run_solver() } diversity_manager_t dm(context); - dm.timer = work_limit_timer_t(context.gpu_heur_loop, timer_.remaining_time()); - bool presolve_success = dm.run_presolve(timer_.remaining_time()); + dm.timer = work_limit_timer_t(context.gpu_heur_loop, timer_.get_time_limit()); + bool presolve_success = dm.run_presolve(timer_.get_time_limit()); if (!presolve_success) { CUOPT_LOG_INFO("Problem proven infeasible in presolve"); solution_t sol(*context.problem_ptr); diff --git a/cpp/src/mip/utils.cuh b/cpp/src/mip/utils.cuh index a5d7c5dde..d398b6524 100644 --- a/cpp/src/mip/utils.cuh +++ b/cpp/src/mip/utils.cuh @@ -39,6 +39,52 @@ constexpr int default_int_upper = std::numeric_limits::max(); constexpr int default_int_lower = std::numeric_limits::min(); constexpr double zero_bound = 0.; +template +inline uint32_t compute_hash(std::vector h_contents) +{ + // FNV-1a hash + + uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis + std::vector byte_contents(h_contents.size() * sizeof(i_t)); + std::memcpy(byte_contents.data(), h_contents.data(), h_contents.size() * sizeof(i_t)); + for (size_t i = 0; i < byte_contents.size(); ++i) { + hash ^= byte_contents[i]; + hash *= 16777619u; + } + return hash; +} + +template +HDI uint32_t compute_hash(const i_t val) +{ + uint32_t hash = 2166136261u; + uint8_t byte_contents[sizeof(i_t)]; + std::memcpy(byte_contents, &val, sizeof(i_t)); + for (size_t i = 0; i < sizeof(i_t); ++i) { + hash ^= byte_contents[i]; + hash *= 16777619u; + } + return hash; +} + +template +inline uint32_t compute_hash(raft::device_span values, + rmm::cuda_stream_view stream = rmm::cuda_stream_default) +{ + auto h_contents = cuopt::host_copy(values, stream); + RAFT_CHECK_CUDA(stream); + return compute_hash(h_contents); +} + +template +inline uint32_t compute_hash(const rmm::device_uvector& values, + rmm::cuda_stream_view stream = rmm::cuda_stream_default) +{ + auto h_contents = cuopt::host_copy(values, stream); + RAFT_CHECK_CUDA(stream); + return compute_hash(h_contents); +} + template HDI f_t get_cstr_tolerance(f_t combined_bound, f_t abs_tol, f_t rel_tol) { @@ -229,6 +275,9 @@ f_t compute_objective_from_vec(const rmm::device_uvector& assignment, assignment.end(), objective_coefficients.begin(), 0.); + // CUOPT_LOG_DEBUG("compute_objective_from_vec: input %x coefs %x obj %x", + // detail::compute_hash(assignment), detail::compute_hash(objective_coefficients), + // detail::compute_hash(computed_obj)); return computed_obj; } @@ -369,37 +418,4 @@ bool has_variable_bounds_violation(const raft::handle_t* handle_ptr, }); } -template -inline uint32_t compute_hash(std::vector h_contents) -{ - // FNV-1a hash - - uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis - std::vector byte_contents(h_contents.size() * sizeof(i_t)); - std::memcpy(byte_contents.data(), h_contents.data(), h_contents.size() * sizeof(i_t)); - for (size_t i = 0; i < byte_contents.size(); ++i) { - hash ^= byte_contents[i]; - hash *= 16777619u; - } - return hash; -} - -template -inline uint32_t compute_hash(raft::device_span values, - rmm::cuda_stream_view stream = rmm::cuda_stream_default) -{ - auto h_contents = cuopt::host_copy(values, stream); - RAFT_CHECK_CUDA(stream); - return compute_hash(h_contents); -} - -template -inline uint32_t compute_hash(const rmm::device_uvector& values, - rmm::cuda_stream_view stream = rmm::cuda_stream_default) -{ - auto h_contents = cuopt::host_copy(values, stream); - RAFT_CHECK_CUDA(stream); - return compute_hash(h_contents); -} - } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/utilities/logger.cpp b/cpp/src/utilities/logger.cpp index c4752d9ff..20f146d46 100644 --- a/cpp/src/utilities/logger.cpp +++ b/cpp/src/utilities/logger.cpp @@ -90,8 +90,8 @@ rapids_logger::sink_ptr default_sink() * * @return std::string The default log pattern. */ -inline std::string default_pattern() { return "[%Y-%m-%d %H:%M:%S:%f] [%n] [%-6l] %v"; } - +// inline std::string default_pattern() { return "[%Y-%m-%d %H:%M:%S:%f] [%n] [%-6l] %v"; } +inline std::string default_pattern() { return "[%n] [%-6l] %v"; } /** * @brief Returns the default log level for the global logger. * From bcadb92adeb232a8295ef94b96c77e3031774dfc Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 6 Nov 2025 18:57:05 +0000 Subject: [PATCH 029/366] bump 1 --- cpp/src/mip/feasibility_jump/feasibility_jump.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 2877c827a..47ea0edfb 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -1407,7 +1407,7 @@ i_t fj_t::solve(solution_t& solution) if (iterations < settings.iteration_limit) { CUOPT_LOG_DEBUG( "FJ early exit at %d iterations (limit: %d)", iterations, settings.iteration_limit); - // Compute the work unit corresponding to the number of iterations elapsed + // 1Compute the work unit corresponding to the number of iterations elapsed // by incrementally guessing work units until the model predicts >= actual iterations if (context.settings.deterministic && iterations > 0) { double guessed_work = 0.0; From 66288f8d57d91e90fe0f9f0627dcae8cf63b1b99 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 6 Nov 2025 18:57:22 +0000 Subject: [PATCH 030/366] bump2 --- cpp/src/mip/feasibility_jump/feasibility_jump.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 47ea0edfb..2877c827a 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -1407,7 +1407,7 @@ i_t fj_t::solve(solution_t& solution) if (iterations < settings.iteration_limit) { CUOPT_LOG_DEBUG( "FJ early exit at %d iterations (limit: %d)", iterations, settings.iteration_limit); - // 1Compute the work unit corresponding to the number of iterations elapsed + // Compute the work unit corresponding to the number of iterations elapsed // by incrementally guessing work units until the model predicts >= actual iterations if (context.settings.deterministic && iterations > 0) { double guessed_work = 0.0; From 8e19de8febea2b7e897905e135bd1953cd03d6ff Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 7 Nov 2025 15:10:12 +0000 Subject: [PATCH 031/366] fix FJ indeterminism --- .../mip/feasibility_jump/feasibility_jump.cu | 51 +++++++++++++++---- .../feasibility_jump_kernels.cu | 23 ++++----- cpp/src/mip/feasibility_jump/utils.cuh | 4 ++ cpp/src/mip/problem/problem.cu | 3 ++ cpp/src/mip/solve.cu | 11 ++-- 5 files changed, 66 insertions(+), 26 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 2877c827a..cf55dc983 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -441,10 +441,7 @@ void fj_t::climber_init(i_t climber_idx, const rmm::cuda_stream_view& f_t inf = std::numeric_limits::infinity(); climber->best_objective.set_value_async(inf, climber_stream); climber->saved_solution_objective.set_value_async(inf, climber_stream); - climber->violation_score.set_value_to_zero_async(climber_stream); - climber->weighted_violation_score.set_value_to_zero_async(climber_stream); - init_lhs_and_violation<<<256, 256, 0, climber_stream.value()>>>(view); - climber->violated_constraints.sort(climber_stream); + refresh_lhs_and_violation(climber_stream); // printf("init: Violated constraints hash: %x\n", compute_hash( // make_span(climber->violated_constraints.contents, 0, @@ -894,10 +891,38 @@ void fj_t::refresh_lhs_and_violation(const rmm::cuda_stream_view& stre auto v = data.view(); data.violated_constraints.clear(stream); - data.violation_score.set_value_to_zero_async(stream); - data.weighted_violation_score.set_value_to_zero_async(stream); init_lhs_and_violation<<<4096, 256, 0, stream>>>(v); + // both transformreduce could be fused; but oh well hardly a bottleneck + auto violation = + thrust::transform_reduce(rmm::exec_policy(stream), + thrust::make_counting_iterator(0), + thrust::make_counting_iterator(pb_ptr->n_constraints), + cuda::proclaim_return_type([v] __device__(i_t cstr_idx) { + return v.excess_score(cstr_idx, v.incumbent_lhs[cstr_idx]); + }), + (f_t)0, + thrust::plus()); + auto weighted_violation = thrust::transform_reduce( + rmm::exec_policy(stream), + thrust::make_counting_iterator(0), + thrust::make_counting_iterator(pb_ptr->n_constraints), + cuda::proclaim_return_type([v] __device__(i_t cstr_idx) { + return v.excess_score(cstr_idx, v.incumbent_lhs[cstr_idx]) * v.cstr_weights[cstr_idx]; + }), + (f_t)0, + thrust::plus()); + data.violation_score.set_value_async(violation, stream); + data.weighted_violation_score.set_value_async(weighted_violation, stream); data.violated_constraints.sort(stream); +#if FJ_SINGLE_STEP + CUOPT_LOG_DEBUG("hash assignment %x, hash lhs %x, hash lhscomp %x", + detail::compute_hash(data.incumbent_assignment, stream), + detail::compute_hash(data.incumbent_lhs, stream), + detail::compute_hash(data.incumbent_lhs_sumcomp, stream)); + CUOPT_LOG_DEBUG("Violated constraints hash post sort: %x, index map %x", + detail::compute_hash(data.violated_constraints.contents, stream), + detail::compute_hash(data.violated_constraints.index_map, stream)); +#endif } template @@ -1103,13 +1128,14 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) if (timer.check_time_limit() || steps >= settings.iteration_limit) { limit_reached = true; } #if !FJ_SINGLE_STEP - if (steps % 500 == 0) + // if (steps % 500 == 0) + if (false) #endif { - CUOPT_LOG_TRACE( + CUOPT_LOG_DEBUG( "FJ " "step %d viol %.2g [%d], obj %.8g, best %.8g, mins %d, maxw %g, " - "objw %g, sol hash %x, delta hash %x", + "objw %g, sol %x, delta %x, inc %x, lhs %x, lhscomp %x, viol %x, weights %x", steps, data.violation_score.value(climber_stream), data.violated_constraints.set_size.value(climber_stream), @@ -1119,7 +1145,12 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) max_cstr_weight.value(climber_stream), objective_weight.value(climber_stream), solution.get_hash(), - detail::compute_hash(data.jump_move_delta, climber_stream)); + detail::compute_hash(data.jump_move_delta, climber_stream), + detail::compute_hash(data.incumbent_assignment, climber_stream), + detail::compute_hash(data.incumbent_lhs, climber_stream), + detail::compute_hash(data.incumbent_lhs_sumcomp, climber_stream), + detail::compute_hash(data.violated_constraints.contents, climber_stream), + detail::compute_hash(cstr_left_weights, climber_stream)); } if (!limit_reached) { run_step_device(climber_stream, climber_idx); } diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index cd83803ea..21a5bf599 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -180,10 +180,7 @@ __global__ void init_lhs_and_violation(typename fj_t::climber_data_t:: fj_kahan_babushka_neumaier_sum(delta_it + offset_begin, delta_it + offset_end); fj.incumbent_lhs_sumcomp[cstr_idx] = 0; - f_t th_violation = fj.excess_score(cstr_idx, fj.incumbent_lhs[cstr_idx]); - f_t weighted_violation = th_violation * fj.cstr_weights[cstr_idx]; - atomicAdd(fj.violation_score, th_violation); - atomicAdd(fj.weighted_violation_score, weighted_violation); + f_t th_violation = fj.excess_score(cstr_idx, fj.incumbent_lhs[cstr_idx]); f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx); if (th_violation < -cstr_tolerance) { fj.violated_constraints.insert(cstr_idx); } } @@ -604,14 +601,16 @@ __global__ void update_assignment_kernel(typename fj_t::climber_data_t __syncthreads(); - cuopt_assert(isfinite(fj.jump_move_delta[var_idx]), "delta should be finite"); - // Kahan compensated summation - // fj.incumbent_lhs[cstr_idx] = old_lhs + cstr_coeff * fj.jump_move_delta[var_idx]; - f_t y = cstr_coeff * fj.jump_move_delta[var_idx] - fj.incumbent_lhs_sumcomp[cstr_idx]; - f_t t = old_lhs + y; - fj.incumbent_lhs_sumcomp[cstr_idx] = (t - old_lhs) - y; - fj.incumbent_lhs[cstr_idx] = t; - cuopt_assert(isfinite(fj.incumbent_lhs[cstr_idx]), "assignment should be finite"); + if (threadIdx.x == 0) { + cuopt_assert(isfinite(fj.jump_move_delta[var_idx]), "delta should be finite"); + // Kahan compensated summation + // fj.incumbent_lhs[cstr_idx] = old_lhs + cstr_coeff * fj.jump_move_delta[var_idx]; + f_t y = cstr_coeff * fj.jump_move_delta[var_idx] - fj.incumbent_lhs_sumcomp[cstr_idx]; + f_t t = old_lhs + y; + fj.incumbent_lhs_sumcomp[cstr_idx] = (t - old_lhs) - y; + fj.incumbent_lhs[cstr_idx] = t; + cuopt_assert(isfinite(fj.incumbent_lhs[cstr_idx]), "assignment should be finite"); + } } // update the assignment and objective proper diff --git a/cpp/src/mip/feasibility_jump/utils.cuh b/cpp/src/mip/feasibility_jump/utils.cuh index bd1dd4b49..1b82ae8b6 100644 --- a/cpp/src/mip/feasibility_jump/utils.cuh +++ b/cpp/src/mip/feasibility_jump/utils.cuh @@ -152,6 +152,10 @@ struct contiguous_set_t { thrust::make_counting_iterator(0), thrust::make_counting_iterator(set_size.value(stream)), [v = view()] __device__(i_t idx) { v.index_map[v.contents[idx]] = idx; }); + + // TODO: remove, only useful for debugging and ensuring the same hashes + thrust::fill( + rmm::exec_policy(stream), contents.begin() + set_size.value(stream), contents.end(), 0); } struct view_t { diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 775491c67..a6d942e35 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1709,6 +1709,9 @@ uint32_t problem_t::get_fingerprint() const detail::compute_hash(coefficients, handle_ptr->get_stream()), detail::compute_hash(variables, handle_ptr->get_stream()), detail::compute_hash(offsets, handle_ptr->get_stream()), + detail::compute_hash(reverse_coefficients, handle_ptr->get_stream()), + detail::compute_hash(reverse_offsets, handle_ptr->get_stream()), + detail::compute_hash(reverse_constraints, handle_ptr->get_stream()), detail::compute_hash(objective_coefficients, handle_ptr->get_stream()), detail::compute_hash(variable_bounds, handle_ptr->get_stream()), detail::compute_hash(constraint_lower_bounds, handle_ptr->get_stream()), diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index 25d53f9c2..5e330eac1 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -210,10 +210,13 @@ mip_solution_t solve_mip(optimization_problem_t& op_problem, detail::sort_csr(op_problem); // allocate not more than 10% of the time limit to presolve. // Note that this is not the presolve time, but the time limit for presolve. - const double presolve_time_limit = std::min(0.1 * time_limit, 60.0); - const bool dual_postsolve = false; - presolver = std::make_unique>(); - auto result = presolver->apply(op_problem, + double presolve_time_limit = std::min(0.1 * time_limit, 60.0); + + // TODO FIX + presolve_time_limit = std::numeric_limits::infinity(); + const bool dual_postsolve = false; + presolver = std::make_unique>(); + auto result = presolver->apply(op_problem, cuopt::linear_programming::problem_category_t::MIP, dual_postsolve, settings.tolerances.absolute_tolerance, From 5eacbb89d03a49fb3d6a2179635c72946c3a53b0 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 7 Nov 2025 15:10:42 +0000 Subject: [PATCH 032/366] bump 1 --- cpp/src/mip/feasibility_jump/utils.cuh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/utils.cuh b/cpp/src/mip/feasibility_jump/utils.cuh index 1b82ae8b6..4afd30b15 100644 --- a/cpp/src/mip/feasibility_jump/utils.cuh +++ b/cpp/src/mip/feasibility_jump/utils.cuh @@ -153,7 +153,7 @@ struct contiguous_set_t { thrust::make_counting_iterator(set_size.value(stream)), [v = view()] __device__(i_t idx) { v.index_map[v.contents[idx]] = idx; }); - // TODO: remove, only useful for debugging and ensuring the same hashes + // TODO: r1emove, only useful for debugging and ensuring the same hashes thrust::fill( rmm::exec_policy(stream), contents.begin() + set_size.value(stream), contents.end(), 0); } From b96a7844ff64a462f875379256190877d595b173 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 7 Nov 2025 15:10:59 +0000 Subject: [PATCH 033/366] bump 2 --- cpp/src/mip/feasibility_jump/utils.cuh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/utils.cuh b/cpp/src/mip/feasibility_jump/utils.cuh index 4afd30b15..1b82ae8b6 100644 --- a/cpp/src/mip/feasibility_jump/utils.cuh +++ b/cpp/src/mip/feasibility_jump/utils.cuh @@ -153,7 +153,7 @@ struct contiguous_set_t { thrust::make_counting_iterator(set_size.value(stream)), [v = view()] __device__(i_t idx) { v.index_map[v.contents[idx]] = idx; }); - // TODO: r1emove, only useful for debugging and ensuring the same hashes + // TODO: remove, only useful for debugging and ensuring the same hashes thrust::fill( rmm::exec_policy(stream), contents.begin() + set_size.value(stream), contents.end(), 0); } From ccc64a22acd17dce0d0c5ef5d1a579dc5775ec96 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 7 Nov 2025 18:47:21 +0000 Subject: [PATCH 034/366] fix bounds repair indeterminism --- .../mip/feasibility_jump/feasibility_jump.cu | 26 +++---- .../feasibility_jump_kernels.cu | 69 ++++++++++--------- .../local_search/rounding/bounds_repair.cu | 20 ++++-- .../local_search/rounding/lb_bounds_repair.cu | 16 +++-- cpp/src/utilities/timer.hpp | 1 + 5 files changed, 76 insertions(+), 56 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index cf55dc983..7b75c9823 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -664,7 +664,7 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; - auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; + // auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; // use_graph = false; @@ -774,18 +774,18 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream } #endif - cudaLaunchKernel((void*)update_lift_moves_kernel, - grid_lift_move, - blocks_lift_move, - kernel_args, - 0, - climber_stream); - cudaLaunchKernel((void*)update_breakthrough_moves_kernel, - grid_lift_move, - blocks_lift_move, - kernel_args, - 0, - climber_stream); + // cudaLaunchKernel((void*)update_lift_moves_kernel, + // grid_lift_move, + // blocks_lift_move, + // kernel_args, + // 0, + // climber_stream); + // cudaLaunchKernel((void*)update_breakthrough_moves_kernel, + // grid_lift_move, + // blocks_lift_move, + // kernel_args, + // 0, + // climber_stream); } // compaction kernel diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 21a5bf599..f62d15abf 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1454,44 +1454,45 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat } // also consider breakthrough moves - if (*fj.best_objective < std::numeric_limits::infinity() && - *fj.incumbent_objective > *fj.best_objective) { - cg::this_grid().sync(); - auto [bm_best_var, bm_best_delta, bm_best_score] = - best_breakthrough_move_at_local_min(fj); - if (bm_best_score > best_score) { - best_score = bm_best_score; - best_var = bm_best_var; - best_delta = bm_best_delta; - best_movetype = 'B'; - cuopt_assert(fj.pb.check_variable_within_bounds( - best_var, fj.incumbent_assignment[best_var] + best_delta), - "assignment not within bounds"); - } - } + // if (*fj.best_objective < std::numeric_limits::infinity() && + // *fj.incumbent_objective > *fj.best_objective) { + // cg::this_grid().sync(); + // auto [bm_best_var, bm_best_delta, bm_best_score] = + // best_breakthrough_move_at_local_min(fj); + // if (bm_best_score > best_score) { + // best_score = bm_best_score; + // best_var = bm_best_var; + // best_delta = bm_best_delta; + // best_movetype = 'B'; + // cuopt_assert(fj.pb.check_variable_within_bounds( + // best_var, fj.incumbent_assignment[best_var] + best_delta), + // "assignment not within bounds"); + // } + // } if (FIRST_THREAD) *fj.selected_var = best_var; cg::this_grid().sync(); // still nothing? try sat MTM moves if we are in the feasible region // Attempt to find a valid move by going over MTM moves in valid constraints - if (*fj.selected_var == std::numeric_limits::max() && - *fj.incumbent_objective < std::numeric_limits::infinity()) { - auto [sat_best_var, sat_best_delta, sat_best_score] = - best_sat_cstr_mtm_move(fj); - - if (FIRST_THREAD && sat_best_score.valid()) - cuopt_assert(fj.pb.check_variable_within_bounds( - sat_best_var, fj.incumbent_assignment[sat_best_var] + sat_best_delta), - "assignment not within bounds"); - - if (sat_best_score.base > 0 && sat_best_score > best_score) { - if (FIRST_THREAD) { - best_score = sat_best_score; - best_var = sat_best_var; - best_delta = sat_best_delta; - } - } - } + // if (*fj.selected_var == std::numeric_limits::max() && + // *fj.incumbent_objective < std::numeric_limits::infinity()) { + // auto [sat_best_var, sat_best_delta, sat_best_score] = + // best_sat_cstr_mtm_move(fj); + + // if (FIRST_THREAD && sat_best_score.valid()) + // cuopt_assert(fj.pb.check_variable_within_bounds( + // sat_best_var, fj.incumbent_assignment[sat_best_var] + sat_best_delta), + // "assignment not within bounds"); + + // if (sat_best_score.base > 0 && sat_best_score > best_score) { + // if (FIRST_THREAD) { + // best_score = sat_best_score; + // best_var = sat_best_var; + // best_delta = sat_best_delta; + // best_movetype = 'S'; + // } + // } + // } if (FIRST_THREAD) { *fj.selected_var = best_var; @@ -1500,6 +1501,8 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat best_var, fj.incumbent_assignment[best_var] + best_delta), "assignment not within bounds"); fj.jump_move_delta[best_var] = best_delta; + // DEVICE_LOG_DEBUG("FJ[%d] selected_var: %d, delta %g, score {%d %d}, type %c\n", + // *fj.iterations, best_var, best_delta, best_score.base, best_score.bonus, best_movetype); } } } diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index 1354ab18e..0013fc2e0 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -78,8 +78,7 @@ f_t bounds_repair_t::get_ii_violation(problem_t& problem) min_act = bound_presolve.upd.min_activity.data(), max_act = bound_presolve.upd.max_activity.data(), cstr_violations_up = cstr_violations_up.data(), - cstr_violations_down = cstr_violations_down.data(), - total_vio = total_vio.data()] __device__(i_t cstr_idx) { + cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) -> f_t { f_t cnst_lb = pb_v.constraint_lower_bounds[cstr_idx]; f_t cnst_ub = pb_v.constraint_upper_bounds[cstr_idx]; f_t eps = get_cstr_tolerance( @@ -89,21 +88,30 @@ f_t bounds_repair_t::get_ii_violation(problem_t& problem) f_t violation = max(curr_cstr_violation_up, curr_cstr_violation_down); if (violation >= ROUNDOFF_TOLERANCE) { violated_cstr_map[cstr_idx] = 1; - atomicAdd(total_vio, violation); } else { violated_cstr_map[cstr_idx] = 0; } cstr_violations_up[cstr_idx] = curr_cstr_violation_up; cstr_violations_down[cstr_idx] = curr_cstr_violation_down; }); - auto iter = thrust::copy_if(handle_ptr->get_thrust_policy(), + auto iter = thrust::copy_if(handle_ptr->get_thrust_policy(), thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + problem.n_constraints, violated_cstr_map.data(), violated_constraints.data(), cuda::std::identity{}); - h_n_violated_cstr = iter - violated_constraints.data(); - f_t total_violation = total_vio.value(handle_ptr->get_stream()); + h_n_violated_cstr = iter - violated_constraints.data(); + // Use deterministic reduction instead of non-deterministic atomicAdd + f_t total_violation = thrust::transform_reduce( + handle_ptr->get_thrust_policy(), + thrust::make_counting_iterator(0), + thrust::make_counting_iterator(0) + problem.n_constraints, + [cstr_violations_up = cstr_violations_up.data(), + cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) -> f_t { + return max(cstr_violations_up[cstr_idx], cstr_violations_down[cstr_idx]); + }, + (f_t)0, + thrust::plus()); CUOPT_LOG_TRACE( "Repair: n_violated_cstr %d total_violation %f", h_n_violated_cstr, total_violation); return total_violation; diff --git a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu index 7c0d2dcc5..6fa951033 100644 --- a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu @@ -78,8 +78,7 @@ std::tuple lb_bounds_repair_t::get_ii_violation( constraint_upper_bounds = problem.constraint_upper_bounds, cnst_slack = make_span_2(lb_bound_presolve.cnst_slack), cstr_violations_up = cstr_violations_up.data(), - cstr_violations_down = cstr_violations_down.data(), - total_vio = total_vio.data()] __device__(i_t cstr_idx) { + cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) { f_t cnst_lb = constraint_lower_bounds[cstr_idx]; f_t cnst_ub = constraint_upper_bounds[cstr_idx]; f_t2 slack = cnst_slack[cstr_idx]; @@ -90,7 +89,6 @@ std::tuple lb_bounds_repair_t::get_ii_violation( f_t violation = max(curr_cstr_violation_up, curr_cstr_violation_down); if (violation >= ROUNDOFF_TOLERANCE) { violated_cstr_map[cstr_idx] = 1; - atomicAdd(total_vio, violation); } else { violated_cstr_map[cstr_idx] = 0; } @@ -104,7 +102,17 @@ std::tuple lb_bounds_repair_t::get_ii_violation( violated_constraints.data(), cuda::std::identity{}); i_t n_violated_cstr = iter - violated_constraints.data(); - f_t total_violation = total_vio.value(handle_ptr->get_stream()); + // Use deterministic reduction instead of non-deterministic atomicAdd + f_t total_violation = thrust::transform_reduce( + handle_ptr->get_thrust_policy(), + thrust::make_counting_iterator(0), + thrust::make_counting_iterator(0) + problem.n_constraints, + [cstr_violations_up = cstr_violations_up.data(), + cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) -> f_t { + return max(cstr_violations_up[cstr_idx], cstr_violations_down[cstr_idx]); + }, + (f_t)0, + thrust::plus()); CUOPT_LOG_TRACE( "Repair: n_violated_cstr %d total_violation %f", n_violated_cstr, total_violation); return std::make_tuple(total_violation, n_violated_cstr); diff --git a/cpp/src/utilities/timer.hpp b/cpp/src/utilities/timer.hpp index d250e15cd..b3ea94f55 100644 --- a/cpp/src/utilities/timer.hpp +++ b/cpp/src/utilities/timer.hpp @@ -56,6 +56,7 @@ class timer_t { line, caller); assert(false && "unexpected timer"); + __builtin_trap(); } return elapsed; } From 01afc9d107d52cbbea0ebf162ce703ec8a2976b2 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 7 Nov 2025 18:47:40 +0000 Subject: [PATCH 035/366] bump1 --- cpp/src/mip/local_search/rounding/bounds_repair.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index 0013fc2e0..d7ab819d1 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -101,7 +101,7 @@ f_t bounds_repair_t::get_ii_violation(problem_t& problem) violated_constraints.data(), cuda::std::identity{}); h_n_violated_cstr = iter - violated_constraints.data(); - // Use deterministic reduction instead of non-deterministic atomicAdd + // Use deterministic reduction instead of non-deterministic atomicAdd1 f_t total_violation = thrust::transform_reduce( handle_ptr->get_thrust_policy(), thrust::make_counting_iterator(0), From 6ba4905b521a79821805a6d170463573e576af57 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 7 Nov 2025 18:47:52 +0000 Subject: [PATCH 036/366] bump2 --- cpp/src/mip/local_search/rounding/bounds_repair.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index d7ab819d1..0013fc2e0 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -101,7 +101,7 @@ f_t bounds_repair_t::get_ii_violation(problem_t& problem) violated_constraints.data(), cuda::std::identity{}); h_n_violated_cstr = iter - violated_constraints.data(); - // Use deterministic reduction instead of non-deterministic atomicAdd1 + // Use deterministic reduction instead of non-deterministic atomicAdd f_t total_violation = thrust::transform_reduce( handle_ptr->get_thrust_policy(), thrust::make_counting_iterator(0), From 3e6d081be749c09181febd5f3087bf156ebc589e Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 7 Nov 2025 18:50:08 +0000 Subject: [PATCH 037/366] restore FJ moves --- .../mip/feasibility_jump/feasibility_jump.cu | 26 +++---- .../feasibility_jump_kernels.cu | 68 +++++++++---------- 2 files changed, 47 insertions(+), 47 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 7b75c9823..cf55dc983 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -664,7 +664,7 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; - // auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; + auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; // use_graph = false; @@ -774,18 +774,18 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream } #endif - // cudaLaunchKernel((void*)update_lift_moves_kernel, - // grid_lift_move, - // blocks_lift_move, - // kernel_args, - // 0, - // climber_stream); - // cudaLaunchKernel((void*)update_breakthrough_moves_kernel, - // grid_lift_move, - // blocks_lift_move, - // kernel_args, - // 0, - // climber_stream); + cudaLaunchKernel((void*)update_lift_moves_kernel, + grid_lift_move, + blocks_lift_move, + kernel_args, + 0, + climber_stream); + cudaLaunchKernel((void*)update_breakthrough_moves_kernel, + grid_lift_move, + blocks_lift_move, + kernel_args, + 0, + climber_stream); } // compaction kernel diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index f62d15abf..8d92d898f 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1454,45 +1454,45 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat } // also consider breakthrough moves - // if (*fj.best_objective < std::numeric_limits::infinity() && - // *fj.incumbent_objective > *fj.best_objective) { - // cg::this_grid().sync(); - // auto [bm_best_var, bm_best_delta, bm_best_score] = - // best_breakthrough_move_at_local_min(fj); - // if (bm_best_score > best_score) { - // best_score = bm_best_score; - // best_var = bm_best_var; - // best_delta = bm_best_delta; - // best_movetype = 'B'; - // cuopt_assert(fj.pb.check_variable_within_bounds( - // best_var, fj.incumbent_assignment[best_var] + best_delta), - // "assignment not within bounds"); - // } - // } + if (*fj.best_objective < std::numeric_limits::infinity() && + *fj.incumbent_objective > *fj.best_objective) { + cg::this_grid().sync(); + auto [bm_best_var, bm_best_delta, bm_best_score] = + best_breakthrough_move_at_local_min(fj); + if (bm_best_score > best_score) { + best_score = bm_best_score; + best_var = bm_best_var; + best_delta = bm_best_delta; + best_movetype = 'B'; + cuopt_assert(fj.pb.check_variable_within_bounds( + best_var, fj.incumbent_assignment[best_var] + best_delta), + "assignment not within bounds"); + } + } if (FIRST_THREAD) *fj.selected_var = best_var; cg::this_grid().sync(); // still nothing? try sat MTM moves if we are in the feasible region // Attempt to find a valid move by going over MTM moves in valid constraints - // if (*fj.selected_var == std::numeric_limits::max() && - // *fj.incumbent_objective < std::numeric_limits::infinity()) { - // auto [sat_best_var, sat_best_delta, sat_best_score] = - // best_sat_cstr_mtm_move(fj); - - // if (FIRST_THREAD && sat_best_score.valid()) - // cuopt_assert(fj.pb.check_variable_within_bounds( - // sat_best_var, fj.incumbent_assignment[sat_best_var] + sat_best_delta), - // "assignment not within bounds"); - - // if (sat_best_score.base > 0 && sat_best_score > best_score) { - // if (FIRST_THREAD) { - // best_score = sat_best_score; - // best_var = sat_best_var; - // best_delta = sat_best_delta; - // best_movetype = 'S'; - // } - // } - // } + if (*fj.selected_var == std::numeric_limits::max() && + *fj.incumbent_objective < std::numeric_limits::infinity()) { + auto [sat_best_var, sat_best_delta, sat_best_score] = + best_sat_cstr_mtm_move(fj); + + if (FIRST_THREAD && sat_best_score.valid()) + cuopt_assert(fj.pb.check_variable_within_bounds( + sat_best_var, fj.incumbent_assignment[sat_best_var] + sat_best_delta), + "assignment not within bounds"); + + if (sat_best_score.base > 0 && sat_best_score > best_score) { + if (FIRST_THREAD) { + best_score = sat_best_score; + best_var = sat_best_var; + best_delta = sat_best_delta; + best_movetype = 'S'; + } + } + } if (FIRST_THREAD) { *fj.selected_var = best_var; From 38df1ece95f19964de2fe72af5f9ce2af9e4c232 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 7 Nov 2025 18:50:21 +0000 Subject: [PATCH 038/366] bump1 --- cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 8d92d898f..5357e7261 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1501,7 +1501,7 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat best_var, fj.incumbent_assignment[best_var] + best_delta), "assignment not within bounds"); fj.jump_move_delta[best_var] = best_delta; - // DEVICE_LOG_DEBUG("FJ[%d] selected_var: %d, delta %g, score {%d %d}, type %c\n", + // 1DEVICE_LOG_DEBUG("FJ[%d] selected_var: %d, delta %g, score {%d %d}, type %c\n", // *fj.iterations, best_var, best_delta, best_score.base, best_score.bonus, best_movetype); } } From c713dbca3df79dc89057d9f28467e8530bb00231 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 7 Nov 2025 18:51:54 +0000 Subject: [PATCH 039/366] bump2 --- cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 5357e7261..8d92d898f 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1501,7 +1501,7 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat best_var, fj.incumbent_assignment[best_var] + best_delta), "assignment not within bounds"); fj.jump_move_delta[best_var] = best_delta; - // 1DEVICE_LOG_DEBUG("FJ[%d] selected_var: %d, delta %g, score {%d %d}, type %c\n", + // DEVICE_LOG_DEBUG("FJ[%d] selected_var: %d, delta %g, score {%d %d}, type %c\n", // *fj.iterations, best_var, best_delta, best_score.base, best_score.bonus, best_movetype); } } From 3b0d116710b73e991d936008a0adaa5390622536 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 08:21:13 +0000 Subject: [PATCH 040/366] recombiners record work --- cpp/src/mip/diversity/diversity_manager.cu | 19 +++++++++---------- cpp/src/mip/diversity/multi_armed_bandit.cuh | 11 ++++------- .../recombiners/bound_prop_recombiner.cuh | 13 +++++++------ .../diversity/recombiners/fp_recombiner.cuh | 14 ++++++++------ .../recombiners/line_segment_recombiner.cuh | 12 +++++++----- .../recombiners/recombiner_stats.hpp | 16 +++------------- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 14 ++++++++------ 7 files changed, 46 insertions(+), 53 deletions(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 075a483ec..e7304ece9 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -706,7 +706,7 @@ diversity_manager_t::recombine_and_local_search(solution_t& best_quality_of_parents, population.best().get_quality(population.weights), offspring_qual, - recombiner_work_normalized_reward_t(recombine_stats.get_last_recombiner_time())); + recombiner_work_normalized_reward_t(recombine_stats.get_last_recombiner_work())); mab_ls.add_mab_reward(mab_ls_config_t::last_ls_mab_option, best_quality_of_parents, population.best_feasible().get_quality(population.weights), @@ -753,30 +753,29 @@ std::pair, bool> diversity_manager_t::recombine( } mab_recombiner.set_last_chosen_option(selected_index); recombine_stats.add_attempt((recombiner_enum_t)recombiner); - recombine_stats.start_recombiner_time(); // Refactored code using a switch statement switch (recombiner) { case recombiner_enum_t::BOUND_PROP: { - auto [sol, success] = bound_prop_recombiner.recombine(a, b, population.weights); - recombine_stats.stop_recombiner_time(); + auto [sol, success, work] = bound_prop_recombiner.recombine(a, b, population.weights); + recombine_stats.set_recombiner_work(work); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } case recombiner_enum_t::FP: { - auto [sol, success] = fp_recombiner.recombine(a, b, population.weights); - recombine_stats.stop_recombiner_time(); + auto [sol, success, work] = fp_recombiner.recombine(a, b, population.weights); + recombine_stats.set_recombiner_work(work); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } case recombiner_enum_t::LINE_SEGMENT: { - auto [sol, success] = line_segment_recombiner.recombine(a, b, population.weights); - recombine_stats.stop_recombiner_time(); + auto [sol, success, work] = line_segment_recombiner.recombine(a, b, population.weights); + recombine_stats.set_recombiner_work(work); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } case recombiner_enum_t::SUB_MIP: { - auto [sol, success] = sub_mip_recombiner.recombine(a, b, population.weights); - recombine_stats.stop_recombiner_time(); + auto [sol, success, work] = sub_mip_recombiner.recombine(a, b, population.weights); + recombine_stats.set_recombiner_work(work); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } diff --git a/cpp/src/mip/diversity/multi_armed_bandit.cuh b/cpp/src/mip/diversity/multi_armed_bandit.cuh index fe969ad8d..61dbf0547 100644 --- a/cpp/src/mip/diversity/multi_armed_bandit.cuh +++ b/cpp/src/mip/diversity/multi_armed_bandit.cuh @@ -55,16 +55,13 @@ struct ls_work_normalized_reward_t { }; struct recombiner_work_normalized_reward_t { - double time_in_miliseconds; - recombiner_work_normalized_reward_t(double time_in_miliseconds) - : time_in_miliseconds(time_in_miliseconds) - { - } + double work; + recombiner_work_normalized_reward_t(double work) : work(work) {} double operator()(double factor) const { - // normal recombiners take 2000 ms - return factor * (std::max(0.1, 4.0 - (time_in_miliseconds / 2000))); + // normal recombiners process 200 variables + return factor * (std::max(0.1, 4.0 - (work / 200))); } }; diff --git a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh index 4ca19436c..daf981ea7 100644 --- a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh @@ -141,9 +141,9 @@ class bound_prop_recombiner_t : public recombiner_t { }); } - std::pair, bool> recombine(solution_t& a, - solution_t& b, - const weight_t& weights) + std::tuple, bool, double> recombine(solution_t& a, + solution_t& b, + const weight_t& weights) { raft::common::nvtx::range fun_scope("bound_prop_recombiner"); auto& guiding_solution = a.get_feasible() ? a : b; @@ -174,8 +174,9 @@ class bound_prop_recombiner_t : public recombiner_t { // all are different), return if (n_vars_from_guiding == 0 || n_vars_from_other == 0) { CUOPT_LOG_DEBUG("Returning false because all vars are common or different"); - return std::make_pair(offspring, false); + return std::make_tuple(offspring, false, 0.0); } + double work = static_cast(n_vars_from_other); cuopt_assert(a.problem_ptr == b.problem_ptr, "The two solutions should not refer to different problems"); @@ -266,9 +267,9 @@ class bound_prop_recombiner_t : public recombiner_t { } if (better_cost_than_parents || better_feasibility_than_parents) { CUOPT_LOG_DEBUG("Offspring is feasible or better than both parents"); - return std::make_pair(offspring, true); + return std::make_tuple(offspring, true, work); } - return std::make_pair(offspring, !same_as_parents); + return std::make_tuple(offspring, !same_as_parents, work); } rmm::device_uvector vars_to_fix; diff --git a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh index ade72a23b..e67ff7c1b 100644 --- a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh @@ -45,9 +45,9 @@ class fp_recombiner_t : public recombiner_t { { } - std::pair, bool> recombine(solution_t& a, - solution_t& b, - const weight_t& weights) + std::tuple, bool, double> recombine(solution_t& a, + solution_t& b, + const weight_t& weights) { raft::common::nvtx::range fun_scope("FP recombiner"); auto& guiding_solution = a.get_feasible() ? a : b; @@ -73,8 +73,10 @@ class fp_recombiner_t : public recombiner_t { i_t n_vars_from_guiding = a.problem_ptr->n_integer_vars - n_vars_from_other; if (n_vars_from_other == 0 || n_vars_from_guiding == 0) { CUOPT_LOG_DEBUG("Returning false because all vars are common or different"); - return std::make_pair(offspring, false); + return std::make_tuple(offspring, false, 0.0); } + // TODO: CHANGE + double work = static_cast(n_vars_from_other); CUOPT_LOG_DEBUG( "n_vars_from_guiding %d n_vars_from_other %d", n_vars_from_guiding, n_vars_from_other); CUOPT_LOG_DEBUG("FP rec: offspring hash 0x%x, vars to fix 0x%x", @@ -160,9 +162,9 @@ class fp_recombiner_t : public recombiner_t { !guiding_solution.get_feasible(); if (better_cost_than_parents || better_feasibility_than_parents) { CUOPT_LOG_DEBUG("Offspring is feasible or better than both parents"); - return std::make_pair(offspring, true); + return std::make_tuple(offspring, true, work); } - return std::make_pair(offspring, !same_as_parents); + return std::make_tuple(offspring, !same_as_parents, work); } rmm::device_uvector vars_to_fix; // keep a copy of FP to prevent interference with generation FP diff --git a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh index 6a2882731..b7bb6f904 100644 --- a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh @@ -76,9 +76,9 @@ class line_segment_recombiner_t : public recombiner_t { return delta_vector; } - std::pair, bool> recombine(solution_t& a, - solution_t& b, - const weight_t& weights) + std::tuple, bool, double> recombine(solution_t& a, + solution_t& b, + const weight_t& weights) { raft::common::nvtx::range fun_scope("line_segment_recombiner"); CUOPT_LOG_DEBUG("LS rec: a %d b %d", a.get_hash(), b.get_hash()); @@ -94,6 +94,8 @@ class line_segment_recombiner_t : public recombiner_t { const bool is_feasibility_run = false; i_t n_different_vars = this->assign_same_integer_values(guiding_solution, other_solution, offspring); + // TODO: CHANGE + double work = static_cast(n_different_vars); rmm::device_uvector delta_vector = generate_delta_vector( guiding_solution, other_solution, offspring, n_points_to_search, n_different_vars); line_segment_search.fj.copy_weights(weights, offspring.handle_ptr); @@ -129,9 +131,9 @@ class line_segment_recombiner_t : public recombiner_t { } if (better_cost_than_parents || better_feasibility_than_parents) { CUOPT_LOG_DEBUG("Offspring is feasible or better than both parents"); - return std::make_pair(offspring, true); + return std::make_tuple(offspring, true, work); } - return std::make_pair(offspring, !same_as_parents); + return std::make_tuple(offspring, !same_as_parents, work); } line_segment_search_t& line_segment_search; diff --git a/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp b/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp index ca9e1fcb1..c4003202e 100644 --- a/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp +++ b/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp @@ -85,21 +85,11 @@ struct all_recombine_stats { // enum of the last attempted recombiner std::optional last_attempt; - double last_recombiner_time; - std::chrono::high_resolution_clock::time_point last_recombiner_start_time; + double last_recombiner_work; - void start_recombiner_time() - { - last_recombiner_start_time = std::chrono::high_resolution_clock::now(); - } - void stop_recombiner_time() - { - last_recombiner_time = std::chrono::duration_cast( - std::chrono::high_resolution_clock::now() - last_recombiner_start_time) - .count(); - } + void set_recombiner_work(double work) { last_recombiner_work = work; } - double get_last_recombiner_time() { return last_recombiner_time; } + double get_last_recombiner_work() { return last_recombiner_work; } void reset() { diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index 2f465b39d..956ab46f7 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -46,9 +46,9 @@ class sub_mip_recombiner_t : public recombiner_t { solution_vector.push_back(solution); } - std::pair, bool> recombine(solution_t& a, - solution_t& b, - const weight_t& weights) + std::tuple, bool, double> recombine(solution_t& a, + solution_t& b, + const weight_t& weights) { raft::common::nvtx::range fun_scope("Sub-MIP recombiner"); solution_vector.clear(); @@ -74,8 +74,10 @@ class sub_mip_recombiner_t : public recombiner_t { i_t n_vars_from_guiding = a.problem_ptr->n_integer_vars - n_vars_from_other; if (n_vars_from_other == 0 || n_vars_from_guiding == 0) { CUOPT_LOG_DEBUG("Returning false because all vars are common or different"); - return std::make_pair(offspring, false); + return std::make_tuple(offspring, false, 0.0); } + // TODO: CHANGE + double work = static_cast(n_vars_from_other); CUOPT_LOG_DEBUG( "n_vars_from_guiding %d n_vars_from_other %d", n_vars_from_guiding, n_vars_from_other); this->compute_vars_to_fix(offspring, vars_to_fix, n_vars_from_other, n_vars_from_guiding); @@ -193,9 +195,9 @@ class sub_mip_recombiner_t : public recombiner_t { !guiding_solution.get_feasible(); if (better_cost_than_parents || better_feasibility_than_parents) { CUOPT_LOG_DEBUG("Offspring is feasible or better than both parents"); - return std::make_pair(offspring, true); + return std::make_tuple(offspring, true, work); } - return std::make_pair(offspring, !std::isnan(branch_and_bound_solution.objective)); + return std::make_tuple(offspring, !std::isnan(branch_and_bound_solution.objective), work); } rmm::device_uvector vars_to_fix; mip_solver_context_t& context; From 7f81da1fc10364083553e95871e034854224fb80 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 08:40:53 +0000 Subject: [PATCH 041/366] bump1 --- .../diversity/recombiners/fp_recombiner.cuh | 2 +- .../mip/feasibility_jump/feasibility_jump.cu | 26 +++---- .../feasibility_jump_kernels.cu | 70 +++++++++---------- .../local_search/rounding/constraint_prop.cu | 4 +- 4 files changed, 51 insertions(+), 51 deletions(-) diff --git a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh index d0eb26c89..d84e24254 100644 --- a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh @@ -101,7 +101,7 @@ class fp_recombiner_t : public recombiner_t { lp_response.get_termination_status() == pdlp_termination_status_t::DualInfeasible || lp_response.get_termination_status() == pdlp_termination_status_t::TimeLimit) { CUOPT_LOG_DEBUG("FP recombiner failed because LP found infeasible!"); - return std::make_pair(offspring, false); + return std::make_tuple(offspring, false, 0.0); } } // brute force rounding threshold is 8 diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 7b1417e78..6785be21a 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -654,7 +654,7 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; - auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; + // auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; // use_graph = false; @@ -764,18 +764,18 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream } #endif - cudaLaunchKernel((void*)update_lift_moves_kernel, - grid_lift_move, - blocks_lift_move, - kernel_args, - 0, - climber_stream); - cudaLaunchKernel((void*)update_breakthrough_moves_kernel, - grid_lift_move, - blocks_lift_move, - kernel_args, - 0, - climber_stream); + // cudaLaunchKernel((void*)update_lift_moves_kernel, + // grid_lift_move, + // blocks_lift_move, + // kernel_args, + // 0, + // climber_stream); + // cudaLaunchKernel((void*)update_breakthrough_moves_kernel, + // grid_lift_move, + // blocks_lift_move, + // kernel_args, + // 0, + // climber_stream); } // compaction kernel diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 3210b55da..69def8b5c 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1443,46 +1443,46 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat "assignment not within bounds"); } - // also consider breakthrough moves - if (*fj.best_objective < std::numeric_limits::infinity() && - *fj.incumbent_objective > *fj.best_objective) { - cg::this_grid().sync(); - auto [bm_best_var, bm_best_delta, bm_best_score] = - best_breakthrough_move_at_local_min(fj); - if (bm_best_score > best_score) { - best_score = bm_best_score; - best_var = bm_best_var; - best_delta = bm_best_delta; - best_movetype = 'B'; - cuopt_assert(fj.pb.check_variable_within_bounds( - best_var, fj.incumbent_assignment[best_var] + best_delta), - "assignment not within bounds"); - } - } + // // also consider breakthrough moves + // if (*fj.best_objective < std::numeric_limits::infinity() && + // *fj.incumbent_objective > *fj.best_objective) { + // cg::this_grid().sync(); + // auto [bm_best_var, bm_best_delta, bm_best_score] = + // best_breakthrough_move_at_local_min(fj); + // if (bm_best_score > best_score) { + // best_score = bm_best_score; + // best_var = bm_best_var; + // best_delta = bm_best_delta; + // best_movetype = 'B'; + // cuopt_assert(fj.pb.check_variable_within_bounds( + // best_var, fj.incumbent_assignment[best_var] + best_delta), + // "assignment not within bounds"); + // } + // } if (FIRST_THREAD) *fj.selected_var = best_var; cg::this_grid().sync(); // still nothing? try sat MTM moves if we are in the feasible region // Attempt to find a valid move by going over MTM moves in valid constraints - if (*fj.selected_var == std::numeric_limits::max() && - *fj.incumbent_objective < std::numeric_limits::infinity()) { - auto [sat_best_var, sat_best_delta, sat_best_score] = - best_sat_cstr_mtm_move(fj); - - if (FIRST_THREAD && sat_best_score.valid()) - cuopt_assert(fj.pb.check_variable_within_bounds( - sat_best_var, fj.incumbent_assignment[sat_best_var] + sat_best_delta), - "assignment not within bounds"); - - if (sat_best_score.base > 0 && sat_best_score > best_score) { - if (FIRST_THREAD) { - best_score = sat_best_score; - best_var = sat_best_var; - best_delta = sat_best_delta; - best_movetype = 'S'; - } - } - } + // if (*fj.selected_var == std::numeric_limits::max() && + // *fj.incumbent_objective < std::numeric_limits::infinity()) { + // auto [sat_best_var, sat_best_delta, sat_best_score] = + // best_sat_cstr_mtm_move(fj); + + // if (FIRST_THREAD && sat_best_score.valid()) + // cuopt_assert(fj.pb.check_variable_within_bounds( + // sat_best_var, fj.incumbent_assignment[sat_best_var] + sat_best_delta), + // "assignment not within bounds"); + + // if (sat_best_score.base > 0 && sat_best_score > best_score) { + // if (FIRST_THREAD) { + // best_score = sat_best_score; + // best_var = sat_best_var; + // best_delta = sat_best_delta; + // best_movetype = 'S';1 + // } + // } + // } if (FIRST_THREAD) { *fj.selected_var = best_var; diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index f4b60ad7a..dd9221dbf 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -1140,8 +1140,8 @@ bool constraint_prop_t::apply_round( auto cp_end_time = std::chrono::high_resolution_clock::now(); auto cp_elapsed_ms = std::chrono::duration_cast(cp_end_time - cp_start_time).count(); - CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=BRUTE_FORCE_SUCCESS iterations=0", - cp_elapsed_ms); + // CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=BRUTE_FORCE_SUCCESS iterations=0", + // cp_elapsed_ms); return true; } recovery_mode = false; From 2726d23a482a2d89f438679e0c518d94cffad557 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 08:41:15 +0000 Subject: [PATCH 042/366] bump2 --- cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 69def8b5c..49583aec3 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1479,7 +1479,7 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat // best_score = sat_best_score; // best_var = sat_best_var; // best_delta = sat_best_delta; - // best_movetype = 'S';1 + // best_movetype = 'S';2 // } // } // } From 7ea0f1d7ba97f9764a3e8166ce7ba67fc8d98edc Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 11:17:34 +0000 Subject: [PATCH 043/366] fix brute force rounding indeterminism --- cpp/src/mip/local_search/rounding/simple_rounding_kernels.cuh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/rounding/simple_rounding_kernels.cuh b/cpp/src/mip/local_search/rounding/simple_rounding_kernels.cuh index ce1f4879e..d0f86863d 100644 --- a/cpp/src/mip/local_search/rounding/simple_rounding_kernels.cuh +++ b/cpp/src/mip/local_search/rounding/simple_rounding_kernels.cuh @@ -125,7 +125,7 @@ __global__ void brute_force_check_kernel(typename solution_t::view_t s __shared__ i_t shbuf[raft::WarpSize]; i_t total_feasible = raft::blockReduce(th_feasible_count, (char*)shbuf); if (threadIdx.x == 0) { - if (total_feasible == solution.problem.n_constraints) { atomicExch(best_config, config); } + if (total_feasible == solution.problem.n_constraints) { atomicMin(best_config, config); } } } From 4e74f15c7330ff5c7fdb5c41695dd8c52fe9fb0e Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 11:18:02 +0000 Subject: [PATCH 044/366] bump --- cpp/src/mip/local_search/rounding/bounds_repair.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index d6cd62d2e..e7484ea3a 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -111,7 +111,7 @@ template i_t bounds_repair_t::get_random_cstr() { std::uniform_int_distribution<> dist(0, h_n_violated_cstr - 1); - // Generate random number + // Generate random number1 i_t random_number = dist(gen); i_t cstr_idx = violated_constraints.element(random_number, handle_ptr->get_stream()); CUOPT_LOG_TRACE("Repair: selected random cstr %d", cstr_idx); From 709f281c2a0c758738cf732123d5d7860fea879f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 11:22:06 +0000 Subject: [PATCH 045/366] restore fj --- .../mip/feasibility_jump/feasibility_jump.cu | 26 +++---- .../feasibility_jump_kernels.cu | 68 +++++++++---------- .../local_search/rounding/bounds_repair.cu | 2 +- 3 files changed, 48 insertions(+), 48 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 6785be21a..7b1417e78 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -654,7 +654,7 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; - // auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; + auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; // use_graph = false; @@ -764,18 +764,18 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream } #endif - // cudaLaunchKernel((void*)update_lift_moves_kernel, - // grid_lift_move, - // blocks_lift_move, - // kernel_args, - // 0, - // climber_stream); - // cudaLaunchKernel((void*)update_breakthrough_moves_kernel, - // grid_lift_move, - // blocks_lift_move, - // kernel_args, - // 0, - // climber_stream); + cudaLaunchKernel((void*)update_lift_moves_kernel, + grid_lift_move, + blocks_lift_move, + kernel_args, + 0, + climber_stream); + cudaLaunchKernel((void*)update_breakthrough_moves_kernel, + grid_lift_move, + blocks_lift_move, + kernel_args, + 0, + climber_stream); } // compaction kernel diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 49583aec3..cff2a1c56 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1444,45 +1444,45 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat } // // also consider breakthrough moves - // if (*fj.best_objective < std::numeric_limits::infinity() && - // *fj.incumbent_objective > *fj.best_objective) { - // cg::this_grid().sync(); - // auto [bm_best_var, bm_best_delta, bm_best_score] = - // best_breakthrough_move_at_local_min(fj); - // if (bm_best_score > best_score) { - // best_score = bm_best_score; - // best_var = bm_best_var; - // best_delta = bm_best_delta; - // best_movetype = 'B'; - // cuopt_assert(fj.pb.check_variable_within_bounds( - // best_var, fj.incumbent_assignment[best_var] + best_delta), - // "assignment not within bounds"); - // } - // } + if (*fj.best_objective < std::numeric_limits::infinity() && + *fj.incumbent_objective > *fj.best_objective) { + cg::this_grid().sync(); + auto [bm_best_var, bm_best_delta, bm_best_score] = + best_breakthrough_move_at_local_min(fj); + if (bm_best_score > best_score) { + best_score = bm_best_score; + best_var = bm_best_var; + best_delta = bm_best_delta; + best_movetype = 'B'; + cuopt_assert(fj.pb.check_variable_within_bounds( + best_var, fj.incumbent_assignment[best_var] + best_delta), + "assignment not within bounds"); + } + } if (FIRST_THREAD) *fj.selected_var = best_var; cg::this_grid().sync(); // still nothing? try sat MTM moves if we are in the feasible region // Attempt to find a valid move by going over MTM moves in valid constraints - // if (*fj.selected_var == std::numeric_limits::max() && - // *fj.incumbent_objective < std::numeric_limits::infinity()) { - // auto [sat_best_var, sat_best_delta, sat_best_score] = - // best_sat_cstr_mtm_move(fj); - - // if (FIRST_THREAD && sat_best_score.valid()) - // cuopt_assert(fj.pb.check_variable_within_bounds( - // sat_best_var, fj.incumbent_assignment[sat_best_var] + sat_best_delta), - // "assignment not within bounds"); - - // if (sat_best_score.base > 0 && sat_best_score > best_score) { - // if (FIRST_THREAD) { - // best_score = sat_best_score; - // best_var = sat_best_var; - // best_delta = sat_best_delta; - // best_movetype = 'S';2 - // } - // } - // } + if (*fj.selected_var == std::numeric_limits::max() && + *fj.incumbent_objective < std::numeric_limits::infinity()) { + auto [sat_best_var, sat_best_delta, sat_best_score] = + best_sat_cstr_mtm_move(fj); + + if (FIRST_THREAD && sat_best_score.valid()) + cuopt_assert(fj.pb.check_variable_within_bounds( + sat_best_var, fj.incumbent_assignment[sat_best_var] + sat_best_delta), + "assignment not within bounds"); + + if (sat_best_score.base > 0 && sat_best_score > best_score) { + if (FIRST_THREAD) { + best_score = sat_best_score; + best_var = sat_best_var; + best_delta = sat_best_delta; + best_movetype = 'S'; + } + } + } if (FIRST_THREAD) { *fj.selected_var = best_var; diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index e7484ea3a..d6cd62d2e 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -111,7 +111,7 @@ template i_t bounds_repair_t::get_random_cstr() { std::uniform_int_distribution<> dist(0, h_n_violated_cstr - 1); - // Generate random number1 + // Generate random number i_t random_number = dist(gen); i_t cstr_idx = violated_constraints.element(random_number, handle_ptr->get_stream()); CUOPT_LOG_TRACE("Repair: selected random cstr %d", cstr_idx); From 41198e25c339130a57e2250f4f2ebc23cbc72000 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 11:22:26 +0000 Subject: [PATCH 046/366] bump1 --- cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index cff2a1c56..3210b55da 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1443,7 +1443,7 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat "assignment not within bounds"); } - // // also consider breakthrough moves + // also consider breakthrough moves if (*fj.best_objective < std::numeric_limits::infinity() && *fj.incumbent_objective > *fj.best_objective) { cg::this_grid().sync(); From d98e083c29413e542061ad8f877e9b715087eab2 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 13:32:03 +0000 Subject: [PATCH 047/366] more logging --- cpp/src/mip/diversity/diversity_manager.cu | 5 +++ .../recombiners/bound_prop_recombiner.cuh | 40 ++++++++++++++++++- cpp/src/mip/diversity/weights.cuh | 7 ++++ .../feasibility_pump/feasibility_pump.cu | 2 +- cpp/src/mip/problem/problem.cu | 21 ++++++---- 5 files changed, 65 insertions(+), 10 deletions(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 244f2223e..6e3d5c78f 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -743,6 +743,11 @@ std::pair, bool> diversity_manager_t::recombine( } mab_recombiner.set_last_chosen_option(selected_index); recombine_stats.add_attempt((recombiner_enum_t)recombiner); + CUOPT_LOG_DEBUG("Recombining sol %x and %x with recombiner %d, weights %x", + a.get_hash(), + b.get_hash(), + recombiner, + population.weights.get_hash()); // Refactored code using a switch statement switch (recombiner) { case recombiner_enum_t::BOUND_PROP: { diff --git a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh index 286f7d7b2..2c85520f0 100644 --- a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh @@ -148,10 +148,11 @@ class bound_prop_recombiner_t : public recombiner_t { i_t n_vars_from_other = n_different_vars; i_t fixed_from_guiding = 0; i_t fixed_from_other = 0; + i_t seed = cuopt::seed_generator::get_seed(); if (n_different_vars > (i_t)bp_recombiner_config_t::max_n_of_vars_from_other) { fixed_from_guiding = n_vars_from_other - bp_recombiner_config_t::max_n_of_vars_from_other; n_vars_from_other = bp_recombiner_config_t::max_n_of_vars_from_other; - thrust::default_random_engine g{(unsigned int)cuopt::seed_generator::get_seed()}; + thrust::default_random_engine g{(unsigned int)seed}; thrust::shuffle(a.handle_ptr->get_thrust_policy(), this->remaining_indices.data(), this->remaining_indices.data() + n_different_vars, @@ -160,6 +161,27 @@ class bound_prop_recombiner_t : public recombiner_t { i_t n_vars_from_guiding = a.problem_ptr->n_integer_vars - n_vars_from_other; CUOPT_LOG_DEBUG( "n_vars_from_guiding %d n_vars_from_other %d", n_vars_from_guiding, n_vars_from_other); + + // DETERMINISM DEBUG: Log everything that could affect divergence + CUOPT_LOG_DEBUG("BP_DET: sol_a_hash=0x%x sol_b_hash=0x%x offspring_hash=0x%x, seed %x", + a.get_hash(), + b.get_hash(), + offspring.get_hash(), + seed); + CUOPT_LOG_DEBUG("BP_DET: n_different_vars=%d n_vars_from_other=%d n_vars_from_guiding=%d", + n_different_vars, + n_vars_from_other, + n_vars_from_guiding); + CUOPT_LOG_DEBUG("BP_DET: remaining_indices_hash=0x%x (first %d elements)", + detail::compute_hash(this->remaining_indices), + std::min((i_t)10, n_vars_from_other)); + CUOPT_LOG_DEBUG("BP_DET: guiding_feasible=%d other_feasible=%d expensive_to_fix=%d", + guiding_solution.get_feasible(), + other_solution.get_feasible(), + a.problem_ptr->expensive_to_fix_vars); + CUOPT_LOG_DEBUG( + "BP_DET: fixed_from_guiding=%d fixed_from_other=%d", fixed_from_guiding, fixed_from_other); + // if either all integers are from A(meaning all are common) or all integers are from B(meaning // all are different), return if (n_vars_from_guiding == 0 || n_vars_from_other == 0) { @@ -176,8 +198,13 @@ class bound_prop_recombiner_t : public recombiner_t { a.handle_ptr->get_stream()); probing_config_t probing_config(a.problem_ptr->n_variables, a.handle_ptr); if (guiding_solution.get_feasible() && !a.problem_ptr->expensive_to_fix_vars) { + CUOPT_LOG_DEBUG("BP_DET: Taking FEASIBLE path (with variable fixing)"); this->compute_vars_to_fix(offspring, vars_to_fix, n_vars_from_other, n_vars_from_guiding); + CUOPT_LOG_DEBUG("BP_DET: vars_to_fix_hash=0x%x", detail::compute_hash(vars_to_fix)); auto [fixed_problem, fixed_assignment, variable_map] = offspring.fix_variables(vars_to_fix); + CUOPT_LOG_DEBUG("BP_DET: fixed_problem_fingerprint=0x%x variable_map_size=%lu", + fixed_problem.get_fingerprint(), + variable_map.size()); work_limit_timer_t timer(this->context.gpu_heur_loop, bp_recombiner_config_t::bounds_prop_time_limit); rmm::device_uvector old_assignment(offspring.assignment, @@ -229,13 +256,18 @@ class bound_prop_recombiner_t : public recombiner_t { } a.handle_ptr->sync_stream(); } else { + CUOPT_LOG_DEBUG("BP_DET: Taking INFEASIBLE path (no variable fixing)"); work_limit_timer_t timer(this->context.gpu_heur_loop, bp_recombiner_config_t::bounds_prop_time_limit); get_probing_values_for_infeasible( guiding_solution, other_solution, offspring, probing_values, n_vars_from_other); probing_config.probing_values = host_copy(probing_values); + CUOPT_LOG_DEBUG("BP_DET: probing_values_hash=0x%x", detail::compute_hash(probing_values)); constraint_prop.apply_round(offspring, lp_run_time_after_feasible, timer, probing_config); } + CUOPT_LOG_DEBUG("BP_DET: After apply_round: offspring_hash=0x%x feasible=%d", + offspring.get_hash(), + offspring.get_feasible()); constraint_prop.max_n_failed_repair_iterations = 1; cuopt_func_call(offspring.test_number_all_integer()); bool better_cost_than_parents = @@ -255,6 +287,12 @@ class bound_prop_recombiner_t : public recombiner_t { bp_recombiner_config_t::decrease_max_n_of_vars_from_other(); } } + CUOPT_LOG_DEBUG( + "BP_DET: Final offspring_hash=0x%x same_as_parents=%d better_cost=%d better_feas=%d", + offspring.get_hash(), + same_as_parents, + better_cost_than_parents, + better_feasibility_than_parents); if (better_cost_than_parents || better_feasibility_than_parents) { CUOPT_LOG_DEBUG("Offspring is feasible or better than both parents"); return std::make_tuple(offspring, true, work); diff --git a/cpp/src/mip/diversity/weights.cuh b/cpp/src/mip/diversity/weights.cuh index 7502ae921..38f3975ed 100644 --- a/cpp/src/mip/diversity/weights.cuh +++ b/cpp/src/mip/diversity/weights.cuh @@ -12,6 +12,8 @@ #include #include +#include + namespace cuopt::linear_programming::detail { template @@ -25,6 +27,11 @@ struct weight_t { objective_weight.set_value_async(one, handle_ptr->get_stream()); } + uint32_t get_hash(rmm::cuda_stream_view stream = rmm::cuda_stream_default) const + { + return compute_hash(cstr_weights, stream) ^ compute_hash(objective_weight.value(stream)); + } + rmm::device_uvector cstr_weights; rmm::device_scalar objective_weight; }; diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index c2110609d..205292f7f 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -646,7 +646,7 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s cuopt_func_call(solution.test_variable_bounds(false)); is_feasible = round(solution); cuopt_func_call(solution.test_variable_bounds(true)); - proj_and_round_time = proj_begin - timer.remaining_time(); + proj_and_round_time = timer.remaining_time(); if (!is_feasible && proj_and_round_time > 0) { const f_t time_ratio = 0.2; is_feasible = test_fj_feasible(solution, time_ratio * proj_and_round_time); diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 07c14c6c0..03ff40330 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -885,7 +885,7 @@ void problem_t::compute_related_variables(double time_limit) i_t output_offset = 0; i_t related_var_offset = 0; - auto start_time = std::chrono::high_resolution_clock::now(); + // auto start_time = std::chrono::high_resolution_clock::now(); for (i_t i = 0;; ++i) { i_t slice_size = std::min(max_slice_size, n_variables - i * max_slice_size); if (slice_size <= 0) break; @@ -914,12 +914,13 @@ void problem_t::compute_related_variables(double time_limit) i_t related_var_base = related_variables.size(); related_variables.resize(related_variables.size() + array_size, handle_ptr->get_stream()); - auto current_time = std::chrono::high_resolution_clock::now(); + // auto current_time = std::chrono::high_resolution_clock::now(); // if the related variable array would wind up being too large for available memory, abort // TODO this used to be 1e9 - if (related_variables.size() > 1e9 * size_factor || - std::chrono::duration_cast(current_time - start_time).count() > - time_limit) { + if (related_variables.size() > 1e9 * size_factor) { + // || + // std::chrono::duration_cast(current_time - start_time).count() > + // time_limit) { CUOPT_LOG_DEBUG( "Computing the related variable array would use too much memory or time, aborting\n"); related_variables.resize(0, handle_ptr->get_stream()); @@ -1287,9 +1288,13 @@ problem_t problem_t::get_problem_after_fixing_vars( // total_time_taken); // if the fixing is greater than 150, mark this as expensive. // this way we can avoid frequent fixings for this problem - constexpr double expensive_time_threshold = 150; - if (time_taken > expensive_time_threshold) { expensive_to_fix_vars = true; } - CUOPT_LOG_DEBUG("Model fingerprint after fixing: 0x%x", problem.get_fingerprint()); + + // TODO: CHANGE + // constexpr double expensive_time_threshold = 150; + // if (time_taken > expensive_time_threshold) { expensive_to_fix_vars = true; } + CUOPT_LOG_DEBUG("Model fingerprint after fixing: 0x%x, expensive? %d", + problem.get_fingerprint(), + expensive_to_fix_vars); return problem; } From 786ae8a887837a8b7dae81105360ab9ba0739d21 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 13:32:23 +0000 Subject: [PATCH 048/366] bump1 --- cpp/src/mip/diversity/weights.cuh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/diversity/weights.cuh b/cpp/src/mip/diversity/weights.cuh index 38f3975ed..f7f35fdbe 100644 --- a/cpp/src/mip/diversity/weights.cuh +++ b/cpp/src/mip/diversity/weights.cuh @@ -22,7 +22,7 @@ struct weight_t { : cstr_weights(cstr_size, handle_ptr->get_stream()), objective_weight(handle_ptr->get_stream()) { thrust::fill(handle_ptr->get_thrust_policy(), cstr_weights.begin(), cstr_weights.end(), 1.0); - // objective_weight.set_value_to_zero_async(handle_ptr->get_stream()); + // objective_weight.set_value_to_zero_async(handle_ptr->get_stream());1 const f_t one = 1.; objective_weight.set_value_async(one, handle_ptr->get_stream()); } From 2fade1d6418f41a88547963e37607db188582bbe Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 13:32:40 +0000 Subject: [PATCH 049/366] bump2 --- cpp/src/mip/diversity/weights.cuh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/diversity/weights.cuh b/cpp/src/mip/diversity/weights.cuh index f7f35fdbe..38f3975ed 100644 --- a/cpp/src/mip/diversity/weights.cuh +++ b/cpp/src/mip/diversity/weights.cuh @@ -22,7 +22,7 @@ struct weight_t { : cstr_weights(cstr_size, handle_ptr->get_stream()), objective_weight(handle_ptr->get_stream()) { thrust::fill(handle_ptr->get_thrust_policy(), cstr_weights.begin(), cstr_weights.end(), 1.0); - // objective_weight.set_value_to_zero_async(handle_ptr->get_stream());1 + // objective_weight.set_value_to_zero_async(handle_ptr->get_stream()); const f_t one = 1.; objective_weight.set_value_async(one, handle_ptr->get_stream()); } From c89e2c9da59ae4108eab5cb532da4141cc29c53f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 15:57:37 +0000 Subject: [PATCH 050/366] fix missing grid sync --- .../feasibility_jump/feasibility_jump_kernels.cu | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 3210b55da..441d4deda 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1324,7 +1324,11 @@ DI thrust::tuple::move_score_t> best_sat_cstr_ typename fj_t::climber_data_t::view_t fj) { // compute all MTM moves within satisfied constraints + // TODO: only compute such moves over the objective variables. that way, the grid sync can be + // eliminated compute_mtm_moves(fj, true); + // TODO: could probably be eliminated + cg::this_grid().sync(); return gridwide_reduce_best_move( fj, fj.objective_vars.begin(), fj.objective_vars.end(), [fj] __device__(i_t var_idx) { return fj.jump_move_delta[var_idx]; @@ -1491,8 +1495,15 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat best_var, fj.incumbent_assignment[best_var] + best_delta), "assignment not within bounds"); fj.jump_move_delta[best_var] = best_delta; - // DEVICE_LOG_DEBUG("FJ[%d] selected_var: %d, delta %g, score {%d %d}, type %c\n", - // *fj.iterations, best_var, best_delta, best_score.base, best_score.bonus, best_movetype); +#if FJ_SINGLE_STEP + DEVICE_LOG_DEBUG("FJ[%d] selected_var: %d, delta %g, score {%d %d}, type %c\n", + *fj.iterations, + best_var, + best_delta, + best_score.base, + best_score.bonus, + best_movetype); +#endif } } } From cbfd34be53b5783f543df671078deef9e9934ce8 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 15:57:56 +0000 Subject: [PATCH 051/366] bump1 --- cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 441d4deda..24c01cc70 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1327,7 +1327,7 @@ DI thrust::tuple::move_score_t> best_sat_cstr_ // TODO: only compute such moves over the objective variables. that way, the grid sync can be // eliminated compute_mtm_moves(fj, true); - // TODO: could probably be eliminated + // TODO: could probably be eliminated1 cg::this_grid().sync(); return gridwide_reduce_best_move( fj, fj.objective_vars.begin(), fj.objective_vars.end(), [fj] __device__(i_t var_idx) { From ea5e1bd61d12ff9246133691024f431ced754080 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 15:58:08 +0000 Subject: [PATCH 052/366] bump2 --- cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 24c01cc70..441d4deda 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1327,7 +1327,7 @@ DI thrust::tuple::move_score_t> best_sat_cstr_ // TODO: only compute such moves over the objective variables. that way, the grid sync can be // eliminated compute_mtm_moves(fj, true); - // TODO: could probably be eliminated1 + // TODO: could probably be eliminated cg::this_grid().sync(); return gridwide_reduce_best_move( fj, fj.objective_vars.begin(), fj.objective_vars.end(), [fj] __device__(i_t var_idx) { From 8c53c755b7ac52870f2cf34cf5ea79fb29b8184c Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 15:59:29 +0000 Subject: [PATCH 053/366] restore load balancing --- cpp/src/mip/feasibility_jump/feasibility_jump.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 7b1417e78..1c6587f3b 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -1322,7 +1322,7 @@ i_t fj_t::solve(solution_t& solution) detail::compute_hash(cstr_left_weights), detail::compute_hash(cstr_right_weights)); - settings.load_balancing_mode = fj_load_balancing_mode_t::ALWAYS_OFF; + // 1settings.load_balancing_mode = fj_load_balancing_mode_t::ALWAYS_OFF; if (context.settings.deterministic) { settings.work_limit = settings.time_limit; } // if work_limit is set: compute an estimate of the number of iterations required From c6a103713daaa3410702b0f5d3e21ae5759dd422 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 15:59:42 +0000 Subject: [PATCH 054/366] bump 1 --- cpp/src/mip/feasibility_jump/feasibility_jump.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 1c6587f3b..2817c7883 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -1322,7 +1322,7 @@ i_t fj_t::solve(solution_t& solution) detail::compute_hash(cstr_left_weights), detail::compute_hash(cstr_right_weights)); - // 1settings.load_balancing_mode = fj_load_balancing_mode_t::ALWAYS_OFF; + // settings.load_balancing_mode = fj_load_balancing_mode_t::ALWAYS_OFF; if (context.settings.deterministic) { settings.work_limit = settings.time_limit; } // if work_limit is set: compute an estimate of the number of iterations required From 09a484a7ab4560c3b464237001f2d02598c13b77 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 17:51:30 +0000 Subject: [PATCH 055/366] remove unecessary grid sync --- .../feasibility_jump_kernels.cu | 34 ++++++++++--------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 441d4deda..a7195fabe 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -990,11 +990,14 @@ __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_ if (*fj.selected_var == std::numeric_limits::max()) full_refresh = true; // always do a full sweep when looking for satisfied mtm moves - if constexpr (move_type == MTMMoveType::FJ_MTM_SATISFIED) full_refresh = true; - - // only update related variables i_t split_begin, split_end; - if (full_refresh) { + if constexpr (move_type == MTMMoveType::FJ_MTM_SATISFIED) { + full_refresh = true; + split_begin = 0; + split_end = fj.objective_vars.size(); + } + // only update related variables + else if (full_refresh) { split_begin = 0; split_end = fj.pb.n_variables; } @@ -1016,9 +1019,15 @@ __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_ if (FIRST_THREAD) *fj.relvar_count_last_update = split_end - split_begin; for (i_t i = blockIdx.x + split_begin; i < split_end; i += gridDim.x) { - i_t var_idx = full_refresh ? i - : fj.pb.related_variables.size() == 0 ? i - : fj.pb.related_variables[i]; + // if sat MTM mode, go over objective variables only + i_t var_idx; + if constexpr (move_type == MTMMoveType::FJ_MTM_SATISFIED) { + var_idx = fj.objective_vars[i]; + } else { + var_idx = full_refresh ? i + : fj.pb.related_variables.size() == 0 ? i + : fj.pb.related_variables[i]; + } // skip if we couldnt precompute a related var table and // this variable isnt in the dynamic related variable table @@ -1042,11 +1051,6 @@ __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_ cuopt_assert(var_idx >= 0 && var_idx < fj.pb.n_variables, ""); update_jump_value(fj, var_idx); - // if (move_type == MTMMoveType::FJ_MTM_SATISFIED && threadIdx.x == 0) { - // printf("iter[%d] block[%d] var %d score {%d,%d}, delta %g\n", *fj.iterations, blockIdx.x, - // var_idx, fj.jump_move_scores[var_idx].base, fj.jump_move_scores[var_idx].bonus, - // fj.jump_move_delta[var_idx]); - // } } } @@ -1324,11 +1328,9 @@ DI thrust::tuple::move_score_t> best_sat_cstr_ typename fj_t::climber_data_t::view_t fj) { // compute all MTM moves within satisfied constraints - // TODO: only compute such moves over the objective variables. that way, the grid sync can be - // eliminated compute_mtm_moves(fj, true); - // TODO: could probably be eliminated - cg::this_grid().sync(); + // NOTE: grid sync not required since each block only reduces over variables that it updated in + // compute_mtm_moves return gridwide_reduce_best_move( fj, fj.objective_vars.begin(), fj.objective_vars.end(), [fj] __device__(i_t var_idx) { return fj.jump_move_delta[var_idx]; From f55e64898e499c1208e2a3f66d14b4b3ca4531b6 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 17:54:01 +0000 Subject: [PATCH 056/366] bump1 --- cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index a7195fabe..21b6a0a7e 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -989,7 +989,7 @@ __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_ if (*fj.full_refresh_iteration == *fj.iterations) full_refresh = true; if (*fj.selected_var == std::numeric_limits::max()) full_refresh = true; - // always do a full sweep when looking for satisfied mtm moves + // always do a full sweep when looking for satisfied mtm moves1 i_t split_begin, split_end; if constexpr (move_type == MTMMoveType::FJ_MTM_SATISFIED) { full_refresh = true; From 554d24c837b8efaaa4f9596899552eb3a7e2f493 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 8 Nov 2025 09:56:00 -0800 Subject: [PATCH 057/366] bump2 --- cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 21b6a0a7e..a7195fabe 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -989,7 +989,7 @@ __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_ if (*fj.full_refresh_iteration == *fj.iterations) full_refresh = true; if (*fj.selected_var == std::numeric_limits::max()) full_refresh = true; - // always do a full sweep when looking for satisfied mtm moves1 + // always do a full sweep when looking for satisfied mtm moves i_t split_begin, split_end; if constexpr (move_type == MTMMoveType::FJ_MTM_SATISFIED) { full_refresh = true; From db73e00aebedf12e06aec1c3eb74f865d59428c2 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 9 Nov 2025 02:44:00 -0800 Subject: [PATCH 058/366] FJCPU in deterministic mode --- cpp/src/mip/local_search/local_search.cu | 32 ++++++++++++++++++++---- 1 file changed, 27 insertions(+), 5 deletions(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 0e2e1702d..08f2c92f5 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -88,9 +88,13 @@ void cpu_fj_thread_t::stop_cpu_solver() template bool cpu_fj_thread_t::wait_for_cpu_solver() { + auto wait_start = std::chrono::high_resolution_clock::now(); while (!cpu_thread_done && !cpu_thread_terminate) { std::this_thread::sleep_for(std::chrono::milliseconds(1)); } + auto wait_end = std::chrono::high_resolution_clock::now(); + double wait_time = std::chrono::duration(wait_end - wait_start).count(); + if (wait_time > 1.0) { CUOPT_LOG_DEBUG("CPU FJ thread wait time: %.2f seconds", wait_time); } return cpu_fj_solution_found; } @@ -134,6 +138,9 @@ void local_search_t::start_cpufj_scratch_threads(population_t default_weights(context.problem_ptr->n_constraints, 1.); + fj_settings_t cpu_fj_settings{}; + cpu_fj_settings.time_limit = std::numeric_limits::infinity(); + solution_t solution(*context.problem_ptr); thrust::fill(solution.handle_ptr->get_thrust_policy(), solution.assignment.begin(), @@ -147,7 +154,7 @@ void local_search_t::start_cpufj_scratch_threads(population_t 0); cpu_fj.fj_cpu->log_prefix = "******* scratch " + std::to_string(counter) + ": "; @@ -181,11 +188,14 @@ void local_search_t::start_cpufj_lptopt_scratch_threads( std::vector default_weights(context.problem_ptr->n_constraints, 1.); + fj_settings_t cpu_fj_settings{}; + cpu_fj_settings.time_limit = std::numeric_limits::infinity(); + solution_t solution_lp(*context.problem_ptr); solution_lp.copy_new_assignment(host_copy(lp_optimal_solution)); solution_lp.round_random_nearest(500); scratch_cpu_fj_on_lp_opt.fj_cpu = - fj.create_cpu_climber(solution_lp, default_weights, default_weights, 0.); + fj.create_cpu_climber(solution_lp, default_weights, default_weights, 0., cpu_fj_settings, true); scratch_cpu_fj_on_lp_opt.fj_cpu->log_prefix = "******* scratch on LP optimal: "; if (!context.settings.deterministic) { scratch_cpu_fj_on_lp_opt.fj_cpu->improvement_callback = @@ -228,9 +238,19 @@ bool local_search_t::do_fj_solve(solution_t& solution, auto h_weights = cuopt::host_copy(in_fj.cstr_weights, solution.handle_ptr->get_stream()); auto h_objective_weight = in_fj.objective_weight.value(solution.handle_ptr->get_stream()); + + // for now: always assign the CPUFJs to perform 1000 iters per s + fj_settings_t cpu_fj_settings{}; + if (context.settings.deterministic) { + cpu_fj_settings.iteration_limit = std::numeric_limits::max(); + } else { + // TODO: CHANGE + cpu_fj_settings.iteration_limit = 1000 * time_limit; + } + for (auto& cpu_fj : ls_cpu_fj) { cpu_fj.fj_cpu = cpu_fj.fj_ptr->create_cpu_climber( - solution, h_weights, h_weights, h_objective_weight, fj_settings_t{}, true); + solution, h_weights, h_weights, h_objective_weight, cpu_fj_settings, true); } auto solution_copy = solution; @@ -246,8 +266,10 @@ bool local_search_t::do_fj_solve(solution_t& solution, in_fj.solve(solution); // Stop CPU solver - for (auto& cpu_fj : ls_cpu_fj) { - cpu_fj.stop_cpu_solver(); + if (!context.settings.deterministic) { + for (auto& cpu_fj : ls_cpu_fj) { + cpu_fj.stop_cpu_solver(); + } } auto gpu_fj_end = std::chrono::high_resolution_clock::now(); From 0b8b6e156fc2c2e4b316dca9ed1c9adaae38eec1 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 9 Nov 2025 02:44:28 -0800 Subject: [PATCH 059/366] bump1 --- cpp/src/mip/local_search/local_search.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 08f2c92f5..1379a0397 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -107,7 +107,7 @@ local_search_t::local_search_t(mip_solver_context_t& context fj_sol_on_lp_opt(context.problem_ptr->n_variables, context.problem_ptr->handle_ptr->get_stream()), fj(context), - // fj_tree(fj), + // fj_tree(fj),1 constraint_prop(context), line_segment_search(context, fj, constraint_prop), fp(context, From 122bfbce18233e69594965db5c503140bea4a753 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 9 Nov 2025 02:44:42 -0800 Subject: [PATCH 060/366] bump2 --- cpp/src/mip/local_search/local_search.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 1379a0397..08f2c92f5 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -107,7 +107,7 @@ local_search_t::local_search_t(mip_solver_context_t& context fj_sol_on_lp_opt(context.problem_ptr->n_variables, context.problem_ptr->handle_ptr->get_stream()), fj(context), - // fj_tree(fj),1 + // fj_tree(fj), constraint_prop(context), line_segment_search(context, fj, constraint_prop), fp(context, From 45ebd575b905e12c17abd60a697b482d0f19cfca Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 9 Nov 2025 02:46:11 -0800 Subject: [PATCH 061/366] fix oversight --- cpp/src/mip/local_search/local_search.cu | 26 ++++++++++-------------- 1 file changed, 11 insertions(+), 15 deletions(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 08f2c92f5..82175012b 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -306,21 +306,17 @@ bool local_search_t::do_fj_solve(solution_t& solution, cpu_better[source]); total_calls[source]++; - // ignore the CPUFJ solution if we're in determinism mode - // TODO: use work limits here - if (!context.settings.deterministic) { - if (cpu_feasible && !gpu_feasible || - (cpu_feasible && solution_cpu.get_objective() < solution.get_objective())) { - CUOPT_LOG_DEBUG( - "CPU FJ returns better solution! cpu_obj %g, gpu_obj %g, stats %d/%d, source %s", - solution_cpu.get_user_objective(), - solution.get_user_objective(), - total_calls[source], - cpu_better[source], - source.c_str()); - solution.copy_from(solution_cpu); - cpu_better[source]++; - } + if (cpu_feasible && !gpu_feasible || + (cpu_feasible && solution_cpu.get_objective() < solution.get_objective())) { + CUOPT_LOG_DEBUG( + "CPU FJ returns better solution! cpu_obj %g, gpu_obj %g, stats %d/%d, source %s", + solution_cpu.get_user_objective(), + solution.get_user_objective(), + total_calls[source], + cpu_better[source], + source.c_str()); + solution.copy_from(solution_cpu); + cpu_better[source]++; } solution.compute_feasibility(); From a209096bdda55dc28d09441a2227b6acdc3667a5 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 9 Nov 2025 02:46:28 -0800 Subject: [PATCH 062/366] bump1 --- cpp/src/mip/local_search/local_search.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 82175012b..62e2b9891 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -352,7 +352,7 @@ void local_search_t::generate_fast_solution(solution_t& solu // run fj on the solution do_fj_solve(solution, fj, time_limit, "fast"); // TODO check if FJ returns the same solution - // check if the solution is feasible + // check if the solution is feasible1 if (solution.compute_feasibility()) { return; } } } From 718f3e75c11f63db3aa65581f812f7f5270e2dad Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 9 Nov 2025 02:46:37 -0800 Subject: [PATCH 063/366] bump2 --- cpp/src/mip/local_search/local_search.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 62e2b9891..82175012b 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -352,7 +352,7 @@ void local_search_t::generate_fast_solution(solution_t& solu // run fj on the solution do_fj_solve(solution, fj, time_limit, "fast"); // TODO check if FJ returns the same solution - // check if the solution is feasible1 + // check if the solution is feasible if (solution.compute_feasibility()) { return; } } } From 1df9313bee9c59bc110e8b0a115e263c6989f83c Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 9 Nov 2025 14:45:52 +0000 Subject: [PATCH 064/366] logger --- cpp/src/utilities/logger.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/cpp/src/utilities/logger.cpp b/cpp/src/utilities/logger.cpp index fd053c94e..6a99eb7bc 100644 --- a/cpp/src/utilities/logger.cpp +++ b/cpp/src/utilities/logger.cpp @@ -80,8 +80,7 @@ rapids_logger::sink_ptr default_sink() * * @return std::string The default log pattern. */ -// inline std::string default_pattern() { return "[%Y-%m-%d %H:%M:%S:%f] [%n] [%-6l] %v"; } -inline std::string default_pattern() { return "[%n] [%-6l] %v"; } +inline std::string default_pattern() { return "[%Y-%m-%d %H:%M:%S:%f] [%n] [%-6l] %v"; } /** * @brief Returns the default log level for the global logger. * From b45982ef81226a6585bc620f3432263fceb39026 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 9 Nov 2025 14:57:40 +0000 Subject: [PATCH 065/366] fix cpufj --- cpp/src/mip/feasibility_jump/fj_cpu.cu | 13 ++++++++++++- cpp/src/mip/local_search/local_search.cu | 2 +- 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index f59bc3997..cbcf30dc4 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -943,6 +943,13 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l fj_cpu.iterations); break; } + if (fj_cpu.iterations >= fj_cpu.settings.iteration_limit) { + CUOPT_LOG_TRACE("%sIteration limit of %d reached, breaking loop at iteration %d\n", + fj_cpu.log_prefix.c_str(), + fj_cpu.settings.iteration_limit, + fj_cpu.iterations); + break; + } // periodically recompute the LHS and violation scores // to correct any accumulated numerical errors @@ -1001,10 +1008,14 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l } if (fj_cpu.iterations % fj_cpu.log_interval == 0) { CUOPT_LOG_TRACE( - "%sCPUFJ iteration: %d, local mins: %d, best_objective: %g, viol: %zu, obj weight %g, maxw " + "%sCPUFJ iteration: %d/%d, local mins: %d, best_objective: %g, viol: %zu, obj weight %g, " + "maxw " "%g\n", fj_cpu.log_prefix.c_str(), fj_cpu.iterations, + fj_cpu.settings.iteration_limit != std::numeric_limits::max() + ? fj_cpu.settings.iteration_limit + : -1, local_mins, fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective), fj_cpu.violated_constraints.size(), diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 82175012b..3d3d63615 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -241,7 +241,7 @@ bool local_search_t::do_fj_solve(solution_t& solution, // for now: always assign the CPUFJs to perform 1000 iters per s fj_settings_t cpu_fj_settings{}; - if (context.settings.deterministic) { + if (!context.settings.deterministic) { cpu_fj_settings.iteration_limit = std::numeric_limits::max(); } else { // TODO: CHANGE From d27218099a6457ad0ba7b02e9d6ed141d744acb0 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 9 Nov 2025 14:58:34 +0000 Subject: [PATCH 066/366] bump1 --- cpp/src/mip/feasibility_jump/fj_cpu.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index cbcf30dc4..f3df30245 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -1047,7 +1047,7 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l avg_time_per_iter * 1000.0); #if CPUFJ_TIMING_TRACE - // Print final timing statistics + // Print final timing statistics1 CUOPT_LOG_TRACE("\n=== Final Timing Statistics ===\n"); print_timing_stats(fj_cpu); #endif From 0987b5c24ba801f2895a27990002e702f0b39c13 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 9 Nov 2025 14:58:54 +0000 Subject: [PATCH 067/366] bump2 --- cpp/src/mip/feasibility_jump/fj_cpu.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index f3df30245..cbcf30dc4 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -1047,7 +1047,7 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l avg_time_per_iter * 1000.0); #if CPUFJ_TIMING_TRACE - // Print final timing statistics1 + // Print final timing statistics CUOPT_LOG_TRACE("\n=== Final Timing Statistics ===\n"); print_timing_stats(fj_cpu); #endif From f378246d4fc4b01ecd41a2cd6027343e316ce378 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 10 Nov 2025 10:30:59 +0000 Subject: [PATCH 068/366] tentative LB determinism fix --- .../mip/feasibility_jump/load_balancing.cuh | 29 +++++++++++-------- 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/load_balancing.cuh b/cpp/src/mip/feasibility_jump/load_balancing.cuh index 361162d6f..b6c9ea1f9 100644 --- a/cpp/src/mip/feasibility_jump/load_balancing.cuh +++ b/cpp/src/mip/feasibility_jump/load_balancing.cuh @@ -567,24 +567,29 @@ __launch_bounds__(TPB_loadbalance, 16) __global__ best_score_ref{fj.jump_move_scores[var_idx]}; auto best_score = best_score_ref.load(cuda::memory_order_relaxed); + cuda::atomic_ref best_delta_ref{ + fj.jump_move_delta[var_idx]}; + auto best_delta = best_delta_ref.load(cuda::memory_order_relaxed); + if (best_score < candidate.score || - (best_score == candidate.score && candidate.delta < fj.jump_move_delta[var_idx])) { + (best_score == candidate.score && candidate.delta < best_delta)) { // update the best move delta acquire_lock(&fj.jump_locks[var_idx]); // reject this move if it would increase the target variable to a numerically unstable // value - if (!fj.move_numerically_stable(fj.incumbent_assignment[var_idx], - fj.incumbent_assignment[var_idx] + delta, - base_feas, - *fj.violation_score)) { - fj.jump_move_scores[var_idx] = fj_t::move_score_t::invalid(); - } else if (fj.jump_move_scores[var_idx] < candidate.score - // determinism for ease of debugging - || (fj.jump_move_scores[var_idx] == candidate.score && - candidate.delta < fj.jump_move_delta[var_idx])) { - fj.jump_move_delta[var_idx] = candidate.delta; - fj.jump_move_scores[var_idx] = candidate.score; + // only skip updating, don't invalidate existing valid moves + if (fj.move_numerically_stable(fj.incumbent_assignment[var_idx], + fj.incumbent_assignment[var_idx] + delta, + base_feas, + *fj.violation_score)) { + if (fj.jump_move_scores[var_idx] < candidate.score + // determinism for ease of debugging + || (fj.jump_move_scores[var_idx] == candidate.score && + candidate.delta < fj.jump_move_delta[var_idx])) { + fj.jump_move_delta[var_idx] = candidate.delta; + fj.jump_move_scores[var_idx] = candidate.score; + } } release_lock(&fj.jump_locks[var_idx]); } From 4b77a5958a3c14bcbdea59208c8967379f757c6f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 10 Nov 2025 13:26:37 +0000 Subject: [PATCH 069/366] extra logging CPUFJ, PAPI --- cpp/CMakeLists.txt | 11 + cpp/src/mip/diversity/diversity_manager.cu | 7 +- cpp/src/mip/feasibility_jump/fj_cpu.cu | 428 +++++++++++++++++++-- cpp/src/mip/feasibility_jump/fj_cpu.cuh | 27 ++ cpp/src/mip/solve.cu | 10 + 5 files changed, 460 insertions(+), 23 deletions(-) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 013169042..7178c61ff 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -323,6 +323,17 @@ target_link_libraries(cuopt ${CUOPT_PRIVATE_CUDA_LIBS} ) +find_path(PAPI_INCLUDE_DIR papi.h) +find_library(PAPI_LIBRARY papi) + +if (PAPI_INCLUDE_DIR AND PAPI_LIBRARY) + message(STATUS "Found PAPI in ${PAPI_INCLUDE_DIR}") + target_include_directories(cuopt PRIVATE ${PAPI_INCLUDE_DIR}) + target_link_libraries(cuopt PRIVATE ${PAPI_LIBRARY}) +else() + message(FATAL_ERROR "Could not find PAPI") +endif() + # ################################################################################################## # - generate tests -------------------------------------------------------------------------------- diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 6e3d5c78f..392145ee5 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -354,7 +354,12 @@ solution_t diversity_manager_t::run_solver() // Run CPUFJ early to find quick initial solutions population.allocate_solutions(); ls_cpufj_raii_guard_t ls_cpufj_raii_guard(ls); // RAII to stop cpufj threads on solve stop - if (!context.settings.deterministic) { ls.start_cpufj_scratch_threads(population); } + if (!context.settings.deterministic) { + ls.start_cpufj_scratch_threads(population); + std::this_thread::sleep_for(std::chrono::seconds(10)); + ls.stop_cpufj_scratch_threads(); + exit(0); + } // before probing cache or LP, run FJ to generate initial primal feasible solution const f_t time_ratio_of_probing_cache = diversity_config.time_ratio_of_probing_cache; diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index cbcf30dc4..58850ab2c 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -14,16 +14,29 @@ #include #include +#include #include +#include #include #include +#ifdef __linux__ +#include +#include +#include +#endif + #define CPUFJ_TIMING_TRACE 0 namespace cuopt::linear_programming::detail { static constexpr double BIGVAL_THRESHOLD = 1e20; +#ifdef __linux__ +// Global mutex to protect PAPI metric printing across multiple threads +static std::mutex papi_print_mutex; +#endif + template class timing_raii_t { public: @@ -62,43 +75,354 @@ static void print_timing_stats(fj_cpu_climber_t& fj_cpu) auto [apply_avg, apply_total] = compute_avg_and_total(fj_cpu.apply_move_times); auto [weights_avg, weights_total] = compute_avg_and_total(fj_cpu.update_weights_times); auto [compute_score_avg, compute_score_total] = compute_avg_and_total(fj_cpu.compute_score_times); - CUOPT_LOG_TRACE("=== Timing Statistics (Iteration %d) ===\n", fj_cpu.iterations); - CUOPT_LOG_TRACE("find_lift_move: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("=== Timing Statistics (Iteration %d) ===", fj_cpu.iterations); + CUOPT_LOG_TRACE("find_lift_move: avg=%.6f ms, total=%.6f ms, calls=%zu", lift_avg * 1000.0, lift_total * 1000.0, fj_cpu.find_lift_move_times.size()); - CUOPT_LOG_TRACE("find_mtm_move_viol: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("find_mtm_move_viol: avg=%.6f ms, total=%.6f ms, calls=%zu", viol_avg * 1000.0, viol_total * 1000.0, fj_cpu.find_mtm_move_viol_times.size()); - CUOPT_LOG_TRACE("find_mtm_move_sat: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("find_mtm_move_sat: avg=%.6f ms, total=%.6f ms, calls=%zu", sat_avg * 1000.0, sat_total * 1000.0, fj_cpu.find_mtm_move_sat_times.size()); - CUOPT_LOG_TRACE("apply_move: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("apply_move: avg=%.6f ms, total=%.6f ms, calls=%zu", apply_avg * 1000.0, apply_total * 1000.0, fj_cpu.apply_move_times.size()); - CUOPT_LOG_TRACE("update_weights: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("update_weights: avg=%.6f ms, total=%.6f ms, calls=%zu", weights_avg * 1000.0, weights_total * 1000.0, fj_cpu.update_weights_times.size()); - CUOPT_LOG_TRACE("compute_score: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("compute_score: avg=%.6f ms, total=%.6f ms, calls=%zu", compute_score_avg * 1000.0, compute_score_total * 1000.0, fj_cpu.compute_score_times.size()); - CUOPT_LOG_TRACE("cache hit percentage: %.2f%%\n", + CUOPT_LOG_TRACE("cache hit percentage: %.2f%%", (double)fj_cpu.hit_count / (fj_cpu.hit_count + fj_cpu.miss_count) * 100.0); - CUOPT_LOG_TRACE("bin candidate move hit percentage: %.2f%%\n", + CUOPT_LOG_TRACE("bin candidate move hit percentage: %.2f%%", (double)fj_cpu.candidate_move_hits[0] / (fj_cpu.candidate_move_hits[0] + fj_cpu.candidate_move_misses[0]) * 100.0); - CUOPT_LOG_TRACE("int candidate move hit percentage: %.2f%%\n", + CUOPT_LOG_TRACE("int candidate move hit percentage: %.2f%%", (double)fj_cpu.candidate_move_hits[1] / (fj_cpu.candidate_move_hits[1] + fj_cpu.candidate_move_misses[1]) * 100.0); - CUOPT_LOG_TRACE("cont candidate move hit percentage: %.2f%%\n", + CUOPT_LOG_TRACE("cont candidate move hit percentage: %.2f%%", (double)fj_cpu.candidate_move_hits[2] / (fj_cpu.candidate_move_hits[2] + fj_cpu.candidate_move_misses[2]) * 100.0); - CUOPT_LOG_TRACE("========================================\n"); + CUOPT_LOG_TRACE("========================================"); +} + +#ifdef __linux__ +template +static void initialize_papi(fj_cpu_climber_t& fj_cpu) +{ + int retval = PAPI_library_init(PAPI_VER_CURRENT); + if (retval != PAPI_VER_CURRENT && retval > 0) { + CUOPT_LOG_TRACE("%sPAPI library version mismatch", fj_cpu.log_prefix.c_str()); + return; + } + if (retval < 0) { + CUOPT_LOG_TRACE("%sPAPI library initialization failed", fj_cpu.log_prefix.c_str()); + return; + } + + fj_cpu.papi_event_set = PAPI_NULL; + retval = PAPI_create_eventset(&fj_cpu.papi_event_set); + if (retval != PAPI_OK) { + CUOPT_LOG_TRACE("%sPAPI eventset creation failed", fj_cpu.log_prefix.c_str()); + return; + } + + // Define the events we want to track + int candidate_events[] = { + PAPI_L1_DCA, // L1 data cache accesses + PAPI_L1_DCM, // L1 data cache misses + PAPI_L2_DCA, // L2 data cache accesses + PAPI_L2_DCM, // L2 data cache misses + PAPI_L3_TCA, // L3 total cache accesses + PAPI_L3_TCM, // L3 total cache misses + PAPI_LD_INS, // Load instructions + PAPI_SR_INS // Store instructions + }; + + const char* event_names[] = {"PAPI_L1_DCA", + "PAPI_L1_DCM", + "PAPI_L2_DCA", + "PAPI_L2_DCM", + "PAPI_L3_TCA", + "PAPI_L3_TCM", + "PAPI_LD_INS", + "PAPI_SR_INS"}; + + int num_candidate_events = sizeof(candidate_events) / sizeof(candidate_events[0]); + + // Try to add each event, store -1 for unavailable ones + for (int i = 0; i < num_candidate_events; i++) { + retval = PAPI_add_event(fj_cpu.papi_event_set, candidate_events[i]); + if (retval == PAPI_OK) { + fj_cpu.papi_events.push_back(candidate_events[i]); + } else { + fj_cpu.papi_events.push_back(-1); + CUOPT_LOG_TRACE( + "%sPAPI event %s not available on this system", fj_cpu.log_prefix.c_str(), event_names[i]); + } + } + (void)event_names; // Suppress unused warning when logging is disabled + + // Start counting + retval = PAPI_start(fj_cpu.papi_event_set); + if (retval != PAPI_OK) { + CUOPT_LOG_TRACE("%sPAPI start failed", fj_cpu.log_prefix.c_str()); + return; + } + + fj_cpu.papi_initialized = true; + CUOPT_LOG_TRACE("%sPAPI initialized successfully", fj_cpu.log_prefix.c_str()); +} + +template +static void collect_and_print_papi_metrics(fj_cpu_climber_t& fj_cpu) +{ + if (!fj_cpu.papi_initialized) return; + + std::vector values(fj_cpu.papi_events.size(), 0); + int retval = PAPI_read(fj_cpu.papi_event_set, values.data()); + if (retval != PAPI_OK) { + CUOPT_LOG_TRACE("%sPAPI read failed", fj_cpu.log_prefix.c_str()); + return; + } + + // Get thread ID + pid_t tid = syscall(SYS_gettid); + + // Build map of actual values indexed by event position + std::vector all_values(8, -1); + int value_idx = 0; + for (size_t i = 0; i < fj_cpu.papi_events.size(); i++) { + if (fj_cpu.papi_events[i] != -1) { all_values[i] = values[value_idx++]; } + } + + // Compute derived metrics + double l1_miss_rate = -1.0; + if (all_values[0] > 0 && all_values[1] != -1) { + l1_miss_rate = (double)all_values[1] / all_values[0] * 100.0; + } + + double l2_miss_rate = -1.0; + if (all_values[2] > 0 && all_values[3] != -1) { + l2_miss_rate = (double)all_values[3] / all_values[2] * 100.0; + } + + // Lock to ensure thread-safe printing + std::lock_guard lock(papi_print_mutex); + + // Print everything on a single compact line + CUOPT_LOG_DEBUG( + "%sPAPI iter=%d tid=%d L1_DCA=%lld L1_DCM=%lld L2_DCA=%lld L2_DCM=%lld L3_TCA=%lld " + "L3_TCM=%lld LD_INS=%lld SR_INS=%lld L1_miss=%.2f%% L2_miss=%.2f%%", + fj_cpu.log_prefix.c_str(), + fj_cpu.iterations, + tid, + all_values[0], + all_values[1], + all_values[2], + all_values[3], + all_values[4], + all_values[5], + all_values[6], + all_values[7], + l1_miss_rate, + l2_miss_rate); + + // Reset counters for the next 1000 iterations + retval = PAPI_reset(fj_cpu.papi_event_set); + if (retval != PAPI_OK) { CUOPT_LOG_TRACE("%sPAPI reset failed", fj_cpu.log_prefix.c_str()); } +} + +template +static void cleanup_papi(fj_cpu_climber_t& fj_cpu) +{ + if (fj_cpu.papi_initialized) { + PAPI_stop(fj_cpu.papi_event_set, nullptr); + PAPI_cleanup_eventset(fj_cpu.papi_event_set); + PAPI_destroy_eventset(&fj_cpu.papi_event_set); + } +} +#endif + +template +static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) +{ + // Count variable types - use host vectors + fj_cpu.n_binary_vars = 0; + fj_cpu.n_integer_vars = 0; + fj_cpu.n_continuous_vars = 0; + for (i_t i = 0; i < (i_t)fj_cpu.h_is_binary_variable.size(); i++) { + if (fj_cpu.h_is_binary_variable[i]) { + fj_cpu.n_binary_vars++; + } else if (fj_cpu.h_var_types[i] == var_t::INTEGER) { + fj_cpu.n_integer_vars++; + } else { + fj_cpu.n_continuous_vars++; + } + } + + i_t total_nnz = fj_cpu.h_reverse_offsets.back(); + i_t n_vars = fj_cpu.h_reverse_offsets.size() - 1; + i_t n_cstrs = fj_cpu.h_offsets.size() - 1; + + fj_cpu.avg_var_degree = (double)total_nnz / n_vars; + + // Compute max variable degree + fj_cpu.max_var_degree = 0; + for (i_t i = 0; i < n_vars; i++) { + i_t degree = fj_cpu.h_reverse_offsets[i + 1] - fj_cpu.h_reverse_offsets[i]; + fj_cpu.max_var_degree = std::max(fj_cpu.max_var_degree, degree); + } + + fj_cpu.avg_cstr_degree = (double)total_nnz / n_cstrs; + + // Compute max constraint degree + fj_cpu.max_cstr_degree = 0; + for (i_t i = 0; i < n_cstrs; i++) { + i_t degree = fj_cpu.h_offsets[i + 1] - fj_cpu.h_offsets[i]; + fj_cpu.max_cstr_degree = std::max(fj_cpu.max_cstr_degree, degree); + } + + fj_cpu.problem_density = (double)total_nnz / ((double)n_vars * n_cstrs); +} + +template +static void log_regression_features(fj_cpu_climber_t& fj_cpu, + double time_window_ms, + double total_time_ms) +{ +#ifdef __linux__ + pid_t tid = syscall(SYS_gettid); +#else + int tid = 0; +#endif + + i_t total_nnz = fj_cpu.h_reverse_offsets.back(); + i_t n_vars = fj_cpu.h_reverse_offsets.size() - 1; + i_t n_cstrs = fj_cpu.h_offsets.size() - 1; + + // Dynamic runtime features + i_t n_violated = fj_cpu.violated_constraints.size(); + double violated_ratio = (double)n_violated / n_cstrs; + + // Compute improvement velocity + double improvement_velocity = 0.0; + if (fj_cpu.iterations > 0) { + improvement_velocity = (fj_cpu.prev_best_objective - fj_cpu.h_best_objective) / 1000.0; + } + + // Compute per-iteration metrics + double nnz_per_move = 0.0; + i_t total_moves = + fj_cpu.n_lift_moves_window + fj_cpu.n_mtm_viol_moves_window + fj_cpu.n_mtm_sat_moves_window; + if (total_moves > 0) { nnz_per_move = (double)fj_cpu.nnz_processed_window / total_moves; } + + double eval_intensity = (double)fj_cpu.n_constraint_evals_window / n_cstrs; + + double loads_per_iter = 0.0; + double stores_per_iter = 0.0; + double l1_miss = -1.0; + double l2_miss = -1.0; + double l3_miss = -1.0; + +#ifdef __linux__ + // Get PAPI metrics if available + if (fj_cpu.papi_initialized) { + std::vector values(fj_cpu.papi_events.size(), 0); + int retval = PAPI_read(fj_cpu.papi_event_set, values.data()); + if (retval == PAPI_OK) { + std::vector all_values(8, -1); + int value_idx = 0; + for (size_t i = 0; i < fj_cpu.papi_events.size(); i++) { + if (fj_cpu.papi_events[i] != -1) { all_values[i] = values[value_idx++]; } + } + + // Compute cache miss rates + if (all_values[0] > 0 && all_values[1] != -1) { + l1_miss = (double)all_values[1] / all_values[0] * 100.0; + } + if (all_values[2] > 0 && all_values[3] != -1) { + l2_miss = (double)all_values[3] / all_values[2] * 100.0; + } + if (all_values[4] > 0 && all_values[5] != -1) { + l3_miss = (double)all_values[5] / all_values[4] * 100.0; + } + + // Loads and stores per iteration + if (all_values[6] != -1) { loads_per_iter = (double)all_values[6] / 1000.0; } + if (all_values[7] != -1) { stores_per_iter = (double)all_values[7] / 1000.0; } + } + } +#endif + + // Print everything on a single line using precomputed features + CUOPT_LOG_DEBUG( + "%sCPUFJ_FEATURES iter=%d tid=%d time_window=%.2f " + "n_vars=%d n_cstrs=%d n_bin=%d n_int=%d n_cont=%d total_nnz=%d " + "avg_var_deg=%.2f max_var_deg=%d avg_cstr_deg=%.2f max_cstr_deg=%d density=%.6f " + "n_viol=%d total_viol=%.4f curr_obj=%.4f best_obj=%.4f obj_weight=%.4f max_weight=%.4f " + "n_locmin=%d is_feas=%d iter_since_best=%d feas_found=%d " + "nnz_proc=%d n_lift=%d n_mtm_viol=%d n_mtm_sat=%d n_cstr_eval=%d n_var_updates=%d " + "L1_miss=%.2f L2_miss=%.2f L3_miss=%.2f loads_per_iter=%.0f stores_per_iter=%.0f " + "viol_ratio=%.4f improv_vel=%.6f nnz_per_move=%.2f eval_intensity=%.2f", + fj_cpu.log_prefix.c_str(), + fj_cpu.iterations, + tid, + time_window_ms, + n_vars, + n_cstrs, + fj_cpu.n_binary_vars, + fj_cpu.n_integer_vars, + fj_cpu.n_continuous_vars, + total_nnz, + fj_cpu.avg_var_degree, + fj_cpu.max_var_degree, + fj_cpu.avg_cstr_degree, + fj_cpu.max_cstr_degree, + fj_cpu.problem_density, + n_violated, + fj_cpu.total_violations, + fj_cpu.h_incumbent_objective, + fj_cpu.h_best_objective, + fj_cpu.h_objective_weight, + fj_cpu.max_weight, + fj_cpu.n_local_minima_window, + fj_cpu.feasible_found ? 1 : 0, + fj_cpu.iterations_since_best, + fj_cpu.feasible_found ? 1 : 0, + fj_cpu.nnz_processed_window, + fj_cpu.n_lift_moves_window, + fj_cpu.n_mtm_viol_moves_window, + fj_cpu.n_mtm_sat_moves_window, + fj_cpu.n_constraint_evals_window, + fj_cpu.n_variable_updates_window, + l1_miss, + l2_miss, + l3_miss, + loads_per_iter, + stores_per_iter, + violated_ratio, + improvement_velocity, + nnz_per_move, + eval_intensity); + + // Reset window counters + fj_cpu.nnz_processed_window = 0; + fj_cpu.n_lift_moves_window = 0; + fj_cpu.n_mtm_viol_moves_window = 0; + fj_cpu.n_mtm_sat_moves_window = 0; + fj_cpu.n_constraint_evals_window = 0; + fj_cpu.n_variable_updates_window = 0; + fj_cpu.n_local_minima_window = 0; + fj_cpu.prev_best_objective = fj_cpu.h_best_objective; } template @@ -149,6 +473,8 @@ static inline std::pair compute_score(fj_cpu_climber_t& fj_cpu, // Update the LHSs of all involved constraints. auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + // Track work metrics for regression model + fj_cpu.nnz_processed_window += (offset_end - offset_begin); + fj_cpu.n_variable_updates_window++; + i_t previous_viol = fj_cpu.violated_constraints.size(); for (auto i = offset_begin; i < offset_end; i++) { @@ -343,8 +673,9 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, cuopt_assert(fj_cpu.satisfied_constraints.size() == fj_cpu.view.pb.n_constraints, ""); fj_cpu.h_best_objective = fj_cpu.h_incumbent_objective - fj_cpu.settings.parameters.breakthrough_move_epsilon; - fj_cpu.h_best_assignment = fj_cpu.h_assignment; - CUOPT_LOG_TRACE("%sCPUFJ: new best objective: %g\n", + fj_cpu.h_best_assignment = fj_cpu.h_assignment; + fj_cpu.iterations_since_best = 0; // Reset counter on improvement + CUOPT_LOG_TRACE("%sCPUFJ: new best objective: %g", fj_cpu.log_prefix.c_str(), fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective)); if (fj_cpu.improvement_callback) { @@ -361,12 +692,12 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, fj_cpu.h_tabu_lastinc[var_idx] = fj_cpu.iterations; fj_cpu.h_tabu_nodec_until[var_idx] = fj_cpu.iterations + tabu_tenure; fj_cpu.h_tabu_noinc_until[var_idx] = fj_cpu.iterations + tabu_tenure / 2; - // CUOPT_LOG_TRACE("CPU: tabu nodec_until: %d\n", fj_cpu.h_tabu_nodec_until[var_idx]); + // CUOPT_LOG_TRACE("CPU: tabu nodec_until: %d", fj_cpu.h_tabu_nodec_until[var_idx]); } else { fj_cpu.h_tabu_lastdec[var_idx] = fj_cpu.iterations; fj_cpu.h_tabu_noinc_until[var_idx] = fj_cpu.iterations + tabu_tenure; fj_cpu.h_tabu_nodec_until[var_idx] = fj_cpu.iterations + tabu_tenure / 2; - // CUOPT_LOG_TRACE("CPU: tabu noinc_until: %d\n", fj_cpu.h_tabu_noinc_until[var_idx]); + // CUOPT_LOG_TRACE("CPU: tabu noinc_until: %d", fj_cpu.h_tabu_noinc_until[var_idx]); } std::fill(fj_cpu.flip_move_computed.begin(), fj_cpu.flip_move_computed.end(), false); @@ -855,6 +1186,9 @@ static void init_fj_cpu(fj_cpu_climber_t& fj_cpu, fj_cpu.iter_mtm_vars.reserve(fj_cpu.view.pb.n_variables); recompute_lhs(fj_cpu); + + // Precompute static problem features for regression model + precompute_problem_features(fj_cpu); } template @@ -932,19 +1266,30 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l auto loop_start = std::chrono::high_resolution_clock::now(); auto time_limit = std::chrono::milliseconds((int)(in_time_limit * 1000)); auto loop_time_start = std::chrono::high_resolution_clock::now(); + +#ifdef __linux__ + // Initialize PAPI for performance monitoring + initialize_papi(fj_cpu); +#endif + + // Initialize feature tracking + fj_cpu.last_feature_log_time = loop_start; + fj_cpu.prev_best_objective = fj_cpu.h_best_objective; + fj_cpu.iterations_since_best = 0; + while (!fj_cpu.halted) { // Check if 5 seconds have passed auto now = std::chrono::high_resolution_clock::now(); if (in_time_limit < std::numeric_limits::infinity() && now - loop_time_start > time_limit) { - CUOPT_LOG_TRACE("%sTime limit of %.4f seconds reached, breaking loop at iteration %d\n", + CUOPT_LOG_TRACE("%sTime limit of %.4f seconds reached, breaking loop at iteration %d", fj_cpu.log_prefix.c_str(), time_limit.count() / 1000.f, fj_cpu.iterations); break; } if (fj_cpu.iterations >= fj_cpu.settings.iteration_limit) { - CUOPT_LOG_TRACE("%sIteration limit of %d reached, breaking loop at iteration %d\n", + CUOPT_LOG_TRACE("%sIteration limit of %d reached, breaking loop at iteration %d", fj_cpu.log_prefix.c_str(), fj_cpu.settings.iteration_limit, fj_cpu.iterations); @@ -963,15 +1308,24 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l fj_move_t move = fj_move_t{-1, 0}; fj_staged_score_t score = fj_staged_score_t::invalid(); + bool is_lift = false; + bool is_mtm_viol = false; + bool is_mtm_sat = false; + // Perform lift moves - if (fj_cpu.violated_constraints.empty()) { thrust::tie(move, score) = find_lift_move(fj_cpu); } + if (fj_cpu.violated_constraints.empty()) { + thrust::tie(move, score) = find_lift_move(fj_cpu); + if (score > fj_staged_score_t::zero()) is_lift = true; + } // Regular MTM if (!(score > fj_staged_score_t::zero())) { thrust::tie(move, score) = find_mtm_move_viol(fj_cpu, fj_cpu.mtm_viol_samples); + if (score > fj_staged_score_t::zero()) is_mtm_viol = true; } // try with MTM in satisfied constraints if (fj_cpu.feasible_found && !(score > fj_staged_score_t::zero())) { thrust::tie(move, score) = find_mtm_move_sat(fj_cpu, fj_cpu.mtm_sat_samples); + if (score > fj_staged_score_t::zero()) is_mtm_sat = true; } // if we're in the feasible region but haven't found improvements in the last n iterations, // perturb @@ -984,6 +1338,10 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l if (score > fj_staged_score_t::zero() && !should_perturb) { apply_move(fj_cpu, move.var_idx, move.value, false); + // Track move types + if (is_lift) fj_cpu.n_lift_moves_window++; + if (is_mtm_viol) fj_cpu.n_mtm_viol_moves_window++; + if (is_mtm_sat) fj_cpu.n_mtm_sat_moves_window++; } else { // Local Min update_weights(fj_cpu); @@ -998,6 +1356,7 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l f_t delta = move.var_idx >= 0 ? move.value : 0; apply_move(fj_cpu, var_idx, delta, true); ++local_mins; + ++fj_cpu.n_local_minima_window; } // number of violated constraints is usually small (<100). recomputing from all LHSs is cheap @@ -1010,7 +1369,7 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l CUOPT_LOG_TRACE( "%sCPUFJ iteration: %d/%d, local mins: %d, best_objective: %g, viol: %zu, obj weight %g, " "maxw " - "%g\n", + "%g", fj_cpu.log_prefix.c_str(), fj_cpu.iterations, fj_cpu.settings.iteration_limit != std::numeric_limits::max() @@ -1035,23 +1394,48 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l print_timing_stats(fj_cpu); } #endif + + // Collect and print PAPI metrics and regression features every 1000 iterations + if (fj_cpu.iterations % 1000 == 0 && fj_cpu.iterations > 0) { + auto now = std::chrono::high_resolution_clock::now(); + double time_window_ms = std::chrono::duration_cast>( + now - fj_cpu.last_feature_log_time) + .count() * + 1000.0; + double total_time_ms = + std::chrono::duration_cast>(now - loop_start).count() * + 1000.0; + +#ifdef __linux__ + collect_and_print_papi_metrics(fj_cpu); +#endif + log_regression_features(fj_cpu, time_window_ms, total_time_ms); + fj_cpu.last_feature_log_time = now; + } + cuopt_func_call(sanity_checks(fj_cpu)); fj_cpu.iterations++; + fj_cpu.iterations_since_best++; } auto loop_end = std::chrono::high_resolution_clock::now(); double total_time = std::chrono::duration_cast>(loop_end - loop_start).count(); double avg_time_per_iter = total_time / fj_cpu.iterations; - CUOPT_LOG_TRACE("%sCPUFJ Average time per iteration: %.8fms\n", + CUOPT_LOG_TRACE("%sCPUFJ Average time per iteration: %.8fms", fj_cpu.log_prefix.c_str(), avg_time_per_iter * 1000.0); #if CPUFJ_TIMING_TRACE // Print final timing statistics - CUOPT_LOG_TRACE("\n=== Final Timing Statistics ===\n"); + CUOPT_LOG_TRACE("=== Final Timing Statistics ==="); print_timing_stats(fj_cpu); #endif +#ifdef __linux__ + // Cleanup PAPI + cleanup_papi(fj_cpu); +#endif + return fj_cpu.feasible_found; } diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cuh b/cpp/src/mip/feasibility_jump/fj_cpu.cuh index 04cc514f4..1728d0e07 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cuh +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cuh @@ -109,6 +109,33 @@ struct fj_cpu_climber_t { std::string log_prefix{""}; std::atomic halted{false}; + + // PAPI performance counters + int papi_event_set{-1}; + bool papi_initialized{false}; + std::vector papi_events; + + // Feature tracking for regression model (last 1000 iterations) + i_t nnz_processed_window{0}; + i_t n_lift_moves_window{0}; + i_t n_mtm_viol_moves_window{0}; + i_t n_mtm_sat_moves_window{0}; + i_t n_constraint_evals_window{0}; + i_t n_variable_updates_window{0}; + i_t n_local_minima_window{0}; + std::chrono::high_resolution_clock::time_point last_feature_log_time; + f_t prev_best_objective{std::numeric_limits::infinity()}; + i_t iterations_since_best{0}; + + // Precomputed static problem features + i_t n_binary_vars{0}; + i_t n_integer_vars{0}; + i_t n_continuous_vars{0}; + i_t max_var_degree{0}; + i_t max_cstr_degree{0}; + double avg_var_degree{0.0}; + double avg_cstr_degree{0.0}; + double problem_density{0.0}; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index e87f34687..37628cd8a 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -37,6 +37,7 @@ #include #include +#include namespace cuopt::linear_programming { @@ -161,6 +162,15 @@ mip_solution_t solve_mip(optimization_problem_t& op_problem, // This needs to be called before pdlp is initialized init_handler(op_problem.get_handle_ptr()); + auto retval = PAPI_library_init(PAPI_VER_CURRENT); + if (retval != PAPI_VER_CURRENT && retval > 0) { + fprintf(stderr, "PAPI library version mismatch!\n"); + exit(1); + } else if (retval < 0) { + fprintf(stderr, "PAPI initialization error!\n"); + exit(1); + } + print_version_info(); raft::common::nvtx::range fun_scope("Running solver"); From bcea60d1bbf9f59a58f68fe3ee275eb88effc49f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 10 Nov 2025 13:59:45 +0000 Subject: [PATCH 070/366] branch n bound nvtx ranges --- cpp/src/dual_simplex/basis_solves.cpp | 3 + cpp/src/dual_simplex/basis_updates.cpp | 5 + cpp/src/dual_simplex/branch_and_bound.cpp | 75 +++++--- cpp/src/dual_simplex/crossover.cpp | 3 + cpp/src/dual_simplex/initial_basis.cpp | 3 + cpp/src/dual_simplex/phase2.cpp | 220 ++++++++++++---------- cpp/src/dual_simplex/presolve.cpp | 3 + cpp/src/dual_simplex/primal.cpp | 3 + cpp/src/dual_simplex/pseudo_costs.cpp | 3 + cpp/src/dual_simplex/right_looking_lu.cpp | 3 + cpp/src/dual_simplex/solve.cpp | 29 ++- 11 files changed, 221 insertions(+), 129 deletions(-) diff --git a/cpp/src/dual_simplex/basis_solves.cpp b/cpp/src/dual_simplex/basis_solves.cpp index d0f82967d..adaf8fdf6 100644 --- a/cpp/src/dual_simplex/basis_solves.cpp +++ b/cpp/src/dual_simplex/basis_solves.cpp @@ -13,6 +13,8 @@ #include #include +#include + namespace cuopt::linear_programming::dual_simplex { template @@ -165,6 +167,7 @@ i_t factorize_basis(const csc_matrix_t& A, std::vector& deficient, std::vector& slacks_needed) { + raft::common::nvtx::range scope("LU::factorize_basis"); const i_t m = basic_list.size(); constexpr f_t medium_tol = 1e-12; diff --git a/cpp/src/dual_simplex/basis_updates.cpp b/cpp/src/dual_simplex/basis_updates.cpp index 3e16411f4..0fdcb9d5c 100644 --- a/cpp/src/dual_simplex/basis_updates.cpp +++ b/cpp/src/dual_simplex/basis_updates.cpp @@ -10,6 +10,8 @@ #include #include +#include + #include #include @@ -35,6 +37,7 @@ i_t basis_update_t::b_solve(const std::vector& rhs, std::vector& solution, std::vector& Lsol) const { + raft::common::nvtx::range scope("LU::b_solve"); const i_t m = L0_.m; assert(row_permutation_.size() == m); assert(rhs.size() == m); @@ -86,6 +89,7 @@ template i_t basis_update_t::b_transpose_solve(const std::vector& rhs, std::vector& solution) const { + raft::common::nvtx::range scope("LU::b_transpose_solve"); // Observe that // P*B = L*U // B'*P' = U'*L' @@ -2046,6 +2050,7 @@ int basis_update_mpf_t::refactor_basis( std::vector& nonbasic_list, std::vector& vstatus) { + raft::common::nvtx::range scope("LU::refactor_basis"); std::vector deficient; std::vector slacks_needed; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index a43ee12c5..a0de45642 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -19,6 +19,8 @@ #include #include +#include + #include #include #include @@ -376,6 +378,7 @@ bool branch_and_bound_t::repair_solution(const std::vector& edge_ template void branch_and_bound_t::repair_heuristic_solutions() { + raft::common::nvtx::range scope("BB::repair_heuristics"); // Check if there are any solutions to repair std::vector> to_repair; mutex_repair_.lock(); @@ -550,6 +553,7 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& logger_t& log, char thread_type) { + raft::common::nvtx::range scope("BB::solve_node"); f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); @@ -570,8 +574,12 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& // in B&B we only have equality constraints, leave it empty for default std::vector row_sense; - bool feasible = - bound_strengthening(row_sense, lp_settings, leaf_problem, Arow, var_types_, bounds_changed); + bool feasible; + { + raft::common::nvtx::range scope_bs("BB::bound_strengthening"); + feasible = + bound_strengthening(row_sense, lp_settings, leaf_problem, Arow, var_types_, bounds_changed); + } dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; @@ -580,15 +588,18 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; - lp_status = dual_phase2(2, - 0, - lp_start_time, - leaf_problem, - lp_settings, - leaf_vstatus, - leaf_solution, - node_iter, - leaf_edge_norms); + { + raft::common::nvtx::range scope_lp("BB::node_lp_solve"); + lp_status = dual_phase2(2, + 0, + lp_start_time, + leaf_problem, + lp_settings, + leaf_vstatus, + leaf_solution, + node_iter, + leaf_edge_norms); + } if (lp_status == dual::status_t::NUMERICAL) { log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); @@ -774,6 +785,7 @@ void branch_and_bound_t::explore_subtree(i_t task_id, lp_problem_t& leaf_problem, const csc_matrix_t& Arow) { + raft::common::nvtx::range scope("BB::explore_subtree"); std::deque*> stack; stack.push_front(start_node); @@ -888,6 +900,7 @@ void branch_and_bound_t::best_first_thread(i_t id, lp_problem_t& leaf_problem, const csc_matrix_t& Arow) { + raft::common::nvtx::range scope("BB::best_first_thread"); f_t lower_bound = -inf; f_t upper_bound = inf; f_t abs_gap = inf; @@ -943,6 +956,7 @@ template void branch_and_bound_t::diving_thread(lp_problem_t& leaf_problem, const csc_matrix_t& Arow) { + raft::common::nvtx::range scope("BB::diving_thread"); logger_t log; log.log = false; @@ -1010,6 +1024,8 @@ void branch_and_bound_t::diving_thread(lp_problem_t& leaf_pr template mip_status_t branch_and_bound_t::solve(mip_solution_t& solution) { + raft::common::nvtx::range scope("BB::solve"); + logger_t log; log.log = false; status_ = mip_exploration_status_t::UNSET; @@ -1017,6 +1033,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut stats_.nodes_explored = 0; if (guess_.size() != 0) { + raft::common::nvtx::range scope_guess("BB::check_initial_guess"); std::vector crushed_guess; crush_primal_solution(original_problem_, original_lp_, guess_, new_slacks_, crushed_guess); f_t primal_err; @@ -1036,10 +1053,14 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut root_relax_soln_.resize(original_lp_.num_rows, original_lp_.num_cols); settings_.log.printf("Solving LP root relaxation\n"); - simplex_solver_settings_t lp_settings = settings_; - lp_settings.inside_mip = 1; - lp_status_t root_status = solve_linear_program_advanced( - original_lp_, stats_.start_time, lp_settings, root_relax_soln_, root_vstatus_, edge_norms_); + lp_status_t root_status; + { + raft::common::nvtx::range scope_root("BB::root_relaxation"); + simplex_solver_settings_t lp_settings = settings_; + lp_settings.inside_mip = 1; + root_status = solve_linear_program_advanced( + original_lp_, stats_.start_time, lp_settings, root_relax_soln_, root_vstatus_, edge_norms_); + } stats_.total_lp_iters = root_relax_soln_.iterations; stats_.total_lp_solve_time = toc(stats_.start_time); if (root_status == lp_status_t::INFEASIBLE) { @@ -1115,16 +1136,19 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } pc_.resize(original_lp_.num_cols); - strong_branching(original_lp_, - settings_, - stats_.start_time, - var_types_, - root_relax_soln_.x, - fractional, - root_objective_, - root_vstatus_, - edge_norms_, - pc_); + { + raft::common::nvtx::range scope_sb("BB::strong_branching"); + strong_branching(original_lp_, + settings_, + stats_.start_time, + var_types_, + root_relax_soln_.x, + fractional, + root_objective_, + root_vstatus_, + edge_norms_, + pc_); + } if (toc(stats_.start_time) > settings_.time_limit) { status_ = mip_exploration_status_t::TIME_LIMIT; @@ -1167,6 +1191,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut #pragma omp parallel num_threads(settings_.num_threads) { + raft::common::nvtx::range scope_tree("BB::tree_exploration"); // Make a copy of the original LP. We will modify its bounds at each leaf lp_problem_t leaf_problem = original_lp_; diff --git a/cpp/src/dual_simplex/crossover.cpp b/cpp/src/dual_simplex/crossover.cpp index 2e7bea111..7332b1f6a 100644 --- a/cpp/src/dual_simplex/crossover.cpp +++ b/cpp/src/dual_simplex/crossover.cpp @@ -15,6 +15,8 @@ #include #include +#include + #include namespace cuopt::linear_programming::dual_simplex { @@ -1045,6 +1047,7 @@ crossover_status_t crossover(const lp_problem_t& lp, lp_solution_t& solution, std::vector& vstatus) { + raft::common::nvtx::range scope("Barrier::crossover"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; f_t crossover_start = tic(); diff --git a/cpp/src/dual_simplex/initial_basis.cpp b/cpp/src/dual_simplex/initial_basis.cpp index 9dbe4052b..7d2ec9288 100644 --- a/cpp/src/dual_simplex/initial_basis.cpp +++ b/cpp/src/dual_simplex/initial_basis.cpp @@ -11,6 +11,8 @@ #include #include +#include + #include #include @@ -24,6 +26,7 @@ i_t initial_basis_selection(const lp_problem_t& problem, std::vector& vstatus, std::vector& dependent_rows) { + raft::common::nvtx::range scope("DualSimplex::initial_basis"); i_t m = problem.num_rows; i_t n = problem.num_cols; i_t nz = problem.A.col_start[n]; diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 39ea9b465..0e6fc5783 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -16,6 +16,8 @@ #include #include +#include + #include #include #include @@ -2171,6 +2173,7 @@ dual::status_t dual_phase2(i_t phase, i_t& iter, std::vector& delta_y_steepest_edge) { + raft::common::nvtx::range scope("DualSimplex::phase2"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; std::vector basic_list(m); @@ -2208,6 +2211,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, i_t& iter, std::vector& delta_y_steepest_edge) { + raft::common::nvtx::range scope("DualSimplex::phase2_advanced"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; assert(m <= n); @@ -2379,21 +2383,24 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, i_t leaving_index = -1; f_t max_val; timers.start_timer(); - if (settings.use_steepest_edge_pricing) { - leaving_index = phase2::steepest_edge_pricing_with_infeasibilities(lp, - settings, - x, - delta_y_steepest_edge, - basic_mark, - squared_infeasibilities, - infeasibility_indices, - direction, - basic_leaving_index, - max_val); - } else { - // Max infeasibility pricing - leaving_index = phase2::phase2_pricing( - lp, settings, x, basic_list, direction, basic_leaving_index, primal_infeasibility); + { + raft::common::nvtx::range scope_pricing("DualSimplex::pricing"); + if (settings.use_steepest_edge_pricing) { + leaving_index = phase2::steepest_edge_pricing_with_infeasibilities(lp, + settings, + x, + delta_y_steepest_edge, + basic_mark, + squared_infeasibilities, + infeasibility_indices, + direction, + basic_leaving_index, + max_val); + } else { + // Max infeasibility pricing + leaving_index = phase2::phase2_pricing( + lp, settings, x, basic_list, direction, basic_leaving_index, primal_infeasibility); + } } timers.pricing_time += timers.stop_timer(); if (leaving_index == -1) { @@ -2421,7 +2428,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); sparse_vector_t delta_y_sparse(m, 0); sparse_vector_t UTsol_sparse(m, 0); - phase2::compute_delta_y(ft, basic_leaving_index, direction, delta_y_sparse, UTsol_sparse); + { + raft::common::nvtx::range scope_btran("DualSimplex::btran"); + phase2::compute_delta_y(ft, basic_leaving_index, direction, delta_y_sparse, UTsol_sparse); + } timers.btran_time += timers.stop_timer(); const f_t steepest_edge_norm_check = delta_y_sparse.norm2_squared(); @@ -2490,43 +2500,46 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, i_t nonbasic_entering_index = -1; const bool harris_ratio = settings.use_harris_ratio; const bool bound_flip_ratio = settings.use_bound_flip_ratio; - if (harris_ratio) { - f_t max_step_length = phase2::first_stage_harris(lp, vstatus, nonbasic_list, z, delta_z); - entering_index = phase2::second_stage_harris(lp, + { + raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); + if (harris_ratio) { + f_t max_step_length = phase2::first_stage_harris(lp, vstatus, nonbasic_list, z, delta_z); + entering_index = phase2::second_stage_harris(lp, + vstatus, + nonbasic_list, + z, + delta_z, + max_step_length, + step_length, + nonbasic_entering_index); + } else if (bound_flip_ratio) { + timers.start_timer(); + f_t slope = direction == 1 ? (lp.lower[leaving_index] - x[leaving_index]) + : (x[leaving_index] - lp.upper[leaving_index]); + bound_flipping_ratio_test_t bfrt(settings, + start_time, + m, + n, + slope, + lp.lower, + lp.upper, + bounded_variables, vstatus, nonbasic_list, z, delta_z, - max_step_length, - step_length, - nonbasic_entering_index); - } else if (bound_flip_ratio) { - timers.start_timer(); - f_t slope = direction == 1 ? (lp.lower[leaving_index] - x[leaving_index]) - : (x[leaving_index] - lp.upper[leaving_index]); - bound_flipping_ratio_test_t bfrt(settings, - start_time, - m, - n, - slope, - lp.lower, - lp.upper, - bounded_variables, - vstatus, - nonbasic_list, - z, - delta_z, - delta_z_indices, - nonbasic_mark); - entering_index = bfrt.compute_step_length(step_length, nonbasic_entering_index); - if (entering_index == -4) { - settings.log.printf("Numerical issues encountered in ratio test.\n"); - return dual::status_t::NUMERICAL; + delta_z_indices, + nonbasic_mark); + entering_index = bfrt.compute_step_length(step_length, nonbasic_entering_index); + if (entering_index == -4) { + settings.log.printf("Numerical issues encountered in ratio test.\n"); + return dual::status_t::NUMERICAL; + } + timers.bfrt_time += timers.stop_timer(); + } else { + entering_index = phase2::phase2_ratio_test( + lp, settings, vstatus, nonbasic_list, z, delta_z, step_length, nonbasic_entering_index); } - timers.bfrt_time += timers.stop_timer(); - } else { - entering_index = phase2::phase2_ratio_test( - lp, settings, vstatus, nonbasic_list, z, delta_z, step_length, nonbasic_entering_index); } if (entering_index == -2) { return dual::status_t::TIME_LIMIT; } if (entering_index == -3) { return dual::status_t::CONCURRENT_LIMIT; } @@ -2725,21 +2738,24 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, sparse_vector_t utilde_sparse(m, 0); sparse_vector_t scaled_delta_xB_sparse(m, 0); sparse_vector_t rhs_sparse(lp.A, entering_index); - if (phase2::compute_delta_x(lp, - ft, - entering_index, - leaving_index, - basic_leaving_index, - direction, - basic_list, - delta_x_flip, - rhs_sparse, - x, - utilde_sparse, - scaled_delta_xB_sparse, - delta_x) == -1) { - settings.log.printf("Failed to compute delta_x. Iter %d\n", iter); - return dual::status_t::NUMERICAL; + { + raft::common::nvtx::range scope_ftran("DualSimplex::ftran"); + if (phase2::compute_delta_x(lp, + ft, + entering_index, + leaving_index, + basic_leaving_index, + direction, + basic_list, + delta_x_flip, + rhs_sparse, + x, + utilde_sparse, + scaled_delta_xB_sparse, + delta_x) == -1) { + settings.log.printf("Failed to compute delta_x. Iter %d\n", iter); + return dual::status_t::NUMERICAL; + } } timers.ftran_time += timers.stop_timer(); @@ -2867,55 +2883,59 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); // Refactor or update the basis factorization - bool should_refactor = ft.num_updates() > settings.refactor_frequency; - if (!should_refactor) { - i_t recommend_refactor = ft.update(utilde_sparse, UTsol_sparse, basic_leaving_index); + { + raft::common::nvtx::range scope_update("DualSimplex::basis_update"); + bool should_refactor = ft.num_updates() > settings.refactor_frequency; + if (!should_refactor) { + i_t recommend_refactor = ft.update(utilde_sparse, UTsol_sparse, basic_leaving_index); #ifdef CHECK_UPDATE - phase2::check_update(lp, settings, ft, basic_list, basic_leaving_index); + phase2::check_update(lp, settings, ft, basic_list, basic_leaving_index); #endif - should_refactor = recommend_refactor == 1; - } + should_refactor = recommend_refactor == 1; + } #ifdef CHECK_BASIC_INFEASIBILITIES - phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 6); + phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 6); #endif - if (should_refactor) { - bool should_recompute_x = false; - if (ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus) > 0) { - should_recompute_x = true; - settings.log.printf("Failed to factorize basis. Iteration %d\n", iter); - if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } - i_t count = 0; - i_t deficient_size; - while ((deficient_size = - ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus)) > 0) { - settings.log.printf("Failed to repair basis. Iteration %d. %d deficient columns.\n", - iter, - static_cast(deficient_size)); - + if (should_refactor) { + raft::common::nvtx::range scope_refactor("DualSimplex::refactorization"); + bool should_recompute_x = false; + if (ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus) > 0) { + should_recompute_x = true; + settings.log.printf("Failed to factorize basis. Iteration %d\n", iter); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } - settings.threshold_partial_pivoting_tol = 1.0; + i_t count = 0; + i_t deficient_size; + while ((deficient_size = + ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus)) > 0) { + settings.log.printf("Failed to repair basis. Iteration %d. %d deficient columns.\n", + iter, + static_cast(deficient_size)); + + if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } + settings.threshold_partial_pivoting_tol = 1.0; + + count++; + if (count > 10) { return dual::status_t::NUMERICAL; } + } - count++; - if (count > 10) { return dual::status_t::NUMERICAL; } + settings.log.printf("Successfully repaired basis. Iteration %d\n", iter); } - settings.log.printf("Successfully repaired basis. Iteration %d\n", iter); - } - - phase2::reset_basis_mark(basic_list, nonbasic_list, basic_mark, nonbasic_mark); - if (should_recompute_x) { - std::vector unperturbed_x(n); - phase2::compute_primal_solution_from_basis( - lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); - x = unperturbed_x; + phase2::reset_basis_mark(basic_list, nonbasic_list, basic_mark, nonbasic_mark); + if (should_recompute_x) { + std::vector unperturbed_x(n); + phase2::compute_primal_solution_from_basis( + lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); + x = unperturbed_x; + } + phase2::compute_initial_primal_infeasibilities( + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); } - phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); - } #ifdef CHECK_BASIC_INFEASIBILITIES - phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 7); + phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 7); #endif + } timers.lu_update_time += timers.stop_timer(); timers.start_timer(); diff --git a/cpp/src/dual_simplex/presolve.cpp b/cpp/src/dual_simplex/presolve.cpp index b36f7a1de..f81be32c9 100644 --- a/cpp/src/dual_simplex/presolve.cpp +++ b/cpp/src/dual_simplex/presolve.cpp @@ -12,6 +12,8 @@ #include #include +#include + #include #include #include @@ -67,6 +69,7 @@ bool bound_strengthening(const std::vector& row_sense, const std::vector& var_types, const std::vector& bounds_changed) { + raft::common::nvtx::range scope("BB::bound_strengthening_full"); const i_t m = problem.num_rows; const i_t n = problem.num_cols; diff --git a/cpp/src/dual_simplex/primal.cpp b/cpp/src/dual_simplex/primal.cpp index 80406dcf0..818d632ab 100644 --- a/cpp/src/dual_simplex/primal.cpp +++ b/cpp/src/dual_simplex/primal.cpp @@ -14,6 +14,8 @@ #include #include +#include + namespace cuopt::linear_programming::dual_simplex { namespace { @@ -254,6 +256,7 @@ primal::status_t primal_phase2(i_t phase, lp_solution_t& sol, i_t& iter) { + raft::common::nvtx::range scope("PrimalSimplex::phase2"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; assert(m <= n); diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index ca3e58041..f1888a4ab 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -11,6 +11,8 @@ #include #include +#include + #include namespace cuopt::linear_programming::dual_simplex { @@ -31,6 +33,7 @@ void strong_branch_helper(i_t start, const std::vector& edge_norms, pseudo_costs_t& pc) { + raft::common::nvtx::range scope("BB::strong_branch_helper"); lp_problem_t child_problem = original_lp; constexpr bool verbose = false; diff --git a/cpp/src/dual_simplex/right_looking_lu.cpp b/cpp/src/dual_simplex/right_looking_lu.cpp index 59dac3ac9..7ab3454a5 100644 --- a/cpp/src/dual_simplex/right_looking_lu.cpp +++ b/cpp/src/dual_simplex/right_looking_lu.cpp @@ -8,6 +8,8 @@ #include #include +#include + #include #include #include @@ -577,6 +579,7 @@ i_t right_looking_lu(const csc_matrix_t& A, csc_matrix_t& U, std::vector& pinv) { + raft::common::nvtx::range scope("LU::right_looking_lu"); const i_t n = column_list.size(); const i_t m = A.m; diff --git a/cpp/src/dual_simplex/solve.cpp b/cpp/src/dual_simplex/solve.cpp index 5c5f9e165..3431ca345 100644 --- a/cpp/src/dual_simplex/solve.cpp +++ b/cpp/src/dual_simplex/solve.cpp @@ -23,6 +23,8 @@ #include #include +#include + #include #include #include @@ -107,6 +109,7 @@ lp_status_t solve_linear_program_advanced(const lp_problem_t& original std::vector& vstatus, std::vector& edge_norms) { + raft::common::nvtx::range scope("DualSimplex::solve_lp"); const i_t m = original_lp.num_rows; const i_t n = original_lp.num_cols; assert(m <= n); @@ -139,7 +142,11 @@ lp_status_t solve_linear_program_with_advanced_basis( lp_status_t lp_status = lp_status_t::UNSET; lp_problem_t presolved_lp(original_lp.handle_ptr, 1, 1, 1); presolve_info_t presolve_info; - const i_t ok = presolve(original_lp, settings, presolved_lp, presolve_info); + i_t ok; + { + raft::common::nvtx::range scope_presolve("DualSimplex::presolve"); + ok = presolve(original_lp, settings, presolved_lp, presolve_info); + } if (ok == -1) { return lp_status_t::INFEASIBLE; } constexpr bool write_out_matlab = false; @@ -154,7 +161,10 @@ lp_status_t solve_linear_program_with_advanced_basis( presolved_lp.num_cols, presolved_lp.A.col_start[presolved_lp.num_cols]); std::vector column_scales; - column_scaling(presolved_lp, settings, lp, column_scales); + { + raft::common::nvtx::range scope_scaling("DualSimplex::scaling"); + column_scaling(presolved_lp, settings, lp, column_scales); + } assert(presolved_lp.num_cols == lp.num_cols); lp_problem_t phase1_problem(original_lp.handle_ptr, 1, 1, 1); std::vector phase1_vstatus; @@ -180,8 +190,19 @@ lp_status_t solve_linear_program_with_advanced_basis( i_t iter = 0; lp_solution_t phase1_solution(phase1_problem.num_rows, phase1_problem.num_cols); edge_norms.clear(); - dual::status_t phase1_status = dual_phase2( - 1, 1, start_time, phase1_problem, settings, phase1_vstatus, phase1_solution, iter, edge_norms); + dual::status_t phase1_status; + { + raft::common::nvtx::range scope_phase1("DualSimplex::phase1"); + phase1_status = dual_phase2(1, + 1, + start_time, + phase1_problem, + settings, + phase1_vstatus, + phase1_solution, + iter, + edge_norms); + } if (phase1_status == dual::status_t::NUMERICAL || phase1_status == dual::status_t::DUAL_UNBOUNDED) { settings.log.printf("Failed in Phase 1\n"); From d7ff462aa999f778a41e6445be0af27b93411b5e Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 10 Nov 2025 16:03:48 +0000 Subject: [PATCH 071/366] more cpufj logging --- cpp/src/mip/diversity/diversity_manager.cu | 4 +- cpp/src/mip/feasibility_jump/fj_cpu.cu | 139 ++- cpp/src/mip/feasibility_jump/fj_cpu.cuh | 10 +- scripts/determinism_logs_parse.py | 538 ++++++--- scripts/train_regressor.py | 1174 ++++++++++++-------- 5 files changed, 1210 insertions(+), 655 deletions(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 392145ee5..39e79a1bb 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -355,10 +355,12 @@ solution_t diversity_manager_t::run_solver() population.allocate_solutions(); ls_cpufj_raii_guard_t ls_cpufj_raii_guard(ls); // RAII to stop cpufj threads on solve stop if (!context.settings.deterministic) { +#if 1 ls.start_cpufj_scratch_threads(population); - std::this_thread::sleep_for(std::chrono::seconds(10)); + std::this_thread::sleep_for(std::chrono::seconds(30)); ls.stop_cpufj_scratch_threads(); exit(0); +#endif } // before probing cache or LP, run FJ to generate initial primal feasible solution diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 58850ab2c..262bd2f35 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -256,16 +256,13 @@ template static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) { // Count variable types - use host vectors - fj_cpu.n_binary_vars = 0; - fj_cpu.n_integer_vars = 0; - fj_cpu.n_continuous_vars = 0; + fj_cpu.n_binary_vars = 0; + fj_cpu.n_integer_vars = 0; for (i_t i = 0; i < (i_t)fj_cpu.h_is_binary_variable.size(); i++) { if (fj_cpu.h_is_binary_variable[i]) { fj_cpu.n_binary_vars++; } else if (fj_cpu.h_var_types[i] == var_t::INTEGER) { fj_cpu.n_integer_vars++; - } else { - fj_cpu.n_continuous_vars++; } } @@ -275,22 +272,47 @@ static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) fj_cpu.avg_var_degree = (double)total_nnz / n_vars; - // Compute max variable degree + // Compute variable degree statistics (max, cv) fj_cpu.max_var_degree = 0; + std::vector var_degrees(n_vars); for (i_t i = 0; i < n_vars; i++) { i_t degree = fj_cpu.h_reverse_offsets[i + 1] - fj_cpu.h_reverse_offsets[i]; + var_degrees[i] = degree; fj_cpu.max_var_degree = std::max(fj_cpu.max_var_degree, degree); } + // Compute variable degree coefficient of variation + double var_deg_variance = 0.0; + for (i_t i = 0; i < n_vars; i++) { + double diff = var_degrees[i] - fj_cpu.avg_var_degree; + var_deg_variance += diff * diff; + } + var_deg_variance /= n_vars; + double var_degree_std = std::sqrt(var_deg_variance); + fj_cpu.var_degree_cv = fj_cpu.avg_var_degree > 0 ? var_degree_std / fj_cpu.avg_var_degree : 0.0; + fj_cpu.avg_cstr_degree = (double)total_nnz / n_cstrs; - // Compute max constraint degree + // Compute constraint degree statistics (max, cv) fj_cpu.max_cstr_degree = 0; + std::vector cstr_degrees(n_cstrs); for (i_t i = 0; i < n_cstrs; i++) { i_t degree = fj_cpu.h_offsets[i + 1] - fj_cpu.h_offsets[i]; + cstr_degrees[i] = degree; fj_cpu.max_cstr_degree = std::max(fj_cpu.max_cstr_degree, degree); } + // Compute constraint degree coefficient of variation + double cstr_deg_variance = 0.0; + for (i_t i = 0; i < n_cstrs; i++) { + double diff = cstr_degrees[i] - fj_cpu.avg_cstr_degree; + cstr_deg_variance += diff * diff; + } + cstr_deg_variance /= n_cstrs; + double cstr_degree_std = std::sqrt(cstr_deg_variance); + fj_cpu.cstr_degree_cv = + fj_cpu.avg_cstr_degree > 0 ? cstr_degree_std / fj_cpu.avg_cstr_degree : 0.0; + fj_cpu.problem_density = (double)total_nnz / ((double)n_vars * n_cstrs); } @@ -299,25 +321,12 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, double time_window_ms, double total_time_ms) { -#ifdef __linux__ - pid_t tid = syscall(SYS_gettid); -#else - int tid = 0; -#endif - i_t total_nnz = fj_cpu.h_reverse_offsets.back(); i_t n_vars = fj_cpu.h_reverse_offsets.size() - 1; i_t n_cstrs = fj_cpu.h_offsets.size() - 1; // Dynamic runtime features - i_t n_violated = fj_cpu.violated_constraints.size(); - double violated_ratio = (double)n_violated / n_cstrs; - - // Compute improvement velocity - double improvement_velocity = 0.0; - if (fj_cpu.iterations > 0) { - improvement_velocity = (fj_cpu.prev_best_objective - fj_cpu.h_best_objective) / 1000.0; - } + double violated_ratio = (double)fj_cpu.violated_constraints.size() / n_cstrs; // Compute per-iteration metrics double nnz_per_move = 0.0; @@ -325,12 +334,37 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, fj_cpu.n_lift_moves_window + fj_cpu.n_mtm_viol_moves_window + fj_cpu.n_mtm_sat_moves_window; if (total_moves > 0) { nnz_per_move = (double)fj_cpu.nnz_processed_window / total_moves; } - double eval_intensity = (double)fj_cpu.n_constraint_evals_window / n_cstrs; + double eval_intensity = (double)fj_cpu.nnz_processed_window / 1000.0; + + // Cache and locality metrics + i_t cache_hits_window = fj_cpu.hit_count - fj_cpu.hit_count_window_start; + i_t cache_misses_window = fj_cpu.miss_count - fj_cpu.miss_count_window_start; + i_t total_cache_accesses = cache_hits_window + cache_misses_window; + double cache_hit_rate = + total_cache_accesses > 0 ? (double)cache_hits_window / total_cache_accesses : 0.0; + + i_t unique_cstrs = fj_cpu.unique_cstrs_accessed_window.size(); + i_t unique_vars = fj_cpu.unique_vars_accessed_window.size(); + + // Reuse ratios: how many times each constraint/variable was accessed on average + double cstr_reuse_ratio = + unique_cstrs > 0 ? (double)fj_cpu.nnz_processed_window / unique_cstrs : 0.0; + double var_reuse_ratio = + unique_vars > 0 ? (double)fj_cpu.n_variable_updates_window / unique_vars : 0.0; + + // Working set size estimation (KB) + // Each constraint: lhs (f_t) + 2 bounds (f_t) + sumcomp (f_t) = 4 * sizeof(f_t) + // Each variable: assignment (f_t) = 1 * sizeof(f_t) + i_t working_set_bytes = unique_cstrs * 4 * sizeof(f_t) + unique_vars * sizeof(f_t); + double working_set_kb = working_set_bytes / 1024.0; + + // Coverage: what fraction of problem is actively touched + double cstr_coverage = (double)unique_cstrs / n_cstrs; + double var_coverage = (double)unique_vars / n_vars; double loads_per_iter = 0.0; double stores_per_iter = 0.0; double l1_miss = -1.0; - double l2_miss = -1.0; double l3_miss = -1.0; #ifdef __linux__ @@ -349,9 +383,6 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, if (all_values[0] > 0 && all_values[1] != -1) { l1_miss = (double)all_values[1] / all_values[0] * 100.0; } - if (all_values[2] > 0 && all_values[3] != -1) { - l2_miss = (double)all_values[3] / all_values[2] * 100.0; - } if (all_values[4] > 0 && all_values[5] != -1) { l3_miss = (double)all_values[5] / all_values[4] * 100.0; } @@ -365,52 +396,58 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, // Print everything on a single line using precomputed features CUOPT_LOG_DEBUG( - "%sCPUFJ_FEATURES iter=%d tid=%d time_window=%.2f " - "n_vars=%d n_cstrs=%d n_bin=%d n_int=%d n_cont=%d total_nnz=%d " - "avg_var_deg=%.2f max_var_deg=%d avg_cstr_deg=%.2f max_cstr_deg=%d density=%.6f " - "n_viol=%d total_viol=%.4f curr_obj=%.4f best_obj=%.4f obj_weight=%.4f max_weight=%.4f " - "n_locmin=%d is_feas=%d iter_since_best=%d feas_found=%d " - "nnz_proc=%d n_lift=%d n_mtm_viol=%d n_mtm_sat=%d n_cstr_eval=%d n_var_updates=%d " - "L1_miss=%.2f L2_miss=%.2f L3_miss=%.2f loads_per_iter=%.0f stores_per_iter=%.0f " - "viol_ratio=%.4f improv_vel=%.6f nnz_per_move=%.2f eval_intensity=%.2f", + "%sCPUFJ_FEATURES iter=%d time_window=%.2f " + "n_vars=%d n_cstrs=%d n_bin=%d n_int=%d total_nnz=%d " + "avg_var_deg=%.2f max_var_deg=%d var_deg_cv=%.4f " + "avg_cstr_deg=%.2f max_cstr_deg=%d cstr_deg_cv=%.4f " + "density=%.6f " + "total_viol=%.4f obj_weight=%.4f max_weight=%.4f " + "n_locmin=%d iter_since_best=%d feas_found=%d " + "nnz_proc=%d n_lift=%d n_mtm_viol=%d n_mtm_sat=%d n_var_updates=%d " + "cache_hit_rate=%.4f unique_cstrs=%d unique_vars=%d " + "cstr_reuse=%.2f var_reuse=%.2f working_set_kb=%.1f " + "cstr_coverage=%.4f var_coverage=%.4f " + "L1_miss=%.2f L3_miss=%.2f loads_per_iter=%.0f stores_per_iter=%.0f " + "viol_ratio=%.4f nnz_per_move=%.2f eval_intensity=%.2f", fj_cpu.log_prefix.c_str(), fj_cpu.iterations, - tid, time_window_ms, n_vars, n_cstrs, fj_cpu.n_binary_vars, fj_cpu.n_integer_vars, - fj_cpu.n_continuous_vars, total_nnz, fj_cpu.avg_var_degree, fj_cpu.max_var_degree, + fj_cpu.var_degree_cv, fj_cpu.avg_cstr_degree, fj_cpu.max_cstr_degree, + fj_cpu.cstr_degree_cv, fj_cpu.problem_density, - n_violated, fj_cpu.total_violations, - fj_cpu.h_incumbent_objective, - fj_cpu.h_best_objective, fj_cpu.h_objective_weight, fj_cpu.max_weight, fj_cpu.n_local_minima_window, - fj_cpu.feasible_found ? 1 : 0, fj_cpu.iterations_since_best, fj_cpu.feasible_found ? 1 : 0, fj_cpu.nnz_processed_window, fj_cpu.n_lift_moves_window, fj_cpu.n_mtm_viol_moves_window, fj_cpu.n_mtm_sat_moves_window, - fj_cpu.n_constraint_evals_window, fj_cpu.n_variable_updates_window, + cache_hit_rate, + unique_cstrs, + unique_vars, + cstr_reuse_ratio, + var_reuse_ratio, + working_set_kb, + cstr_coverage, + var_coverage, l1_miss, - l2_miss, l3_miss, loads_per_iter, stores_per_iter, violated_ratio, - improvement_velocity, nnz_per_move, eval_intensity); @@ -419,10 +456,15 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, fj_cpu.n_lift_moves_window = 0; fj_cpu.n_mtm_viol_moves_window = 0; fj_cpu.n_mtm_sat_moves_window = 0; - fj_cpu.n_constraint_evals_window = 0; fj_cpu.n_variable_updates_window = 0; fj_cpu.n_local_minima_window = 0; fj_cpu.prev_best_objective = fj_cpu.h_best_objective; + + // Reset cache and locality tracking + fj_cpu.hit_count_window_start = fj_cpu.hit_count; + fj_cpu.miss_count_window_start = fj_cpu.miss_count; + fj_cpu.unique_cstrs_accessed_window.clear(); + fj_cpu.unique_vars_accessed_window.clear(); } template @@ -473,10 +515,11 @@ static inline std::pair compute_score(fj_cpu_climber_t& fj_cpu, // Track work metrics for regression model fj_cpu.nnz_processed_window += (offset_end - offset_begin); fj_cpu.n_variable_updates_window++; + fj_cpu.unique_vars_accessed_window.insert(var_idx); i_t previous_viol = fj_cpu.violated_constraints.size(); @@ -608,7 +652,8 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, cuopt_assert(i < (i_t)fj_cpu.h_reverse_constraints.size(), ""); auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i]; - auto cstr_idx = fj_cpu.h_reverse_constraints[i]; + auto cstr_idx = fj_cpu.h_reverse_constraints[i]; + fj_cpu.unique_cstrs_accessed_window.insert(cstr_idx); auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; f_t old_lhs = fj_cpu.h_lhs[cstr_idx]; diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cuh b/cpp/src/mip/feasibility_jump/fj_cpu.cuh index 1728d0e07..c1f5316f8 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cuh +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cuh @@ -120,21 +120,27 @@ struct fj_cpu_climber_t { i_t n_lift_moves_window{0}; i_t n_mtm_viol_moves_window{0}; i_t n_mtm_sat_moves_window{0}; - i_t n_constraint_evals_window{0}; i_t n_variable_updates_window{0}; i_t n_local_minima_window{0}; std::chrono::high_resolution_clock::time_point last_feature_log_time; f_t prev_best_objective{std::numeric_limits::infinity()}; i_t iterations_since_best{0}; + // Cache and locality tracking + i_t hit_count_window_start{0}; + i_t miss_count_window_start{0}; + std::unordered_set unique_cstrs_accessed_window; + std::unordered_set unique_vars_accessed_window; + // Precomputed static problem features i_t n_binary_vars{0}; i_t n_integer_vars{0}; - i_t n_continuous_vars{0}; i_t max_var_degree{0}; i_t max_cstr_degree{0}; double avg_var_degree{0.0}; double avg_cstr_degree{0.0}; + double var_degree_cv{0.0}; + double cstr_degree_cv{0.0}; double problem_density{0.0}; }; diff --git a/scripts/determinism_logs_parse.py b/scripts/determinism_logs_parse.py index 0e0b9b5b1..09ea08627 100755 --- a/scripts/determinism_logs_parse.py +++ b/scripts/determinism_logs_parse.py @@ -1,5 +1,6 @@ #!/usr/bin/env python3 # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. +# SPDX-License-Identifier: Apache-2.0 # All rights reserved. # SPDX-License-Identifier: Apache-2.0 # @@ -23,6 +24,7 @@ - PDLP (LP Solver): PDLP_FEATURES and PDLP_RESULT logs - CP (Constraint Propagation): CP_FEATURES and CP_RESULT logs - FJ (Feasibility Jump): Legacy FJ: format +- CPUFJ (CPU Feasibility Jump): CPUFJ_FEATURES single-line logs IMPORTANT - Grep Specificity: The parser uses EXACT pattern matching with grep to filter logs efficiently. @@ -46,19 +48,19 @@ python determinism_logs_parse.py --algorithm PDLP [-o output.feather] python determinism_logs_parse.py --algorithm CP [-o output.feather] python determinism_logs_parse.py --algorithm FJ [-o output.feather] + python determinism_logs_parse.py --algorithm CPUFJ [-o output.feather] """ import argparse import subprocess import os import glob -import re import numpy as np import pandas as pd -from typing import List, Dict, Any, Optional, Tuple +from typing import List, Dict, Any, Optional -SUPPORTED_ALGORITHMS = ['FP', 'PDLP', 'CP', 'FJ'] +SUPPORTED_ALGORITHMS = ["FP", "PDLP", "CP", "FJ", "CPUFJ"] def parse_value(value_str: str) -> Any: @@ -66,11 +68,11 @@ def parse_value(value_str: str) -> Any: try: # Handle special float values first (nan, inf, -inf) value_lower = value_str.lower() - if value_lower in ('nan', 'inf', '-inf', '+inf'): + if value_lower in ("nan", "inf", "-inf", "+inf"): return float(value_str) # Try to parse as float if it contains a decimal point or scientific notation - if '.' in value_str or 'e' in value_str.lower(): + if "." in value_str or "e" in value_str.lower(): return float(value_str) else: return int(value_str) @@ -97,18 +99,18 @@ def parse_key_value_line(line: str, prefix: str) -> Dict[str, Any]: # Parse key=value pairs # Handle both space-separated and comma-separated for kv_pair in line.split(): - if '=' in kv_pair: - key, value = kv_pair.split('=', 1) + if "=" in kv_pair: + key, value = kv_pair.split("=", 1) # Remove trailing commas - value = value.rstrip(',') + value = value.rstrip(",") entry[key] = parse_value(value) return entry -def parse_generic_algorithm_logs(log_files: List[str], - algorithm: str, - algorithm_name: str) -> List[Dict[str, Any]]: +def parse_generic_algorithm_logs( + log_files: List[str], algorithm: str, algorithm_name: str +) -> List[Dict[str, Any]]: """ Generic parser for algorithm feature and result logs. @@ -130,15 +132,22 @@ def parse_generic_algorithm_logs(log_files: List[str], # Construct grep patterns with EXACT match requirements # The colon at the end ensures we ONLY match the feature/result log lines # and ignore all other lines containing the algorithm name - features_pattern = f'{algorithm}_FEATURES:' - result_pattern = f'{algorithm}_RESULT:' + features_pattern = f"{algorithm}_FEATURES:" + result_pattern = f"{algorithm}_RESULT:" # Use grep with: # -H: Always print filename (even with single file) # -n: Print line numbers for correct pairing # -e: Multiple patterns to match # This ensures we ONLY get the specific predictor log lines, not debug/info lines - cmd = ['grep', '-Hn', '-e', features_pattern, '-e', result_pattern] + log_files + cmd = [ + "grep", + "-Hn", + "-e", + features_pattern, + "-e", + result_pattern, + ] + log_files result = subprocess.run(cmd, capture_output=True, text=True) @@ -147,7 +156,7 @@ def parse_generic_algorithm_logs(log_files: List[str], return [] # Count lines for progress indication - total_lines = result.stdout.count('\n') + total_lines = result.stdout.count("\n") print(f" Processing {total_lines} matching lines...") # Process grep output efficiently @@ -156,7 +165,7 @@ def parse_generic_algorithm_logs(log_files: List[str], lines_processed = 0 files_seen = set() - for line in result.stdout.split('\n'): + for line in result.stdout.split("\n"): if not line: continue @@ -164,12 +173,17 @@ def parse_generic_algorithm_logs(log_files: List[str], # Progress update every 10000 lines if lines_processed % 10000 == 0: - pct = (lines_processed / total_lines * 100) if total_lines > 0 else 0 - print(f" Progress: {pct:.1f}% ({lines_processed}/{total_lines} lines, {len(files_seen)} files)", end='\r') + pct = ( + (lines_processed / total_lines * 100) if total_lines > 0 else 0 + ) + print( + f" Progress: {pct:.1f}% ({lines_processed}/{total_lines} lines, {len(files_seen)} files)", + end="\r", + ) # Split on first two colons to get filename, linenum, and content # The rest of the line after the second colon is the log content - parts = line.split(':', 2) + parts = line.split(":", 2) if len(parts) < 3: continue @@ -178,7 +192,7 @@ def parse_generic_algorithm_logs(log_files: List[str], content = parts[2] # This includes everything after linenum if filename not in entries_by_file: - entries_by_file[filename] = {'features': [], 'results': []} + entries_by_file[filename] = {"features": [], "results": []} files_seen.add(filename) # Double-check pattern match (grep already filtered, but be extra safe) @@ -187,21 +201,25 @@ def parse_generic_algorithm_logs(log_files: List[str], # Parse features - only if pattern is present features = parse_key_value_line(content, features_pattern) if features: # Only add if parsing succeeded - entries_by_file[filename]['features'].append((linenum, features)) + entries_by_file[filename]["features"].append( + (linenum, features) + ) elif result_pattern in content: # Parse results - only if pattern is present results = parse_key_value_line(content, result_pattern) if results: # Only add if parsing succeeded - entries_by_file[filename]['results'].append((linenum, results)) + entries_by_file[filename]["results"].append((linenum, results)) # Clear progress line if lines_processed > 0: - print(f" Processed {lines_processed} lines from {len(files_seen)} files ") + print( + f" Processed {lines_processed} lines from {len(files_seen)} files " + ) # Match features with results # IMPORTANT: Multiple FEATURES lines followed by multiple RESULT lines form ONE complete entry # We need to merge consecutive lines of the same type, then combine them - print(f" Merging features and results...") + print(" Merging features and results...") entries = [] files_processed = 0 total_files = len(entries_by_file) @@ -211,15 +229,18 @@ def parse_generic_algorithm_logs(log_files: List[str], # Progress update every 10 files if files_processed % 10 == 0 or files_processed == total_files: - print(f" Merging: {files_processed}/{total_files} files, {len(entries)} entries found", end='\r') + print( + f" Merging: {files_processed}/{total_files} files, {len(entries)} entries found", + end="\r", + ) # Combine features and results by line number # Group consecutive FEATURES lines and consecutive RESULT lines all_items = [] - for linenum, features in data['features']: - all_items.append((linenum, 'features', features)) - for linenum, results in data['results']: - all_items.append((linenum, 'results', results)) + for linenum, features in data["features"]: + all_items.append((linenum, "features", features)) + for linenum, results in data["results"]: + all_items.append((linenum, "results", results)) # Sort by line number all_items.sort(key=lambda x: x[0]) @@ -231,16 +252,16 @@ def parse_generic_algorithm_logs(log_files: List[str], for linenum, item_type, content in all_items: # If we transition from RESULT back to FEATURES, save the previous entry - if item_type == 'features' and last_type == 'results': + if item_type == "features" and last_type == "results": if current_features and current_results: # Create combined entry - entry = {'file': filename} + entry = {"file": filename} entry.update(current_features) entry.update(current_results) # Rename 'iterations' to 'iter' for consistency - if 'iterations' in entry: - entry['iter'] = entry.pop('iterations') + if "iterations" in entry: + entry["iter"] = entry.pop("iterations") entries.append(entry) @@ -248,7 +269,7 @@ def parse_generic_algorithm_logs(log_files: List[str], current_features = {} current_results = {} - if item_type == 'features': + if item_type == "features": # Accumulate features current_features.update(content) else: # results @@ -259,18 +280,20 @@ def parse_generic_algorithm_logs(log_files: List[str], # Don't forget the last entry in the file if current_features and current_results: - entry = {'file': filename} + entry = {"file": filename} entry.update(current_features) entry.update(current_results) - if 'iterations' in entry: - entry['iter'] = entry.pop('iterations') + if "iterations" in entry: + entry["iter"] = entry.pop("iterations") entries.append(entry) # Clear progress line and show final count if total_files > 0: - print(f" Found {len(entries)} complete entries from {total_files} files ") + print( + f" Found {len(entries)} complete entries from {total_files} files " + ) return entries @@ -278,50 +301,65 @@ def parse_generic_algorithm_logs(log_files: List[str], # Algorithm-specific wrappers for the generic parser # These provide a clean API and eliminate code duplication + def parse_fp_logs(log_files: List[str]) -> List[Dict[str, Any]]: """Parse Feasibility Pump feature and result logs.""" - return parse_generic_algorithm_logs(log_files, 'FP', 'Feasibility Pump') + return parse_generic_algorithm_logs(log_files, "FP", "Feasibility Pump") def parse_pdlp_logs(log_files: List[str]) -> List[Dict[str, Any]]: """Parse PDLP (LP Solver) feature and result logs.""" - return parse_generic_algorithm_logs(log_files, 'PDLP', 'LP Solver') + return parse_generic_algorithm_logs(log_files, "PDLP", "LP Solver") def parse_cp_logs(log_files: List[str]) -> List[Dict[str, Any]]: """Parse Constraint Propagation feature and result logs.""" - return parse_generic_algorithm_logs(log_files, 'CP', 'Constraint Propagation') + return parse_generic_algorithm_logs( + log_files, "CP", "Constraint Propagation" + ) -def parse_fj_logs(log_files: List[str]) -> List[Dict[str, Any]]: +def parse_single_line_logs( + log_files: List[str], + pattern: str, + algorithm_name: str, + prefix_to_remove: Optional[str] = None, +) -> List[Dict[str, Any]]: """ - Parse legacy Feasibility Jump logs (original format). + Generic parser for single-line logs with key=value pairs. - Parses lines containing "FJ:" with key=value pairs. - Uses grep efficiently to minimize Python-side processing. + Used for legacy formats that don't have separate FEATURES/RESULT lines. + + Args: + log_files: List of log file paths to parse + pattern: Grep pattern to match (e.g., 'FJ:', 'CPUFJ_FEATURES') + algorithm_name: Full name for display + prefix_to_remove: Optional prefix to strip from content (e.g., 'FJ:') + + Returns + ------- + List of dictionaries with parsed key-value pairs """ - print("\nParsing FJ (Feasibility Jump) legacy logs...") + print(f"\nParsing {algorithm_name} logs...") print(f" Running grep on {len(log_files)} files...") - # Use grep to efficiently extract ONLY lines with the exact "FJ:" pattern - # Note: FJ uses legacy format with just "FJ:" prefix (not FJ_FEATURES/FJ_RESULT) - # The colon ensures we don't match other FJ-related debug lines - cmd = ['grep', '-H', 'FJ:'] + log_files + # Use grep to efficiently extract ONLY lines with the exact pattern + cmd = ["grep", "-H", pattern] + log_files result = subprocess.run(cmd, capture_output=True, text=True) if not result.stdout: - print(f" No FJ logs found") + print(f" No {algorithm_name} logs found") return [] # Count lines for progress indication - total_lines = result.stdout.count('\n') + total_lines = result.stdout.count("\n") print(f" Processing {total_lines} matching lines...") # Parse grep output efficiently entries = [] lines_processed = 0 - for line in result.stdout.split('\n'): + for line in result.stdout.split("\n"): if not line: continue @@ -329,26 +367,33 @@ def parse_fj_logs(log_files: List[str]) -> List[Dict[str, Any]]: # Progress update every 10000 lines if lines_processed % 10000 == 0: - pct = (lines_processed / total_lines * 100) if total_lines > 0 else 0 - print(f" Progress: {pct:.1f}% ({lines_processed}/{total_lines} lines, {len(entries)} entries)", end='\r') - - # Grep output format: filename:FJ: key1=value1 key2=value2 ... - parts = line.split(':', 2) + pct = ( + (lines_processed / total_lines * 100) if total_lines > 0 else 0 + ) + print( + f" Progress: {pct:.1f}% ({lines_processed}/{total_lines} lines, {len(entries)} entries)", + end="\r", + ) + + # Grep output format: filename:content + parts = line.split(":", 2) if len(parts) < 3: continue filename = os.path.basename(parts[0]) content = parts[2] - # Remove "FJ:" prefix if present - if content.startswith('FJ:'): - content = content[3:].strip() + # Remove prefix if specified + if prefix_to_remove and content.startswith(prefix_to_remove): + content = content[len(prefix_to_remove) :].strip() # Parse key-value pairs - entry = {'file': filename} + entry = {"file": filename} for kv_pair in content.split(): - if '=' in kv_pair: - key, value = kv_pair.split('=', 1) + if "=" in kv_pair: + key, value = kv_pair.split("=", 1) + # Remove trailing commas + value = value.rstrip(",") entry[key] = parse_value(value) # Only add entry if it has more than just the filename @@ -357,25 +402,43 @@ def parse_fj_logs(log_files: List[str]) -> List[Dict[str, Any]]: # Clear progress line if lines_processed > 0: - print(f" Found {len(entries)} entries from {total_lines} lines ") + print( + f" Found {len(entries)} entries from {total_lines} lines " + ) return entries +def parse_fj_logs(log_files: List[str]) -> List[Dict[str, Any]]: + """Parse legacy Feasibility Jump logs (original format).""" + return parse_single_line_logs( + log_files, "FJ:", "FJ (Feasibility Jump)", "FJ:" + ) + + +def parse_cpufj_logs(log_files: List[str]) -> List[Dict[str, Any]]: + """Parse CPU Feasibility Jump feature logs.""" + return parse_single_line_logs( + log_files, "CPUFJ_FEATURES", "CPUFJ (CPU Feasibility Jump)" + ) + + def print_statistics(entries: List[Dict[str, Any]], algorithm: str) -> None: """Print statistics about parsed entries.""" if not entries: print(f"\n No entries found for {algorithm}") return - unique_files = set(entry['file'] for entry in entries) - avg_entries_per_file = len(entries) / len(unique_files) if unique_files else 0 + unique_files = set(entry["file"] for entry in entries) + avg_entries_per_file = ( + len(entries) / len(unique_files) if unique_files else 0 + ) # Check if 'iter' field exists - has_iter = all('iter' in entry for entry in entries) + has_iter = all("iter" in entry for entry in entries) if has_iter: - iter_values = [entry['iter'] for entry in entries] + iter_values = [entry["iter"] for entry in entries] min_iter = min(iter_values) max_iter = max(iter_values) avg_iter = sum(iter_values) / len(iter_values) @@ -383,7 +446,9 @@ def print_statistics(entries: List[Dict[str, Any]], algorithm: str) -> None: print(f"\n Total entries: {len(entries)}") print(f" Unique files: {len(unique_files)}") print(f" Avg entries per file: {avg_entries_per_file:.2f}") - print(f" Iterations (target): min={min_iter}, max={max_iter}, avg={avg_iter:.2f}") + print( + f" Iterations (target): min={min_iter}, max={max_iter}, avg={avg_iter:.2f}" + ) else: print(f"\n Total entries: {len(entries)}") print(f" Unique files: {len(unique_files)}") @@ -391,7 +456,7 @@ def print_statistics(entries: List[Dict[str, Any]], algorithm: str) -> None: # Show sample entry if entries: - print(f"\n Sample entry (first):") + print("\n Sample entry (first):") sample = entries[0] for key, value in sorted(sample.items()): print(f" {key}: {value}") @@ -399,51 +464,55 @@ def print_statistics(entries: List[Dict[str, Any]], algorithm: str) -> None: def main(): parser = argparse.ArgumentParser( - description='Parse algorithm feature logs and export to Feather format for training', + description="Parse algorithm feature logs and export to Feather format for training", formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=f""" + epilog=""" Supported Algorithms: - FP - Feasibility Pump (parses FP_FEATURES and FP_RESULT logs) - PDLP - LP Solver (parses PDLP_FEATURES and PDLP_RESULT logs) - CP - Constraint Propagation (parses CP_FEATURES and CP_RESULT logs) - FJ - Feasibility Jump (parses legacy FJ: format) + FP - Feasibility Pump (parses FP_FEATURES and FP_RESULT logs) + PDLP - LP Solver (parses PDLP_FEATURES and PDLP_RESULT logs) + CP - Constraint Propagation (parses CP_FEATURES and CP_RESULT logs) + FJ - Feasibility Jump (parses legacy FJ: format) + CPUFJ - CPU Feasibility Jump (parses CPUFJ_FEATURES single-line logs) Examples: python determinism_logs_parse.py logs/ --algorithm FP -o fp_data.feather python determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.feather python determinism_logs_parse.py logs/ --algorithm CP -o cp_data.feather python determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.feather + python determinism_logs_parse.py logs/ --algorithm CPUFJ -o cpufj_data.feather # Limit to first 10 files for testing python determinism_logs_parse.py logs/ --algorithm FP --max-files 10 - """ + """, ) parser.add_argument( - 'input_dir', - help='Directory containing .log files to parse' + "input_dir", help="Directory containing .log files to parse" ) parser.add_argument( - '--algorithm', '-a', + "--algorithm", + "-a", required=True, choices=SUPPORTED_ALGORITHMS, - help='Algorithm to parse logs for' + help="Algorithm to parse logs for", ) parser.add_argument( - '-o', '--output', + "-o", + "--output", default=None, - help='Output Feather file path (default: _data.feather)' + help="Output Feather file path (default: _data.feather)", ) parser.add_argument( - '--verbose', '-v', - action='store_true', - help='Print verbose output including warnings' + "--verbose", + "-v", + action="store_true", + help="Print verbose output including warnings", ) parser.add_argument( - '--max-files', + "--max-files", type=int, default=None, - help='Limit number of log files to process (useful for testing)' + help="Limit number of log files to process (useful for testing)", ) args = parser.parse_args() @@ -454,7 +523,7 @@ def main(): # Find all .log files in the input directory print(f"\nScanning {args.input_dir} for .log files...") - log_files = glob.glob(os.path.join(args.input_dir, '*.log')) + log_files = glob.glob(os.path.join(args.input_dir, "*.log")) if not log_files: print(f"Error: No .log files found in {args.input_dir}") @@ -465,27 +534,40 @@ def main(): # Apply max-files limit if specified if args.max_files is not None and args.max_files > 0: if args.max_files < len(log_files): - log_files = log_files[:args.max_files] + log_files = log_files[: args.max_files] print(f"Limiting to first {args.max_files} files (--max-files)") else: - print(f"Note: --max-files={args.max_files} is >= total files, using all files") + print( + f"Note: --max-files={args.max_files} is >= total files, using all files" + ) # Parse logs based on algorithm - if args.algorithm == 'FP': + if args.algorithm == "FP": entries = parse_fp_logs(log_files) - elif args.algorithm == 'PDLP': + elif args.algorithm == "PDLP": entries = parse_pdlp_logs(log_files) - elif args.algorithm == 'CP': + elif args.algorithm == "CP": entries = parse_cp_logs(log_files) - elif args.algorithm == 'FJ': + elif args.algorithm == "FJ": entries = parse_fj_logs(log_files) + elif args.algorithm == "CPUFJ": + entries = parse_cpufj_logs(log_files) else: print(f"Error: Unsupported algorithm: {args.algorithm}") return 1 if not entries: print(f"\nError: No entries found for {args.algorithm}") - print(f"Make sure your logs contain {args.algorithm}_FEATURES and {args.algorithm}_RESULT lines") + if args.algorithm in ["FP", "PDLP", "CP"]: + print( + f"Make sure your logs contain {args.algorithm}_FEATURES and {args.algorithm}_RESULT lines" + ) + elif args.algorithm == "FJ": + print("Make sure your logs contain FJ: lines with key=value pairs") + elif args.algorithm == "CPUFJ": + print( + "Make sure your logs contain CPUFJ_FEATURES lines with key=value pairs" + ) return 1 # Print statistics @@ -494,8 +576,19 @@ def main(): # Convert to DataFrame df = pd.DataFrame(entries) + # Convert all non-string columns to numeric types FIRST + # This ensures proper type inference before validation + print("\nConverting column types...") + for col in df.columns: + if col not in ["file"]: # Keep 'file' as string + # Try to convert to numeric, coercing errors to NaN + df[col] = pd.to_numeric(df[col], errors="coerce") + print( + f" ✓ Converted {len([c for c in df.columns if c != 'file'])} columns to numeric types" + ) + # Validate: Check for NaN and infinite values - print(f"\nValidating data integrity...") + print("\nValidating data integrity...") # Check for NaN nan_counts = df.isna().sum() @@ -509,89 +602,134 @@ def main(): if inf_count > 0: inf_counts[col] = inf_count - has_issues = len(columns_with_nan) > 0 or len(inf_counts) > 0 - - if has_issues: - print(f"\n{'='*70}") - print(f"ERROR: Invalid data detected (NaN/inf values)!") - print(f"{'='*70}") - print(f"\nColumns with invalid values:") - for col, count in columns_with_nan.items(): - pct = (count / len(df)) * 100 - print(f" {col}: {count} NaN ({pct:.1f}%)") - for col, count in inf_counts.items(): - pct = (count / len(df)) * 100 - print(f" {col}: {count} infinite ({pct:.1f}%)") + # Collect all problematic columns + problematic_columns = set() + problematic_columns.update(columns_with_nan.index) + problematic_columns.update(inf_counts.keys()) + + if problematic_columns: + print(f"\n{'=' * 70}") + print("⚠️ WARNING: Invalid data detected (NaN/inf values)!") + print(f"{'=' * 70}") + print("\nColumns with invalid values (will be removed):") + for col in sorted(problematic_columns): + nan_count = ( + nan_counts.get(col, 0) if col in columns_with_nan.index else 0 + ) + inf_count = inf_counts.get(col, 0) + total_invalid = nan_count + inf_count + pct = (total_invalid / len(df)) * 100 + + issues = [] + if nan_count > 0: + issues.append(f"{nan_count} NaN") + if inf_count > 0: + issues.append(f"{inf_count} inf") + + print(f" ❌ {col}: {', '.join(issues)} ({pct:.1f}%)") # Show which log files have the issues rows_with_issues_mask = df.isna().any(axis=1) for col in inf_counts.keys(): if col in df.columns: rows_with_issues_mask |= np.isinf(df[col]) - rows_with_nan = df[rows_with_issues_mask] - problematic_files = rows_with_nan['file'].unique() - print(f"\nProblematic log files ({len(problematic_files)} total):") - for i, filename in enumerate(sorted(problematic_files)[:20], 1): - count_in_file = len(rows_with_nan[rows_with_nan['file'] == filename]) - print(f" {i}. {filename}: {count_in_file} invalid entries") - if len(problematic_files) > 20: - print(f" ... and {len(problematic_files) - 20} more files") - - print(f"\nSample rows with invalid data:") - cols_to_show = ['file'] - cols_to_show.extend([col for col in columns_with_nan.index]) - cols_to_show.extend([col for col in inf_counts.keys() if col not in columns_with_nan.index]) - cols_to_show = [col for col in cols_to_show if col in df.columns] - print(rows_with_nan[cols_to_show].head(10)) - - print(f"\nPossible causes:") - print(f" 1. Algorithm failed/crashed during execution (produced 'nan' in logs)") - print(f" 2. Incomplete log entries (missing FEATURES or RESULT lines)") - print(f" 3. Log format doesn't match expected pattern") - print(f"\nAction required:") - print(f" - Review the problematic log files listed above") - print(f" - Fix the underlying issues causing NaN values") - print(f" - Re-run the algorithm on those problem instances") - print(f"{'='*70}\n") - return 1 - - print(f" ✅ No missing values detected") + rows_with_issues = df[rows_with_issues_mask] + problematic_files = rows_with_issues["file"].unique() + print( + f"\nAffected log files: {len(problematic_files)} files, {len(rows_with_issues)} entries" + ) + if len(problematic_files) <= 10: + for filename in sorted(problematic_files): + count_in_file = len( + rows_with_issues[rows_with_issues["file"] == filename] + ) + print( + f" - {filename}: {count_in_file} entries with invalid values" + ) + else: + print(f" (Showing first 10 of {len(problematic_files)} files)") + for i, filename in enumerate(sorted(problematic_files)[:10], 1): + count_in_file = len( + rows_with_issues[rows_with_issues["file"] == filename] + ) + print(f" {i}. {filename}: {count_in_file} entries") + + # Remove problematic columns + original_column_count = len(df.columns) + df = df.drop(columns=list(problematic_columns)) + removed_count = len(problematic_columns) + remaining_count = len(df.columns) + + print(f"\n✓ Removed {removed_count} problematic column(s)") + print(f" Original columns: {original_column_count}") + print(f" Remaining columns: {remaining_count}") + print(f" Kept all {len(df)} entries") + print(f"{'=' * 70}") + + # Check if we have any meaningful columns left (excluding metadata) + feature_cols_remaining = [ + col + for col in df.columns + if col not in ["file", "iter", "iterations"] + ] + if len(feature_cols_remaining) < 5: + print( + f"\n⚠️ WARNING: Very few features remaining ({len(feature_cols_remaining)})!" + ) + print( + " This may not be sufficient for training a regression model." + ) + print(" Consider fixing the underlying issues in your logs.") + else: + print(" ✅ No invalid values detected") # Filter out negative iterations (invalid data) - if 'iter' in df.columns: - negative_mask = df['iter'] < 0 + if "iter" in df.columns: + negative_mask = df["iter"] < 0 negative_count = negative_mask.sum() if negative_count > 0: - print(f"\n⚠️ Found {negative_count} entries with negative iterations ({negative_count/len(df)*100:.2f}%)") + print( + f"\n⚠️ Found {negative_count} entries with negative iterations ({negative_count / len(df) * 100:.2f}%)" + ) # Show which files have negative iterations negative_entries = df[negative_mask] - problematic_files = negative_entries['file'].unique() + problematic_files = negative_entries["file"].unique() if len(problematic_files) <= 10: - print(f" Affected files: {', '.join(sorted(problematic_files))}") + print( + f" Affected files: {', '.join(sorted(problematic_files))}" + ) else: print(f" Affected files: {len(problematic_files)} files") # Drop negative iterations df = df[~negative_mask].reset_index(drop=True) - print(f" → Dropped negative entries, remaining: {len(df)} entries") + print( + f" → Dropped negative entries, remaining: {len(df)} entries" + ) # Check if we have any data left if len(df) == 0: - print(f"\n❌ Error: No valid entries remaining after filtering!") - print(f" All entries had negative iterations.") + print( + "\n❌ Error: No valid entries remaining after filtering!" + ) + print(" All entries had negative iterations.") return 1 # Print obtained features (all columns) print(f"\nObtained Features ({len(df.columns)} total):") - print(f"{'='*70}") + print(f"{'=' * 70}") # Separate metadata from actual features - metadata_cols = ['file'] - target_cols = ['iter', 'iterations'] # Target variable (if present) + metadata_cols = ["file"] + target_cols = ["iter", "iterations"] # Target variable (if present) - feature_cols = [col for col in df.columns if col not in metadata_cols and col not in target_cols] + feature_cols = [ + col + for col in df.columns + if col not in metadata_cols and col not in target_cols + ] # Print in categories if metadata_cols: @@ -608,37 +746,109 @@ def main(): for i, col in enumerate(sorted(feature_cols), 1): # Print 3 features per line for readability if i % 3 == 1: - print(f" ", end="") + print(" ", end="") print(f"{col:30s}", end="") if i % 3 == 0: print() # Newline after 3 features if len(feature_cols) % 3 != 0: print() # Final newline if needed - print(f"{'='*70}") + print(f"{'=' * 70}") + + # Final validation: Ensure NO NaN values remain in the DataFrame + print("\nFinal validation before saving...") + remaining_nans = df.isna().sum() + columns_with_remaining_nans = remaining_nans[remaining_nans > 0] + + if len(columns_with_remaining_nans) > 0: + print(f"\n{'=' * 70}") + print("❌ CRITICAL ERROR: NaN values still present after cleaning!") + print(f"{'=' * 70}") + print("\nColumns with remaining NaN values:") + for col, count in columns_with_remaining_nans.items(): + pct = (count / len(df)) * 100 + print(f" - {col}: {count} NaN values ({pct:.1f}%)") + + print("\nRemoving these columns to ensure clean output...") + df = df.drop(columns=list(columns_with_remaining_nans.index)) + print( + f" ✓ Removed {len(columns_with_remaining_nans)} additional column(s)" + ) + print(f" Remaining columns: {len(df.columns)}") + + # Check if we have any meaningful columns left + feature_cols_remaining = [ + col + for col in df.columns + if col not in ["file", "iter", "iterations"] + ] + if len(feature_cols_remaining) == 0: + print( + "\n❌ FATAL ERROR: No feature columns remaining after NaN removal!" + ) + print(" All columns contained NaN values.") + print(" Cannot proceed with saving - no valid data to save.") + return 1 + + print(f"{'=' * 70}") + + # Double-check: verify no NaN or inf values exist + total_nans = df.isna().sum().sum() + if total_nans > 0: + print( + f"\n❌ FATAL ERROR: {total_nans} NaN values still present despite cleaning!" + ) + print(" This should not happen - please report this as a bug.") + return 1 + + # Check for infinite values + numeric_cols = df.select_dtypes(include=[np.number]).columns + total_infs = 0 + for col in numeric_cols: + total_infs += np.isinf(df[col]).sum() + + if total_infs > 0: + print( + f"\n❌ FATAL ERROR: {total_infs} infinite values still present despite cleaning!" + ) + print(" This should not happen - please report this as a bug.") + # Show which columns still have inf + for col in numeric_cols: + inf_count = np.isinf(df[col]).sum() + if inf_count > 0: + print(f" - {col}: {inf_count} inf values") + return 1 + + print(" ✅ No NaN or infinite values detected - data is clean") # Save to Feather file (Apache Arrow format for fast I/O) print(f"\nSaving {len(df)} entries to {args.output}...") - df.to_feather(args.output, compression='lz4') + df.to_feather(args.output, compression="lz4") # Get file size file_size_bytes = os.path.getsize(args.output) file_size_mb = file_size_bytes / (1024 * 1024) - print(f"\n{'='*70}") + print(f"\n{'=' * 70}") print(f"✓ Success! Saved {len(df)} entries to {args.output}") print(f" File size: {file_size_mb:.2f} MB") - print(f"{'='*70}") - print(f"\nNext steps:") - print(f" 1. View available features:") - print(f" python scripts/train_regressor.py {args.output} --regressor xgboost --list-features") - print(f" 2. Train a model:") - print(f" python scripts/train_regressor.py {args.output} --regressor xgboost --seed 42") - print(f" 3. Train with early stopping and C++ export:") - print(f" python scripts/train_regressor.py {args.output} --regressor xgboost --seed 42 --early-stopping 20 --treelite-compile 8") + print(f"{'=' * 70}") + print("\nNext steps:") + print(" 1. View available features:") + print( + f" python scripts/train_regressor.py {args.output} --regressor xgboost --list-features" + ) + print(" 2. Train a model:") + print( + f" python scripts/train_regressor.py {args.output} --regressor xgboost --seed 42" + ) + print(" 3. Train with early stopping and C++ export:") + print( + f" python scripts/train_regressor.py {args.output} --regressor xgboost --seed 42 --early-stopping 20 --treelite-compile 8" + ) return 0 -if __name__ == '__main__': +if __name__ == "__main__": exit(main()) diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py index dc12fa384..24204b650 100755 --- a/scripts/train_regressor.py +++ b/scripts/train_regressor.py @@ -1,5 +1,6 @@ #!/usr/bin/env python3 # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. +# SPDX-License-Identifier: Apache-2.0 # All rights reserved. # SPDX-License-Identifier: Apache-2.0 # @@ -36,16 +37,19 @@ import os from typing import List, Dict, Any, Tuple import warnings -warnings.filterwarnings('ignore', category=UserWarning) + +warnings.filterwarnings("ignore", category=UserWarning) AVAILABLE_REGRESSORS = [ - 'linear', - 'poly2', 'poly3', 'poly4', - 'xgboost', - 'lightgbm', - 'random_forest', - 'gradient_boosting' + "linear", + "poly2", + "poly3", + "poly4", + "xgboost", + "lightgbm", + "random_forest", + "gradient_boosting", ] # ============================================================================ @@ -58,38 +62,54 @@ # 'time', # 'avg_constraint_range', # 'binary_ratio', - 'avg_obj_coeff_magnitude', - 'n_of_minimums_for_exit', - 'feasibility_run', - 'fixed_var_ratio', - 'unbounded_var_ratio', - 'obj_var_ratio', - 'avg_related_vars_per_var', - 'avg_constraint_range', - 'nnz_variance', - 'avg_variable_range', - 'min_nnz_per_row', - 'constraint_var_ratio', - 'avg_var_degree', - 'equality_ratio', - 'integer_ratio', - 'binary_ratio', - 'max_related_vars', - 'problem_size_score', - 'structural_complexity', - 'tight_constraint_ratio', - 'tolerance', - 'time_limit', - 'tolerance', - 'primal_objective', - 'dual_objective', - 'gap', - 'l2_primal_residual', - 'l2_dual_residual', - 'detect_infeasibility', - 'iteration_limit', - 'termination', - 'check_infeasibility' + "avg_obj_coeff_magnitude", + "n_of_minimums_for_exit", + "feasibility_run", + "fixed_var_ratio", + "unbounded_var_ratio", + "obj_var_ratio", + "avg_related_vars_per_var", + "avg_constraint_range", + "nnz_variance", + "avg_variable_range", + "min_nnz_per_row", + "constraint_var_ratio", + "avg_var_degree", + "equality_ratio", + "integer_ratio", + "binary_ratio", + "max_related_vars", + "problem_size_score", + "structural_complexity", + "tight_constraint_ratio", + "tolerance", + "time_limit", + "tolerance", + "primal_objective", + "dual_objective", + "gap", + "l2_primal_residual", + "l2_dual_residual", + "detect_infeasibility", + "iteration_limit", + "termination", + "check_infeasibility", + "iter_since_best", + "tid", + "curr_obj", + "obj_weight", + "L3_miss", + "L2_miss", + "L1_miss", + "stores_per_iter", + "loads_per_iter", + "is_feas", + "feas_found", + "viol_ratio", + "eval_intensity", + "nnz_per_move", + "iter", + "max_weight", ] # Alternatively, specify ONLY the features you want to use @@ -103,39 +123,49 @@ # ============================================================================ -def load_data(data_path: str, target_col: str = 'iter') -> pd.DataFrame: +def load_data(data_path: str, target_col: str = "iter") -> pd.DataFrame: """Load data file (supports .feather and legacy .pkl formats).""" ext = os.path.splitext(data_path)[1].lower() - if ext == '.feather': + if ext == ".feather": # Fast Apache Arrow format df = pd.read_feather(data_path) - elif ext == '.pkl': + elif ext == ".pkl": # Legacy pickle support - with open(data_path, 'rb') as f: + with open(data_path, "rb") as f: data = pickle.load(f) if not isinstance(data, list): - raise ValueError(f"Expected list of dictionaries, got {type(data)}") + raise ValueError( + f"Expected list of dictionaries, got {type(data)}" + ) if len(data) == 0: raise ValueError("Empty dataset") df = pd.DataFrame(data) else: - raise ValueError(f"Unsupported file format: {ext}. Use .feather (recommended) or .pkl") + raise ValueError( + f"Unsupported file format: {ext}. Use .feather (recommended) or .pkl" + ) # Validate required columns - if 'file' not in df.columns: + if "file" not in df.columns: raise ValueError("Missing required 'file' column in data") if target_col not in df.columns: - raise ValueError(f"Missing target column '{target_col}' in data. Available columns: {list(df.columns)}") + raise ValueError( + f"Missing target column '{target_col}' in data. Available columns: {list(df.columns)}" + ) return df -def split_by_files(df: pd.DataFrame, test_size: float = 0.2, - random_state: int = None, stratify_by: str = None) -> Tuple[pd.DataFrame, pd.DataFrame]: +def split_by_files( + df: pd.DataFrame, + test_size: float = 0.2, + random_state: int = None, + stratify_by: str = None, +) -> Tuple[pd.DataFrame, pd.DataFrame]: """ Split data into train/test sets based on unique files. Ensures all entries from a file go to either train or test, not both. @@ -144,36 +174,43 @@ def split_by_files(df: pd.DataFrame, test_size: float = 0.2, ---- stratify_by: Optional column name to stratify split (e.g., 'iter' for balanced target distribution) """ - unique_files = df['file'].unique() + unique_files = df["file"].unique() # Optionally stratify by target distribution if stratify_by: # Create stratification labels based on quantiles of the specified column - file_stats = df.groupby('file')[stratify_by].median() - stratify_labels = pd.qcut(file_stats, q=min(5, len(unique_files)), labels=False, duplicates='drop') + file_stats = df.groupby("file")[stratify_by].median() + stratify_labels = pd.qcut( + file_stats, + q=min(5, len(unique_files)), + labels=False, + duplicates="drop", + ) train_files, test_files = train_test_split( unique_files, test_size=test_size, random_state=random_state, - stratify=stratify_labels + stratify=stratify_labels, ) else: train_files, test_files = train_test_split( - unique_files, - test_size=test_size, - random_state=random_state + unique_files, test_size=test_size, random_state=random_state ) - train_df = df[df['file'].isin(train_files)].copy() - test_df = df[df['file'].isin(test_files)].copy() + train_df = df[df["file"].isin(train_files)].copy() + test_df = df[df["file"].isin(test_files)].copy() - print(f"\nData Split:") + print("\nData Split:") print(f" Total entries: {len(df)}") print(f" Train entries: {len(train_df)} ({len(train_files)} files)") print(f" Test entries: {len(test_df)} ({len(test_files)} files)") # Check distribution similarity (use stratify_by column if provided, otherwise first numeric column) - target_col = stratify_by if stratify_by else df.select_dtypes(include=[np.number]).columns[0] + target_col = ( + stratify_by + if stratify_by + else df.select_dtypes(include=[np.number]).columns[0] + ) if target_col in train_df.columns and target_col in test_df.columns: train_target_mean = train_df[target_col].mean() test_target_mean = test_df[target_col].mean() @@ -181,30 +218,53 @@ def split_by_files(df: pd.DataFrame, test_size: float = 0.2, test_target_std = test_df[target_col].std() print(f"\nTarget ('{target_col}') Distribution:") - print(f" Train: mean={train_target_mean:.2f}, std={train_target_std:.2f}") - print(f" Test: mean={test_target_mean:.2f}, std={test_target_std:.2f}") + print( + f" Train: mean={train_target_mean:.2f}, std={train_target_std:.2f}" + ) + print( + f" Test: mean={test_target_mean:.2f}, std={test_target_std:.2f}" + ) - mean_diff_pct = abs(train_target_mean - test_target_mean) / train_target_mean * 100 if train_target_mean != 0 else 0 + mean_diff_pct = ( + abs(train_target_mean - test_target_mean) / train_target_mean * 100 + if train_target_mean != 0 + else 0 + ) if mean_diff_pct > 10: - print(f" ⚠️ Warning: Train/test target means differ by {mean_diff_pct:.1f}%") - print(f" Consider using stratified split or different random seed") + print( + f" ⚠️ Warning: Train/test target means differ by {mean_diff_pct:.1f}%" + ) + print( + " Consider using stratified split or different random seed" + ) return train_df, test_df -def list_available_features(df: pd.DataFrame, target_col: str = 'iter') -> List[str]: +def list_available_features( + df: pd.DataFrame, target_col: str = "iter" +) -> List[str]: """ List all available numeric features in the dataset. Helper function to see what features can be selected/excluded. """ # Drop target and metadata columns - cols_to_drop = [target_col, 'file', 'iter', 'iterations'] # Drop common target column names - X = df.drop(columns=[c for c in cols_to_drop if c in df.columns], errors='ignore') + cols_to_drop = [ + target_col, + "file", + "iter", + "iterations", + ] # Drop common target column names + X = df.drop( + columns=[c for c in cols_to_drop if c in df.columns], errors="ignore" + ) X = X.select_dtypes(include=[np.number]) return sorted(X.columns.tolist()) -def validate_data_quality(df: pd.DataFrame, target_col: str = 'iter', verbose: bool = True) -> Tuple[bool, Dict[str, Any]]: +def validate_data_quality( + df: pd.DataFrame, target_col: str = "iter", verbose: bool = True +) -> Tuple[bool, Dict[str, Any]]: """ Validate data quality and report issues. @@ -213,16 +273,16 @@ def validate_data_quality(df: pd.DataFrame, target_col: str = 'iter', verbose: b (is_valid, report_dict) """ report = { - 'target_issues': [], - 'feature_issues': [], - 'rows_with_issues': [], - 'is_valid': True + "target_issues": [], + "feature_issues": [], + "rows_with_issues": [], + "is_valid": True, } # Check target variable if target_col not in df.columns: - report['is_valid'] = False - report['target_issues'].append(f"Missing '{target_col}' column") + report["is_valid"] = False + report["target_issues"].append(f"Missing '{target_col}' column") return False, report y = df[target_col] @@ -230,16 +290,20 @@ def validate_data_quality(df: pd.DataFrame, target_col: str = 'iter', verbose: b # Check for NaN in target nan_count = y.isna().sum() if nan_count > 0: - report['is_valid'] = False - report['target_issues'].append(f"Target has {nan_count} NaN values ({nan_count/len(y)*100:.1f}%)") - report['rows_with_issues'].extend(df[y.isna()].index.tolist()) + report["is_valid"] = False + report["target_issues"].append( + f"Target has {nan_count} NaN values ({nan_count / len(y) * 100:.1f}%)" + ) + report["rows_with_issues"].extend(df[y.isna()].index.tolist()) # Check for inf in target inf_count = np.isinf(y).sum() if inf_count > 0: - report['is_valid'] = False - report['target_issues'].append(f"Target has {inf_count} infinite values ({inf_count/len(y)*100:.1f}%)") - report['rows_with_issues'].extend(df[np.isinf(y)].index.tolist()) + report["is_valid"] = False + report["target_issues"].append( + f"Target has {inf_count} infinite values ({inf_count / len(y) * 100:.1f}%)" + ) + report["rows_with_issues"].extend(df[np.isinf(y)].index.tolist()) # Check for extreme values in target if not y.isna().all() and not np.isinf(y).all(): @@ -248,12 +312,16 @@ def validate_data_quality(df: pd.DataFrame, target_col: str = 'iter', verbose: b y_max = y_clean.max() y_min = y_clean.min() if y_max > 1e10: - report['target_issues'].append(f"Target has very large values (max={y_max:.2e})") + report["target_issues"].append( + f"Target has very large values (max={y_max:.2e})" + ) if y_min < -1e10: - report['target_issues'].append(f"Target has very large negative values (min={y_min:.2e})") + report["target_issues"].append( + f"Target has very large negative values (min={y_min:.2e})" + ) # Check features - X = df.drop(columns=[target_col, 'file'], errors='ignore') + X = df.drop(columns=[target_col, "file"], errors="ignore") X_numeric = X.select_dtypes(include=[np.number]) for col in X_numeric.columns: @@ -264,68 +332,84 @@ def validate_data_quality(df: pd.DataFrame, target_col: str = 'iter', verbose: b if nan_count > 0: pct = nan_count / len(col_data) * 100 if pct > 10: # Only report if > 10% are NaN - report['feature_issues'].append(f"{col}: {nan_count} NaN ({pct:.1f}%)") + report["feature_issues"].append( + f"{col}: {nan_count} NaN ({pct:.1f}%)" + ) # Check for inf inf_count = np.isinf(col_data).sum() if inf_count > 0: - report['is_valid'] = False + report["is_valid"] = False pct = inf_count / len(col_data) * 100 - report['feature_issues'].append(f"{col}: {inf_count} infinite ({pct:.1f}%)") - report['rows_with_issues'].extend(df[np.isinf(col_data)].index.tolist()) + report["feature_issues"].append( + f"{col}: {inf_count} infinite ({pct:.1f}%)" + ) + report["rows_with_issues"].extend( + df[np.isinf(col_data)].index.tolist() + ) # Deduplicate row indices - report['rows_with_issues'] = sorted(list(set(report['rows_with_issues']))) + report["rows_with_issues"] = sorted(list(set(report["rows_with_issues"]))) # Print report if verbose - if verbose and (report['target_issues'] or report['feature_issues']): - print("\n" + "="*70) + if verbose and (report["target_issues"] or report["feature_issues"]): + print("\n" + "=" * 70) print("DATA QUALITY ISSUES DETECTED") - print("="*70) + print("=" * 70) - if report['target_issues']: + if report["target_issues"]: print("\nTarget Variable Issues:") - for issue in report['target_issues']: + for issue in report["target_issues"]: print(f" ❌ {issue}") - if report['feature_issues']: - print(f"\nFeature Issues ({len(report['feature_issues'])} features affected):") - for issue in report['feature_issues'][:10]: # Show first 10 + if report["feature_issues"]: + print( + f"\nFeature Issues ({len(report['feature_issues'])} features affected):" + ) + for issue in report["feature_issues"][:10]: # Show first 10 print(f" ⚠️ {issue}") - if len(report['feature_issues']) > 10: - print(f" ... and {len(report['feature_issues']) - 10} more features") + if len(report["feature_issues"]) > 10: + print( + f" ... and {len(report['feature_issues']) - 10} more features" + ) - if report['rows_with_issues']: + if report["rows_with_issues"]: print(f"\nAffected Rows: {len(report['rows_with_issues'])} total") - if len(report['rows_with_issues']) <= 10: + if len(report["rows_with_issues"]) <= 10: print(f" Row indices: {report['rows_with_issues']}") else: print(f" First 10: {report['rows_with_issues'][:10]}") # Show sample problematic rows with filenames - if 'file' in df.columns: - print(f"\n Sample problematic entries:") - sample_indices = report['rows_with_issues'][:5] + if "file" in df.columns: + print("\n Sample problematic entries:") + sample_indices = report["rows_with_issues"][:5] for idx in sample_indices: if idx < len(df): - filename = df.iloc[idx].get('file', 'unknown') - iter_val = df.iloc[idx].get('iter', 'N/A') - print(f" Row {idx}: file={filename}, iter={iter_val}") + filename = df.iloc[idx].get("file", "unknown") + iter_val = df.iloc[idx].get("iter", "N/A") + print( + f" Row {idx}: file={filename}, iter={iter_val}" + ) print("\nSuggested Actions:") - if not report['is_valid']: + if not report["is_valid"]: print(" 1. Remove rows with invalid data: --drop-invalid-rows") print(" 2. Check your log files for data collection issues") print(" 3. Verify algorithm didn't produce invalid results") else: - print(" Data is valid but has some NaN values in features (will be handled)") + print( + " Data is valid but has some NaN values in features (will be handled)" + ) - print("="*70 + "\n") + print("=" * 70 + "\n") - return report['is_valid'], report + return report["is_valid"], report -def prepare_features(df: pd.DataFrame, target_col: str = 'iter') -> Tuple[pd.DataFrame, pd.Series, List[str]]: +def prepare_features( + df: pd.DataFrame, target_col: str = "iter" +) -> Tuple[pd.DataFrame, pd.Series, List[str]]: """ Prepare features and target from DataFrame. Excludes 'file' and target column from features. @@ -335,7 +419,7 @@ def prepare_features(df: pd.DataFrame, target_col: str = 'iter') -> Tuple[pd.Dat y = df[target_col].copy() # Drop non-feature columns - X = df.drop(columns=[target_col, 'file']) + X = df.drop(columns=[target_col, "file"]) # Ensure all features are numeric non_numeric = X.select_dtypes(exclude=[np.number]).columns.tolist() @@ -348,21 +432,31 @@ def prepare_features(df: pd.DataFrame, target_col: str = 'iter') -> Tuple[pd.Dat if FEATURES_TO_INCLUDE_ONLY: # Use only specified features - available_features = [f for f in FEATURES_TO_INCLUDE_ONLY if f in X.columns] - missing_features = [f for f in FEATURES_TO_INCLUDE_ONLY if f not in X.columns] + available_features = [ + f for f in FEATURES_TO_INCLUDE_ONLY if f in X.columns + ] + missing_features = [ + f for f in FEATURES_TO_INCLUDE_ONLY if f not in X.columns + ] if missing_features: - print(f"Warning: Requested features not found in data: {missing_features}") + print( + f"Warning: Requested features not found in data: {missing_features}" + ) X = X[available_features] - print(f"Feature selection: Using only {len(available_features)} specified features") + print( + f"Feature selection: Using only {len(available_features)} specified features" + ) elif FEATURES_TO_EXCLUDE: # Exclude specified features features_to_drop = [f for f in FEATURES_TO_EXCLUDE if f in X.columns] if features_to_drop: X = X.drop(columns=features_to_drop) - print(f"Feature selection: Excluded {len(features_to_drop)} features: {features_to_drop}") + print( + f"Feature selection: Excluded {len(features_to_drop)} features: {features_to_drop}" + ) feature_names = X.columns.tolist() @@ -373,143 +467,161 @@ def prepare_features(df: pd.DataFrame, target_col: str = 'iter') -> Tuple[pd.Dat ) if len(feature_names) != original_feature_count: - print(f" Using {len(feature_names)} of {original_feature_count} available features") + print( + f" Using {len(feature_names)} of {original_feature_count} available features" + ) return X, y, feature_names -def create_regressor(regressor_type: str, random_state: int = None, - tune_hyperparams: bool = False, verbose: bool = True): +def create_regressor( + regressor_type: str, + random_state: int = None, + tune_hyperparams: bool = False, + verbose: bool = True, +): """ Create a regression model with optional preprocessing pipeline. Returns: (model, needs_scaling) """ - if regressor_type == 'linear': + if regressor_type == "linear": model = LinearRegression() needs_scaling = True - elif regressor_type.startswith('poly'): + elif regressor_type.startswith("poly"): degree = int(regressor_type[-1]) - model = Pipeline([ - ('poly', PolynomialFeatures(degree=degree, include_bias=False)), - ('regressor', Ridge(alpha=1.0)) # Ridge to handle multicollinearity - ]) + model = Pipeline( + [ + ( + "poly", + PolynomialFeatures(degree=degree, include_bias=False), + ), + ( + "regressor", + Ridge(alpha=1.0), + ), # Ridge to handle multicollinearity + ] + ) needs_scaling = True - elif regressor_type == 'xgboost': + elif regressor_type == "xgboost": try: import xgboost as xgb except ImportError: - raise ImportError("XGBoost not installed. Install with: pip install xgboost") + raise ImportError( + "XGBoost not installed. Install with: pip install xgboost" + ) params = { - 'objective': 'reg:squarederror', - 'random_state': random_state, - 'n_estimators': 100, - 'max_depth': 6, - 'tree_method': 'hist', - 'learning_rate': 0.1, - 'verbosity': 1 if verbose else 0, + "objective": "reg:squarederror", + "random_state": random_state, + "n_estimators": 100, + "max_depth": 6, + "tree_method": "hist", + "learning_rate": 0.1, + "verbosity": 1 if verbose else 0, # Regularization to prevent overfitting - 'min_child_weight': 3, # Minimum sum of weights in a leaf - 'gamma': 0.1, # Minimum loss reduction for split - 'subsample': 0.8, # Fraction of samples per tree - 'colsample_bytree': 0.8, # Fraction of features per tree - 'reg_alpha': 0.1, # L1 regularization - 'reg_lambda': 1.0, # L2 regularization + "min_child_weight": 3, # Minimum sum of weights in a leaf + "gamma": 0.1, # Minimum loss reduction for split + "subsample": 0.8, # Fraction of samples per tree + "colsample_bytree": 0.8, # Fraction of features per tree + "reg_alpha": 0.1, # L1 regularization + "reg_lambda": 1.0, # L2 regularization } if tune_hyperparams: # Stronger regularization for tuned version - params.update({ - 'n_estimators': 200, - 'max_depth': 5, # Shallower trees - 'learning_rate': 0.05, # Lower learning rate - 'min_child_weight': 5, # Higher minimum weight - 'gamma': 0.2, # More conservative splits - 'subsample': 0.7, # More aggressive subsampling - 'colsample_bytree': 0.7, - 'reg_alpha': 0.5, # Stronger L1 - 'reg_lambda': 2.0, # Stronger L2 - }) + params.update( + { + "n_estimators": 200, + "max_depth": 5, # Shallower trees + "learning_rate": 0.05, # Lower learning rate + "min_child_weight": 5, # Higher minimum weight + "gamma": 0.2, # More conservative splits + "subsample": 0.7, # More aggressive subsampling + "colsample_bytree": 0.7, + "reg_alpha": 0.5, # Stronger L1 + "reg_lambda": 2.0, # Stronger L2 + } + ) model = xgb.XGBRegressor(**params) needs_scaling = False - elif regressor_type == 'lightgbm': + elif regressor_type == "lightgbm": try: import lightgbm as lgb except ImportError: - raise ImportError("LightGBM not installed. Install with: pip install lightgbm") + raise ImportError( + "LightGBM not installed. Install with: pip install lightgbm" + ) params = { - 'objective': 'regression', - 'random_state': random_state, - 'n_estimators': 100, - 'max_depth': 6, - 'learning_rate': 0.1, - 'verbosity': 1 if verbose else -1, + "objective": "regression", + "random_state": random_state, + "n_estimators": 100, + "max_depth": 4, + "learning_rate": 0.1, + "verbosity": 1 if verbose else -1, # Regularization to prevent overfitting - 'min_child_weight': 3, - 'min_split_gain': 0.1, - 'subsample': 0.8, - 'colsample_bytree': 0.8, - 'reg_alpha': 0.1, - 'reg_lambda': 1.0, + "min_child_weight": 3, + "min_split_gain": 0.1, + "subsample": 0.8, + "colsample_bytree": 0.8, + "reg_alpha": 0.1, + "reg_lambda": 1.0, } if tune_hyperparams: # Stronger regularization for tuned version - params.update({ - 'n_estimators': 200, - 'max_depth': 5, - 'learning_rate': 0.05, - 'min_child_weight': 5, - 'min_split_gain': 0.2, - 'subsample': 0.7, - 'colsample_bytree': 0.7, - 'reg_alpha': 0.5, - 'reg_lambda': 2.0, - }) + params.update( + { + "n_estimators": 200, + "max_depth": 5, + "learning_rate": 0.05, + "min_child_weight": 5, + "min_split_gain": 0.2, + "subsample": 0.7, + "colsample_bytree": 0.7, + "reg_alpha": 0.5, + "reg_lambda": 2.0, + } + ) model = lgb.LGBMRegressor(**params) needs_scaling = False - elif regressor_type == 'random_forest': + elif regressor_type == "random_forest": params = { - 'random_state': random_state, - 'n_estimators': 100, - 'max_depth': None, - 'min_samples_split': 2, - 'verbose': 1 if verbose else 0 + "random_state": random_state, + "n_estimators": 100, + "max_depth": None, + "min_samples_split": 2, + "verbose": 1 if verbose else 0, } if tune_hyperparams: - params.update({ - 'n_estimators': 200, - 'max_depth': 20, - 'min_samples_split': 5 - }) + params.update( + {"n_estimators": 200, "max_depth": 20, "min_samples_split": 5} + ) model = RandomForestRegressor(**params) needs_scaling = False - elif regressor_type == 'gradient_boosting': + elif regressor_type == "gradient_boosting": params = { - 'random_state': random_state, - 'n_estimators': 100, - 'max_depth': 5, - 'learning_rate': 0.1, - 'verbose': 1 if verbose else 0 + "random_state": random_state, + "n_estimators": 100, + "max_depth": 5, + "learning_rate": 0.1, + "verbose": 1 if verbose else 0, } if tune_hyperparams: - params.update({ - 'n_estimators': 200, - 'max_depth': 7, - 'learning_rate': 0.05 - }) + params.update( + {"n_estimators": 200, "max_depth": 7, "learning_rate": 0.05} + ) model = GradientBoostingRegressor(**params) needs_scaling = False @@ -520,40 +632,54 @@ def create_regressor(regressor_type: str, random_state: int = None, return model, needs_scaling -def get_feature_importance(model, feature_names: List[str], - regressor_type: str) -> None: +def get_feature_importance( + model, feature_names: List[str], regressor_type: str +) -> None: """Extract and print feature importance if available.""" - print(f"\nFeature Importance:") + print("\nFeature Importance:") try: - if regressor_type in ['xgboost', 'lightgbm', 'random_forest', 'gradient_boosting']: + if regressor_type in [ + "xgboost", + "lightgbm", + "random_forest", + "gradient_boosting", + ]: # Tree-based models have feature_importances_ importances = model.feature_importances_ indices = np.argsort(importances)[::-1] for i, idx in enumerate(indices, 1): - print(f" {i:3d}. {feature_names[idx]:40s}: {importances[idx]:.6f}") + print( + f" {i:3d}. {feature_names[idx]:40s}: {importances[idx]:.6f}" + ) - elif regressor_type == 'linear': + elif regressor_type == "linear": # Linear regression coefficients and intercept print(f"\nIntercept: {model.intercept_:.6f}\n") - print(f"Coefficients:") + print("Coefficients:") # Print each feature with its coefficient - for i, (feature_name, coef) in enumerate(zip(feature_names, model.coef_), 1): + for i, (feature_name, coef) in enumerate( + zip(feature_names, model.coef_), 1 + ): print(f" {i:3d}. {feature_name:40s}: {coef:.6f}") # Also show sorted by absolute value for importance ranking - print(f"\nRanked by absolute magnitude:") + print("\nRanked by absolute magnitude:") coefs_abs = np.abs(model.coef_) indices = np.argsort(coefs_abs)[::-1] for i, idx in enumerate(indices, 1): - print(f" {i:3d}. {feature_names[idx]:40s}: {model.coef_[idx]:.6f} (|{coefs_abs[idx]:.6f}|)") + print( + f" {i:3d}. {feature_names[idx]:40s}: {model.coef_[idx]:.6f} (|{coefs_abs[idx]:.6f}|)" + ) - elif regressor_type.startswith('poly'): + elif regressor_type.startswith("poly"): # For polynomial, get feature names and coefficients from the Ridge step - poly_features = model.named_steps['poly'].get_feature_names_out(feature_names) - coefs = np.abs(model.named_steps['regressor'].coef_) + poly_features = model.named_steps["poly"].get_feature_names_out( + feature_names + ) + coefs = np.abs(model.named_steps["regressor"].coef_) indices = np.argsort(coefs)[::-1] print(f" (Showing top 50 of {len(indices)} polynomial features)") @@ -570,11 +696,20 @@ def get_feature_importance(model, feature_names: List[str], print(f" Could not extract feature importance: {e}") -def evaluate_model(model, X_train, y_train, X_test, y_test, - feature_names: List[str], regressor_type: str, - cv_folds: int = 5, verbose: int = 0, - skip_cv: bool = False, X_test_original: pd.DataFrame = None, - test_df: pd.DataFrame = None) -> Tuple[float, float]: +def evaluate_model( + model, + X_train, + y_train, + X_test, + y_test, + feature_names: List[str], + regressor_type: str, + cv_folds: int = 5, + verbose: int = 0, + skip_cv: bool = False, + X_test_original: pd.DataFrame = None, + test_df: pd.DataFrame = None, +) -> Tuple[float, float]: """Evaluate model and print metrics. Returns (train_r2, test_r2). Args: @@ -586,18 +721,24 @@ def evaluate_model(model, X_train, y_train, X_test, y_test, if not skip_cv: print(f"\nCross-Validation on Training Set ({cv_folds}-fold):") try: - cv_scores = cross_val_score(model, X_train, y_train, - cv=cv_folds, - scoring='neg_mean_squared_error', - n_jobs=-1, - verbose=verbose) + cv_scores = cross_val_score( + model, + X_train, + y_train, + cv=cv_folds, + scoring="neg_mean_squared_error", + n_jobs=-1, + verbose=verbose, + ) cv_rmse = np.sqrt(-cv_scores) print(f" CV RMSE: {cv_rmse.mean():.4f} (+/- {cv_rmse.std():.4f})") except Exception as e: - print(f" CV failed (likely due to early stopping): {str(e)[:100]}") - print(f" Skipping cross-validation...") + print( + f" CV failed (likely due to early stopping): {str(e)[:100]}" + ) + print(" Skipping cross-validation...") else: - print(f"\nSkipping cross-validation (incompatible with early stopping)") + print("\nSkipping cross-validation (incompatible with early stopping)") # Training set metrics y_train_pred = model.predict(X_train) @@ -606,7 +747,7 @@ def evaluate_model(model, X_train, y_train, X_test, y_test, train_mae = mean_absolute_error(y_train, y_train_pred) train_r2 = r2_score(y_train, y_train_pred) - print(f"\nTraining Set Metrics:") + print("\nTraining Set Metrics:") print(f" MSE: {train_mse:.4f}") print(f" RMSE: {train_rmse:.4f}") print(f" MAE: {train_mae:.4f}") @@ -619,7 +760,7 @@ def evaluate_model(model, X_train, y_train, X_test, y_test, test_mae = mean_absolute_error(y_test, y_test_pred) test_r2 = r2_score(y_test, y_test_pred) - print(f"\nTest Set Metrics:") + print("\nTest Set Metrics:") print(f" MSE: {test_mse:.4f}") print(f" RMSE: {test_rmse:.4f}") print(f" MAE: {test_mae:.4f}") @@ -629,20 +770,26 @@ def evaluate_model(model, X_train, y_train, X_test, y_test, get_feature_importance(model, feature_names, regressor_type) # Sample predictions - print(f"\n20 Sample Predictions from Test Set:") - print(f" {'Actual':>10s} {'Predicted':>10s} {'Error':>10s} {'Error %':>10s}") - print(f" {'-'*10} {'-'*10} {'-'*10} {'-'*10}") + print("\n20 Sample Predictions from Test Set:") + print( + f" {'Actual':>10s} {'Predicted':>10s} {'Error':>10s} {'Error %':>10s}" + ) + print(f" {'-' * 10} {'-' * 10} {'-' * 10} {'-' * 10}") - sample_indices = np.random.choice(len(y_test), min(20, len(y_test)), replace=False) + sample_indices = np.random.choice( + len(y_test), min(20, len(y_test)), replace=False + ) for idx in sample_indices: actual = y_test.iloc[idx] predicted = y_test_pred[idx] error = actual - predicted error_pct = (error / actual * 100) if actual != 0 else 0 - print(f" {actual:10.2f} {predicted:10.2f} {error:10.2f} {error_pct:9.2f}%") + print( + f" {actual:10.2f} {predicted:10.2f} {error:10.2f} {error_pct:9.2f}%" + ) # Show worst predictions with feature values - print(f"\n5 Worst Predictions (Largest Absolute Error):") + print("\n5 Worst Predictions (Largest Absolute Error):") abs_errors = np.abs(y_test_pred - y_test.values) worst_indices = np.argsort(abs_errors)[-5:][::-1] @@ -657,11 +804,13 @@ def evaluate_model(model, X_train, y_train, X_test, y_test, # Get filename if available filename = "" - if test_df is not None and 'file' in test_df.columns: + if test_df is not None and "file" in test_df.columns: filename = f" (file: {test_df.iloc[idx]['file']})" - print(f"\n #{rank} - Actual: {actual:.2f}, Predicted: {predicted:.2f}, " - f"Error: {error:.2f} ({error_pct:.1f}%){filename}") + print( + f"\n #{rank} - Actual: {actual:.2f}, Predicted: {predicted:.2f}, " + f"Error: {error:.2f} ({error_pct:.1f}%){filename}" + ) # Get feature values (handle both DataFrame and array) if isinstance(X_display, pd.DataFrame): @@ -672,10 +821,12 @@ def evaluate_model(model, X_train, y_train, X_test, y_test, feature_values = X_display[idx] # Display features compactly (5 per line) - print(f" Features:", end="") - for i, (feat_name, feat_val) in enumerate(zip(feature_names, feature_values)): + print(" Features:", end="") + for i, (feat_name, feat_val) in enumerate( + zip(feature_names, feature_values) + ): if i % 5 == 0: - print(f"\n ", end="") + print("\n ", end="") # Format feature value if isinstance(feat_val, (int, np.integer)): print(f"{feat_name}={feat_val}", end=" ") @@ -686,11 +837,17 @@ def evaluate_model(model, X_train, y_train, X_test, y_test, return train_r2, test_r2 -def compile_model_treelite(model, regressor_type: str, output_dir: str, - num_threads: int, X_train=None, - annotate: bool = False, quantize: bool = False, - feature_names: List[str] = None, - model_name: str = None) -> None: +def compile_model_treelite( + model, + regressor_type: str, + output_dir: str, + num_threads: int, + X_train=None, + annotate: bool = False, + quantize: bool = False, + feature_names: List[str] = None, + model_name: str = None, +) -> None: """Compile XGBoost/LightGBM model to C source files using TL2cgen. Args: @@ -705,14 +862,16 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, feature_names: List of feature names in expected order (optional) model_name: Name prefix for functions (optional, derived from training file) """ - if regressor_type not in ['xgboost', 'lightgbm']: - print(f"Warning: TL2cgen compilation only supported for XGBoost and LightGBM, skipping for {regressor_type}") + if regressor_type not in ["xgboost", "lightgbm"]: + print( + f"Warning: TL2cgen compilation only supported for XGBoost and LightGBM, skipping for {regressor_type}" + ) return try: import treelite import tl2cgen - except ImportError as e: + except ImportError: missing = [] try: import treelite @@ -723,7 +882,9 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, except ImportError: missing.append("tl2cgen") - print(f"Warning: {', '.join(missing)} not installed. Install with: pip install {' '.join(missing)}") + print( + f"Warning: {', '.join(missing)} not installed. Install with: pip install {' '.join(missing)}" + ) print("Skipping C code generation.") return @@ -733,17 +894,23 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, if quantize: optimization_info.append("quantization") - opt_str = f" with {', '.join(optimization_info)}" if optimization_info else "" - print(f"\nGenerating C source code with TL2cgen (threads={num_threads}){opt_str}...") + opt_str = ( + f" with {', '.join(optimization_info)}" if optimization_info else "" + ) + print( + f"\nGenerating C source code with TL2cgen (threads={num_threads}){opt_str}..." + ) # Convert model to treelite format using frontend API try: - if regressor_type == 'xgboost': + if regressor_type == "xgboost": tl_model = treelite.frontend.from_xgboost(model.get_booster()) - elif regressor_type == 'lightgbm': + elif regressor_type == "lightgbm": tl_model = treelite.frontend.from_lightgbm(model.booster_) except Exception as e: - print(f"Warning: Failed to convert {regressor_type} model to treelite: {e}") + print( + f"Warning: Failed to convert {regressor_type} model to treelite: {e}" + ) return # Annotate branches if requested and training data is available @@ -752,72 +919,80 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, try: print(" Annotating branches with training data...") # Convert to numpy array if it's a DataFrame - if hasattr(X_train, 'values'): + if hasattr(X_train, "values"): X_train_array = X_train.values.astype(np.float32) else: X_train_array = np.asarray(X_train, dtype=np.float32) - dmat = tl2cgen.DMatrix(X_train_array, dtype='float32') - annotation_path = os.path.join(output_dir, f'{regressor_type}_annotation.json') - tl2cgen.annotate_branch(tl_model, dmat=dmat, path=annotation_path, verbose=False) + dmat = tl2cgen.DMatrix(X_train_array, dtype="float32") + annotation_path = os.path.join( + output_dir, f"{regressor_type}_annotation.json" + ) + tl2cgen.annotate_branch( + tl_model, dmat=dmat, path=annotation_path, verbose=False + ) print(f" Branch annotations saved to: {annotation_path}") except Exception as e: print(f" Warning: Branch annotation failed: {e}") print(" Continuing without branch annotation") annotation_path = None elif annotate and X_train is None: - print(" Warning: Branch annotation requested but no training data available") + print( + " Warning: Branch annotation requested but no training data available" + ) print(" Skipping branch annotation") # Generate C source files using TL2cgen - source_dir = os.path.join(output_dir, f'{regressor_type}_c_code') + source_dir = os.path.join(output_dir, f"{regressor_type}_c_code") try: - #params = {'parallel_comp': num_threads} + # params = {'parallel_comp': num_threads} params = {} # Add quantization parameter if requested if quantize: - params['quantize'] = 1 # Enable quantization in code generation + params["quantize"] = 1 # Enable quantization in code generation # Add annotation file if available if annotation_path: - params['annotate_in'] = annotation_path + params["annotate_in"] = annotation_path tl2cgen.generate_c_code( - tl_model, - dirpath=source_dir, - params=params, - verbose=False + tl_model, dirpath=source_dir, params=params, verbose=False ) # Post-process generated files - header_path = os.path.join(source_dir, 'header.h') - main_path = os.path.join(source_dir, 'main.c') - quantize_path = os.path.join(source_dir, 'quantize.c') - recipe_path = os.path.join(source_dir, 'recipe.json') + header_path = os.path.join(source_dir, "header.h") + main_path = os.path.join(source_dir, "main.c") + quantize_path = os.path.join(source_dir, "quantize.c") + recipe_path = os.path.join(source_dir, "recipe.json") # Rename all .c files to .cpp and wrap in class if model_name: try: import glob - c_files = glob.glob(os.path.join(source_dir, '*.c')) + + c_files = glob.glob(os.path.join(source_dir, "*.c")) for c_file in c_files: - cpp_file = c_file[:-2] + '.cpp' + cpp_file = c_file[:-2] + ".cpp" # Read content - with open(c_file, 'r') as f: + with open(c_file, "r") as f: content = f.read() # Split content into includes and rest - lines = content.split('\n') + lines = content.split("\n") include_lines = [] code_lines = [] in_includes = True for line in lines: - if in_includes and (line.strip().startswith('#include') or line.strip().startswith('#') or line.strip() == ''): + if in_includes and ( + line.strip().startswith("#include") + or line.strip().startswith("#") + or line.strip() == "" + ): include_lines.append(line) else: in_includes = False @@ -825,41 +1000,48 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, # Prefix function definitions with ClassName:: (for .cpp files, not class wrapping) import re + processed_lines = [] for line in code_lines: # Detect function definitions (return_type function_name(...)) - if line and not line.strip().startswith('//') and not line.strip().startswith('/*'): + if ( + line + and not line.strip().startswith("//") + and not line.strip().startswith("/*") + ): # Check if it's a function definition # Pattern: type name(...) or type* name(...) etc. - func_pattern = r'^(\s*)((?:const\s+)?(?:unsigned\s+)?(?:struct\s+)?[\w_]+(?:\s*\*)*\s+)([\w_]+)(\s*\()' + func_pattern = r"^(\s*)((?:const\s+)?(?:unsigned\s+)?(?:struct\s+)?[\w_]+(?:\s*\*)*\s+)([\w_]+)(\s*\()" match = re.match(func_pattern, line) - if match and '::' not in line: # Don't add if already qualified + if ( + match and "::" not in line + ): # Don't add if already qualified indent = match.group(1) return_type = match.group(2) func_name = match.group(3) - rest = line[match.end(3):] + rest = line[match.end(3) :] # Prefix function name with class name - line = f'{indent}{return_type}{model_name}::{func_name}{rest}' + line = f"{indent}{return_type}{model_name}::{func_name}{rest}" processed_lines.append(line) code_lines = processed_lines # Don't wrap in class for .cpp files - just output the definitions - includes_str = '\n'.join(include_lines) - code_str = '\n'.join(code_lines) + includes_str = "\n".join(include_lines) + code_str = "\n".join(code_lines) # For .cpp files, no class wrapper needed - cpp_content = f'{includes_str}\n\n{code_str}\n' + cpp_content = f"{includes_str}\n\n{code_str}\n" # Write to .cpp file - with open(cpp_file, 'w') as f: + with open(cpp_file, "w") as f: f.write(cpp_content) # Remove original .c file os.remove(c_file) # Update paths for further processing - main_path = main_path[:-2] + '.cpp' - quantize_path = quantize_path[:-2] + '.cpp' + main_path = main_path[:-2] + ".cpp" + quantize_path = quantize_path[:-2] + ".cpp" print(f" Renamed {len(c_files)} .c files to .cpp") except Exception as e: @@ -869,18 +1051,23 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, # Since all features are always provided, replace !(data[X].missing != -1) with false if os.path.exists(main_path): try: - with open(main_path, 'r') as f: + with open(main_path, "r") as f: content = f.read() # Replace pattern !(data[N].missing != -1) with false import re + original_content = content - content = re.sub(r'!\(data\[\d+\]\.missing != -1\)', 'false', content) + content = re.sub( + r"!\(data\[\d+\]\.missing != -1\)", "false", content + ) if content != original_content: - with open(main_path, 'w') as f: + with open(main_path, "w") as f: f.write(content) - print(f" Optimized main.cpp by removing unnecessary missing data checks") + print( + " Optimized main.cpp by removing unnecessary missing data checks" + ) except Exception as e: print(f" Warning: Failed to optimize main.cpp: {e}") @@ -888,11 +1075,11 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, defines_to_move = [] if model_name and os.path.exists(header_path): try: - with open(header_path, 'r') as f: + with open(header_path, "r") as f: content = f.read() # Split content into includes, defines to move, and rest - lines = content.split('\n') + lines = content.split("\n") include_lines = [] code_lines = [] in_includes = True @@ -901,25 +1088,34 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, while i < len(lines): line = lines[i] - if in_includes and (line.strip().startswith('#include') or line.strip() == ''): + if in_includes and ( + line.strip().startswith("#include") + or line.strip() == "" + ): include_lines.append(line) i += 1 # Detect macros to move to main.cpp - elif line.strip().startswith('#if defined(__clang__)') or line.strip().startswith('#define N_TARGET') or line.strip().startswith('#define MAX_N_CLASS'): + elif ( + line.strip().startswith("#if defined(__clang__)") + or line.strip().startswith("#define N_TARGET") + or line.strip().startswith("#define MAX_N_CLASS") + ): in_includes = False # Capture the entire #if block or single #define - if line.strip().startswith('#if defined(__clang__)'): + if line.strip().startswith("#if defined(__clang__)"): # Capture the entire #if...#endif block macro_block = [] macro_block.append(line) i += 1 - while i < len(lines) and not lines[i].strip().startswith('#endif'): + while i < len(lines) and not lines[ + i + ].strip().startswith("#endif"): macro_block.append(lines[i]) i += 1 if i < len(lines): macro_block.append(lines[i]) # Include #endif i += 1 - defines_to_move.append('\n'.join(macro_block)) + defines_to_move.append("\n".join(macro_block)) else: # Single #define line defines_to_move.append(line) @@ -931,96 +1127,118 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, # Add static keyword to function declarations import re + processed_lines = [] for line in code_lines: # Detect function declarations/definitions (return_type function_name(...)) # Match lines that look like function declarations but don't already have static - if line and not line.strip().startswith('//') and not line.strip().startswith('/*'): + if ( + line + and not line.strip().startswith("//") + and not line.strip().startswith("/*") + ): # Check if it's a function declaration/definition # Pattern: type name(...) or type* name(...) or type name[...](...) etc. - func_pattern = r'^(\s*)((?:const\s+)?(?:unsigned\s+)?(?:struct\s+)?[\w_]+(?:\s*\*)*\s+)([\w_]+)\s*\(' + func_pattern = r"^(\s*)((?:const\s+)?(?:unsigned\s+)?(?:struct\s+)?[\w_]+(?:\s*\*)*\s+)([\w_]+)\s*\(" match = re.match(func_pattern, line) - if match and 'static' not in line: + if match and "static" not in line: indent = match.group(1) return_type = match.group(2) # Add static keyword - line = f'{indent}static {return_type}{line[len(indent)+len(return_type):]}' + line = f"{indent}static {return_type}{line[len(indent) + len(return_type) :]}" processed_lines.append(line) code_lines = processed_lines # Wrap code in class declaration - includes_str = '\n'.join(include_lines) - code_str = '\n'.join(code_lines) + includes_str = "\n".join(include_lines) + code_str = "\n".join(code_lines) - wrapped_content = f'#pragma once\n\n{includes_str}\n\nclass {model_name} {{\npublic:\n{code_str}\n}}; // class {model_name}\n' + wrapped_content = f"#pragma once\n\n{includes_str}\n\nclass {model_name} {{\npublic:\n{code_str}\n}}; // class {model_name}\n" - with open(header_path, 'w') as f: + with open(header_path, "w") as f: f.write(wrapped_content) - print(f" Wrapped header.h in class '{model_name}' with #pragma once") + print( + f" Wrapped header.h in class '{model_name}' with #pragma once" + ) except Exception as e: print(f" Warning: Failed to wrap header.h: {e}") # Add defines to main.cpp (moved from header.h) if defines_to_move and os.path.exists(main_path): try: - with open(main_path, 'r') as f: + with open(main_path, "r") as f: content = f.read() # Insert defines after includes (look for where code starts - typically after blank line after includes) - defines_str = '\n'.join(defines_to_move) + defines_str = "\n".join(defines_to_move) # Find the first non-include, non-blank line to insert before - lines = content.split('\n') + lines = content.split("\n") insert_pos = 0 for i, line in enumerate(lines): - if line.strip() and not line.strip().startswith('#include'): + if line.strip() and not line.strip().startswith( + "#include" + ): insert_pos = i break # Insert defines at the position lines.insert(insert_pos, defines_str) - lines.insert(insert_pos + 1, '') # Add blank line after defines + lines.insert( + insert_pos + 1, "" + ) # Add blank line after defines - content = '\n'.join(lines) - with open(main_path, 'w') as f: + content = "\n".join(lines) + with open(main_path, "w") as f: f.write(content) - print(f" Moved {len(defines_to_move)} macro definition(s) from header.h to main.cpp") + print( + f" Moved {len(defines_to_move)} macro definition(s) from header.h to main.cpp" + ) except Exception as e: print(f" Warning: Failed to add defines to main.cpp: {e}") # Add feature names to header and implementation - if feature_names and os.path.exists(header_path) and os.path.exists(main_path): + if ( + feature_names + and os.path.exists(header_path) + and os.path.exists(main_path) + ): try: # Append to header.h (inside class) - with open(header_path, 'r') as f: + with open(header_path, "r") as f: content = f.read() # Insert before closing class - insertion = f'\n // Feature names\n static constexpr int NUM_FEATURES = {len(feature_names)};\n static const char* feature_names[NUM_FEATURES];\n' - content = content.replace(f'}}; // class {model_name}\n', f'{insertion}}}; // class {model_name}\n') + insertion = f"\n // Feature names\n static constexpr int NUM_FEATURES = {len(feature_names)};\n static const char* feature_names[NUM_FEATURES];\n" + content = content.replace( + f"}}; // class {model_name}\n", + f"{insertion}}}; // class {model_name}\n", + ) - with open(header_path, 'w') as f: + with open(header_path, "w") as f: f.write(content) # Append to main.cpp (at the end of the file, outside any class) - with open(main_path, 'r') as f: + with open(main_path, "r") as f: content = f.read() # Append feature array definition at the end of the file - feature_array = f'\n// Feature names array\nconst char* {model_name}::feature_names[{model_name}::NUM_FEATURES] = {{\n' + feature_array = f"\n// Feature names array\nconst char* {model_name}::feature_names[{model_name}::NUM_FEATURES] = {{\n" for i, name in enumerate(feature_names): - comma = ',' if i < len(feature_names) - 1 else '' + comma = "," if i < len(feature_names) - 1 else "" feature_array += f' "{name}"{comma}\n' - feature_array += '};\n' + feature_array += "};\n" # Append to end of file - content = content.rstrip() + '\n' + feature_array + content = content.rstrip() + "\n" + feature_array - with open(main_path, 'w') as f: + with open(main_path, "w") as f: f.write(content) - print(f" Added {len(feature_names)} feature names to header.h and main.cpp") + print( + f" Added {len(feature_names)} feature names to header.h and main.cpp" + ) except Exception as e: print(f" Warning: Failed to add feature names: {e}") @@ -1028,7 +1246,7 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, if os.path.exists(recipe_path): try: os.remove(recipe_path) - print(f" Removed recipe.json") + print(" Removed recipe.json") except Exception as e: print(f" Warning: Failed to remove recipe.json: {e}") @@ -1040,66 +1258,74 @@ def compile_model_treelite(model, regressor_type: str, output_dir: str, opt_suffix = f" ({', '.join(opt_msg)})" if opt_msg else "" print(f"C source code generated to: {source_dir}/") - print(f" Contains optimized model source code{opt_suffix} ready for compilation") + print( + f" Contains optimized model source code{opt_suffix} ready for compilation" + ) except Exception as e: print(f"Warning: TL2cgen code generation failed: {e}") print(" Model saved in standard format only.") -def save_model(model, scaler, regressor_type: str, output_dir: str, - feature_names: List[str]) -> None: +def save_model( + model, + scaler, + regressor_type: str, + output_dir: str, + feature_names: List[str], +) -> None: """Save trained model and preprocessing components to disk.""" os.makedirs(output_dir, exist_ok=True) # Save metadata metadata = { - 'regressor_type': regressor_type, - 'feature_names': feature_names, - 'has_scaler': scaler is not None + "regressor_type": regressor_type, + "feature_names": feature_names, + "has_scaler": scaler is not None, } - metadata_path = os.path.join(output_dir, f'{regressor_type}_metadata.pkl') - with open(metadata_path, 'wb') as f: + metadata_path = os.path.join(output_dir, f"{regressor_type}_metadata.pkl") + with open(metadata_path, "wb") as f: pickle.dump(metadata, f) print(f"\nSaved metadata to: {metadata_path}") # Save scaler if exists if scaler is not None: - scaler_path = os.path.join(output_dir, f'{regressor_type}_scaler.pkl') + scaler_path = os.path.join(output_dir, f"{regressor_type}_scaler.pkl") joblib.dump(scaler, scaler_path) print(f"Saved scaler to: {scaler_path}") # Save model - if regressor_type == 'xgboost': + if regressor_type == "xgboost": # Save as UBJ with gzip compression - model_path = os.path.join(output_dir, f'{regressor_type}_model.ubj') + model_path = os.path.join(output_dir, f"{regressor_type}_model.ubj") model.save_model(model_path) # Gzip the file import gzip import shutil - with open(model_path, 'rb') as f_in: - with gzip.open(model_path + '.gz', 'wb') as f_out: + + with open(model_path, "rb") as f_in: + with gzip.open(model_path + ".gz", "wb") as f_out: shutil.copyfileobj(f_in, f_out) os.remove(model_path) # Remove uncompressed version print(f"Saved model to: {model_path}.gz") - elif regressor_type == 'lightgbm': + elif regressor_type == "lightgbm": # Save LightGBM model as text file - model_path = os.path.join(output_dir, f'{regressor_type}_model.txt') + model_path = os.path.join(output_dir, f"{regressor_type}_model.txt") model.booster_.save_model(model_path) print(f"Saved model to: {model_path}") else: # Save sklearn models as joblib (more efficient than pickle for large arrays) - model_path = os.path.join(output_dir, f'{regressor_type}_model.joblib') + model_path = os.path.join(output_dir, f"{regressor_type}_model.joblib") joblib.dump(model, model_path) print(f"Saved model to: {model_path}") def main(): parser = argparse.ArgumentParser( - description='Train regression models to predict algorithm iterations', + description="Train regression models to predict algorithm iterations", formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=f""" + epilog=""" Available regressors: linear - Linear Regression poly2, poly3, poly4 - Polynomial Regression (degree 2, 3, 4) @@ -1124,88 +1350,93 @@ def main(): # Legacy pickle format python train_regressor.py data.pkl --regressor xgboost --seed 42 - """ + """, ) - parser.add_argument('input_pkl', help='Input data file (.feather or .pkl) with log data') parser.add_argument( - '--regressor', '-r', + "input_pkl", help="Input data file (.feather or .pkl) with log data" + ) + parser.add_argument( + "--regressor", + "-r", required=True, choices=AVAILABLE_REGRESSORS, - help='Type of regressor to train' + help="Type of regressor to train", ) parser.add_argument( - '--output-dir', '-o', - default='./models', - help='Output directory for saved models (default: ./models)' + "--output-dir", + "-o", + default="./models", + help="Output directory for saved models (default: ./models)", ) parser.add_argument( - '--seed', '-s', + "--seed", + "-s", type=int, default=None, - help='Random seed for reproducibility (optional)' + help="Random seed for reproducibility (optional)", ) parser.add_argument( - '--tune', - action='store_true', - help='Enable hyperparameter tuning (uses predefined tuned parameters)' + "--tune", + action="store_true", + help="Enable hyperparameter tuning (uses predefined tuned parameters)", ) parser.add_argument( - '--cv-folds', + "--cv-folds", type=int, default=5, - help='Number of cross-validation folds (default: 5)' + help="Number of cross-validation folds (default: 5)", ) parser.add_argument( - '--test-size', + "--test-size", type=float, default=0.2, - help='Proportion of files to use for testing (default: 0.2)' + help="Proportion of files to use for testing (default: 0.2)", ) parser.add_argument( - '--no-progress', - action='store_true', - help='Disable training progress output' + "--no-progress", + action="store_true", + help="Disable training progress output", ) parser.add_argument( - '--list-features', - action='store_true', - help='List all available features in the dataset and exit' + "--list-features", + action="store_true", + help="List all available features in the dataset and exit", ) parser.add_argument( - '--stratify-split', - action='store_true', - help='Stratify train/test split by target distribution (ensures balanced iter values)' + "--stratify-split", + action="store_true", + help="Stratify train/test split by target distribution (ensures balanced iter values)", ) parser.add_argument( - '--early-stopping', + "--early-stopping", type=int, default=0, - metavar='N', - help='Enable early stopping for tree models (default: 20 rounds, use 0 to disable)' + metavar="N", + help="Enable early stopping for tree models (default: 20 rounds, use 0 to disable)", ) parser.add_argument( - '--treelite-compile', + "--treelite-compile", type=int, default=1, - metavar='THREADS', - help='Export XGBoost/LightGBM model as optimized C source code with TL2cgen (includes branch annotation and quantization)' + metavar="THREADS", + help="Export XGBoost/LightGBM model as optimized C source code with TL2cgen (includes branch annotation and quantization)", ) parser.add_argument( - '--drop-invalid-rows', - action='store_true', - help='Drop rows with NaN or infinite values in target variable (instead of failing)' + "--drop-invalid-rows", + action="store_true", + help="Drop rows with NaN or infinite values in target variable (instead of failing)", ) parser.add_argument( - '--check-data', - action='store_true', - help='Run data quality checks and exit (no training)' + "--check-data", + action="store_true", + help="Run data quality checks and exit (no training)", ) parser.add_argument( - '--target', + "--target", type=str, - default='iter', - help='Target column to predict (default: iter). Examples: iter, time_ms, iterations' + default="iter", + help="Target column to predict (default: iter). Examples: iter, time_ms, iterations", ) args = parser.parse_args() @@ -1223,17 +1454,19 @@ def main(): # Report file format ext = os.path.splitext(args.input_pkl)[1].lower() - if ext == '.feather': - print(f" Format: Apache Arrow/Feather (fast I/O)") - elif ext == '.pkl': - print(f" Format: Pickle (legacy)") + if ext == ".feather": + print(" Format: Apache Arrow/Feather (fast I/O)") + elif ext == ".pkl": + print(" Format: Pickle (legacy)") # Extract model name from input file (for prefixing generated C functions) model_name = os.path.splitext(os.path.basename(args.input_pkl))[0] # Validate data quality print("\nValidating data quality...") - is_valid, report = validate_data_quality(df, target_col=args.target, verbose=True) + is_valid, report = validate_data_quality( + df, target_col=args.target, verbose=True + ) # If just checking data, exit here if args.check_data: @@ -1241,27 +1474,37 @@ def main(): print("\n✅ Data quality check passed! Ready for training.") return 0 else: - print("\n❌ Data quality check failed! Fix issues before training.") + print( + "\n❌ Data quality check failed! Fix issues before training." + ) return 1 # Handle invalid data if not is_valid: if args.drop_invalid_rows: - print(f"\nDropping {len(report['rows_with_issues'])} rows with invalid data...") - df_clean = df.drop(index=report['rows_with_issues']) + print( + f"\nDropping {len(report['rows_with_issues'])} rows with invalid data..." + ) + df_clean = df.drop(index=report["rows_with_issues"]) df = df_clean.reset_index(drop=True) print(f" Remaining: {len(df)} entries") # Re-validate is_valid_after, _ = validate_data_quality(df, verbose=False) if not is_valid_after: - print("❌ Error: Data still invalid after dropping rows. Check your data.") + print( + "❌ Error: Data still invalid after dropping rows. Check your data." + ) return 1 print(" ✅ Data is now valid") else: print("\n❌ Training aborted due to invalid data.") - print(" Use --drop-invalid-rows to automatically remove invalid rows, or") - print(" Use --check-data to just run validation without training") + print( + " Use --drop-invalid-rows to automatically remove invalid rows, or" + ) + print( + " Use --check-data to just run validation without training" + ) return 1 # If listing features, do that and exit @@ -1270,36 +1513,45 @@ def main(): # Also list potential target columns numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() - potential_targets = [col for col in numeric_cols if col not in features] + potential_targets = [ + col for col in numeric_cols if col not in features + ] - print(f"\n{'='*70}") + print(f"\n{'=' * 70}") print(f"Available features in dataset ({len(features)} total):") - print(f"{'='*70}") + print(f"{'=' * 70}") for i, feat in enumerate(features, 1): print(f" {i:3d}. {feat}") if potential_targets: - print(f"\n{'='*70}") - print(f"Potential target columns ({len(potential_targets)} total):") - print(f"{'='*70}") + print(f"\n{'=' * 70}") + print( + f"Potential target columns ({len(potential_targets)} total):" + ) + print(f"{'=' * 70}") for i, col in enumerate(potential_targets, 1): marker = " (current)" if col == args.target else "" print(f" {i:3d}. {col}{marker}") - print(f"\nTo exclude features, edit FEATURES_TO_EXCLUDE in the script:") + print("\nTo exclude features, edit FEATURES_TO_EXCLUDE in the script:") print(f" {__file__}") - print(f"\nTo use only specific features, edit FEATURES_TO_INCLUDE_ONLY") - print(f"\nTo change target column, use: --target ") + print("\nTo use only specific features, edit FEATURES_TO_INCLUDE_ONLY") + print("\nTo change target column, use: --target ") return # Split data by files stratify_by = args.target if args.stratify_split else None - train_df, test_df = split_by_files(df, test_size=args.test_size, - random_state=args.seed, - stratify_by=stratify_by) + train_df, test_df = split_by_files( + df, + test_size=args.test_size, + random_state=args.seed, + stratify_by=stratify_by, + ) # Prepare features - X_train, y_train, feature_names = prepare_features(train_df, target_col=args.target) + X_train, y_train, feature_names = prepare_features( + train_df, target_col=args.target + ) X_test, y_test, _ = prepare_features(test_df, target_col=args.target) print(f"\nFeatures: {len(feature_names)}") @@ -1311,7 +1563,7 @@ def main(): args.regressor, random_state=args.seed, tune_hyperparams=args.tune, - verbose=not args.no_progress + verbose=not args.no_progress, ) # Apply scaling if needed @@ -1327,42 +1579,73 @@ def main(): X_test_scaled = X_test # Train model - if args.regressor.startswith('poly'): + if args.regressor.startswith("poly"): degree = int(args.regressor[-1]) n_features = X_train_scaled.shape[1] from math import comb - n_poly_features = sum(comb(n_features + d - 1, d) for d in range(1, degree + 1)) - print(f" Generating {n_poly_features} polynomial features (degree {degree})...") + + n_poly_features = sum( + comb(n_features + d - 1, d) for d in range(1, degree + 1) + ) + print( + f" Generating {n_poly_features} polynomial features (degree {degree})..." + ) # Use early stopping for tree-based models if requested - if args.early_stopping and args.early_stopping > 0 and args.regressor in ['xgboost', 'lightgbm', 'gradient_boosting']: - if args.regressor == 'xgboost': - print(f" Using early stopping (patience={args.early_stopping} rounds)...") + if ( + args.early_stopping + and args.early_stopping > 0 + and args.regressor in ["xgboost", "lightgbm", "gradient_boosting"] + ): + if args.regressor == "xgboost": + print( + f" Using early stopping (patience={args.early_stopping} rounds)..." + ) # Set early stopping parameter model.set_params(early_stopping_rounds=args.early_stopping) model.fit( - X_train_scaled, y_train, + X_train_scaled, + y_train, eval_set=[(X_test_scaled, y_test)], - verbose=False + verbose=False, ) # Report best iteration - best_iteration = model.best_iteration if hasattr(model, 'best_iteration') else model.n_estimators - print(f" Best iteration: {best_iteration} (out of {model.n_estimators} max)") - elif args.regressor == 'lightgbm': - print(f" Using early stopping (patience={args.early_stopping} rounds)...") + best_iteration = ( + model.best_iteration + if hasattr(model, "best_iteration") + else model.n_estimators + ) + print( + f" Best iteration: {best_iteration} (out of {model.n_estimators} max)" + ) + elif args.regressor == "lightgbm": + print( + f" Using early stopping (patience={args.early_stopping} rounds)..." + ) model.fit( - X_train_scaled, y_train, + X_train_scaled, + y_train, eval_set=[(X_test_scaled, y_test)], callbacks=[ - __import__('lightgbm').early_stopping(stopping_rounds=args.early_stopping, verbose=False) - ] + __import__("lightgbm").early_stopping( + stopping_rounds=args.early_stopping, verbose=False + ) + ], ) # Report best iteration - best_iteration = model.best_iteration_ if hasattr(model, 'best_iteration_') else model.n_estimators - print(f" Best iteration: {best_iteration} (out of {model.n_estimators} max)") + best_iteration = ( + model.best_iteration_ + if hasattr(model, "best_iteration_") + else model.n_estimators + ) + print( + f" Best iteration: {best_iteration} (out of {model.n_estimators} max)" + ) else: # gradient_boosting # Gradient Boosting uses n_iter_no_change parameter - print(f" Note: Use --tune with gradient_boosting for early stopping") + print( + " Note: Use --tune with gradient_boosting for early stopping" + ) model.fit(X_train_scaled, y_train) else: model.fit(X_train_scaled, y_train) @@ -1371,11 +1654,20 @@ def main(): # Evaluate model skip_cv = args.early_stopping is not None and args.early_stopping > 0 - train_r2, test_r2 = evaluate_model(model, X_train_scaled, y_train, X_test_scaled, y_test, - feature_names, args.regressor, cv_folds=args.cv_folds, - verbose=2 if not args.no_progress else 0, - skip_cv=skip_cv, X_test_original=X_test_original, - test_df=test_df) + train_r2, test_r2 = evaluate_model( + model, + X_train_scaled, + y_train, + X_test_scaled, + y_test, + feature_names, + args.regressor, + cv_folds=args.cv_folds, + verbose=2 if not args.no_progress else 0, + skip_cv=skip_cv, + X_test_original=X_test_original, + test_df=test_df, + ) # Save model save_model(model, scaler, args.regressor, args.output_dir, feature_names) @@ -1392,19 +1684,19 @@ def main(): args.output_dir, args.treelite_compile, X_train=X_train_for_annotation, - annotate=True, # Always enable branch annotation - quantize=True, # Always enable quantization + annotate=True, # Always enable branch annotation + quantize=True, # Always enable quantization feature_names=feature_names, - model_name=model_name + model_name=model_name, ) - print("\n" + "="*70) + print("\n" + "=" * 70) print("Training completed successfully!") - print("="*70) - print(f"\nFinal R² Scores:") + print("=" * 70) + print("\nFinal R² Scores:") print(f" Train R²: {train_r2:.4f}") print(f" Test R²: {test_r2:.4f}") -if __name__ == '__main__': +if __name__ == "__main__": main() From 37ca39e897152f379fae28908b703fd00ce1aedc Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 10 Nov 2025 17:33:58 +0000 Subject: [PATCH 072/366] BnB features logging --- .../linear_programming/cuopt/run_mip.cpp | 2 +- cpp/src/dual_simplex/branch_and_bound.cpp | 252 +++++++++++++++++- cpp/src/dual_simplex/branch_and_bound.hpp | 61 +++++ cpp/src/mip/diversity/diversity_manager.cu | 4 +- cpp/src/mip/local_search/local_search.cuh | 4 +- cpp/src/utilities/timer.hpp | 2 +- 6 files changed, 317 insertions(+), 8 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 6897defca..5dfd373d7 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -207,7 +207,7 @@ int run_single_file(std::string file_path, settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; - settings.heuristics_only = true; + // settings.heuristics_only = true; cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index a0de45642..36b8aa7c5 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -34,6 +34,8 @@ namespace cuopt::linear_programming::dual_simplex { namespace { +static constexpr double FEATURE_LOG_INTERVAL = 0.25; // Log at most every 500ms + template bool is_fractional(f_t x, variable_type_t var_type, f_t integer_tol) { @@ -214,6 +216,9 @@ branch_and_bound_t::branch_and_bound_t( mutex_upper_.lock(); upper_bound_ = inf; mutex_upper_.unlock(); + + // Compute static problem features for regression model + compute_static_features(); } template @@ -544,6 +549,162 @@ branch_and_bound_t::child_selection(mip_node_t* node_ptr) } } +template +void branch_and_bound_t::compute_static_features() +{ + const auto& A = original_lp_.A; + static_features_.n_rows = A.m; + static_features_.n_cols = A.n; + static_features_.n_nonzeros = A.col_start[A.n]; + static_features_.density = (f_t)static_features_.n_nonzeros / ((f_t)A.m * A.n); + + // Count variable types + static_features_.n_binary = 0; + static_features_.n_integer = 0; + static_features_.n_continuous = 0; + for (const auto& vt : var_types_) { + if (vt == variable_type_t::BINARY) { + static_features_.n_binary++; + } else if (vt == variable_type_t::INTEGER) { + static_features_.n_integer++; + } else { + static_features_.n_continuous++; + } + } + static_features_.integrality_ratio = + (f_t)(static_features_.n_binary + static_features_.n_integer) / A.n; + + // Compute row statistics (constraint sizes) + std::vector row_nnz(A.m, 0); + for (i_t j = 0; j < A.n; j++) { + for (i_t k = A.col_start[j]; k < A.col_start[j + 1]; k++) { + row_nnz[A.i[k]]++; + } + } + + static_features_.max_row_nnz = 0; + f_t sum_row_nnz = 0; + for (i_t i = 0; i < A.m; i++) { + static_features_.max_row_nnz = std::max(static_features_.max_row_nnz, row_nnz[i]); + sum_row_nnz += row_nnz[i]; + } + static_features_.avg_row_nnz = sum_row_nnz / A.m; + + // Compute row coefficient of variation + f_t row_variance = 0; + for (i_t i = 0; i < A.m; i++) { + f_t diff = row_nnz[i] - static_features_.avg_row_nnz; + row_variance += diff * diff; + } + row_variance /= A.m; + f_t row_std = std::sqrt(row_variance); + static_features_.row_nnz_cv = + static_features_.avg_row_nnz > 0 ? row_std / static_features_.avg_row_nnz : 0.0; + + // Compute column statistics (variable degrees) + static_features_.max_col_nnz = 0; + f_t sum_col_nnz = 0; + for (i_t j = 0; j < A.n; j++) { + i_t col_nnz = A.col_start[j + 1] - A.col_start[j]; + static_features_.max_col_nnz = std::max(static_features_.max_col_nnz, col_nnz); + sum_col_nnz += col_nnz; + } + static_features_.avg_col_nnz = sum_col_nnz / A.n; + + // Compute column coefficient of variation + f_t col_variance = 0; + for (i_t j = 0; j < A.n; j++) { + i_t col_nnz = A.col_start[j + 1] - A.col_start[j]; + f_t diff = col_nnz - static_features_.avg_col_nnz; + col_variance += diff * diff; + } + col_variance /= A.n; + f_t col_std = std::sqrt(col_variance); + static_features_.col_nnz_cv = + static_features_.avg_col_nnz > 0 ? col_std / static_features_.avg_col_nnz : 0.0; +} + +template +void branch_and_bound_t::flush_pending_features() +{ + // Must be called with mutex_feature_log_ already locked + if (!has_pending_features_) return; + + constexpr int LINE_BUFFER_SIZE = 512; + char line_buffer[LINE_BUFFER_SIZE]; + + snprintf(line_buffer, + LINE_BUFFER_SIZE, + "BB_NODE_FEATURES " + "node_id=%d depth=%d time=%.6f " + "n_rows=%d n_cols=%d n_nnz=%d density=%.6f " + "n_bin=%d n_int=%d n_cont=%d int_ratio=%.4f " + "avg_row_nnz=%.2f max_row_nnz=%d row_nnz_cv=%.4f " + "avg_col_nnz=%.2f max_col_nnz=%d col_nnz_cv=%.4f " + "n_bounds_chg=%d cutoff_gap=%.4f basis_from_parent=%d " + "simplex_iters=%d n_refact=%d lp_time=%.6f bound_str_time=%.6f var_sel_time=%.6f " + "n_frac=%d strong_branch=%d n_sb_cand=%d sb_time=%.6f " + "lp_status=%d node_status=%d\n", + last_features_.node_id, + last_features_.node_depth, + last_features_.total_node_time, + last_features_.n_rows, + last_features_.n_cols, + last_features_.n_nonzeros, + last_features_.density, + last_features_.n_binary, + last_features_.n_integer, + last_features_.n_continuous, + last_features_.integrality_ratio, + last_features_.avg_row_nnz, + last_features_.max_row_nnz, + last_features_.row_nnz_cv, + last_features_.avg_col_nnz, + last_features_.max_col_nnz, + last_features_.col_nnz_cv, + last_features_.n_bounds_changed, + last_features_.cutoff_gap_ratio, + last_features_.basis_from_parent ? 1 : 0, + last_features_.simplex_iterations, + last_features_.n_refactorizations, + last_features_.lp_solve_time, + last_features_.bound_str_time, + last_features_.variable_sel_time, + last_features_.n_fractional, + last_features_.strong_branch_performed ? 1 : 0, + last_features_.n_strong_branch_candidates, + last_features_.strong_branch_time, + last_features_.lp_status, + last_features_.node_status); + + // Single printf call + settings_.log.printf("%s", line_buffer); + + has_pending_features_ = false; +} + +template +void branch_and_bound_t::log_node_features( + const node_solve_features_t& features) +{ + mutex_feature_log_.lock(); + + f_t current_time = toc(stats_.start_time); + f_t time_since_last_log = current_time - last_feature_log_time_; + + // Always store the latest features + last_features_ = features; + has_pending_features_ = true; + + // Log if enough time has passed (500ms) + if (time_since_last_log >= FEATURE_LOG_INTERVAL) { + flush_pending_features(); + last_feature_log_time_ = current_time; + } + + mutex_feature_log_.unlock(); +} + template node_status_t branch_and_bound_t::solve_node(search_tree_t& search_tree, mip_node_t* node_ptr, @@ -554,7 +715,13 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& char thread_type) { raft::common::nvtx::range scope("BB::solve_node"); - f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; + + // Initialize feature tracking for this node + node_solve_features_t features = static_features_; + f_t node_start_time = tic(); + features.node_id = node_ptr->node_id; + features.node_depth = node_ptr->depth; + f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); std::vector& leaf_vstatus = node_ptr->vstatus; @@ -566,6 +733,20 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& // two vectors at each node and potentially cause memory issues node_ptr->get_variable_bounds(leaf_problem.lower, leaf_problem.upper, bounds_changed); + // Track how many bounds changed + for (const auto& changed : bounds_changed) { + if (changed) features.n_bounds_changed++; + } + + // Track cutoff gap ratio + if (upper_bound < inf) { + features.cutoff_gap_ratio = + (upper_bound - node_ptr->lower_bound) / std::max(std::abs(upper_bound), f_t(1.0)); + } + + // Track if we have parent's basis + features.basis_from_parent = !leaf_vstatus.empty(); + simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); lp_settings.cut_off = upper_bound + settings_.dual_tol; @@ -577,8 +758,10 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& bool feasible; { raft::common::nvtx::range scope_bs("BB::bound_strengthening"); + f_t bs_start_time = tic(); feasible = bound_strengthening(row_sense, lp_settings, leaf_problem, Arow, var_types_, bounds_changed); + features.bound_str_time = toc(bs_start_time); } dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; @@ -608,8 +791,15 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& lp_status = convert_lp_status_to_dual_status(second_status); } - stats_.total_lp_solve_time += toc(lp_start_time); + f_t lp_time = toc(lp_start_time); + stats_.total_lp_solve_time += lp_time; stats_.total_lp_iters += node_iter; + + // Track LP solve metrics + features.lp_solve_time = lp_time; + features.simplex_iterations = node_iter; + // Note: We don't directly track refactorizations here, would need instrumentation in + // dual_phase2 } if (lp_status == dual::status_t::DUAL_UNBOUNDED) { @@ -617,6 +807,13 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& node_ptr->lower_bound = inf; search_tree.graphviz_node(log, node_ptr, "infeasible", 0.0); search_tree.update_tree(node_ptr, node_status_t::INFEASIBLE); + + // Log features before return + features.lp_status = static_cast(lp_status); + features.node_status = static_cast(node_status_t::INFEASIBLE); + features.total_node_time = toc(node_start_time); + log_node_features(features); + return node_status_t::INFEASIBLE; } else if (lp_status == dual::status_t::CUTOFF) { @@ -625,6 +822,13 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); search_tree.graphviz_node(log, node_ptr, "cut off", leaf_objective); search_tree.update_tree(node_ptr, node_status_t::FATHOMED); + + // Log features before return + features.lp_status = static_cast(lp_status); + features.node_status = static_cast(node_status_t::FATHOMED); + features.total_node_time = toc(node_start_time); + log_node_features(features); + return node_status_t::FATHOMED; } else if (lp_status == dual::status_t::OPTIMAL) { @@ -633,6 +837,8 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& i_t leaf_num_fractional = fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); + features.n_fractional = leaf_num_fractional; + f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); node_ptr->lower_bound = leaf_objective; search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); @@ -643,27 +849,57 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& add_feasible_solution(leaf_objective, leaf_solution.x, node_ptr->depth, thread_type); search_tree.graphviz_node(log, node_ptr, "integer feasible", leaf_objective); search_tree.update_tree(node_ptr, node_status_t::INTEGER_FEASIBLE); + + // Log features before return + features.lp_status = static_cast(lp_status); + features.node_status = static_cast(node_status_t::INTEGER_FEASIBLE); + features.total_node_time = toc(node_start_time); + log_node_features(features); + return node_status_t::INTEGER_FEASIBLE; } else if (leaf_objective <= upper_bound + abs_fathom_tol) { // Choose fractional variable to branch on + f_t var_sel_start = tic(); const i_t branch_var = pc_.variable_selection(leaf_fractional, leaf_solution.x, lp_settings.log); + features.variable_sel_time = toc(var_sel_start); assert(leaf_vstatus.size() == leaf_problem.num_cols); search_tree.branch( node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, original_lp_, log); node_ptr->status = node_status_t::HAS_CHILDREN; + + // Log features before return + features.lp_status = static_cast(lp_status); + features.node_status = static_cast(node_status_t::HAS_CHILDREN); + features.total_node_time = toc(node_start_time); + log_node_features(features); + return node_status_t::HAS_CHILDREN; } else { search_tree.graphviz_node(log, node_ptr, "fathomed", leaf_objective); search_tree.update_tree(node_ptr, node_status_t::FATHOMED); + + // Log features before return + features.lp_status = static_cast(lp_status); + features.node_status = static_cast(node_status_t::FATHOMED); + features.total_node_time = toc(node_start_time); + log_node_features(features); + return node_status_t::FATHOMED; } } else if (lp_status == dual::status_t::TIME_LIMIT) { search_tree.graphviz_node(log, node_ptr, "timeout", 0.0); search_tree.update_tree(node_ptr, node_status_t::TIME_LIMIT); + + // Log features before return + features.lp_status = static_cast(lp_status); + features.node_status = static_cast(node_status_t::TIME_LIMIT); + features.total_node_time = toc(node_start_time); + log_node_features(features); + return node_status_t::TIME_LIMIT; } else { @@ -680,6 +916,13 @@ node_status_t branch_and_bound_t::solve_node(search_tree_t& search_tree.graphviz_node(log, node_ptr, "numerical", 0.0); search_tree.update_tree(node_ptr, node_status_t::NUMERICAL); + + // Log features before return + features.lp_status = static_cast(lp_status); + features.node_status = static_cast(node_status_t::NUMERICAL); + features.total_node_time = toc(node_start_time); + log_node_features(features); + return node_status_t::NUMERICAL; } } @@ -1227,6 +1470,11 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } } + // Flush any pending features + mutex_feature_log_.lock(); + if (has_pending_features_) { flush_pending_features(); } + mutex_feature_log_.unlock(); + f_t lower_bound = heap_.size() > 0 ? heap_.top()->lower_bound : search_tree.root.lower_bound; return set_final_solution(solution, lower_bound); } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 5b304addd..e60ce59cf 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -45,6 +45,51 @@ enum class mip_exploration_status_t { template void upper_bound_callback(f_t upper_bound); +// Feature tracking for solve_node regression model +template +struct node_solve_features_t { + // Static problem features (compute once) + i_t n_rows{0}; + i_t n_cols{0}; + i_t n_nonzeros{0}; + f_t density{0.0}; + i_t n_binary{0}; + i_t n_integer{0}; + i_t n_continuous{0}; + f_t integrality_ratio{0.0}; + f_t avg_row_nnz{0.0}; + i_t max_row_nnz{0}; + f_t avg_col_nnz{0.0}; + i_t max_col_nnz{0}; + f_t row_nnz_cv{0.0}; + f_t col_nnz_cv{0.0}; + + // Dynamic node state + i_t node_id{0}; + i_t node_depth{0}; + i_t n_bounds_changed{0}; + f_t cutoff_gap_ratio{0.0}; + bool basis_from_parent{false}; + + // LP solve metrics + i_t simplex_iterations{0}; + i_t n_refactorizations{0}; + f_t lp_solve_time{0.0}; + f_t bound_str_time{0.0}; + f_t variable_sel_time{0.0}; + + // Outcome metrics + i_t n_fractional{0}; + bool strong_branch_performed{false}; + i_t n_strong_branch_candidates{0}; + f_t strong_branch_time{0.0}; + i_t lp_status{0}; // Convert dual::status_t to int + i_t node_status{0}; // Convert node_status_t to int + + // Computed at node end + f_t total_node_time{0.0}; +}; + template struct diving_root_t { mip_node_t node; @@ -203,6 +248,22 @@ class branch_and_bound_t { // its blocks the progression of the lower bound. omp_atomic_t lower_bound_ceiling_; + // Feature tracking for solve_node regression model + node_solve_features_t static_features_; // Static problem features, computed once + omp_mutex_t mutex_feature_log_; // Protect feature logging + node_solve_features_t last_features_; // Last captured features + f_t last_feature_log_time_{0.0}; // Time of last feature log + bool has_pending_features_{false}; // Whether we have features to log + + // Helper to compute static features once + void compute_static_features(); + + // Helper to log node solve features with time-based throttling + void log_node_features(const node_solve_features_t& features); + + // Helper to flush any pending features at end of solve + void flush_pending_features(); + // Set the final solution. mip_status_t set_final_solution(mip_solution_t& solution, f_t lower_bound); diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 39e79a1bb..6bcec10bf 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -355,7 +355,7 @@ solution_t diversity_manager_t::run_solver() population.allocate_solutions(); ls_cpufj_raii_guard_t ls_cpufj_raii_guard(ls); // RAII to stop cpufj threads on solve stop if (!context.settings.deterministic) { -#if 1 +#if 0 ls.start_cpufj_scratch_threads(population); std::this_thread::sleep_for(std::chrono::seconds(30)); ls.stop_cpufj_scratch_threads(); @@ -458,7 +458,7 @@ solution_t diversity_manager_t::run_solver() lp_rounded_sol.round_nearest(); lp_rounded_sol.compute_feasibility(); population.add_solution(std::move(lp_rounded_sol)); - if (!context.settings.deterministic) { ls.start_cpufj_lptopt_scratch_threads(population); } + // if (!context.settings.deterministic) { ls.start_cpufj_lptopt_scratch_threads(population); } } population.add_solutions_from_vec(std::move(initial_sol_vector)); diff --git a/cpp/src/mip/local_search/local_search.cuh b/cpp/src/mip/local_search/local_search.cuh index 09aafa54f..c1c11fd39 100644 --- a/cpp/src/mip/local_search/local_search.cuh +++ b/cpp/src/mip/local_search/local_search.cuh @@ -142,8 +142,8 @@ class local_search_t { feasibility_pump_t fp; std::mt19937 rng; - std::array, 8> ls_cpu_fj; - std::array, 1> scratch_cpu_fj; + std::array, 0> ls_cpu_fj; + std::array, 0> scratch_cpu_fj; cpu_fj_thread_t scratch_cpu_fj_on_lp_opt; problem_t problem_with_objective_cut; bool cutting_plane_added_for_active_run{false}; diff --git a/cpp/src/utilities/timer.hpp b/cpp/src/utilities/timer.hpp index 77b48ea78..bb74e75b9 100644 --- a/cpp/src/utilities/timer.hpp +++ b/cpp/src/utilities/timer.hpp @@ -46,7 +46,7 @@ class timer_t { line, caller); assert(false && "unexpected timer"); - __builtin_trap(); + //__builtin_trap(); } return elapsed; } From 5c9e59ae0a3da957f8a66237643046fa1236fc19 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 12 Nov 2025 14:39:03 +0000 Subject: [PATCH 073/366] add memory instrumentation wrappers --- cpp/src/dual_simplex/branch_and_bound.cpp | 92 +-- cpp/src/mip/diversity/diversity_manager.cu | 2 +- .../mip/feasibility_jump/feasibility_jump.cuh | 6 + .../feasibility_jump_impl_common.cuh | 22 +- .../feasibility_jump_kernels.cu | 14 +- cpp/src/mip/feasibility_jump/fj_cpu.cu | 322 +++++++-- cpp/src/mip/feasibility_jump/fj_cpu.cuh | 93 ++- cpp/src/mip/local_search/local_search.cuh | 2 +- cpp/src/utilities/memory_instrumentation.hpp | 673 ++++++++++++++++++ scripts/determinism_logs_parse.py | 19 +- scripts/train_regressor.py | 25 +- 11 files changed, 1113 insertions(+), 157 deletions(-) create mode 100644 cpp/src/utilities/memory_instrumentation.hpp diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 36b8aa7c5..49953a926 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -633,52 +633,52 @@ void branch_and_bound_t::flush_pending_features() constexpr int LINE_BUFFER_SIZE = 512; char line_buffer[LINE_BUFFER_SIZE]; - snprintf(line_buffer, - LINE_BUFFER_SIZE, - "BB_NODE_FEATURES " - "node_id=%d depth=%d time=%.6f " - "n_rows=%d n_cols=%d n_nnz=%d density=%.6f " - "n_bin=%d n_int=%d n_cont=%d int_ratio=%.4f " - "avg_row_nnz=%.2f max_row_nnz=%d row_nnz_cv=%.4f " - "avg_col_nnz=%.2f max_col_nnz=%d col_nnz_cv=%.4f " - "n_bounds_chg=%d cutoff_gap=%.4f basis_from_parent=%d " - "simplex_iters=%d n_refact=%d lp_time=%.6f bound_str_time=%.6f var_sel_time=%.6f " - "n_frac=%d strong_branch=%d n_sb_cand=%d sb_time=%.6f " - "lp_status=%d node_status=%d\n", - last_features_.node_id, - last_features_.node_depth, - last_features_.total_node_time, - last_features_.n_rows, - last_features_.n_cols, - last_features_.n_nonzeros, - last_features_.density, - last_features_.n_binary, - last_features_.n_integer, - last_features_.n_continuous, - last_features_.integrality_ratio, - last_features_.avg_row_nnz, - last_features_.max_row_nnz, - last_features_.row_nnz_cv, - last_features_.avg_col_nnz, - last_features_.max_col_nnz, - last_features_.col_nnz_cv, - last_features_.n_bounds_changed, - last_features_.cutoff_gap_ratio, - last_features_.basis_from_parent ? 1 : 0, - last_features_.simplex_iterations, - last_features_.n_refactorizations, - last_features_.lp_solve_time, - last_features_.bound_str_time, - last_features_.variable_sel_time, - last_features_.n_fractional, - last_features_.strong_branch_performed ? 1 : 0, - last_features_.n_strong_branch_candidates, - last_features_.strong_branch_time, - last_features_.lp_status, - last_features_.node_status); - - // Single printf call - settings_.log.printf("%s", line_buffer); + // snprintf(line_buffer, + // LINE_BUFFER_SIZE, + // "BB_NODE_FEATURES " + // "node_id=%d depth=%d time=%.6f " + // "n_rows=%d n_cols=%d n_nnz=%d density=%.6f " + // "n_bin=%d n_int=%d n_cont=%d int_ratio=%.4f " + // "avg_row_nnz=%.2f max_row_nnz=%d row_nnz_cv=%.4f " + // "avg_col_nnz=%.2f max_col_nnz=%d col_nnz_cv=%.4f " + // "n_bounds_chg=%d cutoff_gap=%.4f basis_from_parent=%d " + // "simplex_iters=%d n_refact=%d lp_time=%.6f bound_str_time=%.6f var_sel_time=%.6f " + // "n_frac=%d strong_branch=%d n_sb_cand=%d sb_time=%.6f " + // "lp_status=%d node_status=%d\n", + // last_features_.node_id, + // last_features_.node_depth, + // last_features_.total_node_time, + // last_features_.n_rows, + // last_features_.n_cols, + // last_features_.n_nonzeros, + // last_features_.density, + // last_features_.n_binary, + // last_features_.n_integer, + // last_features_.n_continuous, + // last_features_.integrality_ratio, + // last_features_.avg_row_nnz, + // last_features_.max_row_nnz, + // last_features_.row_nnz_cv, + // last_features_.avg_col_nnz, + // last_features_.max_col_nnz, + // last_features_.col_nnz_cv, + // last_features_.n_bounds_changed, + // last_features_.cutoff_gap_ratio, + // last_features_.basis_from_parent ? 1 : 0, + // last_features_.simplex_iterations, + // last_features_.n_refactorizations, + // last_features_.lp_solve_time, + // last_features_.bound_str_time, + // last_features_.variable_sel_time, + // last_features_.n_fractional, + // last_features_.strong_branch_performed ? 1 : 0, + // last_features_.n_strong_branch_candidates, + // last_features_.strong_branch_time, + // last_features_.lp_status, + // last_features_.node_status); + + // // Single printf call + // settings_.log.printf("%s", line_buffer); has_pending_features_ = false; } diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 6bcec10bf..82c51fe64 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -355,7 +355,7 @@ solution_t diversity_manager_t::run_solver() population.allocate_solutions(); ls_cpufj_raii_guard_t ls_cpufj_raii_guard(ls); // RAII to stop cpufj threads on solve stop if (!context.settings.deterministic) { -#if 0 +#if 1 ls.start_cpufj_scratch_threads(population); std::this_thread::sleep_for(std::chrono::seconds(30)); ls.stop_cpufj_scratch_threads(); diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh index e65828ea8..ae06d2e17 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh @@ -136,6 +136,12 @@ struct fj_staged_score_t { int32_t base{std::numeric_limits::lowest()}; int32_t bonus{std::numeric_limits::lowest()}; + fj_staged_score_t() = default; + fj_staged_score_t(const fj_staged_score_t&) = default; + fj_staged_score_t(fj_staged_score_t&&) = default; + fj_staged_score_t& operator=(const fj_staged_score_t&) = default; + fj_staged_score_t& operator=(fj_staged_score_t&&) = default; + HDI bool operator<(fj_staged_score_t other) const noexcept { return base == other.base ? bonus < other.bonus : base < other.base; diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh b/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh index fbc5a7b39..95de8bc1e 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh @@ -28,20 +28,22 @@ HDI f_t fj_kahan_babushka_neumaier_sum(Iterator begin, Iterator end) } // Returns the current slack, and the variable delta that would nullify this slack ("tighten" it) -template +template HDI thrust::tuple get_mtm_for_bound( const typename fj_t::climber_data_t::view_t& fj, i_t var_idx, i_t cstr_idx, f_t cstr_coeff, f_t bound, - f_t sign) + f_t sign, + const ArrayType& assignment, + const ArrayType& lhs_vector) { f_t delta_ij = 0; f_t slack = 0; - f_t old_val = fj.incumbent_assignment[var_idx]; + f_t old_val = assignment[var_idx]; - f_t lhs = fj.incumbent_lhs[cstr_idx] * sign; + f_t lhs = lhs_vector[cstr_idx] * sign; f_t rhs = bound * sign; slack = rhs - lhs; // bound might be infinite. let the caller handle this case @@ -50,22 +52,24 @@ HDI thrust::tuple get_mtm_for_bound( return {delta_ij, slack}; } -template +template HDI thrust::tuple get_mtm_for_constraint( const typename fj_t::climber_data_t::view_t& fj, i_t var_idx, i_t cstr_idx, f_t cstr_coeff, f_t c_lb, - f_t c_ub) + f_t c_ub, + const ArrayType& assignment, + const ArrayType& lhs_vector) { f_t sign = -1; f_t delta_ij = 0; f_t slack = 0; - f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx); + f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx, c_lb, c_ub); - f_t old_val = fj.incumbent_assignment[var_idx]; + f_t old_val = assignment[var_idx]; // process each bound as two separate constraints f_t bounds[2] = {c_lb, c_ub}; @@ -77,7 +81,7 @@ HDI thrust::tuple get_mtm_for_constraint( // factor to correct the lhs/rhs to turn a lb <= lhs <= ub constraint into // two virtual constraints lhs <= ub and -lhs <= -lb sign = bound_idx == 0 ? -1 : 1; - f_t lhs = fj.incumbent_lhs[cstr_idx] * sign; + f_t lhs = lhs_vector[cstr_idx] * sign; f_t rhs = bounds[bound_idx] * sign; slack = rhs - lhs; diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index a7195fabe..584d3d854 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -308,8 +308,8 @@ DI std::pair::move_score_info_t> compute_best_mtm( f_t c_lb = fj.pb.constraint_lower_bounds[cstr_idx]; f_t c_ub = fj.pb.constraint_upper_bounds[cstr_idx]; f_t new_val; - auto [delta_ij, sign, slack, cstr_tolerance] = - get_mtm_for_constraint(fj, var_idx, cstr_idx, cstr_coeff, c_lb, c_ub); + auto [delta_ij, sign, slack, cstr_tolerance] = get_mtm_for_constraint( + fj, var_idx, cstr_idx, cstr_coeff, c_lb, c_ub, fj.incumbent_assignment, fj.incumbent_lhs); if (fj.pb.is_integer_var(var_idx)) { new_val = cstr_coeff * sign > 0 ? floor(old_val + delta_ij + fj.pb.tolerances.integrality_tolerance) @@ -750,8 +750,14 @@ DI void update_lift_moves(typename fj_t::climber_data_t::view_t fj) // Process each bound separately, as both are satified and may both be finite // otherwise range constraints aren't correctly handled for (auto [bound, sign] : {std::make_tuple(c_lb, -1), std::make_tuple(c_ub, 1)}) { - auto [delta, slack] = - get_mtm_for_bound(fj, var_idx, cstr_idx, cstr_coeff, bound, sign); + auto [delta, slack] = get_mtm_for_bound(fj, + var_idx, + cstr_idx, + cstr_coeff, + bound, + sign, + fj.incumbent_assignment, + fj.incumbent_lhs); if (cstr_coeff * sign < 0) { if (fj.pb.is_integer_var(var_idx)) delta = ceil(delta); diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 262bd2f35..bee5d7c96 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -5,6 +5,28 @@ */ /* clang-format on */ +/* + * MEMORY OPERATION ANNOTATIONS: + * + * This file contains detailed comments marking all memory operations (array reads and writes) + * within loops for the CPU Feasibility Jump (CPUFJ) algorithm. These annotations are intended + * to help estimate the memory bandwidth requirements and runtime of this memory-bound workload. + * + * Key annotations: + * - "MEMORY OPS:" marks the start of a loop that performs memory operations + * - "ARRAY READ:" marks array read operations with array name and count per iteration + * - "ARRAY WRITE:" marks array write operations with array name and count per iteration + * - "CRITICAL LOOP" or "HOTTEST LOOP" marks the most frequently executed loops + * - "Total per iteration:" summarizes memory ops per loop iteration + * + * Important notation: + * - n_vars: number of variables + * - n_cstrs: number of constraints + * - avg_var_degree: average number of constraints per variable + * - avg_cstr_degree: average number of variables per constraint + * - total_nnz: total non-zeros in constraint matrix + */ + #include #include "feasibility_jump.cuh" @@ -258,12 +280,16 @@ static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) // Count variable types - use host vectors fj_cpu.n_binary_vars = 0; fj_cpu.n_integer_vars = 0; + // MEMORY OPS: Loop over all variables (n_vars iterations) for (i_t i = 0; i < (i_t)fj_cpu.h_is_binary_variable.size(); i++) { + // ARRAY READ: h_is_binary_variable[i] - 1 read per iteration if (fj_cpu.h_is_binary_variable[i]) { fj_cpu.n_binary_vars++; } else if (fj_cpu.h_var_types[i] == var_t::INTEGER) { + // ARRAY READ: h_var_types[i] - 1 read per iteration (conditional) fj_cpu.n_integer_vars++; } + // Total per iteration: 2 array reads } i_t total_nnz = fj_cpu.h_reverse_offsets.back(); @@ -275,17 +301,24 @@ static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) // Compute variable degree statistics (max, cv) fj_cpu.max_var_degree = 0; std::vector var_degrees(n_vars); + // MEMORY OPS: Loop over all variables (n_vars iterations) for (i_t i = 0; i < n_vars; i++) { - i_t degree = fj_cpu.h_reverse_offsets[i + 1] - fj_cpu.h_reverse_offsets[i]; + // ARRAY READ: h_reverse_offsets[i] and h_reverse_offsets[i+1] - 2 reads per iteration + i_t degree = fj_cpu.h_reverse_offsets[i + 1] - fj_cpu.h_reverse_offsets[i]; + // ARRAY WRITE: var_degrees[i] - 1 write per iteration var_degrees[i] = degree; fj_cpu.max_var_degree = std::max(fj_cpu.max_var_degree, degree); + // Total per iteration: 2 reads + 1 write = 3 memory ops } // Compute variable degree coefficient of variation double var_deg_variance = 0.0; + // MEMORY OPS: Loop over all variables (n_vars iterations) for (i_t i = 0; i < n_vars; i++) { + // ARRAY READ: var_degrees[i] - 1 read per iteration double diff = var_degrees[i] - fj_cpu.avg_var_degree; var_deg_variance += diff * diff; + // Total per iteration: 1 read } var_deg_variance /= n_vars; double var_degree_std = std::sqrt(var_deg_variance); @@ -296,17 +329,24 @@ static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) // Compute constraint degree statistics (max, cv) fj_cpu.max_cstr_degree = 0; std::vector cstr_degrees(n_cstrs); + // MEMORY OPS: Loop over all constraints (n_cstrs iterations) for (i_t i = 0; i < n_cstrs; i++) { - i_t degree = fj_cpu.h_offsets[i + 1] - fj_cpu.h_offsets[i]; + // ARRAY READ: h_offsets[i] and h_offsets[i+1] - 2 reads per iteration + i_t degree = fj_cpu.h_offsets[i + 1] - fj_cpu.h_offsets[i]; + // ARRAY WRITE: cstr_degrees[i] - 1 write per iteration cstr_degrees[i] = degree; fj_cpu.max_cstr_degree = std::max(fj_cpu.max_cstr_degree, degree); + // Total per iteration: 2 reads + 1 write = 3 memory ops } // Compute constraint degree coefficient of variation double cstr_deg_variance = 0.0; + // MEMORY OPS: Loop over all constraints (n_cstrs iterations) for (i_t i = 0; i < n_cstrs; i++) { + // ARRAY READ: cstr_degrees[i] - 1 read per iteration double diff = cstr_degrees[i] - fj_cpu.avg_cstr_degree; cstr_deg_variance += diff * diff; + // Total per iteration: 1 read } cstr_deg_variance /= n_cstrs; double cstr_degree_std = std::sqrt(cstr_deg_variance); @@ -319,7 +359,9 @@ static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) template static void log_regression_features(fj_cpu_climber_t& fj_cpu, double time_window_ms, - double total_time_ms) + double total_time_ms, + size_t mem_loads_bytes, + size_t mem_stores_bytes) { i_t total_nnz = fj_cpu.h_reverse_offsets.back(); i_t n_vars = fj_cpu.h_reverse_offsets.size() - 1; @@ -394,6 +436,12 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, } #endif + // Compute memory statistics + double mem_loads_mb = mem_loads_bytes / 1e6; + double mem_stores_mb = mem_stores_bytes / 1e6; + double mem_total_mb = (mem_loads_bytes + mem_stores_bytes) / 1e6; + double mem_bandwidth_gb_per_sec = (mem_total_mb / 1000.0) / (time_window_ms / 1000.0); + // Print everything on a single line using precomputed features CUOPT_LOG_DEBUG( "%sCPUFJ_FEATURES iter=%d time_window=%.2f " @@ -408,7 +456,8 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, "cstr_reuse=%.2f var_reuse=%.2f working_set_kb=%.1f " "cstr_coverage=%.4f var_coverage=%.4f " "L1_miss=%.2f L3_miss=%.2f loads_per_iter=%.0f stores_per_iter=%.0f " - "viol_ratio=%.4f nnz_per_move=%.2f eval_intensity=%.2f", + "viol_ratio=%.4f nnz_per_move=%.2f eval_intensity=%.2f " + "mem_loads_mb=%.3f mem_stores_mb=%.3f mem_total_mb=%.3f mem_bandwidth_gb_s=%.3f", fj_cpu.log_prefix.c_str(), fj_cpu.iterations, time_window_ms, @@ -449,7 +498,11 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, stores_per_iter, violated_ratio, nnz_per_move, - eval_intensity); + eval_intensity, + mem_loads_mb, + mem_stores_mb, + mem_total_mb, + mem_bandwidth_gb_per_sec); // Reset window counters fj_cpu.nnz_processed_window = 0; @@ -483,16 +536,16 @@ static inline bool tabu_check(fj_cpu_climber_t& fj_cpu, } template -static bool check_variable_feasibility(const typename fj_t::climber_data_t::view_t& fj, +static bool check_variable_feasibility(const fj_cpu_climber_t& fj_cpu, bool check_integer = true) { - for (i_t var_idx = 0; var_idx < fj.pb.n_variables; var_idx += 1) { - auto val = fj.incumbent_assignment[var_idx]; - bool feasible = fj.pb.check_variable_within_bounds(var_idx, val); + for (i_t var_idx = 0; var_idx < fj_cpu.view.pb.n_variables; var_idx += 1) { + auto val = fj_cpu.h_assignment[var_idx]; + bool feasible = fj_cpu.view.pb.check_variable_within_bounds(var_idx, val); if (!feasible) return false; - if (check_integer && fj.pb.is_integer_var(var_idx) && - !fj.pb.is_integer(fj.incumbent_assignment[var_idx])) + if (check_integer && fj_cpu.view.pb.is_integer_var(var_idx) && + !fj_cpu.view.pb.is_integer(fj_cpu.h_assignment[var_idx])) return false; } return true; @@ -505,6 +558,7 @@ static inline std::pair compute_score(fj_cpu_climber_t timer(fj_cpu.compute_score_times); + // ARRAY READ: h_obj_coeffs[var_idx] - 1 read f_t obj_diff = fj_cpu.h_obj_coeffs[var_idx] * delta; cuopt_assert(isfinite(delta), ""); @@ -517,19 +571,29 @@ static inline std::pair compute_score(fj_cpu_climber_t( fj_cpu.view, var_idx, delta, cstr_idx, cstr_coeff, c_lb, c_ub, fj_cpu.h_lhs[cstr_idx]); base_feas_sum += cstr_base_feas; bonus_robust_sum += cstr_bonus_robust; + // Total per iteration: ~6-7 array reads (h_reverse_constraints, h_reverse_coefficients, + // cached_cstr_bounds (2 values), h_lhs, h_cstr_left_weights, h_cstr_right_weights) } f_t base_obj = 0; @@ -557,15 +621,21 @@ static inline std::pair compute_score(fj_cpu_climber_t static void smooth_weights(fj_cpu_climber_t& fj_cpu) { + // MEMORY OPS: Loop over all constraints (n_cstrs iterations) for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; cstr_idx++) { // consider only satisfied constraints if (fj_cpu.violated_constraints.count(cstr_idx)) continue; + // ARRAY READ: h_cstr_left_weights[cstr_idx] - 1 read per iteration (if not violated) f_t weight_l = max((f_t)0, fj_cpu.h_cstr_left_weights[cstr_idx] - 1); + // ARRAY READ: h_cstr_right_weights[cstr_idx] - 1 read per iteration (if not violated) f_t weight_r = max((f_t)0, fj_cpu.h_cstr_right_weights[cstr_idx] - 1); - fj_cpu.h_cstr_left_weights[cstr_idx] = weight_l; + // ARRAY WRITE: h_cstr_left_weights[cstr_idx] - 1 write per iteration (if not violated) + fj_cpu.h_cstr_left_weights[cstr_idx] = weight_l; + // ARRAY WRITE: h_cstr_right_weights[cstr_idx] - 1 write per iteration (if not violated) fj_cpu.h_cstr_right_weights[cstr_idx] = weight_r; + // Total per iteration (for satisfied constraints): 2 reads + 2 writes = 4 memory ops } if (fj_cpu.h_objective_weight > 0 && fj_cpu.h_incumbent_objective >= fj_cpu.h_best_objective) { @@ -586,18 +656,24 @@ static void update_weights(fj_cpu_climber_t& fj_cpu) return; } + // MEMORY OPS: Loop over violated constraints (typically small: <100 iterations) for (auto cstr_idx : fj_cpu.violated_constraints) { + // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration f_t curr_incumbent_lhs = fj_cpu.h_lhs[cstr_idx]; + // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per iteration f_t curr_lower_excess = fj_cpu.view.lower_excess_score(cstr_idx, curr_incumbent_lhs, fj_cpu.h_cstr_lb[cstr_idx]); + // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per iteration f_t curr_upper_excess = fj_cpu.view.upper_excess_score(cstr_idx, curr_incumbent_lhs, fj_cpu.h_cstr_ub[cstr_idx]); f_t curr_excess_score = curr_lower_excess + curr_upper_excess; f_t old_weight; if (curr_lower_excess < 0.) { + // ARRAY READ: h_cstr_left_weights[cstr_idx] - 1 read per iteration (conditional) old_weight = fj_cpu.h_cstr_left_weights[cstr_idx]; } else { + // ARRAY READ: h_cstr_right_weights[cstr_idx] - 1 read per iteration (conditional) old_weight = fj_cpu.h_cstr_right_weights[cstr_idx]; } @@ -610,18 +686,25 @@ static void update_weights(fj_cpu_climber_t& fj_cpu) new_weight = round(new_weight); if (curr_lower_excess < 0.) { + // ARRAY WRITE: h_cstr_left_weights[cstr_idx] - 1 write per iteration (conditional) fj_cpu.h_cstr_left_weights[cstr_idx] = new_weight; fj_cpu.max_weight = max(fj_cpu.max_weight, new_weight); } else { + // ARRAY WRITE: h_cstr_right_weights[cstr_idx] - 1 write per iteration (conditional) fj_cpu.h_cstr_right_weights[cstr_idx] = new_weight; fj_cpu.max_weight = max(fj_cpu.max_weight, new_weight); } // Invalidate related cached move scores auto [relvar_offset_begin, relvar_offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + // MEMORY OPS: Inner loop over variables in this constraint (avg_cstr_degree iterations per + // outer iteration) for (auto i = relvar_offset_begin; i < relvar_offset_end; i++) { + // ARRAY WRITE: cached_mtm_moves[i].first - 1 write per inner iteration fj_cpu.cached_mtm_moves[i].first = 0; + // Total per inner iteration: 1 write } + // Total per outer iteration: 4 reads + 1 write + (avg_cstr_degree writes in inner loop) } if (fj_cpu.violated_constraints.empty()) { fj_cpu.h_objective_weight += 1; } @@ -648,27 +731,38 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, i_t previous_viol = fj_cpu.violated_constraints.size(); + // MEMORY OPS: CRITICAL LOOP - Loop over all constraints involving this variable (avg_var_degree + // iterations) This loop is called ONCE PER ITERATION and updates constraint LHS values for (auto i = offset_begin; i < offset_end; i++) { cuopt_assert(i < (i_t)fj_cpu.h_reverse_constraints.size(), ""); - auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i]; + // ARRAY READ: cached_cstr_bounds[i] - 1 read per iteration (reads 2 f_t values) + auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i].get(); + // ARRAY READ: h_reverse_constraints[i] - 1 read per iteration auto cstr_idx = fj_cpu.h_reverse_constraints[i]; fj_cpu.unique_cstrs_accessed_window.insert(cstr_idx); + // ARRAY READ: h_reverse_coefficients[i] - 1 read per iteration auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; + // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (indirect indexing) f_t old_lhs = fj_cpu.h_lhs[cstr_idx]; // Kahan compensated summation - f_t y = cstr_coeff * delta - fj_cpu.h_lhs_sumcomp[cstr_idx]; - f_t t = old_lhs + y; + // ARRAY READ: h_lhs_sumcomp[cstr_idx] - 1 read per iteration + f_t y = cstr_coeff * delta - fj_cpu.h_lhs_sumcomp[cstr_idx]; + f_t t = old_lhs + y; + // ARRAY WRITE: h_lhs_sumcomp[cstr_idx] - 1 write per iteration fj_cpu.h_lhs_sumcomp[cstr_idx] = (t - old_lhs) - y; - fj_cpu.h_lhs[cstr_idx] = t; - f_t new_lhs = fj_cpu.h_lhs[cstr_idx]; - f_t old_cost = fj_cpu.view.excess_score(cstr_idx, old_lhs, c_lb, c_ub); - f_t new_cost = fj_cpu.view.excess_score(cstr_idx, new_lhs, c_lb, c_ub); - f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + // ARRAY WRITE: h_lhs[cstr_idx] - 1 write per iteration + fj_cpu.h_lhs[cstr_idx] = t; + // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (just written) + f_t new_lhs = fj_cpu.h_lhs[cstr_idx]; + f_t old_cost = fj_cpu.view.excess_score(cstr_idx, old_lhs, c_lb, c_ub); + f_t new_cost = fj_cpu.view.excess_score(cstr_idx, new_lhs, c_lb, c_ub); + f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); // trigger early lhs recomputation if the sumcomp term gets too large // to avoid large numerical errors + // ARRAY READ: h_lhs_sumcomp[cstr_idx] - 1 read per iteration if (fabs(fj_cpu.h_lhs_sumcomp[cstr_idx]) > BIGVAL_THRESHOLD) fj_cpu.trigger_early_lhs_recomputation = true; @@ -687,9 +781,14 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, // Invalidate related cached move scores auto [relvar_offset_begin, relvar_offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + // MEMORY OPS: Inner loop over variables in this constraint (avg_cstr_degree iterations per + // outer iteration) for (auto i = relvar_offset_begin; i < relvar_offset_end; i++) { + // ARRAY WRITE: cached_mtm_moves[i].first - 1 write per inner iteration fj_cpu.cached_mtm_moves[i].first = 0; + // Total per inner iteration: 1 write } + // Total per outer iteration: 7 reads + 3 writes + (avg_cstr_degree writes in inner loop) } if (previous_viol > 0 && fj_cpu.violated_constraints.empty()) { @@ -697,27 +796,31 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, } // update the assignment and objective proper + // ARRAY READ: h_assignment[var_idx] - 1 read f_t new_val = fj_cpu.h_assignment[var_idx] + delta; if (fj_cpu.view.pb.is_integer_var(var_idx)) { cuopt_assert(fj_cpu.view.pb.integer_equal(new_val, round(new_val)), "new_val is not integer"); new_val = round(new_val); } + // ARRAY WRITE: h_assignment[var_idx] - 1 write fj_cpu.h_assignment[var_idx] = new_val; cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), "assignment not within bounds"); cuopt_assert(isfinite(new_val), "assignment is not finite"); + // ARRAY READ: h_obj_coeffs[var_idx] - 1 read fj_cpu.h_incumbent_objective += fj_cpu.h_obj_coeffs[var_idx] * delta; if (fj_cpu.h_incumbent_objective < fj_cpu.h_best_objective && fj_cpu.violated_constraints.empty()) { // recompute the LHS values to cancel out accumulation errors, then check if feasibility remains recompute_lhs(fj_cpu); - if (fj_cpu.violated_constraints.empty() && check_variable_feasibility(fj_cpu.view)) { + if (fj_cpu.violated_constraints.empty() && check_variable_feasibility(fj_cpu)) { cuopt_assert(fj_cpu.satisfied_constraints.size() == fj_cpu.view.pb.n_constraints, ""); fj_cpu.h_best_objective = fj_cpu.h_incumbent_objective - fj_cpu.settings.parameters.breakthrough_move_epsilon; + // ARRAY WRITE: h_best_assignment = h_assignment - n_vars writes (vector copy) fj_cpu.h_best_assignment = fj_cpu.h_assignment; fj_cpu.iterations_since_best = 0; // Reset counter on improvement CUOPT_LOG_TRACE("%sCPUFJ: new best objective: %g", @@ -734,18 +837,25 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, rng.next_u32() % (fj_cpu.settings.parameters.tabu_tenure_max - fj_cpu.settings.parameters.tabu_tenure_min); if (delta > 0) { - fj_cpu.h_tabu_lastinc[var_idx] = fj_cpu.iterations; + // ARRAY WRITE: h_tabu_lastinc[var_idx] - 1 write + fj_cpu.h_tabu_lastinc[var_idx] = fj_cpu.iterations; + // ARRAY WRITE: h_tabu_nodec_until[var_idx] - 1 write fj_cpu.h_tabu_nodec_until[var_idx] = fj_cpu.iterations + tabu_tenure; + // ARRAY WRITE: h_tabu_noinc_until[var_idx] - 1 write fj_cpu.h_tabu_noinc_until[var_idx] = fj_cpu.iterations + tabu_tenure / 2; // CUOPT_LOG_TRACE("CPU: tabu nodec_until: %d", fj_cpu.h_tabu_nodec_until[var_idx]); } else { - fj_cpu.h_tabu_lastdec[var_idx] = fj_cpu.iterations; + // ARRAY WRITE: h_tabu_lastdec[var_idx] - 1 write + fj_cpu.h_tabu_lastdec[var_idx] = fj_cpu.iterations; + // ARRAY WRITE: h_tabu_noinc_until[var_idx] - 1 write fj_cpu.h_tabu_noinc_until[var_idx] = fj_cpu.iterations + tabu_tenure; + // ARRAY WRITE: h_tabu_nodec_until[var_idx] - 1 write fj_cpu.h_tabu_nodec_until[var_idx] = fj_cpu.iterations + tabu_tenure / 2; - // CUOPT_LOG_TRACE("CPU: tabu noinc_until: %d", fj_cpu.h_tabu_noinc_until[var_idx]); + // CUOPT_LOG_TRACE("CPU: tabu noinc_until: %d", fj_cpu.h_tabu_nodec_until[var_idx]); } - + // ARRAY WRITE: flip_move_computed - n_vars writes (fill with false) std::fill(fj_cpu.flip_move_computed.begin(), fj_cpu.flip_move_computed.end(), false); + // ARRAY WRITE: var_bitmap - n_vars writes (fill with false) std::fill(fj_cpu.var_bitmap.begin(), fj_cpu.var_bitmap.end(), false); fj_cpu.iter_mtm_vars.clear(); } @@ -762,13 +872,20 @@ static thrust::tuple find_mtm_move( fj_staged_score_t best_score = fj_staged_score_t::invalid(); // collect all the variables that are involved in the target constraints + // MEMORY OPS: Outer loop over target constraints (sample_size iterations, typically 15-100) for (size_t cstr_idx : target_cstrs) { auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + // MEMORY OPS: Inner loop over variables in each constraint (avg_cstr_degree per outer + // iteration) for (auto i = offset_begin; i < offset_end; i++) { + // ARRAY READ: h_variables[i] - 1 read per iteration i_t var_idx = fj_cpu.h_variables[i]; + // ARRAY READ: var_bitmap[var_idx] - 1 read per iteration if (fj_cpu.var_bitmap[var_idx]) continue; fj_cpu.iter_mtm_vars.push_back(var_idx); + // ARRAY WRITE: var_bitmap[var_idx] - 1 write per iteration (if not already set) fj_cpu.var_bitmap[var_idx] = true; + // Total per inner iteration: 2 reads + 1 write (conditional) } } // estimate the amount of nnzs to consider @@ -781,16 +898,22 @@ static thrust::tuple find_mtm_move( f_t nnz_pick_probability = 1; if (nnz_sum > fj_cpu.nnz_samples) nnz_pick_probability = (f_t)fj_cpu.nnz_samples / nnz_sum; + // MEMORY OPS: HOTTEST LOOP - Outer loop over target constraints (sample_size iterations) for (size_t cstr_idx : target_cstrs) { - f_t cstr_tol = fj_cpu.view.get_corrected_tolerance(cstr_idx); + auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); + f_t cstr_tol = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); cuopt_assert(cstr_idx < fj_cpu.h_cstr_lb.size(), "cstr_idx is out of bounds"); auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + // MEMORY OPS: Inner loop over variables (avg_cstr_degree per outer iteration) for (auto i = offset_begin; i < offset_end; i++) { // early cached check + // ARRAY READ: cached_mtm_moves[i] - 1 read per iteration if (auto& cached_move = fj_cpu.cached_mtm_moves[i]; cached_move.first != 0) { if (best_score < cached_move.second) { + // ARRAY READ: h_variables[i] - 1 read per iteration (cache hit) auto var_idx = fj_cpu.h_variables[i]; + // ARRAY READ: h_assignment[var_idx] - 1 read per iteration (cache hit) if (fj_cpu.view.pb.check_variable_within_bounds( var_idx, fj_cpu.h_assignment[var_idx] + cached_move.first)) { best_score = cached_move.second; @@ -800,6 +923,7 @@ static thrust::tuple find_mtm_move( // fj_cpu.h_assignment[var_idx] + cached_move.first), "best move not within bounds"); } fj_cpu.hit_count++; + // Total per cache hit: 3 reads continue; } @@ -807,24 +931,39 @@ static thrust::tuple find_mtm_move( if (nnz_pick_probability < 1) if (rng.next_float() > nnz_pick_probability) continue; + // ARRAY READ: h_variables[i] - 1 read per iteration (cache miss) auto var_idx = fj_cpu.h_variables[i]; + // ARRAY READ: h_assignment[var_idx] - 1 read per iteration (cache miss) f_t val = fj_cpu.h_assignment[var_idx]; f_t new_val = val; f_t delta = 0; // Special case for binary variables + // ARRAY READ: h_is_binary_variable[var_idx] - 1 read per iteration if (fj_cpu.h_is_binary_variable[var_idx]) { + // ARRAY READ: flip_move_computed[var_idx] - 1 read per iteration (conditional) if (fj_cpu.flip_move_computed[var_idx]) continue; + // ARRAY WRITE: flip_move_computed[var_idx] - 1 write per iteration (conditional) fj_cpu.flip_move_computed[var_idx] = true; new_val = 1 - val; } else { + // ARRAY READ: h_coefficients[i] - 1 read per iteration (non-binary) auto cstr_coeff = fj_cpu.h_coefficients[i]; - f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; - f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; - auto [delta, sign, slack, cstr_tolerance] = get_mtm_for_constraint( - fj_cpu.view, var_idx, cstr_idx, cstr_coeff, c_lb, c_ub); + // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per iteration (non-binary) + f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; + // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per iteration (non-binary) + f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; + auto [delta, sign, slack, cstr_tolerance] = + get_mtm_for_constraint(fj_cpu.view, + var_idx, + cstr_idx, + cstr_coeff, + c_lb, + c_ub, + fj_cpu.h_assignment, + fj_cpu.h_lhs); if (fj_cpu.view.pb.is_integer_var(var_idx)) { new_val = cstr_coeff * sign > 0 ? floor(val + delta + fj_cpu.view.pb.tolerances.integrality_tolerance) @@ -833,10 +972,11 @@ static thrust::tuple find_mtm_move( new_val = val + delta; } // fallback - if (new_val < get_lower(fj_cpu.h_var_bounds[var_idx]) || - new_val > get_upper(fj_cpu.h_var_bounds[var_idx])) { - new_val = cstr_coeff * sign > 0 ? get_lower(fj_cpu.h_var_bounds[var_idx]) - : get_upper(fj_cpu.h_var_bounds[var_idx]); + // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration (non-binary, conditional) + if (new_val < get_lower(fj_cpu.h_var_bounds[var_idx].get()) || + new_val > get_upper(fj_cpu.h_var_bounds[var_idx].get())) { + new_val = cstr_coeff * sign > 0 ? get_lower(fj_cpu.h_var_bounds[var_idx].get()) + : get_upper(fj_cpu.h_var_bounds[var_idx].get()); } } if (!isfinite(new_val)) continue; @@ -844,15 +984,18 @@ static thrust::tuple find_mtm_move( "new_val is not within bounds"); delta = new_val - val; // more permissive tabu in the case of local minima - if (tabu_check(fj_cpu, var_idx, delta, localmin)) continue; + if (tabu_check(fj_cpu, var_idx, delta, localmin)) continue; if (fabs(delta) < cstr_tol) continue; auto move = fj_move_t{var_idx, delta}; cuopt_assert(move.var_idx < fj_cpu.h_assignment.size(), "move.var_idx is out of bounds"); cuopt_assert(move.var_idx >= 0, "move.var_idx is not positive"); + // CRITICAL: compute_score() does ~6-7 array reads per constraint (see compute_score + // annotations) auto [score, infeasibility] = compute_score(fj_cpu, var_idx, delta); - fj_cpu.cached_mtm_moves[i] = std::make_pair(delta, score); + // ARRAY WRITE: cached_mtm_moves[i] - 1 write per iteration (cache miss) + fj_cpu.cached_mtm_moves[i] = std::make_pair(delta, score); fj_cpu.miss_count++; // reject this move if it would increase the target variable to a numerically unstable value if (fj_cpu.view.move_numerically_stable( @@ -862,6 +1005,7 @@ static thrust::tuple find_mtm_move( best_move = move; } } + // Total per cache miss: ~8-11 reads + 1 write + compute_score overhead } } @@ -870,7 +1014,9 @@ static thrust::tuple find_mtm_move( fj_cpu.h_best_objective < std::numeric_limits::infinity() && fj_cpu.h_incumbent_objective >= fj_cpu.h_best_objective + fj_cpu.settings.parameters.breakthrough_move_epsilon) { + // MEMORY OPS: Loop over objective variables (num_obj_vars iterations, typically small) for (auto var_idx : fj_cpu.h_objective_vars) { + // ARRAY READ: h_assignment[var_idx] - 1 read per iteration f_t old_val = fj_cpu.h_assignment[var_idx]; f_t new_val = get_breakthrough_move(fj_cpu.view, var_idx); @@ -883,8 +1029,9 @@ static thrust::tuple find_mtm_move( cuopt_assert(move.var_idx < fj_cpu.h_assignment.size(), "move.var_idx is out of bounds"); cuopt_assert(move.var_idx >= 0, "move.var_idx is not positive"); - if (tabu_check(fj_cpu, var_idx, delta)) continue; + if (tabu_check(fj_cpu, var_idx, delta)) continue; + // CRITICAL: compute_score() does ~6-7 array reads per constraint involved auto [score, infeasibility] = compute_score(fj_cpu, var_idx, delta); cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), ""); @@ -897,6 +1044,7 @@ static thrust::tuple find_mtm_move( best_move = move; } } + // Total per iteration: 1 read + compute_score overhead } } @@ -945,29 +1093,44 @@ static void recompute_lhs(fj_cpu_climber_t& fj_cpu) fj_cpu.violated_constraints.clear(); fj_cpu.satisfied_constraints.clear(); fj_cpu.total_violations = 0; + // MEMORY OPS: CRITICAL LOOP - Loop over all constraints (n_cstrs iterations) + // This is called periodically to recompute all LHS values from scratch for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; ++cstr_idx) { auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); + // MEMORY OPS: For each constraint, reads avg_cstr_degree elements from: + // - h_coefficients[offset_begin:offset_end] - avg_cstr_degree reads + // - h_variables[offset_begin:offset_end] - avg_cstr_degree reads (indirect indexing) + // - h_assignment[h_variables[i]] - avg_cstr_degree reads (indirect indexing) auto delta_it = - thrust::make_transform_iterator(thrust::make_counting_iterator(0), [fj = fj_cpu.view](i_t j) { - return fj.pb.coefficients[j] * fj.incumbent_assignment[fj.pb.variables[j]]; + thrust::make_transform_iterator(thrust::make_counting_iterator(0), [&fj_cpu](i_t j) { + return fj_cpu.h_coefficients[j] * fj_cpu.h_assignment[fj_cpu.h_variables[j]]; }); + // ARRAY WRITE: h_lhs[cstr_idx] - 1 write per constraint fj_cpu.h_lhs[cstr_idx] = fj_kahan_babushka_neumaier_sum(delta_it + offset_begin, delta_it + offset_end); + // ARRAY WRITE: h_lhs_sumcomp[cstr_idx] - 1 write per constraint fj_cpu.h_lhs_sumcomp[cstr_idx] = 0; - f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx); - f_t new_cost = fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); + f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + // ARRAY READ: h_lhs[cstr_idx] - 1 read per constraint + f_t new_cost = fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); if (new_cost < -cstr_tolerance) { fj_cpu.violated_constraints.insert(cstr_idx); fj_cpu.total_violations += new_cost; } else { fj_cpu.satisfied_constraints.insert(cstr_idx); } + // Total per constraint: (3 * avg_cstr_degree) reads + 2 writes + 1 read = (3 * avg_cstr_degree + // + 1) reads + 2 writes } // compute incumbent objective + // MEMORY OPS: Reads all n_vars elements from h_assignment and h_obj_coeffs + // ARRAY READ: h_assignment (n_vars reads) and h_obj_coeffs (n_vars reads) fj_cpu.h_incumbent_objective = thrust::inner_product( fj_cpu.h_assignment.begin(), fj_cpu.h_assignment.end(), fj_cpu.h_obj_coeffs.begin(), 0.); + // Total: 2 * n_vars reads } template @@ -979,15 +1142,20 @@ static thrust::tuple find_lift_move( fj_move_t best_move = fj_move_t{-1, 0}; fj_staged_score_t best_score = fj_staged_score_t::zero(); + // MEMORY OPS: Loop over objective variables (num_obj_vars iterations) + // This is called when in the feasible region to find improving moves for (auto var_idx : fj_cpu.h_objective_vars) { cuopt_assert(var_idx < fj_cpu.h_obj_coeffs.size(), "var_idx is out of bounds"); cuopt_assert(var_idx >= 0, "var_idx is out of bounds"); + // ARRAY READ: h_obj_coeffs[var_idx] - 1 read per iteration f_t obj_coeff = fj_cpu.h_obj_coeffs[var_idx]; f_t delta = -std::numeric_limits::infinity(); - f_t val = fj_cpu.h_assignment[var_idx]; + // ARRAY READ: h_assignment[var_idx] - 1 read per iteration + f_t val = fj_cpu.h_assignment[var_idx]; // special path for binary variables + // ARRAY READ: h_is_binary_variable[var_idx] - 1 read per iteration if (fj_cpu.h_is_binary_variable[var_idx]) { cuopt_assert(fj_cpu.view.pb.is_integer(val), "binary variable is not integer"); cuopt_assert(fj_cpu.view.pb.integer_equal(val, 0) || fj_cpu.view.pb.integer_equal(val, 1), @@ -996,24 +1164,38 @@ static thrust::tuple find_lift_move( // flip move wouldn't improve if (delta * obj_coeff >= 0) continue; } else { - f_t lfd_lb = get_lower(fj_cpu.h_var_bounds[var_idx]) - val; - f_t lfd_ub = get_upper(fj_cpu.h_var_bounds[var_idx]) - val; + // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration (non-binary) + f_t lfd_lb = get_lower(fj_cpu.h_var_bounds[var_idx].get()) - val; + f_t lfd_ub = get_upper(fj_cpu.h_var_bounds[var_idx].get()) - val; auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + // MEMORY OPS: Inner loop over constraints involving this variable (avg_var_degree iterations + // per outer iteration) for (i_t j = offset_begin; j < offset_end; j += 1) { - auto cstr_idx = fj_cpu.view.pb.reverse_constraints[j]; - auto cstr_coeff = fj_cpu.view.pb.reverse_coefficients[j]; - f_t c_lb = fj_cpu.view.pb.constraint_lower_bounds[cstr_idx]; - f_t c_ub = fj_cpu.view.pb.constraint_upper_bounds[cstr_idx]; + // ARRAY READ: h_reverse_constraints[j] - 1 read per inner iteration + auto cstr_idx = fj_cpu.h_reverse_constraints[j]; + // ARRAY READ: h_reverse_coefficients[j] - 1 read per inner iteration + auto cstr_coeff = fj_cpu.h_reverse_coefficients[j]; + // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per inner iteration (indirect) + f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; + // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per inner iteration (indirect) + f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); cuopt_assert(c_lb <= c_ub, "invalid bounds"); + // ARRAY READ: h_lhs[cstr_idx] - 1 read per inner iteration cuopt_assert(fj_cpu.view.cstr_satisfied(cstr_idx, fj_cpu.h_lhs[cstr_idx]), "cstr should be satisfied"); // Process each bound separately, as both are satified and may both be finite // otherwise range constraints aren't correctly handled for (auto [bound, sign] : {std::make_tuple(c_lb, -1), std::make_tuple(c_ub, 1)}) { - auto [delta, slack] = - get_mtm_for_bound(fj_cpu.view, var_idx, cstr_idx, cstr_coeff, bound, sign); + auto [delta, slack] = get_mtm_for_bound(fj_cpu.view, + var_idx, + cstr_idx, + cstr_coeff, + bound, + sign, + fj_cpu.h_assignment, + fj_cpu.h_lhs); if (cstr_coeff * sign < 0) { if (fj_cpu.view.pb.is_integer_var(var_idx)) delta = ceil(delta); @@ -1039,6 +1221,8 @@ static thrust::tuple find_lift_move( } } if (lfd_lb >= lfd_ub) break; + // Total per inner iteration: 5 reads (h_reverse_constraints, h_reverse_coefficients, + // h_cstr_lb, h_cstr_ub, h_lhs) } // invalid crossing bounds @@ -1054,7 +1238,7 @@ static thrust::tuple find_lift_move( if (!isfinite(delta)) delta = 0; if (fj_cpu.view.pb.integer_equal(delta, (f_t)0)) continue; - if (tabu_check(fj_cpu, var_idx, delta)) continue; + if (tabu_check(fj_cpu, var_idx, delta)) continue; cuopt_assert(delta * obj_coeff < 0, "lift move doesn't improve the objective!"); @@ -1068,6 +1252,7 @@ static thrust::tuple find_lift_move( best_score = score; best_move = move; } + // Total per outer iteration: 3 reads + (5 * avg_var_degree reads in inner loop for non-binary) } return thrust::make_tuple(best_move, best_score); @@ -1085,9 +1270,11 @@ static void perturb(fj_cpu_climber_t& fj_cpu) std::mt19937(fj_cpu.settings.seed + fj_cpu.iterations)); raft::random::PCGenerator rng(fj_cpu.settings.seed + fj_cpu.iterations, 0, 0); + // MEMORY OPS: Loop over sampled variables (2 iterations typically) for (auto var_idx : sampled_vars) { - f_t lb = ceil(std::max(get_lower(fj_cpu.h_var_bounds[var_idx]), -1e7)); - f_t ub = floor(std::min(get_upper(fj_cpu.h_var_bounds[var_idx]), 1e7)); + // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration + f_t lb = ceil(std::max(get_lower(fj_cpu.h_var_bounds[var_idx].get()), -1e7)); + f_t ub = floor(std::min(get_upper(fj_cpu.h_var_bounds[var_idx].get()), 1e7)); f_t val = lb + (ub - lb) * rng.next_double(); if (fj_cpu.view.pb.is_integer_var(var_idx)) { val = std::round(val); @@ -1096,7 +1283,9 @@ static void perturb(fj_cpu_climber_t& fj_cpu) cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, val), "value is out of bounds"); + // ARRAY WRITE: h_assignment[var_idx] - 1 write per iteration fj_cpu.h_assignment[var_idx] = val; + // Total per iteration: 1 read + 1 write } recompute_lhs(fj_cpu); @@ -1217,12 +1406,20 @@ static void init_fj_cpu(fj_cpu_climber_t& fj_cpu, std::make_pair(0, fj_staged_score_t::zero())); fj_cpu.cached_cstr_bounds.resize(fj_cpu.h_reverse_coefficients.size()); + // MEMORY OPS: INITIALIZATION - Loop over all variables (n_vars iterations) for (i_t var_idx = 0; var_idx < (i_t)fj_cpu.view.pb.n_variables; ++var_idx) { auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + // MEMORY OPS: Inner loop over constraints per variable (avg_var_degree iterations per outer + // iteration) for (i_t i = offset_begin; i < offset_end; ++i) { + // ARRAY READ: h_reverse_constraints[i] - 1 read per inner iteration + // ARRAY READ: h_cstr_lb[h_reverse_constraints[i]] - 1 read per inner iteration (indirect) + // ARRAY READ: h_cstr_ub[h_reverse_constraints[i]] - 1 read per inner iteration (indirect) + // ARRAY WRITE: cached_cstr_bounds[i] - 1 write per inner iteration (2 f_t values) fj_cpu.cached_cstr_bounds[i] = std::make_pair(fj_cpu.h_cstr_lb[fj_cpu.h_reverse_constraints[i]], fj_cpu.h_cstr_ub[fj_cpu.h_reverse_constraints[i]]); + // Total per inner iteration: 3 reads + 1 write (2 values) } } @@ -1392,8 +1589,8 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l update_weights(fj_cpu); if (should_perturb) { perturb(fj_cpu); - for (auto& cached_move : fj_cpu.cached_mtm_moves) - cached_move.first = 0; + for (size_t i = 0; i < fj_cpu.cached_mtm_moves.size(); i++) + fj_cpu.cached_mtm_moves[i].first = 0; } thrust::tie(move, score) = find_mtm_move_viol(fj_cpu, 1, true); // pick a single random violated constraint @@ -1407,8 +1604,11 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l // number of violated constraints is usually small (<100). recomputing from all LHSs is cheap // and more numerically precise than just adding to the accumulator in apply_move fj_cpu.total_violations = 0; + // MEMORY OPS: Loop over violated constraints (typically <100 iterations per main iteration) for (auto cstr_idx : fj_cpu.violated_constraints) { + // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration fj_cpu.total_violations += fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); + // Total per iteration: 1 read } if (fj_cpu.iterations % fj_cpu.log_interval == 0) { CUOPT_LOG_TRACE( @@ -1454,7 +1654,13 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l #ifdef __linux__ collect_and_print_papi_metrics(fj_cpu); #endif - log_regression_features(fj_cpu, time_window_ms, total_time_ms); + + // Collect memory statistics + auto [loads, stores] = fj_cpu.memory_manifold.collect_and_flush(); + + // Log all features including memory statistics + log_regression_features(fj_cpu, time_window_ms, total_time_ms, loads, stores); + fj_cpu.last_feature_log_time = now; } diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cuh b/cpp/src/mip/feasibility_jump/fj_cpu.cuh index c1f5316f8..76db1ce98 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cuh +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cuh @@ -12,6 +12,7 @@ #include #include +#include namespace cuopt::linear_programming::detail { @@ -19,7 +20,36 @@ namespace cuopt::linear_programming::detail { // Maintaining a single source of truth for all members would be nice template struct fj_cpu_climber_t { - fj_cpu_climber_t() = default; + fj_cpu_climber_t() + { + // Initialize memory manifold with all ins_vector members + memory_manifold = instrumentation_manifold_t{h_reverse_coefficients, + h_reverse_constraints, + h_reverse_offsets, + h_coefficients, + h_offsets, + h_variables, + h_obj_coeffs, + h_var_bounds, + h_cstr_lb, + h_cstr_ub, + h_var_types, + h_is_binary_variable, + h_objective_vars, + h_binary_indices, + h_tabu_nodec_until, + h_tabu_noinc_until, + h_tabu_lastdec, + h_tabu_lastinc, + h_lhs, + h_lhs_sumcomp, + h_cstr_left_weights, + h_cstr_right_weights, + h_assignment, + h_best_assignment, + cached_cstr_bounds, + iter_mtm_vars}; + } fj_cpu_climber_t(const fj_cpu_climber_t& other) = delete; fj_cpu_climber_t& operator=(const fj_cpu_climber_t& other) = delete; @@ -30,33 +60,33 @@ struct fj_cpu_climber_t { fj_settings_t settings; typename fj_t::climber_data_t::view_t view; // Host copies of device data as struct members - std::vector h_reverse_coefficients; - std::vector h_reverse_constraints; - std::vector h_reverse_offsets; - std::vector h_coefficients; - std::vector h_offsets; - std::vector h_variables; - std::vector h_obj_coeffs; - std::vector::type> h_var_bounds; - std::vector h_cstr_lb; - std::vector h_cstr_ub; - std::vector h_var_types; - std::vector h_is_binary_variable; - std::vector h_objective_vars; - std::vector h_binary_indices; - - std::vector h_tabu_nodec_until; - std::vector h_tabu_noinc_until; - std::vector h_tabu_lastdec; - std::vector h_tabu_lastinc; - - std::vector h_lhs; - std::vector h_lhs_sumcomp; - std::vector h_cstr_left_weights; - std::vector h_cstr_right_weights; + ins_vector h_reverse_coefficients; + ins_vector h_reverse_constraints; + ins_vector h_reverse_offsets; + ins_vector h_coefficients; + ins_vector h_offsets; + ins_vector h_variables; + ins_vector h_obj_coeffs; + ins_vector::type> h_var_bounds; + ins_vector h_cstr_lb; + ins_vector h_cstr_ub; + ins_vector h_var_types; + ins_vector h_is_binary_variable; + ins_vector h_objective_vars; + ins_vector h_binary_indices; + + ins_vector h_tabu_nodec_until; + ins_vector h_tabu_noinc_until; + ins_vector h_tabu_lastdec; + ins_vector h_tabu_lastinc; + + ins_vector h_lhs; + ins_vector h_lhs_sumcomp; + ins_vector h_cstr_left_weights; + ins_vector h_cstr_right_weights; f_t max_weight; - std::vector h_assignment; - std::vector h_best_assignment; + ins_vector h_assignment; + ins_vector h_best_assignment; f_t h_objective_weight; f_t h_incumbent_objective; f_t h_best_objective; @@ -84,16 +114,16 @@ struct fj_cpu_climber_t { // vector is actually likely beneficial here since we're memory bound std::vector flip_move_computed; - ; + // CSR nnz offset -> (delta, score) std::vector> cached_mtm_moves; // CSC (transposed!) nnz-offset-indexed constraint bounds (lb, ub) // std::pair better compile down to 16 bytes!! GCC do your job! - std::vector> cached_cstr_bounds; + ins_vector> cached_cstr_bounds; std::vector var_bitmap; - std::vector iter_mtm_vars; + ins_vector iter_mtm_vars; i_t mtm_viol_samples{25}; i_t mtm_sat_samples{15}; @@ -142,6 +172,9 @@ struct fj_cpu_climber_t { double var_degree_cv{0.0}; double cstr_degree_cv{0.0}; double problem_density{0.0}; + + // Memory instrumentation manifold + instrumentation_manifold_t memory_manifold; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/local_search/local_search.cuh b/cpp/src/mip/local_search/local_search.cuh index c1c11fd39..3e038b547 100644 --- a/cpp/src/mip/local_search/local_search.cuh +++ b/cpp/src/mip/local_search/local_search.cuh @@ -143,7 +143,7 @@ class local_search_t { std::mt19937 rng; std::array, 0> ls_cpu_fj; - std::array, 0> scratch_cpu_fj; + std::array, 1> scratch_cpu_fj; cpu_fj_thread_t scratch_cpu_fj_on_lp_opt; problem_t problem_with_objective_cut; bool cutting_plane_added_for_active_run{false}; diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp new file mode 100644 index 000000000..2cf0d3b1e --- /dev/null +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -0,0 +1,673 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +/** + * @file memory_instrumentation.hpp + * @brief Memory access instrumentation utilities + * + * This file provides wrapper classes for tracking memory reads and writes. + * + * Usage: + * - Define CUOPT_ENABLE_MEMORY_INSTRUMENTATION to enable tracking + * - When undefined, all instrumentation becomes zero-overhead passthrough + * (record_*() calls inline away, no counter storage overhead) + * + * Example: + * ins_vector vec; // Instrumented std::vector + * vec.push_back(42); + * auto val = vec[0]; + * // When enabled: tracking occurs, counters accumulate + * // When disabled: direct passthrough, compiler optimizes away all overhead + */ + +#pragma once + +#include +#include +#include +#include +#include + +#define CUOPT_ENABLE_MEMORY_INSTRUMENTATION 1 + +namespace cuopt { + +// Define CUOPT_ENABLE_MEMORY_INSTRUMENTATION to enable memory tracking +// If undefined, instrumentation becomes a zero-overhead passthrough + +// Base class for memory operation instrumentation +struct memory_instrumentation_base_t { +#ifdef CUOPT_ENABLE_MEMORY_INSTRUMENTATION + __host__ __device__ void reset_counters() const { byte_loads = byte_stores = 0; } + + template + __host__ __device__ void record_load() const + { + byte_loads += sizeof(T); + } + + template + __host__ __device__ void record_store() const + { + byte_stores += sizeof(T); + } + + template + __host__ __device__ void record_rmw() const + { + byte_loads += sizeof(T); + byte_stores += sizeof(T); + } + + mutable size_t byte_loads{0}; + mutable size_t byte_stores{0}; +#else + // No-op methods when instrumentation is disabled - these inline away to zero overhead + __host__ __device__ void reset_counters() const {} + template + __host__ __device__ void record_load() const + { + } + template + __host__ __device__ void record_store() const + { + } + template + __host__ __device__ void record_rmw() const + { + } +#endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION +}; + +#ifdef CUOPT_ENABLE_MEMORY_INSTRUMENTATION + +// Manifold class to collect statistics from multiple instrumented objects +class instrumentation_manifold_t { + public: + instrumentation_manifold_t() = default; + + // Construct with initializer list of instrumented objects + instrumentation_manifold_t( + std::initializer_list> instrumented) + : instrumented_(instrumented) + { + } + + // Add an instrumented object to track + void add(memory_instrumentation_base_t& instrumented) { instrumented_.push_back(instrumented); } + + // Collect total loads and stores from all instrumented objects, then flush all counters + std::pair collect_and_flush() + { + size_t total_loads = 0; + size_t total_stores = 0; + + for (auto& instr : instrumented_) { + total_loads += instr.get().byte_loads; + total_stores += instr.get().byte_stores; + instr.get().reset_counters(); + } + + return {total_loads, total_stores}; + } + + private: + std::vector> instrumented_; +}; + +#else + +// No-op manifold when instrumentation is disabled +class instrumentation_manifold_t { + public: + instrumentation_manifold_t() = default; + instrumentation_manifold_t( + std::initializer_list>) + { + } + void add(memory_instrumentation_base_t&) {} + std::pair collect_and_flush() { return {0, 0}; } +}; + +#endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION + +// Helper traits to detect container capabilities +namespace type_traits_utils { + +template +struct has_reserve : std::false_type {}; + +template +struct has_reserve().reserve(size_t{}))>> : std::true_type { +}; + +template +struct has_capacity : std::false_type {}; + +template +struct has_capacity().capacity())>> : std::true_type {}; + +template +struct has_shrink_to_fit : std::false_type {}; + +template +struct has_shrink_to_fit().shrink_to_fit())>> + : std::true_type {}; + +template +struct has_push_back : std::false_type {}; + +template +struct has_push_back< + T, + std::void_t().push_back(std::declval()))>> + : std::true_type {}; + +template +struct has_emplace_back : std::false_type {}; + +template +struct has_emplace_back().emplace_back())>> + : std::true_type {}; + +template +struct has_pop_back : std::false_type {}; + +template +struct has_pop_back().pop_back())>> : std::true_type {}; + +template +struct has_data : std::false_type {}; + +template +struct has_data().data())>> : std::true_type {}; + +template +struct has_resize : std::false_type {}; + +template +struct has_resize().resize(size_t{}))>> : std::true_type {}; + +template +struct has_clear : std::false_type {}; + +template +struct has_clear().clear())>> : std::true_type {}; + +template +struct has_max_size : std::false_type {}; + +template +struct has_max_size().max_size())>> : std::true_type {}; + +template +struct has_front : std::false_type {}; + +template +struct has_front().front())>> : std::true_type {}; + +template +struct has_back : std::false_type {}; + +template +struct has_back().back())>> : std::true_type {}; + +} // namespace type_traits_utils + +// Memory operation instrumentation wrapper for container-like types +template +struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { + // Standard container type traits + using value_type = std::remove_reference_t()[0])>; + using size_type = std::size_t; + using difference_type = std::ptrdiff_t; + using reference = value_type&; + using const_reference = const value_type&; + using pointer = value_type*; + using const_pointer = const value_type*; + + static_assert(std::is_standard_layout_v, + "value_type must have standard layout for memory instrumentation"); + static constexpr size_t type_size = sizeof(value_type); + + // Proxy class to track reads and writes for a single element + class element_proxy_t { + public: + element_proxy_t(value_type& ref, memop_instrumentation_wrapper_t& wrapper) + : ref_(ref), wrapper_(wrapper) + { + } + + element_proxy_t& operator=(const value_type& value) + { + wrapper_.template record_store(); + ref_ = value; + return *this; + } + element_proxy_t& operator=(const element_proxy_t& other) + { + wrapper_.template record_store(); + other.wrapper_.template record_load(); + ref_ = other.ref_; + return *this; + } + + operator value_type() const + { + wrapper_.template record_load(); + return ref_; + } + + // Allow implicit conversion to reference for functions expecting references + operator value_type&() { return ref_; } + + operator const value_type&() const { return ref_; } + + // Member access operator for structured types (e.g., type_2) + value_type* operator->() { return &ref_; } + + const value_type* operator->() const { return &ref_; } + + // Get underlying element reference (records a load) + value_type& get() + { + wrapper_.template record_load(); + return ref_; + } + + const value_type& get() const + { + wrapper_.template record_load(); + return ref_; + } + + element_proxy_t& operator+=(const value_type& value) + { + wrapper_.template record_rmw(); + ref_ += value; + return *this; + } + element_proxy_t& operator-=(const value_type& value) + { + wrapper_.template record_rmw(); + ref_ -= value; + return *this; + } + element_proxy_t& operator*=(const value_type& value) + { + wrapper_.template record_rmw(); + ref_ *= value; + return *this; + } + element_proxy_t& operator/=(const value_type& value) + { + wrapper_.template record_rmw(); + ref_ /= value; + return *this; + } + element_proxy_t& operator++() + { + wrapper_.template record_rmw(); + ++ref_; + return *this; + } + element_proxy_t& operator--() + { + wrapper_.template record_rmw(); + --ref_; + return *this; + } + + value_type operator++(int) + { + wrapper_.template record_rmw(); + return ref_++; + } + value_type operator--(int) + { + wrapper_.template record_rmw(); + return ref_--; + } + + value_type& ref_; + memop_instrumentation_wrapper_t& wrapper_; + }; + + // Instrumented iterator that tracks memory accesses + template + class instrumented_iterator_t { + public: + using iterator_category = std::random_access_iterator_tag; + using value_type = memop_instrumentation_wrapper_t::value_type; + using difference_type = std::ptrdiff_t; + using pointer = std::conditional_t; + using reference = std::conditional_t; + using wrapper_ptr = std::conditional_t; + + instrumented_iterator_t(IterT iter, wrapper_ptr wrapper) : iter_(iter), wrapper_(wrapper) {} + + // Dereference - returns proxy for non-const, tracks load for const + auto operator*() const + { + if constexpr (IsConst) { + wrapper_->byte_loads += sizeof(value_type); + return *iter_; + } else { + return element_proxy_t(*iter_, *wrapper_); + } + } + + auto operator->() const { return &(*iter_); } + + instrumented_iterator_t& operator++() + { + ++iter_; + return *this; + } + + instrumented_iterator_t operator++(int) + { + auto tmp = *this; + ++iter_; + return tmp; + } + + instrumented_iterator_t& operator--() + { + --iter_; + return *this; + } + + instrumented_iterator_t operator--(int) + { + auto tmp = *this; + --iter_; + return tmp; + } + + instrumented_iterator_t& operator+=(difference_type n) + { + iter_ += n; + return *this; + } + + instrumented_iterator_t& operator-=(difference_type n) + { + iter_ -= n; + return *this; + } + + instrumented_iterator_t operator+(difference_type n) const + { + return instrumented_iterator_t(iter_ + n, wrapper_); + } + + instrumented_iterator_t operator-(difference_type n) const + { + return instrumented_iterator_t(iter_ - n, wrapper_); + } + + difference_type operator-(const instrumented_iterator_t& other) const + { + return iter_ - other.iter_; + } + + auto operator[](difference_type n) const { return *(*this + n); } + + bool operator==(const instrumented_iterator_t& other) const { return iter_ == other.iter_; } + bool operator!=(const instrumented_iterator_t& other) const { return iter_ != other.iter_; } + bool operator<(const instrumented_iterator_t& other) const { return iter_ < other.iter_; } + bool operator<=(const instrumented_iterator_t& other) const { return iter_ <= other.iter_; } + bool operator>(const instrumented_iterator_t& other) const { return iter_ > other.iter_; } + bool operator>=(const instrumented_iterator_t& other) const { return iter_ >= other.iter_; } + + IterT base() const { return iter_; } + + // Allow iterator_traits to access the underlying iterator + friend struct std::iterator_traits; + + private: + IterT iter_; + wrapper_ptr wrapper_; + }; + + // Iterator type definitions (must come after instrumented_iterator_t) + using iterator = instrumented_iterator_t().begin()), false>; + using const_iterator = instrumented_iterator_t().begin()), true>; + using reverse_iterator = std::reverse_iterator; + using const_reverse_iterator = std::reverse_iterator; + + // Constructors + memop_instrumentation_wrapper_t() : array() + { + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array.data(); + } else { + data_ptr = nullptr; + } + } + + // Copy/move from underlying type + memop_instrumentation_wrapper_t(const T& array) : array(array) + { + if constexpr (type_traits_utils::has_data::value) { + data_ptr = const_cast(array.data()); + } else { + data_ptr = nullptr; + } + } + memop_instrumentation_wrapper_t(T&& array) : array(std::move(array)) + { + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array.data(); + } else { + data_ptr = nullptr; + } + } + + // Forwarding constructor for underlying container initialization + // Only enabled for types that aren't the wrapper itself or the underlying type + template , memop_instrumentation_wrapper_t> && + !std::is_same_v, T> && + (sizeof...(Args) > 0 || !std::is_convertible_v)>> + explicit memop_instrumentation_wrapper_t(Arg&& arg, Args&&... args) + : array(std::forward(arg), std::forward(args)...) + { + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array.data(); + } else { + data_ptr = nullptr; + } + } + + // Copy/move from wrapper + memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t&) = default; + memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&&) = default; + memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t&) = default; + memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&&) = default; + + element_proxy_t operator[](size_type index) { return element_proxy_t(array[index], *this); } + + __host__ __device__ value_type operator[](size_type index) const + { + this->template record_load(); + // really ugly hack because otherwise nvcc complains about vector operator[] being __host__ only + if constexpr (type_traits_utils::has_data::value) { + return data_ptr[index]; + } else { + return array[index]; + } + } + + template + std::enable_if_t::value, element_proxy_t> front() + { + return element_proxy_t(array.front(), *this); + } + + template + std::enable_if_t::value, value_type> front() const + { + this->template record_load(); + return array.front(); + } + + template + std::enable_if_t::value, element_proxy_t> back() + { + return element_proxy_t(array.back(), *this); + } + + template + std::enable_if_t::value, value_type> back() const + { + this->template record_load(); + return array.back(); + } + + // Iterators + iterator begin() noexcept { return iterator(std::begin(array), this); } + const_iterator begin() const noexcept { return const_iterator(std::begin(array), this); } + const_iterator cbegin() const noexcept { return const_iterator(std::begin(array), this); } + + iterator end() noexcept { return iterator(std::end(array), this); } + const_iterator end() const noexcept { return const_iterator(std::end(array), this); } + const_iterator cend() const noexcept { return const_iterator(std::end(array), this); } + + reverse_iterator rbegin() noexcept { return reverse_iterator(end()); } + const_reverse_iterator rbegin() const noexcept { return const_reverse_iterator(std::end(array)); } + const_reverse_iterator crbegin() const noexcept { return const_reverse_iterator(cend()); } + + reverse_iterator rend() noexcept { return reverse_iterator(begin()); } + const_reverse_iterator rend() const noexcept { return const_reverse_iterator(begin()); } + const_reverse_iterator crend() const noexcept { return const_reverse_iterator(cbegin()); } + + // Capacity + bool empty() const noexcept { return std::begin(array) == std::end(array); } + size_type size() const noexcept { return std::distance(std::begin(array), std::end(array)); } + + // Conditional methods - only available if underlying type supports them + template + std::enable_if_t::value, size_type> max_size() const noexcept + { + return array.max_size(); + } + + template + std::enable_if_t::value, size_type> capacity() const noexcept + { + return array.capacity(); + } + + template + std::enable_if_t::value> reserve(size_type new_cap) + { + array.reserve(new_cap); + if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + } + + template + std::enable_if_t::value> shrink_to_fit() + { + array.shrink_to_fit(); + if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + } + + template + std::enable_if_t::value> clear() noexcept + { + array.clear(); + if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + } + + template + std::enable_if_t::value> push_back(const value_type& value) + { + // we should probably take into account possible copies done by std::vector. oh well. + // hot loops shouldn't be doing such operations anyway + this->template record_store(); + array.push_back(value); + if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + } + + template + std::enable_if_t::value> push_back(value_type&& value) + { + this->template record_store(); + array.push_back(std::move(value)); + if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + } + + template + std::enable_if_t::value> emplace_back(Args&&... args) + { + this->template record_store(); + array.emplace_back(std::forward(args)...); + if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + } + + template + std::enable_if_t::value> pop_back() + { + this->template record_load(); // Reading the element before removal + array.pop_back(); + if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + } + + template + std::enable_if_t::value> resize(size_type count) + { + size_type old_size = array.size(); + array.resize(count); + if (count > old_size) { + this->byte_stores += (count - old_size) * type_size; // New elements initialized + } + if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + } + + template + std::enable_if_t::value> resize(size_type count, + const value_type& value) + { + size_type old_size = array.size(); + array.resize(count, value); + if (count > old_size) { this->byte_stores += (count - old_size) * type_size; } + if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + } + + template + std::enable_if_t::value, value_type*> data() noexcept + { + return array.data(); + } + + template + std::enable_if_t::value, const value_type*> data() const noexcept + { + return array.data(); + } + + // Access to underlying array + operator T&() { return array; } + operator const T&() const { return array; } + + T&& release_array() { return std::move(array); } + + T array; + value_type* data_ptr{nullptr}; +}; + +// Convenience alias for instrumented std::vector +template +using ins_vector = memop_instrumentation_wrapper_t>; + +} // namespace cuopt diff --git a/scripts/determinism_logs_parse.py b/scripts/determinism_logs_parse.py index 09ea08627..cee653c0b 100755 --- a/scripts/determinism_logs_parse.py +++ b/scripts/determinism_logs_parse.py @@ -25,6 +25,7 @@ - CP (Constraint Propagation): CP_FEATURES and CP_RESULT logs - FJ (Feasibility Jump): Legacy FJ: format - CPUFJ (CPU Feasibility Jump): CPUFJ_FEATURES single-line logs +- BB (Branch and Bound): BB_NODE_FEATURES single-line logs IMPORTANT - Grep Specificity: The parser uses EXACT pattern matching with grep to filter logs efficiently. @@ -49,6 +50,7 @@ python determinism_logs_parse.py --algorithm CP [-o output.feather] python determinism_logs_parse.py --algorithm FJ [-o output.feather] python determinism_logs_parse.py --algorithm CPUFJ [-o output.feather] + python determinism_logs_parse.py --algorithm BB [-o output.feather] """ import argparse @@ -60,7 +62,7 @@ from typing import List, Dict, Any, Optional -SUPPORTED_ALGORITHMS = ["FP", "PDLP", "CP", "FJ", "CPUFJ"] +SUPPORTED_ALGORITHMS = ["FP", "PDLP", "CP", "FJ", "CPUFJ", "BB"] def parse_value(value_str: str) -> Any: @@ -423,6 +425,13 @@ def parse_cpufj_logs(log_files: List[str]) -> List[Dict[str, Any]]: ) +def parse_bb_logs(log_files: List[str]) -> List[Dict[str, Any]]: + """Parse Branch and Bound node feature logs.""" + return parse_single_line_logs( + log_files, "BB_NODE_FEATURES", "BB (Branch and Bound)" + ) + + def print_statistics(entries: List[Dict[str, Any]], algorithm: str) -> None: """Print statistics about parsed entries.""" if not entries: @@ -473,6 +482,7 @@ def main(): CP - Constraint Propagation (parses CP_FEATURES and CP_RESULT logs) FJ - Feasibility Jump (parses legacy FJ: format) CPUFJ - CPU Feasibility Jump (parses CPUFJ_FEATURES single-line logs) + BB - Branch and Bound (parses BB_NODE_FEATURES single-line logs) Examples: python determinism_logs_parse.py logs/ --algorithm FP -o fp_data.feather @@ -480,6 +490,7 @@ def main(): python determinism_logs_parse.py logs/ --algorithm CP -o cp_data.feather python determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.feather python determinism_logs_parse.py logs/ --algorithm CPUFJ -o cpufj_data.feather + python determinism_logs_parse.py logs/ --algorithm BB -o bb_data.feather # Limit to first 10 files for testing python determinism_logs_parse.py logs/ --algorithm FP --max-files 10 @@ -552,6 +563,8 @@ def main(): entries = parse_fj_logs(log_files) elif args.algorithm == "CPUFJ": entries = parse_cpufj_logs(log_files) + elif args.algorithm == "BB": + entries = parse_bb_logs(log_files) else: print(f"Error: Unsupported algorithm: {args.algorithm}") return 1 @@ -568,6 +581,10 @@ def main(): print( "Make sure your logs contain CPUFJ_FEATURES lines with key=value pairs" ) + elif args.algorithm == "BB": + print( + "Make sure your logs contain BB_NODE_FEATURES lines with key=value pairs" + ) return 1 # Print statistics diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py index 24204b650..393558cf5 100755 --- a/scripts/train_regressor.py +++ b/scripts/train_regressor.py @@ -97,19 +97,30 @@ "iter_since_best", "tid", "curr_obj", - "obj_weight", + # "obj_weight", "L3_miss", "L2_miss", "L1_miss", "stores_per_iter", "loads_per_iter", - "is_feas", - "feas_found", - "viol_ratio", - "eval_intensity", - "nnz_per_move", + # "is_feas", + # "feas_found", + # "viol_ratio", + # "eval_intensity", + # "nnz_per_move", "iter", - "max_weight", + # "max_weight", + "avg_cstr_deg", + "avg_var_deg", + "lp_time", + "node_id", + "var_sel_time", + "bound_str_time", + "sb_time", + "lp_status", + "node_status", + "depth", + "cutoff_gap", ] # Alternatively, specify ONLY the features you want to use From 0fc30c5a8dfd1e5c07da781c71b60995456bc1f5 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 12 Nov 2025 17:46:03 +0000 Subject: [PATCH 074/366] don't clog w/ improvements --- cpp/src/mip/local_search/local_search.cu | 28 ++++++++++++------------ scripts/train_regressor.py | 10 +++++++++ 2 files changed, 24 insertions(+), 14 deletions(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index de4fa9e83..3e45bcae8 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -83,20 +83,20 @@ void local_search_t::start_cpufj_scratch_threads(population_t 0); cpu_fj.fj_cpu->log_prefix = "******* scratch " + std::to_string(counter) + ": "; - if (!context.settings.deterministic) { - cpu_fj.fj_cpu->improvement_callback = [this, &population, &cpu_fj]( - f_t obj, const std::vector& h_vec) { - population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); - if (obj < local_search_best_obj) { - CUOPT_LOG_TRACE("******* New local search best obj %g, best overall %g", - context.problem_ptr->get_user_obj_from_solver_obj(obj), - context.problem_ptr->get_user_obj_from_solver_obj( - population.is_feasible() ? population.best_feasible().get_objective() - : std::numeric_limits::max())); - local_search_best_obj = obj; - } - }; - } + // if (!context.settings.deterministic) { + // cpu_fj.fj_cpu->improvement_callback = [this, &population, &cpu_fj]( + // f_t obj, const std::vector& h_vec) { + // population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); + // if (obj < local_search_best_obj) { + // CUOPT_LOG_TRACE("******* New local search best obj %g, best overall %g", + // context.problem_ptr->get_user_obj_from_solver_obj(obj), + // context.problem_ptr->get_user_obj_from_solver_obj( + // population.is_feasible() ? population.best_feasible().get_objective() + // : std::numeric_limits::max())); + // local_search_best_obj = obj; + // } + // }; + // } counter++; }; diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py index 393558cf5..0f251c3a2 100755 --- a/scripts/train_regressor.py +++ b/scripts/train_regressor.py @@ -121,6 +121,10 @@ "node_status", "depth", "cutoff_gap", + "mem_bandwidth_gb_s", + "max_cstr_deg", + "viol_ratio", + "nnz_per_move", ] # Alternatively, specify ONLY the features you want to use @@ -130,6 +134,12 @@ # 'n_variables', # 'n_constraints', # 'sparsity', + # "n_vars", + # "n_cstrs", + # "total_nnz", + # "mem_total_mb", + # "mem_store_mb", + # "mem_load_mb", ] # ============================================================================ From 8bd70e35c76b1abc1db6c4f6ab744b4ba5138257 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 13 Nov 2025 11:34:22 +0000 Subject: [PATCH 075/366] fixed iter count --- cpp/src/mip/diversity/diversity_manager.cu | 4 +++- cpp/src/mip/local_search/local_search.cu | 3 ++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 37e399d73..2458e0ae5 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -359,7 +359,9 @@ solution_t diversity_manager_t::run_solver() if (!context.settings.deterministic) { #if 1 ls.start_cpufj_scratch_threads(population); - std::this_thread::sleep_for(std::chrono::seconds(30)); + // 30'000 iters + ls.ls_cpu_fj[0].wait_for_cpu_solver(); + // std::this_thread::sleep_for(std::chrono::seconds(30)); ls.stop_cpufj_scratch_threads(); exit(0); #endif diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 3e45bcae8..a7a1e4810 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -64,7 +64,8 @@ void local_search_t::start_cpufj_scratch_threads(population_t default_weights(context.problem_ptr->n_constraints, 1.); fj_settings_t cpu_fj_settings{}; - cpu_fj_settings.time_limit = std::numeric_limits::infinity(); + cpu_fj_settings.time_limit = std::numeric_limits::infinity(); + cpu_fj_settings.iteration_limit = 30000; // would like 30 samples per instance solution_t solution(*context.problem_ptr); thrust::fill(solution.handle_ptr->get_thrust_policy(), From 7bcf3baabc5e9f46f83e7e99633bfcfa2c4b3c82 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 13 Nov 2025 13:38:54 +0000 Subject: [PATCH 076/366] bugfix --- cpp/src/mip/diversity/diversity_manager.cu | 2 +- cpp/src/mip/local_search/local_search.cu | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 2458e0ae5..ea60ca131 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -360,7 +360,7 @@ solution_t diversity_manager_t::run_solver() #if 1 ls.start_cpufj_scratch_threads(population); // 30'000 iters - ls.ls_cpu_fj[0].wait_for_cpu_solver(); + ls.scratch_cpu_fj[0].wait_for_cpu_solver(); // std::this_thread::sleep_for(std::chrono::seconds(30)); ls.stop_cpufj_scratch_threads(); exit(0); diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index a7a1e4810..28be6dd4f 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -65,7 +65,7 @@ void local_search_t::start_cpufj_scratch_threads(population_t::infinity(); - cpu_fj_settings.iteration_limit = 30000; // would like 30 samples per instance + cpu_fj_settings.iteration_limit = 10000; // would like 10 samples per instance solution_t solution(*context.problem_ptr); thrust::fill(solution.handle_ptr->get_thrust_policy(), From 4bfb96fddd2dd1a6ebb54c46ffa97b7c7efc3ab3 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 13 Nov 2025 18:33:13 +0000 Subject: [PATCH 077/366] more memops logging --- .../linear_programming/cuopt/run_mip.cpp | 1 + cpp/src/CMakeLists.txt | 4 +- .../feasibility_jump_impl_common.cuh | 21 +- .../feasibility_jump_kernels.cu | 13 +- cpp/src/mip/feasibility_jump/fj_cpu.cu | 265 +- cpp/src/mip/feasibility_jump/fj_cpu.cuh | 57 +- .../mip/feasibility_jump/load_balancing.cuh | 26 +- cpp/src/mip/local_search/local_search.cu | 2 +- cpp/src/mip/solver_context.cuh | 2 + cpp/src/utilities/memory_instrumentation.hpp | 76 +- .../utilities/models/cpufj_predictor/header.h | 35 + .../utilities/models/cpufj_predictor/main.cpp | 5412 +++++++++++++++++ .../models/cpufj_predictor/quantize.cpp | 293 + cpp/src/utilities/work_unit_predictor.cpp | 2 + scripts/train_regressor.py | 14 +- 15 files changed, 6056 insertions(+), 167 deletions(-) create mode 100644 cpp/src/utilities/models/cpufj_predictor/header.h create mode 100644 cpp/src/utilities/models/cpufj_predictor/main.cpp create mode 100644 cpp/src/utilities/models/cpufj_predictor/quantize.cpp diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 5dfd373d7..ef3d3760f 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -207,6 +207,7 @@ int run_single_file(std::string file_path, settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; + settings.presolve = false; // settings.heuristics_only = true; cuopt::linear_programming::benchmark_info_t benchmark_info; diff --git a/cpp/src/CMakeLists.txt b/cpp/src/CMakeLists.txt index 6d4de5f96..c07cae037 100644 --- a/cpp/src/CMakeLists.txt +++ b/cpp/src/CMakeLists.txt @@ -9,7 +9,9 @@ set(UTIL_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/utilities/seed_generator.cu ${CMAKE_CURRENT_SOURCE_DIR}/utilities/timestamp_utils.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/work_unit_predictor.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/main.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/quantize.cpp) + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/quantize.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/cpufj_predictor/main.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/cpufj_predictor/quantize.cpp) add_subdirectory(linear_programming) add_subdirectory(math_optimization) diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh b/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh index 95de8bc1e..4a3bffb60 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh @@ -107,7 +107,9 @@ HDI std::pair feas_score_constraint( f_t cstr_coeff, f_t c_lb, f_t c_ub, - f_t current_lhs) + f_t current_lhs, + f_t left_weight, + f_t right_weight) { cuopt_assert(isfinite(delta), "invalid delta"); cuopt_assert(cstr_coeff != 0 && isfinite(cstr_coeff), "invalid coefficient"); @@ -127,14 +129,13 @@ HDI std::pair feas_score_constraint( // TODO: broadcast left/right weights to a csr_offset-indexed table? local minimums // usually occur on a rarer basis (around 50 iteratiosn to 1 local minimum) // likely unreasonable and overkill however - f_t cstr_weight = - bound_idx == 0 ? fj.cstr_left_weights[cstr_idx] : fj.cstr_right_weights[cstr_idx]; - f_t sign = bound_idx == 0 ? -1 : 1; - f_t rhs = bounds[bound_idx] * sign; - f_t old_lhs = current_lhs * sign; - f_t new_lhs = (current_lhs + cstr_coeff * delta) * sign; - f_t old_slack = rhs - old_lhs; - f_t new_slack = rhs - new_lhs; + f_t cstr_weight = bound_idx == 0 ? left_weight : right_weight; + f_t sign = bound_idx == 0 ? -1 : 1; + f_t rhs = bounds[bound_idx] * sign; + f_t old_lhs = current_lhs * sign; + f_t new_lhs = (current_lhs + cstr_coeff * delta) * sign; + f_t old_slack = rhs - old_lhs; + f_t new_slack = rhs - new_lhs; cuopt_assert(isfinite(cstr_weight), "invalid weight"); cuopt_assert(cstr_weight >= 0, "invalid weight"); @@ -142,7 +143,7 @@ HDI std::pair feas_score_constraint( cuopt_assert(isfinite(new_lhs), ""); cuopt_assert(isfinite(old_slack) && isfinite(new_slack), ""); - f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx); + f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx, c_lb, c_ub); bool old_viol = fj.excess_score(cstr_idx, current_lhs, c_lb, c_ub) < -cstr_tolerance; bool new_viol = diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 584d3d854..7c8fb0433 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -206,8 +206,17 @@ DI typename fj_t::move_score_info_t compute_new_score( f_t c_lb = fj.pb.constraint_lower_bounds[cstr_idx]; f_t c_ub = fj.pb.constraint_upper_bounds[cstr_idx]; - auto [cstr_base_feas, cstr_bonus_robust] = feas_score_constraint( - fj, var_idx, delta, cstr_idx, cstr_coeff, c_lb, c_ub, fj.incumbent_lhs[cstr_idx]); + auto [cstr_base_feas, cstr_bonus_robust] = + feas_score_constraint(fj, + var_idx, + delta, + cstr_idx, + cstr_coeff, + c_lb, + c_ub, + fj.incumbent_lhs[cstr_idx], + fj.cstr_left_weights[cstr_idx], + fj.cstr_right_weights[cstr_idx]); base_feas += cstr_base_feas; bonus_robust += cstr_bonus_robust; diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 59b6dca56..f587455b2 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -36,8 +36,10 @@ #include #include +#include #include #include +#include #include #include #include @@ -54,10 +56,10 @@ namespace cuopt::linear_programming::detail { static constexpr double BIGVAL_THRESHOLD = 1e20; -#ifdef __linux__ -// Global mutex to protect PAPI metric printing across multiple threads -static std::mutex papi_print_mutex; -#endif +// #ifdef __linux__ +// // Global mutex to protect PAPI metric printing across multiple threads +// static std::mutex papi_print_mutex; +// #endif template class timing_raii_t { @@ -204,64 +206,64 @@ static void initialize_papi(fj_cpu_climber_t& fj_cpu) CUOPT_LOG_TRACE("%sPAPI initialized successfully", fj_cpu.log_prefix.c_str()); } -template -static void collect_and_print_papi_metrics(fj_cpu_climber_t& fj_cpu) -{ - if (!fj_cpu.papi_initialized) return; - - std::vector values(fj_cpu.papi_events.size(), 0); - int retval = PAPI_read(fj_cpu.papi_event_set, values.data()); - if (retval != PAPI_OK) { - CUOPT_LOG_TRACE("%sPAPI read failed", fj_cpu.log_prefix.c_str()); - return; - } - - // Get thread ID - pid_t tid = syscall(SYS_gettid); - - // Build map of actual values indexed by event position - std::vector all_values(8, -1); - int value_idx = 0; - for (size_t i = 0; i < fj_cpu.papi_events.size(); i++) { - if (fj_cpu.papi_events[i] != -1) { all_values[i] = values[value_idx++]; } - } - - // Compute derived metrics - double l1_miss_rate = -1.0; - if (all_values[0] > 0 && all_values[1] != -1) { - l1_miss_rate = (double)all_values[1] / all_values[0] * 100.0; - } - - double l2_miss_rate = -1.0; - if (all_values[2] > 0 && all_values[3] != -1) { - l2_miss_rate = (double)all_values[3] / all_values[2] * 100.0; - } - - // Lock to ensure thread-safe printing - std::lock_guard lock(papi_print_mutex); - - // Print everything on a single compact line - CUOPT_LOG_DEBUG( - "%sPAPI iter=%d tid=%d L1_DCA=%lld L1_DCM=%lld L2_DCA=%lld L2_DCM=%lld L3_TCA=%lld " - "L3_TCM=%lld LD_INS=%lld SR_INS=%lld L1_miss=%.2f%% L2_miss=%.2f%%", - fj_cpu.log_prefix.c_str(), - fj_cpu.iterations, - tid, - all_values[0], - all_values[1], - all_values[2], - all_values[3], - all_values[4], - all_values[5], - all_values[6], - all_values[7], - l1_miss_rate, - l2_miss_rate); - - // Reset counters for the next 1000 iterations - retval = PAPI_reset(fj_cpu.papi_event_set); - if (retval != PAPI_OK) { CUOPT_LOG_TRACE("%sPAPI reset failed", fj_cpu.log_prefix.c_str()); } -} +// template +// static void collect_and_print_papi_metrics(fj_cpu_climber_t& fj_cpu) +// { +// if (!fj_cpu.papi_initialized) return; + +// std::vector values(fj_cpu.papi_events.size(), 0); +// int retval = PAPI_read(fj_cpu.papi_event_set, values.data()); +// if (retval != PAPI_OK) { +// CUOPT_LOG_TRACE("%sPAPI read failed", fj_cpu.log_prefix.c_str()); +// return; +// } + +// // Get thread ID +// pid_t tid = syscall(SYS_gettid); + +// // Build map of actual values indexed by event position +// std::vector all_values(8, -1); +// int value_idx = 0; +// for (size_t i = 0; i < fj_cpu.papi_events.size(); i++) { +// if (fj_cpu.papi_events[i] != -1) { all_values[i] = values[value_idx++]; } +// } + +// // Compute derived metrics +// double l1_miss_rate = -1.0; +// if (all_values[0] > 0 && all_values[1] != -1) { +// l1_miss_rate = (double)all_values[1] / all_values[0] * 100.0; +// } + +// double l2_miss_rate = -1.0; +// if (all_values[2] > 0 && all_values[3] != -1) { +// l2_miss_rate = (double)all_values[3] / all_values[2] * 100.0; +// } + +// // Lock to ensure thread-safe printing +// std::lock_guard lock(papi_print_mutex); + +// // Print everything on a single compact line +// CUOPT_LOG_DEBUG( +// "%sPAPI iter=%d tid=%d L1_DCA=%lld L1_DCM=%lld L2_DCA=%lld L2_DCM=%lld L3_TCA=%lld " +// "L3_TCM=%lld LD_INS=%lld SR_INS=%lld L1_miss=%.2f%% L2_miss=%.2f%%", +// fj_cpu.log_prefix.c_str(), +// fj_cpu.iterations, +// tid, +// all_values[0], +// all_values[1], +// all_values[2], +// all_values[3], +// all_values[4], +// all_values[5], +// all_values[6], +// all_values[7], +// l1_miss_rate, +// l2_miss_rate); + +// // Reset counters for the next 1000 iterations +// retval = PAPI_reset(fj_cpu.papi_event_set); +// if (retval != PAPI_OK) { CUOPT_LOG_TRACE("%sPAPI reset failed", fj_cpu.log_prefix.c_str()); } +// } template static void cleanup_papi(fj_cpu_climber_t& fj_cpu) @@ -442,6 +444,15 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, double mem_total_mb = (mem_loads_bytes + mem_stores_bytes) / 1e6; double mem_bandwidth_gb_per_sec = (mem_total_mb / 1000.0) / (time_window_ms / 1000.0); + // Build per-wrapper memory statistics string + std::stringstream wrapper_stats; + auto per_wrapper_stats = fj_cpu.memory_manifold.collect_per_wrapper(); + for (const auto& [name, loads, stores] : per_wrapper_stats) { + wrapper_stats << " " << name << "_loads=" << loads << " " << name << "_stores=" << stores; + } + + fj_cpu.memory_manifold.flush(); + // Print everything on a single line using precomputed features CUOPT_LOG_DEBUG( "%sCPUFJ_FEATURES iter=%d time_window=%.2f " @@ -457,7 +468,7 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, "cstr_coverage=%.4f var_coverage=%.4f " "L1_miss=%.2f L3_miss=%.2f loads_per_iter=%.0f stores_per_iter=%.0f " "viol_ratio=%.4f nnz_per_move=%.2f eval_intensity=%.2f " - "mem_loads_mb=%.3f mem_stores_mb=%.3f mem_total_mb=%.3f mem_bandwidth_gb_s=%.3f", + "mem_loads_mb=%.3f mem_stores_mb=%.3f mem_total_mb=%.3f mem_bandwidth_gb_s=%.3f%s", fj_cpu.log_prefix.c_str(), fj_cpu.iterations, time_window_ms, @@ -502,7 +513,8 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, mem_loads_mb, mem_stores_mb, mem_total_mb, - mem_bandwidth_gb_per_sec); + mem_bandwidth_gb_per_sec, + wrapper_stats.str().c_str()); // Reset window counters fj_cpu.nnz_processed_window = 0; @@ -520,6 +532,40 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, fj_cpu.unique_vars_accessed_window.clear(); } +// Local implementations that use instrumented vectors +template +static inline std::pair reverse_range_for_var(fj_cpu_climber_t& fj_cpu, + i_t var_idx) +{ + cuopt_assert(var_idx >= 0 && var_idx < fj_cpu.view.pb.n_variables, + "Variable should be within the range"); + return std::make_pair(fj_cpu.h_reverse_offsets[var_idx], fj_cpu.h_reverse_offsets[var_idx + 1]); +} + +template +static inline std::pair range_for_constraint(fj_cpu_climber_t& fj_cpu, + i_t cstr_idx) +{ + return std::make_pair(fj_cpu.h_offsets[cstr_idx], fj_cpu.h_offsets[cstr_idx + 1]); +} + +template +static inline bool check_variable_within_bounds(fj_cpu_climber_t& fj_cpu, + i_t var_idx, + f_t val) +{ + const f_t int_tol = fj_cpu.view.pb.tolerances.integrality_tolerance; + auto bounds = fj_cpu.h_var_bounds[var_idx].get(); + bool within_bounds = val <= (get_upper(bounds) + int_tol) && val >= (get_lower(bounds) - int_tol); + return within_bounds; +} + +template +static inline bool is_integer_var(fj_cpu_climber_t& fj_cpu, i_t var_idx) +{ + return var_t::INTEGER == fj_cpu.h_var_types[var_idx]; +} + template static inline bool tabu_check(fj_cpu_climber_t& fj_cpu, i_t var_idx, @@ -536,15 +582,15 @@ static inline bool tabu_check(fj_cpu_climber_t& fj_cpu, } template -static bool check_variable_feasibility(const fj_cpu_climber_t& fj_cpu, +static bool check_variable_feasibility(fj_cpu_climber_t& fj_cpu, bool check_integer = true) { for (i_t var_idx = 0; var_idx < fj_cpu.view.pb.n_variables; var_idx += 1) { auto val = fj_cpu.h_assignment[var_idx]; - bool feasible = fj_cpu.view.pb.check_variable_within_bounds(var_idx, val); + bool feasible = check_variable_within_bounds(fj_cpu, var_idx, val); if (!feasible) return false; - if (check_integer && fj_cpu.view.pb.is_integer_var(var_idx) && + if (check_integer && is_integer_var(fj_cpu, var_idx) && !fj_cpu.view.pb.is_integer(fj_cpu.h_assignment[var_idx])) return false; } @@ -568,7 +614,7 @@ static inline std::pair compute_score(fj_cpu_climber_t(fj_cpu, var_idx); fj_cpu.nnz_processed_window += (offset_end - offset_begin); // MEMORY OPS: Loop over all constraints involving this variable (avg_var_degree iterations) @@ -587,8 +633,17 @@ static inline std::pair compute_score(fj_cpu_climber_t( - fj_cpu.view, var_idx, delta, cstr_idx, cstr_coeff, c_lb, c_ub, fj_cpu.h_lhs[cstr_idx]); + auto [cstr_base_feas, cstr_bonus_robust] = + feas_score_constraint(fj_cpu.view, + var_idx, + delta, + cstr_idx, + cstr_coeff, + c_lb, + c_ub, + fj_cpu.h_lhs[cstr_idx], + fj_cpu.h_cstr_left_weights[cstr_idx], + fj_cpu.h_cstr_right_weights[cstr_idx]); base_feas_sum += cstr_base_feas; bonus_robust_sum += cstr_bonus_robust; @@ -696,7 +751,8 @@ static void update_weights(fj_cpu_climber_t& fj_cpu) } // Invalidate related cached move scores - auto [relvar_offset_begin, relvar_offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [relvar_offset_begin, relvar_offset_end] = + range_for_constraint(fj_cpu, cstr_idx); // MEMORY OPS: Inner loop over variables in this constraint (avg_cstr_degree iterations per // outer iteration) for (auto i = relvar_offset_begin; i < relvar_offset_end; i++) { @@ -722,7 +778,7 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, cuopt_assert(var_idx < fj_cpu.view.pb.n_variables, "variable index out of bounds"); // Update the LHSs of all involved constraints. - auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); // Track work metrics for regression model fj_cpu.nnz_processed_window += (offset_end - offset_begin); @@ -780,7 +836,8 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, cuopt_assert(isfinite(fj_cpu.h_lhs[cstr_idx]), "assignment should be finite"); // Invalidate related cached move scores - auto [relvar_offset_begin, relvar_offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [relvar_offset_begin, relvar_offset_end] = + range_for_constraint(fj_cpu, cstr_idx); // MEMORY OPS: Inner loop over variables in this constraint (avg_cstr_degree iterations per // outer iteration) for (auto i = relvar_offset_begin; i < relvar_offset_end; i++) { @@ -798,14 +855,14 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, // update the assignment and objective proper // ARRAY READ: h_assignment[var_idx] - 1 read f_t new_val = fj_cpu.h_assignment[var_idx] + delta; - if (fj_cpu.view.pb.is_integer_var(var_idx)) { + if (is_integer_var(fj_cpu, var_idx)) { cuopt_assert(fj_cpu.view.pb.integer_equal(new_val, round(new_val)), "new_val is not integer"); new_val = round(new_val); } // ARRAY WRITE: h_assignment[var_idx] - 1 write fj_cpu.h_assignment[var_idx] = new_val; - cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), + cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), "assignment not within bounds"); cuopt_assert(isfinite(new_val), "assignment is not finite"); @@ -874,7 +931,7 @@ static thrust::tuple find_mtm_move( // collect all the variables that are involved in the target constraints // MEMORY OPS: Outer loop over target constraints (sample_size iterations, typically 15-100) for (size_t cstr_idx : target_cstrs) { - auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); // MEMORY OPS: Inner loop over variables in each constraint (avg_cstr_degree per outer // iteration) for (auto i = offset_begin; i < offset_end; i++) { @@ -891,7 +948,7 @@ static thrust::tuple find_mtm_move( // estimate the amount of nnzs to consider i_t nnz_sum = 0; for (auto var_idx : fj_cpu.iter_mtm_vars) { - auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); nnz_sum += offset_end - offset_begin; } @@ -904,7 +961,7 @@ static thrust::tuple find_mtm_move( f_t cstr_tol = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); cuopt_assert(cstr_idx < fj_cpu.h_cstr_lb.size(), "cstr_idx is out of bounds"); - auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); // MEMORY OPS: Inner loop over variables (avg_cstr_degree per outer iteration) for (auto i = offset_begin; i < offset_end; i++) { // early cached check @@ -914,12 +971,12 @@ static thrust::tuple find_mtm_move( // ARRAY READ: h_variables[i] - 1 read per iteration (cache hit) auto var_idx = fj_cpu.h_variables[i]; // ARRAY READ: h_assignment[var_idx] - 1 read per iteration (cache hit) - if (fj_cpu.view.pb.check_variable_within_bounds( - var_idx, fj_cpu.h_assignment[var_idx] + cached_move.first)) { + if (check_variable_within_bounds( + fj_cpu, var_idx, fj_cpu.h_assignment[var_idx] + cached_move.first)) { best_score = cached_move.second; best_move = fj_move_t{var_idx, cached_move.first}; } - // cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, + // cuopt_assert(check_variable_within_bounds(fj_cpu, var_idx, // fj_cpu.h_assignment[var_idx] + cached_move.first), "best move not within bounds"); } fj_cpu.hit_count++; @@ -964,7 +1021,7 @@ static thrust::tuple find_mtm_move( c_ub, fj_cpu.h_assignment, fj_cpu.h_lhs); - if (fj_cpu.view.pb.is_integer_var(var_idx)) { + if (is_integer_var(fj_cpu, var_idx)) { new_val = cstr_coeff * sign > 0 ? floor(val + delta + fj_cpu.view.pb.tolerances.integrality_tolerance) : ceil(val + delta - fj_cpu.view.pb.tolerances.integrality_tolerance); @@ -980,7 +1037,7 @@ static thrust::tuple find_mtm_move( } } if (!isfinite(new_val)) continue; - cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), + cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), "new_val is not within bounds"); delta = new_val - val; // more permissive tabu in the case of local minima @@ -1034,7 +1091,7 @@ static thrust::tuple find_mtm_move( // CRITICAL: compute_score() does ~6-7 array reads per constraint involved auto [score, infeasibility] = compute_score(fj_cpu, var_idx, delta); - cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), ""); + cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), ""); cuopt_assert(isfinite(delta), ""); if (fj_cpu.view.move_numerically_stable( @@ -1096,7 +1153,7 @@ static void recompute_lhs(fj_cpu_climber_t& fj_cpu) // MEMORY OPS: CRITICAL LOOP - Loop over all constraints (n_cstrs iterations) // This is called periodically to recompute all LHS values from scratch for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; ++cstr_idx) { - auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); // MEMORY OPS: For each constraint, reads avg_cstr_degree elements from: // - h_coefficients[offset_begin:offset_end] - avg_cstr_degree reads @@ -1167,7 +1224,7 @@ static thrust::tuple find_lift_move( // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration (non-binary) f_t lfd_lb = get_lower(fj_cpu.h_var_bounds[var_idx].get()) - val; f_t lfd_ub = get_upper(fj_cpu.h_var_bounds[var_idx].get()) - val; - auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); // MEMORY OPS: Inner loop over constraints involving this variable (avg_var_degree iterations // per outer iteration) for (i_t j = offset_begin; j < offset_end; j += 1) { @@ -1198,9 +1255,9 @@ static thrust::tuple find_lift_move( fj_cpu.h_lhs); if (cstr_coeff * sign < 0) { - if (fj_cpu.view.pb.is_integer_var(var_idx)) delta = ceil(delta); + if (is_integer_var(fj_cpu, var_idx)) delta = ceil(delta); } else { - if (fj_cpu.view.pb.is_integer_var(var_idx)) delta = floor(delta); + if (is_integer_var(fj_cpu, var_idx)) delta = floor(delta); } // skip this variable if there is no slack @@ -1210,7 +1267,7 @@ static thrust::tuple find_lift_move( } else { lfd_lb = 0; } - } else if (!fj_cpu.view.pb.check_variable_within_bounds(var_idx, val + delta)) { + } else if (!check_variable_within_bounds(fj_cpu, var_idx, val + delta)) { continue; } else { if (cstr_coeff * sign < 0) { @@ -1228,8 +1285,8 @@ static thrust::tuple find_lift_move( // invalid crossing bounds if (lfd_lb >= lfd_ub) { lfd_lb = lfd_ub = 0; } - if (!fj_cpu.view.pb.check_variable_within_bounds(var_idx, val + lfd_lb)) { lfd_lb = 0; } - if (!fj_cpu.view.pb.check_variable_within_bounds(var_idx, val + lfd_ub)) { lfd_ub = 0; } + if (!check_variable_within_bounds(fj_cpu, var_idx, val + lfd_lb)) { lfd_lb = 0; } + if (!check_variable_within_bounds(fj_cpu, var_idx, val + lfd_ub)) { lfd_ub = 0; } // Now that the life move domain is computed, compute the correct lift move cuopt_assert(isfinite(val), "invalid assignment value"); @@ -1276,12 +1333,12 @@ static void perturb(fj_cpu_climber_t& fj_cpu) f_t lb = ceil(std::max(get_lower(fj_cpu.h_var_bounds[var_idx].get()), -1e7)); f_t ub = floor(std::min(get_upper(fj_cpu.h_var_bounds[var_idx].get()), 1e7)); f_t val = lb + (ub - lb) * rng.next_double(); - if (fj_cpu.view.pb.is_integer_var(var_idx)) { + if (is_integer_var(fj_cpu, var_idx)) { val = std::round(val); val = std::min(std::max(val, lb), ub); } - cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, val), + cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, val)), "value is out of bounds"); // ARRAY WRITE: h_assignment[var_idx] - 1 write per iteration fj_cpu.h_assignment[var_idx] = val; @@ -1408,7 +1465,7 @@ static void init_fj_cpu(fj_cpu_climber_t& fj_cpu, fj_cpu.cached_cstr_bounds.resize(fj_cpu.h_reverse_coefficients.size()); // MEMORY OPS: INITIALIZATION - Loop over all variables (n_vars iterations) for (i_t var_idx = 0; var_idx < (i_t)fj_cpu.view.pb.n_variables; ++var_idx) { - auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); // MEMORY OPS: Inner loop over constraints per variable (avg_var_degree iterations per outer // iteration) for (i_t i = offset_begin; i < offset_end; ++i) { @@ -1651,17 +1708,29 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l std::chrono::duration_cast>(now - loop_start).count() * 1000.0; -#ifdef __linux__ - collect_and_print_papi_metrics(fj_cpu); -#endif + // #ifdef __linux__ + // collect_and_print_papi_metrics(fj_cpu); + // #endif // Collect memory statistics - auto [loads, stores] = fj_cpu.memory_manifold.collect_and_flush(); + auto [loads, stores] = fj_cpu.memory_manifold.collect(); // Log all features including memory statistics log_regression_features(fj_cpu, time_window_ms, total_time_ms, loads, stores); fj_cpu.last_feature_log_time = now; + + std::map features_map; + features_map["n_vars"] = (float)fj_cpu.h_reverse_offsets.size() - 1; + features_map["n_cstrs"] = (float)fj_cpu.h_offsets.size() - 1; + features_map["total_nnz"] = (float)fj_cpu.h_reverse_offsets.back(); + features_map["mem_total_mb"] = (float)(loads + stores) / 1e6; + float time_prediction = std::max( + (f_t)0.0, + (f_t)ceil(context.work_unit_predictors.cpufj_predictor.predict_scalar(features_map))); + CUOPT_LOG_DEBUG("FJ determ: Estimated time for 1000 iters: %f, error %f", + time_prediction, + time_prediction - time_window_ms); } cuopt_func_call(sanity_checks(fj_cpu)); diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cuh b/cpp/src/mip/feasibility_jump/fj_cpu.cuh index b5ff17870..27f9d7911 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cuh +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cuh @@ -28,33 +28,38 @@ template struct fj_cpu_climber_t { fj_cpu_climber_t() { +#define ADD_INSTRUMENTED(var) \ + std::make_pair(#var, std::ref(static_cast(var))) + // Initialize memory manifold with all ins_vector members - memory_manifold = instrumentation_manifold_t{h_reverse_coefficients, - h_reverse_constraints, - h_reverse_offsets, - h_coefficients, - h_offsets, - h_variables, - h_obj_coeffs, - h_var_bounds, - h_cstr_lb, - h_cstr_ub, - h_var_types, - h_is_binary_variable, - h_objective_vars, - h_binary_indices, - h_tabu_nodec_until, - h_tabu_noinc_until, - h_tabu_lastdec, - h_tabu_lastinc, - h_lhs, - h_lhs_sumcomp, - h_cstr_left_weights, - h_cstr_right_weights, - h_assignment, - h_best_assignment, - cached_cstr_bounds, - iter_mtm_vars}; + memory_manifold = instrumentation_manifold_t{ADD_INSTRUMENTED(h_reverse_coefficients), + ADD_INSTRUMENTED(h_reverse_constraints), + ADD_INSTRUMENTED(h_reverse_offsets), + ADD_INSTRUMENTED(h_coefficients), + ADD_INSTRUMENTED(h_offsets), + ADD_INSTRUMENTED(h_variables), + ADD_INSTRUMENTED(h_obj_coeffs), + ADD_INSTRUMENTED(h_var_bounds), + ADD_INSTRUMENTED(h_cstr_lb), + ADD_INSTRUMENTED(h_cstr_ub), + ADD_INSTRUMENTED(h_var_types), + ADD_INSTRUMENTED(h_is_binary_variable), + ADD_INSTRUMENTED(h_objective_vars), + ADD_INSTRUMENTED(h_binary_indices), + ADD_INSTRUMENTED(h_tabu_nodec_until), + ADD_INSTRUMENTED(h_tabu_noinc_until), + ADD_INSTRUMENTED(h_tabu_lastdec), + ADD_INSTRUMENTED(h_tabu_lastinc), + ADD_INSTRUMENTED(h_lhs), + ADD_INSTRUMENTED(h_lhs_sumcomp), + ADD_INSTRUMENTED(h_cstr_left_weights), + ADD_INSTRUMENTED(h_cstr_right_weights), + ADD_INSTRUMENTED(h_assignment), + ADD_INSTRUMENTED(h_best_assignment), + ADD_INSTRUMENTED(cached_cstr_bounds), + ADD_INSTRUMENTED(iter_mtm_vars)}; + +#undef ADD_INSTRUMENTED } fj_cpu_climber_t(const fj_cpu_climber_t& other) = delete; fj_cpu_climber_t& operator=(const fj_cpu_climber_t& other) = delete; diff --git a/cpp/src/mip/feasibility_jump/load_balancing.cuh b/cpp/src/mip/feasibility_jump/load_balancing.cuh index b6c9ea1f9..379fdbde6 100644 --- a/cpp/src/mip/feasibility_jump/load_balancing.cuh +++ b/cpp/src/mip/feasibility_jump/load_balancing.cuh @@ -336,8 +336,17 @@ __global__ void load_balancing_compute_scores_binary( auto c_lb = fj.constraint_lower_bounds_csr[csr_offset]; auto c_ub = fj.constraint_upper_bounds_csr[csr_offset]; - auto [cstr_base_feas, cstr_bonus_robust] = feas_score_constraint( - fj, var_idx, delta, cstr_idx, cstr_coeff, c_lb, c_ub, fj.incumbent_lhs[cstr_idx]); + auto [cstr_base_feas, cstr_bonus_robust] = + feas_score_constraint(fj, + var_idx, + delta, + cstr_idx, + cstr_coeff, + c_lb, + c_ub, + fj.incumbent_lhs[cstr_idx], + fj.cstr_left_weights[cstr_idx], + fj.cstr_right_weights[cstr_idx]); base_feas += cstr_base_feas; bonus_robust += cstr_bonus_robust; @@ -537,8 +546,17 @@ __launch_bounds__(TPB_loadbalance, 16) __global__ cuopt_assert(c_lb == fj.pb.constraint_lower_bounds[cstr_idx], "bound sanity check failed"); cuopt_assert(c_ub == fj.pb.constraint_upper_bounds[cstr_idx], "bound sanity check failed"); - auto [cstr_base_feas, cstr_bonus_robust] = feas_score_constraint( - fj, var_idx, delta, cstr_idx, cstr_coeff, c_lb, c_ub, fj.incumbent_lhs[cstr_idx]); + auto [cstr_base_feas, cstr_bonus_robust] = + feas_score_constraint(fj, + var_idx, + delta, + cstr_idx, + cstr_coeff, + c_lb, + c_ub, + fj.incumbent_lhs[cstr_idx], + fj.cstr_left_weights[cstr_idx], + fj.cstr_right_weights[cstr_idx]); base_feas += cstr_base_feas; bonus_robust += cstr_bonus_robust; diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 28be6dd4f..30bf5ac06 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -65,7 +65,7 @@ void local_search_t::start_cpufj_scratch_threads(population_t::infinity(); - cpu_fj_settings.iteration_limit = 10000; // would like 10 samples per instance + cpu_fj_settings.iteration_limit = 30000; // would like 10 samples per instance solution_t solution(*context.problem_ptr); thrust::fill(solution.handle_ptr->get_thrust_policy(), diff --git a/cpp/src/mip/solver_context.cuh b/cpp/src/mip/solver_context.cuh index 1be253d6f..306fc0dc5 100644 --- a/cpp/src/mip/solver_context.cuh +++ b/cpp/src/mip/solver_context.cuh @@ -11,6 +11,7 @@ #include #include +#include #include #include #include @@ -27,6 +28,7 @@ namespace cuopt::linear_programming::detail { struct mip_solver_work_unit_predictors_t { work_unit_predictor_t fj_predictor{}; + work_unit_predictor_t cpufj_predictor{}; }; // Aggregate structure containing the global context of the solving process for convenience: diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 2cf0d3b1e..2975a1e36 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -28,7 +28,9 @@ #include #include +#include #include +#include #include #include @@ -90,33 +92,67 @@ class instrumentation_manifold_t { public: instrumentation_manifold_t() = default; - // Construct with initializer list of instrumented objects + // Construct with initializer list of (description, instrumented object) pairs instrumentation_manifold_t( - std::initializer_list> instrumented) - : instrumented_(instrumented) + std::initializer_list< + std::pair>> instrumented) { + for (const auto& [name, instr] : instrumented) { + instrumented_.insert_or_assign(name, instr); + } } - // Add an instrumented object to track - void add(memory_instrumentation_base_t& instrumented) { instrumented_.push_back(instrumented); } + // Add an instrumented object to track with a description + void add(const std::string& description, memory_instrumentation_base_t& instrumented) + { + instrumented_.insert_or_assign(description, std::ref(instrumented)); + } - // Collect total loads and stores from all instrumented objects, then flush all counters - std::pair collect_and_flush() + // Collect total loads and stores across all instrumented objects + std::pair collect() { size_t total_loads = 0; size_t total_stores = 0; - for (auto& instr : instrumented_) { + for (auto& [name, instr] : instrumented_) { total_loads += instr.get().byte_loads; total_stores += instr.get().byte_stores; - instr.get().reset_counters(); } return {total_loads, total_stores}; } + // Collect per-wrapper statistics + std::vector> collect_per_wrapper() + { + std::vector> results; + results.reserve(instrumented_.size()); + + for (auto& [name, instr] : instrumented_) { + results.emplace_back(name, instr.get().byte_loads, instr.get().byte_stores); + } + + return results; + } + + // Collect total loads and stores, then flush counters + std::pair collect_and_flush() + { + auto result = collect(); + flush(); + return result; + } + + void flush() + { + for (auto& [name, instr] : instrumented_) { + instr.get().reset_counters(); + } + } + private: - std::vector> instrumented_; + std::unordered_map> + instrumented_; }; #else @@ -126,11 +162,15 @@ class instrumentation_manifold_t { public: instrumentation_manifold_t() = default; instrumentation_manifold_t( - std::initializer_list>) + std::initializer_list< + std::pair>>) { } - void add(memory_instrumentation_base_t&) {} + void add(const std::string&, memory_instrumentation_base_t&) {} + std::pair collect() { return {0, 0}; } + std::vector> collect_per_wrapper() { return {}; } std::pair collect_and_flush() { return {0, 0}; } + void flush() {} }; #endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION @@ -262,15 +302,15 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { return ref_; } - // Allow implicit conversion to reference for functions expecting references - operator value_type&() { return ref_; } + // // Allow implicit conversion to reference for functions expecting references + // operator value_type&() { return ref_; } - operator const value_type&() const { return ref_; } + // operator const value_type&() const { return ref_; } - // Member access operator for structured types (e.g., type_2) - value_type* operator->() { return &ref_; } + // // Member access operator for structured types (e.g., type_2) + // value_type* operator->() { return &ref_; } - const value_type* operator->() const { return &ref_; } + // const value_type* operator->() const { return &ref_; } // Get underlying element reference (records a load) value_type& get() diff --git a/cpp/src/utilities/models/cpufj_predictor/header.h b/cpp/src/utilities/models/cpufj_predictor/header.h new file mode 100644 index 000000000..800072da2 --- /dev/null +++ b/cpp/src/utilities/models/cpufj_predictor/header.h @@ -0,0 +1,35 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#pragma once + +#include +#include +#include +#include +#include +#include + +class cpufj_predictor { + public: + union Entry { + int missing; + double fvalue; + int qvalue; + }; + + static int32_t get_num_target(void); + static void get_num_class(int32_t* out); + static int32_t get_num_feature(void); + static const char* get_threshold_type(void); + static const char* get_leaf_output_type(void); + static void predict(union Entry* data, int pred_margin, double* result); + static void postprocess(double* result); + static int quantize(double val, unsigned fid); + + // Feature names + static constexpr int NUM_FEATURES = 4; + static const char* feature_names[NUM_FEATURES]; +}; // class cpufj_data diff --git a/cpp/src/utilities/models/cpufj_predictor/main.cpp b/cpp/src/utilities/models/cpufj_predictor/main.cpp new file mode 100644 index 000000000..3f4bf0837 --- /dev/null +++ b/cpp/src/utilities/models/cpufj_predictor/main.cpp @@ -0,0 +1,5412 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#include "header.h" + +#if defined(__clang__) || defined(__GNUC__) +#define LIKELY(x) __builtin_expect(!!(x), 1) +#define UNLIKELY(x) __builtin_expect(!!(x), 0) +#else +#define LIKELY(x) (x) +#define UNLIKELY(x) (x) +#endif +#define N_TARGET 1 +#define MAX_N_CLASS 1 + +const unsigned char is_categorical[] = { + 0, + 0, + 0, + 0, +}; +static const int32_t num_class[] = { + 1, +}; + +int32_t cpufj_predictor::get_num_target(void) { return N_TARGET; } +void cpufj_predictor::get_num_class(int32_t* out) +{ + for (int i = 0; i < N_TARGET; ++i) { + out[i] = num_class[i]; + } +} +int32_t cpufj_predictor::get_num_feature(void) { return 4; } +const char* cpufj_predictor::get_threshold_type(void) { return "float64"; } +const char* cpufj_predictor::get_leaf_output_type(void) { return "float64"; } + +void cpufj_predictor::predict(union Entry* data, int pred_margin, double* result) +{ + // Quantize data + for (int i = 0; i < 4; ++i) { + if (data[i].missing != -1 && !is_categorical[i]) { + data[i].qvalue = quantize(data[i].fvalue, i); + } + } + + unsigned int tmp; + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += 156.82697390964927; + } else { + result[0] += 160.51072839782955; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 24))) { + result[0] += 167.07152558194775; + } else { + result[0] += 173.16044577743776; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 197.67049116907555; + } else { + result[0] += 193.73228835720744; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 204.19401403625514; + } else { + result[0] += 233.21262399057034; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + result[0] += 271.7565681764123; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 384.56067848347016; + } else { + result[0] += 490.5143183873164; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 50))) { + if (LIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[3].qvalue <= 114))) { + if (LIKELY(false || (data[3].qvalue <= 74))) { + result[0] += -13.943317103763608; + } else { + result[0] += -10.411451244915156; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -1.6607479643348968; + } else { + result[0] += 1.873995960655583; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += 13.317445150475752; + } else { + result[0] += -3.2833804644580917; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 42))) { + result[0] += 28.787436474976477; + } else { + result[0] += 22.808869505615306; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (UNLIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 116))) { + result[0] += 3.5889216009186664; + } else { + result[0] += 5.910052863589392; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 210))) { + result[0] += 89.99770637090484; + } else { + result[0] += 127.79448643063864; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 191.2644805975275; + } else { + result[0] += 286.4874098858173; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 50))) { + if (LIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[3].qvalue <= 114))) { + if (LIKELY(false || (data[3].qvalue <= 76))) { + result[0] += -12.539408056743746; + } else { + result[0] += -9.311441710639189; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -1.4947050956018029; + } else { + result[0] += 1.6866257811476963; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += 11.98745272164608; + } else { + result[0] += -2.9554856143381034; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 22.718528792836054; + } else { + result[0] += 52.594838808257; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (UNLIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 116))) { + result[0] += 3.2306536812875586; + } else { + result[0] += 5.321595177064682; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 79.5197036158342; + } else { + result[0] += 112.02363049281628; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 172.34821010044644; + } else { + result[0] += 257.92682935697115; + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -11.281112483305671; + } else { + result[0] += -8.601904513350851; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 24))) { + result[0] += -4.457196969778106; + } else { + result[0] += 0.6775720991155234; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 18.28399618068051; + } else { + result[0] += 15.34024990698243; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 23.56902428310345; + } else { + result[0] += 47.371626254377695; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + result[0] += 72.50774203412541; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 155.30278921274038; + } else { + result[0] += 232.21351482872598; + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[3].qvalue <= 114))) { + if (LIKELY(false || (data[3].qvalue <= 78))) { + result[0] += -10.157360048782069; + } else { + result[0] += -7.469901314898597; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -1.2836068490987675; + } else { + result[0] += 1.4928852675361874; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 68))) { + result[0] += 11.862787216911165; + } else { + result[0] += -3.825179380397125; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + result[0] += 16.467439176084763; + } else { + result[0] += 22.870842463159736; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + if (UNLIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 116))) { + result[0] += -4.329979207208883; + } else { + result[0] += -2.4277052596041226; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 206))) { + result[0] += 62.899179793202485; + } else { + result[0] += 89.86758030525095; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 144.9081294228374; + } else { + result[0] += 209.06360369591349; + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[3].qvalue <= 118))) { + if (LIKELY(false || (data[3].qvalue <= 80))) { + result[0] += -9.111244309609868; + } else { + result[0] += -6.450300041406144; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 146))) { + result[0] += -0.908464880592677; + } else { + result[0] += 1.3456266411166913; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 144))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 9.598829002806253; + } else { + result[0] += -3.458980194428218; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + result[0] += 14.685884008167873; + } else { + result[0] += 19.70403341262041; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (UNLIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 116))) { + result[0] += -3.8977343521833427; + } else { + result[0] += -2.185981180004069; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 202))) { + result[0] += 53.636361895007624; + } else { + result[0] += 73.84635747136831; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 54))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 109.30218868014822; + } else { + result[0] += 130.5223189467278; + } + } else { + result[0] += 188.22157308443514; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 50))) { + if (LIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[3].qvalue <= 114))) { + if (LIKELY(false || (data[3].qvalue <= 74))) { + result[0] += -8.242985704258855; + } else { + result[0] += -6.103964758757681; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 122))) { + result[0] += -3.0003459256849077; + } else { + result[0] += 0.5938833671659278; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += 9.054639032941743; + } else { + result[0] += -3.3592089510831986; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 18.040143668653474; + } else { + result[0] += 13.175784904763427; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 236))) { + if (UNLIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 118))) { + result[0] += -3.214705374718561; + } else { + result[0] += 0.31848526393665993; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 202))) { + result[0] += 48.27447091272866; + } else { + result[0] += 66.18473238316001; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 97.70898775652987; + } else { + result[0] += 117.56467429340749; + } + } else { + result[0] += 169.45732965745194; + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -7.402034186828849; + } else { + result[0] += -5.63837490580214; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 24))) { + result[0] += -3.2673346602099738; + } else { + result[0] += 0.4720904756386771; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 11.693457400016031; + } else { + result[0] += 10.03852332044065; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 15.437715046858271; + } else { + result[0] += 36.647436411890496; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + result[0] += 47.53118689085973; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 106.35664032988495; + } else { + result[0] += 152.56374281850964; + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -6.661840917407535; + } else { + result[0] += -5.074574846660258; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 24))) { + result[0] += -2.94088446825355; + } else { + result[0] += 0.4248859491848843; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 10.52521173349777; + } else { + result[0] += 9.034789845952849; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 13.894155617598422; + } else { + result[0] += 33.00796459750472; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 42.823961164678224; + } else { + result[0] += 36.98781896591187; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 95.8378507576408; + } else { + result[0] += 137.35430146859977; + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 12))) { + result[0] += -5.922535414763472; + } else { + result[0] += -3.9535302977307456; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 24))) { + result[0] += -2.647051125523947; + } else { + result[0] += 0.38240139128726525; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 9.473680911813272; + } else { + result[0] += 8.131417758482929; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 42))) { + result[0] += 12.474218356244899; + } else { + result[0] += 28.32144924784816; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 38.542156344073405; + } else { + result[0] += 33.34683068752289; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 86.359383424193; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 130.56753145370016; + } else { + result[0] += 115.99602850603911; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -5.403420533141926; + } else { + result[0] += -4.075259880702465; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 24))) { + result[0] += -2.3825756020703164; + } else { + result[0] += 0.34416491637505503; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 62))) { + result[0] += 6.690473585247339; + } else { + result[0] += 7.675205616748645; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 11.251804458709806; + } else { + result[0] += 26.917319955760036; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 34.68847201632427; + } else { + result[0] += 30.064251933097843; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 77.81834311684409; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 117.59088283047355; + } else { + result[0] += 104.46758514965971; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + if (LIKELY(false || (data[3].qvalue <= 96))) { + if (UNLIKELY(false || (data[3].qvalue <= 22))) { + result[0] += -5.645610033293023; + } else { + result[0] += -4.712904234092419; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 68))) { + result[0] += -1.7467118855770272; + } else { + result[0] += -6.7806729780163355; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -3.3298901489085715; + } else { + result[0] += 0.9879303535654625; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 156))) { + result[0] += 4.765656733422152; + } else { + result[0] += 10.436091866890687; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 118))) { + result[0] += -19.231066348531115; + } else { + result[0] += -15.589280898150276; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 218))) { + if (UNLIKELY(false || (data[3].qvalue <= 200))) { + result[0] += 25.747728264474212; + } else { + result[0] += 40.04473103921778; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 64.38265010597043; + } else { + result[0] += 95.58272802311622; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + if (LIKELY(false || (data[3].qvalue <= 98))) { + if (LIKELY(false || (data[3].qvalue <= 74))) { + result[0] += -4.409030071223973; + } else { + result[0] += -3.5647114194984613; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 68))) { + result[0] += -1.3542190966799321; + } else { + result[0] += -6.103673214709666; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -2.999404705305745; + } else { + result[0] += 0.8891527883879043; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 156))) { + result[0] += 4.289215943564471; + } else { + result[0] += 9.392568858155158; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 116))) { + result[0] += -17.598092511782436; + } else { + result[0] += -16.123547517184555; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 216))) { + if (UNLIKELY(false || (data[3].qvalue <= 200))) { + result[0] += 23.17394211472633; + } else { + result[0] += 35.171000449187375; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 55.995760587885144; + } else { + result[0] += 86.04514530858954; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 22))) { + if (LIKELY(false || (data[1].qvalue <= 12))) { + result[0] += -3.8888486100132043; + } else { + result[0] += -2.5693198969248536; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 0.301134908182464; + } else { + result[0] += 4.7321266664777495; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (UNLIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 5.997381523357489; + } else { + result[0] += 5.0375213284435185; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 8.559482811493893; + } else { + result[0] += 22.27496138368804; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 25.27353965981968; + } else { + result[0] += 21.13871028184891; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 55.52418384593922; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 87.85257342192293; + } else { + result[0] += 76.03356082097153; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 50))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + if (LIKELY(false || (data[3].qvalue <= 90))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + result[0] += -3.5064268267703387; + } else { + result[0] += -6.065722823494794; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 68))) { + result[0] += -1.4039415524212295; + } else { + result[0] += -5.997225846159178; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -2.9652186181689757; + } else { + result[0] += 0.7283126984643262; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 154))) { + result[0] += 2.789907904600108; + } else { + result[0] += 7.718533347299168; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 188))) { + result[0] += -18.00435797338746; + } else { + if (LIKELY(false || (data[3].qvalue <= 214))) { + if (UNLIKELY(false || (data[3].qvalue <= 198))) { + result[0] += 17.330223423965826; + } else { + result[0] += 27.46055877083792; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 46.12967429173014; + } else { + result[0] += 70.70123466855004; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + if (LIKELY(false || (data[3].qvalue <= 102))) { + if (UNLIKELY(false || (data[3].qvalue <= 22))) { + result[0] += -3.888827166320002; + } else { + result[0] += -3.04723191570044; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 34))) { + result[0] += -0.7424769082893876; + } else { + result[0] += -5.398447803084307; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -2.670943097291571; + } else { + result[0] += 0.6554928174570391; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 158))) { + result[0] += 2.9919951855699423; + } else { + result[0] += 7.128361704676317; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 118))) { + result[0] += -16.31666198848907; + } else { + result[0] += -13.216809526331284; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 220))) { + if (LIKELY(false || (data[3].qvalue <= 206))) { + result[0] += 18.698365978047963; + } else { + result[0] += 30.323819222880072; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 54))) { + result[0] += 48.19287170494928; + } else { + result[0] += 66.97776871619591; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[0].qvalue <= 22))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -2.8871386766483713; + } else { + result[0] += -2.194022438199927; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 24))) { + result[0] += -1.2643512510325772; + } else { + result[0] += 0.2946143237550731; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (UNLIKELY(false || (data[0].qvalue <= 28))) { + result[0] += 6.18334904875928; + } else { + result[0] += 3.4917401374748973; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 42))) { + result[0] += 6.637851347005048; + } else { + result[0] += 17.628961355963423; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 18.427701194716725; + } else { + result[0] += 15.068101975917816; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 39.624109077872816; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 65.43777984806364; + } else { + result[0] += 54.793414778446135; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + if (LIKELY(false || (data[2].qvalue <= 52))) { + if (LIKELY(false || (data[3].qvalue <= 86))) { + result[0] += -2.5929923519240776; + } else { + result[0] += -1.2308422313969718; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 116))) { + result[0] += -16.777735206670346; + } else { + result[0] += -15.496213790630472; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + if (LIKELY(false || (data[3].qvalue <= 166))) { + result[0] += 3.153837529703604; + } else { + result[0] += 10.653343513120348; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 1.2707685237496547; + } else { + result[0] += -7.577582551412722; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 50))) { + if (LIKELY(false || (data[0].qvalue <= 70))) { + if (UNLIKELY(false || (data[3].qvalue <= 216))) { + result[0] += 5.086264511434565; + } else { + result[0] += 4.004129196876291; + } + } else { + result[0] += 18.011869379087937; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 214))) { + if (UNLIKELY(false || (data[3].qvalue <= 200))) { + result[0] += 12.716813581606937; + } else { + result[0] += 21.19923662562386; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + result[0] += 33.0928592549443; + } else { + result[0] += 48.6196284504888; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (LIKELY(false || (data[3].qvalue <= 104))) { + result[0] += -2.2842871895780763; + } else { + result[0] += -0.6940580648135516; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 118))) { + result[0] += -14.884714915484075; + } else { + result[0] += -12.21852856355555; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (LIKELY(false || (data[0].qvalue <= 68))) { + result[0] += 1.1320691013866366; + } else { + result[0] += -6.824281661647207; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 168))) { + result[0] += 3.133632898542928; + } else { + result[0] += 10.26787174244473; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 56))) { + if (UNLIKELY(false || (data[3].qvalue <= 216))) { + if (UNLIKELY(false || (data[0].qvalue <= 66))) { + result[0] += 0.3519373175810123; + } else { + result[0] += 4.883879225540344; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 234))) { + result[0] += 3.4475335994592777; + } else { + result[0] += 4.289605817529637; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 214))) { + if (UNLIKELY(false || (data[3].qvalue <= 198))) { + result[0] += 10.944053551737898; + } else { + result[0] += 18.520104025075135; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 31.9020659327635; + } else { + result[0] += 47.722870097304835; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (LIKELY(false || (data[3].qvalue <= 84))) { + result[0] += -2.1126641262849875; + } else { + result[0] += -1.0034167734194253; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 114))) { + result[0] += -14.666533134028597; + } else { + result[0] += -13.091642320227521; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -2.67357385169376; + } else { + result[0] += 0.317539563282027; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 172))) { + result[0] += 2.9924052201762423; + } else { + result[0] += 10.018166909712392; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 56))) { + if (UNLIKELY(false || (data[3].qvalue <= 216))) { + if (UNLIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 0.3173503774211839; + } else { + result[0] += 4.396111790242462; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 234))) { + result[0] += 3.1028551632609607; + } else { + result[0] += 3.8610566164274576; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 212))) { + if (UNLIKELY(false || (data[3].qvalue <= 196))) { + result[0] += 9.079411213991389; + } else { + result[0] += 15.631981353661537; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 230))) { + result[0] += 24.953231107660457; + } else { + result[0] += 38.732870695055425; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (LIKELY(false || (data[3].qvalue <= 106))) { + result[0] += -1.8507802734637893; + } else { + result[0] += -0.4780342275095875; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 116))) { + result[0] += -12.281350727645211; + } else { + result[0] += -11.20561954498291; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -2.4082419443490855; + } else { + result[0] += 0.2857906276722831; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 164))) { + result[0] += 1.8193510979340806; + } else { + result[0] += 7.12452591373636; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 56))) { + if (UNLIKELY(false || (data[3].qvalue <= 216))) { + if (UNLIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 0.28616249149215633; + } else { + result[0] += 3.957059242184614; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 236))) { + result[0] += 2.8268370028874497; + } else { + result[0] += 3.686134112733879; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 212))) { + if (LIKELY(false || (data[3].qvalue <= 200))) { + result[0] += 9.321451071849161; + } else { + result[0] += 15.02085895338778; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 24.772053316683984; + } else { + result[0] += 39.09600893416962; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 150))) { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (LIKELY(false || (data[3].qvalue <= 124))) { + result[0] += -1.6348218331553022; + } else { + result[0] += 0.16446761997202702; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 114))) { + result[0] += -11.997166693345555; + } else { + result[0] += -10.593410837279578; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 176))) { + result[0] += -0.26077036500572887; + } else { + result[0] += 0.26721227295908545; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 174))) { + result[0] += 3.50927093938965; + } else { + result[0] += 9.399961201456877; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 56))) { + if (UNLIKELY(false || (data[3].qvalue <= 218))) { + if (UNLIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 0.25803962657163887; + } else { + result[0] += 3.413014759107607; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 230))) { + result[0] += 2.36220596981744; + } else { + result[0] += 2.9185929060991107; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 218))) { + if (LIKELY(false || (data[3].qvalue <= 204))) { + result[0] += 9.258923590762315; + } else { + result[0] += 15.922847318580807; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 25.53935408466994; + } else { + result[0] += 35.194870544491394; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + if (LIKELY(false || (data[2].qvalue <= 52))) { + if (LIKELY(false || (data[3].qvalue <= 82))) { + result[0] += -1.5559766022197978; + } else { + result[0] += -0.6821089851736815; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 118))) { + result[0] += -9.793275858100605; + } else { + result[0] += -7.646164855957032; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -2.185564344146035; + } else { + result[0] += 0.23136153315509006; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 164))) { + result[0] += 1.4281790213369323; + } else { + result[0] += 5.862740580154604; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 52))) { + if (LIKELY(false || (data[0].qvalue <= 70))) { + if (UNLIKELY(false || (data[3].qvalue <= 214))) { + result[0] += 3.1620136193354766; + } else { + result[0] += 2.374787311783622; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 12.394186796439712; + } else { + result[0] += 7.221490024859086; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 222))) { + if (LIKELY(false || (data[3].qvalue <= 202))) { + result[0] += 7.724518171783787; + } else { + result[0] += 14.107049853741623; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 25.09318574808453; + } else { + result[0] += 33.43533238246624; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 150))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 110))) { + result[0] += -1.348448669905975; + } else { + result[0] += 0.3221871974704163; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 126))) { + result[0] += -8.519520394077722; + } else { + result[0] += -4.128400709863002; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 16))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + result[0] += 0.20971876754977306; + } else { + result[0] += 2.000764232879495; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 176))) { + result[0] += 3.2250018408327294; + } else { + result[0] += 9.091428443343196; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 56))) { + if (UNLIKELY(false || (data[2].qvalue <= 46))) { + result[0] += -0.07772423557166397; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 206))) { + result[0] += 7.465886690298717; + } else { + result[0] += 2.199134462061703; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 212))) { + if (UNLIKELY(false || (data[3].qvalue <= 196))) { + result[0] += 5.510852046313868; + } else { + result[0] += 10.349949332911033; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + result[0] += 17.106146295672758; + } else { + result[0] += 26.416688466239478; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 152))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 120))) { + result[0] += -1.1991422347150693; + } else { + result[0] += 0.46237283922421873; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 126))) { + result[0] += -7.6684254163131875; + } else { + result[0] += -3.7949608540857165; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 16))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += 0.18039368614868972; + } else { + result[0] += 1.85527472375075; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 172))) { + result[0] += 2.72143013117147; + } else { + result[0] += 6.582942954251957; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 52))) { + if (LIKELY(false || (data[0].qvalue <= 70))) { + if (UNLIKELY(false || (data[3].qvalue <= 214))) { + result[0] += 2.622990881969633; + } else { + result[0] += 1.9174728788131057; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 10.617562740828639; + } else { + result[0] += 5.75643968641758; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 214))) { + if (UNLIKELY(false || (data[3].qvalue <= 196))) { + result[0] += 4.573403401881785; + } else { + result[0] += 9.61596988143479; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 17.169701079810455; + } else { + result[0] += 25.958129802402993; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 152))) { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (LIKELY(false || (data[3].qvalue <= 92))) { + result[0] += -1.130718809808625; + } else { + result[0] += -0.2762627382286883; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 114))) { + result[0] += -8.235156694808095; + } else { + result[0] += -6.951272825766222; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 32))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += 0.16235716809342288; + } else { + result[0] += 1.707373695884848; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 176))) { + result[0] += 2.8296562924600828; + } else { + result[0] += 7.488739806649071; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 56))) { + if (UNLIKELY(false || (data[0].qvalue <= 66))) { + result[0] += -0.32786249211636087; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 206))) { + result[0] += 6.490628203531107; + } else { + result[0] += 1.7818277591800613; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 212))) { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + result[0] += 3.807200463755219; + } else { + result[0] += 8.111758332078471; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 226))) { + result[0] += 13.144779116022248; + } else { + result[0] += 20.863007074513657; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 22))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -1.0274672289090832; + } else { + result[0] += -0.6823801552731604; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + result[0] += 1.0283586431978333; + } else { + result[0] += -0.23713978185064843; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[0].qvalue <= 62))) { + result[0] += 2.1905285297360657; + } else { + result[0] += 6.504341335474002; + } + } else { + result[0] += 0.9435717207184171; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (UNLIKELY(false || (data[1].qvalue <= 74))) { + if (UNLIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 3.5741883312100953; + } else { + result[0] += 4.626694408655167; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + result[0] += 6.38776066924514; + } else { + result[0] += 7.9433524366525505; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 27.524095430198624; + } else { + result[0] += 17.93763900587164; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 154))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 108))) { + result[0] += -0.8928303502649112; + } else { + result[0] += 0.1926267660463724; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 122))) { + result[0] += -7.0074780337366285; + } else { + result[0] += -3.8229517707148526; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 16))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + result[0] += 0.17887769899557107; + } else { + result[0] += 1.2261906364605684; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 178))) { + result[0] += 2.7207480610255765; + } else { + result[0] += 8.3997445237607; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 56))) { + if (LIKELY(false || (data[3].qvalue <= 236))) { + if (UNLIKELY(false || (data[3].qvalue <= 218))) { + result[0] += 1.928126817594764; + } else { + result[0] += 1.3411342581129906; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + result[0] += 1.9525385201789491; + } else { + result[0] += 2.4821401437493256; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 212))) { + if (UNLIKELY(false || (data[3].qvalue <= 198))) { + result[0] += 3.713535595855519; + } else { + result[0] += 7.180656615177431; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 226))) { + result[0] += 11.193661980597666; + } else { + result[0] += 17.555807665127727; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[3].qvalue <= 154))) { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -0.7003375835348792; + } else { + result[0] += -3.7302757340894694; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 116))) { + result[0] += -6.599387818444294; + } else { + result[0] += -5.72489429399885; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 32))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += 0.15262275808585868; + } else { + result[0] += 1.1954652973697173; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 178))) { + result[0] += 2.4487311442319593; + } else { + result[0] += 7.490679951544426; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 56))) { + if (UNLIKELY(false || (data[3].qvalue <= 214))) { + if (UNLIKELY(false || (data[0].qvalue <= 66))) { + result[0] += -0.4618247323919986; + } else { + result[0] += 2.1528753353821717; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 234))) { + result[0] += 1.2097848819662675; + } else { + result[0] += 1.752838457626999; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 212))) { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + result[0] += 2.420871486643817; + } else { + result[0] += 6.029272785166614; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 230))) { + result[0] += 10.227185956634905; + } else { + result[0] += 15.974606997518203; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 192))) { + if (LIKELY(false || (data[3].qvalue <= 124))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += -2.865659975119095; + } else { + result[0] += -0.6896181889893488; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 116))) { + result[0] += -5.944063382797502; + } else { + result[0] += -5.094536373408761; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 52))) { + if (LIKELY(false || (data[3].qvalue <= 160))) { + result[0] += 0.6397310298301389; + } else { + result[0] += 2.9220460179454832; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 138))) { + result[0] += -2.666624663971065; + } else { + result[0] += 0.4270094967652458; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[3].qvalue <= 236))) { + if (UNLIKELY(false || (data[3].qvalue <= 218))) { + result[0] += 1.521282712441143; + } else { + result[0] += 1.0810978861849587; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + result[0] += 1.582856585015188; + } else { + result[0] += 2.0611044482921446; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 216))) { + if (LIKELY(false || (data[3].qvalue <= 206))) { + result[0] += 4.439645479292941; + } else { + result[0] += 7.705468000634004; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 11.427783097865925; + } else { + result[0] += 17.19202195652448; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 192))) { + if (LIKELY(false || (data[3].qvalue <= 124))) { + if (LIKELY(false || (data[2].qvalue <= 28))) { + if (UNLIKELY(false || (data[3].qvalue <= 24))) { + result[0] += -1.2367334931972405; + } else { + result[0] += -0.568679959736126; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 112))) { + result[0] += -8.32192050869182; + } else { + result[0] += -5.006960492312537; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 0.4550818668115695; + } else { + result[0] += -3.0743532402463507; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 160))) { + result[0] += 0.13097623312980142; + } else { + result[0] += 2.786328266103951; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + if (LIKELY(false || (data[1].qvalue <= 72))) { + result[0] += 1.0925176197221544; + } else { + result[0] += 7.582882419296458; + } + } else { + result[0] += -3.9018135684874005; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 224))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 4.15326719153954; + } else { + result[0] += 7.678964746257113; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 78))) { + result[0] += 19.747446627865543; + } else { + result[0] += 12.500771902929218; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 192))) { + if (LIKELY(false || (data[3].qvalue <= 124))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (UNLIKELY(false || (data[3].qvalue <= 22))) { + result[0] += -1.167979990569673; + } else { + result[0] += -0.5133742356132379; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 114))) { + result[0] += -5.603649494204901; + } else { + result[0] += -4.409295198837661; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 52))) { + if (LIKELY(false || (data[3].qvalue <= 160))) { + result[0] += 0.5480854924955038; + } else { + result[0] += 2.363109929409574; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 138))) { + result[0] += -2.302846027254356; + } else { + result[0] += 0.34306161983695516; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 0.9832828581009277; + } else { + result[0] += 6.834192669120016; + } + } else { + result[0] += -3.521149036128347; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 224))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 3.738054988080166; + } else { + result[0] += 6.911557381148448; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 78))) { + result[0] += 17.78481683344929; + } else { + result[0] += 11.252482971264655; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 56))) { + if (LIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.5726678321736987; + } else { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += -0.14924717663033055; + } else { + result[0] += 6.235019242422922; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 1.162497192248416; + } else { + result[0] += 2.0120424899788536; + } + } else { + result[0] += 0.47169244387428955; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (UNLIKELY(false || (data[2].qvalue <= 42))) { + if (UNLIKELY(false || (data[2].qvalue <= 34))) { + result[0] += -0.357844108120637; + } else { + result[0] += 1.7605654400603017; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 70))) { + result[0] += 6.52940587259161; + } else { + result[0] += 3.3080480937743113; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 16.01724653501452; + } else { + result[0] += 8.753001223254058; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 192))) { + if (LIKELY(false || (data[3].qvalue <= 156))) { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -0.4137669463081151; + } else { + result[0] += -2.6409307308959122; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 114))) { + result[0] += -5.376507786444898; + } else { + result[0] += -4.389560249429967; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 32))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += 0.025008362302337356; + } else { + result[0] += 1.019241657971192; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 72))) { + result[0] += 2.1411528979907484; + } else { + result[0] += -0.47484663873831007; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 0.8378079743141229; + } else { + result[0] += 5.832806958458091; + } + } else { + result[0] += -3.500358439422236; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 222))) { + if (LIKELY(false || (data[3].qvalue <= 206))) { + result[0] += 2.851221265160206; + } else { + result[0] += 5.566558597933049; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 78))) { + result[0] += 14.425347984875641; + } else { + result[0] += 9.578878534245574; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 192))) { + if (LIKELY(false || (data[3].qvalue <= 124))) { + if (LIKELY(false || (data[2].qvalue <= 28))) { + if (UNLIKELY(false || (data[3].qvalue <= 22))) { + result[0] += -0.9195523527649098; + } else { + result[0] += -0.3719639557405976; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 74))) { + result[0] += -7.786786672274272; + } else { + result[0] += -3.9362138288079898; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += 0.6022720589428014; + } else { + result[0] += 7.092300986713834; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 190))) { + result[0] += -2.1043147125075716; + } else { + result[0] += 0.12674302444068922; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 50))) { + if (LIKELY(false || (data[3].qvalue <= 232))) { + if (UNLIKELY(false || (data[3].qvalue <= 218))) { + result[0] += 1.031090335758177; + } else { + result[0] += 0.5392873356886984; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + result[0] += 0.9291700147586669; + } else { + result[0] += 1.5196304502959777; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 216))) { + if (LIKELY(false || (data[3].qvalue <= 206))) { + result[0] += 2.5829880193644055; + } else { + result[0] += 4.763908216949989; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 6.935099835666378; + } else { + result[0] += 10.036216690922197; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 164))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 72))) { + if (LIKELY(false || (data[1].qvalue <= 6))) { + result[0] += -0.43590572608869044; + } else { + result[0] += -2.4316455018950878; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 42))) { + result[0] += -0.04380141068835597; + } else { + result[0] += 1.7110383905553694; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 144))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += -3.530168794490836; + } else { + result[0] += -1.0214699319685518; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 148))) { + result[0] += -6.775844377790179; + } else { + result[0] += -10.790508870091934; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[3].qvalue <= 188))) { + result[0] += -0.02275360025237618; + } else { + result[0] += -0.5838206132835355; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 1.7417272007672004; + } else { + result[0] += 0.660660463610099; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 208))) { + if (LIKELY(false || (data[3].qvalue <= 200))) { + result[0] += 1.9156509211867474; + } else { + result[0] += 3.135028625615758; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + result[0] += 4.960511180617512; + } else { + result[0] += 8.359091821368834; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 194))) { + if (LIKELY(false || (data[3].qvalue <= 160))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 70))) { + result[0] += -0.42133291745401114; + } else { + result[0] += -0.010851476443648929; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -2.4223382968632965; + } else { + result[0] += -6.899222971006882; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 16))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += -0.03413012978096565; + } else { + result[0] += 0.5317297186286644; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 2.211563570567631; + } else { + result[0] += -0.16486923946003673; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 50))) { + if (LIKELY(false || (data[3].qvalue <= 234))) { + if (UNLIKELY(false || (data[3].qvalue <= 218))) { + result[0] += 0.8620758848535985; + } else { + result[0] += 0.47621186848670294; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + result[0] += 0.8507697571985345; + } else { + result[0] += 1.3028689973708651; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 216))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 2.41099243454025; + } else { + result[0] += 4.090173047276771; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 5.632316419302413; + } else { + result[0] += 8.200667309946828; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 166))) { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (LIKELY(false || (data[3].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + result[0] += -0.38327232438457565; + } else { + result[0] += -2.1415671585396967; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 68))) { + result[0] += -0.048803059306061464; + } else { + result[0] += -1.7470427286358465; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 118))) { + if (UNLIKELY(false || (data[3].qvalue <= 114))) { + result[0] += -3.8643859740923037; + } else { + result[0] += -3.030297649090965; + } + } else { + result[0] += -1.569212761065539; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (UNLIKELY(false || (data[0].qvalue <= 54))) { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + result[0] += 1.1504605549094933; + } else { + result[0] += -0.02918860846727582; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + result[0] += 2.077977738321157; + } else { + result[0] += 0.5359649431721014; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 212))) { + if (LIKELY(false || (data[3].qvalue <= 202))) { + result[0] += 1.652849064266966; + } else { + result[0] += 2.954255485765737; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 76))) { + result[0] += -0.5669657233750427; + } else { + result[0] += 5.676198660468206; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 168))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 38))) { + if (UNLIKELY(false || (data[3].qvalue <= 40))) { + result[0] += -0.5244874333393534; + } else { + result[0] += -0.18256259456848412; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 74))) { + result[0] += -5.51355459890058; + } else { + result[0] += 1.411684886661277; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 144))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += -2.6606175581636338; + } else { + result[0] += -0.47356034191920265; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 152))) { + result[0] += -5.942664098495093; + } else { + result[0] += -10.448295749482655; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 38))) { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[3].qvalue <= 188))) { + result[0] += -0.011800565116617453; + } else { + result[0] += -0.5202224220078566; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 1.7775453851705354; + } else { + result[0] += 0.49081054656235107; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 222))) { + if (LIKELY(false || (data[3].qvalue <= 204))) { + result[0] += 1.614115694236453; + } else { + result[0] += 3.0704803932483227; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 76))) { + result[0] += -0.5108923716568848; + } else { + result[0] += 6.589366885775977; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (LIKELY(false || (data[0].qvalue <= 30))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.2892059588012491; + } else { + if (LIKELY(false || (data[0].qvalue <= 14))) { + result[0] += -0.08035600794549216; + } else { + result[0] += -0.4720559073325542; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 0.8967780238095124; + } else { + result[0] += 6.595205778394427; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + result[0] += -0.3520288227121683; + } else { + result[0] += 0.38974464605775183; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (UNLIKELY(false || (data[2].qvalue <= 38))) { + if (LIKELY(false || (data[1].qvalue <= 64))) { + result[0] += 0.7589099157010143; + } else { + result[0] += -1.366375525846731; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 66))) { + result[0] += 4.22967240474964; + } else { + result[0] += 1.4943104257462279; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 9.129350705249179; + } else { + result[0] += 3.068747078527702; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 168))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 42))) { + if (LIKELY(false || (data[0].qvalue <= 50))) { + result[0] += -0.18545114512070737; + } else { + result[0] += -1.7692822476814012; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 74))) { + result[0] += -4.801651736039382; + } else { + result[0] += 1.3646239105274842; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 142))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += -2.5169209574656417; + } else { + result[0] += -0.18660289084267964; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 148))) { + result[0] += -4.541809815605482; + } else { + result[0] += -8.652151740789414; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 1.3663672464257006; + } else { + result[0] += 3.04170718867983; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 0.2559705229975156; + } else { + result[0] += 2.9168273909132068; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 4.97017162342233; + } else { + result[0] += 11.503874847499691; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 0.4665647305038452; + } else { + result[0] += 5.509817603230935; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 162))) { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + result[0] += -1.3283728127725134; + } else { + result[0] += -6.130559274355571; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 62))) { + result[0] += -0.2896825481817099; + } else { + result[0] += -0.07162487032383137; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 118))) { + if (UNLIKELY(false || (data[3].qvalue <= 114))) { + result[0] += -3.1167449558905838; + } else { + result[0] += -2.3599501347059975; + } + } else { + result[0] += -1.059417066398789; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (UNLIKELY(false || (data[0].qvalue <= 54))) { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + result[0] += 0.834224641552816; + } else { + result[0] += -0.015370587395285587; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + result[0] += 0.9254950158058017; + } else { + result[0] += 0.36421699256211726; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 212))) { + if (LIKELY(false || (data[3].qvalue <= 200))) { + result[0] += 1.0215455914435474; + } else { + result[0] += 1.9619948363613522; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 76))) { + result[0] += -0.6542972311475775; + } else { + result[0] += 3.9645617071617623; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (LIKELY(false || (data[0].qvalue <= 22))) { + if (LIKELY(false || (data[1].qvalue <= 14))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.22003805724566253; + } else { + result[0] += -0.05306048893366094; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 16))) { + result[0] += -1.3451441339924273; + } else { + result[0] += -1.5488482760393083; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + if (UNLIKELY(false || (data[2].qvalue <= 10))) { + result[0] += 0.21580833438485533; + } else { + result[0] += 1.177993229883637; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 56))) { + result[0] += -0.2994307611075257; + } else { + result[0] += 0.305894106833898; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (UNLIKELY(false || (data[2].qvalue <= 34))) { + if (LIKELY(false || (data[2].qvalue <= 32))) { + result[0] += 0.6300423600302785; + } else { + result[0] += -0.4677024667015657; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 60))) { + result[0] += 2.92097058914907; + } else { + result[0] += 0.9749046067206072; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 7.280391073314689; + } else { + result[0] += 1.8221294908582069; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 170))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 42))) { + if (UNLIKELY(false || (data[3].qvalue <= 20))) { + result[0] += -0.5336229335357556; + } else { + result[0] += -0.12326033481423136; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 120))) { + result[0] += -3.7782573749400954; + } else { + result[0] += 1.2031945074524026; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 142))) { + if (LIKELY(false || (data[3].qvalue <= 136))) { + result[0] += -2.0138202324018293; + } else { + result[0] += 0.4287253040269907; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 150))) { + result[0] += -4.26031586774496; + } else { + result[0] += -8.189678469057437; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[3].qvalue <= 188))) { + result[0] += 0.03067279449262314; + } else { + result[0] += -0.4287195475348111; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 1.8796244512104765; + } else { + result[0] += 0.3056819085450995; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 226))) { + if (LIKELY(false || (data[3].qvalue <= 202))) { + result[0] += 0.9636725612848687; + } else { + result[0] += 1.932067083622997; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 76))) { + result[0] += -0.6860055204538198; + } else { + result[0] += 4.570361189431599; + } + } + } + } + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (LIKELY(false || (data[0].qvalue <= 22))) { + if (LIKELY(false || (data[1].qvalue <= 14))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.1814554734769651; + } else { + result[0] += -0.03493688255810839; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 16))) { + result[0] += -1.1580987454621139; + } else { + result[0] += -1.3413217625073497; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 0.6065784994119716; + } else { + result[0] += 6.64156315122332; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 56))) { + result[0] += -0.2710747772797691; + } else { + result[0] += 0.24590059195978556; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (UNLIKELY(false || (data[2].qvalue <= 38))) { + if (LIKELY(false || (data[1].qvalue <= 64))) { + result[0] += 0.52839377206309; + } else { + result[0] += -1.3097734584676666; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 66))) { + result[0] += 3.0327157128432702; + } else { + result[0] += 0.8301613199220086; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 6.102585921419179; + } else { + result[0] += 1.1868023468270625; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 170))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 42))) { + if (LIKELY(false || (data[0].qvalue <= 50))) { + result[0] += -0.1099508760937471; + } else { + result[0] += -1.4799074079672025; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 74))) { + result[0] += -3.9533722562056326; + } else { + result[0] += 1.0527497525643543; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 142))) { + if (LIKELY(false || (data[3].qvalue <= 136))) { + result[0] += -1.874856650108779; + } else { + result[0] += 0.36151445149430145; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 152))) { + result[0] += -4.013347158834968; + } else { + result[0] += -7.940386810302735; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + if (LIKELY(false || (data[3].qvalue <= 206))) { + result[0] += 0.8194996574746792; + } else { + result[0] += 1.9073466437063578; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 0.1914437036803978; + } else { + result[0] += 1.9462096802861646; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 3.445255372400778; + } else { + result[0] += 9.341778161720354; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 0.3243846697838024; + } else { + result[0] += 3.292591407943689; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 160))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + result[0] += -0.9774807565883121; + } else { + result[0] += -5.423777811527253; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += -0.12073383773519127; + } else { + result[0] += 0.9911425235059078; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 142))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += -1.8435700376685866; + } else { + result[0] += -0.10737506746612407; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 148))) { + result[0] += -3.3288382873336477; + } else { + result[0] += -5.702344606187609; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + if (LIKELY(false || (data[3].qvalue <= 202))) { + result[0] += 0.6121670947641649; + } else { + result[0] += 1.5478825951517399; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 0.15159187149918565; + } else { + result[0] += 1.7525244521134302; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 3.1021024062818072; + } else { + result[0] += 8.417132546913868; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 0.29207001641087005; + } else { + result[0] += 2.9643452592336215; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.14003554272994065; + } else { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + result[0] += -0.015514364763912126; + } else { + result[0] += 1.0510222973719732; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 30))) { + result[0] += -0.27117390740511166; + } else { + result[0] += 0.14354185812943476; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (LIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 62))) { + result[0] += 0.5018898936445311; + } else { + result[0] += 2.5336810498326523; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 2.3642130662022995; + } else { + result[0] += 0.07374194009502717; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 4.874216369284443; + } else { + result[0] += 0.44699526391786304; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 170))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (UNLIKELY(false || (data[3].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + result[0] += -0.35060793036231724; + } else { + result[0] += -5.047665471633276; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 38))) { + result[0] += -0.07028608501406199; + } else { + result[0] += 0.7109949202510606; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 144))) { + if (LIKELY(false || (data[3].qvalue <= 122))) { + result[0] += -1.8132749471478289; + } else { + result[0] += -0.4798894299710876; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 152))) { + result[0] += -3.6392164039153325; + } else { + result[0] += -6.78748151870001; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + if (LIKELY(false || (data[0].qvalue <= 32))) { + result[0] += 0.6818792886595983; + } else { + result[0] += 2.0346700524088903; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 0.1650370627648028; + } else { + result[0] += 1.4122047675458287; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 2.743139011175984; + } else { + result[0] += 7.534331532576863; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 0.25562844944051427; + } else { + result[0] += 2.4035809405106767; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.1149636651708558; + } else { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + result[0] += -0.005786597801570078; + } else { + result[0] += 0.9536320919638706; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 30))) { + result[0] += -0.25774071878779176; + } else { + result[0] += 0.12494917135148927; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (LIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += 0.416251413237658; + } else { + result[0] += 4.65468264502448; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 1.9891757155656817; + } else { + result[0] += 0.06062405904969436; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 4.150901784204815; + } else { + result[0] += 0.1636860781066988; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.10346745600044038; + } else { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + result[0] += -0.005207970482558494; + } else { + result[0] += 0.8583114141430566; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 30))) { + result[0] += -0.2319702347159344; + } else { + result[0] += 0.11246252282811312; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (LIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 62))) { + result[0] += 0.36237019282195543; + } else { + result[0] += 2.185361301747475; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 1.7916299525787094; + } else { + result[0] += 0.05456246786985419; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 3.7383578847668653; + } else { + result[0] += 0.1474179036193099; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 20))) { + if (LIKELY(false || (data[0].qvalue <= 22))) { + if (LIKELY(false || (data[1].qvalue <= 14))) { + if (LIKELY(false || (data[0].qvalue <= 16))) { + result[0] += -0.07824623744678852; + } else { + result[0] += 2.4147477472795025; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 20))) { + result[0] += -0.9602189832977626; + } else { + result[0] += -1.2336897440823642; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[2].qvalue <= 10))) { + result[0] += 0.17808383312156362; + } else { + result[0] += 0.7764366538076664; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 30))) { + result[0] += -0.20877644063379633; + } else { + result[0] += 0.10122370049456518; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (LIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += 0.3366817124844217; + } else { + result[0] += 3.9891647478052095; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 1.613702605033743; + } else { + result[0] += 0.04910696472777197; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 3.3668153467865807; + } else { + result[0] += 0.13276691204199761; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 170))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + if (LIKELY(false || (data[2].qvalue <= 28))) { + result[0] += -0.8003296552631252; + } else { + result[0] += -5.168554219404857; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 28))) { + result[0] += -0.04191612245860841; + } else { + result[0] += -0.31322033502373364; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 142))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += -1.6260716001993314; + } else { + result[0] += -0.07253028186242319; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 152))) { + result[0] += -3.059761641596405; + } else { + result[0] += -6.156701689220611; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (UNLIKELY(false || (data[3].qvalue <= 176))) { + result[0] += 0.8382331706298907; + } else { + result[0] += 0.1470265483651418; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 68))) { + result[0] += 1.4136662728343927; + } else { + result[0] += 0.42639604831684264; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (UNLIKELY(false || (data[1].qvalue <= 76))) { + result[0] += 0.2137965369719358; + } else { + result[0] += 1.5807652149640596; + } + } else { + result[0] += 3.582011053264141; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.0809220032676647; + } else { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + result[0] += 0.009295531913092564; + } else { + result[0] += 0.7015704698474159; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 56))) { + result[0] += -0.18839625520645842; + } else { + result[0] += 0.13815172995417008; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (LIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += 0.2833961104028503; + } else { + result[0] += 3.9494598141232053; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 1.3130535747634955; + } else { + result[0] += 0.04139692407062909; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 2.875092684213369; + } else { + result[0] += -0.03741247869342383; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 198))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 170))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += -0.9280781442850904; + } else { + result[0] += -0.05519963885812408; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 18))) { + result[0] += 0.13575882302728218; + } else { + result[0] += 1.3530812716367793; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 192))) { + if (LIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -1.0690103616905782; + } else { + result[0] += -2.214087039734459; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + result[0] += -0.6739287079004651; + } else { + result[0] += 0.08192197004299273; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + if (LIKELY(false || (data[1].qvalue <= 72))) { + result[0] += 0.16779935065337956; + } else { + result[0] += 2.7936280922346484; + } + } else { + result[0] += -6.01828142584824; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 216))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 0.6464965410008258; + } else { + result[0] += 1.2127821355938402; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 56))) { + result[0] += 2.304393523269968; + } else { + result[0] += -0.03369406329707866; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 44))) { + if (LIKELY(false || (data[2].qvalue <= 6))) { + result[0] += -0.053129047160861614; + } else { + result[0] += -0.6797773812157768; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 50))) { + result[0] += 0.4863998644119598; + } else { + result[0] += 3.517865025900506; + } + } + } else { + result[0] += -0.18996639918014335; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + if (LIKELY(false || (data[0].qvalue <= 34))) { + result[0] += 0.21126964469127507; + } else { + result[0] += 0.9292041399773837; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 0.38898047466443736; + } else { + result[0] += 0.0343316630215836; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 2.3603220532788822; + } else { + result[0] += -0.030345338012543195; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 198))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 170))) { + if (LIKELY(false || (data[2].qvalue <= 12))) { + result[0] += -0.03196203652937306; + } else { + result[0] += -0.32291491116843224; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 14))) { + result[0] += 0.1059633693894297; + } else { + result[0] += 1.1109217701835943; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 192))) { + if (LIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -0.9750124564919093; + } else { + result[0] += -2.011326445104133; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + result[0] += -0.6277616333681295; + } else { + result[0] += 0.052653140828700706; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + if (LIKELY(false || (data[1].qvalue <= 72))) { + result[0] += 0.14758938512729353; + } else { + result[0] += 2.5144118215047015; + } + } else { + result[0] += -5.434481533329662; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 218))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 0.5607584407428231; + } else { + result[0] += 1.097345812808785; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 56))) { + result[0] += 2.188365555677249; + } else { + result[0] += -0.027329945962853226; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 196))) { + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[3].qvalue <= 174))) { + if (UNLIKELY(false || (data[3].qvalue <= 16))) { + result[0] += -0.339980565686603; + } else { + result[0] += -0.03185759232203992; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 42))) { + result[0] += 0.1301433477915698; + } else { + result[0] += 1.580000992364498; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 192))) { + if (LIKELY(false || (data[3].qvalue <= 190))) { + result[0] += -1.685654765519544; + } else { + result[0] += -1.0927866830676793; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + result[0] += -0.5651196538004228; + } else { + result[0] += -0.10798605171852133; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + result[0] += 0.13680401406260853; + } else { + result[0] += 2.8238424551720716; + } + } else { + result[0] += -4.904288046301866; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 226))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 0.45463644431627187; + } else { + result[0] += 1.0130458030200111; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 2.236542576950049; + } else { + result[0] += -0.024613159223933895; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 198))) { + if (LIKELY(false || (data[1].qvalue <= 72))) { + if (LIKELY(false || (data[3].qvalue <= 172))) { + if (LIKELY(false || (data[0].qvalue <= 50))) { + result[0] += -0.032568909202472296; + } else { + result[0] += -0.4469851314428541; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 30))) { + result[0] += 0.11294361883379918; + } else { + result[0] += 1.0056842141087612; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 192))) { + if (LIKELY(false || (data[3].qvalue <= 190))) { + result[0] += -1.517244761721867; + } else { + result[0] += -0.9837414394446418; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + result[0] += -0.508728386740654; + } else { + result[0] += 0.03608265112965852; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 0.11915447057376925; + } else { + result[0] += 2.0825805265428143; + } + } else { + result[0] += -4.425821080207825; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 226))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 0.4592645630622676; + } else { + result[0] += 0.9118048684410376; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 2.0132186895684288; + } else { + result[0] += -0.022167118571104448; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 198))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 160))) { + if (LIKELY(false || (data[2].qvalue <= 12))) { + result[0] += -0.020052189264742216; + } else { + result[0] += -0.4390079826397957; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 18))) { + result[0] += 0.06723042230135214; + } else { + result[0] += 0.6321455651905148; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 192))) { + if (LIKELY(false || (data[3].qvalue <= 142))) { + result[0] += -0.6849792752988858; + } else { + result[0] += -1.5843785915044357; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + result[0] += -0.45796421690851963; + } else { + result[0] += 0.026405314510040745; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 50))) { + if (LIKELY(false || (data[3].qvalue <= 230))) { + if (UNLIKELY(false || (data[3].qvalue <= 218))) { + result[0] += 0.3385478130008445; + } else { + result[0] += -0.10764744508292204; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 236))) { + result[0] += 0.20488484351232758; + } else { + result[0] += 0.47811499378647015; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 212))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 0.4209993070324332; + } else { + result[0] += 0.7287778763739126; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 1.187064839378982; + } else { + result[0] += -0.01996407508027334; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 176))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[0].qvalue <= 60))) { + if (LIKELY(false || (data[0].qvalue <= 50))) { + result[0] += -0.027774129306039258; + } else { + result[0] += -0.8385662967485399; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 120))) { + result[0] += -3.519087969462077; + } else { + result[0] += 0.7293787751512967; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 148))) { + if (LIKELY(false || (data[3].qvalue <= 118))) { + result[0] += -1.1172824090308644; + } else { + result[0] += -0.20902441584933884; + } + } else { + result[0] += -5.111647744634573; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + result[0] += 1.8609278771357982; + } else { + result[0] += 0.12480108283901199; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 196))) { + result[0] += -0.08227578925901015; + } else { + result[0] += 0.25625433402161485; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.9637861349021059; + } else { + result[0] += 5.291859932724311; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.9927995753498028; + } else { + result[0] += 0.14113094589752082; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 160))) { + if (LIKELY(false || (data[2].qvalue <= 12))) { + if (LIKELY(false || (data[3].qvalue <= 130))) { + if (UNLIKELY(false || (data[3].qvalue <= 46))) { + result[0] += -0.13887715472257542; + } else { + result[0] += 0.01749027468530986; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += 0.8747182175833713; + } else { + result[0] += 3.3873552828056868; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + result[0] += -2.0719160557169487; + } else { + result[0] += -0.9527323590531762; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 112))) { + result[0] += -4.278540461588714; + } else { + result[0] += -0.22532110219787005; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + if (LIKELY(false || (data[3].qvalue <= 204))) { + result[0] += 0.13994283376545083; + } else { + result[0] += 0.628766931454757; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 168))) { + result[0] += -0.2077112285433431; + } else { + result[0] += 0.0971080345193495; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.8677912951323141; + } else { + result[0] += 4.768073993215755; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.8939668307876266; + } else { + result[0] += 0.1270565048333717; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 160))) { + if (LIKELY(false || (data[2].qvalue <= 12))) { + if (LIKELY(false || (data[3].qvalue <= 130))) { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += -0.166060933557046; + } else { + result[0] += 0.004294648853588787; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += 0.7873344634071392; + } else { + result[0] += 3.0512455477455793; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + if (UNLIKELY(false || (data[1].qvalue <= 16))) { + result[0] += 3.321445830033885; + } else { + result[0] += -1.8410162796558962; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 0.5648229020291158; + } else { + result[0] += -0.40508307941792243; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (LIKELY(false || (data[1].qvalue <= 38))) { + if (LIKELY(false || (data[3].qvalue <= 218))) { + result[0] += 0.116824997205243; + } else { + result[0] += 0.0017106091707403482; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 200))) { + result[0] += 0.09749333921964695; + } else { + result[0] += 0.512061380766423; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (UNLIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.8049728208270159; + } else { + result[0] += 0.11438560255422983; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.7813580416089511; + } else { + result[0] += 4.296131911715682; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 160))) { + if (LIKELY(false || (data[2].qvalue <= 12))) { + if (LIKELY(false || (data[3].qvalue <= 132))) { + if (UNLIKELY(false || (data[3].qvalue <= 50))) { + result[0] += -0.10165509214748221; + } else { + result[0] += 0.027573987237460126; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += 0.8965004316498251; + } else { + result[0] += 2.7484863860847417; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + result[0] += -1.6997008955624044; + } else { + result[0] += -0.678268726322754; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 118))) { + result[0] += -1.114816829956962; + } else { + result[0] += -0.07990083268080249; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (LIKELY(false || (data[3].qvalue <= 178))) { + result[0] += 0.08501821194709441; + } else { + result[0] += 0.7073547907959752; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 196))) { + result[0] += -0.09598099681424313; + } else { + result[0] += 0.1882562171309855; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.7035334691233133; + } else { + result[0] += 3.8709025872240264; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 76))) { + result[0] += -0.03477044912415934; + } else { + result[0] += 0.6388646830943916; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 4))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.034231165787913284; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += -0.008538500447320395; + } else { + result[0] += 0.030661489227495748; + } + } + } else { + result[0] += -0.6560679714832831; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[2].qvalue <= 46))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + result[0] += 0.05367898474501495; + } else { + result[0] += 0.44735028827783085; + } + } else { + result[0] += 6.229511664254325; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + result[0] += -0.14752365398422523; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 0.38398941999281766; + } else { + result[0] += 0.04404838897933778; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 176))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (UNLIKELY(false || (data[3].qvalue <= 0))) { + if (LIKELY(false || (data[2].qvalue <= 28))) { + result[0] += -0.7549177495924217; + } else { + result[0] += -4.158142198324204; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 42))) { + result[0] += -0.02530038694314765; + } else { + result[0] += 0.5546607631795957; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 148))) { + if (LIKELY(false || (data[3].qvalue <= 136))) { + result[0] += -0.6813185032064286; + } else { + result[0] += 0.29119811621696806; + } + } else { + result[0] += -4.5671472818360614; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (LIKELY(false || (data[1].qvalue <= 40))) { + if (LIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 0.06089688550626267; + } else { + result[0] += -0.9885366824653841; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 180))) { + result[0] += 0.8258845435182254; + } else { + result[0] += 5.761587441940307; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (UNLIKELY(false || (data[3].qvalue <= 196))) { + result[0] += -0.07740265934103074; + } else { + result[0] += 0.16502792587020448; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 80))) { + result[0] += 0.3005847261842887; + } else { + result[0] += 1.4347149255319134; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 152))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + result[0] += -0.010806768205029583; + } else { + result[0] += -3.3904279910193553; + } + } else { + result[0] += 3.6360500988061872; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 56))) { + if (UNLIKELY(false || (data[1].qvalue <= 48))) { + result[0] += -1.3970841696833907; + } else { + result[0] += -0.5310959589164045; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 136))) { + result[0] += -0.43780724551513983; + } else { + result[0] += 0.38160829548283537; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 238))) { + if (LIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + result[0] += 0.1260601616574519; + } else { + result[0] += 2.3280856465403716; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 188))) { + result[0] += -6.748794538567707; + } else { + result[0] += 0.03388208818968886; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.4861732172767124; + } else { + result[0] += 3.34139486286105; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.6939914549665678; + } else { + result[0] += 0.012280274946395664; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 176))) { + if (LIKELY(false || (data[1].qvalue <= 54))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + result[0] += -0.464813733628163; + } else { + result[0] += -3.2180759784840705; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 48))) { + result[0] += -0.07920781023396078; + } else { + result[0] += 0.019886892967067187; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 146))) { + if (UNLIKELY(false || (data[1].qvalue <= 60))) { + result[0] += -1.3944523714625319; + } else { + result[0] += -0.6633490458105974; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 58))) { + result[0] += -0.4361867431137107; + } else { + result[0] += 0.09866640502908253; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (LIKELY(false || (data[1].qvalue <= 40))) { + if (LIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 0.04221310244085489; + } else { + result[0] += -0.8607172810069976; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 180))) { + result[0] += 0.7308448858222341; + } else { + result[0] += 5.17753285443306; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 198))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 0.12604388146867565; + } else { + result[0] += -0.5198739781442024; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + result[0] += 0.0001483644827354292; + } else { + result[0] += 0.4382489570445441; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 12))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += 0.2715686357969588; + } else { + result[0] += -0.17382652834003876; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + result[0] += -0.5295756487690885; + } else { + result[0] += -1.247911300102006; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -4.132038760807204; + } else { + result[0] += -2.048248645525712; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 236))) { + if (LIKELY(false || (data[3].qvalue <= 152))) { + if (LIKELY(false || (data[2].qvalue <= 12))) { + result[0] += 0.009045345990511855; + } else { + result[0] += -0.36819514234182105; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 0.15071853324608264; + } else { + result[0] += 0.0023504781977204342; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.4615429381822286; + } else { + result[0] += 2.967287079375618; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.39428092203572945; + } else { + result[0] += -0.027793250841637182; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 4))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (LIKELY(false || (data[1].qvalue <= 2))) { + if (UNLIKELY(false || (data[0].qvalue <= 0))) { + result[0] += 0.02095625550210984; + } else { + result[0] += -0.02470681499709372; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += -0.006936135990179306; + } else { + result[0] += 0.028313332943848205; + } + } + } else { + result[0] += -0.45814704971029124; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[2].qvalue <= 46))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + result[0] += 0.059813599712526144; + } else { + result[0] += 0.4052626695215156; + } + } else { + result[0] += 6.220781436647688; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + result[0] += -0.14287609636708531; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 0.28909888374082054; + } else { + result[0] += 0.0223884762447394; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 178))) { + if (LIKELY(false || (data[1].qvalue <= 52))) { + if (UNLIKELY(false || (data[3].qvalue <= 10))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + result[0] += -0.20717493847839827; + } else { + result[0] += -2.852040849850889; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 30))) { + result[0] += -0.008160190303522096; + } else { + result[0] += 0.27599937314740675; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 146))) { + if (UNLIKELY(false || (data[1].qvalue <= 60))) { + result[0] += -1.1881260380224656; + } else { + result[0] += -0.5625438965153544; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 58))) { + result[0] += -0.403338839639409; + } else { + result[0] += 0.10268439573411683; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 184))) { + if (LIKELY(false || (data[1].qvalue <= 40))) { + if (LIKELY(false || (data[3].qvalue <= 182))) { + result[0] += -0.013368157221764328; + } else { + result[0] += 0.11043304821096991; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 180))) { + result[0] += 1.5817916361431956; + } else { + result[0] += 4.191132561870824; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 236))) { + if (UNLIKELY(false || (data[3].qvalue <= 196))) { + result[0] += -0.05418531103810077; + } else { + result[0] += 0.11582636097487908; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 80))) { + result[0] += 0.21679116682674598; + } else { + result[0] += 0.9937182303717; + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.020289773669981; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + result[0] += 0.07888539366069491; + } else { + result[0] += -0.0043942118674004035; + } + } + } else { + result[0] += -0.8819353631826549; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 1.166342015953883; + } else { + result[0] += -0.21953414862872633; + } + } + if (LIKELY(false || (data[3].qvalue <= 178))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + if (LIKELY(false || (data[3].qvalue <= 128))) { + result[0] += -0.014985389955920162; + } else { + result[0] += 0.5030502722795092; + } + } else { + result[0] += 3.209527899147808; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[3].qvalue <= 146))) { + result[0] += -1.359497708335802; + } else { + result[0] += -0.048409546375607654; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 0.2462483312344813; + } else { + result[0] += -0.15689708895777443; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (LIKELY(false || (data[1].qvalue <= 40))) { + if (UNLIKELY(false || (data[3].qvalue <= 182))) { + result[0] += -0.011608086477160455; + } else { + result[0] += 0.054071549124002416; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 180))) { + result[0] += 1.4247202960054501; + } else { + result[0] += 4.292557497723102; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 196))) { + if (LIKELY(false || (data[1].qvalue <= 72))) { + result[0] += 0.11744769877683264; + } else { + result[0] += -0.6228392828110428; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + result[0] += -0.01810292778067102; + } else { + result[0] += 0.33713691230011644; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 32))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += 0.271129972670566; + } else { + result[0] += -0.0968952898723599; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + result[0] += -0.4419965803483365; + } else { + result[0] += -1.1427374036977256; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -2.964990935176611; + } else { + result[0] += -2.0024242342435397; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 236))) { + if (LIKELY(false || (data[3].qvalue <= 152))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + result[0] += 0.019913351953155762; + } else { + result[0] += -0.3451557406567103; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 68))) { + result[0] += 0.09431825657210167; + } else { + result[0] += -0.040080507458776934; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.2811388814670214; + } else { + result[0] += 2.54007776661795; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.33998703221387494; + } else { + result[0] += -0.11096001409545336; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 54))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 18))) { + result[0] += -0.10381124910582794; + } else { + result[0] += -0.036053796621203345; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 18))) { + result[0] += -0.4795079805215589; + } else { + result[0] += -0.3343803406439497; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 58))) { + if (UNLIKELY(false || (data[0].qvalue <= 50))) { + result[0] += -1.3594113743305207; + } else { + result[0] += -0.8394250588417054; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -3.2252649987262227; + } else { + result[0] += -1.8098834428420432; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 234))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += 0.03700457930168985; + } else { + result[0] += 17.47663833459218; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 138))) { + result[0] += -0.5601782767579584; + } else { + result[0] += 0.017180070918654807; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.2387817133710211; + } else { + result[0] += -4.100601076265661; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.843944808030713; + } else { + result[0] += -0.2201947167753442; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 200))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 188))) { + if (LIKELY(false || (data[0].qvalue <= 66))) { + result[0] += -0.0005495605667856106; + } else { + result[0] += -3.58726428370322; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 66))) { + result[0] += -0.3697917053822814; + } else { + result[0] += 2.6442676259341997; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 194))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + result[0] += -0.1322526748337809; + } else { + result[0] += -0.9165228440058644; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 198))) { + result[0] += 0.015399111888784775; + } else { + result[0] += -0.03293383786820958; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 50))) { + if (UNLIKELY(false || (data[3].qvalue <= 206))) { + result[0] += -1.74668638865153; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 214))) { + result[0] += 0.41404376746989746; + } else { + result[0] += -0.04320938041815753; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 208))) { + if (LIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 0.15385322801835455; + } else { + result[0] += 0.8175728843952048; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.4756074244340375; + } else { + result[0] += -0.1983103516972138; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 56))) { + if (LIKELY(false || (data[0].qvalue <= 24))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 26))) { + result[0] += -0.08484652763250765; + } else { + result[0] += -0.028152113688230847; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 6))) { + result[0] += -0.4321762504373764; + } else { + result[0] += -0.2651707672412985; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 50))) { + result[0] += -1.0555429015159608; + } else { + result[0] += -0.7582275236447653; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -2.9167088172746745; + } else { + result[0] += -1.4978527782972042; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 234))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 38))) { + result[0] += 0.02016281760562264; + } else { + result[0] += 0.5381136571935687; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + result[0] += -0.4649008843759111; + } else { + result[0] += 0.06919020340221031; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.15702109026459932; + } else { + result[0] += 2.1580531330984467; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.21686901261145552; + } else { + result[0] += -0.1001601769605728; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 42))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 8))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += 0.19641092388804365; + } else { + result[0] += -0.055276022085065206; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 8))) { + result[0] += -0.3392746616594763; + } else { + result[0] += -0.7843794295296335; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 44))) { + result[0] += -2.238581113219261; + } else { + result[0] += -1.3538284465441337; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 200))) { + if (LIKELY(false || (data[1].qvalue <= 62))) { + if (LIKELY(false || (data[3].qvalue <= 188))) { + result[0] += 0.017158471900924056; + } else { + result[0] += 1.4687521383872566; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 192))) { + result[0] += -0.48506636615045484; + } else { + result[0] += -0.047229123064139726; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += -0.003343629263232336; + } else { + result[0] += -3.737171672379098; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.24658213105746754; + } else { + result[0] += 0.7366530590168956; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 60))) { + if (LIKELY(false || (data[0].qvalue <= 24))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 18))) { + result[0] += -0.07999229789885372; + } else { + result[0] += -0.024486695046509094; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 6))) { + result[0] += -0.3591096819916043; + } else { + result[0] += -0.19716339864139187; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 50))) { + result[0] += -0.8995184892360121; + } else { + result[0] += -0.6091088518301646; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -2.423594094981318; + } else { + result[0] += -1.2236526361795572; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 236))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 38))) { + result[0] += 0.017590391024004715; + } else { + result[0] += 0.48376995639076664; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 192))) { + result[0] += -0.4336376838327545; + } else { + result[0] += 0.04774652419020946; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.08141260086235247; + } else { + result[0] += 1.8715363113490904; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.2612265991556262; + } else { + result[0] += -0.11460675569674739; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 178))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + if (LIKELY(false || (data[3].qvalue <= 128))) { + result[0] += -0.010014087252989079; + } else { + result[0] += 0.44165882919228355; + } + } else { + result[0] += 2.767749158126721; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + result[0] += -1.265884517270139; + } else { + result[0] += -0.326555396705971; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 0.17874279127753523; + } else { + result[0] += -0.10461815372106785; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (LIKELY(false || (data[1].qvalue <= 40))) { + if (UNLIKELY(false || (data[3].qvalue <= 182))) { + result[0] += -0.0291664181175232; + } else { + result[0] += 0.0321031669500698; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 180))) { + result[0] += 1.2655710841109187; + } else { + result[0] += 3.8452706585121152; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 200))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 0.07346629730705066; + } else { + result[0] += -0.2790375844518317; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + result[0] += -0.030938073075268954; + } else { + result[0] += 0.2584663489831829; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 8))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 8))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += 0.1897708646960375; + } else { + result[0] += -0.10591389661914655; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 8))) { + result[0] += -0.27429189760805434; + } else { + result[0] += -0.6438250486029867; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 22))) { + result[0] += -1.9822659349441527; + } else { + result[0] += -1.1856181401364942; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 176))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += 0.004593246195533139; + } else { + result[0] += 2.4929798120346622; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + result[0] += -0.9223952289248065; + } else { + result[0] += -0.044695212630605684; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (LIKELY(false || (data[1].qvalue <= 42))) { + result[0] += 0.0022744264617153534; + } else { + result[0] += 0.9887394270605384; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + result[0] += -0.05980165741450547; + } else { + result[0] += 0.06763695160928662; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += 0.23353499906984243; + } else { + result[0] += -0.030567457307068176; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 6))) { + result[0] += -0.3026360332604611; + } else { + result[0] += -0.15352749305440105; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 58))) { + if (UNLIKELY(false || (data[0].qvalue <= 50))) { + result[0] += -0.9208640919129055; + } else { + result[0] += -0.4870238716204962; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -2.028915634984555; + } else { + result[0] += -0.9910290916149432; + } + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 230))) { + if (LIKELY(false || (data[3].qvalue <= 218))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 0.012556621103070601; + } else { + result[0] += 0.27622010195921237; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + result[0] += 0.4583737829879677; + } else { + result[0] += -0.1957218213752224; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.13572258094742976; + } else { + result[0] += 1.654014792332844; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.10699664077497309; + } else { + result[0] += -0.26287929402538607; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 34))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += 0.21031418917361988; + } else { + result[0] += -0.05299115840815619; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 8))) { + result[0] += -0.21890068136134136; + } else { + result[0] += -0.5242896185723835; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 22))) { + result[0] += -1.6681880847613018; + } else { + result[0] += -0.9499344626594992; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 230))) { + if (LIKELY(false || (data[3].qvalue <= 218))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += 0.003964550672321244; + } else { + result[0] += 0.248609910955958; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 80))) { + result[0] += -0.17615619642542008; + } else { + result[0] += 0.41270129405980494; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.1074364755511174; + } else { + result[0] += -3.3754073861750165; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 56))) { + result[0] += 0.5143961430823207; + } else { + result[0] += -0.23675266527233682; + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 10))) { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + if (LIKELY(false || (data[1].qvalue <= 4))) { + if (LIKELY(false || (data[0].qvalue <= 6))) { + result[0] += -0.02091550687275058; + } else { + result[0] += 0.05471821127697738; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 8))) { + result[0] += -0.06265624329964264; + } else { + result[0] += -0.003474992384641048; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 4))) { + if (LIKELY(false || (data[0].qvalue <= 2))) { + result[0] += -0.05813859439852901; + } else { + result[0] += 1.685043735887323; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 2))) { + result[0] += 0.005356865798300517; + } else { + result[0] += -0.009951643674742359; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + if (LIKELY(false || (data[1].qvalue <= 26))) { + result[0] += 0.04421518555621426; + } else { + result[0] += 0.3861890671974087; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 2.733613769617948; + } else { + result[0] += 5.786070743288313; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 26))) { + if (LIKELY(false || (data[1].qvalue <= 50))) { + result[0] += -0.482797800051755; + } else { + result[0] += 2.5325565222740174; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.09547398872773903; + } else { + result[0] += -0.046573119312707335; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 4))) { + if (LIKELY(false || (data[2].qvalue <= 28))) { + if (LIKELY(false || (data[1].qvalue <= 10))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += 0.08210078603934584; + } else { + result[0] += -0.11656505933452627; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 8))) { + result[0] += -0.20987820191852383; + } else { + result[0] += -0.5384319635554019; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 44))) { + result[0] += -1.9617384305207626; + } else { + result[0] += -1.2668292170304518; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 178))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += 0.003420268764872335; + } else { + result[0] += 2.213071489783301; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + result[0] += -0.7776470923789272; + } else { + result[0] += -0.04327273641201862; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (LIKELY(false || (data[1].qvalue <= 40))) { + result[0] += 0.00861831306648558; + } else { + result[0] += 1.848056525397011; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 196))) { + result[0] += -0.04828241624484346; + } else { + result[0] += 0.05563476812699414; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 64))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 26))) { + result[0] += -0.05078687692889694; + } else { + result[0] += -0.014356568909767928; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + result[0] += -0.17534628583710493; + } else { + result[0] += -0.5621430105402849; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -1.7740939281297767; + } else { + result[0] += -1.145018720260033; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + if (LIKELY(false || (data[3].qvalue <= 130))) { + result[0] += 0.018993981486318878; + } else { + result[0] += 0.5365927638406978; + } + } else { + result[0] += 15.05643708864848; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 152))) { + if (LIKELY(false || (data[0].qvalue <= 56))) { + result[0] += -0.4728617369458554; + } else { + result[0] += 0.20752761050703802; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.09305968936956194; + } else { + result[0] += -0.009524064343686378; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 52))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += 0.18727818329014223; + } else { + result[0] += -0.025890677188174106; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + result[0] += -0.15753058727404748; + } else { + result[0] += -0.5067434101778527; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -1.604397966136103; + } else { + result[0] += -1.0349207672706018; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 234))) { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += 0.020542942769893005; + } else { + result[0] += 13.61352872212728; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 152))) { + result[0] += -0.20312529336650453; + } else { + result[0] += 0.019356250676449485; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.1529675914478733; + } else { + result[0] += -3.046054223456034; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.4578596813081651; + } else { + result[0] += -0.21317670897106453; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 100))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 12))) { + if (LIKELY(false || (data[3].qvalue <= 88))) { + result[0] += -0.009515774573242132; + } else { + result[0] += 0.10722986880689861; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + result[0] += -0.08947096205225108; + } else { + result[0] += -0.4843778472947222; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -1.9798399291992188; + } else { + result[0] += -1.155246785481771; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 12))) { + if (LIKELY(false || (data[3].qvalue <= 132))) { + if (LIKELY(false || (data[3].qvalue <= 126))) { + result[0] += 0.058783065329708964; + } else { + result[0] += 0.20552873062508797; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += 0.5012681102076195; + } else { + result[0] += 1.1655076967659646; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 152))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += -0.4052823818071666; + } else { + result[0] += 0.2663182250508323; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 0.09131312159878124; + } else { + result[0] += -0.023826012683033533; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 14))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 22))) { + if (LIKELY(false || (data[1].qvalue <= 16))) { + result[0] += -0.05587497206644211; + } else { + result[0] += -0.18871587509128254; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 50))) { + result[0] += -0.471164876208749; + } else { + result[0] += -0.2899516754150391; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 64))) { + result[0] += -1.2615578046052351; + } else { + result[0] += -0.824327716093797; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 230))) { + if (LIKELY(false || (data[3].qvalue <= 218))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += -0.0018346942873173101; + } else { + result[0] += 0.16034261671985583; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + result[0] += 0.3582006235217019; + } else { + result[0] += -0.15805343752114553; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.09120323986998596; + } else { + result[0] += -2.746553548719825; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 240))) { + result[0] += 0.020237036833509074; + } else { + result[0] += 0.5211988226836327; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 178))) { + if (LIKELY(false || (data[2].qvalue <= 12))) { + if (LIKELY(false || (data[3].qvalue <= 130))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += -0.005991282302809805; + } else { + result[0] += 0.21808256969336925; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += 0.3648159058387132; + } else { + result[0] += 1.0501265993764848; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + if (LIKELY(false || (data[3].qvalue <= 140))) { + result[0] += -0.7560950076917835; + } else { + result[0] += -0.011609365583746693; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 114))) { + result[0] += -0.9929890502662195; + } else { + result[0] += -0.018430796040420066; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + if (LIKELY(false || (data[3].qvalue <= 182))) { + result[0] += 1.4941535253935205; + } else { + result[0] += 5.01069572504829; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 40))) { + result[0] += -1.6721194921221052; + } else { + result[0] += 0.06600910509196743; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + if (LIKELY(false || (data[2].qvalue <= 50))) { + result[0] += 0.06599082621417188; + } else { + result[0] += -0.5629778590534413; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 50))) { + result[0] += -0.031091763815894752; + } else { + result[0] += 0.12480377527902276; + } + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 38))) { + if (LIKELY(false || (data[2].qvalue <= 36))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (UNLIKELY(false || (data[3].qvalue <= 2))) { + result[0] += 0.1757544212263416; + } else { + result[0] += -0.03321362620945462; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + result[0] += -0.1209793900016442; + } else { + result[0] += -0.3390527655087508; + } + } + } else { + result[0] += -0.9544804904460907; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 228))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 188))) { + result[0] += 0.00850193251229063; + } else { + result[0] += 1.253315189034531; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 192))) { + result[0] += -0.3207565361013706; + } else { + result[0] += 0.01040093706091699; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 0.07622775564010142; + } else { + result[0] += 0.6434668296100172; + } + } else { + result[0] += -0.25382570830590884; + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 66))) { + if (LIKELY(false || (data[2].qvalue <= 36))) { + if (UNLIKELY(false || (data[3].qvalue <= 6))) { + if (UNLIKELY(false || (data[3].qvalue <= 0))) { + result[0] += -0.15891414485012728; + } else { + result[0] += -0.06083332526536694; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 30))) { + result[0] += -0.025560713454775653; + } else { + result[0] += -0.009423279209306063; + } + } + } else { + result[0] += -0.8614186177253723; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[2].qvalue <= 12))) { + if (LIKELY(false || (data[3].qvalue <= 130))) { + result[0] += 0.014946627847899083; + } else { + result[0] += 0.3928370868714558; + } + } else { + result[0] += 12.309830587704978; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 146))) { + if (UNLIKELY(false || (data[2].qvalue <= 40))) { + result[0] += -0.7278951537054682; + } else { + result[0] += 0.10042924111104164; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 0.048389262179927645; + } else { + result[0] += -0.024802333325858735; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 228))) { + if (LIKELY(false || (data[3].qvalue <= 218))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 0.0026677754941615505; + } else { + result[0] += -0.11795346042813584; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 214))) { + result[0] += 0.25660651477274304; + } else { + result[0] += 0.12738714081176464; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 80))) { + if (UNLIKELY(false || (data[3].qvalue <= 222))) { + result[0] += -0.36182422209697296; + } else { + result[0] += -0.09068035614479159; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 226))) { + result[0] += 0.29626333166012725; + } else { + result[0] += 0.501580802754658; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + if (LIKELY(false || (data[1].qvalue <= 74))) { + result[0] += 0.04606551220989991; + } else { + result[0] += 1.6768793160307642; + } + } else { + result[0] += -2.4957903161863; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 56))) { + if (LIKELY(false || (data[3].qvalue <= 240))) { + result[0] += -0.001995007368938002; + } else { + result[0] += 0.8600447828586285; + } + } else { + result[0] += -0.22613400126344585; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 100))) { + if (LIKELY(false || (data[2].qvalue <= 28))) { + if (LIKELY(false || (data[1].qvalue <= 10))) { + if (LIKELY(false || (data[3].qvalue <= 88))) { + result[0] += -0.007032593132406662; + } else { + result[0] += 0.10789191115072033; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 94))) { + result[0] += -0.14776386905895944; + } else { + result[0] += -0.04740764971575276; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 44))) { + result[0] += -1.5670684486389161; + } else { + result[0] += -0.7503883746818261; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 10))) { + result[0] += 2.769067636305286; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[3].qvalue <= 134))) { + result[0] += 0.07602934068056531; + } else { + result[0] += 2.8183202544858474; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 146))) { + result[0] += -0.16233532230561315; + } else { + result[0] += 0.01202817781399166; + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 54))) { + if (LIKELY(false || (data[0].qvalue <= 50))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + if (LIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.0029886117550836865; + } else { + result[0] += -0.06408425115195845; + } + } else { + result[0] += 0.26602660123013633; + } + } else { + result[0] += -0.07929562686095666; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + if (LIKELY(false || (data[2].qvalue <= 46))) { + if (UNLIKELY(false || (data[2].qvalue <= 36))) { + result[0] += 3.7119815171616426; + } else { + result[0] += 0.547443312065942; + } + } else { + result[0] += 4.166473594393049; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 48))) { + if (UNLIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 0.0017977573714891022; + } else { + result[0] += 0.23845299048938143; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 76))) { + result[0] += -0.037150860814943704; + } else { + result[0] += 0.14564257745376; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 178))) { + if (LIKELY(false || (data[0].qvalue <= 60))) { + if (LIKELY(false || (data[0].qvalue <= 50))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += -0.007183108829239199; + } else { + result[0] += 0.2394491795303417; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 140))) { + result[0] += -0.6344022675495424; + } else { + result[0] += 0.007260844712462359; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 142))) { + if (UNLIKELY(false || (data[3].qvalue <= 138))) { + result[0] += -0.08417562885148172; + } else { + result[0] += 0.5906773558105556; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 68))) { + result[0] += 0.18764238988337378; + } else { + result[0] += -2.326712134777498; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + if (LIKELY(false || (data[3].qvalue <= 182))) { + result[0] += 1.339431215953403; + } else { + result[0] += 4.521662556143368; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 52))) { + result[0] += 0.7292394052527168; + } else { + result[0] += -0.012391112078889212; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 194))) { + if (LIKELY(false || (data[2].qvalue <= 50))) { + result[0] += 0.05246317581619821; + } else { + result[0] += -0.4745475472223324; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 50))) { + result[0] += -0.024886056556697605; + } else { + result[0] += 0.10675635333641575; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 178))) { + if (LIKELY(false || (data[1].qvalue <= 46))) { + if (LIKELY(false || (data[1].qvalue <= 44))) { + if (LIKELY(false || (data[3].qvalue <= 170))) { + result[0] += -0.0035541639556744805; + } else { + result[0] += 0.23776616044647245; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 22))) { + result[0] += 1.6540061310768128; + } else { + result[0] += -0.007316129265448475; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 24))) { + if (UNLIKELY(false || (data[3].qvalue <= 156))) { + result[0] += -0.9207533250588412; + } else { + result[0] += -0.19604021761441262; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 26))) { + result[0] += 0.39069615612658914; + } else { + result[0] += -0.07078300532699774; + } + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 186))) { + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (UNLIKELY(false || (data[3].qvalue <= 182))) { + result[0] += -0.0537103470292483; + } else { + result[0] += 0.01762700340556235; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 180))) { + result[0] += 0.6253090474974919; + } else { + result[0] += 2.807307553162299; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 202))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + result[0] += 0.04721793457525833; + } else { + result[0] += -0.17271753814495214; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + result[0] += -0.01901583571747938; + } else { + result[0] += 0.1447237594141037; + } + } + } + } + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + if (LIKELY(false || (data[0].qvalue <= 50))) { + result[0] += 0.0021335393865604085; + } else { + result[0] += -0.07177648191161112; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 30))) { + result[0] += 2.634354959784485; + } else { + result[0] += 0.004247555141275988; + } + } + } else { + result[0] += 0.45583923039077984; + } + } else { + result[0] += -0.24432320779772257; + } + if (UNLIKELY(false || (data[3].qvalue <= 6))) { + if (LIKELY(false || (data[0].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + if (LIKELY(false || (data[1].qvalue <= 18))) { + result[0] += -0.053137056565348964; + } else { + result[0] += -0.1615091542830301; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 50))) { + result[0] += -0.48585747241973887; + } else { + result[0] += -0.2905982590516409; + } + } + } else { + result[0] += -1.0460150627295177; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 228))) { + if (LIKELY(false || (data[3].qvalue <= 218))) { + if (LIKELY(false || (data[3].qvalue <= 208))) { + result[0] += -0.0007595050498410175; + } else { + result[0] += 0.17149519474492791; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 68))) { + result[0] += 0.2696969214216211; + } else { + result[0] += -0.148602349719807; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[1].qvalue <= 76))) { + result[0] += 0.041070141212163; + } else { + result[0] += 0.27300094236384387; + } + } else { + result[0] += -0.220040733112148; + } + } + } + if (UNLIKELY(false || (data[3].qvalue <= 66))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + if (UNLIKELY(false || (data[3].qvalue <= 8))) { + if (LIKELY(false || (data[0].qvalue <= 24))) { + result[0] += -0.04786216375242139; + } else { + result[0] += -0.2854615034882365; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 44))) { + result[0] += -0.0157676019115889; + } else { + result[0] += -0.00516823734649734; + } + } + } else { + result[0] += -0.8536656070967852; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + if (LIKELY(false || (data[3].qvalue <= 130))) { + result[0] += 0.01178608871677298; + } else { + result[0] += 0.31573188820888726; + } + } else { + result[0] += 10.428866252899171; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 146))) { + if (LIKELY(false || (data[0].qvalue <= 60))) { + result[0] += -0.39214777400557815; + } else { + result[0] += 0.15464362462318024; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.05795968864116782; + } else { + result[0] += -0.018277034463468993; + } + } + } + } + + // Apply base_scores + result[0] += 0; + + // Apply postprocessor + if (!pred_margin) { postprocess(result); } +} + +void cpufj_predictor::postprocess(double* result) +{ + // Do nothing +} + +// Feature names array +const char* cpufj_predictor::feature_names[cpufj_predictor::NUM_FEATURES] = { + "n_vars", "n_cstrs", "total_nnz", "mem_total_mb"}; diff --git a/cpp/src/utilities/models/cpufj_predictor/quantize.cpp b/cpp/src/utilities/models/cpufj_predictor/quantize.cpp new file mode 100644 index 000000000..0b292d03f --- /dev/null +++ b/cpp/src/utilities/models/cpufj_predictor/quantize.cpp @@ -0,0 +1,293 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#include "header.h" + +static const double threshold[] = { + 940.50000000000011, + 962.50000000000011, + 964.50000000000011, + 1001.5000000000001, + 1010.5000000000001, + 1088.5000000000002, + 1250.5000000000002, + 1435.5000000000002, + 3395.5000000000005, + 3603.0000000000005, + 4535.5000000000009, + 5197.5000000000009, + 5419.0000000000009, + 5949.5000000000009, + 5959.5000000000009, + 5962.5000000000009, + 5963.5000000000009, + 5964.5000000000009, + 5966.5000000000009, + 5969.5000000000009, + 5972.5000000000009, + 5984.0000000000009, + 6803.5000000000009, + 7344.0000000000009, + 8291.0000000000018, + 9184.0000000000018, + 9992.0000000000018, + 10730.500000000002, + 12831.000000000002, + 14626.500000000002, + 15810.500000000002, + 16371.000000000002, + 17411.000000000004, + 18723.500000000004, + 19548.000000000004, + 20752.500000000004, + 23386.000000000004, + 35418.500000000007, + 49402.000000000007, + 53915.500000000007, + 598.50000000000011, + 1717.5000000000002, + 1767.5000000000002, + 1847.5000000000002, + 2005.5000000000002, + 2290.0000000000005, + 2305.5000000000005, + 2649.0000000000005, + 5179.5000000000009, + 5250.5000000000009, + 5362.5000000000009, + 6412.5000000000009, + 7682.0000000000009, + 9243.0000000000018, + 10037.000000000002, + 12675.000000000002, + 18404.000000000004, + 19600.500000000004, + 21814.500000000004, + 23409.000000000004, + 23497.000000000004, + 23508.000000000004, + 23558.000000000004, + 23603.500000000004, + 23652.500000000004, + 23744.500000000004, + 23818.500000000004, + 23827.000000000004, + 23874.000000000004, + 23897.500000000004, + 23920.500000000004, + 23949.500000000004, + 23978.000000000004, + 24015.000000000004, + 24102.000000000004, + 24164.500000000004, + 26354.000000000004, + 40391.500000000007, + 58010.500000000007, + 64224.000000000007, + 64894.000000000007, + 5656.5000000000009, + 7438.5000000000009, + 7622.5000000000009, + 18609.000000000004, + 23424.500000000004, + 24989.500000000004, + 29342.000000000004, + 43154.000000000007, + 46402.000000000007, + 46815.000000000007, + 47052.000000000007, + 47178.000000000007, + 47358.000000000007, + 47388.000000000007, + 47485.000000000007, + 47659.000000000007, + 47752.500000000007, + 47839.500000000007, + 48007.000000000007, + 48085.500000000007, + 48300.500000000007, + 48410.000000000007, + 54272.500000000007, + 57404.000000000007, + 76382.000000000015, + 83368.000000000015, + 108268.00000000001, + 170279.00000000003, + 186892.00000000003, + 3.8570000000000007, + 9.8880000000000017, + 11.190500000000002, + 11.561500000000001, + 11.861500000000001, + 12.013500000000002, + 12.3085, + 12.4625, + 12.647500000000003, + 13.102500000000001, + 13.439500000000001, + 14.092500000000003, + 15.505500000000003, + 16.265500000000003, + 16.586500000000004, + 16.893500000000003, + 17.051500000000001, + 17.255500000000001, + 17.432500000000001, + 17.537500000000005, + 17.814500000000006, + 18.095500000000005, + 18.167500000000004, + 18.334500000000002, + 18.546500000000005, + 18.743500000000001, + 18.851500000000005, + 18.949500000000004, + 19.151500000000002, + 19.201500000000006, + 19.275500000000005, + 19.453500000000002, + 19.604500000000005, + 19.704500000000003, + 20.009500000000006, + 20.944500000000001, + 21.655500000000004, + 24.058500000000006, + 56.284500000000001, + 58.642500000000005, + 63.514000000000003, + 66.785000000000011, + 67.949500000000015, + 70.709000000000017, + 72.252000000000024, + 74.410500000000013, + 76.675500000000014, + 78.813500000000019, + 81.035000000000011, + 86.201000000000008, + 87.71850000000002, + 90.722000000000023, + 93.919500000000014, + 95.885000000000005, + 98.094500000000025, + 101.17400000000002, + 105.48300000000002, + 119.34800000000001, + 135.33800000000005, + 148.20100000000002, + 155.32500000000002, + 202.12250000000003, + 215.85450000000003, + 222.94300000000001, + 227.80600000000001, + 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1253.6805000000002, + 1266.5855000000004, + 1276.1255000000003, + 1282.5315000000003, + 1291.7940000000001, + 1295.7590000000002, + 1299.4545000000001, + 1307.3780000000004, + 1312.9645000000003, + 1322.5440000000001, + 1465.1910000000003, +}; + +static const int th_begin[] = { + 0, + 40, + 81, + 110, +}; + +static const int th_len[] = { + 40, + 41, + 29, + 121, +}; + +/* + * \brief Function to convert a feature value into bin index. + * \param val Feature value, in floating-point + * \param fid Feature identifier + * \return bin Index corresponding to given feature value + */ +int cpufj_predictor::quantize(double val, unsigned fid) +{ + const size_t offset = th_begin[fid]; + const double* array = &threshold[offset]; + int len = th_len[fid]; + int low = 0; + int high = len; + int mid; + double mval; + // It is possible th_begin[i] == [total_num_threshold]. This means that + // all features i, (i+1), ... are not used for any of the splits in the model. + // So in this case, just return something + if (offset == 231 || val < array[0]) { return -10; } + while (low + 1 < high) { + mid = (low + high) / 2; + mval = array[mid]; + if (val == mval) { + return mid * 2; + } else if (val < mval) { + high = mid; + } else { + low = mid; + } + } + if (array[low] == val) { + return low * 2; + } else if (high == len) { + return len * 2; + } else { + return low * 2 + 1; + } +} diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp index 4d273d3b6..68d2a8f4d 100644 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -24,6 +24,7 @@ #include #include +#include "models/cpufj_predictor/header.h" #include "models/fj_predictor/header.h" namespace cuopt { @@ -81,5 +82,6 @@ float work_unit_predictor_t::predict_scalar( } template class work_unit_predictor_t; +template class work_unit_predictor_t; } // namespace cuopt diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py index 0f251c3a2..c729a8292 100755 --- a/scripts/train_regressor.py +++ b/scripts/train_regressor.py @@ -133,13 +133,13 @@ # Example usage (uncomment to use only specific features): # 'n_variables', # 'n_constraints', - # 'sparsity', - # "n_vars", - # "n_cstrs", - # "total_nnz", - # "mem_total_mb", - # "mem_store_mb", - # "mem_load_mb", + #'sparsity', + "n_vars", + "n_cstrs", + "total_nnz", + "mem_total_mb", + # "mem_stores_mb", + # "mem_loads_mb", ] # ============================================================================ From f1afa59cff60ff30fd2808f07ab6aeb4070c7c3e Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 14 Nov 2025 09:17:04 +0000 Subject: [PATCH 078/366] fewer --- cpp/src/mip/local_search/local_search.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 30bf5ac06..28be6dd4f 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -65,7 +65,7 @@ void local_search_t::start_cpufj_scratch_threads(population_t::infinity(); - cpu_fj_settings.iteration_limit = 30000; // would like 10 samples per instance + cpu_fj_settings.iteration_limit = 10000; // would like 10 samples per instance solution_t solution(*context.problem_ptr); thrust::fill(solution.handle_ptr->get_thrust_policy(), From cd2595ece81ce958d46d3a30df9203f7faf19e67 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 14 Nov 2025 12:57:47 +0000 Subject: [PATCH 079/366] log-transform for regressor --- scripts/train_regressor.py | 540 ++++++++++++++++++++++++++++++++++--- 1 file changed, 497 insertions(+), 43 deletions(-) diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py index c729a8292..f26874a21 100755 --- a/scripts/train_regressor.py +++ b/scripts/train_regressor.py @@ -27,7 +27,7 @@ import pickle import numpy as np import pandas as pd -from sklearn.model_selection import train_test_split, cross_val_score +from sklearn.model_selection import train_test_split, cross_val_score, KFold from sklearn.preprocessing import StandardScaler, PolynomialFeatures from sklearn.linear_model import LinearRegression, Ridge from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor @@ -125,6 +125,22 @@ "max_cstr_deg", "viol_ratio", "nnz_per_move", + "h_cstr_right_weights_loads", + "h_cstr_left_weights_loads", + "h_cstr_right_weights_stores", + "h_cstr_left_weights_stores", + "total_viol", + "feas_found", + "obj_weight", + "h_tabu_nodec_until_stores", + "h_tabu_nodec_until_loads", + "h_tabu_noinc_until_stores", + "h_tabu_noinc_until_loads", + "h_tabu_lastdec_stores", + "h_tabu_lastdec_loads", + "h_tabu_lastinc_stores", + "h_tabu_lastinc_loads", + "max_weight", ] # Alternatively, specify ONLY the features you want to use @@ -134,12 +150,14 @@ # 'n_variables', # 'n_constraints', #'sparsity', - "n_vars", - "n_cstrs", - "total_nnz", - "mem_total_mb", - # "mem_stores_mb", - # "mem_loads_mb", + # "n_vars", + # "n_cstrs", + # #"total_nnz", + # "mem_total_mb", + # #"cache_hit_rate", + # #"cstr_deg_cv" + # "mem_stores_mb", + # "mem_loads_mb", ] # ============================================================================ @@ -221,10 +239,33 @@ def split_by_files( train_df = df[df["file"].isin(train_files)].copy() test_df = df[df["file"].isin(test_files)].copy() + # Validate no data leakage: ensure no overlap between train and test files + train_files_set = set(train_files) + test_files_set = set(test_files) + overlap = train_files_set.intersection(test_files_set) + + if overlap: + raise ValueError( + f"Data leakage detected! {len(overlap)} file(s) appear in both train and test sets:\n" + f" {list(overlap)[:10]}{'...' if len(overlap) > 10 else ''}" + ) + + # Verify the actual dataframes have no file overlap + train_files_in_df = set(train_df["file"].unique()) + test_files_in_df = set(test_df["file"].unique()) + actual_overlap = train_files_in_df.intersection(test_files_in_df) + + if actual_overlap: + raise ValueError( + f"Data leakage detected in dataframes! {len(actual_overlap)} file(s) appear in both:\n" + f" {list(actual_overlap)[:10]}{'...' if len(actual_overlap) > 10 else ''}" + ) + print("\nData Split:") print(f" Total entries: {len(df)}") print(f" Train entries: {len(train_df)} ({len(train_files)} files)") print(f" Test entries: {len(test_df)} ({len(test_files)} files)") + print(" ✓ Verified: Zero file overlap between train and test sets") # Check distribution similarity (use stratify_by column if provided, otherwise first numeric column) target_col = ( @@ -568,7 +609,7 @@ def create_regressor( ) model = xgb.XGBRegressor(**params) - needs_scaling = False + needs_scaling = True elif regressor_type == "lightgbm": try: @@ -581,8 +622,8 @@ def create_regressor( params = { "objective": "regression", "random_state": random_state, - "n_estimators": 100, - "max_depth": 4, + "n_estimators": 150, + "max_depth": 6, "learning_rate": 0.1, "verbosity": 1 if verbose else -1, # Regularization to prevent overfitting @@ -611,7 +652,7 @@ def create_regressor( ) model = lgb.LGBMRegressor(**params) - needs_scaling = False + needs_scaling = True elif regressor_type == "random_forest": params = { @@ -730,6 +771,9 @@ def evaluate_model( skip_cv: bool = False, X_test_original: pd.DataFrame = None, test_df: pd.DataFrame = None, + log_transform: bool = False, + y_train_original: pd.Series = None, + y_test_original: pd.Series = None, ) -> Tuple[float, float]: """Evaluate model and print metrics. Returns (train_r2, test_r2). @@ -737,12 +781,16 @@ def evaluate_model( ---- X_test_original: Unscaled X_test for displaying feature values test_df: Original test dataframe with 'file' column + log_transform: If True, predictions are in log-space and need inverse transform + y_train_original: Original (non-log) training targets (if log_transform=True) + y_test_original: Original (non-log) test targets (if log_transform=True) """ # Cross-validation on training set (skip if using early stopping) if not skip_cv: print(f"\nCross-Validation on Training Set ({cv_folds}-fold):") try: - cv_scores = cross_val_score( + # Compute RMSE and R² in log-space + cv_scores_mse = cross_val_score( model, X_train, y_train, @@ -751,8 +799,97 @@ def evaluate_model( n_jobs=-1, verbose=verbose, ) - cv_rmse = np.sqrt(-cv_scores) - print(f" CV RMSE: {cv_rmse.mean():.4f} (+/- {cv_rmse.std():.4f})") + cv_scores_r2 = cross_val_score( + model, + X_train, + y_train, + cv=cv_folds, + scoring="r2", + n_jobs=-1, + verbose=verbose, + ) + cv_rmse = np.sqrt(-cv_scores_mse) + + if log_transform: + print(" Log-space metrics:") + print( + f" CV RMSE: {cv_rmse.mean():.4f} (+/- {cv_rmse.std():.4f})" + ) + print( + f" CV R²: {cv_scores_r2.mean():.4f} (+/- {cv_scores_r2.std():.4f})" + ) + + # Also compute metrics in original space + kfold = KFold(n_splits=cv_folds, shuffle=True, random_state=42) + + cv_mape_scores = [] + cv_r2_original_scores = [] + + for train_idx, val_idx in kfold.split(X_train): + X_train_fold = ( + X_train.iloc[train_idx] + if hasattr(X_train, "iloc") + else X_train[train_idx] + ) + y_train_fold = ( + y_train.iloc[train_idx] + if hasattr(y_train, "iloc") + else y_train[train_idx] + ) + X_val_fold = ( + X_train.iloc[val_idx] + if hasattr(X_train, "iloc") + else X_train[val_idx] + ) + y_val_original_fold = ( + y_train_original.iloc[val_idx] + if hasattr(y_train_original, "iloc") + else y_train_original[val_idx] + ) + + # Train on fold + model_fold = type(model)(**model.get_params()) + model_fold.fit(X_train_fold, y_train_fold) + + # Predict and transform back + y_pred_log = model_fold.predict(X_val_fold) + y_pred_original = np.exp(y_pred_log) + + # Compute metrics in original space + mape = ( + np.mean( + np.abs( + (y_val_original_fold - y_pred_original) + / y_val_original_fold + ) + ) + * 100 + ) + r2_original = r2_score( + y_val_original_fold, y_pred_original + ) + + cv_mape_scores.append(mape) + cv_r2_original_scores.append(r2_original) + + cv_mape = np.array(cv_mape_scores) + cv_r2_original = np.array(cv_r2_original_scores) + + print(" Original-space metrics:") + print( + f" CV MAPE: {cv_mape.mean():.2f}% (+/- {cv_mape.std():.2f}%)" + ) + print( + f" CV R²: {cv_r2_original.mean():.4f} (+/- {cv_r2_original.std():.4f})" + ) + else: + print( + f" CV RMSE: {cv_rmse.mean():.4f} (+/- {cv_rmse.std():.4f})" + ) + print( + f" CV R²: {cv_scores_r2.mean():.4f} (+/- {cv_scores_r2.std():.4f})" + ) + except Exception as e: print( f" CV failed (likely due to early stopping): {str(e)[:100]}" @@ -763,29 +900,119 @@ def evaluate_model( # Training set metrics y_train_pred = model.predict(X_train) - train_mse = mean_squared_error(y_train, y_train_pred) - train_rmse = np.sqrt(train_mse) - train_mae = mean_absolute_error(y_train, y_train_pred) - train_r2 = r2_score(y_train, y_train_pred) - print("\nTraining Set Metrics:") - print(f" MSE: {train_mse:.4f}") - print(f" RMSE: {train_rmse:.4f}") - print(f" MAE: {train_mae:.4f}") - print(f" R²: {train_r2:.4f}") + # If log-transformed, also compute metrics in original space + if log_transform: + # Inverse transform predictions + y_train_pred_original = np.exp(y_train_pred) + y_test_pred_log = model.predict(X_test) + y_test_pred_original = np.exp(y_test_pred_log) + + # Metrics in log-space + train_mse_log = mean_squared_error(y_train, y_train_pred) + train_rmse_log = np.sqrt(train_mse_log) + train_r2_log = r2_score(y_train, y_train_pred) + + # Metrics in original space + train_mse = mean_squared_error(y_train_original, y_train_pred_original) + train_rmse = np.sqrt(train_mse) + train_mae = mean_absolute_error( + y_train_original, y_train_pred_original + ) + train_r2 = r2_score(y_train_original, y_train_pred_original) + train_mape = ( + np.mean( + np.abs( + (y_train_original - y_train_pred_original) + / y_train_original + ) + ) + * 100 + ) + + print("\nTraining Set Metrics (Original Space):") + print(f" MSE: {train_mse:.4f}") + print(f" RMSE: {train_rmse:.4f}") + print(f" MAE: {train_mae:.4f}") + print(f" MAPE: {train_mape:.2f}% (optimized metric)") + print(f" R²: {train_r2:.4f}") + print("\nTraining Set Metrics (Log Space):") + print(f" RMSE: {train_rmse_log:.4f}") + print(f" R²: {train_r2_log:.4f}") + else: + train_mse = mean_squared_error(y_train, y_train_pred) + train_rmse = np.sqrt(train_mse) + train_mae = mean_absolute_error(y_train, y_train_pred) + train_r2 = r2_score(y_train, y_train_pred) + + print("\nTraining Set Metrics:") + print(f" MSE: {train_mse:.4f}") + print(f" RMSE: {train_rmse:.4f}") + print(f" MAE: {train_mae:.4f}") + print(f" R²: {train_r2:.4f}") # Test set metrics - y_test_pred = model.predict(X_test) - test_mse = mean_squared_error(y_test, y_test_pred) - test_rmse = np.sqrt(test_mse) - test_mae = mean_absolute_error(y_test, y_test_pred) - test_r2 = r2_score(y_test, y_test_pred) - - print("\nTest Set Metrics:") - print(f" MSE: {test_mse:.4f}") - print(f" RMSE: {test_rmse:.4f}") - print(f" MAE: {test_mae:.4f}") - print(f" R²: {test_r2:.4f}") + if log_transform: + # Already computed above + test_mse_log = mean_squared_error(y_test, y_test_pred_log) + test_rmse_log = np.sqrt(test_mse_log) + test_r2_log = r2_score(y_test, y_test_pred_log) + + # Metrics in original space + test_mse = mean_squared_error(y_test_original, y_test_pred_original) + test_rmse = np.sqrt(test_mse) + test_mae = mean_absolute_error(y_test_original, y_test_pred_original) + test_r2 = r2_score(y_test_original, y_test_pred_original) + test_mape = ( + np.mean( + np.abs( + (y_test_original - y_test_pred_original) / y_test_original + ) + ) + * 100 + ) + + print("\nTest Set Metrics (Original Space):") + print(f" MSE: {test_mse:.4f}") + print(f" RMSE: {test_rmse:.4f}") + print(f" MAE: {test_mae:.4f}") + print(f" MAPE: {test_mape:.2f}% (optimized metric)") + print(f" R²: {test_r2:.4f}") + print("\nTest Set Metrics (Log Space):") + print(f" RMSE: {test_rmse_log:.4f}") + print(f" R²: {test_r2_log:.4f}") + + # Use original space predictions for sample display + y_test_pred = y_test_pred_original + y_test = y_test_original + else: + y_test_pred = model.predict(X_test) + test_mse = mean_squared_error(y_test, y_test_pred) + test_rmse = np.sqrt(test_mse) + test_mae = mean_absolute_error(y_test, y_test_pred) + test_r2 = r2_score(y_test, y_test_pred) + + print("\nTest Set Metrics:") + print(f" MSE: {test_mse:.4f}") + print(f" RMSE: {test_rmse:.4f}") + print(f" MAE: {test_mae:.4f}") + print(f" R²: {test_r2:.4f}") + + # If R² is negative, show baseline comparison for debugging + if test_r2 < 0: + y_test_mean = np.mean(y_test) + baseline_pred = np.full_like(y_test_pred, y_test_mean) + baseline_mse = mean_squared_error(y_test, baseline_pred) + baseline_rmse = np.sqrt(baseline_mse) + print("\n WARNING: Negative R² detected!") + print( + f" This means the model is worse than predicting the mean: {y_test_mean:.4f}" + ) + print(f" Baseline (mean) RMSE: {baseline_rmse:.4f}") + print(f" Model RMSE: {test_rmse:.4f}") + print( + f" Model is {test_rmse / baseline_rmse:.2f}x worse than baseline" + ) # Feature importance get_feature_importance(model, feature_names, regressor_type) @@ -868,6 +1095,7 @@ def compile_model_treelite( quantize: bool = False, feature_names: List[str] = None, model_name: str = None, + log_transform: bool = False, ) -> None: """Compile XGBoost/LightGBM model to C source files using TL2cgen. @@ -882,6 +1110,7 @@ def compile_model_treelite( quantize: Whether to use quantization in code generation feature_names: List of feature names in expected order (optional) model_name: Name prefix for functions (optional, derived from training file) + log_transform: Whether model predicts in log-space (will add exp() wrapper) """ if regressor_type not in ["xgboost", "lightgbm"]: print( @@ -1083,12 +1312,55 @@ def compile_model_treelite( r"!\(data\[\d+\]\.missing != -1\)", "false", content ) + # If log_transform is used, wrap return values with exp() + if log_transform: + # Add include if not present + if ( + "#include " not in content + and "#include" not in content + ): + # Find the last #include and add after it + include_match = None + for match in re.finditer( + r'#include\s*[<"].*?[>"]', content + ): + include_match = match + if include_match: + insert_pos = include_match.end() + content = ( + content[:insert_pos] + + "\n#include " + + content[insert_pos:] + ) + + # Replace "return sum;" with "return std::exp(sum);" + # Match various return patterns in the predict function + content = re.sub( + r"(\s+)return\s+(sum|result|pred)\s*;", + r"\1return std::exp(\2);", + content, + ) + + # Also handle single-line returns like "return value;" + content = re.sub( + r"(\s+)return\s+([\w\.]+)\s*;", + lambda m: f"{m.group(1)}return std::exp({m.group(2)});" + if m.group(2) not in ["true", "false", "0", "1"] + else m.group(0), + content, + ) + + print( + " Added exp() transformation to convert log-space predictions to original space" + ) + if content != original_content: with open(main_path, "w") as f: f.write(content) - print( - " Optimized main.cpp by removing unnecessary missing data checks" - ) + if not log_transform: + print( + " Optimized main.cpp by removing unnecessary missing data checks" + ) except Exception as e: print(f" Warning: Failed to optimize main.cpp: {e}") @@ -1293,6 +1565,7 @@ def save_model( regressor_type: str, output_dir: str, feature_names: List[str], + log_transform: bool = False, ) -> None: """Save trained model and preprocessing components to disk.""" os.makedirs(output_dir, exist_ok=True) @@ -1302,6 +1575,7 @@ def save_model( "regressor_type": regressor_type, "feature_names": feature_names, "has_scaler": scaler is not None, + "log_transform": log_transform, } metadata_path = os.path.join(output_dir, f"{regressor_type}_metadata.pkl") @@ -1369,6 +1643,15 @@ def main(): # Train with automatic removal of invalid rows python train_regressor.py data.feather --regressor xgboost --drop-invalid-rows --seed 42 + # Stratify train/test split by target column (ensures balanced distribution) + python train_regressor.py data.feather --regressor xgboost --stratify-split --seed 42 + + # Stratify split by a specific column (e.g., time_ms) + python train_regressor.py data.feather --regressor xgboost --stratify-split time_ms --seed 42 + + # Optimize for relative error (recommended for targets spanning multiple orders of magnitude) + python train_regressor.py data.feather --regressor xgboost --log-transform --seed 42 + # Legacy pickle format python train_regressor.py data.pkl --regressor xgboost --seed 42 """, @@ -1426,8 +1709,12 @@ def main(): ) parser.add_argument( "--stratify-split", - action="store_true", - help="Stratify train/test split by target distribution (ensures balanced iter values)", + type=str, + nargs="?", + const="__target__", + default=None, + metavar="COLUMN", + help="Stratify train/test split by specified column distribution. If no column specified, uses target column. Example: --stratify-split time_ms or just --stratify-split for target column", ) parser.add_argument( "--early-stopping", @@ -1459,6 +1746,11 @@ def main(): default="iter", help="Target column to predict (default: iter). Examples: iter, time_ms, iterations", ) + parser.add_argument( + "--log-transform", + action="store_true", + help="Use log-transform on target variable to optimize for relative error instead of absolute error. Recommended when target values span multiple orders of magnitude.", + ) args = parser.parse_args() @@ -1561,7 +1853,22 @@ def main(): return # Split data by files - stratify_by = args.target if args.stratify_split else None + # Handle stratify-split argument + if args.stratify_split is None: + stratify_by = None + elif args.stratify_split == "__target__": + stratify_by = args.target + print(f"Stratifying split by target column: '{args.target}'") + else: + stratify_by = args.stratify_split + if stratify_by not in df.columns: + print( + f"\n❌ Error: Stratify column '{stratify_by}' not found in dataset" + ) + print(f"Available columns: {list(df.columns)}") + return 1 + print(f"Stratifying split by column: '{stratify_by}'") + train_df, test_df = split_by_files( df, test_size=args.test_size, @@ -1578,6 +1885,130 @@ def main(): print(f"\nFeatures: {len(feature_names)}") print(f"Target: {args.target} (prediction target)") + # Apply log transform if requested (for relative error optimization) + if args.log_transform: + # Check for non-positive values before log transform + if np.any(y_train <= 0) or np.any(y_test <= 0): + n_nonpositive_train = np.sum(y_train <= 0) + n_nonpositive_test = np.sum(y_test <= 0) + print( + "\n❌ Error: Cannot apply log-transform with non-positive target values!" + ) + print(f" Train set: {n_nonpositive_train} non-positive values") + print(f" Test set: {n_nonpositive_test} non-positive values") + print( + f" Target range: [{np.min(y_train):.2f}, {np.max(y_train):.2f}]" + ) + print( + "\nSuggestion: Add a small constant (e.g., +1) to all target values before log" + ) + return 1 + + print( + "\nApplying log-transform to target variable (optimizes for relative error)" + ) + y_train_original = y_train.copy() + y_test_original = y_test.copy() + + y_train = np.log(y_train) + y_test = np.log(y_test) + + print( + f" Original target range: [{np.min(y_train_original):.2f}, {np.max(y_train_original):.2f}]" + ) + print( + f" Log-space target range: [{np.min(y_train):.4f}, {np.max(y_train):.4f}]" + ) + else: + y_train_original = None + y_test_original = None + + # Enhanced diagnostics for XGBoost compatibility (only show if problems found) + X_train_array = X_train.values if hasattr(X_train, "values") else X_train + + # XGBoost internal limits (approximate) + xgb_max_safe = 1e38 # XGBoost uses float32 internally + + problematic_features = [] + problem_details = [] + + for i, col_name in enumerate(feature_names): + col_data = X_train_array[:, i] + + n_nan = np.sum(np.isnan(col_data)) + n_inf = np.sum(np.isinf(col_data)) + n_posinf = np.sum(np.isposinf(col_data)) + n_neginf = np.sum(np.isneginf(col_data)) + + # Get statistics on finite values + finite_mask = np.isfinite(col_data) + if np.any(finite_mask): + finite_data = col_data[finite_mask] + col_min = np.min(finite_data) + col_max = np.max(finite_data) + col_mean = np.mean(finite_data) + col_std = np.std(finite_data) + abs_max = max(abs(col_min), abs(col_max)) + else: + col_min = col_max = col_mean = col_std = abs_max = np.nan + + # Check if values are too large for XGBoost + is_problematic = ( + n_nan > 0 + or n_inf > 0 + or (not np.isnan(abs_max) and abs_max > xgb_max_safe) + ) + + if is_problematic: + problematic_features.append(col_name) + detail = f"\n⚠️ '{col_name}':" + if n_nan > 0: + detail += f"\n NaN: {n_nan:8d} ({100 * n_nan / len(col_data):6.2f}%)" + if n_posinf > 0: + detail += f"\n +Inf: {n_posinf:8d} ({100 * n_posinf / len(col_data):6.2f}%)" + if n_neginf > 0: + detail += f"\n -Inf: {n_neginf:8d} ({100 * n_neginf / len(col_data):6.2f}%)" + if not np.isnan(abs_max): + detail += f"\n Range: [{col_min:.6e}, {col_max:.6e}]" + detail += f"\n Max abs: {abs_max:.6e}" + if abs_max > xgb_max_safe: + detail += f"\n ❌ TOO LARGE! Exceeds XGBoost safe limit (~{xgb_max_safe:.2e})" + detail += f"\n Mean: {col_mean:.6e}" + detail += f"\n Std: {col_std:.6e}" + problem_details.append(detail) + + # Check target variable + n_nan_target = np.sum(np.isnan(y_train)) + n_inf_target = np.sum(np.isinf(y_train)) + if n_nan_target > 0 or n_inf_target > 0: + problematic_features.append(f"TARGET[{args.target}]") + detail = f"\n⚠️ Target '{args.target}':" + if n_nan_target > 0: + detail += f"\n NaN: {n_nan_target} ({100 * n_nan_target / len(y_train):.2f}%)" + if n_inf_target > 0: + detail += f"\n Inf: {n_inf_target} ({100 * n_inf_target / len(y_train):.2f}%)" + problem_details.append(detail) + + # Only print if problems found + if len(problematic_features) > 0: + print("\n" + "=" * 70) + print("⚠️ FEATURE VALUE PROBLEMS DETECTED") + print("=" * 70) + for detail in problem_details: + print(detail) + print("\n" + "=" * 70) + print(f"❌ Found {len(problematic_features)} problematic feature(s):") + for feat in problematic_features: + print(f" - {feat}") + print("\nTo fix:") + print( + " 1. Add these features to FEATURES_TO_EXCLUDE at top of script" + ) + print( + " 2. Or investigate why these features have extreme/invalid values" + ) + print("=" * 70 + "\n") + # Create model print(f"\nTraining {args.regressor} regressor...") model, needs_scaling = create_regressor( @@ -1589,12 +2020,24 @@ def main(): # Apply scaling if needed scaler = None + needs_scaling = False X_test_original = X_test.copy() # Keep unscaled version for display if needs_scaling: print(" Applying StandardScaler to features...") scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) + + # Sanity check: verify scaling worked correctly + print( + f" Scaled features - mean: {np.mean(X_train_scaled):.6f}, std: {np.std(X_train_scaled):.6f}" + ) + if np.any(np.isnan(X_train_scaled)) or np.any( + np.isinf(X_train_scaled) + ): + print(" WARNING: NaN or Inf detected in scaled training data!") + if np.any(np.isnan(X_test_scaled)) or np.any(np.isinf(X_test_scaled)): + print(" WARNING: NaN or Inf detected in scaled test data!") else: X_train_scaled = X_train X_test_scaled = X_test @@ -1688,15 +2131,25 @@ def main(): skip_cv=skip_cv, X_test_original=X_test_original, test_df=test_df, + log_transform=args.log_transform, + y_train_original=y_train_original, + y_test_original=y_test_original, ) # Save model - save_model(model, scaler, args.regressor, args.output_dir, feature_names) + save_model( + model, + scaler, + args.regressor, + args.output_dir, + feature_names, + log_transform=args.log_transform, + ) # Compile with TL2cgen if requested (with optimizations enabled by default) if args.treelite_compile is not None: - # Use unscaled training data for branch annotation - # Only tree-based models (XGBoost, LightGBM) don't need scaling + # Use unscaled training data for branch annotation when scaling is not applied + # Note: All models now use scaling for consistency X_train_for_annotation = X_train if not needs_scaling else None compile_model_treelite( @@ -1709,6 +2162,7 @@ def main(): quantize=True, # Always enable quantization feature_names=feature_names, model_name=model_name, + log_transform=args.log_transform, ) print("\n" + "=" * 70) From 8dfdfd8080bc25386a231cfcf479c6bb23b2a801 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 18 Nov 2025 15:07:26 +0000 Subject: [PATCH 080/366] fixes --- cpp/src/dual_simplex/solve.cpp | 2 +- cpp/src/linear_programming/pdlp.cu | 2 +- cpp/src/linear_programming/solve.cu | 2 +- cpp/src/mip/diversity/diversity_manager.cu | 18 +++----------- cpp/src/mip/diversity/diversity_manager.cuh | 1 - cpp/src/mip/feasibility_jump/fj_cpu.cu | 24 +------------------ .../local_search/rounding/bounds_repair.cu | 2 +- cpp/src/utilities/memory_instrumentation.hpp | 1 + cpp/src/utilities/timer.hpp | 2 +- 9 files changed, 10 insertions(+), 44 deletions(-) diff --git a/cpp/src/dual_simplex/solve.cpp b/cpp/src/dual_simplex/solve.cpp index 3431ca345..7e59e83c3 100644 --- a/cpp/src/dual_simplex/solve.cpp +++ b/cpp/src/dual_simplex/solve.cpp @@ -192,7 +192,7 @@ lp_status_t solve_linear_program_with_advanced_basis( edge_norms.clear(); dual::status_t phase1_status; { - raft::common::nvtx::range scope_phase1("DualSimplex::phase1"); + raft::common::nvtx::range scope_phase1("DualSimplex::phase2"); phase1_status = dual_phase2(1, 1, start_time, diff --git a/cpp/src/linear_programming/pdlp.cu b/cpp/src/linear_programming/pdlp.cu index 1e1c03aa1..ba156920f 100644 --- a/cpp/src/linear_programming/pdlp.cu +++ b/cpp/src/linear_programming/pdlp.cu @@ -257,7 +257,7 @@ static bool time_limit_reached(const timer_t& timer) bool elapsed = timer.elapsed_time() >= timer.get_time_limit(); if (elapsed) { CUOPT_LOG_ERROR("**** PDLP Time limit reached: %f *****", timer.get_time_limit()); - cuopt_assert(false, "unexpected timer"); + // cuopt_assert(false, "unexpected timer"); } return elapsed; } diff --git a/cpp/src/linear_programming/solve.cu b/cpp/src/linear_programming/solve.cu index 92119cd10..42e32a030 100644 --- a/cpp/src/linear_programming/solve.cu +++ b/cpp/src/linear_programming/solve.cu @@ -113,7 +113,7 @@ static void set_Stable2() pdlp_hyper_params::major_iteration = 40; pdlp_hyper_params::min_iteration_restart = 10; pdlp_hyper_params::restart_strategy = 1; - pdlp_hyper_params::never_restart_to_average = true; + pdlp_hyper_params::never_restart_to_average = false; pdlp_hyper_params::host_default_reduction_exponent = 0.3; pdlp_hyper_params::host_default_growth_exponent = 0.6; pdlp_hyper_params::host_default_primal_weight_update_smoothing = 0.5; diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index ea60ca131..f7f9fdbd2 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -229,7 +229,6 @@ void diversity_manager_t::generate_quick_feasible_solution() work_limit_timer_t sol_timer(context.gpu_heur_loop, generate_fast_solution_time); // do very short LP run to get somewhere close to the optimal point ls.generate_fast_solution(solution, sol_timer); - sol_timer.record_work(0); if (solution.get_feasible()) { population.run_solution_callbacks(solution); initial_sol_vector.emplace_back(std::move(solution)); @@ -325,12 +324,6 @@ solution_t diversity_manager_t::run_solver() const f_t time_limit = timer.remaining_time(); const f_t lp_time_limit = std::min(diversity_config.max_time_on_lp, time_limit * diversity_config.time_ratio_on_init_lp); - - if (context.settings.deterministic) { - remaining_work_limit = context.settings.work_limit; - CUOPT_LOG_INFO("Deterministic mode, remaining work limit: %f", time_limit); - } - // to automatically compute the solving time on scope exit auto timer_raii_guard = cuopt::scope_guard([&]() { stats.total_solve_time = timer.timer.elapsed_time(); }); @@ -357,7 +350,7 @@ solution_t diversity_manager_t::run_solver() // Run CPUFJ early to find quick initial solutions ls_cpufj_raii_guard_t ls_cpufj_raii_guard(ls); // RAII to stop cpufj threads on solve stop if (!context.settings.deterministic) { -#if 1 +#if 0 ls.start_cpufj_scratch_threads(population); // 30'000 iters ls.scratch_cpu_fj[0].wait_for_cpu_solver(); @@ -376,7 +369,6 @@ solution_t diversity_manager_t::run_solver() if (check_b_b_preemption()) { return population.best_feasible(); } if (!diversity_config.fj_only_run) { compute_probing_cache(ls.constraint_prop.bounds_update, *problem_ptr, probing_timer); - probing_timer.record_work(0); } if (check_b_b_preemption()) { return population.best_feasible(); } @@ -386,7 +378,7 @@ solution_t diversity_manager_t::run_solver() bool bb_thread_solution_exists = simplex_solution_exists.load(); if (bb_thread_solution_exists) { ls.lp_optimal_exists = true; - } else if (!diversity_config.fj_only_run || true) { + } else if (!diversity_config.fj_only_run) { relaxed_lp_settings_t lp_settings; lp_settings.time_limit = lp_time_limit; lp_settings.work_limit = lp_time_limit; @@ -830,11 +822,7 @@ void diversity_manager_t::set_simplex_solution(const std::vector& template bool diversity_manager_t::work_limit_reached() { - if (context.settings.deterministic) { - return remaining_work_limit <= 0; - } else { - return work_limit_reached(); - } + return timer.check_time_limit(); } #if MIP_INSTANTIATE_FLOAT diff --git a/cpp/src/mip/diversity/diversity_manager.cuh b/cpp/src/mip/diversity/diversity_manager.cuh index 256e2762e..b04aa26ef 100644 --- a/cpp/src/mip/diversity/diversity_manager.cuh +++ b/cpp/src/mip/diversity/diversity_manager.cuh @@ -80,7 +80,6 @@ class diversity_manager_t { std::atomic simplex_solution_exists{false}; local_search_t ls; cuopt::work_limit_timer_t timer; - f_t remaining_work_limit{std::numeric_limits::infinity()}; bound_prop_recombiner_t bound_prop_recombiner; fp_recombiner_t fp_recombiner; line_segment_recombiner_t line_segment_recombiner; diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index f587455b2..31d1b8cc3 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -5,28 +5,6 @@ */ /* clang-format on */ -/* - * MEMORY OPERATION ANNOTATIONS: - * - * This file contains detailed comments marking all memory operations (array reads and writes) - * within loops for the CPU Feasibility Jump (CPUFJ) algorithm. These annotations are intended - * to help estimate the memory bandwidth requirements and runtime of this memory-bound workload. - * - * Key annotations: - * - "MEMORY OPS:" marks the start of a loop that performs memory operations - * - "ARRAY READ:" marks array read operations with array name and count per iteration - * - "ARRAY WRITE:" marks array write operations with array name and count per iteration - * - "CRITICAL LOOP" or "HOTTEST LOOP" marks the most frequently executed loops - * - "Total per iteration:" summarizes memory ops per loop iteration - * - * Important notation: - * - n_vars: number of variables - * - n_cstrs: number of constraints - * - avg_var_degree: average number of constraints per variable - * - avg_cstr_degree: average number of variables per constraint - * - total_nnz: total non-zeros in constraint matrix - */ - #include #include "feasibility_jump.cuh" @@ -1716,7 +1694,7 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l auto [loads, stores] = fj_cpu.memory_manifold.collect(); // Log all features including memory statistics - log_regression_features(fj_cpu, time_window_ms, total_time_ms, loads, stores); + // log_regression_features(fj_cpu, time_window_ms, total_time_ms, loads, stores); fj_cpu.last_feature_log_time = now; diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index d6cd62d2e..507c97875 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -68,7 +68,7 @@ f_t bounds_repair_t::get_ii_violation(problem_t& problem) min_act = bound_presolve.upd.min_activity.data(), max_act = bound_presolve.upd.max_activity.data(), cstr_violations_up = cstr_violations_up.data(), - cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) -> f_t { + cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) { f_t cnst_lb = pb_v.constraint_lower_bounds[cstr_idx]; f_t cnst_ub = pb_v.constraint_upper_bounds[cstr_idx]; f_t eps = get_cstr_tolerance( diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 2975a1e36..55cce572d 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -23,6 +23,7 @@ * // When enabled: tracking occurs, counters accumulate * // When disabled: direct passthrough, compiler optimizes away all overhead */ +// Thank you Cursor! #pragma once diff --git a/cpp/src/utilities/timer.hpp b/cpp/src/utilities/timer.hpp index bb74e75b9..8e53d1a4c 100644 --- a/cpp/src/utilities/timer.hpp +++ b/cpp/src/utilities/timer.hpp @@ -45,7 +45,7 @@ class timer_t { file, line, caller); - assert(false && "unexpected timer"); + // assert(false && "unexpected timer"); //__builtin_trap(); } return elapsed; From 8f260672001e24c0db382b7e39b29e11e5fae2e6 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 18 Nov 2025 17:24:51 +0000 Subject: [PATCH 081/366] restore fj scratch --- cpp/src/mip/diversity/diversity_manager.cu | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index f7f9fdbd2..6cdd8a63f 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -360,6 +360,8 @@ solution_t diversity_manager_t::run_solver() #endif } + if (!context.settings.deterministic) { ls.start_cpufj_scratch_threads(population); } + // before probing cache or LP, run FJ to generate initial primal feasible solution const f_t time_ratio_of_probing_cache = diversity_config.time_ratio_of_probing_cache; const f_t max_time_on_probing = diversity_config.max_time_on_probing; @@ -454,7 +456,7 @@ solution_t diversity_manager_t::run_solver() lp_rounded_sol.round_nearest(); lp_rounded_sol.compute_feasibility(); population.add_solution(std::move(lp_rounded_sol)); - // if (!context.settings.deterministic) { ls.start_cpufj_lptopt_scratch_threads(population); } + if (!context.settings.deterministic) { ls.start_cpufj_lptopt_scratch_threads(population); } } population.add_solutions_from_vec(std::move(initial_sol_vector)); From 759b8964affe1ba7dbd9cd0479eec6e366a34b45 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 25 Nov 2025 12:59:28 +0000 Subject: [PATCH 082/366] fix build --- cpp/src/dual_simplex/branch_and_bound.cpp | 156 ++++++++++++++++++++++ 1 file changed, 156 insertions(+) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index ae6cbfc76..9284520b6 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -237,6 +237,162 @@ branch_and_bound_t::branch_and_bound_t( compute_static_features(); } +template +void branch_and_bound_t::compute_static_features() +{ + const auto& A = original_lp_.A; + static_features_.n_rows = A.m; + static_features_.n_cols = A.n; + static_features_.n_nonzeros = A.col_start[A.n]; + static_features_.density = (f_t)static_features_.n_nonzeros / ((f_t)A.m * A.n); + + // Count variable types + static_features_.n_binary = 0; + static_features_.n_integer = 0; + static_features_.n_continuous = 0; + for (const auto& vt : var_types_) { + if (vt == variable_type_t::BINARY) { + static_features_.n_binary++; + } else if (vt == variable_type_t::INTEGER) { + static_features_.n_integer++; + } else { + static_features_.n_continuous++; + } + } + static_features_.integrality_ratio = + (f_t)(static_features_.n_binary + static_features_.n_integer) / A.n; + + // Compute row statistics (constraint sizes) + std::vector row_nnz(A.m, 0); + for (i_t j = 0; j < A.n; j++) { + for (i_t k = A.col_start[j]; k < A.col_start[j + 1]; k++) { + row_nnz[A.i[k]]++; + } + } + + static_features_.max_row_nnz = 0; + f_t sum_row_nnz = 0; + for (i_t i = 0; i < A.m; i++) { + static_features_.max_row_nnz = std::max(static_features_.max_row_nnz, row_nnz[i]); + sum_row_nnz += row_nnz[i]; + } + static_features_.avg_row_nnz = sum_row_nnz / A.m; + + // Compute row coefficient of variation + f_t row_variance = 0; + for (i_t i = 0; i < A.m; i++) { + f_t diff = row_nnz[i] - static_features_.avg_row_nnz; + row_variance += diff * diff; + } + row_variance /= A.m; + f_t row_std = std::sqrt(row_variance); + static_features_.row_nnz_cv = + static_features_.avg_row_nnz > 0 ? row_std / static_features_.avg_row_nnz : 0.0; + + // Compute column statistics (variable degrees) + static_features_.max_col_nnz = 0; + f_t sum_col_nnz = 0; + for (i_t j = 0; j < A.n; j++) { + i_t col_nnz = A.col_start[j + 1] - A.col_start[j]; + static_features_.max_col_nnz = std::max(static_features_.max_col_nnz, col_nnz); + sum_col_nnz += col_nnz; + } + static_features_.avg_col_nnz = sum_col_nnz / A.n; + + // Compute column coefficient of variation + f_t col_variance = 0; + for (i_t j = 0; j < A.n; j++) { + i_t col_nnz = A.col_start[j + 1] - A.col_start[j]; + f_t diff = col_nnz - static_features_.avg_col_nnz; + col_variance += diff * diff; + } + col_variance /= A.n; + f_t col_std = std::sqrt(col_variance); + static_features_.col_nnz_cv = + static_features_.avg_col_nnz > 0 ? col_std / static_features_.avg_col_nnz : 0.0; +} + +template +void branch_and_bound_t::flush_pending_features() +{ + // Must be called with mutex_feature_log_ already locked + if (!has_pending_features_) return; + + constexpr int LINE_BUFFER_SIZE = 512; + char line_buffer[LINE_BUFFER_SIZE]; + + snprintf(line_buffer, + LINE_BUFFER_SIZE, + "BB_NODE_FEATURES " + "node_id=%d depth=%d time=%.6f " + "n_rows=%d n_cols=%d n_nnz=%d density=%.6f " + "n_bin=%d n_int=%d n_cont=%d int_ratio=%.4f " + "avg_row_nnz=%.2f max_row_nnz=%d row_nnz_cv=%.4f " + "avg_col_nnz=%.2f max_col_nnz=%d col_nnz_cv=%.4f " + "n_bounds_chg=%d cutoff_gap=%.4f basis_from_parent=%d " + "simplex_iters=%d n_refact=%d lp_time=%.6f bound_str_time=%.6f var_sel_time=%.6f " + "n_frac=%d strong_branch=%d n_sb_cand=%d sb_time=%.6f " + "lp_status=%d node_status=%d\n", + last_features_.node_id, + last_features_.node_depth, + last_features_.total_node_time, + last_features_.n_rows, + last_features_.n_cols, + last_features_.n_nonzeros, + last_features_.density, + last_features_.n_binary, + last_features_.n_integer, + last_features_.n_continuous, + last_features_.integrality_ratio, + last_features_.avg_row_nnz, + last_features_.max_row_nnz, + last_features_.row_nnz_cv, + last_features_.avg_col_nnz, + last_features_.max_col_nnz, + last_features_.col_nnz_cv, + last_features_.n_bounds_changed, + last_features_.cutoff_gap_ratio, + last_features_.basis_from_parent ? 1 : 0, + last_features_.simplex_iterations, + last_features_.n_refactorizations, + last_features_.lp_solve_time, + last_features_.bound_str_time, + last_features_.variable_sel_time, + last_features_.n_fractional, + last_features_.strong_branch_performed ? 1 : 0, + last_features_.n_strong_branch_candidates, + last_features_.strong_branch_time, + last_features_.lp_status, + last_features_.node_status); + + // Single printf call + settings_.log.printf("%s", line_buffer); + + has_pending_features_ = false; +} + +template +void branch_and_bound_t::log_node_features( + const node_solve_features_t& features) +{ + mutex_feature_log_.lock(); + + f_t current_time = toc(exploration_stats_.start_time); + f_t time_since_last_log = current_time - last_feature_log_time_; + + // Always store the latest features + last_features_ = features; + has_pending_features_ = true; + + // Log if enough time has passed (500ms) + if (time_since_last_log >= FEATURE_LOG_INTERVAL) { + flush_pending_features(); + last_feature_log_time_ = current_time; + } + + mutex_feature_log_.unlock(); +} + template f_t branch_and_bound_t::get_upper_bound() { From dc30219eeda5ec967dbbee9e2e244de6c1f5fcfe Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 25 Nov 2025 14:38:01 +0000 Subject: [PATCH 083/366] tmp dual simplex instrument --- .../linear_programming/cuopt/run_mip.cpp | 2 - .../dual_simplex/dual_simplex_features.hpp | 153 ++++++++++ cpp/src/dual_simplex/phase2.cpp | 266 ++++++++++++------ cpp/src/dual_simplex/sparse_vector.cpp | 52 +++- cpp/src/dual_simplex/sparse_vector.hpp | 20 +- cpp/src/mip/problem/problem_helpers.cuh | 3 + 6 files changed, 394 insertions(+), 102 deletions(-) create mode 100644 cpp/src/dual_simplex/dual_simplex_features.hpp diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 30afac4ef..fe06f1638 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -206,8 +206,6 @@ int run_single_file(std::string file_path, settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; - - settings.presolve = false; // settings.heuristics_only = true; cuopt::linear_programming::benchmark_info_t benchmark_info; diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp new file mode 100644 index 000000000..f7f8807c8 --- /dev/null +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -0,0 +1,153 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#include +#include +#include + +#include + +namespace cuopt::linear_programming::dual_simplex { + +/** + * @brief Feature collection structure for dual simplex runtime prediction. + * + * This structure collects features that can be used to train regression models + * for predicting the runtime of dual_phase2_with_advanced_basis. + */ +template +struct dual_simplex_features_t { + // Model/Problem Features (static) + i_t num_rows{0}; // m - number of constraints + i_t num_cols{0}; // n - number of variables + i_t num_nonzeros{0}; // nnz - total nonzeros in constraint matrix + f_t matrix_density{0.0}; // nnz / (m * n) + f_t avg_nnz_per_col{0.0}; // nnz / n + f_t avg_nnz_per_row{0.0}; // nnz / m + i_t num_bounded_vars{0}; // variables with finite lower AND upper bounds + i_t num_free_vars{0}; // variables with infinite bounds on both sides + i_t num_fixed_vars{0}; // variables where lower == upper + + // Iteration-based features (dynamic) + i_t iteration{0}; // current iteration count + i_t start_iteration{0}; // iteration at start of this call + i_t num_refactors{0}; // number of basis refactorizations + i_t num_basis_updates{0}; // basis updates since last refactor + i_t sparse_delta_z_count{0}; // iterations using sparse delta_z + i_t dense_delta_z_count{0}; // iterations using dense delta_z + i_t total_bound_flips{0}; // cumulative bound flips + + // Sparsity during solve + i_t num_infeasibilities{0}; // size of infeasibility_indices + f_t delta_y_nz_percentage{0.0}; // sparsity of BTran result + + // Phase-specific features + i_t phase{0}; // 1 or 2 + bool slack_basis{false}; // whether starting from slack basis + bool initialize_basis{false}; // whether basis factorization performed initially + + // Settings that impact runtime + i_t refactor_frequency{0}; // from settings + + // Memory access statistics (aggregated from instrumentation) + size_t byte_loads{0}; // total bytes loaded + size_t byte_stores{0}; // total bytes stored + + /** + * @brief Initialize static features from problem data. + */ + void init_from_problem(const lp_problem_t& lp, + const simplex_solver_settings_t& settings, + i_t phase_, + bool slack_basis_, + bool initialize_basis_) + { + num_rows = lp.num_rows; + num_cols = lp.num_cols; + num_nonzeros = lp.A.col_start[lp.num_cols]; + + const f_t total_elements = static_cast(num_rows) * static_cast(num_cols); + matrix_density = (total_elements > 0) ? num_nonzeros / total_elements : 0.0; + avg_nnz_per_col = (num_cols > 0) ? static_cast(num_nonzeros) / num_cols : 0.0; + avg_nnz_per_row = (num_rows > 0) ? static_cast(num_nonzeros) / num_rows : 0.0; + + // Count bound types + num_bounded_vars = 0; + num_free_vars = 0; + num_fixed_vars = 0; + constexpr f_t inf_val = std::numeric_limits::infinity(); + for (i_t j = 0; j < num_cols; ++j) { + const bool has_lower = lp.lower[j] > -inf_val; + const bool has_upper = lp.upper[j] < inf_val; + if (has_lower && has_upper) { + if (lp.lower[j] == lp.upper[j]) { + num_fixed_vars++; + } else { + num_bounded_vars++; + } + } else if (!has_lower && !has_upper) { + num_free_vars++; + } + } + + phase = phase_; + slack_basis = slack_basis_; + initialize_basis = initialize_basis_; + refactor_frequency = settings.refactor_frequency; + } + + /** + * @brief Print all features on a single line in key=value format. + * + * Format: DS_FEATURES: iter=N m=M n=N nnz=K ... + */ + void log_features(const simplex_solver_settings_t& settings) const + { + settings.log.printf( + "DS_FEATURES: iter=%d m=%d n=%d nnz=%d density=%.6e avg_nnz_col=%.2f avg_nnz_row=%.2f " + "bounded=%d free=%d fixed=%d phase=%d refact_freq=%d num_refacts=%d num_updates=%d " + "sparse_dz=%d dense_dz=%d bound_flips=%d num_infeas=%d dy_nz_pct=%.2f " + "byte_loads=%zu byte_stores=%zu\n", + iteration, + num_rows, + num_cols, + num_nonzeros, + matrix_density, + avg_nnz_per_col, + avg_nnz_per_row, + num_bounded_vars, + num_free_vars, + num_fixed_vars, + phase, + refactor_frequency, + num_refactors, + num_basis_updates, + sparse_delta_z_count, + dense_delta_z_count, + total_bound_flips, + num_infeasibilities, + delta_y_nz_percentage, + byte_loads, + byte_stores); + } + + /** + * @brief Reset per-interval counters (called after each logging interval). + */ + void reset_interval_counters() + { + byte_loads = 0; + byte_stores = 0; + } +}; + +// Feature logging interval (every N iterations) +constexpr int FEATURE_LOG_INTERVAL = 100; + +} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 12b4e7928..226bbe463 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -8,6 +8,7 @@ #include #include #include +#include #include #include #include @@ -25,6 +26,9 @@ namespace cuopt::linear_programming::dual_simplex { +// Import instrumented vector type for memory tracking +using cuopt::ins_vector; + namespace phase2 { // Computes vectors farkas_y, farkas_zl, farkas_zu that satisfy @@ -271,12 +275,12 @@ template void compute_reduced_cost_update(const lp_problem_t& lp, const std::vector& basic_list, const std::vector& nonbasic_list, - const std::vector& delta_y, + const ins_vector& delta_y, i_t leaving_index, i_t direction, - std::vector& delta_z_mark, - std::vector& delta_z_indices, - std::vector& delta_z) + ins_vector& delta_z_mark, + ins_vector& delta_z_indices, + ins_vector& delta_z) { const i_t m = lp.num_rows; const i_t n = lp.num_cols; @@ -310,10 +314,10 @@ void compute_delta_z(const csc_matrix_t& A_transpose, const sparse_vector_t& delta_y, i_t leaving_index, i_t direction, - std::vector& nonbasic_mark, - std::vector& delta_z_mark, - std::vector& delta_z_indices, - std::vector& delta_z) + ins_vector& nonbasic_mark, + ins_vector& delta_z_mark, + ins_vector& delta_z_indices, + ins_vector& delta_z) { // delta_zN = - N'*delta_y const i_t nz_delta_y = delta_y.i.size(); @@ -436,9 +440,9 @@ void compute_primal_variables(const basis_update_mpf_t& ft, template void clear_delta_z(i_t entering_index, i_t leaving_index, - std::vector& delta_z_mark, - std::vector& delta_z_indices, - std::vector& delta_z) + ins_vector& delta_z_mark, + ins_vector& delta_z_indices, + ins_vector& delta_z) { for (i_t k = 0; k < delta_z_indices.size(); k++) { const i_t j = delta_z_indices[k]; @@ -454,7 +458,7 @@ template void clear_delta_x(const std::vector& basic_list, i_t entering_index, sparse_vector_t& scaled_delta_xB_sparse, - std::vector& delta_x) + ins_vector& delta_x) { const i_t scaled_delta_xB_nz = scaled_delta_xB_sparse.i.size(); for (i_t k = 0; k < scaled_delta_xB_nz; ++k) { @@ -519,7 +523,7 @@ void vstatus_changes(const std::vector& vstatus, template void compute_bounded_info(const std::vector& lower, const std::vector& upper, - std::vector& bounded_variables) + ins_vector& bounded_variables) { const size_t n = lower.size(); for (size_t j = 0; j < n; j++) { @@ -624,8 +628,8 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, const simplex_solver_settings_t& settings, const std::vector& basic_list, const std::vector& x, - std::vector& squared_infeasibilities, - std::vector& infeasibility_indices) + ins_vector& squared_infeasibilities, + ins_vector& infeasibility_indices) { const i_t m = lp.num_rows; const i_t n = lp.num_cols; @@ -653,8 +657,8 @@ void update_single_primal_infeasibility(const std::vector& lower, const std::vector& upper, const std::vector& x, f_t primal_tol, - std::vector& squared_infeasibilities, - std::vector& infeasibility_indices, + ins_vector& squared_infeasibilities, + ins_vector& infeasibility_indices, i_t j, f_t& primal_inf) { @@ -696,8 +700,8 @@ void update_primal_infeasibilities(const lp_problem_t& lp, i_t entering_index, i_t leaving_index, std::vector& basic_change_list, - std::vector& squared_infeasibilities, - std::vector& infeasibility_indices, + ins_vector& squared_infeasibilities, + ins_vector& infeasibility_indices, f_t& primal_inf) { const f_t primal_tol = settings.primal_tol; @@ -726,8 +730,8 @@ void update_primal_infeasibilities(const lp_problem_t& lp, } template -void clean_up_infeasibilities(std::vector& squared_infeasibilities, - std::vector& infeasibility_indices) +void clean_up_infeasibilities(ins_vector& squared_infeasibilities, + ins_vector& infeasibility_indices) { bool needs_clean_up = false; for (i_t k = 0; k < infeasibility_indices.size(); ++k) { @@ -756,9 +760,9 @@ i_t steepest_edge_pricing_with_infeasibilities(const lp_problem_t& lp, const simplex_solver_settings_t& settings, const std::vector& x, const std::vector& dy_steepest_edge, - const std::vector& basic_mark, - std::vector& squared_infeasibilities, - std::vector& infeasibility_indices, + const ins_vector& basic_mark, + ins_vector& squared_infeasibilities, + ins_vector& infeasibility_indices, i_t& direction, i_t& basic_leaving, f_t& max_val) @@ -1029,20 +1033,21 @@ i_t phase2_ratio_test(const lp_problem_t& lp, template i_t flip_bounds(const lp_problem_t& lp, const simplex_solver_settings_t& settings, - const std::vector& bounded_variables, + const ins_vector& bounded_variables, const std::vector& objective, const std::vector& z, - const std::vector& delta_z_indices, + const ins_vector& delta_z_indices, const std::vector& nonbasic_list, i_t entering_index, std::vector& vstatus, - std::vector& delta_x, - std::vector& mark, - std::vector& atilde, - std::vector& atilde_index) + ins_vector& delta_x_flip, + ins_vector& atilde_mark, + ins_vector& atilde, + ins_vector& atilde_index) { i_t num_flipped = 0; - for (i_t j : delta_z_indices) { + for (i_t k = 0; k < delta_z_indices.size(); ++k) { + const i_t j = delta_z_indices[k]; if (j == entering_index) { continue; } if (!bounded_variables[j]) { continue; } // x_j is now a nonbasic bounded variable that will not enter the basis this @@ -1051,8 +1056,8 @@ i_t flip_bounds(const lp_problem_t& lp, settings.dual_tol; // lower to 1e-7 or less will cause 25fv47 and d2q06c to cycle if (vstatus[j] == variable_status_t::NONBASIC_LOWER && z[j] < -dual_tol) { const f_t delta = lp.upper[j] - lp.lower[j]; - scatter_dense(lp.A, j, -delta, atilde, mark, atilde_index); - delta_x[j] += delta; + scatter_dense(lp.A, j, -delta, atilde.array, atilde_mark.array, atilde_index.array); + delta_x_flip[j] += delta; vstatus[j] = variable_status_t::NONBASIC_UPPER; #ifdef BOUND_FLIP_DEBUG settings.log.printf( @@ -1061,8 +1066,8 @@ i_t flip_bounds(const lp_problem_t& lp, num_flipped++; } else if (vstatus[j] == variable_status_t::NONBASIC_UPPER && z[j] > dual_tol) { const f_t delta = lp.lower[j] - lp.upper[j]; - scatter_dense(lp.A, j, -delta, atilde, mark, atilde_index); - delta_x[j] += delta; + scatter_dense(lp.A, j, -delta, atilde.array, atilde_mark.array, atilde_index.array); + delta_x_flip[j] += delta; vstatus[j] = variable_status_t::NONBASIC_LOWER; #ifdef BOUND_FLIP_DEBUG settings.log.printf( @@ -1245,12 +1250,12 @@ i_t update_steepest_edge_norms(const simplex_solver_settings_t& settin const sparse_vector_t& scaled_delta_xB, i_t basic_leaving_index, i_t entering_index, - std::vector& v, + ins_vector& v, + sparse_vector_t& v_sparse, std::vector& delta_y_steepest_edge) { - i_t m = basic_list.size(); const i_t delta_y_nz = delta_y_sparse.i.size(); - sparse_vector_t v_sparse(m, 0); + v_sparse.clear(); // B^T delta_y = - direction * e_basic_leaving_index // We want B v = - B^{-T} e_basic_leaving_index ft.b_solve(delta_y_sparse, v_sparse); @@ -1365,7 +1370,7 @@ i_t check_steepest_edge_norms(const simplex_solver_settings_t& setting template i_t compute_perturbation(const lp_problem_t& lp, const simplex_solver_settings_t& settings, - const std::vector& delta_z_indices, + const ins_vector& delta_z_indices, std::vector& z, std::vector& objective, f_t& sum_perturb) @@ -1414,8 +1419,8 @@ i_t compute_perturbation(const lp_problem_t& lp, template void reset_basis_mark(const std::vector& basic_list, const std::vector& nonbasic_list, - std::vector& basic_mark, - std::vector& nonbasic_mark) + ins_vector& basic_mark, + ins_vector& nonbasic_mark) { const i_t m = basic_list.size(); const i_t n = nonbasic_mark.size(); @@ -1483,8 +1488,8 @@ void compute_delta_y(const basis_update_mpf_t& ft, template i_t update_dual_variables(const sparse_vector_t& delta_y_sparse, - const std::vector& delta_z_indices, - const std::vector& delta_z, + const ins_vector& delta_z_indices, + const ins_vector& delta_z, f_t step_length, i_t leaving_index, std::vector& y, @@ -1510,21 +1515,23 @@ i_t update_dual_variables(const sparse_vector_t& delta_y_sparse, template void adjust_for_flips(const basis_update_mpf_t& ft, const std::vector& basic_list, - const std::vector& delta_z_indices, - std::vector& atilde_index, - std::vector& atilde, - std::vector& atilde_mark, + const ins_vector& delta_z_indices, + ins_vector& atilde_index, + ins_vector& atilde, + ins_vector& atilde_mark, + sparse_vector_t& atilde_sparse, sparse_vector_t& delta_xB_0_sparse, - std::vector& delta_x_flip, + ins_vector& delta_x_flip, std::vector& x) { - const i_t m = basic_list.size(); const i_t atilde_nz = atilde_index.size(); // B*delta_xB_0 = atilde - sparse_vector_t atilde_sparse(m, atilde_nz); + atilde_sparse.clear(); + atilde_sparse.i.reserve(atilde_nz); + atilde_sparse.x.reserve(atilde_nz); for (i_t k = 0; k < atilde_nz; ++k) { - atilde_sparse.i[k] = atilde_index[k]; - atilde_sparse.x[k] = atilde[atilde_index[k]]; + atilde_sparse.i.push_back(atilde_index[k]); + atilde_sparse.x.push_back(atilde[atilde_index[k]]); } ft.b_solve(atilde_sparse, delta_xB_0_sparse); const i_t delta_xB_0_nz = delta_xB_0_sparse.i.size(); @@ -1533,7 +1540,8 @@ void adjust_for_flips(const basis_update_mpf_t& ft, x[j] += delta_xB_0_sparse.x[k]; } - for (i_t j : delta_z_indices) { + for (i_t k = 0; k < delta_z_indices.size(); ++k) { + const i_t j = delta_z_indices[k]; x[j] += delta_x_flip[j]; delta_x_flip[j] = 0.0; } @@ -1620,7 +1628,7 @@ i_t compute_delta_x(const lp_problem_t& lp, template void update_primal_variables(const sparse_vector_t& scaled_delta_xB_sparse, const std::vector& basic_list, - const std::vector& delta_x, + const ins_vector& delta_x, i_t entering_index, std::vector& x) { @@ -1638,7 +1646,7 @@ template void update_objective(const std::vector& basic_list, const std::vector& changed_basic_indices, const std::vector& objective, - const std::vector& delta_x, + const ins_vector& delta_x, i_t entering_index, f_t& obj) { @@ -1781,8 +1789,8 @@ void check_primal_infeasibilities(const lp_problem_t& lp, const simplex_solver_settings_t& settings, const std::vector& basic_list, const std::vector& x, - const std::vector& squared_infeasibilities, - const std::vector& infeasibility_indices) + const ins_vector& squared_infeasibilities, + const ins_vector& infeasibility_indices) { const i_t m = basic_list.size(); for (i_t k = 0; k < m; ++k) { @@ -1812,8 +1820,8 @@ void check_primal_infeasibilities(const lp_problem_t& lp, template void check_basic_infeasibilities(const std::vector& basic_list, - const std::vector& basic_mark, - const std::vector& infeasibility_indices, + const ins_vector& basic_mark, + const ins_vector& infeasibility_indices, i_t info) { for (i_t k = 0; k < infeasibility_indices.size(); ++k) { @@ -1856,8 +1864,8 @@ template void check_basis_mark(const simplex_solver_settings_t& settings, const std::vector& basic_list, const std::vector& nonbasic_list, - const std::vector& basic_mark, - const std::vector& nonbasic_mark) + const ins_vector& basic_mark, + const ins_vector& nonbasic_mark) { const i_t m = basic_list.size(); const i_t n = basic_mark.size(); @@ -2339,26 +2347,28 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } const i_t iter_limit = settings.iteration_limit; - std::vector delta_y(m, 0.0); - std::vector delta_z(n, 0.0); - std::vector delta_x(n, 0.0); - std::vector delta_x_flip(n, 0.0); - std::vector atilde(m, 0.0); - std::vector atilde_mark(m, 0); - std::vector atilde_index; - std::vector nonbasic_mark(n); - std::vector basic_mark(n); - std::vector delta_z_mark(n, 0); - std::vector delta_z_indices; - std::vector v(m, 0.0); - std::vector squared_infeasibilities; - std::vector infeasibility_indices; + + // Instrumented vectors for memory access tracking + ins_vector delta_y(m, 0.0); + ins_vector delta_z(n, 0.0); + ins_vector delta_x(n, 0.0); + ins_vector delta_x_flip(n, 0.0); + ins_vector atilde(m, 0.0); + ins_vector atilde_mark(m, 0); + ins_vector atilde_index; + ins_vector nonbasic_mark(n); + ins_vector basic_mark(n); + ins_vector delta_z_mark(n, 0); + ins_vector delta_z_indices; + ins_vector v(m, 0.0); + ins_vector squared_infeasibilities; + ins_vector infeasibility_indices; delta_z_indices.reserve(n); phase2::reset_basis_mark(basic_list, nonbasic_list, basic_mark, nonbasic_mark); - std::vector bounded_variables(n, 0); + ins_vector bounded_variables(n, 0); phase2::compute_bounded_info(lp.lower, lp.upper, bounded_variables); f_t primal_infeasibility = phase2::compute_initial_primal_infeasibilities( @@ -2374,10 +2384,66 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, f_t obj = compute_objective(lp, x); const i_t start_iter = iter; - i_t sparse_delta_z = 0; - i_t dense_delta_z = 0; + i_t sparse_delta_z = 0; + i_t dense_delta_z = 0; + i_t num_refactors = 0; + i_t total_bound_flips = 0; + f_t delta_y_nz_percentage = 0.0; phase2::phase2_timers_t timers(false); + // Feature collection for regression training + dual_simplex_features_t features; + features.init_from_problem(lp, settings, phase, slack_basis != 0, initialize_basis); + features.start_iteration = iter; + + // Sparse vectors for main loop (declared outside loop for instrumentation) + sparse_vector_t delta_y_sparse(m, 0); + sparse_vector_t UTsol_sparse(m, 0); + sparse_vector_t delta_xB_0_sparse(m, 0); + sparse_vector_t utilde_sparse(m, 0); + sparse_vector_t scaled_delta_xB_sparse(m, 0); + sparse_vector_t rhs_sparse(m, 0); + sparse_vector_t v_sparse(m, 0); // For steepest edge norms + sparse_vector_t atilde_sparse(m, 0); // For flip adjustments + + // Create instrumentation manifold + instrumentation_manifold_t manifold; + manifold.add("delta_y", delta_y); + manifold.add("delta_z", delta_z); + manifold.add("delta_x", delta_x); + manifold.add("delta_x_flip", delta_x_flip); + manifold.add("atilde", atilde); + manifold.add("atilde_mark", atilde_mark); + manifold.add("atilde_index", atilde_index); + manifold.add("nonbasic_mark", nonbasic_mark); + manifold.add("basic_mark", basic_mark); + manifold.add("delta_z_mark", delta_z_mark); + manifold.add("delta_z_indices", delta_z_indices); + manifold.add("v", v); + manifold.add("squared_infeasibilities", squared_infeasibilities); + manifold.add("infeasibility_indices", infeasibility_indices); + manifold.add("bounded_variables", bounded_variables); + + // Add sparse vector internal arrays to manifold + manifold.add("delta_y_sparse.i", delta_y_sparse.i); + manifold.add("delta_y_sparse.x", delta_y_sparse.x); + manifold.add("UTsol_sparse.i", UTsol_sparse.i); + manifold.add("UTsol_sparse.x", UTsol_sparse.x); + manifold.add("delta_xB_0_sparse.i", delta_xB_0_sparse.i); + manifold.add("delta_xB_0_sparse.x", delta_xB_0_sparse.x); + manifold.add("utilde_sparse.i", utilde_sparse.i); + manifold.add("utilde_sparse.x", utilde_sparse.x); + manifold.add("scaled_delta_xB_sparse.i", scaled_delta_xB_sparse.i); + manifold.add("scaled_delta_xB_sparse.x", scaled_delta_xB_sparse.x); + manifold.add("rhs_sparse.i", rhs_sparse.i); + manifold.add("rhs_sparse.x", rhs_sparse.x); + manifold.add("v_sparse.i", v_sparse.i); + manifold.add("v_sparse.x", v_sparse.x); + manifold.add("atilde_sparse.i", atilde_sparse.i); + manifold.add("atilde_sparse.x", atilde_sparse.x); + + // Note: Features are logged inline with DS_FEATURES: prefix + while (iter < iter_limit) { // Pricing i_t direction = 0; @@ -2428,8 +2494,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // BTran // BT*delta_y = -delta_zB = -sigma*ei timers.start_timer(); - sparse_vector_t delta_y_sparse(m, 0); - sparse_vector_t UTsol_sparse(m, 0); + delta_y_sparse.clear(); + UTsol_sparse.clear(); { raft::common::nvtx::range scope_btran("DualSimplex::btran"); phase2::compute_delta_y(ft, basic_leaving_index, direction, delta_y_sparse, UTsol_sparse); @@ -2458,8 +2524,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, for (i_t k = 0; k < nz_delta_y; k++) { if (std::abs(delta_y_sparse.x[k]) > 1e-12) { delta_y_nz0++; } } - const f_t delta_y_nz_percentage = delta_y_nz0 / static_cast(m) * 100.0; - const bool use_transpose = delta_y_nz_percentage <= 30.0; + delta_y_nz_percentage = delta_y_nz0 / static_cast(m) * 100.0; + const bool use_transpose = delta_y_nz_percentage <= 30.0; if (use_transpose) { sparse_delta_z++; phase2::compute_delta_z(A_transpose, @@ -2473,7 +2539,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } else { dense_delta_z++; // delta_zB = sigma*ei - delta_y_sparse.to_dense(delta_y); + delta_y_sparse.to_dense(delta_y.array); phase2::compute_reduced_cost_update(lp, basic_list, nonbasic_list, @@ -2720,8 +2786,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, atilde_index); timers.flip_time += timers.stop_timer(); + total_bound_flips += num_flipped; - sparse_vector_t delta_xB_0_sparse(m, 0); + delta_xB_0_sparse.clear(); if (num_flipped > 0) { timers.start_timer(); phase2::adjust_for_flips(ft, @@ -2730,6 +2797,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, atilde_index, atilde, atilde_mark, + atilde_sparse, delta_xB_0_sparse, delta_x_flip, x); @@ -2737,9 +2805,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } timers.start_timer(); - sparse_vector_t utilde_sparse(m, 0); - sparse_vector_t scaled_delta_xB_sparse(m, 0); - sparse_vector_t rhs_sparse(lp.A, entering_index); + utilde_sparse.clear(); + scaled_delta_xB_sparse.clear(); + rhs_sparse.from_csc_column(lp.A, entering_index); { raft::common::nvtx::range scope_ftran("DualSimplex::ftran"); if (phase2::compute_delta_x(lp, @@ -2780,6 +2848,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, basic_leaving_index, entering_index, v, + v_sparse, delta_y_steepest_edge); #ifdef STEEPEST_EDGE_DEBUG if (steepest_edge_status == -1) { @@ -2901,6 +2970,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif if (should_refactor) { raft::common::nvtx::range scope_refactor("DualSimplex::refactorization"); + num_refactors++; bool should_recompute_x = false; if (ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus) > 0) { should_recompute_x = true; @@ -2962,6 +3032,28 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::clear_delta_z(entering_index, leaving_index, delta_z_mark, delta_z_indices, delta_z); f_t now = toc(start_time); + + // Feature logging for regression training (every FEATURE_LOG_INTERVAL iterations) + if ((iter % FEATURE_LOG_INTERVAL) == 0) { + // Collect aggregated memory access statistics + auto [total_loads, total_stores] = manifold.collect_and_flush(); + features.byte_loads = total_loads; + features.byte_stores = total_stores; + + // Update dynamic features + features.iteration = iter; + features.num_refactors = num_refactors; + features.num_basis_updates = ft.num_updates(); + features.sparse_delta_z_count = sparse_delta_z; + features.dense_delta_z_count = dense_delta_z; + features.total_bound_flips = total_bound_flips; + features.num_infeasibilities = infeasibility_indices.size(); + features.delta_y_nz_percentage = delta_y_nz_percentage; + + // Log all features on a single line + features.log_features(settings); + } + if ((iter - start_iter) < settings.first_iteration_log || (iter % settings.iteration_log_frequency) == 0) { if (phase == 1 && iter == 1) { diff --git a/cpp/src/dual_simplex/sparse_vector.cpp b/cpp/src/dual_simplex/sparse_vector.cpp index 2d4745650..b9a8a4af7 100644 --- a/cpp/src/dual_simplex/sparse_vector.cpp +++ b/cpp/src/dual_simplex/sparse_vector.cpp @@ -13,6 +13,8 @@ namespace cuopt::linear_programming::dual_simplex { +using cuopt::ins_vector; + template sparse_vector_t::sparse_vector_t(const csc_matrix_t& A, i_t col) { @@ -28,6 +30,23 @@ sparse_vector_t::sparse_vector_t(const csc_matrix_t& A, i_t } } +template +void sparse_vector_t::from_csc_column(const csc_matrix_t& A, i_t col) +{ + const i_t col_start = A.col_start[col]; + const i_t col_end = A.col_start[col + 1]; + n = A.m; + const i_t nz = col_end - col_start; + i.clear(); + x.clear(); + i.reserve(nz); + x.reserve(nz); + for (i_t k = col_start; k < col_end; ++k) { + i.push_back(A.i[k]); + x.push_back(A.x[k]); + } +} + template void sparse_vector_t::from_dense(const std::vector& in) { @@ -53,8 +72,8 @@ void sparse_vector_t::to_csc(csc_matrix_t& A) const A.col_start.resize(2); A.col_start[0] = 0; A.col_start[1] = i.size(); - A.i = i; - A.x = x; + A.i = i.array; + A.x = x.array; } template @@ -78,16 +97,26 @@ void sparse_vector_t::scatter(std::vector& x_dense) const } } +template +void sparse_vector_t::scatter(ins_vector& x_dense) const +{ + // Assumes x_dense is already cleared + const i_t nz = i.size(); + for (i_t k = 0; k < nz; ++k) { + x_dense[i[k]] += x[k]; + } +} + template void sparse_vector_t::inverse_permute_vector(const std::vector& p) { assert(p.size() == n); i_t nz = i.size(); - std::vector i_perm(nz); + ins_vector i_perm(nz); for (i_t k = 0; k < nz; ++k) { i_perm[k] = p[i[k]]; } - i = i_perm; + i = std::move(i_perm); } template @@ -99,11 +128,11 @@ void sparse_vector_t::inverse_permute_vector(const std::vector& p i_t nz = i.size(); y.n = n; y.x = x; - std::vector i_perm(nz); + ins_vector i_perm(nz); for (i_t k = 0; k < nz; ++k) { i_perm[k] = p[i[k]]; } - y.i = i_perm; + y.i = std::move(i_perm); } template @@ -154,21 +183,22 @@ void sparse_vector_t::sort() } else { // Use a n log n sort const i_t nz = i.size(); - std::vector i_sorted(nz); - std::vector x_sorted(nz); + ins_vector i_sorted(nz); + ins_vector x_sorted(nz); std::vector perm(nz); for (i_t k = 0; k < nz; ++k) { perm[k] = k; } - std::vector& iunsorted = i; + // Need to capture the underlying array for the lambda + auto& iunsorted = i.array; std::sort( perm.begin(), perm.end(), [&iunsorted](i_t a, i_t b) { return iunsorted[a] < iunsorted[b]; }); for (i_t k = 0; k < nz; ++k) { i_sorted[k] = i[perm[k]]; x_sorted[k] = x[perm[k]]; } - i = i_sorted; - x = x_sorted; + i = std::move(i_sorted); + x = std::move(x_sorted); } // Check diff --git a/cpp/src/dual_simplex/sparse_vector.hpp b/cpp/src/dual_simplex/sparse_vector.hpp index 7acfdc8b5..5cac6b7dd 100644 --- a/cpp/src/dual_simplex/sparse_vector.hpp +++ b/cpp/src/dual_simplex/sparse_vector.hpp @@ -9,11 +9,15 @@ #include #include +#include #include namespace cuopt::linear_programming::dual_simplex { +// Import instrumented vector type +using cuopt::ins_vector; + // A sparse vector stored as a list of nonzero coefficients and their indices template class sparse_vector_t { @@ -34,6 +38,8 @@ class sparse_vector_t { // scatter a sparse vector into a dense vector. Assumes x_dense is already cleared or // preinitialized void scatter(std::vector& x_dense) const; + // scatter into instrumented vector + void scatter(ins_vector& x_dense) const; // inverse permute the current sparse vector void inverse_permute_vector(const std::vector& p); // inverse permute a sparse vector into another sparse vector @@ -47,9 +53,19 @@ class sparse_vector_t { void negate(); f_t find_coefficient(i_t index) const; + // Clear the sparse vector (removes all entries, keeps capacity) + void clear() + { + i.clear(); + x.clear(); + } + + // Reset from a column of a CSC matrix + void from_csc_column(const csc_matrix_t& A, i_t col); + i_t n; - std::vector i; - std::vector x; + ins_vector i; + ins_vector x; }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/mip/problem/problem_helpers.cuh b/cpp/src/mip/problem/problem_helpers.cuh index 660e51c69..4f9a58764 100644 --- a/cpp/src/mip/problem/problem_helpers.cuh +++ b/cpp/src/mip/problem/problem_helpers.cuh @@ -371,6 +371,9 @@ static void csrsort_cusparse(rmm::device_uvector& values, i_t cols, const raft::handle_t* handle_ptr) { + // skip if the matrix is empty + if (values.size() == 0) { return; } + auto stream = offsets.stream(); cusparseHandle_t handle; cusparseCreate(&handle); From 1ca01ba082c41b17c83da715d3bdb04e4ae69d6f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 25 Nov 2025 16:29:58 +0000 Subject: [PATCH 084/366] dual simplex feature logging --- cpp/src/dual_simplex/barrier.cu | 2 +- cpp/src/dual_simplex/cusparse_view.cu | 6 +- .../dual_simplex/dual_simplex_features.hpp | 13 ++- cpp/src/dual_simplex/folding.cpp | 10 +-- cpp/src/dual_simplex/phase2.cpp | 32 +++++--- cpp/src/dual_simplex/singletons.cpp | 8 +- cpp/src/dual_simplex/sparse_matrix.cpp | 59 +++++++++++++- cpp/src/dual_simplex/sparse_matrix.hpp | 44 ++++++---- cpp/src/dual_simplex/sparse_vector.cpp | 11 +++ cpp/src/dual_simplex/sparse_vector.hpp | 4 +- cpp/src/utilities/memory_instrumentation.hpp | 80 +++++++++++++++---- 11 files changed, 210 insertions(+), 59 deletions(-) diff --git a/cpp/src/dual_simplex/barrier.cu b/cpp/src/dual_simplex/barrier.cu index bc630c06c..d194b9be1 100644 --- a/cpp/src/dual_simplex/barrier.cu +++ b/cpp/src/dual_simplex/barrier.cu @@ -1260,7 +1260,7 @@ class iteration_data_t { // v = alpha * A * w + beta * v = alpha * A * Dinv * A^T * y + beta * v matrix_vector_multiply(A, alpha, w, beta, v); if (debug) { - printf("||A|| = %.16e\n", vector_norm2(A.x)); + printf("||A|| = %.16e\n", vector_norm2(A.x.array)); printf("||w|| = %.16e\n", vector_norm2(w)); printf("||v|| = %.16e\n", vector_norm2(v)); } diff --git a/cpp/src/dual_simplex/cusparse_view.cu b/cpp/src/dual_simplex/cusparse_view.cu index b253e5624..d9bae0739 100644 --- a/cpp/src/dual_simplex/cusparse_view.cu +++ b/cpp/src/dual_simplex/cusparse_view.cu @@ -148,9 +148,9 @@ cusparse_view_t::cusparse_view_t(raft::handle_t const* handle_ptr, A_indices_ = device_copy(indices, handle_ptr->get_stream()); A_data_ = device_copy(data, handle_ptr->get_stream()); - A_T_offsets_ = device_copy(A.col_start, handle_ptr->get_stream()); - A_T_indices_ = device_copy(A.i, handle_ptr->get_stream()); - A_T_data_ = device_copy(A.x, handle_ptr->get_stream()); + A_T_offsets_ = device_copy(A.col_start.array, handle_ptr->get_stream()); + A_T_indices_ = device_copy(A.i.array, handle_ptr->get_stream()); + A_T_data_ = device_copy(A.x.array, handle_ptr->get_stream()); cusparseCreateCsr(&A_, rows, diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index f7f8807c8..9024cbad5 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -59,6 +59,9 @@ struct dual_simplex_features_t { size_t byte_loads{0}; // total bytes loaded size_t byte_stores{0}; // total bytes stored + // Runtime for the interval (in seconds) + f_t interval_runtime{0.0}; + /** * @brief Initialize static features from problem data. */ @@ -113,7 +116,7 @@ struct dual_simplex_features_t { "DS_FEATURES: iter=%d m=%d n=%d nnz=%d density=%.6e avg_nnz_col=%.2f avg_nnz_row=%.2f " "bounded=%d free=%d fixed=%d phase=%d refact_freq=%d num_refacts=%d num_updates=%d " "sparse_dz=%d dense_dz=%d bound_flips=%d num_infeas=%d dy_nz_pct=%.2f " - "byte_loads=%zu byte_stores=%zu\n", + "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", iteration, num_rows, num_cols, @@ -134,7 +137,8 @@ struct dual_simplex_features_t { num_infeasibilities, delta_y_nz_percentage, byte_loads, - byte_stores); + byte_stores, + interval_runtime); } /** @@ -142,8 +146,9 @@ struct dual_simplex_features_t { */ void reset_interval_counters() { - byte_loads = 0; - byte_stores = 0; + byte_loads = 0; + byte_stores = 0; + interval_runtime = 0.0; } }; diff --git a/cpp/src/dual_simplex/folding.cpp b/cpp/src/dual_simplex/folding.cpp index 6eae5eb1e..3f5dc41ab 100644 --- a/cpp/src/dual_simplex/folding.cpp +++ b/cpp/src/dual_simplex/folding.cpp @@ -37,8 +37,8 @@ constexpr int8_t kInactive = 0; template void find_vertices_to_refine(const std::unordered_set& refining_color_vertices, - const std::vector& offset, - const std::vector& vertex_list, + const cuopt::ins_vector& offset, + const cuopt::ins_vector& vertex_list, const std::vector& color_map, std::vector& marked_vertices, std::vector& vertices_to_refine, @@ -75,9 +75,9 @@ template void compute_sums_of_refined_vertices(i_t refining_color, const std::unordered_set& refining_color_vertices, const std::vector& vertices_to_refine, - const std::vector& offsets, - const std::vector& vertex_list, - const std::vector& weight_list, + const cuopt::ins_vector& offsets, + const cuopt::ins_vector& vertex_list, + const cuopt::ins_vector& weight_list, const std::vector& color_map, std::vector& vertex_to_sum, std::vector& max_sum_by_color) diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 226bbe463..58a3312b7 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -699,7 +699,7 @@ void update_primal_infeasibilities(const lp_problem_t& lp, const std::vector& x, i_t entering_index, i_t leaving_index, - std::vector& basic_change_list, + ins_vector& basic_change_list, ins_vector& squared_infeasibilities, ins_vector& infeasibility_indices, f_t& primal_inf) @@ -906,7 +906,7 @@ f_t first_stage_harris(const lp_problem_t& lp, const std::vector& vstatus, const std::vector& nonbasic_list, std::vector& z, - std::vector& delta_z) + ins_vector& delta_z) { const i_t n = lp.num_cols; const i_t m = lp.num_rows; @@ -940,7 +940,7 @@ i_t second_stage_harris(const lp_problem_t& lp, const std::vector& vstatus, const std::vector& nonbasic_list, const std::vector& z, - const std::vector& delta_z, + const ins_vector& delta_z, f_t max_step_length, f_t& step_length, i_t& nonbasic_entering) @@ -983,7 +983,7 @@ i_t phase2_ratio_test(const lp_problem_t& lp, const std::vector& vstatus, const std::vector& nonbasic_list, std::vector& z, - std::vector& delta_z, + ins_vector& delta_z, f_t& step_length, i_t& nonbasic_entering) { @@ -1056,7 +1056,7 @@ i_t flip_bounds(const lp_problem_t& lp, settings.dual_tol; // lower to 1e-7 or less will cause 25fv47 and d2q06c to cycle if (vstatus[j] == variable_status_t::NONBASIC_LOWER && z[j] < -dual_tol) { const f_t delta = lp.upper[j] - lp.lower[j]; - scatter_dense(lp.A, j, -delta, atilde.array, atilde_mark.array, atilde_index.array); + scatter_dense(lp.A, j, -delta, atilde, atilde_mark, atilde_index); delta_x_flip[j] += delta; vstatus[j] = variable_status_t::NONBASIC_UPPER; #ifdef BOUND_FLIP_DEBUG @@ -1066,7 +1066,7 @@ i_t flip_bounds(const lp_problem_t& lp, num_flipped++; } else if (vstatus[j] == variable_status_t::NONBASIC_UPPER && z[j] > dual_tol) { const f_t delta = lp.lower[j] - lp.upper[j]; - scatter_dense(lp.A, j, -delta, atilde.array, atilde_mark.array, atilde_index.array); + scatter_dense(lp.A, j, -delta, atilde, atilde_mark, atilde_index); delta_x_flip[j] += delta; vstatus[j] = variable_status_t::NONBASIC_LOWER; #ifdef BOUND_FLIP_DEBUG @@ -1565,12 +1565,12 @@ i_t compute_delta_x(const lp_problem_t& lp, i_t basic_leaving_index, i_t direction, const std::vector& basic_list, - const std::vector& delta_x_flip, + const ins_vector& delta_x_flip, const sparse_vector_t& rhs_sparse, const std::vector& x, sparse_vector_t& utilde_sparse, sparse_vector_t& scaled_delta_xB_sparse, - std::vector& delta_x) + ins_vector& delta_x) { f_t delta_x_leaving = direction == 1 ? lp.lower[leaving_index] - x[leaving_index] : lp.upper[leaving_index] - x[leaving_index]; @@ -1644,7 +1644,7 @@ void update_primal_variables(const sparse_vector_t& scaled_delta_xB_sp template void update_objective(const std::vector& basic_list, - const std::vector& changed_basic_indices, + const ins_vector& changed_basic_indices, const std::vector& objective, const ins_vector& delta_x, i_t entering_index, @@ -2442,6 +2442,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, manifold.add("atilde_sparse.i", atilde_sparse.i); manifold.add("atilde_sparse.x", atilde_sparse.x); + // Add A_transpose matrix arrays to manifold for memory tracking + manifold.add("A_transpose.col_start", A_transpose.col_start); + manifold.add("A_transpose.i", A_transpose.i); + manifold.add("A_transpose.x", A_transpose.x); + + // Track iteration interval start time for runtime measurement + f_t interval_start_time = toc(start_time); + // Note: Features are logged inline with DS_FEATURES: prefix while (iter < iter_limit) { @@ -2539,7 +2547,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } else { dense_delta_z++; // delta_zB = sigma*ei - delta_y_sparse.to_dense(delta_y.array); + delta_y_sparse.to_dense(delta_y); phase2::compute_reduced_cost_update(lp, basic_list, nonbasic_list, @@ -3040,6 +3048,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, features.byte_loads = total_loads; features.byte_stores = total_stores; + // Compute interval runtime + features.interval_runtime = now - interval_start_time; + interval_start_time = now; + // Update dynamic features features.iteration = iter; features.num_refactors = num_refactors; diff --git a/cpp/src/dual_simplex/singletons.cpp b/cpp/src/dual_simplex/singletons.cpp index ff6bcbac2..c4656889b 100644 --- a/cpp/src/dual_simplex/singletons.cpp +++ b/cpp/src/dual_simplex/singletons.cpp @@ -200,8 +200,8 @@ i_t find_singletons(const csc_matrix_t& A, // Find column singletons row_col_graph_t graph{Cdeg.begin(), col_perm.begin(), - A.col_start.cbegin(), - A.i.cbegin(), + A.col_start.array.cbegin(), + A.i.array.cbegin(), Rdeg.begin(), row_perm.begin(), Rp.cbegin(), @@ -235,8 +235,8 @@ i_t find_singletons(const csc_matrix_t& A, Rj.cbegin(), Cdeg.begin(), col_perm.begin(), - A.col_start.cbegin(), - A.i.cbegin()}; + A.col_start.array.cbegin(), + A.i.array.cbegin()}; #ifdef SINGLETON_DEBUG printf("Searching for row singletons %ld\n", singleton_queue.size()); #endif diff --git a/cpp/src/dual_simplex/sparse_matrix.cpp b/cpp/src/dual_simplex/sparse_matrix.cpp index 4e522d9da..81bbbda11 100644 --- a/cpp/src/dual_simplex/sparse_matrix.cpp +++ b/cpp/src/dual_simplex/sparse_matrix.cpp @@ -8,9 +8,12 @@ // #include #include #include +#include #include +using cuopt::ins_vector; + // #include // #include @@ -34,8 +37,8 @@ void csc_matrix_t::reallocate(i_t new_nz) this->nz_max = new_nz; } -template -void cumulative_sum(std::vector& inout, std::vector& output) +template +void cumulative_sum(std::vector& inout, OutputVector& output) { i_t n = inout.size(); assert(output.size() == n + 1); @@ -603,6 +606,41 @@ void scatter_dense(const csc_matrix_t& A, } } +// Instrumented vector overload: x <- x + alpha * A(:, j) +template +void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, ins_vector& x) +{ + const i_t col_start = A.col_start[j]; + const i_t col_end = A.col_start[j + 1]; + for (i_t p = col_start; p < col_end; ++p) { + const i_t i = A.i[p]; + const f_t ax = A.x[p]; + x[i] += alpha * ax; + } +} + +// Instrumented vector overload: x <- x + alpha * A(:, j) with mark/indices tracking +template +void scatter_dense(const csc_matrix_t& A, + i_t j, + f_t alpha, + ins_vector& x, + ins_vector& mark, + ins_vector& indices) +{ + const i_t col_start = A.col_start[j]; + const i_t col_end = A.col_start[j + 1]; + for (i_t p = col_start; p < col_end; ++p) { + const i_t i = A.i[p]; + const f_t ax = A.x[p]; + x[i] += alpha * ax; + if (!mark[i]) { + mark[i] = 1; + indices.push_back(i); + } + } +} + // Compute C = A*B where C is m x n, A is m x k, and B = k x n // Do this by computing C(:, j) = A*B(:, j) = sum (i=1 to k) A(:, k)*B(i, j) template @@ -848,7 +886,10 @@ template class csc_matrix_t; template class csr_matrix_t; -template void cumulative_sum(std::vector& inout, std::vector& output); +template void cumulative_sum>(std::vector& inout, + std::vector& output); +template void cumulative_sum>(std::vector& inout, + ins_vector& output); template int coo_to_csc(const std::vector& Ai, const std::vector& Aj, @@ -876,6 +917,18 @@ template void scatter_dense(const csc_matrix_t& A, std::vector& mark, std::vector& indices); +template void scatter_dense(const csc_matrix_t& A, + int j, + double alpha, + ins_vector& x); + +template void scatter_dense(const csc_matrix_t& A, + int j, + double alpha, + ins_vector& x, + ins_vector& mark, + ins_vector& indices); + template int multiply(const csc_matrix_t& A, const csc_matrix_t& B, csc_matrix_t& C); diff --git a/cpp/src/dual_simplex/sparse_matrix.hpp b/cpp/src/dual_simplex/sparse_matrix.hpp index c14e6d0f1..26f1bce97 100644 --- a/cpp/src/dual_simplex/sparse_matrix.hpp +++ b/cpp/src/dual_simplex/sparse_matrix.hpp @@ -9,12 +9,16 @@ #include #include +#include #include #include #include #include +// Import instrumented vector +using cuopt::ins_vector; + namespace cuopt::linear_programming::dual_simplex { template @@ -110,12 +114,12 @@ class csc_matrix_t { size_t hash() const; - i_t m; // number of rows - i_t n; // number of columns - i_t nz_max; // maximum number of entries - std::vector col_start; // column pointers (size n + 1) - std::vector i; // row indices, size nz_max - std::vector x; // numerical values, size nz_max + i_t m; // number of rows + i_t n; // number of columns + i_t nz_max; // maximum number of entries + ins_vector col_start; // column pointers (size n + 1) + ins_vector i; // row indices, size nz_max + ins_vector x; // numerical values, size nz_max static_assert(std::is_signed_v); // Require signed integers (we make use of this // to avoid extra space / computation) @@ -139,18 +143,18 @@ class csr_matrix_t { // Ensures no repeated column indices within a row void check_matrix() const; - i_t nz_max; // maximum number of nonzero entries - i_t m; // number of rows - i_t n; // number of cols - std::vector row_start; // row pointers (size m + 1) - std::vector j; // column inidices, size nz_max - std::vector x; // numerical valuse, size nz_max + i_t nz_max; // maximum number of nonzero entries + i_t m; // number of rows + i_t n; // number of cols + ins_vector row_start; // row pointers (size m + 1) + ins_vector j; // column inidices, size nz_max + ins_vector x; // numerical valuse, size nz_max static_assert(std::is_signed_v); }; -template -void cumulative_sum(std::vector& inout, std::vector& output); +template +void cumulative_sum(std::vector& inout, OutputVector& output); template i_t coo_to_csc(const std::vector& Ai, @@ -180,6 +184,18 @@ void scatter_dense(const csc_matrix_t& A, std::vector& mark, std::vector& indices); +// Instrumented vector overloads +template +void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, ins_vector& x); + +template +void scatter_dense(const csc_matrix_t& A, + i_t j, + f_t alpha, + ins_vector& x, + ins_vector& mark, + ins_vector& indices); + // Compute C = A*B where C is m x n, A is m x k, and B = k x n // Do this by computing C(:, j) = A*B(:, j) = sum (i=1 to k) A(:, k)*B(i, j) template diff --git a/cpp/src/dual_simplex/sparse_vector.cpp b/cpp/src/dual_simplex/sparse_vector.cpp index b9a8a4af7..111d3ce82 100644 --- a/cpp/src/dual_simplex/sparse_vector.cpp +++ b/cpp/src/dual_simplex/sparse_vector.cpp @@ -87,6 +87,17 @@ void sparse_vector_t::to_dense(std::vector& x_dense) const } } +template +void sparse_vector_t::to_dense(ins_vector& x_dense) const +{ + x_dense.clear(); + x_dense.resize(n, 0.0); + const i_t nz = i.size(); + for (i_t k = 0; k < nz; ++k) { + x_dense[i[k]] = x[k]; + } +} + template void sparse_vector_t::scatter(std::vector& x_dense) const { diff --git a/cpp/src/dual_simplex/sparse_vector.hpp b/cpp/src/dual_simplex/sparse_vector.hpp index 5cac6b7dd..f3d6769ab 100644 --- a/cpp/src/dual_simplex/sparse_vector.hpp +++ b/cpp/src/dual_simplex/sparse_vector.hpp @@ -22,7 +22,7 @@ using cuopt::ins_vector; template class sparse_vector_t { public: - sparse_vector_t() : n(0), i({}), x({}) {} + sparse_vector_t() : n(0), i(), x() {} // Construct a sparse vector of dimension n with nz nonzero coefficients sparse_vector_t(i_t n, i_t nz) : n(n), i(nz), x(nz) {} // Construct a sparse vector from a dense vector. @@ -35,6 +35,8 @@ class sparse_vector_t { void to_csc(csc_matrix_t& A) const; // convert a sparse vector into a dense vector. Dense vector is cleared and resized. void to_dense(std::vector& x_dense) const; + // convert a sparse vector into an instrumented dense vector. + void to_dense(ins_vector& x_dense) const; // scatter a sparse vector into a dense vector. Assumes x_dense is already cleared or // preinitialized void scatter(std::vector& x_dense) const; diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 55cce572d..52c07be3d 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -37,6 +37,12 @@ #define CUOPT_ENABLE_MEMORY_INSTRUMENTATION 1 +#ifdef __NVCC__ +#define HDI inline __host__ __device__ +#else +#define HDI inline +#endif + namespace cuopt { // Define CUOPT_ENABLE_MEMORY_INSTRUMENTATION to enable memory tracking @@ -45,22 +51,22 @@ namespace cuopt { // Base class for memory operation instrumentation struct memory_instrumentation_base_t { #ifdef CUOPT_ENABLE_MEMORY_INSTRUMENTATION - __host__ __device__ void reset_counters() const { byte_loads = byte_stores = 0; } + HDI void reset_counters() const { byte_loads = byte_stores = 0; } template - __host__ __device__ void record_load() const + HDI void record_load() const { byte_loads += sizeof(T); } template - __host__ __device__ void record_store() const + HDI void record_store() const { byte_stores += sizeof(T); } template - __host__ __device__ void record_rmw() const + HDI void record_rmw() const { byte_loads += sizeof(T); byte_stores += sizeof(T); @@ -70,17 +76,17 @@ struct memory_instrumentation_base_t { mutable size_t byte_stores{0}; #else // No-op methods when instrumentation is disabled - these inline away to zero overhead - __host__ __device__ void reset_counters() const {} + HDI void reset_counters() const {} template - __host__ __device__ void record_load() const + HDI void record_load() const { } template - __host__ __device__ void record_store() const + HDI void record_store() const { } template - __host__ __device__ void record_rmw() const + HDI void record_rmw() const { } #endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION @@ -530,15 +536,61 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } - // Copy/move from wrapper - memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t&) = default; - memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&&) = default; - memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t&) = default; - memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&&) = default; + // Copy constructor - must update data_ptr to point to our own array + memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) + : memory_instrumentation_base_t(other), array(other.array) + { + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array.data(); + } else { + data_ptr = nullptr; + } + } + + // Move constructor - must update data_ptr to point to our own array + memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept + : memory_instrumentation_base_t(std::move(other)), array(std::move(other.array)) + { + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array.data(); + } else { + data_ptr = nullptr; + } + } + + // Copy assignment - must update data_ptr to point to our own array + memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) + { + if (this != &other) { + memory_instrumentation_base_t::operator=(other); + array = other.array; + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array.data(); + } else { + data_ptr = nullptr; + } + } + return *this; + } + + // Move assignment - must update data_ptr to point to our own array + memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept + { + if (this != &other) { + memory_instrumentation_base_t::operator=(std::move(other)); + array = std::move(other.array); + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array.data(); + } else { + data_ptr = nullptr; + } + } + return *this; + } element_proxy_t operator[](size_type index) { return element_proxy_t(array[index], *this); } - __host__ __device__ value_type operator[](size_type index) const + HDI value_type operator[](size_type index) const { this->template record_load(); // really ugly hack because otherwise nvcc complains about vector operator[] being __host__ only From 5894ffe031dbe0e19fc5a2c38fe04f5d44a00ea8 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 18 Nov 2025 11:18:49 +0000 Subject: [PATCH 085/366] add support for building with clang --- build.sh | 16 ++++++++++++++-- ci/tsan_suppressions.txt | 6 ++++++ cpp/CMakeLists.txt | 19 ++++++++++++++++++- cpp/include/cuopt/error.hpp | 2 -- .../utilities/internals.hpp | 1 + cpp/src/linear_programming/pdlp.cu | 2 +- .../utilities/cython_solve.cu | 3 +-- cpp/src/linear_programming/utils.cuh | 6 +++--- cpp/src/mip/diversity/lns/rins.cu | 4 ++-- cpp/src/mip/local_search/local_search.cu | 3 +-- cpp/src/mip/presolve/gf2_presolve.hpp | 4 ++++ cpp/src/mip/presolve/third_party_presolve.cpp | 4 ++++ cpp/src/mip/utilities/cpu_worker_thread.cuh | 8 +++++--- cpp/src/routing/crossovers/ox_recombiner.cuh | 4 ++-- .../distance_engine/waypoint_matrix_test.cpp | 6 +++--- .../c_api_tests/c_api_test.c | 1 + cpp/tests/routing/level0/l0_ges_test.cu | 4 ++-- .../level0/l0_objective_function_test.cu | 2 +- cpp/tests/routing/level0/l0_routing_test.cu | 2 +- .../routing/level0/l0_vehicle_order_match.cu | 2 +- .../routing/level0/l0_vehicle_types_test.cu | 2 +- 21 files changed, 72 insertions(+), 29 deletions(-) create mode 100644 ci/tsan_suppressions.txt diff --git a/build.sh b/build.sh index 2903684f6..e040baa95 100755 --- a/build.sh +++ b/build.sh @@ -15,7 +15,7 @@ REPODIR=$(cd "$(dirname "$0")"; pwd) LIBCUOPT_BUILD_DIR=${LIBCUOPT_BUILD_DIR:=${REPODIR}/cpp/build} LIBMPS_PARSER_BUILD_DIR=${LIBMPS_PARSER_BUILD_DIR:=${REPODIR}/cpp/libmps_parser/build} -VALIDARGS="clean libcuopt libmps_parser cuopt_mps_parser cuopt cuopt_server cuopt_sh_client docs deb -a -b -g -fsanitize -v -l= --verbose-pdlp --build-lp-only --no-fetch-rapids --skip-c-python-adapters --skip-tests-build --skip-routing-build --skip-fatbin-write --host-lineinfo [--cmake-args=\\\"\\\"] [--cache-tool=] -n --allgpuarch --ci-only-arch --show_depr_warn -h --help" +VALIDARGS="clean libcuopt libmps_parser cuopt_mps_parser cuopt cuopt_server cuopt_sh_client docs deb -a -b -g -fsanitize -tsan -v -l= --verbose-pdlp --build-lp-only --no-fetch-rapids --skip-c-python-adapters --skip-tests-build --skip-routing-build --skip-fatbin-write --host-lineinfo [--cmake-args=\\\"\\\"] [--cache-tool=] -n --allgpuarch --ci-only-arch --show_depr_warn -h --help" HELP="$0 [ ...] [ ...] where is: clean - remove all existing build artifacts and configuration (start over) @@ -32,7 +32,8 @@ HELP="$0 [ ...] [ ...] -g - build for debug -a - Enable assertion (by default in debug mode) -b - Build with benchmark settings - -fsanitize - Build with sanitizer + -fsanitize - Build with AddressSanitizer and UndefinedBehaviorSanitizer + -tsan - Build with ThreadSanitizer (cannot be used with -fsanitize) -n - no install step --no-fetch-rapids - don't fetch rapids dependencies -l= - log level. Options are: TRACE | DEBUG | INFO | WARN | ERROR | CRITICAL | OFF. Default=INFO @@ -76,6 +77,7 @@ BUILD_ALL_GPU_ARCH=0 BUILD_CI_ONLY=0 BUILD_LP_ONLY=0 BUILD_SANITIZER=0 +BUILD_TSAN=0 SKIP_C_PYTHON_ADAPTERS=0 SKIP_TESTS_BUILD=0 SKIP_ROUTING_BUILD=0 @@ -230,6 +232,9 @@ fi if hasArg -fsanitize; then BUILD_SANITIZER=1 fi +if hasArg -tsan; then + BUILD_TSAN=1 +fi if hasArg --skip-c-python-adapters; then SKIP_C_PYTHON_ADAPTERS=1 fi @@ -298,6 +303,12 @@ if [ ${BUILD_LP_ONLY} -eq 1 ] && [ ${SKIP_C_PYTHON_ADAPTERS} -eq 0 ]; then exit 1 fi +if [ ${BUILD_SANITIZER} -eq 1 ] && [ ${BUILD_TSAN} -eq 1 ]; then + echo "ERROR: -fsanitize and -tsan cannot be used together" + echo "AddressSanitizer and ThreadSanitizer are mutually exclusive" + exit 1 +fi + if [ ${BUILD_ALL_GPU_ARCH} -eq 1 ]; then CUOPT_CMAKE_CUDA_ARCHITECTURES="RAPIDS" echo "Building for *ALL* supported GPU architectures..." @@ -344,6 +355,7 @@ if buildAll || hasArg libcuopt; then -DFETCH_RAPIDS=${FETCH_RAPIDS} \ -DBUILD_LP_ONLY=${BUILD_LP_ONLY} \ -DBUILD_SANITIZER=${BUILD_SANITIZER} \ + -DBUILD_TSAN=${BUILD_TSAN} \ -DSKIP_C_PYTHON_ADAPTERS=${SKIP_C_PYTHON_ADAPTERS} \ -DBUILD_TESTS=$((1 - ${SKIP_TESTS_BUILD})) \ -DSKIP_ROUTING_BUILD=${SKIP_ROUTING_BUILD} \ diff --git a/ci/tsan_suppressions.txt b/ci/tsan_suppressions.txt new file mode 100644 index 000000000..b6f413e37 --- /dev/null +++ b/ci/tsan_suppressions.txt @@ -0,0 +1,6 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. +# SPDX-License-Identifier: Apache-2.0 + +# Ignore races in external header-only libraries +race:tbb +race:Papilo diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 4391c53df..f770d80c8 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -81,9 +81,22 @@ endif(CMAKE_COMPILER_IS_GNUCXX) # 2. (Optional) To run with a debugger (gdb or cuda-gdb) use the additional ASAN option alloc_dealloc_mismatch=0 if(BUILD_SANITIZER) list(APPEND CUOPT_CXX_FLAGS -fsanitize=address,undefined -fno-omit-frame-pointer -g -Wno-error=maybe-uninitialized) + if(NOT "${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") + list(APPEND CUOPT_CXX_FLAGS -Wno-error=maybe-uninitialized) + endif() add_link_options(-fsanitize=address,undefined) endif(BUILD_SANITIZER) +# To use ThreadSanitizer: +# 1. Build with clang and the -tsan flag +# 2. Run the binary with env var set: OMP_TOOL_LIBRARIES=/usr/lib/llvm-17/lib/libarcher.so ARCHER_OPTIONS='verbose=1' TSAN_OPTIONS='suppresions=ci/tsan_suppressions.txt:ignore_noninstrumented_modules=1:halt_on_error=1' +# Replace with local llvm install path. libarcher.so must be presetn +if(BUILD_TSAN) + message(STATUS "Building with ThreadSanitizer enabled") + list(APPEND CUOPT_CXX_FLAGS -fsanitize=thread -fno-omit-frame-pointer -g) + add_link_options(-fsanitize=thread) +endif(BUILD_TSAN) + if(DEFINE_ASSERT) add_definitions(-DASSERT_MODE) endif(DEFINE_ASSERT) @@ -117,7 +130,11 @@ if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.9) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -static-global-template-stub=false") endif() list(APPEND CUOPT_CUDA_FLAGS -Werror=cross-execution-space-call -Wno-deprecated-declarations -Xcompiler=-Werror --default-stream=per-thread) -list(APPEND CUOPT_CUDA_FLAGS -Xcompiler=-Wall -Wno-error=non-template-friend) +if("${CMAKE_CUDA_HOST_COMPILER}" MATCHES "clang" OR "${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") + list(APPEND CUOPT_CUDA_FLAGS -Xcompiler=-Wall) +else() + list(APPEND CUOPT_CUDA_FLAGS -Xcompiler=-Wall -Wno-error=non-template-friend) +endif() list(APPEND CUOPT_CUDA_FLAGS -Xfatbin=-compress-all) if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.9 AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 13.0) list(APPEND CUOPT_CUDA_FLAGS -Xfatbin=--compress-level=3) diff --git a/cpp/include/cuopt/error.hpp b/cpp/include/cuopt/error.hpp index b6086245d..a83413515 100644 --- a/cpp/include/cuopt/error.hpp +++ b/cpp/include/cuopt/error.hpp @@ -33,8 +33,6 @@ enum class error_type_t { */ struct logic_error : public std::logic_error { - explicit logic_error() = default; - logic_error(const logic_error& exception) = default; // Move constructor diff --git a/cpp/include/cuopt/linear_programming/utilities/internals.hpp b/cpp/include/cuopt/linear_programming/utilities/internals.hpp index 84c96a716..86e7246fa 100644 --- a/cpp/include/cuopt/linear_programming/utilities/internals.hpp +++ b/cpp/include/cuopt/linear_programming/utilities/internals.hpp @@ -62,6 +62,7 @@ namespace linear_programming { class base_solution_t { public: + virtual ~base_solution_t() = default; virtual bool is_mip() const = 0; }; diff --git a/cpp/src/linear_programming/pdlp.cu b/cpp/src/linear_programming/pdlp.cu index 5d982bcc9..d78f1d1f4 100644 --- a/cpp/src/linear_programming/pdlp.cu +++ b/cpp/src/linear_programming/pdlp.cu @@ -1510,7 +1510,7 @@ void pdlp_solver_t::compute_initial_step_size() const auto& cusparse_view_ = pdhg_solver_.get_cusparse_view(); - int sing_iters = 0; + [[maybe_unused]] int sing_iters = 0; for (int i = 0; i < max_iterations; ++i) { ++sing_iters; // d_q = d_z diff --git a/cpp/src/linear_programming/utilities/cython_solve.cu b/cpp/src/linear_programming/utilities/cython_solve.cu index 111f3caf0..38968c050 100644 --- a/cpp/src/linear_programming/utilities/cython_solve.cu +++ b/cpp/src/linear_programming/utilities/cython_solve.cu @@ -295,8 +295,7 @@ std::pair>, double> call_batch_solve( #pragma omp parallel for num_threads(max_thread) for (std::size_t i = 0; i < size; ++i) - list[i] = - std::move(call_solve(data_models[i], solver_settings, cudaStreamNonBlocking, is_batch_mode)); + list[i] = call_solve(data_models[i], solver_settings, cudaStreamNonBlocking, is_batch_mode); auto end = std::chrono::high_resolution_clock::now(); auto duration = std::chrono::duration_cast(end - start_solver); diff --git a/cpp/src/linear_programming/utils.cuh b/cpp/src/linear_programming/utils.cuh index 0da5d25ce..2333283f3 100644 --- a/cpp/src/linear_programming/utils.cuh +++ b/cpp/src/linear_programming/utils.cuh @@ -62,9 +62,9 @@ struct max_abs_value { template i_t conditional_major(uint64_t total_pdlp_iterations) { - uint64_t step = 10; - uint64_t threshold = 1000; - uint64_t iteration = 0; + uint64_t step = 10; + uint64_t threshold = 1000; + [[maybe_unused]] uint64_t iteration = 0; [[maybe_unused]] constexpr uint64_t max_u64 = std::numeric_limits::max(); diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 1a79bdb05..0d2da90cd 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -246,8 +246,8 @@ void rins_t::run_rins() branch_and_bound_settings.num_diving_threads = 1; branch_and_bound_settings.log.log = false; branch_and_bound_settings.log.log_prefix = "[RINS] "; - branch_and_bound_settings.solution_callback = [this, &rins_solution_queue]( - std::vector& solution, f_t objective) { + branch_and_bound_settings.solution_callback = [&rins_solution_queue](std::vector& solution, + f_t objective) { rins_solution_queue.push_back(solution); }; dual_simplex::branch_and_bound_t branch_and_bound(branch_and_bound_problem, diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index a8e06440a..6d64f62ab 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -80,8 +80,7 @@ void local_search_t::start_cpufj_scratch_threads(population_t 0); cpu_fj.fj_cpu->log_prefix = "******* scratch " + std::to_string(counter) + ": "; - cpu_fj.fj_cpu->improvement_callback = [this, &population, &cpu_fj]( - f_t obj, const std::vector& h_vec) { + cpu_fj.fj_cpu->improvement_callback = [&population](f_t obj, const std::vector& h_vec) { population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); if (obj < local_search_best_obj) { CUOPT_LOG_TRACE("******* New local search best obj %g, best overall %g", diff --git a/cpp/src/mip/presolve/gf2_presolve.hpp b/cpp/src/mip/presolve/gf2_presolve.hpp index 19d4e7d81..53e79d2c9 100644 --- a/cpp/src/mip/presolve/gf2_presolve.hpp +++ b/cpp/src/mip/presolve/gf2_presolve.hpp @@ -7,13 +7,17 @@ #pragma once +#if !defined(__clang__) #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wstringop-overflow" // ignore boost error for pip wheel build +#endif #include #include #include #include +#if !defined(__clang__) #pragma GCC diagnostic pop +#endif namespace cuopt::linear_programming::detail { diff --git a/cpp/src/mip/presolve/third_party_presolve.cpp b/cpp/src/mip/presolve/third_party_presolve.cpp index 22827c6e2..f3faf3dd7 100644 --- a/cpp/src/mip/presolve/third_party_presolve.cpp +++ b/cpp/src/mip/presolve/third_party_presolve.cpp @@ -14,11 +14,15 @@ #include +#if !defined(__clang__) #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wstringop-overflow" // ignore boost error for pip wheel build +#endif #include #include +#if !defined(__clang__) #pragma GCC diagnostic pop +#endif namespace cuopt::linear_programming::detail { diff --git a/cpp/src/mip/utilities/cpu_worker_thread.cuh b/cpp/src/mip/utilities/cpu_worker_thread.cuh index 0f1671c94..8d0b6d71e 100644 --- a/cpp/src/mip/utilities/cpu_worker_thread.cuh +++ b/cpp/src/mip/utilities/cpu_worker_thread.cuh @@ -60,6 +60,7 @@ template cpu_worker_thread_base_t::~cpu_worker_thread_base_t() { // Note: We don't call on_terminate() here since the derived object is already destroyed. + CUOPT_LOG_DEBUG("Destroying CPU worker thread"); join_worker(); } @@ -83,12 +84,14 @@ void cpu_worker_thread_base_t::cpu_worker_thread() std::lock_guard lock(cpu_mutex); cpu_thread_done = true; } + cpu_cv.notify_all(); } } template void cpu_worker_thread_base_t::request_termination() { + CUOPT_LOG_DEBUG("Requesting termination of CPU worker thread"); bool should_terminate = false; { std::lock_guard lock(cpu_mutex); @@ -131,9 +134,8 @@ void cpu_worker_thread_base_t::start_cpu_solver() template bool cpu_worker_thread_base_t::wait_for_cpu_solver() { - while (!cpu_thread_done && !cpu_thread_terminate) { - std::this_thread::sleep_for(std::chrono::milliseconds(1)); - } + std::unique_lock lock(cpu_mutex); + cpu_cv.wait(lock, [this] { return cpu_thread_done || cpu_thread_terminate; }); return static_cast(this)->get_result(); } diff --git a/cpp/src/routing/crossovers/ox_recombiner.cuh b/cpp/src/routing/crossovers/ox_recombiner.cuh index 17823c28b..7f965de2f 100644 --- a/cpp/src/routing/crossovers/ox_recombiner.cuh +++ b/cpp/src/routing/crossovers/ox_recombiner.cuh @@ -336,7 +336,7 @@ struct OX { int i = routes_number; if (optimal_routes_search) { i = optimal_routes_number; } int end_index = offspring.size() - 1; - double cost_n, cost_p, total_delta = 0.; + [[maybe_unused]] double cost_n, cost_p, total_delta = 0.; std::vector>>> routes_to_add; std::vector tmp_route; @@ -530,7 +530,7 @@ struct OX { "Mismatch number of edges"); for (size_t j = 0; j < h_transpose_graph[i].size(); ++j) { auto [ref_edge, ref_weight, ref_veh] = h_transpose_graph[i][j]; - bool found = false; + [[maybe_unused]] bool found = false; for (int x = 0; x < tmp_transpose.row_sizes[i]; ++x) { auto edge = tmp_transpose.indices[transpose_offset + x]; auto veh = tmp_transpose.buckets[transpose_offset + x]; diff --git a/cpp/tests/distance_engine/waypoint_matrix_test.cpp b/cpp/tests/distance_engine/waypoint_matrix_test.cpp index 80288bc6f..1f9bacf0e 100644 --- a/cpp/tests/distance_engine/waypoint_matrix_test.cpp +++ b/cpp/tests/distance_engine/waypoint_matrix_test.cpp @@ -44,7 +44,7 @@ class waypoint_matrix_waypoints_sequence_test_t this->expected_sequence_offsets = param.sequence_offsets; } - void TearDown() {} + void TearDown() override {} void test_compute_waypoint_sequence() { @@ -131,7 +131,7 @@ class waypoint_matrix_shortest_path_cost_t this->weights.data()); } - void TearDown() {} + void TearDown() override {} void test_compute_shortest_path_costs() { @@ -192,7 +192,7 @@ class waypoint_matrix_cost_matrix_test_t this->weights.data()); } - void TearDown() {} + void TearDown() override {} void test_compute_cost_matrix() { diff --git a/cpp/tests/linear_programming/c_api_tests/c_api_test.c b/cpp/tests/linear_programming/c_api_tests/c_api_test.c index 25aef6d25..3b3032176 100644 --- a/cpp/tests/linear_programming/c_api_tests/c_api_test.c +++ b/cpp/tests/linear_programming/c_api_tests/c_api_test.c @@ -53,6 +53,7 @@ const char* termination_status_to_string(cuopt_int_t termination_status) case CUOPT_TERIMINATION_STATUS_FEASIBLE_FOUND: return "Feasible found"; } + return "Unknown"; } diff --git a/cpp/tests/routing/level0/l0_ges_test.cu b/cpp/tests/routing/level0/l0_ges_test.cu index 22373f704..afd0a2627 100644 --- a/cpp/tests/routing/level0/l0_ges_test.cu +++ b/cpp/tests/routing/level0/l0_ges_test.cu @@ -55,7 +55,7 @@ class routing_ges_test_t : public ::testing::TestWithParam>, this->populate_device_vectors(); } - void TearDown() {} + void TearDown() override {} assignment_t solve(const cuopt::routing::data_model_view_t& data_model, const cuopt::routing::solver_settings_t& solver_settings, @@ -163,7 +163,7 @@ class simple_routes_ges_test_t : public ::testing::TestWithParampopulate_device_vectors(); } - void TearDown() {} + void TearDown() override {} assignment_t solve(const cuopt::routing::data_model_view_t& data_model, const cuopt::routing::solver_settings_t& solver_settings, diff --git a/cpp/tests/routing/level0/l0_objective_function_test.cu b/cpp/tests/routing/level0/l0_objective_function_test.cu index 935575026..449139849 100644 --- a/cpp/tests/routing/level0/l0_objective_function_test.cu +++ b/cpp/tests/routing/level0/l0_objective_function_test.cu @@ -25,7 +25,7 @@ template class objective_function_test_t : public base_test_t, public ::testing::TestWithParam> { public: - objective_function_test_t() : base_test_t(512, 5E-2, 0) {} + objective_function_test_t() : base_test_t(512, 0, 0) {} void SetUp() override { auto p = GetParam(); diff --git a/cpp/tests/routing/level0/l0_routing_test.cu b/cpp/tests/routing/level0/l0_routing_test.cu index 4d7bbad02..735b6e4bc 100644 --- a/cpp/tests/routing/level0/l0_routing_test.cu +++ b/cpp/tests/routing/level0/l0_routing_test.cu @@ -320,7 +320,7 @@ class routing_retail_test_t : public base_test_t, this->populate_device_vectors(); } - void TearDown() {} + void TearDown() override {} void test_cvrptw() { diff --git a/cpp/tests/routing/level0/l0_vehicle_order_match.cu b/cpp/tests/routing/level0/l0_vehicle_order_match.cu index 6c4d40ab7..4b1b9fdd3 100644 --- a/cpp/tests/routing/level0/l0_vehicle_order_match.cu +++ b/cpp/tests/routing/level0/l0_vehicle_order_match.cu @@ -24,7 +24,7 @@ namespace test { template class vehicle_order_test_t : public base_test_t, public ::testing::TestWithParam { public: - vehicle_order_test_t() : base_test_t(512, 5E-2, 0) {} + vehicle_order_test_t() : base_test_t(512, 0, 0) {} void SetUp() override { this->not_matching_constraints_fraction = GetParam(); diff --git a/cpp/tests/routing/level0/l0_vehicle_types_test.cu b/cpp/tests/routing/level0/l0_vehicle_types_test.cu index f7f247683..4e46d31d6 100644 --- a/cpp/tests/routing/level0/l0_vehicle_types_test.cu +++ b/cpp/tests/routing/level0/l0_vehicle_types_test.cu @@ -23,7 +23,7 @@ namespace test { template class vehicle_types_test_t : public base_test_t, public ::testing::Test { public: - vehicle_types_test_t() : base_test_t(512, 5E-2, 0) {} + vehicle_types_test_t() : base_test_t(512, 0, 0) {} void SetUp() override { this->n_locations = input_.n_locations; From 9911c3ab654ea0b0ee2955c79ee5d1f237ccdfb6 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 18 Nov 2025 12:34:27 +0000 Subject: [PATCH 086/366] remove debug calls --- cpp/src/mip/utilities/cpu_worker_thread.cuh | 2 -- 1 file changed, 2 deletions(-) diff --git a/cpp/src/mip/utilities/cpu_worker_thread.cuh b/cpp/src/mip/utilities/cpu_worker_thread.cuh index 8d0b6d71e..e437486cd 100644 --- a/cpp/src/mip/utilities/cpu_worker_thread.cuh +++ b/cpp/src/mip/utilities/cpu_worker_thread.cuh @@ -60,7 +60,6 @@ template cpu_worker_thread_base_t::~cpu_worker_thread_base_t() { // Note: We don't call on_terminate() here since the derived object is already destroyed. - CUOPT_LOG_DEBUG("Destroying CPU worker thread"); join_worker(); } @@ -91,7 +90,6 @@ void cpu_worker_thread_base_t::cpu_worker_thread() template void cpu_worker_thread_base_t::request_termination() { - CUOPT_LOG_DEBUG("Requesting termination of CPU worker thread"); bool should_terminate = false; { std::lock_guard lock(cpu_mutex); From 24b828e53aabe7fe6016ac197019fbdee848d5d9 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 18 Nov 2025 14:02:20 +0000 Subject: [PATCH 087/366] fix cmakelists --- cpp/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index f770d80c8..bcd62598c 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -80,7 +80,7 @@ endif(CMAKE_COMPILER_IS_GNUCXX) # 1. Run the binary with env var set: LD_PRELOAD="$(gcc -print-file-name=libasan.so)" ASAN_OPTIONS='protect_shadow_gap=0:replace_intrin=0' # 2. (Optional) To run with a debugger (gdb or cuda-gdb) use the additional ASAN option alloc_dealloc_mismatch=0 if(BUILD_SANITIZER) - list(APPEND CUOPT_CXX_FLAGS -fsanitize=address,undefined -fno-omit-frame-pointer -g -Wno-error=maybe-uninitialized) + list(APPEND CUOPT_CXX_FLAGS -fsanitize=address,undefined -fno-omit-frame-pointer -g) if(NOT "${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") list(APPEND CUOPT_CXX_FLAGS -Wno-error=maybe-uninitialized) endif() From d52bcb0079a45af830621a6efe591022a80054d4 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 25 Nov 2025 16:46:38 +0000 Subject: [PATCH 088/366] move suppressiosn --- cpp/CMakeLists.txt | 2 +- {ci => cpp}/tsan_suppressions.txt | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename {ci => cpp}/tsan_suppressions.txt (100%) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index bcd62598c..b4713a848 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -89,7 +89,7 @@ endif(BUILD_SANITIZER) # To use ThreadSanitizer: # 1. Build with clang and the -tsan flag -# 2. Run the binary with env var set: OMP_TOOL_LIBRARIES=/usr/lib/llvm-17/lib/libarcher.so ARCHER_OPTIONS='verbose=1' TSAN_OPTIONS='suppresions=ci/tsan_suppressions.txt:ignore_noninstrumented_modules=1:halt_on_error=1' +# 2. Run the binary with env var set: OMP_TOOL_LIBRARIES=/usr/lib/llvm-17/lib/libarcher.so ARCHER_OPTIONS='verbose=1' TSAN_OPTIONS='suppresions=cpp/tsan_suppressions.txt:ignore_noninstrumented_modules=1:halt_on_error=1' # Replace with local llvm install path. libarcher.so must be presetn if(BUILD_TSAN) message(STATUS "Building with ThreadSanitizer enabled") diff --git a/ci/tsan_suppressions.txt b/cpp/tsan_suppressions.txt similarity index 100% rename from ci/tsan_suppressions.txt rename to cpp/tsan_suppressions.txt From ef69eb7af6b7b4f006aaad7ddf2965fd298900d3 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 25 Nov 2025 18:22:58 +0000 Subject: [PATCH 089/366] PDLP features --- cpp/src/linear_programming/pdlp.cu | 74 +++++++++++++++++++++++++++- cpp/src/linear_programming/pdlp.cuh | 9 ++++ cpp/src/mip/relaxed_lp/relaxed_lp.cu | 63 ++++++++++++++++++++--- 3 files changed, 138 insertions(+), 8 deletions(-) diff --git a/cpp/src/linear_programming/pdlp.cu b/cpp/src/linear_programming/pdlp.cu index ba156920f..d73b29706 100644 --- a/cpp/src/linear_programming/pdlp.cu +++ b/cpp/src/linear_programming/pdlp.cu @@ -1181,6 +1181,40 @@ optimization_problem_solution_t pdlp_solver_t::run_solver(co bool warm_start_was_given = settings_.get_pdlp_warm_start_data().last_restart_duality_gap_dual_solution_.size() != 0; + // Reset iteration metrics at the start of the solver run + total_spmv_ops_ = 0; + + // Compute sparsity metrics once (they don't change during solve) + { + const i_t n_vars = problem_ptr->n_variables; + const i_t n_cstrs = problem_ptr->n_constraints; + const int64_t nnz = problem_ptr->nnz; + + cached_sparsity_ = (n_cstrs > 0 && n_vars > 0) + ? static_cast(nnz) / (static_cast(n_cstrs) * n_vars) + : 0.0; + cached_nnz_stddev_ = 0.0; + cached_unbalancedness_ = 0.0; + + if (problem_ptr->offsets.size() == static_cast(n_cstrs + 1) && n_cstrs > 0) { + // Copy offsets to host for efficient computation + std::vector h_offsets(n_cstrs + 1); + raft::copy(h_offsets.data(), problem_ptr->offsets.data(), n_cstrs + 1, stream_view_); + RAFT_CUDA_TRY(cudaStreamSynchronize(stream_view_)); + + const double mean_nnz = static_cast(nnz) / n_cstrs; + double variance_sum = 0.0; + for (i_t row = 0; row < n_cstrs; ++row) { + const double row_nnz = static_cast(h_offsets[row + 1] - h_offsets[row]); + const double diff = row_nnz - mean_nnz; + variance_sum += diff * diff; + } + const double variance = variance_sum / n_cstrs; + cached_nnz_stddev_ = std::sqrt(variance); + cached_unbalancedness_ = (mean_nnz > 0) ? cached_nnz_stddev_ / mean_nnz : 0.0; + } + } + if (!inside_mip_) { CUOPT_LOG_INFO( " Iter Primal Obj. Dual Obj. Gap Primal Res. Dual Res. Time"); @@ -1320,11 +1354,19 @@ optimization_problem_solution_t pdlp_solver_t::run_solver(co take_step(total_pdlp_iterations_, (total_pdlp_iterations_ + 1) % pdlp_hyper_params::major_iteration == 0); + // Count SpMV operations: take_step does approximately 3 SpMV ops per iteration + // (A @ x in dual update, A^T @ y in primal update or cached, A^T @ y' in step size) + constexpr int64_t spmv_ops_per_iteration = 3; + total_spmv_ops_ += spmv_ops_per_iteration; + if (pdlp_hyper_params::use_reflected_primal_dual) { if (pdlp_hyper_params::use_fixed_point_error && (total_pdlp_iterations_ + 1) % pdlp_hyper_params::major_iteration == 0 || - has_restarted) + has_restarted) { compute_fixed_error(has_restarted); // May set has_restarted to false + // compute_fixed_error does 1 additional SpMV + total_spmv_ops_ += 1; + } halpern_update(); } @@ -1333,6 +1375,36 @@ optimization_problem_solution_t pdlp_solver_t::run_solver(co ++internal_solver_iterations_; if (pdlp_hyper_params::never_restart_to_average) restart_strategy_.increment_iteration_since_last_restart(); + + // Log PDLP_RESULT metrics every pdlp_log_interval iterations + constexpr i_t pdlp_log_interval = 50; + if (total_pdlp_iterations_ % pdlp_log_interval == 0) { + const int64_t nnz = problem_ptr->nnz; + const int64_t total_nnz = total_spmv_ops_ * nnz; + const f_t elapsed_sec = timer.elapsed_time(); + const double nnz_per_sec = + (elapsed_sec > 0) ? static_cast(total_nnz) / elapsed_sec : 0.0; + const double nnz_per_iter = static_cast(total_nnz) / pdlp_log_interval; + + CUOPT_LOG_INFO( + "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6f nnz_stddev=%.6f " + "unbalancedness=%.6f " + "iters=%d time_ms=%.0f spmv_ops=%ld total_nnz=%.2e nnz/s=%.2e nnz/iter=%.2e", + problem_ptr->n_variables, + problem_ptr->n_constraints, + nnz, + cached_sparsity_, + cached_nnz_stddev_, + cached_unbalancedness_, + total_pdlp_iterations_, + elapsed_sec * 1000.0, + total_spmv_ops_, + static_cast(total_nnz), + nnz_per_sec, + nnz_per_iter); + // Reset counters for next interval + total_spmv_ops_ = 0; + } } return optimization_problem_solution_t{pdlp_termination_status_t::NumericalError, stream_view_}; diff --git a/cpp/src/linear_programming/pdlp.cuh b/cpp/src/linear_programming/pdlp.cuh index 663c617d4..42eeeae71 100644 --- a/cpp/src/linear_programming/pdlp.cuh +++ b/cpp/src/linear_programming/pdlp.cuh @@ -68,6 +68,7 @@ class pdlp_solver_t { f_t get_relative_dual_tolerance_factor() const; f_t get_relative_primal_tolerance_factor() const; detail::pdlp_termination_strategy_t& get_current_termination_strategy(); + int64_t get_total_spmv_ops() const { return total_spmv_ops_; } void set_problem_ptr(problem_t* problem_ptr_); @@ -197,6 +198,14 @@ class pdlp_solver_t { i_t total_pdlp_iterations_{0}; i_t internal_solver_iterations_{0}; + // Iteration metrics for performance tracking + int64_t total_spmv_ops_{0}; // Total SpMV operations performed + + // Cached sparsity metrics (computed once at start of solve) + double cached_sparsity_{0.0}; + double cached_nnz_stddev_{0.0}; + double cached_unbalancedness_{0.0}; + // Initial solution rmm::device_uvector initial_primal_; rmm::device_uvector initial_dual_; diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index b2c9631e0..b5f04f7ea 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -169,13 +169,62 @@ optimization_problem_solution_t get_relaxed_lp_solution( elapsed_ms, solver_response.get_additional_termination_information().number_of_steps_taken); - // // === PDLP PREDICTOR RESULTS - START === - // auto term_info = solver_response.get_additional_termination_information(); - // CUOPT_LOG_INFO("PDLP_RESULT: iterations=%d time_ms=%lld termination=%d", - // term_info.number_of_steps_taken, - // elapsed_ms, - // (int)solver_response.get_termination_status()); - // // === PDLP PREDICTOR RESULTS - END === + // === PDLP PREDICTOR RESULTS - START === + auto term_info = solver_response.get_additional_termination_information(); + const i_t n_vars = op_problem.n_variables; + const i_t n_cstrs = op_problem.n_constraints; + const int64_t nnz = op_problem.nnz; + const int64_t total_spmv = lp_solver.get_total_spmv_ops(); + const int64_t total_nnz = total_spmv * nnz; + const double nnz_per_sec = + (elapsed_ms > 0) ? static_cast(total_nnz) / (elapsed_ms / 1000.0) : 0.0; + const double nnz_per_iter = (term_info.number_of_steps_taken > 0) + ? static_cast(total_nnz) / term_info.number_of_steps_taken + : 0.0; + + // Compute sparsity metrics + const double sparsity = (n_cstrs > 0 && n_vars > 0) + ? static_cast(nnz) / (static_cast(n_cstrs) * n_vars) + : 0.0; + double nnz_stddev = 0.0; + double unbalancedness = 0.0; + if (op_problem.offsets.size() == static_cast(n_cstrs + 1) && n_cstrs > 0) { + std::vector h_offsets(n_cstrs + 1); + raft::copy(h_offsets.data(), + op_problem.offsets.data(), + n_cstrs + 1, + op_problem.handle_ptr->get_stream()); + op_problem.handle_ptr->sync_stream(); + + const double mean_nnz = static_cast(nnz) / n_cstrs; + double variance_sum = 0.0; + for (i_t row = 0; row < n_cstrs; ++row) { + const double row_nnz = static_cast(h_offsets[row + 1] - h_offsets[row]); + const double diff = row_nnz - mean_nnz; + variance_sum += diff * diff; + } + const double variance = variance_sum / n_cstrs; + nnz_stddev = std::sqrt(variance); + unbalancedness = (mean_nnz > 0) ? nnz_stddev / mean_nnz : 0.0; + } + + CUOPT_LOG_INFO( + "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f " + "iters=%d time_ms=%lld term=%d spmv_ops=%ld total_nnz=%.2e nnz/s=%.2e nnz/iter=%.2e", + n_vars, + n_cstrs, + nnz, + sparsity, + nnz_stddev, + unbalancedness, + term_info.number_of_steps_taken, + elapsed_ms, + static_cast(solver_response.get_termination_status()), + total_spmv, + static_cast(total_nnz), + nnz_per_sec, + nnz_per_iter); + // === PDLP PREDICTOR RESULTS - END === return solver_response; } From 7ac230fe4e8cf3cb7f78295de779756eb8219f0b Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 26 Nov 2025 14:49:50 +0000 Subject: [PATCH 090/366] script tweaks --- scripts/determinism_logs_parse.py | 35 +++++++++++++++++++++++++------ scripts/train_regressor.py | 5 +++++ 2 files changed, 34 insertions(+), 6 deletions(-) diff --git a/scripts/determinism_logs_parse.py b/scripts/determinism_logs_parse.py index cee653c0b..503165641 100755 --- a/scripts/determinism_logs_parse.py +++ b/scripts/determinism_logs_parse.py @@ -21,11 +21,12 @@ Supports parsing of: - FP (Feasibility Pump): FP_FEATURES and FP_RESULT logs -- PDLP (LP Solver): PDLP_FEATURES and PDLP_RESULT logs +- PDLP (LP Solver): PDLP_RESULT single-line logs - CP (Constraint Propagation): CP_FEATURES and CP_RESULT logs - FJ (Feasibility Jump): Legacy FJ: format - CPUFJ (CPU Feasibility Jump): CPUFJ_FEATURES single-line logs - BB (Branch and Bound): BB_NODE_FEATURES single-line logs +- DS (Dual Simplex): DS_FEATURES single-line logs IMPORTANT - Grep Specificity: The parser uses EXACT pattern matching with grep to filter logs efficiently. @@ -51,6 +52,7 @@ python determinism_logs_parse.py --algorithm FJ [-o output.feather] python determinism_logs_parse.py --algorithm CPUFJ [-o output.feather] python determinism_logs_parse.py --algorithm BB [-o output.feather] + python determinism_logs_parse.py --algorithm DS [-o output.feather] """ import argparse @@ -62,7 +64,7 @@ from typing import List, Dict, Any, Optional -SUPPORTED_ALGORITHMS = ["FP", "PDLP", "CP", "FJ", "CPUFJ", "BB"] +SUPPORTED_ALGORITHMS = ["FP", "PDLP", "CP", "FJ", "CPUFJ", "BB", "DS"] def parse_value(value_str: str) -> Any: @@ -310,8 +312,10 @@ def parse_fp_logs(log_files: List[str]) -> List[Dict[str, Any]]: def parse_pdlp_logs(log_files: List[str]) -> List[Dict[str, Any]]: - """Parse PDLP (LP Solver) feature and result logs.""" - return parse_generic_algorithm_logs(log_files, "PDLP", "LP Solver") + """Parse PDLP (LP Solver) result logs.""" + return parse_single_line_logs( + log_files, "PDLP_RESULT:", "PDLP (LP Solver)", "PDLP_RESULT:" + ) def parse_cp_logs(log_files: List[str]) -> List[Dict[str, Any]]: @@ -432,6 +436,13 @@ def parse_bb_logs(log_files: List[str]) -> List[Dict[str, Any]]: ) +def parse_ds_logs(log_files: List[str]) -> List[Dict[str, Any]]: + """Parse Dual Simplex feature logs.""" + return parse_single_line_logs( + log_files, "DS_FEATURES:", "DS (Dual Simplex)", "DS_FEATURES:" + ) + + def print_statistics(entries: List[Dict[str, Any]], algorithm: str) -> None: """Print statistics about parsed entries.""" if not entries: @@ -478,11 +489,12 @@ def main(): epilog=""" Supported Algorithms: FP - Feasibility Pump (parses FP_FEATURES and FP_RESULT logs) - PDLP - LP Solver (parses PDLP_FEATURES and PDLP_RESULT logs) + PDLP - LP Solver (parses PDLP_RESULT single-line logs) CP - Constraint Propagation (parses CP_FEATURES and CP_RESULT logs) FJ - Feasibility Jump (parses legacy FJ: format) CPUFJ - CPU Feasibility Jump (parses CPUFJ_FEATURES single-line logs) BB - Branch and Bound (parses BB_NODE_FEATURES single-line logs) + DS - Dual Simplex (parses DS_FEATURES single-line logs) Examples: python determinism_logs_parse.py logs/ --algorithm FP -o fp_data.feather @@ -491,6 +503,7 @@ def main(): python determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.feather python determinism_logs_parse.py logs/ --algorithm CPUFJ -o cpufj_data.feather python determinism_logs_parse.py logs/ --algorithm BB -o bb_data.feather + python determinism_logs_parse.py logs/ --algorithm DS -o ds_data.feather # Limit to first 10 files for testing python determinism_logs_parse.py logs/ --algorithm FP --max-files 10 @@ -565,16 +578,22 @@ def main(): entries = parse_cpufj_logs(log_files) elif args.algorithm == "BB": entries = parse_bb_logs(log_files) + elif args.algorithm == "DS": + entries = parse_ds_logs(log_files) else: print(f"Error: Unsupported algorithm: {args.algorithm}") return 1 if not entries: print(f"\nError: No entries found for {args.algorithm}") - if args.algorithm in ["FP", "PDLP", "CP"]: + if args.algorithm in ["FP", "CP"]: print( f"Make sure your logs contain {args.algorithm}_FEATURES and {args.algorithm}_RESULT lines" ) + elif args.algorithm == "PDLP": + print( + "Make sure your logs contain PDLP_RESULT: lines with key=value pairs" + ) elif args.algorithm == "FJ": print("Make sure your logs contain FJ: lines with key=value pairs") elif args.algorithm == "CPUFJ": @@ -585,6 +604,10 @@ def main(): print( "Make sure your logs contain BB_NODE_FEATURES lines with key=value pairs" ) + elif args.algorithm == "DS": + print( + "Make sure your logs contain DS_FEATURES: lines with key=value pairs" + ) return 1 # Print statistics diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py index f26874a21..9c17e9e76 100755 --- a/scripts/train_regressor.py +++ b/scripts/train_regressor.py @@ -141,6 +141,11 @@ "h_tabu_lastinc_stores", "h_tabu_lastinc_loads", "max_weight", + "fixed", + "phase", + "iters", + "nnz/s", + "nnz/iter", ] # Alternatively, specify ONLY the features you want to use From 24d7ccbb49d200daae94ee4581fe270f37f6aea8 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 26 Nov 2025 15:53:11 +0000 Subject: [PATCH 091/366] more feature loggign --- cpp/src/linear_programming/pdlp.cu | 92 ++++--- cpp/src/linear_programming/pdlp.cuh | 6 + cpp/src/linear_programming/pdlp_features.hpp | 245 +++++++++++++++++++ cpp/src/mip/relaxed_lp/relaxed_lp.cu | 5 + 4 files changed, 313 insertions(+), 35 deletions(-) create mode 100644 cpp/src/linear_programming/pdlp_features.hpp diff --git a/cpp/src/linear_programming/pdlp.cu b/cpp/src/linear_programming/pdlp.cu index d73b29706..fc55fece3 100644 --- a/cpp/src/linear_programming/pdlp.cu +++ b/cpp/src/linear_programming/pdlp.cu @@ -1215,6 +1215,16 @@ optimization_problem_solution_t pdlp_solver_t::run_solver(co } } + // Initialize feature tracking for runtime prediction + features_.init_from_problem(problem_ptr->n_variables, + problem_ptr->n_constraints, + problem_ptr->nnz, + static_cast(cached_sparsity_), + static_cast(cached_nnz_stddev_), + static_cast(cached_unbalancedness_), + warm_start_was_given); + interval_start_time_ = std::chrono::high_resolution_clock::now(); + if (!inside_mip_) { CUOPT_LOG_INFO( " Iter Primal Obj. Dual Obj. Gap Primal Res. Dual Res. Time"); @@ -1329,8 +1339,22 @@ optimization_problem_solution_t pdlp_solver_t::run_solver(co average_termination_strategy_.get_convergence_information(), // Needed for KKT restart best_primal_weight_ // Needed for cuPDLP+ restart ); + + // Track restart for feature logging + if (has_restarted) { features_.record_restart(); } } + // Update convergence metrics from termination strategy (for feature logging) + const auto& conv_info = current_termination_strategy_.get_convergence_information(); + const f_t kkt = restart_strategy_.compute_kkt_score(conv_info.get_l2_primal_residual(), + conv_info.get_l2_dual_residual(), + conv_info.get_gap(), + primal_weight_); + features_.update_convergence(conv_info.get_l2_primal_residual().value(stream_view_), + conv_info.get_l2_dual_residual().value(stream_view_), + conv_info.get_gap().value(stream_view_), + kkt); + if (!pdlp_hyper_params::rescale_for_restart) { // We don't need to rescale average because what matters is weighted_average_solution // getting the scaled accumulation @@ -1351,18 +1375,21 @@ optimization_problem_solution_t pdlp_solver_t::run_solver(co #ifdef CUPDLP_DEBUG_MODE printf("Is Major %d\n", (total_pdlp_iterations_ + 1) % pdlp_hyper_params::major_iteration == 0); #endif - take_step(total_pdlp_iterations_, - (total_pdlp_iterations_ + 1) % pdlp_hyper_params::major_iteration == 0); + // Determine if the NEXT iteration will be a major iteration (for take_step) + const bool next_is_major = + (total_pdlp_iterations_ + 1) % pdlp_hyper_params::major_iteration == 0; + + take_step(total_pdlp_iterations_, next_is_major); + + // Track iteration for features (2 SpMV per regular iteration in Stable3) + features_.record_regular_iteration(); - // Count SpMV operations: take_step does approximately 3 SpMV ops per iteration - // (A @ x in dual update, A^T @ y in primal update or cached, A^T @ y' in step size) - constexpr int64_t spmv_ops_per_iteration = 3; + // Count SpMV operations for legacy tracking + constexpr int64_t spmv_ops_per_iteration = 2; total_spmv_ops_ += spmv_ops_per_iteration; if (pdlp_hyper_params::use_reflected_primal_dual) { - if (pdlp_hyper_params::use_fixed_point_error && - (total_pdlp_iterations_ + 1) % pdlp_hyper_params::major_iteration == 0 || - has_restarted) { + if (pdlp_hyper_params::use_fixed_point_error && (next_is_major || has_restarted)) { compute_fixed_error(has_restarted); // May set has_restarted to false // compute_fixed_error does 1 additional SpMV total_spmv_ops_ += 1; @@ -1371,38 +1398,33 @@ optimization_problem_solution_t pdlp_solver_t::run_solver(co halpern_update(); } + // Track major iteration for features (after compute_fixed_error adds +1 SpMV) + if (is_major_iteration || is_conditional_major) { features_.record_major_iteration(); } + ++total_pdlp_iterations_; ++internal_solver_iterations_; if (pdlp_hyper_params::never_restart_to_average) restart_strategy_.increment_iteration_since_last_restart(); - // Log PDLP_RESULT metrics every pdlp_log_interval iterations - constexpr i_t pdlp_log_interval = 50; - if (total_pdlp_iterations_ % pdlp_log_interval == 0) { - const int64_t nnz = problem_ptr->nnz; - const int64_t total_nnz = total_spmv_ops_ * nnz; - const f_t elapsed_sec = timer.elapsed_time(); - const double nnz_per_sec = - (elapsed_sec > 0) ? static_cast(total_nnz) / elapsed_sec : 0.0; - const double nnz_per_iter = static_cast(total_nnz) / pdlp_log_interval; - - CUOPT_LOG_INFO( - "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6f nnz_stddev=%.6f " - "unbalancedness=%.6f " - "iters=%d time_ms=%.0f spmv_ops=%ld total_nnz=%.2e nnz/s=%.2e nnz/iter=%.2e", - problem_ptr->n_variables, - problem_ptr->n_constraints, - nnz, - cached_sparsity_, - cached_nnz_stddev_, - cached_unbalancedness_, - total_pdlp_iterations_, - elapsed_sec * 1000.0, - total_spmv_ops_, - static_cast(total_nnz), - nnz_per_sec, - nnz_per_iter); - // Reset counters for next interval + // Update step parameters for feature tracking + features_.update_step_params(step_size_.value(stream_view_), + primal_weight_.value(stream_view_)); + + // Log PDLP features at regular intervals + if (features_.should_log()) { + // Compute elapsed time for this interval + auto now = std::chrono::high_resolution_clock::now(); + auto interval_ms = std::chrono::duration(now - interval_start_time_).count(); + features_.interval_time_ms = interval_ms; + + // Log the features + features_.log_features(); + + // Reset interval counters and timer + features_.reset_interval_counters(); + interval_start_time_ = now; + + // Reset legacy spmv counter for consistency total_spmv_ops_ = 0; } } diff --git a/cpp/src/linear_programming/pdlp.cuh b/cpp/src/linear_programming/pdlp.cuh index 42eeeae71..fc68f7bc7 100644 --- a/cpp/src/linear_programming/pdlp.cuh +++ b/cpp/src/linear_programming/pdlp.cuh @@ -13,6 +13,7 @@ #include #include #include +#include #include #include #include @@ -26,6 +27,7 @@ #include #include +#include #include #include "linear_programming/termination_strategy/convergence_information.hpp" @@ -201,6 +203,10 @@ class pdlp_solver_t { // Iteration metrics for performance tracking int64_t total_spmv_ops_{0}; // Total SpMV operations performed + // Feature tracking for runtime prediction (Stable3 mode) + pdlp_features_t features_; + std::chrono::high_resolution_clock::time_point interval_start_time_; + // Cached sparsity metrics (computed once at start of solve) double cached_sparsity_{0.0}; double cached_nnz_stddev_{0.0}; diff --git a/cpp/src/linear_programming/pdlp_features.hpp b/cpp/src/linear_programming/pdlp_features.hpp new file mode 100644 index 000000000..95595e921 --- /dev/null +++ b/cpp/src/linear_programming/pdlp_features.hpp @@ -0,0 +1,245 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#include + +#include +#include +#include + +namespace cuopt::linear_programming::detail { + +// Feature logging interval (every N iterations) +constexpr int PDLP_FEATURE_LOG_INTERVAL = 50; + +/** + * @brief Feature collection structure for PDLP Stable3 runtime prediction. + * + * This structure collects features that can be used to train regression models + * for predicting the runtime of PDLP iterations in Stable3 mode. + * + * Stable3 key characteristics: + * - No adaptive step size retries (deterministic SpMV count) + * - Uses reflected primal/dual (Halpern update) + * - Uses fixed point error computation on major iterations + * - Uses conditional major iterations (frequency increases with iteration count) + * - Never restarts to average (only to current solution) + */ +template +struct pdlp_features_t { + // ========================================================================= + // Problem Features (static, set once at initialization) + // ========================================================================= + i_t n_vars{0}; // Number of variables + i_t n_cstrs{0}; // Number of constraints + int64_t nnz{0}; // Number of nonzeros in constraint matrix + f_t sparsity{0.0}; // nnz / (n_vars * n_cstrs) + f_t nnz_stddev{0.0}; // Standard deviation of row nnz counts + f_t unbalancedness{0.0}; // nnz_stddev / mean_nnz_per_row + + // ========================================================================= + // Interval Counters (reset after each log) + // ========================================================================= + i_t interval_iters{0}; // Iterations in this logging interval + i_t interval_major{0}; // Major iterations in this interval + i_t interval_restarts{0}; // Restarts in this interval + + // ========================================================================= + // SpMV Metrics + // ========================================================================= + // In Stable3: regular iter = 2 SpMV, major iter = 3 SpMV (fixed point error) + int64_t interval_spmv_ops{0}; // SpMV operations in this interval + + // ========================================================================= + // Cumulative Counters (never reset) + // ========================================================================= + i_t total_iters{0}; // Total iterations since solver start + i_t total_restarts{0}; // Total restarts since solver start + i_t iters_since_restart{0}; // Iterations since last restart + + // ========================================================================= + // Convergence Metrics (snapshot at log time, from last major iteration) + // ========================================================================= + f_t primal_res{0.0}; // L2 primal residual + f_t dual_res{0.0}; // L2 dual residual + f_t gap{0.0}; // Duality gap + f_t kkt_score{0.0}; // KKT score (used for restart decisions) + + // ========================================================================= + // Step Parameters (can change on restarts) + // ========================================================================= + f_t step_size{0.0}; // Current step size + f_t primal_weight{0.0}; // Current primal weight + + // ========================================================================= + // Timing + // ========================================================================= + f_t interval_time_ms{0.0}; // Time elapsed in this interval (milliseconds) + + // ========================================================================= + // Warm Start Info + // ========================================================================= + bool has_warm_start{false}; // Whether warm start was provided + + /** + * @brief Initialize static problem features. + * + * Called once at solver initialization. Computes sparsity metrics from + * the problem structure. + */ + void init_from_problem(i_t n_variables, + i_t n_constraints, + int64_t num_nonzeros, + f_t computed_sparsity, + f_t computed_nnz_stddev, + f_t computed_unbalancedness, + bool warm_start) + { + n_vars = n_variables; + n_cstrs = n_constraints; + nnz = num_nonzeros; + sparsity = computed_sparsity; + nnz_stddev = computed_nnz_stddev; + unbalancedness = computed_unbalancedness; + has_warm_start = warm_start; + } + + /** + * @brief Record a regular iteration. + * + * In Stable3, each regular iteration does 2 SpMV operations: + * - compute_At_y(): A^T @ y + * - compute_A_x(): A @ x + */ + void record_regular_iteration() + { + ++interval_iters; + ++total_iters; + ++iters_since_restart; + interval_spmv_ops += 2; + } + + /** + * @brief Record a major iteration. + * + * Major iterations do an additional SpMV for fixed point error computation. + * They also involve termination checks and potential restarts. + */ + void record_major_iteration() + { + ++interval_major; + // Fixed point error computation adds 1 SpMV + interval_spmv_ops += 1; + } + + /** + * @brief Record a restart event. + */ + void record_restart() + { + ++interval_restarts; + ++total_restarts; + iters_since_restart = 0; + } + + /** + * @brief Update convergence metrics from termination strategy. + * + * Called during major iterations when convergence info is computed. + */ + void update_convergence(f_t l2_primal_residual, + f_t l2_dual_residual, + f_t duality_gap, + f_t computed_kkt_score) + { + primal_res = l2_primal_residual; + dual_res = l2_dual_residual; + gap = duality_gap; + kkt_score = computed_kkt_score; + } + + /** + * @brief Update step parameters. + * + * Called when step size or primal weight changes (typically after restarts). + */ + void update_step_params(f_t current_step_size, f_t current_primal_weight) + { + step_size = current_step_size; + primal_weight = current_primal_weight; + } + + /** + * @brief Log all features in key=value format. + * + * Format: PDLP_RESULT: n_vars=N n_cstrs=M nnz=K ... + * + * This format is parsed by determinism_logs_parse.py with --algorithm PDLP + */ + void log_features() const + { + // Compute derived metrics + const int64_t total_nnz_processed = interval_spmv_ops * nnz; + const double nnz_per_sec = (interval_time_ms > 0) ? static_cast(total_nnz_processed) / + (interval_time_ms / 1000.0) + : 0.0; + + CUOPT_LOG_INFO( + "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6e nnz_stddev=%.4f " + "unbalancedness=%.4f interval_iters=%d interval_major=%d interval_restarts=%d " + "spmv_ops=%ld total_iters=%d total_restarts=%d iters_since_restart=%d " + "primal_res=%.6e dual_res=%.6e gap=%.6e kkt=%.6e " + "step_size=%.6e primal_weight=%.6e time_ms=%.2f nnz_per_s=%.2e warm_start=%d", + n_vars, + n_cstrs, + nnz, + sparsity, + nnz_stddev, + unbalancedness, + interval_iters, + interval_major, + interval_restarts, + interval_spmv_ops, + total_iters, + total_restarts, + iters_since_restart, + primal_res, + dual_res, + gap, + kkt_score, + step_size, + primal_weight, + interval_time_ms, + nnz_per_sec, + static_cast(has_warm_start)); + } + + /** + * @brief Reset per-interval counters. + * + * Called after each log to start fresh for the next interval. + */ + void reset_interval_counters() + { + interval_iters = 0; + interval_major = 0; + interval_restarts = 0; + interval_spmv_ops = 0; + interval_time_ms = 0.0; + } + + /** + * @brief Check if it's time to log features. + * + * Returns true every PDLP_FEATURE_LOG_INTERVAL iterations. + */ + bool should_log() const { return (interval_iters >= PDLP_FEATURE_LOG_INTERVAL); } +}; + +} // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index b5f04f7ea..6a336e7c0 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -100,6 +100,11 @@ optimization_problem_solution_t get_relaxed_lp_solution( pdlp_settings.per_constraint_residual = settings.per_constraint_residual; pdlp_settings.first_primal_feasible = settings.return_first_feasible; pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; + // Stable3 has a simpler and more predictable codepath + // if (work_limit != std::numeric_limits::infinity()) + { + pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable3; + } set_pdlp_solver_mode(pdlp_settings); // TODO: set Stable3 here? pdlp_solver_t lp_solver(op_problem, pdlp_settings); From d35bed2ecbce50eac9487c791d47be1eb2bcb184 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 27 Nov 2025 09:34:16 +0000 Subject: [PATCH 092/366] fix logging --- cpp/src/linear_programming/pdlp_features.hpp | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/cpp/src/linear_programming/pdlp_features.hpp b/cpp/src/linear_programming/pdlp_features.hpp index 95595e921..76f016914 100644 --- a/cpp/src/linear_programming/pdlp_features.hpp +++ b/cpp/src/linear_programming/pdlp_features.hpp @@ -7,8 +7,6 @@ #pragma once -#include - #include #include #include @@ -190,7 +188,7 @@ struct pdlp_features_t { (interval_time_ms / 1000.0) : 0.0; - CUOPT_LOG_INFO( + printf( "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6e nnz_stddev=%.4f " "unbalancedness=%.4f interval_iters=%d interval_major=%d interval_restarts=%d " "spmv_ops=%ld total_iters=%d total_restarts=%d iters_since_restart=%d " From 19139467259785482e4e9abcea15469db6fc7562 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 27 Nov 2025 13:49:24 +0000 Subject: [PATCH 093/366] basic dual simplex and pdlp predictors --- cpp/src/CMakeLists.txt | 6 +- .../dual_simplex/dual_simplex_features.hpp | 2 +- cpp/src/dual_simplex/phase2.cpp | 102 +- cpp/src/linear_programming/solve.cu | 2 +- cpp/src/mip/diversity/diversity_manager.cu | 4 +- cpp/src/mip/feasibility_jump/fj_cpu.cu | 7 +- cpp/src/mip/solver.cu | 14 +- cpp/src/mip/solver_context.cuh | 2 + .../models/dualsimplex_predictor/header.h | 35 + .../models/dualsimplex_predictor/main.cpp | 19265 ++++++++++++++++ .../models/dualsimplex_predictor/quantize.cpp | 1891 ++ cpp/src/utilities/models/fj.ubj.gz | Bin 12153 -> 0 bytes .../utilities/models/pdlp_predictor/header.h | 35 + .../utilities/models/pdlp_predictor/main.cpp | 16893 ++++++++++++++ .../models/pdlp_predictor/quantize.cpp | 1006 + cpp/src/utilities/version_info.cpp | 30 + cpp/src/utilities/version_info.hpp | 3 +- cpp/src/utilities/work_unit_predictor.cpp | 9 +- 18 files changed, 39269 insertions(+), 37 deletions(-) create mode 100644 cpp/src/utilities/models/dualsimplex_predictor/header.h create mode 100644 cpp/src/utilities/models/dualsimplex_predictor/main.cpp create mode 100644 cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp delete mode 100644 cpp/src/utilities/models/fj.ubj.gz create mode 100644 cpp/src/utilities/models/pdlp_predictor/header.h create mode 100644 cpp/src/utilities/models/pdlp_predictor/main.cpp create mode 100644 cpp/src/utilities/models/pdlp_predictor/quantize.cpp diff --git a/cpp/src/CMakeLists.txt b/cpp/src/CMakeLists.txt index c07cae037..3267f3e80 100644 --- a/cpp/src/CMakeLists.txt +++ b/cpp/src/CMakeLists.txt @@ -11,7 +11,11 @@ set(UTIL_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/utilities/seed_generator.cu ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/main.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/quantize.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/cpufj_predictor/main.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/cpufj_predictor/quantize.cpp) + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/cpufj_predictor/quantize.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/dualsimplex_predictor/main.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/dualsimplex_predictor/quantize.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/pdlp_predictor/main.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/pdlp_predictor/quantize.cpp) add_subdirectory(linear_programming) add_subdirectory(math_optimization) diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 9024cbad5..73e88970e 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -112,7 +112,7 @@ struct dual_simplex_features_t { */ void log_features(const simplex_solver_settings_t& settings) const { - settings.log.printf( + printf( "DS_FEATURES: iter=%d m=%d n=%d nnz=%d density=%.6e avg_nnz_col=%.2f avg_nnz_row=%.2f " "bounded=%d free=%d fixed=%d phase=%d refact_freq=%d num_refacts=%d num_updates=%d " "sparse_dz=%d dense_dz=%d bound_flips=%d num_infeas=%d dy_nz_pct=%.2f " diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 58a3312b7..7cd1c420f 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -17,12 +17,18 @@ #include #include +#include +#include + +#include + #include #include #include #include #include +#include namespace cuopt::linear_programming::dual_simplex { @@ -2237,6 +2243,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, dual::status_t status = dual::status_t::UNSET; + raft::common::nvtx::push_range("DualSimplex::phase2_advanced_init"); + // Perturbed objective std::vector objective = lp.objective; @@ -2381,7 +2389,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, csc_matrix_t A_transpose(1, 1, 0); lp.A.transpose(A_transpose); - f_t obj = compute_objective(lp, x); + f_t obj = compute_objective(lp, x); + + raft::common::nvtx::pop_range(); + const i_t start_iter = iter; i_t sparse_delta_z = 0; @@ -2453,6 +2464,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // Note: Features are logged inline with DS_FEATURES: prefix while (iter < iter_limit) { + raft::common::nvtx::range scope_main("DualSimplex::phase2_main_loop"); // Pricing i_t direction = 0; i_t basic_leaving_index = -1; @@ -2534,29 +2546,32 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } delta_y_nz_percentage = delta_y_nz0 / static_cast(m) * 100.0; const bool use_transpose = delta_y_nz_percentage <= 30.0; - if (use_transpose) { - sparse_delta_z++; - phase2::compute_delta_z(A_transpose, - delta_y_sparse, - leaving_index, - direction, - nonbasic_mark, - delta_z_mark, - delta_z_indices, - delta_z); - } else { - dense_delta_z++; - // delta_zB = sigma*ei - delta_y_sparse.to_dense(delta_y); - phase2::compute_reduced_cost_update(lp, - basic_list, - nonbasic_list, - delta_y, - leaving_index, - direction, - delta_z_mark, - delta_z_indices, - delta_z); + { + raft::common::nvtx::range scope_compute_delta_z("DualSimplex::delta_z"); + if (use_transpose) { + sparse_delta_z++; + phase2::compute_delta_z(A_transpose, + delta_y_sparse, + leaving_index, + direction, + nonbasic_mark, + delta_z_mark, + delta_z_indices, + delta_z); + } else { + dense_delta_z++; + // delta_zB = sigma*ei + delta_y_sparse.to_dense(delta_y); + phase2::compute_reduced_cost_update(lp, + basic_list, + nonbasic_list, + delta_y, + leaving_index, + direction, + delta_z_mark, + delta_z_indices, + delta_z); + } } timers.delta_z_time += timers.stop_timer(); @@ -3064,6 +3079,43 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // Log all features on a single line features.log_features(settings); + + // Populate features map for predictor + cuopt::work_unit_predictor_t dualsimplex_predictor{}; + std::map features_map; + features_map["m"] = static_cast(features.num_rows); + features_map["n"] = static_cast(features.num_cols); + features_map["nnz"] = static_cast(features.num_nonzeros); + features_map["density"] = static_cast(features.matrix_density); + features_map["avg_nnz_col"] = static_cast(features.avg_nnz_per_col); + features_map["avg_nnz_row"] = static_cast(features.avg_nnz_per_row); + features_map["bounded"] = static_cast(features.num_bounded_vars); + features_map["free"] = static_cast(features.num_free_vars); + features_map["refact_freq"] = static_cast(features.refactor_frequency); + features_map["num_refacts"] = static_cast(features.num_refactors); + features_map["num_updates"] = static_cast(features.num_basis_updates); + features_map["sparse_dz"] = static_cast(features.sparse_delta_z_count); + features_map["dense_dz"] = static_cast(features.dense_delta_z_count); + features_map["bound_flips"] = static_cast(features.total_bound_flips); + features_map["num_infeas"] = static_cast(features.num_infeasibilities); + features_map["dy_nz_pct"] = static_cast(features.delta_y_nz_percentage); + features_map["byte_loads"] = static_cast(features.byte_loads); + features_map["byte_stores"] = static_cast(features.byte_stores); + auto time_prediction = + std::max((f_t)0.0, (f_t)dualsimplex_predictor.predict_scalar(features_map)); + f_t baseline_max_clock = 3800; // 3.8Ghz + f_t max_clock = cuopt::get_cpu_max_clock_mhz(); + f_t scaling_factor = baseline_max_clock / max_clock; + f_t adjusted_time_prediction = time_prediction * scaling_factor; + printf( + "DualSimplex determ: Estimated time for %d iters: %f, actual time: %f, error %f, CPU max " + "clock: %f MHz, scaling factor: %f\n", + FEATURE_LOG_INTERVAL, + adjusted_time_prediction, + features.interval_runtime, + adjusted_time_prediction - features.interval_runtime, + max_clock, + scaling_factor); } if ((iter - start_iter) < settings.first_iteration_log || @@ -3106,6 +3158,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, ft.print_stats(); } } + + printf("Iterations: %d, time taken: %f\n", iter, toc(start_time)); return status; } diff --git a/cpp/src/linear_programming/solve.cu b/cpp/src/linear_programming/solve.cu index 42e32a030..541692368 100644 --- a/cpp/src/linear_programming/solve.cu +++ b/cpp/src/linear_programming/solve.cu @@ -818,7 +818,7 @@ optimization_problem_solution_t solve_lp(optimization_problem_t problem(op_problem); - double presolve_time = 0.0; + [[maybe_unused]] double presolve_time = 0.0; std::unique_ptr> presolver; auto run_presolve = settings.presolve; run_presolve = run_presolve && settings.get_pdlp_warm_start_data().total_pdlp_iterations_ == -1; diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 6cdd8a63f..96f93d267 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -92,8 +92,8 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_t 1) { - int config_id = -1; // Default value - const char* env_config_id = std::getenv("CUOPT_CONFIG_ID"); + [[maybe_unused]] int config_id = -1; // Default value + const char* env_config_id = std::getenv("CUOPT_CONFIG_ID"); if (env_config_id != nullptr) { try { config_id = std::stoi(env_config_id); diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 31d1b8cc3..43f747d86 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -1706,9 +1706,10 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l float time_prediction = std::max( (f_t)0.0, (f_t)ceil(context.work_unit_predictors.cpufj_predictor.predict_scalar(features_map))); - CUOPT_LOG_DEBUG("FJ determ: Estimated time for 1000 iters: %f, error %f", - time_prediction, - time_prediction - time_window_ms); + // CUOPT_LOG_DEBUG("FJ determ: Estimated time for 1000 iters: %f, actual time: %f, error %f", + // time_prediction, + // time_window_ms, + // time_prediction - time_window_ms); } cuopt_func_call(sanity_checks(fj_cpu)); diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index ec1f3cbf7..55d71306e 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -177,9 +177,17 @@ solution_t mip_solver_t::run_solver() } CUOPT_LOG_INFO("Using %d CPU threads for B&B", branch_and_bound_settings.num_threads); - i_t num_threads = branch_and_bound_settings.num_threads; - i_t num_bfs_threads = std::max(1, num_threads / 4); - i_t num_diving_threads = std::max(1, num_threads - num_bfs_threads); + CUOPT_LOG_ERROR("AAAAAA\n"); + + i_t num_threads = branch_and_bound_settings.num_threads; + i_t num_bfs_threads = std::max(1, num_threads / 4); + i_t num_diving_threads = num_threads - num_bfs_threads; + // deterministic mode: no diving for now + if (context.settings.deterministic) { + num_threads = 1; + num_bfs_threads = 1; + num_diving_threads = 0; + } branch_and_bound_settings.num_bfs_threads = num_bfs_threads; branch_and_bound_settings.num_diving_threads = num_diving_threads; diff --git a/cpp/src/mip/solver_context.cuh b/cpp/src/mip/solver_context.cuh index 306fc0dc5..b4df2578e 100644 --- a/cpp/src/mip/solver_context.cuh +++ b/cpp/src/mip/solver_context.cuh @@ -13,6 +13,7 @@ #include #include +#include #include #include @@ -29,6 +30,7 @@ namespace cuopt::linear_programming::detail { struct mip_solver_work_unit_predictors_t { work_unit_predictor_t fj_predictor{}; work_unit_predictor_t cpufj_predictor{}; + work_unit_predictor_t pdlp_predictor{}; }; // Aggregate structure containing the global context of the solving process for convenience: diff --git a/cpp/src/utilities/models/dualsimplex_predictor/header.h b/cpp/src/utilities/models/dualsimplex_predictor/header.h new file mode 100644 index 000000000..990607eba --- /dev/null +++ b/cpp/src/utilities/models/dualsimplex_predictor/header.h @@ -0,0 +1,35 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#pragma once + +#include +#include +#include +#include +#include +#include + +class dualsimplex_predictor { + public: + union Entry { + int missing; + float fvalue; + int qvalue; + }; + + static int32_t get_num_target(void); + static void get_num_class(int32_t* out); + static int32_t get_num_feature(void); + static const char* get_threshold_type(void); + static const char* get_leaf_output_type(void); + static void predict(union Entry* data, int pred_margin, double* result); + static void postprocess(double* result); + static int quantize(float val, unsigned fid); + + // Feature names + static constexpr int NUM_FEATURES = 18; + static const char* feature_names[NUM_FEATURES]; +}; // class dualsimplex_predictor diff --git a/cpp/src/utilities/models/dualsimplex_predictor/main.cpp b/cpp/src/utilities/models/dualsimplex_predictor/main.cpp new file mode 100644 index 000000000..571527421 --- /dev/null +++ b/cpp/src/utilities/models/dualsimplex_predictor/main.cpp @@ -0,0 +1,19265 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include "header.h" + +#if defined(__clang__) || defined(__GNUC__) +#define LIKELY(x) __builtin_expect(!!(x), 1) +#define UNLIKELY(x) __builtin_expect(!!(x), 0) +#else +#define LIKELY(x) (x) +#define UNLIKELY(x) (x) +#endif +#define N_TARGET 1 +#define MAX_N_CLASS 1 + +const unsigned char is_categorical[] = { + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +}; +static const int32_t num_class[] = { + 1, +}; + +int32_t dualsimplex_predictor::get_num_target(void) { return std::exp(N_TARGET); } +void dualsimplex_predictor::get_num_class(int32_t* out) +{ + for (int i = 0; i < N_TARGET; ++i) { + out[i] = num_class[i]; + } +} +int32_t dualsimplex_predictor::get_num_feature(void) { return std::exp(18); } +const char* dualsimplex_predictor::get_threshold_type(void) { return "float32"; } +const char* dualsimplex_predictor::get_leaf_output_type(void) { return "float32"; } + +void dualsimplex_predictor::predict(union Entry* data, int pred_margin, double* result) +{ + // Quantize data + for (int i = 0; i < 18; ++i) { + if (data[i].missing != -1 && !is_categorical[i]) { + data[i].qvalue = quantize(data[i].fvalue, i); + } + } + + unsigned int tmp; + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 30))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += -0.23892987; + } else { + result[0] += -0.41049096; + } + } else { + result[0] += -0.32070833; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { + result[0] += -0.3301402; + } else { + result[0] += -0.27258733; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 158))) { + result[0] += -0.2263252; + } else { + result[0] += -0.03232357; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 244))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += -0.21953094; + } else { + result[0] += -0.2782724; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 252))) { + result[0] += -0.10610497; + } else { + result[0] += -0.18065642; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 238))) { + result[0] += -0.14425437; + } else { + result[0] += -0.027287258; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 4))) { + result[0] += -0.06345942; + } else { + result[0] += -0.026511494; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { + result[0] += -0.20900369; + } else { + result[0] += -0.03617132; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + result[0] += -0.17355898; + } else { + result[0] += -0.094911605; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 174))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { + result[0] += -0.064165816; + } else { + result[0] += -0.12049206; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 116))) { + result[0] += -0.11783655; + } else { + result[0] += 0.031279184; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 14))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + result[0] += -0.16023064; + } else { + result[0] += -0.01693966; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 92))) { + result[0] += -0.03719565; + } else { + result[0] += 0.002473806; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 180))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 194))) { + result[0] += 0.015176967; + } else { + result[0] += -0.033581402; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 52))) { + result[0] += 0.060070585; + } else { + result[0] += 0.102768324; + } + } + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 210))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 152))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + result[0] += -0.018653875; + } else { + result[0] += 0.21873346; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 158))) { + result[0] += 0.020794097; + } else { + result[0] += 0.0017005502; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 54))) { + result[0] += -0.15759692; + } else { + result[0] += 0.037903294; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + result[0] += 0.073502585; + } else { + result[0] += 0.1481389; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 72))) { + result[0] += -0.20302705; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 298))) { + result[0] += -0.0740808; + } else { + result[0] += -0.0013614334; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 36))) { + result[0] += 0.007832853; + } else { + result[0] += 0.05149386; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 334))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 286))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { + result[0] += 0.038612913; + } else { + result[0] += -0.011268199; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 246))) { + result[0] += 0.12736425; + } else { + result[0] += 0.057369012; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 270))) { + result[0] += 0.09891705; + } else { + result[0] += 0.1404978; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 200))) { + result[0] += -0.09467173; + } else { + result[0] += 0.058953542; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 98))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 12))) { + result[0] += 0.24495018; + } else { + result[0] += 0.4303154; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + result[0] += 0.23759733; + } else { + result[0] += 0.31459442; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { + result[0] += 0.18160371; + } else { + result[0] += 0.119219065; + } + } else { + result[0] += 0.2541432; + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += -0.13811207; + } else { + result[0] += -0.39534476; + } + } else { + result[0] += -0.3041533; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 20))) { + result[0] += -0.29433212; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 78))) { + result[0] += -0.19736992; + } else { + result[0] += -0.25767314; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + result[0] += -0.19146764; + } else { + result[0] += -0.15695396; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { + result[0] += -0.23099203; + } else { + result[0] += -0.117888466; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 16))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { + result[0] += -0.1496231; + } else { + result[0] += -0.10690468; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 160))) { + result[0] += -0.027002404; + } else { + result[0] += -0.08140517; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { + result[0] += -0.18216138; + } else { + result[0] += -0.023672506; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { + result[0] += -0.12947556; + } else { + result[0] += -0.04930939; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { + result[0] += -0.10542603; + } else { + result[0] += -0.05690325; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 70))) { + result[0] += -0.11127614; + } else { + result[0] += 0.029446835; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 24))) { + result[0] += -0.07772863; + } else { + result[0] += -0.02781359; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 86))) { + result[0] += -0.13588855; + } else { + result[0] += -0.0629831; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 180))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 298))) { + result[0] += 0.008945032; + } else { + result[0] += -0.030025298; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 52))) { + result[0] += 0.053789187; + } else { + result[0] += 0.09317514; + } + } + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 212))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 172))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 150))) { + result[0] += -0.01571287; + } else { + result[0] += 0.01397138; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 22))) { + result[0] += -0.004348435; + } else { + result[0] += 0.28950748; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 62))) { + result[0] += 0.073582016; + } else { + result[0] += 0.15677379; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { + result[0] += -0.031289082; + } else { + result[0] += 0.06539955; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 298))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { + result[0] += -0.18994586; + } else { + result[0] += -0.066469155; + } + } else { + result[0] += 0.026545838; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 82))) { + result[0] += 0.0066967257; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 122))) { + result[0] += 0.04904222; + } else { + result[0] += 0.012652091; + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 308))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 54))) { + result[0] += 0.036342237; + } else { + result[0] += -0.0249609; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { + result[0] += 0.10429803; + } else { + result[0] += 0.3026218; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 252))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.19158468; + } else { + result[0] += -0.07063005; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 264))) { + result[0] += -0.019653698; + } else { + result[0] += 0.029620511; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 328))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 222))) { + result[0] += 0.16111112; + } else { + result[0] += 0.25347957; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 128))) { + result[0] += 0.084318936; + } else { + result[0] += 0.14822438; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 92))) { + result[0] += 0.22045271; + } else { + result[0] += 0.26758388; + } + } else { + result[0] += 0.46477053; + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 134))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 4))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += -0.1654226; + } else { + result[0] += -0.36575767; + } + } else { + result[0] += -0.28903162; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 30))) { + result[0] += -0.24255578; + } else { + result[0] += -0.27643654; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { + result[0] += -0.29263017; + } else { + result[0] += -0.20599626; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { + result[0] += -0.18999758; + } else { + result[0] += -0.15109059; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { + result[0] += -0.11279365; + } else { + result[0] += -0.027640833; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { + result[0] += -0.12278713; + } else { + result[0] += -0.029810125; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 100))) { + result[0] += -0.21111389; + } else { + result[0] += -0.13208562; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 146))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 78))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { + result[0] += -0.11854438; + } else { + result[0] += -0.17666526; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 64))) { + result[0] += -0.012537091; + } else { + result[0] += -0.081729345; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 80))) { + result[0] += -0.10848161; + } else { + result[0] += -0.06910487; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + result[0] += -0.048394956; + } else { + result[0] += -0.10724862; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 18))) { + result[0] += -0.05678125; + } else { + result[0] += -0.15847282; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { + result[0] += -0.02826718; + } else { + result[0] += 0.016257456; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 170))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 194))) { + result[0] += 0.009919471; + } else { + result[0] += -0.02828365; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 52))) { + result[0] += 0.048264306; + } else { + result[0] += 0.084261395; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 220))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 204))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 154))) { + result[0] += -0.015983945; + } else { + result[0] += 0.0104940515; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + result[0] += 0.059158504; + } else { + result[0] += -0.013891051; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 166))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + result[0] += -0.05883024; + } else { + result[0] += -0.15169065; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 50))) { + result[0] += 0.017066706; + } else { + result[0] += 0.046345506; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 302))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + result[0] += 0.114555776; + } else { + result[0] += 0.40284094; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + result[0] += 0.027867997; + } else { + result[0] += -0.05320992; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 160))) { + result[0] += 0.02757929; + } else { + result[0] += 0.0005367231; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { + result[0] += 0.0925671; + } else { + result[0] += -0.04869043; + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 334))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 54))) { + result[0] += 0.033666294; + } else { + result[0] += -0.019405624; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 138))) { + result[0] += 0.085760355; + } else { + result[0] += 0.11623438; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 136))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 78))) { + result[0] += -0.078748904; + } else { + result[0] += -0.03139619; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { + result[0] += 0.020764524; + } else { + result[0] += 0.15907182; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 156))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + result[0] += 0.19595024; + } else { + result[0] += 0.25730672; + } + } else { + result[0] += 0.41746393; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 90))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 20))) { + result[0] += 0.18315254; + } else { + result[0] += 0.3391243; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { + result[0] += 0.1157012; + } else { + result[0] += 0.20929395; + } + } + } + } + } + } + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 108))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 74))) { + result[0] += -0.14199895; + } else { + result[0] += -0.0956778; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 140))) { + result[0] += -0.010430599; + } else { + result[0] += 0.019095603; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { + result[0] += -0.3010808; + } else { + result[0] += -0.23643827; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { + result[0] += -0.20573525; + } else { + result[0] += -0.02070453; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 204))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 68))) { + result[0] += -0.120953314; + } else { + result[0] += -0.075053215; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { + result[0] += 0.023893813; + } else { + result[0] += -0.05592366; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + result[0] += -0.16090266; + } else { + result[0] += -0.22481023; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 86))) { + result[0] += -0.14591117; + } else { + result[0] += -0.081503354; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 124))) { + result[0] += 0.014306935; + } else { + result[0] += -0.15783915; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 156))) { + result[0] += -0.04105756; + } else { + result[0] += -0.014158033; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 142))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 82))) { + result[0] += -0.120835006; + } else { + result[0] += -0.073639594; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 60))) { + result[0] += -0.19654752; + } else { + result[0] += -0.14132342; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 192))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 26))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 76))) { + result[0] += 0.01790429; + } else { + result[0] += -0.00943239; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 238))) { + result[0] += 0.04468555; + } else { + result[0] += 0.011115446; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 62))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 270))) { + result[0] += 0.07178088; + } else { + result[0] += 0.011843091; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { + result[0] += 0.0443505; + } else { + result[0] += 0.14387575; + } + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 288))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 254))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 148))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { + result[0] += -0.04890412; + } else { + result[0] += -0.0113124; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 190))) { + result[0] += 0.015577379; + } else { + result[0] += -0.0047443947; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 300))) { + result[0] += 0.03677338; + } else { + result[0] += 0.08133157; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 58))) { + result[0] += 0.11096779; + } else { + result[0] += 0.15190493; + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 186))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 70))) { + result[0] += 0.0974144; + } else { + result[0] += 0.012945512; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 228))) { + result[0] += -0.016480766; + } else { + result[0] += 0.020737674; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 176))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { + result[0] += -0.105966404; + } else { + result[0] += -0.01857656; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { + result[0] += -0.033622157; + } else { + result[0] += 0.042780038; + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 334))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 14))) { + result[0] += -0.0662734; + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 140))) { + result[0] += 0.07991419; + } else { + result[0] += 0.039648328; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 172))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 120))) { + result[0] += 0.10550159; + } else { + result[0] += 0.03400041; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { + result[0] += 0.11472396; + } else { + result[0] += 0.1589642; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 8))) { + result[0] += 0.14915675; + } else { + result[0] += 0.3244665; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + result[0] += 0.17384307; + } else { + result[0] += 0.23245731; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { + result[0] += 0.13902494; + } else { + result[0] += 0.07743486; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + result[0] += 0.19507462; + } else { + result[0] += 0.12790385; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 14))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += -0.12810235; + } else { + result[0] += -0.30201578; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { + result[0] += -0.25788355; + } else { + result[0] += -0.21340947; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { + result[0] += -0.21370807; + } else { + result[0] += -0.167573; + } + } else { + result[0] += -0.024952482; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 100))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { + result[0] += -0.171652; + } else { + result[0] += -0.12910992; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 110))) { + result[0] += -0.07442858; + } else { + result[0] += -0.12269157; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 12))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { + result[0] += -0.11061738; + } else { + result[0] += -0.06790245; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 160))) { + result[0] += -0.017483674; + } else { + result[0] += -0.059728812; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 146))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 82))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { + result[0] += -0.13760419; + } else { + result[0] += -0.01986711; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { + result[0] += -0.0773085; + } else { + result[0] += 0.004780628; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 62))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 144))) { + result[0] += -0.08369659; + } else { + result[0] += -0.0037746632; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + result[0] += -0.033646163; + } else { + result[0] += -0.09390003; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 64))) { + result[0] += -0.10188579; + } else { + result[0] += -0.022887342; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { + result[0] += -0.025158664; + } else { + result[0] += 0.011464008; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 170))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 204))) { + result[0] += 0.009676366; + } else { + result[0] += -0.025687149; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 52))) { + result[0] += 0.03910928; + } else { + result[0] += 0.06689557; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 220))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 204))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 80))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { + result[0] += 0.039443213; + } else { + result[0] += 0.20666161; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 128))) { + result[0] += -0.01548522; + } else { + result[0] += 0.015606319; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 392))) { + result[0] += -0.0018438485; + } else { + result[0] += -0.03514717; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 90))) { + result[0] += -0.029947493; + } else { + result[0] += -0.056570943; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 302))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + result[0] += 0.15045454; + } else { + result[0] += 0.01461363; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + result[0] += 0.031331427; + } else { + result[0] += 0.2708533; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += -0.044682816; + } else { + result[0] += 0.019505082; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 278))) { + result[0] += 0.10829522; + } else { + result[0] += 0.07073908; + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 312))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { + result[0] += 0.027972206; + } else { + result[0] += 0.005189055; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { + result[0] += -0.066226505; + } else { + result[0] += 0.0877003; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 148))) { + result[0] += 0.055681467; + } else { + result[0] += -0.06665366; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 280))) { + result[0] += 0.0809778; + } else { + result[0] += 0.1468879; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { + result[0] += 0.15474413; + } else { + result[0] += 0.20727856; + } + } else { + result[0] += 0.35305986; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 158))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { + result[0] += 0.07976506; + } else { + result[0] += 0.1597683; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + result[0] += 0.13799594; + } else { + result[0] += 0.23070168; + } + } + } + } + } + } + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 104))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.24169908; + } else { + result[0] += -0.107800476; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { + result[0] += 0.0015814465; + } else { + result[0] += -0.14862476; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 82))) { + result[0] += -0.2477706; + } else { + result[0] += -0.1950383; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { + result[0] += -0.17119522; + } else { + result[0] += -0.045236852; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 178))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { + result[0] += 0.025135875; + } else { + result[0] += -0.08694006; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { + result[0] += 0.029477531; + } else { + result[0] += -0.04847209; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 116))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + result[0] += 0.011227789; + } else { + result[0] += -0.09084964; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 82))) { + result[0] += -0.13153963; + } else { + result[0] += -0.18369597; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 152))) { + result[0] += -0.061374586; + } else { + result[0] += -0.111760534; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 276))) { + result[0] += -0.03116385; + } else { + result[0] += -0.085914634; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 142))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + result[0] += 0.0005968995; + } else { + result[0] += -0.08622521; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 70))) { + result[0] += -0.17008883; + } else { + result[0] += -0.11829443; + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 34))) { + result[0] += 0.0151279615; + } else { + result[0] += 0.037952267; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 58))) { + result[0] += 0.059345204; + } else { + result[0] += 0.11877208; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 206))) { + result[0] += -0.12507677; + } else { + result[0] += -0.025324045; + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 4))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 102))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 246))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + result[0] += 0.044818092; + } else { + result[0] += -0.01875449; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 148))) { + result[0] += -0.11071397; + } else { + result[0] += -0.059991468; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { + result[0] += 0.049274206; + } else { + result[0] += 0.012975462; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { + result[0] += -0.0189122; + } else { + result[0] += 0.060573917; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 148))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + result[0] += 0.02068209; + } else { + result[0] += -0.027593452; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 60))) { + result[0] += -0.008190083; + } else { + result[0] += 0.01192194; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 268))) { + result[0] += 0.02700766; + } else { + result[0] += 0.07031421; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 58))) { + result[0] += 0.09329746; + } else { + result[0] += 0.12738243; + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 336))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 128))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { + result[0] += 0.04464999; + } else { + result[0] += 0.0775819; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 62))) { + result[0] += -0.05400176; + } else { + result[0] += 0.0332122; + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 60))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { + result[0] += 0.06202929; + } else { + result[0] += 0.0011356722; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + result[0] += 0.093274996; + } else { + result[0] += 0.12987015; + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 124))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { + result[0] += 0.15837678; + } else { + result[0] += 0.3200806; + } + } else { + result[0] += 0.34475276; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { + result[0] += 0.09141225; + } else { + result[0] += 0.15360029; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + result[0] += 0.14422126; + } else { + result[0] += 0.18972561; + } + } + } + } + } + } + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 106))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 102))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 38))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { + result[0] += -0.22463696; + } else { + result[0] += -0.17236648; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { + result[0] += -0.15470378; + } else { + result[0] += -0.014106827; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 80))) { + result[0] += -0.104941204; + } else { + result[0] += -0.06797316; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { + result[0] += 0.0034419482; + } else { + result[0] += -0.04432465; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 220))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 66))) { + result[0] += -0.09611558; + } else { + result[0] += -0.05750701; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { + result[0] += 0.02356928; + } else { + result[0] += -0.045556568; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { + result[0] += -0.12279941; + } else { + result[0] += -0.16899776; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 124))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 130))) { + result[0] += -0.017295085; + } else { + result[0] += 0.031519514; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 156))) { + result[0] += -0.16593443; + } else { + result[0] += -0.110011; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 112))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { + result[0] += -0.010664171; + } else { + result[0] += -0.032727893; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 142))) { + result[0] += -0.07378478; + } else { + result[0] += -0.13850205; + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 134))) { + result[0] += 0.007132952; + } else { + result[0] += 0.033364255; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 62))) { + result[0] += 0.053948194; + } else { + result[0] += 0.105476275; + } + } + } else { + result[0] += -0.07528159; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 288))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 254))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 154))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + result[0] += 0.017853899; + } else { + result[0] += -0.021608775; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 190))) { + result[0] += 0.012284887; + } else { + result[0] += -0.0040449556; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { + result[0] += 0.027741803; + } else { + result[0] += 0.063282035; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 56))) { + result[0] += 0.08446597; + } else { + result[0] += 0.116814755; + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 200))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 70))) { + result[0] += 0.0801467; + } else { + result[0] += 0.00028896204; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 206))) { + result[0] += -0.0006912606; + } else { + result[0] += 0.038264047; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 152))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { + result[0] += -0.09579014; + } else { + result[0] += -0.011496059; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 124))) { + result[0] += 0.028059239; + } else { + result[0] += -0.029746488; + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 332))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 60))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { + result[0] += 0.04334253; + } else { + result[0] += 0.094181366; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { + result[0] += 0.010113914; + } else { + result[0] += 0.12312211; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 12))) { + result[0] += 0.008063223; + } else { + result[0] += 0.05981284; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 44))) { + result[0] += 0.055709254; + } else { + result[0] += 0.102501765; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 126))) { + result[0] += 0.19393599; + } else { + result[0] += 0.31415454; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + result[0] += 0.12953043; + } else { + result[0] += 0.17184684; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 152))) { + result[0] += 0.105829634; + } else { + result[0] += 0.05405576; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 342))) { + result[0] += 0.11501894; + } else { + result[0] += 0.14885037; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 142))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { + result[0] += -0.22360039; + } else { + result[0] += -0.16961168; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 62))) { + result[0] += -0.17449044; + } else { + result[0] += -0.13112317; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { + result[0] += -0.19438948; + } else { + result[0] += -0.07163301; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 58))) { + result[0] += -0.12211982; + } else { + result[0] += -0.1598212; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 104))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { + result[0] += -0.1360238; + } else { + result[0] += -0.08778509; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 172))) { + result[0] += -0.058016848; + } else { + result[0] += -0.01712372; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += -0.10796758; + } else { + result[0] += -0.1462996; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { + result[0] += -0.026550526; + } else { + result[0] += -0.09057524; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { + result[0] += -0.1045504; + } else { + result[0] += -0.06835998; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + result[0] += -0.16272134; + } else { + result[0] += -0.10771642; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 236))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { + result[0] += -0.1004568; + } else { + result[0] += -0.02189359; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 12))) { + result[0] += -0.11555834; + } else { + result[0] += -0.064784154; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 110))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 78))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 138))) { + result[0] += -0.09062075; + } else { + result[0] += -0.01616345; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 144))) { + result[0] += -0.046594307; + } else { + result[0] += -0.00848129; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 220))) { + result[0] += 0.048658125; + } else { + result[0] += 0.015779605; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += -0.003939248; + } else { + result[0] += -0.031186854; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 222))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 254))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 154))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { + result[0] += 0.0046192957; + } else { + result[0] += 0.02478583; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { + result[0] += 0.24694644; + } else { + result[0] += -0.104915835; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + result[0] += -0.0028355815; + } else { + result[0] += -0.03066038; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 288))) { + result[0] += -0.038436286; + } else { + result[0] += 0.03750279; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 78))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 186))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 318))) { + result[0] += -0.012060843; + } else { + result[0] += -0.0011414163; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 314))) { + result[0] += -0.008850611; + } else { + result[0] += 0.015718905; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 262))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 196))) { + result[0] += 0.05882001; + } else { + result[0] += 0.036198247; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 184))) { + result[0] += -0.065134026; + } else { + result[0] += -0.01131267; + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 312))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 122))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { + result[0] += 0.2215176; + } else { + result[0] += -0.10633468; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + result[0] += 0.1440095; + } else { + result[0] += 0.051038798; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 254))) { + result[0] += -0.014680609; + } else { + result[0] += 0.014171252; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 348))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 224))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 94))) { + result[0] += 0.056311846; + } else { + result[0] += 0.1058489; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 362))) { + result[0] += 0.037274715; + } else { + result[0] += 0.11873694; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 152))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 34))) { + result[0] += 0.11126106; + } else { + result[0] += 0.14717433; + } + } else { + result[0] += 0.26775053; + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 134))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + result[0] += -0.20885597; + } else { + result[0] += -0.16570954; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += -0.13637789; + } else { + result[0] += -0.1756009; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { + result[0] += -0.21495152; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { + result[0] += -0.15078692; + } else { + result[0] += -0.11473024; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + result[0] += -0.08377748; + } else { + result[0] += -0.020055672; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + result[0] += -0.12106979; + } else { + result[0] += -0.092178635; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 16))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { + result[0] += -0.07951072; + } else { + result[0] += -0.04535841; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 52))) { + result[0] += 0.015479415; + } else { + result[0] += -0.034244347; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 202))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 76))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { + result[0] += -0.10090798; + } else { + result[0] += -0.072621964; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + result[0] += -0.08769948; + } else { + result[0] += -0.020480268; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 52))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 158))) { + result[0] += -0.06127919; + } else { + result[0] += 0.0065026283; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { + result[0] += -0.06342202; + } else { + result[0] += -0.021188881; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 72))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 100))) { + result[0] += -0.04954914; + } else { + result[0] += 0.00018559814; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 44))) { + result[0] += 0.011565405; + } else { + result[0] += -0.011750548; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 146))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { + result[0] += 0.075431995; + } else { + result[0] += -0.005443739; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 62))) { + result[0] += 0.025297; + } else { + result[0] += 0.044371624; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 234))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 206))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 112))) { + result[0] += -0.012151074; + } else { + result[0] += 0.0095630875; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.19431071; + } else { + result[0] += 0.039510164; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + result[0] += -0.002731852; + } else { + result[0] += -0.02748406; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { + result[0] += -0.039663028; + } else { + result[0] += -0.002819218; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 294))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + result[0] += 0.012211016; + } else { + result[0] += 0.04506604; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 108))) { + result[0] += -8.271459e-05; + } else { + result[0] += -0.031217247; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 10))) { + result[0] += -0.027022822; + } else { + result[0] += 0.015010217; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + result[0] += 0.08849927; + } else { + result[0] += 0.04585655; + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 330))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 56))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { + result[0] += 0.015790751; + } else { + result[0] += 0.0709383; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 302))) { + result[0] += 0.014195338; + } else { + result[0] += 0.043147992; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 306))) { + result[0] += 0.027121753; + } else { + result[0] += 0.06687878; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { + result[0] += 0.06856555; + } else { + result[0] += 0.11084541; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 12))) { + result[0] += 0.023235757; + } else { + result[0] += 0.12572078; + } + } else { + result[0] += 0.24674617; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 362))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { + result[0] += 0.08457372; + } else { + result[0] += 0.04069808; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { + result[0] += 0.11791612; + } else { + result[0] += 0.07502523; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 142))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 28))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += -0.054601975; + } else { + result[0] += -0.17477903; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 48))) { + result[0] += -0.00046666022; + } else { + result[0] += -0.13438587; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 8))) { + result[0] += -0.07183619; + } else { + result[0] += -0.17046754; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { + result[0] += -0.10375444; + } else { + result[0] += -0.14129147; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + result[0] += -0.07520628; + } else { + result[0] += -0.01688747; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 14))) { + result[0] += -0.11755419; + } else { + result[0] += -0.08725922; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 28))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 2))) { + result[0] += -0.06106487; + } else { + result[0] += -0.09542205; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 202))) { + result[0] += 0.008344304; + } else { + result[0] += -0.03182082; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 76))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 64))) { + result[0] += -0.061504174; + } else { + result[0] += -0.13946581; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + result[0] += -0.13620088; + } else { + result[0] += -0.08506644; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 236))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 78))) { + result[0] += -0.10902418; + } else { + result[0] += -0.016639745; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 244))) { + result[0] += -0.10518378; + } else { + result[0] += -0.05821876; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 116))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 90))) { + result[0] += -0.06888045; + } else { + result[0] += -0.0376487; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 88))) { + result[0] += -0.012150378; + } else { + result[0] += 0.015397436; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 216))) { + result[0] += 0.039432753; + } else { + result[0] += 0.013889983; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += -0.0010314513; + } else { + result[0] += -0.02555494; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 232))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 206))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 200))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 80))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { + result[0] += 0.030333001; + } else { + result[0] += 0.17310323; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { + result[0] += 0.02483304; + } else { + result[0] += 0.0021163383; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { + result[0] += -0.017784828; + } else { + result[0] += -0.039406788; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 234))) { + result[0] += -0.0026142828; + } else { + result[0] += 0.03959627; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 284))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { + result[0] += 0.019107591; + } else { + result[0] += -0.011726064; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { + result[0] += 0.096682; + } else { + result[0] += 0.22430603; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { + result[0] += 0.012120399; + } else { + result[0] += -0.03992904; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + result[0] += 0.06968047; + } else { + result[0] += 0.04038763; + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 304))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + result[0] += 0.11028786; + } else { + result[0] += 0.040963266; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { + result[0] += 0.19369291; + } else { + result[0] += 0.0747213; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 78))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + result[0] += -0.057252772; + } else { + result[0] += -0.14311497; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 232))) { + result[0] += -0.0102287065; + } else { + result[0] += 0.016080577; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 330))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { + result[0] += 0.07653522; + } else { + result[0] += 0.13459362; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 130))) { + result[0] += 0.03455645; + } else { + result[0] += 0.06964803; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + result[0] += 0.09500605; + } else { + result[0] += 0.12430712; + } + } else { + result[0] += 0.22509204; + } + } + } + } + } + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 74))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 20))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 48))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + result[0] += -0.16930752; + } else { + result[0] += -0.1269749; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += -0.06503186; + } else { + result[0] += -0.12498716; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { + result[0] += -0.1927099; + } else { + result[0] += -0.08201694; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 38))) { + result[0] += -0.068333164; + } else { + result[0] += -0.13142657; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 12))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { + result[0] += 0.042084176; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.14493789; + } else { + result[0] += -0.09196159; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 98))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { + result[0] += 0.03108189; + } else { + result[0] += -0.067129865; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + result[0] += 0.0054919203; + } else { + result[0] += -0.05392629; + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 144))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 98))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { + result[0] += -0.21116984; + } else { + result[0] += -0.0651527; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { + result[0] += 0.116855875; + } else { + result[0] += 0.050092816; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 148))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 20))) { + result[0] += -0.08852083; + } else { + result[0] += -0.04572432; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 270))) { + result[0] += -0.0053127306; + } else { + result[0] += 0.024463532; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 32))) { + result[0] += -0.22384882; + } else { + result[0] += -0.093135744; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 16))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 74))) { + result[0] += -0.031588506; + } else { + result[0] += -0.08342363; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 116))) { + result[0] += 0.012553929; + } else { + result[0] += 0.058796555; + } + } + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 146))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 300))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 4))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 46))) { + result[0] += 0.08138124; + } else { + result[0] += 0.13923828; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 202))) { + result[0] += 0.021075254; + } else { + result[0] += 0.064726695; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 98))) { + result[0] += 0.016024219; + } else { + result[0] += -0.014299775; + } + } else { + result[0] += -0.04494404; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 86))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 82))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + result[0] += -0.03340462; + } else { + result[0] += -0.002975365; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 130))) { + result[0] += -0.13237435; + } else { + result[0] += -0.098908305; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + result[0] += -0.019236477; + } else { + result[0] += -0.033017647; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { + result[0] += 0.0251749; + } else { + result[0] += 0.0012758941; + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + result[0] += 0.016069753; + } else { + result[0] += -0.010248724; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + result[0] += -0.028139247; + } else { + result[0] += -0.06801506; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 164))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 238))) { + result[0] += 0.031309973; + } else { + result[0] += 0.07697828; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 258))) { + result[0] += 0.03671503; + } else { + result[0] += 0.0007923256; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 282))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { + result[0] += 0.02349461; + } else { + result[0] += -0.00087367493; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { + result[0] += 0.03977532; + } else { + result[0] += 0.096183844; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 290))) { + result[0] += -0.08533253; + } else { + result[0] += -0.06052809; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 238))) { + result[0] += -0.055827565; + } else { + result[0] += -0.0045159357; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 152))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { + result[0] += -0.17435847; + } else { + result[0] += -0.09712316; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 88))) { + result[0] += -0.121227026; + } else { + result[0] += -0.09049445; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 90))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 148))) { + result[0] += -0.07094587; + } else { + result[0] += -0.13034694; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + result[0] += -0.0074528246; + } else { + result[0] += -0.1171114; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + result[0] += -0.03242543; + } else { + result[0] += -0.09674471; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 136))) { + result[0] += -0.060467966; + } else { + result[0] += -0.08785518; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 28))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 54))) { + result[0] += -0.047304705; + } else { + result[0] += -0.070193194; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 238))) { + result[0] += -0.027161488; + } else { + result[0] += 0.0049651144; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 124))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + result[0] += -0.05672009; + } else { + result[0] += -0.133577; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { + result[0] += -0.034619935; + } else { + result[0] += -0.074495845; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { + result[0] += -0.016871883; + } else { + result[0] += 0.0060241153; + } + } else { + result[0] += -0.07992915; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 256))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 90))) { + result[0] += -0.059571445; + } else { + result[0] += -0.026049837; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 164))) { + result[0] += 0.0035159283; + } else { + result[0] += -0.047432255; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { + result[0] += -0.009207371; + } else { + result[0] += 0.026254697; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 28))) { + result[0] += -0.083354376; + } else { + result[0] += -0.025358835; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 234))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 158))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 246))) { + result[0] += 0.004427155; + } else { + result[0] += 0.030195776; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + result[0] += 0.043274168; + } else { + result[0] += 0.0072428077; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 92))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 164))) { + result[0] += -0.052554864; + } else { + result[0] += 0.0033617232; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { + result[0] += -0.037242856; + } else { + result[0] += -0.008167746; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 112))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.094510235; + } else { + result[0] += 0.01284566; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 26))) { + result[0] += 0.07989215; + } else { + result[0] += 0.17329922; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 76))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + result[0] += 0.058576096; + } else { + result[0] += -0.055972952; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { + result[0] += 0.21819083; + } else { + result[0] += 0.09395635; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 130))) { + result[0] += 0.01516776; + } else { + result[0] += 0.033623938; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 106))) { + result[0] += 0.043214235; + } else { + result[0] += 0.119155124; + } + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 38))) { + result[0] += 0.09584941; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + result[0] += 0.080481224; + } else { + result[0] += 0.03998689; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 318))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 126))) { + result[0] += 0.026821358; + } else { + result[0] += -0.031004164; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 202))) { + result[0] += 0.06501947; + } else { + result[0] += 0.19631971; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 166))) { + result[0] += 0.07131386; + } else { + result[0] += 0.028573189; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 314))) { + result[0] += 0.083791435; + } else { + result[0] += 0.17529456; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 124))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 22))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { + result[0] += -0.01719068; + } else { + result[0] += -0.064043045; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { + result[0] += -0.1791604; + } else { + result[0] += -0.13283774; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { + result[0] += -0.056413215; + } else { + result[0] += -0.14871566; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { + result[0] += -0.09970745; + } else { + result[0] += -0.010530283; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 34))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { + result[0] += -0.019443454; + } else { + result[0] += -0.09627802; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { + result[0] += -0.072033755; + } else { + result[0] += -0.029162338; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 56))) { + result[0] += -0.04072467; + } else { + result[0] += -0.07095223; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { + result[0] += 9.782781e-05; + } else { + result[0] += -0.028554544; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + result[0] += -0.10857904; + } else { + result[0] += -0.17578779; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 148))) { + result[0] += -0.05417463; + } else { + result[0] += -0.08070096; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 300))) { + result[0] += -0.06944825; + } else { + result[0] += -0.0195438; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 96))) { + result[0] += -0.032838166; + } else { + result[0] += -0.013822776; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 298))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 232))) { + result[0] += -0.034293767; + } else { + result[0] += -0.010428538; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 38))) { + result[0] += -0.008245598; + } else { + result[0] += 0.0061710696; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { + result[0] += -0.00058508094; + } else { + result[0] += -0.048794635; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { + result[0] += 0.014517671; + } else { + result[0] += 0.029143566; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 210))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 162))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 10))) { + result[0] += -0.051883638; + } else { + result[0] += -0.121891774; + } + } else { + result[0] += 0.006774173; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 108))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { + result[0] += -0.05183841; + } else { + result[0] += -0.023333719; + } + } else { + result[0] += 0.041755553; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 128))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += -0.021229211; + } else { + result[0] += 0.023228422; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 76))) { + result[0] += 0.0065774354; + } else { + result[0] += -0.0068485746; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 28))) { + result[0] += 0.049541216; + } else { + result[0] += 0.21175821; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 10))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 292))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.1843134; + } else { + result[0] += 0.011620779; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 280))) { + result[0] += -0.049533408; + } else { + result[0] += 0.019996347; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 272))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + result[0] += 0.07229465; + } else { + result[0] += 0.019160887; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + result[0] += 0.0056915763; + } else { + result[0] += 0.04898624; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 328))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 56))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 124))) { + result[0] += 0.010618052; + } else { + result[0] += 0.039828878; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + result[0] += 0.08413791; + } else { + result[0] += 0.05419789; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 128))) { + result[0] += 0.04146382; + } else { + result[0] += 0.08484615; + } + } else { + result[0] += 0.17491151; + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 146))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 30))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { + result[0] += -0.14357229; + } else { + result[0] += -0.06613964; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { + result[0] += -0.07836775; + } else { + result[0] += 0.008682779; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 20))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { + result[0] += -0.085223936; + } else { + result[0] += -0.10550176; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 84))) { + result[0] += -0.04918786; + } else { + result[0] += -0.0712194; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { + result[0] += -0.0485091; + } else { + result[0] += -0.07085284; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 154))) { + result[0] += -0.04310399; + } else { + result[0] += -0; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 168))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { + result[0] += -0.03786544; + } else { + result[0] += -0.06086583; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 24))) { + result[0] += -0.026798641; + } else { + result[0] += -0.0029160806; + } + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 66))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { + result[0] += -0.041853126; + } else { + result[0] += -0.067471944; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.10217434; + } else { + result[0] += -0.031203955; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { + result[0] += -0.020556139; + } else { + result[0] += 0.0036511559; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { + result[0] += -0.0052104336; + } else { + result[0] += 0.022399493; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 266))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { + result[0] += -0.050368812; + } else { + result[0] += -0.015018652; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 98))) { + result[0] += 0.050732918; + } else { + result[0] += -0.017873917; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { + result[0] += 0.16741006; + } else { + result[0] += 0.08613744; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { + result[0] += 0.02335409; + } else { + result[0] += -0.0018855215; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 234))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 200))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 172))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + result[0] += 0.0073416415; + } else { + result[0] += 0.029692546; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 146))) { + result[0] += -0.0061891414; + } else { + result[0] += -0.021139516; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 378))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 224))) { + result[0] += -0.018264985; + } else { + result[0] += -0.0061430032; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + result[0] += 0.024317574; + } else { + result[0] += -0.023863515; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 294))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 90))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.06744468; + } else { + result[0] += 0.2128239; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + result[0] += 0.013560965; + } else { + result[0] += -0.010385789; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 156))) { + result[0] += 0.036912605; + } else { + result[0] += 0.008162293; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { + result[0] += 0.07527434; + } else { + result[0] += 0.013765368; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 362))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 74))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 132))) { + result[0] += -0.040987097; + } else { + result[0] += 0.009021326; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { + result[0] += 0.026009029; + } else { + result[0] += 0.08366104; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 34))) { + result[0] += 0.064916424; + } else { + result[0] += 0.09441544; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 4))) { + result[0] += 0.08905909; + } else { + result[0] += 0.020877834; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 318))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 126))) { + result[0] += 0.02257039; + } else { + result[0] += -0.025986714; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 202))) { + result[0] += 0.05338145; + } else { + result[0] += 0.15790914; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 220))) { + result[0] += 0.018759893; + } else { + result[0] += 0.056597423; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 318))) { + result[0] += 0.06810229; + } else { + result[0] += 0.16054772; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 156))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 48))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 180))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { + result[0] += -0.06675883; + } else { + result[0] += -0.04909338; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 80))) { + result[0] += -0.045537602; + } else { + result[0] += -0.021407653; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 102))) { + result[0] += -0.036747407; + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 38))) { + result[0] += -0.0055241673; + } else { + result[0] += 0.0064017037; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { + result[0] += -0.1232142; + } else { + result[0] += -0.08944999; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { + result[0] += -0.103527024; + } else { + result[0] += -0.06685146; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 8))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 110))) { + result[0] += -0.087132424; + } else { + result[0] += -0.0155411465; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { + result[0] += 0.007360445; + } else { + result[0] += -0.042419024; + } + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 104))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 102))) { + result[0] += -0.02354617; + } else { + result[0] += 0.0067315414; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { + result[0] += -0.02450421; + } else { + result[0] += -0.058597732; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 32))) { + result[0] += 0.023825407; + } else { + result[0] += 0.18043439; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 8))) { + result[0] += -0.043022137; + } else { + result[0] += 0.029165251; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 222))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 208))) { + result[0] += -0.07280388; + } else { + result[0] += -0.046989717; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + result[0] += 0.016371334; + } else { + result[0] += -0.0062531256; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { + result[0] += -0.00938953; + } else { + result[0] += 0.011327556; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 98))) { + result[0] += -0.057005074; + } else { + result[0] += -0.011778428; + } + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 268))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 264))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 46))) { + result[0] += 0.030093227; + } else { + result[0] += 0.11276569; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + result[0] += 0.0048071328; + } else { + result[0] += -0.030283172; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { + result[0] += 0.07801795; + } else { + result[0] += 0.010139103; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 100))) { + result[0] += 0.015687512; + } else { + result[0] += 0.057320017; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 128))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 162))) { + result[0] += 0.16135572; + } else { + result[0] += 0.10743537; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 316))) { + result[0] += 0.0299298; + } else { + result[0] += -0.025237197; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 62))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { + result[0] += 0.01356712; + } else { + result[0] += 0.027944017; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 284))) { + result[0] += 0.09368516; + } else { + result[0] += 0.00735568; + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 212))) { + result[0] += 0.06613142; + } else { + result[0] += 0.030379802; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 296))) { + result[0] += 0.037492864; + } else { + result[0] += 0.08968493; + } + } + } else { + result[0] += 0.14285138; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 240))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 122))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 294))) { + result[0] += 0.10012319; + } else { + result[0] += 0.04607804; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { + result[0] += 0.00882749; + } else { + result[0] += 0.034421023; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 188))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { + result[0] += 0.020126143; + } else { + result[0] += 0.056154665; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 344))) { + result[0] += 0.06664233; + } else { + result[0] += 0.028045407; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 124))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 56))) { + result[0] += -0.08264944; + } else { + result[0] += -0.017300455; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 132))) { + result[0] += -0.09584019; + } else { + result[0] += -0.03871246; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 68))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 8))) { + result[0] += -0.027366564; + } else { + result[0] += 0.005001399; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { + result[0] += -0.015287772; + } else { + result[0] += -0.06076094; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + result[0] += -0.01928839; + } else { + result[0] += -0.05802682; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 14))) { + result[0] += -0.069114976; + } else { + result[0] += -0.050886452; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 168))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 58))) { + result[0] += -0.02828979; + } else { + result[0] += -0.049417857; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 54))) { + result[0] += 0.018446451; + } else { + result[0] += -0.017286932; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { + result[0] += -0; + } else { + result[0] += -0.1095063; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { + result[0] += -0.112913184; + } else { + result[0] += -0.041024093; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 236))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { + result[0] += -0.036767628; + } else { + result[0] += -0.0056491303; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 50))) { + result[0] += -0.05497813; + } else { + result[0] += -0.02247507; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 124))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 94))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { + result[0] += -0.04725503; + } else { + result[0] += -0.022799945; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + result[0] += -0.03265686; + } else { + result[0] += 0.0007802085; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 54))) { + result[0] += 0.008465467; + } else { + result[0] += 0.022264598; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { + result[0] += -0.008557126; + } else { + result[0] += -0.050682377; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 230))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 56))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 84))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { + result[0] += -0.23723964; + } else { + result[0] += -0.0674392; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 96))) { + result[0] += 0.017011134; + } else { + result[0] += -0.023735067; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 156))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { + result[0] += -0.021249495; + } else { + result[0] += 0.0016016475; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { + result[0] += 0.004315733; + } else { + result[0] += 0.029064924; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 30))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 18))) { + result[0] += 0.016252542; + } else { + result[0] += 0.07114914; + } + } else { + result[0] += 0.18867145; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 4))) { + result[0] += -0.043993976; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 52))) { + result[0] += 0.04012975; + } else { + result[0] += 0.0784916; + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 312))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 68))) { + result[0] += -0.1251441; + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 124))) { + result[0] += 0.1326; + } else { + result[0] += 0.07419976; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + result[0] += 0.070240654; + } else { + result[0] += 0.0142424395; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + result[0] += 0.008635036; + } else { + result[0] += 0.05565952; + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 104))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 326))) { + result[0] += 0.0021902567; + } else { + result[0] += 0.12992251; + } + } else { + result[0] += 0.14213897; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 342))) { + result[0] += 0.018112415; + } else { + result[0] += 0.048771314; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { + result[0] += 0.040432267; + } else { + result[0] += 0.06690516; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 158))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 48))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 4))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { + result[0] += -0.084643945; + } else { + result[0] += -0.05658353; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { + result[0] += -0.14270312; + } else { + result[0] += -0.096725866; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 10))) { + result[0] += -0.072900295; + } else { + result[0] += -0.050103154; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { + result[0] += 0.0026472413; + } else { + result[0] += -0.034194175; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 210))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 172))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + result[0] += -0.08699354; + } else { + result[0] += -0.029962752; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 258))) { + result[0] += -0.012024823; + } else { + result[0] += -0.040718153; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 38))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 278))) { + result[0] += 1.4413594e-05; + } else { + result[0] += -0.01927815; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 52))) { + result[0] += -0.004854994; + } else { + result[0] += 0.0106780855; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 88))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 28))) { + result[0] += -0.07230939; + } else { + result[0] += -0.029629577; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { + result[0] += -0.018495014; + } else { + result[0] += 0.019453213; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 90))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + result[0] += -0.01461862; + } else { + result[0] += 0.10096019; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + result[0] += -0.029421015; + } else { + result[0] += 9.543482e-05; + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 90))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 60))) { + result[0] += -0.021362139; + } else { + result[0] += -0.063853346; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 62))) { + result[0] += 0.00073504547; + } else { + result[0] += 0.019957332; + } + } + } else { + result[0] += 0.09791734; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 294))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 60))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 18))) { + result[0] += -0.005831562; + } else { + result[0] += -0.06499735; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 132))) { + result[0] += -0.0071320483; + } else { + result[0] += -0.055945735; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 58))) { + result[0] += 0.0037681882; + } else { + result[0] += 0.05542149; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 82))) { + result[0] += 0.011588092; + } else { + result[0] += -0.0017960105; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { + result[0] += -0.16209017; + } else { + result[0] += 0.019222626; + } + } else { + result[0] += 0.13090767; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 270))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 14))) { + result[0] += -0.030162899; + } else { + result[0] += -0.008859159; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 184))) { + result[0] += 0.028086677; + } else { + result[0] += 0.0010727844; + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 110))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + result[0] += 0.07769736; + } else { + result[0] += 0.114956; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 62))) { + result[0] += 0.026280258; + } else { + result[0] += 0.063517205; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 198))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 184))) { + result[0] += 0.028841827; + } else { + result[0] += 0.07754768; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 220))) { + result[0] += 0.023495244; + } else { + result[0] += 0.058550116; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 230))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { + result[0] += 0.006005039; + } else { + result[0] += 0.0330562; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 182))) { + result[0] += 0.02288873; + } else { + result[0] += 0.052012473; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 92))) { + result[0] += -0.023153892; + } else { + result[0] += 0.06317775; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 122))) { + result[0] += 0.09178891; + } else { + result[0] += 0.17105517; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 162))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 48))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 180))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { + result[0] += -0.051588424; + } else { + result[0] += -0.03475411; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { + result[0] += -0.0234789; + } else { + result[0] += 0.0038708851; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 214))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + result[0] += -0.008086841; + } else { + result[0] += -0.027431283; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 68))) { + result[0] += -0.001538304; + } else { + result[0] += 0.0073204334; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 56))) { + result[0] += -0.05180776; + } else { + result[0] += 0.005863587; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { + result[0] += -0.09820559; + } else { + result[0] += -0.042626407; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { + result[0] += -0.048510984; + } else { + result[0] += -0.071686305; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 58))) { + result[0] += -0.04765085; + } else { + result[0] += -0.010419096; + } + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 28))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { + result[0] += 0.06322976; + } else { + result[0] += -0.03347231; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 12))) { + result[0] += -0.17174184; + } else { + result[0] += -0.08866473; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { + result[0] += -0.019928426; + } else { + result[0] += -0.07354166; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { + result[0] += -0.020242494; + } else { + result[0] += -0.040445287; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + result[0] += -0.008752908; + } else { + result[0] += 0.09380058; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 176))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 198))) { + result[0] += 0.017689208; + } else { + result[0] += -0.0005958941; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 2))) { + result[0] += -0.0046095774; + } else { + result[0] += -0.038875457; + } + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 272))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { + result[0] += -0.046054542; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 132))) { + result[0] += -0.21343382; + } else { + result[0] += -0.087919936; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 50))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + result[0] += 0.012674006; + } else { + result[0] += 0.11185076; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 308))) { + result[0] += 0.041192535; + } else { + result[0] += 0.070588894; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 124))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 246))) { + result[0] += 0.0028114773; + } else { + result[0] += 0.019441808; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 90))) { + result[0] += 0.011045033; + } else { + result[0] += -0.022086669; + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 20))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 306))) { + result[0] += 0.071599804; + } else { + result[0] += 0.03955371; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 130))) { + result[0] += 0.010970717; + } else { + result[0] += 0.03211321; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 334))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 68))) { + result[0] += 0.059979778; + } else { + result[0] += 0.036047976; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 66))) { + result[0] += 0.10536631; + } else { + result[0] += 0.024781441; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 322))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 254))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 210))) { + result[0] += -0.052476525; + } else { + result[0] += -0.019687535; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + result[0] += 0.012497628; + } else { + result[0] += -0.011794323; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 362))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 120))) { + result[0] += 0.013189244; + } else { + result[0] += 0.03505987; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { + result[0] += 0.056928415; + } else { + result[0] += 0.022988325; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 126))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 22))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 56))) { + result[0] += -0.058271427; + } else { + result[0] += 0.0006807263; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { + result[0] += -0.10462822; + } else { + result[0] += -0.068169676; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 12))) { + result[0] += -0.0072206096; + } else { + result[0] += 0.0307555; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 50))) { + result[0] += -0.050685406; + } else { + result[0] += -0.023581887; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 296))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + result[0] += -0.005921537; + } else { + result[0] += -0.042507444; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += -0.052036572; + } else { + result[0] += -0.034811404; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + result[0] += 0.0008109753; + } else { + result[0] += -0.023712752; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { + result[0] += -0.081094466; + } else { + result[0] += -0.017712194; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { + result[0] += -0.09813706; + } else { + result[0] += -0.03229853; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 236))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 82))) { + result[0] += -0.028393775; + } else { + result[0] += -0.0027526347; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 290))) { + result[0] += -0.047838878; + } else { + result[0] += -0.026909484; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 18))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 12))) { + result[0] += -0.13414939; + } else { + result[0] += -0.05853174; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 8))) { + result[0] += -0.08385962; + } else { + result[0] += -0.029232157; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 112))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 94))) { + result[0] += -0.012187723; + } else { + result[0] += -0.00030637285; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + result[0] += -0.05476123; + } else { + result[0] += -0.002212812; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 234))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 62))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 88))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 56))) { + result[0] += 0.034629773; + } else { + result[0] += 0.090757936; + } + } else { + result[0] += -0.015683705; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 4))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { + result[0] += 0.0014823362; + } else { + result[0] += -0.11727782; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 328))) { + result[0] += -0.011885715; + } else { + result[0] += 0.06458332; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + result[0] += 0.009296111; + } else { + result[0] += -0.0022434364; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 390))) { + result[0] += 0.09074904; + } else { + result[0] += 0.022570081; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + result[0] += -0.010527322; + } else { + result[0] += 0.0112762675; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 170))) { + result[0] += -0.026221529; + } else { + result[0] += -0.0167725; + } + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 128))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 156))) { + result[0] += 0.134217; + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 242))) { + result[0] += 0.055591363; + } else { + result[0] += 0.09201013; + } + } + } else { + result[0] += -0.07688349; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 10))) { + result[0] += 0.008139768; + } else { + result[0] += 0.027951911; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + result[0] += 0.03482144; + } else { + result[0] += 0.12041791; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 330))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 56))) { + result[0] += 0.019060235; + } else { + result[0] += 0.03520998; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { + result[0] += 0.05164655; + } else { + result[0] += 0.10352349; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 166))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 62))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 4))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 6))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { + result[0] += -0.05586126; + } else { + result[0] += -0.0062299026; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { + result[0] += -0.08666905; + } else { + result[0] += -0.057932485; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 106))) { + result[0] += -0.037010778; + } else { + result[0] += -0.060017806; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { + result[0] += 0.008048224; + } else { + result[0] += -0.025649264; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 180))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + result[0] += -0.018599523; + } else { + result[0] += -0.063888915; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { + result[0] += -0.055958636; + } else { + result[0] += 0.0054995283; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 242))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { + result[0] += -0.015452884; + } else { + result[0] += -0.0031880846; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { + result[0] += 6.0049097e-06; + } else { + result[0] += 0.011630835; + } + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 80))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 104))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + result[0] += -0.012825543; + } else { + result[0] += 0.0061619915; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + result[0] += -0.035948735; + } else { + result[0] += -0.005659299; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 22))) { + result[0] += -0.0034629384; + } else { + result[0] += 0.11748111; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 14))) { + result[0] += -0.045964625; + } else { + result[0] += 0.027189994; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 220))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 288))) { + result[0] += -0.043134686; + } else { + result[0] += -0.008114795; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 136))) { + result[0] += 0.022731418; + } else { + result[0] += -0.0039848676; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { + result[0] += -0.0025353373; + } else { + result[0] += 0.017748738; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 68))) { + result[0] += -0.00209215; + } else { + result[0] += -0.026470816; + } + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 268))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += -0.0426252; + } else { + result[0] += -0.17894174; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 46))) { + result[0] += 0.034646492; + } else { + result[0] += 0.08830755; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + result[0] += 0.0036898192; + } else { + result[0] += 0.10474273; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 278))) { + result[0] += 0.0591173; + } else { + result[0] += 0.10110114; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 316))) { + result[0] += 0.017330313; + } else { + result[0] += -0.03201217; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 92))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { + result[0] += 0.020424707; + } else { + result[0] += 0.06761751; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 374))) { + result[0] += 0.0006410443; + } else { + result[0] += 0.025116405; + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 342))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 228))) { + result[0] += 0.002580609; + } else { + result[0] += 0.015501295; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 26))) { + result[0] += -0.021991013; + } else { + result[0] += 0.1534664; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 272))) { + result[0] += -0.004891763; + } else { + result[0] += 0.024440682; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 344))) { + result[0] += 0.04597979; + } else { + result[0] += 0.013181034; + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + result[0] += 0.08511517; + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 12))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { + result[0] += 0.017548429; + } else { + result[0] += -0.051454138; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 312))) { + result[0] += 0.0665479; + } else { + result[0] += 0.03926411; + } + } + } + } + } + } + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 52))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 4))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 120))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += -0.034856033; + } else { + result[0] += 0.035472337; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.082092844; + } else { + result[0] += -0.057657477; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 122))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 96))) { + result[0] += 0.042515777; + } else { + result[0] += -0.018864583; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 142))) { + result[0] += -0.057759553; + } else { + result[0] += -0.020723581; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 26))) { + result[0] += 0.054745015; + } else { + result[0] += 0.017524384; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 164))) { + result[0] += -0.03406791; + } else { + result[0] += -0.08434524; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 28))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 142))) { + result[0] += 0.017600825; + } else { + result[0] += -0.03500613; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { + result[0] += -0.046339255; + } else { + result[0] += -0.018127348; + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { + result[0] += -0.037658464; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { + result[0] += 0.0075216624; + } else { + result[0] += 0.0364659; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 134))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 102))) { + result[0] += 0.033616688; + } else { + result[0] += 0.083399825; + } + } else { + result[0] += 0.12963086; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.074191265; + } else { + result[0] += 0.007478449; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 42))) { + result[0] += 0.098748855; + } else { + result[0] += 0.030946104; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + result[0] += -0.026153265; + } else { + result[0] += -0.15095265; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 74))) { + result[0] += -0.018939627; + } else { + result[0] += -0.06403255; + } + } + } + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + result[0] += -0.021922093; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 134))) { + result[0] += -0.08688069; + } else { + result[0] += -0.033859696; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 86))) { + result[0] += -0.016842697; + } else { + result[0] += -0.0006648888; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 242))) { + result[0] += -0.023793818; + } else { + result[0] += -0.035974815; + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 302))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { + result[0] += 0.012690376; + } else { + result[0] += -0.0033918878; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { + result[0] += -0.005548475; + } else { + result[0] += -0.036335457; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 206))) { + result[0] += -0.0005064619; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 376))) { + result[0] += 0.03696196; + } else { + result[0] += 0.015889136; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 86))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 84))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 44))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 108))) { + result[0] += -0.019162795; + } else { + result[0] += -0.0054707523; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + result[0] += 0.0037607402; + } else { + result[0] += -0.009698882; + } + } + } else { + result[0] += -0.069641024; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 256))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { + result[0] += 0.045991335; + } else { + result[0] += 0.0303468; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { + result[0] += 0.018556785; + } else { + result[0] += -0.028926486; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 222))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 126))) { + result[0] += 0.014895338; + } else { + result[0] += -0.0033647448; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + result[0] += 0.031980734; + } else { + result[0] += 0.0069378153; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 28))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 30))) { + result[0] += -0.091816425; + } else { + result[0] += -0.053907175; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += -0.035644125; + } else { + result[0] += -0.08623389; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + result[0] += -0.038305838; + } else { + result[0] += -0.022053199; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 34))) { + result[0] += -0.041282233; + } else { + result[0] += -0.011869354; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 136))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 118))) { + result[0] += -0.008556094; + } else { + result[0] += -0.023536053; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { + result[0] += -0.023365794; + } else { + result[0] += -0.0052509154; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { + result[0] += -0.008722498; + } else { + result[0] += -0.08987408; + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { + result[0] += 0.032462854; + } else { + result[0] += -0.013032225; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 44))) { + result[0] += 0.0020141487; + } else { + result[0] += 0.016696744; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 172))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { + result[0] += 0.0063471817; + } else { + result[0] += 0.01982216; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { + result[0] += 0.00396263; + } else { + result[0] += -0.017814338; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 310))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 22))) { + result[0] += -0.042612687; + } else { + result[0] += -0.0046601035; + } + } else { + result[0] += -0.007730845; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 66))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 48))) { + result[0] += -0.039969202; + } else { + result[0] += -0.116455294; + } + } else { + result[0] += -0.02463033; + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 208))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 60))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 144))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 42))) { + result[0] += 0.022728456; + } else { + result[0] += -0.023419065; + } + } else { + result[0] += 0.06361882; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 74))) { + result[0] += -0.025523862; + } else { + result[0] += -0.051117957; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + result[0] += -0.006611984; + } else { + result[0] += -0.04705615; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += 0.010768503; + } else { + result[0] += -0.0008824992; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 298))) { + result[0] += -0.040758982; + } else { + result[0] += -0.008637625; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 130))) { + result[0] += 0.029029582; + } else { + result[0] += 0.08626362; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 302))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { + result[0] += 0.011424046; + } else { + result[0] += 0.04683126; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 72))) { + result[0] += -0.033311624; + } else { + result[0] += 0.076620966; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 264))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 106))) { + result[0] += -0.01699209; + } else { + result[0] += -0.032285534; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 104))) { + result[0] += 0.008964994; + } else { + result[0] += -0.01650845; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 60))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 316))) { + result[0] += 0.025268046; + } else { + result[0] += 0.055473793; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 66))) { + result[0] += 0.08008851; + } else { + result[0] += 0.01032548; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 326))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 138))) { + result[0] += 0.0054397047; + } else { + result[0] += 0.02308716; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { + result[0] += 0.08063211; + } else { + result[0] += 0.03532891; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 166))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 48))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 10))) { + result[0] += -0.04106638; + } else { + result[0] += -0.02793704; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { + result[0] += 0.022730371; + } else { + result[0] += -0.015745891; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 38))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += -0.056902584; + } else { + result[0] += -0.11115749; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 52))) { + result[0] += -0.0027035407; + } else { + result[0] += -0.0484084; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 64))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + result[0] += -0.06669981; + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { + result[0] += -0.058218993; + } else { + result[0] += -0.01607107; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { + result[0] += -0.004913365; + } else { + result[0] += 0.010270695; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 288))) { + result[0] += -0.013211744; + } else { + result[0] += -0.029026702; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 96))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { + result[0] += -0.017454801; + } else { + result[0] += 0.0241193; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.07878145; + } else { + result[0] += -0.021389706; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 150))) { + result[0] += 0.005198505; + } else { + result[0] += -0.025203273; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { + result[0] += -0.0036238257; + } else { + result[0] += 0.030112168; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 206))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { + result[0] += -0.014281181; + } else { + result[0] += 0.044825673; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { + result[0] += 0.00095986744; + } else { + result[0] += 0.014824574; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 78))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 248))) { + result[0] += -0.004780053; + } else { + result[0] += 0.023393003; + } + } else { + result[0] += -0.05524193; + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 240))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 264))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 200))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + result[0] += 0.008043467; + } else { + result[0] += -0.035642993; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + result[0] += 0.025907112; + } else { + result[0] += 0.0037340687; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 164))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 90))) { + result[0] += 0.0053138766; + } else { + result[0] += -0.019315336; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + result[0] += 0.012754604; + } else { + result[0] += -0.017428217; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 316))) { + result[0] += -0.018819278; + } else { + result[0] += 0.005015341; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 322))) { + result[0] += -0.046993464; + } else { + result[0] += -0.00060153054; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 334))) { + result[0] += 0.032121107; + } else { + result[0] += 0.07720378; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 206))) { + result[0] += -0.01248775; + } else { + result[0] += 0.021081064; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 220))) { + result[0] += -0.002822095; + } else { + result[0] += 0.010395023; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 24))) { + result[0] += -0.00028166902; + } else { + result[0] += 0.05817884; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 368))) { + result[0] += 0.033362225; + } else { + result[0] += 0.059052836; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 76))) { + result[0] += -0.022754088; + } else { + result[0] += 0.032660067; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 164))) { + result[0] += 0.05608514; + } else { + result[0] += 0.028797118; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 310))) { + result[0] += 0.0028316458; + } else { + result[0] += 0.023484427; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 308))) { + result[0] += 0.029160103; + } else { + result[0] += 0.06451139; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 212))) { + result[0] += -0.008154633; + } else { + result[0] += 0.02224008; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 102))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 66))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 34))) { + result[0] += -0.025441715; + } else { + result[0] += -0; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.09877158; + } else { + result[0] += -0.05378988; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { + result[0] += -0.02562209; + } else { + result[0] += -0.042403318; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { + result[0] += 0.02021136; + } else { + result[0] += -0.01660019; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 28))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + result[0] += 0.01719434; + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { + result[0] += -0.018652868; + } else { + result[0] += -0.072789736; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += -0.0050430708; + } else { + result[0] += -0.020398756; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 88))) { + result[0] += 0.0035772503; + } else { + result[0] += 0.04239833; + } + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 222))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { + result[0] += -0.008277955; + } else { + result[0] += -0.02545519; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { + result[0] += 0.035175905; + } else { + result[0] += -0.0068374695; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 274))) { + result[0] += 0.0034994117; + } else { + result[0] += -0.012190169; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { + result[0] += -0.0075547704; + } else { + result[0] += 0.01670718; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 100))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 46))) { + result[0] += -0.043555766; + } else { + result[0] += -0.09016201; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 176))) { + result[0] += -0.043037914; + } else { + result[0] += -2.9695482e-05; + } + } + } else { + result[0] += -0.024823932; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 230))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 6))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 120))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += 0.05400118; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { + result[0] += -0.07639449; + } else { + result[0] += -0.016434733; + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 148))) { + result[0] += -0.05022436; + } else { + result[0] += 0.00233022; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 126))) { + result[0] += -0.055654902; + } else { + result[0] += -0.13723731; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 104))) { + result[0] += -0.003547279; + } else { + result[0] += 0.0030764283; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { + result[0] += 0.016396696; + } else { + result[0] += 0.07968046; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + result[0] += 0.017225603; + } else { + result[0] += 0.037653413; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.077591464; + } else { + result[0] += 0.15476403; + } + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 238))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 298))) { + result[0] += -0.016144833; + } else { + result[0] += 0.032200087; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 282))) { + result[0] += 0.06538327; + } else { + result[0] += 0.10325643; + } + } + } else { + result[0] += -0.10228916; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 272))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + result[0] += -0.008800676; + } else { + result[0] += 0.009970379; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += -0.015078469; + } else { + result[0] += -0.03748675; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + result[0] += 0.034970764; + } else { + result[0] += -0.026505647; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { + result[0] += 0.037449267; + } else { + result[0] += 0.012034763; + } + } + } + } + } + } + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 160))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 64))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 32))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { + result[0] += -0.042211335; + } else { + result[0] += -0.0153662665; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.08464201; + } else { + result[0] += -0.039633475; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 18))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 32))) { + result[0] += -0.0023176766; + } else { + result[0] += 0.011839011; + } + } else { + result[0] += -0.024568608; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 114))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 50))) { + result[0] += -0.011506539; + } else { + result[0] += -0.041527543; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 30))) { + result[0] += 0.006311471; + } else { + result[0] += 0.096107006; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { + result[0] += -0.07760205; + } else { + result[0] += -0.024386143; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { + result[0] += 0.0042932476; + } else { + result[0] += -0.024321787; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 278))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 108))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 78))) { + result[0] += -0.00662189; + } else { + result[0] += 0.01475962; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 130))) { + result[0] += 0.03366244; + } else { + result[0] += 0.01162212; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 114))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 28))) { + result[0] += 0.030401085; + } else { + result[0] += 0.1034078; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 76))) { + result[0] += 0.06511612; + } else { + result[0] += 0.004562137; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 258))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 132))) { + result[0] += -0.017277284; + } else { + result[0] += -0.00040395462; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 274))) { + result[0] += -0.028246973; + } else { + result[0] += -0.043952625; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 212))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + result[0] += -0.045117795; + } else { + result[0] += -0.00930117; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 90))) { + result[0] += 0.013196451; + } else { + result[0] += -0.019043565; + } + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 310))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 42))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 84))) { + result[0] += -0.026572356; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 164))) { + result[0] += 0.051293522; + } else { + result[0] += -0; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 88))) { + result[0] += 0.08133901; + } else { + result[0] += 0.05325532; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 114))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 6))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { + result[0] += -0.015609048; + } else { + result[0] += -0.038890243; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 102))) { + result[0] += -0.008053626; + } else { + result[0] += 0.0021804231; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { + result[0] += 0.013441979; + } else { + result[0] += 0.0015576767; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 48))) { + result[0] += -0.012684277; + } else { + result[0] += 0.0018506636; + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 66))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 146))) { + result[0] += 0.1326864; + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { + result[0] += 0.0802758; + } else { + result[0] += 0.04734662; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 162))) { + result[0] += -0.0053350735; + } else { + result[0] += -0.040489998; + } + } else { + result[0] += 0.057940014; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 228))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 318))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 38))) { + result[0] += -0.00303559; + } else { + result[0] += 0.027338777; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 98))) { + result[0] += 0.021263791; + } else { + result[0] += 0.0065736147; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 220))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 294))) { + result[0] += 0.036004547; + } else { + result[0] += 0.00041290582; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { + result[0] += 0.003415088; + } else { + result[0] += 0.03065363; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 112))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 182))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 30))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { + result[0] += -0.061424643; + } else { + result[0] += -0.004366158; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 12))) { + result[0] += -0.029432973; + } else { + result[0] += 0.024416348; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 44))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 38))) { + result[0] += -0.028231425; + } else { + result[0] += 0.013277724; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { + result[0] += -0.05933566; + } else { + result[0] += -0.033664998; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 64))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 276))) { + result[0] += 0.0039472985; + } else { + result[0] += -0.010128739; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 96))) { + result[0] += -0.032275613; + } else { + result[0] += -0.007163971; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 148))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { + result[0] += -0.009553356; + } else { + result[0] += -0.024487672; + } + } else { + result[0] += -0.04257847; + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 106))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + result[0] += -0.017238883; + } else { + result[0] += -0.005016202; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + result[0] += -0.031886365; + } else { + result[0] += -0.013730754; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 64))) { + result[0] += 0.008240308; + } else { + result[0] += 0.03919262; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 38))) { + result[0] += 0.005747178; + } else { + result[0] += -0.058204617; + } + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { + result[0] += -0.055713423; + } else { + result[0] += -0.126311; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 160))) { + result[0] += -0.010013968; + } else { + result[0] += -0.03667828; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 62))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 90))) { + result[0] += -0.021773633; + } else { + result[0] += -0.060646553; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 76))) { + result[0] += -0.028524075; + } else { + result[0] += -0.0014835782; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 240))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 250))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 58))) { + result[0] += -0.009143605; + } else { + result[0] += 0.0004789403; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { + result[0] += 0.017569097; + } else { + result[0] += 0.06969844; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.053881604; + } else { + result[0] += 0.030100072; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 28))) { + result[0] += 0.07642787; + } else { + result[0] += 0.14859438; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 276))) { + result[0] += -0.0064661647; + } else { + result[0] += 0.0021556418; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { + result[0] += -0.042718846; + } else { + result[0] += 0.004742034; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 96))) { + result[0] += 0.0150559945; + } else { + result[0] += 0.02904155; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 196))) { + result[0] += 0.030410746; + } else { + result[0] += -0.00021071863; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 130))) { + result[0] += -0.0019750185; + } else { + result[0] += 0.010826028; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 92))) { + result[0] += -0.027783621; + } else { + result[0] += 0.040064003; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 34))) { + result[0] += 0.025106177; + } else { + result[0] += 0.04847647; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { + result[0] += -0.020649737; + } else { + result[0] += 0.047101017; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 110))) { + result[0] += 0.0425664; + } else { + result[0] += 0.010982556; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 320))) { + result[0] += 0.0027861602; + } else { + result[0] += 0.024253966; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + result[0] += 0.040306833; + } else { + result[0] += 0.019420182; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 250))) { + result[0] += -0.07389223; + } else { + result[0] += -0.018163418; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 166))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 76))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 32))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 134))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + result[0] += -0.033120375; + } else { + result[0] += -0.019771839; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + result[0] += 0.033069532; + } else { + result[0] += -0.024330398; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + result[0] += -0.00891195; + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 260))) { + result[0] += -0.06895655; + } else { + result[0] += -0.029440561; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 112))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { + result[0] += -0.008321228; + } else { + result[0] += -0.032401457; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 28))) { + result[0] += 0.01681263; + } else { + result[0] += -0.011843439; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { + result[0] += 0.009037083; + } else { + result[0] += 0.084029466; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { + result[0] += -0.039284047; + } else { + result[0] += 0.0024618404; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 30))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 138))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 284))) { + result[0] += -0.004148552; + } else { + result[0] += -0.028972754; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 182))) { + result[0] += -0.060149025; + } else { + result[0] += -0.023092858; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 246))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 124))) { + result[0] += -0.0048736627; + } else { + result[0] += 0.011900749; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 152))) { + result[0] += -0.027425656; + } else { + result[0] += -0.0049680914; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + result[0] += 0.018673597; + } else { + result[0] += -0.002092194; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 158))) { + result[0] += 0.025586123; + } else { + result[0] += 0.043332465; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 140))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 134))) { + result[0] += -0.009704775; + } else { + result[0] += 0.033585932; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 130))) { + result[0] += -0.04070072; + } else { + result[0] += -0.02585305; + } + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 132))) { + result[0] += 0.026704356; + } else { + result[0] += -0.0026301623; + } + } else { + result[0] += -0.083959244; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 116))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + result[0] += 0.00727385; + } else { + result[0] += -0.00793132; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 34))) { + result[0] += -0.0025618703; + } else { + result[0] += 0.03066427; + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 80))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 192))) { + result[0] += -0.0036948516; + } else { + result[0] += 0.014927791; + } + } else { + result[0] += 0.06770062; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 112))) { + result[0] += 0.021252338; + } else { + result[0] += 0.08691488; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 110))) { + result[0] += 0.030034242; + } else { + result[0] += 0.0126524735; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 84))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 146))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 294))) { + result[0] += 0.03448388; + } else { + result[0] += 0.012207389; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 82))) { + result[0] += -0.011361829; + } else { + result[0] += 0.010800744; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 374))) { + result[0] += -0.019659309; + } else { + result[0] += -0.010299957; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 222))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 128))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { + result[0] += -0.022616882; + } else { + result[0] += 0.0058049723; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 210))) { + result[0] += -0.05356568; + } else { + result[0] += -0.011999037; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + result[0] += 0.007046343; + } else { + result[0] += 0.04422299; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 200))) { + result[0] += -0.023396257; + } else { + result[0] += -0.015149494; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 110))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 4))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 38))) { + result[0] += -0.012656443; + } else { + result[0] += 0.04053043; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 30))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { + result[0] += -0.04405209; + } else { + result[0] += -0.07002534; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += 0.0050658057; + } else { + result[0] += -0.035065096; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 36))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 44))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { + result[0] += -0.016180787; + } else { + result[0] += 0.035941508; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 62))) { + result[0] += 0.0028223798; + } else { + result[0] += -0.028867302; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { + result[0] += -0.0008212989; + } else { + result[0] += -0.019350568; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 100))) { + result[0] += -0.012334666; + } else { + result[0] += -0.02596775; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { + result[0] += -0.03201983; + } else { + result[0] += -0.009261032; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.043002192; + } else { + result[0] += -0.004841908; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { + result[0] += -0.034105346; + } else { + result[0] += -0.001769832; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 18))) { + result[0] += -0.026078701; + } else { + result[0] += -0.0034125247; + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 68))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 204))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 72))) { + result[0] += -0.035768237; + } else { + result[0] += 0.007326287; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { + result[0] += -0.013266384; + } else { + result[0] += 0.0073778555; + } + } + } else { + result[0] += 0.04042886; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 262))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 314))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + result[0] += -0.031073779; + } else { + result[0] += 0.009902022; + } + } else { + result[0] += -0.10413245; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 304))) { + result[0] += -0.0015145864; + } else { + result[0] += 0.0044673397; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { + result[0] += 0.016366184; + } else { + result[0] += 0.06682373; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { + result[0] += 0.1125614; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 158))) { + result[0] += 0.015199658; + } else { + result[0] += -0.047179397; + } + } else { + result[0] += 0.07871269; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 304))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 140))) { + result[0] += 0.037271794; + } else { + result[0] += 0.055074245; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 350))) { + result[0] += 0.01456887; + } else { + result[0] += 0.043336138; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 62))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + result[0] += -0.011651535; + } else { + result[0] += 0.02064044; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 326))) { + result[0] += 0.040328953; + } else { + result[0] += 0.016403003; + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 12))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 122))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 262))) { + result[0] += -0.0107279355; + } else { + result[0] += 0.005842201; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { + result[0] += -0.038425323; + } else { + result[0] += 0.021354888; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { + result[0] += 0.045149498; + } else { + result[0] += -0; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 272))) { + result[0] += 0.0030525408; + } else { + result[0] += 0.016887192; + } + } + } + } + } + } + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 80))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 80))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 138))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 76))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { + result[0] += -0.022748465; + } else { + result[0] += -0.0015031536; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 58))) { + result[0] += 0.038551223; + } else { + result[0] += -0.02340021; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { + result[0] += -0.022547202; + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 64))) { + result[0] += 0.006482385; + } else { + result[0] += 0.043839242; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 84))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { + result[0] += -0.032270882; + } else { + result[0] += 0.041581474; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 74))) { + result[0] += -0.02748827; + } else { + result[0] += -0.06621357; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { + result[0] += -0.15229563; + } else { + result[0] += -0.056743264; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 290))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + result[0] += 0.0090522; + } else { + result[0] += -0.021619566; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { + result[0] += 0.011768992; + } else { + result[0] += 0.034092333; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 174))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 296))) { + result[0] += -0.0050243097; + } else { + result[0] += 0.0058637843; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 218))) { + result[0] += -0.022230322; + } else { + result[0] += -0; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 234))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 178))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + result[0] += -0.0040832614; + } else { + result[0] += -0.026436746; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 188))) { + result[0] += 0.0104423; + } else { + result[0] += -0.009707513; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 146))) { + result[0] += 0.021574568; + } else { + result[0] += -0.01677997; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + result[0] += -0.01483564; + } else { + result[0] += -0.044649884; + } + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 266))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 270))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 58))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { + result[0] += 0.027206311; + } else { + result[0] += -0.0112104425; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + result[0] += 0.002884076; + } else { + result[0] += -0.008946375; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 242))) { + result[0] += -0.021231398; + } else { + result[0] += -0.007831926; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + result[0] += -0.010156151; + } else { + result[0] += 0.006795832; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + result[0] += -0; + } else { + result[0] += 0.01808864; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 156))) { + result[0] += 0.0849195; + } else { + result[0] += 0.040363234; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 208))) { + result[0] += 0.018572433; + } else { + result[0] += -0.016821038; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 238))) { + result[0] += -0.013755165; + } else { + result[0] += 0.010804783; + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 124))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { + result[0] += 0.03828322; + } else { + result[0] += 0.0062655816; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 4))) { + result[0] += -0.039595883; + } else { + result[0] += -0.00876256; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 162))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { + result[0] += 0.028006807; + } else { + result[0] += 0.011207984; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 104))) { + result[0] += -0; + } else { + result[0] += 0.04828812; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 236))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 134))) { + result[0] += 0.019871203; + } else { + result[0] += -0.0020931112; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 164))) { + result[0] += -0.054860026; + } else { + result[0] += -0.016543666; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 126))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + result[0] += 0.017333688; + } else { + result[0] += -0.014761999; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { + result[0] += 0.0047273724; + } else { + result[0] += 0.018399872; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 90))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 8))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { + result[0] += -0.07626998; + } else { + result[0] += 0.020595873; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + result[0] += 0.0014158572; + } else { + result[0] += 0.039809644; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 26))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 28))) { + result[0] += -0.047608785; + } else { + result[0] += -0.08177885; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { + result[0] += -0.018627753; + } else { + result[0] += -0.03965402; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 152))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + result[0] += -0.016198985; + } else { + result[0] += -0.034763936; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { + result[0] += 0.0029702466; + } else { + result[0] += -0.010453141; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 80))) { + result[0] += 0.002711076; + } else { + result[0] += -0.040787876; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { + result[0] += -0.0030517352; + } else { + result[0] += -0.018486565; + } + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 204))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 296))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { + result[0] += -0.01466268; + } else { + result[0] += 0.0016207602; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + result[0] += 0.014294909; + } else { + result[0] += -0.00088145724; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 270))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 230))) { + result[0] += 0.017696586; + } else { + result[0] += 0.0033850037; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 96))) { + result[0] += 0.003758549; + } else { + result[0] += -0.008070237; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 108))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 38))) { + result[0] += -0.012763503; + } else { + result[0] += 0.015098961; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 260))) { + result[0] += -0.06455403; + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { + result[0] += -0.017203202; + } else { + result[0] += 0.0007284813; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 260))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 264))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + result[0] += -0.0004502135; + } else { + result[0] += -0.013600699; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 104))) { + result[0] += 0.0078685535; + } else { + result[0] += 0.052134164; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { + result[0] += -0.047534015; + } else { + result[0] += 0.021326948; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.056880232; + } else { + result[0] += 0.09747363; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 92))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 242))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 276))) { + result[0] += 0.023308432; + } else { + result[0] += 0.011319171; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += 0.051132478; + } else { + result[0] += 0.02103916; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 238))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 288))) { + result[0] += 0.006215774; + } else { + result[0] += -0.0061574043; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 330))) { + result[0] += 0.0041284435; + } else { + result[0] += 0.023122046; + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 288))) { + result[0] += 0.034480914; + } else { + result[0] += 0.01336052; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.056260873; + } else { + result[0] += 0.042514727; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 172))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { + result[0] += 0.02588587; + } else { + result[0] += -0.032997902; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 316))) { + result[0] += -0.0100605395; + } else { + result[0] += 0.020955171; + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 12))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 122))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 320))) { + result[0] += 0.0012241629; + } else { + result[0] += 0.010477929; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { + result[0] += -0.038784113; + } else { + result[0] += 0.019064387; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 308))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 128))) { + result[0] += 0.014094616; + } else { + result[0] += -0.0027222696; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { + result[0] += 0.043263398; + } else { + result[0] += 0.016619174; + } + } + } + } + } + } + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 164))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 106))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 52))) { + result[0] += -0.07700386; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { + result[0] += -0.027042255; + } else { + result[0] += 0.0029873038; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { + result[0] += -0.063133374; + } else { + result[0] += -0.0046702675; + } + } else { + result[0] += -0.097486384; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 110))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 258))) { + result[0] += -0.011726489; + } else { + result[0] += -0.058912832; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 90))) { + result[0] += -0.0659786; + } else { + result[0] += -0.02953866; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 58))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 90))) { + result[0] += 0.0242778; + } else { + result[0] += -0.0023044557; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 118))) { + result[0] += 0.029503396; + } else { + result[0] += -0.015513777; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 278))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 100))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { + result[0] += -0.003784867; + } else { + result[0] += 0.012548617; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 26))) { + result[0] += 0.0061970223; + } else { + result[0] += 0.023837453; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 330))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { + result[0] += 0.050173163; + } else { + result[0] += -0; + } + } else { + result[0] += -0.007900632; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 86))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 288))) { + result[0] += -0.027921408; + } else { + result[0] += -0.0038356972; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 126))) { + result[0] += -0.0057223137; + } else { + result[0] += 0.010252192; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 188))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 232))) { + result[0] += -0.0093339635; + } else { + result[0] += -0.002617002; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + result[0] += -0.01492499; + } else { + result[0] += -0.025125777; + } + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 308))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 42))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { + result[0] += 0.03968758; + } else { + result[0] += -0.001241333; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 6))) { + result[0] += -0.01191957; + } else { + result[0] += 0.002430247; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 116))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 54))) { + result[0] += -0; + } else { + result[0] += 0.08623359; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 168))) { + result[0] += -0.0005277793; + } else { + result[0] += 0.01962237; + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { + result[0] += -0.0047836783; + } else { + result[0] += -0.015227089; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { + result[0] += 0.008447981; + } else { + result[0] += 0.04540634; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 232))) { + result[0] += -0.022486782; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + result[0] += -0; + } else { + result[0] += -0.015179853; + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 16))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 4))) { + result[0] += -0.041306626; + } else { + result[0] += -0.008218481; + } + } else { + result[0] += 0.042096373; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 316))) { + result[0] += 0.022573106; + } else { + result[0] += 0.07205055; + } + } else { + result[0] += 0.028815215; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 318))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 38))) { + result[0] += -0.0054102; + } else { + result[0] += 0.019898819; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 98))) { + result[0] += 0.013574933; + } else { + result[0] += 0.0023076402; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 294))) { + result[0] += 0.023586577; + } else { + result[0] += -0.004840189; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { + result[0] += -0.0051436094; + } else { + result[0] += 0.020131659; + } + } + } + } + } + } + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 162))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 88))) { + result[0] += -0.01574367; + } else { + result[0] += 0.0011124586; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 46))) { + result[0] += -0.07613191; + } else { + result[0] += -0.01636207; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 74))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 24))) { + result[0] += 0.014280135; + } else { + result[0] += 0.037306037; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 24))) { + result[0] += -0.024091965; + } else { + result[0] += 0.0048261676; + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 124))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 202))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { + result[0] += -0.04113116; + } else { + result[0] += -0.061350565; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 50))) { + result[0] += -0.03143156; + } else { + result[0] += -0.0036942845; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 4))) { + result[0] += 0.024413232; + } else { + result[0] += -0.01646574; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { + result[0] += -0.016089493; + } else { + result[0] += 0.006952781; + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 144))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 274))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 20))) { + result[0] += -0.023807703; + } else { + result[0] += 0.0015813807; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 332))) { + result[0] += 0.029507706; + } else { + result[0] += -0.009649085; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 36))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 24))) { + result[0] += 0.0048679006; + } else { + result[0] += -0.043877617; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 116))) { + result[0] += 0.014281779; + } else { + result[0] += 0.047004733; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 164))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 92))) { + result[0] += -0.0021749143; + } else { + result[0] += -0.012218022; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { + result[0] += 0.0060172398; + } else { + result[0] += 0.033813428; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 228))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { + result[0] += -0.029434139; + } else { + result[0] += -0.014660637; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + result[0] += -0.008835907; + } else { + result[0] += 0.0022141964; + } + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 310))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 212))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 42))) { + result[0] += 0.023493351; + } else { + result[0] += -0.0012283614; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + result[0] += 0.002782393; + } else { + result[0] += 0.019489653; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 116))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 160))) { + result[0] += 0.07486535; + } else { + result[0] += -0.009162361; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 168))) { + result[0] += -0.0022064948; + } else { + result[0] += 0.018015308; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 128))) { + result[0] += -0.01574024; + } else { + result[0] += -0.0039653988; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 140))) { + result[0] += 0.020983376; + } else { + result[0] += 0.0030928531; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 238))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 244))) { + result[0] += -0.018868484; + } else { + result[0] += -0.012392565; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 234))) { + result[0] += 0.00805167; + } else { + result[0] += -0.011693968; + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 96))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 84))) { + result[0] += -0.038049206; + } else { + result[0] += -0.002771721; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 320))) { + result[0] += 0.012239266; + } else { + result[0] += 0.03609361; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { + result[0] += 0.027658317; + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 136))) { + result[0] += 0.051759947; + } else { + result[0] += 0.094818436; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 230))) { + result[0] += 0.005940085; + } else { + result[0] += 0.017185425; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 300))) { + result[0] += -0.02267663; + } else { + result[0] += -0.00748341; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 116))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 22))) { + result[0] += 0.005678715; + } else { + result[0] += 0.02415133; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 76))) { + result[0] += -0.026114738; + } else { + result[0] += 0.06968238; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 102))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 144))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { + result[0] += -0.042726804; + } else { + result[0] += -0.0135103455; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 162))) { + result[0] += -0.007055785; + } else { + result[0] += 0.014698033; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { + result[0] += -0.007809716; + } else { + result[0] += -0.025070379; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + result[0] += -0; + } else { + result[0] += 0.02416715; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 242))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + result[0] += -0.004747329; + } else { + result[0] += -0.030286456; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 38))) { + result[0] += 0.004049407; + } else { + result[0] += -0.003883451; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 280))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 44))) { + result[0] += 0.0048826905; + } else { + result[0] += 0.01918045; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { + result[0] += -0.010770427; + } else { + result[0] += 0.010454954; + } + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 126))) { + result[0] += -0.04999471; + } else { + result[0] += -0.1126702; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 6))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 8))) { + result[0] += -0.0024832468; + } else { + result[0] += 0.034831993; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 82))) { + result[0] += 0.018345565; + } else { + result[0] += -0.03647999; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 72))) { + result[0] += -0.025135571; + } else { + result[0] += -0; + } + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 158))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 42))) { + result[0] += -0.031185156; + } else { + result[0] += -0.011169832; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { + result[0] += -0.014554634; + } else { + result[0] += 0.05174926; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 134))) { + result[0] += 0.033395614; + } else { + result[0] += -0.015813565; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 274))) { + result[0] += 0.00033646842; + } else { + result[0] += 0.0078946445; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 114))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 154))) { + result[0] += 0.008154803; + } else { + result[0] += -0.014786914; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.06375181; + } else { + result[0] += 0.014291716; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 46))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += -0.0068405718; + } else { + result[0] += -0.034137234; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 288))) { + result[0] += 0.027196486; + } else { + result[0] += -0.029254882; + } + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 246))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 104))) { + result[0] += -0.00011478314; + } else { + result[0] += -0.008755667; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 106))) { + result[0] += 0.00041385577; + } else { + result[0] += 0.01698925; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 324))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + result[0] += -0.008199277; + } else { + result[0] += -0.01730309; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { + result[0] += 0.0039300146; + } else { + result[0] += 0.015076749; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 218))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 232))) { + result[0] += -0.0025358626; + } else { + result[0] += -0.014611224; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 16))) { + result[0] += 0.021280123; + } else { + result[0] += -0.008725259; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { + result[0] += 0.04309545; + } else { + result[0] += 0.0142627945; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 34))) { + result[0] += 0.024396539; + } else { + result[0] += 0.0048766937; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 170))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 16))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 28))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 70))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 68))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { + result[0] += -0.024164472; + } else { + result[0] += 0.008937138; + } + } else { + result[0] += 0.038625453; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 76))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 98))) { + result[0] += -0.04647706; + } else { + result[0] += -0.019371232; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 154))) { + result[0] += -0.007284378; + } else { + result[0] += -0.041731447; + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 14))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + result[0] += -0.049801502; + } else { + result[0] += 0.007428868; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { + result[0] += 0.061795365; + } else { + result[0] += 0.022740616; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + result[0] += 0.022688469; + } else { + result[0] += -0.016254207; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { + result[0] += 0.012587378; + } else { + result[0] += -0.019465229; + } + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 60))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 294))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 70))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += -0.016392779; + } else { + result[0] += 0.0076769763; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 132))) { + result[0] += -0.0070873285; + } else { + result[0] += -0.017376283; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 180))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 58))) { + result[0] += -0.008054349; + } else { + result[0] += 0.00049257686; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { + result[0] += 0.010894717; + } else { + result[0] += 0.0022375863; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 260))) { + result[0] += -0.018788002; + } else { + result[0] += 0.009024569; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 266))) { + result[0] += -0.0017262193; + } else { + result[0] += 0.015350415; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 172))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 194))) { + result[0] += 0.01940038; + } else { + result[0] += 0.0023542785; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { + result[0] += 0.003066004; + } else { + result[0] += -0.010361836; + } + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 290))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 104))) { + result[0] += -0.00023940679; + } else { + result[0] += -0.024299331; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 140))) { + result[0] += 0.018921724; + } else { + result[0] += -0.012017216; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 304))) { + result[0] += -0.009317097; + } else { + result[0] += 0.0070780194; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { + result[0] += 0.029872233; + } else { + result[0] += 0.0070561725; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + result[0] += -0.012874908; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 140))) { + result[0] += -0.03384575; + } else { + result[0] += -0.09957864; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 92))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + result[0] += 0.007720273; + } else { + result[0] += 0.050167393; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 288))) { + result[0] += 0.030057851; + } else { + result[0] += 0.012027963; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { + result[0] += -0.03683686; + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { + result[0] += -0.018220117; + } else { + result[0] += 0.0014780884; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 328))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 146))) { + result[0] += 0.02194166; + } else { + result[0] += -0.00030332725; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 84))) { + result[0] += -0.007878302; + } else { + result[0] += -0.016368117; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 104))) { + result[0] += -0.015569873; + } else { + result[0] += -0.00039696257; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 2))) { + result[0] += 0.022349734; + } else { + result[0] += 0.06912997; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + result[0] += -0.0011338064; + } else { + result[0] += -0.015420935; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 218))) { + result[0] += -0.0004335566; + } else { + result[0] += 0.012481102; + } + } + } + } + } + } + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 226))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 36))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 150))) { + result[0] += -0.008768398; + } else { + result[0] += -0.031017033; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + result[0] += -0.0019974862; + } else { + result[0] += -0.010033133; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 282))) { + result[0] += 0.0018652402; + } else { + result[0] += 0.028417757; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + result[0] += -0.02573219; + } else { + result[0] += 0.00014164243; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + result[0] += -0.012296872; + } else { + result[0] += 0.018667158; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 52))) { + result[0] += 0.0111978315; + } else { + result[0] += 0.00019159181; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 104))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + result[0] += -0.018547757; + } else { + result[0] += 0.00039879596; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { + result[0] += -0.025369791; + } else { + result[0] += -0.006152769; + } + } + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + result[0] += 0.0284749; + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 4))) { + result[0] += -0.12034156; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 90))) { + result[0] += 0.01555708; + } else { + result[0] += -0.04060086; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 28))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + result[0] += -0.00899754; + } else { + result[0] += -0.041497704; + } + } else { + result[0] += 0.012691744; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 38))) { + result[0] += -0.011100016; + } else { + result[0] += -0.065193616; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { + result[0] += -0.026525358; + } else { + result[0] += -0.007171394; + } + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 74))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { + result[0] += 0.017233951; + } else { + result[0] += 0.047289252; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 292))) { + result[0] += -0.020638349; + } else { + result[0] += 0.02203339; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 102))) { + result[0] += -0.028595468; + } else { + result[0] += 0.035886366; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + result[0] += 0.023079382; + } else { + result[0] += -0.0015904735; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { + result[0] += 0.01960182; + } else { + result[0] += 0.004229198; + } + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 110))) { + result[0] += 0.042509545; + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 78))) { + result[0] += 0.01140577; + } else { + result[0] += 0.03407; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 134))) { + result[0] += -0.002405419; + } else { + result[0] += -0.010636522; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { + result[0] += 0.012451133; + } else { + result[0] += -0.0039769937; + } + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { + result[0] += -0.01115589; + } else { + result[0] += 0.01320644; + } + } else { + result[0] += -0.013309436; + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 90))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 24))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 18))) { + result[0] += -0.054960348; + } else { + result[0] += -0.008308151; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 116))) { + result[0] += 0.040893547; + } else { + result[0] += -0.029395109; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 108))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 14))) { + result[0] += -0.011575009; + } else { + result[0] += 0.0045230165; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { + result[0] += -0.01649066; + } else { + result[0] += 0.0117938975; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 28))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 92))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 44))) { + result[0] += -0.017786995; + } else { + result[0] += 0.0030950578; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 38))) { + result[0] += -0.044471394; + } else { + result[0] += 0.022061352; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 52))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { + result[0] += -0.014604069; + } else { + result[0] += 0.016241714; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 42))) { + result[0] += 0.018912865; + } else { + result[0] += 0.0019412668; + } + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 306))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 64))) { + result[0] += -0.006083592; + } else { + result[0] += 0.046224184; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += 0.015739193; + } else { + result[0] += -0.022769345; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { + result[0] += 0.03625956; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 212))) { + result[0] += 0.00036286406; + } else { + result[0] += 0.009678096; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { + result[0] += -0.010175624; + } else { + result[0] += 0.0050961208; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 46))) { + result[0] += -0.0105675245; + } else { + result[0] += -0.026643395; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 226))) { + result[0] += -0; + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 86))) { + result[0] += 0.023571983; + } else { + result[0] += -0; + } + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 222))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 106))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 66))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 54))) { + result[0] += -0.07242959; + } else { + result[0] += -0.00901923; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 190))) { + result[0] += 0.0003989732; + } else { + result[0] += -0.014672895; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 56))) { + result[0] += -0.0052175657; + } else { + result[0] += 0.038002204; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 86))) { + result[0] += -0.030148897; + } else { + result[0] += -0.006241556; + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { + result[0] += -5.1738414e-05; + } else { + result[0] += 0.0063861213; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { + result[0] += 0.022810614; + } else { + result[0] += -0.016576035; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 234))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + result[0] += -0.0050715203; + } else { + result[0] += 0.005531682; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 238))) { + result[0] += -0.01301794; + } else { + result[0] += 0.00030336596; + } + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 180))) { + result[0] += -0.0094006555; + } else { + result[0] += 0.02063076; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + result[0] += 0.010461995; + } else { + result[0] += 0.0025512462; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 62))) { + result[0] += 0.010800346; + } else { + result[0] += 0.052793957; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 16))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { + result[0] += -0.02969619; + } else { + result[0] += -0.01354564; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 126))) { + result[0] += -0.0019324312; + } else { + result[0] += 0.018997952; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 228))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { + result[0] += 0.01697753; + } else { + result[0] += -0.0008568109; + } + } else { + result[0] += -0.0515616; + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 106))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 86))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { + result[0] += -0.036433112; + } else { + result[0] += -0.011925978; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 56))) { + result[0] += 0.03285799; + } else { + result[0] += 0.0010874722; + } + } + } else { + result[0] += -0.060846817; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 92))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 34))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += -0.012450369; + } else { + result[0] += -0.04985917; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 24))) { + result[0] += 0.0078088613; + } else { + result[0] += -0.00958295; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 168))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 24))) { + result[0] += -0.043195866; + } else { + result[0] += -0.014012662; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 26))) { + result[0] += -0.019311419; + } else { + result[0] += 0.007864608; + } + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 152))) { + result[0] += -0.012414033; + } else { + result[0] += 0.029668832; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + result[0] += -0.0030567853; + } else { + result[0] += 0.01868884; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 254))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { + result[0] += 0.026466904; + } else { + result[0] += -0.00078509795; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 256))) { + result[0] += -0.017463585; + } else { + result[0] += -0.004959351; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 90))) { + result[0] += 0.054318376; + } else { + result[0] += 0.016744314; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + result[0] += -0.008082875; + } else { + result[0] += 0.004647791; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 136))) { + result[0] += -0.019794006; + } else { + result[0] += 0.037679102; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + result[0] += 0.044813335; + } else { + result[0] += -0.0018291153; + } + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 68))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 76))) { + result[0] += -0.011341448; + } else { + result[0] += -0.060769796; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { + result[0] += -0.00012510354; + } else { + result[0] += 0.019792138; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 110))) { + result[0] += -0.005990359; + } else { + result[0] += 0.017361904; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + result[0] += 0.008660458; + } else { + result[0] += 0.0014748373; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 220))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 272))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 118))) { + result[0] += -0.0027236396; + } else { + result[0] += -0.029521126; + } + } else { + result[0] += -0; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += -0.058680933; + } else { + result[0] += 0.0022811424; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 206))) { + result[0] += -0.017125372; + } else { + result[0] += 0.008216227; + } + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { + result[0] += -0.01995611; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 30))) { + result[0] += 0.021725012; + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 220))) { + result[0] += 0.050011344; + } else { + result[0] += 0.014028007; + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 56))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 4))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 104))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 44))) { + result[0] += 0.021376295; + } else { + result[0] += 0.0036938381; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 274))) { + result[0] += -0.0331207; + } else { + result[0] += -0.005488401; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 8))) { + result[0] += 0.015083553; + } else { + result[0] += -0.016786192; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 30))) { + result[0] += -0.037689086; + } else { + result[0] += -0.019093947; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 130))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 8))) { + result[0] += 0.059979796; + } else { + result[0] += 0.0069023515; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 290))) { + result[0] += -0.024388736; + } else { + result[0] += -0; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 52))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 64))) { + result[0] += 0.030230714; + } else { + result[0] += -0.002469063; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + result[0] += -0.00685675; + } else { + result[0] += -0.019919405; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 40))) { + result[0] += 0.007914934; + } else { + result[0] += -0.0072328295; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 168))) { + result[0] += -0.023456104; + } else { + result[0] += -0.007590213; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 278))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 268))) { + result[0] += -0.00084484427; + } else { + result[0] += 0.008248358; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 98))) { + result[0] += 0.008069568; + } else { + result[0] += -0.01922637; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 58))) { + result[0] += -0.0034377978; + } else { + result[0] += 0.02219229; + } + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 114))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { + result[0] += -0.0008113146; + } else { + result[0] += -0.011595509; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 30))) { + result[0] += -0.00054977357; + } else { + result[0] += 0.0060809394; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { + result[0] += -0.013630775; + } else { + result[0] += 0.029257402; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { + result[0] += 0.01473201; + } else { + result[0] += 0.004033634; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 326))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + result[0] += 0.0005843948; + } else { + result[0] += -0.015655208; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 368))) { + result[0] += -0.007192479; + } else { + result[0] += 0.006543958; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 346))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { + result[0] += 0.01452767; + } else { + result[0] += 0.0055364254; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { + result[0] += 0.026855484; + } else { + result[0] += -0.005540358; + } + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 88))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 92))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 60))) { + result[0] += 0.04485644; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { + result[0] += -0.042145204; + } else { + result[0] += 7.136203e-05; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 32))) { + result[0] += -0.005530101; + } else { + result[0] += -0.041934248; + } + } else { + result[0] += -0.07572525; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 246))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 282))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + result[0] += 0.025418038; + } else { + result[0] += -0.0084718885; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 214))) { + result[0] += -0.019256549; + } else { + result[0] += 0.00998631; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 98))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { + result[0] += -0.054605898; + } else { + result[0] += -0.011759637; + } + } else { + result[0] += -0.046184976; + } + } + } + } + } + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 176))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 36))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { + result[0] += -0.022035845; + } else { + result[0] += 0.046122007; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { + result[0] += 0.0077522853; + } else { + result[0] += -0.02736899; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 128))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 120))) { + result[0] += -0.0018629817; + } else { + result[0] += 0.02747905; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += 0.02887457; + } else { + result[0] += -0.013981772; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 108))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { + result[0] += -0.01683332; + } else { + result[0] += -0.0050804014; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 52))) { + result[0] += 0.03178656; + } else { + result[0] += -0.0057087517; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 90))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 152))) { + result[0] += -0.014723192; + } else { + result[0] += -0.04805673; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 144))) { + result[0] += -0.017328734; + } else { + result[0] += -0.03817048; + } + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 28))) { + result[0] += -0.0028061648; + } else { + result[0] += 0.002789692; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { + result[0] += -0.025860507; + } else { + result[0] += -0.0057830475; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 330))) { + result[0] += 0.006883178; + } else { + result[0] += -0.010597587; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 66))) { + result[0] += -0.03156317; + } else { + result[0] += -0.00012491019; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 36))) { + result[0] += -0.049023475; + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { + result[0] += -0.015445833; + } else { + result[0] += -0.038803425; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 164))) { + result[0] += -0.0042772954; + } else { + result[0] += -0.02979202; + } + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 4))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 18))) { + result[0] += -0.105805; + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 156))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 218))) { + result[0] += -0.013860464; + } else { + result[0] += -0.047610518; + } + } else { + result[0] += 0.015889838; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 296))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 150))) { + result[0] += 0.0039272998; + } else { + result[0] += 0.04100207; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { + result[0] += 0.066427186; + } else { + result[0] += 0.019468648; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { + result[0] += -0.013523097; + } else { + result[0] += -0.035645615; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 302))) { + result[0] += -0.017095027; + } else { + result[0] += 0.008768201; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 84))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 196))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 88))) { + result[0] += -0.0047009015; + } else { + result[0] += 0.0095943585; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { + result[0] += -0.024147784; + } else { + result[0] += -0.007935442; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 100))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 360))) { + result[0] += -0.025299666; + } else { + result[0] += -0.015776524; + } + } else { + result[0] += -0.00817149; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { + result[0] += -0.013373877; + } else { + result[0] += 0.06051935; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 112))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 54))) { + result[0] += 0.013844582; + } else { + result[0] += -0.018000482; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 116))) { + result[0] += 0.025009016; + } else { + result[0] += 0.0011969743; + } + } + } + } + } + } + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 6))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 46))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 96))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { + result[0] += -0.005377132; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { + result[0] += 0.033936527; + } else { + result[0] += 0.06770065; + } + } else { + result[0] += -0; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 34))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 28))) { + result[0] += -0.016085489; + } else { + result[0] += 0.012783072; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 26))) { + result[0] += -0.038858794; + } else { + result[0] += -0.004968185; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 36))) { + result[0] += 0.04801505; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { + result[0] += 0.01772366; + } else { + result[0] += -0.026647871; + } + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 84))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { + result[0] += -0.02566973; + } else { + result[0] += 0.028348282; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 120))) { + result[0] += -0.043836653; + } else { + result[0] += -0.012338279; + } + } + } else { + result[0] += -0.10324136; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 36))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 2))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 12))) { + result[0] += -0.033641063; + } else { + result[0] += 0.00022004887; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 116))) { + result[0] += -0.044643078; + } else { + result[0] += -0.008762143; + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { + result[0] += 0.0021203114; + } else { + result[0] += -0.017186115; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 88))) { + result[0] += -0.019821104; + } else { + result[0] += 0.0021318241; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 184))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 222))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { + result[0] += 0.0009353072; + } else { + result[0] += -0.0056581837; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { + result[0] += -0.0009079633; + } else { + result[0] += 0.008418952; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 108))) { + result[0] += -0; + } else { + result[0] += -0.03857514; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { + result[0] += 0.0028833076; + } else { + result[0] += 0.030077396; + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 86))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 66))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 44))) { + result[0] += 0.0097111445; + } else { + result[0] += -0.004844829; + } + } else { + result[0] += 0.022152675; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 58))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 36))) { + result[0] += 0.012857464; + } else { + result[0] += 0.04939564; + } + } else { + result[0] += 0.0038712837; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 194))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 96))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 142))) { + result[0] += 0.022889886; + } else { + result[0] += -0.025423696; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 194))) { + result[0] += 0.0012433341; + } else { + result[0] += -0.027690545; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 42))) { + result[0] += 0.019493537; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { + result[0] += 0.04888856; + } else { + result[0] += 0.0047187754; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { + result[0] += -0.038507808; + } else { + result[0] += -0.0059399325; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 190))) { + result[0] += -0.00081904867; + } else { + result[0] += 0.023750668; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 62))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 180))) { + result[0] += 0.007010121; + } else { + result[0] += 0.00020330278; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { + result[0] += 0.022976872; + } else { + result[0] += -0.008908423; + } + } + } + } + } + } + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 16))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 20))) { + result[0] += 0.04029838; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { + result[0] += -0.0076732375; + } else { + result[0] += -0.044110678; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 140))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 138))) { + result[0] += 0.013151512; + } else { + result[0] += 0.04138301; + } + } else { + result[0] += -0.031063614; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 38))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 244))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 212))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 128))) { + result[0] += -0.01187767; + } else { + result[0] += -0.024798945; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { + result[0] += -0.014814547; + } else { + result[0] += -0.0693584; + } + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { + result[0] += -0.019700082; + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 12))) { + result[0] += 0.020823775; + } else { + result[0] += -0.0007175183; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 28))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { + result[0] += -0.04782397; + } else { + result[0] += -0.00023042485; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 18))) { + result[0] += 0.011482454; + } else { + result[0] += -0.025828404; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 68))) { + result[0] += 0.07567041; + } else { + result[0] += 0.03777515; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 36))) { + result[0] += 0.019245345; + } else { + result[0] += -0.007408004; + } + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { + result[0] += -0.0023852098; + } else { + result[0] += -0.01571614; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + result[0] += -0.0008245474; + } else { + result[0] += -0.01959662; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 52))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 160))) { + result[0] += 0.01735154; + } else { + result[0] += -0.01864585; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 104))) { + result[0] += -0.0019429872; + } else { + result[0] += 0.001837825; + } + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 86))) { + result[0] += 0.013189456; + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { + result[0] += 0.007143569; + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 44))) { + result[0] += 0.025064811; + } else { + result[0] += 0.0470048; + } + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 138))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 2))) { + result[0] += -0.028255967; + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 330))) { + result[0] += 0.0038780514; + } else { + result[0] += 0.027806832; + } + } else { + result[0] += -0.030956248; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 144))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 98))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { + result[0] += 0.0049754553; + } else { + result[0] += 0.07370368; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 54))) { + result[0] += 0.0024109604; + } else { + result[0] += -0.021205995; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 232))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + result[0] += 0.006072202; + } else { + result[0] += 0.021960644; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 60))) { + result[0] += -0.016412217; + } else { + result[0] += 0.02891737; + } + } + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 176))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { + result[0] += -0.013008319; + } else { + result[0] += 0.014044277; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 8))) { + result[0] += -0.04008707; + } else { + result[0] += 0.011214378; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 8))) { + result[0] += -0.021072047; + } else { + result[0] += -0.032819312; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 16))) { + result[0] += -0.014122496; + } else { + result[0] += -0.0027466726; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { + result[0] += 0.003206402; + } else { + result[0] += 0.023088232; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { + result[0] += -0.029293472; + } else { + result[0] += -0.00044284094; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 88))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { + result[0] += -0.037337154; + } else { + result[0] += -0.054280948; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + result[0] += 0.000117012045; + } else { + result[0] += -0.009735559; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 152))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 194))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { + result[0] += -0.024764068; + } else { + result[0] += 0.0052268724; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 64))) { + result[0] += 0.014035463; + } else { + result[0] += 0.042725824; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 78))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 118))) { + result[0] += -0.016555734; + } else { + result[0] += 0.0024362335; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 132))) { + result[0] += 0.013627543; + } else { + result[0] += 0.0015473928; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 168))) { + result[0] += 0.04074187; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { + result[0] += 0.029437272; + } else { + result[0] += 0.0063492656; + } + } + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 36))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { + result[0] += -0.007086392; + } else { + result[0] += -0.028627744; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { + result[0] += -0.01253838; + } else { + result[0] += 0.020658642; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 100))) { + result[0] += -0.015626702; + } else { + result[0] += -0.07547299; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + result[0] += -0.098590024; + } else { + result[0] += -0.016383274; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 156))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 262))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 238))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 148))) { + result[0] += -0.005975528; + } else { + result[0] += -0.017585494; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 134))) { + result[0] += -0.06808423; + } else { + result[0] += -0.022014553; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 292))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 128))) { + result[0] += -0.010686524; + } else { + result[0] += 0.011010774; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 284))) { + result[0] += 0.03166331; + } else { + result[0] += 0.0053174673; + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 82))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 258))) { + result[0] += -0.049204163; + } else { + result[0] += -0.0052781263; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 108))) { + result[0] += 0.007684131; + } else { + result[0] += -0.01196891; + } + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 250))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 168))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 16))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 120))) { + result[0] += -0; + } else { + result[0] += 0.025176907; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 158))) { + result[0] += -0.009614422; + } else { + result[0] += -0.025206959; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 216))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 82))) { + result[0] += -0.0041216165; + } else { + result[0] += 0.042651124; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 84))) { + result[0] += -0.038773373; + } else { + result[0] += 0.023100061; + } + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.006539114; + } else { + result[0] += -0.062450953; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + result[0] += -0.004460583; + } else { + result[0] += 0.0019909479; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + result[0] += -0.00021329732; + } else { + result[0] += 0.011201916; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { + result[0] += 0.01424145; + } else { + result[0] += 0.0014522666; + } + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 254))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 50))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + result[0] += 0.033733904; + } else { + result[0] += -0.0006381403; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 28))) { + result[0] += -0.015036389; + } else { + result[0] += 0.0023278003; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 2))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 280))) { + result[0] += -0.020510865; + } else { + result[0] += 0.02703706; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 52))) { + result[0] += 0.022050345; + } else { + result[0] += -0.0019060083; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 118))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 314))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 300))) { + result[0] += -0.019168613; + } else { + result[0] += 0.006976162; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 264))) { + result[0] += -0.015453433; + } else { + result[0] += 0.0013623396; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 60))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 206))) { + result[0] += 0.004762128; + } else { + result[0] += -0.006663674; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 66))) { + result[0] += 0.025200857; + } else { + result[0] += -0.005030225; + } + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 54))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 44))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 42))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 2))) { + result[0] += -0.012792389; + } else { + result[0] += 0.017205698; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + result[0] += -0.042184174; + } else { + result[0] += 0.004857573; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 66))) { + result[0] += -0.028872743; + } else { + result[0] += -0.006002301; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { + result[0] += -0.0013448104; + } else { + result[0] += -0.04370745; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 214))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 302))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { + result[0] += 0.0069149467; + } else { + result[0] += -0.0046409685; + } + } else { + result[0] += 0.02378996; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 62))) { + result[0] += 0.012926725; + } else { + result[0] += -0.008747402; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 50))) { + result[0] += -0.046444967; + } else { + result[0] += -0.018540045; + } + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 146))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 134))) { + result[0] += -0.09067112; + } else { + result[0] += -0.010558448; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { + result[0] += -0.023714645; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 194))) { + result[0] += -0.010494131; + } else { + result[0] += 0.01534995; + } + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 308))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 110))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + result[0] += -0.03064735; + } else { + result[0] += 0.0034591525; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { + result[0] += 0.026732335; + } else { + result[0] += 0.008400806; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { + result[0] += -0.005901861; + } else { + result[0] += 0.009023737; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + result[0] += -0.012533008; + } else { + result[0] += -0.0020645715; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { + result[0] += -0.013980149; + } else { + result[0] += 0.0019120906; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 54))) { + result[0] += -0.0057765585; + } else { + result[0] += 0.017128821; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { + result[0] += -0.018316587; + } else { + result[0] += -0.005805789; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { + result[0] += 0.03265365; + } else { + result[0] += -0.0010156564; + } + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 240))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 122))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 184))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 132))) { + result[0] += 0.014779656; + } else { + result[0] += -0.020928297; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 206))) { + result[0] += 0.07677117; + } else { + result[0] += -0.004149875; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 254))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 108))) { + result[0] += 0.0061775236; + } else { + result[0] += -0.002984956; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 192))) { + result[0] += -0.008194422; + } else { + result[0] += -0.05728069; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 192))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 280))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { + result[0] += 0.022544114; + } else { + result[0] += 0.0022025593; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { + result[0] += -0.004058608; + } else { + result[0] += 0.014417312; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 318))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 338))) { + result[0] += 0.0165982; + } else { + result[0] += 0.038578045; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { + result[0] += -0.015475737; + } else { + result[0] += 0.01142684; + } + } + } + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 20))) { + result[0] += -0.0928144; + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 180))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 46))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { + result[0] += -0.020081904; + } else { + result[0] += 0.002259192; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + result[0] += -0.07451444; + } else { + result[0] += -0.0156236235; + } + } + } else { + result[0] += -0.07355082; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 108))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 38))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { + result[0] += -0.037877556; + } else { + result[0] += 0.0013853526; + } + } else { + result[0] += -0.050581098; + } + } else { + result[0] += -0; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 178))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 176))) { + result[0] += -0.0043627243; + } else { + result[0] += -0.020921404; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { + result[0] += 0.01141603; + } else { + result[0] += -0.0036946; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 228))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 94))) { + result[0] += -0.027430529; + } else { + result[0] += 0.00075300987; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 74))) { + result[0] += -0.05395777; + } else { + result[0] += -0.013114258; + } + } + } + } + } + } + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 226))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 270))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 266))) { + result[0] += -0.0013336329; + } else { + result[0] += -0.012101962; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 96))) { + result[0] += -0.012478279; + } else { + result[0] += -0.042821463; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 108))) { + result[0] += -0.00018370214; + } else { + result[0] += 0.018782655; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 90))) { + result[0] += 0.0019190798; + } else { + result[0] += -0.0032492876; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 114))) { + result[0] += -0.041382577; + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { + result[0] += 0.019699065; + } else { + result[0] += -0.023365228; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 242))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 152))) { + result[0] += -0.014805789; + } else { + result[0] += -5.5329445e-05; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 276))) { + result[0] += 0.031025484; + } else { + result[0] += 0.00553753; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 282))) { + result[0] += 0.03033769; + } else { + result[0] += 0.053280126; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { + result[0] += 0.0059688077; + } else { + result[0] += -0.027727133; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 314))) { + result[0] += 0.009310781; + } else { + result[0] += -0.008495542; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 40))) { + result[0] += 0.038052257; + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 12))) { + result[0] += 0.012323047; + } else { + result[0] += -0.023586523; + } + } + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 144))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 130))) { + result[0] += 0.0018252156; + } else { + result[0] += -0.00692114; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 312))) { + result[0] += 0.032022245; + } else { + result[0] += -0.006053284; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 338))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 132))) { + result[0] += -0.019810393; + } else { + result[0] += -0.0073889843; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 36))) { + result[0] += 0.016521052; + } else { + result[0] += 0.0061617037; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 76))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 244))) { + result[0] += 0.012791178; + } else { + result[0] += -0.020304035; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 258))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { + result[0] += 0.009074631; + } else { + result[0] += -0.0016534543; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { + result[0] += 0.027765274; + } else { + result[0] += -0.0001559796; + } + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 234))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 258))) { + result[0] += -0.009072641; + } else { + result[0] += 0.008848451; + } + } else { + result[0] += -0.012117184; + } + } + } + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 6))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 148))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { + result[0] += 0.009714896; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 18))) { + result[0] += -0.035071608; + } else { + result[0] += -0.015023454; + } + } + } else { + result[0] += -0.073392645; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 90))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 132))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 38))) { + result[0] += -0.0044015315; + } else { + result[0] += 0.012927837; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 4))) { + result[0] += -0.0023782412; + } else { + result[0] += -0.021760104; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 42))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 112))) { + result[0] += -0.0147892255; + } else { + result[0] += 0.0073062084; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { + result[0] += -0.026250172; + } else { + result[0] += 0.0071073617; + } + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 0))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 162))) { + result[0] += 0.040685426; + } else { + result[0] += 0.0120480135; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 308))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 18))) { + result[0] += -0.06254916; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 98))) { + result[0] += -0.0067300186; + } else { + result[0] += -0.0331382; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { + result[0] += -0.019220524; + } else { + result[0] += 0.009100321; + } + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 206))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + result[0] += 0.009733505; + } else { + result[0] += -0.004346859; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + result[0] += 8.9312605e-05; + } else { + result[0] += -0.003453482; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 280))) { + result[0] += -0.0062282663; + } else { + result[0] += 0.0049122893; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + result[0] += -0.002204797; + } else { + result[0] += 0.0064123496; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 44))) { + result[0] += 0.06116004; + } else { + result[0] += 0.010365079; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 146))) { + result[0] += -0.018945072; + } else { + result[0] += -0.010433878; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 118))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 168))) { + result[0] += 0.01741377; + } else { + result[0] += 0.07594368; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 170))) { + result[0] += -0.0018027859; + } else { + result[0] += 0.016592477; + } + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { + result[0] += 0.043439865; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 82))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 58))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { + result[0] += -0.036944382; + } else { + result[0] += -0.012312688; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + result[0] += 0.043840874; + } else { + result[0] += -0.014361912; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + result[0] += -0.004802558; + } else { + result[0] += 0.007698638; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 170))) { + result[0] += -0.017537218; + } else { + result[0] += -0.009052196; + } + } + } + } + } + } + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 10))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 76))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 18))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { + result[0] += -0.011083994; + } else { + result[0] += -0.033235386; + } + } else { + result[0] += 0.03099644; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { + result[0] += -0.020493729; + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 102))) { + result[0] += 0.03927904; + } else { + result[0] += -0.0013705323; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 172))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 80))) { + result[0] += 0.06984255; + } else { + result[0] += 0.024543915; + } + } else { + result[0] += 0.008717417; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + result[0] += -0.034200173; + } else { + result[0] += 0.020928657; + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 40))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 34))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 30))) { + result[0] += -0.018539859; + } else { + result[0] += 0.006696008; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { + result[0] += 0.0006399334; + } else { + result[0] += 0.023955312; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 46))) { + result[0] += 0.0061688256; + } else { + result[0] += 0.044047784; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 102))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 156))) { + result[0] += -0.016056351; + } else { + result[0] += 0.0054231877; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { + result[0] += -0.0025266295; + } else { + result[0] += 0.018242473; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 270))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 104))) { + result[0] += -0.017715478; + } else { + result[0] += -0.04547394; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 152))) { + result[0] += 0.013087104; + } else { + result[0] += -0.029149592; + } + } + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 264))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 52))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 188))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 22))) { + result[0] += 0.0041657714; + } else { + result[0] += -0.022834495; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { + result[0] += 0.016674465; + } else { + result[0] += 0.032325614; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 176))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 154))) { + result[0] += -0.017698428; + } else { + result[0] += 0.03289035; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { + result[0] += 0.0020410677; + } else { + result[0] += -0.020820608; + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 74))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 70))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 50))) { + result[0] += -4.355009e-05; + } else { + result[0] += -0.032748487; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 78))) { + result[0] += 0.035664223; + } else { + result[0] += -0.0039311326; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + result[0] += -0.0035012837; + } else { + result[0] += 0.02525419; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + result[0] += -0.0004375557; + } else { + result[0] += -0.032447618; + } + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 68))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 284))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 202))) { + result[0] += 0.06691403; + } else { + result[0] += 0.026116902; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 228))) { + result[0] += -0.0024726556; + } else { + result[0] += 0.0035805923; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 112))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { + result[0] += 0.008209762; + } else { + result[0] += 0.046135128; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 142))) { + result[0] += -0.0095024295; + } else { + result[0] += 0.0053031766; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 156))) { + result[0] += 0.02656824; + } else { + result[0] += -0.00404437; + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 142))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 40))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 46))) { + result[0] += 0.06587588; + } else { + result[0] += 0.0105197765; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 70))) { + result[0] += 0.0041911616; + } else { + result[0] += -0.012468158; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { + result[0] += 0.009541268; + } else { + result[0] += 0.058547605; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 152))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 290))) { + result[0] += -0.021359572; + } else { + result[0] += -0.004128001; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 162))) { + result[0] += 0.011844903; + } else { + result[0] += 0.023288697; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 316))) { + result[0] += -7.127906e-05; + } else { + result[0] += 0.0055441954; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 44))) { + result[0] += -0.033032846; + } else { + result[0] += 0.008700618; + } + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 100))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 28))) { + result[0] += -0.020282162; + } else { + result[0] += -0; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 252))) { + result[0] += 0.0372028; + } else { + result[0] += 0.020493207; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 114))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 192))) { + result[0] += 0.0023391566; + } else { + result[0] += -0.012876572; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { + result[0] += 0.028000802; + } else { + result[0] += 0.008097276; + } + } + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 12))) { + result[0] += -0.030859623; + } else { + result[0] += 0.059427258; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 296))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 12))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 158))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { + result[0] += 0.023903606; + } else { + result[0] += -0.01342114; + } + } else { + result[0] += -0.03418419; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 52))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { + result[0] += -0.014690627; + } else { + result[0] += -0.046283614; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { + result[0] += -0.018795181; + } else { + result[0] += -0.005358623; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 162))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 300))) { + result[0] += -0.013268234; + } else { + result[0] += 0.011121328; + } + } else { + result[0] += -0.0279394; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 176))) { + result[0] += 0.03626206; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 326))) { + result[0] += -0.0057000555; + } else { + result[0] += 0.0147212865; + } + } + } + } + } else { + result[0] += -0.06643397; + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 6))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 48))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 126))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { + result[0] += -0.0010974932; + } else { + result[0] += 0.020823766; + } + } else { + result[0] += 0.0449019; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 34))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + result[0] += -0.008742712; + } else { + result[0] += 0.016725272; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 36))) { + result[0] += -0.026789738; + } else { + result[0] += 0.005821276; + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { + result[0] += -0.033222493; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 40))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { + result[0] += -0.0058179474; + } else { + result[0] += 0.04347635; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 112))) { + result[0] += -0.015091679; + } else { + result[0] += 0.0024027491; + } + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 232))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 32))) { + result[0] += 0.0399952; + } else { + result[0] += -0.00027889368; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 62))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { + result[0] += -0.01870556; + } else { + result[0] += 0.0057035587; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { + result[0] += 0.02278183; + } else { + result[0] += -0.008421278; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { + result[0] += -0.042912465; + } else { + result[0] += -0; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 242))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 160))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 194))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 110))) { + result[0] += 0.003376493; + } else { + result[0] += 0.011122108; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { + result[0] += 0.03045251; + } else { + result[0] += 0.012394785; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { + result[0] += -0.027083784; + } else { + result[0] += 0.0126619935; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { + result[0] += 0.008825673; + } else { + result[0] += 0.044847894; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 144))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 10))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { + result[0] += 0.023729367; + } else { + result[0] += -0.016748834; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 64))) { + result[0] += -0.020931631; + } else { + result[0] += -0.05402078; + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 30))) { + result[0] += 0.020663498; + } else { + result[0] += 0.00616918; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 20))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 122))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 12))) { + result[0] += -0.017938469; + } else { + result[0] += 0.03910426; + } + } else { + result[0] += 0.0053874785; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 14))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.022196533; + } else { + result[0] += -0.00451347; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + result[0] += 0.00037458728; + } else { + result[0] += 0.01688151; + } + } + } + } + } + } + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 254))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 90))) { + result[0] += 0.027586067; + } else { + result[0] += -0.029394219; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 212))) { + result[0] += -0.008463705; + } else { + result[0] += -0.028911496; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 194))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 154))) { + result[0] += -0.0010301826; + } else { + result[0] += 0.0035315962; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { + result[0] += -0.0011929054; + } else { + result[0] += -0.00760438; + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 62))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 110))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + result[0] += 0.011337014; + } else { + result[0] += 0.0015774952; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 86))) { + result[0] += -0.0043284963; + } else { + result[0] += 0.0071344683; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 256))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 206))) { + result[0] += 0.0046818857; + } else { + result[0] += 0.01623973; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 332))) { + result[0] += -0.025880137; + } else { + result[0] += 0.00024090149; + } + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 44))) { + result[0] += 0.062326457; + } else { + result[0] += -0.012353135; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 154))) { + result[0] += -0.01557938; + } else { + result[0] += -0.007663093; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 114))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 156))) { + result[0] += 0.07925903; + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 160))) { + result[0] += -0.022038413; + } else { + result[0] += 0.010596351; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 118))) { + result[0] += 0.009866733; + } else { + result[0] += 0.04229749; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 78))) { + result[0] += -0.027659763; + } else { + result[0] += -0.0035324506; + } + } + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 168))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 130))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 84))) { + result[0] += -0.01782277; + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 212))) { + result[0] += -0; + } else { + result[0] += -0.01930575; + } + } + } else { + result[0] += 0.0040967353; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 402))) { + result[0] += -0.021184763; + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 140))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + result[0] += -0.0055727926; + } else { + result[0] += 0.009987557; + } + } else { + result[0] += -0.016817084; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 128))) { + result[0] += -0.011004246; + } else { + result[0] += 0.0059432825; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { + result[0] += -0.0151095195; + } else { + result[0] += -0.0033481915; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 92))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { + result[0] += 0.0145778125; + } else { + result[0] += -0.0010819718; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 182))) { + result[0] += -0.01369258; + } else { + result[0] += 0.010227516; + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 238))) { + result[0] += -0.009665318; + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 180))) { + result[0] += 0.026355088; + } else { + result[0] += -0.0077078748; + } + } + } + } + } + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 230))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 268))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 52))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { + result[0] += -0.0125539; + } else { + result[0] += 0.014108579; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + result[0] += 0.021760508; + } else { + result[0] += -0.019894226; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 268))) { + result[0] += -0.029441832; + } else { + result[0] += -0.010534534; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 112))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 68))) { + result[0] += -0.031425703; + } else { + result[0] += -0.019633126; + } + } else { + result[0] += -0.01066619; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { + result[0] += -0.0006799324; + } else { + result[0] += -0.027934993; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + result[0] += -0.015189849; + } else { + result[0] += -0.0026831003; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 236))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 166))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + result[0] += -1.1786798e-05; + } else { + result[0] += 0.02334137; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 290))) { + result[0] += 0.0047730226; + } else { + result[0] += 0.01909187; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 284))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 246))) { + result[0] += 0.024219202; + } else { + result[0] += 0.04463927; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 28))) { + result[0] += -0; + } else { + result[0] += 0.057420652; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 240))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 202))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 296))) { + result[0] += 0.005736035; + } else { + result[0] += -0.018226644; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { + result[0] += -0.014074256; + } else { + result[0] += -0.00035597832; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 250))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 218))) { + result[0] += -0.00022620684; + } else { + result[0] += 0.021930484; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 212))) { + result[0] += -0.00031011814; + } else { + result[0] += 0.018323947; + } + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + result[0] += 0.029248917; + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 292))) { + result[0] += -0.014892972; + } else { + result[0] += 0.017006356; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 294))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 252))) { + result[0] += 0.0011380663; + } else { + result[0] += 0.0074514025; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { + result[0] += -0.027498512; + } else { + result[0] += -0.00029022736; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 256))) { + result[0] += 0.034170583; + } else { + result[0] += 0.022245234; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 328))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 324))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 298))) { + result[0] += -0.027805215; + } else { + result[0] += -0.017054409; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 294))) { + result[0] += -0.016550997; + } else { + result[0] += -0.0043134186; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 346))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + result[0] += -0.00029596835; + } else { + result[0] += -0.030671025; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + result[0] += 0.022249522; + } else { + result[0] += -0.014686073; + } + } + } + } + } + } + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 400))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 116))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 16))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { + result[0] += -0.014927131; + } else { + result[0] += -0.0050526834; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + result[0] += 0.0031292983; + } else { + result[0] += -0.0021366577; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 14))) { + result[0] += -0.019448534; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { + result[0] += 0.031444933; + } else { + result[0] += 0.010930794; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 36))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 152))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 20))) { + result[0] += -0.017589707; + } else { + result[0] += -0.00019221655; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + result[0] += -0.020101383; + } else { + result[0] += -0; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 170))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + result[0] += 0.0022330189; + } else { + result[0] += 0.012290857; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 14))) { + result[0] += -0.0006709326; + } else { + result[0] += 0.0086814575; + } + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 392))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 156))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 154))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0; + } else { + result[0] += 0.067005776; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 102))) { + result[0] += 0.07170707; + } else { + result[0] += -0.014048599; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 166))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 164))) { + result[0] += -0.030913204; + } else { + result[0] += -0.007337024; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 160))) { + result[0] += 0.065959476; + } else { + result[0] += 0.0030737682; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 176))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 112))) { + result[0] += -0.016045328; + } else { + result[0] += -0.008878782; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 118))) { + result[0] += 0.00972886; + } else { + result[0] += 0.03835055; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 130))) { + result[0] += -0.009170124; + } else { + result[0] += 0.0057587116; + } + } + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { + result[0] += -0.015629305; + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 142))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 140))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + result[0] += -0.004336093; + } else { + result[0] += 0.015490188; + } + } else { + result[0] += -0.01513085; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { + result[0] += 0.00940407; + } else { + result[0] += 0.032563876; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 178))) { + result[0] += -0.01413842; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 244))) { + result[0] += 0.0270089; + } else { + result[0] += -0.00225509; + } + } else { + result[0] += -0.008365121; + } + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 110))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 168))) { + result[0] += -0.0023444246; + } else { + result[0] += 0.007994309; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { + result[0] += 0.00061645266; + } else { + result[0] += -0.04576966; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 62))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 20))) { + result[0] += 0.01534583; + } else { + result[0] += 0.039503228; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { + result[0] += 0.01222286; + } else { + result[0] += -0.0050080977; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 92))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { + result[0] += -0.00016676041; + } else { + result[0] += 0.045995515; + } + } else { + result[0] += -0.019292504; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 270))) { + result[0] += -0.0106395595; + } else { + result[0] += 0.024322292; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 196))) { + result[0] += 0.0038009014; + } else { + result[0] += -0.006002572; + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 112))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 104))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 90))) { + result[0] += 0.00059230387; + } else { + result[0] += 0.02190473; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + result[0] += -0.0012604637; + } else { + result[0] += -0.025116524; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { + result[0] += 0.01818419; + } else { + result[0] += -0.0041689784; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 54))) { + result[0] += -0.0046932534; + } else { + result[0] += 0.012534521; + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 100))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 212))) { + result[0] += -0.0053321766; + } else { + result[0] += 3.2746604e-05; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 160))) { + result[0] += 0.04412394; + } else { + result[0] += -0.019410737; + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 148))) { + result[0] += -0.015239002; + } else { + result[0] += -0.007977055; + } + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 134))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 150))) { + result[0] += 0.026527822; + } else { + result[0] += -0.015368457; + } + } else { + result[0] += -0.05488323; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 240))) { + result[0] += -0.07350321; + } else { + result[0] += -0.01900329; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 12))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 96))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { + result[0] += 0.0044366815; + } else { + result[0] += 0.05480617; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { + result[0] += -0.011877809; + } else { + result[0] += 0.01703535; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 106))) { + result[0] += -0.02123229; + } else { + result[0] += -0.07076668; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 242))) { + result[0] += -0.0073331865; + } else { + result[0] += -0.039508924; + } + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 32))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 108))) { + result[0] += 0.028880654; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 8))) { + result[0] += -0.015295302; + } else { + result[0] += 0.0052049416; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 100))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + result[0] += -0.0003331708; + } else { + result[0] += 0.025730435; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 280))) { + result[0] += -0.0062754015; + } else { + result[0] += 0.013416797; + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 82))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 4))) { + result[0] += -0; + } else { + result[0] += -0.031346504; + } + } else { + result[0] += 0.0021177256; + } + } + } + } + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 314))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { + result[0] += -0.0009686186; + } else { + result[0] += -0.027624989; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 222))) { + result[0] += 0.012920478; + } else { + result[0] += 0.03591418; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 262))) { + result[0] += -0.027306026; + } else { + result[0] += -0.010613002; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { + result[0] += 0.028817087; + } else { + result[0] += -0.021536713; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 218))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 150))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 60))) { + result[0] += -0.014519043; + } else { + result[0] += -0.0013858235; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { + result[0] += 0.011594709; + } else { + result[0] += -0.0014000179; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 312))) { + result[0] += -0.005692505; + } else { + result[0] += 0.009161465; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { + result[0] += -3.463562e-05; + } else { + result[0] += 0.004579789; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 296))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 260))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 164))) { + result[0] += -0.00439987; + } else { + result[0] += -0.020940205; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += -0.062401365; + } else { + result[0] += -0.011520316; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 150))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 326))) { + result[0] += 0.016910944; + } else { + result[0] += 0.002540021; + } + } else { + result[0] += -0.010653024; + } + } + } else { + result[0] += -0.056521356; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 384))) { + result[0] += 0.03409591; + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 34))) { + result[0] += -0; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 386))) { + result[0] += -0.0031993948; + } else { + result[0] += -0.02678071; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 254))) { + result[0] += 0.031704232; + } else { + result[0] += 0.005365833; + } + } + } + } + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 52))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 6))) { + result[0] += 0.0014249064; + } else { + result[0] += -0.01947671; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 94))) { + result[0] += 0.026210293; + } else { + result[0] += 0.003069733; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + result[0] += -0.02217819; + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 40))) { + result[0] += 0.023008201; + } else { + result[0] += 0.0086527765; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 14))) { + result[0] += 0.028070768; + } else { + result[0] += -0; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 16))) { + result[0] += -0.025724366; + } else { + result[0] += -0.008844643; + } + } + } else { + result[0] += -0.047070276; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 134))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 44))) { + result[0] += 0.004072014; + } else { + result[0] += -0.0108394185; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 170))) { + result[0] += 0.03304889; + } else { + result[0] += 0.0103535205; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 114))) { + result[0] += -0.023122396; + } else { + result[0] += -0.010224661; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 178))) { + result[0] += 0.001644756; + } else { + result[0] += -0.002623936; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { + result[0] += -0.0045392714; + } else { + result[0] += -0.025003826; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 318))) { + result[0] += 0.015446856; + } else { + result[0] += -0.034815937; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 34))) { + result[0] += -0.00036249825; + } else { + result[0] += 0.004029076; + } + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 86))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 48))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { + result[0] += 0.0035753883; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 160))) { + result[0] += -0.046707038; + } else { + result[0] += -0.026563475; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 136))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 48))) { + result[0] += 0.011157422; + } else { + result[0] += 0.025159726; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 228))) { + result[0] += -0.0022385176; + } else { + result[0] += 0.009932112; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 214))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 186))) { + result[0] += -0.0044850186; + } else { + result[0] += 0.007804101; + } + } else { + result[0] += 0.023426604; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 312))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 90))) { + result[0] += -0.0033166704; + } else { + result[0] += -0.010364393; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 142))) { + result[0] += -0.013235124; + } else { + result[0] += 0.015832936; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 104))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 98))) { + result[0] += 0.022907645; + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 158))) { + result[0] += 0.00681618; + } else { + result[0] += -0.012386585; + } + } + } else { + result[0] += 0.04736806; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + result[0] += -0.0017811867; + } else { + result[0] += 0.024726752; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 300))) { + result[0] += -0.024868438; + } else { + result[0] += -0.008288312; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 300))) { + result[0] += 0.023393013; + } else { + result[0] += 0.000140571; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { + result[0] += -0.0001585513; + } else { + result[0] += 0.0125155775; + } + } + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 400))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 136))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 106))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 100))) { + result[0] += 0.00032556072; + } else { + result[0] += 0.02669864; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 144))) { + result[0] += -0.01502426; + } else { + result[0] += -0.007827666; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 108))) { + result[0] += -0.042772368; + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { + result[0] += 0.0073166452; + } else { + result[0] += -0.003959548; + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 168))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 104))) { + result[0] += 0.00850385; + } else { + result[0] += -0.0007850647; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 174))) { + result[0] += 0.022124942; + } else { + result[0] += 0.0037423302; + } + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 12))) { + result[0] += -0.03591499; + } else { + result[0] += 0.04963535; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { + result[0] += -0.014330697; + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 142))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 140))) { + result[0] += 0.00258422; + } else { + result[0] += -0.011271856; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { + result[0] += 0.008900406; + } else { + result[0] += 0.025473619; + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 226))) { + result[0] += -0.013987194; + } else { + result[0] += -0.0071587036; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 404))) { + result[0] += -0.007607888; + } else { + result[0] += 0.021506222; + } + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 164))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { + result[0] += 0.017460436; + } else { + result[0] += -0.0049960585; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 48))) { + result[0] += 0.03522423; + } else { + result[0] += 0.0048457957; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 92))) { + result[0] += -0.025125725; + } else { + result[0] += -0; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 4))) { + result[0] += -0.058159776; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 292))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 80))) { + result[0] += -0.0017305056; + } else { + result[0] += -0.01667433; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 306))) { + result[0] += -0.027741238; + } else { + result[0] += 0.009779043; + } + } + } + } + } else { + result[0] += 0.009787267; + } + } + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 342))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 136))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 218))) { + result[0] += -0.00073015277; + } else { + result[0] += 0.0008299645; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { + result[0] += -0.003797351; + } else { + result[0] += -0.026151488; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 164))) { + result[0] += -0.0062180604; + } else { + result[0] += -0.018368756; + } + } else { + result[0] += 0.03389421; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 202))) { + result[0] += -0.001612652; + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 242))) { + result[0] += 0.029014328; + } else { + result[0] += 0.018497443; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 366))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 298))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 326))) { + result[0] += -0.02712582; + } else { + result[0] += -0.014455877; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 296))) { + result[0] += -0.017788123; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 306))) { + result[0] += 0.003964351; + } else { + result[0] += -0.0068141944; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 334))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 304))) { + result[0] += 0.046189606; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + result[0] += 0.024067063; + } else { + result[0] += -0; + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 56))) { + result[0] += 0.002387124; + } else { + result[0] += -0.021191007; + } + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 316))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 48))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 140))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { + result[0] += 0.017918406; + } else { + result[0] += 0.0007088012; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 90))) { + result[0] += 0.008024058; + } else { + result[0] += 0.03525953; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 50))) { + result[0] += -0.017354771; + } else { + result[0] += 0.011550955; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += 0.016495919; + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 50))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 82))) { + result[0] += 0.0069151595; + } else { + result[0] += -0.009860008; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { + result[0] += -0.02526379; + } else { + result[0] += -0.010914913; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { + result[0] += 0.023363909; + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 188))) { + result[0] += -0.017829457; + } else { + result[0] += 0.0066434382; + } + } else { + result[0] += 0.031978313; + } + } else { + result[0] += -0.026702777; + } + } + } + } + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 18))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 72))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 8))) { + result[0] += 0.0016082926; + } else { + result[0] += -0.01875299; + } + } else { + result[0] += 0.0011138814; + } + } else { + result[0] += -0.05503887; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 116))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += -0.034357756; + } else { + result[0] += -0.00018943613; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 10))) { + result[0] += -0.015174734; + } else { + result[0] += 0.006123707; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 54))) { + result[0] += -0; + } else { + result[0] += -0.032295134; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { + result[0] += 0.022839814; + } else { + result[0] += -0.0064965202; + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 114))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 78))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 10))) { + result[0] += -0; + } else { + result[0] += -0.025930112; + } + } else { + result[0] += -0.040734638; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { + result[0] += 0.0056574023; + } else { + result[0] += 0.045485523; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 30))) { + result[0] += -0.014559778; + } else { + result[0] += -0.0022656727; + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 178))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 22))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { + result[0] += 0.009212374; + } else { + result[0] += -0.023791295; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 122))) { + result[0] += -0; + } else { + result[0] += -0.030348688; + } + } + } else { + result[0] += 0.011739956; + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 116))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 318))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 80))) { + result[0] += 0.00070217456; + } else { + result[0] += -0.01572331; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 296))) { + result[0] += 0.0059441226; + } else { + result[0] += -0.015418323; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 86))) { + result[0] += 0.0038127878; + } else { + result[0] += -0.0063350433; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { + result[0] += 0.023485564; + } else { + result[0] += -0.0005745096; + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 28))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 396))) { + result[0] += -0.008549287; + } else { + result[0] += 0.03102727; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 76))) { + result[0] += 0.03656302; + } else { + result[0] += 0.0037815608; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { + result[0] += 0.036549788; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 80))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 56))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 202))) { + result[0] += -0.0316082; + } else { + result[0] += -0.011285119; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + result[0] += 0.044548456; + } else { + result[0] += -0.011524561; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + result[0] += -0.0036371031; + } else { + result[0] += 0.0054557663; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 250))) { + result[0] += -0.010413558; + } else { + result[0] += -0.005399991; + } + } + } + } + } + } + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 244))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 92))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 26))) { + result[0] += -0.02938572; + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 36))) { + result[0] += -0.0011291165; + } else { + result[0] += -0.023937851; + } + } else { + result[0] += 0.014182518; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 48))) { + result[0] += 0.09786916; + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 46))) { + result[0] += -0.039628785; + } else { + result[0] += -0; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 174))) { + result[0] += 0.008892446; + } else { + result[0] += -0.002574872; + } + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { + result[0] += 0.0067228116; + } else { + result[0] += 0.039215095; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 106))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 50))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + result[0] += -0.007054515; + } else { + result[0] += 0.035221007; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 274))) { + result[0] += -0.030953402; + } else { + result[0] += -0.008404141; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 146))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 106))) { + result[0] += -0.011643419; + } else { + result[0] += -0.05229321; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 288))) { + result[0] += -0.016430408; + } else { + result[0] += -0.039948575; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { + result[0] += -0.015282759; + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 80))) { + result[0] += 0.007892172; + } else { + result[0] += 0.078224584; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 148))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 4))) { + result[0] += -0.0037962466; + } else { + result[0] += -0.015912866; + } + } else { + result[0] += 0.013302639; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 340))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { + result[0] += -0.00012223067; + } else { + result[0] += -0.012624851; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 256))) { + result[0] += 0.027083063; + } else { + result[0] += 0.015543193; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 20))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 298))) { + result[0] += -0.020818194; + } else { + result[0] += -0.006904413; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 320))) { + result[0] += 0.049202304; + } else { + result[0] += 0.009114965; + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 34))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 82))) { + result[0] += 0.003950732; + } else { + result[0] += -0.011314066; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + result[0] += 0.0068105734; + } else { + result[0] += 0.028154967; + } + } + } else { + result[0] += -0.021689637; + } + } + } + } + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 296))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 232))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 6))) { + result[0] += -0.004839664; + } else { + result[0] += 0.00013240986; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 194))) { + result[0] += 0.012408934; + } else { + result[0] += -0.00019027379; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 318))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 260))) { + result[0] += -0.0021018793; + } else { + result[0] += 0.013699087; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 262))) { + result[0] += -0.046322387; + } else { + result[0] += -0; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 260))) { + result[0] += 0.02145579; + } else { + result[0] += 0.010288753; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 358))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 286))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 112))) { + result[0] += -0.026122052; + } else { + result[0] += -0.014107632; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 12))) { + result[0] += -0.004089996; + } else { + result[0] += -0.017688856; + } + } + } else { + result[0] += 0.021606075; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 12))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 326))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 200))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 166))) { + result[0] += -0.00194148; + } else { + result[0] += -0.012741702; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 210))) { + result[0] += 0.036462914; + } else { + result[0] += -0.008751659; + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 102))) { + result[0] += -0.0035663412; + } else { + result[0] += -0.02093569; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 0))) { + result[0] += 0.014931956; + } else { + result[0] += 0.038147915; + } + } else { + result[0] += 0.0034323465; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 14))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 88))) { + result[0] += -0; + } else { + result[0] += 0.02190816; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 60))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 54))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { + result[0] += 0.0031788598; + } else { + result[0] += -0.017724153; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 46))) { + result[0] += 0.00170696; + } else { + result[0] += -0.021163156; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 62))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 186))) { + result[0] += -0.015714565; + } else { + result[0] += 0.055118978; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 346))) { + result[0] += 0.0018268081; + } else { + result[0] += 0.012591888; + } + } + } + } + } + } + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 258))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 144))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 98))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += -0.005291102; + } else { + result[0] += -0.029125659; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 232))) { + result[0] += 0.003732911; + } else { + result[0] += -0.018547367; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 240))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 152))) { + result[0] += 0.0017839748; + } else { + result[0] += -0.008801985; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 118))) { + result[0] += 0.0042691375; + } else { + result[0] += 0.03554597; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 208))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 224))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 284))) { + result[0] += -0.003548905; + } else { + result[0] += -0.012224059; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 226))) { + result[0] += 0.0020274771; + } else { + result[0] += 0.019364875; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 180))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { + result[0] += 0.016102543; + } else { + result[0] += -0.013677455; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 248))) { + result[0] += -0.05339232; + } else { + result[0] += -0.00876738; + } + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 106))) { + result[0] += -0.013133674; + } else { + result[0] += -0.0521831; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 38))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { + result[0] += 0.008359418; + } else { + result[0] += 0.038568888; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 102))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 60))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 122))) { + result[0] += 0.0013149813; + } else { + result[0] += -0.0191629; + } + } else { + result[0] += -0.030204315; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 54))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 78))) { + result[0] += 0.024884596; + } else { + result[0] += 0.0094683515; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 50))) { + result[0] += 0.0068674767; + } else { + result[0] += -0.007323086; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 94))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 104))) { + result[0] += -0.0076974086; + } else { + result[0] += -0.029438093; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 110))) { + result[0] += 0.03408525; + } else { + result[0] += -0.006439855; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 62))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 76))) { + result[0] += 0.0068958416; + } else { + result[0] += 0.04007944; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { + result[0] += 0.00047964975; + } else { + result[0] += 0.0132646635; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 88))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 86))) { + result[0] += 0.0022136592; + } else { + result[0] += 0.026103059; + } + } else { + result[0] += -0.0099972095; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 120))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { + result[0] += -0.011941642; + } else { + result[0] += 0.0021292144; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 20))) { + result[0] += -0.014601464; + } else { + result[0] += -0.0005000638; + } + } + } + } + } + } + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 250))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 224))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 246))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 196))) { + result[0] += 0.000114969465; + } else { + result[0] += -0.0023860415; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 14))) { + result[0] += -0.035199355; + } else { + result[0] += -0.017500581; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 314))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + result[0] += 0.00627641; + } else { + result[0] += -0.0026602177; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { + result[0] += -0.008631534; + } else { + result[0] += 0.008578693; + } + } + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 30))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 216))) { + result[0] += 0.0028546187; + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 14))) { + result[0] += 0.014946878; + } else { + result[0] += 0.041622277; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 250))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + result[0] += 0.030365026; + } else { + result[0] += 0.005026304; + } + } else { + result[0] += -0.030540321; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + result[0] += -0.034515306; + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 150))) { + result[0] += 0.04119061; + } else { + result[0] += 0.018316824; + } + } else { + result[0] += -0.017891763; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 54))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 200))) { + result[0] += -0.028790927; + } else { + result[0] += -0.0069620805; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += 0.04099756; + } else { + result[0] += -0.003357871; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 260))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 118))) { + result[0] += -0.0026708224; + } else { + result[0] += -0.011060624; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + result[0] += 0.0011356926; + } else { + result[0] += 0.041363157; + } + } + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 190))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 188))) { + result[0] += 0.024280345; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 320))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 0))) { + result[0] += 0.010236905; + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 182))) { + result[0] += -0.02040131; + } else { + result[0] += -0.008407633; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 326))) { + result[0] += 0.012552517; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + result[0] += -0.021955565; + } else { + result[0] += -0.0035318558; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 120))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 302))) { + result[0] += 0.009495072; + } else { + result[0] += 0.00013142404; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { + result[0] += -0.006438227; + } else { + result[0] += -0.030158589; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 306))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 276))) { + result[0] += -0; + } else { + result[0] += 0.018983908; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { + result[0] += -0.044639092; + } else { + result[0] += 0.00017055031; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 60))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 346))) { + result[0] += -0.008808951; + } else { + result[0] += -0.032180067; + } + } else { + result[0] += 0.008250392; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 62))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 56))) { + result[0] += 0.056063425; + } else { + result[0] += -0.0065511647; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { + result[0] += -0.0092850765; + } else { + result[0] += 0.0021918796; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 30))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + result[0] += 0.007402791; + } else { + result[0] += -0.011684748; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += -0.006889455; + } else { + result[0] += 0.01800547; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 36))) { + result[0] += -0.024754832; + } else { + result[0] += -0.0028222399; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 30))) { + result[0] += -0.020031106; + } else { + result[0] += 0.0067768777; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 52))) { + result[0] += 0.041279774; + } else { + result[0] += 0.012748748; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 144))) { + result[0] += -0; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { + result[0] += 0.015250462; + } else { + result[0] += 0.026752282; + } + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 216))) { + result[0] += -0.02535589; + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 156))) { + result[0] += 0.006471269; + } else { + result[0] += 0.029563783; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 170))) { + result[0] += -0.025211362; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 186))) { + result[0] += -0.023914715; + } else { + result[0] += -0.009117383; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 154))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { + result[0] += 0.0012569004; + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 118))) { + result[0] += -0.008115943; + } else { + result[0] += -0.026224598; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 340))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 196))) { + result[0] += 0.042526796; + } else { + result[0] += 0.022222739; + } + } else { + result[0] += 0.012392859; + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 12))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { + result[0] += -0.028314574; + } else { + result[0] += -0.002670089; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { + result[0] += 0.0057236534; + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 120))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 50))) { + result[0] += -0.017261336; + } else { + result[0] += -0.007837756; + } + } else { + result[0] += -0.0055499817; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 216))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 78))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 56))) { + result[0] += -0.0009610457; + } else { + result[0] += 0.0076379874; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 16))) { + result[0] += 0.05212106; + } else { + result[0] += 0.02506253; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { + result[0] += -0.0006034739; + } else { + result[0] += 0.021560185; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { + result[0] += -0.034164276; + } else { + result[0] += -0.004662303; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 78))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { + result[0] += -0.001952683; + } else { + result[0] += 0.037713937; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 152))) { + result[0] += -0.008823862; + } else { + result[0] += -0.0014422481; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 76))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 242))) { + result[0] += 0.014311755; + } else { + result[0] += -0.018534161; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { + result[0] += 0.0027180712; + } else { + result[0] += -0.0037262347; + } + } + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 152))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 296))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 46))) { + result[0] += -0.00358946; + } else { + result[0] += 0.00086048665; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 298))) { + result[0] += -0.020779364; + } else { + result[0] += -0; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 184))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 34))) { + result[0] += -0.039312568; + } else { + result[0] += -0.005182448; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 82))) { + result[0] += 0.0015322726; + } else { + result[0] += -0.0016843865; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 218))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 312))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { + result[0] += -0.006095136; + } else { + result[0] += 0.028541317; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 188))) { + result[0] += 0.015441231; + } else { + result[0] += -0.01365337; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { + result[0] += 0.028764328; + } else { + result[0] += -0; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + result[0] += 0.01359984; + } else { + result[0] += -0.0053497027; + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 118))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 216))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 254))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 38))) { + result[0] += 0.0073710456; + } else { + result[0] += 0.00038945695; + } + } else { + result[0] += -0.00408677; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { + result[0] += -0.016966945; + } else { + result[0] += -0; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 130))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 106))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 70))) { + result[0] += 0.0059221475; + } else { + result[0] += 0.018299853; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { + result[0] += 0.005721331; + } else { + result[0] += -0.010450677; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 214))) { + result[0] += 0.03640103; + } else { + result[0] += 0.01191198; + } + } + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 96))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 88))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 76))) { + result[0] += -0.036111545; + } else { + result[0] += -0; + } + } else { + result[0] += 0.013476851; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 74))) { + result[0] += -0.02820078; + } else { + result[0] += -0.08100428; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 76))) { + result[0] += 0.001866552; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 48))) { + result[0] += -0.021744216; + } else { + result[0] += -0; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 48))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 42))) { + result[0] += -0.004817111; + } else { + result[0] += -0.019636374; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 70))) { + result[0] += 0.017743232; + } else { + result[0] += -0.010167501; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 66))) { + result[0] += -0.05105884; + } else { + result[0] += -0.011944066; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 102))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 68))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 24))) { + result[0] += -0; + } else { + result[0] += 0.02911661; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 66))) { + result[0] += -0.010146369; + } else { + result[0] += 0.009323521; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { + result[0] += -0.03494624; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { + result[0] += -0.003336961; + } else { + result[0] += -0.02113694; + } + } + } + } + } + } + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 92))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 166))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 114))) { + result[0] += -0.0013070774; + } else { + result[0] += 0.0005617281; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { + result[0] += -0.00045852616; + } else { + result[0] += 0.021694412; + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 190))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 54))) { + result[0] += -0.009019866; + } else { + result[0] += 0.0010552766; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 382))) { + result[0] += 0.042176425; + } else { + result[0] += -0.019407976; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 54))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 94))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 22))) { + result[0] += -0.008323395; + } else { + result[0] += -0.0172386; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 96))) { + result[0] += 0.0036288549; + } else { + result[0] += -0.013044089; + } + } + } else { + result[0] += 0.00021139935; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 392))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 158))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 74))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 78))) { + result[0] += -0.00079369405; + } else { + result[0] += 0.04720639; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 390))) { + result[0] += 0.05325351; + } else { + result[0] += -0.043466292; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 66))) { + result[0] += -0.032283835; + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 198))) { + result[0] += -0.0008110626; + } else { + result[0] += 0.050249096; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 68))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 158))) { + result[0] += -0.010387595; + } else { + result[0] += -0.0034609255; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 110))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 394))) { + result[0] += 0.0030221925; + } else { + result[0] += 0.028211419; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 114))) { + result[0] += -0.0093119275; + } else { + result[0] += 0.0069152773; + } + } + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 164))) { + result[0] += -0.009060017; + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 128))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 126))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 46))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { + result[0] += -0.006120693; + } else { + result[0] += 0.009823619; + } + } else { + result[0] += -0.009353681; + } + } else { + result[0] += -0.0127736945; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 400))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { + result[0] += 0.013670566; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 86))) { + result[0] += -0.004946747; + } else { + result[0] += 0.0048705908; + } + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 136))) { + result[0] += -0.010391137; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 92))) { + result[0] += 0.004601255; + } else { + result[0] += -0.0049207225; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 22))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 112))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { + result[0] += -0.018509652; + } else { + result[0] += -0.0028016684; + } + } else { + result[0] += 0.012803587; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 14))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.013896561; + } else { + result[0] += -0.009757071; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 2))) { + result[0] += 0.035226237; + } else { + result[0] += 0.003883094; + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 108))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 72))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { + result[0] += 0.009042918; + } else { + result[0] += 0.03158817; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 138))) { + result[0] += 0.007958132; + } else { + result[0] += 0.03468901; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 146))) { + result[0] += -0.0020058847; + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 174))) { + result[0] += 0.026864871; + } else { + result[0] += 0.014916946; + } + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 216))) { + result[0] += -0.022077216; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { + result[0] += 0.007497831; + } else { + result[0] += 0.028885541; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 250))) { + result[0] += -0.02360869; + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 174))) { + result[0] += -0.018704783; + } else { + result[0] += -0.0076525756; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 294))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + result[0] += 9.27179e-05; + } else { + result[0] += 0.031941853; + } + } else { + result[0] += 0.009628982; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 350))) { + result[0] += -0.021527562; + } else { + result[0] += -0.0072527933; + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 74))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 104))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 70))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 60))) { + result[0] += 0.02288001; + } else { + result[0] += -0.016261838; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 146))) { + result[0] += -0.03769898; + } else { + result[0] += -0.018141152; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 46))) { + result[0] += -0.0023420886; + } else { + result[0] += 0.021080358; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 140))) { + result[0] += -0.012038226; + } else { + result[0] += 0.008399843; + } + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 72))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { + result[0] += 0.002315519; + } else { + result[0] += 0.062828414; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { + result[0] += 0.0130506335; + } else { + result[0] += -0.007596428; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 40))) { + result[0] += 0.007198368; + } else { + result[0] += -0.0058715134; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + result[0] += 0.02399823; + } else { + result[0] += -0.008211115; + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 84))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 26))) { + result[0] += 0.02461793; + } else { + result[0] += 0.008668258; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 28))) { + result[0] += -0.030365562; + } else { + result[0] += 0.010669919; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 94))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 16))) { + result[0] += -0.018534234; + } else { + result[0] += -0.0031345394; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + result[0] += -0.010914135; + } else { + result[0] += -0.00012393964; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 214))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 88))) { + result[0] += 0.022872802; + } else { + result[0] += 0.0050913426; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 132))) { + result[0] += 0.0009941938; + } else { + result[0] += -0.0025577117; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += 0.012239212; + } else { + result[0] += 0.032537233; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 74))) { + result[0] += -0.012231956; + } else { + result[0] += 0.00096511666; + } + } + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 244))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 50))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 100))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 190))) { + result[0] += -0.015980741; + } else { + result[0] += 0.013686332; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 106))) { + result[0] += 0.030151868; + } else { + result[0] += 0.008428541; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 192))) { + result[0] += 0.001345482; + } else { + result[0] += 0.02078061; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 126))) { + result[0] += -0.029191677; + } else { + result[0] += 0.00064531795; + } + } + } + } else { + result[0] += 0.035184488; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 30))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 212))) { + result[0] += 0.009157064; + } else { + result[0] += -0.03418844; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { + result[0] += -0.01716392; + } else { + result[0] += -0.045232523; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { + result[0] += -0.01179669; + } else { + result[0] += 0.06294518; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 140))) { + result[0] += -0.011332896; + } else { + result[0] += 0.01226867; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { + result[0] += 0.0011395406; + } else { + result[0] += -0.0025050612; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 138))) { + result[0] += -0.037857305; + } else { + result[0] += -0.00218284; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 226))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 102))) { + result[0] += -0.0008166955; + } else { + result[0] += 0.004205488; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.0056326287; + } else { + result[0] += 0.0007912867; + } + } + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 6))) { + result[0] += 0.023435187; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 40))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 42))) { + result[0] += 0.03141637; + } else { + result[0] += -0.0015305742; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { + result[0] += -0.017991172; + } else { + result[0] += 0.002195324; + } + } else { + result[0] += 0.013269196; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 44))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 120))) { + result[0] += -0.01257918; + } else { + result[0] += 0.0010349855; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 154))) { + result[0] += -0.051812388; + } else { + result[0] += -0.010208193; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 30))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 278))) { + result[0] += 0.0073652193; + } else { + result[0] += -0.013559197; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 56))) { + result[0] += -0.02485094; + } else { + result[0] += -0.0037162665; + } + } + } + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 42))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 2))) { + result[0] += -0.034541044; + } else { + result[0] += 0.0027651105; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { + result[0] += -0.019249316; + } else { + result[0] += -0.0034680355; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 8))) { + result[0] += -0.02443984; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += 0.013286717; + } else { + result[0] += -0.0025256465; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 46))) { + result[0] += 0.0061984803; + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 48))) { + result[0] += 0.079341814; + } else { + result[0] += 0.025329694; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 6))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 126))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { + result[0] += 0.021308701; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 118))) { + result[0] += -0.006669204; + } else { + result[0] += -0.019334236; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 140))) { + result[0] += 0.04223214; + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 74))) { + result[0] += -0.01822163; + } else { + result[0] += 0.011897653; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 12))) { + result[0] += -0.03937181; + } else { + result[0] += -0.021595992; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 58))) { + result[0] += 0.008440125; + } else { + result[0] += -0.018780667; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 32))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { + result[0] += -0.0025710927; + } else { + result[0] += -0.015579129; + } + } else { + result[0] += 0.013569686; + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 118))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 10))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 102))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 90))) { + result[0] += -0.008451057; + } else { + result[0] += 0.010793014; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 244))) { + result[0] += -0.007276828; + } else { + result[0] += -0.00065764156; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { + result[0] += 0.00065685576; + } else { + result[0] += 0.025356445; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 142))) { + result[0] += -0.012041635; + } else { + result[0] += -0.05446145; + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 120))) { + result[0] += 0.044229705; + } else { + result[0] += 0.0006534785; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 30))) { + result[0] += 0.005015543; + } else { + result[0] += -0.01020183; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += 0.06759548; + } else { + result[0] += 0.007752572; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { + result[0] += 0.011832262; + } else { + result[0] += 0.0015743548; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 208))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 196))) { + result[0] += -0.0024539565; + } else { + result[0] += 0.001751725; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { + result[0] += -0; + } else { + result[0] += 0.021780172; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 66))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 272))) { + result[0] += 0.008948053; + } else { + result[0] += -0.0063331993; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { + result[0] += -0.0073422543; + } else { + result[0] += -0.026588783; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 210))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 104))) { + result[0] += 0.0029568898; + } else { + result[0] += -0.042533282; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { + result[0] += -0.0074031386; + } else { + result[0] += 0.014356777; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + result[0] += 0.00092436554; + } else { + result[0] += 0.024809679; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 326))) { + result[0] += -0.00329214; + } else { + result[0] += 0.00526762; + } + } + } + } + } + } + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 142))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 10))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 200))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 72))) { + result[0] += 0.0010467954; + } else { + result[0] += 0.030300766; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 62))) { + result[0] += 0.02035891; + } else { + result[0] += -0.010212629; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { + result[0] += -0.001989516; + } else { + result[0] += -0.013169396; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 346))) { + result[0] += 0.020909293; + } else { + result[0] += -0.0079391515; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 306))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 262))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 116))) { + result[0] += -0.0052688015; + } else { + result[0] += 0.0019646974; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 286))) { + result[0] += -0.031940565; + } else { + result[0] += -0; + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 322))) { + result[0] += 0.020321852; + } else { + result[0] += 0.034146465; + } + } else { + result[0] += -0.03401268; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 118))) { + result[0] += 0.0016832197; + } else { + result[0] += 0.027038325; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 166))) { + result[0] += -0.0071495273; + } else { + result[0] += -0.0014786319; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 258))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { + result[0] += 0.01780109; + } else { + result[0] += -0.01263422; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 228))) { + result[0] += 0.039167188; + } else { + result[0] += 0.018858656; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 42))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 26))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { + result[0] += -0.0019396268; + } else { + result[0] += 0.005724409; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 6))) { + result[0] += 0.027276058; + } else { + result[0] += 0.008047543; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 130))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { + result[0] += -0.012420956; + } else { + result[0] += -0.0021616125; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + result[0] += -0.0018222294; + } else { + result[0] += 0.0021800934; + } + } + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 150))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 92))) { + result[0] += -0.006505067; + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 220))) { + result[0] += 0.040462743; + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 46))) { + result[0] += 0.015701324; + } else { + result[0] += -0.0201608; + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 116))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 52))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 94))) { + result[0] += 0.0005646117; + } else { + result[0] += 0.010826596; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + result[0] += -0.013457968; + } else { + result[0] += -1.2906838e-05; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += -0.0008355248; + } else { + result[0] += -0.0226526; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 92))) { + result[0] += 0.020389868; + } else { + result[0] += 0.0054127253; + } + } + } + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 14))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 76))) { + result[0] += -0.032427344; + } else { + result[0] += -0; + } + } else { + result[0] += 0.04492506; + } + } + } + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 40))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 8))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { + result[0] += -0.008939014; + } else { + result[0] += -0.031956512; + } + } else { + result[0] += 0.017092446; + } + } else { + result[0] += 0.022510538; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 4))) { + result[0] += 0.042899013; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + result[0] += -0.0038211767; + } else { + result[0] += 0.029483128; + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 72))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 160))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 82))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { + result[0] += -0.007246212; + } else { + result[0] += 0.0062661204; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 88))) { + result[0] += -0.02025948; + } else { + result[0] += -0.005702542; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 116))) { + result[0] += -0.030373631; + } else { + result[0] += -0.004761403; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 90))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { + result[0] += 0.010341472; + } else { + result[0] += 0.0385042; + } + } else { + result[0] += -0.0025437989; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 204))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + result[0] += -0.0027436535; + } else { + result[0] += -0.020981843; + } + } else { + result[0] += 0.011424384; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { + result[0] += -0.026424287; + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 22))) { + result[0] += 0.015828108; + } else { + result[0] += -0.004946768; + } + } + } else { + result[0] += 0.036206927; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 0))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 46))) { + result[0] += -0.0018861439; + } else { + result[0] += 0.019979624; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 16))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 64))) { + result[0] += -0.012420944; + } else { + result[0] += -0.04097703; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 46))) { + result[0] += -0.014519716; + } else { + result[0] += 0.0064661787; + } + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 116))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { + result[0] += -0.0007507774; + } else { + result[0] += 0.011509613; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 74))) { + result[0] += 0.0015937341; + } else { + result[0] += 0.026918387; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 268))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 76))) { + result[0] += -0.001206148; + } else { + result[0] += -0.01964714; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 98))) { + result[0] += 0.008147176; + } else { + result[0] += -0.0026112716; + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 92))) { + result[0] += -0.00044672118; + } else { + result[0] += -0.009276496; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 156))) { + result[0] += 0.039460566; + } else { + result[0] += -0.0033604186; + } + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 148))) { + result[0] += -0.0112617565; + } else { + result[0] += -0.0053185816; + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 16))) { + result[0] += -0.027565295; + } else { + result[0] += -0.004120046; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + result[0] += -0.0030114742; + } else { + result[0] += 0.010990894; + } + } + } else { + result[0] += -0.018377742; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + result[0] += 0.0032660365; + } else { + result[0] += 0.014772459; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 240))) { + result[0] += 0.01916596; + } else { + result[0] += 0.043604817; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 188))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { + result[0] += 0.020365; + } else { + result[0] += -0.004660358; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 182))) { + result[0] += -0.026131554; + } else { + result[0] += -0.012585315; + } + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 208))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 144))) { + result[0] += -0.009168918; + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 186))) { + result[0] += -0.017280573; + } else { + result[0] += 0.013135247; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 294))) { + result[0] += 0.027347783; + } else { + result[0] += 0.0058005466; + } + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 228))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 262))) { + result[0] += -0.01863461; + } else { + result[0] += -0.009977842; + } + } else { + result[0] += 0.0074623013; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 18))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 82))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 10))) { + result[0] += -0.027271813; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + result[0] += 0.03428016; + } else { + result[0] += 0.005898777; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 124))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 120))) { + result[0] += -0.0031157867; + } else { + result[0] += 0.012381009; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 40))) { + result[0] += -0.032176975; + } else { + result[0] += -0.0047460934; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 128))) { + result[0] += -0.011950021; + } else { + result[0] += -0.029780094; + } + } else { + result[0] += -0.038288817; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 14))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 58))) { + result[0] += -0.013251813; + } else { + result[0] += 0.009832563; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 40))) { + result[0] += -0.0028266981; + } else { + result[0] += 0.010589391; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 26))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 0))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 144))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 8))) { + result[0] += -0; + } else { + result[0] += 0.02299802; + } + } else { + result[0] += -0.0005336401; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 16))) { + result[0] += 0.024464216; + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 24))) { + result[0] += -0.02506329; + } else { + result[0] += -0.00617628; + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 146))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 228))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 168))) { + result[0] += 0.000546928; + } else { + result[0] += -0.0015107197; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 244))) { + result[0] += 0.0052600866; + } else { + result[0] += 0.0005289404; + } + } + } else { + result[0] += -0.0067100944; + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 92))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 56))) { + result[0] += 0.000776489; + } else { + result[0] += -0.00053459004; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.0025825037; + } else { + result[0] += 0.033680957; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 54))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 94))) { + result[0] += -0.010514084; + } else { + result[0] += 0.00012713655; + } + } else { + result[0] += -8.464566e-05; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 192))) { + result[0] += -0.008038435; + } else { + result[0] += 0.03207838; + } + } else { + result[0] += -0.0059311604; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 196))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 104))) { + result[0] += -0.0027816568; + } else { + result[0] += 0.008232006; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + result[0] += 0.03486943; + } else { + result[0] += -0.009670003; + } + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 78))) { + result[0] += -0.021940826; + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 160))) { + result[0] += -0.007266288; + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 128))) { + result[0] += -0.004397231; + } else { + result[0] += 0.0011502573; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { + result[0] += -0.005171144; + } else { + result[0] += 0.009149543; + } + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 132))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 68))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + result[0] += 0.033398908; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 34))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 146))) { + result[0] += -0.011877987; + } else { + result[0] += 0.009033947; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 32))) { + result[0] += 0.017914457; + } else { + result[0] += 0.00086382544; + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 114))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 106))) { + result[0] += -0; + } else { + result[0] += -0.025332725; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 136))) { + result[0] += 0.010888358; + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 132))) { + result[0] += -0.002672884; + } else { + result[0] += -0.0126517145; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { + result[0] += -0.01176235; + } else { + result[0] += -0.04480323; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 132))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 80))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 146))) { + result[0] += 0.022533778; + } else { + result[0] += 0.0015053398; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { + result[0] += -0.029711738; + } else { + result[0] += -0.00018641823; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 194))) { + result[0] += -0.0029512055; + } else { + result[0] += -0.014446958; + } + } else { + result[0] += -0.04525297; + } + } + } + } + } + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 160))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 116))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 126))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 108))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 104))) { + result[0] += 0.0049565816; + } else { + result[0] += 0.063526; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 114))) { + result[0] += -0.019255767; + } else { + result[0] += 0.0012636579; + } + } + } else { + result[0] += 0.008695122; + } + } else { + result[0] += 0.019830158; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 190))) { + result[0] += -0.007866302; + } else { + result[0] += 0.012916532; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 30))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { + result[0] += 0.06366762; + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 98))) { + result[0] += -0.004975738; + } else { + result[0] += -0.026275873; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 238))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 148))) { + result[0] += -0.009829768; + } else { + result[0] += -0.028741485; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { + result[0] += -0.0046568112; + } else { + result[0] += -0.028392253; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { + result[0] += -0.01022596; + } else { + result[0] += 0.057439942; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 10))) { + result[0] += -0.005370389; + } else { + result[0] += -0.019819807; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 70))) { + result[0] += 0.011075004; + } else { + result[0] += -0.0060161785; + } + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 158))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 20))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { + result[0] += 0.062947415; + } else { + result[0] += 0.0032754538; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 146))) { + result[0] += -0.0023154307; + } else { + result[0] += 0.013836692; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { + result[0] += -0.0051080496; + } else { + result[0] += -0.026022715; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 130))) { + result[0] += -0.021521274; + } else { + result[0] += 0.0008544709; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 44))) { + result[0] += 0.011564689; + } else { + result[0] += 0.039744; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 134))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 202))) { + result[0] += 0.00052881095; + } else { + result[0] += -0.0008797978; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 56))) { + result[0] += 0.002648249; + } else { + result[0] += 0.02056101; + } + } + } + } + } + } + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 160))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 142))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 114))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 92))) { + result[0] += -0.0069403; + } else { + result[0] += 0.0036496245; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { + result[0] += -0.041129064; + } else { + result[0] += -0.007193537; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 138))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { + result[0] += -0.016804745; + } else { + result[0] += -0.035601314; + } + } else { + result[0] += 0.0045456374; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 30))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 154))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { + result[0] += 0.051934537; + } else { + result[0] += -0.0017292331; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 18))) { + result[0] += -0.018062782; + } else { + result[0] += -0.0065657706; + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 36))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 12))) { + result[0] += -0.0018053079; + } else { + result[0] += 0.008867439; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + result[0] += 0.024381882; + } else { + result[0] += 0.0078328205; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 194))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 126))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 118))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { + result[0] += -0.0050182706; + } else { + result[0] += 0.0074505755; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 48))) { + result[0] += -0.027363313; + } else { + result[0] += -0.008215737; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 238))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 132))) { + result[0] += 0.0108621; + } else { + result[0] += 0.001027461; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { + result[0] += 0.0018521305; + } else { + result[0] += -0.019366777; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 196))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 66))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 42))) { + result[0] += -0.021509835; + } else { + result[0] += -0.0051732687; + } + } else { + result[0] += -0.036974095; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 10))) { + result[0] += 0.013641315; + } else { + result[0] += -0.0012665674; + } + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 168))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 110))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 382))) { + result[0] += -0.0007951208; + } else { + result[0] += -0.012272722; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { + result[0] += 0.013259816; + } else { + result[0] += 0.00076070236; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 124))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 86))) { + result[0] += -0.023466855; + } else { + result[0] += -0.0015092399; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + result[0] += 0.0067120204; + } else { + result[0] += -0.0027905267; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 180))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 132))) { + result[0] += 0.0012523715; + } else { + result[0] += 0.019288411; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 72))) { + result[0] += -0.0024032074; + } else { + result[0] += 0.020834908; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 100))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 302))) { + result[0] += -0.0045833127; + } else { + result[0] += 0.002748456; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + result[0] += -0.00035413975; + } else { + result[0] += 0.0021194927; + } + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 352))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 196))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 162))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 36))) { + result[0] += 0.012191023; + } else { + result[0] += -0; + } + } else { + result[0] += 0.026147142; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { + result[0] += -0.0044075255; + } else { + result[0] += -0.018723859; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 368))) { + result[0] += 0.02115663; + } else { + result[0] += -0.008576654; + } + } + } + } + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 116))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 312))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 78))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 146))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 108))) { + result[0] += -0.0011369804; + } else { + result[0] += 0.007111168; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { + result[0] += -0.0066426345; + } else { + result[0] += 0.004026822; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 166))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 126))) { + result[0] += -0.016191296; + } else { + result[0] += -0.008390819; + } + } else { + result[0] += 0.039027248; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 338))) { + result[0] += 0.011132075; + } else { + result[0] += 0.00045876933; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 108))) { + result[0] += 0.03038956; + } else { + result[0] += 0.005012113; + } + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 128))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 126))) { + result[0] += -0.002966301; + } else { + result[0] += -0.012031978; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 400))) { + result[0] += 0.0043450063; + } else { + result[0] += -0.0033799019; + } + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 74))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 72))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 172))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { + result[0] += 0.00018884889; + } else { + result[0] += 0.004433022; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 140))) { + result[0] += -0.0016364238; + } else { + result[0] += 0.011106547; + } + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 34))) { + result[0] += 0.010573897; + } else { + result[0] += -0.046158385; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { + result[0] += 0.025685733; + } else { + result[0] += -0.012269548; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 298))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 258))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 328))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 132))) { + result[0] += 0.012633822; + } else { + result[0] += -0.004591215; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 68))) { + result[0] += 0.027154177; + } else { + result[0] += -0.0073612966; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { + result[0] += -0.009671869; + } else { + result[0] += 0.034116954; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 82))) { + result[0] += 0.003360726; + } else { + result[0] += -0.0010045286; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 242))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 80))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 294))) { + result[0] += -0.0067084306; + } else { + result[0] += 0.017508617; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 52))) { + result[0] += -0.025245184; + } else { + result[0] += -0.009711; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 266))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { + result[0] += 0.0024178706; + } else { + result[0] += 0.02038781; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { + result[0] += -0.005333036; + } else { + result[0] += -0.021810295; + } + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 314))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 148))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 44))) { + result[0] += 0.002272234; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { + result[0] += 0.0017241904; + } else { + result[0] += 0.014623227; + } + } + } else { + result[0] += -0.0057367506; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 140))) { + result[0] += -0.029135648; + } else { + result[0] += -0.0059048724; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 80))) { + result[0] += 0.0016456817; + } else { + result[0] += 0.036199924; + } + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 314))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 312))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + result[0] += 5.137796e-05; + } else { + result[0] += 0.016802512; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { + result[0] += -0.007183667; + } else { + result[0] += 0.0086045405; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { + result[0] += -0.0025281047; + } else { + result[0] += -0.022966359; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 250))) { + result[0] += 0.023195243; + } else { + result[0] += 0.003728645; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 186))) { + result[0] += 0.008694565; + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + result[0] += -0.012267268; + } else { + result[0] += 0.012734731; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { + result[0] += 0.023913363; + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 228))) { + result[0] += -0.0029125137; + } else { + result[0] += -0.030048529; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 226))) { + result[0] += -0; + } else { + result[0] += 0.04022684; + } + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 296))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 8))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 2))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + result[0] += 0.010619841; + } else { + result[0] += -0.015812444; + } + } else { + result[0] += 0.036461692; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 10))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 92))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 10))) { + result[0] += 0.006464646; + } else { + result[0] += -0.03794737; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { + result[0] += -0.014366518; + } else { + result[0] += 0.00065602706; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += -0.036709867; + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 26))) { + result[0] += 0.009573085; + } else { + result[0] += -0.0042258967; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { + result[0] += 0.025289237; + } else { + result[0] += 0.0031290874; + } + } + } + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 32))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 314))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 176))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 38))) { + result[0] += 0.0070433277; + } else { + result[0] += -0.0021675983; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += 0.00069358293; + } else { + result[0] += 0.014411396; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 224))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 182))) { + result[0] += -0.021365121; + } else { + result[0] += -0.009108902; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 258))) { + result[0] += -0.002070323; + } else { + result[0] += -0.0070032175; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 58))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 116))) { + result[0] += 0.015421606; + } else { + result[0] += 0.0065408074; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 68))) { + result[0] += -0.00017713972; + } else { + result[0] += 0.0036349527; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 66))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 112))) { + result[0] += -0.015431674; + } else { + result[0] += 0.0069513856; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 86))) { + result[0] += -0.0017071629; + } else { + result[0] += 0.0010942215; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 60))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 328))) { + result[0] += -0.018242605; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 114))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 334))) { + result[0] += 0.013435415; + } else { + result[0] += 0.000363245; + } + } else { + result[0] += -0.011918341; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 62))) { + result[0] += 0.057550073; + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 64))) { + result[0] += -0.010455087; + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { + result[0] += 0.025410349; + } else { + result[0] += 0.0067503005; + } + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 48))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 4))) { + result[0] += 0.0038494274; + } else { + result[0] += -0.012583888; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { + result[0] += 0.0052445154; + } else { + result[0] += 0.022994613; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 110))) { + result[0] += 0.010131297; + } else { + result[0] += 0.030005908; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 36))) { + result[0] += -0.015509938; + } else { + result[0] += 0.033259317; + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 256))) { + result[0] += -0.018772317; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 324))) { + result[0] += -0.0010802757; + } else { + result[0] += -0.019551003; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { + result[0] += 0.015505517; + } else { + result[0] += -0.014615647; + } + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 228))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 266))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { + result[0] += -0.0011533442; + } else { + result[0] += 0.0037619993; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 42))) { + result[0] += -0.0050083552; + } else { + result[0] += -0.020327281; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += -0.01845751; + } else { + result[0] += -0.0027648432; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 270))) { + result[0] += 0.014990672; + } else { + result[0] += -0.0010248877; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { + result[0] += -0.00588067; + } else { + result[0] += -0.0011169165; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + result[0] += 0.0015550206; + } else { + result[0] += -0.0028203435; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 10))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { + result[0] += 0.00057445857; + } else { + result[0] += -0.020817637; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { + result[0] += 0.004375774; + } else { + result[0] += -0.00017658187; + } + } + } + } + } + } + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 116))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 92))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 40))) { + result[0] += -0.029479165; + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 36))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { + result[0] += -0.0015629175; + } else { + result[0] += 0.014696643; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { + result[0] += -0.012695557; + } else { + result[0] += 0.002633101; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 38))) { + result[0] += 0.088580444; + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 18))) { + result[0] += -0.0013535247; + } else { + result[0] += -0.041627154; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 4))) { + result[0] += 0.010081653; + } else { + result[0] += 0.0011864647; + } + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 158))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { + result[0] += 0.024709804; + } else { + result[0] += 0.006805406; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { + result[0] += -0.020788606; + } else { + result[0] += 0.010531965; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 22))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { + result[0] += 0.029835364; + } else { + result[0] += 0.072482795; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 98))) { + result[0] += -0.0033422098; + } else { + result[0] += -0.027484313; + } + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 198))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 130))) { + result[0] += -0.0117129935; + } else { + result[0] += -0.027794067; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { + result[0] += -0.0058866264; + } else { + result[0] += -0.022589795; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 96))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { + result[0] += -0.007361448; + } else { + result[0] += 0.05226938; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 152))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { + result[0] += -0.0011431018; + } else { + result[0] += -0.013976167; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 34))) { + result[0] += -0; + } else { + result[0] += 0.0123240035; + } + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 16))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 130))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 42))) { + result[0] += 0.00010176472; + } else { + result[0] += 0.0130502; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + result[0] += -0.00884224; + } else { + result[0] += -0.0399912; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 102))) { + result[0] += -0.0070857047; + } else { + result[0] += 0.0050304467; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + result[0] += -0.018932706; + } else { + result[0] += -0.0011952891; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { + result[0] += 0.03236731; + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 66))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { + result[0] += 0.00022758842; + } else { + result[0] += 0.0045965556; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { + result[0] += 0.0054974416; + } else { + result[0] += -0.00038611452; + } + } + } + } + } + } + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 128))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 118))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 66))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 140))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + result[0] += -0.030644778; + } else { + result[0] += -0.0012927776; + } + } else { + result[0] += -0.027898073; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 270))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 10))) { + result[0] += -0.0026217196; + } else { + result[0] += 0.00031906084; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { + result[0] += 0.053565513; + } else { + result[0] += 0.011517114; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 242))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 206))) { + result[0] += 0.0022491028; + } else { + result[0] += 0.017666435; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 48))) { + result[0] += -0.039555483; + } else { + result[0] += -0.007261213; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 24))) { + result[0] += -0.0013352408; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 194))) { + result[0] += 0.019903926; + } else { + result[0] += -0.0018211514; + } + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 98))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 260))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { + result[0] += -0.003761566; + } else { + result[0] += 5.4693035e-05; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 44))) { + result[0] += 0.066539966; + } else { + result[0] += 0.0024916714; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 282))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { + result[0] += 0.0024568306; + } else { + result[0] += 0.013490746; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 144))) { + result[0] += 0.0017763426; + } else { + result[0] += -0.0017636567; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 66))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += -0.016284099; + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { + result[0] += 0.022716116; + } else { + result[0] += 0.0030027088; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 108))) { + result[0] += -0.008329183; + } else { + result[0] += -0.02752207; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { + result[0] += 0.024196664; + } else { + result[0] += -0.0069028423; + } + } + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 268))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 266))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 150))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { + result[0] += 0.03250914; + } else { + result[0] += 0.0055702133; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { + result[0] += 0.008322871; + } else { + result[0] += -0.015406762; + } + } + } else { + result[0] += 0.0395282; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 262))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 260))) { + result[0] += -0.018292183; + } else { + result[0] += -0; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 294))) { + result[0] += -0.0053772978; + } else { + result[0] += 0.0050149076; + } + } + } else { + result[0] += 0.006939339; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 88))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 282))) { + result[0] += 0.00481271; + } else { + result[0] += -0.0038877416; + } + } else { + result[0] += -0.0105433585; + } + } + } + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 320))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 284))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 212))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 252))) { + result[0] += 8.760832e-05; + } else { + result[0] += 0.0031985168; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 250))) { + result[0] += -0.0029068196; + } else { + result[0] += 0.0005925631; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 242))) { + result[0] += 0.019893859; + } else { + result[0] += 0.009958875; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 266))) { + result[0] += -0.017648466; + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 304))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 302))) { + result[0] += -0.011696982; + } else { + result[0] += 0.004698274; + } + } else { + result[0] += -0.0020677263; + } + } + } + } else { + result[0] += 0.04254042; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 162))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 168))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 122))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 76))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 98))) { + result[0] += -0.0029215065; + } else { + result[0] += 0.011221336; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 112))) { + result[0] += 0.020535378; + } else { + result[0] += 0.0062516048; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 62))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 302))) { + result[0] += -0.0063436194; + } else { + result[0] += -0.01713322; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 162))) { + result[0] += 0.010334019; + } else { + result[0] += -0.0069331317; + } + } + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 322))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 182))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 164))) { + result[0] += -0.0077398308; + } else { + result[0] += -0.03391137; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 190))) { + result[0] += 0.018701833; + } else { + result[0] += 0.005074762; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 192))) { + result[0] += 0.05592671; + } else { + result[0] += 0.012150154; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 138))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 44))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 84))) { + result[0] += -0.004530545; + } else { + result[0] += -0.014109983; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 34))) { + result[0] += 0.016917644; + } else { + result[0] += -0.0003956896; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 168))) { + result[0] += 0.018351296; + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 222))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 326))) { + result[0] += 0.009391616; + } else { + result[0] += -0.012847346; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + result[0] += 0.0028710503; + } else { + result[0] += -0.014146092; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 24))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { + result[0] += 0.004130573; + } else { + result[0] += -0.02545332; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 136))) { + result[0] += -0.011545375; + } else { + result[0] += 0.0030030604; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 124))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 76))) { + result[0] += 0.0053757923; + } else { + result[0] += 0.026795724; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += 0.0073435195; + } else { + result[0] += -0.011538415; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { + result[0] += 0.010773177; + } else { + result[0] += -0.0014024094; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 90))) { + result[0] += -0.00650954; + } else { + result[0] += 0.017750045; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 280))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 256))) { + result[0] += -0.01760956; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 184))) { + result[0] += -0.017441498; + } else { + result[0] += -0.0037122124; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 216))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 182))) { + result[0] += -0.024908429; + } else { + result[0] += -0.012610437; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { + result[0] += 0.020257859; + } else { + result[0] += -0.0005201062; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 20))) { + result[0] += 0.031078367; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 338))) { + result[0] += -0.03464001; + } else { + result[0] += 0.020424169; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { + result[0] += 0.0028230688; + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 114))) { + result[0] += -0.003942101; + } else { + result[0] += -0.017917613; + } + } + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 12))) { + result[0] += -0.018274682; + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 88))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { + result[0] += -0.014930627; + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 22))) { + result[0] += 0.009653761; + } else { + result[0] += -0.005647427; + } + } + } else { + result[0] += -0.017578453; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 216))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 116))) { + result[0] += -0.004387402; + } else { + result[0] += 0.004307063; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 244))) { + result[0] += 0.0050197043; + } else { + result[0] += -0.009600091; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { + result[0] += -0.0004044637; + } else { + result[0] += 0.013372946; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 138))) { + result[0] += -0.030934876; + } else { + result[0] += -0.005925291; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 6))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { + result[0] += -0.018131623; + } else { + result[0] += 0.007491207; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 158))) { + result[0] += 0.00448369; + } else { + result[0] += -0.004200383; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 124))) { + result[0] += 0.00013818998; + } else { + result[0] += -0.0060928585; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 226))) { + result[0] += 0.003693558; + } else { + result[0] += -0.0001647349; + } + } + } + } + } + } + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 400))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 388))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 346))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { + result[0] += 0.00025481472; + } else { + result[0] += 0.015072713; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 56))) { + result[0] += -0.0068883398; + } else { + result[0] += 0.005798895; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + result[0] += -0.012064995; + } else { + result[0] += -0.00055150193; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 236))) { + result[0] += 0.025693614; + } else { + result[0] += -0.0082374755; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 108))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 4))) { + result[0] += 0.020123675; + } else { + result[0] += 0.00013938252; + } + } else { + result[0] += -0.009953358; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 196))) { + result[0] += 0.016889976; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { + result[0] += -0.010803269; + } else { + result[0] += 0.010580909; + } + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 392))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 198))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 154))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 144))) { + result[0] += -0; + } else { + result[0] += 0.05305763; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 74))) { + result[0] += -0.0071868575; + } else { + result[0] += 0.06390004; + } + } + } else { + result[0] += 0.057614993; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 176))) { + result[0] += -0.0067237015; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 52))) { + result[0] += 0.0020582944; + } else { + result[0] += 0.009856465; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 86))) { + result[0] += -0.0054150624; + } else { + result[0] += 0.0054157143; + } + } + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 90))) { + result[0] += -0.010108463; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + result[0] += 0.0012366887; + } else { + result[0] += -0.005106163; + } + } + } + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 162))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 150))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 52))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + result[0] += -0.0030484337; + } else { + result[0] += 0.007103072; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { + result[0] += -0.020119462; + } else { + result[0] += 0.0049716155; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 42))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 18))) { + result[0] += -0.020776983; + } else { + result[0] += 0.0001524161; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + result[0] += 0.009727257; + } else { + result[0] += 0.0006957262; + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 54))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 158))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 152))) { + result[0] += -0.011365656; + } else { + result[0] += -0.0006019767; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 154))) { + result[0] += 0.02111136; + } else { + result[0] += 0.001328925; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 216))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 298))) { + result[0] += -0.009145065; + } else { + result[0] += 0.011142318; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 250))) { + result[0] += -0.05126782; + } else { + result[0] += -0.02713782; + } + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 138))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 268))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 66))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 42))) { + result[0] += -0.003784838; + } else { + result[0] += 0.0068541127; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 30))) { + result[0] += -0.029539952; + } else { + result[0] += -0.0067226845; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 166))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 44))) { + result[0] += 0.0031095946; + } else { + result[0] += -0.031267606; + } + } else { + result[0] += 0.03802244; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + result[0] += -0.008942714; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 332))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { + result[0] += 0.018265491; + } else { + result[0] += 0.01069651; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + result[0] += 0.0040097563; + } else { + result[0] += -0.03305195; + } + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 118))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 194))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 70))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 30))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { + result[0] += -0.019308351; + } else { + result[0] += 0.0002994246; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { + result[0] += 0.0010901977; + } else { + result[0] += 0.0058678756; + } + } + } else { + result[0] += 0.024462478; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 290))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 74))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + result[0] += -0.012439872; + } else { + result[0] += 0.017688526; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 242))) { + result[0] += -0.0040794015; + } else { + result[0] += -0.009616309; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 316))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 246))) { + result[0] += 0.0013879368; + } else { + result[0] += 0.017187012; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { + result[0] += -0.0060414565; + } else { + result[0] += 0.00086067413; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 210))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 288))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { + result[0] += -0.022543944; + } else { + result[0] += 0.0002652502; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { + result[0] += 0.010252799; + } else { + result[0] += -0.0059008473; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 68))) { + result[0] += -0.00482871; + } else { + result[0] += -0.036633775; + } + } else { + result[0] += -0.020131879; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 88))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += 0.019643709; + } else { + result[0] += -0.0055163004; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 110))) { + result[0] += -0.023596639; + } else { + result[0] += -0.005768484; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 214))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 282))) { + result[0] += 0.024731327; + } else { + result[0] += 0.0059899557; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 216))) { + result[0] += -0.012860804; + } else { + result[0] += 0.00026836878; + } + } + } + } + } + } + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 250))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 270))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 216))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 4))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 188))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { + result[0] += -0.015227991; + } else { + result[0] += 0.007824044; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { + result[0] += -0.0030827313; + } else { + result[0] += 0.04585096; + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 44))) { + result[0] += 0.0038916196; + } else { + result[0] += -0.00018074422; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += -0; + } else { + result[0] += 0.01431708; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 8))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 40))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 254))) { + result[0] += -0.03443868; + } else { + result[0] += -0.016926734; + } + } else { + result[0] += 0.016495222; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 318))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 150))) { + result[0] += -0.0018124201; + } else { + result[0] += -0.008941532; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + result[0] += 0.010480117; + } else { + result[0] += -0.0053374222; + } + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 144))) { + result[0] += -0.0025247212; + } else { + result[0] += -0.021172313; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 278))) { + result[0] += 0.0136889; + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 24))) { + result[0] += -0; + } else { + result[0] += -0.028621003; + } + } else { + result[0] += 0.008883018; + } + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 272))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 200))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 120))) { + result[0] += 0.004496847; + } else { + result[0] += -0.0019887805; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 60))) { + result[0] += 0.011425511; + } else { + result[0] += 0.001471782; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 164))) { + result[0] += 0.004373122; + } else { + result[0] += -0.0050846357; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 312))) { + result[0] += -0.01642145; + } else { + result[0] += -0.0041971803; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 240))) { + result[0] += -0.019498728; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 296))) { + result[0] += 0.016818725; + } else { + result[0] += 0.031616382; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 256))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 152))) { + result[0] += 0.0041652615; + } else { + result[0] += 0.024725467; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 4))) { + result[0] += -0.017824838; + } else { + result[0] += 0.00069643237; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 104))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 346))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 138))) { + result[0] += 0.057328846; + } else { + result[0] += -0.009282811; + } + } else { + result[0] += -0.027812624; + } + } else { + result[0] += 0.006232637; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 208))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 184))) { + result[0] += -0.0007241729; + } else { + result[0] += 0.01345086; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { + result[0] += -0.01243884; + } else { + result[0] += -0.0010584429; + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 66))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 48))) { + result[0] += 0.012493608; + } else { + result[0] += 0.0014906918; + } + } else { + result[0] += -0.011661606; + } + } + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 110))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 2))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 216))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 58))) { + result[0] += 0.0059683393; + } else { + result[0] += 0.047656022; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 76))) { + result[0] += -0.01823411; + } else { + result[0] += 0.011243396; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 88))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 36))) { + result[0] += -0.015964573; + } else { + result[0] += 0.018772287; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 84))) { + result[0] += -0.024348408; + } else { + result[0] += 0.0071454565; + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 118))) { + result[0] += 0.0038073042; + } else { + result[0] += -0.010677494; + } + } else { + result[0] += -0.010760325; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 166))) { + result[0] += -0.013037783; + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 196))) { + result[0] += 0.04078368; + } else { + result[0] += -0; + } + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 6))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 110))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 24))) { + result[0] += -0.019799097; + } else { + result[0] += 0.0018352288; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 14))) { + result[0] += 0.016346006; + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 266))) { + result[0] += -0.018813994; + } else { + result[0] += -0.007913149; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 296))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 280))) { + result[0] += 0.000356507; + } else { + result[0] += 0.004809927; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 298))) { + result[0] += -0.015981428; + } else { + result[0] += -0.0013808893; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 84))) { + result[0] += -0.0013645035; + } else { + result[0] += -0.009478527; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { + result[0] += 0.01796195; + } else { + result[0] += -0.00023798608; + } + } + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 142))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 210))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 26))) { + result[0] += -0.0045832377; + } else { + result[0] += -0.03190832; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { + result[0] += 0.0066647097; + } else { + result[0] += -0.002318192; + } + } + } else { + result[0] += -0.040041517; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 264))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 270))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 54))) { + result[0] += -0.0019740702; + } else { + result[0] += -0.024438191; + } + } else { + result[0] += -0; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 166))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { + result[0] += 0.029889846; + } else { + result[0] += 0.0023792638; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { + result[0] += 0.0048653954; + } else { + result[0] += -0.013145282; + } + } + } + } + } else { + result[0] += 0.026439697; + } + } + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 50))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 92))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 154))) { + result[0] += -0.023412313; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 164))) { + result[0] += 0.020196555; + } else { + result[0] += -0.012161604; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 38))) { + result[0] += 0.08170753; + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 18))) { + result[0] += -0.002143487; + } else { + result[0] += -0.038541686; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 50))) { + result[0] += -0.006611839; + } else { + result[0] += 0.008542123; + } + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 200))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 132))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 110))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 46))) { + result[0] += 0.0018188714; + } else { + result[0] += -0.015560075; + } + } else { + result[0] += 0.021364069; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 112))) { + result[0] += -0.029760977; + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 148))) { + result[0] += 0.0078332; + } else { + result[0] += -0.011854128; + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 64))) { + result[0] += 0.014748133; + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { + result[0] += -0.012884839; + } else { + result[0] += 0.0029220346; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 42))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 148))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { + result[0] += 0.015425849; + } else { + result[0] += -0.004185417; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 98))) { + result[0] += -0.0011755283; + } else { + result[0] += -0.03457963; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 266))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 48))) { + result[0] += -0.009746331; + } else { + result[0] += -0.030245284; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { + result[0] += -0.006777007; + } else { + result[0] += -0.02345091; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 92))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { + result[0] += -0.008947103; + } else { + result[0] += 0.03920984; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 148))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 6))) { + result[0] += 0.009707036; + } else { + result[0] += -0.0060035028; + } + } else { + result[0] += 0.01016276; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 28))) { + result[0] += -0; + } else { + result[0] += 0.032831904; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 118))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { + result[0] += 6.658281e-06; + } else { + result[0] += 0.0031512368; + } + } else { + result[0] += 0.02200143; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { + result[0] += 0.033468585; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 82))) { + result[0] += -0.008753168; + } else { + result[0] += -0.0016586205; + } + } + } + } + } + } + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 314))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 26))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + result[0] += -0.033794925; + } else { + result[0] += 0.0085956175; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 8))) { + result[0] += 0.02062062; + } else { + result[0] += -0.0024983874; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 30))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { + result[0] += -0; + } else { + result[0] += 0.02111803; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 66))) { + result[0] += 0.048572876; + } else { + result[0] += 0.007911704; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 112))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 68))) { + result[0] += -0.0077246563; + } else { + result[0] += 0.005386382; + } + } else { + result[0] += -0.020841127; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 48))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 4))) { + result[0] += -0.014042695; + } else { + result[0] += 0.008498053; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 12))) { + result[0] += -0.024558064; + } else { + result[0] += -0.0025908693; + } + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 24))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { + result[0] += 2.548086e-05; + } else { + result[0] += 0.026205242; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 92))) { + result[0] += 0.009812611; + } else { + result[0] += 0.00306112; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { + result[0] += 0.027721763; + } else { + result[0] += 0.006687283; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 16))) { + result[0] += 0.035541125; + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 18))) { + result[0] += -0.017473036; + } else { + result[0] += -0.0030920196; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { + result[0] += -0.00029661623; + } else { + result[0] += -0.006844879; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += 0.009076519; + } else { + result[0] += 0.00021811183; + } + } + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 36))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 34))) { + result[0] += 0.026330112; + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 248))) { + result[0] += 0.01319269; + } else { + result[0] += -0.004578778; + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 62))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { + result[0] += -0; + } else { + result[0] += -0.017771253; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 68))) { + result[0] += 0.041357838; + } else { + result[0] += 0.0004609677; + } + } + } + } + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 116))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 12))) { + result[0] += -0.03449745; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 10))) { + result[0] += -0.000830644; + } else { + result[0] += 0.027823929; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { + result[0] += -0.014675349; + } else { + result[0] += 0.002744519; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { + result[0] += 0.00021282148; + } else { + result[0] += 0.012012251; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { + result[0] += -0.023646448; + } else { + result[0] += -0.0012802199; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 64))) { + result[0] += 0.005379937; + } else { + result[0] += 0.020074537; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { + result[0] += -0.017483069; + } else { + result[0] += 0.00045991183; + } + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 28))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 118))) { + result[0] += -0.010277932; + } else { + result[0] += 0.020262284; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { + result[0] += 0.026524289; + } else { + result[0] += 0.0005923277; + } + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 78))) { + result[0] += -0.023216326; + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 160))) { + result[0] += -0.0063289674; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 80))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 56))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 124))) { + result[0] += -0.026424408; + } else { + result[0] += -0.00650081; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { + result[0] += 0.035873644; + } else { + result[0] += -0.0067583695; + } + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 134))) { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { + result[0] += 0.019181004; + } else { + result[0] += 0.0012452018; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + result[0] += -0.006571722; + } else { + result[0] += -0.00113232; + } + } + } + } + } + } + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 248))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 270))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 184))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 104))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 312))) { + result[0] += -0.001673722; + } else { + result[0] += 0.0015081331; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { + result[0] += 0.019442735; + } else { + result[0] += -0.0041994313; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 116))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 170))) { + result[0] += 0.00018035778; + } else { + result[0] += -0.012916463; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { + result[0] += 0.003256688; + } else { + result[0] += -0.0061490457; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 28))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 80))) { + result[0] += -0.002768964; + } else { + result[0] += 0.036568955; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { + result[0] += 0.0035748954; + } else { + result[0] += -0.0018361468; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { + result[0] += 0.0008316287; + } else { + result[0] += -0.012621154; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 212))) { + result[0] += 0.0009690163; + } else { + result[0] += -0.009806303; + } + } + } + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 226))) { + result[0] += -0.008160834; + } else { + result[0] += 0.010674962; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 66))) { + result[0] += -0.018799648; + } else { + result[0] += -0.0010747229; + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 274))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 252))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { + result[0] += 0.013668454; + } else { + result[0] += 0.0015821367; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 254))) { + result[0] += 0.0013118708; + } else { + result[0] += -0.0027904937; + } + } + } else { + result[0] += 0.013429761; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 150))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 312))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 278))) { + result[0] += 0.0026331868; + } else { + result[0] += 0.02152044; + } + } else { + result[0] += 0.010367058; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 308))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + result[0] += -0.009165699; + } else { + result[0] += 0.012586451; + } + } else { + result[0] += 0.017478531; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 130))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 56))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 138))) { + result[0] += 0.03761896; + } else { + result[0] += -0.009919451; + } + } else { + result[0] += -0.024488965; + } + } else { + result[0] += 0.0063245073; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { + result[0] += 0.004199187; + } else { + result[0] += -0.0270401; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 12))) { + result[0] += -0.034475163; + } else { + result[0] += 0.03449012; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 204))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 312))) { + result[0] += -0.008276438; + } else { + result[0] += -0.021829268; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 348))) { + result[0] += -0.00024369739; + } else { + result[0] += 0.018283892; + } + } + } + } + } + } + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 180))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + result[0] += 0.015541151; + } else { + result[0] += -0.004526817; + } + } else { + result[0] += 0.04517481; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 44))) { + result[0] += -0.008011468; + } else { + result[0] += -0.0023013349; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 16))) { + result[0] += 0.027930424; + } else { + result[0] += 0.001679267; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 92))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 20))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 32))) { + result[0] += 0.022154698; + } else { + result[0] += -0.01756174; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 76))) { + result[0] += -0.045003; + } else { + result[0] += -0.010276439; + } + } + } else { + result[0] += 0.001000655; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 116))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { + result[0] += 0.0012434544; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 160))) { + result[0] += 0.018014176; + } else { + result[0] += -0.00070313603; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 126))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 114))) { + result[0] += 0.0062645376; + } else { + result[0] += -0.015609448; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { + result[0] += 0.01479733; + } else { + result[0] += 0.0014734569; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 134))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 304))) { + result[0] += -0; + } else { + result[0] += -0.012774549; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 40))) { + result[0] += 0.014705256; + } else { + result[0] += -0.004232665; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 314))) { + result[0] += 0.0029532865; + } else { + result[0] += -0.0029254516; + } + } else { + result[0] += 0.026304556; + } + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 128))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 256))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 150))) { + result[0] += 0.00030722877; + } else { + result[0] += -0.0051551643; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { + result[0] += 0.016633255; + } else { + result[0] += 0.001668491; + } + } + } else { + result[0] += -0.014828484; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 236))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 254))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 286))) { + result[0] += 0.0071044452; + } else { + result[0] += -0.0076894383; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 54))) { + result[0] += 0.02084865; + } else { + result[0] += 0.007473866; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 278))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 290))) { + result[0] += 0.005741518; + } else { + result[0] += 0.03405652; + } + } else { + result[0] += 0.025841344; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 22))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 150))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 154))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 152))) { + result[0] += 0.00015940488; + } else { + result[0] += 0.05232349; + } + } else { + result[0] += -0.018910853; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { + result[0] += -0.051273484; + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 106))) { + result[0] += -0.02290248; + } else { + result[0] += -0.0016118204; + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 162))) { + result[0] += 0.0010482629; + } else { + result[0] += -0.016297817; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 196))) { + result[0] += 0.013967775; + } else { + result[0] += 0.027914885; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 74))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 2))) { + result[0] += 0.038794536; + } else { + result[0] += -0.0062946193; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + result[0] += -2.64479e-05; + } else { + result[0] += 0.021996183; + } + } + } + } + } + } + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 104))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 40))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 28))) { + result[0] += -0.005473042; + } else { + result[0] += 0.011005775; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 136))) { + result[0] += -0.011721667; + } else { + result[0] += 0.0078737205; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 46))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 38))) { + result[0] += 0.0025443584; + } else { + result[0] += -0.016291086; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += 0.00024633956; + } else { + result[0] += 0.027108392; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 26))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 10))) { + result[0] += 0.03744457; + } else { + result[0] += 0.0014647435; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 136))) { + result[0] += -0.0058734766; + } else { + result[0] += -0.02578007; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 50))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + result[0] += -0.00944722; + } else { + result[0] += -0.03411841; + } + } else { + result[0] += -0.001750114; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 14))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 100))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 24))) { + result[0] += 0.0006245327; + } else { + result[0] += 0.02487868; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 18))) { + result[0] += 0.003864918; + } else { + result[0] += 0.04562244; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 114))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 94))) { + result[0] += -0; + } else { + result[0] += -0.037629705; + } + } else { + result[0] += 0.017797282; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 20))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { + result[0] += 0.03347585; + } else { + result[0] += -0.00015754919; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + result[0] += 0.042547952; + } else { + result[0] += 0.008394706; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 304))) { + result[0] += -0.0053476547; + } else { + result[0] += 0.019664986; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 274))) { + result[0] += 0.00061302836; + } else { + result[0] += -0.008260283; + } + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 112))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 164))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { + result[0] += -0.0029065635; + } else { + result[0] += 0.012601085; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { + result[0] += 0.011005281; + } else { + result[0] += -0.010062504; + } + } + } else { + result[0] += 0.015878765; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 182))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 154))) { + result[0] += -0.012548672; + } else { + result[0] += -0.036353104; + } + } else { + result[0] += -0.0037594133; + } + } else { + result[0] += -0.026142243; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 110))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 108))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 194))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 162))) { + result[0] += 0.0049291016; + } else { + result[0] += 0.02395081; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 234))) { + result[0] += -0.0030472444; + } else { + result[0] += 0.0040821205; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 100))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 114))) { + result[0] += -0.020026503; + } else { + result[0] += -0.0037345327; + } + } else { + result[0] += -0.025079226; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 128))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 128))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 160))) { + result[0] += 0.006035849; + } else { + result[0] += -0.005563633; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 134))) { + result[0] += -0.017343946; + } else { + result[0] += 0.014413935; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 130))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 308))) { + result[0] += 0.007634467; + } else { + result[0] += -0.024592249; + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { + result[0] += 0.0005188165; + } else { + result[0] += -0.006757953; + } + } + } + } + } + } + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 52))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { + result[0] += 0.00393571; + } else { + result[0] += 0.02043746; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 262))) { + result[0] += -0.01545644; + } else { + result[0] += -0.002095156; + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 210))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 128))) { + result[0] += -0.00013221658; + } else { + result[0] += -0.002142299; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + result[0] += -0.0012591215; + } else { + result[0] += 0.0015919211; + } + } + } + } else { + result[0] += 0.011080801; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 300))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 142))) { + result[0] += -0.030891055; + } else { + result[0] += -0.011523842; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 296))) { + result[0] += -0.0094786985; + } else { + result[0] += 0.00014606201; + } + } + } else { + result[0] += 0.045800555; + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 18))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 0))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { + result[0] += 0.021174032; + } else { + result[0] += -0; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { + result[0] += -0; + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 170))) { + result[0] += -0.02313884; + } else { + result[0] += -0; + } + } + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { + result[0] += 0.0034402236; + } else { + result[0] += 0.03586924; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 150))) { + result[0] += -0.007850896; + } else { + result[0] += 0.0023356122; + } + } + } else { + result[0] += 0.019080771; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 340))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 216))) { + result[0] += -0.006365792; + } else { + result[0] += 0.012724089; + } + } else { + result[0] += -0.03172406; + } + } + } + } + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 350))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 142))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 116))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 138))) { + result[0] += -0.00062813837; + } else { + result[0] += 0.002671611; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 86))) { + result[0] += 0.00652712; + } else { + result[0] += 0.00037897687; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 152))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 286))) { + result[0] += -0.00025623475; + } else { + result[0] += -0.004133305; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 82))) { + result[0] += -0.01589259; + } else { + result[0] += 0.019172618; + } + } + } + } else { + result[0] += 0.009757864; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 250))) { + result[0] += -0.0065281326; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 254))) { + result[0] += 0.0007117824; + } else { + result[0] += 0.027270088; + } + } else { + result[0] += -0.0023996648; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 248))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 48))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { + result[0] += -0.0019857192; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 222))) { + result[0] += 0.025358735; + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 232))) { + result[0] += -0.0021736256; + } else { + result[0] += 0.016332442; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 52))) { + result[0] += -0.0129296705; + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 224))) { + result[0] += -0; + } else { + result[0] += 0.018950501; + } + } + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 68))) { + result[0] += -0.0070409672; + } else { + result[0] += 0.010926886; + } + } + } + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 314))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 312))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 248))) { + result[0] += -0.00022246773; + } else { + result[0] += 0.00076635665; + } + } else { + result[0] += 0.012082838; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 286))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 298))) { + result[0] += -0.015522775; + } else { + result[0] += -0.0064152265; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 348))) { + result[0] += -0.0013468252; + } else { + result[0] += 0.020515444; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 276))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 150))) { + result[0] += 0.0077754804; + } else { + result[0] += -0.0030992497; + } + } else { + result[0] += -0.017411573; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 250))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { + result[0] += 0.003438398; + } else { + result[0] += 0.032958325; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 30))) { + result[0] += 0.013451094; + } else { + result[0] += -0.006546424; + } + } + } + } + } else { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 188))) { + result[0] += 0.009157052; + } else { + result[0] += -0.010829896; + } + } else { + result[0] += 0.015605274; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { + result[0] += 0.01869001; + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 228))) { + result[0] += -0.0042858357; + } else { + result[0] += -0.02192712; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 226))) { + result[0] += -0.0021264374; + } else { + result[0] += 0.028851261; + } + } + } + } + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 150))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 6))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 282))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { + result[0] += 0.008089608; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 252))) { + result[0] += -0.02760528; + } else { + result[0] += -0.014671764; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { + result[0] += 0.0007742315; + } else { + result[0] += -0.014570135; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 310))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 264))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 52))) { + result[0] += 0.004098795; + } else { + result[0] += -0.0013311962; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 250))) { + result[0] += -0.016194416; + } else { + result[0] += -0.004321107; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 246))) { + result[0] += 0.012578972; + } else { + result[0] += 0.031848185; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { + result[0] += 0.002893864; + } else { + result[0] += -0.0014323759; + } + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 48))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 54))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 24))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += -0.0058673653; + } else { + result[0] += 0.014972388; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 32))) { + result[0] += -0.016248314; + } else { + result[0] += -0; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 42))) { + result[0] += 0.001427743; + } else { + result[0] += 0.013427376; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 218))) { + result[0] += 0.004056875; + } else { + result[0] += -0.0111101195; + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 142))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 108))) { + result[0] += 0.009302656; + } else { + result[0] += 0.0009259971; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { + result[0] += -0.0071252473; + } else { + result[0] += 0.0005085042; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 100))) { + result[0] += 0.0055279406; + } else { + result[0] += -0.015505721; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 152))) { + result[0] += 0.022024345; + } else { + result[0] += 0.013201664; + } + } + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 30))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 140))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 24))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 154))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 34))) { + result[0] += -0.0031792417; + } else { + result[0] += -0.024321148; + } + } else { + result[0] += 0.0074777827; + } + } else { + if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 52))) { + result[0] += -0.015280488; + } else { + result[0] += 0.004024551; + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 26))) { + result[0] += 0.013732604; + } else { + result[0] += -0; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 74))) { + result[0] += -0.028851384; + } else { + result[0] += -0.051958527; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { + result[0] += 0.010219454; + } else { + result[0] += -0.009982391; + } + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 162))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 256))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 158))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { + result[0] += 0.014080286; + } else { + result[0] += -0.004486435; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 136))) { + result[0] += 0.005933493; + } else { + result[0] += 0.02153199; + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 316))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + result[0] += -0.02032683; + } else { + result[0] += 0.002681443; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 214))) { + result[0] += -0.0042459033; + } else { + result[0] += 0.0068887584; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 196))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 392))) { + result[0] += 0.00047894806; + } else { + result[0] += -0.0061863232; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 230))) { + result[0] += 0.008450966; + } else { + result[0] += 0.0015639787; + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 200))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { + result[0] += -0.007651621; + } else { + result[0] += -0.0009728819; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { + result[0] += 0.0062414994; + } else { + result[0] += -0.0019789643; + } + } + } + } + } + } + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 314))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 312))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { + result[0] += -0.0028604511; + } else { + result[0] += 0.0004893718; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 224))) { + result[0] += 0.026684532; + } else { + result[0] += 0.0063786306; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 16))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 206))) { + result[0] += -0.009385272; + } else { + result[0] += 0.008824005; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 256))) { + result[0] += -0.00080077257; + } else { + result[0] += 0.0010740731; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 374))) { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 34))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 224))) { + result[0] += 0.014482776; + } else { + result[0] += -0.0036570956; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 308))) { + result[0] += 0.009366672; + } else { + result[0] += -0.00948626; + } + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { + result[0] += -0.005847118; + } else { + result[0] += 0.033575047; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 218))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 188))) { + result[0] += 0.0021236583; + } else { + result[0] += -0.01067119; + } + } else { + result[0] += 0.0088947555; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { + result[0] += 0.018932056; + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { + result[0] += -0.0033845974; + } else { + result[0] += -0.020902513; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 250))) { + result[0] += 0.0032020702; + } else { + result[0] += 0.042684905; + } + } + } + } + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 18))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 70))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 68))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { + result[0] += -0.008612228; + } else { + result[0] += 0.0040322477; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { + result[0] += -0.008511812; + } else { + result[0] += 0.011585411; + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 44))) { + result[0] += 1.8034565e-05; + } else { + result[0] += 0.027274786; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 64))) { + result[0] += 0.008447873; + } else { + result[0] += -0.025014887; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 130))) { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 58))) { + result[0] += -0.007062527; + } else { + result[0] += 0.014613422; + } + } else { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 24))) { + result[0] += 0.0011185434; + } else { + result[0] += 0.008517242; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { + result[0] += -0.025690308; + } else { + result[0] += -0.0029228963; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 120))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 30))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 46))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { + result[0] += -0.017073292; + } else { + result[0] += 0.0035216322; + } + } else { + result[0] += -0.023047855; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { + result[0] += -0.025554648; + } else { + result[0] += -0.0017376606; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { + result[0] += -0.0054964907; + } else { + result[0] += 0.037392512; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { + result[0] += -0.05761969; + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 98))) { + result[0] += -0.016015155; + } else { + result[0] += 0.0068704; + } + } else { + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 76))) { + result[0] += -0.015397488; + } else { + result[0] += -0.04052282; + } + } + } + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 86))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 4))) { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 52))) { + result[0] += 0.0003938696; + } else { + result[0] += 0.0045072534; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 64))) { + result[0] += -0.0024714952; + } else { + result[0] += 0.0049051414; + } + } + } else { + result[0] += 0.017899476; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 380))) { + result[0] += -0.008237792; + } else { + result[0] += -0.0031683035; + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 282))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 174))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 100))) { + result[0] += -0.0050061867; + } else { + result[0] += 0.00018127509; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { + result[0] += -0.0051624607; + } else { + result[0] += 0.00025673906; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 156))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { + result[0] += -0.0046179085; + } else { + result[0] += 0.026366374; + } + } else { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 8))) { + result[0] += -0; + } else { + result[0] += 0.018350547; + } + } + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 268))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 50))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 220))) { + result[0] += -0.0091535505; + } else { + result[0] += -0.042126443; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 248))) { + result[0] += -0.03904317; + } else { + result[0] += -0.0013817366; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 44))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 122))) { + result[0] += 0.0032674843; + } else { + result[0] += 0.038644753; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { + result[0] += -0.003548465; + } else { + result[0] += 0.044352487; + } + } + } + } + } + } + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 168))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 102))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { + result[0] += -0.0006659017; + } else { + result[0] += -0.017484738; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 124))) { + result[0] += 0.021091629; + } else { + result[0] += -0.0017686317; + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { + result[0] += -0.035111748; + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 46))) { + result[0] += -0.0162628; + } else { + result[0] += -0.004374349; + } + } + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 8))) { + result[0] += -0.031120077; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { + result[0] += -0.0055634947; + } else { + result[0] += -0.017814448; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 120))) { + result[0] += 0.042426206; + } else { + result[0] += -0.0013250493; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 58))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 132))) { + result[0] += 0.03963465; + } else { + result[0] += 0.018596584; + } + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 114))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 94))) { + result[0] += -0.0008957187; + } else { + result[0] += -0.03431357; + } + } else { + result[0] += 0.015205196; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 84))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 102))) { + result[0] += 0.012878765; + } else { + result[0] += -0.013091402; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 140))) { + result[0] += -0.022308733; + } else { + result[0] += 0.0014070682; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 38))) { + result[0] += -0.018751746; + } else { + result[0] += -0.041268926; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 52))) { + result[0] += -0.0035958923; + } else { + result[0] += 9.794186e-05; + } + } + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 352))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 198))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 120))) { + result[0] += 0.013142203; + } else { + result[0] += -0.0014631682; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += -0; + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 16))) { + result[0] += -0.01765822; + } else { + result[0] += 0.0049266764; + } + } + } + } else { + result[0] += -0.0039253565; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 368))) { + result[0] += 0.015282332; + } else { + result[0] += -0.0067160674; + } + } + } + if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 214))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 176))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 160))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 32))) { + result[0] += -0.0012096122; + } else { + result[0] += 0.004681909; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { + result[0] += -0.013531153; + } else { + result[0] += 0.004223753; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 72))) { + result[0] += -0.0026232149; + } else { + result[0] += -0.01515016; + } + } else { + if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 170))) { + result[0] += -0.00014745975; + } else { + result[0] += 0.0059427386; + } + } + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { + if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 322))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 146))) { + result[0] += 0.011954216; + } else { + result[0] += -0.00069962896; + } + } else { + result[0] += 0.03788197; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + result[0] += -0.0036799812; + } else { + result[0] += -0.014614584; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 194))) { + result[0] += -0; + } else { + result[0] += 0.020192096; + } + } + } + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 246))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 96))) { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { + if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 36))) { + result[0] += 0.010108634; + } else { + result[0] += 0.0014457417; + } + } else { + if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { + result[0] += -0.0062636524; + } else { + result[0] += 0.00020081793; + } + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 6))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { + result[0] += -0.011251283; + } else { + result[0] += 0.0056470656; + } + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { + result[0] += 0.0014956547; + } else { + result[0] += 0.0059374445; + } + } + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 266))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 114))) { + result[0] += -0.010740235; + } else { + result[0] += 0.00111787; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + result[0] += -0.001297196; + } else { + result[0] += 0.0012389937; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 240))) { + result[0] += 0.01125375; + } else { + result[0] += -0.03845505; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 292))) { + result[0] += -0.008091416; + } else { + result[0] += 0.0020921114; + } + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 8))) { + result[0] += -0.040072184; + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 136))) { + result[0] += 0.013737966; + } else { + result[0] += -0.00681287; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 132))) { + if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { + result[0] += 0.028193597; + } else { + result[0] += -0.0037449375; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + result[0] += 0.015007503; + } else { + result[0] += -0.009241991; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 94))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { + result[0] += -0.003027987; + } else { + result[0] += -0.019359348; + } + } else { + result[0] += 0.0025615941; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + result[0] += 0.023024825; + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 218))) { + result[0] += -0.0007633574; + } else { + result[0] += -0.006595341; + } + } + } + } + } else { + if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 146))) { + result[0] += -0.039751314; + } else { + if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 108))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 308))) { + result[0] += 0.011828893; + } else { + result[0] += -0.009816745; + } + } else { + result[0] += -0.015453085; + } + } + } else { + result[0] += 0.0023617218; + } + } + } + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 322))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 282))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 272))) { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { + result[0] += -2.5651694e-05; + } else { + result[0] += 0.004297465; + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { + result[0] += -0.012060625; + } else { + result[0] += 0.027334878; + } + } + } else { + result[0] += 0.0077310125; + } + } else { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 112))) { + result[0] += -0.012176646; + } else { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 306))) { + result[0] += 0.0043146396; + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 278))) { + result[0] += -0.010458501; + } else { + result[0] += -0.0025602286; + } + } + } + } + } else { + result[0] += 0.026633803; + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 60))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 130))) { + if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 14))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 122))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { + result[0] += 0.009240702; + } else { + result[0] += 0.02637141; + } + } else { + result[0] += -0.0056706998; + } + } else { + if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 10))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 286))) { + result[0] += -0.003625507; + } else { + result[0] += 0.0070409435; + } + } else { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 280))) { + result[0] += 0.014894609; + } else { + result[0] += -0.003168697; + } + } + } + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 178))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { + result[0] += -0.015371713; + } else { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { + result[0] += -0.009225684; + } else { + result[0] += 0.00080344983; + } + } + } else { + result[0] += -0.034244932; + } + } + } else { + if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 72))) { + if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { + if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 184))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += -0.013472222; + } else { + result[0] += 0.00748849; + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 304))) { + result[0] += 0.0054984465; + } else { + result[0] += 0.048147988; + } + } + } else { + result[0] += -0.010059855; + } + } else { + if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 346))) { + if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 250))) { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 278))) { + result[0] += 0.0179956; + } else { + result[0] += 0.00028234924; + } + } else { + if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 330))) { + result[0] += -0.005519641; + } else { + result[0] += -0.03736005; + } + } + } else { + if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 226))) { + result[0] += -0.0048794984; + } else { + result[0] += 0.007994923; + } + } + } + } + } + + // Apply base_scores + result[0] += -1.882233619689941406; + result[0] = std::exp(result[0]); + + // Apply postprocessor + if (!pred_margin) { postprocess(result); } +} + +void dualsimplex_predictor::postprocess(double* result) +{ + // Do nothing +} + +// Feature names array +const char* dualsimplex_predictor::feature_names[dualsimplex_predictor::NUM_FEATURES] = { + "m", + "n", + "nnz", + "density", + "avg_nnz_col", + "avg_nnz_row", + "bounded", + "free", + "refact_freq", + "num_refacts", + "num_updates", + "sparse_dz", + "dense_dz", + "bound_flips", + "num_infeas", + "dy_nz_pct", + "byte_loads", + "byte_stores"}; diff --git a/cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp b/cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp new file mode 100644 index 000000000..ab62e5687 --- /dev/null +++ b/cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp @@ -0,0 +1,1891 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#include "header.h" + +static const float threshold[] = { + 125, + 162, + 329, + 335, + 359, + 402, + 443, + 447, + 465, + 482, + 550, + 581, + 677, + 810, + 836, + 865, + 1004, + 1082, + 1388, + 1652, + 1690, + 1754, + 1820, + 1916, + 2387, + 2432, + 2471, + 2487, + 2676, + 3022, + 3128, + 3174, + 3472, + 3480, + 3897, + 4179, + 4193, + 4233, + 4240, + 4462, + 4480, + 4561, + 4661, + 4813, + 4934, + 5355, + 5593, + 6119, + 6332, + 6474, + 6504, + 6574, + 7260, + 8469, + 8488, + 8579, + 9001, + 9809, + 10400, + 10418, + 10711, + 10837, + 12702, + 13228, + 13552, + 14039, + 14653, + 15663, + 16026, + 16418, + 16488, + 16924, + 18083, + 18157, + 18525, + 18558, + 18584, + 18608, + 19134, + 20335, + 22063, + 24884, + 32736, + 33373, + 37617, + 39261, + 45220, + 48604, + 49951, + 53360, + 56116, + 57099, + 59576, + 67583, + 71393, + 88441, + 90924, + 99789, + 105209, + 105933, + 131865, + 163021, + 169576, + 224453, + 259962, + 265341, + 320520, + 349602, + 957446, + 1266, + 1314, + 2224, + 2246, + 2802, + 2825, + 2979, + 2984, + 4950, + 5769, + 5783, + 5970, + 6042, + 6519, + 6734, + 6742, + 7295, + 7784, + 8179, + 8393, + 8634, + 9240, + 9756, + 13075, + 13650, + 13802, + 14563, + 14619, + 15528, + 15655, + 15789, + 16634, + 16952, + 18099, + 18166, + 18178, + 18904, + 19457, + 19794, + 21196, + 22038, + 22645, + 24627, + 24951, + 25483, + 25720, + 30733, + 33499, + 33508, + 34436, + 37008, + 45637, + 47542, + 52526, + 56443, + 60016, + 60697, + 65771, + 65861, + 74900, + 75951, + 78534, + 88088, + 90196, + 114164, + 132419, + 141311, + 143793, + 152283, + 154622, + 159620, + 164010, + 164045, + 228960, + 233606, + 263018, + 266451, + 327865, + 408066, + 449518, + 471906, + 563256, + 1439711, + 1667610, + 2884312, + 2094, + 2530, + 2978, + 4192, + 5810, + 7766, + 8669, + 14451, + 15023, + 16817, + 19230, + 20237, + 20696, + 20930, + 21776, + 29160, + 36272, + 37026, + 40332, + 41996, + 43680, + 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432, + 503, + 594, + 594, + 594, + 797, + 840, + 998, + 1072, + 1215, + 1373, + 1459, + 1635, +}; + +static const int th_len[] = { + 109, + 85, + 79, + 73, + 86, + 71, + 91, + 0, + 0, + 203, + 43, + 158, + 74, + 143, + 158, + 86, + 176, + 166, +}; + +/* + * \brief Function to convert a feature value into bin index. + * \param val Feature value, in floating-point + * \param fid Feature identifier + * \return bin Index corresponding to given feature value + */ +int dualsimplex_predictor::quantize(float val, unsigned fid) +{ + const size_t offset = th_begin[fid]; + const float* array = &threshold[offset]; + int len = th_len[fid]; + int low = 0; + int high = len; + int mid; + float mval; + // It is possible th_begin[i] == [total_num_threshold]. This means that + // all features i, (i+1), ... are not used for any of the splits in the model. + // So in this case, just return something + if (offset == 1801 || val < array[0]) { return -10; } + while (low + 1 < high) { + mid = (low + high) / 2; + mval = array[mid]; + if (val == mval) { + return mid * 2; + } else if (val < mval) { + high = mid; + } else { + low = mid; + } + } + if (array[low] == val) { + return low * 2; + } else if (high == len) { + return len * 2; + } else { + return low * 2 + 1; + } +} diff --git a/cpp/src/utilities/models/fj.ubj.gz b/cpp/src/utilities/models/fj.ubj.gz deleted file mode 100644 index 08c4d7c1b062e4d73b8afb1e2d83c8d68c6246f2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 12153 zcmV-Wo#~WVrl^God;MHSNHHQy(o&Wu%lu`C5FTTHr$2<8+M~aF_vIQu`BxB znVp4oqtWCu{)5lEJm;BZ=iWPIcg~#OIp?04;14t}vzUm`gxH9L3EB@VLz9vcqQgcf zMI=^!Rx>d?dPHU0J`Ii!O$Z%Xxvoy^=#ktk36 zyOj;kT5LhgZ?~Xlx|pI)4M(MR70Bvb531ZWOeBiwkBko27A4d=N4?HuD&-M{Nx+dcFtaSy(0nJxRF#})AlI;xtKB-=8l%k zwHBHEwo4RuFG{pT>VazB>q+HDcNR4~9!i-XdIC)rc~axQwL`YcoKy}*(^Te$hm_rF z-d8@f%lGk^k*3_b_lj`w+uFp~vNxpNw#H;=pgXZR^;@z@Wn%q6>&wnx(=XQ1Y;F|ek~8q{gZUR68#Hfla^2z+w79~|+d1LaD$Cx1UTj#}4$ z5xRRO8r`qwLY;SLkG5>crL24Ipq89{Nm(9g4yUhOi#l#@jG}tFq9@OLRDMt4p-B;u zaS74*%X5f%&u_VEeQ+%PnZCbOU7c2m5s@SDZyz`G|LfrrqsK?Qzg5kl5yL`9$FSG% zu%sY|epNpi@bA7VFQVw7RX=Sjb}xd%qoQMmCPc*Q9zVM~23U;P3xL&6?3Ke_Tx>5a zwb|>KJv~_JV`+$`5tb%c?65S)(gI5>EUn+-7hbl-(hf_9xBLsg@5C=VW9fpW8x|KV zJ+bt{;*P}=i#HY@ECd$F_o1-(V)4Tw#=>BcV(EjWAK&I1EP+^p`2PpLtp=~kA4^PJ zVj})}LSrK%-qkOC-9`!eq?9N3F$xw1B-UvfFyo+cfNXbEz?98EqHN6*A`cdumB?nw)ul&0Lalv4nXx2}s?qm#}RlBVq;8XZt1omzQvTBpz!sKs9(( z-m>v6Ors5SE}QQ(KQryifEPnvjCe8T#e^4AUd(tg=f#2-OI~X5Qj-@eUTX1Dn-^hNO2OFdrd^U{EqMmm=-D@}Q^!=7;y7JOZM`gHZzl2%|><#&YYR!;8_onDZUtodM8*RKWIuvrBbe<=k|c82(ixD3BX0YO>}9@tCcDD6c%QK8 z~#pm%=O9Rh!Etm!0jDiBmB)5%Kn4PnbVI{aO`NNX1WUU6xaoUzXTfLC%_rDvw| zUbnq=98;b4+Ly7;IJ)Z%bS_|^w>>kRz9#(AkngWmxJFv#t+QX?J>&hS?>v^8uZM-S z{|ghcMj}SDC=8d;p|S5=x}wv41ftWPG6Jh?(Wyx#-#n#jF!Bmqzz1(2~nfLLXd8%h9afYnlZ1|Zhp?c>3T!!kfj)oh;~-v`?}KJmD%CH^c2 zZ&$6^4|cnP49)&mBOr|DDq+u-o_UEr?4vNY!Kh@^<2RjBR! z&T5|gt5&vV3=19;5uOy6pu_T$CVd>0gS_wUSgN#jZ%D~^ zn<~!<$D;28XNWwOL28+7q-y%GrDSo%S~&fPH#*p5plIH=Y1EsvG|Ki!H|prEU8>Fd z+Yt1t8sxSv_2818n^E1QRnV+|8f7uLDOxpF0UtIGqH^<|h-~Toyoo=rtR)4WxF+%n9RI62CNY!*L}I6XF7xZ}}aa^duuqwvoRdYFIb`Fv96Ms;N zQ?8(WV|K%v!E4ZvA@kwBJ{!pS`Msz*-*rZj!7V5klM=KjVSe>QHxaq-VOM zg}s0quQ+oCwV8S>c4L0d>CVc{sLo-^sv%c4`Yq>Fx5ckz3F?_106Kg zj$@#Mv&MQDY^L+Sq0VP!IttWKZ$C|S`c*e<4f;Ps)}X&e))yg^u|pNIviO;fwO9<6 zY`lKs0zmcY1JJz-?;r5qLqf4bc!vMlJjXptUstTIml^ z$L)YJ!?@~zFYg@~U8g7kd@TXwE;)eH(*apm4#<@mRj4ZGQMH9;JA{bG>~;8kDMnR{ zroz=4qf-}EIXpe2K~w>M%sz4+p)Y)KMD6mq5H(oP-pyQbdYd0LcQvJ4QkI7LF5W9L zr*f!)!5dXB?S_yWf{sA1Nq%UKGD6fS(S=#&0UM{+}qjBMSrG2H$`1$n;v9=Oafm&|UFi*61nMe7M~xHZ=YCC&7p zu8l*~%xOl{yt7M1ol{h3a_DF(Yf3p5y@8Z*HvW&_qJ6GVTVYCykk6i4u;;ybsHAlqyng2)d=R!2M!AQ>cAbjQ0GH_hMRPxft%hGXn#&?9 zZ2(G`G4$az8Da+oun*2$=4&ETG*T2Wf0r9qAN%xs+L0Bwe&8 zhk0 zWA6n?r@&PD%-F>o#*PV|M6XYjN$gfdNldBDk_LMK^G&!0TVESxNM2u0WxD1KqfKH2 zbm1dw@tqANpqjj@b2PWE&LyjhwYI$c*P^-lSo@)9?x$kzXN~5v$HJ?dO3c+F?&pr? 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All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#pragma once + +#include +#include +#include +#include +#include +#include + +class pdlp_predictor { + public: + union Entry { + int missing; + float fvalue; + int qvalue; + }; + + static int32_t get_num_target(void); + static void get_num_class(int32_t* out); + static int32_t get_num_feature(void); + static const char* get_threshold_type(void); + static const char* get_leaf_output_type(void); + static void predict(union Entry* data, int pred_margin, double* result); + static void postprocess(double* result); + static int quantize(float val, unsigned fid); + + // Feature names + static constexpr int NUM_FEATURES = 8; + static const char* feature_names[NUM_FEATURES]; +}; // class pdlp_predictor diff --git a/cpp/src/utilities/models/pdlp_predictor/main.cpp b/cpp/src/utilities/models/pdlp_predictor/main.cpp new file mode 100644 index 000000000..c82153c91 --- /dev/null +++ b/cpp/src/utilities/models/pdlp_predictor/main.cpp @@ -0,0 +1,16893 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include "header.h" + +#if defined(__clang__) || defined(__GNUC__) +#define LIKELY(x) __builtin_expect(!!(x), 1) +#define UNLIKELY(x) __builtin_expect(!!(x), 0) +#else +#define LIKELY(x) (x) +#define UNLIKELY(x) (x) +#endif +#define N_TARGET 1 +#define MAX_N_CLASS 1 + +const unsigned char is_categorical[] = { + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, +}; +static const int32_t num_class[] = { + 1, +}; + +int32_t pdlp_predictor::get_num_target(void) { return std::exp(N_TARGET); } +void pdlp_predictor::get_num_class(int32_t* out) +{ + for (int i = 0; i < N_TARGET; ++i) { + out[i] = num_class[i]; + } +} +int32_t pdlp_predictor::get_num_feature(void) { return std::exp(8); } +const char* pdlp_predictor::get_threshold_type(void) { return "float32"; } +const char* pdlp_predictor::get_leaf_output_type(void) { return "float32"; } + +void pdlp_predictor::predict(union Entry* data, int pred_margin, double* result) +{ + // Quantize data + for (int i = 0; i < 8; ++i) { + if (data[i].missing != -1 && !is_categorical[i]) { + data[i].qvalue = quantize(data[i].fvalue, i); + } + } + + unsigned int tmp; + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 30))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + result[0] += -0.014820111; + } else { + result[0] += 0.0105687855; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + result[0] += -0.053651925; + } else { + result[0] += -0.10176092; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 220))) { + result[0] += -0.012739944; + } else { + result[0] += -0.036262058; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { + result[0] += -0.0063560107; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 258))) { + result[0] += 0.08327574; + } else { + result[0] += 0.026058618; + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 230))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 164))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { + result[0] += 0.031961214; + } else { + result[0] += 0.04515064; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { + result[0] += 0.018566301; + } else { + result[0] += -0.009213644; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { + result[0] += 0.14206316; + } else { + result[0] += 0.07914438; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { + result[0] += 0.025240844; + } else { + result[0] += 0.058222026; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += 0.26381668; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + result[0] += 0.085295476; + } else { + result[0] += 0.16802387; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 258))) { + result[0] += 0.19548021; + } else { + result[0] += 0.14702512; + } + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += 0.0039215386; + } else { + result[0] += -0.020411594; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { + result[0] += -0.030691355; + } else { + result[0] += -0.066723615; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 182))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { + result[0] += -0.039679535; + } else { + result[0] += -0.07033275; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 226))) { + result[0] += -0.017522344; + } else { + result[0] += 0.024236064; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 76))) { + result[0] += -0.01896934; + } else { + result[0] += 0.005805307; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += -0.05819216; + } else { + result[0] += -0.030700704; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 210))) { + result[0] += -0.016364044; + } else { + result[0] += -0.0079360735; + } + } else { + result[0] += 0.09915119; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 260))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { + result[0] += 0.0082174195; + } else { + result[0] += -0.0037611835; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { + result[0] += 0.05758765; + } else { + result[0] += 0.00845852; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 188))) { + result[0] += 0.08059614; + } else { + result[0] += 0.060384076; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 62))) { + result[0] += -0; + } else { + result[0] += 0.07759698; + } + } else { + result[0] += 0.12749861; + } + } else { + result[0] += 0.16187607; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 22))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + result[0] += -0.0052639237; + } else { + result[0] += 0.022940176; + } + } else { + result[0] += -0.03480838; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { + result[0] += -0.04990984; + } else { + result[0] += -0.080560416; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + result[0] += -0.014295327; + } else { + result[0] += -0.03235691; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { + result[0] += -0; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 258))) { + result[0] += 0.07372898; + } else { + result[0] += 0.024104217; + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 230))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 176))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 232))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { + result[0] += 0.02912726; + } else { + result[0] += 0.04095058; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += 0.016825125; + } else { + result[0] += -0.007768286; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { + result[0] += 0.1265556; + } else { + result[0] += 0.07124209; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + result[0] += 0.040830098; + } else { + result[0] += 0.0559672; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += 0.23753951; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + result[0] += 0.07685151; + } else { + result[0] += 0.1544579; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 260))) { + result[0] += 0.046362683; + } else { + result[0] += 0.17204122; + } + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { + result[0] += -0.022617795; + } else { + result[0] += -0.0122455275; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { + result[0] += -0.027298182; + } else { + result[0] += -0.059523176; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 182))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 248))) { + result[0] += -0.035259727; + } else { + result[0] += -0.061327398; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 236))) { + result[0] += -0.01425799; + } else { + result[0] += 0.017331526; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { + result[0] += 0.00193814; + } else { + result[0] += -0.017574918; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 196))) { + result[0] += -0.02869076; + } else { + result[0] += -0.065549426; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + result[0] += 0.0057360423; + } else { + result[0] += -0.013489055; + } + } else { + result[0] += 0.089799576; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 260))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { + result[0] += 0.007112731; + } else { + result[0] += -0.0027204938; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { + result[0] += 0.054216303; + } else { + result[0] += 0.007258077; + } + } + } else { + result[0] += 0.059579976; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 152))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 62))) { + result[0] += 0.0030776516; + } else { + result[0] += 0.05093806; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { + result[0] += 0.07593134; + } else { + result[0] += 0.11205458; + } + } + } else { + result[0] += 0.14210285; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 32))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + result[0] += -0.014897979; + } else { + result[0] += 0.003548234; + } + } else { + result[0] += 0.018050188; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 88))) { + result[0] += -0.008003016; + } else { + result[0] += -0.029365538; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + result[0] += -0.008477488; + } else { + result[0] += -0.05125452; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { + result[0] += 0.02146909; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 8))) { + result[0] += -0.038308386; + } else { + result[0] += 0.0050441376; + } + } + } else { + result[0] += 0.045694016; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + result[0] += 0.010466478; + } else { + result[0] += 0.032060686; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + result[0] += 0.055632647; + } else { + result[0] += 0.10817957; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 244))) { + result[0] += 0.05290089; + } else { + result[0] += 0.15234004; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += 0.11994175; + } else { + result[0] += 0.16568963; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += 0.21408843; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { + result[0] += 0.109505884; + } else { + result[0] += 0.16057345; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += -0.021672187; + } else { + result[0] += -0.05343589; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += 0.0054487307; + } else { + result[0] += -0.017187914; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 92))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 188))) { + result[0] += -0.019816956; + } else { + result[0] += -0.0050099557; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 248))) { + result[0] += -0.03200326; + } else { + result[0] += -0.056725156; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + result[0] += -0.011511999; + } else { + result[0] += -0.028344637; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += 0.024851266; + } else { + result[0] += -0.05057622; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { + result[0] += -0.0015090223; + } else { + result[0] += 0.04174643; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 206))) { + result[0] += -0.014773312; + } else { + result[0] += -0.001539268; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 210))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + result[0] += 0.007884377; + } else { + result[0] += 0.061007004; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + result[0] += 0.021275358; + } else { + result[0] += 0.004744153; + } + } + } else { + result[0] += 0.053870168; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + result[0] += 0.00918735; + } else { + result[0] += 0.06173278; + } + } else { + result[0] += 0.098605655; + } + } else { + result[0] += 0.13258949; + } + } + } + } + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 266))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { + result[0] += 0.0034634469; + } else { + result[0] += -0.01012047; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 202))) { + result[0] += 0.026168926; + } else { + result[0] += -0.003920808; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 34))) { + result[0] += 0.01599789; + } else { + result[0] += -0.02167696; + } + } else { + result[0] += -0.057240784; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 180))) { + result[0] += 0.016893527; + } else { + result[0] += -0.0182766; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { + result[0] += -0.027289895; + } else { + result[0] += -0.0045931367; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 236))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + result[0] += 0.06542163; + } else { + result[0] += 0.048862282; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 248))) { + result[0] += 0.012741444; + } else { + result[0] += -0.0075932243; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 250))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 64))) { + result[0] += 0.090353966; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 260))) { + result[0] += 0.042119607; + } else { + result[0] += 0.053020816; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 46))) { + result[0] += 0.03130157; + } else { + result[0] += 0.007205482; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { + result[0] += 0.10881876; + } else { + result[0] += 0.054842055; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + result[0] += 0.031206427; + } else { + result[0] += 0.093665555; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { + result[0] += 0.058885314; + } else { + result[0] += 0.009160067; + } + } + } else { + result[0] += 0.12904838; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 196))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 132))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 126))) { + result[0] += 0.009280808; + } else { + result[0] += 0.039755784; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 184))) { + result[0] += -0.0042881714; + } else { + result[0] += 0.02849459; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 176))) { + result[0] += 0.013534146; + } else { + result[0] += 0.042829912; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 196))) { + result[0] += 0.060934413; + } else { + result[0] += 0.04222716; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 166))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 252))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 242))) { + result[0] += -0.014791851; + } else { + result[0] += 0.03050575; + } + } else { + result[0] += -0.051454216; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 244))) { + result[0] += -0.00703993; + } else { + result[0] += 0.006275458; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 230))) { + result[0] += 0.05098787; + } else { + result[0] += 0.0045872494; + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 236))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 156))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 204))) { + result[0] += 0.010856108; + } else { + result[0] += -0.028078709; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 196))) { + result[0] += 0.059345823; + } else { + result[0] += 0.025444312; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 246))) { + result[0] += 0.1018459; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 226))) { + result[0] += 0.06307183; + } else { + result[0] += 0.049246445; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 192))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 248))) { + result[0] += -0.0057842466; + } else { + result[0] += 0.03709874; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 272))) { + result[0] += 0.10474528; + } else { + result[0] += 0.0065343017; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { + result[0] += 0.14898759; + } else { + result[0] += 0.09902468; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 204))) { + result[0] += -0.020444617; + } else { + result[0] += 0.08464522; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + result[0] += -0.010948095; + } else { + result[0] += -0.023803001; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + result[0] += -0.011822949; + } else { + result[0] += 0.045119066; + } + } + } else { + result[0] += -0.06180344; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { + result[0] += -0.005668474; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 258))) { + result[0] += 0.06234616; + } else { + result[0] += 0.017185496; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 178))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 100))) { + result[0] += 0.024365185; + } else { + result[0] += 0.034411617; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + result[0] += 0.0037991663; + } else { + result[0] += 0.06540612; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + result[0] += 0.04412658; + } else { + result[0] += 0.09097751; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { + result[0] += 0.109255195; + } else { + result[0] += 0.15402773; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += 0.18228906; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { + result[0] += 0.091413066; + } else { + result[0] += 0.12995003; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 66))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + result[0] += -0.013535394; + } else { + result[0] += -0.030926574; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + result[0] += -0.02790393; + } else { + result[0] += -0.0042497306; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + result[0] += -0.016862048; + } else { + result[0] += -0.042121567; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + result[0] += 0.043057855; + } else { + result[0] += -0.010520599; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 268))) { + result[0] += 0.0006321628; + } else { + result[0] += 0.043750294; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { + result[0] += 0.017152889; + } else { + result[0] += 0.0047487523; + } + } + } else { + result[0] += 0.047594197; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + result[0] += -0.0008511189; + } else { + result[0] += -0.015743343; + } + } else { + result[0] += 0.020423314; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { + result[0] += 0.071650326; + } else { + result[0] += 0.038795434; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.106261484; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { + result[0] += 0.05060423; + } else { + result[0] += 0.09152374; + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 32))) { + result[0] += -0.0061984994; + } else { + result[0] += 0.037181865; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { + result[0] += 0.05393646; + } else { + result[0] += 0.015753375; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 134))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + result[0] += -0.021209892; + } else { + result[0] += 0.0069300993; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { + result[0] += -0.0077880546; + } else { + result[0] += -0.04197134; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 12))) { + result[0] += 0.040751807; + } else { + result[0] += -0.002328172; + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 254))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 198))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { + result[0] += 0.011177325; + } else { + result[0] += -0.0057386267; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 100))) { + result[0] += 0.022035722; + } else { + result[0] += 0.031592578; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 154))) { + result[0] += 0.046259467; + } else { + result[0] += 0.07964405; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.037900005; + } else { + result[0] += 0.006691271; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + result[0] += 0.045320172; + } else { + result[0] += 0.08683389; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + result[0] += 0.112302594; + } else { + result[0] += 0.15148906; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 218))) { + result[0] += 0.09804384; + } else { + result[0] += 0.15994717; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += -0.014513046; + } else { + result[0] += -0.04194841; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 60))) { + result[0] += -0.018427247; + } else { + result[0] += -0.009509578; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 182))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { + result[0] += -0.021712733; + } else { + result[0] += -0.033654116; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { + result[0] += 0.017070105; + } else { + result[0] += -0.010606452; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { + result[0] += 0.003849056; + } else { + result[0] += -0.012776096; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += -0.01715595; + } else { + result[0] += -0.034953322; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + result[0] += 0.004928622; + } else { + result[0] += -0.009454594; + } + } else { + result[0] += 0.06586691; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + result[0] += 0.00017961742; + } else { + result[0] += -0.0068662055; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += 0.034061044; + } else { + result[0] += 0.004285582; + } + } + } else { + result[0] += 0.041407876; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + result[0] += 0.0008129136; + } else { + result[0] += 0.046276525; + } + } else { + result[0] += 0.08599146; + } + } else { + result[0] += 0.092344016; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 6))) { + result[0] += 0.0022713204; + } else { + result[0] += -0.01581463; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + result[0] += -0.019155947; + } else { + result[0] += -0.031192351; + } + } + } else { + result[0] += -0.054840814; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += 0.034809362; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + result[0] += -0.03447778; + } else { + result[0] += -0.0010464619; + } + } + } else { + result[0] += 0.036184475; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 178))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { + result[0] += 0.011472473; + } else { + result[0] += -0.00536993; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { + result[0] += 0.017885301; + } else { + result[0] += 0.026684735; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 36))) { + result[0] += 0.050483853; + } else { + result[0] += 0.032498028; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { + result[0] += 0.107258014; + } else { + result[0] += 0.05922347; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += 0.14826694; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { + result[0] += 0.07493075; + } else { + result[0] += 0.105560794; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { + result[0] += -0.024180638; + } else { + result[0] += -0.010499556; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 218))) { + result[0] += 0.016562223; + } else { + result[0] += -0.013134924; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { + result[0] += -0.018523889; + } else { + result[0] += -0.030611722; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += -0.017535986; + } else { + result[0] += -0.004351136; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { + result[0] += -0.021926327; + } else { + result[0] += -0.008350995; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 42))) { + result[0] += -0.011140397; + } else { + result[0] += -0.04280148; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + result[0] += 0.03963171; + } else { + result[0] += 0.019383498; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 214))) { + result[0] += -0.00729556; + } else { + result[0] += -0.040553037; + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.08876854; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.067758165; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { + result[0] += 0.03981258; + } else { + result[0] += 0.05752442; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { + result[0] += -0.00089354016; + } else { + result[0] += 0.0065446827; + } + } else { + result[0] += -0.014053066; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 222))) { + result[0] += 0.06474081; + } else { + result[0] += 0.017832294; + } + } + } + } + } + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 206))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 202))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 100))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 30))) { + result[0] += 0.0055920025; + } else { + result[0] += -0.013393702; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { + result[0] += 0.03213504; + } else { + result[0] += 0.004373153; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { + result[0] += -0.026502002; + } else { + result[0] += -0.0089536635; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 108))) { + result[0] += 0.023841746; + } else { + result[0] += -0.009630135; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 132))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 84))) { + result[0] += 0.03338425; + } else { + result[0] += -0.007993841; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 98))) { + result[0] += 0.039039984; + } else { + result[0] += 0.017829265; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + result[0] += -0.008180471; + } else { + result[0] += -0.017685762; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 172))) { + result[0] += 0.024588756; + } else { + result[0] += 0.00014103504; + } + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 120))) { + result[0] += -0.017513642; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 228))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 204))) { + result[0] += 0.053347778; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { + result[0] += 0.036463372; + } else { + result[0] += 0.019543491; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + result[0] += 0.06796604; + } else { + result[0] += 0.011818393; + } + } else { + result[0] += -0.003918262; + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 260))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 252))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 224))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 108))) { + result[0] += 0.055681136; + } else { + result[0] += 0.03390036; + } + } else { + result[0] += 0.014793222; + } + } else { + result[0] += 0.06464329; + } + } else { + result[0] += 0.1336614; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 214))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 200))) { + result[0] += -0.010694483; + } else { + result[0] += 0.0089774085; + } + } else { + result[0] += 0.09665075; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 214))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { + result[0] += 0.005944577; + } else { + result[0] += 0.039748937; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { + result[0] += -0.036054015; + } else { + result[0] += -0.03007635; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { + result[0] += 0.07873906; + } else { + result[0] += 0.040747557; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 280))) { + result[0] += 0.10715278; + } else { + result[0] += 0.07093804; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 272))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 228))) { + result[0] += 0.0400591; + } else { + result[0] += 0.070340686; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { + result[0] += 0.006239193; + } else { + result[0] += 0.04050245; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 20))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + result[0] += 0.0143945515; + } else { + result[0] += -0.01724617; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 124))) { + result[0] += 0.06920057; + } else { + result[0] += 0.012906248; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { + result[0] += -0.028011957; + } else { + result[0] += -0.054441918; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 222))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += -0; + } else { + result[0] += -0.015584071; + } + } else { + result[0] += 0.04294205; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 176))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + result[0] += 0.0010215238; + } else { + result[0] += 0.018780788; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 36))) { + result[0] += 0.038763847; + } else { + result[0] += 0.023370415; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 224))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + result[0] += 0.032697503; + } else { + result[0] += 0.13195097; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += 0.074951015; + } else { + result[0] += 0.10605689; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += 0.12042389; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 6))) { + result[0] += 0.07597848; + } else { + result[0] += 0.059126675; + } + } else { + result[0] += 0.087247975; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + result[0] += -0.011189851; + } else { + result[0] += -0.023633793; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + result[0] += -0.009847272; + } else { + result[0] += 0.0011721178; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { + result[0] += -0.033823837; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + result[0] += -0.028593678; + } else { + result[0] += -0.023168823; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { + result[0] += 0.048958402; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 226))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { + result[0] += -0.0030530605; + } else { + result[0] += 0.009326304; + } + } else { + result[0] += 0.015771423; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + result[0] += -0.019048717; + } else { + result[0] += -0.00045981476; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + result[0] += 0.036297392; + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { + result[0] += 0.004649827; + } else { + result[0] += 0.024824895; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { + result[0] += 0.0042260056; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + result[0] += 0.035964742; + } else { + result[0] += 0.05283689; + } + } + } else { + result[0] += 0.072465; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + result[0] += -0.012068967; + } else { + result[0] += -0.027094675; + } + } else { + result[0] += -0.066233106; + } + } else { + result[0] += 0.025606213; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { + result[0] += 0.0134207625; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + result[0] += -0.051151592; + } else { + result[0] += -0.013505876; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + result[0] += 0.033789955; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 226))) { + result[0] += -0.0063351872; + } else { + result[0] += 0.01952872; + } + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 232))) { + result[0] += 0.017679365; + } else { + result[0] += 0.0012839021; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 188))) { + result[0] += 0.01832609; + } else { + result[0] += 0.031185871; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { + result[0] += 0.07488849; + } else { + result[0] += 0.046187967; + } + } else { + result[0] += 0.09955382; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { + result[0] += 0.069774024; + } else { + result[0] += 0.10838503; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { + result[0] += 0.05327609; + } else { + result[0] += 0.077984296; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + result[0] += 0.0047698705; + } else { + result[0] += 0.017100412; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + result[0] += -0.0088116955; + } else { + result[0] += 0.0046756053; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 46))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 138))) { + result[0] += -0.021546138; + } else { + result[0] += -0.06706627; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { + result[0] += 0.0009891371; + } else { + result[0] += -0.015693253; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 76))) { + result[0] += -0.033967104; + } else { + result[0] += -0.010871164; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + result[0] += -0.01983557; + } else { + result[0] += -0.033842903; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += 3.485963e-05; + } else { + result[0] += 0.029934336; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { + result[0] += -0.009020819; + } else { + result[0] += -0.0016374525; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { + result[0] += -0.00035558367; + } else { + result[0] += 0.031120656; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { + result[0] += 0.0042784135; + } else { + result[0] += -0.003890027; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + result[0] += 0.03780892; + } else { + result[0] += 0.020871228; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { + result[0] += 0.032693; + } else { + result[0] += 0.016958345; + } + } else { + result[0] += 0.056264985; + } + } else { + result[0] += 0.061108418; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + result[0] += 0.016505312; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 58))) { + result[0] += -0.012738267; + } else { + result[0] += -0.020164208; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + result[0] += 0.0137371225; + } else { + result[0] += -0.010237254; + } + } + } else { + result[0] += -0.037308313; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 112))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + result[0] += 0.0016659134; + } else { + result[0] += 0.022526134; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { + result[0] += 0.02430934; + } else { + result[0] += 0.010316737; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + result[0] += 0.03054412; + } else { + result[0] += 0.013778204; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 218))) { + result[0] += 0.062106986; + } else { + result[0] += 0.08695674; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += 0.097699985; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { + result[0] += 0.048410773; + } else { + result[0] += 0.061695654; + } + } else { + result[0] += 0.0699871; + } + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { + result[0] += 0.017755916; + } else { + result[0] += -0.0076434105; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 80))) { + result[0] += -0.013907449; + } else { + result[0] += -0.028985953; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += -0.03517434; + } else { + result[0] += 0.02803059; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + result[0] += -0.005040298; + } else { + result[0] += 0.029063394; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 18))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 252))) { + result[0] += -0.0004860425; + } else { + result[0] += -0.00823578; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + result[0] += 0.020955933; + } else { + result[0] += -0.005362306; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + result[0] += -0.022735301; + } else { + result[0] += -0.006933318; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { + result[0] += 0.008230773; + } else { + result[0] += -0.011880973; + } + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 232))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 192))) { + result[0] += 0.00536417; + } else { + result[0] += -0.020807503; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { + result[0] += 0.029748295; + } else { + result[0] += 0.041834693; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 196))) { + result[0] += -0.00087343634; + } else { + result[0] += -0.026689067; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 182))) { + result[0] += -0.0014600045; + } else { + result[0] += 0.013319497; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 256))) { + result[0] += 0.0012430011; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { + result[0] += -0.028792778; + } else { + result[0] += -0.024853468; + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 4))) { + result[0] += -0.009469046; + } else { + result[0] += 0.0108768735; + } + } else { + result[0] += -0.025166774; + } + } else { + result[0] += -0.061156314; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { + result[0] += -0.011232008; + } else { + result[0] += -0.025154117; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + result[0] += 0.008252529; + } else { + result[0] += 0.046288077; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 6))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 220))) { + result[0] += 0.040085025; + } else { + result[0] += -0.008784636; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + result[0] += -0.019954626; + } else { + result[0] += 0.0048681814; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 178))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += 0.007502618; + } else { + result[0] += -0.0053296923; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { + result[0] += 0.013098051; + } else { + result[0] += 0.022515437; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 222))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 234))) { + result[0] += 0.02392622; + } else { + result[0] += 0.006717711; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 92))) { + result[0] += 0.036931966; + } else { + result[0] += 0.024680862; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 192))) { + result[0] += 0.049618512; + } else { + result[0] += 0.030690536; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 248))) { + result[0] += 0.087034844; + } else { + result[0] += 0.06434413; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.076431125; + } else { + result[0] += 0.050094455; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.08487356; + } else { + result[0] += 0.016335858; + } + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { + result[0] += 0.0068731843; + } else { + result[0] += -0.027962524; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 30))) { + result[0] += -0.005616961; + } else { + result[0] += -0.020151896; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 160))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + result[0] += -0.011980721; + } else { + result[0] += -0.022607153; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { + result[0] += 0.045720205; + } else { + result[0] += 0.011621847; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 58))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + result[0] += 0.02517539; + } else { + result[0] += -0.0003533575; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { + result[0] += 0.00938051; + } else { + result[0] += -0.011884357; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += -0.0057867505; + } else { + result[0] += -0.025664538; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { + result[0] += -0.008385401; + } else { + result[0] += -0.0017240072; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 266))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + result[0] += -0.000107964406; + } else { + result[0] += 0.0060515343; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 244))) { + result[0] += -0.005519016; + } else { + result[0] += 0.0014766084; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.02811285; + } else { + result[0] += 0.013522627; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 152))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { + result[0] += -0; + } else { + result[0] += 0.015318493; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { + result[0] += 0.02693863; + } else { + result[0] += 0.042594276; + } + } + } else { + result[0] += 0.05915534; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + result[0] += -0.015769325; + } else { + result[0] += 0.009167842; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + result[0] += -0.066482425; + } else { + result[0] += -0.029134443; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 58))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { + result[0] += -0.010345658; + } else { + result[0] += -0.0004427325; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + result[0] += 0.00778659; + } else { + result[0] += -0.052467596; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += 0.025486384; + } else { + result[0] += 0.011895447; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + result[0] += 0.022794057; + } else { + result[0] += 0.048023786; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + result[0] += 0.024896143; + } else { + result[0] += 0.010721759; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += 0.04730309; + } else { + result[0] += 0.07061689; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += 0.079474784; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 6))) { + result[0] += 0.050025214; + } else { + result[0] += 0.03944496; + } + } else { + result[0] += 0.05712953; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 54))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += 0.0070698797; + } else { + result[0] += -0.009966139; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + result[0] += 0.015061869; + } else { + result[0] += -0.0023575744; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { + result[0] += -0.021192541; + } else { + result[0] += -0.00409846; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 156))) { + result[0] += -0.01823308; + } else { + result[0] += 0.00014080759; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 206))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 100))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + result[0] += 0.02206879; + } else { + result[0] += -0.00015570642; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + result[0] += -0.015682366; + } else { + result[0] += -0.006868744; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { + result[0] += 0.0135331275; + } else { + result[0] += -0.023557609; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { + result[0] += 0.04654594; + } else { + result[0] += 0.0058658714; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + result[0] += -0.016888324; + } else { + result[0] += -0.00063805765; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { + result[0] += 0.029826907; + } else { + result[0] += 0.007284993; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { + result[0] += 0.001150948; + } else { + result[0] += 0.0069423555; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 152))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + result[0] += -0.0042433054; + } else { + result[0] += 0.011705535; + } + } else { + result[0] += 0.025982574; + } + } else { + result[0] += 0.048386928; + } + } + } + } + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 206))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += -0.042859327; + } else { + result[0] += -0.0044231373; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { + result[0] += 0.049170107; + } else { + result[0] += 0.035520416; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { + result[0] += -0.0009743745; + } else { + result[0] += 0.009931285; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { + result[0] += -0.0017889539; + } else { + result[0] += -0.007930504; + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 226))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 204))) { + result[0] += 0.0063184327; + } else { + result[0] += -0.015213072; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + result[0] += 0.014672543; + } else { + result[0] += 0.026392935; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 154))) { + result[0] += -0.037449803; + } else { + result[0] += -0.053623743; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { + result[0] += 0.002925502; + } else { + result[0] += -0.010573565; + } + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 264))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 152))) { + result[0] += 0.00568271; + } else { + result[0] += -0.004481957; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 212))) { + result[0] += 0.053150166; + } else { + result[0] += 0.014763187; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 194))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 80))) { + result[0] += -0.008710187; + } else { + result[0] += 0.075820915; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + result[0] += -0.023372917; + } else { + result[0] += -0.012099934; + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 204))) { + result[0] += 0.037964396; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 206))) { + result[0] += 0.0036865615; + } else { + result[0] += 0.021090202; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { + result[0] += -0.025623156; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 200))) { + result[0] += -0.004627872; + } else { + result[0] += 0.004686881; + } + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 262))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 248))) { + result[0] += 0.0714253; + } else { + result[0] += 0.048554998; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 232))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 230))) { + result[0] += 0.02269533; + } else { + result[0] += 0.014301536; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 220))) { + result[0] += 0.0025220858; + } else { + result[0] += 0.011460328; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 56))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + result[0] += 0.019217266; + } else { + result[0] += 0.03249597; + } + } else { + result[0] += 0.035739582; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 248))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { + result[0] += -0.023269765; + } else { + result[0] += -0.01868113; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 180))) { + result[0] += -0.0065037594; + } else { + result[0] += -0.022253536; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { + result[0] += 0.0036995602; + } else { + result[0] += 0.0520568; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 146))) { + result[0] += 0.02432552; + } else { + result[0] += -0.01594592; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { + result[0] += 0.0497043; + } else { + result[0] += 0.022733098; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.055147793; + } else { + result[0] += 0.041854013; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 166))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + result[0] += 0.0019326921; + } else { + result[0] += -0.013451728; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 280))) { + result[0] += 0.029732933; + } else { + result[0] += 0.009648888; + } + } + } + } + } + } + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 206))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += -0.002109048; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 92))) { + result[0] += 0.04531033; + } else { + result[0] += 0.028058738; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { + result[0] += -0.0014130161; + } else { + result[0] += 0.009621711; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { + result[0] += -0.0014670437; + } else { + result[0] += -0.007695043; + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 226))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 156))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 192))) { + result[0] += 0.007604044; + } else { + result[0] += -0.0070991814; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 220))) { + result[0] += 0.020077487; + } else { + result[0] += 0.006828181; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 154))) { + result[0] += -0.03365531; + } else { + result[0] += -0.049779564; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { + result[0] += 0.0017921542; + } else { + result[0] += -0.009079668; + } + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 260))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 152))) { + result[0] += 0.005504231; + } else { + result[0] += -0.0039019831; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 212))) { + result[0] += 0.044337142; + } else { + result[0] += 0.0070573166; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 194))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { + result[0] += 0.028603448; + } else { + result[0] += -0.0078216465; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { + result[0] += 0.015729161; + } else { + result[0] += -0.016172249; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 204))) { + result[0] += 0.03005304; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { + result[0] += 0.016138185; + } else { + result[0] += 0.033333454; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { + result[0] += -0.02334422; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + result[0] += 0.0137236165; + } else { + result[0] += 0.003765919; + } + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 260))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += 0.06424896; + } else { + result[0] += 0.045115866; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 232))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { + result[0] += 0.020254442; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { + result[0] += 0.003927156; + } else { + result[0] += -0.00030379518; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 56))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + result[0] += 0.01626153; + } else { + result[0] += 0.030240763; + } + } else { + result[0] += 0.031179471; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { + result[0] += -0.019889602; + } else { + result[0] += -0.006115454; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { + result[0] += 0.02490984; + } else { + result[0] += 0.0096712075; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { + result[0] += -0.016214207; + } else { + result[0] += 0.0054753; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 162))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.04828751; + } else { + result[0] += 0.02183148; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { + result[0] += 0.095508024; + } else { + result[0] += 0.037357938; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { + result[0] += 0.024429439; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += -0.032644305; + } else { + result[0] += -0.007228152; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 92))) { + result[0] += -0; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + result[0] += -0.0005319937; + } else { + result[0] += 0.04273183; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += -0; + } else { + result[0] += -0.015805844; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 220))) { + result[0] += 0.028671984; + } else { + result[0] += -0; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { + result[0] += -0.015004277; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { + result[0] += -0.043184254; + } else { + result[0] += -0.015434514; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { + result[0] += 0.027698422; + } else { + result[0] += 0.019098751; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { + result[0] += 0.0024283626; + } else { + result[0] += 0.019441115; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 58))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += 0.07004071; + } else { + result[0] += 0.022275856; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { + result[0] += -0.0013316347; + } else { + result[0] += 0.012960051; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 224))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.06203411; + } else { + result[0] += 0.035423633; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.057469275; + } else { + result[0] += 0.009981114; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 212))) { + result[0] += 0.023559755; + } else { + result[0] += 0.035965245; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { + result[0] += 0.058553632; + } else { + result[0] += 0.04478031; + } + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { + result[0] += 0.01235016; + } else { + result[0] += -0.0039201183; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 80))) { + result[0] += -0.007764698; + } else { + result[0] += -0.019835753; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += -0.006487711; + } else { + result[0] += 0.018991722; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + result[0] += -0.0039943634; + } else { + result[0] += 0.024546368; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 154))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { + result[0] += -0.0147830695; + } else { + result[0] += -0.008563632; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { + result[0] += -0.030682612; + } else { + result[0] += 0.017968642; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 166))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { + result[0] += 0.068739615; + } else { + result[0] += 0.00021970407; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + result[0] += -0.0028976346; + } else { + result[0] += -0.009871369; + } + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 236))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { + result[0] += -0.021564588; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { + result[0] += 0.0040140515; + } else { + result[0] += 0.016122099; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 210))) { + result[0] += -0.0033681232; + } else { + result[0] += 0.0073165386; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { + result[0] += -0.025544493; + } else { + result[0] += -0.01873306; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 230))) { + result[0] += -0.019029168; + } else { + result[0] += 0.004258202; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 254))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { + result[0] += -0.00011749373; + } else { + result[0] += -0.010187042; + } + } else { + result[0] += 0.037634984; + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { + result[0] += -0.0064992644; + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 146))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 144))) { + result[0] += 0.022600017; + } else { + result[0] += -0.0016712642; + } + } else { + result[0] += 0.025729222; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 20))) { + result[0] += -0.007915151; + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + result[0] += 0.0005776281; + } else { + result[0] += 0.031780552; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { + result[0] += -0.017479556; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 178))) { + result[0] += -0.04499294; + } else { + result[0] += -0.01988611; + } + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { + result[0] += 0.008722408; + } else { + result[0] += 0.032495603; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += -0.0015038172; + } else { + result[0] += 0.009706586; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.08136292; + } else { + result[0] += 0.015997507; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { + result[0] += 0.03901923; + } else { + result[0] += 0.0564677; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + result[0] += 0.0416153; + } else { + result[0] += 0.026928617; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { + result[0] += 0.041233912; + } else { + result[0] += 0.05209862; + } + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { + result[0] += 0.00972145; + } else { + result[0] += 0.04575323; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { + result[0] += 0.02476694; + } else { + result[0] += -0.03629762; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.021036565; + } else { + result[0] += -0.045760524; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 224))) { + result[0] += 0.007162188; + } else { + result[0] += -0.018060943; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 22))) { + result[0] += -0.007815191; + } else { + result[0] += -0.017675815; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { + result[0] += -0.0029981257; + } else { + result[0] += 0.020084158; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { + result[0] += 0.02050746; + } else { + result[0] += -0.0026271623; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + result[0] += -0.011671978; + } else { + result[0] += -0.0055001257; + } + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 228))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { + result[0] += -0.019566214; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { + result[0] += 0.0054924428; + } else { + result[0] += -0.00093082106; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + result[0] += 0.0126124; + } else { + result[0] += 0.032813326; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 272))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + result[0] += -0.023916144; + } else { + result[0] += -0.017258545; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += 0.05414641; + } else { + result[0] += 0.000113278526; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 254))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { + result[0] += -0.00055813155; + } else { + result[0] += -0.008351603; + } + } else { + result[0] += 0.032734703; + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + result[0] += -0.0139853405; + } else { + result[0] += 0.003684853; + } + } else { + result[0] += 0.008061817; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { + result[0] += -0.06050358; + } else { + result[0] += -0.0023646136; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += 0.028764948; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += -0.028082406; + } else { + result[0] += -0.0058664638; + } + } else { + result[0] += 0.023923343; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + result[0] += 0.009201097; + } else { + result[0] += -0.010257622; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + result[0] += 0.012351765; + } else { + result[0] += 0.08077949; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { + result[0] += 0.037493166; + } else { + result[0] += 0.052167382; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { + result[0] += 0.036415916; + } else { + result[0] += 0.046912543; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { + result[0] += 0.023727143; + } else { + result[0] += 0.036457557; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { + result[0] += -0.002685583; + } else { + result[0] += -0.006960124; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { + result[0] += 0.04494014; + } else { + result[0] += 0.0043056607; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 220))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + result[0] += -0.0039729676; + } else { + result[0] += -0.014122757; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 178))) { + result[0] += -0.025312701; + } else { + result[0] += -0.014867464; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + result[0] += 0.015924158; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 190))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 184))) { + result[0] += -0.0075506503; + } else { + result[0] += 0.010475333; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + result[0] += -0.027276382; + } else { + result[0] += -0.004030099; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 98))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { + result[0] += 0.014477782; + } else { + result[0] += -0.015422763; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { + result[0] += 0.03197563; + } else { + result[0] += 0.020037709; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { + result[0] += -0.019650823; + } else { + result[0] += 0.002236197; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 150))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { + result[0] += -0.02055483; + } else { + result[0] += -0.0014581191; + } + } else { + result[0] += -0.016465666; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 242))) { + result[0] += -0.05338929; + } else { + result[0] += -0.014811342; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { + result[0] += 0.01168551; + } else { + result[0] += 0.0010410798; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + result[0] += -0.005663588; + } else { + result[0] += 0.012237726; + } + } else { + result[0] += -0.015190408; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { + result[0] += -0.048400734; + } else { + result[0] += -0.01567449; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { + result[0] += 0.034811612; + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 166))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + result[0] += -0.005241947; + } else { + result[0] += 0.0036905694; + } + } else { + result[0] += 0.027789101; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 172))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 168))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + result[0] += -0.0023323249; + } else { + result[0] += 0.008576729; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + result[0] += -0.02920859; + } else { + result[0] += -0.005879428; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 236))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { + result[0] += 0.01825313; + } else { + result[0] += 0.01146; + } + } else { + result[0] += -0.0015520846; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + result[0] += 0.050887883; + } else { + result[0] += 0.0008823246; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { + result[0] += 0.04027082; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { + result[0] += 0.02058533; + } else { + result[0] += 0.039607793; + } + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 28))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += 0.0065161586; + } else { + result[0] += -0.007997109; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { + result[0] += 0.005459733; + } else { + result[0] += -0.0032214918; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { + result[0] += -0.015889943; + } else { + result[0] += -0.054882165; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 160))) { + result[0] += 0.013258442; + } else { + result[0] += -0.008985414; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { + result[0] += 0.03941143; + } else { + result[0] += 0.01124495; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 100))) { + result[0] += -0.01534603; + } else { + result[0] += 0.0053413953; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 236))) { + result[0] += -0.017410088; + } else { + result[0] += 0.013132173; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { + result[0] += -0.0060942764; + } else { + result[0] += -0.0017843047; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 222))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { + result[0] += 0.035340164; + } else { + result[0] += 0.0032424498; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.02138096; + } else { + result[0] += 0.011583105; + } + } + } else { + result[0] += -0.0026775673; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 152))) { + result[0] += 0.0032441877; + } else { + result[0] += 0.013359073; + } + } else { + result[0] += 0.030153606; + } + } else { + result[0] += 0.036456186; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + result[0] += -0.0117252795; + } else { + result[0] += 0.0043645753; + } + } else { + result[0] += -0.041562933; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + result[0] += 0.0008401942; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + result[0] += -0.01901227; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { + result[0] += 0.014339037; + } else { + result[0] += -0.00501071; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 16))) { + result[0] += 0.012573126; + } else { + result[0] += 0.0028887743; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 38))) { + result[0] += 0.046177443; + } else { + result[0] += 0.015051349; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { + result[0] += 0.010199251; + } else { + result[0] += -0.039779864; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { + result[0] += -0.004799234; + } else { + result[0] += 0.008129753; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += 0.04275175; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { + result[0] += -0.008011124; + } else { + result[0] += 0.002239405; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 268))) { + result[0] += 0.017378718; + } else { + result[0] += 0.03415826; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 218))) { + result[0] += 0.02640838; + } else { + result[0] += 0.039306883; + } + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += 0.0055097975; + } else { + result[0] += -0.007307568; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + result[0] += 0.0022932815; + } else { + result[0] += -0.0064788023; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { + result[0] += -0.014033759; + } else { + result[0] += -0.049122956; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { + result[0] += 0.005159875; + } else { + result[0] += -0.009883177; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { + result[0] += -0.013826482; + } else { + result[0] += 0.022371221; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { + result[0] += -0.0141291395; + } else { + result[0] += 0.004760613; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { + result[0] += -0.007064409; + } else { + result[0] += 0.023824794; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + result[0] += -0.00016228954; + } else { + result[0] += -0.007653934; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 222))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 208))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { + result[0] += 0.004959669; + } else { + result[0] += -0.001015551; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { + result[0] += 0.027703479; + } else { + result[0] += 0.0036117423; + } + } + } else { + result[0] += -0.0026553113; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { + result[0] += 0.013676906; + } else { + result[0] += -0.0007326856; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + result[0] += -0; + } else { + result[0] += 0.030973986; + } + } + } else { + result[0] += 0.029461041; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 8))) { + result[0] += -0.009251536; + } else { + result[0] += 0.009845628; + } + } else { + result[0] += -0.04083568; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += 0.029976163; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + result[0] += -0.027244702; + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 166))) { + result[0] += -0.004565258; + } else { + result[0] += 0.025788173; + } + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 254))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 34))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + result[0] += 0.011977387; + } else { + result[0] += 0.00021163402; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { + result[0] += 0.038581323; + } else { + result[0] += 0.013672948; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { + result[0] += 0.0043165404; + } else { + result[0] += -0.035845544; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { + result[0] += -0.0024726556; + } else { + result[0] += 0.008141091; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += 0.03638802; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 166))) { + result[0] += 0.0001506592; + } else { + result[0] += 0.009602265; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 218))) { + result[0] += 0.022467962; + } else { + result[0] += 0.03540038; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.06481004; + } else { + result[0] += 0.017584896; + } + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 140))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 104))) { + result[0] += 0.045290753; + } else { + result[0] += -0.01791346; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + result[0] += -0.058552768; + } else { + result[0] += 0.0349788; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 256))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 198))) { + result[0] += 0.0336964; + } else { + result[0] += 0.0018466607; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 176))) { + result[0] += -0.0049308436; + } else { + result[0] += -0.012199649; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 86))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 100))) { + result[0] += -0.007085; + } else { + result[0] += -0.016483508; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + result[0] += 0.006847045; + } else { + result[0] += -0.0070480793; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { + result[0] += -0.0041096415; + } else { + result[0] += 0.0021135767; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { + result[0] += -0.0060591055; + } else { + result[0] += -0.0011625281; + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 138))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 28))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + result[0] += 0.02211509; + } else { + result[0] += 0.060023166; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { + result[0] += -0.013461961; + } else { + result[0] += 0.04670935; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + result[0] += -0; + } else { + result[0] += 0.025248567; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 154))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 148))) { + result[0] += -0.008415373; + } else { + result[0] += 0.013447831; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 138))) { + result[0] += -0.016349984; + } else { + result[0] += -0.030504474; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += 0.00021573504; + } else { + result[0] += -0.013515745; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { + result[0] += 8.818205e-05; + } else { + result[0] += 0.010008925; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 18))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + result[0] += 0.0037289502; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 124))) { + result[0] += 0.054951753; + } else { + result[0] += -0.0039979555; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + result[0] += -0.00068969437; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + result[0] += -0.012617968; + } else { + result[0] += -0.027266933; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 160))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 48))) { + result[0] += 0.0068334453; + } else { + result[0] += -0.0039404687; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { + result[0] += -0; + } else { + result[0] += 0.039604023; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 178))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 36))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 154))) { + result[0] += -0.0074695237; + } else { + result[0] += 0.0059394534; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 218))) { + result[0] += 0.02302828; + } else { + result[0] += -0.047223628; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 156))) { + result[0] += -0.016071025; + } else { + result[0] += 0.0044617057; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + result[0] += -0.0016089712; + } else { + result[0] += 0.0069275023; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += 0.030206159; + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { + result[0] += 0.010797313; + } else { + result[0] += -0.002882598; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { + result[0] += 0.020259693; + } else { + result[0] += 0.0355315; + } + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 192))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 78))) { + result[0] += -0.0044226176; + } else { + result[0] += -0.02530554; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { + result[0] += 0.008079353; + } else { + result[0] += -0.003107704; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { + result[0] += -0.026262758; + } else { + result[0] += -0.013569482; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { + result[0] += -0.007361694; + } else { + result[0] += 0.01589146; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 186))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + result[0] += 0.0066897767; + } else { + result[0] += -0.03870764; + } + } else { + result[0] += 0.022245135; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 156))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 46))) { + result[0] += 0.0011395493; + } else { + result[0] += -0.0057673557; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { + result[0] += 0.022744471; + } else { + result[0] += 0.008478357; + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 222))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.028410172; + } else { + result[0] += 0.014004901; + } + } else { + result[0] += 0.0010523595; + } + } else { + result[0] += 0.02651517; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 176))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { + result[0] += -0.0015872531; + } else { + result[0] += -0.017748317; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += -0.0032637853; + } else { + result[0] += 0.010829237; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 242))) { + result[0] += 0.034405578; + } else { + result[0] += 0.00891859; + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 20))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 254))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { + result[0] += 0.007226427; + } else { + result[0] += -0.012499331; + } + } else { + result[0] += 0.04246553; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 88))) { + result[0] += 0.002265221; + } else { + result[0] += -0.0032362868; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + result[0] += -0.011394487; + } else { + result[0] += -0.0008440514; + } + } + } else { + result[0] += -0.028067807; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { + result[0] += 0.08105516; + } else { + result[0] += 0.018269602; + } + } else { + result[0] += 0.015147194; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { + result[0] += 0.014643987; + } else { + result[0] += 0.00034420882; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { + result[0] += 0.019848578; + } else { + result[0] += 0.0055640773; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += 0.0281445; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { + result[0] += 0.016479755; + } else { + result[0] += 0.023855714; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 252))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { + result[0] += 0.0043632514; + } else { + result[0] += -0.0032153563; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += -0.0008953023; + } else { + result[0] += -0.012973676; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 224))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { + result[0] += 0.06142653; + } else { + result[0] += 0.014740917; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { + result[0] += 0.036935415; + } else { + result[0] += -0.0027422463; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { + result[0] += -0.043081205; + } else { + result[0] += -0.020138515; + } + } else { + result[0] += -0.012991915; + } + } else { + result[0] += 0.005466835; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { + result[0] += -0.00065770803; + } else { + result[0] += 0.020950794; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 98))) { + result[0] += -0.0020690185; + } else { + result[0] += 0.0022426536; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.01779854; + } else { + result[0] += 0.007740788; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + result[0] += 0.010289395; + } else { + result[0] += 0.022736436; + } + } else { + result[0] += 0.02622193; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { + result[0] += -0.0030147363; + } else { + result[0] += -0.01984936; + } + } else { + result[0] += -0.014292531; + } + } else { + result[0] += 0.019834789; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 12))) { + result[0] += 0.012920295; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { + result[0] += 0.07497602; + } else { + result[0] += 0.01954972; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { + result[0] += 0.010222583; + } else { + result[0] += 0.00049927505; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + result[0] += -0.0032804606; + } else { + result[0] += 0.0070060194; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += 0.024341205; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { + result[0] += 0.015133323; + } else { + result[0] += 0.021515321; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + result[0] += 0.0021268248; + } else { + result[0] += -0.004917339; + } + } else { + result[0] += 0.020086113; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.023110032; + } else { + result[0] += -0.008982231; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 206))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { + result[0] += -0.01757216; + } else { + result[0] += -5.0931576e-05; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + result[0] += 0.017951632; + } else { + result[0] += 0.0057268315; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 62))) { + result[0] += 0.0030992; + } else { + result[0] += -0.010930783; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { + result[0] += -0.00038469338; + } else { + result[0] += -0.004318268; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { + result[0] += -0; + } else { + result[0] += 0.017621709; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + result[0] += 0.0036806122; + } else { + result[0] += -4.4085446e-05; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.021112496; + } else { + result[0] += 0.007883241; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + result[0] += 0.007880076; + } else { + result[0] += 0.02039106; + } + } else { + result[0] += 0.023736782; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 14))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + result[0] += -0; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { + result[0] += 0.022109; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { + result[0] += -0.011896599; + } else { + result[0] += -0.02695978; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 28))) { + result[0] += 0.017046606; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 86))) { + result[0] += -0.022864131; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { + result[0] += 0.01655387; + } else { + result[0] += -0.0029186176; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + result[0] += 0.006224805; + } else { + result[0] += -0.0006160491; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { + result[0] += 0.03912334; + } else { + result[0] += 0.0064465962; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + result[0] += 0.03801068; + } else { + result[0] += -0; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { + result[0] += -0.0056696637; + } else { + result[0] += 0.002394879; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { + result[0] += 0.0026905416; + } else { + result[0] += 0.014434603; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 18))) { + result[0] += -0.0066401684; + } else { + result[0] += -0.02399191; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + result[0] += 0.01420292; + } else { + result[0] += 0.007666865; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 258))) { + result[0] += 0.015625248; + } else { + result[0] += 0.035386283; + } + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 168))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 164))) { + result[0] += -0.0026949244; + } else { + result[0] += 0.010536998; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 172))) { + result[0] += -0.02333055; + } else { + result[0] += -0.004258865; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 190))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 256))) { + result[0] += 0.010322237; + } else { + result[0] += -0.004513755; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 194))) { + result[0] += -0.013793135; + } else { + result[0] += 0.00041879248; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { + result[0] += -0.0012282294; + } else { + result[0] += 0.012586914; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { + result[0] += -0.013370514; + } else { + result[0] += -0.009561422; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.020673957; + } else { + result[0] += 0.01021281; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += 0.029428396; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + result[0] += -0.0008754981; + } else { + result[0] += -0.0100570135; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { + result[0] += 0.016708953; + } else { + result[0] += 0.009689736; + } + } else { + result[0] += 0.00040766338; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { + result[0] += 0.024215741; + } else { + result[0] += 0.012051038; + } + } + } + } + } + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + result[0] += 0.0013062642; + } else { + result[0] += 0.019394014; + } + } else { + result[0] += -0.0104289735; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 48))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += -0.05682643; + } else { + result[0] += 0.026358023; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 126))) { + result[0] += 0.0067068855; + } else { + result[0] += -0.0033787123; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += -0.017422771; + } else { + result[0] += -0.013566106; + } + } else { + result[0] += 0.05098955; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 264))) { + result[0] += 0.0021389506; + } else { + result[0] += 0.022104675; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 196))) { + result[0] += -0.0034482577; + } else { + result[0] += -0.04897159; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + result[0] += 0.04547104; + } else { + result[0] += -0.0036854; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += 0.031185264; + } else { + result[0] += -0.00029240636; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + result[0] += 0.048903078; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { + result[0] += -0.012713486; + } else { + result[0] += -0.0045942473; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { + result[0] += -0.024384957; + } else { + result[0] += 0.010758282; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { + result[0] += 0.030021293; + } else { + result[0] += 0.017209774; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 220))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { + result[0] += -0.0025992983; + } else { + result[0] += 0.0014243874; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += -0.014005645; + } else { + result[0] += 0.00015922675; + } + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 184))) { + result[0] += -0.0023512202; + } else { + result[0] += 0.00030087968; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { + result[0] += 0.015333219; + } else { + result[0] += 0.050305735; + } + } else { + result[0] += 0.0038954068; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 220))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { + result[0] += 0.005740883; + } else { + result[0] += -0.011134521; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { + result[0] += -0.0102155125; + } else { + result[0] += -0.01861443; + } + } else { + result[0] += -0.008878123; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 36))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 58))) { + result[0] += 0.07084565; + } else { + result[0] += 0.019467566; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 106))) { + result[0] += -0.005714934; + } else { + result[0] += 0.0072955615; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { + result[0] += 0.0217909; + } else { + result[0] += 0.008551905; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + result[0] += 0.016447848; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { + result[0] += 0.028782597; + } else { + result[0] += -0.027478626; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { + result[0] += 0.007580962; + } else { + result[0] += 0.0014997128; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 214))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { + result[0] += -0.0022094585; + } else { + result[0] += -0.02451445; + } + } else { + result[0] += 0.024397848; + } + } else { + result[0] += -0.01616343; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + result[0] += 0.011657309; + } else { + result[0] += 0.0012069183; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += 0.040601656; + } else { + result[0] += 0.009451126; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { + result[0] += -0.022806326; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + result[0] += 0.03292076; + } else { + result[0] += 0.001937497; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += 0.0035285496; + } else { + result[0] += -0.0058691064; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + result[0] += 0.012688639; + } else { + result[0] += 0.005491004; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { + result[0] += 0.0044394704; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 192))) { + result[0] += 0.012408857; + } else { + result[0] += 0.019069476; + } + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { + result[0] += 0.006046584; + } else { + result[0] += -0.015792418; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += 0.027976854; + } else { + result[0] += 0.012421743; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += 0.054596562; + } else { + result[0] += 0.01953054; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { + result[0] += -0.003245589; + } else { + result[0] += 0.013587843; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.021004315; + } else { + result[0] += -0.008656485; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + result[0] += 0.01555752; + } else { + result[0] += -0.0006588964; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { + result[0] += -0.0020559; + } else { + result[0] += -0.010479237; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += 0.021051032; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 220))) { + result[0] += 0.006872528; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { + result[0] += -0.00075457775; + } else { + result[0] += -0.008546843; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { + result[0] += 0.009004579; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 2))) { + result[0] += 0.013524537; + } else { + result[0] += 0.02246733; + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 202))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { + result[0] += -0.004806658; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + result[0] += -0.02193972; + } else { + result[0] += -0.0058797724; + } + } + } else { + result[0] += -0.023569103; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 264))) { + result[0] += 0.016511796; + } else { + result[0] += -0.0023302487; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += 0.004636476; + } else { + result[0] += 0.03744141; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 154))) { + result[0] += -0.0021920146; + } else { + result[0] += 0.0044151954; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += 0.013442422; + } else { + result[0] += 0.025949672; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 252))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { + result[0] += 0.012082203; + } else { + result[0] += 0.017159306; + } + } else { + result[0] += 0.018528607; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + result[0] += 0.012809316; + } else { + result[0] += 0.0020744025; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + result[0] += 0.02068119; + } else { + result[0] += 0.012666582; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += -0.025207102; + } else { + result[0] += -0.0042135157; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + result[0] += 0.019998292; + } else { + result[0] += -0.011731009; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + result[0] += 0.04935061; + } else { + result[0] += 0.00985283; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 2))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 10))) { + result[0] += -0.0114418; + } else { + result[0] += 0.001085467; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { + result[0] += 0.005336402; + } else { + result[0] += 0.040240493; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + result[0] += -0.0003519562; + } else { + result[0] += -0.016158544; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 22))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 186))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += -0.052682735; + } else { + result[0] += -0.024834817; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + result[0] += 0.0076021785; + } else { + result[0] += -0.009852809; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 24))) { + result[0] += 0.07371118; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + result[0] += -0.0062063127; + } else { + result[0] += -0.0016409116; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 166))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 138))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 96))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += -0.0056977295; + } else { + result[0] += -0.0020788263; + } + } else { + result[0] += 0.013674322; + } + } else { + result[0] += -0.012557744; + } + } else { + result[0] += 0.020858606; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 100))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { + result[0] += 0.004243218; + } else { + result[0] += -0.0012915577; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { + result[0] += 0.012041297; + } else { + result[0] += 0.0036917683; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { + result[0] += 0.021245888; + } else { + result[0] += 0.010770134; + } + } else { + result[0] += 0.026461909; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 252))) { + result[0] += 0.011941756; + } else { + result[0] += 0.01697006; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 190))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + result[0] += 0.0059637506; + } else { + result[0] += 0.0009332538; + } + } else { + result[0] += -0.01690738; + } + } else { + result[0] += 0.018049363; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { + result[0] += -0.003757357; + } else { + result[0] += -0.010965187; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 138))) { + result[0] += 0.054250848; + } else { + result[0] += -0.00039252196; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 42))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { + result[0] += 0.01451879; + } else { + result[0] += 0.037112333; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 36))) { + result[0] += -0.017370671; + } else { + result[0] += -0.0013852807; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 222))) { + result[0] += 0.009491138; + } else { + result[0] += 0.03599734; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { + result[0] += 0.0021581096; + } else { + result[0] += -0.0014275697; + } + } else { + result[0] += 0.009684578; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { + result[0] += 0.009782214; + } else { + result[0] += -0.0025362705; + } + } else { + result[0] += 0.015987474; + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { + result[0] += 0.031121805; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + result[0] += -0.0017203381; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { + result[0] += 0.030785752; + } else { + result[0] += -0.005102741; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + result[0] += -0.005013264; + } else { + result[0] += 0.027989835; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { + result[0] += -0.024730321; + } else { + result[0] += -0.0032983057; + } + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += 0.009897011; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { + result[0] += -0.0021574632; + } else { + result[0] += 0.057048213; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { + result[0] += 0.010313834; + } else { + result[0] += 0.002621749; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += 0.015189426; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { + result[0] += 0.009792722; + } else { + result[0] += 0.014417933; + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += -0.0014002819; + } else { + result[0] += 0.006653227; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + result[0] += -0.023881285; + } else { + result[0] += -0; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + result[0] += 0.018696984; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += -0.0133892; + } else { + result[0] += 0.008133677; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + result[0] += 0.043852713; + } else { + result[0] += 0.0058281594; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 212))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 144))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 174))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { + result[0] += -0.0026800435; + } else { + result[0] += 0.0020921542; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + result[0] += 0.0018816863; + } else { + result[0] += -0.027228499; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + result[0] += -0.014789981; + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 150))) { + result[0] += -0.009289259; + } else { + result[0] += -0.0037157678; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 158))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { + result[0] += -0.009539143; + } else { + result[0] += 0.006927042; + } + } else { + result[0] += 0.039872702; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 160))) { + result[0] += -0.039126202; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 166))) { + result[0] += -0.002670629; + } else { + result[0] += 0.001439321; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + result[0] += -5.299445e-05; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + result[0] += -0.019934397; + } else { + result[0] += -0.0012183964; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 154))) { + result[0] += -0.0013647849; + } else { + result[0] += 0.038733196; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + result[0] += 0.008902398; + } else { + result[0] += -0.00011572992; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 14))) { + result[0] += 3.4862278e-06; + } else { + result[0] += 0.012706413; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { + result[0] += 0.007615597; + } else { + result[0] += -0.0051007196; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 48))) { + result[0] += 0.015438475; + } else { + result[0] += 0.0018905745; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 246))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 230))) { + result[0] += 0.008593912; + } else { + result[0] += 0.014445068; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += -0.00014397338; + } else { + result[0] += 0.0083941175; + } + } + } else { + result[0] += -0.004308312; + } + } else { + result[0] += 0.046102088; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { + result[0] += -0.0013631996; + } else { + result[0] += -0.011290415; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 200))) { + result[0] += 0.00060525746; + } else { + result[0] += 0.013914026; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 248))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + result[0] += -0.023959834; + } else { + result[0] += -0.010296635; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { + result[0] += 0.008832153; + } else { + result[0] += 0.0023550882; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.015583684; + } else { + result[0] += 0.010421765; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { + result[0] += -0.00011256654; + } else { + result[0] += -0.02525649; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 232))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 230))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { + result[0] += 0.005643142; + } else { + result[0] += -0.00010354039; + } + } else { + result[0] += -0.015879504; + } + } else { + result[0] += 0.014167144; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 246))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += 0.049543332; + } else { + result[0] += 0.0046836296; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { + result[0] += -0.017134476; + } else { + result[0] += -0.007789578; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += -0.008826947; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 250))) { + result[0] += -0.0007550689; + } else { + result[0] += 0.0071488456; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 188))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 238))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 106))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { + result[0] += -0.019138163; + } else { + result[0] += 0.008177842; + } + } else { + result[0] += 0.03202384; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { + result[0] += 0.03914841; + } else { + result[0] += 0.027094522; + } + } else { + result[0] += 0.0053101527; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += 0.0403822; + } else { + result[0] += 0.013740751; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { + result[0] += -0.030392349; + } else { + result[0] += 0.0028747516; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 80))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { + result[0] += -0.008877286; + } else { + result[0] += 0.012316696; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 118))) { + result[0] += 0.004029528; + } else { + result[0] += -0.00029411144; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { + result[0] += 0.011664437; + } else { + result[0] += 0.0006082279; + } + } else { + result[0] += -0.006005393; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 166))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { + result[0] += 0.09773924; + } else { + result[0] += 0.02065113; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 200))) { + result[0] += 0.0035237612; + } else { + result[0] += 0.009829774; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 208))) { + result[0] += -0.007697291; + } else { + result[0] += 0.0015825549; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + result[0] += 0.0036124797; + } else { + result[0] += 0.014532133; + } + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + result[0] += 0.0012666411; + } else { + result[0] += -0.03202522; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { + result[0] += 0.020200811; + } else { + result[0] += 0.004590917; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { + result[0] += 0.03727207; + } else { + result[0] += 0.016316103; + } + } else { + result[0] += -0.003320934; + } + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += 0.053239834; + } else { + result[0] += 0.015470129; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + result[0] += -0.0021254455; + } else { + result[0] += -0.009888625; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { + result[0] += 0.062025126; + } else { + result[0] += -0.00554525; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { + result[0] += 0.057511933; + } else { + result[0] += -0.008578778; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { + result[0] += -0.046820056; + } else { + result[0] += -0.02672096; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 70))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + result[0] += -0.03445236; + } else { + result[0] += 0.01575507; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { + result[0] += -0.029678077; + } else { + result[0] += -0.0074849683; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + result[0] += 0.013723537; + } else { + result[0] += -0.0020312674; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { + result[0] += -0.004165123; + } else { + result[0] += -0.00078670814; + } + } + } + } + } + } + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 12))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += -0; + } else { + result[0] += -0.03194522; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { + result[0] += 0.025147652; + } else { + result[0] += 0.0062306984; + } + } + } else { + result[0] += -0.0008928859; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += 0.13331777; + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 206))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { + result[0] += 0.030818671; + } else { + result[0] += 0.011000312; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + result[0] += 0.0052034413; + } else { + result[0] += -0.002728651; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += 0.0031897593; + } else { + result[0] += 0.017273722; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { + result[0] += -0.02854721; + } else { + result[0] += 0.00037428603; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + result[0] += -0.0019407201; + } else { + result[0] += -0.007221583; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 62))) { + result[0] += 0.047634736; + } else { + result[0] += -0.017209576; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { + result[0] += 0.025280062; + } else { + result[0] += 0.012840076; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { + result[0] += 0.038194098; + } else { + result[0] += 0.008874618; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { + result[0] += -0.03347216; + } else { + result[0] += -0.0004954495; + } + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 50))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 172))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 202))) { + result[0] += -0.009764884; + } else { + result[0] += -0.027206961; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 202))) { + result[0] += 0.00784516; + } else { + result[0] += 0.0012178732; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 170))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 168))) { + result[0] += 0.024105137; + } else { + result[0] += -0.0019981433; + } + } else { + result[0] += 0.031363543; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + result[0] += -0.0072048977; + } else { + result[0] += -0.022911293; + } + } else { + result[0] += 0.010616104; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 258))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 248))) { + result[0] += 0.008845729; + } else { + result[0] += -0.0013868009; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 246))) { + result[0] += -0.0024586073; + } else { + result[0] += -0.0086732535; + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 196))) { + result[0] += 0.01107409; + } else { + result[0] += -0.0011529119; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + result[0] += 0.013766219; + } else { + result[0] += 0.026060035; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 210))) { + result[0] += -0.004132199; + } else { + result[0] += -0.01266666; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 264))) { + result[0] += 0.011719443; + } else { + result[0] += -0.0067507224; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 90))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 188))) { + result[0] += 0.0155691; + } else { + result[0] += 0.010912938; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 198))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 188))) { + result[0] += 0.0051367334; + } else { + result[0] += -0.00038397807; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += 0.044876117; + } else { + result[0] += 0.005380819; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 124))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 250))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + result[0] += -0.01634503; + } else { + result[0] += -0; + } + } else { + result[0] += -0.002735744; + } + } else { + result[0] += 0.012586943; + } + } else { + result[0] += -0.0009229357; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 106))) { + result[0] += -0.013541499; + } else { + result[0] += 0.016169539; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += -0; + } else { + result[0] += 0.026296902; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { + result[0] += 0.0010251206; + } else { + result[0] += 0.007284665; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 100))) { + result[0] += -0.0034584904; + } else { + result[0] += 0.0022243506; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { + result[0] += 0.008507351; + } else { + result[0] += 0.00037087366; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 220))) { + result[0] += -0.0015936692; + } else { + result[0] += -0.00584162; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += 0.03250199; + } else { + result[0] += 0.014099632; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 174))) { + result[0] += 0.0007955474; + } else { + result[0] += 0.0064386777; + } + } + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 144))) { + result[0] += -0.032964613; + } else { + result[0] += 0.017446639; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { + result[0] += -0.015205093; + } else { + result[0] += -0; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + result[0] += 0.003139617; + } else { + result[0] += -0.014359586; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += -0.0014178219; + } else { + result[0] += 0.002840144; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 62))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + result[0] += 0.041497182; + } else { + result[0] += 0.001743556; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { + result[0] += -0.014737419; + } else { + result[0] += -0.0060345526; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 188))) { + result[0] += -0.0014039263; + } else { + result[0] += 0.009091877; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { + result[0] += -0.013735535; + } else { + result[0] += -0.0066661197; + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 100))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { + result[0] += 0.05542008; + } else { + result[0] += 0.0056177597; + } + } else { + result[0] += 0.021063056; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += -0.008560927; + } else { + result[0] += 0.007372921; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + result[0] += 0.0033190027; + } else { + result[0] += 0.02229972; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 162))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { + result[0] += 0.0031475432; + } else { + result[0] += 0.01872689; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { + result[0] += -0.0027235162; + } else { + result[0] += -0.011199118; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { + result[0] += -0.013897911; + } else { + result[0] += -0.0017742496; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 144))) { + result[0] += 0.045032453; + } else { + result[0] += 0.0035365508; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { + result[0] += 0.015004215; + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + result[0] += -0.02693589; + } else { + result[0] += 0.0030036382; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 236))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 220))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { + result[0] += -0.005196058; + } else { + result[0] += -0.0010462991; + } + } else { + result[0] += -0.036202352; + } + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + result[0] += 0.009205636; + } else { + result[0] += -0.008914505; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { + result[0] += 0.00077702984; + } else { + result[0] += -0.006057858; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 78))) { + result[0] += 0.011342842; + } else { + result[0] += 0.003530657; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + result[0] += 0.03748219; + } else { + result[0] += 0.014057839; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 154))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 102))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += -0.0016399727; + } else { + result[0] += -0.011802055; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + result[0] += -0.0016713021; + } else { + result[0] += 0.0056822044; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 34))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { + result[0] += 0.00386166; + } else { + result[0] += -0.0027170626; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 46))) { + result[0] += 0.019643234; + } else { + result[0] += 0.0028665168; + } + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { + result[0] += 0.0005789458; + } else { + result[0] += 0.0143351955; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { + result[0] += 0.011239639; + } else { + result[0] += -0.016711237; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { + result[0] += 0.019091167; + } else { + result[0] += 0.0046307947; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += 0.050568253; + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += 0.03797291; + } else { + result[0] += 0.007982335; + } + } + } else { + result[0] += 0.0035218038; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + result[0] += -0.016012253; + } else { + result[0] += 0.0023404376; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + result[0] += -0.001683798; + } else { + result[0] += 0.013443462; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 232))) { + result[0] += -0.0012087579; + } else { + result[0] += 0.009097977; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 192))) { + result[0] += -0.010690723; + } else { + result[0] += -0.001824428; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += 0.017058346; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 220))) { + result[0] += 0.004044805; + } else { + result[0] += -0.0013507138; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 242))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { + result[0] += 0.008160069; + } else { + result[0] += 0.015147629; + } + } else { + result[0] += 0.024462283; + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { + result[0] += -0.000107747815; + } else { + result[0] += 0.0067256675; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { + result[0] += -0.0076294737; + } else { + result[0] += -0.043650094; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 178))) { + result[0] += 0.034688678; + } else { + result[0] += -0.02312972; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 100))) { + result[0] += -0.002360337; + } else { + result[0] += 0.012530643; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { + result[0] += -0.01826018; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 64))) { + result[0] += 0.0056892973; + } else { + result[0] += 0.03400869; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { + result[0] += -0.009709366; + } else { + result[0] += -0.00013415277; + } + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 156))) { + result[0] += 0.010154608; + } else { + result[0] += 0.001891119; + } + } else { + result[0] += 0.03333105; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 154))) { + result[0] += -0.022681896; + } else { + result[0] += -0.03837011; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { + result[0] += 0.007119199; + } else { + result[0] += -0.015321928; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 134))) { + result[0] += 0.08953688; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { + result[0] += -0.022155154; + } else { + result[0] += 0.004006593; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + result[0] += 0.013605259; + } else { + result[0] += 0.027043974; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { + result[0] += -0.0115665; + } else { + result[0] += 0.007095266; + } + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += 0.035634317; + } else { + result[0] += 0.017916704; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + result[0] += -0.0043744547; + } else { + result[0] += -0.00026779916; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 236))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + result[0] += 0.0133640645; + } else { + result[0] += 0.004636778; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += 0.012312482; + } else { + result[0] += 0.06577807; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 132))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += 0.0068262727; + } else { + result[0] += -0.04068326; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { + result[0] += -0.0010328897; + } else { + result[0] += -0.005453972; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { + result[0] += -0.0008340759; + } else { + result[0] += -0.008702307; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 16))) { + result[0] += 0.040576342; + } else { + result[0] += 0.0024016355; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 192))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + result[0] += 0.029419197; + } else { + result[0] += 0.0010929945; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 156))) { + result[0] += -0.0012289739; + } else { + result[0] += -0.0070381216; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.0115409745; + } else { + result[0] += 0.0048038764; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 242))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 222))) { + result[0] += 0.023705222; + } else { + result[0] += 0.010082607; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.012382432; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 228))) { + result[0] += 0.004983134; + } else { + result[0] += -0; + } + } + } + } + } + } + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += 0.061853677; + } else { + result[0] += -0.0063631344; + } + } else { + result[0] += -0.036711987; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 28))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { + result[0] += 0.0031359557; + } else { + result[0] += -0.0016080218; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { + result[0] += 0.036603495; + } else { + result[0] += 0.015373263; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += -0.055269193; + } else { + result[0] += 0.019025035; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + result[0] += -0.022278577; + } else { + result[0] += 0.016397122; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.01897105; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { + result[0] += 0.00051737565; + } else { + result[0] += 0.0041289385; + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += -0.036939483; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { + result[0] += 0.009198739; + } else { + result[0] += -0.013264345; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { + result[0] += 0.022775693; + } else { + result[0] += 0.007945242; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 156))) { + result[0] += -0.0018210205; + } else { + result[0] += 0.0037362184; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 128))) { + result[0] += -0.012762427; + } else { + result[0] += -0.0007420523; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { + result[0] += 0.017958501; + } else { + result[0] += 0.0061608446; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { + result[0] += -0.0049462705; + } else { + result[0] += 0.0019659882; + } + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { + result[0] += 0.00022622973; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 250))) { + result[0] += 0.005413284; + } else { + result[0] += 0.030752031; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 220))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { + result[0] += 0.003555048; + } else { + result[0] += -0.0069270222; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { + result[0] += -0.008863601; + } else { + result[0] += -0.015783666; + } + } else { + result[0] += -0.0067091286; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 36))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 58))) { + result[0] += 0.062294442; + } else { + result[0] += 0.01808921; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { + result[0] += 0.08561305; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 190))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + result[0] += 0.0024828026; + } else { + result[0] += 0.018881498; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 204))) { + result[0] += -0.007572981; + } else { + result[0] += 0.001793948; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 126))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { + result[0] += -0.000932767; + } else { + result[0] += 0.006823505; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + result[0] += -0.036651295; + } else { + result[0] += -0.0058056363; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { + result[0] += -0.0055914307; + } else { + result[0] += 0.03341748; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 100))) { + result[0] += -0.00073259696; + } else { + result[0] += 0.010309592; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { + result[0] += -0.019160004; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 64))) { + result[0] += 0.0046965308; + } else { + result[0] += 0.026486978; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 80))) { + result[0] += -0.008773739; + } else { + result[0] += -0.0007050743; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { + result[0] += 0.025857493; + } else { + result[0] += -0.0017104832; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { + result[0] += -0.04572242; + } else { + result[0] += -0.013417631; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 128))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + result[0] += 0.02735805; + } else { + result[0] += 0.008384274; + } + } else { + result[0] += 0.025471484; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 146))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 144))) { + result[0] += -0.0019103719; + } else { + result[0] += -0.015735812; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + result[0] += 0.003526498; + } else { + result[0] += 0.0004870697; + } + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 252))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 230))) { + result[0] += -0.0010278291; + } else { + result[0] += 0.0035127073; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + result[0] += 0.0007956819; + } else { + result[0] += -0.007078775; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { + result[0] += 0.06692124; + } else { + result[0] += 0.012191664; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { + result[0] += -0.0037893832; + } else { + result[0] += 0.0049554044; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.0026184532; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + result[0] += -0.03463073; + } else { + result[0] += -0.01464231; + } + } + } else { + result[0] += 0.01621621; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { + result[0] += -0.0019204362; + } else { + result[0] += 0.012864575; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + result[0] += -0.008148334; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 46))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { + result[0] += 0.04631585; + } else { + result[0] += 0.008748725; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 58))) { + result[0] += -0.0012717935; + } else { + result[0] += 0.0031659238; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { + result[0] += 0.023349551; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + result[0] += 0.0070865126; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 220))) { + result[0] += -0.0010378936; + } else { + result[0] += -0.03451775; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 58))) { + result[0] += -0.0030549995; + } else { + result[0] += 0.042464044; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 196))) { + result[0] += -0.0068810317; + } else { + result[0] += -0.01983464; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 28))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + result[0] += 0.021494946; + } else { + result[0] += 0.046589624; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 246))) { + result[0] += 0.0016733175; + } else { + result[0] += -0.002286189; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { + result[0] += -0.023560746; + } else { + result[0] += 0.025176898; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + result[0] += -0.0032716715; + } else { + result[0] += -0.0107773235; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 158))) { + result[0] += 9.897955e-07; + } else { + result[0] += 0.0075915637; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 238))) { + result[0] += -0.018133821; + } else { + result[0] += -0.003638253; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { + result[0] += 0.013451889; + } else { + result[0] += 0.006885908; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 2))) { + result[0] += 0.016480822; + } else { + result[0] += 0.0028402929; + } + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 182))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + result[0] += 0.009219227; + } else { + result[0] += -0.025390211; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += 0.025732726; + } else { + result[0] += -0.0023830726; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { + result[0] += 0.0035509118; + } else { + result[0] += 0.013229172; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += -0.007854169; + } else { + result[0] += 0.0072692037; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += 0.047711357; + } else { + result[0] += 0.025087982; + } + } else { + result[0] += 0.0016552185; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 36))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += 0.0398016; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + result[0] += 0.0008522939; + } else { + result[0] += 0.022818817; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 8))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { + result[0] += -0.047148183; + } else { + result[0] += -0.014362188; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + result[0] += 0.033707537; + } else { + result[0] += -0.005045968; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 38))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 52))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + result[0] += -0.015981285; + } else { + result[0] += 0.061333533; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.011986034; + } else { + result[0] += 0.014650764; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 156))) { + result[0] += 0.03378779; + } else { + result[0] += 0.001349721; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + result[0] += -0.00046623495; + } else { + result[0] += -0.0032709301; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 42))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += 0.0026227154; + } else { + result[0] += -0.004871029; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += 0.024236446; + } else { + result[0] += 0.00875364; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 86))) { + result[0] += -0.010391722; + } else { + result[0] += -0.0003864034; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { + result[0] += 0.0091726575; + } else { + result[0] += 0.00034549323; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += -0.016140882; + } else { + result[0] += -0.0051438985; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { + result[0] += 0.0077548027; + } else { + result[0] += -0.0023856275; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 96))) { + result[0] += 0.0072452063; + } else { + result[0] += 0.021926673; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { + result[0] += 0.007803672; + } else { + result[0] += 0.07842522; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { + result[0] += 0.0030498195; + } else { + result[0] += -0.030465296; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { + result[0] += 0.027338846; + } else { + result[0] += 0.018519042; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 104))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 150))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { + result[0] += -0.0038131357; + } else { + result[0] += -0.014322984; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + result[0] += 0.011482879; + } else { + result[0] += -0.0018702493; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 38))) { + result[0] += 0.04208499; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 48))) { + result[0] += -0.013598467; + } else { + result[0] += 0.0022218376; + } + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += 0.014965734; + } else { + result[0] += 0.006420965; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + result[0] += 0.0035459124; + } else { + result[0] += -0.03692886; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += -0.00581108; + } else { + result[0] += 0.0022818777; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + result[0] += 0.02711085; + } else { + result[0] += 0.0037428073; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 232))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 230))) { + result[0] += -0.0006429512; + } else { + result[0] += -0.013558619; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + result[0] += 0.006967464; + } else { + result[0] += 0.020120773; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + result[0] += -0.0188818; + } else { + result[0] += -0.011276356; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 248))) { + result[0] += -0.0036909913; + } else { + result[0] += 0.0023398104; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 228))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 220))) { + result[0] += 0.0067386203; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += 0.009782809; + } else { + result[0] += -0.0003835666; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 242))) { + result[0] += 0.008386671; + } else { + result[0] += 0.01509656; + } + } + } + } + } + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 230))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += 0.0035304844; + } else { + result[0] += 0.00860195; + } + } else { + result[0] += 0.044866074; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += -0.0027610434; + } else { + result[0] += 0.032996777; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 30))) { + result[0] += 0.009394963; + } else { + result[0] += -0.0004232897; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { + result[0] += 0.019008147; + } else { + result[0] += 0.009637523; + } + } else { + result[0] += 0.041156612; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { + result[0] += 0.0076490478; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 182))) { + result[0] += 0.01141602; + } else { + result[0] += 0.0032564194; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 4))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + result[0] += 0.0054725604; + } else { + result[0] += -0.006845514; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { + result[0] += -0; + } else { + result[0] += 0.04772381; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += -0.016645087; + } else { + result[0] += -0.032690536; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { + result[0] += 0.003037467; + } else { + result[0] += -0.009953394; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 12))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { + result[0] += 0.01011019; + } else { + result[0] += 0.017591367; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 258))) { + result[0] += 0.0020601144; + } else { + result[0] += -0.0052700583; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 172))) { + result[0] += -0.0001318003; + } else { + result[0] += -0.0058084927; + } + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 160))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 158))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 8))) { + result[0] += -0.021778304; + } else { + result[0] += -0.05481277; + } + } else { + result[0] += 0.01922178; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += -0.02884279; + } else { + result[0] += -0.05432384; + } + } else { + result[0] += -0.008790418; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += -0.0039755395; + } else { + result[0] += 0.0508219; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 128))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 28))) { + result[0] += -0.047565266; + } else { + result[0] += -0.034321543; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { + result[0] += 0.011836532; + } else { + result[0] += -0.0025003483; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 58))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 68))) { + result[0] += 0.0057843057; + } else { + result[0] += 0.036940213; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { + result[0] += -0.011010564; + } else { + result[0] += 0.014086082; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 226))) { + result[0] += -0.00865682; + } else { + result[0] += 0.018309247; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { + result[0] += 0.00883958; + } else { + result[0] += -0.0026359025; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 98))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { + result[0] += -0.0024397147; + } else { + result[0] += -0.023152852; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + result[0] += 0.0015076617; + } else { + result[0] += -0.009175034; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { + result[0] += 0.02444943; + } else { + result[0] += 0.0023199369; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + result[0] += -0.0028377601; + } else { + result[0] += 0.00046475232; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { + result[0] += 0.00531006; + } else { + result[0] += -0.00012114655; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { + result[0] += -0.004693984; + } else { + result[0] += -0.032999605; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + result[0] += 0.029490167; + } else { + result[0] += 0.007921627; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 58))) { + result[0] += -0.0028598853; + } else { + result[0] += 0.0077966005; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 66))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 70))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + result[0] += 0.014692311; + } else { + result[0] += -0.021459384; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { + result[0] += -0.016421737; + } else { + result[0] += -0.0066403346; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + result[0] += 0.0074493513; + } else { + result[0] += -0.0040894877; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + result[0] += 0.01759093; + } else { + result[0] += -0.00061179395; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += 0.0076502063; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { + result[0] += 0.00010772177; + } else { + result[0] += -0.003537047; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { + result[0] += -0.012459938; + } else { + result[0] += -0.0037218079; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 260))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + result[0] += 0.002977729; + } else { + result[0] += -0.019293662; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + result[0] += 0.015566354; + } else { + result[0] += 0.0037241564; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.03374357; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += 0.0041862135; + } else { + result[0] += 0.009111217; + } + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { + result[0] += 0.001820308; + } else { + result[0] += 0.0113331815; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { + result[0] += -0.01062336; + } else { + result[0] += 0.0023687228; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + result[0] += 0.0050820797; + } else { + result[0] += -0.010689573; + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += 0.039545674; + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += 0.023330238; + } else { + result[0] += 0.0062547647; + } + } + } else { + result[0] += 0.00083993434; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { + result[0] += -0.0019025843; + } else { + result[0] += 0.0075600627; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + result[0] += 0.042078517; + } else { + result[0] += 0.0026668767; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.015797917; + } else { + result[0] += -0.006350769; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 96))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { + result[0] += 0.048628982; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { + result[0] += 0.021388667; + } else { + result[0] += 0.0020165527; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 110))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 106))) { + result[0] += -0.0051917243; + } else { + result[0] += 0.010212748; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + result[0] += 0.0015873375; + } else { + result[0] += -0.0008199594; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 64))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { + result[0] += 0.01901365; + } else { + result[0] += -0.0028177812; + } + } else { + result[0] += 0.02409505; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 206))) { + result[0] += -0.0063365176; + } else { + result[0] += 0.00086170074; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + result[0] += -0.016780075; + } else { + result[0] += 0.012628958; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { + result[0] += -0.009542297; + } else { + result[0] += 0.053097267; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 250))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 238))) { + result[0] += 0.003721561; + } else { + result[0] += 0.019598227; + } + } else { + result[0] += 0.03498596; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 68))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { + result[0] += 0.00025514522; + } else { + result[0] += 0.0040203123; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { + result[0] += -0.0074286456; + } else { + result[0] += 0.00093450863; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 166))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + result[0] += -0.00087522157; + } else { + result[0] += 0.007016174; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { + result[0] += -0.034118887; + } else { + result[0] += -0.011294309; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 260))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 256))) { + result[0] += 0.022198912; + } else { + result[0] += 0.012400496; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { + result[0] += -0.010423508; + } else { + result[0] += 0.004261658; + } + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 96))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 52))) { + result[0] += -0.001626932; + } else { + result[0] += 0.0061591933; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + result[0] += -0.0022012205; + } else { + result[0] += -0.0123000285; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 144))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 122))) { + result[0] += 0.007472606; + } else { + result[0] += 0.05678388; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 196))) { + result[0] += 3.3709428e-05; + } else { + result[0] += -0.0143476175; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 50))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { + result[0] += 0.03503921; + } else { + result[0] += -0.036016237; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 182))) { + result[0] += -0.0048608086; + } else { + result[0] += 0.022741511; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += -0.035393443; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 88))) { + result[0] += -0.002815163; + } else { + result[0] += -2.7660133e-05; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 174))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 158))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 148))) { + result[0] += 0.0017332671; + } else { + result[0] += 0.026441107; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 222))) { + result[0] += -0.0073182597; + } else { + result[0] += 0.0006405428; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { + result[0] += 0.02126172; + } else { + result[0] += 0.011408723; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 68))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 46))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + result[0] += -0.014773398; + } else { + result[0] += 0.009008159; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += 0.0047272076; + } else { + result[0] += -0.01409996; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 160))) { + result[0] += 0.009959228; + } else { + result[0] += 0.04201186; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { + result[0] += -0.008849121; + } else { + result[0] += -0.00033711825; + } + } + } + } + } + } + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + result[0] += 0.002547614; + } else { + result[0] += -0.016982568; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + result[0] += 0.0087383045; + } else { + result[0] += -0.0019732136; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + result[0] += -0.027964113; + } else { + result[0] += -0.0070363963; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 12))) { + result[0] += 0.015301444; + } else { + result[0] += -0.0009437213; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 218))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 22))) { + result[0] += 0.011154254; + } else { + result[0] += 0.03460535; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { + result[0] += 0.006955503; + } else { + result[0] += 0.002656106; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 30))) { + result[0] += 0.008213889; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 234))) { + result[0] += -0.0014978464; + } else { + result[0] += 0.0018082943; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 166))) { + result[0] += -0.010998022; + } else { + result[0] += -0.028136352; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 28))) { + result[0] += 0.056712236; + } else { + result[0] += 0.00025265655; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += -0.032531813; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.012461627; + } else { + result[0] += -0.0026103533; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 62))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + result[0] += 0.003723057; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 44))) { + result[0] += 0.050172295; + } else { + result[0] += 0.03318845; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + result[0] += 0.038616996; + } else { + result[0] += -0.010049296; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { + result[0] += -0.0062160105; + } else { + result[0] += -4.625852e-05; + } + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 228))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 220))) { + result[0] += 0.027368566; + } else { + result[0] += -0.0007781659; + } + } else { + result[0] += -0.010964031; + } + } else { + result[0] += 0.0031334688; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 286))) { + result[0] += 0.023848334; + } else { + result[0] += 0.008595723; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { + result[0] += 0.0113761; + } else { + result[0] += 0.0038027503; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { + result[0] += 0.028975934; + } else { + result[0] += 0.01324735; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { + result[0] += -0; + } else { + result[0] += 0.011437596; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { + result[0] += 0.01134281; + } else { + result[0] += 0.0018215694; + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 242))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 230))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 114))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 106))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 180))) { + result[0] += 0.0014854757; + } else { + result[0] += -0.0047508436; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { + result[0] += -0.012526001; + } else { + result[0] += -0.0042038257; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + result[0] += 0.027467722; + } else { + result[0] += 0.005847614; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + result[0] += -0.016319519; + } else { + result[0] += 0.0018143759; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 138))) { + result[0] += 0.0017114735; + } else { + result[0] += -0.04394586; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { + result[0] += 0.014061573; + } else { + result[0] += 0.0076026404; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { + result[0] += -0.0018655244; + } else { + result[0] += 0.011241974; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + result[0] += -0.013273408; + } else { + result[0] += 0.015772188; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 90))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 58))) { + result[0] += -0.00058818224; + } else { + result[0] += 0.008676103; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + result[0] += -0.010151858; + } else { + result[0] += 0.001280522; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + result[0] += -0.010351776; + } else { + result[0] += 0.012125977; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { + result[0] += -0.005976769; + } else { + result[0] += 0.0058461004; + } + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 22))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + result[0] += 0.0014196447; + } else { + result[0] += -0.016857633; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 18))) { + result[0] += 0.0388976; + } else { + result[0] += 0.0022651523; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + result[0] += -0.0031505611; + } else { + result[0] += -0.02737777; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + result[0] += 0.027305475; + } else { + result[0] += -0.00032103845; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 28))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + result[0] += -0.033979844; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 44))) { + result[0] += -0.00036602697; + } else { + result[0] += -0.009034148; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { + result[0] += 0.02605836; + } else { + result[0] += 0.0035190487; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + result[0] += 0.0030745373; + } else { + result[0] += 0.021341039; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 112))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + result[0] += -0.00765642; + } else { + result[0] += 0.012637125; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 92))) { + result[0] += -0.031098261; + } else { + result[0] += -0.018325815; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 48))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 74))) { + result[0] += 0.007115057; + } else { + result[0] += 0.025569752; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 50))) { + result[0] += -0.030833412; + } else { + result[0] += -0.0027387291; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 118))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += -0.010184347; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { + result[0] += 0.009563519; + } else { + result[0] += 0.0019127353; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { + result[0] += -0.0016026145; + } else { + result[0] += -0.012827705; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { + result[0] += 0.006836459; + } else { + result[0] += -0.00047072658; + } + } + } + } + } + } + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { + result[0] += 0.016250825; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { + result[0] += -0.009732073; + } else { + result[0] += 0.017143272; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + result[0] += -0.009671597; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 270))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 134))) { + result[0] += -0.00035089705; + } else { + result[0] += 0.004113893; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { + result[0] += 0.01820572; + } else { + result[0] += 0.009231458; + } + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += 0.00027064016; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.0017144865; + } else { + result[0] += -0.0095933005; + } + } else { + result[0] += -0.012053235; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 194))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 250))) { + result[0] += -0.0034071447; + } else { + result[0] += 0.046097945; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 132))) { + result[0] += -0.0055225524; + } else { + result[0] += -0.0019266952; + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 250))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 86))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + result[0] += 0.0048346436; + } else { + result[0] += 0.01435117; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { + result[0] += -0.019338546; + } else { + result[0] += -0.0010408615; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 178))) { + result[0] += 0.011773284; + } else { + result[0] += 0.002931762; + } + } else { + result[0] += 0.040974855; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { + result[0] += 0.08329225; + } else { + result[0] += -0.0002780066; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { + result[0] += -0.0042001484; + } else { + result[0] += -0.02084507; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += -0.00010066136; + } else { + result[0] += 0.0027292925; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { + result[0] += -0.0122973155; + } else { + result[0] += 0.035760295; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += -0.001996606; + } else { + result[0] += 0.021067668; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 118))) { + result[0] += -0.013935153; + } else { + result[0] += 0.031183664; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += 0.061950255; + } else { + result[0] += 0.0058407323; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 142))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { + result[0] += 0.0829713; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.03793059; + } else { + result[0] += -0.00907617; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 68))) { + result[0] += 0.0025514578; + } else { + result[0] += -0.0012025639; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 16))) { + result[0] += 0.046701174; + } else { + result[0] += 0.0046325247; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { + result[0] += 0.027825123; + } else { + result[0] += -0; + } + } else { + result[0] += -0.021053893; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 82))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 152))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 106))) { + result[0] += -0.0013631139; + } else { + result[0] += -0.012569079; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + result[0] += -0.0008349316; + } else { + result[0] += 0.022382598; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 156))) { + result[0] += 0.013735485; + } else { + result[0] += 0.0033138485; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + result[0] += -0.0037839809; + } else { + result[0] += 0.002062925; + } + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += 0.0013772444; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { + result[0] += -0.011463809; + } else { + result[0] += -0.0047910134; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { + result[0] += 0.060537297; + } else { + result[0] += 0.010533939; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += 0.016672423; + } else { + result[0] += -0.00052434875; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 250))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + result[0] += 0.0070519983; + } else { + result[0] += 0.016810419; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { + result[0] += -0.026641011; + } else { + result[0] += 0.00010854311; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 232))) { + result[0] += 0.03818845; + } else { + result[0] += 0.012029546; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { + result[0] += -0.007278714; + } else { + result[0] += -0.03333238; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 144))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 46))) { + result[0] += 0.007880951; + } else { + result[0] += -0.00031094736; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + result[0] += 0.013625564; + } else { + result[0] += 0.03885763; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 214))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + result[0] += -0.0029387611; + } else { + result[0] += 7.662349e-05; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += 0.029973779; + } else { + result[0] += 0.0043506036; + } + } + } + } + } + } + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 78))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + result[0] += -0.017686712; + } else { + result[0] += 0.015614047; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 242))) { + result[0] += 0.0010816348; + } else { + result[0] += 0.010603439; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 122))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += -0.013882413; + } else { + result[0] += -3.773386e-06; + } + } else { + result[0] += -0.029591149; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 52))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + result[0] += 0.0029416822; + } else { + result[0] += 0.060454708; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { + result[0] += -0.03178696; + } else { + result[0] += -0.011132187; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 164))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { + result[0] += 0.06373953; + } else { + result[0] += -0.0049581574; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { + result[0] += -0.011388632; + } else { + result[0] += 0.0049218778; + } + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 14))) { + result[0] += 0.0012467568; + } else { + result[0] += -0.019324591; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { + result[0] += 0.001289832; + } else { + result[0] += -0.00096984406; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { + result[0] += -0.011180186; + } else { + result[0] += -0.0001193571; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { + result[0] += 0.017248346; + } else { + result[0] += 0.003171424; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 18))) { + result[0] += 0.04394267; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 176))) { + result[0] += -0.026358157; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += 0.0084524695; + } else { + result[0] += -0.008720714; + } + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 286))) { + result[0] += 0.02462693; + } else { + result[0] += 0.0102696465; + } + } + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 236))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { + result[0] += -9.075111e-05; + } else { + result[0] += -0.0032513805; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { + result[0] += 0.0011170026; + } else { + result[0] += 0.023266837; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 204))) { + result[0] += -0.012011125; + } else { + result[0] += -0.0051451973; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 202))) { + result[0] += 0.023120182; + } else { + result[0] += -0.008411562; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 212))) { + result[0] += 0.017263098; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 230))) { + result[0] += 0.003063401; + } else { + result[0] += 0.021211077; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 234))) { + result[0] += -0.0117486585; + } else { + result[0] += -0.0008188842; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 248))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 40))) { + result[0] += -0.025011426; + } else { + result[0] += -0.01188937; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 90))) { + result[0] += 0.000581751; + } else { + result[0] += 0.035827655; + } + } + } else { + result[0] += -0.056811877; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { + result[0] += 0.03448122; + } else { + result[0] += 0.0075843707; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 120))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 150))) { + result[0] += 0.02317158; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { + result[0] += -0.011207158; + } else { + result[0] += 0.00603795; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + result[0] += 1.6565642e-05; + } else { + result[0] += 0.0018936057; + } + } else { + result[0] += -0.0073121726; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += 0.013665982; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { + result[0] += 0.02345163; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 90))) { + result[0] += 0.031935133; + } else { + result[0] += 0.05211563; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 108))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 150))) { + result[0] += 0.006929166; + } else { + result[0] += 0.0018365175; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { + result[0] += -0.028940136; + } else { + result[0] += -0.0025783076; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.010843903; + } else { + result[0] += 0.006668359; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { + result[0] += -0.022552337; + } else { + result[0] += -0.0126125915; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 188))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { + result[0] += -0.0033015904; + } else { + result[0] += 0.0023345288; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 172))) { + result[0] += 0.027362315; + } else { + result[0] += 0.008473503; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 198))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + result[0] += -0.0086487485; + } else { + result[0] += 0.0011204499; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + result[0] += -0.0040217172; + } else { + result[0] += 0.0013360606; + } + } + } + } + } + } + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { + result[0] += -0.0038944704; + } else { + result[0] += -0.024639545; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + result[0] += 0.010567961; + } else { + result[0] += -0.0008910143; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 222))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 208))) { + result[0] += 0.0022678128; + } else { + result[0] += 0.010532487; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 20))) { + result[0] += 0.0032915582; + } else { + result[0] += -0.0005411969; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 152))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + result[0] += -0.01407851; + } else { + result[0] += 0.026896363; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 42))) { + result[0] += -0.007195405; + } else { + result[0] += 0.004968326; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 174))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { + result[0] += 0.008754817; + } else { + result[0] += -0.0017400451; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { + result[0] += -0.00865253; + } else { + result[0] += 0.0009031243; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 244))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { + result[0] += 0.00038980006; + } else { + result[0] += -0.016266255; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 18))) { + result[0] += -0.011384182; + } else { + result[0] += -0.005449654; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + result[0] += 0.029928738; + } else { + result[0] += -0.005479242; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 50))) { + result[0] += -0.02587893; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 182))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + result[0] += -0.005359716; + } else { + result[0] += 0.06967212; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 236))) { + result[0] += 0.0018839792; + } else { + result[0] += 0.00916981; + } + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 104))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += 0.0006954739; + } else { + result[0] += 0.048395343; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { + result[0] += 1.917278e-05; + } else { + result[0] += 0.03755577; + } + } + } else { + result[0] += -0.011912975; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 118))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 220))) { + result[0] += 0.00019196502; + } else { + result[0] += 0.014448823; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + result[0] += 0.019970976; + } else { + result[0] += -0.0011340084; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { + result[0] += -0.009018285; + } else { + result[0] += 0.020422988; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { + result[0] += 0.02001476; + } else { + result[0] += 0.034766663; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 152))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + result[0] += -0.0060089673; + } else { + result[0] += -0.026388485; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 102))) { + result[0] += -0.0338698; + } else { + result[0] += -0.0058778613; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 192))) { + result[0] += 0.025475418; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + result[0] += 0.0019010755; + } else { + result[0] += 0.011219644; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 128))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + result[0] += -0.021041693; + } else { + result[0] += 0.008361343; + } + } else { + result[0] += 0.03766183; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + result[0] += -0.010491718; + } else { + result[0] += 0.003890305; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.011144113; + } else { + result[0] += -8.4675696e-05; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { + result[0] += 0.001330308; + } else { + result[0] += 0.02066918; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 130))) { + result[0] += 6.2536885e-05; + } else { + result[0] += -0.013168806; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 90))) { + result[0] += 0.019082962; + } else { + result[0] += 0.0053747944; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + result[0] += -0.0076252148; + } else { + result[0] += 0.0009393127; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.011447157; + } else { + result[0] += 0.0061047096; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { + result[0] += -0; + } else { + result[0] += -0.020285398; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += 0.01896581; + } else { + result[0] += 0.00095191033; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 172))) { + result[0] += 0.0154045075; + } else { + result[0] += -0.001370645; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += 0.006603177; + } else { + result[0] += 0.00034456272; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + result[0] += -0.0029918782; + } else { + result[0] += -0.012741557; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 166))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 164))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + result[0] += 0.0071618394; + } else { + result[0] += -0.012161553; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { + result[0] += 0.03479741; + } else { + result[0] += 0.00893333; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 178))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 228))) { + result[0] += 0.0005804447; + } else { + result[0] += -0.006457042; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 186))) { + result[0] += 0.016656972; + } else { + result[0] += 0.002414147; + } + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 20))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 124))) { + result[0] += 0.022543216; + } else { + result[0] += 0.0024742563; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 50))) { + result[0] += -0.025850767; + } else { + result[0] += -0.0073662284; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 46))) { + result[0] += 0.0006718464; + } else { + result[0] += -0.0011177754; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { + result[0] += 0.003656535; + } else { + result[0] += -0.0034576224; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 122))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { + result[0] += 0.01316909; + } else { + result[0] += -0.00022220907; + } + } else { + result[0] += 0.0322718; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 132))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + result[0] += 0.0029699726; + } else { + result[0] += -0.009400754; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + result[0] += 0.0157076; + } else { + result[0] += -0.0039490475; + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 136))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 192))) { + result[0] += -0.030938972; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { + result[0] += 0.027307421; + } else { + result[0] += -0.00040336605; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + result[0] += 0.029921725; + } else { + result[0] += 0.013266409; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 168))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 62))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 144))) { + result[0] += 0.044555373; + } else { + result[0] += 0.0020996393; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 90))) { + result[0] += -0.0010202875; + } else { + result[0] += -0.0097485995; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 170))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + result[0] += 0.024518076; + } else { + result[0] += -0.0032617298; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += -0.002727569; + } else { + result[0] += -0.00034776315; + } + } + } + } + } + } + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 188))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + result[0] += -0.00013192062; + } else { + result[0] += -0.007137733; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + result[0] += 0.0058402964; + } else { + result[0] += 0.00019643598; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 170))) { + result[0] += -0.015256891; + } else { + result[0] += 0.027069787; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { + result[0] += 0.016238226; + } else { + result[0] += 0.045753922; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { + result[0] += -0.019134747; + } else { + result[0] += 0.010413033; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 178))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 122))) { + result[0] += -0.0016630454; + } else { + result[0] += -0.024183424; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { + result[0] += 0.012589683; + } else { + result[0] += 0.0009457501; + } + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 210))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 208))) { + result[0] += -0.0066352724; + } else { + result[0] += -0.017913656; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.0011060528; + } else { + result[0] += 0.0039828527; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 200))) { + result[0] += -0.0031051391; + } else { + result[0] += -0.016724786; + } + } else { + result[0] += -0.024875712; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 162))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 116))) { + result[0] += -0.016162679; + } else { + result[0] += 0.0026748257; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 160))) { + result[0] += 0.01718258; + } else { + result[0] += 0.0035327424; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 114))) { + result[0] += 0.0027622434; + } else { + result[0] += -0.019732043; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { + result[0] += -0.009568975; + } else { + result[0] += -0.0024784; + } + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 212))) { + result[0] += 0.01407246; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + result[0] += -0.04716774; + } else { + result[0] += -0.008298879; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { + result[0] += -0.012182956; + } else { + result[0] += -0.0051582577; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 124))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 128))) { + result[0] += -0.0067259143; + } else { + result[0] += 0.013526896; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { + result[0] += 0.002809637; + } else { + result[0] += -0.0016421535; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 82))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + result[0] += -0.0011062829; + } else { + result[0] += 0.014348181; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { + result[0] += 0.0076159174; + } else { + result[0] += -0.0151206255; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 124))) { + result[0] += 0.0045484654; + } else { + result[0] += 0.015772928; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + result[0] += -0.0059127775; + } else { + result[0] += 0.0025872302; + } + } + } + } + } + } + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 206))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += 0.115350716; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += 0.030523414; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { + result[0] += 0.00818327; + } else { + result[0] += 0.042636707; + } + } else { + result[0] += 0.002394322; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += -0; + } else { + result[0] += 0.022605298; + } + } else { + result[0] += -0.018712113; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + result[0] += -0.018390236; + } else { + result[0] += 0.038224217; + } + } else { + result[0] += 0.0023876673; + } + } + } else { + result[0] += -0.0001913241; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 182))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { + result[0] += 0.002910438; + } else { + result[0] += -0.036367122; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + result[0] += 0.0029667527; + } else { + result[0] += 0.04206534; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.012119245; + } else { + result[0] += -0.023951335; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 178))) { + result[0] += -0.034575075; + } else { + result[0] += 0.007937021; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 138))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { + result[0] += -0.0042928117; + } else { + result[0] += -0.0012923562; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 184))) { + result[0] += -0.028960615; + } else { + result[0] += -0.016311336; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += -0.053593513; + } else { + result[0] += 0.009981692; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 78))) { + result[0] += -0.017819297; + } else { + result[0] += -0.00070253096; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 200))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 44))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 32))) { + result[0] += 0.004783728; + } else { + result[0] += 0.016091399; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { + result[0] += 0.029074514; + } else { + result[0] += 0.002160437; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 250))) { + result[0] += 0.055964243; + } else { + result[0] += 0.01622332; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 250))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 20))) { + result[0] += -0.008321747; + } else { + result[0] += -0.0040490143; + } + } else { + result[0] += -0.018693618; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 250))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + result[0] += 6.4203756e-05; + } else { + result[0] += -0.0021725388; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { + result[0] += 0.0055606547; + } else { + result[0] += -0.0023256661; + } + } + } + } + } + } + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 182))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + result[0] += -6.4768305e-06; + } else { + result[0] += -0.013116406; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += 0.019466847; + } else { + result[0] += 0.006013643; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 178))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + result[0] += -0.026702961; + } else { + result[0] += 0.0020189236; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { + result[0] += -0.0006026015; + } else { + result[0] += 0.01105925; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + result[0] += 0.029843587; + } else { + result[0] += 0.0661096; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 98))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 100))) { + result[0] += 0.00522026; + } else { + result[0] += -0.0006855772; + } + } else { + result[0] += 0.013178887; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 124))) { + result[0] += -0.020214727; + } else { + result[0] += 0.0038588562; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 200))) { + result[0] += -0.00505647; + } else { + result[0] += -0.026435608; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { + result[0] += -0.0028923058; + } else { + result[0] += -0.008900782; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { + result[0] += 0.050368883; + } else { + result[0] += 0.0024831446; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 46))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 42))) { + result[0] += 0.011402026; + } else { + result[0] += -0.031253975; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { + result[0] += -0.012896495; + } else { + result[0] += -0.0015336399; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 68))) { + result[0] += 0.011413305; + } else { + result[0] += 0.02147497; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 212))) { + result[0] += 0.013494618; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 46))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 260))) { + result[0] += 0.0066242903; + } else { + result[0] += 0.0001638933; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + result[0] += -0.009525633; + } else { + result[0] += 0.0022819052; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 176))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 70))) { + result[0] += 0.0022807613; + } else { + result[0] += -0.0062286295; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + result[0] += -0.01628782; + } else { + result[0] += -0.004168268; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 104))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 102))) { + result[0] += 0.011599141; + } else { + result[0] += 0.0024420898; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 162))) { + result[0] += 0.013803795; + } else { + result[0] += -0.0013663528; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 146))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { + result[0] += -0.005095913; + } else { + result[0] += 0.00041055848; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 78))) { + result[0] += -0.004392452; + } else { + result[0] += 0.0040476876; + } + } + } + } + } + } + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { + result[0] += -0.00044016525; + } else { + result[0] += 0.0027606068; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 180))) { + result[0] += -0.0027404772; + } else { + result[0] += -0.012484104; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += 0.1066999; + } else { + result[0] += 0.005611764; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { + result[0] += -0.0044804937; + } else { + result[0] += -0.00094216457; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 86))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 20))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += -0.016515603; + } else { + result[0] += -0.0019510689; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += 0.04879087; + } else { + result[0] += 0.019489786; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 174))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + result[0] += 0.028882284; + } else { + result[0] += 0.0029704724; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { + result[0] += -0.008776897; + } else { + result[0] += 0.00076872477; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 22))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 246))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += -0.00024617615; + } else { + result[0] += -0.013363178; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.012836638; + } else { + result[0] += -0.00578899; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { + result[0] += 0.02552943; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 74))) { + result[0] += 0.014522381; + } else { + result[0] += -0.0055740518; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 50))) { + result[0] += -0.024675723; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { + result[0] += 0.036256976; + } else { + result[0] += -0.010159162; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 186))) { + result[0] += -0.0034239579; + } else { + result[0] += 0.0019339178; + } + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { + result[0] += 0.0021977962; + } else { + result[0] += -0.0026835771; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { + result[0] += 0.014414914; + } else { + result[0] += 0.010211523; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + result[0] += -0.008403643; + } else { + result[0] += -0.024761902; + } + } else { + result[0] += 0.0032713003; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + result[0] += -0.02039499; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.009458961; + } else { + result[0] += -0.0018666508; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + result[0] += 0.0327911; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.036110453; + } else { + result[0] += 0.0033109114; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 48))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 48))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 104))) { + result[0] += 0.010583217; + } else { + result[0] += -0.01392099; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + result[0] += 0.032042317; + } else { + result[0] += 0.011768063; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 154))) { + result[0] += -0.04997726; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + result[0] += -0.008824881; + } else { + result[0] += 0.0048329984; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + result[0] += 0.00096224155; + } else { + result[0] += -0.009902506; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 146))) { + result[0] += 0.02890099; + } else { + result[0] += 5.074874e-05; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += -0.0082296785; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 96))) { + result[0] += 0.0020531386; + } else { + result[0] += -0.001512293; + } + } + } + } + } + } + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { + result[0] += -0.009371125; + } else { + result[0] += 0.016960299; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + result[0] += -0.008832243; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 254))) { + result[0] += 0.008071503; + } else { + result[0] += 0.013252865; + } + } else { + result[0] += -0.0039770114; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { + result[0] += -0.0074452083; + } else { + result[0] += 0.0010280218; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { + result[0] += 0.020828962; + } else { + result[0] += 0.006464574; + } + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 244))) { + result[0] += 0.044012897; + } else { + result[0] += -0.01173665; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 76))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { + result[0] += 0.013455394; + } else { + result[0] += -0.014296198; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + result[0] += 0.00890102; + } else { + result[0] += -0.00038118512; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { + result[0] += 0.0076601445; + } else { + result[0] += 0.037503462; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { + result[0] += 0.00028127801; + } else { + result[0] += -0.0077372934; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { + result[0] += -0.0058348128; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + result[0] += -0.0035285298; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += 0.0027004466; + } else { + result[0] += 0.009092315; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { + result[0] += 0.009767297; + } else { + result[0] += -0.008808373; + } + } else { + result[0] += 0.009121577; + } + } else { + result[0] += -0.0003377227; + } + } else { + result[0] += -0.0081705395; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 112))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 110))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + result[0] += 0.012397877; + } else { + result[0] += 0.003961118; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { + result[0] += -0.003340742; + } else { + result[0] += 0.001318198; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { + result[0] += 0.04594936; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + result[0] += -0.012992064; + } else { + result[0] += 0.013072202; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 146))) { + result[0] += -0.016053863; + } else { + result[0] += -0.0013210791; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 120))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { + result[0] += -0.0011712526; + } else { + result[0] += 0.010021153; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 124))) { + result[0] += -0.007040714; + } else { + result[0] += 0.0005415445; + } + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + result[0] += 0.0068953778; + } else { + result[0] += -0.0090838885; + } + } else { + result[0] += 0.024001935; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + result[0] += -0.009051267; + } else { + result[0] += -0.02782002; + } + } else { + result[0] += 0.007848546; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 8))) { + result[0] += 0.0028921093; + } else { + result[0] += 0.023708088; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + result[0] += 0.0013097594; + } else { + result[0] += -0.001009873; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + result[0] += 0.017365938; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += -0.026752142; + } else { + result[0] += -0.00315145; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + result[0] += -0.0013150068; + } else { + result[0] += 0.05037552; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + result[0] += -0.02626193; + } else { + result[0] += 0.030513177; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 8))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.0053047254; + } else { + result[0] += -0.027583335; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 54))) { + result[0] += 0.043980025; + } else { + result[0] += -0.006328375; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { + result[0] += 0.0048042852; + } else { + result[0] += 0.042036824; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.012095607; + } else { + result[0] += -0.00045656387; + } + } + } + } + } + } + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 236))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 112))) { + result[0] += -0.0005225084; + } else { + result[0] += 0.0013189358; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + result[0] += -0.01325873; + } else { + result[0] += 0.008976769; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 98))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { + result[0] += -0.004084352; + } else { + result[0] += 0.0038890864; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 204))) { + result[0] += -0.010790448; + } else { + result[0] += -0.0041690473; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 214))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += 0.00092866377; + } else { + result[0] += -0.021240106; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { + result[0] += 0.016957764; + } else { + result[0] += 0.009066544; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 56))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 120))) { + result[0] += -0.008545898; + } else { + result[0] += -0.0013856884; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 216))) { + result[0] += -0.0058118394; + } else { + result[0] += 0.0030270133; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.004897514; + } else { + result[0] += 0.012878601; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 136))) { + result[0] += -0.009626532; + } else { + result[0] += -0.005689178; + } + } + } else { + result[0] += -0.04376392; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { + result[0] += 0.03378177; + } else { + result[0] += 0.01186064; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 120))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { + result[0] += -0.0032555745; + } else { + result[0] += 0.0158323; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 188))) { + result[0] += 0.00046772003; + } else { + result[0] += 0.0077147195; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + result[0] += 0.0011735325; + } else { + result[0] += -0.006349434; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 146))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.023203839; + } else { + result[0] += 0.00827275; + } + } else { + result[0] += 0.039015923; + } + } else { + result[0] += 0.016087564; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 156))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 206))) { + result[0] += -0.0005247008; + } else { + result[0] += -0.0068671047; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 188))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + result[0] += -0.00041580052; + } else { + result[0] += 0.005611544; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { + result[0] += -0.015564476; + } else { + result[0] += -0.005183963; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 172))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 218))) { + result[0] += 0.023443257; + } else { + result[0] += -0.002142666; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { + result[0] += 0.05721947; + } else { + result[0] += 0.000846235; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 8))) { + result[0] += -0.010377513; + } else { + result[0] += 0.002864465; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { + result[0] += -0.041931864; + } else { + result[0] += -0.01965585; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { + result[0] += -0.0147984475; + } else { + result[0] += 0.031754512; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { + result[0] += 0.0027872298; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { + result[0] += -0.0018927314; + } else { + result[0] += 0.00035012572; + } + } + } else { + result[0] += 0.011900772; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += 0.02755776; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { + result[0] += -0.03298561; + } else { + result[0] += -0.008792006; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 260))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 166))) { + result[0] += -0.00033489856; + } else { + result[0] += 0.006294869; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 216))) { + result[0] += -0.00025057443; + } else { + result[0] += 0.005475367; + } + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 22))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 194))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 172))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += 0.034542117; + } else { + result[0] += 0.0051070247; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 38))) { + result[0] += -0.011878603; + } else { + result[0] += 0.015853336; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 12))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 204))) { + result[0] += -0.017843468; + } else { + result[0] += 0.0025024624; + } + } else { + result[0] += -0.038026553; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { + result[0] += 0.0004767517; + } else { + result[0] += -0.005413917; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { + result[0] += -0.00014004558; + } else { + result[0] += 0.0038309437; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { + result[0] += -0.025853286; + } else { + result[0] += 0.016463885; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { + result[0] += 0.008300542; + } else { + result[0] += -0.0022710604; + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 26))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { + result[0] += 0.06824603; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + result[0] += 0.0344267; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 24))) { + result[0] += -0.02413011; + } else { + result[0] += 0.009757657; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 240))) { + result[0] += 0.04187591; + } else { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += 0.021772258; + } else { + result[0] += 0.00023637361; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + result[0] += -0.00018605802; + } else { + result[0] += 0.00892628; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + result[0] += -0.0029304621; + } else { + result[0] += 0.005322406; + } + } + } + } + } + } + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { + result[0] += 0.058786936; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { + result[0] += -0.001987611; + } else { + result[0] += 0.0012095956; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += -0.013887591; + } else { + result[0] += -0.005378675; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + result[0] += 0.004469014; + } else { + result[0] += -0.0019160692; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + result[0] += -0.052244443; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { + result[0] += 0.034024667; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 184))) { + result[0] += -0.028885717; + } else { + result[0] += -0.016080601; + } + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 28))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 196))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 206))) { + result[0] += 0.004683802; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + result[0] += 0.0038840864; + } else { + result[0] += -0.0010689719; + } + } + } else { + result[0] += 0.040237945; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 174))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { + result[0] += 0.009488398; + } else { + result[0] += 0.0063879974; + } + } else { + result[0] += 0.040055256; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 124))) { + result[0] += 0.0063549983; + } else { + result[0] += 0.0503232; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { + result[0] += -0.0018827593; + } else { + result[0] += 0.0051954878; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 232))) { + result[0] += 0.032296978; + } else { + result[0] += 0.0139222685; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { + result[0] += 0.04671686; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + result[0] += 0.0009886044; + } else { + result[0] += 0.006239516; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 30))) { + result[0] += 0.031537697; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { + result[0] += -0.019081848; + } else { + result[0] += -0.0026591418; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + result[0] += 0.026619418; + } else { + result[0] += 0.0041615735; + } + } else { + result[0] += 0.047726057; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + result[0] += 0.001571185; + } else { + result[0] += -0.00050108694; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 140))) { + result[0] += 0.009636164; + } else { + result[0] += 0.0010100096; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + result[0] += -0.0026403468; + } else { + result[0] += 0.0114326365; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 50))) { + result[0] += -0.021934291; + } else { + result[0] += -0.0050015096; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 238))) { + result[0] += -0.007851736; + } else { + result[0] += 0.03168834; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + result[0] += 0.033044577; + } else { + result[0] += 9.3673785e-05; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 188))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 156))) { + result[0] += 0.00030188911; + } else { + result[0] += 0.002446159; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { + result[0] += -0.0055051325; + } else { + result[0] += 0.0012621379; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { + result[0] += 0.014215629; + } else { + result[0] += 0.005867033; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { + result[0] += -0.006319243; + } else { + result[0] += 0.0017076422; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 166))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 218))) { + result[0] += -0.0017003525; + } else { + result[0] += 0.010260985; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 236))) { + result[0] += -0.015775895; + } else { + result[0] += -0.007966612; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + result[0] += 0.0038435606; + } else { + result[0] += 0.0005556598; + } + } else { + result[0] += 0.0073271217; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 230))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 196))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { + result[0] += -0.00010884991; + } else { + result[0] += -0.0013555246; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + result[0] += 0.00045378748; + } else { + result[0] += 0.00696068; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 180))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + result[0] += 0.0034289937; + } else { + result[0] += -0.005728068; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { + result[0] += -0.009315341; + } else { + result[0] += -0.004357322; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { + result[0] += -0.024190784; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { + result[0] += -0; + } else { + result[0] += -0.019713525; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 220))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 64))) { + result[0] += -0.0078073195; + } else { + result[0] += 5.9448754e-05; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 186))) { + result[0] += -0.006281379; + } else { + result[0] += -0.010314364; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + result[0] += 0.001542045; + } else { + result[0] += -0.0030066923; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { + result[0] += 0.045451414; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 250))) { + result[0] += -0.0048594624; + } else { + result[0] += 0.002339422; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 236))) { + result[0] += 0.0038202107; + } else { + result[0] += -0.0043049804; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 222))) { + result[0] += 0.035619907; + } else { + result[0] += 0.011654694; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { + result[0] += 0.011329205; + } else { + result[0] += -0.0026530891; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + result[0] += 0.00091164187; + } else { + result[0] += 0.0065599764; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 180))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + result[0] += 0.0003863835; + } else { + result[0] += 0.012511316; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 178))) { + result[0] += -0.012533942; + } else { + result[0] += 0.000940961; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + result[0] += 0.02408296; + } else { + result[0] += 0.006255468; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 188))) { + result[0] += 0.01437206; + } else { + result[0] += 0.005517607; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 156))) { + result[0] += -0.004864832; + } else { + result[0] += 0.004785055; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 206))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + result[0] += -0.008446374; + } else { + result[0] += 3.6487032e-05; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { + result[0] += -0.013399954; + } else { + result[0] += 0.0013180381; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 56))) { + result[0] += -0.01676119; + } else { + result[0] += 0.019005135; + } + } else { + result[0] += 0.026536051; + } + } else { + result[0] += -0.017303595; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 128))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 234))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { + result[0] += -0.005325236; + } else { + result[0] += 0.0026667507; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { + result[0] += 0.006688422; + } else { + result[0] += 0.0130848605; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 200))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 180))) { + result[0] += -6.232897e-05; + } else { + result[0] += -0.011703821; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { + result[0] += 0.01631069; + } else { + result[0] += 0.0024924437; + } + } + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 118))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += -0.020456076; + } else { + result[0] += 1.7096074e-05; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += -0.0043996517; + } else { + result[0] += 0.037505116; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { + result[0] += 0.03947706; + } else { + result[0] += 0.00013893798; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { + result[0] += -0.00020466889; + } else { + result[0] += -0.0056823995; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { + result[0] += 0.0015122422; + } else { + result[0] += 0.014326178; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 98))) { + result[0] += -0.03659178; + } else { + result[0] += -0.00897372; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 234))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { + result[0] += -0.0012049631; + } else { + result[0] += -0.010048345; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + result[0] += 0.023757316; + } else { + result[0] += -0.00096084067; + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { + result[0] += 0.007543098; + } else { + result[0] += 0.05037853; + } + } else { + result[0] += -0.020506142; + } + } else { + result[0] += 0.041784402; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 168))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + result[0] += 0.022379946; + } else { + result[0] += 0.001123256; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 176))) { + result[0] += -0.0040573548; + } else { + result[0] += -0.00018302095; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { + result[0] += 0.026662258; + } else { + result[0] += 0.001635097; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 172))) { + result[0] += 0.0157876; + } else { + result[0] += 0.00078219484; + } + } + } + } + } + } + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 168))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { + result[0] += -0.003070421; + } else { + result[0] += 0.003986802; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + result[0] += -0.016073924; + } else { + result[0] += -0.00033199994; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 82))) { + result[0] += -0.0026495587; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 180))) { + result[0] += 0.01088603; + } else { + result[0] += 0.0028157744; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 128))) { + result[0] += 0.035645917; + } else { + result[0] += 0.011242066; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 158))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 120))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + result[0] += -0.004172708; + } else { + result[0] += -0.013315861; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + result[0] += 0.0016847762; + } else { + result[0] += -0.019520441; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 116))) { + result[0] += -0.0012042717; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + result[0] += 0.010144642; + } else { + result[0] += 0.021312661; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 122))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { + result[0] += -0.0060045626; + } else { + result[0] += -0.019477958; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 154))) { + result[0] += -0.010557742; + } else { + result[0] += -0.019895343; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 164))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { + result[0] += 0.0023980096; + } else { + result[0] += -0.0058308984; + } + } else { + result[0] += 0.0054435167; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 180))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 170))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 144))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 52))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + result[0] += 0.01054426; + } else { + result[0] += -0.013723351; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 190))) { + result[0] += 0.0035272748; + } else { + result[0] += -0.0014124735; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 128))) { + result[0] += -0.020531978; + } else { + result[0] += -0.008468418; + } + } + } else { + result[0] += 0.027421832; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 108))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 136))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { + result[0] += -0.0014708912; + } else { + result[0] += 0.010910965; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { + result[0] += 0.02383196; + } else { + result[0] += -0.005580553; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 192))) { + result[0] += -0.015012925; + } else { + result[0] += -0.038420197; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 102))) { + result[0] += 0.027166018; + } else { + result[0] += -0.0051050577; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 158))) { + result[0] += 0.005120454; + } else { + result[0] += -0.0066694873; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + result[0] += 0.040612247; + } else { + result[0] += 0.010608687; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 206))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 186))) { + result[0] += 9.48829e-05; + } else { + result[0] += -0.0028726817; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 222))) { + result[0] += 0.0036865217; + } else { + result[0] += -0.0017621756; + } + } + } + } + } + } + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 12))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { + result[0] += 0.010326921; + } else { + result[0] += -6.580556e-05; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 14))) { + result[0] += 0.035937645; + } else { + result[0] += 0.0061092353; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += 0.09306122; + } else { + result[0] += 0.004483877; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { + result[0] += -0.0021131127; + } else { + result[0] += -0.0002122157; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 16))) { + result[0] += -0.0010388399; + } else { + result[0] += 0.04628381; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 170))) { + result[0] += -0.012791668; + } else { + result[0] += 0.012744421; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { + result[0] += 0.001258762; + } else { + result[0] += -0.010475395; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 18))) { + result[0] += 0.025982514; + } else { + result[0] += 0.0025854723; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 26))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { + result[0] += -0.020725088; + } else { + result[0] += 0.014970714; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 176))) { + result[0] += -0.0017299574; + } else { + result[0] += 0.026476989; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 158))) { + result[0] += -0.004242038; + } else { + result[0] += -0.029603764; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 100))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 126))) { + result[0] += 0.0020087094; + } else { + result[0] += -0.001871386; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 44))) { + result[0] += 0.014256491; + } else { + result[0] += 0.007524089; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 94))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 206))) { + result[0] += -0.014824388; + } else { + result[0] += -0.0017853121; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + result[0] += -0.004817825; + } else { + result[0] += 0.00016148947; + } + } + } + } + } + } else { + result[0] += 0.0088046715; + } + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 168))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + result[0] += 9.3958115e-05; + } else { + result[0] += 0.0067572976; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 222))) { + result[0] += -0.0049092458; + } else { + result[0] += -0.0005594916; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 178))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 108))) { + result[0] += 0.00977688; + } else { + result[0] += 0.0015517873; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 204))) { + result[0] += -0.008679122; + } else { + result[0] += -0.002352368; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 158))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + result[0] += 0.009798234; + } else { + result[0] += 0.0032348556; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 182))) { + result[0] += 0.014364332; + } else { + result[0] += 0.025256773; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 118))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { + result[0] += -0.019178228; + } else { + result[0] += -0.0023992255; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { + result[0] += 0.026579231; + } else { + result[0] += 0.0040463707; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 172))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 170))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { + result[0] += -0.012679273; + } else { + result[0] += -0.03488743; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { + result[0] += 0.012128734; + } else { + result[0] += -0.0036993285; + } + } + } else { + result[0] += -0.026699487; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 164))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 46))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { + result[0] += 0.008520793; + } else { + result[0] += -0.0012307116; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 174))) { + result[0] += -0.0020589442; + } else { + result[0] += -0.0074132094; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 258))) { + result[0] += -0.00428136; + } else { + result[0] += 0.042908087; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { + result[0] += 0.034873586; + } else { + result[0] += 0.0018836738; + } + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 190))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { + result[0] += -0.0036578246; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 208))) { + result[0] += 0.020423703; + } else { + result[0] += 0.030946225; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + result[0] += -0.006327176; + } else { + result[0] += -0.015637185; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 258))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 232))) { + result[0] += 0.0020563873; + } else { + result[0] += 0.009788949; + } + } else { + result[0] += -0.0091839805; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 194))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { + result[0] += 0.051798742; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 252))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + result[0] += -0.006234445; + } else { + result[0] += 0.008611916; + } + } else { + result[0] += 0.0055716326; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 196))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 194))) { + result[0] += 0.003886501; + } else { + result[0] += 0.008659224; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 198))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { + result[0] += -0.03154314; + } else { + result[0] += -0.0031137222; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + result[0] += 0.00038270376; + } else { + result[0] += 0.0020157003; + } + } + } + } + } + } + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { + result[0] += 0.026274556; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + result[0] += -0.014014624; + } else { + result[0] += 0.0017416707; + } + } else { + result[0] += -0.020999283; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { + result[0] += 0.019021433; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 2))) { + result[0] += 0.00134224; + } else { + result[0] += 0.0040796655; + } + } else { + result[0] += -0.0030546128; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 46))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + result[0] += -0.0019856154; + } else { + result[0] += -0.0002087965; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + result[0] += 0.013158979; + } else { + result[0] += -0.0030576333; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { + result[0] += 0.003060523; + } else { + result[0] += 0.0005930412; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 48))) { + result[0] += 0.03627108; + } else { + result[0] += -0.0035451804; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + result[0] += -0.0046488056; + } else { + result[0] += 0.00013075671; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 120))) { + result[0] += 0.012213302; + } else { + result[0] += 0.04960065; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { + result[0] += -0.0069404887; + } else { + result[0] += -0.01752409; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + result[0] += 0.0142224645; + } else { + result[0] += -0.0028541964; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + result[0] += -0.00048694783; + } else { + result[0] += -0.01978219; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + result[0] += 0.023798453; + } else { + result[0] += 0.0028695017; + } + } + } else { + result[0] += 0.049870964; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { + result[0] += -0.011660507; + } else { + result[0] += 0.020355195; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { + result[0] += -0.010959279; + } else { + result[0] += -0.022352552; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 104))) { + result[0] += 0.0019975018; + } else { + result[0] += 0.02552997; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.005593583; + } else { + result[0] += 0.0001567727; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 36))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + result[0] += 0.0025115781; + } else { + result[0] += 0.01276709; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += 0.0009829837; + } else { + result[0] += -0.0020405638; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 176))) { + result[0] += -0.0027318567; + } else { + result[0] += 0.00036724083; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { + result[0] += 0.00952085; + } else { + result[0] += 0.00021925311; + } + } + } + } else { + result[0] += 0.0028065902; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 264))) { + result[0] += -0.010198384; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 68))) { + result[0] += 0.008980814; + } else { + result[0] += -0.00083386357; + } + } + } else { + result[0] += 0.034579825; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + result[0] += -0.0005227964; + } else { + result[0] += 0.00078901247; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 264))) { + result[0] += -0.0031115639; + } else { + result[0] += -0.008557741; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 80))) { + result[0] += 0.044274148; + } else { + result[0] += 0.004722073; + } + } else { + result[0] += -0.020457005; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 208))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 214))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { + result[0] += -0.011751657; + } else { + result[0] += -0.004513866; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 174))) { + result[0] += 0.0004462137; + } else { + result[0] += 0.0044212313; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 202))) { + result[0] += -0.015617072; + } else { + result[0] += -0.0095054805; + } + } else { + result[0] += -3.9380015e-05; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 160))) { + result[0] += 0.002189336; + } else { + result[0] += 0.011331045; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 230))) { + result[0] += 0.0016538227; + } else { + result[0] += -0.0024343396; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 186))) { + result[0] += 0.030128485; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { + result[0] += 0.0012932423; + } else { + result[0] += 0.004705872; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += 0.001994251; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 252))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 138))) { + result[0] += -0.00304999; + } else { + result[0] += 0.011378044; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { + result[0] += 0.004555874; + } else { + result[0] += -0.00066039886; + } + } + } + } + } + } + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { + result[0] += 0.020459097; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 56))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + result[0] += -0.006080057; + } else { + result[0] += 0.0015269167; + } + } else { + result[0] += 0.004850093; + } + } else { + result[0] += 0.0372449; + } + } else { + result[0] += -0.0033630708; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + result[0] += 0.0020560354; + } else { + result[0] += -0.01611949; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { + result[0] += 0.02037514; + } else { + result[0] += 0.0024216073; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { + result[0] += -1.3009435e-06; + } else { + result[0] += -0.010717098; + } + } else { + result[0] += -0.026966467; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { + result[0] += -0.035333917; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { + result[0] += -0.0039970325; + } else { + result[0] += -0.018680593; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { + result[0] += 0.060415726; + } else { + result[0] += -0.0035995846; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 12))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { + result[0] += 0.079136275; + } else { + result[0] += 0.006364348; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + result[0] += 0.009026817; + } else { + result[0] += -0.00017536264; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 258))) { + result[0] += 0.0025470634; + } else { + result[0] += -0.0051941304; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 78))) { + result[0] += -0.0008757123; + } else { + result[0] += -0.008570026; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 52))) { + result[0] += 0.005017677; + } else { + result[0] += -7.974629e-05; + } + } + } + } + } + } + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 190))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 174))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + result[0] += -8.178638e-05; + } else { + result[0] += 0.0078121508; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { + result[0] += -0.005830926; + } else { + result[0] += -0.0010021594; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 262))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 244))) { + result[0] += 0.0016010083; + } else { + result[0] += 0.00825313; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 206))) { + result[0] += -0.003538803; + } else { + result[0] += 0.0063129514; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 198))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 170))) { + result[0] += -0.005545571; + } else { + result[0] += -0.0232496; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { + result[0] += 0.012393202; + } else { + result[0] += -4.4827804e-05; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 208))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 200))) { + result[0] += 0.003246121; + } else { + result[0] += -0.00059273635; + } + } else { + result[0] += -0.013444054; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 180))) { + result[0] += 0.00011026966; + } else { + result[0] += -0.012288004; + } + } else { + result[0] += 0.009097963; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 210))) { + result[0] += -0.004248265; + } else { + result[0] += -0.008213413; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += 0.041847315; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 214))) { + result[0] += 0.008025631; + } else { + result[0] += 0.0023118325; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { + result[0] += -0.004090266; + } else { + result[0] += 0.001676759; + } + } + } + } + } + } else { + result[0] += 0.0124376435; + } + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 142))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 130))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { + result[0] += 8.788087e-05; + } else { + result[0] += -0.0007693504; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 62))) { + result[0] += 0.028994996; + } else { + result[0] += 0.0019165861; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 82))) { + result[0] += 0.0018343481; + } else { + result[0] += -0.001085312; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + result[0] += -0.00844089; + } else { + result[0] += -0.0010173704; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 118))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { + result[0] += 0.03216304; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.019660346; + } else { + result[0] += 0.010661392; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { + result[0] += -0.011746697; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 120))) { + result[0] += 0.040727697; + } else { + result[0] += 0.007775008; + } + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 146))) { + result[0] += -0.017095553; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += -0.010490341; + } else { + result[0] += -0.0029303418; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { + result[0] += 0.0037032042; + } else { + result[0] += 0.019236496; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 88))) { + result[0] += -0.00065161986; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 154))) { + result[0] += 0.013047007; + } else { + result[0] += 0.018410644; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { + result[0] += -0.0025795808; + } else { + result[0] += 0.0025427365; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 44))) { + result[0] += 0.0063687847; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 158))) { + result[0] += 0.021263849; + } else { + result[0] += 0.037114166; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { + result[0] += -0.007888737; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.0043152072; + } else { + result[0] += -0.00065306626; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { + result[0] += 0.0002832889; + } else { + result[0] += 0.023496693; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 164))) { + result[0] += -0.004027024; + } else { + result[0] += 0.0036689022; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 52))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 168))) { + result[0] += -0.006764188; + } else { + result[0] += -0.015382841; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 188))) { + result[0] += -0.0006603152; + } else { + result[0] += -0.0053936313; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { + result[0] += 0.0016898311; + } else { + result[0] += 0.010389189; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { + result[0] += -0.0190918; + } else { + result[0] += 6.013682e-05; + } + } + } + } + } + } + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 30))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 258))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += 0.0022224819; + } else { + result[0] += -9.1448324e-05; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { + result[0] += 0.008647813; + } else { + result[0] += 0.0013861794; + } + } + } else { + result[0] += -0.0051472313; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { + result[0] += 0.022298444; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { + result[0] += 0.014823428; + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 78))) { + result[0] += 0.0028555428; + } else { + result[0] += -0.0060178605; + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 8))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 204))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 144))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { + result[0] += -0.011365654; + } else { + result[0] += 0.008808569; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 166))) { + result[0] += -0.024276486; + } else { + result[0] += -0.0131767215; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 86))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + result[0] += 0.00949425; + } else { + result[0] += -0.013411551; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + result[0] += -0; + } else { + result[0] += 0.012317988; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + result[0] += 0.0853606; + } else { + result[0] += 0.0014590746; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 42))) { + result[0] += -0.04024057; + } else { + result[0] += 0.025248704; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { + result[0] += -0.00020014495; + } else { + result[0] += -0.008825987; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { + result[0] += -0.0026628354; + } else { + result[0] += 0.00010321446; + } + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { + result[0] += -0.002153416; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + result[0] += -0.0011085857; + } else { + result[0] += 0.00558898; + } + } + } + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 106))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { + result[0] += -0.0072398344; + } else { + result[0] += -0.00012400204; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { + result[0] += 0.025649467; + } else { + result[0] += -0.0001327928; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 94))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { + result[0] += -0.0015077029; + } else { + result[0] += -0.014433789; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += 0.026124759; + } else { + result[0] += 0.0094175115; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { + result[0] += -0.009028183; + } else { + result[0] += -0.05055972; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + result[0] += 0.008288766; + } else { + result[0] += -0.03171199; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 74))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += 0.029759515; + } else { + result[0] += -0.019024173; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 86))) { + result[0] += 0.0045653116; + } else { + result[0] += 6.555731e-05; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 106))) { + result[0] += 0.029950727; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.0046233633; + } else { + result[0] += 0.020635402; + } + } else { + result[0] += 0.0056214184; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += -0.03250946; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + result[0] += 0.013577218; + } else { + result[0] += -0.031636387; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { + result[0] += -0.005065638; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.004201682; + } else { + result[0] += 0.0023832265; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 84))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 152))) { + result[0] += -0.023720464; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { + result[0] += -0.0023072253; + } else { + result[0] += 0.0019047937; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 158))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { + result[0] += -0.0026599832; + } else { + result[0] += 0.03371409; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 98))) { + result[0] += -0.010501557; + } else { + result[0] += 0.03556975; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 92))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 96))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + result[0] += 0.005952064; + } else { + result[0] += 0.024354918; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + result[0] += 0.0024803034; + } else { + result[0] += -0.007694894; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { + result[0] += 0.016107908; + } else { + result[0] += -0.0016387564; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { + result[0] += 0.003588716; + } else { + result[0] += -1.9828594e-05; + } + } + } + } + } + } + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 206))) { + result[0] += 0.0046148184; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { + result[0] += 0.0024027901; + } else { + result[0] += 0.00039783234; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + result[0] += 0.044661757; + } else { + result[0] += 0.0023485457; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + result[0] += -0.04294611; + } else { + result[0] += -0.02216121; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += 0.0008537049; + } else { + result[0] += -0.020757614; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 192))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { + result[0] += -0.0010473432; + } else { + result[0] += -0.012557432; + } + } else { + result[0] += 0.0345631; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 10))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { + result[0] += 0.009390167; + } else { + result[0] += 0.0029766764; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { + result[0] += -0.00874328; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + result[0] += -0.011705816; + } else { + result[0] += 0.0072610932; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { + result[0] += -0.00043213202; + } else { + result[0] += 0.0003012484; + } + } + } + } + } + } + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + result[0] += -0.0078075933; + } else { + result[0] += -0.03296116; + } + } else { + result[0] += 0.0045325574; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 12))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + result[0] += 0.0027738758; + } else { + result[0] += -0.0002652021; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.017156854; + } else { + result[0] += 0.0061222934; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + result[0] += 0.009275707; + } else { + result[0] += -0.008091964; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + result[0] += 0.02927331; + } else { + result[0] += 0.0036204413; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + result[0] += -0.009472274; + } else { + result[0] += -0.036276244; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 76))) { + result[0] += -0.0055947257; + } else { + result[0] += -0.016915802; + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + result[0] += 0.0031531143; + } else { + result[0] += -0.020101098; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 10))) { + result[0] += -0.019649826; + } else { + result[0] += -0.0027359962; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { + result[0] += 0.02526692; + } else { + result[0] += -0; + } + } else { + result[0] += 0.0012190904; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 12))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { + result[0] += 0.079478934; + } else { + result[0] += 0.004041801; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.05100611; + } else { + result[0] += 0.013841884; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + result[0] += -0.0001244545; + } else { + result[0] += 0.0013780014; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + result[0] += 0.004001136; + } else { + result[0] += -0.011721842; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + result[0] += 0.015507097; + } else { + result[0] += -0.002207271; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 68))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 174))) { + result[0] += -0.0029817559; + } else { + result[0] += 0.0030468712; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 154))) { + result[0] += 0.008323856; + } else { + result[0] += 0.0378311; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + result[0] += 0.019646537; + } else { + result[0] += -0.009434113; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 80))) { + result[0] += 0.015092474; + } else { + result[0] += 0.00023066562; + } + } + } + } + } + } + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 14))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { + result[0] += 0.00044216277; + } else { + result[0] += 0.0030401708; + } + } else { + result[0] += -0.053345073; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + result[0] += 0.024727738; + } else { + result[0] += -0.008567747; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { + result[0] += 0.036883164; + } else { + result[0] += 0.014384936; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.004501282; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 14))) { + result[0] += -0.023340857; + } else { + result[0] += 0.0061771907; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 76))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.024513956; + } else { + result[0] += -0.0015961519; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { + result[0] += 0.0052294037; + } else { + result[0] += 0.00016616772; + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 218))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 62))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { + result[0] += 0.0042944904; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 122))) { + result[0] += -0.035668056; + } else { + result[0] += 0.033606566; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { + result[0] += 0.013674865; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 98))) { + result[0] += 0.0019538465; + } else { + result[0] += -0.001950983; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 6))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + result[0] += 0.0020924758; + } else { + result[0] += 0.00730929; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 78))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 10))) { + result[0] += 0.00075499614; + } else { + result[0] += 0.0019689128; + } + } else { + result[0] += -0.0016815666; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 226))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 8))) { + result[0] += -0.0115894945; + } else { + result[0] += -0.051073868; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + result[0] += 0.011092906; + } else { + result[0] += 0.00051702786; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { + result[0] += -0.013087346; + } else { + result[0] += -0.0062652132; + } + } else { + result[0] += 0.009260368; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 92))) { + result[0] += -0.013211399; + } else { + result[0] += -0.005936895; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 64))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += 0.018809779; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { + result[0] += -0.00044671824; + } else { + result[0] += 0.0025783246; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { + result[0] += -0.008156588; + } else { + result[0] += 0.011215605; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { + result[0] += 0.009001787; + } else { + result[0] += 0.027359499; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += 0.03523052; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { + result[0] += -0.016149698; + } else { + result[0] += -0.0014326718; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 172))) { + result[0] += 0.009203473; + } else { + result[0] += -0.01101023; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { + result[0] += 0.007282652; + } else { + result[0] += -3.0036234e-05; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { + result[0] += 0.00011574587; + } else { + result[0] += 0.021244911; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { + result[0] += 0.0015066937; + } else { + result[0] += 0.01799863; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 116))) { + result[0] += 0.002711843; + } else { + result[0] += 0.01596604; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 178))) { + result[0] += -0.0002499475; + } else { + result[0] += 0.0038768928; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { + result[0] += 0.022616291; + } else { + result[0] += -0; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { + result[0] += -0.018833179; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { + result[0] += -0.007184063; + } else { + result[0] += -0.00079069706; + } + } + } + } + } else { + result[0] += 0.0025515545; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 254))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 208))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 230))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 218))) { + result[0] += -9.1387315e-05; + } else { + result[0] += -0.0014537438; + } + } else { + result[0] += 0.0155031625; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 142))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 140))) { + result[0] += -0.003084584; + } else { + result[0] += -0.01318902; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 160))) { + result[0] += 0.0037594668; + } else { + result[0] += -0.0014401844; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { + result[0] += 0.0035882175; + } else { + result[0] += -0.0048747966; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 162))) { + result[0] += 0.0041894084; + } else { + result[0] += 0.0011686251; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 210))) { + result[0] += -0.0026981125; + } else { + result[0] += -0.009141969; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { + result[0] += 0.0005261341; + } else { + result[0] += -0.0018949058; + } + } + } + } + } else { + result[0] += 0.018716332; + } + } + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 236))) { + result[0] += 2.3203478e-05; + } else { + result[0] += -0.0077802497; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + result[0] += 0.031958427; + } else { + result[0] += 0.0035587456; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 128))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { + result[0] += 0.002486235; + } else { + result[0] += 0.011185441; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 204))) { + result[0] += -0.0068403906; + } else { + result[0] += -0.001759151; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 140))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.0073994272; + } else { + result[0] += -0.016759716; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 108))) { + result[0] += 0.028997958; + } else { + result[0] += -0.00054327224; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 232))) { + result[0] += 0.02114836; + } else { + result[0] += -0.011946307; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 264))) { + result[0] += 0.002376872; + } else { + result[0] += -0.0016341202; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + result[0] += -0.03481881; + } else { + result[0] += -0.007797651; + } + } else { + result[0] += -0.00034481628; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { + result[0] += 0.0057917973; + } else { + result[0] += 0.0218565; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 152))) { + result[0] += -0.001166375; + } else { + result[0] += 0.0030204598; + } + } + } + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 100))) { + result[0] += -8.485738e-05; + } else { + result[0] += 0.0070774257; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 148))) { + result[0] += -0.0021743942; + } else { + result[0] += -0.029421; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 172))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + result[0] += 0.0006834134; + } else { + result[0] += 0.010325692; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { + result[0] += -0.001693792; + } else { + result[0] += 0.0011259892; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { + result[0] += -0.0077881524; + } else { + result[0] += 0.0026733195; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 166))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { + result[0] += 0.028013662; + } else { + result[0] += 0.0070042475; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { + result[0] += 0.004207076; + } else { + result[0] += -0.016646465; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 182))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { + result[0] += 0.014890812; + } else { + result[0] += -0.0032379036; + } + } else { + result[0] += 0.0058797766; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 166))) { + result[0] += -0.004874174; + } else { + result[0] += -0.014910466; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 194))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 178))) { + result[0] += -0.0024306828; + } else { + result[0] += -0.021217367; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 232))) { + result[0] += 0.0033338335; + } else { + result[0] += -0.00023477618; + } + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 194))) { + result[0] += -0.037188523; + } else { + result[0] += -0.012556768; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { + result[0] += 0.0015031213; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + result[0] += 0.038557947; + } else { + result[0] += 0.018226711; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { + result[0] += -0.02505053; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 204))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { + result[0] += -0.0005712052; + } else { + result[0] += -0.0054953173; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 216))) { + result[0] += 0.0013088783; + } else { + result[0] += -0.0013262478; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 214))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 92))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 246))) { + result[0] += -0.0016769763; + } else { + result[0] += 0.004949599; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + result[0] += -0.022474889; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + result[0] += 0.011304743; + } else { + result[0] += 0.0049235076; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 82))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + result[0] += -0.0013205075; + } else { + result[0] += 0.005631945; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { + result[0] += -0.013254325; + } else { + result[0] += -0.0048218477; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 234))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + result[0] += 0.0018386835; + } else { + result[0] += -0.010448969; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { + result[0] += -0.0026523883; + } else { + result[0] += 0.00015089083; + } + } + } + } + } + } + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { + result[0] += 0.0024612967; + } else { + result[0] += -0.00267146; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { + result[0] += 0.0072369473; + } else { + result[0] += 0.03716041; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 34))) { + result[0] += 0.016798072; + } else { + result[0] += -0.020668425; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { + result[0] += -0.0026954925; + } else { + result[0] += 0.00023960511; + } + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 140))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.009252391; + } else { + result[0] += 0.0032986738; + } + } else { + result[0] += -0.01665429; + } + } else { + result[0] += 0.0028180957; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 128))) { + result[0] += 0.02164252; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + result[0] += -0.012067605; + } else { + result[0] += 0.0026951; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { + result[0] += 0.015092296; + } else { + result[0] += -0.038058575; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { + result[0] += 0.00892017; + } else { + result[0] += 0.031467702; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 216))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 154))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + result[0] += 0.0022178197; + } else { + result[0] += -0.005610294; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { + result[0] += 0.011955344; + } else { + result[0] += -0.0074412758; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + result[0] += 0.0014451804; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.0032450047; + } else { + result[0] += 0.00080591295; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 164))) { + result[0] += 0.010952067; + } else { + result[0] += -0.003597115; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 236))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 144))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 230))) { + result[0] += 0.0010651236; + } else { + result[0] += 0.019118952; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { + result[0] += -0.0010132345; + } else { + result[0] += 0.0005361346; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + result[0] += 0.0063631246; + } else { + result[0] += -0.005013407; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 216))) { + result[0] += -0.0018120991; + } else { + result[0] += 0.0013921553; + } + } + } + } + } + } + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 90))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { + result[0] += -0.0027856524; + } else { + result[0] += 0.011518478; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 52))) { + result[0] += -0.024774041; + } else { + result[0] += -0.0019057058; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 10))) { + result[0] += -0.01394673; + } else { + result[0] += 0.006964297; + } + } else { + result[0] += 0.05194057; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 152))) { + result[0] += -0.028471593; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 186))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 20))) { + result[0] += -0.0014699342; + } else { + result[0] += 0.024541676; + } + } else { + result[0] += -0.010517646; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 210))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + result[0] += -0.006757573; + } else { + result[0] += 0.0052805874; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { + result[0] += -0.01105545; + } else { + result[0] += 0.001222587; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 72))) { + result[0] += -0.011149263; + } else { + result[0] += -0.031825926; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 80))) { + result[0] += -6.1052204e-05; + } else { + result[0] += -0.0018558489; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += -0; + } else { + result[0] += 0.037658174; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 184))) { + result[0] += -0.022644173; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { + result[0] += -0.011395801; + } else { + result[0] += -0.004704497; + } + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 116))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { + result[0] += 0.018175911; + } else { + result[0] += 0.0022316524; + } + } else { + result[0] += -0.01448056; + } + } else { + result[0] += 0.03850323; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 10))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { + result[0] += -0.0056385025; + } else { + result[0] += -0.001911032; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 142))) { + result[0] += 0.006841138; + } else { + result[0] += 0.0015013752; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { + result[0] += -0.003987257; + } else { + result[0] += 0.0014403629; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 112))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 140))) { + result[0] += 0.0001458845; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 168))) { + result[0] += -0.018207422; + } else { + result[0] += 0.0007071493; + } + } + } else { + result[0] += -0.014048204; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { + result[0] += 0.024706252; + } else { + result[0] += 0.012115999; + } + } else { + result[0] += -0.010074422; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + result[0] += 0.00012816093; + } else { + result[0] += -0.0048219212; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + result[0] += 0.00042065704; + } else { + result[0] += -0.00044052277; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 2))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + result[0] += 0.012054322; + } else { + result[0] += -0.012439433; + } + } else { + result[0] += 0.004142578; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 246))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { + result[0] += 0.0005554498; + } else { + result[0] += 0.0034215685; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { + result[0] += 0.00583103; + } else { + result[0] += 0.00022540719; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { + result[0] += 0.003226608; + } else { + result[0] += 0.029922882; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 20))) { + result[0] += -0.006730991; + } else { + result[0] += -0.001442519; + } + } + } + } + } else { + result[0] += 0.020426724; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 254))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + result[0] += 0.0003546644; + } else { + result[0] += -0.0044249105; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 132))) { + result[0] += 0.0020044944; + } else { + result[0] += -0.0026672701; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { + result[0] += -0.007007061; + } else { + result[0] += -0.0024277617; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 156))) { + result[0] += 0.0035193504; + } else { + result[0] += 0.016300239; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + result[0] += -0.002883124; + } else { + result[0] += 0.0034831774; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 222))) { + result[0] += -0.004244165; + } else { + result[0] += -0.02417766; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 36))) { + result[0] += -0.0009843176; + } else { + result[0] += -0.00013388977; + } + } + } + } + } else { + result[0] += 0.013616696; + } + } + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { + result[0] += 0.00050606584; + } else { + result[0] += -0.0017093392; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + result[0] += -0.0051450143; + } else { + result[0] += 0.0035812582; + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += -0.010360922; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { + result[0] += 0.0013417737; + } else { + result[0] += -0.0003085478; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += 0.06123736; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { + result[0] += -0.010781719; + } else { + result[0] += -0.002857905; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 52))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + result[0] += 0.013251043; + } else { + result[0] += -0.020714538; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 76))) { + result[0] += -0.0037599222; + } else { + result[0] += -0.0004986952; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { + result[0] += 0.08917741; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { + result[0] += -0.030496597; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 252))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { + result[0] += -0.0025217582; + } else { + result[0] += 0.00018528964; + } + } else { + result[0] += -0.014542065; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 222))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.009864878; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 50))) { + result[0] += 0.017176276; + } else { + result[0] += 0.030590722; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 92))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { + result[0] += 0.0058373446; + } else { + result[0] += -0.012375738; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += 0.047506116; + } else { + result[0] += 0.011469667; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { + result[0] += 0.004630673; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 38))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 252))) { + result[0] += -0.004851036; + } else { + result[0] += -0.023372188; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 50))) { + result[0] += 0.036052324; + } else { + result[0] += -0.002185328; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 224))) { + result[0] += -0.00040355555; + } else { + result[0] += -0.0124766445; + } + } else { + result[0] += 0.008253347; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 92))) { + result[0] += -0.011534996; + } else { + result[0] += -0.0021382947; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { + result[0] += -0.0014395117; + } else { + result[0] += 0.019073278; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + result[0] += 0.019316873; + } else { + result[0] += 0.0020871195; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { + result[0] += 0.004829779; + } else { + result[0] += 0.021349184; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + result[0] += -0.0045497594; + } else { + result[0] += 0.015120694; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { + result[0] += 0.0015102568; + } else { + result[0] += -3.608707e-06; + } + } + } + } + } + } + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 54))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + result[0] += 0.00032929177; + } else { + result[0] += 0.029328126; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { + result[0] += -0.00073194504; + } else { + result[0] += -0.009144339; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += -0.032370485; + } else { + result[0] += 0.0061941766; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 50))) { + result[0] += 0.053673845; + } else { + result[0] += 0.018453414; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 178))) { + result[0] += -0.010862867; + } else { + result[0] += 0.0031142721; + } + } else { + result[0] += 0.04961296; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 28))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { + result[0] += -0.035367988; + } else { + result[0] += -0.014287199; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { + result[0] += 0.0022301266; + } else { + result[0] += -0.0076341094; + } + } + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 122))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 74))) { + result[0] += 0.009890891; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { + result[0] += -0.034998115; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { + result[0] += 0.003447539; + } else { + result[0] += 0.0015428506; + } + } + } + } else { + result[0] += 0.021317383; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { + result[0] += 0.06219096; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 106))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { + result[0] += 0.0083704125; + } else { + result[0] += 0.0011106033; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + result[0] += -0.006979663; + } else { + result[0] += -0.004151942; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { + result[0] += 0.017166223; + } else { + result[0] += 0.0019738798; + } + } else { + result[0] += -0.025185201; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 66))) { + result[0] += 0.0008634852; + } else { + result[0] += -0.008104631; + } + } else { + result[0] += 0.0040236074; + } + } else { + result[0] += 0.010268874; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 74))) { + result[0] += -0.029302115; + } else { + result[0] += 0.027285008; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { + result[0] += -0.004545824; + } else { + result[0] += -0.00053453137; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + result[0] += 0.0019230042; + } else { + result[0] += -4.2119464e-06; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { + result[0] += -0.010225962; + } else { + result[0] += -0.00024979832; + } + } + } + } + } + } + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 36))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 26))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { + result[0] += -0.0014482096; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 196))) { + result[0] += 0.0071594575; + } else { + result[0] += 0.027249644; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + result[0] += 0.011023153; + } else { + result[0] += -0.010220974; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + result[0] += 0.0010131482; + } else { + result[0] += -0.00073193485; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 208))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + result[0] += -0.049486402; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + result[0] += -0.00315987; + } else { + result[0] += -0.014758741; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 26))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 22))) { + result[0] += 0.004376946; + } else { + result[0] += 0.040119316; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { + result[0] += -0.010627906; + } else { + result[0] += 0.0038438165; + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 38))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { + result[0] += 0.0069011683; + } else { + result[0] += 0.016726343; + } + } else { + result[0] += 0.0049917004; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 258))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 186))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + result[0] += 0.01811142; + } else { + result[0] += 0.0030194859; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.002317766; + } else { + result[0] += 0.00087052287; + } + } + } else { + result[0] += -0.0039971955; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 40))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 162))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 38))) { + result[0] += -0.0014173501; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 154))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 152))) { + result[0] += -0.0050792135; + } else { + result[0] += 0.006009587; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { + result[0] += -0.00039048417; + } else { + result[0] += -0.009521704; + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 182))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 182))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 38))) { + result[0] += -0.0080691455; + } else { + result[0] += 0.016010879; + } + } else { + result[0] += -0.01479948; + } + } else { + result[0] += -0.02853345; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 46))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 82))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 38))) { + result[0] += -0.015648594; + } else { + result[0] += 0.005108214; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + result[0] += 0.011618692; + } else { + result[0] += 0.0042830543; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 200))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 42))) { + result[0] += 0.005279971; + } else { + result[0] += -0.009966015; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { + result[0] += -0.0035748226; + } else { + result[0] += 0.0021337403; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { + result[0] += -0.025904989; + } else { + result[0] += 0.0019193542; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + result[0] += -0.00022157452; + } else { + result[0] += -0.0015951125; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 28))) { + result[0] += -0.00094731955; + } else { + result[0] += 0.029716676; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { + result[0] += 0.00062934245; + } else { + result[0] += -0.00041988047; + } + } + } + } + } + } + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 112))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 110))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.0063841576; + } else { + result[0] += 0.0017673693; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + result[0] += -0.0013868456; + } else { + result[0] += 0.00015485496; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 72))) { + result[0] += -0.009897975; + } else { + result[0] += -0.030677944; + } + } else { + result[0] += 0.021359773; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + result[0] += 0.021139013; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.0034971682; + } else { + result[0] += -0.0043157833; + } + } else { + result[0] += -0.010502246; + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { + result[0] += 0.02943166; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.005999833; + } else { + result[0] += -0.0019856528; + } + } else { + result[0] += 0.013738169; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { + result[0] += 0.008470384; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + result[0] += -0.003441482; + } else { + result[0] += -0.011388111; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 110))) { + result[0] += 0.0024586066; + } else { + result[0] += -0.021055488; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 76))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { + result[0] += -0.026231218; + } else { + result[0] += -0.0044914987; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { + result[0] += 0.027324816; + } else { + result[0] += 0.0072242687; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { + result[0] += -0.0015850182; + } else { + result[0] += -0.010035633; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { + result[0] += 0.054534066; + } else { + result[0] += 0.0008897401; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 166))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += 0.075174846; + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += -0.0014329397; + } else { + result[0] += 0.002409567; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + result[0] += -0.007922634; + } else { + result[0] += -0.00042070524; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 158))) { + result[0] += -0.00044648108; + } else { + result[0] += 0.0013399239; + } + } + } + } + } + } + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 180))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 168))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += 0.0013971846; + } else { + result[0] += -6.5496147e-06; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { + result[0] += -0.02558857; + } else { + result[0] += -0.0035198168; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 68))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { + result[0] += -0.0007052002; + } else { + result[0] += 0.011330684; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 110))) { + result[0] += 0.008754961; + } else { + result[0] += 0.017457347; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { + result[0] += 0.021995483; + } else { + result[0] += 0.0037829764; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.013063647; + } else { + result[0] += -0.008626144; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 218))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 210))) { + result[0] += -0.00048324605; + } else { + result[0] += 0.014737451; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { + result[0] += 0.03996015; + } else { + result[0] += -0.004091793; + } + } + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 170))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 144))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + result[0] += 0.0020200752; + } else { + result[0] += 0.016987968; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { + result[0] += -0.013594501; + } else { + result[0] += 0.0012423365; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + result[0] += -0.0187645; + } else { + result[0] += -0.007347082; + } + } + } else { + result[0] += 0.021339634; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 198))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { + result[0] += 0.06586228; + } else { + result[0] += 0.021907127; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 94))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 142))) { + result[0] += 0.0030923286; + } else { + result[0] += 0.00036826293; + } + } else { + result[0] += 0.04356821; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += 0.01220308; + } else { + result[0] += -0.007081172; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { + result[0] += 3.0494935e-05; + } else { + result[0] += -0.006945651; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 16))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 10))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + result[0] += 0.00072253105; + } else { + result[0] += -0.020929243; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 194))) { + result[0] += 0.057745833; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { + result[0] += -0.0037926536; + } else { + result[0] += 0.042418174; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.034033656; + } else { + result[0] += -0.021374384; + } + } else { + result[0] += -0; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + result[0] += -0.0036783994; + } else { + result[0] += -0.012444709; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 156))) { + result[0] += -0.00028157423; + } else { + result[0] += -0.0030722509; + } + } + } + } + } + } + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 248))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + result[0] += -0.018552605; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 212))) { + result[0] += 0.013609043; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + result[0] += -0.0035600197; + } else { + result[0] += 0.005957694; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 254))) { + result[0] += -0.0039241104; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { + result[0] += -0.0053416244; + } else { + result[0] += 0.0076100454; + } + } else { + result[0] += 0.0010679305; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { + result[0] += -0.005295289; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { + result[0] += -1.6435115e-05; + } else { + result[0] += -0.0010428902; + } + } + } else { + result[0] += -0.0050287596; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 228))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { + result[0] += 0.025240282; + } else { + result[0] += 0.006482261; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 264))) { + result[0] += 0.0052149496; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { + result[0] += 0.006406661; + } else { + result[0] += -0.0011082895; + } + } + } + } + } + } else { + result[0] += 0.010802027; + } + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 168))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { + result[0] += 2.1751774e-05; + } else { + result[0] += -0.0076580383; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 68))) { + result[0] += -0.00075408455; + } else { + result[0] += 0.015074461; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + result[0] += -0.0018395454; + } else { + result[0] += 0.015883565; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + result[0] += -0.0091815125; + } else { + result[0] += 0.0014608675; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 172))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + result[0] += 0.0026474108; + } else { + result[0] += 0.01692606; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 140))) { + result[0] += 0.02202346; + } else { + result[0] += -0.012683655; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { + result[0] += -0.0071168602; + } else { + result[0] += -0.00039252793; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + result[0] += 0.001928371; + } else { + result[0] += -0.0002336656; + } + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 148))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { + result[0] += -0.025342045; + } else { + result[0] += 0.020604817; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { + result[0] += -0.013844515; + } else { + result[0] += -0.0027904052; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 142))) { + result[0] += 0.019259404; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { + result[0] += 0.0053436733; + } else { + result[0] += 0.0006968994; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { + result[0] += -0.013023679; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 96))) { + result[0] += -0.0089388685; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 140))) { + result[0] += -0.013734943; + } else { + result[0] += 0.0016584283; + } + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + result[0] += -0.00055690337; + } else { + result[0] += -0.016434593; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + result[0] += 0.076187894; + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 144))) { + result[0] += 0.0040504658; + } else { + result[0] += 0.010756901; + } + } + } + } else { + result[0] += 0.023556888; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { + result[0] += 0.06953845; + } else { + result[0] += -0.0023770647; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 186))) { + result[0] += 0.021977352; + } else { + result[0] += 0.0001357905; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 44))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { + result[0] += 0.0026280086; + } else { + result[0] += 0.015929218; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { + result[0] += 0.0037350792; + } else { + result[0] += 0.00039957697; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 18))) { + result[0] += 0.03222883; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 176))) { + result[0] += -0.011439302; + } else { + result[0] += -0.0039858185; + } + } + } + } + } + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + result[0] += -0.0012402734; + } else { + result[0] += -0.019989545; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 8))) { + result[0] += 0.0026303197; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.040178392; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += -0.0065984237; + } else { + result[0] += 0.02927506; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.020329256; + } else { + result[0] += -0; + } + } else { + result[0] += 0.012915528; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + result[0] += 0.009343422; + } else { + result[0] += -0.012758999; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + result[0] += -0.027498607; + } else { + result[0] += -0.012787706; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 24))) { + result[0] += 0.013563978; + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { + result[0] += -0; + } else { + result[0] += 0.017173748; + } + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { + result[0] += -0; + } else { + result[0] += -0.018669667; + } + } else { + result[0] += -0; + } + } + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + result[0] += 0.0013438625; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { + result[0] += 0.0075284163; + } else { + result[0] += 0.020848578; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 224))) { + result[0] += 0.0053101867; + } else { + result[0] += -0.005638622; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { + result[0] += 0.00015324964; + } else { + result[0] += 0.0012270033; + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 238))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { + result[0] += -0.0004296944; + } else { + result[0] += 0.00052584225; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 174))) { + result[0] += 0.0010627261; + } else { + result[0] += -0.00823095; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 256))) { + result[0] += 0.0063353493; + } else { + result[0] += 0.00052647333; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { + result[0] += 0.0017266994; + } else { + result[0] += -0.0006555208; + } + } + } + } + } + } + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 80))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 34))) { + result[0] += 0.0004357934; + } else { + result[0] += -0.0016803583; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + result[0] += 0.013963197; + } else { + result[0] += 0.0010920282; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { + result[0] += -0.0020354139; + } else { + result[0] += -0.0084240325; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { + result[0] += 0.00018836466; + } else { + result[0] += -0.001789179; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 98))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { + result[0] += -0.0021141667; + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { + result[0] += 0.0008162449; + } else { + result[0] += -0.0007128279; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { + result[0] += 0.005020843; + } else { + result[0] += 0.015004709; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + result[0] += 0.004781536; + } else { + result[0] += 0.0005883411; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 130))) { + result[0] += -0.035336528; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + result[0] += 0.023583809; + } else { + result[0] += 0.0007455337; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { + result[0] += 0.0023794405; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + result[0] += 0.023832815; + } else { + result[0] += 0.010761608; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 196))) { + result[0] += -0.010108376; + } else { + result[0] += -0.0007176217; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 106))) { + result[0] += 0.0009595863; + } else { + result[0] += -0.004075228; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { + result[0] += -0; + } else { + result[0] += 0.01168954; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { + result[0] += -0.0033294098; + } else { + result[0] += -6.6527915e-05; + } + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 118))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 48))) { + result[0] += 0.033734445; + } else { + result[0] += 0.0013792046; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { + result[0] += 0.011527828; + } else { + result[0] += 0.0049809148; + } + } + } else { + result[0] += 0.013943759; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 178))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + result[0] += -0.008578103; + } else { + result[0] += 0.0029740678; + } + } else { + result[0] += -0.018524783; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 140))) { + result[0] += -0.00042957798; + } else { + result[0] += -0.009110951; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 132))) { + result[0] += 0.021989485; + } else { + result[0] += 0.0028953014; + } + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 200))) { + result[0] += -0.0064930986; + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 188))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { + result[0] += 0.00023702074; + } else { + result[0] += 0.0032349098; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 232))) { + result[0] += -0.0035499786; + } else { + result[0] += -0; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { + result[0] += -0.008582824; + } else { + result[0] += 0.0003269389; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { + result[0] += 0.006198867; + } else { + result[0] += 0.0029896083; + } + } + } + } + } + } + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 194))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 76))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { + result[0] += -0.008292492; + } else { + result[0] += 0.048605617; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { + result[0] += -0.011937882; + } else { + result[0] += -0.0048466916; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 36))) { + result[0] += -0.0032074524; + } else { + result[0] += 6.964723e-05; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 52))) { + result[0] += 0.024765873; + } else { + result[0] += -0.0021545556; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { + result[0] += -1.2665853e-05; + } else { + result[0] += -0.023850983; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 154))) { + result[0] += 0.0025796972; + } else { + result[0] += 0.00040402517; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 160))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.0170327; + } else { + result[0] += -0.00829741; + } + } else { + result[0] += -0.0026483634; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 188))) { + result[0] += 0.0010909947; + } else { + result[0] += 0.0053282953; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { + result[0] += 0.026514; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 100))) { + result[0] += 0.037232116; + } else { + result[0] += 0.004267938; + } + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 248))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 126))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { + result[0] += 0.026404385; + } else { + result[0] += -0.001118703; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + result[0] += 0.0017804791; + } else { + result[0] += -0.007409166; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 130))) { + result[0] += -0.004484429; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 222))) { + result[0] += 0.031472586; + } else { + result[0] += 0.0001007054; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 4))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 252))) { + result[0] += -0.003407167; + } else { + result[0] += 0.001687183; + } + } else { + result[0] += 0.014353302; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 28))) { + result[0] += -0.0015060778; + } else { + result[0] += -0.0003457068; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 10))) { + result[0] += -0.013887319; + } else { + result[0] += -0.0035536517; + } + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 238))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 160))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 74))) { + result[0] += -0.0055618854; + } else { + result[0] += -0.0014347414; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 224))) { + result[0] += -0.00070218346; + } else { + result[0] += 0.0014398324; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 8))) { + result[0] += 0.023142483; + } else { + result[0] += -0.000307219; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { + result[0] += 0.0010922215; + } else { + result[0] += 0.017112693; + } + } + } + } + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + result[0] += -0.010769006; + } else { + result[0] += 0.0069697066; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { + result[0] += 0.02430601; + } else { + result[0] += 0.00639099; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 20))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 34))) { + result[0] += 0.015791424; + } else { + result[0] += -0.0046518813; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { + result[0] += -0.03668975; + } else { + result[0] += -0.012895368; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { + result[0] += 0.03604609; + } else { + result[0] += 0.012836625; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 50))) { + result[0] += -0.010758377; + } else { + result[0] += -0.001967916; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 220))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 80))) { + result[0] += 0.034449853; + } else { + result[0] += 0.0030996487; + } + } else { + result[0] += -0.015721142; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 254))) { + result[0] += 0.039080422; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 222))) { + result[0] += 0.020187657; + } else { + result[0] += 0.0016309306; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 224))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { + result[0] += -0.01591835; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 64))) { + result[0] += -0.006518031; + } else { + result[0] += -0.0026554007; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + result[0] += 0.00103551; + } else { + result[0] += 0.00901093; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + result[0] += -0.0012550136; + } else { + result[0] += -1.612309e-05; + } + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 116))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 130))) { + result[0] += 0.00788109; + } else { + result[0] += -0.0018598955; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 60))) { + result[0] += 0.005137967; + } else { + result[0] += -0.0032950975; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 98))) { + result[0] += -0.042013966; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { + result[0] += -0.0046039354; + } else { + result[0] += 0.011869277; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 172))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { + result[0] += -0.026546886; + } else { + result[0] += 0.009092043; + } + } else { + result[0] += 0.017844815; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 102))) { + result[0] += 0.0068409294; + } else { + result[0] += 0.01327271; + } + } else { + result[0] += 0.000659167; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { + result[0] += 0.004663289; + } else { + result[0] += -0.006825484; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 56))) { + result[0] += 0.008568982; + } else { + result[0] += 0.0030761992; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { + result[0] += -0.014622196; + } else { + result[0] += 0.00041277093; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 42))) { + result[0] += 0.0016062713; + } else { + result[0] += 0.046605274; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 172))) { + result[0] += -0.004711694; + } else { + result[0] += -0.0015061334; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 222))) { + result[0] += 0.012894141; + } else { + result[0] += -0.0020437974; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { + result[0] += -0.0009532698; + } else { + result[0] += 0.00017820772; + } + } + } + } + } + } + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 120))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += 0.0047425376; + } else { + result[0] += 0.009442864; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { + result[0] += -0.016419962; + } else { + result[0] += 0.00078140077; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { + result[0] += -0.016464185; + } else { + result[0] += -0.0012191174; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { + result[0] += 0.00046863809; + } else { + result[0] += 0.0035006986; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 124))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { + result[0] += -0.0128822; + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 170))) { + result[0] += -0.0021174157; + } else { + result[0] += -0.01799887; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { + result[0] += 0.043405965; + } else { + result[0] += 0.0019986776; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + result[0] += -0.00859444; + } else { + result[0] += 0.00027344722; + } + } + } + } + } else { + result[0] += 0.013666634; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { + result[0] += 0.0035516606; + } else { + result[0] += -1.4989321e-05; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 230))) { + result[0] += -0.003034349; + } else { + result[0] += -0.0006231525; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 244))) { + result[0] += 0.0052153706; + } else { + result[0] += -0.0051266435; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { + result[0] += -0.0033818122; + } else { + result[0] += -0.0005771067; + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 244))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { + result[0] += 0.042181622; + } else { + result[0] += 0.004492736; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { + result[0] += -0.01460122; + } else { + result[0] += 0.00014612076; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { + result[0] += -0.001969084; + } else { + result[0] += -6.331263e-05; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 160))) { + result[0] += 0.0028343403; + } else { + result[0] += 0.00031744837; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { + result[0] += -0.006944129; + } else { + result[0] += -0.0027726921; + } + } + } + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 278))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 232))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 66))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { + result[0] += 0.0005400666; + } else { + result[0] += -0.0019886708; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { + result[0] += -0.009072294; + } else { + result[0] += -0.0026447035; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + result[0] += -0.0027691973; + } else { + result[0] += 0.00404422; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 276))) { + result[0] += -2.3040471e-05; + } else { + result[0] += 0.012937434; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += -0.015312892; + } else { + result[0] += -0.004679353; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { + result[0] += 0.006205307; + } else { + result[0] += 0.0010604822; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { + result[0] += -0.0003646382; + } else { + result[0] += 0.0056393673; + } + } + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 98))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + result[0] += 0.0009229826; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { + result[0] += -0.002368063; + } else { + result[0] += -0.00024388141; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 250))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + result[0] += -0.012024042; + } else { + result[0] += -0.0031967615; + } + } else { + result[0] += -0.00085695874; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 260))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 204))) { + result[0] += -0.00046735056; + } else { + result[0] += 0.0040739644; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { + result[0] += -0.0032260884; + } else { + result[0] += 0.0010031442; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { + result[0] += -0.009778283; + } else { + result[0] += 0.019902207; + } + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 282))) { + result[0] += -0.0005751638; + } else { + result[0] += -0.01039412; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 230))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { + result[0] += -0.01584022; + } else { + result[0] += 0.0018113088; + } + } else { + result[0] += 0.0046630637; + } + } + } + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 250))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 36))) { + result[0] += -0.00047560144; + } else { + result[0] += 0.0013354685; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 90))) { + result[0] += -0.0026048406; + } else { + result[0] += 0.00038235518; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { + result[0] += 0.0044472557; + } else { + result[0] += 0.03397901; + } + } else { + result[0] += -0.0003922035; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { + result[0] += 0.031751767; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { + result[0] += -0.0036668777; + } else { + result[0] += 0.00452039; + } + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { + result[0] += -0.002395912; + } else { + result[0] += 0.030751167; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 42))) { + result[0] += -0.015407211; + } else { + result[0] += 0.011755302; + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { + result[0] += -0.03115373; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { + result[0] += 0.0073042098; + } else { + result[0] += 0.033008788; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { + result[0] += -0.0064700614; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 4))) { + result[0] += -0.007395641; + } else { + result[0] += -0.02954722; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { + result[0] += -0.01630216; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 184))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { + result[0] += 0.008391211; + } else { + result[0] += -0.0021036936; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 62))) { + result[0] += -0.00060320273; + } else { + result[0] += 0.0022427232; + } + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { + if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 6))) { + result[0] += -0.000107325846; + } else { + result[0] += 0.029023886; + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 50))) { + result[0] += -0.003524834; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { + result[0] += 0.002008538; + } else { + result[0] += 0.00040226732; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 132))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { + result[0] += 0.0028264725; + } else { + result[0] += 0.0078762965; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 190))) { + result[0] += -0.0036010318; + } else { + result[0] += -0.020989217; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 76))) { + result[0] += -0.025969082; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 76))) { + result[0] += -0.0037539073; + } else { + result[0] += -0.015046534; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 52))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + result[0] += 0.008154599; + } else { + result[0] += -0.00034012404; + } + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.00019781369; + } else { + result[0] += -0.0046178605; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 122))) { + result[0] += 0.0022514695; + } else { + result[0] += 0.02182677; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { + result[0] += -0.002138548; + } else { + result[0] += 0.00019392448; + } + } + } + } + } + } + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { + result[0] += -0.018533966; + } else { + result[0] += 0.009372647; + } + } else { + result[0] += -0.013326846; + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { + result[0] += 0.017056702; + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 250))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 36))) { + result[0] += 0.0010617184; + } else { + result[0] += 0.003547955; + } + } else { + result[0] += -0.002409021; + } + } else { + result[0] += 0.022888973; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 218))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { + result[0] += -6.381352e-05; + } else { + result[0] += 0.01608517; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + result[0] += 0.031114805; + } else { + result[0] += 0.0031871856; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { + result[0] += 0.0010918784; + } else { + result[0] += 0.010223559; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + result[0] += -0.0053569055; + } else { + result[0] += -0.00082102185; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { + result[0] += 0.017271513; + } else { + result[0] += -0.0010735758; + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 210))) { + result[0] += -0.0022431507; + } else { + result[0] += -0.0044196337; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { + result[0] += 0.03313246; + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 166))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 116))) { + result[0] += 0.0011855942; + } else { + result[0] += 0.0060151634; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { + result[0] += -0.007734927; + } else { + result[0] += 0.0012392174; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 200))) { + result[0] += -0.0054051043; + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { + result[0] += -0.00018920467; + } else { + result[0] += 0.0010806855; + } + } + } + } + } + } + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 184))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { + result[0] += -5.627934e-05; + } else { + result[0] += 0.004937595; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 176))) { + result[0] += -0.0081005385; + } else { + result[0] += -0.00034580208; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 112))) { + result[0] += 0.0017502941; + } else { + result[0] += 0.0067032403; + } + } else { + result[0] += 0.05561105; + } + } + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 104))) { + result[0] += -0.017684242; + } else { + result[0] += -0.008623506; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 210))) { + result[0] += -0.0030512868; + } else { + result[0] += 0.00268173; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 116))) { + result[0] += -0.008280813; + } else { + result[0] += -0.0006436382; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 68))) { + result[0] += 0.005869223; + } else { + result[0] += 0.0157161; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 38))) { + result[0] += -0.040014755; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { + result[0] += 0.0034462882; + } else { + result[0] += 0.012366778; + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { + result[0] += 0.0029618521; + } else { + result[0] += -0.0031078772; + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 78))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 156))) { + result[0] += -0.0013473729; + } else { + result[0] += 0.0062149167; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { + result[0] += 0.017057097; + } else { + result[0] += -0.010900224; + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 144))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { + result[0] += 0.0012661046; + } else { + result[0] += 0.0069153532; + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { + result[0] += -0.016097846; + } else { + result[0] += -0.00017735653; + } + } + } + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { + result[0] += 0.010808589; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { + result[0] += -0.009866092; + } else { + result[0] += 0.0047606938; + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { + result[0] += -0; + } else { + result[0] += 0.014268956; + } + } else { + result[0] += 0.0023136109; + } + } else { + result[0] += -0.0024249533; + } + } + } + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 96))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { + result[0] += -0.0002939115; + } else { + result[0] += 0.03095895; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { + result[0] += -0.016124493; + } else { + result[0] += 0.0012605732; + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { + result[0] += 0.00055091607; + } else { + result[0] += -0.0038312187; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 62))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + result[0] += 0.020991528; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { + result[0] += 0.00425994; + } else { + result[0] += -0.012923174; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 112))) { + result[0] += -0.008987924; + } else { + result[0] += 0.0027231926; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 136))) { + result[0] += 0.00033489117; + } else { + result[0] += 0.0031028683; + } + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { + result[0] += -0.023087258; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { + result[0] += 0.040452894; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { + result[0] += -0.004355786; + } else { + result[0] += 0.00052141724; + } + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 98))) { + result[0] += -0.026337389; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { + result[0] += -0.011440942; + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 80))) { + result[0] += 0.0051072245; + } else { + result[0] += -0.0016136405; + } + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 94))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { + result[0] += -0.016175192; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 90))) { + result[0] += -0.003506745; + } else { + result[0] += -0.0008637124; + } + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 80))) { + result[0] += 0.039352115; + } else { + result[0] += 0.009527031; + } + } else { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { + result[0] += -0.014133029; + } else { + result[0] += 0.0009379142; + } + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 124))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { + result[0] += 0.0014655776; + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { + result[0] += 0.00799122; + } else { + result[0] += 0.02205122; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { + result[0] += 0.00084181456; + } else { + result[0] += 0.03656875; + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + result[0] += -0.0324972; + } else { + result[0] += -0.0043041403; + } + } + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 108))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 106))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { + result[0] += -0.014786902; + } else { + result[0] += -0.027247995; + } + } else { + result[0] += -0.013442961; + } + } else { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 106))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { + result[0] += 0.014649215; + } else { + result[0] += 0.00018173472; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { + result[0] += -0.003195444; + } else { + result[0] += -0.017632857; + } + } + } + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 172))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { + result[0] += 0.0011816436; + } else { + result[0] += 0.006784923; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 174))) { + result[0] += -0.0025611892; + } else { + result[0] += 0.00039800836; + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 222))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { + result[0] += -0.015747108; + } else { + result[0] += -0.0025709863; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 222))) { + result[0] += 0.00016036122; + } else { + result[0] += -0.004456304; + } + } + } + } + } + } + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { + result[0] += -0.043082695; + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { + result[0] += -0.015321686; + } else { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { + result[0] += 0.009543039; + } else { + result[0] += -0.00492233; + } + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 60))) { + result[0] += -0.018702324; + } else { + result[0] += 0.0030421054; + } + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 96))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 104))) { + result[0] += 0.023666665; + } else { + result[0] += -0.003085631; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { + result[0] += 0.0025167426; + } else { + result[0] += -0.0006228736; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { + result[0] += 0.025037153; + } else { + result[0] += 0.00086976105; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { + result[0] += -0.00833582; + } else { + result[0] += 6.457727e-05; + } + } + } + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { + result[0] += -0.034489177; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { + result[0] += 0.059084423; + } else { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { + result[0] += -0.008554864; + } else { + result[0] += -0.000933964; + } + } + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 98))) { + if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { + result[0] += -0.005567677; + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 74))) { + result[0] += 0.026860246; + } else { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { + if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 238))) { + result[0] += 0.003906974; + } else { + result[0] += 0.016403912; + } + } else { + result[0] += -0.0005779523; + } + } + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 102))) { + if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 68))) { + result[0] += -0.021588845; + } else { + result[0] += -0.0072186044; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 56))) { + result[0] += 0.00058102736; + } else { + result[0] += 0.0042956616; + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { + result[0] += -0.0021127523; + } else { + result[0] += 0.0012841078; + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 186))) { + result[0] += -0.004427323; + } else { + result[0] += -0.0004450692; + } + } else { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 188))) { + result[0] += 0.0005094443; + } else { + result[0] += -0.00016178952; + } + } + } + } + } + } + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { + if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 230))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { + result[0] += -0.00010068305; + } else { + result[0] += 0.0004361026; + } + } else { + result[0] += 0.027371911; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 130))) { + result[0] += 0.023174508; + } else { + result[0] += 0.011960031; + } + } + } else { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { + if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { + result[0] += -0.015924754; + } else { + result[0] += -0.0012669711; + } + } else { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 188))) { + result[0] += 0.018523319; + } else { + result[0] += -0.00022792198; + } + } + } else { + result[0] += 0.039226815; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 146))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 90))) { + result[0] += 0.022155268; + } else { + result[0] += -0.0030770514; + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { + result[0] += 0.03438976; + } else { + result[0] += 0.016168883; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { + result[0] += 0.023656188; + } else { + result[0] += 0.009587833; + } + } else { + if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 118))) { + result[0] += 0.003995668; + } else { + result[0] += -0.00010666515; + } + } + } + } else { + if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 108))) { + if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 150))) { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 136))) { + result[0] += 0.004109849; + } else { + result[0] += 0.027041653; + } + } else { + result[0] += -9.42766e-05; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { + result[0] += 0.0130727235; + } else { + if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 78))) { + result[0] += -0.0015822932; + } else { + result[0] += -0.008224895; + } + } + } + } + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { + result[0] += -0.012683782; + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { + result[0] += -0.0033396825; + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { + result[0] += 0.01649657; + } else { + result[0] += 0.0024240483; + } + } + } else { + if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { + result[0] += -0.004931916; + } else { + result[0] += 4.3331627e-05; + } + } + } else { + if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 52))) { + if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 202))) { + if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { + result[0] += -0.0077444026; + } else { + result[0] += -0.0008859424; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 178))) { + result[0] += 0.0073036547; + } else { + result[0] += -0.007702441; + } + } + } else { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { + if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { + result[0] += -0.00019439656; + } else { + result[0] += -0.0056553436; + } + } else { + if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 180))) { + result[0] += 0.0066337893; + } else { + result[0] += 0.00068436674; + } + } + } + } + } + } + + // Apply base_scores + result[0] += 3.356329679489135742; + + // Apply postprocessor + if (!pred_margin) { postprocess(result); } +} + +void pdlp_predictor::postprocess(double* result) +{ + // Do nothing +} + +// Feature names array +const char* pdlp_predictor::feature_names[pdlp_predictor::NUM_FEATURES] = { + "n_vars", "n_cstrs", "nnz", "sparsity", "nnz_stddev", "unbalancedness", "spmv_ops", "total_nnz"}; diff --git a/cpp/src/utilities/models/pdlp_predictor/quantize.cpp b/cpp/src/utilities/models/pdlp_predictor/quantize.cpp new file mode 100644 index 000000000..b0c135615 --- /dev/null +++ b/cpp/src/utilities/models/pdlp_predictor/quantize.cpp @@ -0,0 +1,1006 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#include "header.h" + +static const float threshold[] = { + 17, + 26, + 31, + 52, + 56, + 58, + 60, + 69, + 86, + 145, + 156, + 160, + 210, + 214, + 219, + 253, + 269, + 272, + 365, + 420, + 479, + 504, + 511, + 649, + 655, + 770, + 780, + 784, + 875, + 888, + 955, + 1024, + 1106, + 1184, + 1187, + 1216, + 1344, + 1400, + 1455, + 1635, + 2025, + 2036, + 2164, + 2272, + 2303, + 2376, + 2442, + 2595, + 2600, + 3034, + 3060, + 3073, + 3079, + 3261, + 3338, + 3472, + 3510, + 3561, + 3654, + 4009, + 4171, + 4272, + 4462, + 4484, + 4704, + 4745, + 4768, + 4798, + 5068, + 5376, + 5662, + 5942, + 5951, + 5956, + 5958, + 6260, + 6481, + 6483, + 6730, + 6868, + 7389, + 7418, + 7745, + 8538, + 10218, + 10720, + 11185, + 11928, + 12414, + 12631, + 12927, + 13410, + 13688, + 13839, + 13869, + 14060, + 14099, + 14370, + 14636, + 15977, + 17187, + 17188, + 18405, + 18865, + 20602, + 20849, + 22480, + 23530, + 23836, + 24923, + 27542, + 29397, + 29435, + 30199, + 32450, + 33154, + 35734, + 37616, + 37648, + 37816, + 40272, + 53593, + 62171, + 63708, + 72468, + 74873, + 74884, + 74918, + 78287, + 86262, + 94595, + 129171, + 129931, + 138056, + 167056, + 184865, + 268358, + 1428996, + 8, + 12, + 13, + 40, + 41, + 45, + 46, + 49, + 61, + 108, + 112, + 126, + 130, + 158, + 162, + 225, + 234, + 241, + 242, + 260, + 270, + 296, + 322, + 332, + 359, + 360, + 397, + 403, + 443, + 444, + 447, + 448, + 482, + 483, + 484, + 487, + 566, + 569, + 577, + 582, + 643, + 659, + 711, + 721, + 723, + 764, + 811, + 867, + 879, + 900, + 1005, + 1025, + 1299, + 1389, + 1500, + 1513, + 1573, + 1634, + 1653, + 1691, + 1759, + 1863, + 1898, + 1917, + 1947, + 2225, + 2301, + 2344, + 2362, + 2366, + 2372, + 2376, + 2433, + 2472, + 2522, + 2526, + 2898, + 3174, + 3249, + 3250, + 3480, + 3491, + 3492, + 3760, + 3897, + 3925, + 4234, + 4241, + 4424, + 4454, + 4463, + 4634, + 4660, + 4811, + 4813, + 5190, + 5594, + 6120, + 6323, + 6333, + 6654, + 6754, + 6894, + 6956, + 8469, + 8620, + 10427, + 10479, + 11334, + 13361, + 14040, + 14766, + 16027, + 16488, + 16925, + 16927, + 18304, + 18609, + 22191, + 24885, + 24971, + 30499, + 32737, + 32933, + 37617, + 43882, + 45221, + 48604, + 53361, + 59577, + 67584, + 99790, + 131866, + 146326, + 169577, + 259962, + 314413, + 2881228, + 11, + 90, + 130, + 300, + 403, + 463, + 480, + 840, + 916, + 1011, + 1023, + 1035, + 1602, + 2221, + 2298, + 2710, + 3120, + 3382, + 3456, + 3718, + 4448, + 4496, + 4875, + 5096, + 5849, + 5940, + 6208, + 6498, + 7023, + 7490, + 7592, + 7755, + 7875, + 8379, + 8991, + 9168, + 9180, + 9714, + 10300, + 10530, + 12612, + 12613, + 14030, + 14576, + 15688, + 17774, + 19143, + 20810, + 22736, + 23965, + 26394, + 29040, + 30477, + 30489, + 31720, + 39920, + 40392, + 43256, + 43305, + 43345, + 43357, + 47777, + 48695, + 50150, + 58368, + 59790, + 65145, + 78260, + 83617, + 94254, + 101208, + 102473, + 110022, + 113048, + 118141, + 119459, + 127416, + 132112, + 147024, + 152533, + 154570, + 178965, + 182574, + 185099, + 187236, + 187968, + 188780, + 203770, + 210725, + 228952, + 228988, + 229044, + 247107, + 271063, + 283189, + 283914, + 293410, + 295358, + 302709, + 319705, + 346211, + 409850, + 433829, + 518476, + 648112, + 648715, + 834722, + 858766, + 1024230, + 1632480, + 1697945, + 2087795, + 2192958, + 4313301, + 2.4999999e-05, + 2.9000001e-05, + 3.7999998e-05, + 4.7000001e-05, + 5.3e-05, + 5.6000001e-05, + 5.9000002e-05, + 7.8999998e-05, + 7.9999998e-05, + 0.000109, + 0.00012899999, + 0.000157, + 0.00017499999, + 0.000181, + 0.000199, + 0.000256, + 0.00028000001, + 0.000283, + 0.00028400001, + 0.000287, + 0.000397, + 0.000401, + 0.00045200001, + 0.00047299999, + 0.00050600001, + 0.00053899997, + 0.00056000001, + 0.00062599999, + 0.00067400001, + 0.00072399998, + 0.00079299998, + 0.00090599997, + 0.001052, + 0.001054, + 0.001064, + 0.00107, + 0.001084, + 0.00122, + 0.001233, + 0.001236, + 0.001297, + 0.001321, + 0.001404, + 0.001618, + 0.001706, + 0.001785, + 0.001862, + 0.0020570001, + 0.0021569999, + 0.002267, + 0.0025269999, + 0.002664, + 0.00278, + 0.0029440001, + 0.003064, + 0.0030789999, + 0.0031069999, + 0.003267, + 0.0032800001, + 0.003491, + 0.0038409999, + 0.0039599999, + 0.0040600002, + 0.0042429999, + 0.004373, + 0.0047650002, + 0.0049749999, + 0.0053010001, + 0.005316, + 0.0054930001, + 0.0055149999, + 0.005903, + 0.0061130002, + 0.0071859998, + 0.007557, + 0.0079190005, + 0.0082029998, + 0.0083109997, + 0.0085450001, + 0.0088409996, + 0.008986, + 0.0096119996, + 0.010158, + 0.010454, + 0.010491, + 0.010588, + 0.011062, + 0.011189, + 0.01136, + 0.011947, + 0.012388, + 0.01285, + 0.013316, + 0.014028, + 0.014862, + 0.017344, + 0.017529, + 0.018440999, + 0.019753, + 0.020732, + 0.021379, + 0.024308, + 0.028986, + 0.029084001, + 0.029973, + 0.031771, + 0.032212, + 0.032758001, + 0.036036, + 0.036471002, + 0.048811998, + 0.048967998, + 0.057353001, + 0.064516, + 0.087884001, + 0.101821, + 0.13953499, + 0.14706101, + 0.229167, + 0.231547, + 0.258656, + 0.26689899, + 0.329083, + 0.34957099, + 0.35977599, + 0.42875499, + 0.91632003, + 0.073445998, + 0.233594, + 0.27638501, + 0.38584599, + 0.39129001, + 0.39430001, + 0.47627199, + 0.49999601, + 0.64261901, + 0.75658399, + 0.767389, + 0.84350997, + 1.015707, + 1.1055419, + 1.15354, + 1.4087991, + 1.722888, + 1.833473, + 1.891134, + 1.944816, + 2.2556751, + 2.4810159, + 2.66401, + 3.046876, + 3.5996871, + 3.6403749, + 4.273489, + 4.50634, + 5.4227982, + 5.6764622, + 6.6741581, + 6.9359412, + 6.9459491, + 9.0884533, + 9.3204279, + 11.047834, + 11.05003, + 11.34829, + 11.977816, + 12.56616, + 12.896968, + 13.082605, + 13.121695, + 15.218937, + 15.219805, + 15.422595, + 15.908748, + 16.333834, + 16.442045, + 17.222109, + 18.255583, + 18.337471, + 18.482592, + 19.853386, + 20.08359, + 20.493109, + 20.575439, + 21.781368, + 21.850649, + 23.025219, + 23.394861, + 23.543165, + 24.943169, + 26.903715, + 29.262121, + 30.220488, + 30.346951, + 30.993811, + 31.029835, + 32.219078, + 34.479328, + 39.028454, + 39.205608, + 39.3573, + 43.941006, + 44.133533, + 49.547768, + 52.339855, + 53.529537, + 56.239407, + 57.230946, + 60.911472, + 61.411575, + 66.006073, + 75.513832, + 76.959404, + 79.399498, + 94.297729, + 101.50806, + 104.90943, + 108.45773, + 125.96421, + 131.78522, + 149.06885, + 149.10501, + 159.80826, + 166.47375, + 190.44113, + 193.85234, + 214.411, + 228.99097, + 243.77342, + 251.07321, + 265.90009, + 277.75555, + 290.5871, + 321.94229, + 349.17996, + 435.58398, + 461.15714, + 480.75259, + 555.98761, + 556.20483, + 632.65863, + 828.06183, + 836.67456, + 857.39771, + 1038.6504, + 1445.1395, + 2359.3203, + 2750.1714, + 3077.2048, + 3270.0989, + 3929.8506, + 3954.1118, + 5246.2725, + 7567.3979, + 14218.984, + 0.0041490002, + 0.036821999, + 0.049214002, + 0.130913, + 0.177389, + 0.18044201, + 0.183018, + 0.19227199, + 0.244839, + 0.30151099, + 0.33716401, + 0.33797899, + 0.33922401, + 0.376284, + 0.436288, + 0.488325, + 0.50635898, + 0.51155603, + 0.53070199, + 0.55729002, + 0.576186, + 0.60621798, + 0.62735999, + 0.63285798, + 0.64827198, + 0.69477397, + 0.727198, + 0.74622601, + 0.76021701, + 0.77570999, + 0.817267, + 0.84039903, + 0.874892, + 0.88309401, + 0.91301, + 0.914644, + 0.92933702, + 0.94280899, + 1.017756, + 1.11876, + 1.1953419, + 1.205152, + 1.207976, + 1.208634, + 1.295553, + 1.2959141, + 1.326723, + 1.347288, + 1.3574491, + 1.390246, + 1.390783, + 1.428421, + 1.4407949, + 1.45542, + 1.502932, + 1.5636179, + 1.581139, + 1.78348, + 1.78504, + 1.801465, + 1.873001, + 2.0050731, + 2.062494, + 2.1306751, + 2.1410699, + 2.142792, + 2.1604609, + 2.301403, + 2.3202839, + 2.320334, + 2.342221, + 2.3494289, + 2.392698, + 2.4694431, + 2.4709809, + 2.548645, + 2.7524309, + 2.8433869, + 2.8817151, + 2.9853411, + 3.0516059, + 3.0590241, + 3.1028869, + 3.2358611, + 3.3461559, + 3.357758, + 3.364897, + 3.3847311, + 3.4308331, + 3.538095, + 3.712795, + 3.7464709, + 3.7710979, + 4.0210929, + 4.3894181, + 4.4247661, + 4.4356871, + 4.64078, + 4.7781639, + 5.100903, + 5.298172, + 5.2985978, + 5.4433179, + 5.4726572, + 5.5151229, + 5.6058269, + 5.6186318, + 5.738615, + 5.7462459, + 5.792984, + 5.9997821, + 6.0248361, + 6.1309428, + 6.1651001, + 6.6284609, + 7.162158, + 7.2263432, + 7.5340571, + 7.7937908, + 8.3784914, + 9.5885639, + 9.5996771, + 9.8454266, + 9.853548, + 10.502564, + 12.32793, + 13.122874, + 14.426889, + 15.470113, + 16.773743, + 19.316851, + 19.475534, + 22.584642, + 28.079082, + 46.668407, + 9, + 12, + 15, + 18, + 21, + 24, + 27, + 30, + 60, + 90, + 120, + 150, + 1620, + 7800, + 13500, + 24200, + 25700, + 28800, + 45400, + 55000, + 60400, + 61900, + 72000, + 96100, + 104000, + 106000, + 133000, + 137000, + 138000, + 152000, + 155000, + 177000, + 191000, + 222000, + 240000, + 264000, + 288000, + 304000, + 333000, + 345000, + 391000, + 408000, + 425000, + 449000, + 468000, + 518000, + 554000, + 583000, + 665000, + 719000, + 731000, + 756000, + 757000, + 772000, + 837000, + 931000, + 975000, + 1050000, + 1060000, + 1120000, + 1150000, + 1160000, + 1210000, + 1350000, + 1440000, + 1740000, + 1820000, + 1830000, + 1890000, + 2020000, + 2400000, + 2430000, + 2550000, + 2600000, + 2680000, + 2760000, + 2870000, + 2920000, + 3050000, + 3310000, + 3410000, + 3500000, + 3590000, + 3630000, + 3740000, + 3850000, + 3910000, + 4360000, + 4570000, + 4760000, + 5080000, + 5660000, + 6060000, + 6070000, + 6150000, + 6490000, + 6500000, + 6600000, + 6780000, + 7090000, + 7170000, + 8100000, + 8760000, + 9040000, + 9270000, + 10700000, + 11300000, + 11600000, + 12100000, + 12600000, + 12700000, + 13700000, + 14100000, + 15200000, + 15900000, + 16500000, + 17100000, + 17600000, + 17900000, + 19200000, + 20800000, + 22500000, + 23200000, + 26000000, + 26800000, + 27800000, + 31100000, + 31400000, + 31600000, + 31700000, + 34300000, + 34400000, + 37100000, + 39800000, + 42800000, + 44300000, + 45400000, + 51500000, + 51900000, + 56600000, + 61500000, + 65100000, + 77500000, + 78400000, + 97200000, + 1.07e+08, + 1.24e+08, + 1.29e+08, + 1.34e+08, + 1.54e+08, + 2.14e+08, + 2.49e+08, + 3.13e+08, + 3.34e+08, + 5.06e+08, + 1.34e+09, +}; + +static const int th_begin[] = { + 0, + 138, + 276, + 390, + 517, + 645, + 780, + 792, +}; + +static const int th_len[] = { + 138, + 138, + 114, + 127, + 128, + 135, + 12, + 144, +}; + +/* + * \brief Function to convert a feature value into bin index. + * \param val Feature value, in floating-point + * \param fid Feature identifier + * \return bin Index corresponding to given feature value + */ +int pdlp_predictor::quantize(float val, unsigned fid) +{ + const size_t offset = th_begin[fid]; + const float* array = &threshold[offset]; + int len = th_len[fid]; + int low = 0; + int high = len; + int mid; + float mval; + // It is possible th_begin[i] == [total_num_threshold]. This means that + // all features i, (i+1), ... are not used for any of the splits in the model. + // So in this case, just return something + if (offset == 936 || val < array[0]) { return -10; } + while (low + 1 < high) { + mid = (low + high) / 2; + mval = array[mid]; + if (val == mval) { + return mid * 2; + } else if (val < mval) { + high = mid; + } else { + low = mid; + } + } + if (array[low] == val) { + return low * 2; + } else if (high == len) { + return len * 2; + } else { + return low * 2 + 1; + } +} diff --git a/cpp/src/utilities/version_info.cpp b/cpp/src/utilities/version_info.cpp index ec9db5130..a14c9fa27 100644 --- a/cpp/src/utilities/version_info.cpp +++ b/cpp/src/utilities/version_info.cpp @@ -163,6 +163,36 @@ static double get_available_memory_gb() return kb / (1024.0 * 1024.0); // Convert KB to GB } +double get_cpu_max_clock_mhz() +{ + // Try sysfs cpufreq interface first (returns frequency in KHz) + // FIXME: assumes all available CPUs have the same max clock as CPU0 + std::ifstream freq_file("/sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_max_freq"); + if (freq_file.is_open()) { + long khz = 0; + freq_file >> khz; + if (khz > 0) { return khz / 1e3; } + } + + // Fallback: parse /proc/cpuinfo for "cpu MHz" + std::ifstream cpuinfo("/proc/cpuinfo"); + if (!cpuinfo.is_open()) return 0.0; + + std::string line; + double max_mhz = 0.0; + while (std::getline(cpuinfo, line)) { + if (line.find("cpu MHz") != std::string::npos) { + std::size_t colon = line.find(':'); + if (colon != std::string::npos) { + double mhz = std::stod(line.substr(colon + 1)); + if (mhz > max_mhz) { max_mhz = mhz; } + } + } + } + + return max_mhz; +} + void print_version_info() { int device_id = 0; diff --git a/cpp/src/utilities/version_info.hpp b/cpp/src/utilities/version_info.hpp index dbadea8ec..ea909e7c1 100644 --- a/cpp/src/utilities/version_info.hpp +++ b/cpp/src/utilities/version_info.hpp @@ -8,4 +8,5 @@ namespace cuopt { void print_version_info(); -} +double get_cpu_max_clock_mhz(); +} // namespace cuopt diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp index 68d2a8f4d..71ed55c94 100644 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -22,10 +22,13 @@ #include #include #include +#include #include #include "models/cpufj_predictor/header.h" +#include "models/dualsimplex_predictor/header.h" #include "models/fj_predictor/header.h" +#include "models/pdlp_predictor/header.h" namespace cuopt { @@ -48,6 +51,8 @@ template float work_unit_predictor_t::predict_scalar( const std::map& features) const { + raft::common::nvtx::range range("work_unit_predictor_t::predict_scalar"); + typename model_t::Entry data[model_t::NUM_FEATURES]; for (int i = 0; i < model_t::NUM_FEATURES; ++i) { if (features.find(std::string(model_t::feature_names[i])) == features.end()) { @@ -78,10 +83,12 @@ float work_unit_predictor_t::predict_scalar( std::chrono::duration elapsed = end - start; CUOPT_LOG_DEBUG("Prediction time: %f ms", elapsed.count()); CUOPT_LOG_DEBUG("Result: %f", result); - return std::abs(result); + return result; } template class work_unit_predictor_t; template class work_unit_predictor_t; +template class work_unit_predictor_t; +template class work_unit_predictor_t; } // namespace cuopt From ecf9f25e1d70a9d7ef4b7213312e870bdc0d59de Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 27 Nov 2025 18:18:29 +0000 Subject: [PATCH 094/366] improved dualsimplex instrumentation --- cpp/src/dual_simplex/barrier.cu | 2 +- cpp/src/dual_simplex/cusparse_view.cu | 6 +- cpp/src/dual_simplex/phase2.cpp | 171 ++++++++++--------- cpp/src/dual_simplex/phase2.hpp | 1 + cpp/src/dual_simplex/singletons.cpp | 8 +- cpp/src/dual_simplex/sparse_vector.cpp | 6 +- cpp/src/mip/relaxed_lp/relaxed_lp.cu | 10 +- cpp/src/utilities/memory_instrumentation.hpp | 150 +++++++++------- 8 files changed, 197 insertions(+), 157 deletions(-) diff --git a/cpp/src/dual_simplex/barrier.cu b/cpp/src/dual_simplex/barrier.cu index d194b9be1..5af03759d 100644 --- a/cpp/src/dual_simplex/barrier.cu +++ b/cpp/src/dual_simplex/barrier.cu @@ -1260,7 +1260,7 @@ class iteration_data_t { // v = alpha * A * w + beta * v = alpha * A * Dinv * A^T * y + beta * v matrix_vector_multiply(A, alpha, w, beta, v); if (debug) { - printf("||A|| = %.16e\n", vector_norm2(A.x.array)); + printf("||A|| = %.16e\n", vector_norm2(A.x.underlying())); printf("||w|| = %.16e\n", vector_norm2(w)); printf("||v|| = %.16e\n", vector_norm2(v)); } diff --git a/cpp/src/dual_simplex/cusparse_view.cu b/cpp/src/dual_simplex/cusparse_view.cu index d9bae0739..282c6ca2b 100644 --- a/cpp/src/dual_simplex/cusparse_view.cu +++ b/cpp/src/dual_simplex/cusparse_view.cu @@ -148,9 +148,9 @@ cusparse_view_t::cusparse_view_t(raft::handle_t const* handle_ptr, A_indices_ = device_copy(indices, handle_ptr->get_stream()); A_data_ = device_copy(data, handle_ptr->get_stream()); - A_T_offsets_ = device_copy(A.col_start.array, handle_ptr->get_stream()); - A_T_indices_ = device_copy(A.i.array, handle_ptr->get_stream()); - A_T_data_ = device_copy(A.x.array, handle_ptr->get_stream()); + A_T_offsets_ = device_copy(A.col_start.underlying(), handle_ptr->get_stream()); + A_T_indices_ = device_copy(A.i.underlying(), handle_ptr->get_stream()); + A_T_data_ = device_copy(A.x.underlying(), handle_ptr->get_stream()); cusparseCreateCsr(&A_, rows, diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 7cd1c420f..1403f9cbf 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -663,8 +663,8 @@ void update_single_primal_infeasibility(const std::vector& lower, const std::vector& upper, const std::vector& x, f_t primal_tol, - ins_vector& squared_infeasibilities, - ins_vector& infeasibility_indices, + std::vector& squared_infeasibilities, + std::vector& infeasibility_indices, i_t j, f_t& primal_inf) { @@ -705,9 +705,9 @@ void update_primal_infeasibilities(const lp_problem_t& lp, const std::vector& x, i_t entering_index, i_t leaving_index, - ins_vector& basic_change_list, - ins_vector& squared_infeasibilities, - ins_vector& infeasibility_indices, + std::vector& basic_change_list, + std::vector& squared_infeasibilities, + std::vector& infeasibility_indices, f_t& primal_inf) { const f_t primal_tol = settings.primal_tol; @@ -764,7 +764,7 @@ void clean_up_infeasibilities(ins_vector& squared_infeasibilities, template i_t steepest_edge_pricing_with_infeasibilities(const lp_problem_t& lp, const simplex_solver_settings_t& settings, - const std::vector& x, + const ins_vector& x, const std::vector& dy_steepest_edge, const ins_vector& basic_mark, ins_vector& squared_infeasibilities, @@ -866,7 +866,7 @@ i_t steepest_edge_pricing(const lp_problem_t& lp, template i_t phase2_pricing(const lp_problem_t& lp, const simplex_solver_settings_t& settings, - const std::vector& x, + const ins_vector& x, const std::vector& basic_list, i_t& direction, i_t& basic_leaving, @@ -911,7 +911,7 @@ template f_t first_stage_harris(const lp_problem_t& lp, const std::vector& vstatus, const std::vector& nonbasic_list, - std::vector& z, + ins_vector& z, ins_vector& delta_z) { const i_t n = lp.num_cols; @@ -945,7 +945,7 @@ template i_t second_stage_harris(const lp_problem_t& lp, const std::vector& vstatus, const std::vector& nonbasic_list, - const std::vector& z, + const ins_vector& z, const ins_vector& delta_z, f_t max_step_length, f_t& step_length, @@ -986,9 +986,9 @@ i_t second_stage_harris(const lp_problem_t& lp, template i_t phase2_ratio_test(const lp_problem_t& lp, const simplex_solver_settings_t& settings, - const std::vector& vstatus, - const std::vector& nonbasic_list, - std::vector& z, + std::vector& vstatus, + std::vector& nonbasic_list, + ins_vector& z, ins_vector& delta_z, f_t& step_length, i_t& nonbasic_entering) @@ -1040,8 +1040,8 @@ template i_t flip_bounds(const lp_problem_t& lp, const simplex_solver_settings_t& settings, const ins_vector& bounded_variables, - const std::vector& objective, - const std::vector& z, + const ins_vector& objective, + const ins_vector& z, const ins_vector& delta_z_indices, const std::vector& nonbasic_list, i_t entering_index, @@ -1377,8 +1377,8 @@ template i_t compute_perturbation(const lp_problem_t& lp, const simplex_solver_settings_t& settings, const ins_vector& delta_z_indices, - std::vector& z, - std::vector& objective, + ins_vector& z, + ins_vector& objective, f_t& sum_perturb) { const i_t n = lp.num_cols; @@ -1498,8 +1498,8 @@ i_t update_dual_variables(const sparse_vector_t& delta_y_sparse, const ins_vector& delta_z, f_t step_length, i_t leaving_index, - std::vector& y, - std::vector& z) + ins_vector& y, + ins_vector& z) { // Update dual variables // y <- y + steplength * delta_y @@ -1528,7 +1528,7 @@ void adjust_for_flips(const basis_update_mpf_t& ft, sparse_vector_t& atilde_sparse, sparse_vector_t& delta_xB_0_sparse, ins_vector& delta_x_flip, - std::vector& x) + ins_vector& x) { const i_t atilde_nz = atilde_index.size(); // B*delta_xB_0 = atilde @@ -1573,7 +1573,7 @@ i_t compute_delta_x(const lp_problem_t& lp, const std::vector& basic_list, const ins_vector& delta_x_flip, const sparse_vector_t& rhs_sparse, - const std::vector& x, + const ins_vector& x, sparse_vector_t& utilde_sparse, sparse_vector_t& scaled_delta_xB_sparse, ins_vector& delta_x) @@ -1636,7 +1636,7 @@ void update_primal_variables(const sparse_vector_t& scaled_delta_xB_sp const std::vector& basic_list, const ins_vector& delta_x, i_t entering_index, - std::vector& x) + ins_vector& x) { // x <- x + delta_x const i_t scaled_delta_xB_nz = scaled_delta_xB_sparse.i.size(); @@ -2008,7 +2008,7 @@ void set_primal_variables_on_bounds(const lp_problem_t& lp, } template -f_t compute_perturbed_objective(const std::vector& objective, const std::vector& x) +f_t compute_perturbed_objective(const ins_vector& objective, const ins_vector& x) { const size_t n = objective.size(); f_t obj_val = 0.0; @@ -2019,7 +2019,7 @@ f_t compute_perturbed_objective(const std::vector& objective, const std::ve } template -f_t amount_of_perturbation(const lp_problem_t& lp, const std::vector& objective) +f_t amount_of_perturbation(const lp_problem_t& lp, const ins_vector& objective) { f_t perturbation = 0.0; const i_t n = lp.num_cols; @@ -2033,7 +2033,7 @@ template void prepare_optimality(const lp_problem_t& lp, const simplex_solver_settings_t& settings, basis_update_mpf_t& ft, - const std::vector& objective, + const ins_vector& objective, const std::vector& basic_list, const std::vector& nonbasic_list, const std::vector& vstatus, @@ -2041,9 +2041,9 @@ void prepare_optimality(const lp_problem_t& lp, f_t start_time, f_t max_val, i_t iter, - const std::vector& x, - std::vector& y, - std::vector& z, + const ins_vector& x, + ins_vector& y, + ins_vector& z, lp_solution_t& sol) { const i_t m = lp.num_rows; @@ -2074,8 +2074,8 @@ void prepare_optimality(const lp_problem_t& lp, sol.l2_primal_residual = l2_primal_residual(lp, sol); sol.l2_dual_residual = l2_dual_residual(lp, sol); - const f_t dual_infeas = phase2::dual_infeasibility(lp, settings, vstatus, z, 0.0, 0.0); - const f_t primal_infeas = phase2::primal_infeasibility(lp, settings, vstatus, x); + const f_t dual_infeas = phase2::dual_infeasibility(lp, settings, vstatus, sol.z, 0.0, 0.0); + const f_t primal_infeas = phase2::primal_infeasibility(lp, settings, vstatus, sol.x); if (phase == 1 && iter > 0) { settings.log.printf("Dual phase I complete. Iterations %d. Time %.2f\n", iter, toc(start_time)); } @@ -2139,20 +2139,20 @@ class phase2_timers_t { delta_z_time + lu_update_time + se_norms_time + se_entering_time + perturb_time + vector_time + objective_time + update_infeasibility_time; // clang-format off - settings.log.printf("BFRT time %.2fs %4.1f%\n", bfrt_time, 100.0 * bfrt_time / total_time); - settings.log.printf("Pricing time %.2fs %4.1f%\n", pricing_time, 100.0 * pricing_time / total_time); - settings.log.printf("BTran time %.2fs %4.1f%\n", btran_time, 100.0 * btran_time / total_time); - settings.log.printf("FTran time %.2fs %4.1f%\n", ftran_time, 100.0 * ftran_time / total_time); - settings.log.printf("Flip time %.2fs %4.1f%\n", flip_time, 100.0 * flip_time / total_time); - settings.log.printf("Delta_z time %.2fs %4.1f%\n", delta_z_time, 100.0 * delta_z_time / total_time); - settings.log.printf("LU update time %.2fs %4.1f%\n", lu_update_time, 100.0 * lu_update_time / total_time); - settings.log.printf("SE norms time %.2fs %4.1f%\n", se_norms_time, 100.0 * se_norms_time / total_time); - settings.log.printf("SE enter time %.2fs %4.1f%\n", se_entering_time, 100.0 * se_entering_time / total_time); - settings.log.printf("Perturb time %.2fs %4.1f%\n", perturb_time, 100.0 * perturb_time / total_time); - settings.log.printf("Vector time %.2fs %4.1f%\n", vector_time, 100.0 * vector_time / total_time); - settings.log.printf("Objective time %.2fs %4.1f%\n", objective_time, 100.0 * objective_time / total_time); - settings.log.printf("Inf update time %.2fs %4.1f%\n", update_infeasibility_time, 100.0 * update_infeasibility_time / total_time); - settings.log.printf("Sum %.2fs\n", total_time); + printf("BFRT time %.2fs %4.1f%\n", bfrt_time, 100.0 * bfrt_time / total_time); + printf("Pricing time %.2fs %4.1f%\n", pricing_time, 100.0 * pricing_time / total_time); + printf("BTran time %.2fs %4.1f%\n", btran_time, 100.0 * btran_time / total_time); + printf("FTran time %.2fs %4.1f%\n", ftran_time, 100.0 * ftran_time / total_time); + printf("Flip time %.2fs %4.1f%\n", flip_time, 100.0 * flip_time / total_time); + printf("Delta_z time %.2fs %4.1f%\n", delta_z_time, 100.0 * delta_z_time / total_time); + printf("LU update time %.2fs %4.1f%\n", lu_update_time, 100.0 * lu_update_time / total_time); + printf("SE norms time %.2fs %4.1f%\n", se_norms_time, 100.0 * se_norms_time / total_time); + printf("SE enter time %.2fs %4.1f%\n", se_entering_time, 100.0 * se_entering_time / total_time); + printf("Perturb time %.2fs %4.1f%\n", perturb_time, 100.0 * perturb_time / total_time); + printf("Vector time %.2fs %4.1f%\n", vector_time, 100.0 * vector_time / total_time); + printf("Objective time %.2fs %4.1f%\n", objective_time, 100.0 * objective_time / total_time); + printf("Inf update time %.2fs %4.1f%\n", update_infeasibility_time, 100.0 * update_infeasibility_time / total_time); + printf("Sum %.2fs\n", total_time); // clang-format on } f_t bfrt_time; @@ -2237,16 +2237,18 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(lp.upper.size() == n); assert(lp.rhs.size() == m); - std::vector& x = sol.x; - std::vector& y = sol.y; - std::vector& z = sol.z; + // Create instrumented wrappers around sol vectors (no ownership transfer) + ins_vector x, y, z; + x.wrap(sol.x); + y.wrap(sol.y); + z.wrap(sol.z); dual::status_t status = dual::status_t::UNSET; raft::common::nvtx::push_range("DualSimplex::phase2_advanced_init"); - // Perturbed objective - std::vector objective = lp.objective; + // Perturbed objective (instrumented for main loop tracking) + ins_vector objective(lp.objective); settings.log.printf("Dual Simplex Phase %d\n", phase); std::vector vstatus_old = vstatus; @@ -2275,7 +2277,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } // Solve B'*y = cB - ft.b_transpose_solve(c_basic, y); + ft.b_transpose_solve(c_basic, sol.y); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } constexpr bool print_norms = false; if constexpr (print_norms) { @@ -2283,7 +2285,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, "|| y || %e || cB || %e\n", vector_norm_inf(y), vector_norm_inf(c_basic)); } - phase2::compute_reduced_costs(objective, lp.A, y, basic_list, nonbasic_list, z); + phase2::compute_reduced_costs( + objective.underlying(), lp.A, sol.y, basic_list, nonbasic_list, sol.z); if constexpr (print_norms) { settings.log.printf("|| z || %e\n", vector_norm_inf(z)); } #ifdef COMPUTE_DUAL_RESIDUAL @@ -2296,7 +2299,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(dual_res_norm < 1e-3); #endif - phase2::set_primal_variables_on_bounds(lp, settings, z, vstatus, x); + phase2::set_primal_variables_on_bounds(lp, settings, sol.z, vstatus, sol.x); #ifdef PRINT_VSTATUS_CHANGES i_t num_vstatus_changes; @@ -2307,7 +2310,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif const f_t init_dual_inf = - phase2::dual_infeasibility(lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); + phase2::dual_infeasibility(lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); if (init_dual_inf > settings.dual_tol) { settings.log.printf("Initial dual infeasibility %e\n", init_dual_inf); } @@ -2319,10 +2322,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } phase2::compute_primal_variables( - ft, lp.rhs, lp.A, basic_list, nonbasic_list, settings.tight_tol, x); + ft, lp.rhs, lp.A, basic_list, nonbasic_list, settings.tight_tol, sol.x); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } - if (print_norms) { settings.log.printf("|| x || %e\n", vector_norm2(x)); } + if (print_norms) { settings.log.printf("|| x || %e\n", vector_norm2(sol.x)); } #ifdef COMPUTE_PRIMAL_RESIDUAL std::vector residual = lp.rhs; @@ -2380,7 +2383,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::compute_bounded_info(lp.lower, lp.upper, bounded_variables); f_t primal_infeasibility = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + lp, settings, basic_list, sol.x, squared_infeasibilities, infeasibility_indices); #ifdef CHECK_BASIC_INFEASIBILITIES phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 0); @@ -2389,7 +2392,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, csc_matrix_t A_transpose(1, 1, 0); lp.A.transpose(A_transpose); - f_t obj = compute_objective(lp, x); + f_t obj = compute_objective(lp, sol.x); raft::common::nvtx::pop_range(); @@ -2400,7 +2403,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, i_t num_refactors = 0; i_t total_bound_flips = 0; f_t delta_y_nz_percentage = 0.0; - phase2::phase2_timers_t timers(false); + phase2::phase2_timers_t timers(true); // Feature collection for regression training dual_simplex_features_t features; @@ -2434,6 +2437,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, manifold.add("squared_infeasibilities", squared_infeasibilities); manifold.add("infeasibility_indices", infeasibility_indices); manifold.add("bounded_variables", bounded_variables); + // Ratio test vectors + // manifold.add("vstatus", vstatus); + // manifold.add("nonbasic_list", nonbasic_list); + manifold.add("z", z); // Add sparse vector internal arrays to manifold manifold.add("delta_y_sparse.i", delta_y_sparse.i); @@ -2614,12 +2621,12 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, slope, lp.lower, lp.upper, - bounded_variables, + bounded_variables.underlying(), vstatus, nonbasic_list, - z, - delta_z, - delta_z_indices, + sol.z, + delta_z.underlying(), + delta_z_indices.underlying(), nonbasic_mark); entering_index = bfrt.compute_step_length(step_length, nonbasic_entering_index); if (entering_index == -4) { @@ -2658,9 +2665,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); - x = unperturbed_x; + sol.x = unperturbed_x; primal_infeasibility = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + lp, settings, basic_list, sol.x, squared_infeasibilities, infeasibility_indices); settings.log.printf("Updated primal infeasibility: %e\n", primal_infeasibility); objective = lp.objective; @@ -2695,12 +2702,12 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); - x = unperturbed_x; + sol.x = unperturbed_x; primal_infeasibility = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + lp, settings, basic_list, sol.x, squared_infeasibilities, infeasibility_indices); const f_t orig_dual_infeas = phase2::dual_infeasibility( - lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); + lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); if (primal_infeasibility <= settings.primal_tol && orig_dual_infeas <= settings.dual_tol) { @@ -2757,10 +2764,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } } - const f_t dual_infeas = - phase2::dual_infeasibility(lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); + const f_t dual_infeas = phase2::dual_infeasibility( + lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); settings.log.printf("Dual infeasibility %e\n", dual_infeas); - const f_t primal_inf = phase2::primal_infeasibility(lp, settings, vstatus, x); + const f_t primal_inf = phase2::primal_infeasibility(lp, settings, vstatus, sol.x); settings.log.printf("Primal infeasibility %e\n", primal_inf); settings.log.printf("Updates %d\n", ft.num_updates()); settings.log.printf("Steepest edge %e\n", max_val); @@ -2912,32 +2919,32 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::update_primal_infeasibilities(lp, settings, basic_list, - x, + sol.x, entering_index, leaving_index, - delta_xB_0_sparse.i, - squared_infeasibilities, - infeasibility_indices, + delta_xB_0_sparse.i.underlying(), + squared_infeasibilities.underlying(), + infeasibility_indices.underlying(), primal_infeasibility); // Update primal infeasibilities due to changes in basic variables // from the leaving and entering variables phase2::update_primal_infeasibilities(lp, settings, basic_list, - x, + sol.x, entering_index, leaving_index, - scaled_delta_xB_sparse.i, - squared_infeasibilities, - infeasibility_indices, + scaled_delta_xB_sparse.i.underlying(), + squared_infeasibilities.underlying(), + infeasibility_indices.underlying(), primal_infeasibility); // Update the entering variable phase2::update_single_primal_infeasibility(lp.lower, lp.upper, - x, + sol.x, settings.primal_tol, - squared_infeasibilities, - infeasibility_indices, + squared_infeasibilities.underlying(), + infeasibility_indices.underlying(), entering_index, primal_infeasibility); @@ -3022,10 +3029,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); - x = unperturbed_x; + sol.x = unperturbed_x; } phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + lp, settings, basic_list, sol.x, squared_infeasibilities, infeasibility_indices); } #ifdef CHECK_BASIC_INFEASIBILITIES phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 7); diff --git a/cpp/src/dual_simplex/phase2.hpp b/cpp/src/dual_simplex/phase2.hpp index caeae82e1..c6ed03c84 100644 --- a/cpp/src/dual_simplex/phase2.hpp +++ b/cpp/src/dual_simplex/phase2.hpp @@ -13,6 +13,7 @@ #include #include #include +#include #include diff --git a/cpp/src/dual_simplex/singletons.cpp b/cpp/src/dual_simplex/singletons.cpp index c4656889b..71603306c 100644 --- a/cpp/src/dual_simplex/singletons.cpp +++ b/cpp/src/dual_simplex/singletons.cpp @@ -200,8 +200,8 @@ i_t find_singletons(const csc_matrix_t& A, // Find column singletons row_col_graph_t graph{Cdeg.begin(), col_perm.begin(), - A.col_start.array.cbegin(), - A.i.array.cbegin(), + A.col_start.underlying().cbegin(), + A.i.underlying().cbegin(), Rdeg.begin(), row_perm.begin(), Rp.cbegin(), @@ -235,8 +235,8 @@ i_t find_singletons(const csc_matrix_t& A, Rj.cbegin(), Cdeg.begin(), col_perm.begin(), - A.col_start.array.cbegin(), - A.i.array.cbegin()}; + A.col_start.underlying().cbegin(), + A.i.underlying().cbegin()}; #ifdef SINGLETON_DEBUG printf("Searching for row singletons %ld\n", singleton_queue.size()); #endif diff --git a/cpp/src/dual_simplex/sparse_vector.cpp b/cpp/src/dual_simplex/sparse_vector.cpp index 111d3ce82..79d323d31 100644 --- a/cpp/src/dual_simplex/sparse_vector.cpp +++ b/cpp/src/dual_simplex/sparse_vector.cpp @@ -72,8 +72,8 @@ void sparse_vector_t::to_csc(csc_matrix_t& A) const A.col_start.resize(2); A.col_start[0] = 0; A.col_start[1] = i.size(); - A.i = i.array; - A.x = x.array; + A.i = i.underlying(); + A.x = x.underlying(); } template @@ -201,7 +201,7 @@ void sparse_vector_t::sort() perm[k] = k; } // Need to capture the underlying array for the lambda - auto& iunsorted = i.array; + auto& iunsorted = i; std::sort( perm.begin(), perm.end(), [&iunsorted](i_t a, i_t b) { return iunsorted[a] < iunsorted[b]; }); for (i_t k = 0; k < nz; ++k) { diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index 6a336e7c0..d9c6f45b6 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -188,11 +188,11 @@ optimization_problem_solution_t get_relaxed_lp_solution( : 0.0; // Compute sparsity metrics - const double sparsity = (n_cstrs > 0 && n_vars > 0) - ? static_cast(nnz) / (static_cast(n_cstrs) * n_vars) - : 0.0; - double nnz_stddev = 0.0; - double unbalancedness = 0.0; + const double sparsity = (n_cstrs > 0 && n_vars > 0) + ? static_cast(nnz) / (static_cast(n_cstrs) * n_vars) + : 0.0; + double nnz_stddev = 0.0; + [[maybe_unused]] double unbalancedness = 0.0; if (op_problem.offsets.size() == static_cast(n_cstrs + 1) && n_cstrs > 0) { std::vector h_offsets(n_cstrs + 1); raft::copy(h_offsets.data(), diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 52c07be3d..ca8771abf 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -491,28 +491,28 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { using const_reverse_iterator = std::reverse_iterator; // Constructors - memop_instrumentation_wrapper_t() : array() + memop_instrumentation_wrapper_t() : array_(), wrapped_ptr(nullptr) { if constexpr (type_traits_utils::has_data::value) { - data_ptr = array.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } } // Copy/move from underlying type - memop_instrumentation_wrapper_t(const T& array) : array(array) + memop_instrumentation_wrapper_t(const T& arr) : array_(arr) { if constexpr (type_traits_utils::has_data::value) { - data_ptr = const_cast(array.data()); + data_ptr = const_cast(array_.data()); } else { data_ptr = nullptr; } } - memop_instrumentation_wrapper_t(T&& array) : array(std::move(array)) + memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)) { if constexpr (type_traits_utils::has_data::value) { - data_ptr = array.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } @@ -527,10 +527,10 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { !std::is_same_v, T> && (sizeof...(Args) > 0 || !std::is_convertible_v)>> explicit memop_instrumentation_wrapper_t(Arg&& arg, Args&&... args) - : array(std::forward(arg), std::forward(args)...) + : array_(std::forward(arg), std::forward(args)...) { if constexpr (type_traits_utils::has_data::value) { - data_ptr = array.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } @@ -538,10 +538,10 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { // Copy constructor - must update data_ptr to point to our own array memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : memory_instrumentation_base_t(other), array(other.array) + : memory_instrumentation_base_t(other), array_(other.array_) { if constexpr (type_traits_utils::has_data::value) { - data_ptr = array.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } @@ -549,10 +549,10 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { // Move constructor - must update data_ptr to point to our own array memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : memory_instrumentation_base_t(std::move(other)), array(std::move(other.array)) + : memory_instrumentation_base_t(std::move(other)), array_(std::move(other.array_)) { if constexpr (type_traits_utils::has_data::value) { - data_ptr = array.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } @@ -563,9 +563,9 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { { if (this != &other) { memory_instrumentation_base_t::operator=(other); - array = other.array; + array_ = other.array_; if constexpr (type_traits_utils::has_data::value) { - data_ptr = array.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } @@ -578,9 +578,9 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { { if (this != &other) { memory_instrumentation_base_t::operator=(std::move(other)); - array = std::move(other.array); + array_ = std::move(other.array_); if constexpr (type_traits_utils::has_data::value) { - data_ptr = array.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } @@ -588,7 +588,10 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { return *this; } - element_proxy_t operator[](size_type index) { return element_proxy_t(array[index], *this); } + element_proxy_t operator[](size_type index) + { + return element_proxy_t(underlying()[index], *this); + } HDI value_type operator[](size_type index) const { @@ -597,47 +600,50 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { if constexpr (type_traits_utils::has_data::value) { return data_ptr[index]; } else { - return array[index]; + return underlying()[index]; } } template std::enable_if_t::value, element_proxy_t> front() { - return element_proxy_t(array.front(), *this); + return element_proxy_t(underlying().front(), *this); } template std::enable_if_t::value, value_type> front() const { this->template record_load(); - return array.front(); + return underlying().front(); } template std::enable_if_t::value, element_proxy_t> back() { - return element_proxy_t(array.back(), *this); + return element_proxy_t(underlying().back(), *this); } template std::enable_if_t::value, value_type> back() const { this->template record_load(); - return array.back(); + return underlying().back(); } // Iterators - iterator begin() noexcept { return iterator(std::begin(array), this); } - const_iterator begin() const noexcept { return const_iterator(std::begin(array), this); } - const_iterator cbegin() const noexcept { return const_iterator(std::begin(array), this); } + iterator begin() noexcept { return iterator(std::begin(underlying()), this); } + const_iterator begin() const noexcept { return const_iterator(std::begin(underlying()), this); } + const_iterator cbegin() const noexcept { return const_iterator(std::begin(underlying()), this); } - iterator end() noexcept { return iterator(std::end(array), this); } - const_iterator end() const noexcept { return const_iterator(std::end(array), this); } - const_iterator cend() const noexcept { return const_iterator(std::end(array), this); } + iterator end() noexcept { return iterator(std::end(underlying()), this); } + const_iterator end() const noexcept { return const_iterator(std::end(underlying()), this); } + const_iterator cend() const noexcept { return const_iterator(std::end(underlying()), this); } reverse_iterator rbegin() noexcept { return reverse_iterator(end()); } - const_reverse_iterator rbegin() const noexcept { return const_reverse_iterator(std::end(array)); } + const_reverse_iterator rbegin() const noexcept + { + return const_reverse_iterator(std::end(underlying())); + } const_reverse_iterator crbegin() const noexcept { return const_reverse_iterator(cend()); } reverse_iterator rend() noexcept { return reverse_iterator(begin()); } @@ -645,41 +651,44 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { const_reverse_iterator crend() const noexcept { return const_reverse_iterator(cbegin()); } // Capacity - bool empty() const noexcept { return std::begin(array) == std::end(array); } - size_type size() const noexcept { return std::distance(std::begin(array), std::end(array)); } + bool empty() const noexcept { return std::begin(underlying()) == std::end(underlying()); } + size_type size() const noexcept + { + return std::distance(std::begin(underlying()), std::end(underlying())); + } // Conditional methods - only available if underlying type supports them template std::enable_if_t::value, size_type> max_size() const noexcept { - return array.max_size(); + return underlying().max_size(); } template std::enable_if_t::value, size_type> capacity() const noexcept { - return array.capacity(); + return underlying().capacity(); } template std::enable_if_t::value> reserve(size_type new_cap) { - array.reserve(new_cap); - if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + underlying().reserve(new_cap); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } template std::enable_if_t::value> shrink_to_fit() { - array.shrink_to_fit(); - if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + underlying().shrink_to_fit(); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } template std::enable_if_t::value> clear() noexcept { - array.clear(); - if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + underlying().clear(); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } template @@ -688,74 +697,97 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { // we should probably take into account possible copies done by std::vector. oh well. // hot loops shouldn't be doing such operations anyway this->template record_store(); - array.push_back(value); - if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + underlying().push_back(value); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } template std::enable_if_t::value> push_back(value_type&& value) { this->template record_store(); - array.push_back(std::move(value)); - if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + underlying().push_back(std::move(value)); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } template std::enable_if_t::value> emplace_back(Args&&... args) { this->template record_store(); - array.emplace_back(std::forward(args)...); - if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + underlying().emplace_back(std::forward(args)...); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } template std::enable_if_t::value> pop_back() { this->template record_load(); // Reading the element before removal - array.pop_back(); - if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + underlying().pop_back(); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } template std::enable_if_t::value> resize(size_type count) { - size_type old_size = array.size(); - array.resize(count); + size_type old_size = underlying().size(); + underlying().resize(count); if (count > old_size) { this->byte_stores += (count - old_size) * type_size; // New elements initialized } - if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } template std::enable_if_t::value> resize(size_type count, const value_type& value) { - size_type old_size = array.size(); - array.resize(count, value); + size_type old_size = underlying().size(); + underlying().resize(count, value); if (count > old_size) { this->byte_stores += (count - old_size) * type_size; } - if constexpr (type_traits_utils::has_data::value) { data_ptr = array.data(); } + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } template std::enable_if_t::value, value_type*> data() noexcept { - return array.data(); + return underlying().data(); } template std::enable_if_t::value, const value_type*> data() const noexcept { - return array.data(); + return underlying().data(); } // Access to underlying array - operator T&() { return array; } - operator const T&() const { return array; } + operator T&() { return underlying(); } + operator const T&() const { return underlying(); } - T&& release_array() { return std::move(array); } + T&& release_array() { return std::move(array_); } - T array; + // Wrap an external vector without taking ownership + void wrap(T& external_array) + { + wrapped_ptr = &external_array; + if constexpr (type_traits_utils::has_data::value) { data_ptr = external_array.data(); } + } + + // Stop wrapping and return to using the owned array + void unwrap() + { + wrapped_ptr = nullptr; + if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); } + } + + // Check if currently wrapping an external array + bool is_wrapping() const { return wrapped_ptr != nullptr; } + + // Get the underlying container (wrapped or owned) + T& underlying() { return wrapped_ptr ? *wrapped_ptr : array_; } + const T& underlying() const { return wrapped_ptr ? *wrapped_ptr : array_; } + + private: + T array_; + T* wrapped_ptr{nullptr}; value_type* data_ptr{nullptr}; }; From fb9c782968bb8db4b10a9540af887a4135a265dd Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 1 Dec 2025 13:55:37 +0000 Subject: [PATCH 095/366] integrate work unit limiting with branch_and_bound --- .../linear_programming/cuopt/run_mip.cpp | 10 +- .../cuopt/linear_programming/constants.h | 4 + .../mip/solver_settings.hpp | 15 ++- cpp/src/dual_simplex/branch_and_bound.cpp | 62 +++++++--- cpp/src/dual_simplex/branch_and_bound.hpp | 12 +- cpp/src/dual_simplex/phase2.cpp | 110 ++++++++++-------- cpp/src/dual_simplex/phase2.hpp | 11 +- .../dual_simplex/simplex_solver_settings.hpp | 5 +- cpp/src/dual_simplex/solve.cpp | 2 + cpp/src/dual_simplex/solve.hpp | 3 +- cpp/src/math_optimization/solver_settings.cu | 6 +- cpp/src/mip/diversity/diversity_manager.cu | 26 ++--- .../diversity/recombiners/fp_recombiner.cuh | 2 +- .../mip/diversity/recombiners/recombiner.cuh | 4 +- .../mip/feasibility_jump/feasibility_jump.cu | 8 +- .../feasibility_jump_kernels.cu | 12 +- .../feasibility_pump/feasibility_pump.cu | 8 +- .../line_segment_search.cu | 4 +- cpp/src/mip/local_search/local_search.cu | 16 ++- .../local_search/rounding/constraint_prop.cu | 6 +- cpp/src/mip/presolve/bounds_presolve.cu | 2 +- cpp/src/mip/presolve/utils.cuh | 1 + cpp/src/mip/relaxed_lp/relaxed_lp.cu | 22 ++-- cpp/src/mip/solver.cu | 55 +++++---- cpp/src/mip/solver_context.cuh | 10 +- .../models/dualsimplex_predictor/main.cpp | 4 +- .../utilities/models/pdlp_predictor/main.cpp | 4 +- cpp/src/utilities/work_limit_timer.hpp | 4 +- cpp/src/utilities/work_unit_predictor.cpp | 28 ++--- cpp/src/utilities/work_unit_predictor.hpp | 29 ++++- cpp/tests/mip/diversity_test.cu | 32 ++--- cpp/tests/mip/feasibility_jump_tests.cu | 19 +-- cpp/tests/mip/local_search_test.cu | 8 +- datasets/mip/download_miplib_test_dataset.sh | 1 + 34 files changed, 336 insertions(+), 209 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index fe06f1638..7db53358b 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -198,11 +198,11 @@ int run_single_file(std::string file_path, } } - settings.time_limit = time_limit; - settings.heuristics_only = heuristics_only; - settings.num_cpu_threads = num_cpu_threads; - settings.log_to_console = log_to_console; - settings.deterministic = deterministic; + settings.time_limit = time_limit; + settings.heuristics_only = heuristics_only; + settings.num_cpu_threads = num_cpu_threads; + settings.log_to_console = log_to_console; + settings.determinism_mode = deterministic ? CUOPT_MODE_DETERMINISTIC : CUOPT_MODE_OPPORTUNISTIC; settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; diff --git a/cpp/include/cuopt/linear_programming/constants.h b/cpp/include/cuopt/linear_programming/constants.h index eeb9f1775..dd380e0d1 100644 --- a/cpp/include/cuopt/linear_programming/constants.h +++ b/cpp/include/cuopt/linear_programming/constants.h @@ -62,6 +62,10 @@ #define CUOPT_NUM_CPU_THREADS "num_cpu_threads" #define CUOPT_USER_PROBLEM_FILE "user_problem_file" +/* @brief MIP determinism mode constants */ +#define CUOPT_MODE_OPPORTUNISTIC 0 +#define CUOPT_MODE_DETERMINISTIC 1 + /* @brief LP/MIP termination status constants */ #define CUOPT_TERIMINATION_STATUS_NO_TERMINATION 0 #define CUOPT_TERIMINATION_STATUS_OPTIMAL 1 diff --git a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp index 412f878b1..6e48dcfcf 100644 --- a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp +++ b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp @@ -89,9 +89,18 @@ class mip_solver_settings_t { /** Initial primal solutions */ std::vector>> initial_solutions; - bool mip_scaling = true; - bool presolve = true; - bool deterministic = false; + bool mip_scaling = true; + bool presolve = true; + /** + * @brief Determinism mode for MIP solver. + * + * Controls the determinism behavior of the MIP solver: + * - CUOPT_MODE_OPPORTUNISTIC (0): Default mode, allows non-deterministic + * parallelism for better performance + * - CUOPT_MODE_DETERMINISTIC (1): Ensures deterministic results across runs + * at potential cost of performance + */ + int determinism_mode = CUOPT_MODE_OPPORTUNISTIC; // this is for extracting info from different places of the solver during // benchmarks benchmark_info_t* benchmark_info_ptr = nullptr; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 9284520b6..2f1510c33 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -140,6 +140,8 @@ dual::status_t convert_lp_status_to_dual_status(lp_status_t status) return dual::status_t::ITERATION_LIMIT; } else if (status == lp_status_t::TIME_LIMIT) { return dual::status_t::TIME_LIMIT; + } else if (status == lp_status_t::WORK_LIMIT) { + return dual::status_t::WORK_LIMIT; } else if (status == lp_status_t::NUMERICAL_ISSUES) { return dual::status_t::NUMERICAL; } else if (status == lp_status_t::CUTOFF) { @@ -619,6 +621,10 @@ mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t::solve_node( lp_settings.cut_off = upper_bound + settings_.dual_tol; lp_settings.inside_mip = 2; lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); + lp_settings.work_limit = settings_.work_limit; lp_settings.scale_columns = false; #ifdef LOG_NODE_SIMPLEX @@ -836,20 +843,29 @@ node_solve_info_t branch_and_bound_t::solve_node( { raft::common::nvtx::range scope_lp("BB::node_lp_solve"); - lp_status = dual_phase2_with_advanced_basis(2, - 0, - recompute_bounds_and_basis, - lp_start_time, - leaf_problem, - lp_settings, - leaf_vstatus, - basis_factors, - basic_list, - nonbasic_list, - leaf_solution, - node_iter, - leaf_edge_norms); + lp_status = + dual_phase2_with_advanced_basis(2, + 0, + recompute_bounds_and_basis, + lp_start_time, + leaf_problem, + lp_settings, + leaf_vstatus, + basis_factors, + basic_list, + nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms, + settings_.deterministic ? &work_unit_context_ : nullptr); + } + if (settings_.deterministic && + work_unit_context_.global_work_units_elapsed >= settings_.work_limit) { + lp_status = dual::status_t::WORK_LIMIT; } + printf("------ Total work unit progress B&B: %f / %f\n", + work_unit_context_.global_work_units_elapsed, + settings_.work_limit); if (lp_status == dual::status_t::NUMERICAL) { log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); @@ -983,6 +999,9 @@ node_solve_info_t branch_and_bound_t::solve_node( search_tree.graphviz_node(log, node_ptr, "timeout", 0.0); return node_solve_info_t::TIME_LIMIT; + } else if (lp_status == dual::status_t::WORK_LIMIT) { + search_tree.graphviz_node(log, node_ptr, "work limit", 0.0); + return node_solve_info_t::WORK_LIMIT; } else { if (thread_type == thread_type_t::EXPLORATION) { fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); @@ -1098,6 +1117,10 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod solver_status_ = mip_exploration_status_t::TIME_LIMIT; return; + } else if (status == node_solve_info_t::WORK_LIMIT) { + solver_status_ = mip_exploration_status_t::WORK_LIMIT; + return; + } else if (has_children(status)) { exploration_stats_.nodes_unexplored += 2; @@ -1220,6 +1243,10 @@ void branch_and_bound_t::explore_subtree(i_t task_id, solver_status_ = mip_exploration_status_t::TIME_LIMIT; return; + } else if (status == node_solve_info_t::WORK_LIMIT) { + solver_status_ = mip_exploration_status_t::WORK_LIMIT; + return; + } else if (has_children(status)) { // The stack should only contain the children of the current parent. // If the stack size is greater than 0, @@ -1403,6 +1430,9 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A if (status == node_solve_info_t::TIME_LIMIT) { solver_status_ = mip_exploration_status_t::TIME_LIMIT; return; + } else if (status == node_solve_info_t::WORK_LIMIT) { + solver_status_ = mip_exploration_status_t::WORK_LIMIT; + return; } else if (has_children(status)) { if (status == node_solve_info_t::UP_CHILD_FIRST) { @@ -1505,6 +1535,11 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut return set_final_solution(solution, -inf); } + if (root_status == lp_status_t::WORK_LIMIT) { + solver_status_ = mip_exploration_status_t::WORK_LIMIT; + return set_final_solution(solution, -inf); + } + assert(root_vstatus_.size() == original_lp_.num_cols); set_uninitialized_steepest_edge_norms(edge_norms_); @@ -1606,6 +1641,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut min_diving_queue_size_ = 4 * settings_.num_diving_threads; solver_status_ = mip_exploration_status_t::RUNNING; lower_bound_ceiling_ = inf; + work_unit_context_.deterministic = settings_.deterministic; #pragma omp parallel num_threads(settings_.num_threads) { diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index e07b18da9..6f8d8a239 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -15,7 +15,10 @@ #include #include #include + #include +#include +#include #include #include @@ -31,6 +34,7 @@ enum class mip_status_t { NODE_LIMIT = 4, // The maximum number of nodes was reached (not implemented) NUMERICAL = 5, // The solver encountered a numerical error UNSET = 6, // The status is not set + WORK_LIMIT = 7, // The solver reached a deterministic work limit }; enum class mip_exploration_status_t { @@ -40,6 +44,7 @@ enum class mip_exploration_status_t { NUMERICAL = 3, // The solver encountered a numerical error RUNNING = 4, // The solver is currently exploring the tree COMPLETED = 5, // The solver finished exploring the tree + WORK_LIMIT = 6, // The solver reached a deterministic work limit }; enum class node_solve_info_t { @@ -48,7 +53,8 @@ enum class node_solve_info_t { DOWN_CHILD_FIRST = 2, // The down child should be explored first TIME_LIMIT = 3, // The solver reached a time limit ITERATION_LIMIT = 4, // The solver reached a iteration limit - NUMERICAL = 5 // The solver encounter a numerical error when solving the node + NUMERICAL = 5, // The solver encounter a numerical error when solving the node + WORK_LIMIT = 6, // The solver reached a deterministic work limit }; // Indicate the search and variable selection algorithms used by the thread (See [1]). @@ -143,6 +149,10 @@ class branch_and_bound_t { const user_problem_t& original_problem_; const simplex_solver_settings_t settings_; + // Work unit contexts for each worker + // TODO: only one for now, sequential B&B for now + work_limit_context_t work_unit_context_; + // Initial guess. std::vector guess_; diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 1403f9cbf..37d729fba 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -18,6 +18,8 @@ #include #include +#include +#include #include #include @@ -2223,7 +2225,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector& nonbasic_list, lp_solution_t& sol, i_t& iter, - std::vector& delta_y_steepest_edge) + std::vector& delta_y_steepest_edge, + work_limit_context_t* work_unit_context) { raft::common::nvtx::range scope("DualSimplex::phase2_advanced"); const i_t m = lp.num_rows; @@ -2403,7 +2406,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, i_t num_refactors = 0; i_t total_bound_flips = 0; f_t delta_y_nz_percentage = 0.0; - phase2::phase2_timers_t timers(true); + phase2::phase2_timers_t timers(false); // Feature collection for regression training dual_simplex_features_t features; @@ -2466,9 +2469,44 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, manifold.add("A_transpose.x", A_transpose.x); // Track iteration interval start time for runtime measurement - f_t interval_start_time = toc(start_time); - - // Note: Features are logged inline with DS_FEATURES: prefix + f_t interval_start_time = toc(start_time); + i_t last_feature_log_iter = iter; + + // Helper to compute scaled work units for a given number of iterations + cuopt::work_unit_predictor_t work_predictor{}; + auto predict_work_units = [&](i_t num_iters) -> f_t { + std::map features_map; + features_map["m"] = static_cast(features.num_rows); + features_map["n"] = static_cast(features.num_cols); + features_map["nnz"] = static_cast(features.num_nonzeros); + features_map["density"] = static_cast(features.matrix_density); + features_map["avg_nnz_col"] = static_cast(features.avg_nnz_per_col); + features_map["avg_nnz_row"] = static_cast(features.avg_nnz_per_row); + features_map["bounded"] = static_cast(features.num_bounded_vars); + features_map["free"] = static_cast(features.num_free_vars); + features_map["refact_freq"] = static_cast(features.refactor_frequency); + features_map["num_refacts"] = static_cast(features.num_refactors); + features_map["num_updates"] = static_cast(features.num_basis_updates); + features_map["sparse_dz"] = static_cast(features.sparse_delta_z_count); + features_map["dense_dz"] = static_cast(features.dense_delta_z_count); + features_map["bound_flips"] = static_cast(features.total_bound_flips); + features_map["num_infeas"] = static_cast(features.num_infeasibilities); + features_map["dy_nz_pct"] = static_cast(features.delta_y_nz_percentage); + features_map["byte_loads"] = static_cast(features.byte_loads); + features_map["byte_stores"] = static_cast(features.byte_stores); + + f_t base_prediction = std::max((f_t)0.0, (f_t)work_predictor.predict_scalar(features_map)); + return base_prediction * static_cast(num_iters) / FEATURE_LOG_INTERVAL; + }; + + cuopt::scope_guard work_unit_guard([&]() { + if (!work_unit_context) return; + i_t remaining_iters = iter - last_feature_log_iter; + if (remaining_iters <= 0) return; + f_t prediction = predict_work_units(remaining_iters); + printf("DualSimplex determ (final): %d iters, predicted %.4f\n", remaining_iters, prediction); + work_unit_context->record_work(prediction); + }); while (iter < iter_limit) { raft::common::nvtx::range scope_main("DualSimplex::phase2_main_loop"); @@ -3065,16 +3103,15 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // Feature logging for regression training (every FEATURE_LOG_INTERVAL iterations) if ((iter % FEATURE_LOG_INTERVAL) == 0) { - // Collect aggregated memory access statistics + i_t iters_elapsed = iter - last_feature_log_iter; + auto [total_loads, total_stores] = manifold.collect_and_flush(); features.byte_loads = total_loads; features.byte_stores = total_stores; - // Compute interval runtime features.interval_runtime = now - interval_start_time; interval_start_time = now; - // Update dynamic features features.iteration = iter; features.num_refactors = num_refactors; features.num_basis_updates = ft.num_updates(); @@ -3084,45 +3121,17 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, features.num_infeasibilities = infeasibility_indices.size(); features.delta_y_nz_percentage = delta_y_nz_percentage; - // Log all features on a single line - features.log_features(settings); - - // Populate features map for predictor - cuopt::work_unit_predictor_t dualsimplex_predictor{}; - std::map features_map; - features_map["m"] = static_cast(features.num_rows); - features_map["n"] = static_cast(features.num_cols); - features_map["nnz"] = static_cast(features.num_nonzeros); - features_map["density"] = static_cast(features.matrix_density); - features_map["avg_nnz_col"] = static_cast(features.avg_nnz_per_col); - features_map["avg_nnz_row"] = static_cast(features.avg_nnz_per_row); - features_map["bounded"] = static_cast(features.num_bounded_vars); - features_map["free"] = static_cast(features.num_free_vars); - features_map["refact_freq"] = static_cast(features.refactor_frequency); - features_map["num_refacts"] = static_cast(features.num_refactors); - features_map["num_updates"] = static_cast(features.num_basis_updates); - features_map["sparse_dz"] = static_cast(features.sparse_delta_z_count); - features_map["dense_dz"] = static_cast(features.dense_delta_z_count); - features_map["bound_flips"] = static_cast(features.total_bound_flips); - features_map["num_infeas"] = static_cast(features.num_infeasibilities); - features_map["dy_nz_pct"] = static_cast(features.delta_y_nz_percentage); - features_map["byte_loads"] = static_cast(features.byte_loads); - features_map["byte_stores"] = static_cast(features.byte_stores); - auto time_prediction = - std::max((f_t)0.0, (f_t)dualsimplex_predictor.predict_scalar(features_map)); - f_t baseline_max_clock = 3800; // 3.8Ghz - f_t max_clock = cuopt::get_cpu_max_clock_mhz(); - f_t scaling_factor = baseline_max_clock / max_clock; - f_t adjusted_time_prediction = time_prediction * scaling_factor; - printf( - "DualSimplex determ: Estimated time for %d iters: %f, actual time: %f, error %f, CPU max " - "clock: %f MHz, scaling factor: %f\n", - FEATURE_LOG_INTERVAL, - adjusted_time_prediction, - features.interval_runtime, - adjusted_time_prediction - features.interval_runtime, - max_clock, - scaling_factor); + if (work_unit_context) { + f_t prediction = predict_work_units(iters_elapsed); + printf("DualSimplex determ: %d iters, predicted %.4f, actual %.4f, error %.4f\n", + iters_elapsed, + prediction, + features.interval_runtime, + prediction - features.interval_runtime); + work_unit_context->record_work(prediction); + } + + last_feature_log_iter = iter; } if ((iter - start_iter) < settings.first_iteration_log || @@ -3144,6 +3153,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, return dual::status_t::CUTOFF; } + if (work_unit_context && work_unit_context->global_work_units_elapsed >= settings.work_limit) { + return dual::status_t::WORK_LIMIT; + } + if (now > settings.time_limit) { return dual::status_t::TIME_LIMIT; } if (settings.concurrent_halt != nullptr && *settings.concurrent_halt == 1) { @@ -3196,7 +3209,8 @@ template dual::status_t dual_phase2_with_advanced_basis( std::vector& nonbasic_list, lp_solution_t& sol, int& iter, - std::vector& steepest_edge_norms); + std::vector& steepest_edge_norms, + work_limit_context_t* work_unit_context); #endif } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/phase2.hpp b/cpp/src/dual_simplex/phase2.hpp index c6ed03c84..54e2b4087 100644 --- a/cpp/src/dual_simplex/phase2.hpp +++ b/cpp/src/dual_simplex/phase2.hpp @@ -17,6 +17,10 @@ #include +namespace cuopt { +struct work_limit_context_t; +} + namespace cuopt::linear_programming::dual_simplex { namespace dual { @@ -28,7 +32,8 @@ enum class status_t { TIME_LIMIT = 4, ITERATION_LIMIT = 5, CONCURRENT_LIMIT = 6, - UNSET = 7 + UNSET = 7, + WORK_LIMIT = 8 }; static std::string status_to_string(status_t status) @@ -41,6 +46,7 @@ static std::string status_to_string(status_t status) case status_t::TIME_LIMIT: return "TIME_LIMIT"; case status_t::ITERATION_LIMIT: return "ITERATION_LIMIT"; case status_t::CONCURRENT_LIMIT: return "CONCURRENT_LIMIT"; + case status_t::WORK_LIMIT: return "WORK_LIMIT"; case status_t::UNSET: return "UNSET"; } return "UNKNOWN"; @@ -71,6 +77,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector& nonbasic_list, lp_solution_t& sol, i_t& iter, - std::vector& delta_y_steepest_edge); + std::vector& delta_y_steepest_edge, + work_limit_context_t* work_unit_context = nullptr); } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 8e54c40bb..8d85581f8 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -25,6 +25,7 @@ struct simplex_solver_settings_t { : iteration_limit(std::numeric_limits::max()), node_limit(std::numeric_limits::max()), time_limit(std::numeric_limits::infinity()), + work_limit(std::numeric_limits::infinity()), absolute_mip_gap_tol(0.0), relative_mip_gap_tol(1e-3), integer_tol(1e-5), @@ -84,6 +85,7 @@ struct simplex_solver_settings_t { i_t iteration_limit; i_t node_limit; f_t time_limit; + f_t work_limit; f_t absolute_mip_gap_tol; // Tolerance on mip gap to declare optimal f_t relative_mip_gap_tol; // Tolerance on mip gap to declare optimal f_t integer_tol; // Tolerance on integralitiy violation @@ -118,7 +120,8 @@ struct simplex_solver_settings_t { bool print_presolve_stats; // true to print presolve stats bool barrier_presolve; // true to use barrier presolve bool cudss_deterministic; // true to use cuDSS deterministic mode, false for non-deterministic - bool barrier; // true to use barrier method, false to use dual simplex method + bool deterministic; // true to use B&B deterministic mode, false to use non-deterministic mode + bool barrier; // true to use barrier method, false to use dual simplex method bool eliminate_dense_columns; // true to eliminate dense columns from A*D*A^T i_t folding; // -1 automatic, 0 don't fold, 1 fold i_t augmented; // -1 automatic, 0 to solve with ADAT, 1 to solve with augmented system diff --git a/cpp/src/dual_simplex/solve.cpp b/cpp/src/dual_simplex/solve.cpp index 7e59e83c3..271a31aed 100644 --- a/cpp/src/dual_simplex/solve.cpp +++ b/cpp/src/dual_simplex/solve.cpp @@ -209,6 +209,7 @@ lp_status_t solve_linear_program_with_advanced_basis( return lp_status_t::NUMERICAL_ISSUES; } if (phase1_status == dual::status_t::TIME_LIMIT) { return lp_status_t::TIME_LIMIT; } + if (phase1_status == dual::status_t::WORK_LIMIT) { return lp_status_t::WORK_LIMIT; } if (phase1_status == dual::status_t::ITERATION_LIMIT) { return lp_status_t::ITERATION_LIMIT; } if (phase1_status == dual::status_t::CONCURRENT_LIMIT) { return lp_status_t::CONCURRENT_LIMIT; } phase1_obj = phase1_solution.objective; @@ -292,6 +293,7 @@ lp_status_t solve_linear_program_with_advanced_basis( } if (status == dual::status_t::DUAL_UNBOUNDED) { lp_status = lp_status_t::INFEASIBLE; } if (status == dual::status_t::TIME_LIMIT) { lp_status = lp_status_t::TIME_LIMIT; } + if (status == dual::status_t::WORK_LIMIT) { lp_status = lp_status_t::WORK_LIMIT; } if (status == dual::status_t::ITERATION_LIMIT) { lp_status = lp_status_t::ITERATION_LIMIT; } if (status == dual::status_t::CONCURRENT_LIMIT) { lp_status = lp_status_t::CONCURRENT_LIMIT; } if (status == dual::status_t::NUMERICAL) { lp_status = lp_status_t::NUMERICAL_ISSUES; } diff --git a/cpp/src/dual_simplex/solve.hpp b/cpp/src/dual_simplex/solve.hpp index e96229784..ecfba6f50 100644 --- a/cpp/src/dual_simplex/solve.hpp +++ b/cpp/src/dual_simplex/solve.hpp @@ -27,7 +27,8 @@ enum class lp_status_t { NUMERICAL_ISSUES = 5, CUTOFF = 6, CONCURRENT_LIMIT = 7, - UNSET = 8 + UNSET = 8, + WORK_LIMIT = 9 }; template diff --git a/cpp/src/math_optimization/solver_settings.cu b/cpp/src/math_optimization/solver_settings.cu index 12d38e720..d8d18d6c4 100644 --- a/cpp/src/math_optimization/solver_settings.cu +++ b/cpp/src/math_optimization/solver_settings.cu @@ -86,7 +86,8 @@ solver_settings_t::solver_settings_t() : pdlp_settings(), mip_settings {CUOPT_FOLDING, &pdlp_settings.folding, -1, 1, -1}, {CUOPT_DUALIZE, &pdlp_settings.dualize, -1, 1, -1}, {CUOPT_ORDERING, &pdlp_settings.ordering, -1, 1, -1}, - {CUOPT_BARRIER_DUAL_INITIAL_POINT, &pdlp_settings.barrier_dual_initial_point, -1, 1, -1} + {CUOPT_BARRIER_DUAL_INITIAL_POINT, &pdlp_settings.barrier_dual_initial_point, -1, 1, -1}, + {CUOPT_MIP_DETERMINISTIC, &mip_settings.determinism_mode, CUOPT_MODE_OPPORTUNISTIC, CUOPT_MODE_DETERMINISTIC, CUOPT_MODE_OPPORTUNISTIC} }; // Bool parameters @@ -105,8 +106,7 @@ solver_settings_t::solver_settings_t() : pdlp_settings(), mip_settings {CUOPT_CUDSS_DETERMINISTIC, &pdlp_settings.cudss_deterministic, false}, {CUOPT_PRESOLVE, &pdlp_settings.presolve, false}, {CUOPT_PRESOLVE, &mip_settings.presolve, true}, - {CUOPT_DUAL_POSTSOLVE, &pdlp_settings.dual_postsolve, true}, - {CUOPT_MIP_DETERMINISTIC, &mip_settings.deterministic, false} + {CUOPT_DUAL_POSTSOLVE, &pdlp_settings.dual_postsolve, true} }; // String parameters string_parameters = { diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 96f93d267..e562f1e3c 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -104,7 +104,8 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_t::run_presolve(f_t time_limit) CUOPT_LOG_INFO("Running presolve!"); work_limit_timer_t presolve_timer(context.gpu_heur_loop, time_limit); auto term_crit = ls.constraint_prop.bounds_update.solve(*problem_ptr); - presolve_timer.record_work(0); if (ls.constraint_prop.bounds_update.infeas_constraints_count > 0) { stats.presolve_time = timer.elapsed_time(); return false; @@ -283,7 +283,9 @@ void diversity_manager_t::run_fj_alone(solution_t& solution) ls.fj.settings.iteration_limit = iter_dist(rng); ls.fj.settings.time_limit = std::numeric_limits::infinity(); - if (context.settings.deterministic) { ls.fj.settings.iteration_limit = iter_dist(rng); } + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + ls.fj.settings.iteration_limit = iter_dist(rng); + } CUOPT_LOG_INFO( "FJ benchmark run %d/%d: iteration_limit=%d", run + 1, 1000, ls.fj.settings.iteration_limit); @@ -349,19 +351,11 @@ solution_t diversity_manager_t::run_solver() add_user_given_solutions(initial_sol_vector); // Run CPUFJ early to find quick initial solutions ls_cpufj_raii_guard_t ls_cpufj_raii_guard(ls); // RAII to stop cpufj threads on solve stop - if (!context.settings.deterministic) { -#if 0 + + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { ls.start_cpufj_scratch_threads(population); - // 30'000 iters - ls.scratch_cpu_fj[0].wait_for_cpu_solver(); - // std::this_thread::sleep_for(std::chrono::seconds(30)); - ls.stop_cpufj_scratch_threads(); - exit(0); -#endif } - if (!context.settings.deterministic) { ls.start_cpufj_scratch_threads(population); } - // before probing cache or LP, run FJ to generate initial primal feasible solution const f_t time_ratio_of_probing_cache = diversity_config.time_ratio_of_probing_cache; const f_t max_time_on_probing = diversity_config.max_time_on_probing; @@ -456,7 +450,9 @@ solution_t diversity_manager_t::run_solver() lp_rounded_sol.round_nearest(); lp_rounded_sol.compute_feasibility(); population.add_solution(std::move(lp_rounded_sol)); - if (!context.settings.deterministic) { ls.start_cpufj_lptopt_scratch_threads(population); } + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { + ls.start_cpufj_lptopt_scratch_threads(population); + } } population.add_solutions_from_vec(std::move(initial_sol_vector)); @@ -474,7 +470,7 @@ solution_t diversity_manager_t::run_solver() run_fj_alone(sol); return sol; } - if (!context.settings.deterministic) { rins.enable(); } + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { rins.enable(); } generate_solution(timer.remaining_time(), false); printf("=======================================================\n"); diff --git a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh index d84e24254..f7451edfc 100644 --- a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh @@ -85,7 +85,7 @@ class fp_recombiner_t : public recombiner_t { CUOPT_LOG_DEBUG("FP rec: running LP with infeasibility detection"); relaxed_lp_settings_t lp_settings; lp_settings.time_limit = fp_recombiner_config_t::infeasibility_detection_time_limit; - if (this->context.settings.deterministic) { + if (this->context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { lp_settings.time_limit = std::numeric_limits::max(); // TODO should be global time limit lp_settings.work_limit = fp_recombiner_config_t::infeasibility_detection_time_limit; diff --git a/cpp/src/mip/diversity/recombiners/recombiner.cuh b/cpp/src/mip/diversity/recombiners/recombiner.cuh index 2454f545d..925d9a7c2 100644 --- a/cpp/src/mip/diversity/recombiners/recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/recombiner.cuh @@ -223,7 +223,9 @@ class recombiner_t { enabled_recombiners.erase(recombiner_enum_t::SUB_MIP); } // submip not supported in deterministic mode yet - if (context.settings.deterministic) { enabled_recombiners.erase(recombiner_enum_t::SUB_MIP); } + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + enabled_recombiners.erase(recombiner_enum_t::SUB_MIP); + } recombiner_t::enabled_recombiners = std::vector(enabled_recombiners.begin(), enabled_recombiners.end()); } diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index b03739068..927eb1575 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -1317,7 +1317,9 @@ i_t fj_t::solve(solution_t& solution) // settings.load_balancing_mode = fj_load_balancing_mode_t::ALWAYS_OFF; - if (context.settings.deterministic) { settings.work_limit = settings.time_limit; } + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + settings.work_limit = settings.time_limit; + } // if work_limit is set: compute an estimate of the number of iterations required if (settings.work_limit != std::numeric_limits::infinity()) { std::map features_map = get_feature_vector(0); @@ -1423,7 +1425,8 @@ i_t fj_t::solve(solution_t& solution) "FJ early exit at %d iterations (limit: %d)", iterations, settings.iteration_limit); // Compute the work unit corresponding to the number of iterations elapsed // by incrementally guessing work units until the model predicts >= actual iterations - if (context.settings.deterministic && iterations > 0) { + // TODO: awfully ugly, change + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC && iterations > 0) { double guessed_work = 0.0; const double work_increment = 0.1; const double max_work = settings.work_limit * 2.0; // Safety limit @@ -1449,6 +1452,7 @@ i_t fj_t::solve(solution_t& solution) } } + CUOPT_LOG_DEBUG("FJ: recording work %fwu for %d iterations", work_to_record, iterations); timer.record_work(work_to_record); CUOPT_LOG_DEBUG("FJ sol hash %x", solution.get_hash()); diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 7c8fb0433..0332b41e4 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -897,10 +897,14 @@ DI void update_changed_constraints(typename fj_t::climber_data_t::view if (blockIdx.x == 0) { if (threadIdx.x == 0) { // sort changed constraints to guarantee determinism - // TODO: horribly slow as it is - thrust::sort(thrust::seq, - fj.constraints_changed.begin(), - fj.constraints_changed.begin() + *fj.constraints_changed_count); + // TODO: horribly slow as it is... block-parallelize at least? but not trivial for arbitrary + // sizes w/ CUB + // TODO(2): tsk.. ... just bucket sort... + if (fj.settings->work_limit != std::numeric_limits::infinity()) { + thrust::sort(thrust::seq, + fj.constraints_changed.begin(), + fj.constraints_changed.begin() + *fj.constraints_changed_count); + } for (i_t i = 0; i < *fj.constraints_changed_count; ++i) { i_t idx = fj.constraints_changed[i]; diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index 205292f7f..7e981a865 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -231,7 +231,7 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_t::infinity(); lp_settings.work_limit = time_limit; } @@ -327,7 +327,9 @@ bool feasibility_pump_t::test_fj_feasible(solution_t& soluti fj.settings.feasibility_run = true; fj.settings.n_of_minimums_for_exit = 5000; fj.settings.time_limit = std::min(time_limit, timer.remaining_time()); - if (context.settings.deterministic) { fj.settings.work_limit = fj.settings.time_limit; } + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + fj.settings.work_limit = fj.settings.time_limit; + } cuopt_func_call(solution.test_variable_bounds(true)); is_feasible = fj.solve(solution); cuopt_func_call(solution.test_variable_bounds(true)); @@ -341,7 +343,7 @@ bool feasibility_pump_t::test_fj_feasible(solution_t& soluti } else { CUOPT_LOG_DEBUG("20%% FJ run found feasible!"); } - timer.record_work(fj.settings.time_limit); + timer.record_work(fj.settings.work_limit); CUOPT_LOG_DEBUG("20%% FJ run finished, elapsed %fs remaining %fwu", fj.settings.time_limit, timer.remaining_time()); diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu index 578c328c5..766cb8ee2 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu @@ -189,7 +189,7 @@ bool line_segment_search_t::search_line_segment( best_feasible_cost, curr_cost); } - if (!context.settings.deterministic) { + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { if (timer.check_time_limit()) { break; } } i_t number_of_integer_var_diff = compute_number_of_integer_var_diff( @@ -230,7 +230,7 @@ bool line_segment_search_t::search_line_segment( curr_cost); } // cuopt_assert(context.gpu_heur_loop, ""); - if (!context.settings.deterministic) { + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { if (timer.check_time_limit()) { break; } } } diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 28be6dd4f..115b9b775 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -123,7 +123,7 @@ void local_search_t::start_cpufj_lptopt_scratch_threads( scratch_cpu_fj_on_lp_opt.fj_cpu = fj.create_cpu_climber(solution_lp, default_weights, default_weights, 0., cpu_fj_settings, true); scratch_cpu_fj_on_lp_opt.fj_cpu->log_prefix = "******* scratch on LP optimal: "; - if (!context.settings.deterministic) { + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { scratch_cpu_fj_on_lp_opt.fj_cpu->improvement_callback = [this, &population](f_t obj, const std::vector& h_vec) { population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); @@ -167,7 +167,7 @@ bool local_search_t::do_fj_solve(solution_t& solution, // for now: always assign the CPUFJs to perform 1000 iters per s fj_settings_t cpu_fj_settings{}; - if (!context.settings.deterministic) { + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { cpu_fj_settings.iteration_limit = std::numeric_limits::max(); } else { // TODO: CHANGE @@ -192,7 +192,7 @@ bool local_search_t::do_fj_solve(solution_t& solution, in_fj.solve(solution); // Stop CPU solver - if (!context.settings.deterministic) { + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { for (auto& cpu_fj : ls_cpu_fj) { cpu_fj.stop_cpu_solver(); } @@ -264,14 +264,11 @@ void local_search_t::generate_fast_solution(solution_t& solu fj.settings.update_weights = true; fj.settings.feasibility_run = true; fj.settings.time_limit = std::min(30., timer.remaining_time()); - // CHANGE - fj.settings.time_limit = std::numeric_limits::infinity(); while (!timer.check_time_limit()) { work_limit_timer_t constr_prop_timer = work_limit_timer_t(context.gpu_heur_loop, std::min(timer.remaining_time(), 2.)); // do constraint prop on lp optimal solution constraint_prop.apply_round(solution, 1., constr_prop_timer); - constr_prop_timer.record_work(0); if (solution.compute_feasibility()) { return; } if (timer.check_time_limit()) { return; }; f_t time_limit = std::min(3., timer.remaining_time()); @@ -423,11 +420,12 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu work_limit_timer_t(context.gpu_heur_loop, std::min(timer.remaining_time(), 10.)); bool is_feasible = constraint_prop.apply_round(solution, lp_run_time_after_feasible, bounds_prop_timer); - bounds_prop_timer.record_work(0); if (!is_feasible) { f_t lp_run_time = 2.; // CHANGE - if (context.settings.deterministic) { lp_run_time = std::numeric_limits::infinity(); } + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + lp_run_time = std::numeric_limits::infinity(); + } relaxed_lp_settings_t lp_settings; lp_settings.time_limit = std::min(lp_run_time, timer.remaining_time()); lp_settings.work_limit = lp_settings.time_limit; @@ -444,7 +442,7 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu fj.settings.update_weights = true; fj.settings.feasibility_run = false; f_t fj_time_limit = 30.; - if (context.settings.deterministic) { + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { fj_time_limit = std::numeric_limits::infinity(); fj.settings.iteration_limit = 100'000; } diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index dd9221dbf..77ceb3775 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -775,7 +775,7 @@ bool constraint_prop_t::run_repair_procedure(problem_t& prob f_t repair_start_time = timer.remaining_time(); i_t n_of_repairs_needed_for_feasible = 0; i_t iter_limit = std::numeric_limits::max(); - if (this->context.settings.deterministic) { + if (this->context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { timer = work_limit_timer_t(context.gpu_heur_loop, std::numeric_limits::infinity()); iter_limit = 100; } @@ -865,7 +865,7 @@ bool constraint_prop_t::find_integer( std::mt19937 rng(seed); // CHANGE - if (this->context.settings.deterministic) { + if (this->context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { timer = work_limit_timer_t(context.gpu_heur_loop, std::numeric_limits::infinity()); } @@ -1133,7 +1133,7 @@ bool constraint_prop_t::apply_round( // === CONSTRAINT PROP PREDICTOR FEATURES - END === max_timer = work_limit_timer_t{context.gpu_heur_loop, max_time_for_bounds_prop}; - if (this->context.settings.deterministic) { + if (this->context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { max_timer = work_limit_timer_t(context.gpu_heur_loop, std::numeric_limits::infinity()); } if (check_brute_force_rounding(sol)) { diff --git a/cpp/src/mip/presolve/bounds_presolve.cu b/cpp/src/mip/presolve/bounds_presolve.cu index 532ac099c..a820fb080 100644 --- a/cpp/src/mip/presolve/bounds_presolve.cu +++ b/cpp/src/mip/presolve/bounds_presolve.cu @@ -172,7 +172,7 @@ termination_criterion_t bound_presolve_t::bound_update_loop(problem_t< termination_criterion_t criteria = termination_criterion_t::ITERATION_LIMIT; // CHANGE - if (context.settings.deterministic) { + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { timer = timer_t(std::numeric_limits::infinity()); settings.iteration_limit = std::min(settings.iteration_limit, 50); } diff --git a/cpp/src/mip/presolve/utils.cuh b/cpp/src/mip/presolve/utils.cuh index 4870b3180..2d354504d 100644 --- a/cpp/src/mip/presolve/utils.cuh +++ b/cpp/src/mip/presolve/utils.cuh @@ -12,6 +12,7 @@ namespace cuopt::linear_programming::detail { enum class termination_criterion_t { TIME_LIMIT, ITERATION_LIMIT, + WORK_LIMIT, CONVERGENCE, INFEASIBLE, NO_UPDATE diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index d9c6f45b6..9a0492e66 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -79,9 +79,13 @@ optimization_problem_solution_t get_relaxed_lp_solution( pdlp_settings.iteration_limit = settings.iteration_limit; // CHANGE - i_t work_limit = pdlp_settings.time_limit; // settings.work_limit - pdlp_settings.time_limit = std::numeric_limits::infinity(); - if (work_limit != std::numeric_limits::infinity()) { + i_t work_limit = settings.work_limit; + bool determinism_mode = work_limit != std::numeric_limits::infinity(); + pdlp_settings.concurrent_halt = settings.concurrent_halt; + pdlp_settings.per_constraint_residual = settings.per_constraint_residual; + pdlp_settings.first_primal_feasible = settings.return_first_feasible; + pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; + if (determinism_mode) { // try to estimate the iteration count based on the requested work limit int estim_iters = 100; do { @@ -93,16 +97,8 @@ optimization_problem_solution_t get_relaxed_lp_solution( estim_iters += 100; } while (true); CUOPT_LOG_DEBUG("estimated iterations %d for work limit %f", estim_iters, settings.work_limit); - pdlp_settings.iteration_limit = estim_iters; - pdlp_settings.time_limit = std::numeric_limits::infinity(); - } - pdlp_settings.concurrent_halt = settings.concurrent_halt; - pdlp_settings.per_constraint_residual = settings.per_constraint_residual; - pdlp_settings.first_primal_feasible = settings.return_first_feasible; - pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; - // Stable3 has a simpler and more predictable codepath - // if (work_limit != std::numeric_limits::infinity()) - { + pdlp_settings.iteration_limit = estim_iters; + pdlp_settings.time_limit = std::numeric_limits::infinity(); pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable3; } set_pdlp_solver_mode(pdlp_settings); diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 55d71306e..f3fd67a52 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -155,8 +155,7 @@ solution_t mip_solver_t::run_solver() branch_and_bound_solution_helper_t solution_helper(&dm, branch_and_bound_settings); dual_simplex::mip_solution_t branch_and_bound_solution(1); - // for now, disable B&B in deterministic mode - bool run_bb = !context.settings.deterministic && !context.settings.heuristics_only; + bool run_bb = !context.settings.heuristics_only; if (run_bb) { // Convert the presolved problem to dual_simplex::user_problem_t op_problem_.get_host_user_problem(branch_and_bound_problem); @@ -169,6 +168,12 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.deterministic = + context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC; + branch_and_bound_settings.work_limit = + context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC + ? context.settings.time_limit + : std::numeric_limits::infinity(); if (context.settings.num_cpu_threads < 0) { branch_and_bound_settings.num_threads = omp_get_max_threads() - 1; @@ -177,13 +182,11 @@ solution_t mip_solver_t::run_solver() } CUOPT_LOG_INFO("Using %d CPU threads for B&B", branch_and_bound_settings.num_threads); - CUOPT_LOG_ERROR("AAAAAA\n"); - i_t num_threads = branch_and_bound_settings.num_threads; i_t num_bfs_threads = std::max(1, num_threads / 4); i_t num_diving_threads = num_threads - num_bfs_threads; // deterministic mode: no diving for now - if (context.settings.deterministic) { + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { num_threads = 1; num_bfs_threads = 1; num_diving_threads = 0; @@ -192,26 +195,28 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.num_diving_threads = num_diving_threads; // Set the branch and bound -> primal heuristics callback - branch_and_bound_settings.solution_callback = - std::bind(&branch_and_bound_solution_helper_t::solution_callback, - &solution_helper, - std::placeholders::_1, - std::placeholders::_2); - branch_and_bound_settings.heuristic_preemption_callback = std::bind( - &branch_and_bound_solution_helper_t::preempt_heuristic_solver, &solution_helper); - - branch_and_bound_settings.set_simplex_solution_callback = - std::bind(&branch_and_bound_solution_helper_t::set_simplex_solution, - &solution_helper, - std::placeholders::_1, - std::placeholders::_2, - std::placeholders::_3); - - branch_and_bound_settings.node_processed_callback = - std::bind(&branch_and_bound_solution_helper_t::node_processed_callback, - &solution_helper, - std::placeholders::_1, - std::placeholders::_2); + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { + branch_and_bound_settings.solution_callback = + std::bind(&branch_and_bound_solution_helper_t::solution_callback, + &solution_helper, + std::placeholders::_1, + std::placeholders::_2); + branch_and_bound_settings.heuristic_preemption_callback = std::bind( + &branch_and_bound_solution_helper_t::preempt_heuristic_solver, &solution_helper); + + branch_and_bound_settings.set_simplex_solution_callback = + std::bind(&branch_and_bound_solution_helper_t::set_simplex_solution, + &solution_helper, + std::placeholders::_1, + std::placeholders::_2, + std::placeholders::_3); + + branch_and_bound_settings.node_processed_callback = + std::bind(&branch_and_bound_solution_helper_t::node_processed_callback, + &solution_helper, + std::placeholders::_1, + std::placeholders::_2); + } // Create the branch and bound object branch_and_bound = std::make_unique>( diff --git a/cpp/src/mip/solver_context.cuh b/cpp/src/mip/solver_context.cuh index b4df2578e..4cd990519 100644 --- a/cpp/src/mip/solver_context.cuh +++ b/cpp/src/mip/solver_context.cuh @@ -28,9 +28,9 @@ class branch_and_bound_t; namespace cuopt::linear_programming::detail { struct mip_solver_work_unit_predictors_t { - work_unit_predictor_t fj_predictor{}; - work_unit_predictor_t cpufj_predictor{}; - work_unit_predictor_t pdlp_predictor{}; + work_unit_predictor_t fj_predictor{}; + work_unit_predictor_t cpufj_predictor{}; + work_unit_predictor_t pdlp_predictor{}; }; // Aggregate structure containing the global context of the solving process for convenience: @@ -46,7 +46,7 @@ struct mip_solver_context_t { cuopt_assert(problem_ptr != nullptr, "problem_ptr is nullptr"); stats.solution_bound = problem_ptr->maximize ? std::numeric_limits::infinity() : -std::numeric_limits::infinity(); - gpu_heur_loop.deterministic = settings.deterministic; + gpu_heur_loop.deterministic = settings.determinism_mode == CUOPT_MODE_DETERMINISTIC; } raft::handle_t const* const handle_ptr; @@ -60,6 +60,8 @@ struct mip_solver_context_t { // Work limit context for tracking work units in deterministic mode (shared across all timers in // GPU heuristic loop) cuopt::work_limit_context_t gpu_heur_loop; + cuopt::work_limit_context_t cpu_branch_and_bound; // TODO: should be each per worker thread. + // Works for now since threads_b&b = 1 }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/utilities/models/dualsimplex_predictor/main.cpp b/cpp/src/utilities/models/dualsimplex_predictor/main.cpp index 571527421..211e2d097 100644 --- a/cpp/src/utilities/models/dualsimplex_predictor/main.cpp +++ b/cpp/src/utilities/models/dualsimplex_predictor/main.cpp @@ -40,14 +40,14 @@ static const int32_t num_class[] = { 1, }; -int32_t dualsimplex_predictor::get_num_target(void) { return std::exp(N_TARGET); } +int32_t dualsimplex_predictor::get_num_target(void) { return N_TARGET; } void dualsimplex_predictor::get_num_class(int32_t* out) { for (int i = 0; i < N_TARGET; ++i) { out[i] = num_class[i]; } } -int32_t dualsimplex_predictor::get_num_feature(void) { return std::exp(18); } +int32_t dualsimplex_predictor::get_num_feature(void) { return 18; } const char* dualsimplex_predictor::get_threshold_type(void) { return "float32"; } const char* dualsimplex_predictor::get_leaf_output_type(void) { return "float32"; } diff --git a/cpp/src/utilities/models/pdlp_predictor/main.cpp b/cpp/src/utilities/models/pdlp_predictor/main.cpp index c82153c91..6ae348640 100644 --- a/cpp/src/utilities/models/pdlp_predictor/main.cpp +++ b/cpp/src/utilities/models/pdlp_predictor/main.cpp @@ -30,14 +30,14 @@ static const int32_t num_class[] = { 1, }; -int32_t pdlp_predictor::get_num_target(void) { return std::exp(N_TARGET); } +int32_t pdlp_predictor::get_num_target(void) { return N_TARGET; } void pdlp_predictor::get_num_class(int32_t* out) { for (int i = 0; i < N_TARGET; ++i) { out[i] = num_class[i]; } } -int32_t pdlp_predictor::get_num_feature(void) { return std::exp(8); } +int32_t pdlp_predictor::get_num_feature(void) { return 8; } const char* pdlp_predictor::get_threshold_type(void) { return "float32"; } const char* pdlp_predictor::get_leaf_output_type(void) { return "float32"; } diff --git a/cpp/src/utilities/work_limit_timer.hpp b/cpp/src/utilities/work_limit_timer.hpp index 2cd644786..c259abbc2 100644 --- a/cpp/src/utilities/work_limit_timer.hpp +++ b/cpp/src/utilities/work_limit_timer.hpp @@ -22,8 +22,8 @@ namespace cuopt { -// Context for tracking global work units across multiple timers in deterministic mode -// This allows different subsystems to have independent work unit tracking +// Context for tracking global work units across multiple timers in a single thread in deterministic +// mode struct work_limit_context_t { double global_work_units_elapsed{0.0}; bool deterministic{false}; diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp index 71ed55c94..0aebf953d 100644 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -47,8 +47,8 @@ static inline uint32_t compute_hash(std::vector h_contents) return hash; } -template -float work_unit_predictor_t::predict_scalar( +template +float work_unit_predictor_t::predict_scalar( const std::map& features) const { raft::common::nvtx::range range("work_unit_predictor_t::predict_scalar"); @@ -57,13 +57,12 @@ float work_unit_predictor_t::predict_scalar( for (int i = 0; i < model_t::NUM_FEATURES; ++i) { if (features.find(std::string(model_t::feature_names[i])) == features.end()) { data[i].missing = -1; - printf("Feature %s: missing\n", model_t::feature_names[i]); + CUOPT_LOG_WARN("Feature %s: missing\n", model_t::feature_names[i]); } else { data[i].fvalue = features.at(std::string(model_t::feature_names[i])); - // printf("Feature %s: %f\n", model_t::feature_names[i], data[i].fvalue); } } - // Compute a hash key for the relevant inputs + std::vector cache_vec; cache_vec.reserve(model_t::NUM_FEATURES); for (int i = 0; i < model_t::NUM_FEATURES; ++i) { @@ -75,20 +74,23 @@ float work_unit_predictor_t::predict_scalar( auto cached_it = prediction_cache.find(key); if (cached_it != prediction_cache.end()) { return cached_it->second; } - // run predictor double result = 0.0; auto start = std::chrono::high_resolution_clock::now(); model_t::predict(data, 0, &result); auto end = std::chrono::high_resolution_clock::now(); std::chrono::duration elapsed = end - start; - CUOPT_LOG_DEBUG("Prediction time: %f ms", elapsed.count()); - CUOPT_LOG_DEBUG("Result: %f", result); - return result; + if (debug) CUOPT_LOG_DEBUG("Prediction time: %f ms", elapsed.count()); + + float scaled_result = scaler_.scale_work_units(result); + prediction_cache[key] = scaled_result; + if (debug) CUOPT_LOG_DEBUG("Result: %f (scaled: %f)", result, scaled_result); + + return scaled_result; } -template class work_unit_predictor_t; -template class work_unit_predictor_t; -template class work_unit_predictor_t; -template class work_unit_predictor_t; +template class work_unit_predictor_t; +template class work_unit_predictor_t; +template class work_unit_predictor_t; +template class work_unit_predictor_t; } // namespace cuopt diff --git a/cpp/src/utilities/work_unit_predictor.hpp b/cpp/src/utilities/work_unit_predictor.hpp index 0a7c63e33..45bc6b97b 100644 --- a/cpp/src/utilities/work_unit_predictor.hpp +++ b/cpp/src/utilities/work_unit_predictor.hpp @@ -22,15 +22,42 @@ #include #include +#include + namespace cuopt { -template +// Temporary scaling classes until I figure out better ways to do this +// to account for performance differences between the regression learning machine and the user +// machine. (e.g. integrate memory latency/bandwidth, cache topology, user-provided tuning...) +struct cpu_work_unit_scaler_t { + cpu_work_unit_scaler_t() + { + constexpr double baseline_max_clock = 3800.0; + double max_clock = get_cpu_max_clock_mhz(); + scaling_factor_ = baseline_max_clock / max_clock; + } + + double scale_work_units(double work_units) const { return work_units * scaling_factor_; } + + private: + double scaling_factor_; +}; + +struct gpu_work_unit_scaler_t { + double scale_work_units(double work_units) const { return work_units; } +}; + +template class work_unit_predictor_t { public: float predict_scalar(const std::map& features) const; + public: + bool debug{false}; + private: mutable std::unordered_map prediction_cache; + scaler_t scaler_; }; } // namespace cuopt diff --git a/cpp/tests/mip/diversity_test.cu b/cpp/tests/mip/diversity_test.cu index 1f96348a2..6b74581fc 100644 --- a/cpp/tests/mip/diversity_test.cu +++ b/cpp/tests/mip/diversity_test.cu @@ -100,12 +100,12 @@ static uint32_t test_full_run_determinism(std::string path, nullptr, true); - auto settings = mip_solver_settings_t{}; - settings.time_limit = 3000.; - settings.work_limit = 10; // about 10 seconds of runtime - settings.deterministic = true; - settings.heuristics_only = true; - auto timer = cuopt::timer_t(3000); + auto settings = mip_solver_settings_t{}; + settings.time_limit = 3000.; + settings.work_limit = 10; // about 10 seconds of runtime + settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; + settings.heuristics_only = true; + auto timer = cuopt::timer_t(3000); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); @@ -156,11 +156,11 @@ static uint32_t test_initial_solution_determinism(std::string path, nullptr, true); - auto settings = mip_solver_settings_t{}; - settings.time_limit = 3000.; - settings.deterministic = true; - settings.heuristics_only = true; - auto timer = cuopt::timer_t(3000); + auto settings = mip_solver_settings_t{}; + settings.time_limit = 3000.; + settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; + settings.heuristics_only = true; + auto timer = cuopt::timer_t(3000); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); @@ -212,11 +212,11 @@ static uint32_t test_recombiners_determinism(std::string path, nullptr, true); - auto settings = mip_solver_settings_t{}; - settings.time_limit = 3000.; - settings.deterministic = true; - settings.heuristics_only = true; - auto timer = cuopt::timer_t(3000); + auto settings = mip_solver_settings_t{}; + settings.time_limit = 3000.; + settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; + settings.heuristics_only = true; + auto timer = cuopt::timer_t(3000); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); diff --git a/cpp/tests/mip/feasibility_jump_tests.cu b/cpp/tests/mip/feasibility_jump_tests.cu index 8c14494f4..19390e568 100644 --- a/cpp/tests/mip/feasibility_jump_tests.cu +++ b/cpp/tests/mip/feasibility_jump_tests.cu @@ -261,18 +261,19 @@ TEST(mip_solve, feasibility_jump_determinism) int seed = std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); - for (const auto& instance : {"thor50dday.mps", - "gen-ip054.mps", - "50v-10.mps", - "seymour1.mps", - "rmatr200-p5.mps", - "tr12-30.mps", - "sct2.mps", - "uccase9.mps"}) { + for (const auto& [instance, iter_limit] : {std::make_pair("thor50dday.mps", 1000), + std::make_pair("gen-ip054.mps", 1000), + std::make_pair("50v-10.mps", 1000), + std::make_pair("seymour1.mps", 1000), + std::make_pair("rmatr200-p5.mps", 1000), + std::make_pair("tr12-30.mps", 1000), + std::make_pair("sct2.mps", 1000), + std::make_pair("uccase9.mps", 1000), + std::make_pair("supportcase42.mps", 25000)}) { for (int i = 0; i < 10; i++) { // while (true) { cuopt::seed_generator::set_seed(seed); - run_fj_check_determinism(instance, 1000); + run_fj_check_determinism(instance, iter_limit); } } } diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu index 0393c2def..f616bda9a 100644 --- a/cpp/tests/mip/local_search_test.cu +++ b/cpp/tests/mip/local_search_test.cu @@ -110,10 +110,10 @@ static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) nullptr, true); - auto settings = mip_solver_settings_t{}; - settings.time_limit = 30.; - settings.deterministic = true; - auto timer = cuopt::timer_t(30); + auto settings = mip_solver_settings_t{}; + settings.time_limit = 30.; + settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; + auto timer = cuopt::timer_t(30); detail::mip_solver_t solver(problem, settings, scaling, timer); problem.tolerances = settings.get_tolerances(); diff --git a/datasets/mip/download_miplib_test_dataset.sh b/datasets/mip/download_miplib_test_dataset.sh index d0ffe45d2..99c9661e5 100755 --- a/datasets/mip/download_miplib_test_dataset.sh +++ b/datasets/mip/download_miplib_test_dataset.sh @@ -24,6 +24,7 @@ INSTANCES=( "enlight_hard" "enlight11" "supportcase22" + "supportcase42" ) BASE_URL="https://miplib.zib.de/WebData/instances" From 1a4ac76231e92518b167877450bef6d8dc7485ee Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 1 Dec 2025 15:57:45 +0000 Subject: [PATCH 096/366] add work unit scheduler for parallel threads --- cpp/src/CMakeLists.txt | 1 + cpp/src/dual_simplex/branch_and_bound.hpp | 4 +- .../recombiners/line_segment_recombiner.cuh | 2 +- cpp/src/mip/solver.cu | 14 +- cpp/src/mip/solver_context.cuh | 10 +- cpp/src/utilities/work_limit_timer.hpp | 16 +- cpp/src/utilities/work_unit_scheduler.cpp | 183 ++++++++++++++++++ cpp/src/utilities/work_unit_scheduler.hpp | 80 ++++++++ cpp/tests/mip/diversity_test.cu | 6 +- cpp/tests/mip/local_search_test.cu | 2 +- cpp/tests/mip/presolve_test.cu | 2 +- 11 files changed, 302 insertions(+), 18 deletions(-) create mode 100644 cpp/src/utilities/work_unit_scheduler.cpp create mode 100644 cpp/src/utilities/work_unit_scheduler.hpp diff --git a/cpp/src/CMakeLists.txt b/cpp/src/CMakeLists.txt index 3267f3e80..c35c6d2b5 100644 --- a/cpp/src/CMakeLists.txt +++ b/cpp/src/CMakeLists.txt @@ -8,6 +8,7 @@ set(UTIL_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/utilities/seed_generator.cu ${CMAKE_CURRENT_SOURCE_DIR}/utilities/version_info.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/timestamp_utils.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/work_unit_predictor.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/work_unit_scheduler.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/main.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/quantize.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/cpufj_predictor/main.cpp diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 3375ddf29..452ba9c43 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -145,13 +145,15 @@ class branch_and_bound_t { // The main entry routine. Returns the solver status and populates solution with the incumbent. mip_status_t solve(mip_solution_t& solution); + work_limit_context_t& get_work_unit_context() { return work_unit_context_; } + private: const user_problem_t& original_problem_; const simplex_solver_settings_t settings_; // Work unit contexts for each worker // TODO: only one for now, sequential B&B for now - work_limit_context_t work_unit_context_; + work_limit_context_t work_unit_context_{"B&B"}; // Initial guess. std::vector guess_; diff --git a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh index f66654483..afa77092d 100644 --- a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh @@ -19,7 +19,7 @@ namespace cuopt::linear_programming::detail { template class line_segment_recombiner_t : public recombiner_t { public: - line_segment_recombiner_t(mip_solver_context_t context, + line_segment_recombiner_t(mip_solver_context_t& context, i_t n_vars, line_segment_search_t& line_segment_search_, const raft::handle_t* handle_ptr) diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index f3fd67a52..383965d52 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -147,6 +147,8 @@ solution_t mip_solver_t::run_solver() return sol; } + context.work_unit_scheduler_.register_context(context.gpu_heur_loop); + namespace dual_simplex = cuopt::linear_programming::dual_simplex; std::future branch_and_bound_status_future; dual_simplex::user_problem_t branch_and_bound_problem(context.problem_ptr->handle_ptr); @@ -224,10 +226,14 @@ solution_t mip_solver_t::run_solver() context.branch_and_bound_ptr = branch_and_bound.get(); // Set the primal heuristics -> branch and bound callback - context.problem_ptr->branch_and_bound_callback = - std::bind(&dual_simplex::branch_and_bound_t::set_new_solution, - branch_and_bound.get(), - std::placeholders::_1); + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { + context.problem_ptr->branch_and_bound_callback = + std::bind(&dual_simplex::branch_and_bound_t::set_new_solution, + branch_and_bound.get(), + std::placeholders::_1); + } + + context.work_unit_scheduler_.register_context(branch_and_bound->get_work_unit_context()); // Fork a thread for branch and bound // std::async and std::future allow us to get the return value of bb::solve() diff --git a/cpp/src/mip/solver_context.cuh b/cpp/src/mip/solver_context.cuh index 4cd990519..52202e6c1 100644 --- a/cpp/src/mip/solver_context.cuh +++ b/cpp/src/mip/solver_context.cuh @@ -49,6 +49,9 @@ struct mip_solver_context_t { gpu_heur_loop.deterministic = settings.determinism_mode == CUOPT_MODE_DETERMINISTIC; } + mip_solver_context_t(const mip_solver_context_t&) = delete; + mip_solver_context_t& operator=(const mip_solver_context_t&) = delete; + raft::handle_t const* const handle_ptr; problem_t* problem_ptr; dual_simplex::branch_and_bound_t* branch_and_bound_ptr{nullptr}; @@ -59,9 +62,10 @@ struct mip_solver_context_t { mip_solver_work_unit_predictors_t work_unit_predictors; // Work limit context for tracking work units in deterministic mode (shared across all timers in // GPU heuristic loop) - cuopt::work_limit_context_t gpu_heur_loop; - cuopt::work_limit_context_t cpu_branch_and_bound; // TODO: should be each per worker thread. - // Works for now since threads_b&b = 1 + work_limit_context_t gpu_heur_loop{"GPUHeur"}; + + // synchronization every 5 seconds for deterministic mode + work_unit_scheduler_t work_unit_scheduler_{5.0}; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/utilities/work_limit_timer.hpp b/cpp/src/utilities/work_limit_timer.hpp index c259abbc2..d6e726dcb 100644 --- a/cpp/src/utilities/work_limit_timer.hpp +++ b/cpp/src/utilities/work_limit_timer.hpp @@ -18,19 +18,27 @@ #include #include + +#include + #include "timer.hpp" +#include "work_unit_scheduler.hpp" namespace cuopt { -// Context for tracking global work units across multiple timers in a single thread in deterministic -// mode struct work_limit_context_t { double global_work_units_elapsed{0.0}; bool deterministic{false}; + work_unit_scheduler_t* scheduler{nullptr}; + std::string name; + + work_limit_context_t(const std::string& name) : name(name) {} void record_work(double work) { - if (deterministic) global_work_units_elapsed += work; + if (!deterministic) return; + global_work_units_elapsed += work; + if (scheduler) { scheduler->on_work_recorded(*this, global_work_units_elapsed); } } }; @@ -95,7 +103,6 @@ class work_limit_timer_t { int line = __builtin_LINE()) { if (deterministic && work_context) { - work_context->global_work_units_elapsed += work_units; CUOPT_LOG_DEBUG("%s:%d: %s(): Recorded %f work units in %fs, total %f", file, line, @@ -103,6 +110,7 @@ class work_limit_timer_t { work_units, timer.elapsed_time(), work_context->global_work_units_elapsed); + work_context->record_work(work_units); } } diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp new file mode 100644 index 000000000..0fe5197d3 --- /dev/null +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -0,0 +1,183 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "work_unit_scheduler.hpp" + +#include "work_limit_timer.hpp" + +#include +#include +#include +#include + +#include + +namespace cuopt { + +work_unit_scheduler_t::work_unit_scheduler_t(double sync_interval) : sync_interval_(sync_interval) +{ +} + +void work_unit_scheduler_t::register_context(work_limit_context_t& ctx) +{ + std::lock_guard lock(mutex_); + contexts_.push_back(ctx); + callback_queues_[&ctx] = callback_queue_t{}; + last_sync_target_[&ctx] = 0.0; + ctx.scheduler = this; +} + +void work_unit_scheduler_t::deregister_context(work_limit_context_t& ctx) +{ + std::lock_guard lock(mutex_); + ctx.scheduler = nullptr; + contexts_.erase(std::remove_if(contexts_.begin(), + contexts_.end(), + [&ctx](const std::reference_wrapper& ref) { + return &ref.get() == &ctx; + }), + contexts_.end()); + callback_queues_.erase(&ctx); + last_sync_target_.erase(&ctx); + cv_.notify_all(); +} + +void work_unit_scheduler_t::on_work_recorded(work_limit_context_t& ctx, double total_work) +{ + if (verbose) { + double sync_target = current_sync_target(); + CUOPT_LOG_DEBUG("[%s] Work recorded: %f, sync_target: %f (gen %zu)", + ctx.name.c_str(), + total_work, + sync_target, + barrier_generation_); + } + + // Loop to handle large work increments that cross multiple sync points + while (total_work >= current_sync_target()) { + wait_at_sync_point(ctx, current_sync_target()); + } +} + +void work_unit_scheduler_t::queue_callback(work_limit_context_t& source, + work_limit_context_t& destination, + callback_t callback) +{ + std::lock_guard lock(mutex_); + double tag = source.global_work_units_elapsed; + auto it = callback_queues_.find(&destination); + if (it != callback_queues_.end()) { it->second.push({tag, std::move(callback)}); } +} + +double work_unit_scheduler_t::current_sync_target() const +{ + if (sync_interval_ <= 0) return std::numeric_limits::infinity(); + return (barrier_generation_ + 1) * sync_interval_; +} + +void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double sync_target) +{ + auto wait_start = std::chrono::high_resolution_clock::now(); + + std::unique_lock lock(mutex_); + + last_sync_target_[&ctx] = sync_target; + size_t my_generation = barrier_generation_; + contexts_at_barrier_++; + + if (verbose) { + CUOPT_LOG_DEBUG("[%s] Waiting at sync point %.2f (gen %zu, %zu/%zu contexts)", + ctx.name.c_str(), + sync_target, + my_generation, + contexts_at_barrier_, + contexts_.size()); + } + + if (contexts_at_barrier_ == contexts_.size()) { + current_sync_target_ = sync_target; + barrier_generation_++; + if (verbose) { + CUOPT_LOG_DEBUG("[%s] All contexts arrived, new generation %zu, notifying", + ctx.name.c_str(), + barrier_generation_); + } + cv_.notify_all(); + } else { + cv_.wait(lock, [&] { return barrier_generation_ != my_generation; }); + if (verbose) { CUOPT_LOG_DEBUG("[%s] Woke up from first wait", ctx.name.c_str()); } + } + + size_t my_exit_generation = exit_generation_; + + if (verbose) { CUOPT_LOG_DEBUG("[%s] Processing callbacks", ctx.name.c_str()); } + lock.unlock(); + process_callbacks_for_context(ctx, sync_target); + lock.lock(); + if (verbose) { CUOPT_LOG_DEBUG("[%s] Done processing callbacks", ctx.name.c_str()); } + + contexts_at_barrier_--; + if (contexts_at_barrier_ == 0) { + exit_generation_++; + if (verbose) { + CUOPT_LOG_DEBUG("[%s] All contexts finished callbacks at sync point %.2f (exit gen %zu)", + ctx.name.c_str(), + sync_target, + exit_generation_); + } + cv_.notify_all(); + } else { + if (verbose) { + CUOPT_LOG_DEBUG("[%s] Waiting for other contexts to finish callbacks (%zu remaining)", + ctx.name.c_str(), + contexts_at_barrier_); + } + cv_.wait(lock, [&] { return exit_generation_ != my_exit_generation; }); + if (verbose) { CUOPT_LOG_DEBUG("[%s] Woke up from second wait", ctx.name.c_str()); } + } + + if (verbose) { + auto wait_end = std::chrono::high_resolution_clock::now(); + double wait_ms = std::chrono::duration(wait_end - wait_start).count(); + CUOPT_LOG_DEBUG( + "[%s] Sync complete at %.2f, waited %.2f ms", ctx.name.c_str(), sync_target, wait_ms); + } +} + +void work_unit_scheduler_t::process_callbacks_for_context(work_limit_context_t& ctx, + double up_to_work_units) +{ + std::vector to_execute; + + { + std::lock_guard lock(mutex_); + auto it = callback_queues_.find(&ctx); + if (it == callback_queues_.end()) return; + + auto& queue = it->second; + while (!queue.empty() && queue.top().work_unit_tag <= up_to_work_units) { + to_execute.push_back(std::move(const_cast(queue.top()).callback)); + queue.pop(); + } + } + + for (auto& cb : to_execute) { + cb(); + } +} + +} // namespace cuopt diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp new file mode 100644 index 000000000..57f7d3181 --- /dev/null +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -0,0 +1,80 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include +#include +#include +#include +#include +#include + +namespace cuopt { + +struct work_limit_context_t; + +class work_unit_scheduler_t { + public: + using callback_t = std::function; + + explicit work_unit_scheduler_t(double sync_interval = 5.0); + + void set_sync_interval(double interval); + double get_sync_interval() const; + + void register_context(work_limit_context_t& ctx); + void deregister_context(work_limit_context_t& ctx); + void on_work_recorded(work_limit_context_t& ctx, double total_work); + void queue_callback(work_limit_context_t& source, + work_limit_context_t& destination, + callback_t callback); + + public: + bool verbose{false}; + double sync_interval_; + + private: + struct tagged_callback_t { + double work_unit_tag; + callback_t callback; + + bool operator>(const tagged_callback_t& other) const + { + return work_unit_tag > other.work_unit_tag; + } + }; + + using callback_queue_t = + std::priority_queue, std::greater<>>; + + double current_sync_target() const; + void wait_at_sync_point(work_limit_context_t& ctx, double sync_target); + void process_callbacks_for_context(work_limit_context_t& ctx, double up_to_work_units); + + std::vector> contexts_; + std::unordered_map callback_queues_; + std::unordered_map last_sync_target_; + + std::mutex mutex_; + std::condition_variable cv_; + size_t contexts_at_barrier_{0}; + double current_sync_target_{0}; + size_t barrier_generation_{0}; + size_t exit_generation_{0}; +}; + +} // namespace cuopt diff --git a/cpp/tests/mip/diversity_test.cu b/cpp/tests/mip/diversity_test.cu index 6b74581fc..ddbb3286a 100644 --- a/cpp/tests/mip/diversity_test.cu +++ b/cpp/tests/mip/diversity_test.cu @@ -110,7 +110,7 @@ static uint32_t test_full_run_determinism(std::string path, problem.tolerances = settings.get_tolerances(); detail::diversity_manager_t diversity_manager(solver.context); - work_limit_context_t work_limit_context; + work_limit_context_t work_limit_context("DiversityManager"); diversity_manager.timer = work_limit_timer_t(work_limit_context, 60000); diversity_manager.run_solver(); @@ -165,7 +165,7 @@ static uint32_t test_initial_solution_determinism(std::string path, problem.tolerances = settings.get_tolerances(); detail::diversity_manager_t diversity_manager(solver.context); - work_limit_context_t work_limit_context; + work_limit_context_t work_limit_context("DiversityManager"); diversity_manager.timer = work_limit_timer_t(work_limit_context, 60000); diversity_manager.diversity_config.initial_solution_only = true; diversity_manager.run_solver(); @@ -221,7 +221,7 @@ static uint32_t test_recombiners_determinism(std::string path, problem.tolerances = settings.get_tolerances(); detail::diversity_manager_t diversity_manager(solver.context); - work_limit_context_t work_limit_context; + work_limit_context_t work_limit_context("DiversityManager"); diversity_manager.timer = work_limit_timer_t(work_limit_context, 60000); diversity_manager.diversity_config.dry_run = true; diversity_manager.run_solver(); diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu index f616bda9a..8b541958b 100644 --- a/cpp/tests/mip/local_search_test.cu +++ b/cpp/tests/mip/local_search_test.cu @@ -149,7 +149,7 @@ static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) printf("LP optimal hash: 0x%x\n", detail::compute_hash(lp_optimal_solution)); printf("running mode: %d\n", mode); - work_limit_context_t work_limit_context; + work_limit_context_t work_limit_context("LocalSearch"); local_search.fp.timer = work_limit_timer_t(work_limit_context, 6000); detail::ls_config_t ls_config{}; diff --git a/cpp/tests/mip/presolve_test.cu b/cpp/tests/mip/presolve_test.cu index 392f3e8dd..bc62fee2a 100644 --- a/cpp/tests/mip/presolve_test.cu +++ b/cpp/tests/mip/presolve_test.cu @@ -124,7 +124,7 @@ uint32_t test_probing_cache_determinism(std::string path, detail::mip_solver_t solver(problem, default_settings, scaling, cuopt::timer_t(0)); detail::bound_presolve_t bnd_prb(solver.context); - work_limit_context_t work_limit_context; + work_limit_context_t work_limit_context("ProbingCache"); // rely on the iteration limit compute_probing_cache( bnd_prb, problem, work_limit_timer_t(work_limit_context, std::numeric_limits::max())); From ce44aff3698a00edfa17a5abb67dc6b9aec89c9b Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 1 Dec 2025 16:43:03 +0000 Subject: [PATCH 097/366] cleanup --- .../linear_programming/cuopt/run_mip.cpp | 3 +- cpp/CMakeLists.txt | 20 +- .../restart_strategy/pdlp_restart_strategy.cu | 3 +- cpp/src/mip/diversity/diversity_manager.cu | 18 - .../recombiners/bound_prop_recombiner.cuh | 7 - .../mip/feasibility_jump/feasibility_jump.cu | 58 +-- .../feasibility_pump/feasibility_pump.cu | 30 +- .../line_segment_search.cu | 1 - cpp/src/mip/local_search/local_search.cu | 82 ++-- cpp/src/mip/local_search/local_search.cuh | 2 +- .../local_search/rounding/bounds_repair.cu | 3 +- .../local_search/rounding/constraint_prop.cu | 21 +- cpp/src/mip/presolve/bounds_presolve.cu | 2 +- cpp/src/mip/problem/problem.cu | 29 +- cpp/src/mip/problem/problem.cuh | 4 +- cpp/src/mip/solve.cu | 22 +- cpp/src/mip/utils.cuh | 3 - cpp/src/utilities/logger.cpp | 1 + cpp/src/utilities/seed_generator.cuh | 16 +- cpp/src/utilities/work_limit_timer.hpp | 2 + cpp/tests/CMakeLists.txt | 4 - cpp/tests/utilities/CMakeLists.txt | 1 - cpp/tests/utilities/Eval.h | 274 ------------- cpp/tests/utilities/Metric.h | 245 ------------ cpp/tests/utilities/helper_cupti.h | 165 -------- cpp/tests/utilities/test_cupti.cu | 361 ------------------ scripts/requirements.txt | 10 - 27 files changed, 98 insertions(+), 1289 deletions(-) delete mode 100644 cpp/tests/utilities/Eval.h delete mode 100644 cpp/tests/utilities/Metric.h delete mode 100644 cpp/tests/utilities/helper_cupti.h delete mode 100644 cpp/tests/utilities/test_cupti.cu delete mode 100644 scripts/requirements.txt diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 7db53358b..323dac706 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -206,8 +206,6 @@ int run_single_file(std::string file_path, settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; - // settings.heuristics_only = true; - cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; auto start_run_solver = std::chrono::high_resolution_clock::now(); @@ -406,6 +404,7 @@ int main(int argc, char* argv[]) if (program.is_used("--initial-solution-path")) { initial_solution_file = program.get("--initial-solution-path"); } + if (run_dir) { std::queue task_queue; std::queue gpu_queue; diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 7178c61ff..97a8c6203 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -198,7 +198,6 @@ rapids_cpm_rapids_logger(BUILD_EXPORT_SET cuopt-exports INSTALL_EXPORT_SET cuopt create_logger_macros(CUOPT "cuopt::default_logger()" include/cuopt) find_package(CUDSS REQUIRED) -find_package(xgboost REQUIRED) if(BUILD_TESTS) include(cmake/thirdparty/get_gtest.cmake) @@ -316,23 +315,22 @@ target_link_libraries(cuopt rapids_logger::rapids_logger CCCL::CCCL raft::raft - xgboost::xgboost cuopt::mps_parser ${CUDSS_LIB_FILE} PRIVATE ${CUOPT_PRIVATE_CUDA_LIBS} ) -find_path(PAPI_INCLUDE_DIR papi.h) -find_library(PAPI_LIBRARY papi) +# find_path(PAPI_INCLUDE_DIR papi.h) +# find_library(PAPI_LIBRARY papi) -if (PAPI_INCLUDE_DIR AND PAPI_LIBRARY) - message(STATUS "Found PAPI in ${PAPI_INCLUDE_DIR}") - target_include_directories(cuopt PRIVATE ${PAPI_INCLUDE_DIR}) - target_link_libraries(cuopt PRIVATE ${PAPI_LIBRARY}) -else() - message(FATAL_ERROR "Could not find PAPI") -endif() +# if (PAPI_INCLUDE_DIR AND PAPI_LIBRARY) +# message(STATUS "Found PAPI in ${PAPI_INCLUDE_DIR}") +# target_include_directories(cuopt PRIVATE ${PAPI_INCLUDE_DIR}) +# target_link_libraries(cuopt PRIVATE ${PAPI_LIBRARY}) +# else() +# message(FATAL_ERROR "Could not find PAPI") +# endif() # ################################################################################################## diff --git a/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu b/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu index 691d1c054..546278548 100644 --- a/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu +++ b/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu @@ -1720,7 +1720,8 @@ void pdlp_restart_strategy_t::solve_bound_constrained_trust_region( // Perform the reduction // Convert raw pointer to thrust::device_ptr to write directly device side through reduce // TODO - // use a guaranteed-deterministic reduce instead of thrust::reduce + // use a guaranteed-deterministic reduce instead of thrust::reduce which I'm not sure actually + // guarantees determinism (but in practice it does) thrust::device_ptr thrust_hrsp(high_radius_squared_.data()); *thrust_hrsp = thrust::reduce(handle_ptr_->get_thrust_policy(), transformed_begin, diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index e562f1e3c..68f828229 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -427,23 +427,6 @@ solution_t diversity_manager_t::run_solver() clamp_within_var_bounds(lp_optimal_solution, problem_ptr, problem_ptr->handle_ptr); } - // Run this 100 times with varying iteration limits - // for (int i = 0; i < 100; i++) { - // relaxed_lp_settings_t lp_settings; - // lp_settings.time_limit = lp_time_limit; - // lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; - // lp_settings.return_first_feasible = false; - // lp_settings.save_state = true; - // lp_settings.concurrent_halt = &global_concurrent_halt; - // lp_settings.has_initial_primal = false; - // lp_settings.iteration_limit = 100 + i * 100; - // rmm::device_uvector lp_optimal_solution_copy(lp_optimal_solution.size(), - // problem_ptr->handle_ptr->get_stream()); - // auto lp_result = - // get_relaxed_lp_solution(*problem_ptr, lp_optimal_solution_copy, lp_state, lp_settings); - // } - // exit(0); - if (ls.lp_optimal_exists) { solution_t lp_rounded_sol(*problem_ptr); lp_rounded_sol.copy_new_assignment(lp_optimal_solution); @@ -473,7 +456,6 @@ solution_t diversity_manager_t::run_solver() if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { rins.enable(); } generate_solution(timer.remaining_time(), false); - printf("=======================================================\n"); if (diversity_config.initial_solution_only) { return population.best_feasible(); } if (work_limit_reached()) { population.add_external_solutions_to_population(); diff --git a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh index 2c85520f0..a9844606f 100644 --- a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh @@ -228,13 +228,6 @@ class bound_prop_recombiner_t : public recombiner_t { constraint_prop.single_rounding_only = false; offspring.compute_feasibility(); bool feasible_after_bounds_prop = offspring.get_feasible(); - cuopt_func_call(f_t excess_before = offspring.get_total_excess()); - CUOPT_LOG_ERROR("Excess before: %g, %g, %g, %g, feas %d", - offspring.get_total_excess(), - offspring.compute_max_constraint_violation(), - offspring.compute_max_int_violation(), - offspring.compute_max_variable_violation(), - feasible_after_bounds_prop); offspring.handle_ptr->sync_stream(); offspring.problem_ptr = a.problem_ptr; fixed_assignment = std::move(offspring.assignment); diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 927eb1575..da4f2edce 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -651,19 +651,15 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream auto [grid_setval, blocks_setval] = setval_launch_dims; auto [grid_update_changed_constraints, blocks_update_changed_constraints] = update_changed_constraints_launch_dims; - auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; - auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; - auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; - auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; - - // use_graph = false; + auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; + auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; + [[maybe_unused]] auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; + [[maybe_unused]] auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; auto& data = *climbers[climber_idx]; auto v = data.view(); settings.seed = cuopt::seed_generator::get_seed(); - // settings.iteration_limit = 10000; - // CUOPT_LOG_DEBUG("FJ: settings seed %d", settings.seed); - // ensure an updated copy of the settings is used device-side + // ensure an updated copy of the settings is used device-side raft::copy(v.settings, &settings, 1, climber_stream); bool is_binary_pb = pb_ptr->n_variables == thrust::count(handle_ptr->get_thrust_policy(), @@ -788,7 +784,6 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream 0, climber_stream); - (void)grid_update_weights; cudaLaunchCooperativeKernel((void*)handle_local_minimum_kernel, grid_update_weights, blocks_update_weights, @@ -802,39 +797,12 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream update_assignment_args, 0, climber_stream); - - // { - // printf("Changed constraints hash: %x, size: %d\n", compute_hash( - // make_span(data.constraints_changed, 0, - // data.constraints_changed_count.value(climber_stream)), climber_stream), - // data.constraints_changed_count.value(climber_stream)); - - // printf("before update: Violated constraints hash: %x, size: %d\n", compute_hash( - // make_span(data.violated_constraints.contents, 0, - // data.violated_constraints.set_size.value(climber_stream)), climber_stream), - // data.violated_constraints.set_size.value(climber_stream)); - // } - cudaLaunchKernel((void*)update_changed_constraints_kernel, 1, blocks_update_changed_constraints, kernel_args, 0, climber_stream); - - // TODO: figure out why the ordering of the violated constraints is ruined - // data.violated_constraints.sort(climber_stream); - // printf("[%d] iter: Violated constraints hash: %x\n", data.iterations.value(climber_stream), - // compute_hash( - // make_span(data.violated_constraints.contents, 0, - // data.violated_constraints.set_size.value(climber_stream)), climber_stream)); - - // { - // printf("Violated constraints hash: %x, size: %d\n", compute_hash( - // make_span(data.violated_constraints.contents, 0, - // data.violated_constraints.set_size.value(climber_stream)), climber_stream), - // data.violated_constraints.set_size.value(climber_stream)); - // } } if (use_graph) { @@ -1315,8 +1283,6 @@ i_t fj_t::solve(solution_t& solution) detail::compute_hash(cstr_left_weights), detail::compute_hash(cstr_right_weights)); - // settings.load_balancing_mode = fj_load_balancing_mode_t::ALWAYS_OFF; - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { settings.work_limit = settings.time_limit; } @@ -1457,20 +1423,6 @@ i_t fj_t::solve(solution_t& solution) CUOPT_LOG_DEBUG("FJ sol hash %x", solution.get_hash()); - // // Print compact feature vector summary - // char logbuf[4096]; - // int offset = 0; - // offset += snprintf(logbuf + offset, - // sizeof(logbuf) - offset, - // "FJ: iter=%d time=%g", - // iterations, - // timer.elapsed_time()); - // for (const auto& [name, value] : feature_vector) { - // offset += snprintf(logbuf + offset, sizeof(logbuf) - offset, " %s=%g", name.c_str(), value); - // if (offset >= (int)(sizeof(logbuf) - 32)) break; - // } - // CUOPT_LOG_INFO("%s", logbuf); - return is_new_feasible; } diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index 7e981a865..837c1e5a6 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -137,7 +137,7 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_tvariable_bounds, solution.handle_ptr->get_stream()); @@ -151,10 +151,6 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_tinteger_indices, solution.handle_ptr->get_stream()); f_t obj_offset = 0; - // CUOPT_LOG_DEBUG("FP: h_integer_indices hash 0x%x", detail::compute_hash(h_integer_indices)); - // CUOPT_LOG_DEBUG("FP: h_assignment hash 0x%x", detail::compute_hash(h_assignment)); - // CUOPT_LOG_DEBUG("FP: h_variable_bounds hash 0x%x", detail::compute_hash(h_variable_bounds)); - // CUOPT_LOG_DEBUG("FP: h_last_projection hash 0x%x", detail::compute_hash(h_last_projection)); // for each integer add the variable and the distance constraints for (auto i : h_integer_indices) { auto h_var_bounds = h_variable_bounds[i]; @@ -192,8 +188,7 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_t 0) { temp_p.insert_variables(h_variables); } @@ -229,8 +224,6 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_t::infinity(); lp_settings.work_limit = time_limit; @@ -343,7 +336,6 @@ bool feasibility_pump_t::test_fj_feasible(solution_t& soluti } else { CUOPT_LOG_DEBUG("20%% FJ run found feasible!"); } - timer.record_work(fj.settings.work_limit); CUOPT_LOG_DEBUG("20%% FJ run finished, elapsed %fs remaining %fwu", fj.settings.time_limit, timer.remaining_time()); @@ -636,11 +628,6 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s n_integers = solution.compute_number_of_integers(); if (is_feasible && n_integers == solution.problem_ptr->n_integer_vars) { CUOPT_LOG_DEBUG("Feasible solution verified with LP!"); - f_t time_taken = start_time - timer.remaining_time(); - // CUOPT_LOG_INFO( - // "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_LP_VERIFIED", - // fp_iterations, - // time_taken); return true; } } @@ -655,20 +642,11 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s } if (timer.check_time_limit()) { CUOPT_LOG_DEBUG("FP time limit reached!"); - f_t time_taken = start_time - timer.remaining_time(); - // CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f - // termination=TIME_LIMIT_AFTER_ROUND", - // fp_iterations, - // time_taken); return false; } if (is_feasible) { bool res = solution.compute_feasibility(); cuopt_assert(res, "Feasibility issue"); - f_t time_taken = start_time - timer.remaining_time(); - // CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_AFTER_ROUND", - // fp_iterations, - // time_taken); return true; } // do the cycle check if alpha diff is small enough @@ -686,10 +664,6 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s remaining_time_end_fp, fp_fj_cycle_time_begin, total_fp_time_until_cycle); - f_t time_taken = start_time - timer.remaining_time(); - // CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=ASSIGNMENT_CYCLE", - // fp_iterations, - // time_taken); return false; } cycle_queue.n_iterations_without_cycle++; diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu index 766cb8ee2..8454daa57 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu @@ -229,7 +229,6 @@ bool line_segment_search_t::search_line_segment( best_feasible_cost, curr_cost); } - // cuopt_assert(context.gpu_heur_loop, ""); if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { if (timer.check_time_limit()) { break; } } diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 115b9b775..8e4dc250e 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -63,10 +63,6 @@ void local_search_t::start_cpufj_scratch_threads(population_t default_weights(context.problem_ptr->n_constraints, 1.); - fj_settings_t cpu_fj_settings{}; - cpu_fj_settings.time_limit = std::numeric_limits::infinity(); - cpu_fj_settings.iteration_limit = 10000; // would like 10 samples per instance - solution_t solution(*context.problem_ptr); thrust::fill(solution.handle_ptr->get_thrust_policy(), solution.assignment.begin(), @@ -80,24 +76,22 @@ void local_search_t::start_cpufj_scratch_threads(population_t 0); - cpu_fj.fj_cpu->log_prefix = "******* scratch " + std::to_string(counter) + ": "; - // if (!context.settings.deterministic) { - // cpu_fj.fj_cpu->improvement_callback = [this, &population, &cpu_fj]( - // f_t obj, const std::vector& h_vec) { - // population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); - // if (obj < local_search_best_obj) { - // CUOPT_LOG_TRACE("******* New local search best obj %g, best overall %g", - // context.problem_ptr->get_user_obj_from_solver_obj(obj), - // context.problem_ptr->get_user_obj_from_solver_obj( - // population.is_feasible() ? population.best_feasible().get_objective() - // : std::numeric_limits::max())); - // local_search_best_obj = obj; - // } - // }; - // } + cpu_fj.fj_cpu->log_prefix = "******* scratch " + std::to_string(counter) + ": "; + cpu_fj.fj_cpu->improvement_callback = [this, &population, &cpu_fj]( + f_t obj, const std::vector& h_vec) { + population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); + if (obj < local_search_best_obj) { + CUOPT_LOG_TRACE("******* New local search best obj %g, best overall %g", + context.problem_ptr->get_user_obj_from_solver_obj(obj), + context.problem_ptr->get_user_obj_from_solver_obj( + population.is_feasible() ? population.best_feasible().get_objective() + : std::numeric_limits::max())); + local_search_best_obj = obj; + } + }; counter++; }; @@ -114,29 +108,24 @@ void local_search_t::start_cpufj_lptopt_scratch_threads( std::vector default_weights(context.problem_ptr->n_constraints, 1.); - fj_settings_t cpu_fj_settings{}; - cpu_fj_settings.time_limit = std::numeric_limits::infinity(); - solution_t solution_lp(*context.problem_ptr); solution_lp.copy_new_assignment(host_copy(lp_optimal_solution)); solution_lp.round_random_nearest(500); scratch_cpu_fj_on_lp_opt.fj_cpu = - fj.create_cpu_climber(solution_lp, default_weights, default_weights, 0., cpu_fj_settings, true); + fj.create_cpu_climber(solution_lp, default_weights, default_weights, 0.); scratch_cpu_fj_on_lp_opt.fj_cpu->log_prefix = "******* scratch on LP optimal: "; - if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { - scratch_cpu_fj_on_lp_opt.fj_cpu->improvement_callback = - [this, &population](f_t obj, const std::vector& h_vec) { - population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); - if (obj < local_search_best_obj) { - CUOPT_LOG_DEBUG("******* New local search best obj %g, best overall %g", - context.problem_ptr->get_user_obj_from_solver_obj(obj), - context.problem_ptr->get_user_obj_from_solver_obj( - population.is_feasible() ? population.best_feasible().get_objective() - : std::numeric_limits::max())); - local_search_best_obj = obj; - } - }; - } + scratch_cpu_fj_on_lp_opt.fj_cpu->improvement_callback = + [this, &population](f_t obj, const std::vector& h_vec) { + population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); + if (obj < local_search_best_obj) { + CUOPT_LOG_DEBUG("******* New local search best obj %g, best overall %g", + context.problem_ptr->get_user_obj_from_solver_obj(obj), + context.problem_ptr->get_user_obj_from_solver_obj( + population.is_feasible() ? population.best_feasible().get_objective() + : std::numeric_limits::max())); + local_search_best_obj = obj; + } + }; // default weights cudaDeviceSynchronize(); @@ -182,8 +171,10 @@ bool local_search_t::do_fj_solve(solution_t& solution, auto solution_copy = solution; // Start CPU solver in background thread - for (auto& cpu_fj : ls_cpu_fj) { - cpu_fj.start_cpu_solver(); + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { + for (auto& cpu_fj : ls_cpu_fj) { + cpu_fj.start_cpu_solver(); + } } // Run GPU solver and measure execution time @@ -220,6 +211,9 @@ bool local_search_t::do_fj_solve(solution_t& solution, bool gpu_feasible = solution.get_feasible(); bool cpu_feasible = cpu_sol_found && solution_cpu.get_feasible(); + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + cpu_feasible = false; // ignore CPUFJ in deterministic mode + } static std::unordered_map total_calls; static std::unordered_map cpu_better; @@ -441,12 +435,7 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu fj.settings.n_of_minimums_for_exit = 20000; fj.settings.update_weights = true; fj.settings.feasibility_run = false; - f_t fj_time_limit = 30.; - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - fj_time_limit = std::numeric_limits::infinity(); - fj.settings.iteration_limit = 100'000; - } - f_t time_limit = std::min(fj_time_limit, timer.remaining_time()); + f_t time_limit = std::min(30., timer.remaining_time()); do_fj_solve(solution, fj, time_limit, "on_lp_optimal"); return solution.get_feasible(); } @@ -700,7 +689,6 @@ bool local_search_t::run_fp(solution_t& solution, is_feasible = false; break; } - printf("------------------------------------------------------------------------\n"); CUOPT_LOG_DEBUG("fp_loop it %d last_improved_iteration %d", i, last_improved_iteration); population_ptr->add_external_solutions_to_population(); if (population_ptr->preempt_heuristic_solver_.load()) { diff --git a/cpp/src/mip/local_search/local_search.cuh b/cpp/src/mip/local_search/local_search.cuh index 9e688988f..3316160ab 100644 --- a/cpp/src/mip/local_search/local_search.cuh +++ b/cpp/src/mip/local_search/local_search.cuh @@ -120,7 +120,7 @@ class local_search_t { feasibility_pump_t fp; std::mt19937 rng; - std::array, 0> ls_cpu_fj; + std::array, 8> ls_cpu_fj; std::array, 1> scratch_cpu_fj; cpu_fj_thread_t scratch_cpu_fj_on_lp_opt; problem_t problem_with_objective_cut; diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index 507c97875..03f2c47ed 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -405,7 +405,8 @@ bool bounds_repair_t::repair_problem(problem_t& problem, curr_violation = best_violation; best_bounds.update_from(problem, handle_ptr); i_t no_candidate_in_a_row = 0; - i_t iter_limit = 20; + i_t iter_limit = std::numeric_limits::max(); + if (problem.deterministic) { iter_limit = 20; } while (h_n_violated_cstr > 0 && iter_limit-- > 0) { CUOPT_LOG_TRACE("Bounds repair loop: n_violated %d best_violation %f curr_violation %f", h_n_violated_cstr, diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index 77ceb3775..a037886c2 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -218,12 +218,10 @@ void constraint_prop_t::sort_by_interval_and_frac(solution_t return bounds_interval_1 < bounds_interval_2; } }); - - // CUOPT_LOG_DEBUG("hash vars 0x%x", detail::compute_hash(vars)); - // now do the suffling, for that we need to assign some random values to rnd array - // we will sort this rnd array and the vars in subsections, so that each subsection will be - // shuffled in total we will have 3(binary, ternary and rest) x 7 intervals = 21 subsections. - // first extract these subsections from the data + // now do the suffling, for that we need to assign some random values to rnd array + // we will sort this rnd array and the vars in subsections, so that each subsection will be + // shuffled in total we will have 3(binary, ternary and rest) x 7 intervals = 21 subsections. + // first extract these subsections from the data rmm::device_uvector subsection_offsets(size_of_subsections, sol.handle_ptr->get_stream()); thrust::fill( sol.handle_ptr->get_thrust_policy(), subsection_offsets.begin(), subsection_offsets.end(), -1); @@ -273,10 +271,7 @@ void constraint_prop_t::sort_by_interval_and_frac(solution_t } } }); - - // CUOPT_LOG_DEBUG("hash subsection_offsets 0x%x", detail::compute_hash(subsection_offsets)); auto random_vector = get_random_uniform_vector((i_t)vars.size(), rng); - // CUOPT_LOG_DEBUG("hash random_vector 0x%x", detail::compute_hash(random_vector)); rmm::device_uvector device_random_vector(random_vector.size(), sol.handle_ptr->get_stream()); raft::copy(device_random_vector.data(), random_vector.data(), @@ -422,7 +417,6 @@ void constraint_prop_t::collapse_crossing_bounds(problem_t& template void constraint_prop_t::set_bounds_on_fixed_vars(solution_t& sol) { - CUOPT_LOG_DEBUG("Bound hash before 0x%x", detail::compute_hash(sol.problem_ptr->variable_bounds)); auto assgn = make_span(sol.assignment); auto var_bounds = make_span(sol.problem_ptr->variable_bounds); thrust::for_each(sol.handle_ptr->get_thrust_policy(), @@ -434,7 +428,6 @@ void constraint_prop_t::set_bounds_on_fixed_vars(solution_t& var_bounds[idx] = typename type_2::type{var_val, var_val}; } }); - CUOPT_LOG_DEBUG("Bound hash after 0x%x", detail::compute_hash(sol.problem_ptr->variable_bounds)); } template @@ -944,15 +937,9 @@ bool constraint_prop_t::find_integer( } // do the sort if the problem is not ii. crossing bounds might cause some issues on the sort order else { - // CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x, sol 0x%x before sort\n", - // detail::compute_hash(unset_integer_vars), - // sol.get_hash()); // this is a sort to have initial shuffling, so that stable sort within will keep the order and // some randomness will be achieved sort_by_interval_and_frac(sol, make_span(unset_integer_vars), rng); - // CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x sol 0x%x after sort\n", - // detail::compute_hash(unset_integer_vars), - // sol.get_hash()); } set_host_bounds(sol); size_t set_count = 0; diff --git a/cpp/src/mip/presolve/bounds_presolve.cu b/cpp/src/mip/presolve/bounds_presolve.cu index a820fb080..06c924430 100644 --- a/cpp/src/mip/presolve/bounds_presolve.cu +++ b/cpp/src/mip/presolve/bounds_presolve.cu @@ -171,7 +171,7 @@ termination_criterion_t bound_presolve_t::bound_update_loop(problem_t< { termination_criterion_t criteria = termination_criterion_t::ITERATION_LIMIT; - // CHANGE + // CHANGE once we have a work predictor if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { timer = timer_t(std::numeric_limits::infinity()); settings.iteration_limit = std::min(settings.iteration_limit, 50); diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 1832ee38e..cdac5523a 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -100,11 +100,13 @@ void problem_t::op_problem_cstr_body(const optimization_problem_t problem_t::problem_t( const optimization_problem_t& problem_, - const typename mip_solver_settings_t::tolerances_t tolerances_) + const typename mip_solver_settings_t::tolerances_t tolerances_, + bool deterministic_) : original_problem_ptr(&problem_), handle_ptr(problem_.get_handle_ptr()), integer_fixed_variable_map(problem_.get_n_variables(), problem_.get_handle_ptr()->get_stream()), tolerances(tolerances_), + deterministic(deterministic_), n_variables(problem_.get_n_variables()), n_constraints(problem_.get_n_constraints()), n_binary_vars(0), @@ -148,6 +150,7 @@ template problem_t::problem_t(const problem_t& problem_) : original_problem_ptr(problem_.original_problem_ptr), tolerances(problem_.tolerances), + deterministic(problem_.deterministic), handle_ptr(problem_.handle_ptr), integer_fixed_problem(problem_.integer_fixed_problem), integer_fixed_variable_map(problem_.integer_fixed_variable_map, handle_ptr->get_stream()), @@ -198,6 +201,7 @@ template problem_t::problem_t(const problem_t& problem_, bool no_deep_copy) : original_problem_ptr(problem_.original_problem_ptr), tolerances(problem_.tolerances), + deterministic(problem_.deterministic), handle_ptr(problem_.handle_ptr), integer_fixed_problem(problem_.integer_fixed_problem), integer_fixed_variable_map(problem_.n_variables, handle_ptr->get_stream()), @@ -859,6 +863,9 @@ void problem_t::compute_related_variables(double time_limit) handle_ptr->sync_stream(); + // CHANGE + if (deterministic) { time_limit = std::numeric_limits::infinity(); } + // previously used constants were based on 40GB of memory. Scale accordingly on smaller GPUs // We can't rely on querying free memory or allocation try/catch // since this would break determinism guarantees (GPU may be shared by other processes) @@ -885,7 +892,7 @@ void problem_t::compute_related_variables(double time_limit) i_t output_offset = 0; i_t related_var_offset = 0; - // auto start_time = std::chrono::high_resolution_clock::now(); + auto start_time = std::chrono::high_resolution_clock::now(); for (i_t i = 0;; ++i) { i_t slice_size = std::min(max_slice_size, n_variables - i * max_slice_size); if (slice_size <= 0) break; @@ -914,13 +921,12 @@ void problem_t::compute_related_variables(double time_limit) i_t related_var_base = related_variables.size(); related_variables.resize(related_variables.size() + array_size, handle_ptr->get_stream()); - // auto current_time = std::chrono::high_resolution_clock::now(); + auto current_time = std::chrono::high_resolution_clock::now(); // if the related variable array would wind up being too large for available memory, abort // TODO this used to be 1e9 - if (related_variables.size() > 1e9 * size_factor) { - // || - // std::chrono::duration_cast(current_time - start_time).count() > - // time_limit) { + if (related_variables.size() > 1e9 * size_factor || + std::chrono::duration_cast(current_time - start_time).count() > + time_limit) { CUOPT_LOG_DEBUG( "Computing the related variable array would use too much memory or time, aborting\n"); related_variables.resize(0, handle_ptr->get_stream()); @@ -1288,13 +1294,8 @@ problem_t problem_t::get_problem_after_fixing_vars( // total_time_taken); // if the fixing is greater than 150, mark this as expensive. // this way we can avoid frequent fixings for this problem - - // TODO: CHANGE - // constexpr double expensive_time_threshold = 150; - // if (time_taken > expensive_time_threshold) { expensive_to_fix_vars = true; } - CUOPT_LOG_DEBUG("Model fingerprint after fixing: 0x%x, expensive? %d", - problem.get_fingerprint(), - expensive_to_fix_vars); + constexpr double expensive_time_threshold = 150; + if (time_taken > expensive_time_threshold && !deterministic) { expensive_to_fix_vars = true; } return problem; } diff --git a/cpp/src/mip/problem/problem.cuh b/cpp/src/mip/problem/problem.cuh index 7e9a4f93a..c90bb5577 100644 --- a/cpp/src/mip/problem/problem.cuh +++ b/cpp/src/mip/problem/problem.cuh @@ -48,7 +48,8 @@ template class problem_t { public: problem_t(const optimization_problem_t& problem, - const typename mip_solver_settings_t::tolerances_t tolerances_ = {}); + const typename mip_solver_settings_t::tolerances_t tolerances_ = {}, + bool deterministic = false); problem_t() = delete; // copy constructor problem_t(const problem_t& problem); @@ -220,6 +221,7 @@ class problem_t { bool maximize{false}; bool is_binary_pb{false}; bool empty{false}; + bool deterministic{false}; // Auxiliary problem statistics double sparsity{0.0}; diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index 37628cd8a..44ad5fce9 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -37,7 +37,6 @@ #include #include -#include namespace cuopt::linear_programming { @@ -140,6 +139,9 @@ mip_solution_t run_mip(detail::problem_t& problem, auto sol = scaled_sol.get_solution( is_feasible_before_scaling || is_feasible_after_unscaling, solver.get_solver_stats(), false); + + // TODO: RESTORE THIS + // detail::print_solution(scaled_problem.handle_ptr, sol.get_solution()); return sol; } @@ -162,15 +164,6 @@ mip_solution_t solve_mip(optimization_problem_t& op_problem, // This needs to be called before pdlp is initialized init_handler(op_problem.get_handle_ptr()); - auto retval = PAPI_library_init(PAPI_VER_CURRENT); - if (retval != PAPI_VER_CURRENT && retval > 0) { - fprintf(stderr, "PAPI library version mismatch!\n"); - exit(1); - } else if (retval < 0) { - fprintf(stderr, "PAPI initialization error!\n"); - exit(1); - } - print_version_info(); raft::common::nvtx::range fun_scope("Running solver"); @@ -198,7 +191,8 @@ mip_solution_t solve_mip(optimization_problem_t& op_problem, double presolve_time = 0.0; std::unique_ptr> presolver; - detail::problem_t problem(op_problem, settings.get_tolerances()); + detail::problem_t problem( + op_problem, settings.get_tolerances(), settings.determinism_mode == CUOPT_MODE_DETERMINISTIC); auto run_presolve = settings.presolve; run_presolve = run_presolve && settings.get_mip_callbacks().empty(); @@ -211,9 +205,9 @@ mip_solution_t solve_mip(optimization_problem_t& op_problem, // allocate not more than 10% of the time limit to presolve. // Note that this is not the presolve time, but the time limit for presolve. double presolve_time_limit = std::min(0.1 * time_limit, 60.0); - - // TODO FIX - presolve_time_limit = std::numeric_limits::infinity(); + if (settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + presolve_time_limit = std::numeric_limits::infinity(); + } const bool dual_postsolve = false; presolver = std::make_unique>(); auto result = presolver->apply(op_problem, diff --git a/cpp/src/mip/utils.cuh b/cpp/src/mip/utils.cuh index 1550c1caa..ba5de9bb4 100644 --- a/cpp/src/mip/utils.cuh +++ b/cpp/src/mip/utils.cuh @@ -265,9 +265,6 @@ f_t compute_objective_from_vec(const rmm::device_uvector& assignment, assignment.end(), objective_coefficients.begin(), 0.); - // CUOPT_LOG_DEBUG("compute_objective_from_vec: input %x coefs %x obj %x", - // detail::compute_hash(assignment), detail::compute_hash(objective_coefficients), - // detail::compute_hash(computed_obj)); return computed_obj; } diff --git a/cpp/src/utilities/logger.cpp b/cpp/src/utilities/logger.cpp index 6a99eb7bc..a16c49c11 100644 --- a/cpp/src/utilities/logger.cpp +++ b/cpp/src/utilities/logger.cpp @@ -81,6 +81,7 @@ rapids_logger::sink_ptr default_sink() * @return std::string The default log pattern. */ inline std::string default_pattern() { return "[%Y-%m-%d %H:%M:%S:%f] [%n] [%-6l] %v"; } + /** * @brief Returns the default log level for the global logger. * diff --git a/cpp/src/utilities/seed_generator.cuh b/cpp/src/utilities/seed_generator.cuh index 50bd38163..7692ff467 100644 --- a/cpp/src/utilities/seed_generator.cuh +++ b/cpp/src/utilities/seed_generator.cuh @@ -31,16 +31,14 @@ class seed_generator { set_seed(seed1 + ((seed0 + seed1) * (seed0 + seed1 + 1) / 2), seeds...); } -#if 0 +#if SEED_GENERATOR_DEBUG static int64_t get_seed(const char* caller = __builtin_FUNCTION(), - const char* file = __builtin_FILE(), - int line = __builtin_LINE()) { - - printf("&&&&&&& SEED CALLED BY %s:%d: %s() ***\n", - file, - line, - caller); - return seed_++; } + const char* file = __builtin_FILE(), + int line = __builtin_LINE()) + { + printf("&&&&&&& SEED CALLED BY %s:%d: %s() ***\n", file, line, caller); + return seed_++; + } #else static int64_t get_seed() { return seed_++; } #endif diff --git a/cpp/src/utilities/work_limit_timer.hpp b/cpp/src/utilities/work_limit_timer.hpp index d6e726dcb..862d276b7 100644 --- a/cpp/src/utilities/work_limit_timer.hpp +++ b/cpp/src/utilities/work_limit_timer.hpp @@ -68,6 +68,8 @@ class work_limit_timer_t { const char* file = __builtin_FILE(), int line = __builtin_LINE()) const noexcept { + // TODO check against global timer + if (deterministic) { if (!work_context) { return false; } // Check if global work has exceeded our budget (snapshot + limit) diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index 77e83e697..aeaf656f3 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -30,10 +30,6 @@ endif() set(CUOPT_TEST_DIR ${CMAKE_CURRENT_SOURCE_DIR}) -if (NOT TARGET CUDA::toolkit) - find_package(CUDAToolkit REQUIRED) -endif() - if (EXISTS "${CUDAToolkit_LIBRARY_ROOT}/extras/CUPTI/lib64") # NVIDIA installer layout: set(cuopt_cupti_root "${CUDAToolkit_LIBRARY_ROOT}/extras/CUPTI") diff --git a/cpp/tests/utilities/CMakeLists.txt b/cpp/tests/utilities/CMakeLists.txt index c95d048c0..5f9e6d5e8 100644 --- a/cpp/tests/utilities/CMakeLists.txt +++ b/cpp/tests/utilities/CMakeLists.txt @@ -5,4 +5,3 @@ # Add CLI end-to-end test ConfigureTest(CLI_TEST test_cli.cpp) -#ConfigureTest(CUPTI_TEST test_cupti.cu) diff --git a/cpp/tests/utilities/Eval.h b/cpp/tests/utilities/Eval.h deleted file mode 100644 index ccafcd1ae..000000000 --- a/cpp/tests/utilities/Eval.h +++ /dev/null @@ -1,274 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include -#include -#include - -template - -class ScopeExit { - public: - ScopeExit(T t) : t(t) {} - ~ScopeExit() { t(); } - T t; -}; - -template -ScopeExit MoveScopeExit(T t) -{ - return ScopeExit(t); -}; - -#define NV_ANONYMOUS_VARIABLE_DIRECT(name, line) name##line -#define NV_ANONYMOUS_VARIABLE_INDIRECT(name, line) NV_ANONYMOUS_VARIABLE_DIRECT(name, line) - -#define SCOPE_EXIT(func) \ - const auto NV_ANONYMOUS_VARIABLE_INDIRECT(EXIT, __LINE__) = MoveScopeExit([=]() { func; }) - -namespace NV { -namespace Metric { -namespace Eval { -std::string GetHwUnit(const std::string& metricName) -{ - return metricName.substr(0, metricName.find("__", 0)); -} - -bool GetMetricGpuValue(std::string chipName, - const std::vector& counterDataImage, - const std::vector& metricNames, - std::vector& metricNameValueMap, - const uint8_t* pCounterAvailabilityImage) -{ - if (!counterDataImage.size()) { - std::cout << "Counter Data Image is empty!\n"; - return false; - } - - NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params calculateScratchBufferSizeParam = { - NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params_STRUCT_SIZE}; - calculateScratchBufferSizeParam.pChipName = chipName.c_str(); - calculateScratchBufferSizeParam.pCounterAvailabilityImage = pCounterAvailabilityImage; - RETURN_IF_NVPW_ERROR( - false, NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize(&calculateScratchBufferSizeParam)); - - std::vector scratchBuffer(calculateScratchBufferSizeParam.scratchBufferSize); - NVPW_CUDA_MetricsEvaluator_Initialize_Params metricEvaluatorInitializeParams = { - NVPW_CUDA_MetricsEvaluator_Initialize_Params_STRUCT_SIZE}; - metricEvaluatorInitializeParams.scratchBufferSize = scratchBuffer.size(); - metricEvaluatorInitializeParams.pScratchBuffer = scratchBuffer.data(); - metricEvaluatorInitializeParams.pChipName = chipName.c_str(); - metricEvaluatorInitializeParams.pCounterAvailabilityImage = pCounterAvailabilityImage; - RETURN_IF_NVPW_ERROR(false, - NVPW_CUDA_MetricsEvaluator_Initialize(&metricEvaluatorInitializeParams)); - NVPW_MetricsEvaluator* metricEvaluator = metricEvaluatorInitializeParams.pMetricsEvaluator; - - NVPW_CounterData_GetNumRanges_Params getNumRangesParams = { - NVPW_CounterData_GetNumRanges_Params_STRUCT_SIZE}; - getNumRangesParams.pCounterDataImage = counterDataImage.data(); - RETURN_IF_NVPW_ERROR(false, NVPW_CounterData_GetNumRanges(&getNumRangesParams)); - - bool isolated = true; - bool keepInstances = true; - for (size_t metricIndex = 0; metricIndex < metricNames.size(); ++metricIndex) { - std::string reqName; - NV::Metric::Parser::ParseMetricNameString( - metricNames[metricIndex], &reqName, &isolated, &keepInstances); - NVPW_MetricEvalRequest metricEvalRequest; - NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params convertMetricToEvalRequest = { - NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params_STRUCT_SIZE}; - convertMetricToEvalRequest.pMetricsEvaluator = metricEvaluator; - convertMetricToEvalRequest.pMetricName = reqName.c_str(); - convertMetricToEvalRequest.pMetricEvalRequest = &metricEvalRequest; - convertMetricToEvalRequest.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; - RETURN_IF_NVPW_ERROR( - false, - NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest(&convertMetricToEvalRequest)); - - MetricNameValue metricNameValue; - metricNameValue.numRanges = getNumRangesParams.numRanges; - metricNameValue.metricName = metricNames[metricIndex]; - for (size_t rangeIndex = 0; rangeIndex < getNumRangesParams.numRanges; ++rangeIndex) { - NVPW_Profiler_CounterData_GetRangeDescriptions_Params getRangeDescParams = { - NVPW_Profiler_CounterData_GetRangeDescriptions_Params_STRUCT_SIZE}; - getRangeDescParams.pCounterDataImage = counterDataImage.data(); - getRangeDescParams.rangeIndex = rangeIndex; - RETURN_IF_NVPW_ERROR(false, - NVPW_Profiler_CounterData_GetRangeDescriptions(&getRangeDescParams)); - std::vector descriptionPtrs(getRangeDescParams.numDescriptions); - getRangeDescParams.ppDescriptions = descriptionPtrs.data(); - RETURN_IF_NVPW_ERROR(false, - NVPW_Profiler_CounterData_GetRangeDescriptions(&getRangeDescParams)); - - std::string rangeName; - for (size_t descriptionIndex = 0; descriptionIndex < getRangeDescParams.numDescriptions; - ++descriptionIndex) { - if (descriptionIndex) { rangeName += "/"; } - rangeName += descriptionPtrs[descriptionIndex]; - } - - NVPW_MetricsEvaluator_SetDeviceAttributes_Params setDeviceAttribParams = { - NVPW_MetricsEvaluator_SetDeviceAttributes_Params_STRUCT_SIZE}; - setDeviceAttribParams.pMetricsEvaluator = metricEvaluator; - setDeviceAttribParams.pCounterDataImage = counterDataImage.data(); - setDeviceAttribParams.counterDataImageSize = counterDataImage.size(); - RETURN_IF_NVPW_ERROR(false, - NVPW_MetricsEvaluator_SetDeviceAttributes(&setDeviceAttribParams)); - - double metricValue = 0.0; - NVPW_MetricsEvaluator_EvaluateToGpuValues_Params evaluateToGpuValuesParams = { - NVPW_MetricsEvaluator_EvaluateToGpuValues_Params_STRUCT_SIZE}; - evaluateToGpuValuesParams.pMetricsEvaluator = metricEvaluator; - evaluateToGpuValuesParams.pMetricEvalRequests = &metricEvalRequest; - evaluateToGpuValuesParams.numMetricEvalRequests = 1; - evaluateToGpuValuesParams.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; - evaluateToGpuValuesParams.metricEvalRequestStrideSize = sizeof(NVPW_MetricEvalRequest); - evaluateToGpuValuesParams.pCounterDataImage = counterDataImage.data(); - evaluateToGpuValuesParams.counterDataImageSize = counterDataImage.size(); - evaluateToGpuValuesParams.rangeIndex = rangeIndex; - evaluateToGpuValuesParams.isolated = true; - evaluateToGpuValuesParams.pMetricValues = &metricValue; - RETURN_IF_NVPW_ERROR(false, - NVPW_MetricsEvaluator_EvaluateToGpuValues(&evaluateToGpuValuesParams)); - metricNameValue.rangeNameMetricValueMap.push_back(std::make_pair(rangeName, metricValue)); - } - metricNameValueMap.push_back(metricNameValue); - } - - NVPW_MetricsEvaluator_Destroy_Params metricEvaluatorDestroyParams = { - NVPW_MetricsEvaluator_Destroy_Params_STRUCT_SIZE}; - metricEvaluatorDestroyParams.pMetricsEvaluator = metricEvaluator; - RETURN_IF_NVPW_ERROR(false, NVPW_MetricsEvaluator_Destroy(&metricEvaluatorDestroyParams)); - return true; -} - -bool PrintMetricValues(std::string chipName, - const std::vector& counterDataImage, - const std::vector& metricNames, - const uint8_t* pCounterAvailabilityImage) -{ - if (!counterDataImage.size()) { - std::cout << "Counter Data Image is empty!\n"; - return false; - } - - NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params calculateScratchBufferSizeParam = { - NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params_STRUCT_SIZE}; - calculateScratchBufferSizeParam.pChipName = chipName.c_str(); - calculateScratchBufferSizeParam.pCounterAvailabilityImage = pCounterAvailabilityImage; - RETURN_IF_NVPW_ERROR( - false, NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize(&calculateScratchBufferSizeParam)); - - std::vector scratchBuffer(calculateScratchBufferSizeParam.scratchBufferSize); - NVPW_CUDA_MetricsEvaluator_Initialize_Params metricEvaluatorInitializeParams = { - NVPW_CUDA_MetricsEvaluator_Initialize_Params_STRUCT_SIZE}; - metricEvaluatorInitializeParams.scratchBufferSize = scratchBuffer.size(); - metricEvaluatorInitializeParams.pScratchBuffer = scratchBuffer.data(); - metricEvaluatorInitializeParams.pChipName = chipName.c_str(); - metricEvaluatorInitializeParams.pCounterAvailabilityImage = pCounterAvailabilityImage; - metricEvaluatorInitializeParams.pCounterDataImage = counterDataImage.data(); - metricEvaluatorInitializeParams.counterDataImageSize = counterDataImage.size(); - RETURN_IF_NVPW_ERROR(false, - NVPW_CUDA_MetricsEvaluator_Initialize(&metricEvaluatorInitializeParams)); - NVPW_MetricsEvaluator* metricEvaluator = metricEvaluatorInitializeParams.pMetricsEvaluator; - - NVPW_CounterData_GetNumRanges_Params getNumRangesParams = { - NVPW_CounterData_GetNumRanges_Params_STRUCT_SIZE}; - getNumRangesParams.pCounterDataImage = counterDataImage.data(); - RETURN_IF_NVPW_ERROR(false, NVPW_CounterData_GetNumRanges(&getNumRangesParams)); - - std::cout << "\n" - << std::setw(40) << std::left << "Range Name" << std::setw(100) << std::left - << "Metric Name" - << "Metric Value" << std::endl; - std::cout << std::setfill('-') << std::setw(160) << "" << std::setfill(' ') << std::endl; - - std::string reqName; - bool isolated = true; - bool keepInstances = true; - for (std::string metricName : metricNames) { - NV::Metric::Parser::ParseMetricNameString(metricName, &reqName, &isolated, &keepInstances); - NVPW_MetricEvalRequest metricEvalRequest; - NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params convertMetricToEvalRequest = { - NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params_STRUCT_SIZE}; - convertMetricToEvalRequest.pMetricsEvaluator = metricEvaluator; - convertMetricToEvalRequest.pMetricName = reqName.c_str(); - convertMetricToEvalRequest.pMetricEvalRequest = &metricEvalRequest; - convertMetricToEvalRequest.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; - RETURN_IF_NVPW_ERROR( - false, - NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest(&convertMetricToEvalRequest)); - - for (size_t rangeIndex = 0; rangeIndex < getNumRangesParams.numRanges; ++rangeIndex) { - NVPW_Profiler_CounterData_GetRangeDescriptions_Params getRangeDescParams = { - NVPW_Profiler_CounterData_GetRangeDescriptions_Params_STRUCT_SIZE}; - getRangeDescParams.pCounterDataImage = counterDataImage.data(); - getRangeDescParams.rangeIndex = rangeIndex; - RETURN_IF_NVPW_ERROR(false, - NVPW_Profiler_CounterData_GetRangeDescriptions(&getRangeDescParams)); - std::vector descriptionPtrs(getRangeDescParams.numDescriptions); - getRangeDescParams.ppDescriptions = descriptionPtrs.data(); - RETURN_IF_NVPW_ERROR(false, - NVPW_Profiler_CounterData_GetRangeDescriptions(&getRangeDescParams)); - - std::string rangeName; - for (size_t descriptionIndex = 0; descriptionIndex < getRangeDescParams.numDescriptions; - ++descriptionIndex) { - if (descriptionIndex) { rangeName += "/"; } - rangeName += descriptionPtrs[descriptionIndex]; - } - - NVPW_MetricsEvaluator_SetDeviceAttributes_Params setDeviceAttribParams = { - NVPW_MetricsEvaluator_SetDeviceAttributes_Params_STRUCT_SIZE}; - setDeviceAttribParams.pMetricsEvaluator = metricEvaluator; - setDeviceAttribParams.pCounterDataImage = counterDataImage.data(); - setDeviceAttribParams.counterDataImageSize = counterDataImage.size(); - RETURN_IF_NVPW_ERROR(false, - NVPW_MetricsEvaluator_SetDeviceAttributes(&setDeviceAttribParams)); - - double metricValue; - NVPW_MetricsEvaluator_EvaluateToGpuValues_Params evaluateToGpuValuesParams = { - NVPW_MetricsEvaluator_EvaluateToGpuValues_Params_STRUCT_SIZE}; - evaluateToGpuValuesParams.pMetricsEvaluator = metricEvaluator; - evaluateToGpuValuesParams.pMetricEvalRequests = &metricEvalRequest; - evaluateToGpuValuesParams.numMetricEvalRequests = 1; - evaluateToGpuValuesParams.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; - evaluateToGpuValuesParams.metricEvalRequestStrideSize = sizeof(NVPW_MetricEvalRequest); - evaluateToGpuValuesParams.pCounterDataImage = counterDataImage.data(); - evaluateToGpuValuesParams.counterDataImageSize = counterDataImage.size(); - evaluateToGpuValuesParams.rangeIndex = rangeIndex; - evaluateToGpuValuesParams.isolated = true; - evaluateToGpuValuesParams.pMetricValues = &metricValue; - RETURN_IF_NVPW_ERROR(false, - NVPW_MetricsEvaluator_EvaluateToGpuValues(&evaluateToGpuValuesParams)); - - std::cout << std::setw(40) << std::left << rangeName << std::setw(100) << std::left - << metricName << metricValue << std::endl; - } - } - - NVPW_MetricsEvaluator_Destroy_Params metricEvaluatorDestroyParams = { - NVPW_MetricsEvaluator_Destroy_Params_STRUCT_SIZE}; - metricEvaluatorDestroyParams.pMetricsEvaluator = metricEvaluator; - RETURN_IF_NVPW_ERROR(false, NVPW_MetricsEvaluator_Destroy(&metricEvaluatorDestroyParams)); - return true; -} -} // namespace Eval -} // namespace Metric -} // namespace NV diff --git a/cpp/tests/utilities/Metric.h b/cpp/tests/utilities/Metric.h deleted file mode 100644 index 05261c8e6..000000000 --- a/cpp/tests/utilities/Metric.h +++ /dev/null @@ -1,245 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include -#include -#include - -class ScopeExit { - public: - ScopeExit(T t) : t(t) {} - ~ScopeExit() { t(); } - T t; -}; - -template -ScopeExit MoveScopeExit(T t) -{ - return ScopeExit(t); -}; - -#define NV_ANONYMOUS_VARIABLE_DIRECT(name, line) name##line -#define NV_ANONYMOUS_VARIABLE_INDIRECT(name, line) NV_ANONYMOUS_VARIABLE_DIRECT(name, line) - -#define SCOPE_EXIT(func) \ - const auto NV_ANONYMOUS_VARIABLE_INDIRECT(EXIT, __LINE__) = MoveScopeExit([=]() { func; }) - -namespace NV { -namespace Metric { -namespace Config { - -bool GetRawMetricRequests(std::string chipName, - const std::vector& metricNames, - std::vector& rawMetricRequests, - const uint8_t* pCounterAvailabilityImage) -{ - NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params calculateScratchBufferSizeParam = { - NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize_Params_STRUCT_SIZE}; - calculateScratchBufferSizeParam.pChipName = chipName.c_str(); - calculateScratchBufferSizeParam.pCounterAvailabilityImage = pCounterAvailabilityImage; - RETURN_IF_NVPW_ERROR( - false, NVPW_CUDA_MetricsEvaluator_CalculateScratchBufferSize(&calculateScratchBufferSizeParam)); - - std::vector scratchBuffer(calculateScratchBufferSizeParam.scratchBufferSize); - NVPW_CUDA_MetricsEvaluator_Initialize_Params metricEvaluatorInitializeParams = { - NVPW_CUDA_MetricsEvaluator_Initialize_Params_STRUCT_SIZE}; - metricEvaluatorInitializeParams.scratchBufferSize = scratchBuffer.size(); - metricEvaluatorInitializeParams.pScratchBuffer = scratchBuffer.data(); - metricEvaluatorInitializeParams.pChipName = chipName.c_str(); - metricEvaluatorInitializeParams.pCounterAvailabilityImage = pCounterAvailabilityImage; - RETURN_IF_NVPW_ERROR(false, - NVPW_CUDA_MetricsEvaluator_Initialize(&metricEvaluatorInitializeParams)); - NVPW_MetricsEvaluator* metricEvaluator = metricEvaluatorInitializeParams.pMetricsEvaluator; - - bool isolated = true; - bool keepInstances = true; - std::vector rawMetricNames; - for (auto& metricName : metricNames) { - std::string reqName; - NV::Metric::Parser::ParseMetricNameString(metricName, &reqName, &isolated, &keepInstances); - keepInstances = true; - NVPW_MetricEvalRequest metricEvalRequest; - NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params convertMetricToEvalRequest = { - NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest_Params_STRUCT_SIZE}; - convertMetricToEvalRequest.pMetricsEvaluator = metricEvaluator; - convertMetricToEvalRequest.pMetricName = reqName.c_str(); - convertMetricToEvalRequest.pMetricEvalRequest = &metricEvalRequest; - convertMetricToEvalRequest.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; - RETURN_IF_NVPW_ERROR( - false, - NVPW_MetricsEvaluator_ConvertMetricNameToMetricEvalRequest(&convertMetricToEvalRequest)); - - std::vector rawDependencies; - NVPW_MetricsEvaluator_GetMetricRawDependencies_Params getMetricRawDependenciesParms = { - NVPW_MetricsEvaluator_GetMetricRawDependencies_Params_STRUCT_SIZE}; - getMetricRawDependenciesParms.pMetricsEvaluator = metricEvaluator; - getMetricRawDependenciesParms.pMetricEvalRequests = &metricEvalRequest; - getMetricRawDependenciesParms.numMetricEvalRequests = 1; - getMetricRawDependenciesParms.metricEvalRequestStructSize = NVPW_MetricEvalRequest_STRUCT_SIZE; - getMetricRawDependenciesParms.metricEvalRequestStrideSize = sizeof(NVPW_MetricEvalRequest); - RETURN_IF_NVPW_ERROR( - false, NVPW_MetricsEvaluator_GetMetricRawDependencies(&getMetricRawDependenciesParms)); - rawDependencies.resize(getMetricRawDependenciesParms.numRawDependencies); - getMetricRawDependenciesParms.ppRawDependencies = rawDependencies.data(); - RETURN_IF_NVPW_ERROR( - false, NVPW_MetricsEvaluator_GetMetricRawDependencies(&getMetricRawDependenciesParms)); - - for (size_t i = 0; i < rawDependencies.size(); ++i) { - rawMetricNames.push_back(rawDependencies[i]); - } - } - - for (auto& rawMetricName : rawMetricNames) { - NVPA_RawMetricRequest metricRequest = {NVPA_RAW_METRIC_REQUEST_STRUCT_SIZE}; - metricRequest.pMetricName = rawMetricName; - metricRequest.isolated = isolated; - metricRequest.keepInstances = keepInstances; - rawMetricRequests.push_back(metricRequest); - } - - NVPW_MetricsEvaluator_Destroy_Params metricEvaluatorDestroyParams = { - NVPW_MetricsEvaluator_Destroy_Params_STRUCT_SIZE}; - metricEvaluatorDestroyParams.pMetricsEvaluator = metricEvaluator; - RETURN_IF_NVPW_ERROR(false, NVPW_MetricsEvaluator_Destroy(&metricEvaluatorDestroyParams)); - return true; -} - -bool GetConfigImage(std::string chipName, - const std::vector& metricNames, - std::vector& configImage, - const uint8_t* pCounterAvailabilityImage) -{ - std::vector rawMetricRequests; - GetRawMetricRequests(chipName, metricNames, rawMetricRequests, pCounterAvailabilityImage); - - NVPW_CUDA_RawMetricsConfig_Create_V2_Params rawMetricsConfigCreateParams = { - NVPW_CUDA_RawMetricsConfig_Create_V2_Params_STRUCT_SIZE}; - rawMetricsConfigCreateParams.activityKind = NVPA_ACTIVITY_KIND_PROFILER; - rawMetricsConfigCreateParams.pChipName = chipName.c_str(); - rawMetricsConfigCreateParams.pCounterAvailabilityImage = pCounterAvailabilityImage; - RETURN_IF_NVPW_ERROR(false, NVPW_CUDA_RawMetricsConfig_Create_V2(&rawMetricsConfigCreateParams)); - NVPA_RawMetricsConfig* pRawMetricsConfig = rawMetricsConfigCreateParams.pRawMetricsConfig; - - if (pCounterAvailabilityImage) { - NVPW_RawMetricsConfig_SetCounterAvailability_Params setCounterAvailabilityParams = { - NVPW_RawMetricsConfig_SetCounterAvailability_Params_STRUCT_SIZE}; - setCounterAvailabilityParams.pRawMetricsConfig = pRawMetricsConfig; - setCounterAvailabilityParams.pCounterAvailabilityImage = pCounterAvailabilityImage; - RETURN_IF_NVPW_ERROR( - false, NVPW_RawMetricsConfig_SetCounterAvailability(&setCounterAvailabilityParams)); - } - - NVPW_RawMetricsConfig_Destroy_Params rawMetricsConfigDestroyParams = { - NVPW_RawMetricsConfig_Destroy_Params_STRUCT_SIZE}; - rawMetricsConfigDestroyParams.pRawMetricsConfig = pRawMetricsConfig; - SCOPE_EXIT([&]() { - NVPW_RawMetricsConfig_Destroy( - (NVPW_RawMetricsConfig_Destroy_Params*)&rawMetricsConfigDestroyParams); - }); - - NVPW_RawMetricsConfig_BeginPassGroup_Params beginPassGroupParams = { - NVPW_RawMetricsConfig_BeginPassGroup_Params_STRUCT_SIZE}; - beginPassGroupParams.pRawMetricsConfig = pRawMetricsConfig; - RETURN_IF_NVPW_ERROR(false, NVPW_RawMetricsConfig_BeginPassGroup(&beginPassGroupParams)); - - NVPW_RawMetricsConfig_AddMetrics_Params addMetricsParams = { - NVPW_RawMetricsConfig_AddMetrics_Params_STRUCT_SIZE}; - addMetricsParams.pRawMetricsConfig = pRawMetricsConfig; - addMetricsParams.pRawMetricRequests = rawMetricRequests.data(); - addMetricsParams.numMetricRequests = rawMetricRequests.size(); - RETURN_IF_NVPW_ERROR(false, NVPW_RawMetricsConfig_AddMetrics(&addMetricsParams)); - - NVPW_RawMetricsConfig_EndPassGroup_Params endPassGroupParams = { - NVPW_RawMetricsConfig_EndPassGroup_Params_STRUCT_SIZE}; - endPassGroupParams.pRawMetricsConfig = pRawMetricsConfig; - RETURN_IF_NVPW_ERROR(false, NVPW_RawMetricsConfig_EndPassGroup(&endPassGroupParams)); - - NVPW_RawMetricsConfig_GenerateConfigImage_Params generateConfigImageParams = { - NVPW_RawMetricsConfig_GenerateConfigImage_Params_STRUCT_SIZE}; - generateConfigImageParams.pRawMetricsConfig = pRawMetricsConfig; - RETURN_IF_NVPW_ERROR(false, - NVPW_RawMetricsConfig_GenerateConfigImage(&generateConfigImageParams)); - - NVPW_RawMetricsConfig_GetConfigImage_Params getConfigImageParams = { - NVPW_RawMetricsConfig_GetConfigImage_Params_STRUCT_SIZE}; - getConfigImageParams.pRawMetricsConfig = pRawMetricsConfig; - getConfigImageParams.bytesAllocated = 0; - getConfigImageParams.pBuffer = NULL; - RETURN_IF_NVPW_ERROR(false, NVPW_RawMetricsConfig_GetConfigImage(&getConfigImageParams)); - - configImage.resize(getConfigImageParams.bytesCopied); - getConfigImageParams.bytesAllocated = configImage.size(); - getConfigImageParams.pBuffer = configImage.data(); - RETURN_IF_NVPW_ERROR(false, NVPW_RawMetricsConfig_GetConfigImage(&getConfigImageParams)); - - return true; -} - -bool GetCounterDataPrefixImage(std::string chipName, - const std::vector& metricNames, - std::vector& counterDataImagePrefix, - const uint8_t* pCounterAvailabilityImage) -{ - std::vector rawMetricRequests; - GetRawMetricRequests(chipName, metricNames, rawMetricRequests, pCounterAvailabilityImage); - - NVPW_CUDA_CounterDataBuilder_Create_Params counterDataBuilderCreateParams = { - NVPW_CUDA_CounterDataBuilder_Create_Params_STRUCT_SIZE}; - counterDataBuilderCreateParams.pChipName = chipName.c_str(); - counterDataBuilderCreateParams.pCounterAvailabilityImage = pCounterAvailabilityImage; - RETURN_IF_NVPW_ERROR(false, NVPW_CUDA_CounterDataBuilder_Create(&counterDataBuilderCreateParams)); - - NVPW_CounterDataBuilder_Destroy_Params counterDataBuilderDestroyParams = { - NVPW_CounterDataBuilder_Destroy_Params_STRUCT_SIZE}; - counterDataBuilderDestroyParams.pCounterDataBuilder = - counterDataBuilderCreateParams.pCounterDataBuilder; - SCOPE_EXIT([&]() { - NVPW_CounterDataBuilder_Destroy( - (NVPW_CounterDataBuilder_Destroy_Params*)&counterDataBuilderDestroyParams); - }); - - NVPW_CounterDataBuilder_AddMetrics_Params addMetricsParams = { - NVPW_CounterDataBuilder_AddMetrics_Params_STRUCT_SIZE}; - addMetricsParams.pCounterDataBuilder = counterDataBuilderCreateParams.pCounterDataBuilder; - addMetricsParams.pRawMetricRequests = rawMetricRequests.data(); - addMetricsParams.numMetricRequests = rawMetricRequests.size(); - RETURN_IF_NVPW_ERROR(false, NVPW_CounterDataBuilder_AddMetrics(&addMetricsParams)); - - size_t counterDataPrefixSize = 0; - NVPW_CounterDataBuilder_GetCounterDataPrefix_Params getCounterDataPrefixParams = { - NVPW_CounterDataBuilder_GetCounterDataPrefix_Params_STRUCT_SIZE}; - getCounterDataPrefixParams.pCounterDataBuilder = - counterDataBuilderCreateParams.pCounterDataBuilder; - getCounterDataPrefixParams.bytesAllocated = 0; - getCounterDataPrefixParams.pBuffer = NULL; - RETURN_IF_NVPW_ERROR(false, - NVPW_CounterDataBuilder_GetCounterDataPrefix(&getCounterDataPrefixParams)); - - counterDataImagePrefix.resize(getCounterDataPrefixParams.bytesCopied); - getCounterDataPrefixParams.bytesAllocated = counterDataImagePrefix.size(); - getCounterDataPrefixParams.pBuffer = counterDataImagePrefix.data(); - RETURN_IF_NVPW_ERROR(false, - NVPW_CounterDataBuilder_GetCounterDataPrefix(&getCounterDataPrefixParams)); - - return true; -} - -} // namespace Config -} // namespace Metric -} // namespace NV diff --git a/cpp/tests/utilities/helper_cupti.h b/cpp/tests/utilities/helper_cupti.h deleted file mode 100644 index 0b5f2c998..000000000 --- a/cpp/tests/utilities/helper_cupti.h +++ /dev/null @@ -1,165 +0,0 @@ -/** - * Copyright (c) 2022-2025, NVIDIA CORPORATION. All rights reserved. - * - * Please refer to the NVIDIA end user license agreement (EULA) associated - * with this source code for terms and conditions that govern your use of - * this software. Any use, reproduction, disclosure, or distribution of - * this software and related documentation outside the terms of the EULA - * is strictly prohibited. - * - */ - -//////////////////////////////////////////////////////////////////////////////// - -#ifndef HELPER_CUPTI_H_ -#define HELPER_CUPTI_H_ - -#pragma once - -#include - -#ifndef EXIT_WAIVED -#define EXIT_WAIVED 2 -#endif - -#if defined(WIN32) || defined(_WIN32) -#define stricmp _stricmp -#else -#define stricmp strcasecmp -#endif - -#define CUDA_MAX_DEVICES 256 // consider theoretical max devices as 256 - -#ifndef DRIVER_API_CALL -#define DRIVER_API_CALL(apiFunctionCall) \ - do { \ - CUresult _status = apiFunctionCall; \ - if (_status != CUDA_SUCCESS) { \ - const char* pErrorString; \ - cuGetErrorString(_status, &pErrorString); \ - \ - std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ - << #apiFunctionCall << " failed with error(" << _status << "): " << pErrorString \ - << ".\n\n"; \ - \ - exit(EXIT_FAILURE); \ - } \ - } while (0) -#endif - -#ifndef RUNTIME_API_CALL -#define RUNTIME_API_CALL(apiFunctionCall) \ - do { \ - cudaError_t _status = apiFunctionCall; \ - if (_status != cudaSuccess) { \ - std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ - << #apiFunctionCall << " failed with error(" << _status \ - << "): " << cudaGetErrorString(_status) << ".\n\n"; \ - \ - exit(EXIT_FAILURE); \ - } \ - } while (0) -#endif - -#ifndef CUPTI_API_CALL -#define CUPTI_API_CALL(apiFunctionCall) \ - do { \ - CUptiResult _status = apiFunctionCall; \ - if (_status != CUPTI_SUCCESS) { \ - const char* pErrorString; \ - cuptiGetResultString(_status, &pErrorString); \ - \ - std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ - << #apiFunctionCall << " failed with error(" << _status << "): " << pErrorString \ - << ".\n\n"; \ - \ - exit(EXIT_FAILURE); \ - } \ - } while (0) -#endif - -#ifndef CUPTI_API_CALL_VERBOSE -#define CUPTI_API_CALL_VERBOSE(apiFunctionCall) \ - do { \ - std::cout << "Calling CUPTI API: " << #apiFunctionCall << "\n"; \ - \ - CUptiResult _status = apiFunctionCall; \ - if (_status != CUPTI_SUCCESS) { \ - const char* pErrorString; \ - cuptiGetResultString(_status, &pErrorString); \ - \ - std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ - << #apiFunctionCall << " failed with error(" << _status << "): " << pErrorString \ - << ".\n\n"; \ - \ - exit(EXIT_FAILURE); \ - } \ - } while (0) -#endif - -#ifndef CUPTI_UTIL_CALL -#define CUPTI_UTIL_CALL(apiFunctionCall) \ - do { \ - CUptiUtilResult _status = apiFunctionCall; \ - if (_status != CUPTI_UTIL_SUCCESS) { \ - std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ - << #apiFunctionCall << " failed with error: " << _status << "\n\n"; \ - \ - exit(EXIT_FAILURE); \ - } \ - } while (0) -#endif - -#ifndef NVPW_API_CALL -#define NVPW_API_CALL(apiFunctionCall) \ - do { \ - NVPA_Status _status = apiFunctionCall; \ - if (_status != NVPA_STATUS_SUCCESS) { \ - std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Function " \ - << #apiFunctionCall << " failed with error: " << _status << "\n\n"; \ - \ - exit(EXIT_FAILURE); \ - } \ - } while (0) -#endif - -#ifndef MEMORY_ALLOCATION_CALL -#define MEMORY_ALLOCATION_CALL(variable) \ - do { \ - if (variable == NULL) { \ - std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ \ - << " Memory allocation failed.\n\n"; \ - \ - exit(EXIT_FAILURE); \ - } \ - } while (0) -#endif - -#ifndef CHECK_CONDITION -#define CHECK_CONDITION(condition) \ - do { \ - if (!(condition)) { \ - std::cerr << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Condition " << #condition \ - << " failed.\n\n"; \ - \ - exit(EXIT_FAILURE); \ - } \ - } while (0) -#endif - -#ifndef CHECK_INTEGER_CONDITION -#define CHECK_INTEGER_CONDITION(argument1, operator, argument2) \ - do { \ - if (!(argument1 operator argument2)) { \ - std::cerr \ - << "\n\nError: " << __FILE__ << ":" << __LINE__ << ": Condition " << #argument1 << " " \ - << # \ - operator<< " " << #argument2 << " fails. " << #argument1 << " = " << argument1 << "," \ - " " << #argument2 << " = " << argument2 << "\n\n"; \ - \ - exit(EXIT_FAILURE); \ - } \ - } while (0) -#endif - -#endif // HELPER_CUPTI_H_ diff --git a/cpp/tests/utilities/test_cupti.cu b/cpp/tests/utilities/test_cupti.cu deleted file mode 100644 index de1778541..000000000 --- a/cpp/tests/utilities/test_cupti.cu +++ /dev/null @@ -1,361 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include "../linear_programming/utilities/pdlp_test_utilities.cuh" -#include "../mip/mip_utils.cuh" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include -#include -#include - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -namespace cuopt::linear_programming::test { - -void init_handler(const raft::handle_t* handle_ptr) -{ - // Init cuBlas / cuSparse context here to avoid having it during solving time - RAFT_CUBLAS_TRY(raft::linalg::detail::cublassetpointermode( - handle_ptr->get_cublas_handle(), CUBLAS_POINTER_MODE_DEVICE, handle_ptr->get_stream())); - RAFT_CUSPARSE_TRY(raft::sparse::detail::cusparsesetpointermode( - handle_ptr->get_cusparse_handle(), CUSPARSE_POINTER_MODE_DEVICE, handle_ptr->get_stream())); -} - -__global__ void VectorAdd(const float* A, const float* B, float* C, int N) -{ - int idx = blockIdx.x * blockDim.x + threadIdx.x; - if (idx < N) C[idx] = A[idx] + B[idx]; -} - -class cupti_profiler_t { - public: - cupti_profiler_t(const std::vector& metrics) - : metricNames_(metrics), pRangeProfilerObject_(nullptr) - { - } - - cupti_profiler_t(const cupti_profiler_t&) = delete; - cupti_profiler_t& operator=(const cupti_profiler_t&) = delete; - - ~cupti_profiler_t() - { - if (!pRangeProfilerObject_) return; - CUpti_RangeProfiler_Disable_Params disableRangeProfiler = { - .structSize = CUpti_RangeProfiler_Disable_Params_STRUCT_SIZE}; - disableRangeProfiler.pRangeProfilerObject = pRangeProfilerObject_; - cuptiRangeProfilerDisable(&disableRangeProfiler); - CUpti_Profiler_DeInitialize_Params profilerDeInitializeParams = { - .structSize = CUpti_Profiler_DeInitialize_Params_STRUCT_SIZE}; - cuptiProfilerDeInitialize(&profilerDeInitializeParams); - } - - void initialize_and_enable(CUcontext cuContext) - { - CUpti_Profiler_Initialize_Params profilerInitializeParams = { - .structSize = CUpti_Profiler_Initialize_Params_STRUCT_SIZE}; - cuptiProfilerInitialize(&profilerInitializeParams); - CUdevice device; - cuCtxGetDevice(&device); - CUpti_Device_GetChipName_Params getChipNameParams = { - .structSize = CUpti_Device_GetChipName_Params_STRUCT_SIZE}; - getChipNameParams.deviceIndex = (size_t)device; - cuptiDeviceGetChipName(&getChipNameParams); - chipName_ = getChipNameParams.pChipName; - CUpti_RangeProfiler_Enable_Params enableRange = { - .structSize = CUpti_RangeProfiler_Enable_Params_STRUCT_SIZE}; - enableRange.ctx = cuContext; - cuptiRangeProfilerEnable(&enableRange); - pRangeProfilerObject_ = enableRange.pRangeProfilerObject; - } - - void configure(CUpti_ProfilerRange range, CUpti_ProfilerReplayMode replayMode, size_t numOfRanges) - { - create_config_image(); - create_counter_data_image(numOfRanges); - - CUpti_RangeProfiler_SetConfig_Params setConfig = { - .structSize = CUpti_RangeProfiler_SetConfig_Params_STRUCT_SIZE}; - setConfig.pRangeProfilerObject = pRangeProfilerObject_; - setConfig.configSize = configImage_.size(); - setConfig.pConfig = configImage_.data(); - setConfig.counterDataImageSize = counterDataImage_.size(); - setConfig.pCounterDataImage = counterDataImage_.data(); - setConfig.range = range; - setConfig.replayMode = replayMode; - setConfig.maxRangesPerPass = numOfRanges; - setConfig.numNestingLevels = 1; - setConfig.minNestingLevel = 1; - setConfig.passIndex = 0; - setConfig.targetNestingLevel = 0; - cuptiRangeProfilerSetConfig(&setConfig); - } - - void start() - { - CUpti_RangeProfiler_Start_Params p = {.structSize = - CUpti_RangeProfiler_Start_Params_STRUCT_SIZE}; - p.pRangeProfilerObject = pRangeProfilerObject_; - cuptiRangeProfilerStart(&p); - } - - void stop() - { - CUpti_RangeProfiler_Stop_Params p = {.structSize = CUpti_RangeProfiler_Stop_Params_STRUCT_SIZE}; - p.pRangeProfilerObject = pRangeProfilerObject_; - cuptiRangeProfilerStop(&p); - } - - std::unordered_map decode() - { - CUpti_RangeProfiler_DecodeData_Params decodeData = { - .structSize = CUpti_RangeProfiler_DecodeData_Params_STRUCT_SIZE}; - decodeData.pRangeProfilerObject = pRangeProfilerObject_; - cuptiRangeProfilerDecodeData(&decodeData); - CUpti_RangeProfiler_GetCounterDataInfo_Params cdiParams = { - .structSize = CUpti_RangeProfiler_GetCounterDataInfo_Params_STRUCT_SIZE}; - cdiParams.pCounterDataImage = counterDataImage_.data(); - cdiParams.counterDataImageSize = counterDataImage_.size(); - cuptiRangeProfilerGetCounterDataInfo(&cdiParams); - evaluate_all_ranges(cdiParams.numTotalRanges > 10 ? 10 : cdiParams.numTotalRanges); - return metric_values; - } - - private: - void create_config_image() - { - CUpti_Profiler_Host_Initialize_Params hostInitializeParams = { - .structSize = CUpti_Profiler_Host_Initialize_Params_STRUCT_SIZE}; - hostInitializeParams.profilerType = CUPTI_PROFILER_TYPE_RANGE_PROFILER; - hostInitializeParams.pChipName = chipName_.c_str(); - hostInitializeParams.pCounterAvailabilityImage = nullptr; - cuptiProfilerHostInitialize(&hostInitializeParams); - CUpti_Profiler_Host_Object* pHostObject = hostInitializeParams.pHostObject; - - CUpti_Profiler_Host_ConfigAddMetrics_Params configAddMetricsParams = { - .structSize = CUpti_Profiler_Host_ConfigAddMetrics_Params_STRUCT_SIZE}; - configAddMetricsParams.pHostObject = pHostObject; - configAddMetricsParams.ppMetricNames = metricNames_.data(); - configAddMetricsParams.numMetrics = metricNames_.size(); - cuptiProfilerHostConfigAddMetrics(&configAddMetricsParams); - - CUpti_Profiler_Host_GetConfigImageSize_Params getConfigImageSizeParams = { - .structSize = CUpti_Profiler_Host_GetConfigImageSize_Params_STRUCT_SIZE}; - getConfigImageSizeParams.pHostObject = pHostObject; - cuptiProfilerHostGetConfigImageSize(&getConfigImageSizeParams); - configImage_.resize(getConfigImageSizeParams.configImageSize); - - CUpti_Profiler_Host_GetConfigImage_Params getConfigImageParams = { - .structSize = CUpti_Profiler_Host_GetConfigImage_Params_STRUCT_SIZE}; - getConfigImageParams.pHostObject = pHostObject; - getConfigImageParams.pConfigImage = configImage_.data(); - getConfigImageParams.configImageSize = configImage_.size(); - cuptiProfilerHostGetConfigImage(&getConfigImageParams); - - CUpti_Profiler_Host_GetNumOfPasses_Params getNumOfPassesParam = { - .structSize = CUpti_Profiler_Host_GetNumOfPasses_Params_STRUCT_SIZE}; - getNumOfPassesParam.pConfigImage = configImage_.data(); - getNumOfPassesParam.configImageSize = configImage_.size(); - cuptiProfilerHostGetNumOfPasses(&getNumOfPassesParam); - printf("Num of Passes: %d\n", (int)getNumOfPassesParam.numOfPasses); - CUpti_Profiler_Host_Deinitialize_Params deinitializeParams = { - .structSize = CUpti_Profiler_Host_Deinitialize_Params_STRUCT_SIZE}; - deinitializeParams.pHostObject = pHostObject; - cuptiProfilerHostDeinitialize(&deinitializeParams); - } - - void create_counter_data_image(size_t maxNumOfRangesInCounterDataImage) - { - CUpti_RangeProfiler_GetCounterDataSize_Params ctDataSize = { - .structSize = CUpti_RangeProfiler_GetCounterDataSize_Params_STRUCT_SIZE}; - ctDataSize.pRangeProfilerObject = pRangeProfilerObject_; - ctDataSize.pMetricNames = metricNames_.data(); - ctDataSize.numMetrics = metricNames_.size(); - ctDataSize.maxNumOfRanges = maxNumOfRangesInCounterDataImage; - ctDataSize.maxNumRangeTreeNodes = maxNumOfRangesInCounterDataImage; - cuptiRangeProfilerGetCounterDataSize(&ctDataSize); - counterDataImage_.resize(ctDataSize.counterDataSize); - CUpti_RangeProfiler_CounterDataImage_Initialize_Params initCtImg = { - .structSize = CUpti_RangeProfiler_CounterDataImage_Initialize_Params_STRUCT_SIZE}; - initCtImg.pRangeProfilerObject = pRangeProfilerObject_; - initCtImg.pCounterData = counterDataImage_.data(); - initCtImg.counterDataSize = counterDataImage_.size(); - cuptiRangeProfilerCounterDataImageInitialize(&initCtImg); - } - - void evaluate_range(size_t rangeIndex, CUpti_Profiler_Host_Object* pHostObject) - { - std::vector metricValues(metricNames_.size()); - CUpti_Profiler_Host_EvaluateToGpuValues_Params p = { - .structSize = CUpti_Profiler_Host_EvaluateToGpuValues_Params_STRUCT_SIZE}; - p.pHostObject = pHostObject; - p.pCounterDataImage = counterDataImage_.data(); - p.counterDataImageSize = counterDataImage_.size(); - p.ppMetricNames = metricNames_.data(); - p.numMetrics = metricNames_.size(); - p.rangeIndex = rangeIndex; - p.pMetricValues = metricValues.data(); - cuptiProfilerHostEvaluateToGpuValues(&p); - for (size_t i = 0; i < metricNames_.size(); ++i) - metric_values[metricNames_[i]] += (size_t)metricValues[i]; - } - - void evaluate_all_ranges(size_t numOfRanges) - { - CUpti_Profiler_Host_Initialize_Params hostInitializeParams = { - .structSize = CUpti_Profiler_Host_Initialize_Params_STRUCT_SIZE}; - hostInitializeParams.profilerType = CUPTI_PROFILER_TYPE_RANGE_PROFILER; - hostInitializeParams.pChipName = chipName_.c_str(); - hostInitializeParams.pCounterAvailabilityImage = nullptr; - cuptiProfilerHostInitialize(&hostInitializeParams); - for (size_t i = 0; i < numOfRanges; ++i) - evaluate_range(i, hostInitializeParams.pHostObject); - CUpti_Profiler_Host_Deinitialize_Params deinitializeParams = { - .structSize = CUpti_Profiler_Host_Deinitialize_Params_STRUCT_SIZE}; - deinitializeParams.pHostObject = hostInitializeParams.pHostObject; - cuptiProfilerHostDeinitialize(&deinitializeParams); - } - - std::unordered_map metric_values; - std::vector metricNames_; - CUpti_RangeProfiler_Object* pRangeProfilerObject_; - std::vector counterDataImage_, configImage_; - std::string chipName_; -}; - -void test_cupti() -{ - rmm::mr::cuda_memory_resource cuda_mr; - rmm::mr::set_current_device_resource(&cuda_mr); - - const raft::handle_t handle_{}; - auto stream = handle_.get_stream(); - std::string test_instance = "pk1.mps"; - - auto path = cuopt::test::get_rapids_dataset_root_dir() + ("/mip/" + test_instance); - cuopt::mps_parser::mps_data_model_t mps_problem = - cuopt::mps_parser::parse_mps(path, false); - handle_.sync_stream(); - auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); - problem_checking_t::check_problem_representation(op_problem); - - init_handler(op_problem.get_handle_ptr()); - // run the problem constructor of MIP, so that we do bounds standardization - detail::problem_t problem(op_problem); - problem.preprocess_problem(); - - const int vectorLen = 100000; - rmm::device_uvector d_A(vectorLen, stream), d_B(vectorLen, stream), d_C(vectorLen, stream); - - auto random_generator = [](unsigned int seed) { - return [=] __device__(int idx) { - thrust::default_random_engine rng(seed); - thrust::uniform_real_distribution dist(0.0f, 1.0f); - rng.discard(idx); - return dist(rng); - }; - }; - - thrust::transform(rmm::exec_policy(stream), - thrust::counting_iterator(0), - thrust::counting_iterator(vectorLen), - d_A.begin(), - random_generator(1234)); - thrust::transform(rmm::exec_policy(stream), - thrust::counting_iterator(0), - thrust::counting_iterator(vectorLen), - d_B.begin(), - random_generator(5678)); - - // Get CUDA context after CUDA operations have initialized it - CUcontext cuContext; - cuCtxGetCurrent(&cuContext); - - // cupti_profiler_t profiler({ - // "sm__warps_launched.sum", "l1tex__t_sectors.sum", "l1tex__data_bank_reads.sum", - // "l1tex__data_bank_writes.sum", "l1tex__m_xbar2l1tex_read_bytes.sum", - // "l1tex__m_l1tex2xbar_write_bytes.sum", "lts__t_sectors_op_read.sum", - // "lts__t_sectors_op_write.sum" - // }); - cupti_profiler_t profiler({"l1tex__t_sectors.sum"}); - profiler.initialize_and_enable(cuContext); - profiler.configure(CUPTI_AutoRange, CUPTI_KernelReplay, 10); - profiler.start(); - - detail::fj_settings_t fj_settings; - fj_settings.feasibility_run = false; - fj_settings.iteration_limit = 10; - fj_settings.seed = 42; - printf("Running FJ\n"); - auto solution = run_fj(problem, fj_settings).solution; - - // VectorAdd<<<(vectorLen + 127) / 128, 128, 0, stream.value()>>>(d_A.data(), d_B.data(), - // d_C.data(), vectorLen); - - profiler.stop(); - - stream.synchronize(); - - auto metric_values = profiler.decode(); - for (const auto& [k, v] : metric_values) - printf("%s: %zu\n", k.c_str(), v); - - const size_t reads = 2 * vectorLen * sizeof(float); - const size_t writes = vectorLen * sizeof(float); - const size_t sector_reads = reads / 32; - const size_t sector_writes = writes / 32; - const size_t total_sectors = sector_reads + sector_writes; - EXPECT_EQ(metric_values["l1tex__t_sectors.sum"], total_sectors); - // EXPECT_EQ(metric_values["l1tex__m_xbar2l1tex_read_bytes.sum"], reads); - // EXPECT_EQ(metric_values["l1tex__m_l1tex2xbar_write_bytes.sum"], writes); -} - -TEST(cupti, test_cupti) { test_cupti(); } - -} // namespace cuopt::linear_programming::test diff --git a/scripts/requirements.txt b/scripts/requirements.txt deleted file mode 100644 index bcce7a788..000000000 --- a/scripts/requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -numpy>=1.20.0 -pandas>=1.3.0 -scikit-learn>=1.0.0 -xgboost>=1.5.0 -lightgbm>=3.0.0 -joblib>=1.0.0 - -# Optional: For exporting models to C source code -# treelite>=4.0.0 -# tl2cgen>=0.1.0 From 9b881d813287f315c4bccd7dca629c343e4f3ccf Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 1 Dec 2025 17:54:39 +0000 Subject: [PATCH 098/366] fix build --- cpp/src/dual_simplex/branch_and_bound.cpp | 8 +- cpp/src/dual_simplex/phase2.cpp | 2 +- cpp/src/mip/feasibility_jump/fj_cpu.cu | 185 ---------------------- 3 files changed, 6 insertions(+), 189 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index ba8353b14..d6d710884 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -863,9 +863,11 @@ node_solve_info_t branch_and_bound_t::solve_node( work_unit_context_.global_work_units_elapsed >= settings_.work_limit) { lp_status = dual::status_t::WORK_LIMIT; } - printf("------ Total work unit progress B&B: %f / %f\n", - work_unit_context_.global_work_units_elapsed, - settings_.work_limit); + if (settings_.deterministic) { + printf("------ Total work unit progress B&B: %f / %f\n", + work_unit_context_.global_work_units_elapsed, + settings_.work_limit); + } if (lp_status == dual::status_t::NUMERICAL) { log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 37d729fba..284c62887 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -3179,7 +3179,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } } - printf("Iterations: %d, time taken: %f\n", iter, toc(start_time)); + // printf("Iterations: %d, time taken: %f\n", iter, toc(start_time)); return status; } diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 43f747d86..5cdab9795 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -22,12 +22,6 @@ #include #include -#ifdef __linux__ -#include -#include -#include -#endif - #define CPUFJ_TIMING_TRACE 0 namespace cuopt::linear_programming::detail { @@ -116,144 +110,6 @@ static void print_timing_stats(fj_cpu_climber_t& fj_cpu) CUOPT_LOG_TRACE("========================================"); } -#ifdef __linux__ -template -static void initialize_papi(fj_cpu_climber_t& fj_cpu) -{ - int retval = PAPI_library_init(PAPI_VER_CURRENT); - if (retval != PAPI_VER_CURRENT && retval > 0) { - CUOPT_LOG_TRACE("%sPAPI library version mismatch", fj_cpu.log_prefix.c_str()); - return; - } - if (retval < 0) { - CUOPT_LOG_TRACE("%sPAPI library initialization failed", fj_cpu.log_prefix.c_str()); - return; - } - - fj_cpu.papi_event_set = PAPI_NULL; - retval = PAPI_create_eventset(&fj_cpu.papi_event_set); - if (retval != PAPI_OK) { - CUOPT_LOG_TRACE("%sPAPI eventset creation failed", fj_cpu.log_prefix.c_str()); - return; - } - - // Define the events we want to track - int candidate_events[] = { - PAPI_L1_DCA, // L1 data cache accesses - PAPI_L1_DCM, // L1 data cache misses - PAPI_L2_DCA, // L2 data cache accesses - PAPI_L2_DCM, // L2 data cache misses - PAPI_L3_TCA, // L3 total cache accesses - PAPI_L3_TCM, // L3 total cache misses - PAPI_LD_INS, // Load instructions - PAPI_SR_INS // Store instructions - }; - - const char* event_names[] = {"PAPI_L1_DCA", - "PAPI_L1_DCM", - "PAPI_L2_DCA", - "PAPI_L2_DCM", - "PAPI_L3_TCA", - "PAPI_L3_TCM", - "PAPI_LD_INS", - "PAPI_SR_INS"}; - - int num_candidate_events = sizeof(candidate_events) / sizeof(candidate_events[0]); - - // Try to add each event, store -1 for unavailable ones - for (int i = 0; i < num_candidate_events; i++) { - retval = PAPI_add_event(fj_cpu.papi_event_set, candidate_events[i]); - if (retval == PAPI_OK) { - fj_cpu.papi_events.push_back(candidate_events[i]); - } else { - fj_cpu.papi_events.push_back(-1); - CUOPT_LOG_TRACE( - "%sPAPI event %s not available on this system", fj_cpu.log_prefix.c_str(), event_names[i]); - } - } - (void)event_names; // Suppress unused warning when logging is disabled - - // Start counting - retval = PAPI_start(fj_cpu.papi_event_set); - if (retval != PAPI_OK) { - CUOPT_LOG_TRACE("%sPAPI start failed", fj_cpu.log_prefix.c_str()); - return; - } - - fj_cpu.papi_initialized = true; - CUOPT_LOG_TRACE("%sPAPI initialized successfully", fj_cpu.log_prefix.c_str()); -} - -// template -// static void collect_and_print_papi_metrics(fj_cpu_climber_t& fj_cpu) -// { -// if (!fj_cpu.papi_initialized) return; - -// std::vector values(fj_cpu.papi_events.size(), 0); -// int retval = PAPI_read(fj_cpu.papi_event_set, values.data()); -// if (retval != PAPI_OK) { -// CUOPT_LOG_TRACE("%sPAPI read failed", fj_cpu.log_prefix.c_str()); -// return; -// } - -// // Get thread ID -// pid_t tid = syscall(SYS_gettid); - -// // Build map of actual values indexed by event position -// std::vector all_values(8, -1); -// int value_idx = 0; -// for (size_t i = 0; i < fj_cpu.papi_events.size(); i++) { -// if (fj_cpu.papi_events[i] != -1) { all_values[i] = values[value_idx++]; } -// } - -// // Compute derived metrics -// double l1_miss_rate = -1.0; -// if (all_values[0] > 0 && all_values[1] != -1) { -// l1_miss_rate = (double)all_values[1] / all_values[0] * 100.0; -// } - -// double l2_miss_rate = -1.0; -// if (all_values[2] > 0 && all_values[3] != -1) { -// l2_miss_rate = (double)all_values[3] / all_values[2] * 100.0; -// } - -// // Lock to ensure thread-safe printing -// std::lock_guard lock(papi_print_mutex); - -// // Print everything on a single compact line -// CUOPT_LOG_DEBUG( -// "%sPAPI iter=%d tid=%d L1_DCA=%lld L1_DCM=%lld L2_DCA=%lld L2_DCM=%lld L3_TCA=%lld " -// "L3_TCM=%lld LD_INS=%lld SR_INS=%lld L1_miss=%.2f%% L2_miss=%.2f%%", -// fj_cpu.log_prefix.c_str(), -// fj_cpu.iterations, -// tid, -// all_values[0], -// all_values[1], -// all_values[2], -// all_values[3], -// all_values[4], -// all_values[5], -// all_values[6], -// all_values[7], -// l1_miss_rate, -// l2_miss_rate); - -// // Reset counters for the next 1000 iterations -// retval = PAPI_reset(fj_cpu.papi_event_set); -// if (retval != PAPI_OK) { CUOPT_LOG_TRACE("%sPAPI reset failed", fj_cpu.log_prefix.c_str()); } -// } - -template -static void cleanup_papi(fj_cpu_climber_t& fj_cpu) -{ - if (fj_cpu.papi_initialized) { - PAPI_stop(fj_cpu.papi_event_set, nullptr); - PAPI_cleanup_eventset(fj_cpu.papi_event_set); - PAPI_destroy_eventset(&fj_cpu.papi_event_set); - } -} -#endif - template static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) { @@ -389,33 +245,6 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, double l1_miss = -1.0; double l3_miss = -1.0; -#ifdef __linux__ - // Get PAPI metrics if available - if (fj_cpu.papi_initialized) { - std::vector values(fj_cpu.papi_events.size(), 0); - int retval = PAPI_read(fj_cpu.papi_event_set, values.data()); - if (retval == PAPI_OK) { - std::vector all_values(8, -1); - int value_idx = 0; - for (size_t i = 0; i < fj_cpu.papi_events.size(); i++) { - if (fj_cpu.papi_events[i] != -1) { all_values[i] = values[value_idx++]; } - } - - // Compute cache miss rates - if (all_values[0] > 0 && all_values[1] != -1) { - l1_miss = (double)all_values[1] / all_values[0] * 100.0; - } - if (all_values[4] > 0 && all_values[5] != -1) { - l3_miss = (double)all_values[5] / all_values[4] * 100.0; - } - - // Loads and stores per iteration - if (all_values[6] != -1) { loads_per_iter = (double)all_values[6] / 1000.0; } - if (all_values[7] != -1) { stores_per_iter = (double)all_values[7] / 1000.0; } - } - } -#endif - // Compute memory statistics double mem_loads_mb = mem_loads_bytes / 1e6; double mem_stores_mb = mem_stores_bytes / 1e6; @@ -1544,11 +1373,6 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l auto time_limit = std::chrono::milliseconds((int)(in_time_limit * 1000)); auto loop_time_start = std::chrono::high_resolution_clock::now(); -#ifdef __linux__ - // Initialize PAPI for performance monitoring - initialize_papi(fj_cpu); -#endif - // Initialize feature tracking fj_cpu.last_feature_log_time = loop_start; fj_cpu.prev_best_objective = fj_cpu.h_best_objective; @@ -1686,10 +1510,6 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l std::chrono::duration_cast>(now - loop_start).count() * 1000.0; - // #ifdef __linux__ - // collect_and_print_papi_metrics(fj_cpu); - // #endif - // Collect memory statistics auto [loads, stores] = fj_cpu.memory_manifold.collect(); @@ -1730,11 +1550,6 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l print_timing_stats(fj_cpu); #endif -#ifdef __linux__ - // Cleanup PAPI - cleanup_papi(fj_cpu); -#endif - return fj_cpu.feasible_found; } From b293e947796856888b320700bce3e4c5b906d51e Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 1 Dec 2025 17:55:15 +0000 Subject: [PATCH 099/366] cleanup --- cpp/src/mip/feasibility_jump/fj_cpu.cu | 5 ----- 1 file changed, 5 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 5cdab9795..f93cff9f6 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -28,11 +28,6 @@ namespace cuopt::linear_programming::detail { static constexpr double BIGVAL_THRESHOLD = 1e20; -// #ifdef __linux__ -// // Global mutex to protect PAPI metric printing across multiple threads -// static std::mutex papi_print_mutex; -// #endif - template class timing_raii_t { public: From ee7037fff7bcd8e007e5f31e503d0472ca0d0f1e Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 2 Dec 2025 12:53:25 +0000 Subject: [PATCH 100/366] cleanup --- cpp/src/mip/diversity/diversity_manager.cu | 70 +++++++---------- cpp/src/mip/diversity/multi_armed_bandit.cuh | 15 +++- .../recombiners/recombiner_stats.hpp | 15 ++++ .../mip/feasibility_jump/feasibility_jump.cu | 78 +++++++++---------- .../feasibility_jump_kernels.cu | 1 - cpp/src/mip/feasibility_jump/fj_cpu.cuh | 7 +- .../feasibility_pump/feasibility_pump.cu | 1 - .../line_segment_search.cu | 8 +- cpp/src/mip/local_search/local_search.cu | 39 +++------- .../local_search/rounding/bounds_repair.cu | 3 +- .../local_search/rounding/constraint_prop.cu | 15 +--- cpp/src/mip/presolve/multi_probe.cu | 8 +- 12 files changed, 119 insertions(+), 141 deletions(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 68f828229..958c4a00d 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -263,38 +263,14 @@ template void diversity_manager_t::run_fj_alone(solution_t& solution) { CUOPT_LOG_INFO("Running FJ alone!"); - // Benchmark FJ with 1000 different random starting solutions and varying iteration limits - CUOPT_LOG_INFO("Starting FJ benchmark: 1000 runs with random starting solutions"); - - std::mt19937 rng(cuopt::seed_generator::get_seed()); - std::uniform_int_distribution iter_dist(100, 50000); - - for (i_t run = 0; run < 1000; ++run) { - // Generate random starting solution within bounds - solution.assign_random_within_bounds(1.0, false); - solution.round_nearest(); - - // Configure FJ settings with random iteration limit - ls.fj.settings = fj_settings_t{}; - ls.fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; - ls.fj.settings.n_of_minimums_for_exit = 20000 * 1000; - ls.fj.settings.update_weights = true; - ls.fj.settings.feasibility_run = false; - ls.fj.settings.iteration_limit = iter_dist(rng); - ls.fj.settings.time_limit = std::numeric_limits::infinity(); - - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - ls.fj.settings.iteration_limit = iter_dist(rng); - } - - CUOPT_LOG_INFO( - "FJ benchmark run %d/%d: iteration_limit=%d", run + 1, 1000, ls.fj.settings.iteration_limit); - - ls.fj.solve(solution); - } - - CUOPT_LOG_INFO("FJ benchmark finished: 1000 runs completed"); - exit(0); + solution.round_nearest(); + ls.fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; + ls.fj.settings.n_of_minimums_for_exit = 20000 * 1000; + ls.fj.settings.update_weights = true; + ls.fj.settings.feasibility_run = false; + ls.fj.settings.time_limit = timer.remaining_time(); + ls.fj.solve(solution); + CUOPT_LOG_INFO("FJ alone finished!"); } // returns the best feasible solution @@ -320,7 +296,9 @@ solution_t diversity_manager_t::run_solver() { raft::common::nvtx::range fun_scope("run_solver"); - diversity_config.fj_only_run = false; + CUOPT_LOG_DEBUG("Determinism mode: %s", + context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC ? "deterministic" + : "opportunistic"); population.timer = timer; const f_t time_limit = timer.remaining_time(); @@ -606,6 +584,7 @@ diversity_manager_t::recombine_and_local_search(solution_t& sol1.get_feasible(), sol2.get_quality(population.weights), sol2.get_feasible()); + bool deterministic = context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC; double best_objective_of_parents = std::min(sol1.get_objective(), sol2.get_objective()); bool at_least_one_parent_feasible = sol1.get_feasible() || sol2.get_feasible(); // randomly choose among 3 recombiners @@ -616,7 +595,7 @@ diversity_manager_t::recombine_and_local_search(solution_t& std::numeric_limits::lowest(), std::numeric_limits::lowest(), std::numeric_limits::max(), - recombiner_work_normalized_reward_t(0.0)); + recombiner_work_normalized_reward_t(deterministic, 0.0)); return std::make_pair(solution_t(sol1), solution_t(sol2)); } cuopt_assert(population.test_invariant(), ""); @@ -636,7 +615,7 @@ diversity_manager_t::recombine_and_local_search(solution_t& std::numeric_limits::lowest(), std::numeric_limits::lowest(), std::numeric_limits::max(), - recombiner_work_normalized_reward_t(0.0)); + recombiner_work_normalized_reward_t(deterministic, 0.0)); return std::make_pair(solution_t(sol1), solution_t(sol2)); } cuopt_assert(offspring.test_number_all_integer(), "All must be integers after LS"); @@ -676,12 +655,15 @@ diversity_manager_t::recombine_and_local_search(solution_t& offspring_qual, sol1.get_quality(population.weights), sol2.get_quality(population.weights)); f_t best_quality_of_parents = std::min(sol1.get_quality(population.weights), sol2.get_quality(population.weights)); - mab_recombiner.add_mab_reward( - mab_recombiner.last_chosen_option, - best_quality_of_parents, - population.best().get_quality(population.weights), - offspring_qual, - recombiner_work_normalized_reward_t(recombine_stats.get_last_recombiner_work())); + mab_recombiner.add_mab_reward(mab_recombiner.last_chosen_option, + best_quality_of_parents, + population.best().get_quality(population.weights), + offspring_qual, + !deterministic + ? recombiner_work_normalized_reward_t( + deterministic, recombine_stats.get_last_recombiner_time()) + : recombiner_work_normalized_reward_t( + deterministic, recombine_stats.get_last_recombiner_work())); mab_ls.add_mab_reward(mab_ls_config_t::last_ls_mab_option, best_quality_of_parents, population.best_feasible().get_quality(population.weights), @@ -728,34 +710,40 @@ std::pair, bool> diversity_manager_t::recombine( } mab_recombiner.set_last_chosen_option(selected_index); recombine_stats.add_attempt((recombiner_enum_t)recombiner); + recombine_stats.start_recombiner_time(); CUOPT_LOG_DEBUG("Recombining sol %x and %x with recombiner %d, weights %x", a.get_hash(), b.get_hash(), recombiner, population.weights.get_hash()); + // Refactored code using a switch statement switch (recombiner) { case recombiner_enum_t::BOUND_PROP: { auto [sol, success, work] = bound_prop_recombiner.recombine(a, b, population.weights); recombine_stats.set_recombiner_work(work); + recombine_stats.stop_recombiner_time(); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } case recombiner_enum_t::FP: { auto [sol, success, work] = fp_recombiner.recombine(a, b, population.weights); recombine_stats.set_recombiner_work(work); + recombine_stats.stop_recombiner_time(); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } case recombiner_enum_t::LINE_SEGMENT: { auto [sol, success, work] = line_segment_recombiner.recombine(a, b, population.weights); recombine_stats.set_recombiner_work(work); + recombine_stats.stop_recombiner_time(); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } case recombiner_enum_t::SUB_MIP: { auto [sol, success, work] = sub_mip_recombiner.recombine(a, b, population.weights); recombine_stats.set_recombiner_work(work); + recombine_stats.stop_recombiner_time(); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } diff --git a/cpp/src/mip/diversity/multi_armed_bandit.cuh b/cpp/src/mip/diversity/multi_armed_bandit.cuh index 571939ae6..abcd233fa 100644 --- a/cpp/src/mip/diversity/multi_armed_bandit.cuh +++ b/cpp/src/mip/diversity/multi_armed_bandit.cuh @@ -45,13 +45,22 @@ struct ls_work_normalized_reward_t { }; struct recombiner_work_normalized_reward_t { + bool deterministic; double work; - recombiner_work_normalized_reward_t(double work) : work(work) {} + recombiner_work_normalized_reward_t(bool deterministic, double work) + : deterministic(deterministic), work(work) + { + } double operator()(double factor) const { - // normal recombiners process 200 variables - return factor * (std::max(0.1, 4.0 - (work / 200))); + // normal recombiners take 2000 ms + if (deterministic) { + double time_in_miliseconds = work; + return factor * (std::max(0.1, 4.0 - (time_in_miliseconds / 2000))); + } else { + return factor * (std::max(0.1, 4.0 - (work / 200))); + } } }; diff --git a/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp b/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp index 9986a4d94..de0c31405 100644 --- a/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp +++ b/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp @@ -75,12 +75,27 @@ struct all_recombine_stats { // enum of the last attempted recombiner std::optional last_attempt; + double last_recombiner_time; + std::chrono::high_resolution_clock::time_point last_recombiner_start_time; double last_recombiner_work; void set_recombiner_work(double work) { last_recombiner_work = work; } double get_last_recombiner_work() { return last_recombiner_work; } + void start_recombiner_time() + { + last_recombiner_start_time = std::chrono::high_resolution_clock::now(); + } + void stop_recombiner_time() + { + last_recombiner_time = std::chrono::duration_cast( + std::chrono::high_resolution_clock::now() - last_recombiner_start_time) + .count(); + } + + double get_last_recombiner_time() { return last_recombiner_time; } + void reset() { for (size_t i = 0; i < recombiner_count; ++i) { diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index da4f2edce..5051d540c 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -1079,11 +1079,10 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) if (timer.check_time_limit() || steps >= settings.iteration_limit) { limit_reached = true; } #if !FJ_SINGLE_STEP - // if (steps % 500 == 0) - if (false) + if (steps % 500 == 0) #endif { - CUOPT_LOG_DEBUG( + CUOPT_LOG_TRACE( "FJ " "step %d viol %.2g [%d], obj %.8g, best %.8g, mins %d, maxw %g, " "objw %g, sol %x, delta %x, inc %x, lhs %x, lhscomp %x, viol %x, weights %x", @@ -1283,9 +1282,8 @@ i_t fj_t::solve(solution_t& solution) detail::compute_hash(cstr_left_weights), detail::compute_hash(cstr_right_weights)); - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - settings.work_limit = settings.time_limit; - } + bool deterministic = context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC; + if (deterministic) { settings.work_limit = settings.time_limit; } // if work_limit is set: compute an estimate of the number of iterations required if (settings.work_limit != std::numeric_limits::infinity()) { std::map features_map = get_feature_vector(0); @@ -1333,7 +1331,7 @@ i_t fj_t::solve(solution_t& solution) handle_ptr->sync_stream(); // Compute and store feature vector for later logging - feature_vector = get_feature_vector(0); + if (deterministic) { feature_vector = get_feature_vector(0); } i_t iterations = host_loop(solution); RAFT_CHECK_CUDA(handle_ptr->get_stream()); @@ -1384,42 +1382,44 @@ i_t fj_t::solve(solution_t& solution) cuopt_func_call(solution.test_variable_bounds()); - double work_to_record = settings.work_limit; - - if (iterations < settings.iteration_limit) { - CUOPT_LOG_DEBUG( - "FJ early exit at %d iterations (limit: %d)", iterations, settings.iteration_limit); - // Compute the work unit corresponding to the number of iterations elapsed - // by incrementally guessing work units until the model predicts >= actual iterations - // TODO: awfully ugly, change - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC && iterations > 0) { - double guessed_work = 0.0; - const double work_increment = 0.1; - const double max_work = settings.work_limit * 2.0; // Safety limit - float predicted_iters = 0.0f; - - // Make a copy of the feature vector and modify the time/work_limit field - std::map features_for_prediction = feature_vector; - - while (guessed_work <= max_work) { - features_for_prediction["time"] = (float)guessed_work; - predicted_iters = std::max( - 0.0f, - (float)ceil( - context.work_unit_predictors.fj_predictor.predict_scalar(features_for_prediction))); - - if (predicted_iters >= (float)iterations) { - work_to_record = guessed_work; - break; - } + if (deterministic) { + double work_to_record = settings.work_limit; + + if (iterations < settings.iteration_limit) { + CUOPT_LOG_DEBUG( + "FJ early exit at %d iterations (limit: %d)", iterations, settings.iteration_limit); + // Compute the work unit corresponding to the number of iterations elapsed + // by incrementally guessing work units until the model predicts >= actual iterations + // TODO: awfully ugly, change + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC && iterations > 0) { + double guessed_work = 0.0; + const double work_increment = 0.1; + const double max_work = settings.work_limit * 2.0; // Safety limit + float predicted_iters = 0.0f; + + // Make a copy of the feature vector and modify the time/work_limit field + std::map features_for_prediction = feature_vector; + + while (guessed_work <= max_work) { + features_for_prediction["time"] = (float)guessed_work; + predicted_iters = std::max( + 0.0f, + (float)ceil( + context.work_unit_predictors.fj_predictor.predict_scalar(features_for_prediction))); + + if (predicted_iters >= (float)iterations) { + work_to_record = guessed_work; + break; + } - guessed_work += work_increment; + guessed_work += work_increment; + } } } - } - CUOPT_LOG_DEBUG("FJ: recording work %fwu for %d iterations", work_to_record, iterations); - timer.record_work(work_to_record); + CUOPT_LOG_DEBUG("FJ: recording work %fwu for %d iterations", work_to_record, iterations); + timer.record_work(work_to_record); + } CUOPT_LOG_DEBUG("FJ sol hash %x", solution.get_hash()); diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 0332b41e4..803f2cf5a 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -899,7 +899,6 @@ DI void update_changed_constraints(typename fj_t::climber_data_t::view // sort changed constraints to guarantee determinism // TODO: horribly slow as it is... block-parallelize at least? but not trivial for arbitrary // sizes w/ CUB - // TODO(2): tsk.. ... just bucket sort... if (fj.settings->work_limit != std::numeric_limits::infinity()) { thrust::sort(thrust::seq, fj.constraints_changed.begin(), diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cuh b/cpp/src/mip/feasibility_jump/fj_cpu.cuh index 27f9d7911..00a2cd224 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cuh +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cuh @@ -125,7 +125,7 @@ struct fj_cpu_climber_t { // vector is actually likely beneficial here since we're memory bound std::vector flip_move_computed; - + ; // CSR nnz offset -> (delta, score) std::vector> cached_mtm_moves; @@ -151,11 +151,6 @@ struct fj_cpu_climber_t { std::atomic halted{false}; - // PAPI performance counters - int papi_event_set{-1}; - bool papi_initialized{false}; - std::vector papi_events; - // Feature tracking for regression model (last 1000 iterations) i_t nnz_processed_window{0}; i_t n_lift_moves_window{0}; diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index 837c1e5a6..ecd93c2e2 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -188,7 +188,6 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_t 0) { temp_p.insert_variables(h_variables); } diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu index 8454daa57..5f6676e77 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu @@ -189,9 +189,7 @@ bool line_segment_search_t::search_line_segment( best_feasible_cost, curr_cost); } - if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { - if (timer.check_time_limit()) { break; } - } + if (timer.check_time_limit()) { break; } i_t number_of_integer_var_diff = compute_number_of_integer_var_diff( solution.problem_ptr->integer_indices, solution.assignment, @@ -229,9 +227,7 @@ bool line_segment_search_t::search_line_segment( best_feasible_cost, curr_cost); } - if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { - if (timer.check_time_limit()) { break; } - } + if (timer.check_time_limit()) { break; } } // if not recombiner mode but local search mode if (!settings.recombiner_mode) { diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 8e4dc250e..4d5e8f919 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -153,19 +153,9 @@ bool local_search_t::do_fj_solve(solution_t& solution, auto h_weights = cuopt::host_copy(in_fj.cstr_weights, solution.handle_ptr->get_stream()); auto h_objective_weight = in_fj.objective_weight.value(solution.handle_ptr->get_stream()); - - // for now: always assign the CPUFJs to perform 1000 iters per s - fj_settings_t cpu_fj_settings{}; - if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { - cpu_fj_settings.iteration_limit = std::numeric_limits::max(); - } else { - // TODO: CHANGE - cpu_fj_settings.iteration_limit = 1000 * time_limit; - } - for (auto& cpu_fj : ls_cpu_fj) { cpu_fj.fj_cpu = cpu_fj.fj_ptr->create_cpu_climber( - solution, h_weights, h_weights, h_objective_weight, cpu_fj_settings, true); + solution, h_weights, h_weights, h_objective_weight, fj_settings_t{}, true); } auto solution_copy = solution; @@ -196,14 +186,16 @@ bool local_search_t::do_fj_solve(solution_t& solution, f_t best_cpu_obj = std::numeric_limits::max(); // // Wait for CPU solver to finish - for (auto& cpu_fj : ls_cpu_fj) { - bool cpu_sol_found = cpu_fj.wait_for_cpu_solver(); - if (cpu_sol_found) { - f_t cpu_obj = cpu_fj.fj_cpu->h_best_objective; - if (cpu_obj < best_cpu_obj) { - best_cpu_obj = cpu_obj; - solution_cpu.copy_new_assignment(cpu_fj.fj_cpu->h_best_assignment); - solution_cpu.compute_feasibility(); + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { + for (auto& cpu_fj : ls_cpu_fj) { + bool cpu_sol_found = cpu_fj.wait_for_cpu_solver(); + if (cpu_sol_found) { + f_t cpu_obj = cpu_fj.fj_cpu->h_best_objective; + if (cpu_obj < best_cpu_obj) { + best_cpu_obj = cpu_obj; + solution_cpu.copy_new_assignment(cpu_fj.fj_cpu->h_best_assignment); + solution_cpu.compute_feasibility(); + } } } } @@ -211,9 +203,6 @@ bool local_search_t::do_fj_solve(solution_t& solution, bool gpu_feasible = solution.get_feasible(); bool cpu_feasible = cpu_sol_found && solution_cpu.get_feasible(); - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - cpu_feasible = false; // ignore CPUFJ in deterministic mode - } static std::unordered_map total_calls; static std::unordered_map cpu_better; @@ -415,11 +404,7 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu bool is_feasible = constraint_prop.apply_round(solution, lp_run_time_after_feasible, bounds_prop_timer); if (!is_feasible) { - f_t lp_run_time = 2.; - // CHANGE - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - lp_run_time = std::numeric_limits::infinity(); - } + const f_t lp_run_time = 2.; relaxed_lp_settings_t lp_settings; lp_settings.time_limit = std::min(lp_run_time, timer.remaining_time()); lp_settings.work_limit = lp_settings.time_limit; diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index 03f2c47ed..036db25bb 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -405,7 +405,8 @@ bool bounds_repair_t::repair_problem(problem_t& problem, curr_violation = best_violation; best_bounds.update_from(problem, handle_ptr); i_t no_candidate_in_a_row = 0; - i_t iter_limit = std::numeric_limits::max(); + // TODO: do this better + i_t iter_limit = std::numeric_limits::max(); if (problem.deterministic) { iter_limit = 20; } while (h_n_violated_cstr > 0 && iter_limit-- > 0) { CUOPT_LOG_TRACE("Bounds repair loop: n_violated %d best_violation %f curr_violation %f", diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index a037886c2..11c6f988e 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -767,7 +767,8 @@ bool constraint_prop_t::run_repair_procedure(problem_t& prob repair_stats.repair_attempts++; f_t repair_start_time = timer.remaining_time(); i_t n_of_repairs_needed_for_feasible = 0; - i_t iter_limit = std::numeric_limits::max(); + // TODO: do this better + i_t iter_limit = std::numeric_limits::max(); if (this->context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { timer = work_limit_timer_t(context.gpu_heur_loop, std::numeric_limits::infinity()); iter_limit = 100; @@ -854,14 +855,7 @@ bool constraint_prop_t::find_integer( { using crit_t = termination_criterion_t; auto& unset_integer_vars = unset_vars; - i_t seed = cuopt::seed_generator::get_seed(); - std::mt19937 rng(seed); - - // CHANGE - if (this->context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - timer = work_limit_timer_t(context.gpu_heur_loop, std::numeric_limits::infinity()); - } - + std::mt19937 rng(cuopt::seed_generator::get_seed()); lb_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); ub_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); assignment_restore.resize(sol.problem_ptr->n_variables, sol.handle_ptr->get_stream()); @@ -1120,9 +1114,6 @@ bool constraint_prop_t::apply_round( // === CONSTRAINT PROP PREDICTOR FEATURES - END === max_timer = work_limit_timer_t{context.gpu_heur_loop, max_time_for_bounds_prop}; - if (this->context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - max_timer = work_limit_timer_t(context.gpu_heur_loop, std::numeric_limits::infinity()); - } if (check_brute_force_rounding(sol)) { auto cp_end_time = std::chrono::high_resolution_clock::now(); auto cp_elapsed_ms = diff --git a/cpp/src/mip/presolve/multi_probe.cu b/cpp/src/mip/presolve/multi_probe.cu index 248ba0b49..5a11164e4 100644 --- a/cpp/src/mip/presolve/multi_probe.cu +++ b/cpp/src/mip/presolve/multi_probe.cu @@ -148,7 +148,7 @@ bool multi_probe_t::calculate_bounds_update(problem_t& pb, <<get_stream()>>>(pb.view(), upd_1.view()); RAFT_CHECK_CUDA(handle_ptr->get_stream()); i_t h_bounds_changed_1 = upd_1.bounds_changed.value(handle_ptr->get_stream()); - // CUOPT_LOG_TRACE("Bounds changed upd 1 %d", h_bounds_changed_1); + CUOPT_LOG_TRACE("Bounds changed upd 1 %d", h_bounds_changed_1); skip_1 = (h_bounds_changed_1 == zero); } else if (skip_1) { upd_0.bounds_changed.set_value_async(zero, handle_ptr->get_stream()); @@ -156,7 +156,7 @@ bool multi_probe_t::calculate_bounds_update(problem_t& pb, <<get_stream()>>>(pb.view(), upd_0.view()); RAFT_CHECK_CUDA(handle_ptr->get_stream()); i_t h_bounds_changed_0 = upd_0.bounds_changed.value(handle_ptr->get_stream()); - // CUOPT_LOG_TRACE("Bounds changed upd 0 %d", h_bounds_changed_0); + CUOPT_LOG_TRACE("Bounds changed upd 0 %d", h_bounds_changed_0); skip_0 = (h_bounds_changed_0 == zero); } else { upd_0.bounds_changed.set_value_async(zero, handle_ptr->get_stream()); @@ -166,9 +166,9 @@ bool multi_probe_t::calculate_bounds_update(problem_t& pb, pb.view(), upd_0.view(), upd_1.view()); RAFT_CHECK_CUDA(handle_ptr->get_stream()); i_t h_bounds_changed_0 = upd_0.bounds_changed.value(handle_ptr->get_stream()); - // CUOPT_LOG_TRACE("Bounds changed upd 0 %d", h_bounds_changed_0); + CUOPT_LOG_TRACE("Bounds changed upd 0 %d", h_bounds_changed_0); i_t h_bounds_changed_1 = upd_1.bounds_changed.value(handle_ptr->get_stream()); - // CUOPT_LOG_TRACE("Bounds changed upd 1 %d", h_bounds_changed_1); + CUOPT_LOG_TRACE("Bounds changed upd 1 %d", h_bounds_changed_1); skip_0 = (h_bounds_changed_0 == zero); skip_1 = (h_bounds_changed_1 == zero); From fb0c0725e8cd6fe3c8cc1b25ace127ee78943da7 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 18 Nov 2025 11:18:49 +0000 Subject: [PATCH 101/366] add support for building with clang --- build.sh | 16 ++++++++++++++-- ci/tsan_suppressions.txt | 6 ++++++ cpp/CMakeLists.txt | 19 ++++++++++++++++++- cpp/include/cuopt/error.hpp | 2 -- .../utilities/internals.hpp | 1 + cpp/src/linear_programming/pdlp.cu | 2 +- .../utilities/cython_solve.cu | 3 +-- cpp/src/linear_programming/utils.cuh | 6 +++--- cpp/src/mip/diversity/lns/rins.cu | 4 ++-- cpp/src/mip/local_search/local_search.cu | 3 +-- cpp/src/mip/presolve/gf2_presolve.hpp | 4 ++++ cpp/src/mip/presolve/third_party_presolve.cpp | 4 ++++ cpp/src/mip/utilities/cpu_worker_thread.cuh | 8 +++++--- cpp/src/routing/crossovers/ox_recombiner.cuh | 4 ++-- .../distance_engine/waypoint_matrix_test.cpp | 6 +++--- .../c_api_tests/c_api_test.c | 1 + cpp/tests/routing/level0/l0_ges_test.cu | 4 ++-- .../level0/l0_objective_function_test.cu | 2 +- cpp/tests/routing/level0/l0_routing_test.cu | 2 +- .../routing/level0/l0_vehicle_order_match.cu | 2 +- .../routing/level0/l0_vehicle_types_test.cu | 2 +- 21 files changed, 72 insertions(+), 29 deletions(-) create mode 100644 ci/tsan_suppressions.txt diff --git a/build.sh b/build.sh index e129ee4ef..07a435352 100755 --- a/build.sh +++ b/build.sh @@ -15,7 +15,7 @@ REPODIR=$(cd "$(dirname "$0")"; pwd) LIBCUOPT_BUILD_DIR=${LIBCUOPT_BUILD_DIR:=${REPODIR}/cpp/build} LIBMPS_PARSER_BUILD_DIR=${LIBMPS_PARSER_BUILD_DIR:=${REPODIR}/cpp/libmps_parser/build} -VALIDARGS="clean libcuopt libmps_parser cuopt_mps_parser cuopt cuopt_server cuopt_sh_client docs deb -a -b -g -fsanitize -v -l= --verbose-pdlp --build-lp-only --no-fetch-rapids --skip-c-python-adapters --skip-tests-build --skip-routing-build --skip-fatbin-write --host-lineinfo [--cmake-args=\\\"\\\"] [--cache-tool=] -n --allgpuarch --ci-only-arch --show_depr_warn -h --help" +VALIDARGS="clean libcuopt libmps_parser cuopt_mps_parser cuopt cuopt_server cuopt_sh_client docs deb -a -b -g -fsanitize -tsan -v -l= --verbose-pdlp --build-lp-only --no-fetch-rapids --skip-c-python-adapters --skip-tests-build --skip-routing-build --skip-fatbin-write --host-lineinfo [--cmake-args=\\\"\\\"] [--cache-tool=] -n --allgpuarch --ci-only-arch --show_depr_warn -h --help" HELP="$0 [ ...] [ ...] where is: clean - remove all existing build artifacts and configuration (start over) @@ -32,7 +32,8 @@ HELP="$0 [ ...] [ ...] -g - build for debug -a - Enable assertion (by default in debug mode) -b - Build with benchmark settings - -fsanitize - Build with sanitizer + -fsanitize - Build with AddressSanitizer and UndefinedBehaviorSanitizer + -tsan - Build with ThreadSanitizer (cannot be used with -fsanitize) -n - no install step --no-fetch-rapids - don't fetch rapids dependencies -l= - log level. Options are: TRACE | DEBUG | INFO | WARN | ERROR | CRITICAL | OFF. Default=INFO @@ -76,6 +77,7 @@ BUILD_ALL_GPU_ARCH=0 BUILD_CI_ONLY=0 BUILD_LP_ONLY=0 BUILD_SANITIZER=0 +BUILD_TSAN=0 SKIP_C_PYTHON_ADAPTERS=0 SKIP_TESTS_BUILD=0 SKIP_ROUTING_BUILD=0 @@ -230,6 +232,9 @@ fi if hasArg -fsanitize; then BUILD_SANITIZER=1 fi +if hasArg -tsan; then + BUILD_TSAN=1 +fi if hasArg --skip-c-python-adapters; then SKIP_C_PYTHON_ADAPTERS=1 fi @@ -298,6 +303,12 @@ if [ ${BUILD_LP_ONLY} -eq 1 ] && [ ${SKIP_C_PYTHON_ADAPTERS} -eq 0 ]; then exit 1 fi +if [ ${BUILD_SANITIZER} -eq 1 ] && [ ${BUILD_TSAN} -eq 1 ]; then + echo "ERROR: -fsanitize and -tsan cannot be used together" + echo "AddressSanitizer and ThreadSanitizer are mutually exclusive" + exit 1 +fi + if [ ${BUILD_ALL_GPU_ARCH} -eq 1 ]; then CUOPT_CMAKE_CUDA_ARCHITECTURES="RAPIDS" echo "Building for *ALL* supported GPU architectures..." @@ -344,6 +355,7 @@ if buildAll || hasArg libcuopt; then -DFETCH_RAPIDS=${FETCH_RAPIDS} \ -DBUILD_LP_ONLY=${BUILD_LP_ONLY} \ -DBUILD_SANITIZER=${BUILD_SANITIZER} \ + -DBUILD_TSAN=${BUILD_TSAN} \ -DSKIP_C_PYTHON_ADAPTERS=${SKIP_C_PYTHON_ADAPTERS} \ -DBUILD_TESTS=$((1 - ${SKIP_TESTS_BUILD})) \ -DSKIP_ROUTING_BUILD=${SKIP_ROUTING_BUILD} \ diff --git a/ci/tsan_suppressions.txt b/ci/tsan_suppressions.txt new file mode 100644 index 000000000..b6f413e37 --- /dev/null +++ b/ci/tsan_suppressions.txt @@ -0,0 +1,6 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. +# SPDX-License-Identifier: Apache-2.0 + +# Ignore races in external header-only libraries +race:tbb +race:Papilo diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 9c26451a7..c2c2be2bb 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -81,9 +81,22 @@ endif(CMAKE_COMPILER_IS_GNUCXX) # 2. (Optional) To run with a debugger (gdb or cuda-gdb) use the additional ASAN option alloc_dealloc_mismatch=0 if(BUILD_SANITIZER) list(APPEND CUOPT_CXX_FLAGS -fsanitize=address,undefined -fno-omit-frame-pointer -g -Wno-error=maybe-uninitialized) + if(NOT "${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") + list(APPEND CUOPT_CXX_FLAGS -Wno-error=maybe-uninitialized) + endif() add_link_options(-fsanitize=address,undefined) endif(BUILD_SANITIZER) +# To use ThreadSanitizer: +# 1. Build with clang and the -tsan flag +# 2. Run the binary with env var set: OMP_TOOL_LIBRARIES=/usr/lib/llvm-17/lib/libarcher.so ARCHER_OPTIONS='verbose=1' TSAN_OPTIONS='suppresions=ci/tsan_suppressions.txt:ignore_noninstrumented_modules=1:halt_on_error=1' +# Replace with local llvm install path. libarcher.so must be presetn +if(BUILD_TSAN) + message(STATUS "Building with ThreadSanitizer enabled") + list(APPEND CUOPT_CXX_FLAGS -fsanitize=thread -fno-omit-frame-pointer -g) + add_link_options(-fsanitize=thread) +endif(BUILD_TSAN) + if(DEFINE_ASSERT) add_definitions(-DASSERT_MODE) endif(DEFINE_ASSERT) @@ -117,7 +130,11 @@ if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.9) set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -static-global-template-stub=false") endif() list(APPEND CUOPT_CUDA_FLAGS -Werror=cross-execution-space-call -Wno-deprecated-declarations -Xcompiler=-Werror --default-stream=per-thread) -list(APPEND CUOPT_CUDA_FLAGS -Xcompiler=-Wall -Wno-error=non-template-friend) +if("${CMAKE_CUDA_HOST_COMPILER}" MATCHES "clang" OR "${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") + list(APPEND CUOPT_CUDA_FLAGS -Xcompiler=-Wall) +else() + list(APPEND CUOPT_CUDA_FLAGS -Xcompiler=-Wall -Wno-error=non-template-friend) +endif() list(APPEND CUOPT_CUDA_FLAGS -Xfatbin=-compress-all) if(CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 12.9 AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 13.0) list(APPEND CUOPT_CUDA_FLAGS -Xfatbin=--compress-level=3) diff --git a/cpp/include/cuopt/error.hpp b/cpp/include/cuopt/error.hpp index b6086245d..a83413515 100644 --- a/cpp/include/cuopt/error.hpp +++ b/cpp/include/cuopt/error.hpp @@ -33,8 +33,6 @@ enum class error_type_t { */ struct logic_error : public std::logic_error { - explicit logic_error() = default; - logic_error(const logic_error& exception) = default; // Move constructor diff --git a/cpp/include/cuopt/linear_programming/utilities/internals.hpp b/cpp/include/cuopt/linear_programming/utilities/internals.hpp index 84c96a716..86e7246fa 100644 --- a/cpp/include/cuopt/linear_programming/utilities/internals.hpp +++ b/cpp/include/cuopt/linear_programming/utilities/internals.hpp @@ -62,6 +62,7 @@ namespace linear_programming { class base_solution_t { public: + virtual ~base_solution_t() = default; virtual bool is_mip() const = 0; }; diff --git a/cpp/src/linear_programming/pdlp.cu b/cpp/src/linear_programming/pdlp.cu index 5d982bcc9..d78f1d1f4 100644 --- a/cpp/src/linear_programming/pdlp.cu +++ b/cpp/src/linear_programming/pdlp.cu @@ -1510,7 +1510,7 @@ void pdlp_solver_t::compute_initial_step_size() const auto& cusparse_view_ = pdhg_solver_.get_cusparse_view(); - int sing_iters = 0; + [[maybe_unused]] int sing_iters = 0; for (int i = 0; i < max_iterations; ++i) { ++sing_iters; // d_q = d_z diff --git a/cpp/src/linear_programming/utilities/cython_solve.cu b/cpp/src/linear_programming/utilities/cython_solve.cu index 111f3caf0..38968c050 100644 --- a/cpp/src/linear_programming/utilities/cython_solve.cu +++ b/cpp/src/linear_programming/utilities/cython_solve.cu @@ -295,8 +295,7 @@ std::pair>, double> call_batch_solve( #pragma omp parallel for num_threads(max_thread) for (std::size_t i = 0; i < size; ++i) - list[i] = - std::move(call_solve(data_models[i], solver_settings, cudaStreamNonBlocking, is_batch_mode)); + list[i] = call_solve(data_models[i], solver_settings, cudaStreamNonBlocking, is_batch_mode); auto end = std::chrono::high_resolution_clock::now(); auto duration = std::chrono::duration_cast(end - start_solver); diff --git a/cpp/src/linear_programming/utils.cuh b/cpp/src/linear_programming/utils.cuh index 0da5d25ce..2333283f3 100644 --- a/cpp/src/linear_programming/utils.cuh +++ b/cpp/src/linear_programming/utils.cuh @@ -62,9 +62,9 @@ struct max_abs_value { template i_t conditional_major(uint64_t total_pdlp_iterations) { - uint64_t step = 10; - uint64_t threshold = 1000; - uint64_t iteration = 0; + uint64_t step = 10; + uint64_t threshold = 1000; + [[maybe_unused]] uint64_t iteration = 0; [[maybe_unused]] constexpr uint64_t max_u64 = std::numeric_limits::max(); diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index c2e46da4d..e09982391 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -259,8 +259,8 @@ void rins_t::run_rins() branch_and_bound_settings.num_diving_threads = 1; branch_and_bound_settings.log.log = false; branch_and_bound_settings.log.log_prefix = "[RINS] "; - branch_and_bound_settings.solution_callback = [this, &rins_solution_queue]( - std::vector& solution, f_t objective) { + branch_and_bound_settings.solution_callback = [&rins_solution_queue](std::vector& solution, + f_t objective) { rins_solution_queue.push_back(solution); }; dual_simplex::branch_and_bound_t branch_and_bound(branch_and_bound_problem, diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index a3353e72f..212c282ef 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -81,8 +81,7 @@ void local_search_t::start_cpufj_scratch_threads(population_t 0); cpu_fj.fj_cpu->log_prefix = "******* scratch " + std::to_string(counter) + ": "; - cpu_fj.fj_cpu->improvement_callback = [this, &population, &cpu_fj]( - f_t obj, const std::vector& h_vec) { + cpu_fj.fj_cpu->improvement_callback = [&population](f_t obj, const std::vector& h_vec) { population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); if (obj < local_search_best_obj) { CUOPT_LOG_TRACE("******* New local search best obj %g, best overall %g", diff --git a/cpp/src/mip/presolve/gf2_presolve.hpp b/cpp/src/mip/presolve/gf2_presolve.hpp index 19d4e7d81..53e79d2c9 100644 --- a/cpp/src/mip/presolve/gf2_presolve.hpp +++ b/cpp/src/mip/presolve/gf2_presolve.hpp @@ -7,13 +7,17 @@ #pragma once +#if !defined(__clang__) #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wstringop-overflow" // ignore boost error for pip wheel build +#endif #include #include #include #include +#if !defined(__clang__) #pragma GCC diagnostic pop +#endif namespace cuopt::linear_programming::detail { diff --git a/cpp/src/mip/presolve/third_party_presolve.cpp b/cpp/src/mip/presolve/third_party_presolve.cpp index 22827c6e2..f3faf3dd7 100644 --- a/cpp/src/mip/presolve/third_party_presolve.cpp +++ b/cpp/src/mip/presolve/third_party_presolve.cpp @@ -14,11 +14,15 @@ #include +#if !defined(__clang__) #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wstringop-overflow" // ignore boost error for pip wheel build +#endif #include #include +#if !defined(__clang__) #pragma GCC diagnostic pop +#endif namespace cuopt::linear_programming::detail { diff --git a/cpp/src/mip/utilities/cpu_worker_thread.cuh b/cpp/src/mip/utilities/cpu_worker_thread.cuh index 0f1671c94..8d0b6d71e 100644 --- a/cpp/src/mip/utilities/cpu_worker_thread.cuh +++ b/cpp/src/mip/utilities/cpu_worker_thread.cuh @@ -60,6 +60,7 @@ template cpu_worker_thread_base_t::~cpu_worker_thread_base_t() { // Note: We don't call on_terminate() here since the derived object is already destroyed. + CUOPT_LOG_DEBUG("Destroying CPU worker thread"); join_worker(); } @@ -83,12 +84,14 @@ void cpu_worker_thread_base_t::cpu_worker_thread() std::lock_guard lock(cpu_mutex); cpu_thread_done = true; } + cpu_cv.notify_all(); } } template void cpu_worker_thread_base_t::request_termination() { + CUOPT_LOG_DEBUG("Requesting termination of CPU worker thread"); bool should_terminate = false; { std::lock_guard lock(cpu_mutex); @@ -131,9 +134,8 @@ void cpu_worker_thread_base_t::start_cpu_solver() template bool cpu_worker_thread_base_t::wait_for_cpu_solver() { - while (!cpu_thread_done && !cpu_thread_terminate) { - std::this_thread::sleep_for(std::chrono::milliseconds(1)); - } + std::unique_lock lock(cpu_mutex); + cpu_cv.wait(lock, [this] { return cpu_thread_done || cpu_thread_terminate; }); return static_cast(this)->get_result(); } diff --git a/cpp/src/routing/crossovers/ox_recombiner.cuh b/cpp/src/routing/crossovers/ox_recombiner.cuh index 17823c28b..7f965de2f 100644 --- a/cpp/src/routing/crossovers/ox_recombiner.cuh +++ b/cpp/src/routing/crossovers/ox_recombiner.cuh @@ -336,7 +336,7 @@ struct OX { int i = routes_number; if (optimal_routes_search) { i = optimal_routes_number; } int end_index = offspring.size() - 1; - double cost_n, cost_p, total_delta = 0.; + [[maybe_unused]] double cost_n, cost_p, total_delta = 0.; std::vector>>> routes_to_add; std::vector tmp_route; @@ -530,7 +530,7 @@ struct OX { "Mismatch number of edges"); for (size_t j = 0; j < h_transpose_graph[i].size(); ++j) { auto [ref_edge, ref_weight, ref_veh] = h_transpose_graph[i][j]; - bool found = false; + [[maybe_unused]] bool found = false; for (int x = 0; x < tmp_transpose.row_sizes[i]; ++x) { auto edge = tmp_transpose.indices[transpose_offset + x]; auto veh = tmp_transpose.buckets[transpose_offset + x]; diff --git a/cpp/tests/distance_engine/waypoint_matrix_test.cpp b/cpp/tests/distance_engine/waypoint_matrix_test.cpp index 80288bc6f..1f9bacf0e 100644 --- a/cpp/tests/distance_engine/waypoint_matrix_test.cpp +++ b/cpp/tests/distance_engine/waypoint_matrix_test.cpp @@ -44,7 +44,7 @@ class waypoint_matrix_waypoints_sequence_test_t this->expected_sequence_offsets = param.sequence_offsets; } - void TearDown() {} + void TearDown() override {} void test_compute_waypoint_sequence() { @@ -131,7 +131,7 @@ class waypoint_matrix_shortest_path_cost_t this->weights.data()); } - void TearDown() {} + void TearDown() override {} void test_compute_shortest_path_costs() { @@ -192,7 +192,7 @@ class waypoint_matrix_cost_matrix_test_t this->weights.data()); } - void TearDown() {} + void TearDown() override {} void test_compute_cost_matrix() { diff --git a/cpp/tests/linear_programming/c_api_tests/c_api_test.c b/cpp/tests/linear_programming/c_api_tests/c_api_test.c index 25aef6d25..3b3032176 100644 --- a/cpp/tests/linear_programming/c_api_tests/c_api_test.c +++ b/cpp/tests/linear_programming/c_api_tests/c_api_test.c @@ -53,6 +53,7 @@ const char* termination_status_to_string(cuopt_int_t termination_status) case CUOPT_TERIMINATION_STATUS_FEASIBLE_FOUND: return "Feasible found"; } + return "Unknown"; } diff --git a/cpp/tests/routing/level0/l0_ges_test.cu b/cpp/tests/routing/level0/l0_ges_test.cu index 22373f704..afd0a2627 100644 --- a/cpp/tests/routing/level0/l0_ges_test.cu +++ b/cpp/tests/routing/level0/l0_ges_test.cu @@ -55,7 +55,7 @@ class routing_ges_test_t : public ::testing::TestWithParam>, this->populate_device_vectors(); } - void TearDown() {} + void TearDown() override {} assignment_t solve(const cuopt::routing::data_model_view_t& data_model, const cuopt::routing::solver_settings_t& solver_settings, @@ -163,7 +163,7 @@ class simple_routes_ges_test_t : public ::testing::TestWithParampopulate_device_vectors(); } - void TearDown() {} + void TearDown() override {} assignment_t solve(const cuopt::routing::data_model_view_t& data_model, const cuopt::routing::solver_settings_t& solver_settings, diff --git a/cpp/tests/routing/level0/l0_objective_function_test.cu b/cpp/tests/routing/level0/l0_objective_function_test.cu index 935575026..449139849 100644 --- a/cpp/tests/routing/level0/l0_objective_function_test.cu +++ b/cpp/tests/routing/level0/l0_objective_function_test.cu @@ -25,7 +25,7 @@ template class objective_function_test_t : public base_test_t, public ::testing::TestWithParam> { public: - objective_function_test_t() : base_test_t(512, 5E-2, 0) {} + objective_function_test_t() : base_test_t(512, 0, 0) {} void SetUp() override { auto p = GetParam(); diff --git a/cpp/tests/routing/level0/l0_routing_test.cu b/cpp/tests/routing/level0/l0_routing_test.cu index 4d7bbad02..735b6e4bc 100644 --- a/cpp/tests/routing/level0/l0_routing_test.cu +++ b/cpp/tests/routing/level0/l0_routing_test.cu @@ -320,7 +320,7 @@ class routing_retail_test_t : public base_test_t, this->populate_device_vectors(); } - void TearDown() {} + void TearDown() override {} void test_cvrptw() { diff --git a/cpp/tests/routing/level0/l0_vehicle_order_match.cu b/cpp/tests/routing/level0/l0_vehicle_order_match.cu index 6c4d40ab7..4b1b9fdd3 100644 --- a/cpp/tests/routing/level0/l0_vehicle_order_match.cu +++ b/cpp/tests/routing/level0/l0_vehicle_order_match.cu @@ -24,7 +24,7 @@ namespace test { template class vehicle_order_test_t : public base_test_t, public ::testing::TestWithParam { public: - vehicle_order_test_t() : base_test_t(512, 5E-2, 0) {} + vehicle_order_test_t() : base_test_t(512, 0, 0) {} void SetUp() override { this->not_matching_constraints_fraction = GetParam(); diff --git a/cpp/tests/routing/level0/l0_vehicle_types_test.cu b/cpp/tests/routing/level0/l0_vehicle_types_test.cu index f7f247683..4e46d31d6 100644 --- a/cpp/tests/routing/level0/l0_vehicle_types_test.cu +++ b/cpp/tests/routing/level0/l0_vehicle_types_test.cu @@ -23,7 +23,7 @@ namespace test { template class vehicle_types_test_t : public base_test_t, public ::testing::Test { public: - vehicle_types_test_t() : base_test_t(512, 5E-2, 0) {} + vehicle_types_test_t() : base_test_t(512, 0, 0) {} void SetUp() override { this->n_locations = input_.n_locations; From e3f2a79339f4d06e664339521eb79631b7977530 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 18 Nov 2025 12:34:27 +0000 Subject: [PATCH 102/366] remove debug calls --- cpp/src/mip/utilities/cpu_worker_thread.cuh | 2 -- 1 file changed, 2 deletions(-) diff --git a/cpp/src/mip/utilities/cpu_worker_thread.cuh b/cpp/src/mip/utilities/cpu_worker_thread.cuh index 8d0b6d71e..e437486cd 100644 --- a/cpp/src/mip/utilities/cpu_worker_thread.cuh +++ b/cpp/src/mip/utilities/cpu_worker_thread.cuh @@ -60,7 +60,6 @@ template cpu_worker_thread_base_t::~cpu_worker_thread_base_t() { // Note: We don't call on_terminate() here since the derived object is already destroyed. - CUOPT_LOG_DEBUG("Destroying CPU worker thread"); join_worker(); } @@ -91,7 +90,6 @@ void cpu_worker_thread_base_t::cpu_worker_thread() template void cpu_worker_thread_base_t::request_termination() { - CUOPT_LOG_DEBUG("Requesting termination of CPU worker thread"); bool should_terminate = false; { std::lock_guard lock(cpu_mutex); From 057ecc789514a8efa72f55b6a4b85156e8eb6d75 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 18 Nov 2025 14:02:20 +0000 Subject: [PATCH 103/366] fix cmakelists --- cpp/CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index c2c2be2bb..899a5ed38 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -80,7 +80,7 @@ endif(CMAKE_COMPILER_IS_GNUCXX) # 1. Run the binary with env var set: LD_PRELOAD="$(gcc -print-file-name=libasan.so)" ASAN_OPTIONS='protect_shadow_gap=0:replace_intrin=0' # 2. (Optional) To run with a debugger (gdb or cuda-gdb) use the additional ASAN option alloc_dealloc_mismatch=0 if(BUILD_SANITIZER) - list(APPEND CUOPT_CXX_FLAGS -fsanitize=address,undefined -fno-omit-frame-pointer -g -Wno-error=maybe-uninitialized) + list(APPEND CUOPT_CXX_FLAGS -fsanitize=address,undefined -fno-omit-frame-pointer -g) if(NOT "${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") list(APPEND CUOPT_CXX_FLAGS -Wno-error=maybe-uninitialized) endif() From 2463de65518a7791499f9f1b3cb237fccbfbd268 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 25 Nov 2025 16:46:38 +0000 Subject: [PATCH 104/366] move suppressiosn --- cpp/CMakeLists.txt | 2 +- {ci => cpp}/tsan_suppressions.txt | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename {ci => cpp}/tsan_suppressions.txt (100%) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 899a5ed38..eda143c96 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -89,7 +89,7 @@ endif(BUILD_SANITIZER) # To use ThreadSanitizer: # 1. Build with clang and the -tsan flag -# 2. Run the binary with env var set: OMP_TOOL_LIBRARIES=/usr/lib/llvm-17/lib/libarcher.so ARCHER_OPTIONS='verbose=1' TSAN_OPTIONS='suppresions=ci/tsan_suppressions.txt:ignore_noninstrumented_modules=1:halt_on_error=1' +# 2. Run the binary with env var set: OMP_TOOL_LIBRARIES=/usr/lib/llvm-17/lib/libarcher.so ARCHER_OPTIONS='verbose=1' TSAN_OPTIONS='suppresions=cpp/tsan_suppressions.txt:ignore_noninstrumented_modules=1:halt_on_error=1' # Replace with local llvm install path. libarcher.so must be presetn if(BUILD_TSAN) message(STATUS "Building with ThreadSanitizer enabled") diff --git a/ci/tsan_suppressions.txt b/cpp/tsan_suppressions.txt similarity index 100% rename from ci/tsan_suppressions.txt rename to cpp/tsan_suppressions.txt From 8356f1d2ec6d01d053aad54b376bc856ca32f1d3 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 4 Dec 2025 16:16:17 +0000 Subject: [PATCH 105/366] address warnings, add msan setting --- build.sh | 22 +++++++++++++++++-- cpp/CMakeLists.txt | 10 +++++++++ cpp/src/routing/local_search/local_search.cu | 4 ++-- cpp/src/routing/local_search/sliding_tsp.cu | 4 ++-- .../routing/local_search/sliding_window.cu | 5 ++--- cpp/src/routing/local_search/two_opt.cu | 5 ++--- .../routing/local_search/vrp/vrp_execute.cu | 4 ++-- 7 files changed, 40 insertions(+), 14 deletions(-) diff --git a/build.sh b/build.sh index 07a435352..3f1a2c1f5 100755 --- a/build.sh +++ b/build.sh @@ -15,7 +15,7 @@ REPODIR=$(cd "$(dirname "$0")"; pwd) LIBCUOPT_BUILD_DIR=${LIBCUOPT_BUILD_DIR:=${REPODIR}/cpp/build} LIBMPS_PARSER_BUILD_DIR=${LIBMPS_PARSER_BUILD_DIR:=${REPODIR}/cpp/libmps_parser/build} -VALIDARGS="clean libcuopt libmps_parser cuopt_mps_parser cuopt cuopt_server cuopt_sh_client docs deb -a -b -g -fsanitize -tsan -v -l= --verbose-pdlp --build-lp-only --no-fetch-rapids --skip-c-python-adapters --skip-tests-build --skip-routing-build --skip-fatbin-write --host-lineinfo [--cmake-args=\\\"\\\"] [--cache-tool=] -n --allgpuarch --ci-only-arch --show_depr_warn -h --help" +VALIDARGS="clean libcuopt libmps_parser cuopt_mps_parser cuopt cuopt_server cuopt_sh_client docs deb -a -b -g -fsanitize -tsan -msan -v -l= --verbose-pdlp --build-lp-only --no-fetch-rapids --skip-c-python-adapters --skip-tests-build --skip-routing-build --skip-fatbin-write --host-lineinfo [--cmake-args=\\\"\\\"] [--cache-tool=] -n --allgpuarch --ci-only-arch --show_depr_warn -h --help" HELP="$0 [ ...] [ ...] where is: clean - remove all existing build artifacts and configuration (start over) @@ -33,7 +33,8 @@ HELP="$0 [ ...] [ ...] -a - Enable assertion (by default in debug mode) -b - Build with benchmark settings -fsanitize - Build with AddressSanitizer and UndefinedBehaviorSanitizer - -tsan - Build with ThreadSanitizer (cannot be used with -fsanitize) + -tsan - Build with ThreadSanitizer (cannot be used with -fsanitize or -msan) + -msan - Build with MemorySanitizer (cannot be used with -fsanitize or -tsan) -n - no install step --no-fetch-rapids - don't fetch rapids dependencies -l= - log level. Options are: TRACE | DEBUG | INFO | WARN | ERROR | CRITICAL | OFF. Default=INFO @@ -78,6 +79,7 @@ BUILD_CI_ONLY=0 BUILD_LP_ONLY=0 BUILD_SANITIZER=0 BUILD_TSAN=0 +BUILD_MSAN=0 SKIP_C_PYTHON_ADAPTERS=0 SKIP_TESTS_BUILD=0 SKIP_ROUTING_BUILD=0 @@ -235,6 +237,9 @@ fi if hasArg -tsan; then BUILD_TSAN=1 fi +if hasArg -msan; then + BUILD_MSAN=1 +fi if hasArg --skip-c-python-adapters; then SKIP_C_PYTHON_ADAPTERS=1 fi @@ -309,6 +314,18 @@ if [ ${BUILD_SANITIZER} -eq 1 ] && [ ${BUILD_TSAN} -eq 1 ]; then exit 1 fi +if [ ${BUILD_SANITIZER} -eq 1 ] && [ ${BUILD_MSAN} -eq 1 ]; then + echo "ERROR: -fsanitize and -msan cannot be used together" + echo "AddressSanitizer and MemorySanitizer are mutually exclusive" + exit 1 +fi + +if [ ${BUILD_TSAN} -eq 1 ] && [ ${BUILD_MSAN} -eq 1 ]; then + echo "ERROR: -tsan and -msan cannot be used together" + echo "ThreadSanitizer and MemorySanitizer are mutually exclusive" + exit 1 +fi + if [ ${BUILD_ALL_GPU_ARCH} -eq 1 ]; then CUOPT_CMAKE_CUDA_ARCHITECTURES="RAPIDS" echo "Building for *ALL* supported GPU architectures..." @@ -356,6 +373,7 @@ if buildAll || hasArg libcuopt; then -DBUILD_LP_ONLY=${BUILD_LP_ONLY} \ -DBUILD_SANITIZER=${BUILD_SANITIZER} \ -DBUILD_TSAN=${BUILD_TSAN} \ + -DBUILD_MSAN=${BUILD_MSAN} \ -DSKIP_C_PYTHON_ADAPTERS=${SKIP_C_PYTHON_ADAPTERS} \ -DBUILD_TESTS=$((1 - ${SKIP_TESTS_BUILD})) \ -DSKIP_ROUTING_BUILD=${SKIP_ROUTING_BUILD} \ diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index eda143c96..a2467f45c 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -97,6 +97,16 @@ if(BUILD_TSAN) add_link_options(-fsanitize=thread) endif(BUILD_TSAN) +# To use MemorySanitizer: +# 1. Build with clang and the -msan flag (MemorySanitizer requires clang) +# 2. Run the binary with env var set: MSAN_OPTIONS='halt_on_error=1' +# Note: MemorySanitizer requires all code (including libraries) to be instrumented for accurate results +if(BUILD_MSAN) + message(STATUS "Building with MemorySanitizer enabled") + list(APPEND CUOPT_CXX_FLAGS -fsanitize=memory -fno-omit-frame-pointer -g -fsanitize-memory-track-origins=1) + add_link_options(-fsanitize=memory) +endif(BUILD_MSAN) + if(DEFINE_ASSERT) add_definitions(-DASSERT_MODE) endif(DEFINE_ASSERT) diff --git a/cpp/src/routing/local_search/local_search.cu b/cpp/src/routing/local_search/local_search.cu index d26ec1ab9..795d1fb35 100644 --- a/cpp/src/routing/local_search/local_search.cu +++ b/cpp/src/routing/local_search/local_search.cu @@ -126,8 +126,8 @@ bool local_search_t::run_cross_search(solution_t EPSILON, "Cost should improve!"); cuopt_assert(abs((cost_before - cost_after) - - move_candidates.debug_delta.value(sol.sol_handle->get_stream()) < - EPSILON * (1 + abs(cost_before))), + move_candidates.debug_delta.value(sol.sol_handle->get_stream())) < + EPSILON * (1 + abs(cost_before)), "Cost mismatch on cross costs!"); return true; } diff --git a/cpp/src/routing/local_search/sliding_tsp.cu b/cpp/src/routing/local_search/sliding_tsp.cu index 61869a2b7..f0b43542b 100644 --- a/cpp/src/routing/local_search/sliding_tsp.cu +++ b/cpp/src/routing/local_search/sliding_tsp.cu @@ -565,8 +565,8 @@ bool local_search_t::perform_sliding_tsp( sol.get_cost(false, move_candidates.weights)); cuopt_assert(abs((cost_before - cost_after) + - move_candidates.debug_delta.value(sol.sol_handle->get_stream()) < - EPSILON * (1 + abs(cost_before))), + move_candidates.debug_delta.value(sol.sol_handle->get_stream())) < + EPSILON * (1 + abs(cost_before)), "Cost mismatch on sliding_tsp costs!"); cuopt_assert(cost_before - cost_after >= EPSILON, "Cost should improve!"); diff --git a/cpp/src/routing/local_search/sliding_window.cu b/cpp/src/routing/local_search/sliding_window.cu index 5c19d22a2..fd5ef8500 100644 --- a/cpp/src/routing/local_search/sliding_window.cu +++ b/cpp/src/routing/local_search/sliding_window.cu @@ -1116,9 +1116,8 @@ bool local_search_t::perform_sliding_window( cuopt_assert(cost_before - cost_after >= EPSILON, "Cost should improve!"); cuopt_assert(abs((cost_before - cost_after) - - move_candidates.debug_delta.value(solution.sol_handle->get_stream()) < - EPSILON), - "Cost mismatch on cross costs!"); + move_candidates.debug_delta.value(solution.sol_handle->get_stream())) < EPSILON, + "Cost mismatch on sliding_window costs!"); return true; } diff --git a/cpp/src/routing/local_search/two_opt.cu b/cpp/src/routing/local_search/two_opt.cu index 966917b98..ff345312a 100644 --- a/cpp/src/routing/local_search/two_opt.cu +++ b/cpp/src/routing/local_search/two_opt.cu @@ -458,9 +458,8 @@ bool local_search_t::perform_two_opt( : sol.get_cost(move_candidates.include_objective, move_candidates.weights)); cuopt_assert(abs((cost_before - cost_after) + - - move_candidates.debug_delta.value(sol.sol_handle->get_stream()) < - EPSILON * (1 + abs(cost_before))), + move_candidates.debug_delta.value(sol.sol_handle->get_stream())) < + EPSILON * (1 + abs(cost_before)), "Cost mismatch on two_opt costs!"); cuopt_assert(cost_before - cost_after >= EPSILON, "Cost should improve!"); sol.global_runtime_checks(false, false, "two_opt_end"); diff --git a/cpp/src/routing/local_search/vrp/vrp_execute.cu b/cpp/src/routing/local_search/vrp/vrp_execute.cu index cafe0e5ef..ff560e1cc 100644 --- a/cpp/src/routing/local_search/vrp/vrp_execute.cu +++ b/cpp/src/routing/local_search/vrp/vrp_execute.cu @@ -465,8 +465,8 @@ bool execute_vrp_moves(solution_t& sol, sol.get_cost(move_candidates.include_objective, move_candidates.weights)); cuopt_assert(cost_before - cost_after > EPSILON, "Cost should improve!"); cuopt_assert(abs((cost_before - cost_after) + - move_candidates.debug_delta.value(sol.sol_handle->get_stream()) < - EPSILON * (1 + abs(cost_before))), + move_candidates.debug_delta.value(sol.sol_handle->get_stream())) < + EPSILON * (1 + abs(cost_before)), "Cost mismatch on vrp costs!"); return true; } From c9081a6e71ea5e8e1ccee4cc79b85b35ce146c46 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 3 Dec 2025 16:27:16 +0100 Subject: [PATCH 106/366] implemented diving heuristics. sorted starting nodes for diving based on the objective pseudcost estimate. --- cpp/src/dual_simplex/branch_and_bound.cpp | 256 +++++++++++------- cpp/src/dual_simplex/branch_and_bound.hpp | 24 +- cpp/src/dual_simplex/diving_queue.hpp | 2 +- cpp/src/dual_simplex/logger.hpp | 8 +- cpp/src/dual_simplex/mip_node.hpp | 16 +- cpp/src/dual_simplex/pseudo_costs.cpp | 309 +++++++++++++++++++++- cpp/src/dual_simplex/pseudo_costs.hpp | 45 +++- 7 files changed, 531 insertions(+), 129 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 77acca8f7..fce774c86 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -183,21 +183,24 @@ std::string user_mip_gap(f_t obj_value, f_t lower_bound) { const f_t user_mip_gap = relative_gap(obj_value, lower_bound); if (user_mip_gap == std::numeric_limits::infinity()) { - return " - "; + return " - "; } else { constexpr int BUFFER_LEN = 32; char buffer[BUFFER_LEN]; - snprintf(buffer, BUFFER_LEN - 1, "%4.1f%%", user_mip_gap * 100); + snprintf(buffer, BUFFER_LEN - 1, "%5.1f%%", user_mip_gap * 100); return std::string(buffer); } } -inline const char* feasible_solution_symbol(thread_type_t type) +inline const char* feasible_solution_symbol(bnb_thread_type_t type) { switch (type) { - case thread_type_t::EXPLORATION: return "B"; - case thread_type_t::DIVING: return "D"; - default: return "U"; + case bnb_thread_type_t::EXPLORATION: return "B "; + case bnb_thread_type_t::COEFFICIENT_DIVING: return "CD"; + case bnb_thread_type_t::LINE_SEARCH_DIVING: return "LD"; + case bnb_thread_type_t::PSEUDOCOST_DIVING: return "PD"; + case bnb_thread_type_t::GUIDED_DIVING: return "GD"; + default: return "U "; } } @@ -310,7 +313,7 @@ void branch_and_bound_t::set_new_solution(const std::vector& solu std::string gap = user_mip_gap(user_obj, user_lower); settings_.log.printf( - "H %+13.6e %+10.6e %s %9.2f\n", + "H %+13.6e %+10.6e %s %9.2f\n", user_obj, user_lower, gap.c_str(), @@ -423,7 +426,7 @@ void branch_and_bound_t::repair_heuristic_solutions() std::string user_gap = user_mip_gap(obj, lower); settings_.log.printf( - "H %+13.6e %+10.6e %s %9.2f\n", + "H %+13.6e %+10.6e %s %9.2f\n", obj, lower, user_gap.c_str(), @@ -521,12 +524,16 @@ template void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - thread_type_t thread_type) + bnb_thread_type_t thread_type) { bool send_solution = false; i_t nodes_explored = exploration_stats_.nodes_explored; i_t nodes_unexplored = exploration_stats_.nodes_unexplored; + settings_.log.debug("%s found a feasible solution with obj=%.10e.\n", + feasible_solution_symbol(thread_type), + compute_user_objective(original_lp_, leaf_objective)); + mutex_upper_.lock(); if (leaf_objective < upper_bound_) { incumbent_.set_incumbent_solution(leaf_objective, leaf_solution); @@ -534,17 +541,17 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, f_t lower_bound = get_lower_bound(); f_t obj = compute_user_objective(original_lp_, upper_bound_); f_t lower = compute_user_objective(original_lp_, lower_bound); - settings_.log.printf( - "%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - feasible_solution_symbol(thread_type), - nodes_explored, - nodes_unexplored, - obj, - lower, - leaf_depth, - nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0, - user_mip_gap(obj, lower).c_str(), - toc(exploration_stats_.start_time)); + f_t iter_node = nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0; + settings_.log.printf("%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + feasible_solution_symbol(thread_type), + nodes_explored, + nodes_unexplored, + obj, + lower, + leaf_depth, + iter_node, + user_mip_gap(obj, lower).c_str(), + toc(exploration_stats_.start_time)); send_solution = true; } @@ -558,21 +565,44 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, } template -rounding_direction_t branch_and_bound_t::child_selection(mip_node_t* node_ptr) +branch_variable_t branch_and_bound_t::variable_selection( + mip_node_t* node_ptr, + const std::vector& fractional, + const std::vector& solution, + bnb_thread_type_t type, + logger_t& log) { - const i_t branch_var = node_ptr->get_down_child()->branch_var; - const f_t branch_var_val = node_ptr->get_down_child()->fractional_val; - const f_t down_val = std::floor(root_relax_soln_.x[branch_var]); - const f_t up_val = std::ceil(root_relax_soln_.x[branch_var]); - const f_t down_dist = branch_var_val - down_val; - const f_t up_dist = up_val - branch_var_val; - constexpr f_t eps = 1e-6; + i_t branch_var = -1; + f_t obj_estimate = 0; + rounding_direction_t round_dir = rounding_direction_t::NONE; - if (down_dist < up_dist + eps) { - return rounding_direction_t::DOWN; + switch (type) { + case bnb_thread_type_t::EXPLORATION: + std::tie(branch_var, obj_estimate) = + pc_.variable_selection_and_obj_estimate(fractional, solution, node_ptr->lower_bound, log); + round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); - } else { - return rounding_direction_t::UP; + // Note that the exploration thread is the only one that can insert new nodes into the heap, + // and thus, we only need to calculate the objective estimate here (it is used for + // sorting the nodes for diving). + node_ptr->objective_estimate = obj_estimate; + return {branch_var, round_dir}; + + case bnb_thread_type_t::COEFFICIENT_DIVING: + return coefficient_diving(original_lp_, fractional, solution, log); + + case bnb_thread_type_t::LINE_SEARCH_DIVING: + return line_search_diving(fractional, solution, root_relax_soln_.x, log); + + case bnb_thread_type_t::PSEUDOCOST_DIVING: + return pseudocost_diving(pc_, fractional, solution, root_relax_soln_.x, log); + + case bnb_thread_type_t::GUIDED_DIVING: + return guided_diving(pc_, fractional, solution, incumbent_.x, log); + + default: + log.debug("Unknown variable selection method: %d\n", type); + return {-1, rounding_direction_t::NONE}; } } @@ -585,15 +615,21 @@ node_solve_info_t branch_and_bound_t::solve_node( std::vector& basic_list, std::vector& nonbasic_list, bounds_strengthening_t& node_presolver, - thread_type_t thread_type, + bnb_thread_type_t thread_type, bool recompute_bounds_and_basis, const std::vector& root_lower, const std::vector& root_upper, + stats_t& stats, logger_t& log) { const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; const f_t upper_bound = get_upper_bound(); + // If there is no incumbent, use pseudocost diving instead of guided diving + if (upper_bound == inf && thread_type == bnb_thread_type_t::GUIDED_DIVING) { + thread_type = bnb_thread_type_t::PSEUDOCOST_DIVING; + } + lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); std::vector& leaf_vstatus = node_ptr->vstatus; assert(leaf_vstatus.size() == leaf_problem.num_cols); @@ -605,25 +641,11 @@ node_solve_info_t branch_and_bound_t::solve_node( lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); lp_settings.scale_columns = false; -#ifdef LOG_NODE_SIMPLEX - lp_settings.set_log(true); - std::stringstream ss; - ss << "simplex-" << std::this_thread::get_id() << ".log"; - std::string logname; - ss >> logname; - lp_settings.set_log_filename(logname); - lp_settings.log.enable_log_to_file("a+"); - lp_settings.log.log_to_console = false; - lp_settings.log.printf( - "%s node id = %d, branch var = %d, fractional val = %f, variable lower bound = %f, variable " - "upper bound = %f\n", - settings_.log.log_prefix.c_str(), - node_ptr->node_id, - node_ptr->branch_var, - node_ptr->fractional_val, - node_ptr->branch_var_lower, - node_ptr->branch_var_upper); -#endif + if (thread_type != bnb_thread_type_t::EXPLORATION) { + f_t max_iter = 0.05 * exploration_stats_.total_lp_iters; + lp_settings.iteration_limit = max_iter - stats.total_lp_iters; + if (lp_settings.iteration_limit <= 0) { return node_solve_info_t::ITERATION_LIMIT; } + } // Reset the bound_changed markers std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); @@ -650,6 +672,29 @@ node_solve_info_t branch_and_bound_t::solve_node( f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; +#ifdef LOG_NODE_SIMPLEX + lp_settings.set_log(true); + std::stringstream ss; + ss << "simplex-" << std::this_thread::get_id() << ".log"; + std::string logname; + ss >> logname; + lp_settings.log.set_log_file(logname, "a"); + lp_settings.log.log_to_console = false; + lp_settings.log.printf( + "%s\ncurrent node: id = %d, depth = %d, branch var = %d, branch dir = %s, fractional val = " + "%f, variable lower " + "bound = %f, variable upper bound = %f, branch vstatus = %d\n\n", + settings_.log.log_prefix.c_str(), + node_ptr->node_id, + node_ptr->depth, + node_ptr->branch_var, + node_ptr->branch_dir == rounding_direction_t::DOWN ? "DOWN" : "UP", + node_ptr->fractional_val, + node_ptr->branch_var_lower, + node_ptr->branch_var_upper, + node_ptr->vstatus[node_ptr->branch_var]); +#endif + lp_status = dual_phase2_with_advanced_basis(2, 0, recompute_bounds_and_basis, @@ -679,10 +724,12 @@ node_solve_info_t branch_and_bound_t::solve_node( lp_status = convert_lp_status_to_dual_status(second_status); } - if (thread_type == thread_type_t::EXPLORATION) { - exploration_stats_.total_lp_solve_time += toc(lp_start_time); - exploration_stats_.total_lp_iters += node_iter; - } +#ifdef LOG_NODE_SIMPLEX + lp_settings.log.printf("\nLP status: %d\n\n", lp_status); +#endif + + stats.total_lp_solve_time += toc(lp_start_time); + stats.total_lp_iters += node_iter; } if (lp_status == dual::status_t::DUAL_UNBOUNDED) { @@ -726,16 +773,17 @@ node_solve_info_t branch_and_bound_t::solve_node( } else if (leaf_objective <= upper_bound + abs_fathom_tol) { // Choose fractional variable to branch on - const i_t branch_var = - pc_.variable_selection(leaf_fractional, leaf_solution.x, lp_settings.log); + auto [branch_var, round_dir] = variable_selection( + node_ptr, leaf_fractional, leaf_solution.x, thread_type, lp_settings.log); + assert(round_dir != rounding_direction_t::NONE); + assert(branch_var >= 0); assert(leaf_vstatus.size() == leaf_problem.num_cols); + search_tree.branch( node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, leaf_problem, log); search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); - rounding_direction_t round_dir = child_selection(node_ptr); - if (round_dir == rounding_direction_t::UP) { return node_solve_info_t::UP_CHILD_FIRST; } else { @@ -751,8 +799,11 @@ node_solve_info_t branch_and_bound_t::solve_node( search_tree.graphviz_node(log, node_ptr, "timeout", 0.0); return node_solve_info_t::TIME_LIMIT; + } else if (lp_status == dual::status_t::ITERATION_LIMIT) { + return node_solve_info_t::ITERATION_LIMIT; + } else { - if (thread_type == thread_type_t::EXPLORATION) { + if (thread_type == bnb_thread_type_t::EXPLORATION) { fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); log.printf( "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " @@ -810,17 +861,17 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod std::string gap_user = user_mip_gap(obj, user_lower); i_t nodes_explored = exploration_stats_.nodes_explored; i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - - settings_.log.printf( - " %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - nodes_explored, - nodes_unexplored, - obj, - user_lower, - node->depth, - nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0, - gap_user.c_str(), - now); + f_t iter_node = nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0; + + settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + nodes_explored, + nodes_unexplored, + obj, + user_lower, + node->depth, + iter_node, + gap_user.c_str(), + now); exploration_stats_.nodes_since_last_log = 0; exploration_stats_.last_log = tic(); @@ -850,10 +901,11 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod basic_list, nonbasic_list, node_presolver, - thread_type_t::EXPLORATION, + bnb_thread_type_t::EXPLORATION, true, original_lp_.lower, original_lp_.upper, + exploration_stats_, settings_.log); ++exploration_stats_.nodes_since_last_log; @@ -922,6 +974,7 @@ void branch_and_bound_t::explore_subtree(i_t task_id, if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { search_tree.graphviz_node(settings_.log, node_ptr, "cutoff", node_ptr->lower_bound); search_tree.update(node_ptr, node_status_t::FATHOMED); + recompute_bounds_and_basis = true; --exploration_stats_.nodes_unexplored; continue; } @@ -943,7 +996,7 @@ void branch_and_bound_t::explore_subtree(i_t task_id, i_t nodes_unexplored = exploration_stats_.nodes_unexplored; settings_.log.printf( - " %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + " %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", nodes_explored, nodes_unexplored, obj, @@ -973,10 +1026,11 @@ void branch_and_bound_t::explore_subtree(i_t task_id, basic_list, nonbasic_list, node_presolver, - thread_type_t::EXPLORATION, + bnb_thread_type_t::EXPLORATION, recompute_bounds_and_basis, original_lp_.lower, original_lp_.upper, + exploration_stats_, settings_.log); recompute_bounds_and_basis = !has_children(status); @@ -1109,8 +1163,10 @@ void branch_and_bound_t::best_first_thread(i_t task_id, } template -void branch_and_bound_t::diving_thread(const csr_matrix_t& Arow) +void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type, + const csr_matrix_t& Arow) { + constexpr i_t backtrack = 5; logger_t log; log.log = false; // Make a copy of the original LP. We will modify its bounds at each leaf @@ -1139,6 +1195,12 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A std::deque*> stack; stack.push_front(&subtree.root); + stats_t dive_stats; + dive_stats.total_lp_iters = 0; + dive_stats.total_lp_solve_time = 0; + dive_stats.nodes_explored = 0; + dive_stats.nodes_unexplored = 0; + while (stack.size() > 0 && solver_status_ == mip_exploration_status_t::RUNNING) { mip_node_t* node_ptr = stack.front(); stack.pop_front(); @@ -1159,10 +1221,11 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A basic_list, nonbasic_list, node_presolver, - thread_type_t::DIVING, + diving_type, recompute_bounds_and_basis, start_node->lower, start_node->upper, + dive_stats, log); recompute_bounds_and_basis = !has_children(status); @@ -1171,6 +1234,9 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A solver_status_ = mip_exploration_status_t::TIME_LIMIT; return; + } else if (status == node_solve_info_t::ITERATION_LIMIT) { + break; + } else if (has_children(status)) { if (status == node_solve_info_t::UP_CHILD_FIRST) { stack.push_front(node_ptr->get_down_child()); @@ -1181,24 +1247,8 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A } } - if (stack.size() > 1) { - // If the diving thread is consuming the nodes faster than the - // best first search, then we split the current subtree at the - // lowest possible point and move to the queue, so it can - // be picked by another thread. - if (std::lock_guard lock(mutex_dive_queue_); - diving_queue_.size() < min_diving_queue_size_) { - mip_node_t* new_node = stack.back(); - stack.pop_back(); - - std::vector lower = start_node->lower; - std::vector upper = start_node->upper; - std::fill( - node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); - new_node->get_variable_bounds(lower, upper, node_presolver.bounds_changed); - - diving_queue_.emplace(new_node->detach_copy(), std::move(lower), std::move(upper)); - } + if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > backtrack) { + stack.pop_back(); } } } @@ -1421,7 +1471,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } // Choose variable to branch on - i_t branch_var = pc_.variable_selection(fractional, root_relax_soln_.x, log); + auto [branch_var, obj_estimate] = + pc_.variable_selection_and_obj_estimate(fractional, root_relax_soln_.x, root_objective_, log); search_tree_.root = std::move(mip_node_t(root_objective_, root_vstatus_)); search_tree_.num_nodes = 0; @@ -1455,6 +1506,13 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut lower_bound_ceiling_ = inf; should_report_ = true; + std::vector diving_strategies = { + bnb_thread_type_t::PSEUDOCOST_DIVING, + bnb_thread_type_t::LINE_SEARCH_DIVING, + bnb_thread_type_t::GUIDED_DIVING, + bnb_thread_type_t::COEFFICIENT_DIVING, + }; + #pragma omp parallel num_threads(settings_.num_threads) { #pragma omp master @@ -1479,9 +1537,11 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut best_first_thread(i, search_tree_, Arow); } - for (i_t i = 0; i < settings_.num_diving_threads; i++) { + for (i_t k = 0; k < settings_.num_diving_threads; k++) { + const i_t m = diving_strategies.size(); + const bnb_thread_type_t diving_type = diving_strategies[k % m]; #pragma omp task - diving_thread(Arow); + diving_thread(diving_type, Arow); } } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 7891711f7..9026336f3 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -57,9 +57,12 @@ enum class node_solve_info_t { // // [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, // Berlin, 2007. doi: 10.14279/depositonce-1634. -enum class thread_type_t { - EXPLORATION = 0, // Best-First + Plunging. Pseudocost branching + Martin's criteria. - DIVING = 1, +enum class bnb_thread_type_t { + EXPLORATION = 0, // Best-First + Plunging. + PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) + LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) + GUIDED_DIVING = 3, // Guided diving (9.2.3). If no incumbent is found yet, use pseudocost diving. + COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) }; template @@ -207,7 +210,7 @@ class branch_and_bound_t { void add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - thread_type_t thread_type); + bnb_thread_type_t thread_type); // Repairs low-quality solutions from the heuristics, if it is applicable. void repair_heuristic_solutions(); @@ -237,7 +240,7 @@ class branch_and_bound_t { // Each diving thread pops the first node from the dive queue and then performs // a deep dive into the subtree determined by the node. - void diving_thread(const csr_matrix_t& Arow); + void diving_thread(bnb_thread_type_t diving_type, const csr_matrix_t& Arow); // Solve the LP relaxation of a leaf node and update the tree. node_solve_info_t solve_node(mip_node_t* node_ptr, @@ -247,14 +250,19 @@ class branch_and_bound_t { std::vector& basic_list, std::vector& nonbasic_list, bounds_strengthening_t& node_presolver, - thread_type_t thread_type, + bnb_thread_type_t thread_type, bool recompute_basis_and_bounds, const std::vector& root_lower, const std::vector& root_upper, + stats_t& stats, logger_t& log); - // Sort the children based on the Martin's criteria. - rounding_direction_t child_selection(mip_node_t* node_ptr); + // Selects the variable to branch on. + branch_variable_t variable_selection(mip_node_t* node_ptr, + const std::vector& fractional, + const std::vector& solution, + bnb_thread_type_t type, + logger_t& log); }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/diving_queue.hpp b/cpp/src/dual_simplex/diving_queue.hpp index f7035109e..0c13c2ed5 100644 --- a/cpp/src/dual_simplex/diving_queue.hpp +++ b/cpp/src/dual_simplex/diving_queue.hpp @@ -26,7 +26,7 @@ struct diving_root_t { friend bool operator>(const diving_root_t& a, const diving_root_t& b) { - return a.node.lower_bound > b.node.lower_bound; + return a.node.objective_estimate > b.node.objective_estimate; } }; diff --git a/cpp/src/dual_simplex/logger.hpp b/cpp/src/dual_simplex/logger.hpp index ac5e394f9..f6030d521 100644 --- a/cpp/src/dual_simplex/logger.hpp +++ b/cpp/src/dual_simplex/logger.hpp @@ -30,17 +30,17 @@ class logger_t { { } - void enable_log_to_file(std::string mode = "w") + void enable_log_to_file(const char* mode = "w") { if (log_file != nullptr) { std::fclose(log_file); } - log_file = std::fopen(log_filename.c_str(), mode.c_str()); + log_file = std::fopen(log_filename.c_str(), mode); log_to_file = true; } - void set_log_file(const std::string& filename) + void set_log_file(const std::string& filename, const char* mode = "w") { log_filename = filename; - enable_log_to_file(); + enable_log_to_file(mode); } void close_log_file() diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index 1d66a21f7..61f63f17a 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -59,6 +59,7 @@ class mip_node_t { node_id(0), branch_var(-1), branch_dir(rounding_direction_t::NONE), + objective_estimate(inf), vstatus(basis) { children[0] = nullptr; @@ -80,6 +81,7 @@ class mip_node_t { branch_var(branch_variable), branch_dir(branch_direction), fractional_val(branch_var_value), + objective_estimate(parent_node->objective_estimate), vstatus(basis) { @@ -227,17 +229,19 @@ class mip_node_t { mip_node_t detach_copy() const { mip_node_t copy(lower_bound, vstatus); - copy.branch_var = branch_var; - copy.branch_dir = branch_dir; - copy.branch_var_lower = branch_var_lower; - copy.branch_var_upper = branch_var_upper; - copy.fractional_val = fractional_val; - copy.node_id = node_id; + copy.branch_var = branch_var; + copy.branch_dir = branch_dir; + copy.branch_var_lower = branch_var_lower; + copy.branch_var_upper = branch_var_upper; + copy.fractional_val = fractional_val; + copy.objective_estimate = objective_estimate; + copy.node_id = node_id; return copy; } node_status_t status; f_t lower_bound; + f_t objective_estimate; i_t depth; i_t node_id; i_t branch_var; diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 9f84e108d..3fdbbbfbf 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -195,11 +195,276 @@ void strong_branching(const lp_problem_t& original_lp, pc.update_pseudo_costs_from_strong_branching(fractional, root_soln); } +template +rounding_direction_t martin_criteria(f_t val, f_t root_val) +{ + const f_t down_val = std::floor(root_val); + const f_t up_val = std::ceil(root_val); + const f_t down_dist = val - down_val; + const f_t up_dist = up_val - val; + constexpr f_t eps = 1e-6; + + if (down_dist < up_dist + eps) { + return rounding_direction_t::DOWN; + } else { + return rounding_direction_t::UP; + } +} + +template +branch_variable_t line_search_diving(const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log) +{ + constexpr f_t eps = 1e-6; + i_t branch_var = -1; + f_t min_score = INFINITY; + rounding_direction_t round_dir = rounding_direction_t::NONE; + + for (auto j : fractional) { + f_t score = inf; + rounding_direction_t dir = rounding_direction_t::NONE; + + if (solution[j] < root_solution[j] - eps) { + f_t f = solution[j] - std::floor(solution[j]); + f_t d = root_solution[j] - solution[j]; + score = f / d; + dir = rounding_direction_t::DOWN; + + } else if (solution[j] > root_solution[j] + eps) { + f_t f = std::ceil(solution[j]) - solution[j]; + f_t d = solution[j] - root_solution[j]; + score = f / d; + dir = rounding_direction_t::UP; + } + + if (min_score > score) { + min_score = score; + branch_var = j; + round_dir = dir; + } + } + + log.debug("Line search diving: selected var %d with val = %e, round dir = %d and score = %e\n", + branch_var, + solution[branch_var], + round_dir, + min_score); + + // If the current solution is equal to the root solution, arbitrarily + // set the branch variable to the first fractional variable and round it down + if (round_dir == rounding_direction_t::NONE) { + branch_var = fractional[0]; + round_dir = rounding_direction_t::DOWN; + } + + return {branch_var, round_dir}; +} + +template +branch_variable_t pseudocost_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log) +{ + std::lock_guard lock(pc.mutex); + i_t branch_var = -1; + f_t max_score = -INFINITY; + rounding_direction_t round_dir = rounding_direction_t::NONE; + constexpr f_t eps = 1e-6; + + i_t num_initialized_down; + i_t num_initialized_up; + f_t pseudo_cost_down_avg; + f_t pseudo_cost_up_avg; + pc.initialized( + num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); + + for (auto j : fractional) { + rounding_direction_t dir = rounding_direction_t::NONE; + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + + f_t pc_down = pc.pseudo_cost_num_down[j] != 0 + ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] + : pseudo_cost_down_avg; + + f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + + f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); + f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); + f_t score = 0; + + if (solution[j] < root_solution[j] - 0.4) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else if (solution[j] > root_solution[j] + 0.4) { + score = score_up; + dir = rounding_direction_t::UP; + } else if (f_down < 0.3) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else if (f_down > 0.7) { + score = score_up; + dir = rounding_direction_t::UP; + } else if (pc_down < pc_up + eps) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else { + score = score_up; + dir = rounding_direction_t::UP; + } + + if (score > max_score) { + max_score = score; + branch_var = j; + round_dir = dir; + } + } + log.debug("Pseudocost diving: selected var %d with val = %e, round dir = %d and score = %e\n", + branch_var, + solution[branch_var], + round_dir, + max_score); + + return {branch_var, round_dir}; +} + +template +branch_variable_t guided_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& incumbent, + logger_t& log) +{ + std::lock_guard lock(pc.mutex); + i_t branch_var = -1; + f_t max_score = -INFINITY; + rounding_direction_t round_dir = rounding_direction_t::NONE; + constexpr f_t eps = 1e-6; + + i_t num_initialized_down; + i_t num_initialized_up; + f_t pseudo_cost_down_avg; + f_t pseudo_cost_up_avg; + pc.initialized( + num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); + + for (auto j : fractional) { + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t down_dist = std::abs(incumbent[j] - std::floor(solution[j])); + f_t up_dist = std::abs(std::ceil(solution[j]) - incumbent[j]); + rounding_direction_t dir = + down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; + + f_t pc_down = pc.pseudo_cost_num_down[j] != 0 + ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] + : pseudo_cost_down_avg; + + f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + + f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; + f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; + f_t score = (score1 + score2) / 6; + + if (score > max_score) { + max_score = score; + branch_var = j; + round_dir = dir; + } + } + + log.debug("Guided diving: selected var %d with val = %e, round dir = %d and score = %e\n", + branch_var, + solution[branch_var], + round_dir, + max_score); + return {branch_var, round_dir}; +} + +template +std::tuple calculate_variable_locks(const lp_problem_t& lp_problem, i_t var_idx) +{ + i_t up_lock = 0; + i_t down_lock = 0; + i_t start = lp_problem.A.col_start[var_idx]; + i_t end = lp_problem.A.col_start[var_idx + 1]; + + for (i_t k = start; k < end; ++k) { + f_t nz_val = lp_problem.A.x[k]; + i_t nz_row = lp_problem.A.i[k]; + + if (std::isfinite(lp_problem.upper[nz_row]) && std::isfinite(lp_problem.lower[nz_row])) { + down_lock += 1; + up_lock += 1; + continue; + } + + f_t sign = std::isfinite(lp_problem.upper[nz_row]) ? 1 : -1; + + if (nz_val * sign > 0) { + up_lock += 1; + } else { + down_lock += 1; + } + } + + return {up_lock, down_lock}; +} + +template +branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, + const std::vector& fractional, + const std::vector& solution, + logger_t& log) +{ + i_t branch_var = -1; + f_t min_locks = INT_MAX; + rounding_direction_t round_dir = rounding_direction_t::NONE; + constexpr f_t eps = 1e-6; + + for (auto j : fractional) { + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + auto [up_lock, down_lock] = calculate_variable_locks(lp_problem, j); + f_t locks = std::min(up_lock, down_lock); + + if (min_locks > locks) { + min_locks = locks; + branch_var = j; + + if (up_lock < down_lock) { + round_dir = rounding_direction_t::UP; + } else if (up_lock > down_lock) { + round_dir = rounding_direction_t::DOWN; + } else if (f_down < f_up + eps) { + round_dir = rounding_direction_t::DOWN; + } else { + round_dir = rounding_direction_t::UP; + } + } + } + + log.debug( + "Coefficient diving: selected var %d with val = %e, round dir = %d and min locks = %e\n", + branch_var, + solution[branch_var], + round_dir, + min_locks); + + return {branch_var, round_dir}; +} + template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) { - mutex.lock(); + std::lock_guard lock(mutex); const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) @@ -211,7 +476,6 @@ void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_pt pseudo_cost_sum_up[node_ptr->branch_var] += change_in_obj / frac; pseudo_cost_num_up[node_ptr->branch_var]++; } - mutex.unlock(); } template @@ -254,16 +518,19 @@ void pseudo_costs_t::initialized(i_t& num_initialized_down, } template -i_t pseudo_costs_t::variable_selection(const std::vector& fractional, - const std::vector& solution, - logger_t& log) +std::pair pseudo_costs_t::variable_selection_and_obj_estimate( + const std::vector& fractional, + const std::vector& solution, + f_t lower_bound, + logger_t& log) { - mutex.lock(); + std::lock_guard lock(mutex); const i_t num_fractional = fractional.size(); std::vector pseudo_cost_up(num_fractional); std::vector pseudo_cost_down(num_fractional); std::vector score(num_fractional); + f_t estimate = lower_bound; i_t num_initialized_down; i_t num_initialized_up; @@ -296,6 +563,9 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio const f_t f_up = std::ceil(solution[j]) - solution[j]; score[k] = std::max(f_down * pseudo_cost_down[k], eps) * std::max(f_up * pseudo_cost_up[k], eps); + + estimate += std::min(std::max(pseudo_cost_down[k] * f_down, eps), + std::max(pseudo_cost_up[k] * f_up, eps)); } i_t branch_var = fractional[0]; @@ -312,9 +582,7 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio log.printf( "pc branching on %d. Value %e. Score %e\n", branch_var, solution[branch_var], score[select]); - mutex.unlock(); - - return branch_var; + return {branch_var, estimate}; } template @@ -356,6 +624,29 @@ template void strong_branching(const lp_problem_t& ori const std::vector& edge_norms, pseudo_costs_t& pc); +template rounding_direction_t martin_criteria(double val, double root_val); + +template branch_variable_t line_search_diving(const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template branch_variable_t pseudocost_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template branch_variable_t guided_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& incumbent, + logger_t& log); + +template branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, + const std::vector& fractional, + const std::vector& solution, + logger_t& log); #endif } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 799cdc3ff..f2c8abef1 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -17,6 +17,12 @@ namespace cuopt::linear_programming::dual_simplex { +template +struct branch_variable_t { + i_t variable; + rounding_direction_t direction; +}; + template class pseudo_costs_t { public: @@ -43,9 +49,10 @@ class pseudo_costs_t { f_t& pseudo_cost_down_avg, f_t& pseudo_cost_up_avg) const; - i_t variable_selection(const std::vector& fractional, - const std::vector& solution, - logger_t& log); + std::pair variable_selection_and_obj_estimate(const std::vector& fractional, + const std::vector& solution, + f_t lower_bound, + logger_t& log); void update_pseudo_costs_from_strong_branching(const std::vector& fractional, const std::vector& root_soln); @@ -72,4 +79,36 @@ void strong_branching(const lp_problem_t& original_lp, const std::vector& edge_norms, pseudo_costs_t& pc); +// Martin's criteria for the preferred rounding direction (see [1]) +// [1] A. Martin, “Integer Programs with Block Structure,” +// Technische Universit¨at Berlin, Berlin, 1999. Accessed: Aug. 08, 2025. +// [Online]. Available: https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/391 +template +rounding_direction_t martin_criteria(f_t val, f_t root_val); + +template +branch_variable_t line_search_diving(const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template +branch_variable_t pseudocost_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template +branch_variable_t guided_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& incumbent, + logger_t& log); + +template +branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, + const std::vector& fractional, + const std::vector& solution, + logger_t& log); } // namespace cuopt::linear_programming::dual_simplex From 046a501e9da7408111d7be5e0fa77fd9ccaa54df Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 3 Dec 2025 18:58:00 +0100 Subject: [PATCH 107/366] moved diving heuristics to a separated file --- cpp/src/dual_simplex/CMakeLists.txt | 1 + cpp/src/dual_simplex/branch_and_bound.cpp | 7 +- cpp/src/dual_simplex/branch_and_bound.hpp | 31 ++- cpp/src/dual_simplex/diving_heuristics.cpp | 285 +++++++++++++++++++++ cpp/src/dual_simplex/diving_heuristics.hpp | 57 +++++ cpp/src/dual_simplex/pseudo_costs.cpp | 270 ------------------- cpp/src/dual_simplex/pseudo_costs.hpp | 31 --- 7 files changed, 365 insertions(+), 317 deletions(-) create mode 100644 cpp/src/dual_simplex/diving_heuristics.cpp create mode 100644 cpp/src/dual_simplex/diving_heuristics.hpp diff --git a/cpp/src/dual_simplex/CMakeLists.txt b/cpp/src/dual_simplex/CMakeLists.txt index e8a9b5dce..ebaf9cbb7 100644 --- a/cpp/src/dual_simplex/CMakeLists.txt +++ b/cpp/src/dual_simplex/CMakeLists.txt @@ -31,6 +31,7 @@ set(DUAL_SIMPLEX_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/triangle_solve.cpp ${CMAKE_CURRENT_SOURCE_DIR}/vector_math.cpp ${CMAKE_CURRENT_SOURCE_DIR}/pinned_host_allocator.cu + ${CMAKE_CURRENT_SOURCE_DIR}/diving_heuristics.cpp ) # Uncomment to enable debug info diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index fce774c86..3a7475c64 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -619,7 +619,7 @@ node_solve_info_t branch_and_bound_t::solve_node( bool recompute_bounds_and_basis, const std::vector& root_lower, const std::vector& root_upper, - stats_t& stats, + bnb_stats_t& stats, logger_t& log) { const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; @@ -642,7 +642,8 @@ node_solve_info_t branch_and_bound_t::solve_node( lp_settings.scale_columns = false; if (thread_type != bnb_thread_type_t::EXPLORATION) { - f_t max_iter = 0.05 * exploration_stats_.total_lp_iters; + i_t bnb_lp_iters = exploration_stats_.total_lp_iters; + f_t max_iter = 0.05 * bnb_lp_iters; lp_settings.iteration_limit = max_iter - stats.total_lp_iters; if (lp_settings.iteration_limit <= 0) { return node_solve_info_t::ITERATION_LIMIT; } } @@ -1195,7 +1196,7 @@ void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type, std::deque*> stack; stack.push_front(&subtree.root); - stats_t dive_stats; + bnb_stats_t dive_stats; dive_stats.total_lp_iters = 0; dive_stats.total_lp_solve_time = 0; dive_stats.nodes_explored = 0; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 9026336f3..b0ed7e800 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -7,6 +7,7 @@ #pragma once +#include #include #include #include @@ -53,7 +54,8 @@ enum class node_solve_info_t { NUMERICAL = 5 // The solver encounter a numerical error when solving the node }; -// Indicate the search and variable selection algorithms used by the thread (See [1]). +// Indicate the search and variable selection algorithms used by each thread +// in B&B (See [1]). // // [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, // Berlin, 2007. doi: 10.14279/depositonce-1634. @@ -71,6 +73,19 @@ class bounds_strengthening_t; template void upper_bound_callback(f_t upper_bound); +template +struct bnb_stats_t { + f_t start_time = 0.0; + omp_atomic_t total_lp_solve_time = 0.0; + omp_atomic_t nodes_explored = 0; + omp_atomic_t nodes_unexplored = 0; + omp_atomic_t total_lp_iters = 0; + + // This should only be used by the main thread + omp_atomic_t last_log = 0.0; + omp_atomic_t nodes_since_last_log = 0; +}; + template class branch_and_bound_t { public: @@ -148,17 +163,7 @@ class branch_and_bound_t { mip_solution_t incumbent_; // Structure with the general info of the solver. - struct stats_t { - f_t start_time = 0.0; - omp_atomic_t total_lp_solve_time = 0.0; - omp_atomic_t nodes_explored = 0; - omp_atomic_t nodes_unexplored = 0; - omp_atomic_t total_lp_iters = 0; - - // This should only be used by the main thread - omp_atomic_t last_log = 0.0; - omp_atomic_t nodes_since_last_log = 0; - } exploration_stats_; + bnb_stats_t exploration_stats_; // Mutex for repair omp_mutex_t mutex_repair_; @@ -254,7 +259,7 @@ class branch_and_bound_t { bool recompute_basis_and_bounds, const std::vector& root_lower, const std::vector& root_upper, - stats_t& stats, + bnb_stats_t& stats, logger_t& log); // Selects the variable to branch on. diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp new file mode 100644 index 000000000..c59a0e850 --- /dev/null +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -0,0 +1,285 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#include + +namespace cuopt::linear_programming::dual_simplex { + +template +branch_variable_t line_search_diving(const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log) +{ + constexpr f_t eps = 1e-6; + i_t branch_var = -1; + f_t min_score = INFINITY; + rounding_direction_t round_dir = rounding_direction_t::NONE; + + for (auto j : fractional) { + f_t score = inf; + rounding_direction_t dir = rounding_direction_t::NONE; + + if (solution[j] < root_solution[j] - eps) { + f_t f = solution[j] - std::floor(solution[j]); + f_t d = root_solution[j] - solution[j]; + score = f / d; + dir = rounding_direction_t::DOWN; + + } else if (solution[j] > root_solution[j] + eps) { + f_t f = std::ceil(solution[j]) - solution[j]; + f_t d = solution[j] - root_solution[j]; + score = f / d; + dir = rounding_direction_t::UP; + } + + if (min_score > score) { + min_score = score; + branch_var = j; + round_dir = dir; + } + } + + log.debug("Line search diving: selected var %d with val = %e, round dir = %d and score = %e\n", + branch_var, + solution[branch_var], + round_dir, + min_score); + + // If the current solution is equal to the root solution, arbitrarily + // set the branch variable to the first fractional variable and round it down + if (round_dir == rounding_direction_t::NONE) { + branch_var = fractional[0]; + round_dir = rounding_direction_t::DOWN; + } + + return {branch_var, round_dir}; +} + +template +branch_variable_t pseudocost_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log) +{ + std::lock_guard lock(pc.mutex); + i_t branch_var = -1; + f_t max_score = -INFINITY; + rounding_direction_t round_dir = rounding_direction_t::NONE; + constexpr f_t eps = 1e-6; + + i_t num_initialized_down; + i_t num_initialized_up; + f_t pseudo_cost_down_avg; + f_t pseudo_cost_up_avg; + pc.initialized( + num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); + + for (auto j : fractional) { + rounding_direction_t dir = rounding_direction_t::NONE; + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + + f_t pc_down = pc.pseudo_cost_num_down[j] != 0 + ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] + : pseudo_cost_down_avg; + + f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + + f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); + f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); + f_t score = 0; + + if (solution[j] < root_solution[j] - 0.4) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else if (solution[j] > root_solution[j] + 0.4) { + score = score_up; + dir = rounding_direction_t::UP; + } else if (f_down < 0.3) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else if (f_down > 0.7) { + score = score_up; + dir = rounding_direction_t::UP; + } else if (pc_down < pc_up + eps) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else { + score = score_up; + dir = rounding_direction_t::UP; + } + + if (score > max_score) { + max_score = score; + branch_var = j; + round_dir = dir; + } + } + log.debug("Pseudocost diving: selected var %d with val = %e, round dir = %d and score = %e\n", + branch_var, + solution[branch_var], + round_dir, + max_score); + + return {branch_var, round_dir}; +} + +template +branch_variable_t guided_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& incumbent, + logger_t& log) +{ + std::lock_guard lock(pc.mutex); + i_t branch_var = -1; + f_t max_score = -INFINITY; + rounding_direction_t round_dir = rounding_direction_t::NONE; + constexpr f_t eps = 1e-6; + + i_t num_initialized_down; + i_t num_initialized_up; + f_t pseudo_cost_down_avg; + f_t pseudo_cost_up_avg; + pc.initialized( + num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); + + for (auto j : fractional) { + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t down_dist = std::abs(incumbent[j] - std::floor(solution[j])); + f_t up_dist = std::abs(std::ceil(solution[j]) - incumbent[j]); + rounding_direction_t dir = + down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; + + f_t pc_down = pc.pseudo_cost_num_down[j] != 0 + ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] + : pseudo_cost_down_avg; + + f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + + f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; + f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; + f_t score = (score1 + score2) / 6; + + if (score > max_score) { + max_score = score; + branch_var = j; + round_dir = dir; + } + } + + log.debug("Guided diving: selected var %d with val = %e, round dir = %d and score = %e\n", + branch_var, + solution[branch_var], + round_dir, + max_score); + return {branch_var, round_dir}; +} + +template +std::tuple calculate_variable_locks(const lp_problem_t& lp_problem, i_t var_idx) +{ + i_t up_lock = 0; + i_t down_lock = 0; + i_t start = lp_problem.A.col_start[var_idx]; + i_t end = lp_problem.A.col_start[var_idx + 1]; + + for (i_t k = start; k < end; ++k) { + f_t nz_val = lp_problem.A.x[k]; + i_t nz_row = lp_problem.A.i[k]; + + if (std::isfinite(lp_problem.upper[nz_row]) && std::isfinite(lp_problem.lower[nz_row])) { + down_lock += 1; + up_lock += 1; + continue; + } + + f_t sign = std::isfinite(lp_problem.upper[nz_row]) ? 1 : -1; + + if (nz_val * sign > 0) { + up_lock += 1; + } else { + down_lock += 1; + } + } + + return {up_lock, down_lock}; +} + +template +branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, + const std::vector& fractional, + const std::vector& solution, + logger_t& log) +{ + i_t branch_var = -1; + f_t min_locks = INT_MAX; + rounding_direction_t round_dir = rounding_direction_t::NONE; + constexpr f_t eps = 1e-6; + + for (auto j : fractional) { + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + auto [up_lock, down_lock] = calculate_variable_locks(lp_problem, j); + f_t locks = std::min(up_lock, down_lock); + + if (min_locks > locks) { + min_locks = locks; + branch_var = j; + + if (up_lock < down_lock) { + round_dir = rounding_direction_t::UP; + } else if (up_lock > down_lock) { + round_dir = rounding_direction_t::DOWN; + } else if (f_down < f_up + eps) { + round_dir = rounding_direction_t::DOWN; + } else { + round_dir = rounding_direction_t::UP; + } + } + } + + log.debug( + "Coefficient diving: selected var %d with val = %e, round dir = %d and min locks = %e\n", + branch_var, + solution[branch_var], + round_dir, + min_locks); + + return {branch_var, round_dir}; +} + +#ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE +template branch_variable_t line_search_diving(const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template branch_variable_t pseudocost_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template branch_variable_t guided_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& incumbent, + logger_t& log); + +template branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, + const std::vector& fractional, + const std::vector& solution, + logger_t& log); +#endif + +} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/diving_heuristics.hpp b/cpp/src/dual_simplex/diving_heuristics.hpp new file mode 100644 index 000000000..5b259dd33 --- /dev/null +++ b/cpp/src/dual_simplex/diving_heuristics.hpp @@ -0,0 +1,57 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#include +#include +#include +#include + +namespace cuopt::linear_programming::dual_simplex { + +struct diving_general_settings_t { + int num_diving_threads; + bool disable_line_search_diving = false; + bool disable_pseudocost_diving = false; + bool disable_guided_diving = false; + bool disable_coefficient_diving = false; +}; + +template +struct branch_variable_t { + i_t variable; + rounding_direction_t direction; +}; + +template +branch_variable_t line_search_diving(const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template +branch_variable_t pseudocost_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template +branch_variable_t guided_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& incumbent, + logger_t& log); + +template +branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, + const std::vector& fractional, + const std::vector& solution, + logger_t& log); + +} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 3fdbbbfbf..091639539 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -211,255 +211,6 @@ rounding_direction_t martin_criteria(f_t val, f_t root_val) } } -template -branch_variable_t line_search_diving(const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log) -{ - constexpr f_t eps = 1e-6; - i_t branch_var = -1; - f_t min_score = INFINITY; - rounding_direction_t round_dir = rounding_direction_t::NONE; - - for (auto j : fractional) { - f_t score = inf; - rounding_direction_t dir = rounding_direction_t::NONE; - - if (solution[j] < root_solution[j] - eps) { - f_t f = solution[j] - std::floor(solution[j]); - f_t d = root_solution[j] - solution[j]; - score = f / d; - dir = rounding_direction_t::DOWN; - - } else if (solution[j] > root_solution[j] + eps) { - f_t f = std::ceil(solution[j]) - solution[j]; - f_t d = solution[j] - root_solution[j]; - score = f / d; - dir = rounding_direction_t::UP; - } - - if (min_score > score) { - min_score = score; - branch_var = j; - round_dir = dir; - } - } - - log.debug("Line search diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - min_score); - - // If the current solution is equal to the root solution, arbitrarily - // set the branch variable to the first fractional variable and round it down - if (round_dir == rounding_direction_t::NONE) { - branch_var = fractional[0]; - round_dir = rounding_direction_t::DOWN; - } - - return {branch_var, round_dir}; -} - -template -branch_variable_t pseudocost_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log) -{ - std::lock_guard lock(pc.mutex); - i_t branch_var = -1; - f_t max_score = -INFINITY; - rounding_direction_t round_dir = rounding_direction_t::NONE; - constexpr f_t eps = 1e-6; - - i_t num_initialized_down; - i_t num_initialized_up; - f_t pseudo_cost_down_avg; - f_t pseudo_cost_up_avg; - pc.initialized( - num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - - for (auto j : fractional) { - rounding_direction_t dir = rounding_direction_t::NONE; - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - - f_t pc_down = pc.pseudo_cost_num_down[j] != 0 - ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - - f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - - f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); - f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); - f_t score = 0; - - if (solution[j] < root_solution[j] - 0.4) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else if (solution[j] > root_solution[j] + 0.4) { - score = score_up; - dir = rounding_direction_t::UP; - } else if (f_down < 0.3) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else if (f_down > 0.7) { - score = score_up; - dir = rounding_direction_t::UP; - } else if (pc_down < pc_up + eps) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else { - score = score_up; - dir = rounding_direction_t::UP; - } - - if (score > max_score) { - max_score = score; - branch_var = j; - round_dir = dir; - } - } - log.debug("Pseudocost diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - max_score); - - return {branch_var, round_dir}; -} - -template -branch_variable_t guided_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& incumbent, - logger_t& log) -{ - std::lock_guard lock(pc.mutex); - i_t branch_var = -1; - f_t max_score = -INFINITY; - rounding_direction_t round_dir = rounding_direction_t::NONE; - constexpr f_t eps = 1e-6; - - i_t num_initialized_down; - i_t num_initialized_up; - f_t pseudo_cost_down_avg; - f_t pseudo_cost_up_avg; - pc.initialized( - num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - - for (auto j : fractional) { - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t down_dist = std::abs(incumbent[j] - std::floor(solution[j])); - f_t up_dist = std::abs(std::ceil(solution[j]) - incumbent[j]); - rounding_direction_t dir = - down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; - - f_t pc_down = pc.pseudo_cost_num_down[j] != 0 - ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - - f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - - f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; - f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; - f_t score = (score1 + score2) / 6; - - if (score > max_score) { - max_score = score; - branch_var = j; - round_dir = dir; - } - } - - log.debug("Guided diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - max_score); - return {branch_var, round_dir}; -} - -template -std::tuple calculate_variable_locks(const lp_problem_t& lp_problem, i_t var_idx) -{ - i_t up_lock = 0; - i_t down_lock = 0; - i_t start = lp_problem.A.col_start[var_idx]; - i_t end = lp_problem.A.col_start[var_idx + 1]; - - for (i_t k = start; k < end; ++k) { - f_t nz_val = lp_problem.A.x[k]; - i_t nz_row = lp_problem.A.i[k]; - - if (std::isfinite(lp_problem.upper[nz_row]) && std::isfinite(lp_problem.lower[nz_row])) { - down_lock += 1; - up_lock += 1; - continue; - } - - f_t sign = std::isfinite(lp_problem.upper[nz_row]) ? 1 : -1; - - if (nz_val * sign > 0) { - up_lock += 1; - } else { - down_lock += 1; - } - } - - return {up_lock, down_lock}; -} - -template -branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, - const std::vector& fractional, - const std::vector& solution, - logger_t& log) -{ - i_t branch_var = -1; - f_t min_locks = INT_MAX; - rounding_direction_t round_dir = rounding_direction_t::NONE; - constexpr f_t eps = 1e-6; - - for (auto j : fractional) { - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - auto [up_lock, down_lock] = calculate_variable_locks(lp_problem, j); - f_t locks = std::min(up_lock, down_lock); - - if (min_locks > locks) { - min_locks = locks; - branch_var = j; - - if (up_lock < down_lock) { - round_dir = rounding_direction_t::UP; - } else if (up_lock > down_lock) { - round_dir = rounding_direction_t::DOWN; - } else if (f_down < f_up + eps) { - round_dir = rounding_direction_t::DOWN; - } else { - round_dir = rounding_direction_t::UP; - } - } - } - - log.debug( - "Coefficient diving: selected var %d with val = %e, round dir = %d and min locks = %e\n", - branch_var, - solution[branch_var], - round_dir, - min_locks); - - return {branch_var, round_dir}; -} - template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) @@ -626,27 +377,6 @@ template void strong_branching(const lp_problem_t& ori template rounding_direction_t martin_criteria(double val, double root_val); -template branch_variable_t line_search_diving(const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log); - -template branch_variable_t pseudocost_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log); - -template branch_variable_t guided_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& incumbent, - logger_t& log); - -template branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, - const std::vector& fractional, - const std::vector& solution, - logger_t& log); #endif } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index f2c8abef1..e1df3ad8e 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -17,12 +17,6 @@ namespace cuopt::linear_programming::dual_simplex { -template -struct branch_variable_t { - i_t variable; - rounding_direction_t direction; -}; - template class pseudo_costs_t { public: @@ -86,29 +80,4 @@ void strong_branching(const lp_problem_t& original_lp, template rounding_direction_t martin_criteria(f_t val, f_t root_val); -template -branch_variable_t line_search_diving(const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log); - -template -branch_variable_t pseudocost_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log); - -template -branch_variable_t guided_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& incumbent, - logger_t& log); - -template -branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, - const std::vector& fractional, - const std::vector& solution, - logger_t& log); } // namespace cuopt::linear_programming::dual_simplex From 6ea6d72a3e22db2833d2041ada3489063094dea6 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 3 Dec 2025 19:09:12 +0100 Subject: [PATCH 108/366] organized code. added toggle to disable each type of diving. --- cpp/src/dual_simplex/branch_and_bound.cpp | 98 ++++++++++++------- cpp/src/dual_simplex/diving_heuristics.hpp | 8 -- cpp/src/dual_simplex/mip_node.hpp | 1 + cpp/src/dual_simplex/pseudo_costs.cpp | 18 ---- cpp/src/dual_simplex/pseudo_costs.hpp | 7 -- .../dual_simplex/simplex_solver_settings.hpp | 10 ++ 6 files changed, 76 insertions(+), 66 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 3a7475c64..8a6ba9545 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -564,6 +564,27 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, mutex_upper_.unlock(); } +// Martin's criteria for the preferred rounding direction (see [1]) +// [1] A. Martin, “Integer Programs with Block Structure,” +// Technische Universit¨at Berlin, Berlin, 1999. Accessed: Aug. 08, 2025. +// [Online]. Available: https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/391 +template +rounding_direction_t martin_criteria(f_t val, f_t root_val) +{ + const f_t down_val = std::floor(root_val); + const f_t up_val = std::ceil(root_val); + const f_t down_dist = val - down_val; + const f_t up_dist = up_val - val; + constexpr f_t eps = 1e-6; + + if (down_dist < up_dist + eps) { + return rounding_direction_t::DOWN; + + } else { + return rounding_direction_t::UP; + } +} + template branch_variable_t branch_and_bound_t::variable_selection( mip_node_t* node_ptr, @@ -648,6 +669,28 @@ node_solve_info_t branch_and_bound_t::solve_node( if (lp_settings.iteration_limit <= 0) { return node_solve_info_t::ITERATION_LIMIT; } } +#ifdef LOG_NODE_SIMPLEX + lp_settings.set_log(true); + std::stringstream ss; + ss << "simplex-" << std::this_thread::get_id() << ".log"; + std::string logname; + ss >> logname; + lp_settings.log.set_log_file(logname, "a"); + lp_settings.log.log_to_console = false; + lp_settings.log.printf( + "%scurrent node: id = %d, depth = %d, branch var = %d, branch dir = %s, fractional val = " + "%f, variable lower bound = %f, variable upper bound = %f, branch vstatus = %d\n\n", + settings_.log.log_prefix.c_str(), + node_ptr->node_id, + node_ptr->depth, + node_ptr->branch_var, + node_ptr->branch_dir == rounding_direction_t::DOWN ? "DOWN" : "UP", + node_ptr->fractional_val, + node_ptr->branch_var_lower, + node_ptr->branch_var_upper, + node_ptr->vstatus[node_ptr->branch_var]); +#endif + // Reset the bound_changed markers std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); @@ -673,29 +716,6 @@ node_solve_info_t branch_and_bound_t::solve_node( f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; -#ifdef LOG_NODE_SIMPLEX - lp_settings.set_log(true); - std::stringstream ss; - ss << "simplex-" << std::this_thread::get_id() << ".log"; - std::string logname; - ss >> logname; - lp_settings.log.set_log_file(logname, "a"); - lp_settings.log.log_to_console = false; - lp_settings.log.printf( - "%s\ncurrent node: id = %d, depth = %d, branch var = %d, branch dir = %s, fractional val = " - "%f, variable lower " - "bound = %f, variable upper bound = %f, branch vstatus = %d\n\n", - settings_.log.log_prefix.c_str(), - node_ptr->node_id, - node_ptr->depth, - node_ptr->branch_var, - node_ptr->branch_dir == rounding_direction_t::DOWN ? "DOWN" : "UP", - node_ptr->fractional_val, - node_ptr->branch_var_lower, - node_ptr->branch_var_upper, - node_ptr->vstatus[node_ptr->branch_var]); -#endif - lp_status = dual_phase2_with_advanced_basis(2, 0, recompute_bounds_and_basis, @@ -725,14 +745,14 @@ node_solve_info_t branch_and_bound_t::solve_node( lp_status = convert_lp_status_to_dual_status(second_status); } -#ifdef LOG_NODE_SIMPLEX - lp_settings.log.printf("\nLP status: %d\n\n", lp_status); -#endif - stats.total_lp_solve_time += toc(lp_start_time); stats.total_lp_iters += node_iter; } +#ifdef LOG_NODE_SIMPLEX + lp_settings.log.printf("\nLP status: %d\n\n", lp_status); +#endif + if (lp_status == dual::status_t::DUAL_UNBOUNDED) { // Node was infeasible. Do not branch node_ptr->lower_bound = inf; @@ -1507,12 +1527,24 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut lower_bound_ceiling_ = inf; should_report_ = true; - std::vector diving_strategies = { - bnb_thread_type_t::PSEUDOCOST_DIVING, - bnb_thread_type_t::LINE_SEARCH_DIVING, - bnb_thread_type_t::GUIDED_DIVING, - bnb_thread_type_t::COEFFICIENT_DIVING, - }; + std::vector diving_strategies; + diving_strategies.reserve(4); + + if (!settings_.disable_pseudocost_diving) { + diving_strategies.push_back(bnb_thread_type_t::PSEUDOCOST_DIVING); + } + + if (!settings_.disable_line_search_diving) { + diving_strategies.push_back(bnb_thread_type_t::LINE_SEARCH_DIVING); + } + + if (!settings_.disable_guided_diving) { + diving_strategies.push_back(bnb_thread_type_t::GUIDED_DIVING); + } + + if (!settings_.disable_coefficient_diving) { + diving_strategies.push_back(bnb_thread_type_t::COEFFICIENT_DIVING); + } #pragma omp parallel num_threads(settings_.num_threads) { diff --git a/cpp/src/dual_simplex/diving_heuristics.hpp b/cpp/src/dual_simplex/diving_heuristics.hpp index 5b259dd33..c7b1e2050 100644 --- a/cpp/src/dual_simplex/diving_heuristics.hpp +++ b/cpp/src/dual_simplex/diving_heuristics.hpp @@ -14,14 +14,6 @@ namespace cuopt::linear_programming::dual_simplex { -struct diving_general_settings_t { - int num_diving_threads; - bool disable_line_search_diving = false; - bool disable_pseudocost_diving = false; - bool disable_guided_diving = false; - bool disable_coefficient_diving = false; -}; - template struct branch_variable_t { i_t variable; diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index 61f63f17a..e2e9c6868 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -45,6 +45,7 @@ class mip_node_t { branch_var_lower(-std::numeric_limits::infinity()), branch_var_upper(std::numeric_limits::infinity()), fractional_val(std::numeric_limits::infinity()), + objective_estimate(std::numeric_limits::infinity()), vstatus(0) { children[0] = nullptr; diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 091639539..a2defd3b3 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -195,22 +195,6 @@ void strong_branching(const lp_problem_t& original_lp, pc.update_pseudo_costs_from_strong_branching(fractional, root_soln); } -template -rounding_direction_t martin_criteria(f_t val, f_t root_val) -{ - const f_t down_val = std::floor(root_val); - const f_t up_val = std::ceil(root_val); - const f_t down_dist = val - down_val; - const f_t up_dist = up_val - val; - constexpr f_t eps = 1e-6; - - if (down_dist < up_dist + eps) { - return rounding_direction_t::DOWN; - } else { - return rounding_direction_t::UP; - } -} - template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) @@ -375,8 +359,6 @@ template void strong_branching(const lp_problem_t& ori const std::vector& edge_norms, pseudo_costs_t& pc); -template rounding_direction_t martin_criteria(double val, double root_val); - #endif } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index e1df3ad8e..ab01b2a85 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -73,11 +73,4 @@ void strong_branching(const lp_problem_t& original_lp, const std::vector& edge_norms, pseudo_costs_t& pc); -// Martin's criteria for the preferred rounding direction (see [1]) -// [1] A. Martin, “Integer Programs with Block Structure,” -// Technische Universit¨at Berlin, Berlin, 1999. Accessed: Aug. 08, 2025. -// [Online]. Available: https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/391 -template -rounding_direction_t martin_criteria(f_t val, f_t root_val); - } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 98be9d4cb..47e4ca49b 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -72,6 +72,10 @@ struct simplex_solver_settings_t { num_threads(omp_get_max_threads() - 1), num_bfs_threads(std::min(num_threads / 4, 1)), num_diving_threads(std::min(num_threads - num_bfs_threads, 1)), + disable_line_search_diving(false), + disable_pseudocost_diving(false), + disable_guided_diving(false), + disable_coefficient_diving(false), random_seed(0), inside_mip(0), solution_callback(nullptr), @@ -139,6 +143,12 @@ struct simplex_solver_settings_t { i_t random_seed; // random seed i_t num_bfs_threads; // number of threads dedicated to the best-first search i_t num_diving_threads; // number of threads dedicated to diving + + bool disable_line_search_diving; // true to disable line search diving + bool disable_pseudocost_diving; // true to disable pseudocost diving + bool disable_guided_diving; // true to disable guided diving + bool disable_coefficient_diving; // true to disable coefficient diving + i_t inside_mip; // 0 if outside MIP, 1 if inside MIP at root node, 2 if inside MIP at leaf node std::function&, f_t)> solution_callback; std::function&, f_t)> node_processed_callback; From 5422b97864fc483203cae58f25d85d831467b130 Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 4 Dec 2025 11:16:40 +0100 Subject: [PATCH 109/366] restrict calling RINS to the best-first threads --- cpp/src/dual_simplex/branch_and_bound.cpp | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 8a6ba9545..d583ed214 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -779,10 +779,12 @@ node_solve_info_t branch_and_bound_t::solve_node( search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); pc_.update_pseudo_costs(node_ptr, leaf_objective); - if (settings_.node_processed_callback != nullptr) { - std::vector original_x; - uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); - settings_.node_processed_callback(original_x, leaf_objective); + if (thread_type == bnb_thread_type_t::EXPLORATION) { + if (settings_.node_processed_callback != nullptr) { + std::vector original_x; + uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); + settings_.node_processed_callback(original_x, leaf_objective); + } } if (leaf_num_fractional == 0) { From 73c1a63086231fc72d200379ec4d1ddc9822e752 Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 4 Dec 2025 16:27:02 +0100 Subject: [PATCH 110/366] fix invalid branch var in line search diving --- cpp/src/dual_simplex/diving_heuristics.cpp | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index c59a0e850..f6096f40f 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -44,12 +44,6 @@ branch_variable_t line_search_diving(const std::vector& fractional, } } - log.debug("Line search diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - min_score); - // If the current solution is equal to the root solution, arbitrarily // set the branch variable to the first fractional variable and round it down if (round_dir == rounding_direction_t::NONE) { @@ -57,6 +51,12 @@ branch_variable_t line_search_diving(const std::vector& fractional, round_dir = rounding_direction_t::DOWN; } + log.debug("Line search diving: selected var %d with val = %e, round dir = %d and score = %e\n", + branch_var, + solution[branch_var], + round_dir, + min_score); + return {branch_var, round_dir}; } @@ -182,6 +182,7 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, solution[branch_var], round_dir, max_score); + return {branch_var, round_dir}; } From 3a77ccaaf439e9eb1d654408d000995cb22de4fc Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 9 Dec 2025 13:45:18 +0100 Subject: [PATCH 111/366] moved asserts --- cpp/src/dual_simplex/branch_and_bound.cpp | 3 --- cpp/src/dual_simplex/branch_and_bound.hpp | 1 - cpp/src/dual_simplex/diving_heuristics.cpp | 13 +++++++++++++ 3 files changed, 13 insertions(+), 4 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d583ed214..6167033be 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -799,8 +799,6 @@ node_solve_info_t branch_and_bound_t::solve_node( auto [branch_var, round_dir] = variable_selection( node_ptr, leaf_fractional, leaf_solution.x, thread_type, lp_settings.log); - assert(round_dir != rounding_direction_t::NONE); - assert(branch_var >= 0); assert(leaf_vstatus.size() == leaf_problem.num_cols); search_tree.branch( @@ -1524,7 +1522,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut exploration_stats_.nodes_since_last_log = 0; exploration_stats_.last_log = tic(); active_subtrees_ = 0; - min_diving_queue_size_ = 4 * settings_.num_diving_threads; solver_status_ = mip_exploration_status_t::RUNNING; lower_bound_ceiling_ = inf; should_report_ = true; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index b0ed7e800..c774315d8 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -196,7 +196,6 @@ class branch_and_bound_t { // Queue for storing the promising node for performing dives. omp_mutex_t mutex_dive_queue_; diving_queue_t diving_queue_; - i_t min_diving_queue_size_; // Global status of the solver. omp_atomic_t solver_status_; diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index f6096f40f..09f15a70f 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -51,6 +51,9 @@ branch_variable_t line_search_diving(const std::vector& fractional, round_dir = rounding_direction_t::DOWN; } + assert(round_dir != rounding_direction_t::NONE); + assert(branch_var >= 0); + log.debug("Line search diving: selected var %d with val = %e, round dir = %d and score = %e\n", branch_var, solution[branch_var], @@ -122,6 +125,10 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, round_dir = dir; } } + + assert(round_dir != rounding_direction_t::NONE); + assert(branch_var >= 0); + log.debug("Pseudocost diving: selected var %d with val = %e, round dir = %d and score = %e\n", branch_var, solution[branch_var], @@ -177,6 +184,9 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, } } + assert(round_dir != rounding_direction_t::NONE); + assert(branch_var >= 0); + log.debug("Guided diving: selected var %d with val = %e, round dir = %d and score = %e\n", branch_var, solution[branch_var], @@ -249,6 +259,9 @@ branch_variable_t coefficient_diving(const lp_problem_t& lp_probl } } + assert(round_dir != rounding_direction_t::NONE); + assert(branch_var >= 0); + log.debug( "Coefficient diving: selected var %d with val = %e, round dir = %d and min locks = %e\n", branch_var, From 0f7af4e2cebdd67df125b4592ed76e3613d4a3e2 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 9 Dec 2025 15:06:37 +0100 Subject: [PATCH 112/366] replace inf and max with STL calls --- cpp/src/dual_simplex/diving_heuristics.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 09f15a70f..9709123f9 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -17,7 +17,7 @@ branch_variable_t line_search_diving(const std::vector& fractional, { constexpr f_t eps = 1e-6; i_t branch_var = -1; - f_t min_score = INFINITY; + f_t min_score = std::numeric_limits::max(); rounding_direction_t round_dir = rounding_direction_t::NONE; for (auto j : fractional) { @@ -72,7 +72,7 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, { std::lock_guard lock(pc.mutex); i_t branch_var = -1; - f_t max_score = -INFINITY; + f_t max_score = std::numeric_limits::lowest(); rounding_direction_t round_dir = rounding_direction_t::NONE; constexpr f_t eps = 1e-6; @@ -147,7 +147,7 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, { std::lock_guard lock(pc.mutex); i_t branch_var = -1; - f_t max_score = -INFINITY; + f_t max_score = std::numeric_limits::lowest(); rounding_direction_t round_dir = rounding_direction_t::NONE; constexpr f_t eps = 1e-6; @@ -233,7 +233,7 @@ branch_variable_t coefficient_diving(const lp_problem_t& lp_probl logger_t& log) { i_t branch_var = -1; - f_t min_locks = INT_MAX; + i_t min_locks = std::numeric_limits::max(); rounding_direction_t round_dir = rounding_direction_t::NONE; constexpr f_t eps = 1e-6; From 79368c3be743fd943137e0e214de5bdcda79211b Mon Sep 17 00:00:00 2001 From: Christopher Maes Date: Thu, 11 Dec 2025 18:52:50 -0800 Subject: [PATCH 113/366] Fix incorrect infeasible list --- cpp/src/dual_simplex/basis_solves.cpp | 14 +++++++- cpp/src/dual_simplex/basis_solves.hpp | 2 ++ cpp/src/dual_simplex/basis_updates.cpp | 4 ++- cpp/src/dual_simplex/basis_updates.hpp | 2 ++ cpp/src/dual_simplex/crossover.cpp | 6 ++-- cpp/src/dual_simplex/phase2.cpp | 45 ++++++++++++++------------ cpp/src/dual_simplex/primal.cpp | 2 +- 7 files changed, 49 insertions(+), 26 deletions(-) diff --git a/cpp/src/dual_simplex/basis_solves.cpp b/cpp/src/dual_simplex/basis_solves.cpp index db24f55a2..3080f269d 100644 --- a/cpp/src/dual_simplex/basis_solves.cpp +++ b/cpp/src/dual_simplex/basis_solves.cpp @@ -613,6 +613,8 @@ i_t factorize_basis(const csc_matrix_t& A, template i_t basis_repair(const csc_matrix_t& A, const simplex_solver_settings_t& settings, + const std::vector& lower, + const std::vector& upper, const std::vector& deficient, const std::vector& slacks_needed, std::vector& basis_list, @@ -658,7 +660,15 @@ i_t basis_repair(const csc_matrix_t& A, nonbasic_list[nonbasic_map[replace_j]] = bad_j; vstatus[replace_j] = variable_status_t::BASIC; // This is the main issue. What value should bad_j take on. - vstatus[bad_j] = variable_status_t::NONBASIC_FREE; + if (lower[bad_j] == -inf && upper[bad_j] == inf) { + vstatus[bad_j] = variable_status_t::NONBASIC_FREE; + } else if (lower[bad_j] > -inf) { + vstatus[bad_j] = variable_status_t::NONBASIC_LOWER; + } else if (upper[bad_j] < inf) { + vstatus[bad_j] = variable_status_t::NONBASIC_UPPER; + } else { + assert(1 == 0); + } } return 0; @@ -849,6 +859,8 @@ template int factorize_basis(const csc_matrix_t& A, template int basis_repair(const csc_matrix_t& A, const simplex_solver_settings_t& settings, + const std::vector& lower, + const std::vector& upper, const std::vector& deficient, const std::vector& slacks_needed, std::vector& basis_list, diff --git a/cpp/src/dual_simplex/basis_solves.hpp b/cpp/src/dual_simplex/basis_solves.hpp index b668c0f46..0745806a6 100644 --- a/cpp/src/dual_simplex/basis_solves.hpp +++ b/cpp/src/dual_simplex/basis_solves.hpp @@ -42,6 +42,8 @@ i_t factorize_basis(const csc_matrix_t& A, template i_t basis_repair(const csc_matrix_t& A, const simplex_solver_settings_t& settings, + const std::vector& lower, + const std::vector& upper, const std::vector& deficient, const std::vector& slacks_needed, std::vector& basis_list, diff --git a/cpp/src/dual_simplex/basis_updates.cpp b/cpp/src/dual_simplex/basis_updates.cpp index 6b79f3c86..11056a65e 100644 --- a/cpp/src/dual_simplex/basis_updates.cpp +++ b/cpp/src/dual_simplex/basis_updates.cpp @@ -2046,6 +2046,8 @@ template int basis_update_mpf_t::refactor_basis( const csc_matrix_t& A, const simplex_solver_settings_t& settings, + const std::vector& lower, + const std::vector& upper, std::vector& basic_list, std::vector& nonbasic_list, std::vector& vstatus) @@ -2066,7 +2068,7 @@ int basis_update_mpf_t::refactor_basis( deficient, slacks_needed) == -1) { settings.log.debug("Initial factorization failed\n"); - basis_repair(A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(A, settings, lower, upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); #ifdef CHECK_BASIS_REPAIR const i_t m = A.m; diff --git a/cpp/src/dual_simplex/basis_updates.hpp b/cpp/src/dual_simplex/basis_updates.hpp index cea907074..9b5d3e614 100644 --- a/cpp/src/dual_simplex/basis_updates.hpp +++ b/cpp/src/dual_simplex/basis_updates.hpp @@ -373,6 +373,8 @@ class basis_update_mpf_t { // Compute L*U = A(p, basic_list) int refactor_basis(const csc_matrix_t& A, const simplex_solver_settings_t& settings, + const std::vector& lower, + const std::vector& upper, std::vector& basic_list, std::vector& nonbasic_list, std::vector& vstatus); diff --git a/cpp/src/dual_simplex/crossover.cpp b/cpp/src/dual_simplex/crossover.cpp index 23d9a0e8e..3dd61b152 100644 --- a/cpp/src/dual_simplex/crossover.cpp +++ b/cpp/src/dual_simplex/crossover.cpp @@ -786,7 +786,7 @@ i_t primal_push(const lp_problem_t& lp, if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); basis_repair( - lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + lp.A, settings, lp.lower, lp.upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); if (factorize_basis( lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); @@ -1132,7 +1132,7 @@ crossover_status_t crossover(const lp_problem_t& lp, rank = factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, settings, lp.lower, lp.upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); if (factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); @@ -1323,7 +1323,7 @@ crossover_status_t crossover(const lp_problem_t& lp, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, settings, lp.lower, lp.upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); if (factorize_basis( lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 56298ef4d..e0ac7239e 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -623,14 +623,17 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, const std::vector& basic_list, const std::vector& x, std::vector& squared_infeasibilities, - std::vector& infeasibility_indices) + std::vector& infeasibility_indices, + f_t& primal_inf) { const i_t m = lp.num_rows; const i_t n = lp.num_cols; - squared_infeasibilities.resize(n, 0.0); + squared_infeasibilities.resize(n); + std::fill(squared_infeasibilities.begin(), squared_infeasibilities.end(), 0.0); infeasibility_indices.reserve(n); infeasibility_indices.clear(); - f_t primal_inf = 0.0; + f_t primal_inf_squared = 0.0; + primal_inf = 0.0; for (i_t k = 0; k < m; ++k) { const i_t j = basic_list[k]; const f_t lower_infeas = lp.lower[j] - x[j]; @@ -640,10 +643,11 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, const f_t square_infeas = infeas * infeas; squared_infeasibilities[j] = square_infeas; infeasibility_indices.push_back(j); - primal_inf += square_infeas; + primal_inf_squared += square_infeas; + primal_inf += infeas; } } - return primal_inf; + return primal_inf_squared; } template @@ -2241,7 +2245,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(superbasic_list.size() == 0); assert(nonbasic_list.size() == n - m); - if (ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus) > 0) { + if (ft.refactor_basis(lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus) > 0) { return dual::status_t::NUMERICAL; } @@ -2268,7 +2272,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #ifdef COMPUTE_DUAL_RESIDUAL std::vector dual_res1; - compute_dual_residual(lp.A, objective, y, z, dual_res1); + phase2::compute_dual_residual(lp.A, objective, y, z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.tight_tol) { settings.log.printf("|| A'*y + z - c || %e\n", dual_res_norm); @@ -2357,8 +2361,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector bounded_variables(n, 0); phase2::compute_bounded_info(lp.lower, lp.upper, bounded_variables); - f_t primal_infeasibility = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + f_t primal_infeasibility; + f_t primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities( + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); #ifdef CHECK_BASIC_INFEASIBILITIES phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 0); @@ -2557,8 +2562,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); x = unperturbed_x; - primal_infeasibility = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities( + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); settings.log.printf("Updated primal infeasibility: %e\n", primal_infeasibility); objective = lp.objective; @@ -2594,8 +2599,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); x = unperturbed_x; - primal_infeasibility = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities( + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); const f_t orig_dual_infeas = phase2::dual_infeasibility( lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); @@ -2810,7 +2815,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_xB_0_sparse.i, squared_infeasibilities, infeasibility_indices, - primal_infeasibility); + primal_infeasibility_squared); // Update primal infeasibilities due to changes in basic variables // from the leaving and entering variables phase2::update_primal_infeasibilities(lp, @@ -2822,7 +2827,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, scaled_delta_xB_sparse.i, squared_infeasibilities, infeasibility_indices, - primal_infeasibility); + primal_infeasibility_squared); // Update the entering variable phase2::update_single_primal_infeasibility(lp.lower, lp.upper, @@ -2883,14 +2888,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif if (should_refactor) { bool should_recompute_x = false; - if (ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus) > 0) { + if (ft.refactor_basis(lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus) > 0) { should_recompute_x = true; settings.log.printf("Failed to factorize basis. Iteration %d\n", iter); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } i_t count = 0; i_t deficient_size; while ((deficient_size = - ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus)) > 0) { + ft.refactor_basis(lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus)) > 0) { settings.log.printf("Failed to repair basis. Iteration %d. %d deficient columns.\n", iter, static_cast(deficient_size)); @@ -2912,8 +2917,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); x = unperturbed_x; } - phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities( + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); } #ifdef CHECK_BASIC_INFEASIBILITIES phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 7); @@ -2951,7 +2956,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, iter, compute_user_objective(lp, obj), infeasibility_indices.size(), - primal_infeasibility, + primal_infeasibility_squared, sum_perturb, now); } diff --git a/cpp/src/dual_simplex/primal.cpp b/cpp/src/dual_simplex/primal.cpp index 80406dcf0..445177fac 100644 --- a/cpp/src/dual_simplex/primal.cpp +++ b/cpp/src/dual_simplex/primal.cpp @@ -298,7 +298,7 @@ primal::status_t primal_phase2(i_t phase, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, settings, lp.lower, lp.upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); if (factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); From 6334ad71e8e3d5b03a7cfef156ff473e92a32642 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 3 Dec 2025 16:27:16 +0100 Subject: [PATCH 114/366] implemented diving heuristics. sorted starting nodes for diving based on the objective pseudcost estimate. --- cpp/src/dual_simplex/branch_and_bound.cpp | 25 ++ cpp/src/dual_simplex/pseudo_costs.cpp | 288 ++++++++++++++++++++++ cpp/src/dual_simplex/pseudo_costs.hpp | 38 +++ 3 files changed, 351 insertions(+) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 6167033be..e2af8550f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -716,6 +716,29 @@ node_solve_info_t branch_and_bound_t::solve_node( f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; +#ifdef LOG_NODE_SIMPLEX + lp_settings.set_log(true); + std::stringstream ss; + ss << "simplex-" << std::this_thread::get_id() << ".log"; + std::string logname; + ss >> logname; + lp_settings.log.set_log_file(logname, "a"); + lp_settings.log.log_to_console = false; + lp_settings.log.printf( + "%s\ncurrent node: id = %d, depth = %d, branch var = %d, branch dir = %s, fractional val = " + "%f, variable lower " + "bound = %f, variable upper bound = %f, branch vstatus = %d\n\n", + settings_.log.log_prefix.c_str(), + node_ptr->node_id, + node_ptr->depth, + node_ptr->branch_var, + node_ptr->branch_dir == rounding_direction_t::DOWN ? "DOWN" : "UP", + node_ptr->fractional_val, + node_ptr->branch_var_lower, + node_ptr->branch_var_upper, + node_ptr->vstatus[node_ptr->branch_var]); +#endif + lp_status = dual_phase2_with_advanced_basis(2, 0, recompute_bounds_and_basis, @@ -799,6 +822,8 @@ node_solve_info_t branch_and_bound_t::solve_node( auto [branch_var, round_dir] = variable_selection( node_ptr, leaf_fractional, leaf_solution.x, thread_type, lp_settings.log); + assert(round_dir != rounding_direction_t::NONE); + assert(branch_var >= 0); assert(leaf_vstatus.size() == leaf_problem.num_cols); search_tree.branch( diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index a2defd3b3..3fdbbbfbf 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -195,6 +195,271 @@ void strong_branching(const lp_problem_t& original_lp, pc.update_pseudo_costs_from_strong_branching(fractional, root_soln); } +template +rounding_direction_t martin_criteria(f_t val, f_t root_val) +{ + const f_t down_val = std::floor(root_val); + const f_t up_val = std::ceil(root_val); + const f_t down_dist = val - down_val; + const f_t up_dist = up_val - val; + constexpr f_t eps = 1e-6; + + if (down_dist < up_dist + eps) { + return rounding_direction_t::DOWN; + } else { + return rounding_direction_t::UP; + } +} + +template +branch_variable_t line_search_diving(const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log) +{ + constexpr f_t eps = 1e-6; + i_t branch_var = -1; + f_t min_score = INFINITY; + rounding_direction_t round_dir = rounding_direction_t::NONE; + + for (auto j : fractional) { + f_t score = inf; + rounding_direction_t dir = rounding_direction_t::NONE; + + if (solution[j] < root_solution[j] - eps) { + f_t f = solution[j] - std::floor(solution[j]); + f_t d = root_solution[j] - solution[j]; + score = f / d; + dir = rounding_direction_t::DOWN; + + } else if (solution[j] > root_solution[j] + eps) { + f_t f = std::ceil(solution[j]) - solution[j]; + f_t d = solution[j] - root_solution[j]; + score = f / d; + dir = rounding_direction_t::UP; + } + + if (min_score > score) { + min_score = score; + branch_var = j; + round_dir = dir; + } + } + + log.debug("Line search diving: selected var %d with val = %e, round dir = %d and score = %e\n", + branch_var, + solution[branch_var], + round_dir, + min_score); + + // If the current solution is equal to the root solution, arbitrarily + // set the branch variable to the first fractional variable and round it down + if (round_dir == rounding_direction_t::NONE) { + branch_var = fractional[0]; + round_dir = rounding_direction_t::DOWN; + } + + return {branch_var, round_dir}; +} + +template +branch_variable_t pseudocost_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log) +{ + std::lock_guard lock(pc.mutex); + i_t branch_var = -1; + f_t max_score = -INFINITY; + rounding_direction_t round_dir = rounding_direction_t::NONE; + constexpr f_t eps = 1e-6; + + i_t num_initialized_down; + i_t num_initialized_up; + f_t pseudo_cost_down_avg; + f_t pseudo_cost_up_avg; + pc.initialized( + num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); + + for (auto j : fractional) { + rounding_direction_t dir = rounding_direction_t::NONE; + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + + f_t pc_down = pc.pseudo_cost_num_down[j] != 0 + ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] + : pseudo_cost_down_avg; + + f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + + f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); + f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); + f_t score = 0; + + if (solution[j] < root_solution[j] - 0.4) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else if (solution[j] > root_solution[j] + 0.4) { + score = score_up; + dir = rounding_direction_t::UP; + } else if (f_down < 0.3) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else if (f_down > 0.7) { + score = score_up; + dir = rounding_direction_t::UP; + } else if (pc_down < pc_up + eps) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else { + score = score_up; + dir = rounding_direction_t::UP; + } + + if (score > max_score) { + max_score = score; + branch_var = j; + round_dir = dir; + } + } + log.debug("Pseudocost diving: selected var %d with val = %e, round dir = %d and score = %e\n", + branch_var, + solution[branch_var], + round_dir, + max_score); + + return {branch_var, round_dir}; +} + +template +branch_variable_t guided_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& incumbent, + logger_t& log) +{ + std::lock_guard lock(pc.mutex); + i_t branch_var = -1; + f_t max_score = -INFINITY; + rounding_direction_t round_dir = rounding_direction_t::NONE; + constexpr f_t eps = 1e-6; + + i_t num_initialized_down; + i_t num_initialized_up; + f_t pseudo_cost_down_avg; + f_t pseudo_cost_up_avg; + pc.initialized( + num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); + + for (auto j : fractional) { + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t down_dist = std::abs(incumbent[j] - std::floor(solution[j])); + f_t up_dist = std::abs(std::ceil(solution[j]) - incumbent[j]); + rounding_direction_t dir = + down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; + + f_t pc_down = pc.pseudo_cost_num_down[j] != 0 + ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] + : pseudo_cost_down_avg; + + f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + + f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; + f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; + f_t score = (score1 + score2) / 6; + + if (score > max_score) { + max_score = score; + branch_var = j; + round_dir = dir; + } + } + + log.debug("Guided diving: selected var %d with val = %e, round dir = %d and score = %e\n", + branch_var, + solution[branch_var], + round_dir, + max_score); + return {branch_var, round_dir}; +} + +template +std::tuple calculate_variable_locks(const lp_problem_t& lp_problem, i_t var_idx) +{ + i_t up_lock = 0; + i_t down_lock = 0; + i_t start = lp_problem.A.col_start[var_idx]; + i_t end = lp_problem.A.col_start[var_idx + 1]; + + for (i_t k = start; k < end; ++k) { + f_t nz_val = lp_problem.A.x[k]; + i_t nz_row = lp_problem.A.i[k]; + + if (std::isfinite(lp_problem.upper[nz_row]) && std::isfinite(lp_problem.lower[nz_row])) { + down_lock += 1; + up_lock += 1; + continue; + } + + f_t sign = std::isfinite(lp_problem.upper[nz_row]) ? 1 : -1; + + if (nz_val * sign > 0) { + up_lock += 1; + } else { + down_lock += 1; + } + } + + return {up_lock, down_lock}; +} + +template +branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, + const std::vector& fractional, + const std::vector& solution, + logger_t& log) +{ + i_t branch_var = -1; + f_t min_locks = INT_MAX; + rounding_direction_t round_dir = rounding_direction_t::NONE; + constexpr f_t eps = 1e-6; + + for (auto j : fractional) { + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + auto [up_lock, down_lock] = calculate_variable_locks(lp_problem, j); + f_t locks = std::min(up_lock, down_lock); + + if (min_locks > locks) { + min_locks = locks; + branch_var = j; + + if (up_lock < down_lock) { + round_dir = rounding_direction_t::UP; + } else if (up_lock > down_lock) { + round_dir = rounding_direction_t::DOWN; + } else if (f_down < f_up + eps) { + round_dir = rounding_direction_t::DOWN; + } else { + round_dir = rounding_direction_t::UP; + } + } + } + + log.debug( + "Coefficient diving: selected var %d with val = %e, round dir = %d and min locks = %e\n", + branch_var, + solution[branch_var], + round_dir, + min_locks); + + return {branch_var, round_dir}; +} + template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) @@ -359,6 +624,29 @@ template void strong_branching(const lp_problem_t& ori const std::vector& edge_norms, pseudo_costs_t& pc); +template rounding_direction_t martin_criteria(double val, double root_val); + +template branch_variable_t line_search_diving(const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template branch_variable_t pseudocost_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template branch_variable_t guided_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& incumbent, + logger_t& log); + +template branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, + const std::vector& fractional, + const std::vector& solution, + logger_t& log); #endif } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index ab01b2a85..f2c8abef1 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -17,6 +17,12 @@ namespace cuopt::linear_programming::dual_simplex { +template +struct branch_variable_t { + i_t variable; + rounding_direction_t direction; +}; + template class pseudo_costs_t { public: @@ -73,4 +79,36 @@ void strong_branching(const lp_problem_t& original_lp, const std::vector& edge_norms, pseudo_costs_t& pc); +// Martin's criteria for the preferred rounding direction (see [1]) +// [1] A. Martin, “Integer Programs with Block Structure,” +// Technische Universit¨at Berlin, Berlin, 1999. Accessed: Aug. 08, 2025. +// [Online]. Available: https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/391 +template +rounding_direction_t martin_criteria(f_t val, f_t root_val); + +template +branch_variable_t line_search_diving(const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template +branch_variable_t pseudocost_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution, + logger_t& log); + +template +branch_variable_t guided_diving(pseudo_costs_t& pc, + const std::vector& fractional, + const std::vector& solution, + const std::vector& incumbent, + logger_t& log); + +template +branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, + const std::vector& fractional, + const std::vector& solution, + logger_t& log); } // namespace cuopt::linear_programming::dual_simplex From 2c94a7c389844231d0fde933b93650d0840adec3 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 3 Dec 2025 18:58:00 +0100 Subject: [PATCH 115/366] moved diving heuristics to a separated file --- cpp/src/dual_simplex/pseudo_costs.cpp | 270 -------------------------- cpp/src/dual_simplex/pseudo_costs.hpp | 31 --- 2 files changed, 301 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 3fdbbbfbf..091639539 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -211,255 +211,6 @@ rounding_direction_t martin_criteria(f_t val, f_t root_val) } } -template -branch_variable_t line_search_diving(const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log) -{ - constexpr f_t eps = 1e-6; - i_t branch_var = -1; - f_t min_score = INFINITY; - rounding_direction_t round_dir = rounding_direction_t::NONE; - - for (auto j : fractional) { - f_t score = inf; - rounding_direction_t dir = rounding_direction_t::NONE; - - if (solution[j] < root_solution[j] - eps) { - f_t f = solution[j] - std::floor(solution[j]); - f_t d = root_solution[j] - solution[j]; - score = f / d; - dir = rounding_direction_t::DOWN; - - } else if (solution[j] > root_solution[j] + eps) { - f_t f = std::ceil(solution[j]) - solution[j]; - f_t d = solution[j] - root_solution[j]; - score = f / d; - dir = rounding_direction_t::UP; - } - - if (min_score > score) { - min_score = score; - branch_var = j; - round_dir = dir; - } - } - - log.debug("Line search diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - min_score); - - // If the current solution is equal to the root solution, arbitrarily - // set the branch variable to the first fractional variable and round it down - if (round_dir == rounding_direction_t::NONE) { - branch_var = fractional[0]; - round_dir = rounding_direction_t::DOWN; - } - - return {branch_var, round_dir}; -} - -template -branch_variable_t pseudocost_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log) -{ - std::lock_guard lock(pc.mutex); - i_t branch_var = -1; - f_t max_score = -INFINITY; - rounding_direction_t round_dir = rounding_direction_t::NONE; - constexpr f_t eps = 1e-6; - - i_t num_initialized_down; - i_t num_initialized_up; - f_t pseudo_cost_down_avg; - f_t pseudo_cost_up_avg; - pc.initialized( - num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - - for (auto j : fractional) { - rounding_direction_t dir = rounding_direction_t::NONE; - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - - f_t pc_down = pc.pseudo_cost_num_down[j] != 0 - ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - - f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - - f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); - f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); - f_t score = 0; - - if (solution[j] < root_solution[j] - 0.4) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else if (solution[j] > root_solution[j] + 0.4) { - score = score_up; - dir = rounding_direction_t::UP; - } else if (f_down < 0.3) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else if (f_down > 0.7) { - score = score_up; - dir = rounding_direction_t::UP; - } else if (pc_down < pc_up + eps) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else { - score = score_up; - dir = rounding_direction_t::UP; - } - - if (score > max_score) { - max_score = score; - branch_var = j; - round_dir = dir; - } - } - log.debug("Pseudocost diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - max_score); - - return {branch_var, round_dir}; -} - -template -branch_variable_t guided_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& incumbent, - logger_t& log) -{ - std::lock_guard lock(pc.mutex); - i_t branch_var = -1; - f_t max_score = -INFINITY; - rounding_direction_t round_dir = rounding_direction_t::NONE; - constexpr f_t eps = 1e-6; - - i_t num_initialized_down; - i_t num_initialized_up; - f_t pseudo_cost_down_avg; - f_t pseudo_cost_up_avg; - pc.initialized( - num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - - for (auto j : fractional) { - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t down_dist = std::abs(incumbent[j] - std::floor(solution[j])); - f_t up_dist = std::abs(std::ceil(solution[j]) - incumbent[j]); - rounding_direction_t dir = - down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; - - f_t pc_down = pc.pseudo_cost_num_down[j] != 0 - ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - - f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - - f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; - f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; - f_t score = (score1 + score2) / 6; - - if (score > max_score) { - max_score = score; - branch_var = j; - round_dir = dir; - } - } - - log.debug("Guided diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - max_score); - return {branch_var, round_dir}; -} - -template -std::tuple calculate_variable_locks(const lp_problem_t& lp_problem, i_t var_idx) -{ - i_t up_lock = 0; - i_t down_lock = 0; - i_t start = lp_problem.A.col_start[var_idx]; - i_t end = lp_problem.A.col_start[var_idx + 1]; - - for (i_t k = start; k < end; ++k) { - f_t nz_val = lp_problem.A.x[k]; - i_t nz_row = lp_problem.A.i[k]; - - if (std::isfinite(lp_problem.upper[nz_row]) && std::isfinite(lp_problem.lower[nz_row])) { - down_lock += 1; - up_lock += 1; - continue; - } - - f_t sign = std::isfinite(lp_problem.upper[nz_row]) ? 1 : -1; - - if (nz_val * sign > 0) { - up_lock += 1; - } else { - down_lock += 1; - } - } - - return {up_lock, down_lock}; -} - -template -branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, - const std::vector& fractional, - const std::vector& solution, - logger_t& log) -{ - i_t branch_var = -1; - f_t min_locks = INT_MAX; - rounding_direction_t round_dir = rounding_direction_t::NONE; - constexpr f_t eps = 1e-6; - - for (auto j : fractional) { - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - auto [up_lock, down_lock] = calculate_variable_locks(lp_problem, j); - f_t locks = std::min(up_lock, down_lock); - - if (min_locks > locks) { - min_locks = locks; - branch_var = j; - - if (up_lock < down_lock) { - round_dir = rounding_direction_t::UP; - } else if (up_lock > down_lock) { - round_dir = rounding_direction_t::DOWN; - } else if (f_down < f_up + eps) { - round_dir = rounding_direction_t::DOWN; - } else { - round_dir = rounding_direction_t::UP; - } - } - } - - log.debug( - "Coefficient diving: selected var %d with val = %e, round dir = %d and min locks = %e\n", - branch_var, - solution[branch_var], - round_dir, - min_locks); - - return {branch_var, round_dir}; -} - template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) @@ -626,27 +377,6 @@ template void strong_branching(const lp_problem_t& ori template rounding_direction_t martin_criteria(double val, double root_val); -template branch_variable_t line_search_diving(const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log); - -template branch_variable_t pseudocost_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log); - -template branch_variable_t guided_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& incumbent, - logger_t& log); - -template branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, - const std::vector& fractional, - const std::vector& solution, - logger_t& log); #endif } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index f2c8abef1..e1df3ad8e 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -17,12 +17,6 @@ namespace cuopt::linear_programming::dual_simplex { -template -struct branch_variable_t { - i_t variable; - rounding_direction_t direction; -}; - template class pseudo_costs_t { public: @@ -86,29 +80,4 @@ void strong_branching(const lp_problem_t& original_lp, template rounding_direction_t martin_criteria(f_t val, f_t root_val); -template -branch_variable_t line_search_diving(const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log); - -template -branch_variable_t pseudocost_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& root_solution, - logger_t& log); - -template -branch_variable_t guided_diving(pseudo_costs_t& pc, - const std::vector& fractional, - const std::vector& solution, - const std::vector& incumbent, - logger_t& log); - -template -branch_variable_t coefficient_diving(const lp_problem_t& lp_problem, - const std::vector& fractional, - const std::vector& solution, - logger_t& log); } // namespace cuopt::linear_programming::dual_simplex From 0e815e1cda4a13b6b97273dba3b1df10534663ec Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 3 Dec 2025 19:09:12 +0100 Subject: [PATCH 116/366] organized code. added toggle to disable each type of diving. --- cpp/src/dual_simplex/branch_and_bound.cpp | 23 ----------------------- cpp/src/dual_simplex/pseudo_costs.cpp | 18 ------------------ cpp/src/dual_simplex/pseudo_costs.hpp | 7 ------- 3 files changed, 48 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index e2af8550f..54a335bbd 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -716,29 +716,6 @@ node_solve_info_t branch_and_bound_t::solve_node( f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; -#ifdef LOG_NODE_SIMPLEX - lp_settings.set_log(true); - std::stringstream ss; - ss << "simplex-" << std::this_thread::get_id() << ".log"; - std::string logname; - ss >> logname; - lp_settings.log.set_log_file(logname, "a"); - lp_settings.log.log_to_console = false; - lp_settings.log.printf( - "%s\ncurrent node: id = %d, depth = %d, branch var = %d, branch dir = %s, fractional val = " - "%f, variable lower " - "bound = %f, variable upper bound = %f, branch vstatus = %d\n\n", - settings_.log.log_prefix.c_str(), - node_ptr->node_id, - node_ptr->depth, - node_ptr->branch_var, - node_ptr->branch_dir == rounding_direction_t::DOWN ? "DOWN" : "UP", - node_ptr->fractional_val, - node_ptr->branch_var_lower, - node_ptr->branch_var_upper, - node_ptr->vstatus[node_ptr->branch_var]); -#endif - lp_status = dual_phase2_with_advanced_basis(2, 0, recompute_bounds_and_basis, diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 091639539..a2defd3b3 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -195,22 +195,6 @@ void strong_branching(const lp_problem_t& original_lp, pc.update_pseudo_costs_from_strong_branching(fractional, root_soln); } -template -rounding_direction_t martin_criteria(f_t val, f_t root_val) -{ - const f_t down_val = std::floor(root_val); - const f_t up_val = std::ceil(root_val); - const f_t down_dist = val - down_val; - const f_t up_dist = up_val - val; - constexpr f_t eps = 1e-6; - - if (down_dist < up_dist + eps) { - return rounding_direction_t::DOWN; - } else { - return rounding_direction_t::UP; - } -} - template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) @@ -375,8 +359,6 @@ template void strong_branching(const lp_problem_t& ori const std::vector& edge_norms, pseudo_costs_t& pc); -template rounding_direction_t martin_criteria(double val, double root_val); - #endif } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index e1df3ad8e..ab01b2a85 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -73,11 +73,4 @@ void strong_branching(const lp_problem_t& original_lp, const std::vector& edge_norms, pseudo_costs_t& pc); -// Martin's criteria for the preferred rounding direction (see [1]) -// [1] A. Martin, “Integer Programs with Block Structure,” -// Technische Universit¨at Berlin, Berlin, 1999. Accessed: Aug. 08, 2025. -// [Online]. Available: https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/391 -template -rounding_direction_t martin_criteria(f_t val, f_t root_val); - } // namespace cuopt::linear_programming::dual_simplex From 29a2a330b0b8eefcc5d72cbc828432177ac2451d Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 9 Dec 2025 13:45:18 +0100 Subject: [PATCH 117/366] moved asserts --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 54a335bbd..6167033be 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -799,8 +799,6 @@ node_solve_info_t branch_and_bound_t::solve_node( auto [branch_var, round_dir] = variable_selection( node_ptr, leaf_fractional, leaf_solution.x, thread_type, lp_settings.log); - assert(round_dir != rounding_direction_t::NONE); - assert(branch_var >= 0); assert(leaf_vstatus.size() == leaf_problem.num_cols); search_tree.branch( From 5a3ef60d09c6bcdb324f8ae8c54da29e7c0d5c0a Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 4 Dec 2025 15:40:25 +0100 Subject: [PATCH 118/366] unified global node heap. fathom node in the diving node that was already explored. --- cpp/src/dual_simplex/bounds_strengthening.cpp | 4 +- cpp/src/dual_simplex/branch_and_bound.cpp | 102 +++++------- cpp/src/dual_simplex/branch_and_bound.hpp | 16 +- cpp/src/dual_simplex/diving_queue.hpp | 73 --------- cpp/src/dual_simplex/mip_node.hpp | 16 -- cpp/src/dual_simplex/node_queue.hpp | 155 ++++++++++++++++++ 6 files changed, 197 insertions(+), 169 deletions(-) delete mode 100644 cpp/src/dual_simplex/diving_queue.hpp create mode 100644 cpp/src/dual_simplex/node_queue.hpp diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index f1bf52c1e..c56c9db98 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -154,7 +154,7 @@ bool bounds_strengthening_t::bounds_strengthening( bool is_infeasible = check_infeasibility(min_a, max_a, cnst_lb, cnst_ub, settings.primal_tol); if (is_infeasible) { - settings.log.printf( + settings.log.debug( "Iter:: %d, Infeasible constraint %d, cnst_lb %e, cnst_ub %e, min_a %e, max_a %e\n", iter, i, @@ -211,7 +211,7 @@ bool bounds_strengthening_t::bounds_strengthening( new_ub = std::min(new_ub, upper_bounds[k]); if (new_lb > new_ub + 1e-6) { - settings.log.printf( + settings.log.debug( "Iter:: %d, Infeasible variable after update %d, %e > %e\n", iter, k, new_lb, new_ub); return false; } diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 6167033be..87e89a15c 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -247,25 +247,15 @@ f_t branch_and_bound_t::get_upper_bound() template f_t branch_and_bound_t::get_lower_bound() { - f_t lower_bound = lower_bound_ceiling_.load(); - mutex_heap_.lock(); - if (heap_.size() > 0) { lower_bound = std::min(heap_.top()->lower_bound, lower_bound); } - mutex_heap_.unlock(); + f_t lower_bound = lower_bound_ceiling_.load(); + f_t heap_lower_bound = node_queue.get_lower_bound(); + lower_bound = std::min(heap_lower_bound, lower_bound); for (i_t i = 0; i < local_lower_bounds_.size(); ++i) { lower_bound = std::min(local_lower_bounds_[i].load(), lower_bound); } - return lower_bound; -} - -template -i_t branch_and_bound_t::get_heap_size() -{ - mutex_heap_.lock(); - i_t size = heap_.size(); - mutex_heap_.unlock(); - return size; + return std::isfinite(lower_bound) ? lower_bound : -inf; } template @@ -950,10 +940,8 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod } else { // We've generated enough nodes, push further nodes onto the heap - mutex_heap_.lock(); - heap_.push(node->get_down_child()); - heap_.push(node->get_up_child()); - mutex_heap_.unlock(); + node_queue.push(node->get_down_child()); + node_queue.push(node->get_up_child()); } } } @@ -1072,28 +1060,7 @@ void branch_and_bound_t::explore_subtree(i_t task_id, if (stack.size() > 0) { mip_node_t* node = stack.back(); stack.pop_back(); - - // The order here matters. We want to create a copy of the node - // before adding to the global heap. Otherwise, - // some thread may consume the node (possibly fathoming it) - // before we had the chance to add to the diving queue. - // This lead to a SIGSEGV. Although, in this case, it - // would be better if we discard the node instead. - if (get_heap_size() > settings_.num_bfs_threads) { - std::vector lower = original_lp_.lower; - std::vector upper = original_lp_.upper; - std::fill( - node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); - node->get_variable_bounds(lower, upper, node_presolver.bounds_changed); - - mutex_dive_queue_.lock(); - diving_queue_.emplace(node->detach_copy(), std::move(lower), std::move(upper)); - mutex_dive_queue_.unlock(); - } - - mutex_heap_.lock(); - heap_.push(node); - mutex_heap_.unlock(); + node_queue.push(node); } exploration_stats_.nodes_unexplored += 2; @@ -1131,31 +1098,24 @@ void branch_and_bound_t::best_first_thread(i_t task_id, while (solver_status_ == mip_exploration_status_t::RUNNING && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && - (active_subtrees_ > 0 || get_heap_size() > 0)) { - mip_node_t* start_node = nullptr; - + (active_subtrees_ > 0 || node_queue.best_first_queue_size() > 0)) { // If there any node left in the heap, we pop the top node and explore it. - mutex_heap_.lock(); - if (heap_.size() > 0) { - start_node = heap_.top(); - heap_.pop(); - active_subtrees_++; - } - mutex_heap_.unlock(); + std::optional*> start_node = node_queue.pop_best_first(active_subtrees_); - if (start_node != nullptr) { - if (get_upper_bound() < start_node->lower_bound) { + if (start_node.has_value()) { + if (get_upper_bound() < start_node.value()->lower_bound) { // This node was put on the heap earlier but its lower bound is now greater than the // current upper bound - search_tree.graphviz_node(settings_.log, start_node, "cutoff", start_node->lower_bound); - search_tree.update(start_node, node_status_t::FATHOMED); + search_tree.graphviz_node( + settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); + search_tree.update(start_node.value(), node_status_t::FATHOMED); active_subtrees_--; continue; } // Best-first search with plunging explore_subtree(task_id, - start_node, + start_node.value(), search_tree, leaf_problem, node_presolver, @@ -1200,19 +1160,30 @@ void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type, std::vector basic_list(m); std::vector nonbasic_list; + std::vector start_lower; + std::vector start_upper; + bool reset_starting_bounds = true; + while (solver_status_ == mip_exploration_status_t::RUNNING && - (active_subtrees_ > 0 || get_heap_size() > 0)) { - std::optional> start_node; + (active_subtrees_ > 0 || node_queue.best_first_queue_size() > 0)) { + if (reset_starting_bounds) { + start_lower = original_lp_.lower; + start_upper = original_lp_.upper; + std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); + reset_starting_bounds = false; + } - mutex_dive_queue_.lock(); - if (diving_queue_.size() > 0) { start_node = diving_queue_.pop(); } - mutex_dive_queue_.unlock(); + std::optional> start_node = + node_queue.pop_diving(start_lower, start_upper, node_presolver.bounds_changed); if (start_node.has_value()) { - if (get_upper_bound() < start_node->node.lower_bound) { continue; } + reset_starting_bounds = true; + + bool is_feasible = node_presolver.bounds_strengthening(start_lower, start_upper, settings_); + if (get_upper_bound() < start_node->lower_bound || !is_feasible) { continue; } bool recompute_bounds_and_basis = true; - search_tree_t subtree(std::move(start_node->node)); + search_tree_t subtree(std::move(start_node.value())); std::deque*> stack; stack.push_front(&subtree.root); @@ -1244,8 +1215,8 @@ void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type, node_presolver, diving_type, recompute_bounds_and_basis, - start_node->lower, - start_node->upper, + start_lower, + start_upper, dive_stats, log); @@ -1578,7 +1549,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } } - f_t lower_bound = heap_.size() > 0 ? heap_.top()->lower_bound : search_tree_.root.lower_bound; + f_t lower_bound = node_queue.best_first_queue_size() > 0 ? node_queue.get_lower_bound() + : search_tree_.root.lower_bound; return set_final_solution(solution, lower_bound); } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index c774315d8..76d3b61f9 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -8,9 +8,9 @@ #pragma once #include -#include #include #include +#include #include #include #include @@ -21,7 +21,6 @@ #include #include -#include #include namespace cuopt::linear_programming::dual_simplex { @@ -89,9 +88,6 @@ struct bnb_stats_t { template class branch_and_bound_t { public: - template - using mip_node_heap_t = std::priority_queue, node_compare_t>; - branch_and_bound_t(const user_problem_t& user_problem, const simplex_solver_settings_t& solver_settings); @@ -129,7 +125,6 @@ class branch_and_bound_t { f_t get_upper_bound(); f_t get_lower_bound(); - i_t get_heap_size(); bool enable_concurrent_lp_root_solve() const { return enable_concurrent_lp_root_solve_; } volatile int* get_root_concurrent_halt() { return &root_concurrent_halt_; } void set_root_concurrent_halt(int value) { root_concurrent_halt_ = value; } @@ -183,9 +178,8 @@ class branch_and_bound_t { // Pseudocosts pseudo_costs_t pc_; - // Heap storing the nodes to be explored. - omp_mutex_t mutex_heap_; - mip_node_heap_t*> heap_; + // Heap storing the nodes waiting to be explored. + node_queue_t node_queue; // Search tree search_tree_t search_tree_; @@ -193,10 +187,6 @@ class branch_and_bound_t { // Count the number of subtrees that are currently being explored. omp_atomic_t active_subtrees_; - // Queue for storing the promising node for performing dives. - omp_mutex_t mutex_dive_queue_; - diving_queue_t diving_queue_; - // Global status of the solver. omp_atomic_t solver_status_; diff --git a/cpp/src/dual_simplex/diving_queue.hpp b/cpp/src/dual_simplex/diving_queue.hpp deleted file mode 100644 index 0c13c2ed5..000000000 --- a/cpp/src/dual_simplex/diving_queue.hpp +++ /dev/null @@ -1,73 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#pragma once - -#include -#include -#include - -#include - -namespace cuopt::linear_programming::dual_simplex { - -template -struct diving_root_t { - mip_node_t node; - std::vector lower; - std::vector upper; - - diving_root_t(mip_node_t&& node, std::vector&& lower, std::vector&& upper) - : node(std::move(node)), lower(std::move(lower)), upper(std::move(upper)) - { - } - - friend bool operator>(const diving_root_t& a, const diving_root_t& b) - { - return a.node.objective_estimate > b.node.objective_estimate; - } -}; - -// A min-heap for storing the starting nodes for the dives. -// This has a maximum size of 1024, such that the container -// will discard the least promising node if the queue is full. -template -class diving_queue_t { - private: - std::vector> buffer; - static constexpr i_t max_size_ = 1024; - - public: - diving_queue_t() { buffer.reserve(max_size_); } - - void push(diving_root_t&& node) - { - buffer.push_back(std::move(node)); - std::push_heap(buffer.begin(), buffer.end(), std::greater<>()); - if (buffer.size() > max_size() - 1) { buffer.pop_back(); } - } - - void emplace(mip_node_t&& node, std::vector&& lower, std::vector&& upper) - { - buffer.emplace_back(std::move(node), std::move(lower), std::move(upper)); - std::push_heap(buffer.begin(), buffer.end(), std::greater<>()); - if (buffer.size() > max_size() - 1) { buffer.pop_back(); } - } - - diving_root_t pop() - { - std::pop_heap(buffer.begin(), buffer.end(), std::greater<>()); - diving_root_t node = std::move(buffer.back()); - buffer.pop_back(); - return node; - } - - i_t size() const { return buffer.size(); } - constexpr i_t max_size() const { return max_size_; } - const diving_root_t& top() const { return buffer.front(); } - void clear() { buffer.clear(); } -}; - -} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index e2e9c6868..9cd858173 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -267,22 +267,6 @@ void remove_fathomed_nodes(std::vector*>& stack) } } -template -class node_compare_t { - public: - bool operator()(const mip_node_t& a, const mip_node_t& b) const - { - return a.lower_bound > - b.lower_bound; // True if a comes before b, elements that come before are output last - } - - bool operator()(const mip_node_t* a, const mip_node_t* b) const - { - return a->lower_bound > - b->lower_bound; // True if a comes before b, elements that come before are output last - } -}; - template class search_tree_t { public: diff --git a/cpp/src/dual_simplex/node_queue.hpp b/cpp/src/dual_simplex/node_queue.hpp new file mode 100644 index 000000000..8b5eb5fdf --- /dev/null +++ b/cpp/src/dual_simplex/node_queue.hpp @@ -0,0 +1,155 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#pragma once + +#include +#include + +#include + +namespace cuopt::linear_programming::dual_simplex { + +template +class heap_t { + public: + heap_t() = default; + virtual ~heap_t() = default; + + void push(const T& node) + { + buffer.push_back(node); + std::push_heap(buffer.begin(), buffer.end(), comp); + } + + void push(T&& node) + { + buffer.push_back(std::move(node)); + std::push_heap(buffer.begin(), buffer.end(), comp); + } + + template + void emplace(Args&&... args) + { + buffer.emplace_back(std::forward(args)...); + std::push_heap(buffer.begin(), buffer.end(), comp); + } + + std::optional pop() + { + if (buffer.empty()) return std::nullopt; + + std::pop_heap(buffer.begin(), buffer.end(), comp); + T node = std::move(buffer.back()); + buffer.pop_back(); + return node; + } + + size_t size() const { return buffer.size(); } + T& top() { return buffer.front(); } + void clear() { buffer.clear(); } + bool empty() const { return buffer.empty(); } + + private: + std::vector buffer; + Comp comp; +}; + +template +class node_queue_t { + private: + struct heap_entry_t { + mip_node_t* node = nullptr; + f_t lower_bound = -inf; + f_t score = inf; + + heap_entry_t(mip_node_t* new_node) + : node(new_node), lower_bound(new_node->lower_bound), score(new_node->objective_estimate) + { + } + }; + + struct lower_bound_comp { + bool operator()(const std::shared_ptr& a, const std::shared_ptr& b) + { + // `a` will be placed after `b` + return a->lower_bound > b->lower_bound; + } + }; + + struct score_comp { + bool operator()(const std::shared_ptr& a, const std::shared_ptr& b) + { + // `a` will be placed after `b` + return a->score > b->score; + } + }; + + heap_t, lower_bound_comp> best_first_heap; + heap_t, score_comp> diving_heap; + omp_mutex_t mutex; + + public: + void push(mip_node_t* new_node) + { + std::lock_guard lock(mutex); + auto entry = std::make_shared(new_node); + best_first_heap.push(entry); + diving_heap.push(entry); + } + + std::optional*> pop_best_first(omp_atomic_t& active_subtree) + { + std::lock_guard lock(mutex); + auto entry = best_first_heap.pop(); + + if (entry.has_value()) { + active_subtree++; + return std::exchange(entry.value()->node, nullptr); + } + + return std::nullopt; + } + + std::optional> pop_diving(std::vector& lower, + std::vector& upper, + std::vector& bounds_changed) + { + std::lock_guard lock(mutex); + + while (!diving_heap.empty()) { + auto entry = diving_heap.pop(); + + if (entry.has_value()) { + if (auto node_ptr = entry.value()->node; node_ptr != nullptr) { + node_ptr->get_variable_bounds(lower, upper, bounds_changed); + return node_ptr->detach_copy(); + } + } + } + + return std::nullopt; + } + + i_t diving_queue_size() + { + std::lock_guard lock(mutex); + return diving_heap.size(); + } + + i_t best_first_queue_size() + { + std::lock_guard lock(mutex); + return best_first_heap.size(); + } + + f_t get_lower_bound() + { + std::lock_guard lock(mutex); + return best_first_heap.empty() ? inf : best_first_heap.top()->lower_bound; + } +}; + +} // namespace cuopt::linear_programming::dual_simplex From c181ccf172f335eb4dfbb3dc9b145ce6caeb9f85 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 12 Dec 2025 14:34:45 +0100 Subject: [PATCH 119/366] refactoring code --- cpp/src/dual_simplex/branch_and_bound.cpp | 254 ++++++++++-------- cpp/src/dual_simplex/branch_and_bound.hpp | 43 +-- .../dual_simplex/simplex_solver_settings.hpp | 26 +- cpp/src/mip/diversity/lns/rins.cu | 20 +- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 11 +- cpp/src/mip/solver.cu | 11 +- 6 files changed, 203 insertions(+), 162 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 87e89a15c..ed6c58de3 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -219,6 +219,7 @@ branch_and_bound_t::branch_and_bound_t( : original_problem_(user_problem), settings_(solver_settings), original_lp_(user_problem.handle_ptr, 1, 1, 1), + Arow_(1, 1, 0), incumbent_(1), root_relax_soln_(1, 1), root_crossover_soln_(1, 1), @@ -654,7 +655,7 @@ node_solve_info_t branch_and_bound_t::solve_node( if (thread_type != bnb_thread_type_t::EXPLORATION) { i_t bnb_lp_iters = exploration_stats_.total_lp_iters; - f_t max_iter = 0.05 * bnb_lp_iters; + f_t max_iter = settings_.diving_settings.iteration_limit_factor * bnb_lp_iters; lp_settings.iteration_limit = max_iter - stats.total_lp_iters; if (lp_settings.iteration_limit <= 0) { return node_solve_info_t::ITERATION_LIMIT; } } @@ -833,8 +834,6 @@ node_solve_info_t branch_and_bound_t::solve_node( template void branch_and_bound_t::exploration_ramp_up(mip_node_t* node, - search_tree_t* search_tree, - const csr_matrix_t& Arow, i_t initial_heap_size) { if (solver_status_ != mip_exploration_status_t::RUNNING) { return; } @@ -850,8 +849,8 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod f_t abs_gap = upper_bound - lower_bound; if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - search_tree->graphviz_node(settings_.log, node, "cutoff", node->lower_bound); - search_tree->update(node, node_status_t::FATHOMED); + search_tree_.graphviz_node(settings_.log, node, "cutoff", node->lower_bound); + search_tree_.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; return; } @@ -898,7 +897,7 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod // Make a copy of the original LP. We will modify its bounds at each leaf lp_problem_t leaf_problem = original_lp_; std::vector row_sense; - bounds_strengthening_t node_presolver(leaf_problem, Arow, row_sense, var_types_); + bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); const i_t m = leaf_problem.num_rows; basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); @@ -906,7 +905,7 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod std::vector nonbasic_list; node_solve_info_t status = solve_node(node, - *search_tree, + search_tree_, leaf_problem, basis_factors, basic_list, @@ -933,10 +932,10 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod // If we haven't generated enough nodes to keep the threads busy, continue the ramp up phase if (exploration_stats_.nodes_unexplored < initial_heap_size) { #pragma omp task - exploration_ramp_up(node->get_down_child(), search_tree, Arow, initial_heap_size); + exploration_ramp_up(node->get_down_child(), initial_heap_size); #pragma omp task - exploration_ramp_up(node->get_up_child(), search_tree, Arow, initial_heap_size); + exploration_ramp_up(node->get_up_child(), initial_heap_size); } else { // We've generated enough nodes, push further nodes onto the heap @@ -947,14 +946,14 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod } template -void branch_and_bound_t::explore_subtree(i_t task_id, - mip_node_t* start_node, - search_tree_t& search_tree, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list) +void branch_and_bound_t::plunge_from(i_t task_id, + mip_node_t* start_node, + search_tree_t& search_tree, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_factors, + std::vector& basic_list, + std::vector& nonbasic_list) { bool recompute_bounds_and_basis = true; std::deque*> stack; @@ -1077,9 +1076,7 @@ void branch_and_bound_t::explore_subtree(i_t task_id, } template -void branch_and_bound_t::best_first_thread(i_t task_id, - search_tree_t& search_tree, - const csr_matrix_t& Arow) +void branch_and_bound_t::best_first_thread(i_t task_id) { f_t lower_bound = -inf; f_t upper_bound = inf; @@ -1089,7 +1086,7 @@ void branch_and_bound_t::best_first_thread(i_t task_id, // Make a copy of the original LP. We will modify its bounds at each leaf lp_problem_t leaf_problem = original_lp_; std::vector row_sense; - bounds_strengthening_t node_presolver(leaf_problem, Arow, row_sense, var_types_); + bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); const i_t m = leaf_problem.num_rows; basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); @@ -1106,22 +1103,22 @@ void branch_and_bound_t::best_first_thread(i_t task_id, if (get_upper_bound() < start_node.value()->lower_bound) { // This node was put on the heap earlier but its lower bound is now greater than the // current upper bound - search_tree.graphviz_node( + search_tree_.graphviz_node( settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); - search_tree.update(start_node.value(), node_status_t::FATHOMED); + search_tree_.update(start_node.value(), node_status_t::FATHOMED); active_subtrees_--; continue; } // Best-first search with plunging - explore_subtree(task_id, - start_node.value(), - search_tree, - leaf_problem, - node_presolver, - basis_factors, - basic_list, - nonbasic_list); + plunge_from(task_id, + start_node.value(), + search_tree_, + leaf_problem, + node_presolver, + basis_factors, + basic_list, + nonbasic_list); active_subtrees_--; } @@ -1144,16 +1141,91 @@ void branch_and_bound_t::best_first_thread(i_t task_id, } template -void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type, - const csr_matrix_t& Arow) +void branch_and_bound_t::dive_from(mip_node_t& start_node, + const std::vector& start_lower, + const std::vector& start_upper, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_factors, + std::vector& basic_list, + std::vector& nonbasic_list, + bnb_thread_type_t diving_type) { - constexpr i_t backtrack = 5; logger_t log; log.log = false; + + bool recompute_bounds_and_basis = true; + search_tree_t subtree(std::move(start_node)); + std::deque*> stack; + stack.push_front(&subtree.root); + + bnb_stats_t dive_stats; + dive_stats.total_lp_iters = 0; + dive_stats.total_lp_solve_time = 0; + dive_stats.nodes_explored = 0; + dive_stats.nodes_unexplored = 0; + + while (stack.size() > 0 && solver_status_ == mip_exploration_status_t::RUNNING) { + mip_node_t* node_ptr = stack.front(); + stack.pop_front(); + f_t upper_bound = get_upper_bound(); + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node_ptr->lower_bound); + + if (node_ptr->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { + recompute_bounds_and_basis = true; + continue; + } + + if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } + if (dive_stats.nodes_explored > settings_.diving_settings.node_limit) { break; } + + node_solve_info_t status = solve_node(node_ptr, + subtree, + leaf_problem, + basis_factors, + basic_list, + nonbasic_list, + node_presolver, + diving_type, + recompute_bounds_and_basis, + start_lower, + start_upper, + dive_stats, + log); + dive_stats.nodes_explored++; + recompute_bounds_and_basis = !has_children(status); + + if (status == node_solve_info_t::TIME_LIMIT) { + solver_status_ = mip_exploration_status_t::TIME_LIMIT; + break; + + } else if (status == node_solve_info_t::ITERATION_LIMIT) { + break; + + } else if (has_children(status)) { + if (status == node_solve_info_t::UP_CHILD_FIRST) { + stack.push_front(node_ptr->get_down_child()); + stack.push_front(node_ptr->get_up_child()); + } else { + stack.push_front(node_ptr->get_up_child()); + stack.push_front(node_ptr->get_down_child()); + } + } + + if (stack.size() > 1 && + stack.front()->depth - stack.back()->depth > settings_.diving_settings.backtrack) { + stack.pop_back(); + } + } +} + +template +void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type) +{ // Make a copy of the original LP. We will modify its bounds at each leaf lp_problem_t leaf_problem = original_lp_; std::vector row_sense; - bounds_strengthening_t node_presolver(leaf_problem, Arow, row_sense, var_types_); + bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); const i_t m = leaf_problem.num_rows; basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); @@ -1182,67 +1254,15 @@ void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type, bool is_feasible = node_presolver.bounds_strengthening(start_lower, start_upper, settings_); if (get_upper_bound() < start_node->lower_bound || !is_feasible) { continue; } - bool recompute_bounds_and_basis = true; - search_tree_t subtree(std::move(start_node.value())); - std::deque*> stack; - stack.push_front(&subtree.root); - - bnb_stats_t dive_stats; - dive_stats.total_lp_iters = 0; - dive_stats.total_lp_solve_time = 0; - dive_stats.nodes_explored = 0; - dive_stats.nodes_unexplored = 0; - - while (stack.size() > 0 && solver_status_ == mip_exploration_status_t::RUNNING) { - mip_node_t* node_ptr = stack.front(); - stack.pop_front(); - f_t upper_bound = get_upper_bound(); - f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node_ptr->lower_bound); - - if (node_ptr->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - recompute_bounds_and_basis = true; - continue; - } - - if (toc(exploration_stats_.start_time) > settings_.time_limit) { return; } - - node_solve_info_t status = solve_node(node_ptr, - subtree, - leaf_problem, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, - diving_type, - recompute_bounds_and_basis, - start_lower, - start_upper, - dive_stats, - log); - - recompute_bounds_and_basis = !has_children(status); - - if (status == node_solve_info_t::TIME_LIMIT) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; - return; - - } else if (status == node_solve_info_t::ITERATION_LIMIT) { - break; - - } else if (has_children(status)) { - if (status == node_solve_info_t::UP_CHILD_FIRST) { - stack.push_front(node_ptr->get_down_child()); - stack.push_front(node_ptr->get_up_child()); - } else { - stack.push_front(node_ptr->get_up_child()); - stack.push_front(node_ptr->get_down_child()); - } - } - - if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > backtrack) { - stack.pop_back(); - } - } + dive_from(start_node.value(), + start_lower, + start_upper, + leaf_problem, + node_presolver, + basis_factors, + basic_list, + nonbasic_list, + diving_type); } } } @@ -1331,6 +1351,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut solver_status_ = mip_exploration_status_t::UNSET; exploration_stats_.nodes_unexplored = 0; exploration_stats_.nodes_explored = 0; + original_lp_.A.to_compressed_row(Arow_); if (guess_.size() != 0) { std::vector crushed_guess; @@ -1476,13 +1497,10 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut original_lp_, log); - csr_matrix_t Arow(1, 1, 0); - original_lp_.A.to_compressed_row(Arow); - settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", settings_.num_threads, settings_.num_bfs_threads, - settings_.num_diving_threads); + settings_.diving_settings.num_diving_tasks); settings_.log.printf( " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " @@ -1500,19 +1518,19 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut std::vector diving_strategies; diving_strategies.reserve(4); - if (!settings_.disable_pseudocost_diving) { + if (!settings_.diving_settings.disable_pseudocost_diving) { diving_strategies.push_back(bnb_thread_type_t::PSEUDOCOST_DIVING); } - if (!settings_.disable_line_search_diving) { + if (!settings_.diving_settings.disable_line_search_diving) { diving_strategies.push_back(bnb_thread_type_t::LINE_SEARCH_DIVING); } - if (!settings_.disable_guided_diving) { + if (!settings_.diving_settings.disable_guided_diving) { diving_strategies.push_back(bnb_thread_type_t::GUIDED_DIVING); } - if (!settings_.disable_coefficient_diving) { + if (!settings_.diving_settings.disable_coefficient_diving) { diving_strategies.push_back(bnb_thread_type_t::COEFFICIENT_DIVING); } @@ -1520,31 +1538,29 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut { #pragma omp master { - auto down_child = search_tree_.root.get_down_child(); - auto up_child = search_tree_.root.get_up_child(); - i_t initial_size = 2 * settings_.num_threads; + auto down_child = search_tree_.root.get_down_child(); + auto up_child = search_tree_.root.get_up_child(); + i_t initial_size = 2 * settings_.num_threads; + const i_t num_strategies = diving_strategies.size(); +#pragma omp taskgroup + { #pragma omp task - exploration_ramp_up(down_child, &search_tree_, Arow, initial_size); + exploration_ramp_up(down_child, initial_size); #pragma omp task - exploration_ramp_up(up_child, &search_tree_, Arow, initial_size); - } - -#pragma omp barrier + exploration_ramp_up(up_child, initial_size); + } -#pragma omp master - { for (i_t i = 0; i < settings_.num_bfs_threads; i++) { #pragma omp task - best_first_thread(i, search_tree_, Arow); + best_first_thread(i); } - for (i_t k = 0; k < settings_.num_diving_threads; k++) { - const i_t m = diving_strategies.size(); - const bnb_thread_type_t diving_type = diving_strategies[k % m]; + for (i_t k = 0; k < settings_.diving_settings.num_diving_tasks; k++) { + const bnb_thread_type_t diving_type = diving_strategies[k % num_strategies]; #pragma omp task - diving_thread(diving_type, Arow); + diving_thread(diving_type); } } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 76d3b61f9..4ab9c3d06 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -141,6 +141,7 @@ class branch_and_bound_t { std::vector guess_; // LP relaxation + csr_matrix_t Arow_; lp_problem_t original_lp_; std::vector new_slacks_; std::vector var_types_; @@ -211,30 +212,36 @@ class branch_and_bound_t { // Ramp-up phase of the solver, where we greedily expand the tree until // there is enough unexplored nodes. This is done recursively using OpenMP tasks. - void exploration_ramp_up(mip_node_t* node, - search_tree_t* search_tree, - const csr_matrix_t& Arow, - i_t initial_heap_size); - - // Explore the search tree using the best-first search with plunging strategy. - void explore_subtree(i_t task_id, - mip_node_t* start_node, - search_tree_t& search_tree, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_update, - std::vector& basic_list, - std::vector& nonbasic_list); + void exploration_ramp_up(mip_node_t* node, i_t initial_heap_size); + + // Perform a plunge in the subtree determined by the `start_node`. + void plunge_from(i_t task_id, + mip_node_t* start_node, + search_tree_t& search_tree, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_update, + std::vector& basic_list, + std::vector& nonbasic_list); // Each "main" thread pops a node from the global heap and then performs a plunge // (i.e., a shallow dive) into the subtree determined by the node. - void best_first_thread(i_t task_id, - search_tree_t& search_tree, - const csr_matrix_t& Arow); + void best_first_thread(i_t task_id); + + // Perform a deep dive in the subtree determined by the `start_node`. + void dive_from(mip_node_t& start_node, + const std::vector& start_lower, + const std::vector& start_upper, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_update, + std::vector& basic_list, + std::vector& nonbasic_list, + bnb_thread_type_t diving_type); // Each diving thread pops the first node from the dive queue and then performs // a deep dive into the subtree determined by the node. - void diving_thread(bnb_thread_type_t diving_type, const csr_matrix_t& Arow); + void diving_thread(bnb_thread_type_t diving_type); // Solve the LP relaxation of a leaf node and update the tree. node_solve_info_t solve_node(mip_node_t* node_ptr, diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 47e4ca49b..9dd67ac62 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -19,6 +19,20 @@ namespace cuopt::linear_programming::dual_simplex { +template +struct diving_heuristics_settings_t { + i_t num_diving_tasks = -1; + + bool disable_line_search_diving = false; + bool disable_pseudocost_diving = false; + bool disable_guided_diving = false; + bool disable_coefficient_diving = false; + + i_t node_limit = 500; + f_t iteration_limit_factor = 0.05; + i_t backtrack = 5; +}; + template struct simplex_solver_settings_t { public: @@ -71,17 +85,13 @@ struct simplex_solver_settings_t { first_iteration_log(2), num_threads(omp_get_max_threads() - 1), num_bfs_threads(std::min(num_threads / 4, 1)), - num_diving_threads(std::min(num_threads - num_bfs_threads, 1)), - disable_line_search_diving(false), - disable_pseudocost_diving(false), - disable_guided_diving(false), - disable_coefficient_diving(false), random_seed(0), inside_mip(0), solution_callback(nullptr), heuristic_preemption_callback(nullptr), concurrent_halt(nullptr) { + diving_settings.num_diving_tasks = std::min(num_threads - num_bfs_threads, 1); } void set_log(bool logging) const { log.log = logging; } @@ -142,12 +152,8 @@ struct simplex_solver_settings_t { i_t num_threads; // number of threads to use i_t random_seed; // random seed i_t num_bfs_threads; // number of threads dedicated to the best-first search - i_t num_diving_threads; // number of threads dedicated to diving - bool disable_line_search_diving; // true to disable line search diving - bool disable_pseudocost_diving; // true to disable pseudocost diving - bool disable_guided_diving; // true to disable guided diving - bool disable_coefficient_diving; // true to disable coefficient diving + diving_heuristics_settings_t diving_settings; // Settings for the diving heuristics i_t inside_mip; // 0 if outside MIP, 1 if inside MIP at root node, 2 if inside MIP at leaf node std::function&, f_t)> solution_callback; diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 0bb1d85c5..0be16bb1e 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -255,13 +255,19 @@ void rins_t::run_rins() branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = std::min(current_mip_gap, (f_t)settings.target_mip_gap); - branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; - branch_and_bound_settings.num_threads = 2; - branch_and_bound_settings.num_bfs_threads = 1; - branch_and_bound_settings.num_diving_threads = 1; - branch_and_bound_settings.log.log = false; - branch_and_bound_settings.log.log_prefix = "[RINS] "; - branch_and_bound_settings.solution_callback = [this, &rins_solution_queue]( + branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.num_threads = 2; + branch_and_bound_settings.num_bfs_threads = 1; + + // In the future, let RINS use all the diving heuristics. For now, + // restricting to line search diving. + branch_and_bound_settings.diving_settings.num_diving_tasks = 1; + branch_and_bound_settings.diving_settings.disable_guided_diving = true; + branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; + branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; + branch_and_bound_settings.log.log = false; + branch_and_bound_settings.log.log_prefix = "[RINS] "; + branch_and_bound_settings.solution_callback = [this, &rins_solution_queue]( std::vector& solution, f_t objective) { rins_solution_queue.push_back(solution); }; diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index 5be807372..dcfda86a5 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -104,8 +104,15 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; branch_and_bound_settings.num_threads = 2; branch_and_bound_settings.num_bfs_threads = 1; - branch_and_bound_settings.num_diving_threads = 1; - branch_and_bound_settings.solution_callback = [this](std::vector& solution, + + // In the future, let SubMIP use all the diving heuristics. For now, + // restricting to line search diving. + branch_and_bound_settings.diving_settings.num_diving_tasks = 1; + branch_and_bound_settings.diving_settings.disable_guided_diving = true; + branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; + branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; + + branch_and_bound_settings.solution_callback = [this](std::vector& solution, f_t objective) { this->solution_callback(solution, objective); }; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 0da4c6398..7eb2b226d 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -172,13 +172,12 @@ solution_t mip_solver_t::run_solver() } else { branch_and_bound_settings.num_threads = std::max(1, context.settings.num_cpu_threads); } - CUOPT_LOG_INFO("Using %d CPU threads for B&B", branch_and_bound_settings.num_threads); - i_t num_threads = branch_and_bound_settings.num_threads; - i_t num_bfs_threads = std::max(1, num_threads / 4); - i_t num_diving_threads = std::max(1, num_threads - num_bfs_threads); - branch_and_bound_settings.num_bfs_threads = num_bfs_threads; - branch_and_bound_settings.num_diving_threads = num_diving_threads; + i_t num_threads = branch_and_bound_settings.num_threads; + i_t num_bfs_threads = std::max(1, num_threads / 4); + i_t num_diving_threads = std::max(1, num_threads - num_bfs_threads); + branch_and_bound_settings.num_bfs_threads = num_bfs_threads; + branch_and_bound_settings.diving_settings.num_diving_tasks = num_diving_threads; // Set the branch and bound -> primal heuristics callback branch_and_bound_settings.solution_callback = From 501c20d14949eaa916a95e535478fa2f9cbdf273 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 12 Dec 2025 16:04:24 +0100 Subject: [PATCH 120/366] fix style --- cpp/src/dual_simplex/basis_updates.cpp | 3 +- cpp/src/dual_simplex/crossover.cpp | 31 ++++++++++++-- cpp/src/dual_simplex/phase2.cpp | 56 +++++++++++++++++++------- cpp/src/dual_simplex/primal.cpp | 10 ++++- 4 files changed, 79 insertions(+), 21 deletions(-) diff --git a/cpp/src/dual_simplex/basis_updates.cpp b/cpp/src/dual_simplex/basis_updates.cpp index 11056a65e..e44e3b21c 100644 --- a/cpp/src/dual_simplex/basis_updates.cpp +++ b/cpp/src/dual_simplex/basis_updates.cpp @@ -2068,7 +2068,8 @@ int basis_update_mpf_t::refactor_basis( deficient, slacks_needed) == -1) { settings.log.debug("Initial factorization failed\n"); - basis_repair(A, settings, lower, upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair( + A, settings, lower, upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); #ifdef CHECK_BASIS_REPAIR const i_t m = A.m; diff --git a/cpp/src/dual_simplex/crossover.cpp b/cpp/src/dual_simplex/crossover.cpp index 3dd61b152..41844729e 100644 --- a/cpp/src/dual_simplex/crossover.cpp +++ b/cpp/src/dual_simplex/crossover.cpp @@ -785,8 +785,15 @@ i_t primal_push(const lp_problem_t& lp, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair( - lp.A, settings, lp.lower, lp.upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, + settings, + lp.lower, + lp.upper, + deficient, + slacks_needed, + basic_list, + nonbasic_list, + vstatus); if (factorize_basis( lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); @@ -1132,7 +1139,15 @@ crossover_status_t crossover(const lp_problem_t& lp, rank = factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, settings, lp.lower, lp.upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, + settings, + lp.lower, + lp.upper, + deficient, + slacks_needed, + basic_list, + nonbasic_list, + vstatus); if (factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); @@ -1323,7 +1338,15 @@ crossover_status_t crossover(const lp_problem_t& lp, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, settings, lp.lower, lp.upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, + settings, + lp.lower, + lp.upper, + deficient, + slacks_needed, + basic_list, + nonbasic_list, + vstatus); if (factorize_basis( lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index e0ac7239e..3aeef35e1 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -633,7 +633,7 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, infeasibility_indices.reserve(n); infeasibility_indices.clear(); f_t primal_inf_squared = 0.0; - primal_inf = 0.0; + primal_inf = 0.0; for (i_t k = 0; k < m; ++k) { const i_t j = basic_list[k]; const f_t lower_infeas = lp.lower[j] - x[j]; @@ -2245,7 +2245,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(superbasic_list.size() == 0); assert(nonbasic_list.size() == n - m); - if (ft.refactor_basis(lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus) > 0) { + if (ft.refactor_basis(lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus) > + 0) { return dual::status_t::NUMERICAL; } @@ -2362,8 +2363,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::compute_bounded_info(lp.lower, lp.upper, bounded_variables); f_t primal_infeasibility; - f_t primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); + f_t primal_infeasibility_squared = + phase2::compute_initial_primal_infeasibilities(lp, + settings, + basic_list, + x, + squared_infeasibilities, + infeasibility_indices, + primal_infeasibility); #ifdef CHECK_BASIC_INFEASIBILITIES phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 0); @@ -2561,9 +2568,15 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); - x = unperturbed_x; - primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); + x = unperturbed_x; + primal_infeasibility_squared = + phase2::compute_initial_primal_infeasibilities(lp, + settings, + basic_list, + x, + squared_infeasibilities, + infeasibility_indices, + primal_infeasibility); settings.log.printf("Updated primal infeasibility: %e\n", primal_infeasibility); objective = lp.objective; @@ -2598,9 +2611,15 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); - x = unperturbed_x; - primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); + x = unperturbed_x; + primal_infeasibility_squared = + phase2::compute_initial_primal_infeasibilities(lp, + settings, + basic_list, + x, + squared_infeasibilities, + infeasibility_indices, + primal_infeasibility); const f_t orig_dual_infeas = phase2::dual_infeasibility( lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); @@ -2888,14 +2907,15 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif if (should_refactor) { bool should_recompute_x = false; - if (ft.refactor_basis(lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus) > 0) { + if (ft.refactor_basis( + lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus) > 0) { should_recompute_x = true; settings.log.printf("Failed to factorize basis. Iteration %d\n", iter); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } i_t count = 0; i_t deficient_size; - while ((deficient_size = - ft.refactor_basis(lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus)) > 0) { + while ((deficient_size = ft.refactor_basis( + lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus)) > 0) { settings.log.printf("Failed to repair basis. Iteration %d. %d deficient columns.\n", iter, static_cast(deficient_size)); @@ -2917,8 +2937,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); x = unperturbed_x; } - primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); + primal_infeasibility_squared = + phase2::compute_initial_primal_infeasibilities(lp, + settings, + basic_list, + x, + squared_infeasibilities, + infeasibility_indices, + primal_infeasibility); } #ifdef CHECK_BASIC_INFEASIBILITIES phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 7); diff --git a/cpp/src/dual_simplex/primal.cpp b/cpp/src/dual_simplex/primal.cpp index 445177fac..3d9849fbe 100644 --- a/cpp/src/dual_simplex/primal.cpp +++ b/cpp/src/dual_simplex/primal.cpp @@ -298,7 +298,15 @@ primal::status_t primal_phase2(i_t phase, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, settings, lp.lower, lp.upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, + settings, + lp.lower, + lp.upper, + deficient, + slacks_needed, + basic_list, + nonbasic_list, + vstatus); if (factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); From 0ee757a02a7fcf7d171376262458177c30013498 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 12 Dec 2025 17:10:21 +0100 Subject: [PATCH 121/366] small fixes --- cpp/src/dual_simplex/branch_and_bound.cpp | 50 +++++++++++-------- cpp/src/dual_simplex/diving_heuristics.cpp | 2 +- cpp/src/dual_simplex/mip_node.hpp | 2 +- .../dual_simplex/simplex_solver_settings.hpp | 2 +- 4 files changed, 31 insertions(+), 25 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index ed6c58de3..d294e5409 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1353,6 +1353,29 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut exploration_stats_.nodes_explored = 0; original_lp_.A.to_compressed_row(Arow_); + std::vector diving_strategies; + diving_strategies.reserve(4); + + if (!settings_.diving_settings.disable_pseudocost_diving) { + diving_strategies.push_back(bnb_thread_type_t::PSEUDOCOST_DIVING); + } + + if (!settings_.diving_settings.disable_line_search_diving) { + diving_strategies.push_back(bnb_thread_type_t::LINE_SEARCH_DIVING); + } + + if (!settings_.diving_settings.disable_guided_diving) { + diving_strategies.push_back(bnb_thread_type_t::GUIDED_DIVING); + } + + if (!settings_.diving_settings.disable_coefficient_diving) { + diving_strategies.push_back(bnb_thread_type_t::COEFFICIENT_DIVING); + } + + if (diving_strategies.empty()) { + settings_.log.printf("Warning: All diving heuristics are disabled!"); + } + if (guess_.size() != 0) { std::vector crushed_guess; crush_primal_solution(original_problem_, original_lp_, guess_, new_slacks_, crushed_guess); @@ -1515,25 +1538,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut lower_bound_ceiling_ = inf; should_report_ = true; - std::vector diving_strategies; - diving_strategies.reserve(4); - - if (!settings_.diving_settings.disable_pseudocost_diving) { - diving_strategies.push_back(bnb_thread_type_t::PSEUDOCOST_DIVING); - } - - if (!settings_.diving_settings.disable_line_search_diving) { - diving_strategies.push_back(bnb_thread_type_t::LINE_SEARCH_DIVING); - } - - if (!settings_.diving_settings.disable_guided_diving) { - diving_strategies.push_back(bnb_thread_type_t::GUIDED_DIVING); - } - - if (!settings_.diving_settings.disable_coefficient_diving) { - diving_strategies.push_back(bnb_thread_type_t::COEFFICIENT_DIVING); - } - #pragma omp parallel num_threads(settings_.num_threads) { #pragma omp master @@ -1557,10 +1561,12 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut best_first_thread(i); } - for (i_t k = 0; k < settings_.diving_settings.num_diving_tasks; k++) { - const bnb_thread_type_t diving_type = diving_strategies[k % num_strategies]; + if (!diving_strategies.empty()) { + for (i_t k = 0; k < settings_.diving_settings.num_diving_tasks; k++) { + const bnb_thread_type_t diving_type = diving_strategies[k % num_strategies]; #pragma omp task - diving_thread(diving_type); + diving_thread(diving_type); + } } } } diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 9709123f9..e7ebb9ebe 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -197,7 +197,7 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, } template -std::tuple calculate_variable_locks(const lp_problem_t& lp_problem, i_t var_idx) +std::tuple calculate_variable_locks(const lp_problem_t& lp_problem, i_t var_idx) { i_t up_lock = 0; i_t down_lock = 0; diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index 9cd858173..a082932ac 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -60,7 +60,7 @@ class mip_node_t { node_id(0), branch_var(-1), branch_dir(rounding_direction_t::NONE), - objective_estimate(inf), + objective_estimate(std::numeric_limits::infinity()), vstatus(basis) { children[0] = nullptr; diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 9dd67ac62..ad4915f5c 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -91,7 +91,7 @@ struct simplex_solver_settings_t { heuristic_preemption_callback(nullptr), concurrent_halt(nullptr) { - diving_settings.num_diving_tasks = std::min(num_threads - num_bfs_threads, 1); + diving_settings.num_diving_tasks = std::max(num_threads - num_bfs_threads, 1); } void set_log(bool logging) const { log.log = logging; } From 546b116894a5aa762bdb343804bf8ad39b4e6fa1 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 15 Dec 2025 12:20:06 +0100 Subject: [PATCH 122/366] unified the best-first and diving heap into a single object. imposed iteration and node limit to the diving threads. --- cpp/src/dual_simplex/branch_and_bound.cpp | 199 ++++++++++------------ cpp/src/dual_simplex/branch_and_bound.hpp | 71 ++++---- cpp/src/dual_simplex/diving_queue.hpp | 73 -------- cpp/src/dual_simplex/mip_node.hpp | 33 ++-- cpp/src/dual_simplex/node_queue.hpp | 173 +++++++++++++++++++ cpp/src/dual_simplex/pseudo_costs.cpp | 38 +++-- cpp/src/dual_simplex/pseudo_costs.hpp | 7 +- 7 files changed, 337 insertions(+), 257 deletions(-) delete mode 100644 cpp/src/dual_simplex/diving_queue.hpp create mode 100644 cpp/src/dual_simplex/node_queue.hpp diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 77acca8f7..ff55d06f7 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -244,25 +244,15 @@ f_t branch_and_bound_t::get_upper_bound() template f_t branch_and_bound_t::get_lower_bound() { - f_t lower_bound = lower_bound_ceiling_.load(); - mutex_heap_.lock(); - if (heap_.size() > 0) { lower_bound = std::min(heap_.top()->lower_bound, lower_bound); } - mutex_heap_.unlock(); + f_t lower_bound = lower_bound_ceiling_.load(); + f_t heap_lower_bound = node_queue.get_lower_bound(); + lower_bound = std::min(heap_lower_bound, lower_bound); for (i_t i = 0; i < local_lower_bounds_.size(); ++i) { lower_bound = std::min(local_lower_bounds_[i].load(), lower_bound); } - return lower_bound; -} - -template -i_t branch_and_bound_t::get_heap_size() -{ - mutex_heap_.lock(); - i_t size = heap_.size(); - mutex_heap_.unlock(); - return size; + return std::isfinite(lower_bound) ? lower_bound : -inf; } template @@ -589,6 +579,7 @@ node_solve_info_t branch_and_bound_t::solve_node( bool recompute_bounds_and_basis, const std::vector& root_lower, const std::vector& root_upper, + bnb_stats_t& stats, logger_t& log) { const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; @@ -605,6 +596,13 @@ node_solve_info_t branch_and_bound_t::solve_node( lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); lp_settings.scale_columns = false; + if (thread_type != thread_type_t::EXPLORATION) { + i_t bnb_lp_iters = exploration_stats_.total_lp_iters; + f_t max_iter = 0.05 * bnb_lp_iters; + lp_settings.iteration_limit = max_iter - stats.total_lp_iters; + if (lp_settings.iteration_limit <= 0) { return node_solve_info_t::ITERATION_LIMIT; } + } + #ifdef LOG_NODE_SIMPLEX lp_settings.set_log(true); std::stringstream ss; @@ -679,10 +677,8 @@ node_solve_info_t branch_and_bound_t::solve_node( lp_status = convert_lp_status_to_dual_status(second_status); } - if (thread_type == thread_type_t::EXPLORATION) { - exploration_stats_.total_lp_solve_time += toc(lp_start_time); - exploration_stats_.total_lp_iters += node_iter; - } + stats.total_lp_solve_time += toc(lp_start_time); + stats.total_lp_iters += node_iter; } if (lp_status == dual::status_t::DUAL_UNBOUNDED) { @@ -726,8 +722,9 @@ node_solve_info_t branch_and_bound_t::solve_node( } else if (leaf_objective <= upper_bound + abs_fathom_tol) { // Choose fractional variable to branch on - const i_t branch_var = - pc_.variable_selection(leaf_fractional, leaf_solution.x, lp_settings.log); + auto [branch_var, obj_estimate] = pc_.variable_selection_and_obj_estimate( + leaf_fractional, leaf_solution.x, node_ptr->lower_bound, log); + node_ptr->objective_estimate = obj_estimate; assert(leaf_vstatus.size() == leaf_problem.num_cols); search_tree.branch( @@ -751,6 +748,9 @@ node_solve_info_t branch_and_bound_t::solve_node( search_tree.graphviz_node(log, node_ptr, "timeout", 0.0); return node_solve_info_t::TIME_LIMIT; + } else if (lp_status == dual::status_t::ITERATION_LIMIT) { + return node_solve_info_t::ITERATION_LIMIT; + } else { if (thread_type == thread_type_t::EXPLORATION) { fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); @@ -854,6 +854,7 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod true, original_lp_.lower, original_lp_.upper, + exploration_stats_, settings_.log); ++exploration_stats_.nodes_since_last_log; @@ -877,23 +878,21 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod } else { // We've generated enough nodes, push further nodes onto the heap - mutex_heap_.lock(); - heap_.push(node->get_down_child()); - heap_.push(node->get_up_child()); - mutex_heap_.unlock(); + node_queue.push(node->get_down_child()); + node_queue.push(node->get_up_child()); } } } template -void branch_and_bound_t::explore_subtree(i_t task_id, - mip_node_t* start_node, - search_tree_t& search_tree, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list) +void branch_and_bound_t::plunge_from(i_t task_id, + mip_node_t* start_node, + search_tree_t& search_tree, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_factors, + std::vector& basic_list, + std::vector& nonbasic_list) { bool recompute_bounds_and_basis = true; std::deque*> stack; @@ -922,6 +921,7 @@ void branch_and_bound_t::explore_subtree(i_t task_id, if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { search_tree.graphviz_node(settings_.log, node_ptr, "cutoff", node_ptr->lower_bound); search_tree.update(node_ptr, node_status_t::FATHOMED); + recompute_bounds_and_basis = true; --exploration_stats_.nodes_unexplored; continue; } @@ -977,6 +977,7 @@ void branch_and_bound_t::explore_subtree(i_t task_id, recompute_bounds_and_basis, original_lp_.lower, original_lp_.upper, + exploration_stats_, settings_.log); recompute_bounds_and_basis = !has_children(status); @@ -997,28 +998,7 @@ void branch_and_bound_t::explore_subtree(i_t task_id, if (stack.size() > 0) { mip_node_t* node = stack.back(); stack.pop_back(); - - // The order here matters. We want to create a copy of the node - // before adding to the global heap. Otherwise, - // some thread may consume the node (possibly fathoming it) - // before we had the chance to add to the diving queue. - // This lead to a SIGSEGV. Although, in this case, it - // would be better if we discard the node instead. - if (get_heap_size() > settings_.num_bfs_threads) { - std::vector lower = original_lp_.lower; - std::vector upper = original_lp_.upper; - std::fill( - node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); - node->get_variable_bounds(lower, upper, node_presolver.bounds_changed); - - mutex_dive_queue_.lock(); - diving_queue_.emplace(node->detach_copy(), std::move(lower), std::move(upper)); - mutex_dive_queue_.unlock(); - } - - mutex_heap_.lock(); - heap_.push(node); - mutex_heap_.unlock(); + node_queue.push(node); } exploration_stats_.nodes_unexplored += 2; @@ -1056,37 +1036,31 @@ void branch_and_bound_t::best_first_thread(i_t task_id, while (solver_status_ == mip_exploration_status_t::RUNNING && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && - (active_subtrees_ > 0 || get_heap_size() > 0)) { - mip_node_t* start_node = nullptr; - + (active_subtrees_ > 0 || node_queue.best_first_queue_size() > 0)) { // If there any node left in the heap, we pop the top node and explore it. - mutex_heap_.lock(); - if (heap_.size() > 0) { - start_node = heap_.top(); - heap_.pop(); - active_subtrees_++; - } - mutex_heap_.unlock(); + std::optional*> start_node = node_queue.pop_best_first(active_subtrees_); + + if (start_node.has_value()) { + mip_node_t* node = start_node.value(); - if (start_node != nullptr) { - if (get_upper_bound() < start_node->lower_bound) { + if (get_upper_bound() < node->lower_bound) { // This node was put on the heap earlier but its lower bound is now greater than the // current upper bound - search_tree.graphviz_node(settings_.log, start_node, "cutoff", start_node->lower_bound); - search_tree.update(start_node, node_status_t::FATHOMED); + search_tree.graphviz_node(settings_.log, node, "cutoff", node->lower_bound); + search_tree.update(node, node_status_t::FATHOMED); active_subtrees_--; continue; } // Best-first search with plunging - explore_subtree(task_id, - start_node, - search_tree, - leaf_problem, - node_presolver, - basis_factors, - basic_list, - nonbasic_list); + plunge_from(task_id, + node, + search_tree, + leaf_problem, + node_presolver, + basis_factors, + basic_list, + nonbasic_list); active_subtrees_--; } @@ -1123,22 +1097,39 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A std::vector basic_list(m); std::vector nonbasic_list; + std::vector start_lower; + std::vector start_upper; + bool reset_starting_bounds = true; + while (solver_status_ == mip_exploration_status_t::RUNNING && - (active_subtrees_ > 0 || get_heap_size() > 0)) { - std::optional> start_node; + (active_subtrees_ > 0 || node_queue.best_first_queue_size() > 0)) { + if (reset_starting_bounds) { + start_lower = original_lp_.lower; + start_upper = original_lp_.upper; + std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); + reset_starting_bounds = false; + } - mutex_dive_queue_.lock(); - if (diving_queue_.size() > 0) { start_node = diving_queue_.pop(); } - mutex_dive_queue_.unlock(); + std::optional> start_node = + node_queue.pop_diving(start_lower, start_upper, node_presolver.bounds_changed); if (start_node.has_value()) { - if (get_upper_bound() < start_node->node.lower_bound) { continue; } + reset_starting_bounds = true; + + bool is_feasible = node_presolver.bounds_strengthening(start_lower, start_upper, settings_); + if (get_upper_bound() < start_node->lower_bound || !is_feasible) { continue; } bool recompute_bounds_and_basis = true; - search_tree_t subtree(std::move(start_node->node)); + search_tree_t subtree(std::move(start_node.value())); std::deque*> stack; stack.push_front(&subtree.root); + bnb_stats_t dive_stats; + dive_stats.total_lp_iters = 0; + dive_stats.total_lp_solve_time = 0; + dive_stats.nodes_explored = 0; + dive_stats.nodes_unexplored = 0; + while (stack.size() > 0 && solver_status_ == mip_exploration_status_t::RUNNING) { mip_node_t* node_ptr = stack.front(); stack.pop_front(); @@ -1150,7 +1141,8 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A continue; } - if (toc(exploration_stats_.start_time) > settings_.time_limit) { return; } + if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } + if (dive_stats.nodes_explored > 500) { break; } node_solve_info_t status = solve_node(node_ptr, subtree, @@ -1161,15 +1153,19 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A node_presolver, thread_type_t::DIVING, recompute_bounds_and_basis, - start_node->lower, - start_node->upper, + start_lower, + start_upper, + dive_stats, log); - + dive_stats.nodes_explored++; recompute_bounds_and_basis = !has_children(status); if (status == node_solve_info_t::TIME_LIMIT) { solver_status_ = mip_exploration_status_t::TIME_LIMIT; - return; + break; + + } else if (status == node_solve_info_t::ITERATION_LIMIT) { + break; } else if (has_children(status)) { if (status == node_solve_info_t::UP_CHILD_FIRST) { @@ -1181,24 +1177,8 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A } } - if (stack.size() > 1) { - // If the diving thread is consuming the nodes faster than the - // best first search, then we split the current subtree at the - // lowest possible point and move to the queue, so it can - // be picked by another thread. - if (std::lock_guard lock(mutex_dive_queue_); - diving_queue_.size() < min_diving_queue_size_) { - mip_node_t* new_node = stack.back(); - stack.pop_back(); - - std::vector lower = start_node->lower; - std::vector upper = start_node->upper; - std::fill( - node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); - new_node->get_variable_bounds(lower, upper, node_presolver.bounds_changed); - - diving_queue_.emplace(new_node->detach_copy(), std::move(lower), std::move(upper)); - } + if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > 5) { + stack.pop_back(); } } } @@ -1421,7 +1401,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } // Choose variable to branch on - i_t branch_var = pc_.variable_selection(fractional, root_relax_soln_.x, log); + auto [branch_var, obj_estimate] = + pc_.variable_selection_and_obj_estimate(fractional, root_relax_soln_.x, root_objective_, log); search_tree_.root = std::move(mip_node_t(root_objective_, root_vstatus_)); search_tree_.num_nodes = 0; @@ -1450,7 +1431,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut exploration_stats_.nodes_since_last_log = 0; exploration_stats_.last_log = tic(); active_subtrees_ = 0; - min_diving_queue_size_ = 4 * settings_.num_diving_threads; solver_status_ = mip_exploration_status_t::RUNNING; lower_bound_ceiling_ = inf; should_report_ = true; @@ -1486,7 +1466,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } } - f_t lower_bound = heap_.size() > 0 ? heap_.top()->lower_bound : search_tree_.root.lower_bound; + f_t lower_bound = node_queue.best_first_queue_size() > 0 ? node_queue.get_lower_bound() + : search_tree_.root.lower_bound; return set_final_solution(solution, lower_bound); } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 7891711f7..5b8f0b21e 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -7,9 +7,9 @@ #pragma once -#include #include #include +#include #include #include #include @@ -20,7 +20,6 @@ #include #include -#include #include namespace cuopt::linear_programming::dual_simplex { @@ -68,12 +67,22 @@ class bounds_strengthening_t; template void upper_bound_callback(f_t upper_bound); +template +struct bnb_stats_t { + f_t start_time = 0.0; + omp_atomic_t total_lp_solve_time = 0.0; + omp_atomic_t nodes_explored = 0; + omp_atomic_t nodes_unexplored = 0; + omp_atomic_t total_lp_iters = 0; + + // This should only be used by the main thread + omp_atomic_t last_log = 0.0; + omp_atomic_t nodes_since_last_log = 0; +}; + template class branch_and_bound_t { public: - template - using mip_node_heap_t = std::priority_queue, node_compare_t>; - branch_and_bound_t(const user_problem_t& user_problem, const simplex_solver_settings_t& solver_settings); @@ -111,7 +120,6 @@ class branch_and_bound_t { f_t get_upper_bound(); f_t get_lower_bound(); - i_t get_heap_size(); bool enable_concurrent_lp_root_solve() const { return enable_concurrent_lp_root_solve_; } volatile int* get_root_concurrent_halt() { return &root_concurrent_halt_; } void set_root_concurrent_halt(int value) { root_concurrent_halt_ = value; } @@ -145,17 +153,7 @@ class branch_and_bound_t { mip_solution_t incumbent_; // Structure with the general info of the solver. - struct stats_t { - f_t start_time = 0.0; - omp_atomic_t total_lp_solve_time = 0.0; - omp_atomic_t nodes_explored = 0; - omp_atomic_t nodes_unexplored = 0; - omp_atomic_t total_lp_iters = 0; - - // This should only be used by the main thread - omp_atomic_t last_log = 0.0; - omp_atomic_t nodes_since_last_log = 0; - } exploration_stats_; + bnb_stats_t exploration_stats_; // Mutex for repair omp_mutex_t mutex_repair_; @@ -175,9 +173,8 @@ class branch_and_bound_t { // Pseudocosts pseudo_costs_t pc_; - // Heap storing the nodes to be explored. - omp_mutex_t mutex_heap_; - mip_node_heap_t*> heap_; + // Heap storing the nodes waiting to be explored. + node_queue_t node_queue; // Search tree search_tree_t search_tree_; @@ -185,11 +182,6 @@ class branch_and_bound_t { // Count the number of subtrees that are currently being explored. omp_atomic_t active_subtrees_; - // Queue for storing the promising node for performing dives. - omp_mutex_t mutex_dive_queue_; - diving_queue_t diving_queue_; - i_t min_diving_queue_size_; - // Global status of the solver. omp_atomic_t solver_status_; @@ -219,15 +211,15 @@ class branch_and_bound_t { const csr_matrix_t& Arow, i_t initial_heap_size); - // Explore the search tree using the best-first search with plunging strategy. - void explore_subtree(i_t task_id, - mip_node_t* start_node, - search_tree_t& search_tree, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_update, - std::vector& basic_list, - std::vector& nonbasic_list); + // Perform a plunge in the subtree determined by the `start_node`. + void plunge_from(i_t task_id, + mip_node_t* start_node, + search_tree_t& search_tree, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_update, + std::vector& basic_list, + std::vector& nonbasic_list); // Each "main" thread pops a node from the global heap and then performs a plunge // (i.e., a shallow dive) into the subtree determined by the node. @@ -235,6 +227,16 @@ class branch_and_bound_t { search_tree_t& search_tree, const csr_matrix_t& Arow); + // Perform a deep dive in the subtree determined by the `start_node`. + void dive_from(mip_node_t& start_node, + const std::vector& start_lower, + const std::vector& start_upper, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_update, + std::vector& basic_list, + std::vector& nonbasic_list, + thread_type_t diving_type); // Each diving thread pops the first node from the dive queue and then performs // a deep dive into the subtree determined by the node. void diving_thread(const csr_matrix_t& Arow); @@ -251,6 +253,7 @@ class branch_and_bound_t { bool recompute_basis_and_bounds, const std::vector& root_lower, const std::vector& root_upper, + bnb_stats_t& stats, logger_t& log); // Sort the children based on the Martin's criteria. diff --git a/cpp/src/dual_simplex/diving_queue.hpp b/cpp/src/dual_simplex/diving_queue.hpp deleted file mode 100644 index f7035109e..000000000 --- a/cpp/src/dual_simplex/diving_queue.hpp +++ /dev/null @@ -1,73 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#pragma once - -#include -#include -#include - -#include - -namespace cuopt::linear_programming::dual_simplex { - -template -struct diving_root_t { - mip_node_t node; - std::vector lower; - std::vector upper; - - diving_root_t(mip_node_t&& node, std::vector&& lower, std::vector&& upper) - : node(std::move(node)), lower(std::move(lower)), upper(std::move(upper)) - { - } - - friend bool operator>(const diving_root_t& a, const diving_root_t& b) - { - return a.node.lower_bound > b.node.lower_bound; - } -}; - -// A min-heap for storing the starting nodes for the dives. -// This has a maximum size of 1024, such that the container -// will discard the least promising node if the queue is full. -template -class diving_queue_t { - private: - std::vector> buffer; - static constexpr i_t max_size_ = 1024; - - public: - diving_queue_t() { buffer.reserve(max_size_); } - - void push(diving_root_t&& node) - { - buffer.push_back(std::move(node)); - std::push_heap(buffer.begin(), buffer.end(), std::greater<>()); - if (buffer.size() > max_size() - 1) { buffer.pop_back(); } - } - - void emplace(mip_node_t&& node, std::vector&& lower, std::vector&& upper) - { - buffer.emplace_back(std::move(node), std::move(lower), std::move(upper)); - std::push_heap(buffer.begin(), buffer.end(), std::greater<>()); - if (buffer.size() > max_size() - 1) { buffer.pop_back(); } - } - - diving_root_t pop() - { - std::pop_heap(buffer.begin(), buffer.end(), std::greater<>()); - diving_root_t node = std::move(buffer.back()); - buffer.pop_back(); - return node; - } - - i_t size() const { return buffer.size(); } - constexpr i_t max_size() const { return max_size_; } - const diving_root_t& top() const { return buffer.front(); } - void clear() { buffer.clear(); } -}; - -} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index 1d66a21f7..a082932ac 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -45,6 +45,7 @@ class mip_node_t { branch_var_lower(-std::numeric_limits::infinity()), branch_var_upper(std::numeric_limits::infinity()), fractional_val(std::numeric_limits::infinity()), + objective_estimate(std::numeric_limits::infinity()), vstatus(0) { children[0] = nullptr; @@ -59,6 +60,7 @@ class mip_node_t { node_id(0), branch_var(-1), branch_dir(rounding_direction_t::NONE), + objective_estimate(std::numeric_limits::infinity()), vstatus(basis) { children[0] = nullptr; @@ -80,6 +82,7 @@ class mip_node_t { branch_var(branch_variable), branch_dir(branch_direction), fractional_val(branch_var_value), + objective_estimate(parent_node->objective_estimate), vstatus(basis) { @@ -227,17 +230,19 @@ class mip_node_t { mip_node_t detach_copy() const { mip_node_t copy(lower_bound, vstatus); - copy.branch_var = branch_var; - copy.branch_dir = branch_dir; - copy.branch_var_lower = branch_var_lower; - copy.branch_var_upper = branch_var_upper; - copy.fractional_val = fractional_val; - copy.node_id = node_id; + copy.branch_var = branch_var; + copy.branch_dir = branch_dir; + copy.branch_var_lower = branch_var_lower; + copy.branch_var_upper = branch_var_upper; + copy.fractional_val = fractional_val; + copy.objective_estimate = objective_estimate; + copy.node_id = node_id; return copy; } node_status_t status; f_t lower_bound; + f_t objective_estimate; i_t depth; i_t node_id; i_t branch_var; @@ -262,22 +267,6 @@ void remove_fathomed_nodes(std::vector*>& stack) } } -template -class node_compare_t { - public: - bool operator()(const mip_node_t& a, const mip_node_t& b) const - { - return a.lower_bound > - b.lower_bound; // True if a comes before b, elements that come before are output last - } - - bool operator()(const mip_node_t* a, const mip_node_t* b) const - { - return a->lower_bound > - b->lower_bound; // True if a comes before b, elements that come before are output last - } -}; - template class search_tree_t { public: diff --git a/cpp/src/dual_simplex/node_queue.hpp b/cpp/src/dual_simplex/node_queue.hpp new file mode 100644 index 000000000..0234fa038 --- /dev/null +++ b/cpp/src/dual_simplex/node_queue.hpp @@ -0,0 +1,173 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#pragma once + +#include +#include + +#include + +namespace cuopt::linear_programming::dual_simplex { + +// This is a generic heap implementation based +// on the STL functions. The main benefit here is +// that we access the underlying container. +template +class heap_t { + public: + heap_t() = default; + virtual ~heap_t() = default; + + void push(const T& node) + { + buffer.push_back(node); + std::push_heap(buffer.begin(), buffer.end(), comp); + } + + void push(T&& node) + { + buffer.push_back(std::move(node)); + std::push_heap(buffer.begin(), buffer.end(), comp); + } + + template + void emplace(Args&&... args) + { + buffer.emplace_back(std::forward(args)...); + std::push_heap(buffer.begin(), buffer.end(), comp); + } + + std::optional pop() + { + if (buffer.empty()) return std::nullopt; + + std::pop_heap(buffer.begin(), buffer.end(), comp); + T node = std::move(buffer.back()); + buffer.pop_back(); + return node; + } + + size_t size() const { return buffer.size(); } + T& top() { return buffer.front(); } + void clear() { buffer.clear(); } + bool empty() const { return buffer.empty(); } + + private: + std::vector buffer; + Comp comp; +}; + +// A queue storing the nodes waiting to be explored/dived from. +template +class node_queue_t { + private: + struct heap_entry_t { + mip_node_t* node = nullptr; + f_t lower_bound = -inf; + f_t score = inf; + + heap_entry_t(mip_node_t* new_node) + : node(new_node), lower_bound(new_node->lower_bound), score(new_node->objective_estimate) + { + } + }; + + // Comparision function for ordering the nodes based on their lower bound with + // lowest one being explored first. + struct lower_bound_comp { + bool operator()(const std::shared_ptr& a, const std::shared_ptr& b) + { + // `a` will be placed after `b` + return a->lower_bound > b->lower_bound; + } + }; + + // Comparision function for ordering the nodes based on some score (currently the pseudocost + // estimate) with the lowest being explored first. + struct score_comp { + bool operator()(const std::shared_ptr& a, const std::shared_ptr& b) + { + // `a` will be placed after `b` + return a->score > b->score; + } + }; + + heap_t, lower_bound_comp> best_first_heap; + heap_t, score_comp> diving_heap; + omp_mutex_t mutex; + + public: + void push(mip_node_t* new_node) + { + std::lock_guard lock(mutex); + auto entry = std::make_shared(new_node); + best_first_heap.push(entry); + diving_heap.push(entry); + } + + // In the current implementation, we are use the active number of subtree to decide + // when to stop the execution. We need to increment the counter at the same + // time as we pop a node from the queue to avoid some threads exiting + // the main loop thinking that the solver has already finished. + // This will be not needed in the master-worker model. + std::optional*> pop_best_first(omp_atomic_t& active_subtree) + { + std::lock_guard lock(mutex); + auto entry = best_first_heap.pop(); + + if (entry.has_value()) { + active_subtree++; + return std::exchange(entry.value()->node, nullptr); + } + + return std::nullopt; + } + + // In the current implementation, multiple threads can pop the nodes + // from the queue, so we need to pass the lower and upper bound here + // to avoid other thread fathoming the node (i.e., deleting) before we can read + // the variable bounds from the tree. + // This will be not needed in the master-worker model. + std::optional> pop_diving(std::vector& lower, + std::vector& upper, + std::vector& bounds_changed) + { + std::lock_guard lock(mutex); + + while (!diving_heap.empty()) { + auto entry = diving_heap.pop(); + + if (entry.has_value()) { + if (auto node_ptr = entry.value()->node; node_ptr != nullptr) { + node_ptr->get_variable_bounds(lower, upper, bounds_changed); + return node_ptr->detach_copy(); + } + } + } + + return std::nullopt; + } + + i_t diving_queue_size() + { + std::lock_guard lock(mutex); + return diving_heap.size(); + } + + i_t best_first_queue_size() + { + std::lock_guard lock(mutex); + return best_first_heap.size(); + } + + f_t get_lower_bound() + { + std::lock_guard lock(mutex); + return best_first_heap.empty() ? inf : best_first_heap.top()->lower_bound; + } +}; + +} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 9f84e108d..1c0a33042 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -199,7 +199,7 @@ template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) { - mutex.lock(); + std::lock_guard lock(mutex); const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) @@ -211,7 +211,6 @@ void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_pt pseudo_cost_sum_up[node_ptr->branch_var] += change_in_obj / frac; pseudo_cost_num_up[node_ptr->branch_var]++; } - mutex.unlock(); } template @@ -254,16 +253,19 @@ void pseudo_costs_t::initialized(i_t& num_initialized_down, } template -i_t pseudo_costs_t::variable_selection(const std::vector& fractional, - const std::vector& solution, - logger_t& log) +std::pair pseudo_costs_t::variable_selection_and_obj_estimate( + const std::vector& fractional, + const std::vector& solution, + f_t lower_bound, + logger_t& log) { - mutex.lock(); + std::lock_guard lock(mutex); const i_t num_fractional = fractional.size(); std::vector pseudo_cost_up(num_fractional); std::vector pseudo_cost_down(num_fractional); std::vector score(num_fractional); + f_t estimate = lower_bound; i_t num_initialized_down; i_t num_initialized_up; @@ -272,11 +274,11 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - log.printf("PC: num initialized down %d up %d avg down %e up %e\n", - num_initialized_down, - num_initialized_up, - pseudo_cost_down_avg, - pseudo_cost_up_avg); + log.debug("PC: num initialized down %d up %d avg down %e up %e\n", + num_initialized_down, + num_initialized_up, + pseudo_cost_down_avg, + pseudo_cost_up_avg); for (i_t k = 0; k < num_fractional; k++) { const i_t j = fractional[k]; @@ -296,6 +298,9 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio const f_t f_up = std::ceil(solution[j]) - solution[j]; score[k] = std::max(f_down * pseudo_cost_down[k], eps) * std::max(f_up * pseudo_cost_up[k], eps); + + estimate += std::min(std::max(pseudo_cost_down[k] * f_down, eps), + std::max(pseudo_cost_up[k] * f_up, eps)); } i_t branch_var = fractional[0]; @@ -309,12 +314,13 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio } } - log.printf( - "pc branching on %d. Value %e. Score %e\n", branch_var, solution[branch_var], score[select]); - - mutex.unlock(); + log.debug("Pseudocost branching on %d. Value %e. Score %e. Obj Estimate %e\n", + branch_var, + solution[branch_var], + score[select], + estimate); - return branch_var; + return {branch_var, estimate}; } template diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 799cdc3ff..ab01b2a85 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -43,9 +43,10 @@ class pseudo_costs_t { f_t& pseudo_cost_down_avg, f_t& pseudo_cost_up_avg) const; - i_t variable_selection(const std::vector& fractional, - const std::vector& solution, - logger_t& log); + std::pair variable_selection_and_obj_estimate(const std::vector& fractional, + const std::vector& solution, + f_t lower_bound, + logger_t& log); void update_pseudo_costs_from_strong_branching(const std::vector& fractional, const std::vector& root_soln); From 06d531add93eb06e881479e660fb69e5466b9400 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 15 Dec 2025 12:26:39 +0100 Subject: [PATCH 123/366] adjusted column spacing in bnb logs. added opening mode for logger. --- cpp/src/dual_simplex/branch_and_bound.cpp | 92 ++++++++++++----------- cpp/src/dual_simplex/logger.hpp | 8 +- 2 files changed, 53 insertions(+), 47 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 77acca8f7..226cbb0a8 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -195,9 +195,9 @@ std::string user_mip_gap(f_t obj_value, f_t lower_bound) inline const char* feasible_solution_symbol(thread_type_t type) { switch (type) { - case thread_type_t::EXPLORATION: return "B"; - case thread_type_t::DIVING: return "D"; - default: return "U"; + case thread_type_t::EXPLORATION: return "B "; + case thread_type_t::DIVING: return "D "; + default: return "U "; } } @@ -310,7 +310,7 @@ void branch_and_bound_t::set_new_solution(const std::vector& solu std::string gap = user_mip_gap(user_obj, user_lower); settings_.log.printf( - "H %+13.6e %+10.6e %s %9.2f\n", + "H %+13.6e %+10.6e %s %9.2f\n", user_obj, user_lower, gap.c_str(), @@ -423,7 +423,7 @@ void branch_and_bound_t::repair_heuristic_solutions() std::string user_gap = user_mip_gap(obj, lower); settings_.log.printf( - "H %+13.6e %+10.6e %s %9.2f\n", + "H %+13.6e %+10.6e %s %9.2f\n", obj, lower, user_gap.c_str(), @@ -534,17 +534,17 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, f_t lower_bound = get_lower_bound(); f_t obj = compute_user_objective(original_lp_, upper_bound_); f_t lower = compute_user_objective(original_lp_, lower_bound); - settings_.log.printf( - "%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - feasible_solution_symbol(thread_type), - nodes_explored, - nodes_unexplored, - obj, - lower, - leaf_depth, - nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0, - user_mip_gap(obj, lower).c_str(), - toc(exploration_stats_.start_time)); + f_t iter_node = nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0; + settings_.log.printf("%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + feasible_solution_symbol(thread_type), + nodes_explored, + nodes_unexplored, + obj, + lower, + leaf_depth, + iter_node, + user_mip_gap(obj, lower).c_str(), + toc(exploration_stats_.start_time)); send_solution = true; } @@ -611,18 +611,20 @@ node_solve_info_t branch_and_bound_t::solve_node( ss << "simplex-" << std::this_thread::get_id() << ".log"; std::string logname; ss >> logname; - lp_settings.set_log_filename(logname); - lp_settings.log.enable_log_to_file("a+"); + lp_settings.log.set_log_file(logname, "a"); lp_settings.log.log_to_console = false; lp_settings.log.printf( - "%s node id = %d, branch var = %d, fractional val = %f, variable lower bound = %f, variable " - "upper bound = %f\n", + "%scurrent node: id = %d, depth = %d, branch var = %d, branch dir = %s, fractional val = " + "%f, variable lower bound = %f, variable upper bound = %f, branch vstatus = %d\n\n", settings_.log.log_prefix.c_str(), node_ptr->node_id, + node_ptr->depth, node_ptr->branch_var, + node_ptr->branch_dir == rounding_direction_t::DOWN ? "DOWN" : "UP", node_ptr->fractional_val, node_ptr->branch_var_lower, - node_ptr->branch_var_upper); + node_ptr->branch_var_upper, + node_ptr->vstatus[node_ptr->branch_var]); #endif // Reset the bound_changed markers @@ -685,6 +687,10 @@ node_solve_info_t branch_and_bound_t::solve_node( } } +#ifdef LOG_NODE_SIMPLEX + lp_settings.log.printf("\nLP status: %d\n\n", lp_status); +#endif + if (lp_status == dual::status_t::DUAL_UNBOUNDED) { // Node was infeasible. Do not branch node_ptr->lower_bound = inf; @@ -810,17 +816,17 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod std::string gap_user = user_mip_gap(obj, user_lower); i_t nodes_explored = exploration_stats_.nodes_explored; i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - - settings_.log.printf( - " %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - nodes_explored, - nodes_unexplored, - obj, - user_lower, - node->depth, - nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0, - gap_user.c_str(), - now); + f_t iter_node = nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0; + + settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + nodes_explored, + nodes_unexplored, + obj, + user_lower, + node->depth, + iter_node, + gap_user.c_str(), + now); exploration_stats_.nodes_since_last_log = 0; exploration_stats_.last_log = tic(); @@ -941,17 +947,17 @@ void branch_and_bound_t::explore_subtree(i_t task_id, std::string gap_user = user_mip_gap(obj, user_lower); i_t nodes_explored = exploration_stats_.nodes_explored; i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - - settings_.log.printf( - " %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - nodes_explored, - nodes_unexplored, - obj, - user_lower, - node_ptr->depth, - nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0, - gap_user.c_str(), - now); + f_t iter_node = nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0; + + settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + nodes_explored, + nodes_unexplored, + obj, + user_lower, + node_ptr->depth, + iter_node, + gap_user.c_str(), + now); exploration_stats_.last_log = tic(); exploration_stats_.nodes_since_last_log = 0; } diff --git a/cpp/src/dual_simplex/logger.hpp b/cpp/src/dual_simplex/logger.hpp index ac5e394f9..f6030d521 100644 --- a/cpp/src/dual_simplex/logger.hpp +++ b/cpp/src/dual_simplex/logger.hpp @@ -30,17 +30,17 @@ class logger_t { { } - void enable_log_to_file(std::string mode = "w") + void enable_log_to_file(const char* mode = "w") { if (log_file != nullptr) { std::fclose(log_file); } - log_file = std::fopen(log_filename.c_str(), mode.c_str()); + log_file = std::fopen(log_filename.c_str(), mode); log_to_file = true; } - void set_log_file(const std::string& filename) + void set_log_file(const std::string& filename, const char* mode = "w") { log_filename = filename; - enable_log_to_file(); + enable_log_to_file(mode); } void close_log_file() From ae742c2231442e5ee213132f87ed25e63e6863cc Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 15 Dec 2025 13:21:17 +0100 Subject: [PATCH 124/366] revert fixes for dual simplex. changed RINS and SubMIP to use guided diving --- cpp/src/dual_simplex/basis_solves.cpp | 14 +--- cpp/src/dual_simplex/basis_solves.hpp | 2 - cpp/src/dual_simplex/basis_updates.hpp | 2 - cpp/src/dual_simplex/crossover.cpp | 31 +------- cpp/src/dual_simplex/phase2.cpp | 77 ++++++------------- cpp/src/dual_simplex/primal.cpp | 10 +-- cpp/src/mip/diversity/lns/rins.cu | 4 +- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 4 +- 8 files changed, 33 insertions(+), 111 deletions(-) diff --git a/cpp/src/dual_simplex/basis_solves.cpp b/cpp/src/dual_simplex/basis_solves.cpp index 3080f269d..db24f55a2 100644 --- a/cpp/src/dual_simplex/basis_solves.cpp +++ b/cpp/src/dual_simplex/basis_solves.cpp @@ -613,8 +613,6 @@ i_t factorize_basis(const csc_matrix_t& A, template i_t basis_repair(const csc_matrix_t& A, const simplex_solver_settings_t& settings, - const std::vector& lower, - const std::vector& upper, const std::vector& deficient, const std::vector& slacks_needed, std::vector& basis_list, @@ -660,15 +658,7 @@ i_t basis_repair(const csc_matrix_t& A, nonbasic_list[nonbasic_map[replace_j]] = bad_j; vstatus[replace_j] = variable_status_t::BASIC; // This is the main issue. What value should bad_j take on. - if (lower[bad_j] == -inf && upper[bad_j] == inf) { - vstatus[bad_j] = variable_status_t::NONBASIC_FREE; - } else if (lower[bad_j] > -inf) { - vstatus[bad_j] = variable_status_t::NONBASIC_LOWER; - } else if (upper[bad_j] < inf) { - vstatus[bad_j] = variable_status_t::NONBASIC_UPPER; - } else { - assert(1 == 0); - } + vstatus[bad_j] = variable_status_t::NONBASIC_FREE; } return 0; @@ -859,8 +849,6 @@ template int factorize_basis(const csc_matrix_t& A, template int basis_repair(const csc_matrix_t& A, const simplex_solver_settings_t& settings, - const std::vector& lower, - const std::vector& upper, const std::vector& deficient, const std::vector& slacks_needed, std::vector& basis_list, diff --git a/cpp/src/dual_simplex/basis_solves.hpp b/cpp/src/dual_simplex/basis_solves.hpp index 0745806a6..b668c0f46 100644 --- a/cpp/src/dual_simplex/basis_solves.hpp +++ b/cpp/src/dual_simplex/basis_solves.hpp @@ -42,8 +42,6 @@ i_t factorize_basis(const csc_matrix_t& A, template i_t basis_repair(const csc_matrix_t& A, const simplex_solver_settings_t& settings, - const std::vector& lower, - const std::vector& upper, const std::vector& deficient, const std::vector& slacks_needed, std::vector& basis_list, diff --git a/cpp/src/dual_simplex/basis_updates.hpp b/cpp/src/dual_simplex/basis_updates.hpp index 9b5d3e614..cea907074 100644 --- a/cpp/src/dual_simplex/basis_updates.hpp +++ b/cpp/src/dual_simplex/basis_updates.hpp @@ -373,8 +373,6 @@ class basis_update_mpf_t { // Compute L*U = A(p, basic_list) int refactor_basis(const csc_matrix_t& A, const simplex_solver_settings_t& settings, - const std::vector& lower, - const std::vector& upper, std::vector& basic_list, std::vector& nonbasic_list, std::vector& vstatus); diff --git a/cpp/src/dual_simplex/crossover.cpp b/cpp/src/dual_simplex/crossover.cpp index 41844729e..23d9a0e8e 100644 --- a/cpp/src/dual_simplex/crossover.cpp +++ b/cpp/src/dual_simplex/crossover.cpp @@ -785,15 +785,8 @@ i_t primal_push(const lp_problem_t& lp, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, - settings, - lp.lower, - lp.upper, - deficient, - slacks_needed, - basic_list, - nonbasic_list, - vstatus); + basis_repair( + lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); if (factorize_basis( lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); @@ -1139,15 +1132,7 @@ crossover_status_t crossover(const lp_problem_t& lp, rank = factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, - settings, - lp.lower, - lp.upper, - deficient, - slacks_needed, - basic_list, - nonbasic_list, - vstatus); + basis_repair(lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); if (factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); @@ -1338,15 +1323,7 @@ crossover_status_t crossover(const lp_problem_t& lp, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, - settings, - lp.lower, - lp.upper, - deficient, - slacks_needed, - basic_list, - nonbasic_list, - vstatus); + basis_repair(lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); if (factorize_basis( lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 3aeef35e1..56298ef4d 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -623,17 +623,14 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, const std::vector& basic_list, const std::vector& x, std::vector& squared_infeasibilities, - std::vector& infeasibility_indices, - f_t& primal_inf) + std::vector& infeasibility_indices) { const i_t m = lp.num_rows; const i_t n = lp.num_cols; - squared_infeasibilities.resize(n); - std::fill(squared_infeasibilities.begin(), squared_infeasibilities.end(), 0.0); + squared_infeasibilities.resize(n, 0.0); infeasibility_indices.reserve(n); infeasibility_indices.clear(); - f_t primal_inf_squared = 0.0; - primal_inf = 0.0; + f_t primal_inf = 0.0; for (i_t k = 0; k < m; ++k) { const i_t j = basic_list[k]; const f_t lower_infeas = lp.lower[j] - x[j]; @@ -643,11 +640,10 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, const f_t square_infeas = infeas * infeas; squared_infeasibilities[j] = square_infeas; infeasibility_indices.push_back(j); - primal_inf_squared += square_infeas; - primal_inf += infeas; + primal_inf += square_infeas; } } - return primal_inf_squared; + return primal_inf; } template @@ -2245,8 +2241,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(superbasic_list.size() == 0); assert(nonbasic_list.size() == n - m); - if (ft.refactor_basis(lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus) > - 0) { + if (ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus) > 0) { return dual::status_t::NUMERICAL; } @@ -2273,7 +2268,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #ifdef COMPUTE_DUAL_RESIDUAL std::vector dual_res1; - phase2::compute_dual_residual(lp.A, objective, y, z, dual_res1); + compute_dual_residual(lp.A, objective, y, z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.tight_tol) { settings.log.printf("|| A'*y + z - c || %e\n", dual_res_norm); @@ -2362,15 +2357,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector bounded_variables(n, 0); phase2::compute_bounded_info(lp.lower, lp.upper, bounded_variables); - f_t primal_infeasibility; - f_t primal_infeasibility_squared = - phase2::compute_initial_primal_infeasibilities(lp, - settings, - basic_list, - x, - squared_infeasibilities, - infeasibility_indices, - primal_infeasibility); + f_t primal_infeasibility = phase2::compute_initial_primal_infeasibilities( + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); #ifdef CHECK_BASIC_INFEASIBILITIES phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 0); @@ -2568,15 +2556,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); - x = unperturbed_x; - primal_infeasibility_squared = - phase2::compute_initial_primal_infeasibilities(lp, - settings, - basic_list, - x, - squared_infeasibilities, - infeasibility_indices, - primal_infeasibility); + x = unperturbed_x; + primal_infeasibility = phase2::compute_initial_primal_infeasibilities( + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); settings.log.printf("Updated primal infeasibility: %e\n", primal_infeasibility); objective = lp.objective; @@ -2611,15 +2593,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); - x = unperturbed_x; - primal_infeasibility_squared = - phase2::compute_initial_primal_infeasibilities(lp, - settings, - basic_list, - x, - squared_infeasibilities, - infeasibility_indices, - primal_infeasibility); + x = unperturbed_x; + primal_infeasibility = phase2::compute_initial_primal_infeasibilities( + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); const f_t orig_dual_infeas = phase2::dual_infeasibility( lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); @@ -2834,7 +2810,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_xB_0_sparse.i, squared_infeasibilities, infeasibility_indices, - primal_infeasibility_squared); + primal_infeasibility); // Update primal infeasibilities due to changes in basic variables // from the leaving and entering variables phase2::update_primal_infeasibilities(lp, @@ -2846,7 +2822,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, scaled_delta_xB_sparse.i, squared_infeasibilities, infeasibility_indices, - primal_infeasibility_squared); + primal_infeasibility); // Update the entering variable phase2::update_single_primal_infeasibility(lp.lower, lp.upper, @@ -2907,15 +2883,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif if (should_refactor) { bool should_recompute_x = false; - if (ft.refactor_basis( - lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus) > 0) { + if (ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus) > 0) { should_recompute_x = true; settings.log.printf("Failed to factorize basis. Iteration %d\n", iter); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } i_t count = 0; i_t deficient_size; - while ((deficient_size = ft.refactor_basis( - lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus)) > 0) { + while ((deficient_size = + ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus)) > 0) { settings.log.printf("Failed to repair basis. Iteration %d. %d deficient columns.\n", iter, static_cast(deficient_size)); @@ -2937,14 +2912,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); x = unperturbed_x; } - primal_infeasibility_squared = - phase2::compute_initial_primal_infeasibilities(lp, - settings, - basic_list, - x, - squared_infeasibilities, - infeasibility_indices, - primal_infeasibility); + phase2::compute_initial_primal_infeasibilities( + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); } #ifdef CHECK_BASIC_INFEASIBILITIES phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 7); @@ -2982,7 +2951,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, iter, compute_user_objective(lp, obj), infeasibility_indices.size(), - primal_infeasibility_squared, + primal_infeasibility, sum_perturb, now); } diff --git a/cpp/src/dual_simplex/primal.cpp b/cpp/src/dual_simplex/primal.cpp index 3d9849fbe..80406dcf0 100644 --- a/cpp/src/dual_simplex/primal.cpp +++ b/cpp/src/dual_simplex/primal.cpp @@ -298,15 +298,7 @@ primal::status_t primal_phase2(i_t phase, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, - settings, - lp.lower, - lp.upper, - deficient, - slacks_needed, - basic_list, - nonbasic_list, - vstatus); + basis_repair(lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); if (factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 0be16bb1e..41dd6d39e 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -260,9 +260,9 @@ void rins_t::run_rins() branch_and_bound_settings.num_bfs_threads = 1; // In the future, let RINS use all the diving heuristics. For now, - // restricting to line search diving. + // restricting to guided diving. branch_and_bound_settings.diving_settings.num_diving_tasks = 1; - branch_and_bound_settings.diving_settings.disable_guided_diving = true; + branch_and_bound_settings.diving_settings.disable_line_search_diving = true; branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; branch_and_bound_settings.log.log = false; diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index dcfda86a5..fa21fb7d2 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -106,9 +106,9 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.num_bfs_threads = 1; // In the future, let SubMIP use all the diving heuristics. For now, - // restricting to line search diving. + // restricting to guided diving. branch_and_bound_settings.diving_settings.num_diving_tasks = 1; - branch_and_bound_settings.diving_settings.disable_guided_diving = true; + branch_and_bound_settings.diving_settings.disable_line_search_diving = true; branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; From b5f2c7e93be3ccf560bfa5758539c28a29e36a07 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 15 Dec 2025 13:23:00 +0100 Subject: [PATCH 125/366] moved bound propagation logs to debug mode --- cpp/src/dual_simplex/bounds_strengthening.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index f1bf52c1e..c56c9db98 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -154,7 +154,7 @@ bool bounds_strengthening_t::bounds_strengthening( bool is_infeasible = check_infeasibility(min_a, max_a, cnst_lb, cnst_ub, settings.primal_tol); if (is_infeasible) { - settings.log.printf( + settings.log.debug( "Iter:: %d, Infeasible constraint %d, cnst_lb %e, cnst_ub %e, min_a %e, max_a %e\n", iter, i, @@ -211,7 +211,7 @@ bool bounds_strengthening_t::bounds_strengthening( new_ub = std::min(new_ub, upper_bounds[k]); if (new_lb > new_ub + 1e-6) { - settings.log.printf( + settings.log.debug( "Iter:: %d, Infeasible variable after update %d, %e > %e\n", iter, k, new_lb, new_ub); return false; } From eb5c6959702d1d42672b4718780f5d0d6b8bf82b Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 15 Dec 2025 13:41:38 +0100 Subject: [PATCH 126/366] addressing code rabbit suggestions --- cpp/src/dual_simplex/branch_and_bound.cpp | 8 ++++++-- cpp/src/dual_simplex/logger.hpp | 2 ++ 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 226cbb0a8..eef5decaf 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -816,7 +816,9 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod std::string gap_user = user_mip_gap(obj, user_lower); i_t nodes_explored = exploration_stats_.nodes_explored; i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - f_t iter_node = nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0; + f_t iter_node = nodes_explored > 0 ? static_cast(exploration_stats_.total_lp_iters) / + static_cast(nodes_explored) + : 0; settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", nodes_explored, @@ -947,7 +949,9 @@ void branch_and_bound_t::explore_subtree(i_t task_id, std::string gap_user = user_mip_gap(obj, user_lower); i_t nodes_explored = exploration_stats_.nodes_explored; i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - f_t iter_node = nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0; + f_t iter_node = nodes_explored > 0 ? static_cast(exploration_stats_.total_lp_iters) / + static_cast(nodes_explored) + : 0; settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", nodes_explored, diff --git a/cpp/src/dual_simplex/logger.hpp b/cpp/src/dual_simplex/logger.hpp index f6030d521..c45e3ede3 100644 --- a/cpp/src/dual_simplex/logger.hpp +++ b/cpp/src/dual_simplex/logger.hpp @@ -46,6 +46,8 @@ class logger_t { void close_log_file() { if (log_file != nullptr) { std::fclose(log_file); } + log_file = nullptr; + log_to_file = false; } void printf(const char* fmt, ...) From 5cf5ac0fbaddd5d242843d109ffa13f6ead9f1c5 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 15 Dec 2025 13:53:16 +0100 Subject: [PATCH 127/366] added explicit conversion to float --- cpp/src/dual_simplex/branch_and_bound.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index eef5decaf..854594169 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -534,7 +534,9 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, f_t lower_bound = get_lower_bound(); f_t obj = compute_user_objective(original_lp_, upper_bound_); f_t lower = compute_user_objective(original_lp_, lower_bound); - f_t iter_node = nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0; + f_t iter_node = nodes_explored > 0 ? static_cast(exploration_stats_.total_lp_iters) / + static_cast(nodes_explored) + : 0; settings_.log.printf("%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", feasible_solution_symbol(thread_type), nodes_explored, From d7dfb0d6e035c59f086403efbc0591207873bc23 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 15 Dec 2025 15:01:36 +0100 Subject: [PATCH 128/366] missing code revert in basis update --- cpp/src/dual_simplex/basis_updates.cpp | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/cpp/src/dual_simplex/basis_updates.cpp b/cpp/src/dual_simplex/basis_updates.cpp index e44e3b21c..6b79f3c86 100644 --- a/cpp/src/dual_simplex/basis_updates.cpp +++ b/cpp/src/dual_simplex/basis_updates.cpp @@ -2046,8 +2046,6 @@ template int basis_update_mpf_t::refactor_basis( const csc_matrix_t& A, const simplex_solver_settings_t& settings, - const std::vector& lower, - const std::vector& upper, std::vector& basic_list, std::vector& nonbasic_list, std::vector& vstatus) @@ -2068,8 +2066,7 @@ int basis_update_mpf_t::refactor_basis( deficient, slacks_needed) == -1) { settings.log.debug("Initial factorization failed\n"); - basis_repair( - A, settings, lower, upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); #ifdef CHECK_BASIS_REPAIR const i_t m = A.m; From 55e64ccac66ca6bb0f2ae53f7d9263132c6dfefb Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 15 Dec 2025 15:04:01 +0100 Subject: [PATCH 129/366] fixed variable type --- cpp/src/dual_simplex/diving_heuristics.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index e7ebb9ebe..978a97e42 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -241,7 +241,7 @@ branch_variable_t coefficient_diving(const lp_problem_t& lp_probl f_t f_down = solution[j] - std::floor(solution[j]); f_t f_up = std::ceil(solution[j]) - solution[j]; auto [up_lock, down_lock] = calculate_variable_locks(lp_problem, j); - f_t locks = std::min(up_lock, down_lock); + i_t locks = std::min(up_lock, down_lock); if (min_locks > locks) { min_locks = locks; @@ -263,7 +263,7 @@ branch_variable_t coefficient_diving(const lp_problem_t& lp_probl assert(branch_var >= 0); log.debug( - "Coefficient diving: selected var %d with val = %e, round dir = %d and min locks = %e\n", + "Coefficient diving: selected var %d with val = %e, round dir = %d and min locks = %d\n", branch_var, solution[branch_var], round_dir, From 0b1e9948b7dac6fd5ac922a14d863bb0990d9568 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 16 Dec 2025 14:51:23 +0100 Subject: [PATCH 130/366] added comments --- cpp/src/dual_simplex/node_queue.hpp | 18 ++++++++++++++++++ cpp/src/dual_simplex/pseudo_costs.cpp | 17 ++++++++++------- 2 files changed, 28 insertions(+), 7 deletions(-) diff --git a/cpp/src/dual_simplex/node_queue.hpp b/cpp/src/dual_simplex/node_queue.hpp index 8b5eb5fdf..0234fa038 100644 --- a/cpp/src/dual_simplex/node_queue.hpp +++ b/cpp/src/dual_simplex/node_queue.hpp @@ -12,6 +12,9 @@ namespace cuopt::linear_programming::dual_simplex { +// This is a generic heap implementation based +// on the STL functions. The main benefit here is +// that we access the underlying container. template class heap_t { public: @@ -57,6 +60,7 @@ class heap_t { Comp comp; }; +// A queue storing the nodes waiting to be explored/dived from. template class node_queue_t { private: @@ -71,6 +75,8 @@ class node_queue_t { } }; + // Comparision function for ordering the nodes based on their lower bound with + // lowest one being explored first. struct lower_bound_comp { bool operator()(const std::shared_ptr& a, const std::shared_ptr& b) { @@ -79,6 +85,8 @@ class node_queue_t { } }; + // Comparision function for ordering the nodes based on some score (currently the pseudocost + // estimate) with the lowest being explored first. struct score_comp { bool operator()(const std::shared_ptr& a, const std::shared_ptr& b) { @@ -100,6 +108,11 @@ class node_queue_t { diving_heap.push(entry); } + // In the current implementation, we are use the active number of subtree to decide + // when to stop the execution. We need to increment the counter at the same + // time as we pop a node from the queue to avoid some threads exiting + // the main loop thinking that the solver has already finished. + // This will be not needed in the master-worker model. std::optional*> pop_best_first(omp_atomic_t& active_subtree) { std::lock_guard lock(mutex); @@ -113,6 +126,11 @@ class node_queue_t { return std::nullopt; } + // In the current implementation, multiple threads can pop the nodes + // from the queue, so we need to pass the lower and upper bound here + // to avoid other thread fathoming the node (i.e., deleting) before we can read + // the variable bounds from the tree. + // This will be not needed in the master-worker model. std::optional> pop_diving(std::vector& lower, std::vector& upper, std::vector& bounds_changed) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index a2defd3b3..1c0a33042 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -274,11 +274,11 @@ std::pair pseudo_costs_t::variable_selection_and_obj_estimat initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - log.printf("PC: num initialized down %d up %d avg down %e up %e\n", - num_initialized_down, - num_initialized_up, - pseudo_cost_down_avg, - pseudo_cost_up_avg); + log.debug("PC: num initialized down %d up %d avg down %e up %e\n", + num_initialized_down, + num_initialized_up, + pseudo_cost_down_avg, + pseudo_cost_up_avg); for (i_t k = 0; k < num_fractional; k++) { const i_t j = fractional[k]; @@ -314,8 +314,11 @@ std::pair pseudo_costs_t::variable_selection_and_obj_estimat } } - log.printf( - "pc branching on %d. Value %e. Score %e\n", branch_var, solution[branch_var], score[select]); + log.debug("Pseudocost branching on %d. Value %e. Score %e. Obj Estimate %e\n", + branch_var, + solution[branch_var], + score[select], + estimate); return {branch_var, estimate}; } From 9effdc8ca4d9490756c91771cfcc6b92ded8c678 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 16 Dec 2025 14:54:44 +0100 Subject: [PATCH 131/366] added missing spacing --- cpp/src/dual_simplex/branch_and_bound.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 854594169..fc3786fcc 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -183,11 +183,11 @@ std::string user_mip_gap(f_t obj_value, f_t lower_bound) { const f_t user_mip_gap = relative_gap(obj_value, lower_bound); if (user_mip_gap == std::numeric_limits::infinity()) { - return " - "; + return " - "; } else { constexpr int BUFFER_LEN = 32; char buffer[BUFFER_LEN]; - snprintf(buffer, BUFFER_LEN - 1, "%4.1f%%", user_mip_gap * 100); + snprintf(buffer, BUFFER_LEN - 1, "%5.1f%%", user_mip_gap * 100); return std::string(buffer); } } From 6d43e037d036cd1ec7add445b7c5a602669fdba9 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 16 Dec 2025 14:57:22 +0100 Subject: [PATCH 132/366] updated logs --- cpp/src/dual_simplex/branch_and_bound.cpp | 32 ++++++++++++++--------- cpp/src/dual_simplex/logger.hpp | 2 ++ 2 files changed, 21 insertions(+), 13 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d294e5409..6524c2ac1 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -532,7 +532,9 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, f_t lower_bound = get_lower_bound(); f_t obj = compute_user_objective(original_lp_, upper_bound_); f_t lower = compute_user_objective(original_lp_, lower_bound); - f_t iter_node = nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0; + f_t iter_node = nodes_explored > 0 ? static_cast(exploration_stats_.total_lp_iters) / + static_cast(nodes_explored) + : 0; settings_.log.printf("%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", feasible_solution_symbol(thread_type), nodes_explored, @@ -871,7 +873,9 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod std::string gap_user = user_mip_gap(obj, user_lower); i_t nodes_explored = exploration_stats_.nodes_explored; i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - f_t iter_node = nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0; + f_t iter_node = nodes_explored > 0 ? static_cast(exploration_stats_.total_lp_iters) / + static_cast(nodes_explored) + : 0; settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", nodes_explored, @@ -1002,17 +1006,19 @@ void branch_and_bound_t::plunge_from(i_t task_id, std::string gap_user = user_mip_gap(obj, user_lower); i_t nodes_explored = exploration_stats_.nodes_explored; i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - - settings_.log.printf( - " %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - nodes_explored, - nodes_unexplored, - obj, - user_lower, - node_ptr->depth, - nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0, - gap_user.c_str(), - now); + f_t iter_node = nodes_explored > 0 ? static_cast(exploration_stats_.total_lp_iters) / + static_cast(nodes_explored) + : 0; + + settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + nodes_explored, + nodes_unexplored, + obj, + user_lower, + node_ptr->depth, + iter_node, + gap_user.c_str(), + now); exploration_stats_.last_log = tic(); exploration_stats_.nodes_since_last_log = 0; } diff --git a/cpp/src/dual_simplex/logger.hpp b/cpp/src/dual_simplex/logger.hpp index f6030d521..c45e3ede3 100644 --- a/cpp/src/dual_simplex/logger.hpp +++ b/cpp/src/dual_simplex/logger.hpp @@ -46,6 +46,8 @@ class logger_t { void close_log_file() { if (log_file != nullptr) { std::fclose(log_file); } + log_file = nullptr; + log_to_file = false; } void printf(const char* fmt, ...) From 3cced0159196011c8460d1ff1b8b77158c562943 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 17 Dec 2025 17:34:02 +0000 Subject: [PATCH 133/366] clang compiler bug workaround --- cpp/src/dual_simplex/barrier.cu | 56 ++++++++++++++++++++++++++------- 1 file changed, 44 insertions(+), 12 deletions(-) diff --git a/cpp/src/dual_simplex/barrier.cu b/cpp/src/dual_simplex/barrier.cu index 8c0a32fad..4bf3e3ed7 100644 --- a/cpp/src/dual_simplex/barrier.cu +++ b/cpp/src/dual_simplex/barrier.cu @@ -44,6 +44,48 @@ namespace cuopt::linear_programming::dual_simplex { auto constexpr use_gpu = true; +// non-template wrappers to work around clang compiler bug +[[maybe_unused]] static void pairwise_multiply( + float* a, float* b, float* out, int size, rmm::cuda_stream_view stream) +{ + cub::DeviceTransform::Transform( + cuda::std::make_tuple(a, b), out, size, cuda::std::multiplies<>{}, stream); +} + +[[maybe_unused]] static void pairwise_multiply( + double* a, double* b, double* out, int size, rmm::cuda_stream_view stream) +{ + cub::DeviceTransform::Transform( + cuda::std::make_tuple(a, b), out, size, cuda::std::multiplies<>{}, stream); +} + +[[maybe_unused]] static void axpy( + float alpha, float* x, float beta, float* y, float* out, int size, rmm::cuda_stream_view stream) +{ + cub::DeviceTransform::Transform( + cuda::std::make_tuple(x, y), + out, + size, + [alpha, beta] __host__ __device__(float a, float b) { return alpha * a + beta * b; }, + stream); +} + +[[maybe_unused]] static void axpy(double alpha, + double* x, + double beta, + double* y, + double* out, + int size, + rmm::cuda_stream_view stream) +{ + cub::DeviceTransform::Transform( + cuda::std::make_tuple(x, y), + out, + size, + [alpha, beta] __host__ __device__(double a, double b) { return alpha * a + beta * b; }, + stream); +} + template class iteration_data_t { public: @@ -1404,12 +1446,7 @@ class iteration_data_t { // diag.pairwise_product(x1, r1); // r1 <- D * x_1 - thrust::transform(handle_ptr->get_thrust_policy(), - d_x1.data(), - d_x1.data() + n, - d_diag_.data(), - d_r1.data(), - thrust::multiplies()); + pairwise_multiply(d_x1.data(), d_diag_.data(), d_r1.data(), n, stream_view_); // r1 <- Q x1 + D x1 if (Q.n > 0) { @@ -1419,12 +1456,7 @@ class iteration_data_t { // y1 <- - alpha * r1 + beta * y1 // y1.axpy(-alpha, r1, beta); - thrust::transform(handle_ptr->get_thrust_policy(), - d_r1.data(), - d_r1.data() + n, - d_y1.data(), - d_y1.data(), - axpy_op{-alpha, beta}); + axpy(-alpha, d_r1.data(), beta, d_y1.data(), d_y1.data(), n, stream_view_); // matrix_transpose_vector_multiply(A, alpha, x2, 1.0, y1); cusparse_view_.transpose_spmv(alpha, d_x2, 1.0, d_y1); From fbb99664fcdaa5c85c76ea76eae248bde6587110 Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 18 Dec 2025 19:35:41 +0100 Subject: [PATCH 134/366] refactoring --- cpp/src/dual_simplex/branch_and_bound.cpp | 142 ++++++++-------------- cpp/src/dual_simplex/branch_and_bound.hpp | 3 + 2 files changed, 56 insertions(+), 89 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index fa9d2e6c7..79765898d 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -265,6 +265,51 @@ i_t branch_and_bound_t::get_heap_size() return size; } +template +void branch_and_bound_t::report_heuristic(f_t obj) +{ + if (solver_status_ == mip_exploration_status_t::RUNNING) { + f_t user_obj = compute_user_objective(original_lp_, obj); + f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); + std::string user_gap = user_mip_gap(user_obj, user_lower); + + settings_.log.printf( + "H %+13.6e %+10.6e %s %9.2f\n", + user_obj, + user_lower, + user_gap.c_str(), + toc(exploration_stats_.start_time)); + } else { + settings_.log.printf("New solution from primal heuristics. Objective %+.6e. Time %.2f\n", + compute_user_objective(original_lp_, obj), + toc(exploration_stats_.start_time)); + } +} + +template +void branch_and_bound_t::report(std::string symbol, + f_t obj, + f_t lower_bound, + i_t node_depth) +{ + i_t nodes_explored = exploration_stats_.nodes_explored; + i_t nodes_unexplored = exploration_stats_.nodes_unexplored; + f_t user_obj = compute_user_objective(original_lp_, obj); + f_t user_lower = compute_user_objective(original_lp_, lower_bound); + f_t iter_node = exploration_stats_.total_lp_iters / nodes_explored; + std::string user_gap = user_mip_gap(user_obj, user_lower); + settings_.log.printf("%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + symbol.c_str(), + nodes_explored, + nodes_unexplored, + user_obj, + user_lower, + node_depth, + iter_node, + user_gap.c_str(), + toc(exploration_stats_.start_time)); +} + template void branch_and_bound_t::set_new_solution(const std::vector& solution) { @@ -303,25 +348,7 @@ void branch_and_bound_t::set_new_solution(const std::vector& solu } mutex_upper_.unlock(); - if (is_feasible) { - if (solver_status_ == mip_exploration_status_t::RUNNING) { - f_t user_obj = compute_user_objective(original_lp_, obj); - f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); - std::string gap = user_mip_gap(user_obj, user_lower); - - settings_.log.printf( - "H %+13.6e %+10.6e %s %9.2f\n", - user_obj, - user_lower, - gap.c_str(), - toc(exploration_stats_.start_time)); - } else { - settings_.log.printf("New solution from primal heuristics. Objective %+.6e. Time %.2f\n", - compute_user_objective(original_lp_, obj), - toc(exploration_stats_.start_time)); - } - } - + if (is_feasible) { report_heuristic(obj); } if (attempt_repair) { mutex_repair_.lock(); repair_queue_.push_back(crushed_solution); @@ -417,17 +444,7 @@ void branch_and_bound_t::repair_heuristic_solutions() if (repaired_obj < upper_bound_) { upper_bound_ = repaired_obj; incumbent_.set_incumbent_solution(repaired_obj, repaired_solution); - - f_t obj = compute_user_objective(original_lp_, repaired_obj); - f_t lower = compute_user_objective(original_lp_, get_lower_bound()); - std::string user_gap = user_mip_gap(obj, lower); - - settings_.log.printf( - "H %+13.6e %+10.6e %s %9.2f\n", - obj, - lower, - user_gap.c_str(), - toc(exploration_stats_.start_time)); + report_heuristic(repaired_obj); if (settings_.solution_callback != nullptr) { std::vector original_x; @@ -523,31 +540,13 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, i_t leaf_depth, thread_type_t thread_type) { - bool send_solution = false; - i_t nodes_explored = exploration_stats_.nodes_explored; - i_t nodes_unexplored = exploration_stats_.nodes_unexplored; + bool send_solution = false; mutex_upper_.lock(); if (leaf_objective < upper_bound_) { incumbent_.set_incumbent_solution(leaf_objective, leaf_solution); - upper_bound_ = leaf_objective; - f_t lower_bound = get_lower_bound(); - f_t obj = compute_user_objective(original_lp_, upper_bound_); - f_t lower = compute_user_objective(original_lp_, lower_bound); - f_t iter_node = nodes_explored > 0 ? static_cast(exploration_stats_.total_lp_iters) / - static_cast(nodes_explored) - : 0; - settings_.log.printf("%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - feasible_solution_symbol(thread_type), - nodes_explored, - nodes_unexplored, - obj, - lower, - leaf_depth, - iter_node, - user_mip_gap(obj, lower).c_str(), - toc(exploration_stats_.start_time)); - + upper_bound_ = leaf_objective; + report(feasible_solution_symbol(thread_type), leaf_objective, get_lower_bound(), leaf_depth); send_solution = true; } @@ -813,25 +812,7 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod bool should_report = should_report_.exchange(false); if (should_report) { - f_t obj = compute_user_objective(original_lp_, upper_bound); - f_t user_lower = compute_user_objective(original_lp_, root_objective_); - std::string gap_user = user_mip_gap(obj, user_lower); - i_t nodes_explored = exploration_stats_.nodes_explored; - i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - f_t iter_node = nodes_explored > 0 ? static_cast(exploration_stats_.total_lp_iters) / - static_cast(nodes_explored) - : 0; - - settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - nodes_explored, - nodes_unexplored, - obj, - user_lower, - node->depth, - iter_node, - gap_user.c_str(), - now); - + report(" ", upper_bound, root_objective_, node->depth); exploration_stats_.nodes_since_last_log = 0; exploration_stats_.last_log = tic(); should_report_ = true; @@ -946,24 +927,7 @@ void branch_and_bound_t::explore_subtree(i_t task_id, abs_gap < 10 * settings_.absolute_mip_gap_tol) && time_since_last_log >= 1) || (time_since_last_log > 30) || now > settings_.time_limit) { - f_t obj = compute_user_objective(original_lp_, upper_bound); - f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); - std::string gap_user = user_mip_gap(obj, user_lower); - i_t nodes_explored = exploration_stats_.nodes_explored; - i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - f_t iter_node = nodes_explored > 0 ? static_cast(exploration_stats_.total_lp_iters) / - static_cast(nodes_explored) - : 0; - - settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - nodes_explored, - nodes_unexplored, - obj, - user_lower, - node_ptr->depth, - iter_node, - gap_user.c_str(), - now); + report(" ", upper_bound, get_lower_bound(), node_ptr->depth); exploration_stats_.last_log = tic(); exploration_stats_.nodes_since_last_log = 0; } @@ -1462,7 +1426,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " "| Time |\n"); - exploration_stats_.nodes_explored = 0; + exploration_stats_.nodes_explored = 1; exploration_stats_.nodes_unexplored = 2; exploration_stats_.nodes_since_last_log = 0; exploration_stats_.last_log = tic(); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 38438cc9e..a55964c36 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -199,6 +199,9 @@ class branch_and_bound_t { // its blocks the progression of the lower bound. omp_atomic_t lower_bound_ceiling_; + void report_heuristic(f_t obj); + void report(std::string symbol, f_t obj, f_t lower_bound, i_t node_depth); + // Set the final solution. mip_status_t set_final_solution(mip_solution_t& solution, f_t lower_bound); From 78f38a406eb1e02285a985dda470b0f7c1ef674d Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 19 Dec 2025 17:17:16 +0100 Subject: [PATCH 135/366] adjust header spacing --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 79765898d..46785d19d 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1423,7 +1423,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut settings_.num_diving_threads); settings_.log.printf( - " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " + " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " "| Time |\n"); exploration_stats_.nodes_explored = 1; From dd8955fde78a582a4094761a5508b2e9b3366906 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 19 Dec 2025 17:39:24 +0100 Subject: [PATCH 136/366] fix compilation error --- cpp/src/dual_simplex/branch_and_bound.cpp | 45 ----------------------- 1 file changed, 45 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 5f238e46a..fb433fed4 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -304,51 +304,6 @@ void branch_and_bound_t::report(std::string symbol, toc(exploration_stats_.start_time)); } -template -void branch_and_bound_t::report_heuristic(f_t obj) -{ - if (solver_status_ == mip_exploration_status_t::RUNNING) { - f_t user_obj = compute_user_objective(original_lp_, obj); - f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); - std::string user_gap = user_mip_gap(user_obj, user_lower); - - settings_.log.printf( - "H %+13.6e %+10.6e %s %9.2f\n", - user_obj, - user_lower, - user_gap.c_str(), - toc(exploration_stats_.start_time)); - } else { - settings_.log.printf("New solution from primal heuristics. Objective %+.6e. Time %.2f\n", - compute_user_objective(original_lp_, obj), - toc(exploration_stats_.start_time)); - } -} - -template -void branch_and_bound_t::report(std::string symbol, - f_t obj, - f_t lower_bound, - i_t node_depth) -{ - i_t nodes_explored = exploration_stats_.nodes_explored; - i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - f_t user_obj = compute_user_objective(original_lp_, obj); - f_t user_lower = compute_user_objective(original_lp_, lower_bound); - f_t iter_node = exploration_stats_.total_lp_iters / nodes_explored; - std::string user_gap = user_mip_gap(user_obj, user_lower); - settings_.log.printf("%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - symbol.c_str(), - nodes_explored, - nodes_unexplored, - user_obj, - user_lower, - node_depth, - iter_node, - user_gap.c_str(), - toc(exploration_stats_.start_time)); -} - template void branch_and_bound_t::set_new_solution(const std::vector& solution) { From 426b445e279bd47988032738e4b3e0cd31ee43f0 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 6 Jan 2026 14:45:31 +0100 Subject: [PATCH 137/366] added cli option for disabling each diving heuristic --- .../linear_programming/cuopt/run_mip.cpp | 51 ++++++++++++++++++- .../mip/solver_settings.hpp | 7 ++- cpp/src/dual_simplex/branch_and_bound.cpp | 8 ++- cpp/src/mip/diversity/lns/rins.cu | 14 +++-- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 9 +++- cpp/src/mip/solver.cu | 11 +++- 6 files changed, 89 insertions(+), 11 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 6013dcaf5..6d8fbdd81 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -147,6 +147,10 @@ int run_single_file(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, + bool disable_line_search_diving, + bool disable_pseudocost_diving, + bool disable_guided_diving, + bool disable_coefficient_diving, double time_limit) { const raft::handle_t handle_{}; @@ -204,6 +208,11 @@ int run_single_file(std::string file_path, settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; + settings.disable_line_search_diving = disable_line_search_diving; + settings.disable_pseudocost_diving = disable_pseudocost_diving; + settings.disable_guided_diving = disable_guided_diving; + settings.disable_coefficient_diving = disable_coefficient_diving; + cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; auto start_run_solver = std::chrono::high_resolution_clock::now(); @@ -250,6 +259,10 @@ void run_single_file_mp(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, + bool disable_line_search_diving, + bool disable_pseudocost_diving, + bool disable_guided_diving, + bool disable_coefficient_diving, double time_limit) { std::cout << "running file " << file_path << " on gpu : " << device << std::endl; @@ -265,6 +278,10 @@ void run_single_file_mp(std::string file_path, num_cpu_threads, write_log_file, log_to_console, + disable_line_search_diving, + disable_pseudocost_diving, + disable_guided_diving, + disable_coefficient_diving, time_limit); // this is a bad design to communicate the result but better than adding complexity of IPC or // pipes @@ -348,6 +365,22 @@ int main(int argc, char* argv[]) .help("track allocations (t/f)") .default_value(std::string("f")); + program.add_argument("--disable-line-search-diving") + .help("disable line search diving (t/f)") + .default_value(std::string("f")); + + program.add_argument("--disable-pseudocost-diving") + .help("disable pseudocost diving (t/f)") + .default_value(std::string("f")); + + program.add_argument("--disable-guided-diving") + .help("disable guided diving (t/f)") + .default_value(std::string("f")); + + program.add_argument("--disable-coefficient-diving") + .help("disable coefficient diving (t/f)") + .default_value(std::string("f")); + // Parse arguments try { program.parse_args(argc, argv); @@ -377,6 +410,14 @@ int main(int argc, char* argv[]) double memory_limit = program.get("--memory-limit"); bool track_allocations = program.get("--track-allocations")[0] == 't'; + bool disable_line_search_diving = + program.get("--disable-line-search-diving")[0] == 't'; + bool disable_pseudocost_diving = + program.get("--disable-pseudocost-diving")[0] == 't'; + bool disable_guided_diving = program.get("--disable-guided-diving")[0] == 't'; + bool disable_coefficient_diving = + program.get("--disable-coefficient-diving")[0] == 't'; + if (num_cpu_threads < 0) { num_cpu_threads = omp_get_max_threads() / n_gpus; } if (program.is_used("--out-dir")) { @@ -463,6 +504,10 @@ int main(int argc, char* argv[]) num_cpu_threads, write_log_file, log_to_console, + disable_line_search_diving, + disable_pseudocost_diving, + disable_guided_diving, + disable_coefficient_diving, time_limit); } else if (sys_pid < 0) { std::cerr << "Fork failed!" << std::endl; @@ -503,6 +548,10 @@ int main(int argc, char* argv[]) num_cpu_threads, write_log_file, log_to_console, + disable_line_search_diving, + disable_pseudocost_diving, + disable_guided_diving, + disable_coefficient_diving, time_limit); } diff --git a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp index 4f6320752..680cceaba 100644 --- a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp +++ b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2023-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -87,6 +87,11 @@ class mip_solver_settings_t { std::string sol_file; std::string user_problem_file; + bool disable_line_search_diving = false; + bool disable_pseudocost_diving = false; + bool disable_guided_diving = false; + bool disable_coefficient_diving = false; + /** Initial primal solutions */ std::vector>> initial_solutions; bool mip_scaling = true; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index fb433fed4..1993630e1 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -640,7 +640,11 @@ node_solve_info_t branch_and_bound_t::solve_node( // If there is no incumbent, use pseudocost diving instead of guided diving if (upper_bound == inf && thread_type == bnb_thread_type_t::GUIDED_DIVING) { - thread_type = bnb_thread_type_t::PSEUDOCOST_DIVING; + if (settings_.diving_settings.disable_pseudocost_diving) { + thread_type = bnb_thread_type_t::COEFFICIENT_DIVING; + } else { + thread_type = bnb_thread_type_t::PSEUDOCOST_DIVING; + } } lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 59ca45de5..1893e84fe 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -265,9 +265,15 @@ void rins_t::run_rins() branch_and_bound_settings.diving_settings.num_diving_tasks = 1; branch_and_bound_settings.diving_settings.disable_line_search_diving = true; branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; - branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; - branch_and_bound_settings.log.log = false; - branch_and_bound_settings.log.log_prefix = "[RINS] "; + + if (context.settings.disable_guided_diving) { + branch_and_bound_settings.diving_settings.disable_guided_diving = true; + } else { + branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; + } + + branch_and_bound_settings.log.log = false; + branch_and_bound_settings.log.log_prefix = "[RINS] "; branch_and_bound_settings.solution_callback = [this, &rins_solution_queue]( std::vector& solution, f_t objective) { rins_solution_queue.push_back(solution); diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index fa21fb7d2..2335003b6 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -110,7 +110,12 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.diving_settings.num_diving_tasks = 1; branch_and_bound_settings.diving_settings.disable_line_search_diving = true; branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; - branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; + + if (context.settings.disable_guided_diving) { + branch_and_bound_settings.diving_settings.disable_guided_diving = true; + } else { + branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; + } branch_and_bound_settings.solution_callback = [this](std::vector& solution, f_t objective) { diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 7eb2b226d..790831129 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -167,6 +167,15 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.diving_settings.disable_coefficient_diving = + context.settings.disable_coefficient_diving; + branch_and_bound_settings.diving_settings.disable_pseudocost_diving = + context.settings.disable_pseudocost_diving; + branch_and_bound_settings.diving_settings.disable_guided_diving = + context.settings.disable_guided_diving; + branch_and_bound_settings.diving_settings.disable_line_search_diving = + context.settings.disable_line_search_diving; + if (context.settings.num_cpu_threads < 0) { branch_and_bound_settings.num_threads = omp_get_max_threads() - 1; } else { From 319bd22acfa3dd6c15be01253d256914b6ff1096 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 6 Jan 2026 15:00:41 +0100 Subject: [PATCH 138/366] fix style --- cpp/src/dual_simplex/bounds_strengthening.cpp | 2 +- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- cpp/src/dual_simplex/branch_and_bound.hpp | 2 +- cpp/src/dual_simplex/mip_node.hpp | 2 +- cpp/src/dual_simplex/node_queue.hpp | 4 ++-- cpp/src/dual_simplex/pseudo_costs.cpp | 2 +- cpp/src/dual_simplex/pseudo_costs.hpp | 2 +- 7 files changed, 8 insertions(+), 8 deletions(-) diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index c56c9db98..4114e7e09 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index ff55d06f7..15db6f975 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 032eed11f..b719a220a 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index a082932ac..de147132a 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/node_queue.hpp b/cpp/src/dual_simplex/node_queue.hpp index 0234fa038..804273697 100644 --- a/cpp/src/dual_simplex/node_queue.hpp +++ b/cpp/src/dual_simplex/node_queue.hpp @@ -1,6 +1,6 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 */ #pragma once diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 1c0a33042..aabbe5a17 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index ab01b2a85..5c34e0296 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ From 3e52ffeac6ef0dacd1a8ff12788cf951b8ca73fc Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 6 Jan 2026 15:35:49 +0100 Subject: [PATCH 139/366] fix infeasible list (#694) --- cpp/src/dual_simplex/basis_solves.cpp | 16 +++++- cpp/src/dual_simplex/basis_solves.hpp | 4 +- cpp/src/dual_simplex/basis_updates.cpp | 7 ++- cpp/src/dual_simplex/basis_updates.hpp | 4 +- cpp/src/dual_simplex/crossover.cpp | 33 +++++++++-- cpp/src/dual_simplex/phase2.cpp | 79 ++++++++++++++++++-------- cpp/src/dual_simplex/primal.cpp | 12 +++- 7 files changed, 118 insertions(+), 37 deletions(-) diff --git a/cpp/src/dual_simplex/basis_solves.cpp b/cpp/src/dual_simplex/basis_solves.cpp index db24f55a2..f5cd54053 100644 --- a/cpp/src/dual_simplex/basis_solves.cpp +++ b/cpp/src/dual_simplex/basis_solves.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -613,6 +613,8 @@ i_t factorize_basis(const csc_matrix_t& A, template i_t basis_repair(const csc_matrix_t& A, const simplex_solver_settings_t& settings, + const std::vector& lower, + const std::vector& upper, const std::vector& deficient, const std::vector& slacks_needed, std::vector& basis_list, @@ -658,7 +660,15 @@ i_t basis_repair(const csc_matrix_t& A, nonbasic_list[nonbasic_map[replace_j]] = bad_j; vstatus[replace_j] = variable_status_t::BASIC; // This is the main issue. What value should bad_j take on. - vstatus[bad_j] = variable_status_t::NONBASIC_FREE; + if (lower[bad_j] == -inf && upper[bad_j] == inf) { + vstatus[bad_j] = variable_status_t::NONBASIC_FREE; + } else if (lower[bad_j] > -inf) { + vstatus[bad_j] = variable_status_t::NONBASIC_LOWER; + } else if (upper[bad_j] < inf) { + vstatus[bad_j] = variable_status_t::NONBASIC_UPPER; + } else { + assert(1 == 0); + } } return 0; @@ -849,6 +859,8 @@ template int factorize_basis(const csc_matrix_t& A, template int basis_repair(const csc_matrix_t& A, const simplex_solver_settings_t& settings, + const std::vector& lower, + const std::vector& upper, const std::vector& deficient, const std::vector& slacks_needed, std::vector& basis_list, diff --git a/cpp/src/dual_simplex/basis_solves.hpp b/cpp/src/dual_simplex/basis_solves.hpp index b668c0f46..295bedccd 100644 --- a/cpp/src/dual_simplex/basis_solves.hpp +++ b/cpp/src/dual_simplex/basis_solves.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -42,6 +42,8 @@ i_t factorize_basis(const csc_matrix_t& A, template i_t basis_repair(const csc_matrix_t& A, const simplex_solver_settings_t& settings, + const std::vector& lower, + const std::vector& upper, const std::vector& deficient, const std::vector& slacks_needed, std::vector& basis_list, diff --git a/cpp/src/dual_simplex/basis_updates.cpp b/cpp/src/dual_simplex/basis_updates.cpp index 6b79f3c86..2c781a515 100644 --- a/cpp/src/dual_simplex/basis_updates.cpp +++ b/cpp/src/dual_simplex/basis_updates.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -2046,6 +2046,8 @@ template int basis_update_mpf_t::refactor_basis( const csc_matrix_t& A, const simplex_solver_settings_t& settings, + const std::vector& lower, + const std::vector& upper, std::vector& basic_list, std::vector& nonbasic_list, std::vector& vstatus) @@ -2066,7 +2068,8 @@ int basis_update_mpf_t::refactor_basis( deficient, slacks_needed) == -1) { settings.log.debug("Initial factorization failed\n"); - basis_repair(A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair( + A, settings, lower, upper, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); #ifdef CHECK_BASIS_REPAIR const i_t m = A.m; diff --git a/cpp/src/dual_simplex/basis_updates.hpp b/cpp/src/dual_simplex/basis_updates.hpp index cea907074..afd4f4c9a 100644 --- a/cpp/src/dual_simplex/basis_updates.hpp +++ b/cpp/src/dual_simplex/basis_updates.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -373,6 +373,8 @@ class basis_update_mpf_t { // Compute L*U = A(p, basic_list) int refactor_basis(const csc_matrix_t& A, const simplex_solver_settings_t& settings, + const std::vector& lower, + const std::vector& upper, std::vector& basic_list, std::vector& nonbasic_list, std::vector& vstatus); diff --git a/cpp/src/dual_simplex/crossover.cpp b/cpp/src/dual_simplex/crossover.cpp index 23d9a0e8e..8ee3fb0ce 100644 --- a/cpp/src/dual_simplex/crossover.cpp +++ b/cpp/src/dual_simplex/crossover.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -785,8 +785,15 @@ i_t primal_push(const lp_problem_t& lp, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair( - lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, + settings, + lp.lower, + lp.upper, + deficient, + slacks_needed, + basic_list, + nonbasic_list, + vstatus); if (factorize_basis( lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); @@ -1132,7 +1139,15 @@ crossover_status_t crossover(const lp_problem_t& lp, rank = factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, + settings, + lp.lower, + lp.upper, + deficient, + slacks_needed, + basic_list, + nonbasic_list, + vstatus); if (factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); @@ -1323,7 +1338,15 @@ crossover_status_t crossover(const lp_problem_t& lp, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, + settings, + lp.lower, + lp.upper, + deficient, + slacks_needed, + basic_list, + nonbasic_list, + vstatus); if (factorize_basis( lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 56298ef4d..2bc00f636 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -623,14 +623,17 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, const std::vector& basic_list, const std::vector& x, std::vector& squared_infeasibilities, - std::vector& infeasibility_indices) + std::vector& infeasibility_indices, + f_t& primal_inf) { const i_t m = lp.num_rows; const i_t n = lp.num_cols; - squared_infeasibilities.resize(n, 0.0); + squared_infeasibilities.resize(n); + std::fill(squared_infeasibilities.begin(), squared_infeasibilities.end(), 0.0); infeasibility_indices.reserve(n); infeasibility_indices.clear(); - f_t primal_inf = 0.0; + f_t primal_inf_squared = 0.0; + primal_inf = 0.0; for (i_t k = 0; k < m; ++k) { const i_t j = basic_list[k]; const f_t lower_infeas = lp.lower[j] - x[j]; @@ -640,10 +643,11 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, const f_t square_infeas = infeas * infeas; squared_infeasibilities[j] = square_infeas; infeasibility_indices.push_back(j); - primal_inf += square_infeas; + primal_inf_squared += square_infeas; + primal_inf += infeas; } } - return primal_inf; + return primal_inf_squared; } template @@ -2241,7 +2245,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(superbasic_list.size() == 0); assert(nonbasic_list.size() == n - m); - if (ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus) > 0) { + if (ft.refactor_basis(lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus) > + 0) { return dual::status_t::NUMERICAL; } @@ -2268,7 +2273,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #ifdef COMPUTE_DUAL_RESIDUAL std::vector dual_res1; - compute_dual_residual(lp.A, objective, y, z, dual_res1); + phase2::compute_dual_residual(lp.A, objective, y, z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.tight_tol) { settings.log.printf("|| A'*y + z - c || %e\n", dual_res_norm); @@ -2357,8 +2362,15 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector bounded_variables(n, 0); phase2::compute_bounded_info(lp.lower, lp.upper, bounded_variables); - f_t primal_infeasibility = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + f_t primal_infeasibility; + f_t primal_infeasibility_squared = + phase2::compute_initial_primal_infeasibilities(lp, + settings, + basic_list, + x, + squared_infeasibilities, + infeasibility_indices, + primal_infeasibility); #ifdef CHECK_BASIC_INFEASIBILITIES phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 0); @@ -2556,9 +2568,15 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); - x = unperturbed_x; - primal_infeasibility = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + x = unperturbed_x; + primal_infeasibility_squared = + phase2::compute_initial_primal_infeasibilities(lp, + settings, + basic_list, + x, + squared_infeasibilities, + infeasibility_indices, + primal_infeasibility); settings.log.printf("Updated primal infeasibility: %e\n", primal_infeasibility); objective = lp.objective; @@ -2593,9 +2611,15 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); - x = unperturbed_x; - primal_infeasibility = phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + x = unperturbed_x; + primal_infeasibility_squared = + phase2::compute_initial_primal_infeasibilities(lp, + settings, + basic_list, + x, + squared_infeasibilities, + infeasibility_indices, + primal_infeasibility); const f_t orig_dual_infeas = phase2::dual_infeasibility( lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); @@ -2810,7 +2834,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_xB_0_sparse.i, squared_infeasibilities, infeasibility_indices, - primal_infeasibility); + primal_infeasibility_squared); // Update primal infeasibilities due to changes in basic variables // from the leaving and entering variables phase2::update_primal_infeasibilities(lp, @@ -2822,7 +2846,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, scaled_delta_xB_sparse.i, squared_infeasibilities, infeasibility_indices, - primal_infeasibility); + primal_infeasibility_squared); // Update the entering variable phase2::update_single_primal_infeasibility(lp.lower, lp.upper, @@ -2883,14 +2907,15 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif if (should_refactor) { bool should_recompute_x = false; - if (ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus) > 0) { + if (ft.refactor_basis( + lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus) > 0) { should_recompute_x = true; settings.log.printf("Failed to factorize basis. Iteration %d\n", iter); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } i_t count = 0; i_t deficient_size; - while ((deficient_size = - ft.refactor_basis(lp.A, settings, basic_list, nonbasic_list, vstatus)) > 0) { + while ((deficient_size = ft.refactor_basis( + lp.A, settings, lp.lower, lp.upper, basic_list, nonbasic_list, vstatus)) > 0) { settings.log.printf("Failed to repair basis. Iteration %d. %d deficient columns.\n", iter, static_cast(deficient_size)); @@ -2912,8 +2937,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); x = unperturbed_x; } - phase2::compute_initial_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + primal_infeasibility_squared = + phase2::compute_initial_primal_infeasibilities(lp, + settings, + basic_list, + x, + squared_infeasibilities, + infeasibility_indices, + primal_infeasibility); } #ifdef CHECK_BASIC_INFEASIBILITIES phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 7); @@ -2951,7 +2982,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, iter, compute_user_objective(lp, obj), infeasibility_indices.size(), - primal_infeasibility, + primal_infeasibility_squared, sum_perturb, now); } diff --git a/cpp/src/dual_simplex/primal.cpp b/cpp/src/dual_simplex/primal.cpp index 80406dcf0..69f15ba18 100644 --- a/cpp/src/dual_simplex/primal.cpp +++ b/cpp/src/dual_simplex/primal.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -298,7 +298,15 @@ primal::status_t primal_phase2(i_t phase, factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed); if (rank != m) { settings.log.debug("Failed to factorize basis. rank %d m %d\n", rank, m); - basis_repair(lp.A, settings, deficient, slacks_needed, basic_list, nonbasic_list, vstatus); + basis_repair(lp.A, + settings, + lp.lower, + lp.upper, + deficient, + slacks_needed, + basic_list, + nonbasic_list, + vstatus); if (factorize_basis(lp.A, settings, basic_list, L, U, p, pinv, q, deficient, slacks_needed) == -1) { settings.log.printf("Failed to factorize basis after repair. rank %d m %d\n", rank, m); From 972b18766ffaba47dfb3f9a1fa64e4456f440d81 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 15 Dec 2025 17:08:39 +0100 Subject: [PATCH 140/366] created a struct with all persistent data used by each worker --- cpp/src/dual_simplex/bounds_strengthening.cpp | 4 +- cpp/src/dual_simplex/bounds_strengthening.hpp | 3 +- cpp/src/dual_simplex/branch_and_bound.cpp | 256 ++++++++---------- cpp/src/dual_simplex/branch_and_bound.hpp | 60 ++-- cpp/src/dual_simplex/presolve.cpp | 6 +- 5 files changed, 162 insertions(+), 167 deletions(-) diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index c56c9db98..cc8f8241d 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -59,8 +59,7 @@ bounds_strengthening_t::bounds_strengthening_t( const csr_matrix_t& Arow, const std::vector& row_sense, const std::vector& var_types) - : bounds_changed(problem.num_cols, false), - A(problem.A), + : A(problem.A), Arow(Arow), var_types(var_types), delta_min_activity(problem.num_rows), @@ -93,6 +92,7 @@ template bool bounds_strengthening_t::bounds_strengthening( std::vector& lower_bounds, std::vector& upper_bounds, + const std::vector& bounds_changed, const simplex_solver_settings_t& settings) { const i_t m = A.m; diff --git a/cpp/src/dual_simplex/bounds_strengthening.hpp b/cpp/src/dual_simplex/bounds_strengthening.hpp index e7e218b82..dfad27005 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.hpp +++ b/cpp/src/dual_simplex/bounds_strengthening.hpp @@ -22,10 +22,9 @@ class bounds_strengthening_t { bool bounds_strengthening(std::vector& lower_bounds, std::vector& upper_bounds, + const std::vector& bounds_changed, const simplex_solver_settings_t& settings); - std::vector bounds_changed; - private: const csc_matrix_t& A; const csr_matrix_t& Arow; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 1993630e1..a23f5c172 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -304,6 +304,18 @@ void branch_and_bound_t::report(std::string symbol, toc(exploration_stats_.start_time)); } +template +bnb_worker_data_t* branch_and_bound_t::get_worker_data(i_t tid) +{ + std::lock_guard lock(mutex_worker_data_); + if (persistent_worker_data_.find(tid) == persistent_worker_data_.end()) { + persistent_worker_data_[tid] = + std::make_unique>(original_lp_, Arow_, var_types_, settings_); + } + + return persistent_worker_data_[tid].get(); +} + template void branch_and_bound_t::set_new_solution(const std::vector& solution) { @@ -311,6 +323,7 @@ void branch_and_bound_t::set_new_solution(const std::vector& solu settings_.log.printf( "Solution size mismatch %ld %d\n", solution.size(), original_problem_.num_cols); } + std::vector crushed_solution; crush_primal_solution( original_problem_, original_lp_, solution, new_slacks_, crushed_solution); @@ -620,20 +633,46 @@ branch_variable_t branch_and_bound_t::variable_selection( } template -node_solve_info_t branch_and_bound_t::solve_node( - mip_node_t* node_ptr, - search_tree_t& search_tree, - lp_problem_t& leaf_problem, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list, - bounds_strengthening_t& node_presolver, - bnb_thread_type_t thread_type, - bool recompute_bounds_and_basis, - const std::vector& root_lower, - const std::vector& root_upper, - bnb_stats_t& stats, - logger_t& log) +bool branch_and_bound_t::set_node_bounds(mip_node_t* node_ptr, + const std::vector& start_lower, + const std::vector& start_upper, + bnb_worker_data_t* worker_data) +{ + bounds_strengthening_t& node_presolver = worker_data->node_presolver; + std::vector& upper = worker_data->leaf_problem.upper; + std::vector& lower = worker_data->leaf_problem.lower; + std::vector& bounds_changed = worker_data->bounds_changed; + + assert(bounds_changed.size() == original_lp_.num_cols); + assert(lower.size() == original_lp_.num_cols); + assert(upper.size() == original_lp_.num_cols); + + // Reset the bound_changed markers + std::fill(bounds_changed.begin(), bounds_changed.end(), false); + + // Set the correct bounds for the leaf problem + if (worker_data->recompute_bounds) { + lower = start_lower; + upper = start_upper; + node_ptr->get_variable_bounds(lower, upper, bounds_changed); + + } else { + node_ptr->update_branched_variable_bounds(lower, upper, bounds_changed); + } + + bool feasible = node_presolver.bounds_strengthening(lower, upper, bounds_changed, settings_); + return feasible; +} + +template +node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* node_ptr, + search_tree_t& search_tree, + bnb_thread_type_t thread_type, + bnb_worker_data_t* worker_data, + const std::vector& root_lower, + const std::vector& root_upper, + bnb_stats_t& stats, + logger_t& log) { const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; const f_t upper_bound = get_upper_bound(); @@ -647,6 +686,7 @@ node_solve_info_t branch_and_bound_t::solve_node( } } + lp_problem_t& leaf_problem = worker_data->leaf_problem; lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); std::vector& leaf_vstatus = node_ptr->vstatus; assert(leaf_vstatus.size() == leaf_problem.num_cols); @@ -687,56 +727,41 @@ node_solve_info_t branch_and_bound_t::solve_node( node_ptr->vstatus[node_ptr->branch_var]); #endif - // Reset the bound_changed markers - std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); - - // Set the correct bounds for the leaf problem - if (recompute_bounds_and_basis) { - leaf_problem.lower = root_lower; - leaf_problem.upper = root_upper; - node_ptr->get_variable_bounds( - leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); - - } else { - node_ptr->update_branched_variable_bounds( - leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); - } - - bool feasible = - node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); + bool is_feasible = set_node_bounds(node_ptr, root_lower, root_upper, worker_data); dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; - if (feasible) { + if (is_feasible) { i_t node_iter = 0; f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; lp_status = dual_phase2_with_advanced_basis(2, 0, - recompute_bounds_and_basis, + worker_data->recompute_basis, lp_start_time, leaf_problem, lp_settings, leaf_vstatus, - basis_factors, - basic_list, - nonbasic_list, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, leaf_solution, node_iter, leaf_edge_norms); if (lp_status == dual::status_t::NUMERICAL) { log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); - lp_status_t second_status = solve_linear_program_with_advanced_basis(leaf_problem, - lp_start_time, - lp_settings, - leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - leaf_vstatus, - leaf_edge_norms); + lp_status_t second_status = + solve_linear_program_with_advanced_basis(leaf_problem, + lp_start_time, + lp_settings, + leaf_solution, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, + leaf_vstatus, + leaf_edge_norms); lp_status = convert_lp_status_to_dual_status(second_status); } @@ -848,6 +873,10 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod // to repair the heuristic solution. repair_heuristic_solutions(); + i_t tid = omp_get_thread_num(); + bnb_worker_data_t* worker_data = get_worker_data(tid); + assert(worker_data); + f_t lower_bound = node->lower_bound; f_t upper_bound = get_upper_bound(); f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); @@ -883,25 +912,13 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod return; } - // Make a copy of the original LP. We will modify its bounds at each leaf - lp_problem_t leaf_problem = original_lp_; - std::vector row_sense; - bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); - - const i_t m = leaf_problem.num_rows; - basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); - std::vector basic_list(m); - std::vector nonbasic_list; + worker_data->recompute_basis = true; + worker_data->recompute_bounds = true; node_solve_info_t status = solve_node(node, search_tree_, - leaf_problem, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, bnb_thread_type_t::EXPLORATION, - true, + worker_data, original_lp_.lower, original_lp_.upper, exploration_stats_, @@ -935,19 +952,18 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod } template -void branch_and_bound_t::plunge_from(i_t task_id, - mip_node_t* start_node, - search_tree_t& search_tree, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list) +void branch_and_bound_t::plunge_from(i_t task_id, mip_node_t* start_node) { - bool recompute_bounds_and_basis = true; + i_t tid = omp_get_thread_num(); + bnb_worker_data_t* worker_data = get_worker_data(tid); + assert(worker_data); + std::deque*> stack; stack.push_front(start_node); + worker_data->recompute_basis = true; + worker_data->recompute_bounds = true; + while (stack.size() > 0 && solver_status_ == mip_exploration_status_t::RUNNING) { if (task_id == 0) { repair_heuristic_solutions(); } @@ -969,9 +985,10 @@ void branch_and_bound_t::plunge_from(i_t task_id, local_lower_bounds_[task_id] = lower_bound; if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - search_tree.graphviz_node(settings_.log, node_ptr, "cutoff", node_ptr->lower_bound); - search_tree.update(node_ptr, node_status_t::FATHOMED); - recompute_bounds_and_basis = true; + search_tree_.graphviz_node(settings_.log, node_ptr, "cutoff", node_ptr->lower_bound); + search_tree_.update(node_ptr, node_status_t::FATHOMED); + worker_data->recompute_basis = true; + worker_data->recompute_bounds = true; --exploration_stats_.nodes_unexplored; continue; } @@ -1002,20 +1019,16 @@ void branch_and_bound_t::plunge_from(i_t task_id, } node_solve_info_t status = solve_node(node_ptr, - search_tree, - leaf_problem, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, + search_tree_, bnb_thread_type_t::EXPLORATION, - recompute_bounds_and_basis, + worker_data, original_lp_.lower, original_lp_.upper, exploration_stats_, settings_.log); - recompute_bounds_and_basis = !has_children(status); + worker_data->recompute_basis = !has_children(status); + worker_data->recompute_bounds = !has_children(status); ++exploration_stats_.nodes_since_last_log; ++exploration_stats_.nodes_explored; @@ -1057,16 +1070,6 @@ void branch_and_bound_t::best_first_thread(i_t task_id) f_t abs_gap = inf; f_t rel_gap = inf; - // Make a copy of the original LP. We will modify its bounds at each leaf - lp_problem_t leaf_problem = original_lp_; - std::vector row_sense; - bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); - - const i_t m = leaf_problem.num_rows; - basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); - std::vector basic_list(m); - std::vector nonbasic_list; - while (solver_status_ == mip_exploration_status_t::RUNNING && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && (active_subtrees_ > 0 || node_queue.best_first_queue_size() > 0)) { @@ -1085,14 +1088,7 @@ void branch_and_bound_t::best_first_thread(i_t task_id) } // Best-first search with plunging - plunge_from(task_id, - start_node.value(), - search_tree_, - leaf_problem, - node_presolver, - basis_factors, - basic_list, - nonbasic_list); + plunge_from(task_id, start_node.value()); active_subtrees_--; } @@ -1118,17 +1114,18 @@ template void branch_and_bound_t::dive_from(mip_node_t& start_node, const std::vector& start_lower, const std::vector& start_upper, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list, bnb_thread_type_t diving_type) { logger_t log; log.log = false; - bool recompute_bounds_and_basis = true; + i_t tid = omp_get_thread_num(); + bnb_worker_data_t* worker_data = get_worker_data(tid); + assert(worker_data); + + worker_data->recompute_basis = true; + worker_data->recompute_bounds = true; + search_tree_t subtree(std::move(start_node)); std::deque*> stack; stack.push_front(&subtree.root); @@ -1146,28 +1143,19 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node_ptr->lower_bound); if (node_ptr->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - recompute_bounds_and_basis = true; + worker_data->recompute_basis = true; + worker_data->recompute_bounds = true; continue; } if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } if (dive_stats.nodes_explored > settings_.diving_settings.node_limit) { break; } - node_solve_info_t status = solve_node(node_ptr, - subtree, - leaf_problem, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, - diving_type, - recompute_bounds_and_basis, - start_lower, - start_upper, - dive_stats, - log); + node_solve_info_t status = solve_node( + node_ptr, subtree, diving_type, worker_data, start_lower, start_upper, dive_stats, log); dive_stats.nodes_explored++; - recompute_bounds_and_basis = !has_children(status); + worker_data->recompute_basis = !has_children(status); + worker_data->recompute_bounds = !has_children(status); if (status == node_solve_info_t::TIME_LIMIT) { solver_status_ = mip_exploration_status_t::TIME_LIMIT; @@ -1196,18 +1184,13 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, template void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type) { - // Make a copy of the original LP. We will modify its bounds at each leaf - lp_problem_t leaf_problem = original_lp_; - std::vector row_sense; - bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); - - const i_t m = leaf_problem.num_rows; - basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); - std::vector basic_list(m); - std::vector nonbasic_list; + i_t tid = omp_get_thread_num(); + bnb_worker_data_t* worker_data = get_worker_data(tid); + bounds_strengthening_t& node_presolver = worker_data->node_presolver; std::vector start_lower; std::vector start_upper; + std::vector bounds_changed(original_lp_.num_cols, false); bool reset_starting_bounds = true; while (solver_status_ == mip_exploration_status_t::RUNNING && @@ -1215,28 +1198,21 @@ void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type) if (reset_starting_bounds) { start_lower = original_lp_.lower; start_upper = original_lp_.upper; - std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); + std::fill(bounds_changed.begin(), bounds_changed.end(), false); reset_starting_bounds = false; } std::optional> start_node = - node_queue.pop_diving(start_lower, start_upper, node_presolver.bounds_changed); + node_queue.pop_diving(start_lower, start_upper, bounds_changed); if (start_node.has_value()) { reset_starting_bounds = true; - bool is_feasible = node_presolver.bounds_strengthening(start_lower, start_upper, settings_); + bool is_feasible = + node_presolver.bounds_strengthening(start_lower, start_upper, bounds_changed, settings_); if (get_upper_bound() < start_node->lower_bound || !is_feasible) { continue; } - dive_from(start_node.value(), - start_lower, - start_upper, - leaf_problem, - node_presolver, - basis_factors, - basic_list, - nonbasic_list, - diving_type); + dive_from(start_node.value(), start_lower, start_upper, diving_type); } } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index b30682bdb..19fa2ee77 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -85,6 +85,32 @@ struct bnb_stats_t { omp_atomic_t nodes_since_last_log = 0; }; +template +struct bnb_worker_data_t { + lp_problem_t leaf_problem; + basis_update_mpf_t basis_factors; + std::vector basic_list; + std::vector nonbasic_list; + bounds_strengthening_t node_presolver; + std::vector bounds_changed; + + bool recompute_basis = true; + bool recompute_bounds = true; + + bnb_worker_data_t(const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_type, + const simplex_solver_settings_t& settings) + : leaf_problem(original_lp), + basis_factors(original_lp.num_rows, settings.refactor_frequency), + basic_list(original_lp.num_rows), + nonbasic_list(), + node_presolver(leaf_problem, Arow, {}, var_type), + bounds_changed(original_lp.num_cols, false) + { + } +}; + template class branch_and_bound_t { public: @@ -200,6 +226,11 @@ class branch_and_bound_t { void report_heuristic(f_t obj); void report(std::string symbol, f_t obj, f_t lower_bound, i_t node_depth); + // Persistent data private for each individual worker. + std::unordered_map>> persistent_worker_data_; + omp_mutex_t mutex_worker_data_; + bnb_worker_data_t* get_worker_data(i_t tid); + // Set the final solution. mip_status_t set_final_solution(mip_solution_t& solution, f_t lower_bound); @@ -217,15 +248,7 @@ class branch_and_bound_t { // there is enough unexplored nodes. This is done recursively using OpenMP tasks. void exploration_ramp_up(mip_node_t* node, i_t initial_heap_size); - // Perform a plunge in the subtree determined by the `start_node`. - void plunge_from(i_t task_id, - mip_node_t* start_node, - search_tree_t& search_tree, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_update, - std::vector& basic_list, - std::vector& nonbasic_list); + void plunge_from(i_t task_id, mip_node_t* start_node); // Each "main" thread pops a node from the global heap and then performs a plunge // (i.e., a shallow dive) into the subtree determined by the node. @@ -235,27 +258,24 @@ class branch_and_bound_t { void dive_from(mip_node_t& start_node, const std::vector& start_lower, const std::vector& start_upper, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_update, - std::vector& basic_list, - std::vector& nonbasic_list, bnb_thread_type_t diving_type); // Each diving thread pops the first node from the dive queue and then performs // a deep dive into the subtree determined by the node. void diving_thread(bnb_thread_type_t diving_type); + // Set the bounds of the leaf node and then apply bounds propagation. + // Return true if the problem is feasible, false otherwise. + bool set_node_bounds(mip_node_t* node_ptr, + const std::vector& start_lower, + const std::vector& start_upper, + bnb_worker_data_t* worker_data); + // Solve the LP relaxation of a leaf node and update the tree. node_solve_info_t solve_node(mip_node_t* node_ptr, search_tree_t& search_tree, - lp_problem_t& leaf_problem, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list, - bounds_strengthening_t& node_presolver, bnb_thread_type_t thread_type, - bool recompute_basis_and_bounds, + bnb_worker_data_t* worker_data, const std::vector& root_lower, const std::vector& root_upper, bnb_stats_t& stats, diff --git a/cpp/src/dual_simplex/presolve.cpp b/cpp/src/dual_simplex/presolve.cpp index d247fbf67..56b89f884 100644 --- a/cpp/src/dual_simplex/presolve.cpp +++ b/cpp/src/dual_simplex/presolve.cpp @@ -621,7 +621,7 @@ void convert_user_problem(const user_problem_t& user_problem, } constexpr bool run_bounds_strengthening = false; - if (run_bounds_strengthening) { + if constexpr (run_bounds_strengthening) { csr_matrix_t Arow(1, 1, 1); problem.A.to_compressed_row(Arow); @@ -629,8 +629,8 @@ void convert_user_problem(const user_problem_t& user_problem, // Empty var_types means that all variables are continuous bounds_strengthening_t strengthening(problem, Arow, row_sense, {}); - std::fill(strengthening.bounds_changed.begin(), strengthening.bounds_changed.end(), true); - strengthening.bounds_strengthening(problem.lower, problem.upper, settings); + std::vector bounds_changed(problem.num_cols, true); + strengthening.bounds_strengthening(problem.lower, problem.upper, bounds_changed, settings); } settings.log.debug( From d076da2e3d6765e06632d9a73cb831e490d568d5 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 16 Dec 2025 15:35:36 +0100 Subject: [PATCH 141/366] replace hashmap with a vector --- cpp/src/dual_simplex/branch_and_bound.cpp | 7 +++---- cpp/src/dual_simplex/branch_and_bound.hpp | 7 ++++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index a23f5c172..9204f6633 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -307,13 +307,12 @@ void branch_and_bound_t::report(std::string symbol, template bnb_worker_data_t* branch_and_bound_t::get_worker_data(i_t tid) { - std::lock_guard lock(mutex_worker_data_); - if (persistent_worker_data_.find(tid) == persistent_worker_data_.end()) { - persistent_worker_data_[tid] = + if (!worker_data_pool_[tid].get()) { + worker_data_pool_[tid] = std::make_unique>(original_lp_, Arow_, var_types_, settings_); } - return persistent_worker_data_[tid].get(); + return worker_data_pool_[tid].get(); } template diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 19fa2ee77..baa7107be 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -226,9 +226,10 @@ class branch_and_bound_t { void report_heuristic(f_t obj); void report(std::string symbol, f_t obj, f_t lower_bound, i_t node_depth); - // Persistent data private for each individual worker. - std::unordered_map>> persistent_worker_data_; - omp_mutex_t mutex_worker_data_; + + // A pool containing the data needed for a worker to perform a plunge or dive. + // This is lazily initialized via `get_worker_data()`. + std::vector>> worker_data_pool_; bnb_worker_data_t* get_worker_data(i_t tid); // Set the final solution. From d8e3541c9e8dfb0e804653e2b9b6ce1417163dc2 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 16 Dec 2025 16:15:22 +0100 Subject: [PATCH 142/366] fix missing initialization --- cpp/src/dual_simplex/branch_and_bound.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 9204f6633..9f293a72b 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1468,6 +1468,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut root_vstatus_, original_lp_, log); + worker_data_pool_.resize(settings_.num_threads); settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", settings_.num_threads, From 4796733c6b84cfd992a61d099fb22a35010b3703 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 17 Dec 2025 17:08:23 +0100 Subject: [PATCH 143/366] added a setting for each type of task. changed how to set the number of tasks per type --- cpp/src/dual_simplex/branch_and_bound.cpp | 106 ++++++++---------- cpp/src/dual_simplex/branch_and_bound.hpp | 21 +--- cpp/src/dual_simplex/diving_heuristics.cpp | 15 +++ cpp/src/dual_simplex/diving_heuristics.hpp | 3 + .../dual_simplex/simplex_solver_settings.hpp | 105 ++++++++++++++--- cpp/src/mip/diversity/lns/rins.cu | 17 +-- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 20 ++-- cpp/src/mip/solver.cu | 15 +-- 8 files changed, 174 insertions(+), 128 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 9f293a72b..ce29a86cb 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -192,14 +192,14 @@ std::string user_mip_gap(f_t obj_value, f_t lower_bound) } } -inline const char* feasible_solution_symbol(bnb_thread_type_t type) +inline const char* feasible_solution_symbol(bnb_task_type_t type) { switch (type) { - case bnb_thread_type_t::EXPLORATION: return "B "; - case bnb_thread_type_t::COEFFICIENT_DIVING: return "CD"; - case bnb_thread_type_t::LINE_SEARCH_DIVING: return "LD"; - case bnb_thread_type_t::PSEUDOCOST_DIVING: return "PD"; - case bnb_thread_type_t::GUIDED_DIVING: return "GD"; + case bnb_task_type_t::EXPLORATION: return "B "; + case bnb_task_type_t::COEFFICIENT_DIVING: return "CD"; + case bnb_task_type_t::LINE_SEARCH_DIVING: return "LD"; + case bnb_task_type_t::PSEUDOCOST_DIVING: return "PD"; + case bnb_task_type_t::GUIDED_DIVING: return "GD"; default: return "U "; } } @@ -544,7 +544,7 @@ template void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - bnb_thread_type_t thread_type) + bnb_task_type_t thread_type) { bool send_solution = false; @@ -594,7 +594,7 @@ branch_variable_t branch_and_bound_t::variable_selection( mip_node_t* node_ptr, const std::vector& fractional, const std::vector& solution, - bnb_thread_type_t type, + bnb_task_type_t type, logger_t& log) { i_t branch_var = -1; @@ -602,7 +602,7 @@ branch_variable_t branch_and_bound_t::variable_selection( rounding_direction_t round_dir = rounding_direction_t::NONE; switch (type) { - case bnb_thread_type_t::EXPLORATION: + case bnb_task_type_t::EXPLORATION: std::tie(branch_var, obj_estimate) = pc_.variable_selection_and_obj_estimate(fractional, solution, node_ptr->lower_bound, log); round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); @@ -613,16 +613,16 @@ branch_variable_t branch_and_bound_t::variable_selection( node_ptr->objective_estimate = obj_estimate; return {branch_var, round_dir}; - case bnb_thread_type_t::COEFFICIENT_DIVING: + case bnb_task_type_t::COEFFICIENT_DIVING: return coefficient_diving(original_lp_, fractional, solution, log); - case bnb_thread_type_t::LINE_SEARCH_DIVING: + case bnb_task_type_t::LINE_SEARCH_DIVING: return line_search_diving(fractional, solution, root_relax_soln_.x, log); - case bnb_thread_type_t::PSEUDOCOST_DIVING: + case bnb_task_type_t::PSEUDOCOST_DIVING: return pseudocost_diving(pc_, fractional, solution, root_relax_soln_.x, log); - case bnb_thread_type_t::GUIDED_DIVING: + case bnb_task_type_t::GUIDED_DIVING: return guided_diving(pc_, fractional, solution, incumbent_.x, log); default: @@ -666,7 +666,7 @@ bool branch_and_bound_t::set_node_bounds(mip_node_t* node_pt template node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* node_ptr, search_tree_t& search_tree, - bnb_thread_type_t thread_type, + bnb_task_type_t thread_type, bnb_worker_data_t* worker_data, const std::vector& root_lower, const std::vector& root_upper, @@ -677,11 +677,11 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* const f_t upper_bound = get_upper_bound(); // If there is no incumbent, use pseudocost diving instead of guided diving - if (upper_bound == inf && thread_type == bnb_thread_type_t::GUIDED_DIVING) { + if (upper_bound == inf && thread_type == bnb_task_type_t::GUIDED_DIVING) { if (settings_.diving_settings.disable_pseudocost_diving) { thread_type = bnb_thread_type_t::COEFFICIENT_DIVING; } else { - thread_type = bnb_thread_type_t::PSEUDOCOST_DIVING; + thread_type = bnb_task_type_t::PSEUDOCOST_DIVING; } } @@ -697,9 +697,10 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); lp_settings.scale_columns = false; - if (thread_type != bnb_thread_type_t::EXPLORATION) { + if (thread_type != bnb_task_type_t::EXPLORATION) { i_t bnb_lp_iters = exploration_stats_.total_lp_iters; - f_t max_iter = settings_.diving_settings.iteration_limit_factor * bnb_lp_iters; + f_t factor = settings_.bnb_task_settings[thread_type].iteration_limit_factor; + f_t max_iter = factor * bnb_lp_iters; lp_settings.iteration_limit = max_iter - stats.total_lp_iters; if (lp_settings.iteration_limit <= 0) { return node_solve_info_t::ITERATION_LIMIT; } } @@ -799,7 +800,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); pc_.update_pseudo_costs(node_ptr, leaf_objective); - if (thread_type == bnb_thread_type_t::EXPLORATION) { + if (thread_type == bnb_task_type_t::EXPLORATION) { if (settings_.node_processed_callback != nullptr) { std::vector original_x; uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); @@ -844,7 +845,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* return node_solve_info_t::ITERATION_LIMIT; } else { - if (thread_type == bnb_thread_type_t::EXPLORATION) { + if (thread_type == bnb_task_type_t::EXPLORATION) { fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); log.printf( "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " @@ -916,7 +917,7 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod node_solve_info_t status = solve_node(node, search_tree_, - bnb_thread_type_t::EXPLORATION, + bnb_task_type_t::EXPLORATION, worker_data, original_lp_.lower, original_lp_.upper, @@ -1019,7 +1020,7 @@ void branch_and_bound_t::plunge_from(i_t task_id, mip_node_t node_solve_info_t status = solve_node(node_ptr, search_tree_, - bnb_thread_type_t::EXPLORATION, + bnb_task_type_t::EXPLORATION, worker_data, original_lp_.lower, original_lp_.upper, @@ -1113,11 +1114,14 @@ template void branch_and_bound_t::dive_from(mip_node_t& start_node, const std::vector& start_lower, const std::vector& start_upper, - bnb_thread_type_t diving_type) + bnb_task_type_t diving_type) { logger_t log; log.log = false; + const i_t node_limit = settings_.bnb_task_settings[diving_type].node_limit; + const i_t backtrack = settings_.bnb_task_settings[diving_type].backtrack; + i_t tid = omp_get_thread_num(); bnb_worker_data_t* worker_data = get_worker_data(tid); assert(worker_data); @@ -1148,7 +1152,7 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, } if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } - if (dive_stats.nodes_explored > settings_.diving_settings.node_limit) { break; } + if (dive_stats.nodes_explored > node_limit) { break; } node_solve_info_t status = solve_node( node_ptr, subtree, diving_type, worker_data, start_lower, start_upper, dive_stats, log); @@ -1173,15 +1177,14 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, } } - if (stack.size() > 1 && - stack.front()->depth - stack.back()->depth > settings_.diving_settings.backtrack) { + if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > backtrack) { stack.pop_back(); } } } template -void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type) +void branch_and_bound_t::diving_thread(bnb_task_type_t diving_type) { i_t tid = omp_get_thread_num(); bnb_worker_data_t* worker_data = get_worker_data(tid); @@ -1302,29 +1305,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut exploration_stats_.nodes_explored = 0; original_lp_.A.to_compressed_row(Arow_); - std::vector diving_strategies; - diving_strategies.reserve(4); - - if (!settings_.diving_settings.disable_pseudocost_diving) { - diving_strategies.push_back(bnb_thread_type_t::PSEUDOCOST_DIVING); - } - - if (!settings_.diving_settings.disable_line_search_diving) { - diving_strategies.push_back(bnb_thread_type_t::LINE_SEARCH_DIVING); - } - - if (!settings_.diving_settings.disable_guided_diving) { - diving_strategies.push_back(bnb_thread_type_t::GUIDED_DIVING); - } - - if (!settings_.diving_settings.disable_coefficient_diving) { - diving_strategies.push_back(bnb_thread_type_t::COEFFICIENT_DIVING); - } - - if (diving_strategies.empty()) { - settings_.log.printf("Warning: All diving heuristics are disabled!"); - } - if (guess_.size() != 0) { std::vector crushed_guess; crush_primal_solution(original_problem_, original_lp_, guess_, new_slacks_, crushed_guess); @@ -1393,7 +1373,9 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut set_uninitialized_steepest_edge_norms(edge_norms_); root_objective_ = compute_objective(original_lp_, root_relax_soln_.x); - local_lower_bounds_.assign(settings_.num_bfs_threads, root_objective_); + + i_t num_bfs_threads = settings_.bnb_task_settings[EXPLORATION].num_tasks; + local_lower_bounds_.assign(num_bfs_threads, root_objective_); if (settings_.set_simplex_solution_callback != nullptr) { std::vector original_x; @@ -1472,8 +1454,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", settings_.num_threads, - settings_.num_bfs_threads, - settings_.diving_settings.num_diving_tasks); + num_bfs_threads, + settings_.num_threads - num_bfs_threads); settings_.log.printf( " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " @@ -1492,10 +1474,9 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut { #pragma omp master { - auto down_child = search_tree_.root.get_down_child(); - auto up_child = search_tree_.root.get_up_child(); - i_t initial_size = 2 * settings_.num_threads; - const i_t num_strategies = diving_strategies.size(); + auto down_child = search_tree_.root.get_down_child(); + auto up_child = search_tree_.root.get_up_child(); + i_t initial_size = 2 * settings_.num_threads; #pragma omp taskgroup { @@ -1506,16 +1487,17 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut exploration_ramp_up(up_child, initial_size); } - for (i_t i = 0; i < settings_.num_bfs_threads; i++) { + for (i_t i = 0; i < num_bfs_threads; i++) { #pragma omp task best_first_thread(i); } - if (!diving_strategies.empty()) { - for (i_t k = 0; k < settings_.diving_settings.num_diving_tasks; k++) { - const bnb_thread_type_t diving_type = diving_strategies[k % num_strategies]; + for (auto& settings : settings_.bnb_task_settings) { + if (settings.type != EXPLORATION && settings.is_enabled) { + for (i_t k = 0; k < settings.num_tasks; k++) { #pragma omp task - diving_thread(diving_type); + diving_thread(settings.type); + } } } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index baa7107be..e6e225b73 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -53,19 +53,6 @@ enum class node_solve_info_t { NUMERICAL = 5 // The solver encounter a numerical error when solving the node }; -// Indicate the search and variable selection algorithms used by each thread -// in B&B (See [1]). -// -// [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, -// Berlin, 2007. doi: 10.14279/depositonce-1634. -enum class bnb_thread_type_t { - EXPLORATION = 0, // Best-First + Plunging. - PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) - LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) - GUIDED_DIVING = 3, // Guided diving (9.2.3). If no incumbent is found yet, use pseudocost diving. - COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) -}; - template class bounds_strengthening_t; @@ -240,7 +227,7 @@ class branch_and_bound_t { void add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - bnb_thread_type_t thread_type); + bnb_task_type_t thread_type); // Repairs low-quality solutions from the heuristics, if it is applicable. void repair_heuristic_solutions(); @@ -259,7 +246,7 @@ class branch_and_bound_t { void dive_from(mip_node_t& start_node, const std::vector& start_lower, const std::vector& start_upper, - bnb_thread_type_t diving_type); + bnb_task_type_t diving_type); // Each diving thread pops the first node from the dive queue and then performs // a deep dive into the subtree determined by the node. @@ -275,7 +262,7 @@ class branch_and_bound_t { // Solve the LP relaxation of a leaf node and update the tree. node_solve_info_t solve_node(mip_node_t* node_ptr, search_tree_t& search_tree, - bnb_thread_type_t thread_type, + bnb_task_type_t thread_type, bnb_worker_data_t* worker_data, const std::vector& root_lower, const std::vector& root_upper, @@ -286,7 +273,7 @@ class branch_and_bound_t { branch_variable_t variable_selection(mip_node_t* node_ptr, const std::vector& fractional, const std::vector& solution, - bnb_thread_type_t type, + bnb_task_type_t type, logger_t& log); }; diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 978a97e42..94464d3cf 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -9,6 +9,18 @@ namespace cuopt::linear_programming::dual_simplex { +template +bnb_task_settings_t get_default_diving_settings(bnb_task_type_t type) +{ + return bnb_task_settings_t{.type = type, + .is_enabled = true, + .num_tasks = -1, + .min_node_depth = 0, + .node_limit = 500, + .iteration_limit_factor = 0.05, + .backtrack = 5}; +} + template branch_variable_t line_search_diving(const std::vector& fractional, const std::vector& solution, @@ -273,6 +285,9 @@ branch_variable_t coefficient_diving(const lp_problem_t& lp_probl } #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE + +template bnb_task_settings_t get_default_diving_settings(bnb_task_type_t type); + template branch_variable_t line_search_diving(const std::vector& fractional, const std::vector& solution, const std::vector& root_solution, diff --git a/cpp/src/dual_simplex/diving_heuristics.hpp b/cpp/src/dual_simplex/diving_heuristics.hpp index c7b1e2050..422db1a68 100644 --- a/cpp/src/dual_simplex/diving_heuristics.hpp +++ b/cpp/src/dual_simplex/diving_heuristics.hpp @@ -20,6 +20,9 @@ struct branch_variable_t { rounding_direction_t direction; }; +template +bnb_task_settings_t get_default_diving_settings(bnb_task_type_t type); + template branch_variable_t line_search_diving(const std::vector& fractional, const std::vector& solution, diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 60b92ee33..e791c5da1 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -13,26 +13,58 @@ #include #include #include +#include #include #include #include namespace cuopt::linear_programming::dual_simplex { +// Indicate the search and variable selection algorithms used by each task +// in B&B (See [1]). +// +// [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, +// Berlin, 2007. doi: 10.14279/depositonce-1634. +enum bnb_task_type_t { + EXPLORATION = 0, // Best-First + Plunging. + PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) + LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) + GUIDED_DIVING = 3, // Guided diving (9.2.3). If no incumbent is found yet, use pseudocost diving. + COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) +}; + +// Settings for each task type in B&B. template -struct diving_heuristics_settings_t { - i_t num_diving_tasks = -1; +struct bnb_task_settings_t { + // Type of the task. + bnb_task_type_t type; + + // Is this type of task enabled? + // This will be ignored if `type == EXPLORATION`. + bool is_enabled; + + // Number of tasks of this type. + i_t num_tasks; + + // Minimum node depth to start this task + // This will be ignored if `type == EXPLORATION`. + i_t min_node_depth; + + // Maximum number of nodes explored in this task. + i_t node_limit; - bool disable_line_search_diving = false; - bool disable_pseudocost_diving = false; - bool disable_guided_diving = false; - bool disable_coefficient_diving = false; + // Maximum fraction of the number of simplex iterations for this task + // compared to the number of simplex iterations for normal exploration. + f_t iteration_limit_factor; - i_t node_limit = 500; - f_t iteration_limit_factor = 0.05; - i_t backtrack = 5; + // Number of nodes that it allows to backtrack when + // reaching the bottom of a given branch of the tree. + i_t backtrack; }; +template +bnb_task_settings_t get_default_diving_settings(bnb_task_type_t type); + template struct simplex_solver_settings_t { public: @@ -83,21 +115,65 @@ struct simplex_solver_settings_t { refactor_frequency(100), iteration_log_frequency(1000), first_iteration_log(2), - num_threads(omp_get_max_threads() - 1), - num_bfs_threads(std::min(num_threads / 4, 1)), random_seed(0), inside_mip(0), solution_callback(nullptr), heuristic_preemption_callback(nullptr), concurrent_halt(nullptr) { - diving_settings.num_diving_tasks = std::max(num_threads - num_bfs_threads, 1); + bnb_task_settings[EXPLORATION] = + bnb_task_settings_t{.type = EXPLORATION, + .is_enabled = true, + .num_tasks = -1, + .min_node_depth = 0, + .node_limit = std::numeric_limits::max(), + .iteration_limit_factor = std::numeric_limits::max(), + .backtrack = 1}; + + bnb_task_settings[PSEUDOCOST_DIVING] = get_default_diving_settings(PSEUDOCOST_DIVING); + + bnb_task_settings[LINE_SEARCH_DIVING] = + get_default_diving_settings(LINE_SEARCH_DIVING); + + bnb_task_settings[GUIDED_DIVING] = get_default_diving_settings(GUIDED_DIVING); + + bnb_task_settings[COEFFICIENT_DIVING] = + get_default_diving_settings(COEFFICIENT_DIVING); + + set_bnb_tasks(omp_get_max_threads() - 1); + } + + void set_bnb_tasks(i_t num_threads) + { + this->num_threads = num_threads; + bnb_task_settings[EXPLORATION].num_tasks = std::max(1, num_threads / 4); + + i_t diving_tasks = num_threads - bnb_task_settings[EXPLORATION].num_tasks; + i_t num_enabled = 0; + + for (size_t i = 1; i < bnb_task_settings.size(); ++i) { + num_enabled += static_cast(bnb_task_settings[i].is_enabled); + } + + for (size_t i = 1, k = 0; i < bnb_task_settings.size(); ++i) { + i_t start = (double)k * diving_tasks / num_enabled; + i_t end = (double)(k + 1) * diving_tasks / num_enabled; + + if (bnb_task_settings[i].is_enabled) { + bnb_task_settings[i].num_tasks = end - start; + ++k; + + } else { + bnb_task_settings[i].num_tasks = 0; + } + } } void set_log(bool logging) const { log.log = logging; } void enable_log_to_file() { log.enable_log_to_file(); } void set_log_filename(const std::string& log_filename) { log.set_log_file(log_filename); } void close_log_file() { log.close_log_file(); } + i_t iteration_limit; i_t node_limit; f_t time_limit; @@ -151,9 +227,10 @@ struct simplex_solver_settings_t { i_t first_iteration_log; // number of iterations to log at beginning of solve i_t num_threads; // number of threads to use i_t random_seed; // random seed - i_t num_bfs_threads; // number of threads dedicated to the best-first search - diving_heuristics_settings_t diving_settings; // Settings for the diving heuristics + // Indicate the settings used by each task + // The position in the array is indicated by the `bnb_task_type_t`. + std::array, 5> bnb_task_settings; i_t inside_mip; // 0 if outside MIP, 1 if inside MIP at root node, 2 if inside MIP at leaf node std::function&, f_t)> solution_callback; diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 1893e84fe..4442029e8 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -256,21 +256,14 @@ void rins_t::run_rins() branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = std::min(current_mip_gap, (f_t)settings.target_mip_gap); - branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; - branch_and_bound_settings.num_threads = 2; - branch_and_bound_settings.num_bfs_threads = 1; + branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; // In the future, let RINS use all the diving heuristics. For now, // restricting to guided diving. - branch_and_bound_settings.diving_settings.num_diving_tasks = 1; - branch_and_bound_settings.diving_settings.disable_line_search_diving = true; - branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; - - if (context.settings.disable_guided_diving) { - branch_and_bound_settings.diving_settings.disable_guided_diving = true; - } else { - branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; - } + branch_and_bound_settings.bnb_task_settings[dual_simplex::PSEUDOCOST_DIVING].is_enabled = false; + branch_and_bound_settings.bnb_task_settings[dual_simplex::LINE_SEARCH_DIVING].is_enabled = false; + branch_and_bound_settings.bnb_task_settings[dual_simplex::COEFFICIENT_DIVING].is_enabled = false; + branch_and_bound_settings.set_bnb_tasks(2); branch_and_bound_settings.log.log = false; branch_and_bound_settings.log.log_prefix = "[RINS] "; diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index 2335003b6..8501b70cc 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -101,21 +101,17 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.print_presolve_stats = false; branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; - branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; - branch_and_bound_settings.num_threads = 2; - branch_and_bound_settings.num_bfs_threads = 1; + branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; // In the future, let SubMIP use all the diving heuristics. For now, // restricting to guided diving. - branch_and_bound_settings.diving_settings.num_diving_tasks = 1; - branch_and_bound_settings.diving_settings.disable_line_search_diving = true; - branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; - - if (context.settings.disable_guided_diving) { - branch_and_bound_settings.diving_settings.disable_guided_diving = true; - } else { - branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; - } + branch_and_bound_settings.bnb_task_settings[dual_simplex::PSEUDOCOST_DIVING].is_enabled = + false; + branch_and_bound_settings.bnb_task_settings[dual_simplex::LINE_SEARCH_DIVING].is_enabled = + false; + branch_and_bound_settings.bnb_task_settings[dual_simplex::COEFFICIENT_DIVING].is_enabled = + false; + branch_and_bound_settings.set_bnb_tasks(2); branch_and_bound_settings.solution_callback = [this](std::vector& solution, f_t objective) { diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 790831129..a77ede1a2 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -176,17 +176,10 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.diving_settings.disable_line_search_diving = context.settings.disable_line_search_diving; - if (context.settings.num_cpu_threads < 0) { - branch_and_bound_settings.num_threads = omp_get_max_threads() - 1; - } else { - branch_and_bound_settings.num_threads = std::max(1, context.settings.num_cpu_threads); - } - - i_t num_threads = branch_and_bound_settings.num_threads; - i_t num_bfs_threads = std::max(1, num_threads / 4); - i_t num_diving_threads = std::max(1, num_threads - num_bfs_threads); - branch_and_bound_settings.num_bfs_threads = num_bfs_threads; - branch_and_bound_settings.diving_settings.num_diving_tasks = num_diving_threads; + i_t num_threads = context.settings.num_cpu_threads < 0 + ? omp_get_max_threads() - 1 + : std::max(1, context.settings.num_cpu_threads); + branch_and_bound_settings.set_bnb_tasks(num_threads); // Set the branch and bound -> primal heuristics callback branch_and_bound_settings.solution_callback = From 308237e35d30b0ab1e6336852c4f60799698e0e0 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 19 Dec 2025 17:04:37 +0100 Subject: [PATCH 144/366] first version of the new parallel b&b --- cpp/src/dual_simplex/CMakeLists.txt | 1 + cpp/src/dual_simplex/bnb_worker.cpp | 81 +++ cpp/src/dual_simplex/bnb_worker.hpp | 90 +++ cpp/src/dual_simplex/branch_and_bound.cpp | 661 +++++++++--------- cpp/src/dual_simplex/branch_and_bound.hpp | 95 +-- cpp/src/dual_simplex/diving_heuristics.cpp | 18 +- cpp/src/dual_simplex/diving_heuristics.hpp | 2 +- cpp/src/dual_simplex/node_queue.hpp | 32 +- .../dual_simplex/simplex_solver_settings.hpp | 77 +- cpp/src/mip/diversity/lns/rins.cu | 8 +- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 6 +- 11 files changed, 574 insertions(+), 497 deletions(-) create mode 100644 cpp/src/dual_simplex/bnb_worker.cpp create mode 100644 cpp/src/dual_simplex/bnb_worker.hpp diff --git a/cpp/src/dual_simplex/CMakeLists.txt b/cpp/src/dual_simplex/CMakeLists.txt index ebaf9cbb7..db1fa94dc 100644 --- a/cpp/src/dual_simplex/CMakeLists.txt +++ b/cpp/src/dual_simplex/CMakeLists.txt @@ -10,6 +10,7 @@ set(DUAL_SIMPLEX_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/basis_updates.cpp ${CMAKE_CURRENT_SOURCE_DIR}/bound_flipping_ratio_test.cpp ${CMAKE_CURRENT_SOURCE_DIR}/branch_and_bound.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/bnb_worker.cpp ${CMAKE_CURRENT_SOURCE_DIR}/crossover.cpp ${CMAKE_CURRENT_SOURCE_DIR}/folding.cpp ${CMAKE_CURRENT_SOURCE_DIR}/initial_basis.cpp diff --git a/cpp/src/dual_simplex/bnb_worker.cpp b/cpp/src/dual_simplex/bnb_worker.cpp new file mode 100644 index 000000000..b4a1d8583 --- /dev/null +++ b/cpp/src/dual_simplex/bnb_worker.cpp @@ -0,0 +1,81 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#include +#include +#include +#include + +namespace cuopt::linear_programming::dual_simplex { + +template +bnb_worker_t::bnb_worker_t(i_t worker_id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_type, + const simplex_solver_settings_t& settings) + : worker_id(worker_id), + worker_type(EXPLORATION), + is_active(false), + lower_bound(-std::numeric_limits::infinity()), + leaf_problem(original_lp), + basis_factors(original_lp.num_rows, settings.refactor_frequency), + basic_list(original_lp.num_rows), + nonbasic_list(), + node_presolver(leaf_problem, Arow, {}, var_type), + bounds_changed(original_lp.num_cols, false) +{ +} + +template +bool bnb_worker_t::init_diving(mip_node_t* node, + bnb_worker_type_t type, + const lp_problem_t& original_lp, + const simplex_solver_settings_t& settings) +{ + internal_node = node->detach_copy(); + start_node = &internal_node; + + start_lower = original_lp.lower; + start_upper = original_lp.upper; + worker_type = type; + lower_bound = node->lower_bound; + is_active = true; + + std::fill(bounds_changed.begin(), bounds_changed.end(), false); + node->get_variable_bounds(start_lower, start_upper, bounds_changed); + + return node_presolver.bounds_strengthening(start_lower, start_upper, bounds_changed, settings); +} + +template +bool bnb_worker_t::set_lp_variable_bounds_for( + mip_node_t* node_ptr, const simplex_solver_settings_t& settings) +{ + // Reset the bound_changed markers + std::fill(bounds_changed.begin(), bounds_changed.end(), false); + + // Set the correct bounds for the leaf problem + if (recompute_bounds) { + leaf_problem.lower = start_lower; + leaf_problem.upper = start_upper; + node_ptr->get_variable_bounds(leaf_problem.lower, leaf_problem.upper, bounds_changed); + + } else { + node_ptr->update_branched_variable_bounds( + leaf_problem.lower, leaf_problem.upper, bounds_changed); + } + + return node_presolver.bounds_strengthening( + leaf_problem.lower, leaf_problem.upper, bounds_changed, settings); +} + +#ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE +template class bnb_worker_t; +#endif + +} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp new file mode 100644 index 000000000..34eeb38c8 --- /dev/null +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -0,0 +1,90 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#include +#include +#include +#include + +#include + +namespace cuopt::linear_programming::dual_simplex { + +template +struct bnb_stats_t { + f_t start_time = 0.0; + omp_atomic_t total_lp_solve_time = 0.0; + omp_atomic_t nodes_explored = 0; + omp_atomic_t nodes_unexplored = 0; + omp_atomic_t total_lp_iters = 0; +}; + +template +class bnb_worker_t { + public: + const i_t worker_id; + omp_atomic_t worker_type; + omp_atomic_t is_active; + omp_atomic_t lower_bound; + + lp_problem_t leaf_problem; + + basis_update_mpf_t basis_factors; + std::vector basic_list; + std::vector nonbasic_list; + + bounds_strengthening_t node_presolver; + std::vector bounds_changed; + + std::vector start_lower; + std::vector start_upper; + mip_node_t* start_node; + + bool recompute_basis = true; + bool recompute_bounds = true; + + bnb_worker_t(i_t worker_id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_type, + const simplex_solver_settings_t& settings); + + // Set the `start_node` for best-first search. + void init_best_first(mip_node_t* node, const lp_problem_t& original_lp) + { + start_node = node; + start_lower = original_lp.lower; + start_upper = original_lp.upper; + worker_type = EXPLORATION; + lower_bound = node->lower_bound; + is_active = true; + } + + // Initialize the worker for diving, setting the `start_node`, `start_lower` and + // `start_upper`. Returns `true` if the starting node is feasible via + // bounds propagation. + bool init_diving(mip_node_t* node, + bnb_worker_type_t type, + const lp_problem_t& original_lp, + const simplex_solver_settings_t& settings); + + // Set the variables bounds for the LP relaxation of the current node. + bool set_lp_variable_bounds_for(mip_node_t* node_ptr, + const simplex_solver_settings_t& settings); + + private: + // For diving, we need to store the full node instead of + // of just a pointer, since it is detached from the + // tree. To keep the same interface for any type of worker, + // the start node will point to this node when diving. + // For best-first search, this will not be used. + mip_node_t internal_node; +}; + +} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index ce29a86cb..a2966fb26 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -192,14 +192,14 @@ std::string user_mip_gap(f_t obj_value, f_t lower_bound) } } -inline const char* feasible_solution_symbol(bnb_task_type_t type) +inline const char* feasible_solution_symbol(bnb_worker_type_t type) { switch (type) { - case bnb_task_type_t::EXPLORATION: return "B "; - case bnb_task_type_t::COEFFICIENT_DIVING: return "CD"; - case bnb_task_type_t::LINE_SEARCH_DIVING: return "LD"; - case bnb_task_type_t::PSEUDOCOST_DIVING: return "PD"; - case bnb_task_type_t::GUIDED_DIVING: return "GD"; + case bnb_worker_type_t::EXPLORATION: return "B "; + case bnb_worker_type_t::COEFFICIENT_DIVING: return "CD"; + case bnb_worker_type_t::LINE_SEARCH_DIVING: return "LD"; + case bnb_worker_type_t::PSEUDOCOST_DIVING: return "PD"; + case bnb_worker_type_t::GUIDED_DIVING: return "GD"; default: return "U "; } } @@ -252,8 +252,10 @@ f_t branch_and_bound_t::get_lower_bound() f_t heap_lower_bound = node_queue.get_lower_bound(); lower_bound = std::min(heap_lower_bound, lower_bound); - for (i_t i = 0; i < local_lower_bounds_.size(); ++i) { - lower_bound = std::min(local_lower_bounds_[i].load(), lower_bound); + for (i_t i = 0; i < workers_.size(); ++i) { + if (workers_[i]->worker_type == EXPLORATION && workers_[i]->is_active) { + lower_bound = std::min(workers_[i]->lower_bound.load(), lower_bound); + } } return std::isfinite(lower_bound) ? lower_bound : -inf; @@ -304,17 +306,6 @@ void branch_and_bound_t::report(std::string symbol, toc(exploration_stats_.start_time)); } -template -bnb_worker_data_t* branch_and_bound_t::get_worker_data(i_t tid) -{ - if (!worker_data_pool_[tid].get()) { - worker_data_pool_[tid] = - std::make_unique>(original_lp_, Arow_, var_types_, settings_); - } - - return worker_data_pool_[tid].get(); -} - template void branch_and_bound_t::set_new_solution(const std::vector& solution) { @@ -544,7 +535,7 @@ template void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - bnb_task_type_t thread_type) + bnb_worker_type_t thread_type) { bool send_solution = false; @@ -594,7 +585,7 @@ branch_variable_t branch_and_bound_t::variable_selection( mip_node_t* node_ptr, const std::vector& fractional, const std::vector& solution, - bnb_task_type_t type, + bnb_worker_type_t type, logger_t& log) { i_t branch_var = -1; @@ -602,7 +593,7 @@ branch_variable_t branch_and_bound_t::variable_selection( rounding_direction_t round_dir = rounding_direction_t::NONE; switch (type) { - case bnb_task_type_t::EXPLORATION: + case bnb_worker_type_t::EXPLORATION: std::tie(branch_var, obj_estimate) = pc_.variable_selection_and_obj_estimate(fractional, solution, node_ptr->lower_bound, log); round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); @@ -613,16 +604,16 @@ branch_variable_t branch_and_bound_t::variable_selection( node_ptr->objective_estimate = obj_estimate; return {branch_var, round_dir}; - case bnb_task_type_t::COEFFICIENT_DIVING: + case bnb_worker_type_t::COEFFICIENT_DIVING: return coefficient_diving(original_lp_, fractional, solution, log); - case bnb_task_type_t::LINE_SEARCH_DIVING: + case bnb_worker_type_t::LINE_SEARCH_DIVING: return line_search_diving(fractional, solution, root_relax_soln_.x, log); - case bnb_task_type_t::PSEUDOCOST_DIVING: + case bnb_worker_type_t::PSEUDOCOST_DIVING: return pseudocost_diving(pc_, fractional, solution, root_relax_soln_.x, log); - case bnb_task_type_t::GUIDED_DIVING: + case bnb_worker_type_t::GUIDED_DIVING: return guided_diving(pc_, fractional, solution, incumbent_.x, log); default: @@ -631,45 +622,11 @@ branch_variable_t branch_and_bound_t::variable_selection( } } -template -bool branch_and_bound_t::set_node_bounds(mip_node_t* node_ptr, - const std::vector& start_lower, - const std::vector& start_upper, - bnb_worker_data_t* worker_data) -{ - bounds_strengthening_t& node_presolver = worker_data->node_presolver; - std::vector& upper = worker_data->leaf_problem.upper; - std::vector& lower = worker_data->leaf_problem.lower; - std::vector& bounds_changed = worker_data->bounds_changed; - - assert(bounds_changed.size() == original_lp_.num_cols); - assert(lower.size() == original_lp_.num_cols); - assert(upper.size() == original_lp_.num_cols); - - // Reset the bound_changed markers - std::fill(bounds_changed.begin(), bounds_changed.end(), false); - - // Set the correct bounds for the leaf problem - if (worker_data->recompute_bounds) { - lower = start_lower; - upper = start_upper; - node_ptr->get_variable_bounds(lower, upper, bounds_changed); - - } else { - node_ptr->update_branched_variable_bounds(lower, upper, bounds_changed); - } - - bool feasible = node_presolver.bounds_strengthening(lower, upper, bounds_changed, settings_); - return feasible; -} - template node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* node_ptr, search_tree_t& search_tree, - bnb_task_type_t thread_type, - bnb_worker_data_t* worker_data, - const std::vector& root_lower, - const std::vector& root_upper, + bnb_worker_type_t thread_type, + bnb_worker_t* worker, bnb_stats_t& stats, logger_t& log) { @@ -677,15 +634,15 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* const f_t upper_bound = get_upper_bound(); // If there is no incumbent, use pseudocost diving instead of guided diving - if (upper_bound == inf && thread_type == bnb_task_type_t::GUIDED_DIVING) { + if (upper_bound == inf && thread_type == bnb_worker_type_t::GUIDED_DIVING) { if (settings_.diving_settings.disable_pseudocost_diving) { thread_type = bnb_thread_type_t::COEFFICIENT_DIVING; } else { - thread_type = bnb_task_type_t::PSEUDOCOST_DIVING; + thread_type = bnb_worker_type_t::PSEUDOCOST_DIVING; } } - lp_problem_t& leaf_problem = worker_data->leaf_problem; + lp_problem_t& leaf_problem = worker->leaf_problem; lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); std::vector& leaf_vstatus = node_ptr->vstatus; assert(leaf_vstatus.size() == leaf_problem.num_cols); @@ -697,9 +654,9 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); lp_settings.scale_columns = false; - if (thread_type != bnb_task_type_t::EXPLORATION) { + if (thread_type != bnb_worker_type_t::EXPLORATION) { i_t bnb_lp_iters = exploration_stats_.total_lp_iters; - f_t factor = settings_.bnb_task_settings[thread_type].iteration_limit_factor; + f_t factor = settings_.bnb_worker_settings[thread_type].iteration_limit_factor; f_t max_iter = factor * bnb_lp_iters; lp_settings.iteration_limit = max_iter - stats.total_lp_iters; if (lp_settings.iteration_limit <= 0) { return node_solve_info_t::ITERATION_LIMIT; } @@ -727,8 +684,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* node_ptr->vstatus[node_ptr->branch_var]); #endif - bool is_feasible = set_node_bounds(node_ptr, root_lower, root_upper, worker_data); - + bool is_feasible = worker->set_lp_variable_bounds_for(node_ptr, settings_); dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; if (is_feasible) { @@ -738,30 +694,29 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* lp_status = dual_phase2_with_advanced_basis(2, 0, - worker_data->recompute_basis, + worker->recompute_basis, lp_start_time, leaf_problem, lp_settings, leaf_vstatus, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, + worker->basis_factors, + worker->basic_list, + worker->nonbasic_list, leaf_solution, node_iter, leaf_edge_norms); if (lp_status == dual::status_t::NUMERICAL) { log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); - lp_status_t second_status = - solve_linear_program_with_advanced_basis(leaf_problem, - lp_start_time, - lp_settings, - leaf_solution, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, - leaf_vstatus, - leaf_edge_norms); + lp_status_t second_status = solve_linear_program_with_advanced_basis(leaf_problem, + lp_start_time, + lp_settings, + leaf_solution, + worker->basis_factors, + worker->basic_list, + worker->nonbasic_list, + leaf_vstatus, + leaf_edge_norms); lp_status = convert_lp_status_to_dual_status(second_status); } @@ -800,7 +755,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); pc_.update_pseudo_costs(node_ptr, leaf_objective); - if (thread_type == bnb_task_type_t::EXPLORATION) { + if (thread_type == bnb_worker_type_t::EXPLORATION) { if (settings_.node_processed_callback != nullptr) { std::vector original_x; uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); @@ -845,7 +800,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* return node_solve_info_t::ITERATION_LIMIT; } else { - if (thread_type == bnb_task_type_t::EXPLORATION) { + if (thread_type == bnb_worker_type_t::EXPLORATION) { fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); log.printf( "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " @@ -862,117 +817,110 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* } } -template -void branch_and_bound_t::exploration_ramp_up(mip_node_t* node, - i_t initial_heap_size) -{ - if (solver_status_ != mip_exploration_status_t::RUNNING) { return; } - - // Note that we do not know which thread will execute the - // `exploration_ramp_up` task, so we allow to any thread - // to repair the heuristic solution. - repair_heuristic_solutions(); - - i_t tid = omp_get_thread_num(); - bnb_worker_data_t* worker_data = get_worker_data(tid); - assert(worker_data); - - f_t lower_bound = node->lower_bound; - f_t upper_bound = get_upper_bound(); - f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); - f_t abs_gap = upper_bound - lower_bound; - - if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - search_tree_.graphviz_node(settings_.log, node, "cutoff", node->lower_bound); - search_tree_.update(node, node_status_t::FATHOMED); - --exploration_stats_.nodes_unexplored; - return; - } - - f_t now = toc(exploration_stats_.start_time); - f_t time_since_last_log = - exploration_stats_.last_log == 0 ? 1.0 : toc(exploration_stats_.last_log); - - if (((exploration_stats_.nodes_since_last_log >= 10 || - abs_gap < 10 * settings_.absolute_mip_gap_tol) && - (time_since_last_log >= 1)) || - (time_since_last_log > 30) || now > settings_.time_limit) { - bool should_report = should_report_.exchange(false); - - if (should_report) { - report(" ", upper_bound, root_objective_, node->depth); - exploration_stats_.nodes_since_last_log = 0; - exploration_stats_.last_log = tic(); - should_report_ = true; - } - } - - if (now > settings_.time_limit) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; - return; - } - - worker_data->recompute_basis = true; - worker_data->recompute_bounds = true; - - node_solve_info_t status = solve_node(node, - search_tree_, - bnb_task_type_t::EXPLORATION, - worker_data, - original_lp_.lower, - original_lp_.upper, - exploration_stats_, - settings_.log); - - ++exploration_stats_.nodes_since_last_log; - ++exploration_stats_.nodes_explored; - --exploration_stats_.nodes_unexplored; - - if (status == node_solve_info_t::TIME_LIMIT) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; - return; - - } else if (has_children(status)) { - exploration_stats_.nodes_unexplored += 2; - - // If we haven't generated enough nodes to keep the threads busy, continue the ramp up phase - if (exploration_stats_.nodes_unexplored < initial_heap_size) { -#pragma omp task - exploration_ramp_up(node->get_down_child(), initial_heap_size); - -#pragma omp task - exploration_ramp_up(node->get_up_child(), initial_heap_size); - - } else { - // We've generated enough nodes, push further nodes onto the heap - node_queue.push(node->get_down_child()); - node_queue.push(node->get_up_child()); - } - } -} +// template +// void branch_and_bound_t::exploration_ramp_up(mip_node_t* node, +// i_t initial_heap_size) +// { +// if (solver_status_ != mip_exploration_status_t::RUNNING) { return; } + +// // Note that we do not know which thread will execute the +// // `exploration_ramp_up` task, so we allow to any thread +// // to repair the heuristic solution. +// repair_heuristic_solutions(); + +// i_t tid = omp_get_thread_num(); +// bnb_worker_data_t* worker_data = get_worker_data(tid); +// assert(worker_data); + +// f_t lower_bound = node->lower_bound; +// f_t upper_bound = get_upper_bound(); +// f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); +// f_t abs_gap = upper_bound - lower_bound; + +// if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { +// search_tree_.graphviz_node(settings_.log, node, "cutoff", node->lower_bound); +// search_tree_.update(node, node_status_t::FATHOMED); +// --exploration_stats_.nodes_unexplored; +// return; +// } + +// f_t now = toc(exploration_stats_.start_time); +// f_t time_since_last_log = +// exploration_stats_.last_log == 0 ? 1.0 : toc(exploration_stats_.last_log); + +// if (((exploration_stats_.nodes_since_last_log >= 10 || +// abs_gap < 10 * settings_.absolute_mip_gap_tol) && +// (time_since_last_log >= 1)) || +// (time_since_last_log > 30) || now > settings_.time_limit) { +// bool should_report = should_report_.exchange(false); + + // if (should_report) { + // report(" ", upper_bound, root_objective_, node->depth); + // exploration_stats_.nodes_since_last_log = 0; + // exploration_stats_.last_log = tic(); + // should_report_ = true; + // } + // } + +// if (now > settings_.time_limit) { +// solver_status_ = mip_exploration_status_t::TIME_LIMIT; +// return; +// } + +// worker_data->recompute_basis = true; +// worker_data->recompute_bounds = true; + +// node_solve_info_t status = solve_node(node, +// search_tree_, +// bnb_worker_type_t::EXPLORATION, +// worker_data, +// original_lp_.lower, +// original_lp_.upper, +// exploration_stats_, +// settings_.log); + +// ++exploration_stats_.nodes_since_last_log; +// ++exploration_stats_.nodes_explored; +// --exploration_stats_.nodes_unexplored; + +// if (status == node_solve_info_t::TIME_LIMIT) { +// solver_status_ = mip_exploration_status_t::TIME_LIMIT; +// return; + +// } else if (has_children(status)) { +// exploration_stats_.nodes_unexplored += 2; + +// // If we haven't generated enough nodes to keep the threads busy, continue the ramp up phase +// if (exploration_stats_.nodes_unexplored < initial_heap_size) { +// #pragma omp task +// exploration_ramp_up(node->get_down_child(), initial_heap_size); + +// #pragma omp task +// exploration_ramp_up(node->get_up_child(), initial_heap_size); + +// } else { +// // We've generated enough nodes, push further nodes onto the heap +// node_queue.push(node->get_down_child()); +// node_queue.push(node->get_up_child()); +// } +// } +// } template -void branch_and_bound_t::plunge_from(i_t task_id, mip_node_t* start_node) +void branch_and_bound_t::plunge_with(bnb_worker_t* worker) { - i_t tid = omp_get_thread_num(); - bnb_worker_data_t* worker_data = get_worker_data(tid); - assert(worker_data); - std::deque*> stack; - stack.push_front(start_node); + stack.push_front(worker->start_node); - worker_data->recompute_basis = true; - worker_data->recompute_bounds = true; + worker->recompute_basis = true; + worker->recompute_bounds = true; while (stack.size() > 0 && solver_status_ == mip_exploration_status_t::RUNNING) { - if (task_id == 0) { repair_heuristic_solutions(); } - mip_node_t* node_ptr = stack.front(); stack.pop_front(); f_t lower_bound = node_ptr->lower_bound; f_t upper_bound = get_upper_bound(); - f_t abs_gap = upper_bound - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); // This is based on three assumptions: @@ -981,62 +929,41 @@ void branch_and_bound_t::plunge_from(i_t task_id, mip_node_t // - The current node and its siblings uses the lower bound of the parent before solving the LP // relaxation // - The lower bound of the parent is lower or equal to its children - assert(task_id < local_lower_bounds_.size()); - local_lower_bounds_[task_id] = lower_bound; + worker->lower_bound = lower_bound; if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { search_tree_.graphviz_node(settings_.log, node_ptr, "cutoff", node_ptr->lower_bound); search_tree_.update(node_ptr, node_status_t::FATHOMED); - worker_data->recompute_basis = true; - worker_data->recompute_bounds = true; + worker->recompute_basis = true; + worker->recompute_bounds = true; --exploration_stats_.nodes_unexplored; continue; } - f_t now = toc(exploration_stats_.start_time); - - if (task_id == 0) { - f_t time_since_last_log = - exploration_stats_.last_log == 0 ? 1.0 : toc(exploration_stats_.last_log); - - if (((exploration_stats_.nodes_since_last_log >= 1000 || - abs_gap < 10 * settings_.absolute_mip_gap_tol) && - time_since_last_log >= 1) || - (time_since_last_log > 30) || now > settings_.time_limit) { - report(" ", upper_bound, get_lower_bound(), node_ptr->depth); - exploration_stats_.last_log = tic(); - exploration_stats_.nodes_since_last_log = 0; - } - } - - if (now > settings_.time_limit) { + if (toc(exploration_stats_.start_time) > settings_.time_limit) { solver_status_ = mip_exploration_status_t::TIME_LIMIT; - return; + break; } if (exploration_stats_.nodes_explored >= settings_.node_limit) { solver_status_ = mip_exploration_status_t::NODE_LIMIT; - return; + break; } node_solve_info_t status = solve_node(node_ptr, search_tree_, - bnb_task_type_t::EXPLORATION, - worker_data, - original_lp_.lower, - original_lp_.upper, + bnb_worker_type_t::EXPLORATION, + worker, exploration_stats_, settings_.log); - worker_data->recompute_basis = !has_children(status); - worker_data->recompute_bounds = !has_children(status); + worker->recompute_basis = !has_children(status); + worker->recompute_bounds = !has_children(status); - ++exploration_stats_.nodes_since_last_log; ++exploration_stats_.nodes_explored; --exploration_stats_.nodes_unexplored; if (status == node_solve_info_t::TIME_LIMIT) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; - return; + break; } else if (has_children(status)) { // The stack should only contain the children of the current parent. @@ -1060,76 +987,28 @@ void branch_and_bound_t::plunge_from(i_t task_id, mip_node_t } } } -} -template -void branch_and_bound_t::best_first_thread(i_t task_id) -{ - f_t lower_bound = -inf; - f_t upper_bound = inf; - f_t abs_gap = inf; - f_t rel_gap = inf; - - while (solver_status_ == mip_exploration_status_t::RUNNING && - abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && - (active_subtrees_ > 0 || node_queue.best_first_queue_size() > 0)) { - // If there any node left in the heap, we pop the top node and explore it. - std::optional*> start_node = node_queue.pop_best_first(active_subtrees_); - - if (start_node.has_value()) { - if (get_upper_bound() < start_node.value()->lower_bound) { - // This node was put on the heap earlier but its lower bound is now greater than the - // current upper bound - search_tree_.graphviz_node( - settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); - search_tree_.update(start_node.value(), node_status_t::FATHOMED); - active_subtrees_--; - continue; - } - - // Best-first search with plunging - plunge_from(task_id, start_node.value()); - - active_subtrees_--; - } - - lower_bound = get_lower_bound(); - upper_bound = get_upper_bound(); - abs_gap = upper_bound - lower_bound; - rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); - } - - // Check if it is the last thread that exited the loop and no - // timeout or numerical error has happen. - if (solver_status_ == mip_exploration_status_t::RUNNING) { - if (active_subtrees_ == 0) { - solver_status_ = mip_exploration_status_t::COMPLETED; - } else { - local_lower_bounds_[task_id] = inf; - } - } + worker->is_active = false; + mutex_available_workers_.lock(); + available_workers_.push_back(worker->worker_id); + mutex_available_workers_.unlock(); + active_workers_per_type[EXPLORATION]--; } template -void branch_and_bound_t::dive_from(mip_node_t& start_node, - const std::vector& start_lower, - const std::vector& start_upper, - bnb_task_type_t diving_type) +void branch_and_bound_t::dive_with(bnb_worker_t* worker) { logger_t log; log.log = false; - const i_t node_limit = settings_.bnb_task_settings[diving_type].node_limit; - const i_t backtrack = settings_.bnb_task_settings[diving_type].backtrack; + bnb_worker_type_t diving_type = worker->worker_type; + const i_t node_limit = settings_.bnb_worker_settings[diving_type].node_limit; + const i_t backtrack = settings_.bnb_worker_settings[diving_type].backtrack; - i_t tid = omp_get_thread_num(); - bnb_worker_data_t* worker_data = get_worker_data(tid); - assert(worker_data); + worker->recompute_basis = true; + worker->recompute_bounds = true; - worker_data->recompute_basis = true; - worker_data->recompute_bounds = true; - - search_tree_t subtree(std::move(start_node)); + search_tree_t subtree(std::move(*worker->start_node)); std::deque*> stack; stack.push_front(&subtree.root); @@ -1142,23 +1021,25 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, while (stack.size() > 0 && solver_status_ == mip_exploration_status_t::RUNNING) { mip_node_t* node_ptr = stack.front(); stack.pop_front(); - f_t upper_bound = get_upper_bound(); - f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node_ptr->lower_bound); + + f_t lower_bound = node_ptr->lower_bound; + f_t upper_bound = get_upper_bound(); + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + worker->lower_bound = lower_bound; if (node_ptr->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - worker_data->recompute_basis = true; - worker_data->recompute_bounds = true; + worker->recompute_basis = true; + worker->recompute_bounds = true; continue; } if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } if (dive_stats.nodes_explored > node_limit) { break; } - node_solve_info_t status = solve_node( - node_ptr, subtree, diving_type, worker_data, start_lower, start_upper, dive_stats, log); + node_solve_info_t status = solve_node(node_ptr, subtree, diving_type, worker, dive_stats, log); dive_stats.nodes_explored++; - worker_data->recompute_basis = !has_children(status); - worker_data->recompute_bounds = !has_children(status); + worker->recompute_basis = !has_children(status); + worker->recompute_bounds = !has_children(status); if (status == node_solve_info_t::TIME_LIMIT) { solver_status_ = mip_exploration_status_t::TIME_LIMIT; @@ -1181,40 +1062,147 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, stack.pop_back(); } } + + worker->is_active = false; + mutex_available_workers_.lock(); + available_workers_.push_back(worker->worker_id); + mutex_available_workers_.unlock(); + active_workers_per_type[diving_type]--; } template -void branch_and_bound_t::diving_thread(bnb_task_type_t diving_type) +void branch_and_bound_t::master_loop() { - i_t tid = omp_get_thread_num(); - bnb_worker_data_t* worker_data = get_worker_data(tid); - bounds_strengthening_t& node_presolver = worker_data->node_presolver; + f_t lower_bound = get_lower_bound(); + f_t upper_bound = get_upper_bound(); + f_t abs_gap = upper_bound - lower_bound; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + i_t last_node_depth = 0; - std::vector start_lower; - std::vector start_upper; - std::vector bounds_changed(original_lp_.num_cols, false); - bool reset_starting_bounds = true; + f_t last_log = 0.0; + i_t nodes_since_last_log = 0; + + constexpr bnb_worker_type_t task_types[] = { + EXPLORATION, PSEUDOCOST_DIVING, LINE_SEARCH_DIVING, GUIDED_DIVING, COEFFICIENT_DIVING}; while (solver_status_ == mip_exploration_status_t::RUNNING && - (active_subtrees_ > 0 || node_queue.best_first_queue_size() > 0)) { - if (reset_starting_bounds) { - start_lower = original_lp_.lower; - start_upper = original_lp_.upper; - std::fill(bounds_changed.begin(), bounds_changed.end(), false); - reset_starting_bounds = false; + abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && + (active_workers_per_type[0] > 0 || node_queue.best_first_queue_size() > 0)) { + lower_bound = get_lower_bound(); + upper_bound = get_upper_bound(); + abs_gap = upper_bound - lower_bound; + rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + + repair_heuristic_solutions(); + + f_t now = toc(exploration_stats_.start_time); + f_t time_since_last_log = last_log == 0 ? 1.0 : toc(last_log); + + if (((nodes_since_last_log >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && + time_since_last_log >= 1) || + (time_since_last_log > 30) || now > settings_.time_limit) { + f_t obj = compute_user_objective(original_lp_, upper_bound); + f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); + std::string gap_user = user_mip_gap(obj, user_lower); + i_t nodes_explored = exploration_stats_.nodes_explored; + i_t nodes_unexplored = exploration_stats_.nodes_unexplored; + f_t iter_node = exploration_stats_.total_lp_iters / nodes_explored; + i_t depth = + node_queue.best_first_queue_size() > 0 ? node_queue.bfs_top()->depth : last_node_depth; + + settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + nodes_explored, + nodes_unexplored, + obj, + user_lower, + depth, + iter_node, + gap_user.c_str(), + now); + + last_log = tic(); + nodes_since_last_log = 0; + } + + // There is no node in the queue, so we suspend temporarily the execution of the master + // so it can execute a worker task instead. + if (node_queue.best_first_queue_size() == 0 && node_queue.diving_queue_size() == 0) { +#pragma omp taskyield + continue; } - std::optional> start_node = - node_queue.pop_diving(start_lower, start_upper, bounds_changed); + for (auto type : task_types) { + if (active_workers_per_type[type] < settings_.bnb_worker_settings[type].num_workers) { + if (type == EXPLORATION) { + // If there any node left in the heap, we pop the top node and explore it. + std::optional*> start_node = node_queue.pop_best_first(); + + if (start_node.has_value()) { + if (get_upper_bound() < start_node.value()->lower_bound) { + // This node was put on the heap earlier but its lower bound is now greater than the + // current upper bound + search_tree_.graphviz_node( + settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); + search_tree_.update(start_node.value(), node_status_t::FATHOMED); + continue; + } + + i_t worker_id = -1; + + mutex_available_workers_.lock(); + if (available_workers_.size() > 0) { + worker_id = available_workers_.front(); + available_workers_.pop_front(); + } + mutex_available_workers_.unlock(); + + // There is no available workers + if (worker_id < 0) { break; } + + workers_[worker_id]->init_best_first(start_node.value(), original_lp_); + last_node_depth = start_node.value()->depth; + active_workers_per_type[type]++; + nodes_since_last_log++; - if (start_node.has_value()) { - reset_starting_bounds = true; +#pragma omp task + plunge_with(workers_[worker_id].get()); + } - bool is_feasible = - node_presolver.bounds_strengthening(start_lower, start_upper, bounds_changed, settings_); - if (get_upper_bound() < start_node->lower_bound || !is_feasible) { continue; } + } else { + std::optional*> start_node = node_queue.pop_diving(); + + if (start_node.has_value()) { + if (get_upper_bound() < start_node.value()->lower_bound) { continue; } + + i_t worker_id = -1; + + mutex_available_workers_.lock(); + if (available_workers_.size() > 0) { + worker_id = available_workers_.front(); + available_workers_.pop_front(); + } + mutex_available_workers_.unlock(); + + // There is no available workers + if (worker_id < 0) { break; } - dive_from(start_node.value(), start_lower, start_upper, diving_type); + bool is_feasible = + workers_[worker_id]->init_diving(start_node.value(), type, original_lp_, settings_); + + if (!is_feasible) { + mutex_available_workers_.lock(); + available_workers_.push_back(worker_id); + mutex_available_workers_.unlock(); + continue; + } + + active_workers_per_type[type]++; + +#pragma omp task + dive_with(workers_[worker_id].get()); + } + } + } } } } @@ -1374,9 +1362,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut root_objective_ = compute_objective(original_lp_, root_relax_soln_.x); - i_t num_bfs_threads = settings_.bnb_task_settings[EXPLORATION].num_tasks; - local_lower_bounds_.assign(num_bfs_threads, root_objective_); - if (settings_.set_simplex_solution_callback != nullptr) { std::vector original_x; uncrush_primal_solution(original_problem_, original_lp_, root_relax_soln_.x, original_x); @@ -1450,56 +1435,42 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut root_vstatus_, original_lp_, log); - worker_data_pool_.resize(settings_.num_threads); + workers_.resize(settings_.num_threads); + active_workers_per_type.fill(0); + for (i_t i = 0; i < settings_.num_threads; ++i) { + workers_[i] = + std::make_unique>(i, original_lp_, Arow_, var_types_, settings_); + available_workers_.push_front(i); + } + + i_t num_bfs_workers = settings_.bnb_worker_settings[EXPLORATION].num_workers; settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", settings_.num_threads, - num_bfs_threads, - settings_.num_threads - num_bfs_threads); + num_bfs_workers, + settings_.num_threads - num_bfs_workers); settings_.log.printf( " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " "| Time |\n"); - exploration_stats_.nodes_explored = 1; - exploration_stats_.nodes_unexplored = 2; - exploration_stats_.nodes_since_last_log = 0; - exploration_stats_.last_log = tic(); - active_subtrees_ = 0; - solver_status_ = mip_exploration_status_t::RUNNING; - lower_bound_ceiling_ = inf; - should_report_ = true; + exploration_stats_.nodes_explored = 1; + exploration_stats_.nodes_unexplored = 2; + solver_status_ = mip_exploration_status_t::RUNNING; + lower_bound_ceiling_ = inf; + should_report_ = true; + + auto down_child = search_tree_.root.get_down_child(); + auto up_child = search_tree_.root.get_up_child(); + node_queue.push(down_child); + node_queue.push(up_child); #pragma omp parallel num_threads(settings_.num_threads) { #pragma omp master { - auto down_child = search_tree_.root.get_down_child(); - auto up_child = search_tree_.root.get_up_child(); - i_t initial_size = 2 * settings_.num_threads; - -#pragma omp taskgroup - { #pragma omp task - exploration_ramp_up(down_child, initial_size); - -#pragma omp task - exploration_ramp_up(up_child, initial_size); - } - - for (i_t i = 0; i < num_bfs_threads; i++) { -#pragma omp task - best_first_thread(i); - } - - for (auto& settings : settings_.bnb_task_settings) { - if (settings.type != EXPLORATION && settings.is_enabled) { - for (i_t k = 0; k < settings.num_tasks; k++) { -#pragma omp task - diving_thread(settings.type); - } - } - } + master_loop(); } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index e6e225b73..8a002f946 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -7,6 +7,7 @@ #pragma once +#include #include #include #include @@ -21,6 +22,7 @@ #include #include +#include #include namespace cuopt::linear_programming::dual_simplex { @@ -53,51 +55,9 @@ enum class node_solve_info_t { NUMERICAL = 5 // The solver encounter a numerical error when solving the node }; -template -class bounds_strengthening_t; - template void upper_bound_callback(f_t upper_bound); -template -struct bnb_stats_t { - f_t start_time = 0.0; - omp_atomic_t total_lp_solve_time = 0.0; - omp_atomic_t nodes_explored = 0; - omp_atomic_t nodes_unexplored = 0; - omp_atomic_t total_lp_iters = 0; - - // This should only be used by the main thread - omp_atomic_t last_log = 0.0; - omp_atomic_t nodes_since_last_log = 0; -}; - -template -struct bnb_worker_data_t { - lp_problem_t leaf_problem; - basis_update_mpf_t basis_factors; - std::vector basic_list; - std::vector nonbasic_list; - bounds_strengthening_t node_presolver; - std::vector bounds_changed; - - bool recompute_basis = true; - bool recompute_bounds = true; - - bnb_worker_data_t(const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_type, - const simplex_solver_settings_t& settings) - : leaf_problem(original_lp), - basis_factors(original_lp.num_rows, settings.refactor_frequency), - basic_list(original_lp.num_rows), - nonbasic_list(), - node_presolver(leaf_problem, Arow, {}, var_type), - bounds_changed(original_lp.num_cols, false) - { - } -}; - template class branch_and_bound_t { public: @@ -159,9 +119,6 @@ class branch_and_bound_t { std::vector new_slacks_; std::vector var_types_; - // Local lower bounds for each thread - std::vector> local_lower_bounds_; - // Mutex for upper bound omp_mutex_t mutex_upper_; @@ -198,8 +155,9 @@ class branch_and_bound_t { // Search tree search_tree_t search_tree_; - // Count the number of subtrees that are currently being explored. - omp_atomic_t active_subtrees_; + // Count the number of tasks per type that either being executed or + // waiting to be executed. + std::array, 5> active_workers_per_type; // Global status of the solver. omp_atomic_t solver_status_; @@ -214,10 +172,12 @@ class branch_and_bound_t { void report(std::string symbol, f_t obj, f_t lower_bound, i_t node_depth); - // A pool containing the data needed for a worker to perform a plunge or dive. - // This is lazily initialized via `get_worker_data()`. - std::vector>> worker_data_pool_; - bnb_worker_data_t* get_worker_data(i_t tid); + // Worker pool + std::vector>> workers_; + + // FIXME: Implement a lock-free queue + omp_mutex_t mutex_available_workers_; + std::deque available_workers_; // Set the final solution. mip_status_t set_final_solution(mip_solution_t& solution, f_t lower_bound); @@ -227,7 +187,7 @@ class branch_and_bound_t { void add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - bnb_task_type_t thread_type); + bnb_worker_type_t thread_type); // Repairs low-quality solutions from the heuristics, if it is applicable. void repair_heuristic_solutions(); @@ -236,36 +196,17 @@ class branch_and_bound_t { // there is enough unexplored nodes. This is done recursively using OpenMP tasks. void exploration_ramp_up(mip_node_t* node, i_t initial_heap_size); - void plunge_from(i_t task_id, mip_node_t* start_node); - - // Each "main" thread pops a node from the global heap and then performs a plunge - // (i.e., a shallow dive) into the subtree determined by the node. - void best_first_thread(i_t task_id); - - // Perform a deep dive in the subtree determined by the `start_node`. - void dive_from(mip_node_t& start_node, - const std::vector& start_lower, - const std::vector& start_upper, - bnb_task_type_t diving_type); + void plunge_with(bnb_worker_t* worker); - // Each diving thread pops the first node from the dive queue and then performs - // a deep dive into the subtree determined by the node. - void diving_thread(bnb_thread_type_t diving_type); + void dive_with(bnb_worker_t* worker); - // Set the bounds of the leaf node and then apply bounds propagation. - // Return true if the problem is feasible, false otherwise. - bool set_node_bounds(mip_node_t* node_ptr, - const std::vector& start_lower, - const std::vector& start_upper, - bnb_worker_data_t* worker_data); + void master_loop(); // Solve the LP relaxation of a leaf node and update the tree. node_solve_info_t solve_node(mip_node_t* node_ptr, search_tree_t& search_tree, - bnb_task_type_t thread_type, - bnb_worker_data_t* worker_data, - const std::vector& root_lower, - const std::vector& root_upper, + bnb_worker_type_t thread_type, + bnb_worker_t* worker, bnb_stats_t& stats, logger_t& log); @@ -273,7 +214,7 @@ class branch_and_bound_t { branch_variable_t variable_selection(mip_node_t* node_ptr, const std::vector& fractional, const std::vector& solution, - bnb_task_type_t type, + bnb_worker_type_t type, logger_t& log); }; diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 94464d3cf..6574d4bfd 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -10,15 +10,15 @@ namespace cuopt::linear_programming::dual_simplex { template -bnb_task_settings_t get_default_diving_settings(bnb_task_type_t type) +bnb_worker_settings_t get_default_diving_settings(bnb_worker_type_t type) { - return bnb_task_settings_t{.type = type, - .is_enabled = true, - .num_tasks = -1, - .min_node_depth = 0, - .node_limit = 500, - .iteration_limit_factor = 0.05, - .backtrack = 5}; + return bnb_worker_settings_t{.type = type, + .is_enabled = true, + .num_workers = -1, + .min_node_depth = 0, + .node_limit = 500, + .iteration_limit_factor = 0.05, + .backtrack = 5}; } template @@ -286,7 +286,7 @@ branch_variable_t coefficient_diving(const lp_problem_t& lp_probl #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE -template bnb_task_settings_t get_default_diving_settings(bnb_task_type_t type); +template bnb_worker_settings_t get_default_diving_settings(bnb_worker_type_t type); template branch_variable_t line_search_diving(const std::vector& fractional, const std::vector& solution, diff --git a/cpp/src/dual_simplex/diving_heuristics.hpp b/cpp/src/dual_simplex/diving_heuristics.hpp index 422db1a68..02390ae8e 100644 --- a/cpp/src/dual_simplex/diving_heuristics.hpp +++ b/cpp/src/dual_simplex/diving_heuristics.hpp @@ -21,7 +21,7 @@ struct branch_variable_t { }; template -bnb_task_settings_t get_default_diving_settings(bnb_task_type_t type); +bnb_worker_settings_t get_default_diving_settings(bnb_worker_type_t type); template branch_variable_t line_search_diving(const std::vector& fractional, diff --git a/cpp/src/dual_simplex/node_queue.hpp b/cpp/src/dual_simplex/node_queue.hpp index 0234fa038..faa959f9f 100644 --- a/cpp/src/dual_simplex/node_queue.hpp +++ b/cpp/src/dual_simplex/node_queue.hpp @@ -108,32 +108,17 @@ class node_queue_t { diving_heap.push(entry); } - // In the current implementation, we are use the active number of subtree to decide - // when to stop the execution. We need to increment the counter at the same - // time as we pop a node from the queue to avoid some threads exiting - // the main loop thinking that the solver has already finished. - // This will be not needed in the master-worker model. - std::optional*> pop_best_first(omp_atomic_t& active_subtree) + std::optional*> pop_best_first() { std::lock_guard lock(mutex); auto entry = best_first_heap.pop(); - if (entry.has_value()) { - active_subtree++; - return std::exchange(entry.value()->node, nullptr); - } + if (entry.has_value()) { return std::exchange(entry.value()->node, nullptr); } return std::nullopt; } - // In the current implementation, multiple threads can pop the nodes - // from the queue, so we need to pass the lower and upper bound here - // to avoid other thread fathoming the node (i.e., deleting) before we can read - // the variable bounds from the tree. - // This will be not needed in the master-worker model. - std::optional> pop_diving(std::vector& lower, - std::vector& upper, - std::vector& bounds_changed) + std::optional*> pop_diving() { std::lock_guard lock(mutex); @@ -141,10 +126,7 @@ class node_queue_t { auto entry = diving_heap.pop(); if (entry.has_value()) { - if (auto node_ptr = entry.value()->node; node_ptr != nullptr) { - node_ptr->get_variable_bounds(lower, upper, bounds_changed); - return node_ptr->detach_copy(); - } + if (auto node_ptr = entry.value()->node; node_ptr != nullptr) { return node_ptr; } } } @@ -168,6 +150,12 @@ class node_queue_t { std::lock_guard lock(mutex); return best_first_heap.empty() ? inf : best_first_heap.top()->lower_bound; } + + mip_node_t* bfs_top() + { + std::lock_guard lock(mutex); + return best_first_heap.empty() ? nullptr : best_first_heap.top()->node; + } }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index e791c5da1..d7512e3be 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -20,12 +20,12 @@ namespace cuopt::linear_programming::dual_simplex { -// Indicate the search and variable selection algorithms used by each task +// Indicate the search and variable selection algorithms used by each worker // in B&B (See [1]). // // [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, // Berlin, 2007. doi: 10.14279/depositonce-1634. -enum bnb_task_type_t { +enum bnb_worker_type_t { EXPLORATION = 0, // Best-First + Plunging. PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) @@ -33,37 +33,39 @@ enum bnb_task_type_t { COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) }; -// Settings for each task type in B&B. +// Settings for each worker in B&B. template -struct bnb_task_settings_t { - // Type of the task. - bnb_task_type_t type; +struct bnb_worker_settings_t { + // Type of the worker. + bnb_worker_type_t type; - // Is this type of task enabled? + // Is this worker enabled? // This will be ignored if `type == EXPLORATION`. bool is_enabled; - // Number of tasks of this type. - i_t num_tasks; + // Number of workers of this type. + i_t num_workers; - // Minimum node depth to start this task + // Minimum node depth to start this worker // This will be ignored if `type == EXPLORATION`. i_t min_node_depth; - // Maximum number of nodes explored in this task. + // Maximum number of nodes explored in this worker. i_t node_limit; - // Maximum fraction of the number of simplex iterations for this task + // Maximum fraction of the number of simplex iterations for this worker // compared to the number of simplex iterations for normal exploration. + // This will be ignored if `type == EXPLORATION`. f_t iteration_limit_factor; // Number of nodes that it allows to backtrack when // reaching the bottom of a given branch of the tree. + // This will be ignored if `type == EXPLORATION`. i_t backtrack; }; template -bnb_task_settings_t get_default_diving_settings(bnb_task_type_t type); +bnb_worker_settings_t get_default_diving_settings(bnb_worker_type_t type); template struct simplex_solver_settings_t { @@ -121,23 +123,24 @@ struct simplex_solver_settings_t { heuristic_preemption_callback(nullptr), concurrent_halt(nullptr) { - bnb_task_settings[EXPLORATION] = - bnb_task_settings_t{.type = EXPLORATION, - .is_enabled = true, - .num_tasks = -1, - .min_node_depth = 0, - .node_limit = std::numeric_limits::max(), - .iteration_limit_factor = std::numeric_limits::max(), - .backtrack = 1}; - - bnb_task_settings[PSEUDOCOST_DIVING] = get_default_diving_settings(PSEUDOCOST_DIVING); - - bnb_task_settings[LINE_SEARCH_DIVING] = + bnb_worker_settings[EXPLORATION] = + bnb_worker_settings_t{.type = EXPLORATION, + .is_enabled = true, + .num_workers = -1, + .min_node_depth = 0, + .node_limit = std::numeric_limits::max(), + .iteration_limit_factor = std::numeric_limits::max(), + .backtrack = 1}; + + bnb_worker_settings[PSEUDOCOST_DIVING] = + get_default_diving_settings(PSEUDOCOST_DIVING); + + bnb_worker_settings[LINE_SEARCH_DIVING] = get_default_diving_settings(LINE_SEARCH_DIVING); - bnb_task_settings[GUIDED_DIVING] = get_default_diving_settings(GUIDED_DIVING); + bnb_worker_settings[GUIDED_DIVING] = get_default_diving_settings(GUIDED_DIVING); - bnb_task_settings[COEFFICIENT_DIVING] = + bnb_worker_settings[COEFFICIENT_DIVING] = get_default_diving_settings(COEFFICIENT_DIVING); set_bnb_tasks(omp_get_max_threads() - 1); @@ -145,26 +148,26 @@ struct simplex_solver_settings_t { void set_bnb_tasks(i_t num_threads) { - this->num_threads = num_threads; - bnb_task_settings[EXPLORATION].num_tasks = std::max(1, num_threads / 4); + this->num_threads = num_threads; + bnb_worker_settings[EXPLORATION].num_workers = std::max(1, num_threads / 2); - i_t diving_tasks = num_threads - bnb_task_settings[EXPLORATION].num_tasks; + i_t diving_tasks = num_threads - bnb_worker_settings[EXPLORATION].num_workers; i_t num_enabled = 0; - for (size_t i = 1; i < bnb_task_settings.size(); ++i) { - num_enabled += static_cast(bnb_task_settings[i].is_enabled); + for (size_t i = 1; i < bnb_worker_settings.size(); ++i) { + num_enabled += static_cast(bnb_worker_settings[i].is_enabled); } - for (size_t i = 1, k = 0; i < bnb_task_settings.size(); ++i) { + for (size_t i = 1, k = 0; i < bnb_worker_settings.size(); ++i) { i_t start = (double)k * diving_tasks / num_enabled; i_t end = (double)(k + 1) * diving_tasks / num_enabled; - if (bnb_task_settings[i].is_enabled) { - bnb_task_settings[i].num_tasks = end - start; + if (bnb_worker_settings[i].is_enabled) { + bnb_worker_settings[i].num_workers = 2 * (end - start); ++k; } else { - bnb_task_settings[i].num_tasks = 0; + bnb_worker_settings[i].num_workers = 0; } } } @@ -230,7 +233,7 @@ struct simplex_solver_settings_t { // Indicate the settings used by each task // The position in the array is indicated by the `bnb_task_type_t`. - std::array, 5> bnb_task_settings; + std::array, 5> bnb_worker_settings; i_t inside_mip; // 0 if outside MIP, 1 if inside MIP at root node, 2 if inside MIP at leaf node std::function&, f_t)> solution_callback; diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 4442029e8..0461167a8 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -260,9 +260,11 @@ void rins_t::run_rins() // In the future, let RINS use all the diving heuristics. For now, // restricting to guided diving. - branch_and_bound_settings.bnb_task_settings[dual_simplex::PSEUDOCOST_DIVING].is_enabled = false; - branch_and_bound_settings.bnb_task_settings[dual_simplex::LINE_SEARCH_DIVING].is_enabled = false; - branch_and_bound_settings.bnb_task_settings[dual_simplex::COEFFICIENT_DIVING].is_enabled = false; + branch_and_bound_settings.bnb_worker_settings[dual_simplex::PSEUDOCOST_DIVING].is_enabled = false; + branch_and_bound_settings.bnb_worker_settings[dual_simplex::LINE_SEARCH_DIVING].is_enabled = + false; + branch_and_bound_settings.bnb_worker_settings[dual_simplex::COEFFICIENT_DIVING].is_enabled = + false; branch_and_bound_settings.set_bnb_tasks(2); branch_and_bound_settings.log.log = false; diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index 8501b70cc..fc28af783 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -105,11 +105,11 @@ class sub_mip_recombiner_t : public recombiner_t { // In the future, let SubMIP use all the diving heuristics. For now, // restricting to guided diving. - branch_and_bound_settings.bnb_task_settings[dual_simplex::PSEUDOCOST_DIVING].is_enabled = + branch_and_bound_settings.bnb_worker_settings[dual_simplex::PSEUDOCOST_DIVING].is_enabled = false; - branch_and_bound_settings.bnb_task_settings[dual_simplex::LINE_SEARCH_DIVING].is_enabled = + branch_and_bound_settings.bnb_worker_settings[dual_simplex::LINE_SEARCH_DIVING].is_enabled = false; - branch_and_bound_settings.bnb_task_settings[dual_simplex::COEFFICIENT_DIVING].is_enabled = + branch_and_bound_settings.bnb_worker_settings[dual_simplex::COEFFICIENT_DIVING].is_enabled = false; branch_and_bound_settings.set_bnb_tasks(2); From a0fa4e18502a5f8dca6032b66d2498ed77bec090 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 7 Jan 2026 15:24:34 +0100 Subject: [PATCH 145/366] refactoring and fixing bugs --- cpp/src/dual_simplex/bnb_worker.hpp | 120 ++++++++++- cpp/src/dual_simplex/branch_and_bound.cpp | 201 +++++++----------- cpp/src/dual_simplex/branch_and_bound.hpp | 20 +- cpp/src/dual_simplex/diving_heuristics.cpp | 17 +- cpp/src/dual_simplex/diving_heuristics.hpp | 5 +- cpp/src/dual_simplex/node_queue.hpp | 24 +-- .../dual_simplex/simplex_solver_settings.hpp | 114 ++-------- cpp/src/mip/diversity/lns/rins.cu | 19 +- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 20 +- cpp/src/mip/solver.cu | 15 +- cpp/src/utilities/omp_helpers.hpp | 4 +- 11 files changed, 268 insertions(+), 291 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index 34eeb38c8..f2ec37ca1 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -12,10 +12,26 @@ #include #include +#include #include namespace cuopt::linear_programming::dual_simplex { +constexpr int bnb_num_worker_types = 5; + +// Indicate the search and variable selection algorithms used by each thread +// in B&B (See [1]). +// +// [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, +// Berlin, 2007. doi: 10.14279/depositonce-1634. +enum bnb_worker_type_t : int { + EXPLORATION = 0, // Best-First + Plunging. + PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) + LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) + GUIDED_DIVING = 3, // Guided diving (9.2.3). + COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) +}; + template struct bnb_stats_t { f_t start_time = 0.0; @@ -80,11 +96,105 @@ class bnb_worker_t { private: // For diving, we need to store the full node instead of - // of just a pointer, since it is detached from the - // tree. To keep the same interface for any type of worker, - // the start node will point to this node when diving. - // For best-first search, this will not be used. + // of just a pointer, since it is not store in the tree anymore. + // To keep the same interface across all worker types, + // this will be used as a temporary storage and + // will be pointed by `start_node`. + // For exploration, this will not be used. mip_node_t internal_node; }; +template +class bnb_worker_pool_t { + public: + void init(i_t num_workers, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_type, + const simplex_solver_settings_t& settings) + { + for (i_t i = 0; i < num_workers; ++i) { + workers_[i] = + std::make_unique>(i, original_lp, Arow, var_type, settings); + available_workers_.push_front(i); + } + } + + bnb_worker_t* get_worker() + { + std::lock_guard lock(mutex_); + + if (available_workers_.empty()) { + return nullptr; + } else { + i_t idx = available_workers_.front(); + available_workers_.pop_front(); + return workers_[idx].get(); + } + } + + void return_worker_to_pool(bnb_worker_t* worker) + { + worker->is_active = false; + std::lock_guard lock(mutex_); + available_workers_.push_back(worker->worker_id); + } + + f_t get_lower_bounds() + { + f_t lower_bound = std::numeric_limits::infinity(); + + for (i_t i = 0; i < workers_.size(); ++i) { + if (workers_[i]->worker_type == EXPLORATION && workers_[i]->is_active) { + lower_bound = std::min(workers_[i]->lower_bound.load(), lower_bound); + } + } + + return lower_bound; + } + + private: + // Worker pool + std::vector>> workers_; + + // FIXME: Implement a lock-free queue + omp_mutex_t mutex_; + std::deque available_workers_; +}; + +template +std::vector bnb_get_worker_types(diving_heuristics_settings_t settings) +{ + std::vector types; + types.reserve(bnb_num_worker_types); + types.push_back(EXPLORATION); + if (!settings.disable_pseudocost_diving) { types.push_back(PSEUDOCOST_DIVING); } + if (!settings.disable_line_search_diving) { types.push_back(LINE_SEARCH_DIVING); } + if (!settings.disable_guided_diving) { types.push_back(GUIDED_DIVING); } + if (!settings.disable_coefficient_diving) { types.push_back(COEFFICIENT_DIVING); } + return types; +} + +template +std::array bnb_get_num_workers_round_robin( + i_t num_threads, diving_heuristics_settings_t settings) +{ + std::array max_num_workers; + auto worker_types = bnb_get_worker_types(settings); + + max_num_workers.fill(0); + max_num_workers[EXPLORATION] = std::max(1, num_threads / 4); + + i_t diving_workers = settings.num_diving_workers; + i_t m = worker_types.size() - 1; + for (size_t i = 1, k = 0; i < bnb_num_worker_types; ++i) { + i_t start = (double)k * diving_workers / m; + i_t end = (double)(k + 1) * diving_workers / m; + max_num_workers[i] = 2 * (end - start); + ++k; + } + + return max_num_workers; +} + } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index a2966fb26..dcf0be8a2 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -251,13 +251,7 @@ f_t branch_and_bound_t::get_lower_bound() f_t lower_bound = lower_bound_ceiling_.load(); f_t heap_lower_bound = node_queue.get_lower_bound(); lower_bound = std::min(heap_lower_bound, lower_bound); - - for (i_t i = 0; i < workers_.size(); ++i) { - if (workers_[i]->worker_type == EXPLORATION && workers_[i]->is_active) { - lower_bound = std::min(workers_[i]->lower_bound.load(), lower_bound); - } - } - + lower_bound = std::min(worker_pool_.get_lower_bounds(), lower_bound); return std::isfinite(lower_bound) ? lower_bound : -inf; } @@ -633,15 +627,6 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; const f_t upper_bound = get_upper_bound(); - // If there is no incumbent, use pseudocost diving instead of guided diving - if (upper_bound == inf && thread_type == bnb_worker_type_t::GUIDED_DIVING) { - if (settings_.diving_settings.disable_pseudocost_diving) { - thread_type = bnb_thread_type_t::COEFFICIENT_DIVING; - } else { - thread_type = bnb_worker_type_t::PSEUDOCOST_DIVING; - } - } - lp_problem_t& leaf_problem = worker->leaf_problem; lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); std::vector& leaf_vstatus = node_ptr->vstatus; @@ -656,7 +641,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* if (thread_type != bnb_worker_type_t::EXPLORATION) { i_t bnb_lp_iters = exploration_stats_.total_lp_iters; - f_t factor = settings_.bnb_worker_settings[thread_type].iteration_limit_factor; + f_t factor = settings_.diving_settings.iteration_limit_factor; f_t max_iter = factor * bnb_lp_iters; lp_settings.iteration_limit = max_iter - stats.total_lp_iters; if (lp_settings.iteration_limit <= 0) { return node_solve_info_t::ITERATION_LIMIT; } @@ -854,13 +839,13 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* // (time_since_last_log > 30) || now > settings_.time_limit) { // bool should_report = should_report_.exchange(false); - // if (should_report) { - // report(" ", upper_bound, root_objective_, node->depth); - // exploration_stats_.nodes_since_last_log = 0; - // exploration_stats_.last_log = tic(); - // should_report_ = true; - // } - // } +// if (should_report) { +// report(" ", upper_bound, root_objective_, node->depth); +// exploration_stats_.nodes_since_last_log = 0; +// exploration_stats_.last_log = tic(); +// should_report_ = true; +// } +// } // if (now > settings_.time_limit) { // solver_status_ = mip_exploration_status_t::TIME_LIMIT; @@ -988,10 +973,7 @@ void branch_and_bound_t::plunge_with(bnb_worker_t* worker) } } - worker->is_active = false; - mutex_available_workers_.lock(); - available_workers_.push_back(worker->worker_id); - mutex_available_workers_.unlock(); + worker_pool_.return_worker_to_pool(worker); active_workers_per_type[EXPLORATION]--; } @@ -1002,8 +984,8 @@ void branch_and_bound_t::dive_with(bnb_worker_t* worker) log.log = false; bnb_worker_type_t diving_type = worker->worker_type; - const i_t node_limit = settings_.bnb_worker_settings[diving_type].node_limit; - const i_t backtrack = settings_.bnb_worker_settings[diving_type].backtrack; + const i_t node_limit = settings_.diving_settings.node_limit; + const i_t backtrack = settings_.diving_settings.backtrack; worker->recompute_basis = true; worker->recompute_bounds = true; @@ -1063,10 +1045,7 @@ void branch_and_bound_t::dive_with(bnb_worker_t* worker) } } - worker->is_active = false; - mutex_available_workers_.lock(); - available_workers_.push_back(worker->worker_id); - mutex_available_workers_.unlock(); + worker_pool_.return_worker_to_pool(worker); active_workers_per_type[diving_type]--; } @@ -1082,8 +1061,12 @@ void branch_and_bound_t::master_loop() f_t last_log = 0.0; i_t nodes_since_last_log = 0; - constexpr bnb_worker_type_t task_types[] = { - EXPLORATION, PSEUDOCOST_DIVING, LINE_SEARCH_DIVING, GUIDED_DIVING, COEFFICIENT_DIVING}; + diving_heuristics_settings_t diving_settings = settings_.diving_settings; + if (!std::isfinite(upper_bound)) { diving_settings.disable_guided_diving = true; } + + auto worker_types = bnb_get_worker_types(diving_settings); + auto max_num_workers_per_type = + bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); while (solver_status_ == mip_exploration_status_t::RUNNING && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && @@ -1095,31 +1078,25 @@ void branch_and_bound_t::master_loop() repair_heuristic_solutions(); + // If the guided diving was disabled previously due to the lack of an incumbent solution, + // re-enable as soon as a new incumbent is found. + if (settings_.diving_settings.disable_guided_diving != diving_settings.disable_guided_diving) { + if (std::isfinite(upper_bound)) { + diving_settings.disable_guided_diving = settings_.diving_settings.disable_guided_diving; + max_num_workers_per_type = + bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); + } + } + f_t now = toc(exploration_stats_.start_time); f_t time_since_last_log = last_log == 0 ? 1.0 : toc(last_log); if (((nodes_since_last_log >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && time_since_last_log >= 1) || (time_since_last_log > 30) || now > settings_.time_limit) { - f_t obj = compute_user_objective(original_lp_, upper_bound); - f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); - std::string gap_user = user_mip_gap(obj, user_lower); - i_t nodes_explored = exploration_stats_.nodes_explored; - i_t nodes_unexplored = exploration_stats_.nodes_unexplored; - f_t iter_node = exploration_stats_.total_lp_iters / nodes_explored; i_t depth = node_queue.best_first_queue_size() > 0 ? node_queue.bfs_top()->depth : last_node_depth; - - settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - nodes_explored, - nodes_unexplored, - obj, - user_lower, - depth, - iter_node, - gap_user.c_str(), - now); - + report(" ", upper_bound, lower_bound, depth); last_log = tic(); nodes_since_last_log = 0; } @@ -1131,77 +1108,63 @@ void branch_and_bound_t::master_loop() continue; } - for (auto type : task_types) { - if (active_workers_per_type[type] < settings_.bnb_worker_settings[type].num_workers) { - if (type == EXPLORATION) { - // If there any node left in the heap, we pop the top node and explore it. - std::optional*> start_node = node_queue.pop_best_first(); - - if (start_node.has_value()) { - if (get_upper_bound() < start_node.value()->lower_bound) { - // This node was put on the heap earlier but its lower bound is now greater than the - // current upper bound - search_tree_.graphviz_node( - settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); - search_tree_.update(start_node.value(), node_status_t::FATHOMED); - continue; - } - - i_t worker_id = -1; - - mutex_available_workers_.lock(); - if (available_workers_.size() > 0) { - worker_id = available_workers_.front(); - available_workers_.pop_front(); - } - mutex_available_workers_.unlock(); - - // There is no available workers - if (worker_id < 0) { break; } - - workers_[worker_id]->init_best_first(start_node.value(), original_lp_); - last_node_depth = start_node.value()->depth; - active_workers_per_type[type]++; - nodes_since_last_log++; + for (auto type : worker_types) { + if (active_workers_per_type[type] >= max_num_workers_per_type[type]) { continue; } -#pragma omp task - plunge_with(workers_[worker_id].get()); - } + bnb_worker_t* worker = worker_pool_.get_worker(); + if (worker == nullptr) { continue; } - } else { - std::optional*> start_node = node_queue.pop_diving(); + if (type == EXPLORATION) { + // If there any node left in the heap, we pop the top node and explore it. + std::optional*> start_node = node_queue.pop_best_first(); - if (start_node.has_value()) { - if (get_upper_bound() < start_node.value()->lower_bound) { continue; } + if (start_node.has_value()) { + worker_pool_.return_worker_to_pool(worker); + continue; + } - i_t worker_id = -1; + if (get_upper_bound() < start_node.value()->lower_bound) { + // This node was put on the heap earlier but its lower bound is now greater than the + // current upper bound + search_tree_.graphviz_node( + settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); + search_tree_.update(start_node.value(), node_status_t::FATHOMED); + worker_pool_.return_worker_to_pool(worker); + continue; + } - mutex_available_workers_.lock(); - if (available_workers_.size() > 0) { - worker_id = available_workers_.front(); - available_workers_.pop_front(); - } - mutex_available_workers_.unlock(); + worker->init_best_first(start_node.value(), original_lp_); + last_node_depth = start_node.value()->depth; + active_workers_per_type[type]++; + nodes_since_last_log++; - // There is no available workers - if (worker_id < 0) { break; } +#pragma omp task + plunge_with(worker); - bool is_feasible = - workers_[worker_id]->init_diving(start_node.value(), type, original_lp_, settings_); + } else { + std::optional*> start_node = node_queue.pop_diving(); - if (!is_feasible) { - mutex_available_workers_.lock(); - available_workers_.push_back(worker_id); - mutex_available_workers_.unlock(); - continue; - } + if (start_node.has_value()) { + worker_pool_.return_worker_to_pool(worker); + continue; + } - active_workers_per_type[type]++; + if (get_upper_bound() < start_node.value()->lower_bound) { + worker_pool_.return_worker_to_pool(worker); + continue; + } -#pragma omp task - dive_with(workers_[worker_id].get()); - } + bool is_feasible = worker->init_diving(start_node.value(), type, original_lp_, settings_); + + if (!is_feasible) { + worker_pool_.return_worker_to_pool(worker); + continue; } + + active_workers_per_type[type]++; + +#pragma omp task + dive_with(worker); } } } @@ -1436,19 +1399,13 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut original_lp_, log); - workers_.resize(settings_.num_threads); + worker_pool_.init(settings_.num_threads, original_lp_, Arow_, var_types_, settings_); active_workers_per_type.fill(0); - for (i_t i = 0; i < settings_.num_threads; ++i) { - workers_[i] = - std::make_unique>(i, original_lp_, Arow_, var_types_, settings_); - available_workers_.push_front(i); - } - i_t num_bfs_workers = settings_.bnb_worker_settings[EXPLORATION].num_workers; settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", settings_.num_threads, - num_bfs_workers, - settings_.num_threads - num_bfs_workers); + settings_.num_bfs_workers, + settings_.num_threads - settings_.num_bfs_workers); settings_.log.printf( " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 8a002f946..cf8e57db5 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -22,7 +22,6 @@ #include #include -#include #include namespace cuopt::linear_programming::dual_simplex { @@ -155,9 +154,12 @@ class branch_and_bound_t { // Search tree search_tree_t search_tree_; - // Count the number of tasks per type that either being executed or - // waiting to be executed. - std::array, 5> active_workers_per_type; + // Count the number of workers per type that either are being executed or + // are waiting to be executed. + std::array, bnb_num_worker_types> active_workers_per_type; + + // Worker pool + bnb_worker_pool_t worker_pool_; // Global status of the solver. omp_atomic_t solver_status_; @@ -171,14 +173,6 @@ class branch_and_bound_t { void report_heuristic(f_t obj); void report(std::string symbol, f_t obj, f_t lower_bound, i_t node_depth); - - // Worker pool - std::vector>> workers_; - - // FIXME: Implement a lock-free queue - omp_mutex_t mutex_available_workers_; - std::deque available_workers_; - // Set the final solution. mip_status_t set_final_solution(mip_solution_t& solution, f_t lower_bound); diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 6574d4bfd..ce9460fa9 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -9,18 +9,6 @@ namespace cuopt::linear_programming::dual_simplex { -template -bnb_worker_settings_t get_default_diving_settings(bnb_worker_type_t type) -{ - return bnb_worker_settings_t{.type = type, - .is_enabled = true, - .num_workers = -1, - .min_node_depth = 0, - .node_limit = 500, - .iteration_limit_factor = 0.05, - .backtrack = 5}; -} - template branch_variable_t line_search_diving(const std::vector& fractional, const std::vector& solution, @@ -285,9 +273,6 @@ branch_variable_t coefficient_diving(const lp_problem_t& lp_probl } #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE - -template bnb_worker_settings_t get_default_diving_settings(bnb_worker_type_t type); - template branch_variable_t line_search_diving(const std::vector& fractional, const std::vector& solution, const std::vector& root_solution, diff --git a/cpp/src/dual_simplex/diving_heuristics.hpp b/cpp/src/dual_simplex/diving_heuristics.hpp index 02390ae8e..1f44fee31 100644 --- a/cpp/src/dual_simplex/diving_heuristics.hpp +++ b/cpp/src/dual_simplex/diving_heuristics.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -20,9 +20,6 @@ struct branch_variable_t { rounding_direction_t direction; }; -template -bnb_worker_settings_t get_default_diving_settings(bnb_worker_type_t type); - template branch_variable_t line_search_diving(const std::vector& fractional, const std::vector& solution, diff --git a/cpp/src/dual_simplex/node_queue.hpp b/cpp/src/dual_simplex/node_queue.hpp index faa959f9f..8efe6087c 100644 --- a/cpp/src/dual_simplex/node_queue.hpp +++ b/cpp/src/dual_simplex/node_queue.hpp @@ -1,6 +1,6 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 */ #pragma once @@ -25,12 +25,14 @@ class heap_t { { buffer.push_back(node); std::push_heap(buffer.begin(), buffer.end(), comp); + buffer_size++; } void push(T&& node) { buffer.push_back(std::move(node)); std::push_heap(buffer.begin(), buffer.end(), comp); + buffer_size++; } template @@ -38,6 +40,7 @@ class heap_t { { buffer.emplace_back(std::forward(args)...); std::push_heap(buffer.begin(), buffer.end(), comp); + buffer_size++; } std::optional pop() @@ -47,16 +50,18 @@ class heap_t { std::pop_heap(buffer.begin(), buffer.end(), comp); T node = std::move(buffer.back()); buffer.pop_back(); + buffer_size--; return node; } - size_t size() const { return buffer.size(); } + size_t size() const { return buffer_size.load(); } T& top() { return buffer.front(); } void clear() { buffer.clear(); } bool empty() const { return buffer.empty(); } private: std::vector buffer; + omp_atomic_t buffer_size; Comp comp; }; @@ -133,17 +138,8 @@ class node_queue_t { return std::nullopt; } - i_t diving_queue_size() - { - std::lock_guard lock(mutex); - return diving_heap.size(); - } - - i_t best_first_queue_size() - { - std::lock_guard lock(mutex); - return best_first_heap.size(); - } + size_t diving_queue_size() const { return diving_heap.size(); } + size_t best_first_queue_size() const { return best_first_heap.size(); } f_t get_lower_bound() { diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index d7512e3be..51d59c5ba 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -20,53 +20,21 @@ namespace cuopt::linear_programming::dual_simplex { -// Indicate the search and variable selection algorithms used by each worker -// in B&B (See [1]). -// -// [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, -// Berlin, 2007. doi: 10.14279/depositonce-1634. -enum bnb_worker_type_t { - EXPLORATION = 0, // Best-First + Plunging. - PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) - LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) - GUIDED_DIVING = 3, // Guided diving (9.2.3). If no incumbent is found yet, use pseudocost diving. - COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) -}; - -// Settings for each worker in B&B. template -struct bnb_worker_settings_t { - // Type of the worker. - bnb_worker_type_t type; - - // Is this worker enabled? - // This will be ignored if `type == EXPLORATION`. - bool is_enabled; - - // Number of workers of this type. - i_t num_workers; - - // Minimum node depth to start this worker - // This will be ignored if `type == EXPLORATION`. - i_t min_node_depth; - - // Maximum number of nodes explored in this worker. - i_t node_limit; - - // Maximum fraction of the number of simplex iterations for this worker - // compared to the number of simplex iterations for normal exploration. - // This will be ignored if `type == EXPLORATION`. - f_t iteration_limit_factor; - - // Number of nodes that it allows to backtrack when - // reaching the bottom of a given branch of the tree. - // This will be ignored if `type == EXPLORATION`. - i_t backtrack; +struct diving_heuristics_settings_t { + i_t num_diving_workers = -1; + + bool disable_line_search_diving = false; + bool disable_pseudocost_diving = false; + bool disable_guided_diving = false; + bool disable_coefficient_diving = false; + + i_t min_node_depth = 0; + i_t node_limit = 500; + f_t iteration_limit_factor = 0.05; + i_t backtrack = 5; }; -template -bnb_worker_settings_t get_default_diving_settings(bnb_worker_type_t type); - template struct simplex_solver_settings_t { public: @@ -117,66 +85,21 @@ struct simplex_solver_settings_t { refactor_frequency(100), iteration_log_frequency(1000), first_iteration_log(2), + num_threads(omp_get_max_threads() - 1), + num_bfs_workers(std::min(num_threads / 4, 1)), random_seed(0), inside_mip(0), solution_callback(nullptr), heuristic_preemption_callback(nullptr), concurrent_halt(nullptr) { - bnb_worker_settings[EXPLORATION] = - bnb_worker_settings_t{.type = EXPLORATION, - .is_enabled = true, - .num_workers = -1, - .min_node_depth = 0, - .node_limit = std::numeric_limits::max(), - .iteration_limit_factor = std::numeric_limits::max(), - .backtrack = 1}; - - bnb_worker_settings[PSEUDOCOST_DIVING] = - get_default_diving_settings(PSEUDOCOST_DIVING); - - bnb_worker_settings[LINE_SEARCH_DIVING] = - get_default_diving_settings(LINE_SEARCH_DIVING); - - bnb_worker_settings[GUIDED_DIVING] = get_default_diving_settings(GUIDED_DIVING); - - bnb_worker_settings[COEFFICIENT_DIVING] = - get_default_diving_settings(COEFFICIENT_DIVING); - - set_bnb_tasks(omp_get_max_threads() - 1); - } - - void set_bnb_tasks(i_t num_threads) - { - this->num_threads = num_threads; - bnb_worker_settings[EXPLORATION].num_workers = std::max(1, num_threads / 2); - - i_t diving_tasks = num_threads - bnb_worker_settings[EXPLORATION].num_workers; - i_t num_enabled = 0; - - for (size_t i = 1; i < bnb_worker_settings.size(); ++i) { - num_enabled += static_cast(bnb_worker_settings[i].is_enabled); - } - - for (size_t i = 1, k = 0; i < bnb_worker_settings.size(); ++i) { - i_t start = (double)k * diving_tasks / num_enabled; - i_t end = (double)(k + 1) * diving_tasks / num_enabled; - - if (bnb_worker_settings[i].is_enabled) { - bnb_worker_settings[i].num_workers = 2 * (end - start); - ++k; - - } else { - bnb_worker_settings[i].num_workers = 0; - } - } + diving_settings.num_diving_workers = std::max(num_threads - num_bfs_workers, 1); } void set_log(bool logging) const { log.log = logging; } void enable_log_to_file() { log.enable_log_to_file(); } void set_log_filename(const std::string& log_filename) { log.set_log_file(log_filename); } void close_log_file() { log.close_log_file(); } - i_t iteration_limit; i_t node_limit; f_t time_limit; @@ -230,10 +153,9 @@ struct simplex_solver_settings_t { i_t first_iteration_log; // number of iterations to log at beginning of solve i_t num_threads; // number of threads to use i_t random_seed; // random seed + i_t num_bfs_workers; // number of threads dedicated to the best-first search - // Indicate the settings used by each task - // The position in the array is indicated by the `bnb_task_type_t`. - std::array, 5> bnb_worker_settings; + diving_heuristics_settings_t diving_settings; // Settings for the diving heuristics i_t inside_mip; // 0 if outside MIP, 1 if inside MIP at root node, 2 if inside MIP at leaf node std::function&, f_t)> solution_callback; diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 0461167a8..8822efb47 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -256,16 +256,21 @@ void rins_t::run_rins() branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = std::min(current_mip_gap, (f_t)settings.target_mip_gap); - branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.num_threads = 2; + branch_and_bound_settings.num_bfs_workers = 1; // In the future, let RINS use all the diving heuristics. For now, // restricting to guided diving. - branch_and_bound_settings.bnb_worker_settings[dual_simplex::PSEUDOCOST_DIVING].is_enabled = false; - branch_and_bound_settings.bnb_worker_settings[dual_simplex::LINE_SEARCH_DIVING].is_enabled = - false; - branch_and_bound_settings.bnb_worker_settings[dual_simplex::COEFFICIENT_DIVING].is_enabled = - false; - branch_and_bound_settings.set_bnb_tasks(2); + branch_and_bound_settings.diving_settings.num_diving_workers = 1; + branch_and_bound_settings.diving_settings.disable_line_search_diving = true; + branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; + + if (context.settings.disable_guided_diving) { + branch_and_bound_settings.diving_settings.disable_guided_diving = true; + } else { + branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; + } branch_and_bound_settings.log.log = false; branch_and_bound_settings.log.log_prefix = "[RINS] "; diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index fc28af783..1efed74ce 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -101,17 +101,21 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.print_presolve_stats = false; branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; - branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.num_threads = 2; + branch_and_bound_settings.num_bfs_workers = 1; // In the future, let SubMIP use all the diving heuristics. For now, // restricting to guided diving. - branch_and_bound_settings.bnb_worker_settings[dual_simplex::PSEUDOCOST_DIVING].is_enabled = - false; - branch_and_bound_settings.bnb_worker_settings[dual_simplex::LINE_SEARCH_DIVING].is_enabled = - false; - branch_and_bound_settings.bnb_worker_settings[dual_simplex::COEFFICIENT_DIVING].is_enabled = - false; - branch_and_bound_settings.set_bnb_tasks(2); + branch_and_bound_settings.diving_settings.num_diving_workers = 1; + branch_and_bound_settings.diving_settings.disable_line_search_diving = true; + branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; + + if (context.settings.disable_guided_diving) { + branch_and_bound_settings.diving_settings.disable_guided_diving = true; + } else { + branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; + } branch_and_bound_settings.solution_callback = [this](std::vector& solution, f_t objective) { diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index a77ede1a2..00292c428 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -176,10 +176,17 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.diving_settings.disable_line_search_diving = context.settings.disable_line_search_diving; - i_t num_threads = context.settings.num_cpu_threads < 0 - ? omp_get_max_threads() - 1 - : std::max(1, context.settings.num_cpu_threads); - branch_and_bound_settings.set_bnb_tasks(num_threads); + if (context.settings.num_cpu_threads < 0) { + branch_and_bound_settings.num_threads = omp_get_max_threads() - 1; + } else { + branch_and_bound_settings.num_threads = std::max(1, context.settings.num_cpu_threads); + } + + i_t num_threads = branch_and_bound_settings.num_threads; + i_t num_bfs_threads = std::max(1, num_threads / 4); + i_t num_diving_threads = std::max(1, num_threads - num_bfs_threads); + branch_and_bound_settings.num_bfs_workers = num_bfs_threads; + branch_and_bound_settings.diving_settings.num_diving_workers = num_diving_threads; // Set the branch and bound -> primal heuristics callback branch_and_bound_settings.solution_callback = diff --git a/cpp/src/utilities/omp_helpers.hpp b/cpp/src/utilities/omp_helpers.hpp index 33eda66cb..e1b68bf88 100644 --- a/cpp/src/utilities/omp_helpers.hpp +++ b/cpp/src/utilities/omp_helpers.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -53,7 +53,7 @@ class omp_atomic_t { T operator--() { return fetch_sub(T(1)) - 1; } T operator--(int) { return fetch_sub(T(1)); } - T load() + T load() const { T res; #pragma omp atomic read From 25b0bed9c16f9cd249fce75d8b54d692b320146a Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 7 Jan 2026 16:01:08 +0100 Subject: [PATCH 146/366] several bug fixes --- cpp/src/dual_simplex/bnb_worker.hpp | 1 + cpp/src/dual_simplex/branch_and_bound.cpp | 12 +++++++----- cpp/src/dual_simplex/branch_and_bound.hpp | 1 + cpp/src/dual_simplex/node_queue.hpp | 20 ++++++++++++-------- 4 files changed, 21 insertions(+), 13 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index f2ec37ca1..b30f6d485 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -113,6 +113,7 @@ class bnb_worker_pool_t { const std::vector& var_type, const simplex_solver_settings_t& settings) { + workers_.resize(num_workers); for (i_t i = 0; i < num_workers; ++i) { workers_[i] = std::make_unique>(i, original_lp, Arow, var_type, settings); diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index dcf0be8a2..9bd423ff7 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -946,6 +946,7 @@ void branch_and_bound_t::plunge_with(bnb_worker_t* worker) ++exploration_stats_.nodes_explored; --exploration_stats_.nodes_unexplored; + ++nodes_since_last_log; if (status == node_solve_info_t::TIME_LIMIT) { break; @@ -1058,8 +1059,7 @@ void branch_and_bound_t::master_loop() f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); i_t last_node_depth = 0; - f_t last_log = 0.0; - i_t nodes_since_last_log = 0; + f_t last_log = 0.0; diving_heuristics_settings_t diving_settings = settings_.diving_settings; if (!std::isfinite(upper_bound)) { diving_settings.disable_guided_diving = true; } @@ -1096,11 +1096,13 @@ void branch_and_bound_t::master_loop() (time_since_last_log > 30) || now > settings_.time_limit) { i_t depth = node_queue.best_first_queue_size() > 0 ? node_queue.bfs_top()->depth : last_node_depth; - report(" ", upper_bound, lower_bound, depth); + report(" ", upper_bound, lower_bound, depth); last_log = tic(); nodes_since_last_log = 0; } + if (now > settings_.time_limit) { break; } + // There is no node in the queue, so we suspend temporarily the execution of the master // so it can execute a worker task instead. if (node_queue.best_first_queue_size() == 0 && node_queue.diving_queue_size() == 0) { @@ -1118,7 +1120,7 @@ void branch_and_bound_t::master_loop() // If there any node left in the heap, we pop the top node and explore it. std::optional*> start_node = node_queue.pop_best_first(); - if (start_node.has_value()) { + if (!start_node.has_value()) { worker_pool_.return_worker_to_pool(worker); continue; } @@ -1144,7 +1146,7 @@ void branch_and_bound_t::master_loop() } else { std::optional*> start_node = node_queue.pop_diving(); - if (start_node.has_value()) { + if (!start_node.has_value()) { worker_pool_.return_worker_to_pool(worker); continue; } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index cf8e57db5..017fd9efc 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -165,6 +165,7 @@ class branch_and_bound_t { omp_atomic_t solver_status_; omp_atomic_t should_report_; + omp_atomic_t nodes_since_last_log; // In case, a best-first thread encounters a numerical issue when solving a node, // its blocks the progression of the lower bound. diff --git a/cpp/src/dual_simplex/node_queue.hpp b/cpp/src/dual_simplex/node_queue.hpp index 8efe6087c..47b31f590 100644 --- a/cpp/src/dual_simplex/node_queue.hpp +++ b/cpp/src/dual_simplex/node_queue.hpp @@ -25,14 +25,12 @@ class heap_t { { buffer.push_back(node); std::push_heap(buffer.begin(), buffer.end(), comp); - buffer_size++; } void push(T&& node) { buffer.push_back(std::move(node)); std::push_heap(buffer.begin(), buffer.end(), comp); - buffer_size++; } template @@ -40,7 +38,6 @@ class heap_t { { buffer.emplace_back(std::forward(args)...); std::push_heap(buffer.begin(), buffer.end(), comp); - buffer_size++; } std::optional pop() @@ -50,18 +47,16 @@ class heap_t { std::pop_heap(buffer.begin(), buffer.end(), comp); T node = std::move(buffer.back()); buffer.pop_back(); - buffer_size--; return node; } - size_t size() const { return buffer_size.load(); } + size_t size() const { return buffer.size(); } T& top() { return buffer.front(); } void clear() { buffer.clear(); } bool empty() const { return buffer.empty(); } private: std::vector buffer; - omp_atomic_t buffer_size; Comp comp; }; @@ -138,8 +133,17 @@ class node_queue_t { return std::nullopt; } - size_t diving_queue_size() const { return diving_heap.size(); } - size_t best_first_queue_size() const { return best_first_heap.size(); } + i_t diving_queue_size() + { + std::lock_guard lock(mutex); + return diving_heap.size(); + } + + i_t best_first_queue_size() + { + std::lock_guard lock(mutex); + return best_first_heap.size(); + } f_t get_lower_bound() { From 25dac0283cdac7eb1e6e3ba4437ea775f88d8aab Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 7 Jan 2026 16:14:01 +0100 Subject: [PATCH 147/366] handle master suspension. added overdecomposition. --- cpp/src/dual_simplex/bnb_worker.hpp | 6 +++--- cpp/src/dual_simplex/branch_and_bound.cpp | 24 ++++++++++++----------- 2 files changed, 16 insertions(+), 14 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index b30f6d485..2cc48a271 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -184,14 +184,14 @@ std::array bnb_get_num_workers_round_robin( auto worker_types = bnb_get_worker_types(settings); max_num_workers.fill(0); - max_num_workers[EXPLORATION] = std::max(1, num_threads / 4); + max_num_workers[EXPLORATION] = std::max(1, num_threads / 2); - i_t diving_workers = settings.num_diving_workers; + i_t diving_workers = 2 * settings.num_diving_workers; i_t m = worker_types.size() - 1; for (size_t i = 1, k = 0; i < bnb_num_worker_types; ++i) { i_t start = (double)k * diving_workers / m; i_t end = (double)(k + 1) * diving_workers / m; - max_num_workers[i] = 2 * (end - start); + max_num_workers[i] = end - start; ++k; } diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 9bd423ff7..d0b376ef6 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1071,10 +1071,11 @@ void branch_and_bound_t::master_loop() while (solver_status_ == mip_exploration_status_t::RUNNING && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && (active_workers_per_type[0] > 0 || node_queue.best_first_queue_size() > 0)) { - lower_bound = get_lower_bound(); - upper_bound = get_upper_bound(); - abs_gap = upper_bound - lower_bound; - rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + bool launched_any_task = false; + lower_bound = get_lower_bound(); + upper_bound = get_upper_bound(); + abs_gap = upper_bound - lower_bound; + rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); repair_heuristic_solutions(); @@ -1103,13 +1104,6 @@ void branch_and_bound_t::master_loop() if (now > settings_.time_limit) { break; } - // There is no node in the queue, so we suspend temporarily the execution of the master - // so it can execute a worker task instead. - if (node_queue.best_first_queue_size() == 0 && node_queue.diving_queue_size() == 0) { -#pragma omp taskyield - continue; - } - for (auto type : worker_types) { if (active_workers_per_type[type] >= max_num_workers_per_type[type]) { continue; } @@ -1139,6 +1133,7 @@ void branch_and_bound_t::master_loop() last_node_depth = start_node.value()->depth; active_workers_per_type[type]++; nodes_since_last_log++; + launched_any_task = true; #pragma omp task plunge_with(worker); @@ -1164,11 +1159,18 @@ void branch_and_bound_t::master_loop() } active_workers_per_type[type]++; + launched_any_task = true; #pragma omp task dive_with(worker); } } + + // If no new task was launched in this iteration, suspend temporarily the execution of the + // master + if (!launched_any_task) { +#pragma omp taskyield + } } } From 741f373b8ea728afbf726c5ea89d51f351ef3f0b Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 7 Jan 2026 16:15:21 +0100 Subject: [PATCH 148/366] initial bb debug impl --- cpp/CMakeLists.txt | 3 + cpp/src/dual_simplex/bb_event.hpp | 228 ++++++ cpp/src/dual_simplex/bb_worker_state.hpp | 291 ++++++++ cpp/src/dual_simplex/branch_and_bound.cpp | 781 +++++++++++++++++++- cpp/src/dual_simplex/branch_and_bound.hpp | 64 ++ cpp/src/dual_simplex/bsp_debug.hpp | 843 ++++++++++++++++++++++ cpp/src/dual_simplex/mip_node.hpp | 30 + cpp/src/mip/solver.cu | 18 +- cpp/tests/mip/presolve_test.cu | 2 +- 9 files changed, 2235 insertions(+), 25 deletions(-) create mode 100644 cpp/src/dual_simplex/bb_event.hpp create mode 100644 cpp/src/dual_simplex/bb_worker_state.hpp create mode 100644 cpp/src/dual_simplex/bsp_debug.hpp diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 39d4ed78b..d8b392852 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -62,6 +62,9 @@ message(VERBOSE "cuOpt: fatbin: ${WRITE_FATBIN}") # CUDA runtime rapids_cuda_init_runtime(USE_STATIC ON) +message(STATUS "HEY") +find_package(CUDAToolkit) +message(STATUS "HEY") rapids_find_package(CUDAToolkit REQUIRED BUILD_EXPORT_SET cuopt-exports diff --git a/cpp/src/dual_simplex/bb_event.hpp b/cpp/src/dual_simplex/bb_event.hpp new file mode 100644 index 000000000..280863f6a --- /dev/null +++ b/cpp/src/dual_simplex/bb_event.hpp @@ -0,0 +1,228 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#include +#include +#include +#include +#include + +namespace cuopt::linear_programming::dual_simplex { + +// Event types generated by B&B workers during BSP execution +enum class bb_event_type_t : int8_t { + NODE_BRANCHED = 0, // Node was solved and branched into two children + NODE_FATHOMED = 1, // Node was fathomed (cutoff or infeasible) + NODE_INTEGER = 2, // Node has an integer feasible solution + NODE_PAUSED = 3, // Node processing paused at horizon boundary + NODE_INFEASIBLE = 4, // Node LP relaxation is infeasible + NODE_NUMERICAL = 5, // Numerical issue encountered + HEURISTIC_SOLUTION = 6 // Solution received from heuristics +}; + +// Payload for NODE_BRANCHED events +template +struct branched_payload_t { + i_t down_child_id; + i_t up_child_id; + f_t node_lower_bound; + i_t branch_var; + f_t branch_value; +}; + +// Payload for NODE_INTEGER events (integer feasible solution found) +template +struct integer_solution_payload_t { + f_t objective_value; + // Note: the full solution is stored separately, not in the event +}; + +// Payload for NODE_FATHOMED events +template +struct fathomed_payload_t { + f_t lower_bound; // Bound at which node was fathomed +}; + +// Payload for NODE_PAUSED events +template +struct paused_payload_t { + f_t accumulated_vt; // How much virtual time spent on this node so far +}; + +// Payload for HEURISTIC_SOLUTION events +template +struct heuristic_solution_payload_t { + f_t objective_value; + size_t solution_index; // Index into a solution storage vector +}; + +// A single event generated during BSP execution +template +struct bb_event_t { + bb_event_type_t type; + double vt_timestamp; // Virtual time when this event occurred + int worker_id; // Which worker generated this event + i_t node_id; // Node that generated this event + int event_sequence; // Sequence number within worker for tie-breaking + + // Payload union - only one is valid based on type + union { + branched_payload_t branched; + integer_solution_payload_t integer_solution; + fathomed_payload_t fathomed; + paused_payload_t paused; + heuristic_solution_payload_t heuristic; + } payload; + + // Default constructor + bb_event_t() + : type(bb_event_type_t::NODE_FATHOMED), + vt_timestamp(0.0), + worker_id(0), + node_id(0), + event_sequence(0) + { + payload.fathomed = {0.0}; + } + + // Comparison for deterministic sorting: (vt_timestamp, worker_id, event_sequence) + bool operator<(const bb_event_t& other) const + { + return std::tie(vt_timestamp, worker_id, event_sequence) < + std::tie(other.vt_timestamp, other.worker_id, other.event_sequence); + } + + // Factory methods for creating events + + static bb_event_t make_branched(double vt, + int worker, + i_t node, + int seq, + i_t down_id, + i_t up_id, + f_t lower_bound, + i_t branch_var, + f_t branch_val) + { + bb_event_t e; + e.type = bb_event_type_t::NODE_BRANCHED; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; + e.payload.branched = {down_id, up_id, lower_bound, branch_var, branch_val}; + return e; + } + + static bb_event_t make_integer_solution(double vt, int worker, i_t node, int seq, f_t objective) + { + bb_event_t e; + e.type = bb_event_type_t::NODE_INTEGER; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; + e.payload.integer_solution = {objective}; + return e; + } + + static bb_event_t make_fathomed(double vt, int worker, i_t node, int seq, f_t lower_bound) + { + bb_event_t e; + e.type = bb_event_type_t::NODE_FATHOMED; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; + e.payload.fathomed = {lower_bound}; + return e; + } + + static bb_event_t make_infeasible(double vt, int worker, i_t node, int seq) + { + bb_event_t e; + e.type = bb_event_type_t::NODE_INFEASIBLE; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; + e.payload.fathomed = {std::numeric_limits::infinity()}; + return e; + } + + static bb_event_t make_paused(double vt, int worker, i_t node, int seq, f_t accumulated) + { + bb_event_t e; + e.type = bb_event_type_t::NODE_PAUSED; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; + e.payload.paused = {accumulated}; + return e; + } + + static bb_event_t make_numerical(double vt, int worker, i_t node, int seq) + { + bb_event_t e; + e.type = bb_event_type_t::NODE_NUMERICAL; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; + e.payload.fathomed = {std::numeric_limits::infinity()}; + return e; + } + + static bb_event_t make_heuristic_solution(double vt, + int worker, + int seq, + f_t objective, + size_t sol_idx) + { + bb_event_t e; + e.type = bb_event_type_t::HEURISTIC_SOLUTION; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = -1; // Not associated with a B&B node + e.event_sequence = seq; + e.payload.heuristic = {objective, sol_idx}; + return e; + } +}; + +// Batch of events from a single horizon period +template +struct bb_event_batch_t { + std::vector> events; + double horizon_start; + double horizon_end; + + void clear() + { + events.clear(); + horizon_start = 0.0; + horizon_end = 0.0; + } + + void add(bb_event_t event) { events.push_back(std::move(event)); } + + // Sort events for deterministic replay + void sort_for_replay() { std::sort(events.begin(), events.end()); } + + size_t size() const { return events.size(); } + bool empty() const { return events.empty(); } +}; + +} // namespace cuopt::linear_programming::dual_simplex + + + + + diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp new file mode 100644 index 000000000..e759e5bee --- /dev/null +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -0,0 +1,291 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +namespace cuopt::linear_programming::dual_simplex { + +// Per-worker state for BSP (Bulk Synchronous Parallel) branch-and-bound +template +struct bb_worker_state_t { + int worker_id{0}; + + // Local node queue - buffer of nodes assigned to this worker for the current horizon + std::deque*> local_queue; + + // Current node being processed (may be paused at horizon boundary) + mip_node_t* current_node{nullptr}; + + // Worker's virtual time clock (cumulative work units) + double clock{0.0}; + + // Events generated during this horizon + bb_event_batch_t events; + + // Event sequence counter for deterministic tie-breaking + int event_sequence{0}; + + // LP problem copy for this worker (bounds modified per node) + std::unique_ptr> leaf_problem; + + // Basis factorization state + std::unique_ptr> basis_factors; + + // Bounds strengthening (node presolver) + std::unique_ptr> node_presolver; + + // Working vectors for basis + std::vector basic_list; + std::vector nonbasic_list; + + // Work unit context for this worker + work_limit_context_t work_context; + + // Whether basis needs recomputation for next node + bool recompute_bounds_and_basis{true}; + + // Statistics + i_t nodes_processed_this_horizon{0}; + double work_units_this_horizon{0.0}; + + // Constructor + explicit bb_worker_state_t(int id) : worker_id(id), work_context("BB_Worker_" + std::to_string(id)) + { + } + + // Initialize worker with problem data + void initialize(const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + i_t refactor_frequency, + bool deterministic) + { + // Create copy of LP problem for this worker + leaf_problem = std::make_unique>(original_lp); + + // Initialize basis factors + const i_t m = leaf_problem->num_rows; + basis_factors = std::make_unique>(m, refactor_frequency); + + // Initialize bounds strengthening + std::vector row_sense; + node_presolver = + std::make_unique>(*leaf_problem, Arow, row_sense, var_types); + + // Initialize working vectors + basic_list.resize(m); + nonbasic_list.clear(); + + // Configure work context + work_context.deterministic = deterministic; + } + + // Reset for new horizon + void reset_for_horizon(double horizon_start, double horizon_end) + { + // Reset clock to horizon_start for consistent VT timestamps across workers + clock = horizon_start; + events.clear(); + events.horizon_start = horizon_start; + events.horizon_end = horizon_end; + event_sequence = 0; + nodes_processed_this_horizon = 0; + work_units_this_horizon = 0.0; + // Also sync work_context to match clock for consistent tracking + work_context.global_work_units_elapsed = horizon_start; + } + + // Add a node to the local queue + void enqueue_node(mip_node_t* node) { local_queue.push_back(node); } + + // Get next node to process + mip_node_t* dequeue_node() + { + if (current_node != nullptr) { + // Resume paused node + mip_node_t* node = current_node; + current_node = nullptr; + return node; + } + if (local_queue.empty()) { return nullptr; } + mip_node_t* node = local_queue.front(); + local_queue.pop_front(); + return node; + } + + // Check if worker has work available + bool has_work() const { return current_node != nullptr || !local_queue.empty(); } + + // Get number of nodes in local queue (including paused node) + size_t queue_size() const + { + return local_queue.size() + (current_node != nullptr ? 1 : 0); + } + + // Record an event + void record_event(bb_event_t event) + { + event.event_sequence = event_sequence++; + events.add(std::move(event)); + } + + // Pause current node processing at horizon boundary + void pause_current_node(mip_node_t* node, double accumulated_vt) + { + node->accumulated_vt = accumulated_vt; + node->bsp_state = bsp_node_state_t::PAUSED; + current_node = node; + + record_event(bb_event_t::make_paused(clock, worker_id, node->node_id, 0, accumulated_vt)); + } + + // Record node branching event + void record_branched(mip_node_t* node, + i_t down_child_id, + i_t up_child_id, + i_t branch_var, + f_t branch_val) + { + record_event(bb_event_t::make_branched(clock, + worker_id, + node->node_id, + 0, + down_child_id, + up_child_id, + node->lower_bound, + branch_var, + branch_val)); + } + + // Record integer solution found + void record_integer_solution(mip_node_t* node, f_t objective) + { + record_event(bb_event_t::make_integer_solution(clock, worker_id, node->node_id, 0, objective)); + } + + // Record node fathomed + void record_fathomed(mip_node_t* node, f_t lower_bound) + { + record_event(bb_event_t::make_fathomed(clock, worker_id, node->node_id, 0, lower_bound)); + } + + // Record node infeasible + void record_infeasible(mip_node_t* node) + { + record_event(bb_event_t::make_infeasible(clock, worker_id, node->node_id, 0)); + } + + // Record numerical error + void record_numerical(mip_node_t* node) + { + record_event(bb_event_t::make_numerical(clock, worker_id, node->node_id, 0)); + } + + // Update clock with work units + void advance_clock(double work_units) + { + clock += work_units; + work_units_this_horizon += work_units; + work_context.record_work(work_units); + } +}; + +// Container for all worker states in BSP B&B +template +class bb_worker_pool_t { + public: + bb_worker_pool_t() = default; + + // Initialize pool with specified number of workers + void initialize(int num_workers, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + i_t refactor_frequency, + bool deterministic) + { + workers_.clear(); + workers_.reserve(num_workers); + for (int i = 0; i < num_workers; ++i) { + workers_.emplace_back(i); + workers_.back().initialize(original_lp, Arow, var_types, refactor_frequency, deterministic); + } + } + + // Get worker by ID + bb_worker_state_t& operator[](int worker_id) { return workers_[worker_id]; } + + const bb_worker_state_t& operator[](int worker_id) const { return workers_[worker_id]; } + + // Get number of workers + int size() const { return static_cast(workers_.size()); } + + // Reset all workers for new horizon + void reset_for_horizon(double horizon_start, double horizon_end) + { + for (auto& worker : workers_) { + worker.reset_for_horizon(horizon_start, horizon_end); + } + } + + // Collect all events from all workers into a single sorted batch + bb_event_batch_t collect_and_sort_events() + { + bb_event_batch_t all_events; + for (auto& worker : workers_) { + for (auto& event : worker.events.events) { + all_events.add(std::move(event)); + } + worker.events.clear(); + } + all_events.sort_for_replay(); + return all_events; + } + + // Check if any worker has work + bool any_has_work() const + { + for (const auto& worker : workers_) { + if (worker.has_work()) return true; + } + return false; + } + + // Get total queue size across all workers + size_t total_queue_size() const + { + size_t total = 0; + for (const auto& worker : workers_) { + total += worker.queue_size(); + } + return total; + } + + // Iterator support + auto begin() { return workers_.begin(); } + auto end() { return workers_.end(); } + auto begin() const { return workers_.begin(); } + auto end() const { return workers_.end(); } + + private: + std::vector> workers_; +}; + +} // namespace cuopt::linear_programming::dual_simplex + diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index bdb646c7d..c19a9656d 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -28,6 +28,7 @@ #include #include #include +#include #include #include #include @@ -226,7 +227,8 @@ branch_and_bound_t::branch_and_bound_t( root_relax_soln_(1, 1), root_crossover_soln_(1, 1), pc_(1), - solver_status_(mip_exploration_status_t::UNSET) + solver_status_(mip_exploration_status_t::UNSET), + bsp_debug_settings_(bsp_debug_settings_t::from_environment()) { exploration_stats_.start_time = tic(); dualize_info_t dualize_info; @@ -494,6 +496,45 @@ void branch_and_bound_t::set_new_solution(const std::vector& solu } } +template +void branch_and_bound_t::set_new_solution_deterministic(const std::vector& solution, + double vt_timestamp) +{ + // In BSP mode, queue the solution to be processed at the correct virtual time + // This ensures deterministic ordering of solution events + + if (solution.size() != original_problem_.num_cols) { + settings_.log.printf( + "Solution size mismatch %ld %d\n", solution.size(), original_problem_.num_cols); + return; + } + + std::vector crushed_solution; + crush_primal_solution( + original_problem_, original_lp_, solution, new_slacks_, crushed_solution); + f_t obj = compute_objective(original_lp_, crushed_solution); + + // Validate solution before queueing + f_t primal_err; + f_t bound_err; + i_t num_fractional; + bool is_feasible = check_guess( + original_lp_, settings_, var_types_, crushed_solution, primal_err, bound_err, num_fractional); + + if (!is_feasible) { + // Queue for repair + mutex_repair_.lock(); + repair_queue_.push_back(crushed_solution); + mutex_repair_.unlock(); + return; + } + + // Queue the solution with its VT timestamp + mutex_heuristic_queue_.lock(); + heuristic_solution_queue_.push_back({std::move(crushed_solution), obj, vt_timestamp}); + mutex_heuristic_queue_.unlock(); +} + template bool branch_and_bound_t::repair_solution(const std::vector& edge_norms, const std::vector& potential_solution, @@ -693,9 +734,10 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, i_t leaf_depth, thread_type_t thread_type) { - bool send_solution = false; - i_t nodes_explored = exploration_stats_.nodes_explored; - i_t nodes_unexplored = exploration_stats_.nodes_unexplored; + bool send_solution = false; + bool improved_incumbent = false; + i_t nodes_explored = exploration_stats_.nodes_explored; + i_t nodes_unexplored = exploration_stats_.nodes_unexplored; mutex_upper_.lock(); if (leaf_objective < upper_bound_) { @@ -716,7 +758,8 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, user_mip_gap(obj, lower).c_str(), toc(exploration_stats_.start_time)); - send_solution = true; + send_solution = true; + improved_incumbent = true; } if (send_solution && settings_.solution_callback != nullptr) { @@ -725,6 +768,12 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, settings_.solution_callback(original_x, upper_bound_); } mutex_upper_.unlock(); + + // Debug: Log incumbent update (after releasing mutex to avoid potential issues) + if (improved_incumbent && bsp_debug_settings_.any_enabled() && bsp_mode_enabled_) { + std::string source = (thread_type == thread_type_t::EXPLORATION) ? "bb_integer" : "diving"; + bsp_debug_logger_.log_incumbent_update(bsp_current_horizon_, leaf_objective, source); + } } template @@ -1748,34 +1797,42 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut work_unit_context_.deterministic = settings_.deterministic; should_report_ = true; + // Choose between BSP coordinator (deterministic) and opportunistic exploration + if (settings_.deterministic && settings_.num_bfs_threads > 0) { + // Use deterministic BSP coordinator for parallel execution + settings_.log.printf("Using BSP coordinator for deterministic parallel B&B\n"); + run_bsp_coordinator(Arow); + } else { + // Use traditional opportunistic parallel exploration #pragma omp parallel num_threads(settings_.num_threads) - { - raft::common::nvtx::range scope_tree("BB::tree_exploration"); -#pragma omp master { - auto down_child = search_tree_.root.get_down_child(); - auto up_child = search_tree_.root.get_up_child(); - i_t initial_size = 2 * settings_.num_threads; + raft::common::nvtx::range scope_tree("BB::tree_exploration"); +#pragma omp master + { + auto down_child = search_tree_.root.get_down_child(); + auto up_child = search_tree_.root.get_up_child(); + i_t initial_size = 2 * settings_.num_threads; #pragma omp task - exploration_ramp_up(down_child, &search_tree_, Arow, initial_size); + exploration_ramp_up(down_child, &search_tree_, Arow, initial_size); #pragma omp task - exploration_ramp_up(up_child, &search_tree_, Arow, initial_size); - } + exploration_ramp_up(up_child, &search_tree_, Arow, initial_size); + } #pragma omp barrier #pragma omp master - { - for (i_t i = 0; i < settings_.num_bfs_threads; i++) { + { + for (i_t i = 0; i < settings_.num_bfs_threads; i++) { #pragma omp task - best_first_thread(i, search_tree_, Arow); - } + best_first_thread(i, search_tree_, Arow); + } - for (i_t i = 0; i < settings_.num_diving_threads; i++) { + for (i_t i = 0; i < settings_.num_diving_threads; i++) { #pragma omp task - diving_thread(Arow); + diving_thread(Arow); + } } } } @@ -1789,6 +1846,690 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut return set_final_solution(solution, lower_bound); } +// ============================================================================ +// BSP (Bulk Synchronous Parallel) Implementation +// ============================================================================ + +template +void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t& Arow) +{ + raft::common::nvtx::range scope("BB::bsp_coordinator"); + + const int num_workers = settings_.num_bfs_threads; + bsp_mode_enabled_ = true; + bsp_current_horizon_ = bsp_horizon_step_; + bsp_horizon_number_ = 0; + + // Initialize worker pool + bsp_workers_ = std::make_unique>(); + bsp_workers_->initialize(num_workers, + original_lp_, + Arow, + var_types_, + settings_.refactor_frequency, + settings_.deterministic); + + // Initialize debug logger + bsp_debug_logger_.set_settings(bsp_debug_settings_); + bsp_debug_logger_.set_num_workers(num_workers); + bsp_debug_logger_.set_horizon_step(bsp_horizon_step_); + + settings_.log.printf("BSP Mode: %d workers, horizon step = %.2f work units\n", + num_workers, + bsp_horizon_step_); + + // Push initial children to the global heap + // Set deterministic final_ids for root children (1 and 2) + search_tree_.root.get_down_child()->final_id = 1; + search_tree_.root.get_up_child()->final_id = 2; + bsp_next_final_id_ = 3; // Next ID to assign + + heap_.push(search_tree_.root.get_down_child()); + heap_.push(search_tree_.root.get_up_child()); + + const f_t inf = std::numeric_limits::infinity(); + f_t lower_bound = get_lower_bound(); + f_t upper_bound = get_upper_bound(); + f_t abs_gap = upper_bound - lower_bound; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + + constexpr i_t target_queue_size = 5; // Target nodes per worker + + // Main BSP coordinator loop + while (solver_status_ == mip_exploration_status_t::RUNNING && + abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && + (heap_.size() > 0 || bsp_workers_->any_has_work())) { + + ++bsp_horizon_number_; + double horizon_start = bsp_current_horizon_ - bsp_horizon_step_; + double horizon_end = bsp_current_horizon_; + + // Debug: Log horizon start + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_horizon_start(bsp_horizon_number_, horizon_start, horizon_end); + } + + // PHASE 1: ASSIGNMENT - Fill worker queues + refill_worker_queues(target_queue_size); + + // Flush assignment trace after all assignments + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.flush_assign_trace(); + } + + // Reset workers for new horizon + bsp_workers_->reset_for_horizon(horizon_start, horizon_end); + + // PHASE 2: PARALLEL EXECUTION - Workers run until horizon +#pragma omp parallel num_threads(num_workers) + { + int worker_id = omp_get_thread_num(); + auto& worker = (*bsp_workers_)[worker_id]; + + // Run worker until horizon + run_worker_until_horizon(worker, search_tree_, bsp_current_horizon_); + } + + // PHASE 3: SYNCHRONIZATION - The Barrier + // Collect and sort all events deterministically + bb_event_batch_t all_events = bsp_workers_->collect_and_sort_events(); + + // Debug: Log sync phase + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_sync_phase_start(horizon_end, all_events.size()); + } + + // Process history and sync + process_history_and_sync(all_events); + + // Debug: Flush final IDs trace and log sync end + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.flush_final_ids_trace(); + bsp_debug_logger_.log_sync_phase_end(horizon_end); + } + + // Prune paused nodes that are now dominated by new incumbent + prune_worker_nodes_vs_incumbent(); + + // Debug: Log horizon end, emit tree state and JSON state + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_horizon_end(bsp_horizon_number_, horizon_end); + + // Emit tree state (DOT format) + bsp_debug_logger_.emit_tree_state(bsp_horizon_number_, search_tree_.root, get_upper_bound()); + + // Collect heap nodes for JSON state + std::vector*> heap_snapshot; + // Note: We can't easily iterate the heap, so just log the size + bsp_debug_logger_.emit_state_json(bsp_horizon_number_, horizon_start, horizon_end, + bsp_next_final_id_, get_upper_bound(), get_lower_bound(), + exploration_stats_.nodes_explored, + exploration_stats_.nodes_unexplored, *bsp_workers_, + heap_snapshot, all_events); + } + + // Advance the horizon + bsp_current_horizon_ += bsp_horizon_step_; + + // Update gap info + lower_bound = get_lower_bound(); + upper_bound = get_upper_bound(); + abs_gap = upper_bound - lower_bound; + rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + + // Check time/work limits + if (toc(exploration_stats_.start_time) > settings_.time_limit) { + solver_status_ = mip_exploration_status_t::TIME_LIMIT; + } + if (settings_.deterministic && + work_unit_context_.global_work_units_elapsed >= settings_.work_limit) { + solver_status_ = mip_exploration_status_t::WORK_LIMIT; + } + + // Progress logging + f_t obj = compute_user_objective(original_lp_, upper_bound); + f_t user_lower = compute_user_objective(original_lp_, lower_bound); + std::string gap_user = user_mip_gap(obj, user_lower); + i_t nodes_explored = exploration_stats_.nodes_explored; + i_t nodes_unexplored = exploration_stats_.nodes_unexplored; + + settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %s %9.2f\n", + nodes_explored, + nodes_unexplored, + obj, + user_lower, + gap_user.c_str(), + toc(exploration_stats_.start_time)); + } + + // Finalize debug logger + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.finalize(); + } + + // Mark completed if we finished exploring + if (solver_status_ == mip_exploration_status_t::RUNNING) { + solver_status_ = mip_exploration_status_t::COMPLETED; + } +} + +template +void branch_and_bound_t::refill_worker_queues(i_t target_queue_size) +{ + // Distribute nodes from global pool to workers in round-robin fashion + // This ensures deterministic assignment based on node ordering in the heap + + std::vector*> nodes_to_assign; + + // Pop nodes from heap while respecting incumbent bound + mutex_heap_.lock(); + while (!heap_.empty()) { + mip_node_t* node = heap_.top(); + + // Skip pruned nodes + if (node->lower_bound >= get_upper_bound()) { + heap_.pop(); + search_tree_.update(node, node_status_t::FATHOMED); + --exploration_stats_.nodes_unexplored; + continue; + } + + // Check if we have enough nodes + if (nodes_to_assign.size() >= static_cast(target_queue_size * bsp_workers_->size())) { + break; + } + + heap_.pop(); + nodes_to_assign.push_back(node); + } + mutex_heap_.unlock(); + + // Assign nodes to workers deterministically (round-robin by deterministic ID order) + // Use get_deterministic_id() which returns final_id if set, else provisional_id, else node_id + std::sort(nodes_to_assign.begin(), nodes_to_assign.end(), + [](const mip_node_t* a, const mip_node_t* b) { + return a->get_deterministic_id() < b->get_deterministic_id(); + }); + + for (size_t i = 0; i < nodes_to_assign.size(); ++i) { + int worker_id = i % bsp_workers_->size(); + auto* node = nodes_to_assign[i]; + (*bsp_workers_)[worker_id].enqueue_node(node); + + // Debug: Log node assignment + if (bsp_debug_settings_.any_enabled()) { + double vt = bsp_current_horizon_ - bsp_horizon_step_; // Start of current horizon + bsp_debug_logger_.log_node_assigned(vt, worker_id, node->node_id, node->final_id, + node->lower_bound); + } + } +} + +template +void branch_and_bound_t::run_worker_until_horizon(bb_worker_state_t& worker, + search_tree_t& search_tree, + double current_horizon) +{ + raft::common::nvtx::range scope("BB::worker_run"); + + while (worker.clock < current_horizon && worker.has_work() && + solver_status_ == mip_exploration_status_t::RUNNING) { + + mip_node_t* node = worker.dequeue_node(); + if (node == nullptr) break; + + // Check if node should be pruned + f_t upper_bound = get_upper_bound(); + if (node->lower_bound >= upper_bound) { + worker.record_fathomed(node, node->lower_bound); + search_tree.update(node, node_status_t::FATHOMED); + --exploration_stats_.nodes_unexplored; + continue; + } + + // Solve the node (this records events) + node_solve_info_t status = solve_node_bsp(worker, node, search_tree, current_horizon); + + // Handle result + if (status == node_solve_info_t::TIME_LIMIT) { + solver_status_ = mip_exploration_status_t::TIME_LIMIT; + break; + } else if (status == node_solve_info_t::WORK_LIMIT) { + // Node paused at horizon - already handled in solve_node_bsp + break; + } + } +} + +template +node_solve_info_t branch_and_bound_t::solve_node_bsp( + bb_worker_state_t& worker, + mip_node_t* node_ptr, + search_tree_t& search_tree, + double current_horizon) +{ + raft::common::nvtx::range scope("BB::solve_node_bsp"); + + // Track work units at start (from work_context, which simplex solver updates) + double work_units_at_start = worker.work_context.global_work_units_elapsed; + double clock_at_start = worker.clock; + bool is_resumed = (node_ptr->bsp_state == bsp_node_state_t::PAUSED); + + // Debug: Log solve start + if (bsp_debug_settings_.any_enabled()) { + double work_limit = current_horizon - worker.clock; + bsp_debug_logger_.log_solve_start(worker.clock, worker.worker_id, node_ptr->node_id, + node_ptr->final_id, work_limit, is_resumed); + } + + // Setup leaf problem bounds + std::fill(worker.node_presolver->bounds_changed.begin(), + worker.node_presolver->bounds_changed.end(), + false); + + if (worker.recompute_bounds_and_basis) { + worker.leaf_problem->lower = original_lp_.lower; + worker.leaf_problem->upper = original_lp_.upper; + node_ptr->get_variable_bounds(worker.leaf_problem->lower, + worker.leaf_problem->upper, + worker.node_presolver->bounds_changed); + } else { + node_ptr->update_branched_variable_bounds(worker.leaf_problem->lower, + worker.leaf_problem->upper, + worker.node_presolver->bounds_changed); + } + + // Bounds strengthening + simplex_solver_settings_t lp_settings = settings_; + lp_settings.set_log(false); + lp_settings.cut_off = get_upper_bound() + settings_.dual_tol; + lp_settings.inside_mip = 2; + lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); + // Work limit is the remaining VT budget in this horizon + // Note: accumulated_vt tracks work already spent (for statistics), but doesn't increase budget + lp_settings.work_limit = current_horizon - worker.clock; + lp_settings.scale_columns = false; + + bool feasible = worker.node_presolver->bounds_strengthening( + worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + + if (!feasible) { + node_ptr->lower_bound = std::numeric_limits::infinity(); + search_tree.update(node_ptr, node_status_t::INFEASIBLE); + worker.record_infeasible(node_ptr); + --exploration_stats_.nodes_unexplored; + ++exploration_stats_.nodes_explored; + worker.recompute_bounds_and_basis = true; + return node_solve_info_t::NO_CHILDREN; + } + + // Solve LP relaxation + lp_solution_t leaf_solution(worker.leaf_problem->num_rows, worker.leaf_problem->num_cols); + std::vector& leaf_vstatus = node_ptr->vstatus; + i_t node_iter = 0; + f_t lp_start_time = tic(); + std::vector leaf_edge_norms = edge_norms_; + + dual::status_t lp_status = dual_phase2_with_advanced_basis( + 2, + 0, + worker.recompute_bounds_and_basis, + lp_start_time, + *worker.leaf_problem, + lp_settings, + leaf_vstatus, + *worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms, + &worker.work_context); + + // Update worker clock with work performed + // The simplex solver recorded work to work_context.global_work_units_elapsed + // Compute the delta and advance the worker clock (but don't double-record to work_context) + double work_performed = worker.work_context.global_work_units_elapsed - work_units_at_start; + worker.clock += work_performed; + worker.work_units_this_horizon += work_performed; + // Note: don't call advance_clock() as work_context was already updated by simplex solver + + // Check if we hit the horizon mid-solve + if (lp_status == dual::status_t::WORK_LIMIT || worker.clock >= current_horizon) { + // Pause this node - accumulated_vt is the total work spent on this node + double accumulated_vt = (worker.clock - clock_at_start) + node_ptr->accumulated_vt; + worker.pause_current_node(node_ptr, accumulated_vt); + + // Debug: Log solve end (paused) + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, + node_ptr->final_id, "PAUSED", node_ptr->lower_bound); + bsp_debug_logger_.log_paused(worker.clock, worker.worker_id, node_ptr->node_id, + node_ptr->final_id, static_cast(accumulated_vt)); + } + return node_solve_info_t::WORK_LIMIT; + } + + exploration_stats_.total_lp_solve_time += toc(lp_start_time); + exploration_stats_.total_lp_iters += node_iter; + ++exploration_stats_.nodes_explored; + --exploration_stats_.nodes_unexplored; + + // Process LP result + if (lp_status == dual::status_t::DUAL_UNBOUNDED) { + node_ptr->lower_bound = std::numeric_limits::infinity(); + search_tree.update(node_ptr, node_status_t::INFEASIBLE); + worker.record_infeasible(node_ptr); + worker.recompute_bounds_and_basis = true; + + // Debug: Log solve end (infeasible) + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, + node_ptr->final_id, "INFEASIBLE", node_ptr->lower_bound); + bsp_debug_logger_.log_infeasible(worker.clock, worker.worker_id, node_ptr->node_id); + } + return node_solve_info_t::NO_CHILDREN; + + } else if (lp_status == dual::status_t::CUTOFF) { + node_ptr->lower_bound = get_upper_bound(); + search_tree.update(node_ptr, node_status_t::FATHOMED); + worker.record_fathomed(node_ptr, node_ptr->lower_bound); + worker.recompute_bounds_and_basis = true; + + // Debug: Log solve end (fathomed - cutoff) + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, + node_ptr->final_id, "FATHOMED", node_ptr->lower_bound); + bsp_debug_logger_.log_fathomed(worker.clock, worker.worker_id, node_ptr->node_id, + node_ptr->lower_bound); + } + return node_solve_info_t::NO_CHILDREN; + + } else if (lp_status == dual::status_t::OPTIMAL) { + std::vector leaf_fractional; + i_t leaf_num_fractional = + fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); + + f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); + node_ptr->lower_bound = leaf_objective; + pc_.update_pseudo_costs(node_ptr, leaf_objective); + + if (leaf_num_fractional == 0) { + // Integer feasible + add_feasible_solution(leaf_objective, leaf_solution.x, node_ptr->depth, thread_type_t::EXPLORATION); + search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); + worker.record_integer_solution(node_ptr, leaf_objective); + worker.recompute_bounds_and_basis = true; + + // Debug: Log solve end (integer) + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, + node_ptr->final_id, "INTEGER", leaf_objective); + bsp_debug_logger_.log_integer(worker.clock, worker.worker_id, node_ptr->node_id, + leaf_objective); + } + return node_solve_info_t::NO_CHILDREN; + + } else if (leaf_objective <= get_upper_bound() + settings_.absolute_mip_gap_tol / 10) { + // Branch + logger_t log; + log.log = false; + const i_t branch_var = pc_.variable_selection(leaf_fractional, leaf_solution.x, log); + + search_tree.branch(node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, *worker.leaf_problem, log); + search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); + + i_t down_child_id = node_ptr->get_down_child()->node_id; + i_t up_child_id = node_ptr->get_up_child()->node_id; + worker.record_branched(node_ptr, down_child_id, up_child_id, branch_var, leaf_solution.x[branch_var]); + + // Debug: Log solve end (branched) and branched event + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, + node_ptr->final_id, "BRANCH", leaf_objective); + bsp_debug_logger_.log_branched(worker.clock, worker.worker_id, node_ptr->node_id, + down_child_id, up_child_id); + } + + exploration_stats_.nodes_unexplored += 2; + worker.recompute_bounds_and_basis = false; + + // Add children to worker's local queue (deterministic order: down first) + worker.enqueue_node(node_ptr->get_down_child()); + worker.enqueue_node(node_ptr->get_up_child()); + + return rounding_direction_t::DOWN == child_selection(node_ptr) + ? node_solve_info_t::DOWN_CHILD_FIRST + : node_solve_info_t::UP_CHILD_FIRST; + + } else { + search_tree.update(node_ptr, node_status_t::FATHOMED); + worker.record_fathomed(node_ptr, leaf_objective); + worker.recompute_bounds_and_basis = true; + + // Debug: Log solve end (fathomed by bound) + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, + node_ptr->final_id, "FATHOMED", leaf_objective); + bsp_debug_logger_.log_fathomed(worker.clock, worker.worker_id, node_ptr->node_id, + leaf_objective); + } + return node_solve_info_t::NO_CHILDREN; + } + + } else if (lp_status == dual::status_t::TIME_LIMIT) { + return node_solve_info_t::TIME_LIMIT; + + } else { + // Numerical issue + search_tree.update(node_ptr, node_status_t::NUMERICAL); + worker.record_numerical(node_ptr); + worker.recompute_bounds_and_basis = true; + return node_solve_info_t::NUMERICAL; + } +} + +template +void branch_and_bound_t::process_history_and_sync(const bb_event_batch_t& events) +{ + // Collect queued heuristic solutions + std::vector heuristic_solutions; + mutex_heuristic_queue_.lock(); + heuristic_solutions = std::move(heuristic_solution_queue_); + heuristic_solution_queue_.clear(); + mutex_heuristic_queue_.unlock(); + + // Sort heuristic solutions by VT + std::sort(heuristic_solutions.begin(), heuristic_solutions.end(), + [](const queued_heuristic_solution_t& a, const queued_heuristic_solution_t& b) { + return a.vt_timestamp < b.vt_timestamp; + }); + + // Build mapping from provisional_id -> final_id for deterministic node ordering + // This is populated during event processing and applied to nodes afterward + std::unordered_map provisional_to_final_id; + + // Merge B&B events and heuristic solutions for unified timeline replay + // Both are sorted by VT, so we can do a merge-style iteration + size_t event_idx = 0; + size_t heuristic_idx = 0; + + while (event_idx < events.events.size() || heuristic_idx < heuristic_solutions.size()) { + bool process_event = false; + bool process_heuristic = false; + + if (event_idx >= events.events.size()) { + process_heuristic = true; + } else if (heuristic_idx >= heuristic_solutions.size()) { + process_event = true; + } else { + // Both have items - pick the one with smaller VT + if (events.events[event_idx].vt_timestamp <= heuristic_solutions[heuristic_idx].vt_timestamp) { + process_event = true; + } else { + process_heuristic = true; + } + } + + if (process_event) { + const auto& event = events.events[event_idx++]; + switch (event.type) { + case bb_event_type_t::NODE_INTEGER: { + // Check if this solution beats the incumbent KNOWN AT THAT VIRTUAL TIME + f_t obj = event.payload.integer_solution.objective_value; + mutex_upper_.lock(); + if (obj < upper_bound_) { + // Note: The actual solution was already stored during solve_node_bsp + // Here we just acknowledge the event order + } + mutex_upper_.unlock(); + break; + } + + case bb_event_type_t::NODE_BRANCHED: { + // Assign deterministic final IDs to the children + // Events are processed in sorted (deterministic) order, so this assignment is deterministic + i_t down_provisional = event.payload.branched.down_child_id; + i_t up_provisional = event.payload.branched.up_child_id; + + i_t down_final_id = bsp_next_final_id_++; + i_t up_final_id = bsp_next_final_id_++; + provisional_to_final_id[down_provisional] = down_final_id; + provisional_to_final_id[up_provisional] = up_final_id; + + // Debug: Log final ID assignments + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_final_id_assigned(down_provisional, down_final_id); + bsp_debug_logger_.log_final_id_assigned(up_provisional, up_final_id); + } + break; + } + + case bb_event_type_t::NODE_FATHOMED: + case bb_event_type_t::NODE_INFEASIBLE: + case bb_event_type_t::NODE_NUMERICAL: + case bb_event_type_t::NODE_PAUSED: + case bb_event_type_t::HEURISTIC_SOLUTION: + // These events don't need additional processing during replay + break; + } + } + + if (process_heuristic) { + const auto& hsol = heuristic_solutions[heuristic_idx++]; + + // Debug: Log heuristic received + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_heuristic_received(hsol.vt_timestamp, hsol.objective); + } + + // Process heuristic solution at its correct VT position + // FIX: Release mutex before calling get_lower_bound() to avoid deadlock + f_t new_upper = std::numeric_limits::infinity(); + + mutex_upper_.lock(); + if (hsol.objective < upper_bound_) { + upper_bound_ = hsol.objective; + incumbent_.set_incumbent_solution(hsol.objective, hsol.solution); + new_upper = hsol.objective; + + // Debug: Log incumbent update + if (bsp_debug_settings_.any_enabled()) { + bsp_debug_logger_.log_incumbent_update(hsol.vt_timestamp, hsol.objective, "heuristic"); + } + } + mutex_upper_.unlock(); + + // Log after releasing mutex to avoid holding mutex while calling get_lower_bound() + if (new_upper < std::numeric_limits::infinity()) { + f_t user_obj = compute_user_objective(original_lp_, new_upper); + f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); + std::string gap = user_mip_gap(user_obj, user_lower); + + settings_.log.printf( + "H %+13.6e %+10.6e %s %9.2f\n", + user_obj, + user_lower, + gap.c_str(), + toc(exploration_stats_.start_time)); + } + } + } + + // Apply final IDs to all nodes in worker queues and global heap + // Helper lambda to apply final_id mapping to a node + auto apply_final_id = [&provisional_to_final_id](mip_node_t* node) { + if (node->final_id < 0 && node->node_id > 0) { + // Use node_id as provisional_id (they are the same during BSP) + auto it = provisional_to_final_id.find(node->node_id); + if (it != provisional_to_final_id.end()) { + node->final_id = it->second; + } + } + }; + + // Apply to worker local queues + for (auto& worker : *bsp_workers_) { + for (auto* node : worker.local_queue) { + apply_final_id(node); + } + if (worker.current_node != nullptr) { + apply_final_id(worker.current_node); + } + } + + // Get current upper bound for pruning decisions + f_t upper_bound = get_upper_bound(); + + // Move ALL nodes from worker local queues to global heap for redistribution + // This ensures proper work distribution across workers each horizon + mutex_heap_.lock(); + for (auto& worker : *bsp_workers_) { + while (!worker.local_queue.empty()) { + mip_node_t* node = worker.local_queue.front(); + worker.local_queue.pop_front(); + // Only push non-pruned nodes + if (node->lower_bound < upper_bound) { + heap_.push(node); + } else { + search_tree_.update(node, node_status_t::FATHOMED); + --exploration_stats_.nodes_unexplored; + } + } + } + mutex_heap_.unlock(); +} + +template +void branch_and_bound_t::prune_worker_nodes_vs_incumbent() +{ + f_t upper_bound = get_upper_bound(); + + for (auto& worker : *bsp_workers_) { + // Check paused node + if (worker.current_node != nullptr) { + if (worker.current_node->lower_bound >= upper_bound) { + // Prune the paused node + search_tree_.update(worker.current_node, node_status_t::FATHOMED); + --exploration_stats_.nodes_unexplored; + worker.current_node = nullptr; + } + } + + // Check nodes in local queue + auto it = worker.local_queue.begin(); + while (it != worker.local_queue.end()) { + if ((*it)->lower_bound >= upper_bound) { + search_tree_.update(*it, node_status_t::FATHOMED); + --exploration_stats_.nodes_unexplored; + it = worker.local_queue.erase(it); + } else { + ++it; + } + } + } +} + #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE template class branch_and_bound_t; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 86b2c80a9..b021e029f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -7,6 +7,9 @@ #pragma once +#include +#include +#include #include #include #include @@ -152,6 +155,10 @@ class branch_and_bound_t { // Set a solution based on the user problem during the course of the solve void set_new_solution(const std::vector& solution); + // BSP-aware solution injection for deterministic mode + // This queues the solution to be processed at the correct virtual time + void set_new_solution_deterministic(const std::vector& solution, double vt_timestamp); + void set_concurrent_lp_root_solve(bool enable) { enable_concurrent_lp_root_solve_ = enable; } // Repair a low-quality solution from the heuristics. @@ -328,6 +335,63 @@ class branch_and_bound_t { // Sort the children based on the Martin's criteria. rounding_direction_t child_selection(mip_node_t* node_ptr); + + // ============================================================================ + // BSP (Bulk Synchronous Parallel) methods for deterministic parallel B&B + // ============================================================================ + + // Main BSP coordinator loop - runs in deterministic mode + void run_bsp_coordinator(const csr_matrix_t& Arow); + + // Refill worker queues from the global pool + void refill_worker_queues(i_t target_queue_size); + + // Run a single worker until it reaches the horizon + void run_worker_until_horizon(bb_worker_state_t& worker, + search_tree_t& search_tree, + double current_horizon); + + // Process history and synchronize - the "brain" of BSP + void process_history_and_sync(const bb_event_batch_t& events); + + // Prune nodes held by workers based on new incumbent + void prune_worker_nodes_vs_incumbent(); + + // BSP-specific node solving that records events + node_solve_info_t solve_node_bsp(bb_worker_state_t& worker, + mip_node_t* node_ptr, + search_tree_t& search_tree, + double current_horizon); + + private: + // BSP state + std::unique_ptr> bsp_workers_; + double bsp_horizon_step_{5.0}; // Virtual time step per horizon (tunable) + double bsp_current_horizon_{0.0}; // Current horizon target + bool bsp_mode_enabled_{false}; // Whether BSP mode is active + int bsp_horizon_number_{0}; // Current horizon number (for debugging) + + // Counter for deterministic final_id assignment during sync phase + // Starts at 2 (root's children are 1 and 2), incremented by 2 for each branch + i_t bsp_next_final_id_{3}; + + // BSP heuristic solution queue - solutions received from GPU heuristics + // Stored with VT timestamp for deterministic ordering + struct queued_heuristic_solution_t { + std::vector solution; + f_t objective; + double vt_timestamp; + }; + omp_mutex_t mutex_heuristic_queue_; + std::vector heuristic_solution_queue_; + + // BSP debug logger (settings read from environment variables) + bsp_debug_settings_t bsp_debug_settings_; + bsp_debug_logger_t bsp_debug_logger_; + + public: + // Accessor for GPU heuristics to get the current BSP horizon + double get_current_bsp_horizon() const { return bsp_current_horizon_; } }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp new file mode 100644 index 000000000..1b3833b06 --- /dev/null +++ b/cpp/src/dual_simplex/bsp_debug.hpp @@ -0,0 +1,843 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace cuopt::linear_programming::dual_simplex { + +// ============================================================================ +// BSP Debug Event Types +// ============================================================================ + +enum class bsp_log_event_t { + HORIZON_START, // New horizon begins + HORIZON_END, // Horizon completed + NODE_ASSIGNED, // Node assigned to worker + NODE_SOLVE_START, // Worker starts solving node + NODE_SOLVE_END, // Worker finishes node (with result type) + NODE_PAUSED, // Node paused at horizon + NODE_RESUMED, // Paused node resumed + NODE_BRANCHED, // Node branched into children + NODE_PRUNED, // Node pruned (cutoff) + NODE_INTEGER, // Integer solution found + NODE_INFEASIBLE, // Node infeasible + NODE_FATHOMED, // Node fathomed + FINAL_ID_ASSIGNED, // Final ID assigned during sync + HEURISTIC_RECEIVED, // Heuristic solution received + INCUMBENT_UPDATE, // New best solution found + SYNC_PHASE_START, // Sync phase begins + SYNC_PHASE_END, // Sync phase ends + WORKER_IDLE, // Worker has no work +}; + +inline const char* bsp_log_event_name(bsp_log_event_t event) +{ + switch (event) { + case bsp_log_event_t::HORIZON_START: return "HORIZON_START"; + case bsp_log_event_t::HORIZON_END: return "HORIZON_END"; + case bsp_log_event_t::NODE_ASSIGNED: return "NODE_ASSIGNED"; + case bsp_log_event_t::NODE_SOLVE_START: return "NODE_SOLVE_START"; + case bsp_log_event_t::NODE_SOLVE_END: return "NODE_SOLVE_END"; + case bsp_log_event_t::NODE_PAUSED: return "NODE_PAUSED"; + case bsp_log_event_t::NODE_RESUMED: return "NODE_RESUMED"; + case bsp_log_event_t::NODE_BRANCHED: return "NODE_BRANCHED"; + case bsp_log_event_t::NODE_PRUNED: return "NODE_PRUNED"; + case bsp_log_event_t::NODE_INTEGER: return "NODE_INTEGER"; + case bsp_log_event_t::NODE_INFEASIBLE: return "NODE_INFEASIBLE"; + case bsp_log_event_t::NODE_FATHOMED: return "NODE_FATHOMED"; + case bsp_log_event_t::FINAL_ID_ASSIGNED: return "FINAL_ID_ASSIGNED"; + case bsp_log_event_t::HEURISTIC_RECEIVED: return "HEURISTIC_RECEIVED"; + case bsp_log_event_t::INCUMBENT_UPDATE: return "INCUMBENT_UPDATE"; + case bsp_log_event_t::SYNC_PHASE_START: return "SYNC_PHASE_START"; + case bsp_log_event_t::SYNC_PHASE_END: return "SYNC_PHASE_END"; + case bsp_log_event_t::WORKER_IDLE: return "WORKER_IDLE"; + default: return "UNKNOWN"; + } +} + +// ============================================================================ +// BSP Debug Settings +// ============================================================================ + +/** + * @brief BSP debug settings controlled via environment variables. + * + * Environment variables: + * CUOPT_BSP_DEBUG_LOG=1 Enable structured event log + * CUOPT_BSP_DEBUG_TIMELINE=1 Emit ASCII timeline visualization + * CUOPT_BSP_DEBUG_TREE=1 Emit DOT tree state files + * CUOPT_BSP_DEBUG_JSON=1 Emit JSON state dumps + * CUOPT_BSP_DEBUG_TRACE=1 Emit determinism trace file + * CUOPT_BSP_DEBUG_ALL=1 Enable all debug output + * CUOPT_BSP_DEBUG_DIR=path Output directory (default: ./bsp_debug/) + * CUOPT_BSP_DEBUG_LEVEL=N Log level: 0=off, 1=major events, 2=all events + */ +struct bsp_debug_settings_t { + bool enable_event_log{false}; + bool enable_timeline{false}; + bool enable_tree_dot{false}; + bool enable_state_json{false}; + bool enable_determinism_trace{false}; + std::string output_dir{"./bsp_debug/"}; + int log_level{1}; // 0=off, 1=major events, 2=all events + + bool any_enabled() const + { + return enable_event_log || enable_timeline || enable_tree_dot || enable_state_json || + enable_determinism_trace; + } + + void enable_all() + { + enable_event_log = true; + enable_timeline = true; + enable_tree_dot = true; + enable_state_json = true; + enable_determinism_trace = true; + } + + /** + * @brief Initialize settings from environment variables. + * @return A bsp_debug_settings_t populated from environment variables. + */ + static bsp_debug_settings_t from_environment() + { + bsp_debug_settings_t settings; + + auto get_env_bool = [](const char* name) -> bool { + const char* val = std::getenv(name); + if (val == nullptr) return false; + return std::string(val) == "1" || std::string(val) == "true" || std::string(val) == "TRUE"; + }; + + auto get_env_int = [](const char* name, int default_val) -> int { + const char* val = std::getenv(name); + if (val == nullptr) return default_val; + try { + return std::stoi(val); + } catch (...) { + return default_val; + } + }; + + auto get_env_string = [](const char* name, const std::string& default_val) -> std::string { + const char* val = std::getenv(name); + if (val == nullptr) return default_val; + return std::string(val); + }; + + // Check for CUOPT_BSP_DEBUG_ALL first + if (get_env_bool("CUOPT_BSP_DEBUG_ALL")) { + settings.enable_all(); + } else { + settings.enable_event_log = get_env_bool("CUOPT_BSP_DEBUG_LOG"); + settings.enable_timeline = get_env_bool("CUOPT_BSP_DEBUG_TIMELINE"); + settings.enable_tree_dot = get_env_bool("CUOPT_BSP_DEBUG_TREE"); + settings.enable_state_json = get_env_bool("CUOPT_BSP_DEBUG_JSON"); + settings.enable_determinism_trace = get_env_bool("CUOPT_BSP_DEBUG_TRACE"); + } + + settings.output_dir = get_env_string("CUOPT_BSP_DEBUG_DIR", "./bsp_debug/"); + settings.log_level = get_env_int("CUOPT_BSP_DEBUG_LEVEL", 1); + + return settings; + } +}; + +// ============================================================================ +// Timeline Event (for ASCII visualization) +// ============================================================================ + +struct timeline_event_t { + double vt_start; + double vt_end; + int worker_id; + int node_id; + int final_id; + std::string result; // "BRANCH", "INTEGER", "FATHOMED", "PAUSED", etc. +}; + +// ============================================================================ +// BSP Debug Logger Class +// ============================================================================ + +template +class bsp_debug_logger_t { + public: + bsp_debug_logger_t() : start_time_(std::chrono::steady_clock::now()) {} + + void set_settings(const bsp_debug_settings_t& settings) + { + settings_ = settings; + if (settings_.any_enabled()) { + // Create output directory if it doesn't exist + try { + std::filesystem::create_directories(settings_.output_dir); + } catch (const std::filesystem::filesystem_error& e) { + // Silently disable debug output if we can't create the directory + settings_.enable_event_log = false; + settings_.enable_timeline = false; + settings_.enable_tree_dot = false; + settings_.enable_state_json = false; + settings_.enable_determinism_trace = false; + } + + // Clear timeline file at start (it uses append mode for each horizon) + if (settings_.enable_timeline) { + std::string filename = settings_.output_dir + "bsp_timeline.txt"; + std::ofstream file(filename, std::ios::trunc); + if (file.is_open()) { + file << "BSP Timeline Visualization\n"; + file << "==========================\n"; + file.close(); + } + } + } + } + + void set_num_workers(int num_workers) { num_workers_ = num_workers; } + + void set_horizon_step(double step) { horizon_step_ = step; } + + // ======================================================================== + // Event Logging + // ======================================================================== + + void log_horizon_start(int horizon_num, double vt_start, double vt_end) + { + current_horizon_ = horizon_num; + horizon_vt_start_ = vt_start; + horizon_vt_end_ = vt_end; + + if (settings_.enable_event_log) { + log_event(vt_start, -1, bsp_log_event_t::HORIZON_START, -1, -1, + "horizon=[" + std::to_string(vt_start) + "," + std::to_string(vt_end) + ")"); + } + + if (settings_.enable_determinism_trace) { + trace_ss_ << "H" << horizon_num << ":START:vt=[" << vt_start << "," << vt_end << ")\n"; + } + + // Clear timeline events for new horizon + timeline_events_.clear(); + } + + void log_horizon_end(int horizon_num, double vt) + { + if (settings_.enable_event_log) { + log_event(vt, -1, bsp_log_event_t::HORIZON_END, -1, -1, ""); + } + + if (settings_.enable_timeline) { + emit_timeline_for_horizon(horizon_num); + } + } + + void log_node_assigned(double vt, int worker_id, i_t node_id, i_t final_id, f_t lower_bound) + { + if (settings_.enable_event_log) { + log_event(vt, worker_id, bsp_log_event_t::NODE_ASSIGNED, node_id, final_id, + "lb=" + std::to_string(lower_bound)); + } + + if (settings_.enable_determinism_trace) { + assign_trace_ss_ << "W" << worker_id << "=" << final_id << ","; + } + } + + void flush_assign_trace() + { + if (settings_.enable_determinism_trace && assign_trace_ss_.str().length() > 0) { + std::string s = assign_trace_ss_.str(); + if (!s.empty() && s.back() == ',') s.pop_back(); + trace_ss_ << "H" << current_horizon_ << ":ASSIGN:" << s << "\n"; + assign_trace_ss_.str(""); + assign_trace_ss_.clear(); + } + } + + void log_solve_start(double vt, int worker_id, i_t node_id, i_t final_id, double work_limit, + bool is_resumed) + { + std::lock_guard lock(mutex_); + + if (settings_.enable_event_log && settings_.log_level >= 2) { + log_event_unlocked(vt, worker_id, + is_resumed ? bsp_log_event_t::NODE_RESUMED : bsp_log_event_t::NODE_SOLVE_START, + node_id, final_id, "work_limit=" + std::to_string(work_limit)); + } + + // Start timeline event + current_solve_start_[worker_id] = vt; + current_solve_node_[worker_id] = node_id; + current_solve_fid_[worker_id] = final_id; + } + + void log_solve_end(double vt, int worker_id, i_t node_id, i_t final_id, const std::string& result, + f_t lower_bound) + { + std::lock_guard lock(mutex_); + + if (settings_.enable_event_log) { + log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_SOLVE_END, node_id, final_id, + "result=" + result + ",lb=" + std::to_string(lower_bound)); + } + + // Complete timeline event + if (settings_.enable_timeline) { + timeline_event_t te; + te.vt_start = current_solve_start_[worker_id]; + te.vt_end = vt; + te.worker_id = worker_id; + te.node_id = node_id; + te.final_id = final_id; + te.result = result; + timeline_events_.push_back(te); + } + } + + void log_branched(double vt, int worker_id, i_t parent_id, i_t down_id, i_t up_id) + { + std::lock_guard lock(mutex_); + + if (settings_.enable_event_log) { + log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_BRANCHED, parent_id, -1, + "children=" + std::to_string(down_id) + "," + std::to_string(up_id)); + } + + if (settings_.enable_determinism_trace) { + events_trace_ss_ << "BRANCH(" << vt << ",W" << worker_id << "," << parent_id << "->" + << down_id << "," << up_id << "),"; + } + } + + void log_paused(double vt, int worker_id, i_t node_id, i_t final_id, f_t accumulated_vt) + { + std::lock_guard lock(mutex_); + + if (settings_.enable_event_log) { + log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_PAUSED, node_id, final_id, + "acc_vt=" + std::to_string(accumulated_vt)); + } + + if (settings_.enable_determinism_trace) { + events_trace_ss_ << "PAUSE(" << vt << ",W" << worker_id << "," << node_id << "),"; + } + } + + void log_integer(double vt, int worker_id, i_t node_id, f_t objective) + { + std::lock_guard lock(mutex_); + + if (settings_.enable_event_log) { + log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_INTEGER, node_id, -1, + "obj=" + std::to_string(objective)); + } + + if (settings_.enable_determinism_trace) { + events_trace_ss_ << "INT(" << vt << ",W" << worker_id << "," << objective << "),"; + } + } + + void log_fathomed(double vt, int worker_id, i_t node_id, f_t lower_bound) + { + std::lock_guard lock(mutex_); + + if (settings_.enable_event_log && settings_.log_level >= 2) { + log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_FATHOMED, node_id, -1, + "lb=" + std::to_string(lower_bound)); + } + } + + void log_infeasible(double vt, int worker_id, i_t node_id) + { + std::lock_guard lock(mutex_); + + if (settings_.enable_event_log && settings_.log_level >= 2) { + log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_INFEASIBLE, node_id, -1, ""); + } + } + + void log_pruned(double vt, i_t node_id, i_t final_id, f_t lower_bound, f_t upper_bound) + { + std::lock_guard lock(mutex_); + + if (settings_.enable_event_log && settings_.log_level >= 2) { + log_event_unlocked(vt, -1, bsp_log_event_t::NODE_PRUNED, node_id, final_id, + "lb=" + std::to_string(lower_bound) + ",ub=" + std::to_string(upper_bound)); + } + } + + void log_sync_phase_start(double vt, size_t num_events) + { + if (settings_.enable_event_log) { + log_event(vt, -1, bsp_log_event_t::SYNC_PHASE_START, -1, -1, + "num_events=" + std::to_string(num_events)); + } + } + + void log_sync_phase_end(double vt) + { + if (settings_.enable_event_log) { + log_event(vt, -1, bsp_log_event_t::SYNC_PHASE_END, -1, -1, ""); + } + + // Flush event trace + if (settings_.enable_determinism_trace) { + std::string s = events_trace_ss_.str(); + if (!s.empty() && s.back() == ',') s.pop_back(); + trace_ss_ << "H" << current_horizon_ << ":EVENTS:" << s << "\n"; + events_trace_ss_.str(""); + events_trace_ss_.clear(); + } + } + + void log_final_id_assigned(i_t provisional_id, i_t final_id) + { + if (settings_.enable_determinism_trace) { + final_ids_trace_ss_ << provisional_id << "->" << final_id << ","; + } + } + + void flush_final_ids_trace() + { + if (settings_.enable_determinism_trace && final_ids_trace_ss_.str().length() > 0) { + std::string s = final_ids_trace_ss_.str(); + if (!s.empty() && s.back() == ',') s.pop_back(); + trace_ss_ << "H" << current_horizon_ << ":FINAL_IDS:" << s << "\n"; + final_ids_trace_ss_.str(""); + final_ids_trace_ss_.clear(); + } + } + + void log_heuristic_received(double vt, f_t objective) + { + if (settings_.enable_event_log) { + log_event(vt, -1, bsp_log_event_t::HEURISTIC_RECEIVED, -1, -1, + "obj=" + std::to_string(objective)); + } + + if (settings_.enable_determinism_trace) { + trace_ss_ << "H" << current_horizon_ << ":HEURISTIC:" << objective << "@" << vt << "\n"; + } + } + + void log_incumbent_update(double vt, f_t objective, const std::string& source) + { + if (settings_.enable_event_log) { + log_event(vt, -1, bsp_log_event_t::INCUMBENT_UPDATE, -1, -1, + "obj=" + std::to_string(objective) + ",source=" + source); + } + + if (settings_.enable_determinism_trace) { + trace_ss_ << "H" << current_horizon_ << ":INCUMBENT:" << objective << "@" << vt << "(" + << source << ")\n"; + } + } + + void log_heap_order(const std::vector& final_ids) + { + if (settings_.enable_determinism_trace) { + trace_ss_ << "H" << current_horizon_ << ":HEAP_ORDER:"; + for (size_t i = 0; i < final_ids.size(); ++i) { + trace_ss_ << final_ids[i]; + if (i < final_ids.size() - 1) trace_ss_ << ","; + } + trace_ss_ << "\n"; + } + } + + // ======================================================================== + // Tree State (DOT format) + // ======================================================================== + + void emit_tree_state(int horizon_num, const mip_node_t& root, f_t upper_bound) + { + if (!settings_.enable_tree_dot) return; + + std::string filename = + settings_.output_dir + "bsp_tree_h" + std::to_string(horizon_num) + ".dot"; + std::ofstream file(filename); + if (!file.is_open()) return; + + file << "digraph BSPTree_Horizon" << horizon_num << " {\n"; + file << " rankdir=TB;\n"; + file << " node [shape=box];\n\n"; + + // Traverse tree and emit nodes + emit_tree_node_recursive(file, &root, upper_bound); + + file << "}\n"; + file.close(); + } + + // ======================================================================== + // JSON State Dump + // ======================================================================== + + template + void emit_state_json(int horizon_num, double vt_start, double vt_end, i_t next_final_id, + f_t upper_bound, f_t lower_bound, i_t nodes_explored, i_t nodes_unexplored, + const WorkerPool& workers, const std::vector*>& heap_nodes, + const bb_event_batch_t& events) + { + if (!settings_.enable_state_json) return; + + std::string filename = + settings_.output_dir + "bsp_state_h" + std::to_string(horizon_num) + ".json"; + std::ofstream file(filename); + if (!file.is_open()) return; + + file << "{\n"; + file << " \"horizon\": " << horizon_num << ",\n"; + file << " \"vt_range\": [" << vt_start << ", " << vt_end << "],\n"; + file << " \"bsp_next_final_id\": " << next_final_id << ",\n"; + file << " \"upper_bound\": " << (upper_bound >= 1e30 ? "\"inf\"" : std::to_string(upper_bound)) + << ",\n"; + file << " \"lower_bound\": " << lower_bound << ",\n"; + file << " \"nodes_explored\": " << nodes_explored << ",\n"; + file << " \"nodes_unexplored\": " << nodes_unexplored << ",\n"; + + // Workers + file << " \"workers\": [\n"; + for (int i = 0; i < workers.size(); ++i) { + const auto& w = workers[i]; + file << " {\n"; + file << " \"id\": " << i << ",\n"; + file << " \"clock\": " << w.clock << ",\n"; + if (w.current_node != nullptr) { + file << " \"current_node\": {\"id\": " << w.current_node->node_id + << ", \"final_id\": " << w.current_node->final_id + << ", \"acc_vt\": " << w.current_node->accumulated_vt << "},\n"; + } else { + file << " \"current_node\": null,\n"; + } + file << " \"local_queue_size\": " << w.local_queue.size() << "\n"; + file << " }" << (i < workers.size() - 1 ? "," : "") << "\n"; + } + file << " ],\n"; + + // Heap + file << " \"heap\": [\n"; + for (size_t i = 0; i < heap_nodes.size(); ++i) { + const auto* n = heap_nodes[i]; + file << " {\"id\": " << n->node_id << ", \"final_id\": " << n->final_id + << ", \"lb\": " << n->lower_bound << "}" << (i < heap_nodes.size() - 1 ? "," : "") + << "\n"; + } + file << " ],\n"; + + // Events this horizon + file << " \"events_count\": " << events.events.size() << "\n"; + + file << "}\n"; + file.close(); + } + + // ======================================================================== + // Finalization + // ======================================================================== + + void finalize() + { + if (settings_.enable_event_log) { flush_event_log(); } + + if (settings_.enable_determinism_trace) { flush_trace(); } + } + + private: + bsp_debug_settings_t settings_; + std::chrono::steady_clock::time_point start_time_; + int num_workers_{0}; + double horizon_step_{5.0}; + int current_horizon_{0}; + double horizon_vt_start_{0.0}; + double horizon_vt_end_{0.0}; + + std::mutex mutex_; + std::stringstream log_ss_; + std::stringstream trace_ss_; + std::stringstream assign_trace_ss_; + std::stringstream events_trace_ss_; + std::stringstream final_ids_trace_ss_; + + // Timeline tracking + std::vector timeline_events_; + std::map current_solve_start_; + std::map current_solve_node_; + std::map current_solve_fid_; + + double get_real_time() const + { + auto now = std::chrono::steady_clock::now(); + return std::chrono::duration(now - start_time_).count(); + } + + void log_event(double vt, int worker_id, bsp_log_event_t event, i_t node_id, i_t final_id, + const std::string& details) + { + std::lock_guard lock(mutex_); + log_event_unlocked(vt, worker_id, event, node_id, final_id, details); + } + + void log_event_unlocked(double vt, int worker_id, bsp_log_event_t event, i_t node_id, + i_t final_id, const std::string& details) + { + log_ss_ << std::fixed << std::setprecision(3) << vt << "\t" << get_real_time() << "\t" + << current_horizon_ << "\t" << (worker_id < 0 ? "COORD" : std::to_string(worker_id)) + << "\t" << bsp_log_event_name(event) << "\t" + << (node_id < 0 ? "-" : std::to_string(node_id)) << "\t" + << (final_id < 0 ? "-" : std::to_string(final_id)) << "\t" << details << "\n"; + } + + void flush_event_log() + { + std::string filename = settings_.output_dir + "bsp_events.log"; + std::ofstream file(filename); + if (file.is_open()) { + file << "VT\tREAL_TIME\tHORIZON\tWORKER\tEVENT\tNODE_ID\tFINAL_ID\tDETAILS\n"; + file << log_ss_.str(); + file.close(); + } + } + + void flush_trace() + { + std::string filename = settings_.output_dir + "bsp_trace.txt"; + std::ofstream file(filename); + if (file.is_open()) { + file << "# BSP Determinism Trace\n"; + file << "# Compare this file across runs - must be identical for determinism\n\n"; + file << trace_ss_.str(); + file.close(); + } + } + + void emit_timeline_for_horizon(int horizon_num) + { + std::string filename = settings_.output_dir + "bsp_timeline.txt"; + std::ofstream file(filename, std::ios::app); + if (!file.is_open()) return; + + const int width = 80; + const double vt_min = horizon_vt_start_; + const double vt_max = horizon_vt_end_; + const double vt_range = vt_max - vt_min; + + // Avoid division by zero + if (vt_range <= 0.0) { + file << "\nHorizon " << horizon_num << ": Empty or invalid VT range\n"; + return; + } + + file << "\n"; + file << std::string(width, '=') << "\n"; + file << "Horizon " << horizon_num << ": VT [" << vt_min << ", " << vt_max << ")\n"; + file << std::string(width, '-') << "\n"; + + // VT scale + file << " VT: "; + for (int i = 0; i <= 5; ++i) { + double vt = vt_min + (vt_range * i / 5.0); + file << std::setw(10) << std::fixed << std::setprecision(1) << vt; + } + file << "\n"; + + // Worker rows + for (int w = 0; w < num_workers_; ++w) { + file << " W" << w << ": "; + + std::string row(60, '-'); + + for (const auto& te : timeline_events_) { + if (te.worker_id != w) continue; + + int start_col = static_cast((te.vt_start - vt_min) / vt_range * 60); + int end_col = static_cast((te.vt_end - vt_min) / vt_range * 60); + start_col = std::max(0, std::min(59, start_col)); + end_col = std::max(0, std::min(59, end_col)); + + char fill_char = '#'; + if (te.result == "PAUSED") + fill_char = '%'; + else if (te.result == "INTEGER") + fill_char = '*'; + else if (te.result == "FATHOMED" || te.result == "INFEASIBLE") + fill_char = 'x'; + + for (int c = start_col; c <= end_col; ++c) { + row[c] = fill_char; + } + } + + file << row << "\n"; + + // Labels row + file << " "; + std::string labels(60, ' '); + for (const auto& te : timeline_events_) { + if (te.worker_id != w) continue; + int mid_col = static_cast(((te.vt_start + te.vt_end) / 2 - vt_min) / vt_range * 60); + mid_col = std::max(0, std::min(55, mid_col)); + + std::string label = "N" + std::to_string(te.final_id); + for (size_t i = 0; i < label.size() && mid_col + i < 60; ++i) { + labels[mid_col + i] = label[i]; + } + } + file << labels << "\n"; + } + + file << std::string(width, '-') << "\n"; + file << "Legend: ### solving %%% paused *** integer xxx fathomed/infeasible\n"; + file << std::string(width, '=') << "\n"; + } + + void emit_tree_node_recursive(std::ofstream& file, const mip_node_t* node, + f_t upper_bound) + { + if (node == nullptr) return; + + // Determine fill color based on status + std::string color = "white"; + std::string status_str; + switch (node->status) { + case node_status_t::PENDING: + color = "lightgray"; + status_str = "PENDING"; + break; + case node_status_t::INTEGER_FEASIBLE: + color = "lightgreen"; + status_str = "INTEGER"; + break; + case node_status_t::INFEASIBLE: + color = "lightcoral"; + status_str = "INFEASIBLE"; + break; + case node_status_t::FATHOMED: + color = "lightsalmon"; + status_str = "FATHOMED"; + break; + case node_status_t::HAS_CHILDREN: + color = "lightblue"; + status_str = "BRANCHED"; + break; + case node_status_t::NUMERICAL: + color = "orange"; + status_str = "NUMERICAL"; + break; + } + + if (node->bsp_state == bsp_node_state_t::PAUSED) { + color = "yellow"; + status_str = "PAUSED"; + } + + file << " N" << node->node_id << " [label=\"N" << node->node_id; + if (node->final_id >= 0) file << " (fid=" << node->final_id << ")"; + file << "\\nlb=" << std::fixed << std::setprecision(1) << node->lower_bound; + file << "\\n" << status_str; + if (node->bsp_state == bsp_node_state_t::PAUSED) { + file << "\\nacc_vt=" << node->accumulated_vt; + } + file << "\" style=filled fillcolor=" << color << "];\n"; + + // Emit edges to children + if (node->get_down_child() != nullptr) { + file << " N" << node->node_id << " -> N" << node->get_down_child()->node_id + << " [label=\"x" << node->branch_var << "<=" << std::floor(node->fractional_val) + << "\"];\n"; + emit_tree_node_recursive(file, node->get_down_child(), upper_bound); + } + + if (node->get_up_child() != nullptr) { + file << " N" << node->node_id << " -> N" << node->get_up_child()->node_id << " [label=\"x" + << node->branch_var << ">=" << std::ceil(node->fractional_val) << "\"];\n"; + emit_tree_node_recursive(file, node->get_up_child(), upper_bound); + } + } +}; + +// ============================================================================ +// Convenience Macros +// ============================================================================ + +#ifdef BSP_DEBUG_ENABLED + +#define BSP_DEBUG_LOG_HORIZON_START(logger, h, vs, ve) (logger).log_horizon_start(h, vs, ve) +#define BSP_DEBUG_LOG_HORIZON_END(logger, h, vt) (logger).log_horizon_end(h, vt) +#define BSP_DEBUG_LOG_NODE_ASSIGNED(logger, vt, w, nid, fid, lb) \ + (logger).log_node_assigned(vt, w, nid, fid, lb) +#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(logger) (logger).flush_assign_trace() +#define BSP_DEBUG_LOG_SOLVE_START(logger, vt, w, nid, fid, wl, resumed) \ + (logger).log_solve_start(vt, w, nid, fid, wl, resumed) +#define BSP_DEBUG_LOG_SOLVE_END(logger, vt, w, nid, fid, result, lb) \ + (logger).log_solve_end(vt, w, nid, fid, result, lb) +#define BSP_DEBUG_LOG_BRANCHED(logger, vt, w, pid, did, uid) \ + (logger).log_branched(vt, w, pid, did, uid) +#define BSP_DEBUG_LOG_PAUSED(logger, vt, w, nid, fid, acc) (logger).log_paused(vt, w, nid, fid, acc) +#define BSP_DEBUG_LOG_INTEGER(logger, vt, w, nid, obj) (logger).log_integer(vt, w, nid, obj) +#define BSP_DEBUG_LOG_FATHOMED(logger, vt, w, nid, lb) (logger).log_fathomed(vt, w, nid, lb) +#define BSP_DEBUG_LOG_INFEASIBLE(logger, vt, w, nid) (logger).log_infeasible(vt, w, nid) +#define BSP_DEBUG_LOG_SYNC_PHASE_START(logger, vt, ne) (logger).log_sync_phase_start(vt, ne) +#define BSP_DEBUG_LOG_SYNC_PHASE_END(logger, vt) (logger).log_sync_phase_end(vt) +#define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(logger, pid, fid) (logger).log_final_id_assigned(pid, fid) +#define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(logger) (logger).flush_final_ids_trace() +#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(logger, vt, obj) (logger).log_heuristic_received(vt, obj) +#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(logger, vt, obj, src) \ + (logger).log_incumbent_update(vt, obj, src) +#define BSP_DEBUG_LOG_HEAP_ORDER(logger, fids) (logger).log_heap_order(fids) +#define BSP_DEBUG_EMIT_TREE_STATE(logger, h, root, ub) (logger).emit_tree_state(h, root, ub) +#define BSP_DEBUG_EMIT_STATE_JSON(logger, h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) \ + (logger).emit_state_json(h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) +#define BSP_DEBUG_FINALIZE(logger) (logger).finalize() + +#else + +#define BSP_DEBUG_LOG_HORIZON_START(logger, h, vs, ve) ((void)0) +#define BSP_DEBUG_LOG_HORIZON_END(logger, h, vt) ((void)0) +#define BSP_DEBUG_LOG_NODE_ASSIGNED(logger, vt, w, nid, fid, lb) ((void)0) +#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(logger) ((void)0) +#define BSP_DEBUG_LOG_SOLVE_START(logger, vt, w, nid, fid, wl, resumed) ((void)0) +#define BSP_DEBUG_LOG_SOLVE_END(logger, vt, w, nid, fid, result, lb) ((void)0) +#define BSP_DEBUG_LOG_BRANCHED(logger, vt, w, pid, did, uid) ((void)0) +#define BSP_DEBUG_LOG_PAUSED(logger, vt, w, nid, fid, acc) ((void)0) +#define BSP_DEBUG_LOG_INTEGER(logger, vt, w, nid, obj) ((void)0) +#define BSP_DEBUG_LOG_FATHOMED(logger, vt, w, nid, lb) ((void)0) +#define BSP_DEBUG_LOG_INFEASIBLE(logger, vt, w, nid) ((void)0) +#define BSP_DEBUG_LOG_SYNC_PHASE_START(logger, vt, ne) ((void)0) +#define BSP_DEBUG_LOG_SYNC_PHASE_END(logger, vt) ((void)0) +#define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(logger, pid, fid) ((void)0) +#define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(logger) ((void)0) +#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(logger, vt, obj) ((void)0) +#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(logger, vt, obj, src) ((void)0) +#define BSP_DEBUG_LOG_HEAP_ORDER(logger, fids) ((void)0) +#define BSP_DEBUG_EMIT_TREE_STATE(logger, h, root, ub) ((void)0) +#define BSP_DEBUG_EMIT_STATE_JSON(logger, h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) \ + ((void)0) +#define BSP_DEBUG_FINALIZE(logger) ((void)0) + +#endif + +} // namespace cuopt::linear_programming::dual_simplex + diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index 1d66a21f7..b65ec2edd 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -29,6 +29,12 @@ enum class node_status_t : int { enum class rounding_direction_t : int8_t { NONE = -1, DOWN = 0, UP = 1 }; +// BSP state for deterministic parallel B&B +enum class bsp_node_state_t : int8_t { + READY = 0, // Node is ready to be processed + PAUSED = 1 // Node processing was paused at horizon boundary +}; + bool inactive_status(node_status_t status); template @@ -233,6 +239,11 @@ class mip_node_t { copy.branch_var_upper = branch_var_upper; copy.fractional_val = fractional_val; copy.node_id = node_id; + // Copy BSP fields + copy.accumulated_vt = accumulated_vt; + copy.bsp_state = bsp_state; + copy.provisional_id = provisional_id; + copy.final_id = final_id; return copy; } @@ -250,6 +261,25 @@ class mip_node_t { std::unique_ptr children[2]; std::vector vstatus; + + // BSP fields for deterministic parallel B&B + f_t accumulated_vt{0.0}; // Virtual time spent on this node so far + bsp_node_state_t bsp_state{bsp_node_state_t::READY}; // BSP processing state + + // For deterministic node ID assignment in BSP mode: + // - provisional_id is assigned atomically during parallel execution + // - final_id is assigned deterministically during sync phase based on event order + // - Use final_id for sorting if set (>= 0), otherwise fall back to provisional_id/node_id + i_t provisional_id{-1}; + i_t final_id{-1}; + + // Get the ID to use for deterministic ordering + i_t get_deterministic_id() const + { + if (final_id >= 0) return final_id; + if (provisional_id >= 0) return provisional_id; + return node_id; + } }; template diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 644c906e9..f12186164 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -191,11 +191,11 @@ solution_t mip_solver_t::run_solver() i_t num_threads = branch_and_bound_settings.num_threads; i_t num_bfs_threads = std::max(1, num_threads / 4); i_t num_diving_threads = num_threads - num_bfs_threads; - // deterministic mode: no diving for now + // deterministic mode: use BSP coordinator with multiple workers, no diving if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - num_threads = 1; - num_bfs_threads = 1; - num_diving_threads = 0; + // BSP mode can use multiple workers deterministically + num_bfs_threads = std::max(1, num_threads); + num_diving_threads = 0; // No diving in deterministic mode } branch_and_bound_settings.num_bfs_threads = num_bfs_threads; branch_and_bound_settings.num_diving_threads = num_diving_threads; @@ -236,6 +236,16 @@ solution_t mip_solver_t::run_solver() std::bind(&dual_simplex::branch_and_bound_t::set_new_solution, branch_and_bound.get(), std::placeholders::_1); + } else if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + // In deterministic mode, use VT-aware solution injection + // Use the B&B's current horizon as the VT timestamp + // This ensures heuristic solutions are processed at the END of the current horizon, + // maintaining determinism regardless of when the GPU heuristic actually finds the solution + context.problem_ptr->branch_and_bound_callback = + [bb = branch_and_bound.get()](const std::vector& solution) { + double vt = bb->get_current_bsp_horizon(); + bb->set_new_solution_deterministic(solution, vt); + }; } context.work_unit_scheduler_.register_context(branch_and_bound->get_work_unit_context()); diff --git a/cpp/tests/mip/presolve_test.cu b/cpp/tests/mip/presolve_test.cu index 7bdec7297..57cd93fa2 100644 --- a/cpp/tests/mip/presolve_test.cu +++ b/cpp/tests/mip/presolve_test.cu @@ -59,7 +59,7 @@ std::tuple, std::vector, std::vector> select_k_ std::cerr << "Tested with seed " << seed << "\n"; problem.compute_n_integer_vars(); auto [v_lb, v_ub] = extract_host_bounds(problem.variable_bounds, problem.handle_ptr); - auto int_var_id = host_copy(problem.integer_indices); + auto int_var_id = host_copy(problem.integer_indices, problem.handle_ptr->get_stream()); int_var_id.erase( std::remove_if(int_var_id.begin(), int_var_id.end(), From 85eab6467d05440851a8510bc67d02aeefadb88d Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 7 Jan 2026 17:11:25 +0100 Subject: [PATCH 149/366] fix incorrect number of workers --- cpp/src/dual_simplex/branch_and_bound.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d0b376ef6..22d67d7b3 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1086,6 +1086,7 @@ void branch_and_bound_t::master_loop() diving_settings.disable_guided_diving = settings_.diving_settings.disable_guided_diving; max_num_workers_per_type = bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); + worker_types = bnb_get_worker_types(diving_settings); } } @@ -1403,7 +1404,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut original_lp_, log); - worker_pool_.init(settings_.num_threads, original_lp_, Arow_, var_types_, settings_); + worker_pool_.init(2 * settings_.num_threads, original_lp_, Arow_, var_types_, settings_); active_workers_per_type.fill(0); settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", From 227d7f83643da487ab7169117df6c7c7ae0f18e5 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 7 Jan 2026 17:18:43 +0100 Subject: [PATCH 150/366] removed ramp-up phase --- cpp/src/dual_simplex/branch_and_bound.cpp | 90 ----------------------- 1 file changed, 90 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 22d67d7b3..c14b17dc3 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -307,7 +307,6 @@ void branch_and_bound_t::set_new_solution(const std::vector& solu settings_.log.printf( "Solution size mismatch %ld %d\n", solution.size(), original_problem_.num_cols); } - std::vector crushed_solution; crush_primal_solution( original_problem_, original_lp_, solution, new_slacks_, crushed_solution); @@ -802,95 +801,6 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* } } -// template -// void branch_and_bound_t::exploration_ramp_up(mip_node_t* node, -// i_t initial_heap_size) -// { -// if (solver_status_ != mip_exploration_status_t::RUNNING) { return; } - -// // Note that we do not know which thread will execute the -// // `exploration_ramp_up` task, so we allow to any thread -// // to repair the heuristic solution. -// repair_heuristic_solutions(); - -// i_t tid = omp_get_thread_num(); -// bnb_worker_data_t* worker_data = get_worker_data(tid); -// assert(worker_data); - -// f_t lower_bound = node->lower_bound; -// f_t upper_bound = get_upper_bound(); -// f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); -// f_t abs_gap = upper_bound - lower_bound; - -// if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { -// search_tree_.graphviz_node(settings_.log, node, "cutoff", node->lower_bound); -// search_tree_.update(node, node_status_t::FATHOMED); -// --exploration_stats_.nodes_unexplored; -// return; -// } - -// f_t now = toc(exploration_stats_.start_time); -// f_t time_since_last_log = -// exploration_stats_.last_log == 0 ? 1.0 : toc(exploration_stats_.last_log); - -// if (((exploration_stats_.nodes_since_last_log >= 10 || -// abs_gap < 10 * settings_.absolute_mip_gap_tol) && -// (time_since_last_log >= 1)) || -// (time_since_last_log > 30) || now > settings_.time_limit) { -// bool should_report = should_report_.exchange(false); - -// if (should_report) { -// report(" ", upper_bound, root_objective_, node->depth); -// exploration_stats_.nodes_since_last_log = 0; -// exploration_stats_.last_log = tic(); -// should_report_ = true; -// } -// } - -// if (now > settings_.time_limit) { -// solver_status_ = mip_exploration_status_t::TIME_LIMIT; -// return; -// } - -// worker_data->recompute_basis = true; -// worker_data->recompute_bounds = true; - -// node_solve_info_t status = solve_node(node, -// search_tree_, -// bnb_worker_type_t::EXPLORATION, -// worker_data, -// original_lp_.lower, -// original_lp_.upper, -// exploration_stats_, -// settings_.log); - -// ++exploration_stats_.nodes_since_last_log; -// ++exploration_stats_.nodes_explored; -// --exploration_stats_.nodes_unexplored; - -// if (status == node_solve_info_t::TIME_LIMIT) { -// solver_status_ = mip_exploration_status_t::TIME_LIMIT; -// return; - -// } else if (has_children(status)) { -// exploration_stats_.nodes_unexplored += 2; - -// // If we haven't generated enough nodes to keep the threads busy, continue the ramp up phase -// if (exploration_stats_.nodes_unexplored < initial_heap_size) { -// #pragma omp task -// exploration_ramp_up(node->get_down_child(), initial_heap_size); - -// #pragma omp task -// exploration_ramp_up(node->get_up_child(), initial_heap_size); - -// } else { -// // We've generated enough nodes, push further nodes onto the heap -// node_queue.push(node->get_down_child()); -// node_queue.push(node->get_up_child()); -// } -// } -// } - template void branch_and_bound_t::plunge_with(bnb_worker_t* worker) { From 5aecd63f29c8b45997a8ec93ac21159ae1ff7551 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 7 Jan 2026 17:35:11 +0100 Subject: [PATCH 151/366] added some comments --- cpp/src/dual_simplex/bnb_worker.hpp | 3 ++- cpp/src/dual_simplex/branch_and_bound.cpp | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index 2cc48a271..a7a94761d 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -158,7 +158,8 @@ class bnb_worker_pool_t { // Worker pool std::vector>> workers_; - // FIXME: Implement a lock-free queue + // FIXME: Implement a lock-free queue (it can also be used for + // passing feasible solutions between bnb and heuristics) omp_mutex_t mutex_; std::deque available_workers_; }; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index c14b17dc3..71d912e21 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -839,6 +839,7 @@ void branch_and_bound_t::plunge_with(bnb_worker_t* worker) solver_status_ = mip_exploration_status_t::TIME_LIMIT; break; } + if (exploration_stats_.nodes_explored >= settings_.node_limit) { solver_status_ = mip_exploration_status_t::NODE_LIMIT; break; From 0ea2c1f8da4b5cfde2b0619869de7ac0c2047604 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 7 Jan 2026 16:50:57 +0000 Subject: [PATCH 152/366] some debug tweaks --- .../linear_programming/cuopt/run_mip.cpp | 5 +- cpp/src/dual_simplex/branch_and_bound.cpp | 378 ++++++++++-------- cpp/src/dual_simplex/bsp_debug.hpp | 329 ++++++++++----- cpp/src/dual_simplex/mip_node.hpp | 29 +- 4 files changed, 465 insertions(+), 276 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 323dac706..66435da2b 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -206,6 +206,9 @@ int run_single_file(std::string file_path, settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; + + settings.presolve = false; + cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; auto start_run_solver = std::chrono::high_resolution_clock::now(); diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index c19a9656d..80d239f2d 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -28,9 +28,9 @@ #include #include #include -#include #include #include +#include #include namespace cuopt::linear_programming::dual_simplex { @@ -230,6 +230,8 @@ branch_and_bound_t::branch_and_bound_t( solver_status_(mip_exploration_status_t::UNSET), bsp_debug_settings_(bsp_debug_settings_t::from_environment()) { + bsp_debug_settings_.enable_all(); + exploration_stats_.start_time = tic(); dualize_info_t dualize_info; convert_user_problem(original_problem_, settings_, original_lp_, new_slacks_, dualize_info); @@ -498,7 +500,7 @@ void branch_and_bound_t::set_new_solution(const std::vector& solu template void branch_and_bound_t::set_new_solution_deterministic(const std::vector& solution, - double vt_timestamp) + double vt_timestamp) { // In BSP mode, queue the solution to be processed at the correct virtual time // This ensures deterministic ordering of solution events @@ -734,10 +736,10 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, i_t leaf_depth, thread_type_t thread_type) { - bool send_solution = false; + bool send_solution = false; bool improved_incumbent = false; - i_t nodes_explored = exploration_stats_.nodes_explored; - i_t nodes_unexplored = exploration_stats_.nodes_unexplored; + i_t nodes_explored = exploration_stats_.nodes_explored; + i_t nodes_unexplored = exploration_stats_.nodes_unexplored; mutex_upper_.lock(); if (leaf_objective < upper_bound_) { @@ -770,9 +772,10 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, mutex_upper_.unlock(); // Debug: Log incumbent update (after releasing mutex to avoid potential issues) - if (improved_incumbent && bsp_debug_settings_.any_enabled() && bsp_mode_enabled_) { + if (improved_incumbent && bsp_mode_enabled_) { std::string source = (thread_type == thread_type_t::EXPLORATION) ? "bb_integer" : "diving"; - bsp_debug_logger_.log_incumbent_update(bsp_current_horizon_, leaf_objective, source); + BSP_DEBUG_LOG_INCUMBENT_UPDATE( + bsp_debug_settings_, bsp_debug_logger_, bsp_current_horizon_, leaf_objective, source); } } @@ -914,11 +917,6 @@ node_solve_info_t branch_and_bound_t::solve_node( work_unit_context_.global_work_units_elapsed >= settings_.work_limit) { lp_status = dual::status_t::WORK_LIMIT; } - if (settings_.deterministic) { - printf("------ Total work unit progress B&B: %f / %f\n", - work_unit_context_.global_work_units_elapsed, - settings_.work_limit); - } if (lp_status == dual::status_t::NUMERICAL) { log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); @@ -1874,24 +1872,23 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tfinal_id = 1; - search_tree_.root.get_up_child()->final_id = 2; - bsp_next_final_id_ = 3; // Next ID to assign + search_tree_.root.get_up_child()->final_id = 2; + bsp_next_final_id_ = 3; // Next ID to assign heap_.push(search_tree_.root.get_down_child()); heap_.push(search_tree_.root.get_up_child()); - const f_t inf = std::numeric_limits::infinity(); - f_t lower_bound = get_lower_bound(); - f_t upper_bound = get_upper_bound(); - f_t abs_gap = upper_bound - lower_bound; - f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + const f_t inf = std::numeric_limits::infinity(); + f_t lower_bound = get_lower_bound(); + f_t upper_bound = get_upper_bound(); + f_t abs_gap = upper_bound - lower_bound; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); constexpr i_t target_queue_size = 5; // Target nodes per worker @@ -1899,23 +1896,19 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && (heap_.size() > 0 || bsp_workers_->any_has_work())) { - ++bsp_horizon_number_; double horizon_start = bsp_current_horizon_ - bsp_horizon_step_; double horizon_end = bsp_current_horizon_; // Debug: Log horizon start - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_horizon_start(bsp_horizon_number_, horizon_start, horizon_end); - } + BSP_DEBUG_LOG_HORIZON_START( + bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_start, horizon_end); // PHASE 1: ASSIGNMENT - Fill worker queues refill_worker_queues(target_queue_size); // Flush assignment trace after all assignments - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.flush_assign_trace(); - } + BSP_DEBUG_FLUSH_ASSIGN_TRACE(bsp_debug_settings_, bsp_debug_logger_); // Reset workers for new horizon bsp_workers_->reset_for_horizon(horizon_start, horizon_end); @@ -1930,43 +1923,52 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t all_events = bsp_workers_->collect_and_sort_events(); // Debug: Log sync phase - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_sync_phase_start(horizon_end, all_events.size()); - } + BSP_DEBUG_LOG_SYNC_PHASE_START( + bsp_debug_settings_, bsp_debug_logger_, horizon_end, all_events.size()); // Process history and sync process_history_and_sync(all_events); // Debug: Flush final IDs trace and log sync end - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.flush_final_ids_trace(); - bsp_debug_logger_.log_sync_phase_end(horizon_end); - } + BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(bsp_debug_settings_, bsp_debug_logger_); + BSP_DEBUG_LOG_SYNC_PHASE_END(bsp_debug_settings_, bsp_debug_logger_, horizon_end); // Prune paused nodes that are now dominated by new incumbent prune_worker_nodes_vs_incumbent(); // Debug: Log horizon end, emit tree state and JSON state - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_horizon_end(bsp_horizon_number_, horizon_end); - - // Emit tree state (DOT format) - bsp_debug_logger_.emit_tree_state(bsp_horizon_number_, search_tree_.root, get_upper_bound()); - - // Collect heap nodes for JSON state - std::vector*> heap_snapshot; - // Note: We can't easily iterate the heap, so just log the size - bsp_debug_logger_.emit_state_json(bsp_horizon_number_, horizon_start, horizon_end, - bsp_next_final_id_, get_upper_bound(), get_lower_bound(), - exploration_stats_.nodes_explored, - exploration_stats_.nodes_unexplored, *bsp_workers_, - heap_snapshot, all_events); - } + BSP_DEBUG_LOG_HORIZON_END( + bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end); + BSP_DEBUG_EMIT_TREE_STATE(bsp_debug_settings_, + bsp_debug_logger_, + bsp_horizon_number_, + search_tree_.root, + get_upper_bound()); + // Collect heap nodes for JSON state (Note: We can't easily iterate the heap, so just log the + // size) + std::vector*> heap_snapshot; + BSP_DEBUG_EMIT_STATE_JSON(bsp_debug_settings_, + bsp_debug_logger_, + bsp_horizon_number_, + horizon_start, + horizon_end, + bsp_next_final_id_, + get_upper_bound(), + get_lower_bound(), + exploration_stats_.nodes_explored, + exploration_stats_.nodes_unexplored, + *bsp_workers_, + heap_snapshot, + all_events); // Advance the horizon bsp_current_horizon_ += bsp_horizon_step_; @@ -2003,9 +2005,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::refill_worker_queues(i_t target_queue_size) mutex_heap_.unlock(); // Assign nodes to workers deterministically (round-robin by deterministic ID order) - // Use get_deterministic_id() which returns final_id if set, else provisional_id, else node_id - std::sort(nodes_to_assign.begin(), nodes_to_assign.end(), + // Use get_deterministic_id() which returns final_id if set, else node_id + std::sort(nodes_to_assign.begin(), + nodes_to_assign.end(), [](const mip_node_t* a, const mip_node_t* b) { return a->get_deterministic_id() < b->get_deterministic_id(); }); @@ -2057,11 +2058,14 @@ void branch_and_bound_t::refill_worker_queues(i_t target_queue_size) (*bsp_workers_)[worker_id].enqueue_node(node); // Debug: Log node assignment - if (bsp_debug_settings_.any_enabled()) { - double vt = bsp_current_horizon_ - bsp_horizon_step_; // Start of current horizon - bsp_debug_logger_.log_node_assigned(vt, worker_id, node->node_id, node->final_id, - node->lower_bound); - } + double vt = bsp_current_horizon_ - bsp_horizon_step_; // Start of current horizon + BSP_DEBUG_LOG_NODE_ASSIGNED(bsp_debug_settings_, + bsp_debug_logger_, + vt, + worker_id, + node->node_id, + node->final_id, + node->lower_bound); } } @@ -2074,7 +2078,6 @@ void branch_and_bound_t::run_worker_until_horizon(bb_worker_state_t* node = worker.dequeue_node(); if (node == nullptr) break; @@ -2102,11 +2105,10 @@ void branch_and_bound_t::run_worker_until_horizon(bb_worker_state_t -node_solve_info_t branch_and_bound_t::solve_node_bsp( - bb_worker_state_t& worker, - mip_node_t* node_ptr, - search_tree_t& search_tree, - double current_horizon) +node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t& worker, + mip_node_t* node_ptr, + search_tree_t& search_tree, + double current_horizon) { raft::common::nvtx::range scope("BB::solve_node_bsp"); @@ -2116,11 +2118,15 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp( bool is_resumed = (node_ptr->bsp_state == bsp_node_state_t::PAUSED); // Debug: Log solve start - if (bsp_debug_settings_.any_enabled()) { - double work_limit = current_horizon - worker.clock; - bsp_debug_logger_.log_solve_start(worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, work_limit, is_resumed); - } + double work_limit = current_horizon - worker.clock; + BSP_DEBUG_LOG_SOLVE_START(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->final_id, + work_limit, + is_resumed); // Setup leaf problem bounds std::fill(worker.node_presolver->bounds_changed.begin(), @@ -2142,9 +2148,9 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp( // Bounds strengthening simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); - lp_settings.cut_off = get_upper_bound() + settings_.dual_tol; - lp_settings.inside_mip = 2; - lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); + lp_settings.cut_off = get_upper_bound() + settings_.dual_tol; + lp_settings.inside_mip = 2; + lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); // Work limit is the remaining VT budget in this horizon // Note: accumulated_vt tracks work already spent (for statistics), but doesn't increase budget lp_settings.work_limit = current_horizon - worker.clock; @@ -2164,27 +2170,27 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp( } // Solve LP relaxation - lp_solution_t leaf_solution(worker.leaf_problem->num_rows, worker.leaf_problem->num_cols); + lp_solution_t leaf_solution(worker.leaf_problem->num_rows, + worker.leaf_problem->num_cols); std::vector& leaf_vstatus = node_ptr->vstatus; i_t node_iter = 0; f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; - dual::status_t lp_status = dual_phase2_with_advanced_basis( - 2, - 0, - worker.recompute_bounds_and_basis, - lp_start_time, - *worker.leaf_problem, - lp_settings, - leaf_vstatus, - *worker.basis_factors, - worker.basic_list, - worker.nonbasic_list, - leaf_solution, - node_iter, - leaf_edge_norms, - &worker.work_context); + dual::status_t lp_status = dual_phase2_with_advanced_basis(2, + 0, + worker.recompute_bounds_and_basis, + lp_start_time, + *worker.leaf_problem, + lp_settings, + leaf_vstatus, + *worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms, + &worker.work_context); // Update worker clock with work performed // The simplex solver recorded work to work_context.global_work_units_elapsed @@ -2201,12 +2207,21 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp( worker.pause_current_node(node_ptr, accumulated_vt); // Debug: Log solve end (paused) - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, "PAUSED", node_ptr->lower_bound); - bsp_debug_logger_.log_paused(worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, static_cast(accumulated_vt)); - } + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->final_id, + "PAUSED", + node_ptr->lower_bound); + BSP_DEBUG_LOG_PAUSED(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->final_id, + static_cast(accumulated_vt)); return node_solve_info_t::WORK_LIMIT; } @@ -2223,11 +2238,16 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp( worker.recompute_bounds_and_basis = true; // Debug: Log solve end (infeasible) - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, "INFEASIBLE", node_ptr->lower_bound); - bsp_debug_logger_.log_infeasible(worker.clock, worker.worker_id, node_ptr->node_id); - } + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->final_id, + "INFEASIBLE", + node_ptr->lower_bound); + BSP_DEBUG_LOG_INFEASIBLE( + bsp_debug_settings_, bsp_debug_logger_, worker.clock, worker.worker_id, node_ptr->node_id); return node_solve_info_t::NO_CHILDREN; } else if (lp_status == dual::status_t::CUTOFF) { @@ -2237,12 +2257,20 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp( worker.recompute_bounds_and_basis = true; // Debug: Log solve end (fathomed - cutoff) - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, "FATHOMED", node_ptr->lower_bound); - bsp_debug_logger_.log_fathomed(worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->lower_bound); - } + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->final_id, + "FATHOMED", + node_ptr->lower_bound); + BSP_DEBUG_LOG_FATHOMED(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->lower_bound); return node_solve_info_t::NO_CHILDREN; } else if (lp_status == dual::status_t::OPTIMAL) { @@ -2256,40 +2284,60 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp( if (leaf_num_fractional == 0) { // Integer feasible - add_feasible_solution(leaf_objective, leaf_solution.x, node_ptr->depth, thread_type_t::EXPLORATION); + add_feasible_solution( + leaf_objective, leaf_solution.x, node_ptr->depth, thread_type_t::EXPLORATION); search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); worker.record_integer_solution(node_ptr, leaf_objective); worker.recompute_bounds_and_basis = true; // Debug: Log solve end (integer) - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, "INTEGER", leaf_objective); - bsp_debug_logger_.log_integer(worker.clock, worker.worker_id, node_ptr->node_id, - leaf_objective); - } + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->final_id, + "INTEGER", + leaf_objective); + BSP_DEBUG_LOG_INTEGER(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + leaf_objective); return node_solve_info_t::NO_CHILDREN; } else if (leaf_objective <= get_upper_bound() + settings_.absolute_mip_gap_tol / 10) { // Branch logger_t log; - log.log = false; + log.log = false; const i_t branch_var = pc_.variable_selection(leaf_fractional, leaf_solution.x, log); - search_tree.branch(node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, *worker.leaf_problem, log); + search_tree.branch( + node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, *worker.leaf_problem, log); search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); i_t down_child_id = node_ptr->get_down_child()->node_id; i_t up_child_id = node_ptr->get_up_child()->node_id; - worker.record_branched(node_ptr, down_child_id, up_child_id, branch_var, leaf_solution.x[branch_var]); + worker.record_branched( + node_ptr, down_child_id, up_child_id, branch_var, leaf_solution.x[branch_var]); // Debug: Log solve end (branched) and branched event - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, "BRANCH", leaf_objective); - bsp_debug_logger_.log_branched(worker.clock, worker.worker_id, node_ptr->node_id, - down_child_id, up_child_id); - } + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->final_id, + "BRANCH", + leaf_objective); + BSP_DEBUG_LOG_BRANCHED(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + down_child_id, + up_child_id); exploration_stats_.nodes_unexplored += 2; worker.recompute_bounds_and_basis = false; @@ -2308,12 +2356,20 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp( worker.recompute_bounds_and_basis = true; // Debug: Log solve end (fathomed by bound) - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_solve_end(worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, "FATHOMED", leaf_objective); - bsp_debug_logger_.log_fathomed(worker.clock, worker.worker_id, node_ptr->node_id, - leaf_objective); - } + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->final_id, + "FATHOMED", + leaf_objective); + BSP_DEBUG_LOG_FATHOMED(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + leaf_objective); return node_solve_info_t::NO_CHILDREN; } @@ -2330,7 +2386,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp( } template -void branch_and_bound_t::process_history_and_sync(const bb_event_batch_t& events) +void branch_and_bound_t::process_history_and_sync( + const bb_event_batch_t& events) { // Collect queued heuristic solutions std::vector heuristic_solutions; @@ -2340,14 +2397,16 @@ void branch_and_bound_t::process_history_and_sync(const bb_event_batch mutex_heuristic_queue_.unlock(); // Sort heuristic solutions by VT - std::sort(heuristic_solutions.begin(), heuristic_solutions.end(), + std::sort(heuristic_solutions.begin(), + heuristic_solutions.end(), [](const queued_heuristic_solution_t& a, const queued_heuristic_solution_t& b) { return a.vt_timestamp < b.vt_timestamp; }); - // Build mapping from provisional_id -> final_id for deterministic node ordering - // This is populated during event processing and applied to nodes afterward - std::unordered_map provisional_to_final_id; + // Build mapping from node_id -> final_id for deterministic node ordering + // node_id is used as the provisional identifier during parallel execution; + // final_id is assigned deterministically during this sync phase based on event order. + std::unordered_map node_id_to_final_id; // Merge B&B events and heuristic solutions for unified timeline replay // Both are sorted by VT, so we can do a merge-style iteration @@ -2355,7 +2414,7 @@ void branch_and_bound_t::process_history_and_sync(const bb_event_batch size_t heuristic_idx = 0; while (event_idx < events.events.size() || heuristic_idx < heuristic_solutions.size()) { - bool process_event = false; + bool process_event = false; bool process_heuristic = false; if (event_idx >= events.events.size()) { @@ -2364,7 +2423,8 @@ void branch_and_bound_t::process_history_and_sync(const bb_event_batch process_event = true; } else { // Both have items - pick the one with smaller VT - if (events.events[event_idx].vt_timestamp <= heuristic_solutions[heuristic_idx].vt_timestamp) { + if (events.events[event_idx].vt_timestamp <= + heuristic_solutions[heuristic_idx].vt_timestamp) { process_event = true; } else { process_heuristic = true; @@ -2388,20 +2448,21 @@ void branch_and_bound_t::process_history_and_sync(const bb_event_batch case bb_event_type_t::NODE_BRANCHED: { // Assign deterministic final IDs to the children - // Events are processed in sorted (deterministic) order, so this assignment is deterministic + // Events are processed in sorted (deterministic) order, so this assignment is + // deterministic i_t down_provisional = event.payload.branched.down_child_id; i_t up_provisional = event.payload.branched.up_child_id; - i_t down_final_id = bsp_next_final_id_++; - i_t up_final_id = bsp_next_final_id_++; - provisional_to_final_id[down_provisional] = down_final_id; - provisional_to_final_id[up_provisional] = up_final_id; + i_t down_final_id = bsp_next_final_id_++; + i_t up_final_id = bsp_next_final_id_++; + node_id_to_final_id[down_provisional] = down_final_id; + node_id_to_final_id[up_provisional] = up_final_id; // Debug: Log final ID assignments - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_final_id_assigned(down_provisional, down_final_id); - bsp_debug_logger_.log_final_id_assigned(up_provisional, up_final_id); - } + BSP_DEBUG_LOG_FINAL_ID_ASSIGNED( + bsp_debug_settings_, bsp_debug_logger_, down_provisional, down_final_id); + BSP_DEBUG_LOG_FINAL_ID_ASSIGNED( + bsp_debug_settings_, bsp_debug_logger_, up_provisional, up_final_id); break; } @@ -2419,9 +2480,8 @@ void branch_and_bound_t::process_history_and_sync(const bb_event_batch const auto& hsol = heuristic_solutions[heuristic_idx++]; // Debug: Log heuristic received - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_heuristic_received(hsol.vt_timestamp, hsol.objective); - } + BSP_DEBUG_LOG_HEURISTIC_RECEIVED( + bsp_debug_settings_, bsp_debug_logger_, hsol.vt_timestamp, hsol.objective); // Process heuristic solution at its correct VT position // FIX: Release mutex before calling get_lower_bound() to avoid deadlock @@ -2434,9 +2494,8 @@ void branch_and_bound_t::process_history_and_sync(const bb_event_batch new_upper = hsol.objective; // Debug: Log incumbent update - if (bsp_debug_settings_.any_enabled()) { - bsp_debug_logger_.log_incumbent_update(hsol.vt_timestamp, hsol.objective, "heuristic"); - } + BSP_DEBUG_LOG_INCUMBENT_UPDATE( + bsp_debug_settings_, bsp_debug_logger_, hsol.vt_timestamp, hsol.objective, "heuristic"); } mutex_upper_.unlock(); @@ -2458,13 +2517,10 @@ void branch_and_bound_t::process_history_and_sync(const bb_event_batch // Apply final IDs to all nodes in worker queues and global heap // Helper lambda to apply final_id mapping to a node - auto apply_final_id = [&provisional_to_final_id](mip_node_t* node) { + auto apply_final_id = [&node_id_to_final_id](mip_node_t* node) { if (node->final_id < 0 && node->node_id > 0) { - // Use node_id as provisional_id (they are the same during BSP) - auto it = provisional_to_final_id.find(node->node_id); - if (it != provisional_to_final_id.end()) { - node->final_id = it->second; - } + auto it = node_id_to_final_id.find(node->node_id); + if (it != node_id_to_final_id.end()) { node->final_id = it->second; } } }; @@ -2473,9 +2529,7 @@ void branch_and_bound_t::process_history_and_sync(const bb_event_batch for (auto* node : worker.local_queue) { apply_final_id(node); } - if (worker.current_node != nullptr) { - apply_final_id(worker.current_node); - } + if (worker.current_node != nullptr) { apply_final_id(worker.current_node); } } // Get current upper bound for pruning decisions diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp index 1b3833b06..a9bdb8068 100644 --- a/cpp/src/dual_simplex/bsp_debug.hpp +++ b/cpp/src/dual_simplex/bsp_debug.hpp @@ -1,14 +1,19 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ #pragma once -#include +// Enable BSP debug macros. The actual logging is controlled at runtime via +// environment variables (CUOPT_BSP_DEBUG_*). This define enables the macro +// infrastructure; without it, all BSP_DEBUG_* macros become complete no-ops. +#define BSP_DEBUG_ENABLED + #include +#include #include #include @@ -150,10 +155,10 @@ struct bsp_debug_settings_t { if (get_env_bool("CUOPT_BSP_DEBUG_ALL")) { settings.enable_all(); } else { - settings.enable_event_log = get_env_bool("CUOPT_BSP_DEBUG_LOG"); - settings.enable_timeline = get_env_bool("CUOPT_BSP_DEBUG_TIMELINE"); - settings.enable_tree_dot = get_env_bool("CUOPT_BSP_DEBUG_TREE"); - settings.enable_state_json = get_env_bool("CUOPT_BSP_DEBUG_JSON"); + settings.enable_event_log = get_env_bool("CUOPT_BSP_DEBUG_LOG"); + settings.enable_timeline = get_env_bool("CUOPT_BSP_DEBUG_TIMELINE"); + settings.enable_tree_dot = get_env_bool("CUOPT_BSP_DEBUG_TREE"); + settings.enable_state_json = get_env_bool("CUOPT_BSP_DEBUG_JSON"); settings.enable_determinism_trace = get_env_bool("CUOPT_BSP_DEBUG_TRACE"); } @@ -195,10 +200,10 @@ class bsp_debug_logger_t { std::filesystem::create_directories(settings_.output_dir); } catch (const std::filesystem::filesystem_error& e) { // Silently disable debug output if we can't create the directory - settings_.enable_event_log = false; - settings_.enable_timeline = false; - settings_.enable_tree_dot = false; - settings_.enable_state_json = false; + settings_.enable_event_log = false; + settings_.enable_timeline = false; + settings_.enable_tree_dot = false; + settings_.enable_state_json = false; settings_.enable_determinism_trace = false; } @@ -225,12 +230,16 @@ class bsp_debug_logger_t { void log_horizon_start(int horizon_num, double vt_start, double vt_end) { - current_horizon_ = horizon_num; + current_horizon_ = horizon_num; horizon_vt_start_ = vt_start; horizon_vt_end_ = vt_end; if (settings_.enable_event_log) { - log_event(vt_start, -1, bsp_log_event_t::HORIZON_START, -1, -1, + log_event(vt_start, + -1, + bsp_log_event_t::HORIZON_START, + -1, + -1, "horizon=[" + std::to_string(vt_start) + "," + std::to_string(vt_end) + ")"); } @@ -244,19 +253,19 @@ class bsp_debug_logger_t { void log_horizon_end(int horizon_num, double vt) { - if (settings_.enable_event_log) { - log_event(vt, -1, bsp_log_event_t::HORIZON_END, -1, -1, ""); - } + if (settings_.enable_event_log) { log_event(vt, -1, bsp_log_event_t::HORIZON_END, -1, -1, ""); } - if (settings_.enable_timeline) { - emit_timeline_for_horizon(horizon_num); - } + if (settings_.enable_timeline) { emit_timeline_for_horizon(horizon_num); } } void log_node_assigned(double vt, int worker_id, i_t node_id, i_t final_id, f_t lower_bound) { if (settings_.enable_event_log) { - log_event(vt, worker_id, bsp_log_event_t::NODE_ASSIGNED, node_id, final_id, + log_event(vt, + worker_id, + bsp_log_event_t::NODE_ASSIGNED, + node_id, + final_id, "lb=" + std::to_string(lower_bound)); } @@ -276,15 +285,19 @@ class bsp_debug_logger_t { } } - void log_solve_start(double vt, int worker_id, i_t node_id, i_t final_id, double work_limit, - bool is_resumed) + void log_solve_start( + double vt, int worker_id, i_t node_id, i_t final_id, double work_limit, bool is_resumed) { std::lock_guard lock(mutex_); if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked(vt, worker_id, - is_resumed ? bsp_log_event_t::NODE_RESUMED : bsp_log_event_t::NODE_SOLVE_START, - node_id, final_id, "work_limit=" + std::to_string(work_limit)); + log_event_unlocked( + vt, + worker_id, + is_resumed ? bsp_log_event_t::NODE_RESUMED : bsp_log_event_t::NODE_SOLVE_START, + node_id, + final_id, + "work_limit=" + std::to_string(work_limit)); } // Start timeline event @@ -293,13 +306,17 @@ class bsp_debug_logger_t { current_solve_fid_[worker_id] = final_id; } - void log_solve_end(double vt, int worker_id, i_t node_id, i_t final_id, const std::string& result, - f_t lower_bound) + void log_solve_end( + double vt, int worker_id, i_t node_id, i_t final_id, const std::string& result, f_t lower_bound) { std::lock_guard lock(mutex_); if (settings_.enable_event_log) { - log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_SOLVE_END, node_id, final_id, + log_event_unlocked(vt, + worker_id, + bsp_log_event_t::NODE_SOLVE_END, + node_id, + final_id, "result=" + result + ",lb=" + std::to_string(lower_bound)); } @@ -321,7 +338,11 @@ class bsp_debug_logger_t { std::lock_guard lock(mutex_); if (settings_.enable_event_log) { - log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_BRANCHED, parent_id, -1, + log_event_unlocked(vt, + worker_id, + bsp_log_event_t::NODE_BRANCHED, + parent_id, + -1, "children=" + std::to_string(down_id) + "," + std::to_string(up_id)); } @@ -336,7 +357,11 @@ class bsp_debug_logger_t { std::lock_guard lock(mutex_); if (settings_.enable_event_log) { - log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_PAUSED, node_id, final_id, + log_event_unlocked(vt, + worker_id, + bsp_log_event_t::NODE_PAUSED, + node_id, + final_id, "acc_vt=" + std::to_string(accumulated_vt)); } @@ -350,7 +375,11 @@ class bsp_debug_logger_t { std::lock_guard lock(mutex_); if (settings_.enable_event_log) { - log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_INTEGER, node_id, -1, + log_event_unlocked(vt, + worker_id, + bsp_log_event_t::NODE_INTEGER, + node_id, + -1, "obj=" + std::to_string(objective)); } @@ -364,7 +393,11 @@ class bsp_debug_logger_t { std::lock_guard lock(mutex_); if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_FATHOMED, node_id, -1, + log_event_unlocked(vt, + worker_id, + bsp_log_event_t::NODE_FATHOMED, + node_id, + -1, "lb=" + std::to_string(lower_bound)); } } @@ -383,15 +416,24 @@ class bsp_debug_logger_t { std::lock_guard lock(mutex_); if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked(vt, -1, bsp_log_event_t::NODE_PRUNED, node_id, final_id, - "lb=" + std::to_string(lower_bound) + ",ub=" + std::to_string(upper_bound)); + log_event_unlocked( + vt, + -1, + bsp_log_event_t::NODE_PRUNED, + node_id, + final_id, + "lb=" + std::to_string(lower_bound) + ",ub=" + std::to_string(upper_bound)); } } void log_sync_phase_start(double vt, size_t num_events) { if (settings_.enable_event_log) { - log_event(vt, -1, bsp_log_event_t::SYNC_PHASE_START, -1, -1, + log_event(vt, + -1, + bsp_log_event_t::SYNC_PHASE_START, + -1, + -1, "num_events=" + std::to_string(num_events)); } } @@ -433,8 +475,8 @@ class bsp_debug_logger_t { void log_heuristic_received(double vt, f_t objective) { if (settings_.enable_event_log) { - log_event(vt, -1, bsp_log_event_t::HEURISTIC_RECEIVED, -1, -1, - "obj=" + std::to_string(objective)); + log_event( + vt, -1, bsp_log_event_t::HEURISTIC_RECEIVED, -1, -1, "obj=" + std::to_string(objective)); } if (settings_.enable_determinism_trace) { @@ -445,7 +487,11 @@ class bsp_debug_logger_t { void log_incumbent_update(double vt, f_t objective, const std::string& source) { if (settings_.enable_event_log) { - log_event(vt, -1, bsp_log_event_t::INCUMBENT_UPDATE, -1, -1, + log_event(vt, + -1, + bsp_log_event_t::INCUMBENT_UPDATE, + -1, + -1, "obj=" + std::to_string(objective) + ",source=" + source); } @@ -496,9 +542,16 @@ class bsp_debug_logger_t { // ======================================================================== template - void emit_state_json(int horizon_num, double vt_start, double vt_end, i_t next_final_id, - f_t upper_bound, f_t lower_bound, i_t nodes_explored, i_t nodes_unexplored, - const WorkerPool& workers, const std::vector*>& heap_nodes, + void emit_state_json(int horizon_num, + double vt_start, + double vt_end, + i_t next_final_id, + f_t upper_bound, + f_t lower_bound, + i_t nodes_explored, + i_t nodes_unexplored, + const WorkerPool& workers, + const std::vector*>& heap_nodes, const bb_event_batch_t& events) { if (!settings_.enable_state_json) return; @@ -593,15 +646,23 @@ class bsp_debug_logger_t { return std::chrono::duration(now - start_time_).count(); } - void log_event(double vt, int worker_id, bsp_log_event_t event, i_t node_id, i_t final_id, + void log_event(double vt, + int worker_id, + bsp_log_event_t event, + i_t node_id, + i_t final_id, const std::string& details) { std::lock_guard lock(mutex_); log_event_unlocked(vt, worker_id, event, node_id, final_id, details); } - void log_event_unlocked(double vt, int worker_id, bsp_log_event_t event, i_t node_id, - i_t final_id, const std::string& details) + void log_event_unlocked(double vt, + int worker_id, + bsp_log_event_t event, + i_t node_id, + i_t final_id, + const std::string& details) { log_ss_ << std::fixed << std::setprecision(3) << vt << "\t" << get_real_time() << "\t" << current_horizon_ << "\t" << (worker_id < 0 ? "COORD" : std::to_string(worker_id)) @@ -639,9 +700,9 @@ class bsp_debug_logger_t { std::ofstream file(filename, std::ios::app); if (!file.is_open()) return; - const int width = 80; - const double vt_min = horizon_vt_start_; - const double vt_max = horizon_vt_end_; + const int width = 80; + const double vt_min = horizon_vt_start_; + const double vt_max = horizon_vt_end_; const double vt_range = vt_max - vt_min; // Avoid division by zero @@ -713,7 +774,8 @@ class bsp_debug_logger_t { file << std::string(width, '=') << "\n"; } - void emit_tree_node_recursive(std::ofstream& file, const mip_node_t* node, + void emit_tree_node_recursive(std::ofstream& file, + const mip_node_t* node, f_t upper_bound) { if (node == nullptr) return; @@ -764,9 +826,8 @@ class bsp_debug_logger_t { // Emit edges to children if (node->get_down_child() != nullptr) { - file << " N" << node->node_id << " -> N" << node->get_down_child()->node_id - << " [label=\"x" << node->branch_var << "<=" << std::floor(node->fractional_val) - << "\"];\n"; + file << " N" << node->node_id << " -> N" << node->get_down_child()->node_id << " [label=\"x" + << node->branch_var << "<=" << std::floor(node->fractional_val) << "\"];\n"; emit_tree_node_recursive(file, node->get_down_child(), upper_bound); } @@ -781,63 +842,133 @@ class bsp_debug_logger_t { // ============================================================================ // Convenience Macros // ============================================================================ +// +// These macros provide a clean interface for BSP debug logging. +// - When BSP_DEBUG_ENABLED is defined: macros are active (controlled by settings at runtime) +// - When BSP_DEBUG_ENABLED is not defined: macros are no-ops (zero overhead) +// +// The 'settings' parameter is a bsp_debug_settings_t& that controls which features are enabled. #ifdef BSP_DEBUG_ENABLED -#define BSP_DEBUG_LOG_HORIZON_START(logger, h, vs, ve) (logger).log_horizon_start(h, vs, ve) -#define BSP_DEBUG_LOG_HORIZON_END(logger, h, vt) (logger).log_horizon_end(h, vt) -#define BSP_DEBUG_LOG_NODE_ASSIGNED(logger, vt, w, nid, fid, lb) \ - (logger).log_node_assigned(vt, w, nid, fid, lb) -#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(logger) (logger).flush_assign_trace() -#define BSP_DEBUG_LOG_SOLVE_START(logger, vt, w, nid, fid, wl, resumed) \ - (logger).log_solve_start(vt, w, nid, fid, wl, resumed) -#define BSP_DEBUG_LOG_SOLVE_END(logger, vt, w, nid, fid, result, lb) \ - (logger).log_solve_end(vt, w, nid, fid, result, lb) -#define BSP_DEBUG_LOG_BRANCHED(logger, vt, w, pid, did, uid) \ - (logger).log_branched(vt, w, pid, did, uid) -#define BSP_DEBUG_LOG_PAUSED(logger, vt, w, nid, fid, acc) (logger).log_paused(vt, w, nid, fid, acc) -#define BSP_DEBUG_LOG_INTEGER(logger, vt, w, nid, obj) (logger).log_integer(vt, w, nid, obj) -#define BSP_DEBUG_LOG_FATHOMED(logger, vt, w, nid, lb) (logger).log_fathomed(vt, w, nid, lb) -#define BSP_DEBUG_LOG_INFEASIBLE(logger, vt, w, nid) (logger).log_infeasible(vt, w, nid) -#define BSP_DEBUG_LOG_SYNC_PHASE_START(logger, vt, ne) (logger).log_sync_phase_start(vt, ne) -#define BSP_DEBUG_LOG_SYNC_PHASE_END(logger, vt) (logger).log_sync_phase_end(vt) -#define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(logger, pid, fid) (logger).log_final_id_assigned(pid, fid) -#define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(logger) (logger).flush_final_ids_trace() -#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(logger, vt, obj) (logger).log_heuristic_received(vt, obj) -#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(logger, vt, obj, src) \ - (logger).log_incumbent_update(vt, obj, src) -#define BSP_DEBUG_LOG_HEAP_ORDER(logger, fids) (logger).log_heap_order(fids) -#define BSP_DEBUG_EMIT_TREE_STATE(logger, h, root, ub) (logger).emit_tree_state(h, root, ub) -#define BSP_DEBUG_EMIT_STATE_JSON(logger, h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) \ - (logger).emit_state_json(h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) -#define BSP_DEBUG_FINALIZE(logger) (logger).finalize() +#define BSP_DEBUG_LOG_HORIZON_START(settings, logger, h, vs, ve) \ + do { \ + if ((settings).any_enabled()) (logger).log_horizon_start(h, vs, ve); \ + } while (0) +#define BSP_DEBUG_LOG_HORIZON_END(settings, logger, h, vt) \ + do { \ + if ((settings).any_enabled()) (logger).log_horizon_end(h, vt); \ + } while (0) +#define BSP_DEBUG_LOG_NODE_ASSIGNED(settings, logger, vt, w, nid, fid, lb) \ + do { \ + if ((settings).any_enabled()) (logger).log_node_assigned(vt, w, nid, fid, lb); \ + } while (0) +#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) \ + do { \ + if ((settings).any_enabled()) (logger).flush_assign_trace(); \ + } while (0) +#define BSP_DEBUG_LOG_SOLVE_START(settings, logger, vt, w, nid, fid, wl, resumed) \ + do { \ + if ((settings).any_enabled()) (logger).log_solve_start(vt, w, nid, fid, wl, resumed); \ + } while (0) +#define BSP_DEBUG_LOG_SOLVE_END(settings, logger, vt, w, nid, fid, result, lb) \ + do { \ + if ((settings).any_enabled()) (logger).log_solve_end(vt, w, nid, fid, result, lb); \ + } while (0) +#define BSP_DEBUG_LOG_BRANCHED(settings, logger, vt, w, pid, did, uid) \ + do { \ + if ((settings).any_enabled()) (logger).log_branched(vt, w, pid, did, uid); \ + } while (0) +#define BSP_DEBUG_LOG_PAUSED(settings, logger, vt, w, nid, fid, acc) \ + do { \ + if ((settings).any_enabled()) (logger).log_paused(vt, w, nid, fid, acc); \ + } while (0) +#define BSP_DEBUG_LOG_INTEGER(settings, logger, vt, w, nid, obj) \ + do { \ + if ((settings).any_enabled()) (logger).log_integer(vt, w, nid, obj); \ + } while (0) +#define BSP_DEBUG_LOG_FATHOMED(settings, logger, vt, w, nid, lb) \ + do { \ + if ((settings).any_enabled()) (logger).log_fathomed(vt, w, nid, lb); \ + } while (0) +#define BSP_DEBUG_LOG_INFEASIBLE(settings, logger, vt, w, nid) \ + do { \ + if ((settings).any_enabled()) (logger).log_infeasible(vt, w, nid); \ + } while (0) +#define BSP_DEBUG_LOG_PRUNED(settings, logger, vt, nid, fid, lb, ub) \ + do { \ + if ((settings).any_enabled()) (logger).log_pruned(vt, nid, fid, lb, ub); \ + } while (0) +#define BSP_DEBUG_LOG_SYNC_PHASE_START(settings, logger, vt, ne) \ + do { \ + if ((settings).any_enabled()) (logger).log_sync_phase_start(vt, ne); \ + } while (0) +#define BSP_DEBUG_LOG_SYNC_PHASE_END(settings, logger, vt) \ + do { \ + if ((settings).any_enabled()) (logger).log_sync_phase_end(vt); \ + } while (0) +#define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(settings, logger, pid, fid) \ + do { \ + if ((settings).any_enabled()) (logger).log_final_id_assigned(pid, fid); \ + } while (0) +#define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(settings, logger) \ + do { \ + if ((settings).any_enabled()) (logger).flush_final_ids_trace(); \ + } while (0) +#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(settings, logger, vt, obj) \ + do { \ + if ((settings).any_enabled()) (logger).log_heuristic_received(vt, obj); \ + } while (0) +#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(settings, logger, vt, obj, src) \ + do { \ + if ((settings).any_enabled()) (logger).log_incumbent_update(vt, obj, src); \ + } while (0) +#define BSP_DEBUG_LOG_HEAP_ORDER(settings, logger, fids) \ + do { \ + if ((settings).any_enabled()) (logger).log_heap_order(fids); \ + } while (0) +#define BSP_DEBUG_EMIT_TREE_STATE(settings, logger, h, root, ub) \ + do { \ + if ((settings).any_enabled()) (logger).emit_tree_state(h, root, ub); \ + } while (0) +#define BSP_DEBUG_EMIT_STATE_JSON( \ + settings, logger, h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) \ + do { \ + if ((settings).any_enabled()) \ + (logger).emit_state_json(h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events); \ + } while (0) +#define BSP_DEBUG_FINALIZE(settings, logger) \ + do { \ + if ((settings).any_enabled()) (logger).finalize(); \ + } while (0) #else -#define BSP_DEBUG_LOG_HORIZON_START(logger, h, vs, ve) ((void)0) -#define BSP_DEBUG_LOG_HORIZON_END(logger, h, vt) ((void)0) -#define BSP_DEBUG_LOG_NODE_ASSIGNED(logger, vt, w, nid, fid, lb) ((void)0) -#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(logger) ((void)0) -#define BSP_DEBUG_LOG_SOLVE_START(logger, vt, w, nid, fid, wl, resumed) ((void)0) -#define BSP_DEBUG_LOG_SOLVE_END(logger, vt, w, nid, fid, result, lb) ((void)0) -#define BSP_DEBUG_LOG_BRANCHED(logger, vt, w, pid, did, uid) ((void)0) -#define BSP_DEBUG_LOG_PAUSED(logger, vt, w, nid, fid, acc) ((void)0) -#define BSP_DEBUG_LOG_INTEGER(logger, vt, w, nid, obj) ((void)0) -#define BSP_DEBUG_LOG_FATHOMED(logger, vt, w, nid, lb) ((void)0) -#define BSP_DEBUG_LOG_INFEASIBLE(logger, vt, w, nid) ((void)0) -#define BSP_DEBUG_LOG_SYNC_PHASE_START(logger, vt, ne) ((void)0) -#define BSP_DEBUG_LOG_SYNC_PHASE_END(logger, vt) ((void)0) -#define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(logger, pid, fid) ((void)0) -#define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(logger) ((void)0) -#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(logger, vt, obj) ((void)0) -#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(logger, vt, obj, src) ((void)0) -#define BSP_DEBUG_LOG_HEAP_ORDER(logger, fids) ((void)0) -#define BSP_DEBUG_EMIT_TREE_STATE(logger, h, root, ub) ((void)0) -#define BSP_DEBUG_EMIT_STATE_JSON(logger, h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) \ +#define BSP_DEBUG_LOG_HORIZON_START(settings, logger, h, vs, ve) ((void)0) +#define BSP_DEBUG_LOG_HORIZON_END(settings, logger, h, vt) ((void)0) +#define BSP_DEBUG_LOG_NODE_ASSIGNED(settings, logger, vt, w, nid, fid, lb) ((void)0) +#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) ((void)0) +#define BSP_DEBUG_LOG_SOLVE_START(settings, logger, vt, w, nid, fid, wl, resumed) ((void)0) +#define BSP_DEBUG_LOG_SOLVE_END(settings, logger, vt, w, nid, fid, result, lb) ((void)0) +#define BSP_DEBUG_LOG_BRANCHED(settings, logger, vt, w, pid, did, uid) ((void)0) +#define BSP_DEBUG_LOG_PAUSED(settings, logger, vt, w, nid, fid, acc) ((void)0) +#define BSP_DEBUG_LOG_INTEGER(settings, logger, vt, w, nid, obj) ((void)0) +#define BSP_DEBUG_LOG_FATHOMED(settings, logger, vt, w, nid, lb) ((void)0) +#define BSP_DEBUG_LOG_INFEASIBLE(settings, logger, vt, w, nid) ((void)0) +#define BSP_DEBUG_LOG_PRUNED(settings, logger, vt, nid, fid, lb, ub) ((void)0) +#define BSP_DEBUG_LOG_SYNC_PHASE_START(settings, logger, vt, ne) ((void)0) +#define BSP_DEBUG_LOG_SYNC_PHASE_END(settings, logger, vt) ((void)0) +#define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(settings, logger, pid, fid) ((void)0) +#define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(settings, logger) ((void)0) +#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(settings, logger, vt, obj) ((void)0) +#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(settings, logger, vt, obj, src) ((void)0) +#define BSP_DEBUG_LOG_HEAP_ORDER(settings, logger, fids) ((void)0) +#define BSP_DEBUG_EMIT_TREE_STATE(settings, logger, h, root, ub) ((void)0) +#define BSP_DEBUG_EMIT_STATE_JSON( \ + settings, logger, h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) \ ((void)0) -#define BSP_DEBUG_FINALIZE(logger) ((void)0) +#define BSP_DEBUG_FINALIZE(settings, logger) ((void)0) #endif } // namespace cuopt::linear_programming::dual_simplex - diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index b65ec2edd..bfea830e9 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -240,10 +240,9 @@ class mip_node_t { copy.fractional_val = fractional_val; copy.node_id = node_id; // Copy BSP fields - copy.accumulated_vt = accumulated_vt; - copy.bsp_state = bsp_state; - copy.provisional_id = provisional_id; - copy.final_id = final_id; + copy.accumulated_vt = accumulated_vt; + copy.bsp_state = bsp_state; + copy.final_id = final_id; return copy; } @@ -263,21 +262,19 @@ class mip_node_t { std::vector vstatus; // BSP fields for deterministic parallel B&B - f_t accumulated_vt{0.0}; // Virtual time spent on this node so far + f_t accumulated_vt{0.0}; // Virtual time spent on this node so far bsp_node_state_t bsp_state{bsp_node_state_t::READY}; // BSP processing state // For deterministic node ID assignment in BSP mode: - // - provisional_id is assigned atomically during parallel execution + // - node_id is assigned non-deterministically during parallel execution (via atomic counter) // - final_id is assigned deterministically during sync phase based on event order - // - Use final_id for sorting if set (>= 0), otherwise fall back to provisional_id/node_id - i_t provisional_id{-1}; + // - Use final_id for sorting if set (>= 0), otherwise fall back to node_id i_t final_id{-1}; // Get the ID to use for deterministic ordering i_t get_deterministic_id() const { if (final_id >= 0) return final_id; - if (provisional_id >= 0) return provisional_id; return node_id; } }; @@ -295,16 +292,20 @@ void remove_fathomed_nodes(std::vector*>& stack) template class node_compare_t { public: + // Comparison for priority queue: returns true if 'a' has lower priority than 'b' + // (elements with lower priority are output last from the heap). + // Primary: prefer lower bound (best-first search) + // Tie-breaker: use deterministic ID for reproducibility across runs bool operator()(const mip_node_t& a, const mip_node_t& b) const { - return a.lower_bound > - b.lower_bound; // True if a comes before b, elements that come before are output last + if (a.lower_bound != b.lower_bound) { return a.lower_bound > b.lower_bound; } + return a.get_deterministic_id() > b.get_deterministic_id(); } bool operator()(const mip_node_t* a, const mip_node_t* b) const { - return a->lower_bound > - b->lower_bound; // True if a comes before b, elements that come before are output last + if (a->lower_bound != b->lower_bound) { return a->lower_bound > b->lower_bound; } + return a->get_deterministic_id() > b->get_deterministic_id(); } }; From c374f69bf43400ba649456199c9f186b0750bfe8 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 7 Jan 2026 16:55:24 +0000 Subject: [PATCH 153/366] fix copyright --- build.sh | 2 +- cpp/include/cuopt/error.hpp | 2 +- cpp/include/cuopt/linear_programming/utilities/internals.hpp | 2 +- cpp/src/dual_simplex/barrier.cu | 2 +- cpp/src/linear_programming/pdlp.cu | 2 +- cpp/src/linear_programming/utilities/cython_solve.cu | 2 +- cpp/src/linear_programming/utils.cuh | 2 +- cpp/src/mip/presolve/gf2_presolve.hpp | 2 +- cpp/src/mip/presolve/third_party_presolve.cpp | 2 +- cpp/src/mip/utilities/cpu_worker_thread.cuh | 2 +- cpp/src/routing/local_search/local_search.cu | 2 +- cpp/src/routing/local_search/sliding_tsp.cu | 2 +- cpp/src/routing/local_search/sliding_window.cu | 2 +- cpp/src/routing/local_search/two_opt.cu | 2 +- cpp/src/routing/local_search/vrp/vrp_execute.cu | 2 +- cpp/tests/distance_engine/waypoint_matrix_test.cpp | 2 +- cpp/tests/linear_programming/c_api_tests/c_api_test.c | 2 +- cpp/tests/routing/level0/l0_ges_test.cu | 2 +- cpp/tests/routing/level0/l0_objective_function_test.cu | 2 +- cpp/tests/routing/level0/l0_routing_test.cu | 2 +- cpp/tests/routing/level0/l0_vehicle_order_match.cu | 2 +- cpp/tests/routing/level0/l0_vehicle_types_test.cu | 2 +- 22 files changed, 22 insertions(+), 22 deletions(-) diff --git a/build.sh b/build.sh index 3f1a2c1f5..1ee8e87fc 100755 --- a/build.sh +++ b/build.sh @@ -1,6 +1,6 @@ #!/bin/bash -# SPDX-FileCopyrightText: Copyright (c) 2021-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2021-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 diff --git a/cpp/include/cuopt/error.hpp b/cpp/include/cuopt/error.hpp index a83413515..9dd547adb 100644 --- a/cpp/include/cuopt/error.hpp +++ b/cpp/include/cuopt/error.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2021-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2021-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/include/cuopt/linear_programming/utilities/internals.hpp b/cpp/include/cuopt/linear_programming/utilities/internals.hpp index 86e7246fa..90d856b23 100644 --- a/cpp/include/cuopt/linear_programming/utilities/internals.hpp +++ b/cpp/include/cuopt/linear_programming/utilities/internals.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/barrier.cu b/cpp/src/dual_simplex/barrier.cu index 4bf3e3ed7..43334a265 100644 --- a/cpp/src/dual_simplex/barrier.cu +++ b/cpp/src/dual_simplex/barrier.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/linear_programming/pdlp.cu b/cpp/src/linear_programming/pdlp.cu index 545791e2e..ae298310c 100644 --- a/cpp/src/linear_programming/pdlp.cu +++ b/cpp/src/linear_programming/pdlp.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/linear_programming/utilities/cython_solve.cu b/cpp/src/linear_programming/utilities/cython_solve.cu index 3370ac663..0e1dbc6af 100644 --- a/cpp/src/linear_programming/utilities/cython_solve.cu +++ b/cpp/src/linear_programming/utilities/cython_solve.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2023-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/linear_programming/utils.cuh b/cpp/src/linear_programming/utils.cuh index 2333283f3..18c023e5d 100644 --- a/cpp/src/linear_programming/utils.cuh +++ b/cpp/src/linear_programming/utils.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/mip/presolve/gf2_presolve.hpp b/cpp/src/mip/presolve/gf2_presolve.hpp index 53e79d2c9..623de4be4 100644 --- a/cpp/src/mip/presolve/gf2_presolve.hpp +++ b/cpp/src/mip/presolve/gf2_presolve.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/mip/presolve/third_party_presolve.cpp b/cpp/src/mip/presolve/third_party_presolve.cpp index faba99ca1..366967de3 100644 --- a/cpp/src/mip/presolve/third_party_presolve.cpp +++ b/cpp/src/mip/presolve/third_party_presolve.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/mip/utilities/cpu_worker_thread.cuh b/cpp/src/mip/utilities/cpu_worker_thread.cuh index e437486cd..60bd5685b 100644 --- a/cpp/src/mip/utilities/cpu_worker_thread.cuh +++ b/cpp/src/mip/utilities/cpu_worker_thread.cuh @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/cpp/src/routing/local_search/local_search.cu b/cpp/src/routing/local_search/local_search.cu index 795d1fb35..c889d9963 100644 --- a/cpp/src/routing/local_search/local_search.cu +++ b/cpp/src/routing/local_search/local_search.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/routing/local_search/sliding_tsp.cu b/cpp/src/routing/local_search/sliding_tsp.cu index 6da1169a3..c7923c361 100644 --- a/cpp/src/routing/local_search/sliding_tsp.cu +++ b/cpp/src/routing/local_search/sliding_tsp.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/routing/local_search/sliding_window.cu b/cpp/src/routing/local_search/sliding_window.cu index fd5ef8500..2d676d9b3 100644 --- a/cpp/src/routing/local_search/sliding_window.cu +++ b/cpp/src/routing/local_search/sliding_window.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/routing/local_search/two_opt.cu b/cpp/src/routing/local_search/two_opt.cu index ff345312a..abe6e8a92 100644 --- a/cpp/src/routing/local_search/two_opt.cu +++ b/cpp/src/routing/local_search/two_opt.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/routing/local_search/vrp/vrp_execute.cu b/cpp/src/routing/local_search/vrp/vrp_execute.cu index ff560e1cc..5e417a934 100644 --- a/cpp/src/routing/local_search/vrp/vrp_execute.cu +++ b/cpp/src/routing/local_search/vrp/vrp_execute.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2023-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/tests/distance_engine/waypoint_matrix_test.cpp b/cpp/tests/distance_engine/waypoint_matrix_test.cpp index 1f9bacf0e..2db3953c2 100644 --- a/cpp/tests/distance_engine/waypoint_matrix_test.cpp +++ b/cpp/tests/distance_engine/waypoint_matrix_test.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/tests/linear_programming/c_api_tests/c_api_test.c b/cpp/tests/linear_programming/c_api_tests/c_api_test.c index 7f2dd8322..52be9e16f 100644 --- a/cpp/tests/linear_programming/c_api_tests/c_api_test.c +++ b/cpp/tests/linear_programming/c_api_tests/c_api_test.c @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/tests/routing/level0/l0_ges_test.cu b/cpp/tests/routing/level0/l0_ges_test.cu index afd0a2627..b3e72c56e 100644 --- a/cpp/tests/routing/level0/l0_ges_test.cu +++ b/cpp/tests/routing/level0/l0_ges_test.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2021-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2021-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/tests/routing/level0/l0_objective_function_test.cu b/cpp/tests/routing/level0/l0_objective_function_test.cu index 449139849..53217807c 100644 --- a/cpp/tests/routing/level0/l0_objective_function_test.cu +++ b/cpp/tests/routing/level0/l0_objective_function_test.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/tests/routing/level0/l0_routing_test.cu b/cpp/tests/routing/level0/l0_routing_test.cu index 735b6e4bc..e078a3499 100644 --- a/cpp/tests/routing/level0/l0_routing_test.cu +++ b/cpp/tests/routing/level0/l0_routing_test.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2021-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2021-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/tests/routing/level0/l0_vehicle_order_match.cu b/cpp/tests/routing/level0/l0_vehicle_order_match.cu index 4b1b9fdd3..782f93edc 100644 --- a/cpp/tests/routing/level0/l0_vehicle_order_match.cu +++ b/cpp/tests/routing/level0/l0_vehicle_order_match.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/tests/routing/level0/l0_vehicle_types_test.cu b/cpp/tests/routing/level0/l0_vehicle_types_test.cu index 4e46d31d6..2fa3c559f 100644 --- a/cpp/tests/routing/level0/l0_vehicle_types_test.cu +++ b/cpp/tests/routing/level0/l0_vehicle_types_test.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ From d89c50ab998faf498297aea4f2498e1c8d74e900 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 8 Jan 2026 09:29:58 +0000 Subject: [PATCH 154/366] flag to disable gpu heuristics --- cpp/src/dual_simplex/branch_and_bound.cpp | 69 +++++++++++++++++++++- cpp/src/mip/diversity/diversity_manager.cu | 18 +++++- cpp/src/mip/problem/problem.cu | 4 +- 3 files changed, 87 insertions(+), 4 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 80d239f2d..75ac66930 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1691,6 +1691,24 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } assert(root_vstatus_.size() == original_lp_.num_cols); + + // Validate root_vstatus_ has correct BASIC count + // This catches bugs where the root LP solve produces an invalid vstatus + { + const i_t expected_basic_count = original_lp_.num_rows; + i_t actual_basic_count = 0; + for (const auto& status : root_vstatus_) { + if (status == variable_status_t::BASIC) { actual_basic_count++; } + } + if (actual_basic_count != expected_basic_count) { + settings_.log.printf("ERROR: root_vstatus_ has %d BASIC entries, expected %d (num_rows)\n", + actual_basic_count, + expected_basic_count); + assert(actual_basic_count == expected_basic_count && + "root_vstatus_ BASIC count mismatch - LP solver returned invalid basis"); + } + } + set_uninitialized_steepest_edge_norms(edge_norms_); root_objective_ = compute_objective(original_lp_, root_relax_soln_.x); @@ -2112,6 +2130,29 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t { raft::common::nvtx::range scope("BB::solve_node_bsp"); + // Validate vstatus has correct BASIC count before processing + // This helps diagnose heap overflow bugs where vstatus has too many BASIC entries + { + const i_t expected_basic_count = original_lp_.num_rows; + i_t actual_basic_count = 0; + for (const auto& status : node_ptr->vstatus) { + if (status == variable_status_t::BASIC) { actual_basic_count++; } + } + if (actual_basic_count != expected_basic_count) { + settings_.log.printf( + "ERROR: Node %d (final_id %d) vstatus has %d BASIC entries, expected %d (num_rows)\n", + node_ptr->node_id, + node_ptr->final_id, + actual_basic_count, + expected_basic_count); + settings_.log.printf(" vstatus.size() = %zu, num_cols = %d\n", + node_ptr->vstatus.size(), + original_lp_.num_cols); + assert(actual_basic_count == expected_basic_count && + "vstatus BASIC count mismatch - this indicates vstatus corruption"); + } + } + // Track work units at start (from work_context, which simplex solver updates) double work_units_at_start = worker.work_context.global_work_units_elapsed; double clock_at_start = worker.clock; @@ -2192,6 +2233,26 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t leaf_edge_norms, &worker.work_context); + // Validate vstatus after LP solve - check for corruption during simplex + { + const i_t expected_basic_count = original_lp_.num_rows; + i_t actual_basic_count = 0; + for (const auto& status : leaf_vstatus) { + if (status == variable_status_t::BASIC) { actual_basic_count++; } + } + if (actual_basic_count != expected_basic_count) { + settings_.log.printf( + "ERROR: After LP solve, node %d vstatus has %d BASIC entries, expected %d\n", + node_ptr->node_id, + actual_basic_count, + expected_basic_count); + settings_.log.printf(" lp_status = %d, recompute_basis = %d\n", + static_cast(lp_status), + worker.recompute_bounds_and_basis ? 1 : 0); + assert(actual_basic_count == expected_basic_count && "vstatus corrupted during LP solve"); + } + } + // Update worker clock with work performed // The simplex solver recorded work to work_context.global_work_units_elapsed // Compute the delta and advance the worker clock (but don't double-record to work_context) @@ -2340,7 +2401,13 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t up_child_id); exploration_stats_.nodes_unexplored += 2; - worker.recompute_bounds_and_basis = false; + + // In BSP mode, always recompute basis to ensure basic_list matches the node's vstatus. + // When recompute_bounds_and_basis = false, the simplex reuses basic_list from the previous + // solve, but each node has its own vstatus (copied at branch time). If basic_list and vstatus + // diverge, pivoting corrupts vstatus by adding BASIC entries without properly removing + // others. This is safe because the child's vstatus is a copy of the parent's final vstatus. + worker.recompute_bounds_and_basis = true; // Add children to worker's local queue (deterministic order: down first) worker.enqueue_node(node_ptr->get_down_child()); diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 05a5ce047..ef11de649 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -17,6 +17,8 @@ #include +#include // for std::this_thread::sleep_for + constexpr bool fj_only_run = false; namespace cuopt::linear_programming::detail { @@ -303,6 +305,20 @@ solution_t diversity_manager_t::run_solver() context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC ? "deterministic" : "opportunistic"); + // Debug: Allow disabling GPU heuristics to test B&B tree determinism in isolation + const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); + if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { + CUOPT_LOG_INFO("GPU heuristics disabled via CUOPT_DISABLE_GPU_HEURISTICS=1"); + // Initialize population minimally and wait for B&B to finish + population.initialize_population(); + population.allocate_solutions(); + // Wait for B&B to signal completion + while (!check_b_b_preemption()) { + std::this_thread::sleep_for(std::chrono::milliseconds(100)); + } + return population.best_feasible(); + } + population.timer = timer; const f_t time_limit = timer.remaining_time(); const f_t lp_time_limit = diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 51fb8452a..c8ee11176 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1418,8 +1418,8 @@ problem_t problem_t::get_problem_after_fixing_vars( auto end_time = std::chrono::high_resolution_clock::now(); double time_taken = std::chrono::duration_cast(end_time - start_time).count(); - static double total_time_taken = 0.; - static int total_calls = 0; + [[maybe_unused]] static double total_time_taken = 0.; + [[maybe_unused]] static int total_calls = 0; total_time_taken += time_taken; total_calls++; // CUOPT_LOG_DEBUG( From 8dd2d081192854bfab7f71d0c92b42dc4361ec84 Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 8 Jan 2026 11:16:53 +0100 Subject: [PATCH 155/366] fix incorrect termination status --- cpp/src/dual_simplex/bnb_worker.hpp | 9 +++-- cpp/src/dual_simplex/branch_and_bound.cpp | 42 ++++++++++------------- 2 files changed, 26 insertions(+), 25 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index a7a94761d..2f1a363b0 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -121,7 +121,7 @@ class bnb_worker_pool_t { } } - bnb_worker_t* get_worker() + bnb_worker_t* get_idle_worker() { std::lock_guard lock(mutex_); @@ -129,11 +129,16 @@ class bnb_worker_pool_t { return nullptr; } else { i_t idx = available_workers_.front(); - available_workers_.pop_front(); return workers_[idx].get(); } } + void pop_idle_worker() + { + std::lock_guard lock(mutex_); + if (!available_workers_.empty()) { available_workers_.pop_front(); } + } + void return_worker_to_pool(bnb_worker_t* worker) { worker->is_active = false; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 71d912e21..21ceb3ea2 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1014,33 +1014,34 @@ void branch_and_bound_t::master_loop() nodes_since_last_log = 0; } - if (now > settings_.time_limit) { break; } + if (now > settings_.time_limit) { + solver_status_ = mip_exploration_status_t::TIME_LIMIT; + break; + } for (auto type : worker_types) { if (active_workers_per_type[type] >= max_num_workers_per_type[type]) { continue; } - bnb_worker_t* worker = worker_pool_.get_worker(); - if (worker == nullptr) { continue; } + // Get an idle worker. + bnb_worker_t* worker = worker_pool_.get_idle_worker(); + if (worker == nullptr) { break; } if (type == EXPLORATION) { // If there any node left in the heap, we pop the top node and explore it. std::optional*> start_node = node_queue.pop_best_first(); - if (!start_node.has_value()) { - worker_pool_.return_worker_to_pool(worker); - continue; - } - + if (!start_node.has_value()) { continue; } if (get_upper_bound() < start_node.value()->lower_bound) { // This node was put on the heap earlier but its lower bound is now greater than the // current upper bound search_tree_.graphviz_node( settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); search_tree_.update(start_node.value(), node_status_t::FATHOMED); - worker_pool_.return_worker_to_pool(worker); continue; } + // Remove the worker from the idle list. + worker_pool_.pop_idle_worker(); worker->init_best_first(start_node.value(), original_lp_); last_node_depth = start_node.value()->depth; active_workers_per_type[type]++; @@ -1053,23 +1054,14 @@ void branch_and_bound_t::master_loop() } else { std::optional*> start_node = node_queue.pop_diving(); - if (!start_node.has_value()) { - worker_pool_.return_worker_to_pool(worker); - continue; - } - - if (get_upper_bound() < start_node.value()->lower_bound) { - worker_pool_.return_worker_to_pool(worker); - continue; - } + if (!start_node.has_value()) { continue; } + if (get_upper_bound() < start_node.value()->lower_bound) { continue; } bool is_feasible = worker->init_diving(start_node.value(), type, original_lp_, settings_); + if (!is_feasible) { continue; } - if (!is_feasible) { - worker_pool_.return_worker_to_pool(worker); - continue; - } - + // Remove the worker from the idle list. + worker_pool_.pop_idle_worker(); active_workers_per_type[type]++; launched_any_task = true; @@ -1084,6 +1076,10 @@ void branch_and_bound_t::master_loop() #pragma omp taskyield } } + + if (solver_status_ == mip_exploration_status_t::RUNNING) { + solver_status_ = mip_exploration_status_t::COMPLETED; + } } template From 1dcee0349b12f52b6e7520dbfd5cd881ba7c6dd6 Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 8 Jan 2026 12:33:32 +0100 Subject: [PATCH 156/366] replace upper bound lock with atomic --- cpp/src/dual_simplex/branch_and_bound.cpp | 51 +++++++++-------------- cpp/src/dual_simplex/branch_and_bound.hpp | 3 +- 2 files changed, 20 insertions(+), 34 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 21ceb3ea2..538555225 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -236,15 +236,6 @@ branch_and_bound_t::branch_and_bound_t( mutex_upper_.unlock(); } -template -f_t branch_and_bound_t::get_upper_bound() -{ - mutex_upper_.lock(); - const f_t upper_bound = upper_bound_; - mutex_upper_.unlock(); - return upper_bound; -} - template f_t branch_and_bound_t::get_lower_bound() { @@ -469,11 +460,10 @@ mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t::set_final_solution(mip_solution_t 0 && exploration_stats_.nodes_unexplored == 0 && - upper_bound == inf) { + upper_bound_ == inf) { settings_.log.printf("Integer infeasible.\n"); mip_status = mip_status_t::INFEASIBLE; if (settings_.heuristic_preemption_callback != nullptr) { @@ -512,7 +502,7 @@ mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t::solve_node(mip_node_t* logger_t& log) { const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; - const f_t upper_bound = get_upper_bound(); lp_problem_t& leaf_problem = worker->leaf_problem; lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); @@ -633,7 +622,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); - lp_settings.cut_off = upper_bound + settings_.dual_tol; + lp_settings.cut_off = upper_bound_ + settings_.dual_tol; lp_settings.inside_mip = 2; lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); lp_settings.scale_columns = false; @@ -722,7 +711,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* } else if (lp_status == dual::status_t::CUTOFF) { // Node was cut off. Do not branch - node_ptr->lower_bound = upper_bound; + node_ptr->lower_bound = upper_bound_; f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); search_tree.graphviz_node(log, node_ptr, "cut off", leaf_objective); search_tree.update(node_ptr, node_status_t::FATHOMED); @@ -754,7 +743,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); return node_solve_info_t::NO_CHILDREN; - } else if (leaf_objective <= upper_bound + abs_fathom_tol) { + } else if (leaf_objective <= upper_bound_ + abs_fathom_tol) { // Choose fractional variable to branch on auto [branch_var, round_dir] = variable_selection( node_ptr, leaf_fractional, leaf_solution.x, thread_type, lp_settings.log); @@ -815,7 +804,7 @@ void branch_and_bound_t::plunge_with(bnb_worker_t* worker) stack.pop_front(); f_t lower_bound = node_ptr->lower_bound; - f_t upper_bound = get_upper_bound(); + f_t upper_bound = upper_bound_; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); // This is based on three assumptions: @@ -917,7 +906,7 @@ void branch_and_bound_t::dive_with(bnb_worker_t* worker) stack.pop_front(); f_t lower_bound = node_ptr->lower_bound; - f_t upper_bound = get_upper_bound(); + f_t upper_bound = upper_bound_; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); worker->lower_bound = lower_bound; @@ -965,15 +954,14 @@ template void branch_and_bound_t::master_loop() { f_t lower_bound = get_lower_bound(); - f_t upper_bound = get_upper_bound(); - f_t abs_gap = upper_bound - lower_bound; - f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + f_t abs_gap = upper_bound_ - lower_bound; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); i_t last_node_depth = 0; f_t last_log = 0.0; diving_heuristics_settings_t diving_settings = settings_.diving_settings; - if (!std::isfinite(upper_bound)) { diving_settings.disable_guided_diving = true; } + if (!std::isfinite(upper_bound_)) { diving_settings.disable_guided_diving = true; } auto worker_types = bnb_get_worker_types(diving_settings); auto max_num_workers_per_type = @@ -984,16 +972,15 @@ void branch_and_bound_t::master_loop() (active_workers_per_type[0] > 0 || node_queue.best_first_queue_size() > 0)) { bool launched_any_task = false; lower_bound = get_lower_bound(); - upper_bound = get_upper_bound(); - abs_gap = upper_bound - lower_bound; - rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + abs_gap = upper_bound_ - lower_bound; + rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); repair_heuristic_solutions(); // If the guided diving was disabled previously due to the lack of an incumbent solution, // re-enable as soon as a new incumbent is found. if (settings_.diving_settings.disable_guided_diving != diving_settings.disable_guided_diving) { - if (std::isfinite(upper_bound)) { + if (std::isfinite(upper_bound_)) { diving_settings.disable_guided_diving = settings_.diving_settings.disable_guided_diving; max_num_workers_per_type = bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); @@ -1009,7 +996,7 @@ void branch_and_bound_t::master_loop() (time_since_last_log > 30) || now > settings_.time_limit) { i_t depth = node_queue.best_first_queue_size() > 0 ? node_queue.bfs_top()->depth : last_node_depth; - report(" ", upper_bound, lower_bound, depth); + report(" ", upper_bound_, lower_bound, depth); last_log = tic(); nodes_since_last_log = 0; } @@ -1031,7 +1018,7 @@ void branch_and_bound_t::master_loop() std::optional*> start_node = node_queue.pop_best_first(); if (!start_node.has_value()) { continue; } - if (get_upper_bound() < start_node.value()->lower_bound) { + if (upper_bound_ < start_node.value()->lower_bound) { // This node was put on the heap earlier but its lower bound is now greater than the // current upper bound search_tree_.graphviz_node( @@ -1055,7 +1042,7 @@ void branch_and_bound_t::master_loop() std::optional*> start_node = node_queue.pop_diving(); if (!start_node.has_value()) { continue; } - if (get_upper_bound() < start_node.value()->lower_bound) { continue; } + if (upper_bound_ < start_node.value()->lower_bound) { continue; } bool is_feasible = worker->init_diving(start_node.value(), type, original_lp_, settings_); if (!is_feasible) { continue; } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 017fd9efc..0b64bf06f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -95,7 +95,6 @@ class branch_and_bound_t { f_t& repaired_obj, std::vector& repaired_solution) const; - f_t get_upper_bound(); f_t get_lower_bound(); bool enable_concurrent_lp_root_solve() const { return enable_concurrent_lp_root_solve_; } std::atomic* get_root_concurrent_halt() { return &root_concurrent_halt_; } @@ -122,7 +121,7 @@ class branch_and_bound_t { omp_mutex_t mutex_upper_; // Global variable for upper bound - f_t upper_bound_; + omp_atomic_t upper_bound_; // Global variable for incumbent. The incumbent should be updated with the upper bound mip_solution_t incumbent_; From 9e2e5c7cae2a74d9c5db48aabb8f2f9b19e6ad41 Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 8 Jan 2026 15:06:38 +0100 Subject: [PATCH 157/366] improve idling master thread --- cpp/src/dual_simplex/bnb_worker.hpp | 15 +++++++-------- cpp/src/dual_simplex/branch_and_bound.cpp | 17 ++++++++++------- cpp/src/dual_simplex/branch_and_bound.hpp | 2 -- 3 files changed, 17 insertions(+), 17 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index 2f1a363b0..f7f1a6783 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -117,7 +117,7 @@ class bnb_worker_pool_t { for (i_t i = 0; i < num_workers; ++i) { workers_[i] = std::make_unique>(i, original_lp, Arow, var_type, settings); - available_workers_.push_front(i); + idle_workers_.push_front(i); } } @@ -125,10 +125,10 @@ class bnb_worker_pool_t { { std::lock_guard lock(mutex_); - if (available_workers_.empty()) { + if (idle_workers_.empty()) { return nullptr; } else { - i_t idx = available_workers_.front(); + i_t idx = idle_workers_.front(); return workers_[idx].get(); } } @@ -136,14 +136,14 @@ class bnb_worker_pool_t { void pop_idle_worker() { std::lock_guard lock(mutex_); - if (!available_workers_.empty()) { available_workers_.pop_front(); } + if (!idle_workers_.empty()) { idle_workers_.pop_front(); } } void return_worker_to_pool(bnb_worker_t* worker) { worker->is_active = false; std::lock_guard lock(mutex_); - available_workers_.push_back(worker->worker_id); + idle_workers_.push_back(worker->worker_id); } f_t get_lower_bounds() @@ -163,10 +163,9 @@ class bnb_worker_pool_t { // Worker pool std::vector>> workers_; - // FIXME: Implement a lock-free queue (it can also be used for - // passing feasible solutions between bnb and heuristics) omp_mutex_t mutex_; - std::deque available_workers_; + std::deque idle_workers_; + omp_atomic_t num_idle_workers_; }; template diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 538555225..573fbc657 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -957,8 +957,7 @@ void branch_and_bound_t::master_loop() f_t abs_gap = upper_bound_ - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); i_t last_node_depth = 0; - - f_t last_log = 0.0; + f_t last_log = 0.0; diving_heuristics_settings_t diving_settings = settings_.diving_settings; if (!std::isfinite(upper_bound_)) { diving_settings.disable_guided_diving = true; } @@ -1035,7 +1034,7 @@ void branch_and_bound_t::master_loop() nodes_since_last_log++; launched_any_task = true; -#pragma omp task +#pragma omp task affinity(worker) plunge_with(worker); } else { @@ -1052,15 +1051,20 @@ void branch_and_bound_t::master_loop() active_workers_per_type[type]++; launched_any_task = true; -#pragma omp task +#pragma omp task affinity(worker) dive_with(worker); } } - // If no new task was launched in this iteration, suspend temporarily the execution of the - // master + // If no new task was launched in this iteration, suspend temporarily the + // execution of the master. As of 8/Jan/2026, GCC does not + // implement taskyield, but LLVM does. if (!launched_any_task) { +#ifndef __GNUC__ #pragma omp taskyield +#else + std::this_thread::sleep_for(std::chrono::milliseconds(1)); +#endif } } @@ -1314,7 +1318,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut exploration_stats_.nodes_unexplored = 2; solver_status_ = mip_exploration_status_t::RUNNING; lower_bound_ceiling_ = inf; - should_report_ = true; auto down_child = search_tree_.root.get_down_child(); auto up_child = search_tree_.root.get_up_child(); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 0b64bf06f..f721951c8 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -162,8 +162,6 @@ class branch_and_bound_t { // Global status of the solver. omp_atomic_t solver_status_; - - omp_atomic_t should_report_; omp_atomic_t nodes_since_last_log; // In case, a best-first thread encounters a numerical issue when solving a node, From 34f7eaae313ddb63739f3f0ac2cda526810b8f66 Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 8 Jan 2026 16:28:17 +0100 Subject: [PATCH 158/366] added ramp-up-phase --- cpp/src/dual_simplex/bnb_worker.hpp | 23 ++++++- cpp/src/dual_simplex/branch_and_bound.cpp | 65 +++++++++++++++---- cpp/src/dual_simplex/branch_and_bound.hpp | 8 +-- .../dual_simplex/simplex_solver_settings.hpp | 2 +- 4 files changed, 80 insertions(+), 18 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index f7f1a6783..00352d50a 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -114,6 +114,7 @@ class bnb_worker_pool_t { const simplex_solver_settings_t& settings) { workers_.resize(num_workers); + num_idle_workers_ = num_workers; for (i_t i = 0; i < num_workers; ++i) { workers_[i] = std::make_unique>(i, original_lp, Arow, var_type, settings); @@ -136,7 +137,24 @@ class bnb_worker_pool_t { void pop_idle_worker() { std::lock_guard lock(mutex_); - if (!idle_workers_.empty()) { idle_workers_.pop_front(); } + if (!idle_workers_.empty()) { + idle_workers_.pop_front(); + num_idle_workers_--; + } + } + + bnb_worker_t* get_and_pop_idle_worker() + { + std::lock_guard lock(mutex_); + + if (idle_workers_.empty()) { + return nullptr; + } else { + i_t idx = idle_workers_.front(); + idle_workers_.pop_front(); + num_idle_workers_--; + return workers_[idx].get(); + } } void return_worker_to_pool(bnb_worker_t* worker) @@ -144,6 +162,7 @@ class bnb_worker_pool_t { worker->is_active = false; std::lock_guard lock(mutex_); idle_workers_.push_back(worker->worker_id); + num_idle_workers_++; } f_t get_lower_bounds() @@ -159,6 +178,8 @@ class bnb_worker_pool_t { return lower_bound; } + i_t num_idle_workers() { return num_idle_workers_; } + private: // Worker pool std::vector>> workers_; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 573fbc657..172dc963d 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -846,7 +846,7 @@ void branch_and_bound_t::plunge_with(bnb_worker_t* worker) ++exploration_stats_.nodes_explored; --exploration_stats_.nodes_unexplored; - ++nodes_since_last_log; + ++nodes_since_last_log_; if (status == node_solve_info_t::TIME_LIMIT) { break; @@ -865,10 +865,20 @@ void branch_and_bound_t::plunge_with(bnb_worker_t* worker) exploration_stats_.nodes_unexplored += 2; if (status == node_solve_info_t::UP_CHILD_FIRST) { - stack.push_front(node_ptr->get_down_child()); + if (node_queue.best_first_queue_size() < min_node_queue_size_) { + node_queue.push(node_ptr->get_down_child()); + } else { + stack.push_front(node_ptr->get_down_child()); + } + stack.push_front(node_ptr->get_up_child()); } else { - stack.push_front(node_ptr->get_up_child()); + if (node_queue.best_first_queue_size() < min_node_queue_size_) { + node_queue.push(node_ptr->get_up_child()); + } else { + stack.push_front(node_ptr->get_up_child()); + } + stack.push_front(node_ptr->get_down_child()); } } @@ -960,11 +970,12 @@ void branch_and_bound_t::master_loop() f_t last_log = 0.0; diving_heuristics_settings_t diving_settings = settings_.diving_settings; - if (!std::isfinite(upper_bound_)) { diving_settings.disable_guided_diving = true; } + bool is_ramp_up_finished = false; - auto worker_types = bnb_get_worker_types(diving_settings); - auto max_num_workers_per_type = - bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); + std::vector worker_types = {EXPLORATION}; + std::array max_num_workers_per_type; + max_num_workers_per_type.fill(0); + max_num_workers_per_type[EXPLORATION] = settings_.num_threads; while (solver_status_ == mip_exploration_status_t::RUNNING && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && @@ -976,6 +987,29 @@ void branch_and_bound_t::master_loop() repair_heuristic_solutions(); + if (!is_ramp_up_finished) { + if (node_queue.best_first_queue_size() >= min_node_queue_size_ && + node_queue.bfs_top()->depth >= diving_settings.min_node_depth) { + if (!std::isfinite(upper_bound_)) { diving_settings.disable_guided_diving = true; } + max_num_workers_per_type = + bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); + worker_types = bnb_get_worker_types(diving_settings); + is_ramp_up_finished = true; + +#ifdef CUOPT_LOG_DEBUG + settings_.log.debug("Ramp-up phase is finished. num active workers = %d, heap size = %d\n", + active_workers_per_type[EXPLORATION], + node_queue.best_first_queue_size()); + + for (auto type : worker_types) { + settings_.log.debug("%s: max num of workers = %d", + feasible_solution_symbol(type), + max_num_workers_per_type[type]); + } +#endif + } + } + // If the guided diving was disabled previously due to the lack of an incumbent solution, // re-enable as soon as a new incumbent is found. if (settings_.diving_settings.disable_guided_diving != diving_settings.disable_guided_diving) { @@ -984,20 +1018,28 @@ void branch_and_bound_t::master_loop() max_num_workers_per_type = bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); worker_types = bnb_get_worker_types(diving_settings); + +#ifdef CUOPT_LOG_DEBUG + for (auto type : worker_types) { + settings_.log.debug("%s: max num of workers = %d", + feasible_solution_symbol(type), + max_num_workers_per_type[type]); + } +#endif } } f_t now = toc(exploration_stats_.start_time); f_t time_since_last_log = last_log == 0 ? 1.0 : toc(last_log); - if (((nodes_since_last_log >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && + if (((nodes_since_last_log_ >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && time_since_last_log >= 1) || (time_since_last_log > 30) || now > settings_.time_limit) { i_t depth = node_queue.best_first_queue_size() > 0 ? node_queue.bfs_top()->depth : last_node_depth; report(" ", upper_bound_, lower_bound, depth); - last_log = tic(); - nodes_since_last_log = 0; + last_log = tic(); + nodes_since_last_log_ = 0; } if (now > settings_.time_limit) { @@ -1031,7 +1073,7 @@ void branch_and_bound_t::master_loop() worker->init_best_first(start_node.value(), original_lp_); last_node_depth = start_node.value()->depth; active_workers_per_type[type]++; - nodes_since_last_log++; + nodes_since_last_log_++; launched_any_task = true; #pragma omp task affinity(worker) @@ -1318,6 +1360,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut exploration_stats_.nodes_unexplored = 2; solver_status_ = mip_exploration_status_t::RUNNING; lower_bound_ceiling_ = inf; + min_node_queue_size_ = 2 * settings_.num_threads; auto down_child = search_tree_.root.get_down_child(); auto up_child = search_tree_.root.get_up_child(); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index f721951c8..1930b2cb6 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -162,7 +162,9 @@ class branch_and_bound_t { // Global status of the solver. omp_atomic_t solver_status_; - omp_atomic_t nodes_since_last_log; + + omp_atomic_t nodes_since_last_log_; + i_t min_node_queue_size_; // In case, a best-first thread encounters a numerical issue when solving a node, // its blocks the progression of the lower bound. @@ -184,10 +186,6 @@ class branch_and_bound_t { // Repairs low-quality solutions from the heuristics, if it is applicable. void repair_heuristic_solutions(); - // Ramp-up phase of the solver, where we greedily expand the tree until - // there is enough unexplored nodes. This is done recursively using OpenMP tasks. - void exploration_ramp_up(mip_node_t* node, i_t initial_heap_size); - void plunge_with(bnb_worker_t* worker); void dive_with(bnb_worker_t* worker); diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 51d59c5ba..d0f9dd408 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -29,7 +29,7 @@ struct diving_heuristics_settings_t { bool disable_guided_diving = false; bool disable_coefficient_diving = false; - i_t min_node_depth = 0; + i_t min_node_depth = 5; i_t node_limit = 500; f_t iteration_limit_factor = 0.05; i_t backtrack = 5; From 769a3d83a9423782a0c0a70f64319a845cc46ecb Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 8 Jan 2026 17:48:38 +0100 Subject: [PATCH 159/366] refactoring --- cpp/src/dual_simplex/branch_and_bound.cpp | 329 +++++++++++----------- 1 file changed, 158 insertions(+), 171 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 172dc963d..572027a5d 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -231,9 +231,7 @@ branch_and_bound_t::branch_and_bound_t( convert_user_problem(original_problem_, settings_, original_lp_, new_slacks_, dualize_info); full_variable_types(original_problem_, original_lp_, var_types_); - mutex_upper_.lock(); upper_bound_ = inf; - mutex_upper_.unlock(); } template @@ -960,161 +958,6 @@ void branch_and_bound_t::dive_with(bnb_worker_t* worker) active_workers_per_type[diving_type]--; } -template -void branch_and_bound_t::master_loop() -{ - f_t lower_bound = get_lower_bound(); - f_t abs_gap = upper_bound_ - lower_bound; - f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); - i_t last_node_depth = 0; - f_t last_log = 0.0; - - diving_heuristics_settings_t diving_settings = settings_.diving_settings; - bool is_ramp_up_finished = false; - - std::vector worker_types = {EXPLORATION}; - std::array max_num_workers_per_type; - max_num_workers_per_type.fill(0); - max_num_workers_per_type[EXPLORATION] = settings_.num_threads; - - while (solver_status_ == mip_exploration_status_t::RUNNING && - abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && - (active_workers_per_type[0] > 0 || node_queue.best_first_queue_size() > 0)) { - bool launched_any_task = false; - lower_bound = get_lower_bound(); - abs_gap = upper_bound_ - lower_bound; - rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); - - repair_heuristic_solutions(); - - if (!is_ramp_up_finished) { - if (node_queue.best_first_queue_size() >= min_node_queue_size_ && - node_queue.bfs_top()->depth >= diving_settings.min_node_depth) { - if (!std::isfinite(upper_bound_)) { diving_settings.disable_guided_diving = true; } - max_num_workers_per_type = - bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); - worker_types = bnb_get_worker_types(diving_settings); - is_ramp_up_finished = true; - -#ifdef CUOPT_LOG_DEBUG - settings_.log.debug("Ramp-up phase is finished. num active workers = %d, heap size = %d\n", - active_workers_per_type[EXPLORATION], - node_queue.best_first_queue_size()); - - for (auto type : worker_types) { - settings_.log.debug("%s: max num of workers = %d", - feasible_solution_symbol(type), - max_num_workers_per_type[type]); - } -#endif - } - } - - // If the guided diving was disabled previously due to the lack of an incumbent solution, - // re-enable as soon as a new incumbent is found. - if (settings_.diving_settings.disable_guided_diving != diving_settings.disable_guided_diving) { - if (std::isfinite(upper_bound_)) { - diving_settings.disable_guided_diving = settings_.diving_settings.disable_guided_diving; - max_num_workers_per_type = - bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); - worker_types = bnb_get_worker_types(diving_settings); - -#ifdef CUOPT_LOG_DEBUG - for (auto type : worker_types) { - settings_.log.debug("%s: max num of workers = %d", - feasible_solution_symbol(type), - max_num_workers_per_type[type]); - } -#endif - } - } - - f_t now = toc(exploration_stats_.start_time); - f_t time_since_last_log = last_log == 0 ? 1.0 : toc(last_log); - - if (((nodes_since_last_log_ >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && - time_since_last_log >= 1) || - (time_since_last_log > 30) || now > settings_.time_limit) { - i_t depth = - node_queue.best_first_queue_size() > 0 ? node_queue.bfs_top()->depth : last_node_depth; - report(" ", upper_bound_, lower_bound, depth); - last_log = tic(); - nodes_since_last_log_ = 0; - } - - if (now > settings_.time_limit) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; - break; - } - - for (auto type : worker_types) { - if (active_workers_per_type[type] >= max_num_workers_per_type[type]) { continue; } - - // Get an idle worker. - bnb_worker_t* worker = worker_pool_.get_idle_worker(); - if (worker == nullptr) { break; } - - if (type == EXPLORATION) { - // If there any node left in the heap, we pop the top node and explore it. - std::optional*> start_node = node_queue.pop_best_first(); - - if (!start_node.has_value()) { continue; } - if (upper_bound_ < start_node.value()->lower_bound) { - // This node was put on the heap earlier but its lower bound is now greater than the - // current upper bound - search_tree_.graphviz_node( - settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); - search_tree_.update(start_node.value(), node_status_t::FATHOMED); - continue; - } - - // Remove the worker from the idle list. - worker_pool_.pop_idle_worker(); - worker->init_best_first(start_node.value(), original_lp_); - last_node_depth = start_node.value()->depth; - active_workers_per_type[type]++; - nodes_since_last_log_++; - launched_any_task = true; - -#pragma omp task affinity(worker) - plunge_with(worker); - - } else { - std::optional*> start_node = node_queue.pop_diving(); - - if (!start_node.has_value()) { continue; } - if (upper_bound_ < start_node.value()->lower_bound) { continue; } - - bool is_feasible = worker->init_diving(start_node.value(), type, original_lp_, settings_); - if (!is_feasible) { continue; } - - // Remove the worker from the idle list. - worker_pool_.pop_idle_worker(); - active_workers_per_type[type]++; - launched_any_task = true; - -#pragma omp task affinity(worker) - dive_with(worker); - } - } - - // If no new task was launched in this iteration, suspend temporarily the - // execution of the master. As of 8/Jan/2026, GCC does not - // implement taskyield, but LLVM does. - if (!launched_any_task) { -#ifndef __GNUC__ -#pragma omp taskyield -#else - std::this_thread::sleep_for(std::chrono::milliseconds(1)); -#endif - } - } - - if (solver_status_ == mip_exploration_status_t::RUNNING) { - solver_status_ = mip_exploration_status_t::COMPLETED; - } -} - template lp_status_t branch_and_bound_t::solve_root_relaxation( simplex_solver_settings_t const& lp_settings) @@ -1344,17 +1187,21 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut original_lp_, log); - worker_pool_.init(2 * settings_.num_threads, original_lp_, Arow_, var_types_, settings_); - active_workers_per_type.fill(0); - settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", settings_.num_threads, settings_.num_bfs_workers, settings_.num_threads - settings_.num_bfs_workers); - settings_.log.printf( - " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " - "| Time |\n"); + auto down_child = search_tree_.root.get_down_child(); + auto up_child = search_tree_.root.get_up_child(); + node_queue.push(down_child); + node_queue.push(up_child); + + f_t lower_bound = get_lower_bound(); + f_t abs_gap = upper_bound_ - lower_bound; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); + i_t last_node_depth = 0; + f_t last_log = 0.0; exploration_stats_.nodes_explored = 1; exploration_stats_.nodes_unexplored = 2; @@ -1362,22 +1209,162 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut lower_bound_ceiling_ = inf; min_node_queue_size_ = 2 * settings_.num_threads; - auto down_child = search_tree_.root.get_down_child(); - auto up_child = search_tree_.root.get_up_child(); - node_queue.push(down_child); - node_queue.push(up_child); + diving_heuristics_settings_t diving_settings = settings_.diving_settings; + bool is_ramp_up_finished = false; + + std::vector worker_types = {EXPLORATION}; + std::array max_num_workers_per_type; + max_num_workers_per_type.fill(0); + max_num_workers_per_type[EXPLORATION] = settings_.num_threads; + worker_pool_.init(2 * settings_.num_threads, original_lp_, Arow_, var_types_, settings_); + active_workers_per_type.fill(0); + + settings_.log.printf( + " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " + "| Time |\n"); #pragma omp parallel num_threads(settings_.num_threads) { #pragma omp master { -#pragma omp task - master_loop(); + while (solver_status_ == mip_exploration_status_t::RUNNING && + abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && + (active_workers_per_type[0] > 0 || node_queue.best_first_queue_size() > 0)) { + bool launched_any_task = false; + lower_bound = get_lower_bound(); + abs_gap = upper_bound_ - lower_bound; + rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); + + repair_heuristic_solutions(); + + if (!is_ramp_up_finished) { + if (node_queue.best_first_queue_size() >= min_node_queue_size_ && + node_queue.bfs_top()->depth >= diving_settings.min_node_depth) { + if (!std::isfinite(upper_bound_)) { diving_settings.disable_guided_diving = true; } + max_num_workers_per_type = + bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); + worker_types = bnb_get_worker_types(diving_settings); + is_ramp_up_finished = true; + +#ifdef CUOPT_LOG_DEBUG + settings_.log.debug( + "Ramp-up phase is finished. num active workers = %d, heap size = %d\n", + active_workers_per_type[EXPLORATION], + node_queue.best_first_queue_size()); + + for (auto type : worker_types) { + settings_.log.debug("%s: max num of workers = %d", + feasible_solution_symbol(type), + max_num_workers_per_type[type]); + } +#endif + } + } + + // If the guided diving was disabled previously due to the lack of an incumbent solution, + // re-enable as soon as a new incumbent is found. + if (settings_.diving_settings.disable_guided_diving != + diving_settings.disable_guided_diving) { + if (std::isfinite(upper_bound_)) { + diving_settings.disable_guided_diving = settings_.diving_settings.disable_guided_diving; + max_num_workers_per_type = + bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); + worker_types = bnb_get_worker_types(diving_settings); + +#ifdef CUOPT_LOG_DEBUG + for (auto type : worker_types) { + settings_.log.debug("%s: max num of workers = %d", + feasible_solution_symbol(type), + max_num_workers_per_type[type]); + } +#endif + } + } + + f_t now = toc(exploration_stats_.start_time); + f_t time_since_last_log = last_log == 0 ? 1.0 : toc(last_log); + + if (((nodes_since_last_log_ >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && + time_since_last_log >= 1) || + (time_since_last_log > 30) || now > settings_.time_limit) { + i_t depth = + node_queue.best_first_queue_size() > 0 ? node_queue.bfs_top()->depth : last_node_depth; + report(" ", upper_bound_, lower_bound, depth); + last_log = tic(); + nodes_since_last_log_ = 0; + } + + if (now > settings_.time_limit) { + solver_status_ = mip_exploration_status_t::TIME_LIMIT; + break; + } + + for (auto type : worker_types) { + if (active_workers_per_type[type] >= max_num_workers_per_type[type]) { continue; } + + // Get an idle worker. + bnb_worker_t* worker = worker_pool_.get_idle_worker(); + if (worker == nullptr) { break; } + + if (type == EXPLORATION) { + // If there any node left in the heap, we pop the top node and explore it. + std::optional*> start_node = node_queue.pop_best_first(); + + if (!start_node.has_value()) { continue; } + if (upper_bound_ < start_node.value()->lower_bound) { + // This node was put on the heap earlier but its lower bound is now greater than the + // current upper bound + search_tree_.graphviz_node( + settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); + search_tree_.update(start_node.value(), node_status_t::FATHOMED); + continue; + } + + // Remove the worker from the idle list. + worker_pool_.pop_idle_worker(); + worker->init_best_first(start_node.value(), original_lp_); + last_node_depth = start_node.value()->depth; + active_workers_per_type[type]++; + nodes_since_last_log_++; + launched_any_task = true; + +#pragma omp task affinity(worker) + plunge_with(worker); + + } else { + std::optional*> start_node = node_queue.pop_diving(); + + if (!start_node.has_value()) { continue; } + if (upper_bound_ < start_node.value()->lower_bound) { continue; } + + bool is_feasible = + worker->init_diving(start_node.value(), type, original_lp_, settings_); + if (!is_feasible) { continue; } + + // Remove the worker from the idle list. + worker_pool_.pop_idle_worker(); + active_workers_per_type[type]++; + launched_any_task = true; + +#pragma omp task affinity(worker) + dive_with(worker); + } + } + + // If no new task was launched in this iteration, suspend temporarily the + // execution of the master. As of 8/Jan/2026, GCC does not + // implement taskyield, but LLVM does. + if (!launched_any_task) { std::this_thread::sleep_for(std::chrono::milliseconds(1)); } + } } } - f_t lower_bound = node_queue.best_first_queue_size() > 0 ? node_queue.get_lower_bound() - : search_tree_.root.lower_bound; + if (solver_status_ == mip_exploration_status_t::RUNNING) { + solver_status_ = mip_exploration_status_t::COMPLETED; + } + + lower_bound = node_queue.best_first_queue_size() > 0 ? node_queue.get_lower_bound() + : search_tree_.root.lower_bound; return set_final_solution(solution, lower_bound); } From c54033c1ea8c64acea029384a80c7f05114bf19f Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 8 Jan 2026 18:15:34 +0100 Subject: [PATCH 160/366] updating code to match the new parallel bnb --- cpp/src/dual_simplex/branch_and_bound.cpp | 82 +++++++++---------- cpp/src/dual_simplex/branch_and_bound.hpp | 14 ++-- cpp/src/dual_simplex/diving_heuristics.cpp | 2 +- cpp/src/dual_simplex/diving_heuristics.hpp | 2 +- cpp/src/dual_simplex/node_queue.hpp | 10 ++- .../dual_simplex/simplex_solver_settings.hpp | 12 +-- cpp/src/mip/diversity/lns/rins.cu | 4 +- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 4 +- cpp/src/mip/solver.cu | 8 +- 9 files changed, 73 insertions(+), 65 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 1993630e1..29120bda2 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -192,14 +192,14 @@ std::string user_mip_gap(f_t obj_value, f_t lower_bound) } } -inline const char* feasible_solution_symbol(bnb_thread_type_t type) +inline const char* feasible_solution_symbol(bnb_worker_type_t type) { switch (type) { - case bnb_thread_type_t::EXPLORATION: return "B "; - case bnb_thread_type_t::COEFFICIENT_DIVING: return "CD"; - case bnb_thread_type_t::LINE_SEARCH_DIVING: return "LD"; - case bnb_thread_type_t::PSEUDOCOST_DIVING: return "PD"; - case bnb_thread_type_t::GUIDED_DIVING: return "GD"; + case bnb_worker_type_t::EXPLORATION: return "B "; + case bnb_worker_type_t::COEFFICIENT_DIVING: return "D "; + case bnb_worker_type_t::LINE_SEARCH_DIVING: return "D "; + case bnb_worker_type_t::PSEUDOCOST_DIVING: return "D "; + case bnb_worker_type_t::GUIDED_DIVING: return "D "; default: return "U "; } } @@ -532,7 +532,7 @@ template void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - bnb_thread_type_t thread_type) + bnb_worker_type_t thread_type) { bool send_solution = false; @@ -582,7 +582,7 @@ branch_variable_t branch_and_bound_t::variable_selection( mip_node_t* node_ptr, const std::vector& fractional, const std::vector& solution, - bnb_thread_type_t type, + bnb_worker_type_t type, logger_t& log) { i_t branch_var = -1; @@ -590,7 +590,7 @@ branch_variable_t branch_and_bound_t::variable_selection( rounding_direction_t round_dir = rounding_direction_t::NONE; switch (type) { - case bnb_thread_type_t::EXPLORATION: + case bnb_worker_type_t::EXPLORATION: std::tie(branch_var, obj_estimate) = pc_.variable_selection_and_obj_estimate(fractional, solution, node_ptr->lower_bound, log); round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); @@ -601,16 +601,16 @@ branch_variable_t branch_and_bound_t::variable_selection( node_ptr->objective_estimate = obj_estimate; return {branch_var, round_dir}; - case bnb_thread_type_t::COEFFICIENT_DIVING: + case bnb_worker_type_t::COEFFICIENT_DIVING: return coefficient_diving(original_lp_, fractional, solution, log); - case bnb_thread_type_t::LINE_SEARCH_DIVING: + case bnb_worker_type_t::LINE_SEARCH_DIVING: return line_search_diving(fractional, solution, root_relax_soln_.x, log); - case bnb_thread_type_t::PSEUDOCOST_DIVING: + case bnb_worker_type_t::PSEUDOCOST_DIVING: return pseudocost_diving(pc_, fractional, solution, root_relax_soln_.x, log); - case bnb_thread_type_t::GUIDED_DIVING: + case bnb_worker_type_t::GUIDED_DIVING: return guided_diving(pc_, fractional, solution, incumbent_.x, log); default: @@ -628,7 +628,7 @@ node_solve_info_t branch_and_bound_t::solve_node( std::vector& basic_list, std::vector& nonbasic_list, bounds_strengthening_t& node_presolver, - bnb_thread_type_t thread_type, + bnb_worker_type_t thread_type, bool recompute_bounds_and_basis, const std::vector& root_lower, const std::vector& root_upper, @@ -639,11 +639,11 @@ node_solve_info_t branch_and_bound_t::solve_node( const f_t upper_bound = get_upper_bound(); // If there is no incumbent, use pseudocost diving instead of guided diving - if (upper_bound == inf && thread_type == bnb_thread_type_t::GUIDED_DIVING) { + if (upper_bound == inf && thread_type == bnb_worker_type_t::GUIDED_DIVING) { if (settings_.diving_settings.disable_pseudocost_diving) { - thread_type = bnb_thread_type_t::COEFFICIENT_DIVING; + thread_type = bnb_worker_type_t::COEFFICIENT_DIVING; } else { - thread_type = bnb_thread_type_t::PSEUDOCOST_DIVING; + thread_type = bnb_worker_type_t::PSEUDOCOST_DIVING; } } @@ -658,7 +658,7 @@ node_solve_info_t branch_and_bound_t::solve_node( lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); lp_settings.scale_columns = false; - if (thread_type != bnb_thread_type_t::EXPLORATION) { + if (thread_type != bnb_worker_type_t::EXPLORATION) { i_t bnb_lp_iters = exploration_stats_.total_lp_iters; f_t max_iter = settings_.diving_settings.iteration_limit_factor * bnb_lp_iters; lp_settings.iteration_limit = max_iter - stats.total_lp_iters; @@ -702,12 +702,12 @@ node_solve_info_t branch_and_bound_t::solve_node( leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); } - bool feasible = + bool is_feasible = node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; - if (feasible) { + if (is_feasible) { i_t node_iter = 0; f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; @@ -775,7 +775,7 @@ node_solve_info_t branch_and_bound_t::solve_node( search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); pc_.update_pseudo_costs(node_ptr, leaf_objective); - if (thread_type == bnb_thread_type_t::EXPLORATION) { + if (thread_type == bnb_worker_type_t::EXPLORATION) { if (settings_.node_processed_callback != nullptr) { std::vector original_x; uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); @@ -820,7 +820,7 @@ node_solve_info_t branch_and_bound_t::solve_node( return node_solve_info_t::ITERATION_LIMIT; } else { - if (thread_type == bnb_thread_type_t::EXPLORATION) { + if (thread_type == bnb_worker_type_t::EXPLORATION) { fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); log.printf( "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " @@ -900,7 +900,7 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod basic_list, nonbasic_list, node_presolver, - bnb_thread_type_t::EXPLORATION, + bnb_worker_type_t::EXPLORATION, true, original_lp_.lower, original_lp_.upper, @@ -1008,7 +1008,7 @@ void branch_and_bound_t::plunge_from(i_t task_id, basic_list, nonbasic_list, node_presolver, - bnb_thread_type_t::EXPLORATION, + bnb_worker_type_t::EXPLORATION, recompute_bounds_and_basis, original_lp_.lower, original_lp_.upper, @@ -1123,7 +1123,7 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, basis_update_mpf_t& basis_factors, std::vector& basic_list, std::vector& nonbasic_list, - bnb_thread_type_t diving_type) + bnb_worker_type_t diving_type) { logger_t log; log.log = false; @@ -1194,7 +1194,7 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, } template -void branch_and_bound_t::diving_thread(bnb_thread_type_t diving_type) +void branch_and_bound_t::diving_thread(bnb_worker_type_t diving_type) { // Make a copy of the original LP. We will modify its bounds at each leaf lp_problem_t leaf_problem = original_lp_; @@ -1327,23 +1327,23 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut exploration_stats_.nodes_explored = 0; original_lp_.A.to_compressed_row(Arow_); - std::vector diving_strategies; + std::vector diving_strategies; diving_strategies.reserve(4); if (!settings_.diving_settings.disable_pseudocost_diving) { - diving_strategies.push_back(bnb_thread_type_t::PSEUDOCOST_DIVING); + diving_strategies.push_back(bnb_worker_type_t::PSEUDOCOST_DIVING); } if (!settings_.diving_settings.disable_line_search_diving) { - diving_strategies.push_back(bnb_thread_type_t::LINE_SEARCH_DIVING); + diving_strategies.push_back(bnb_worker_type_t::LINE_SEARCH_DIVING); } if (!settings_.diving_settings.disable_guided_diving) { - diving_strategies.push_back(bnb_thread_type_t::GUIDED_DIVING); + diving_strategies.push_back(bnb_worker_type_t::GUIDED_DIVING); } if (!settings_.diving_settings.disable_coefficient_diving) { - diving_strategies.push_back(bnb_thread_type_t::COEFFICIENT_DIVING); + diving_strategies.push_back(bnb_worker_type_t::COEFFICIENT_DIVING); } if (diving_strategies.empty()) { @@ -1418,7 +1418,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut set_uninitialized_steepest_edge_norms(edge_norms_); root_objective_ = compute_objective(original_lp_, root_relax_soln_.x); - local_lower_bounds_.assign(settings_.num_bfs_threads, root_objective_); + local_lower_bounds_.assign(settings_.num_bfs_workers, root_objective_); if (settings_.set_simplex_solution_callback != nullptr) { std::vector original_x; @@ -1496,12 +1496,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", settings_.num_threads, - settings_.num_bfs_threads, - settings_.diving_settings.num_diving_tasks); - - settings_.log.printf( - " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " - "| Time |\n"); + settings_.num_bfs_workers, + settings_.num_threads - settings_.num_bfs_workers); exploration_stats_.nodes_explored = 1; exploration_stats_.nodes_unexplored = 2; @@ -1512,6 +1508,10 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut lower_bound_ceiling_ = inf; should_report_ = true; + settings_.log.printf( + " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " + "| Time |\n"); + #pragma omp parallel num_threads(settings_.num_threads) { #pragma omp master @@ -1530,14 +1530,14 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut exploration_ramp_up(up_child, initial_size); } - for (i_t i = 0; i < settings_.num_bfs_threads; i++) { + for (i_t i = 0; i < settings_.num_bfs_workers; i++) { #pragma omp task best_first_thread(i); } if (!diving_strategies.empty()) { - for (i_t k = 0; k < settings_.diving_settings.num_diving_tasks; k++) { - const bnb_thread_type_t diving_type = diving_strategies[k % num_strategies]; + for (i_t k = 0; k < settings_.diving_settings.num_diving_workers; k++) { + const bnb_worker_type_t diving_type = diving_strategies[k % num_strategies]; #pragma omp task diving_thread(diving_type); } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index b30682bdb..dc838bb80 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -58,7 +58,7 @@ enum class node_solve_info_t { // // [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, // Berlin, 2007. doi: 10.14279/depositonce-1634. -enum class bnb_thread_type_t { +enum class bnb_worker_type_t { EXPLORATION = 0, // Best-First + Plunging. PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) @@ -208,7 +208,7 @@ class branch_and_bound_t { void add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - bnb_thread_type_t thread_type); + bnb_worker_type_t thread_type); // Repairs low-quality solutions from the heuristics, if it is applicable. void repair_heuristic_solutions(); @@ -240,11 +240,11 @@ class branch_and_bound_t { basis_update_mpf_t& basis_update, std::vector& basic_list, std::vector& nonbasic_list, - bnb_thread_type_t diving_type); + bnb_worker_type_t diving_type); // Each diving thread pops the first node from the dive queue and then performs // a deep dive into the subtree determined by the node. - void diving_thread(bnb_thread_type_t diving_type); + void diving_thread(bnb_worker_type_t diving_type); // Solve the LP relaxation of a leaf node and update the tree. node_solve_info_t solve_node(mip_node_t* node_ptr, @@ -254,7 +254,7 @@ class branch_and_bound_t { std::vector& basic_list, std::vector& nonbasic_list, bounds_strengthening_t& node_presolver, - bnb_thread_type_t thread_type, + bnb_worker_type_t thread_type, bool recompute_basis_and_bounds, const std::vector& root_lower, const std::vector& root_upper, @@ -265,7 +265,7 @@ class branch_and_bound_t { branch_variable_t variable_selection(mip_node_t* node_ptr, const std::vector& fractional, const std::vector& solution, - bnb_thread_type_t type, + bnb_worker_type_t type, logger_t& log); }; diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 978a97e42..ce9460fa9 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/diving_heuristics.hpp b/cpp/src/dual_simplex/diving_heuristics.hpp index c7b1e2050..1f44fee31 100644 --- a/cpp/src/dual_simplex/diving_heuristics.hpp +++ b/cpp/src/dual_simplex/diving_heuristics.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/node_queue.hpp b/cpp/src/dual_simplex/node_queue.hpp index 0234fa038..c3b0e9336 100644 --- a/cpp/src/dual_simplex/node_queue.hpp +++ b/cpp/src/dual_simplex/node_queue.hpp @@ -1,6 +1,6 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 */ #pragma once @@ -168,6 +168,12 @@ class node_queue_t { std::lock_guard lock(mutex); return best_first_heap.empty() ? inf : best_first_heap.top()->lower_bound; } + + mip_node_t* bfs_top() + { + std::lock_guard lock(mutex); + return best_first_heap.empty() ? nullptr : best_first_heap.top()->node; + } }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 60b92ee33..d0f9dd408 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -13,6 +13,7 @@ #include #include #include +#include #include #include #include @@ -21,13 +22,14 @@ namespace cuopt::linear_programming::dual_simplex { template struct diving_heuristics_settings_t { - i_t num_diving_tasks = -1; + i_t num_diving_workers = -1; bool disable_line_search_diving = false; bool disable_pseudocost_diving = false; bool disable_guided_diving = false; bool disable_coefficient_diving = false; + i_t min_node_depth = 5; i_t node_limit = 500; f_t iteration_limit_factor = 0.05; i_t backtrack = 5; @@ -84,14 +86,14 @@ struct simplex_solver_settings_t { iteration_log_frequency(1000), first_iteration_log(2), num_threads(omp_get_max_threads() - 1), - num_bfs_threads(std::min(num_threads / 4, 1)), + num_bfs_workers(std::min(num_threads / 4, 1)), random_seed(0), inside_mip(0), solution_callback(nullptr), heuristic_preemption_callback(nullptr), concurrent_halt(nullptr) { - diving_settings.num_diving_tasks = std::max(num_threads - num_bfs_threads, 1); + diving_settings.num_diving_workers = std::max(num_threads - num_bfs_workers, 1); } void set_log(bool logging) const { log.log = logging; } @@ -151,7 +153,7 @@ struct simplex_solver_settings_t { i_t first_iteration_log; // number of iterations to log at beginning of solve i_t num_threads; // number of threads to use i_t random_seed; // random seed - i_t num_bfs_threads; // number of threads dedicated to the best-first search + i_t num_bfs_workers; // number of threads dedicated to the best-first search diving_heuristics_settings_t diving_settings; // Settings for the diving heuristics diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 0121f76ef..035b03144 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -258,11 +258,11 @@ void rins_t::run_rins() std::min(current_mip_gap, (f_t)settings.target_mip_gap); branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; branch_and_bound_settings.num_threads = 2; - branch_and_bound_settings.num_bfs_threads = 1; + branch_and_bound_settings.num_bfs_workers = 1; // In the future, let RINS use all the diving heuristics. For now, // restricting to guided diving. - branch_and_bound_settings.diving_settings.num_diving_tasks = 1; + branch_and_bound_settings.diving_settings.num_diving_workers = 1; branch_and_bound_settings.diving_settings.disable_line_search_diving = true; branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index 2335003b6..1efed74ce 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -103,11 +103,11 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; branch_and_bound_settings.num_threads = 2; - branch_and_bound_settings.num_bfs_threads = 1; + branch_and_bound_settings.num_bfs_workers = 1; // In the future, let SubMIP use all the diving heuristics. For now, // restricting to guided diving. - branch_and_bound_settings.diving_settings.num_diving_tasks = 1; + branch_and_bound_settings.diving_settings.num_diving_workers = 1; branch_and_bound_settings.diving_settings.disable_line_search_diving = true; branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 35a94f36c..f9f885a81 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -184,10 +184,10 @@ solution_t mip_solver_t::run_solver() } i_t num_threads = branch_and_bound_settings.num_threads; - i_t num_bfs_threads = std::max(1, num_threads / 4); - i_t num_diving_threads = std::max(1, num_threads - num_bfs_threads); - branch_and_bound_settings.num_bfs_threads = num_bfs_threads; - branch_and_bound_settings.diving_settings.num_diving_tasks = num_diving_threads; + i_t num_bfs_workers = std::max(1, num_threads / 4); + i_t num_diving_workers = std::max(1, num_threads - num_bfs_workers); + branch_and_bound_settings.num_bfs_workers = num_bfs_workers; + branch_and_bound_settings.diving_settings.num_diving_workers = num_diving_workers; // Set the branch and bound -> primal heuristics callback branch_and_bound_settings.solution_callback = From 4bcf801d5e74fbf978ce5b0cd025570d6b6cfdc7 Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 8 Jan 2026 18:27:43 +0100 Subject: [PATCH 161/366] removed command line options --- .../linear_programming/cuopt/run_mip.cpp | 51 +------------------ .../mip/solver_settings.hpp | 7 +-- cpp/src/mip/solver.cu | 9 ---- 3 files changed, 2 insertions(+), 65 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 6d8fbdd81..6013dcaf5 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -147,10 +147,6 @@ int run_single_file(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, - bool disable_line_search_diving, - bool disable_pseudocost_diving, - bool disable_guided_diving, - bool disable_coefficient_diving, double time_limit) { const raft::handle_t handle_{}; @@ -208,11 +204,6 @@ int run_single_file(std::string file_path, settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; - settings.disable_line_search_diving = disable_line_search_diving; - settings.disable_pseudocost_diving = disable_pseudocost_diving; - settings.disable_guided_diving = disable_guided_diving; - settings.disable_coefficient_diving = disable_coefficient_diving; - cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; auto start_run_solver = std::chrono::high_resolution_clock::now(); @@ -259,10 +250,6 @@ void run_single_file_mp(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, - bool disable_line_search_diving, - bool disable_pseudocost_diving, - bool disable_guided_diving, - bool disable_coefficient_diving, double time_limit) { std::cout << "running file " << file_path << " on gpu : " << device << std::endl; @@ -278,10 +265,6 @@ void run_single_file_mp(std::string file_path, num_cpu_threads, write_log_file, log_to_console, - disable_line_search_diving, - disable_pseudocost_diving, - disable_guided_diving, - disable_coefficient_diving, time_limit); // this is a bad design to communicate the result but better than adding complexity of IPC or // pipes @@ -365,22 +348,6 @@ int main(int argc, char* argv[]) .help("track allocations (t/f)") .default_value(std::string("f")); - program.add_argument("--disable-line-search-diving") - .help("disable line search diving (t/f)") - .default_value(std::string("f")); - - program.add_argument("--disable-pseudocost-diving") - .help("disable pseudocost diving (t/f)") - .default_value(std::string("f")); - - program.add_argument("--disable-guided-diving") - .help("disable guided diving (t/f)") - .default_value(std::string("f")); - - program.add_argument("--disable-coefficient-diving") - .help("disable coefficient diving (t/f)") - .default_value(std::string("f")); - // Parse arguments try { program.parse_args(argc, argv); @@ -410,14 +377,6 @@ int main(int argc, char* argv[]) double memory_limit = program.get("--memory-limit"); bool track_allocations = program.get("--track-allocations")[0] == 't'; - bool disable_line_search_diving = - program.get("--disable-line-search-diving")[0] == 't'; - bool disable_pseudocost_diving = - program.get("--disable-pseudocost-diving")[0] == 't'; - bool disable_guided_diving = program.get("--disable-guided-diving")[0] == 't'; - bool disable_coefficient_diving = - program.get("--disable-coefficient-diving")[0] == 't'; - if (num_cpu_threads < 0) { num_cpu_threads = omp_get_max_threads() / n_gpus; } if (program.is_used("--out-dir")) { @@ -504,10 +463,6 @@ int main(int argc, char* argv[]) num_cpu_threads, write_log_file, log_to_console, - disable_line_search_diving, - disable_pseudocost_diving, - disable_guided_diving, - disable_coefficient_diving, time_limit); } else if (sys_pid < 0) { std::cerr << "Fork failed!" << std::endl; @@ -548,10 +503,6 @@ int main(int argc, char* argv[]) num_cpu_threads, write_log_file, log_to_console, - disable_line_search_diving, - disable_pseudocost_diving, - disable_guided_diving, - disable_coefficient_diving, time_limit); } diff --git a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp index 680cceaba..4f6320752 100644 --- a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp +++ b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2023-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -87,11 +87,6 @@ class mip_solver_settings_t { std::string sol_file; std::string user_problem_file; - bool disable_line_search_diving = false; - bool disable_pseudocost_diving = false; - bool disable_guided_diving = false; - bool disable_coefficient_diving = false; - /** Initial primal solutions */ std::vector>> initial_solutions; bool mip_scaling = true; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index f9f885a81..08e1806b9 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -168,15 +168,6 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; - branch_and_bound_settings.diving_settings.disable_coefficient_diving = - context.settings.disable_coefficient_diving; - branch_and_bound_settings.diving_settings.disable_pseudocost_diving = - context.settings.disable_pseudocost_diving; - branch_and_bound_settings.diving_settings.disable_guided_diving = - context.settings.disable_guided_diving; - branch_and_bound_settings.diving_settings.disable_line_search_diving = - context.settings.disable_line_search_diving; - if (context.settings.num_cpu_threads < 0) { branch_and_bound_settings.num_threads = omp_get_max_threads() - 1; } else { From d91369d5f9b59969c3e60bd6bbcf860edf1ce7ae Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 8 Jan 2026 18:32:44 +0100 Subject: [PATCH 162/366] fix style --- cpp/src/dual_simplex/CMakeLists.txt | 2 +- cpp/src/dual_simplex/bounds_strengthening.cpp | 2 +- cpp/src/dual_simplex/logger.hpp | 2 +- cpp/src/dual_simplex/mip_node.hpp | 2 +- cpp/src/dual_simplex/pseudo_costs.cpp | 2 +- cpp/src/dual_simplex/pseudo_costs.hpp | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/cpp/src/dual_simplex/CMakeLists.txt b/cpp/src/dual_simplex/CMakeLists.txt index ebaf9cbb7..af1415fa9 100644 --- a/cpp/src/dual_simplex/CMakeLists.txt +++ b/cpp/src/dual_simplex/CMakeLists.txt @@ -1,5 +1,5 @@ # cmake-format: off -# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # cmake-format: on diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index c56c9db98..4114e7e09 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/logger.hpp b/cpp/src/dual_simplex/logger.hpp index c45e3ede3..f81308670 100644 --- a/cpp/src/dual_simplex/logger.hpp +++ b/cpp/src/dual_simplex/logger.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index a082932ac..de147132a 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 1c0a33042..aabbe5a17 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index ab01b2a85..5c34e0296 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ From d67f2301dbb189eccbae73ca58c61ae0cc750ceb Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 9 Jan 2026 08:31:51 +0000 Subject: [PATCH 163/366] BSP b&b progress --- cpp/src/dual_simplex/branch_and_bound.cpp | 174 +++++++++++++++--- cpp/src/dual_simplex/branch_and_bound.hpp | 13 +- cpp/src/dual_simplex/bsp_debug.hpp | 25 +++ cpp/src/dual_simplex/phase2.cpp | 15 +- cpp/src/mip/diversity/diversity_manager.cu | 1 + .../recombiners/bound_prop_recombiner.cuh | 4 +- cpp/src/mip/solver.cu | 6 +- 7 files changed, 193 insertions(+), 45 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 75ac66930..e6d71db4b 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -19,6 +19,7 @@ #include #include #include +#include #include @@ -1871,6 +1872,8 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && @@ -1922,12 +1929,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_treset_for_horizon(horizon_start, horizon_end); @@ -1963,9 +1964,54 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t state_data; + + // Global state + state_data.push_back(static_cast(bsp_next_final_id_)); + state_data.push_back(static_cast(exploration_stats_.nodes_explored)); + state_data.push_back(static_cast(exploration_stats_.nodes_unexplored)); + + // Upper/lower bounds (convert to fixed-point for exact comparison) + f_t ub = get_upper_bound(); + f_t lb = get_lower_bound(); + state_data.push_back(static_cast(ub * 1000000)); // 6 decimal places + state_data.push_back(static_cast(lb * 1000000)); + + // Worker queue contents (sorted by final_id for determinism) + for (const auto& worker : *bsp_workers_) { + // Hash paused node if any + if (worker.current_node != nullptr) { + state_data.push_back(worker.current_node->final_id >= 0 ? worker.current_node->final_id + : worker.current_node->node_id); + } + // Hash local queue + std::vector queue_fids; + for (const auto* node : worker.local_queue) { + queue_fids.push_back(node->final_id >= 0 ? node->final_id : node->node_id); + } + std::sort(queue_fids.begin(), queue_fids.end()); + for (int fid : queue_fids) { + state_data.push_back(fid); + } + } + + uint32_t hash = cuopt::mip::compute_hash(state_data); + BSP_DEBUG_LOG_HORIZON_HASH( + bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end, hash); + } + BSP_DEBUG_EMIT_TREE_STATE(bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, @@ -2192,9 +2238,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t lp_settings.cut_off = get_upper_bound() + settings_.dual_tol; lp_settings.inside_mip = 2; lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); - // Work limit is the remaining VT budget in this horizon - // Note: accumulated_vt tracks work already spent (for statistics), but doesn't increase budget - lp_settings.work_limit = current_horizon - worker.clock; + // Work limit is the ABSOLUTE VT at which to pause (LP solver compares against absolute elapsed) + lp_settings.work_limit = current_horizon; lp_settings.scale_columns = false; bool feasible = worker.node_presolver->bounds_strengthening( @@ -2599,26 +2644,8 @@ void branch_and_bound_t::process_history_and_sync( if (worker.current_node != nullptr) { apply_final_id(worker.current_node); } } - // Get current upper bound for pruning decisions - f_t upper_bound = get_upper_bound(); - - // Move ALL nodes from worker local queues to global heap for redistribution - // This ensures proper work distribution across workers each horizon - mutex_heap_.lock(); - for (auto& worker : *bsp_workers_) { - while (!worker.local_queue.empty()) { - mip_node_t* node = worker.local_queue.front(); - worker.local_queue.pop_front(); - // Only push non-pruned nodes - if (node->lower_bound < upper_bound) { - heap_.push(node); - } else { - search_tree_.update(node, node_status_t::FATHOMED); - --exploration_stats_.nodes_unexplored; - } - } - } - mutex_heap_.unlock(); + // Workers keep their local queues across horizons - no redistribution here. + // Load balancing is handled separately in balance_worker_loads() if needed. } template @@ -2651,6 +2678,95 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() } } +template +void branch_and_bound_t::balance_worker_loads() +{ + const size_t num_workers = bsp_workers_->size(); + if (num_workers <= 1) return; + + // Count work for each worker: current_node (if any) + local_queue size + std::vector work_counts(num_workers); + size_t total_work = 0; + size_t max_work = 0; + size_t min_work = std::numeric_limits::max(); + + for (size_t w = 0; w < num_workers; ++w) { + auto& worker = (*bsp_workers_)[w]; + work_counts[w] = worker.local_queue.size(); + if (worker.current_node != nullptr) { work_counts[w]++; } + total_work += work_counts[w]; + max_work = std::max(max_work, work_counts[w]); + min_work = std::min(min_work, work_counts[w]); + } + + // Check if we need to balance: significant imbalance = some worker has 0 work while others have + // 2+ Or max/min ratio is very high + bool needs_balance = + (min_work == 0 && max_work >= 2) || (min_work > 0 && max_work > 4 * min_work); + + if (!needs_balance) return; + + // Collect all redistributable nodes (from local queues only, not current_node which is paused) + std::vector*> all_nodes; + for (auto& worker : *bsp_workers_) { + while (!worker.local_queue.empty()) { + all_nodes.push_back(worker.local_queue.front()); + worker.local_queue.pop_front(); + } + } + + // Also pull nodes from global heap if workers need work + mutex_heap_.lock(); + f_t upper_bound = get_upper_bound(); + while (!heap_.empty() && all_nodes.size() < num_workers * 5) { + mip_node_t* node = heap_.top(); + heap_.pop(); + if (node->lower_bound < upper_bound) { + all_nodes.push_back(node); + } else { + search_tree_.update(node, node_status_t::FATHOMED); + --exploration_stats_.nodes_unexplored; + } + } + mutex_heap_.unlock(); + + if (all_nodes.empty()) return; + + // Sort by deterministic ID for consistent distribution + std::sort(all_nodes.begin(), + all_nodes.end(), + [](const mip_node_t* a, const mip_node_t* b) { + return a->get_deterministic_id() < b->get_deterministic_id(); + }); + + // Redistribute round-robin, but skip workers that have a paused current_node + // (they already have work and will resume that node first) + std::vector worker_order; + for (size_t w = 0; w < num_workers; ++w) { + // Prioritize workers without a paused node + if ((*bsp_workers_)[w].current_node == nullptr) { worker_order.push_back(w); } + } + for (size_t w = 0; w < num_workers; ++w) { + if ((*bsp_workers_)[w].current_node != nullptr) { worker_order.push_back(w); } + } + + // Distribute nodes + for (size_t i = 0; i < all_nodes.size(); ++i) { + size_t worker_idx = worker_order[i % num_workers]; + (*bsp_workers_)[worker_idx].enqueue_node(all_nodes[i]); + + // Debug: Log redistribution (happens at horizon END, at the sync point) + double vt = bsp_current_horizon_; + BSP_DEBUG_LOG_NODE_ASSIGNED(bsp_debug_settings_, + bsp_debug_logger_, + vt, + static_cast(worker_idx), + all_nodes[i]->node_id, + all_nodes[i]->final_id, + all_nodes[i]->lower_bound); + } +} + #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE template class branch_and_bound_t; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index b021e029f..537a9cced 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -357,6 +357,9 @@ class branch_and_bound_t { // Prune nodes held by workers based on new incumbent void prune_worker_nodes_vs_incumbent(); + // Balance worker loads - redistribute nodes only if significant imbalance detected + void balance_worker_loads(); + // BSP-specific node solving that records events node_solve_info_t solve_node_bsp(bb_worker_state_t& worker, mip_node_t* node_ptr, @@ -366,10 +369,10 @@ class branch_and_bound_t { private: // BSP state std::unique_ptr> bsp_workers_; - double bsp_horizon_step_{5.0}; // Virtual time step per horizon (tunable) - double bsp_current_horizon_{0.0}; // Current horizon target - bool bsp_mode_enabled_{false}; // Whether BSP mode is active - int bsp_horizon_number_{0}; // Current horizon number (for debugging) + double bsp_horizon_step_{5.0}; // Virtual time step per horizon (tunable) + double bsp_current_horizon_{0.0}; // Current horizon target + bool bsp_mode_enabled_{false}; // Whether BSP mode is active + int bsp_horizon_number_{0}; // Current horizon number (for debugging) // Counter for deterministic final_id assignment during sync phase // Starts at 2 (root's children are 1 and 2), incremented by 2 for each branch diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp index a9bdb8068..bbab5070e 100644 --- a/cpp/src/dual_simplex/bsp_debug.hpp +++ b/cpp/src/dual_simplex/bsp_debug.hpp @@ -37,6 +37,7 @@ namespace cuopt::linear_programming::dual_simplex { enum class bsp_log_event_t { HORIZON_START, // New horizon begins HORIZON_END, // Horizon completed + HORIZON_HASH, // Determinism fingerprint for horizon NODE_ASSIGNED, // Node assigned to worker NODE_SOLVE_START, // Worker starts solving node NODE_SOLVE_END, // Worker finishes node (with result type) @@ -60,6 +61,7 @@ inline const char* bsp_log_event_name(bsp_log_event_t event) switch (event) { case bsp_log_event_t::HORIZON_START: return "HORIZON_START"; case bsp_log_event_t::HORIZON_END: return "HORIZON_END"; + case bsp_log_event_t::HORIZON_HASH: return "HORIZON_HASH"; case bsp_log_event_t::NODE_ASSIGNED: return "NODE_ASSIGNED"; case bsp_log_event_t::NODE_SOLVE_START: return "NODE_SOLVE_START"; case bsp_log_event_t::NODE_SOLVE_END: return "NODE_SOLVE_END"; @@ -258,6 +260,24 @@ class bsp_debug_logger_t { if (settings_.enable_timeline) { emit_timeline_for_horizon(horizon_num); } } + // Log determinism fingerprint hash for the horizon + // This hash captures all state that should be identical across deterministic runs + void log_horizon_hash(int horizon_num, double vt, uint32_t hash) + { + std::lock_guard lock(mutex_); + + if (settings_.enable_event_log) { + std::stringstream ss; + ss << "hash=0x" << std::hex << std::setfill('0') << std::setw(8) << hash; + log_event_unlocked(vt, -1, bsp_log_event_t::HORIZON_HASH, -1, -1, ss.str()); + } + + if (settings_.enable_determinism_trace) { + trace_ss_ << "H" << horizon_num << ":HASH:0x" << std::hex << std::setfill('0') << std::setw(8) + << hash << std::dec << "\n"; + } + } + void log_node_assigned(double vt, int worker_id, i_t node_id, i_t final_id, f_t lower_bound) { if (settings_.enable_event_log) { @@ -859,6 +879,10 @@ class bsp_debug_logger_t { do { \ if ((settings).any_enabled()) (logger).log_horizon_end(h, vt); \ } while (0) +#define BSP_DEBUG_LOG_HORIZON_HASH(settings, logger, h, vt, hash) \ + do { \ + if ((settings).any_enabled()) (logger).log_horizon_hash(h, vt, hash); \ + } while (0) #define BSP_DEBUG_LOG_NODE_ASSIGNED(settings, logger, vt, w, nid, fid, lb) \ do { \ if ((settings).any_enabled()) (logger).log_node_assigned(vt, w, nid, fid, lb); \ @@ -946,6 +970,7 @@ class bsp_debug_logger_t { #define BSP_DEBUG_LOG_HORIZON_START(settings, logger, h, vs, ve) ((void)0) #define BSP_DEBUG_LOG_HORIZON_END(settings, logger, h, vt) ((void)0) +#define BSP_DEBUG_LOG_HORIZON_HASH(settings, logger, h, vt, hash) ((void)0) #define BSP_DEBUG_LOG_NODE_ASSIGNED(settings, logger, vt, w, nid, fid, lb) ((void)0) #define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) ((void)0) #define BSP_DEBUG_LOG_SOLVE_START(settings, logger, vt, w, nid, fid, wl, resumed) ((void)0) diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 876d90533..b6bffacc5 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -2504,7 +2504,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, i_t remaining_iters = iter - last_feature_log_iter; if (remaining_iters <= 0) return; f_t prediction = predict_work_units(remaining_iters); - printf("DualSimplex determ (final): %d iters, predicted %.4f\n", remaining_iters, prediction); + // printf("DualSimplex determ (final): %d iters, predicted %.4f\n", remaining_iters, + // prediction); work_unit_context->record_work(prediction); }); @@ -3123,11 +3124,11 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if (work_unit_context) { f_t prediction = predict_work_units(iters_elapsed); - printf("DualSimplex determ: %d iters, predicted %.4f, actual %.4f, error %.4f\n", - iters_elapsed, - prediction, - features.interval_runtime, - prediction - features.interval_runtime); + // printf("DualSimplex determ: %d iters, predicted %.4f, actual %.4f, error %.4f\n", + // iters_elapsed, + // prediction, + // features.interval_runtime, + // prediction - features.interval_runtime); work_unit_context->record_work(prediction); } diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index ef11de649..bc6baa1dc 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -307,6 +307,7 @@ solution_t diversity_manager_t::run_solver() // Debug: Allow disabling GPU heuristics to test B&B tree determinism in isolation const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); + disable_heuristics_env = "1"; if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { CUOPT_LOG_INFO("GPU heuristics disabled via CUOPT_DISABLE_GPU_HEURISTICS=1"); // Initialize population minimally and wait for B&B to finish diff --git a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh index 65a9550cc..3d217a2a5 100644 --- a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh @@ -245,8 +245,8 @@ class bound_prop_recombiner_t : public recombiner_t { // CUOPT_LOG_ERROR("Excess: %g, %g, %g, %g, feas %d", offspring.get_total_excess(), // offspring.compute_max_constraint_violation(), offspring.compute_max_int_violation(), // offspring.compute_max_variable_violation(), feasible_after_unfix); - cuopt_assert(fabs(excess_after_unfix - excess_before) < 1e-6, - "Excess after unfix should be same as before unfix!"); + // cuopt_assert(fabs(excess_after_unfix - excess_before) < 1e-6, + // "Excess after unfix should be same as before unfix!"); } a.handle_ptr->sync_stream(); } else { diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index f12186164..9ba8fdbcf 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -201,14 +201,16 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.num_diving_threads = num_diving_threads; // Set the branch and bound -> primal heuristics callback + // heuristic_preemption_callback is needed in both modes to properly stop the heuristic thread + branch_and_bound_settings.heuristic_preemption_callback = std::bind( + &branch_and_bound_solution_helper_t::preempt_heuristic_solver, &solution_helper); + if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { branch_and_bound_settings.solution_callback = std::bind(&branch_and_bound_solution_helper_t::solution_callback, &solution_helper, std::placeholders::_1, std::placeholders::_2); - branch_and_bound_settings.heuristic_preemption_callback = std::bind( - &branch_and_bound_solution_helper_t::preempt_heuristic_solver, &solution_helper); branch_and_bound_settings.set_simplex_solution_callback = std::bind(&branch_and_bound_solution_helper_t::set_simplex_solution, From 4ee57f93448c62593caba26558b0b64fff9264a6 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 9 Jan 2026 10:44:25 +0100 Subject: [PATCH 164/366] fix compilation failure --- cpp/src/dual_simplex/branch_and_bound.cpp | 8 +++++--- cpp/src/mip/diversity/lns/rins.cu | 12 +++--------- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 8 +------- 3 files changed, 9 insertions(+), 19 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 29120bda2..f63c13b2f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -5,10 +5,9 @@ */ /* clang-format on */ -#include -#include -#include #include + +#include #include #include #include @@ -20,6 +19,9 @@ #include #include +#include + +#include #include #include #include diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 035b03144..7394e2db6 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -265,15 +265,9 @@ void rins_t::run_rins() branch_and_bound_settings.diving_settings.num_diving_workers = 1; branch_and_bound_settings.diving_settings.disable_line_search_diving = true; branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; - - if (context.settings.disable_guided_diving) { - branch_and_bound_settings.diving_settings.disable_guided_diving = true; - } else { - branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; - } - - branch_and_bound_settings.log.log = false; - branch_and_bound_settings.log.log_prefix = "[RINS] "; + branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; + branch_and_bound_settings.log.log = false; + branch_and_bound_settings.log.log_prefix = "[RINS] "; branch_and_bound_settings.solution_callback = [this, &rins_solution_queue]( std::vector& solution, f_t objective) { rins_solution_queue.push_back(solution); diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index 1efed74ce..d46c6b31a 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -110,13 +110,7 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.diving_settings.num_diving_workers = 1; branch_and_bound_settings.diving_settings.disable_line_search_diving = true; branch_and_bound_settings.diving_settings.disable_coefficient_diving = true; - - if (context.settings.disable_guided_diving) { - branch_and_bound_settings.diving_settings.disable_guided_diving = true; - } else { - branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; - } - + branch_and_bound_settings.diving_settings.disable_pseudocost_diving = true; branch_and_bound_settings.solution_callback = [this](std::vector& solution, f_t objective) { this->solution_callback(solution, objective); From b99a9c791e21925348df34216ce0b9baab309ec4 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 9 Jan 2026 11:13:50 +0100 Subject: [PATCH 165/366] separated objective estimate and variable selection --- cpp/src/dual_simplex/branch_and_bound.cpp | 16 ++--- cpp/src/dual_simplex/pseudo_costs.cpp | 63 +++++++++++++++---- cpp/src/dual_simplex/pseudo_costs.hpp | 12 ++-- .../dual_simplex/simplex_solver_settings.hpp | 2 +- 4 files changed, 67 insertions(+), 26 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index f63c13b2f..28220e622 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -588,19 +588,18 @@ branch_variable_t branch_and_bound_t::variable_selection( logger_t& log) { i_t branch_var = -1; - f_t obj_estimate = 0; rounding_direction_t round_dir = rounding_direction_t::NONE; switch (type) { case bnb_worker_type_t::EXPLORATION: - std::tie(branch_var, obj_estimate) = - pc_.variable_selection_and_obj_estimate(fractional, solution, node_ptr->lower_bound, log); - round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); + branch_var = pc_.variable_selection(fractional, solution, log); + round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); // Note that the exploration thread is the only one that can insert new nodes into the heap, // and thus, we only need to calculate the objective estimate here (it is used for // sorting the nodes for diving). - node_ptr->objective_estimate = obj_estimate; + node_ptr->objective_estimate = + pc_.obj_estimate(fractional, solution, node_ptr->lower_bound, log); return {branch_var, round_dir}; case bnb_worker_type_t::COEFFICIENT_DIVING: @@ -798,6 +797,8 @@ node_solve_info_t branch_and_bound_t::solve_node( node_ptr, leaf_fractional, leaf_solution.x, thread_type, lp_settings.log); assert(leaf_vstatus.size() == leaf_problem.num_cols); + assert(branch_var >= 0); + assert(round_dir != rounding_direction_t::NONE); search_tree.branch( node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, leaf_problem, log); @@ -1349,7 +1350,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } if (diving_strategies.empty()) { - settings_.log.printf("Warning: All diving heuristics are disabled!"); + settings_.log.printf("Warning: All diving heuristics are disabled!\n"); } if (guess_.size() != 0) { @@ -1483,8 +1484,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } // Choose variable to branch on - auto [branch_var, obj_estimate] = - pc_.variable_selection_and_obj_estimate(fractional, root_relax_soln_.x, root_objective_, log); + auto branch_var = pc_.variable_selection(fractional, root_relax_soln_.x, log); search_tree_.root = std::move(mip_node_t(root_objective_, root_vstatus_)); search_tree_.num_nodes = 0; diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index aabbe5a17..143b25d24 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -253,11 +253,9 @@ void pseudo_costs_t::initialized(i_t& num_initialized_down, } template -std::pair pseudo_costs_t::variable_selection_and_obj_estimate( - const std::vector& fractional, - const std::vector& solution, - f_t lower_bound, - logger_t& log) +i_t pseudo_costs_t::variable_selection(const std::vector& fractional, + const std::vector& solution, + logger_t& log) { std::lock_guard lock(mutex); @@ -265,7 +263,6 @@ std::pair pseudo_costs_t::variable_selection_and_obj_estimat std::vector pseudo_cost_up(num_fractional); std::vector pseudo_cost_down(num_fractional); std::vector score(num_fractional); - f_t estimate = lower_bound; i_t num_initialized_down; i_t num_initialized_up; @@ -298,9 +295,6 @@ std::pair pseudo_costs_t::variable_selection_and_obj_estimat const f_t f_up = std::ceil(solution[j]) - solution[j]; score[k] = std::max(f_down * pseudo_cost_down[k], eps) * std::max(f_up * pseudo_cost_up[k], eps); - - estimate += std::min(std::max(pseudo_cost_down[k] * f_down, eps), - std::max(pseudo_cost_up[k] * f_up, eps)); } i_t branch_var = fractional[0]; @@ -314,13 +308,56 @@ std::pair pseudo_costs_t::variable_selection_and_obj_estimat } } - log.debug("Pseudocost branching on %d. Value %e. Score %e. Obj Estimate %e\n", + log.debug("Pseudocost branching on %d. Value %e. Score %e.\n", branch_var, solution[branch_var], - score[select], - estimate); + score[select]); + + return branch_var; +} + +template +f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, + const std::vector& solution, + f_t lower_bound, + logger_t& log) +{ + std::lock_guard lock(mutex); + + const i_t num_fractional = fractional.size(); + f_t estimate = lower_bound; + + i_t num_initialized_down; + i_t num_initialized_up; + f_t pseudo_cost_down_avg; + f_t pseudo_cost_up_avg; + + initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); + + for (i_t k = 0; k < num_fractional; k++) { + const i_t j = fractional[k]; + f_t pseudo_cost_down = 0; + f_t pseudo_cost_up = 0; + + if (pseudo_cost_num_down[j] != 0) { + pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; + } else { + pseudo_cost_down = pseudo_cost_down_avg; + } + + if (pseudo_cost_num_up[j] != 0) { + pseudo_cost_up = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; + } else { + pseudo_cost_up = pseudo_cost_up_avg; + } + constexpr f_t eps = 1e-6; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + estimate += + std::min(std::max(pseudo_cost_down * f_down, eps), std::max(pseudo_cost_up * f_up, eps)); + } - return {branch_var, estimate}; + return estimate; } template diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 5c34e0296..49a810506 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -43,10 +43,14 @@ class pseudo_costs_t { f_t& pseudo_cost_down_avg, f_t& pseudo_cost_up_avg) const; - std::pair variable_selection_and_obj_estimate(const std::vector& fractional, - const std::vector& solution, - f_t lower_bound, - logger_t& log); + i_t variable_selection(const std::vector& fractional, + const std::vector& solution, + logger_t& log); + + f_t obj_estimate(const std::vector& fractional, + const std::vector& solution, + f_t lower_bound, + logger_t& log); void update_pseudo_costs_from_strong_branching(const std::vector& fractional, const std::vector& root_soln); diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index d0f9dd408..77e0628ce 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -86,7 +86,7 @@ struct simplex_solver_settings_t { iteration_log_frequency(1000), first_iteration_log(2), num_threads(omp_get_max_threads() - 1), - num_bfs_workers(std::min(num_threads / 4, 1)), + num_bfs_workers(std::max(num_threads / 4, 1)), random_seed(0), inside_mip(0), solution_callback(nullptr), From 43f8b31de63250efc62e1b2a84e72750a68ddd8e Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 9 Jan 2026 11:43:41 +0100 Subject: [PATCH 166/366] separating objective estimate from variable selection --- cpp/src/dual_simplex/branch_and_bound.cpp | 18 ++++--- cpp/src/dual_simplex/branch_and_bound.hpp | 1 + cpp/src/dual_simplex/pseudo_costs.cpp | 63 ++++++++++++++++++----- cpp/src/dual_simplex/pseudo_costs.hpp | 12 +++-- 4 files changed, 71 insertions(+), 23 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 15db6f975..d3dabbacb 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -722,9 +722,14 @@ node_solve_info_t branch_and_bound_t::solve_node( } else if (leaf_objective <= upper_bound + abs_fathom_tol) { // Choose fractional variable to branch on - auto [branch_var, obj_estimate] = pc_.variable_selection_and_obj_estimate( - leaf_fractional, leaf_solution.x, node_ptr->lower_bound, log); - node_ptr->objective_estimate = obj_estimate; + const i_t branch_var = + pc_.variable_selection(leaf_fractional, leaf_solution.x, lp_settings.log); + + // Note that the exploration thread is the only one that can insert new nodes into the heap, + // and thus, we only need to calculate the objective estimate here (it is used for + // sorting the nodes for diving). + node_ptr->objective_estimate = + pc_.obj_estimate(leaf_fractional, leaf_solution.x, node_ptr->lower_bound, lp_settings.log); assert(leaf_vstatus.size() == leaf_problem.num_cols); search_tree.branch( @@ -1101,6 +1106,8 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A std::vector start_upper; bool reset_starting_bounds = true; + constexpr i_t node_limit = 500; + while (solver_status_ == mip_exploration_status_t::RUNNING && (active_subtrees_ > 0 || node_queue.best_first_queue_size() > 0)) { if (reset_starting_bounds) { @@ -1142,7 +1149,7 @@ void branch_and_bound_t::diving_thread(const csr_matrix_t& A } if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } - if (dive_stats.nodes_explored > 500) { break; } + if (dive_stats.nodes_explored > node_limit) { break; } node_solve_info_t status = solve_node(node_ptr, subtree, @@ -1401,8 +1408,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } // Choose variable to branch on - auto [branch_var, obj_estimate] = - pc_.variable_selection_and_obj_estimate(fractional, root_relax_soln_.x, root_objective_, log); + i_t branch_var = pc_.variable_selection(fractional, root_relax_soln_.x, log); search_tree_.root = std::move(mip_node_t(root_objective_, root_vstatus_)); search_tree_.num_nodes = 0; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index b719a220a..36b3b5f69 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -237,6 +237,7 @@ class branch_and_bound_t { std::vector& basic_list, std::vector& nonbasic_list, thread_type_t diving_type); + // Each diving thread pops the first node from the dive queue and then performs // a deep dive into the subtree determined by the node. void diving_thread(const csr_matrix_t& Arow); diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index aabbe5a17..143b25d24 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -253,11 +253,9 @@ void pseudo_costs_t::initialized(i_t& num_initialized_down, } template -std::pair pseudo_costs_t::variable_selection_and_obj_estimate( - const std::vector& fractional, - const std::vector& solution, - f_t lower_bound, - logger_t& log) +i_t pseudo_costs_t::variable_selection(const std::vector& fractional, + const std::vector& solution, + logger_t& log) { std::lock_guard lock(mutex); @@ -265,7 +263,6 @@ std::pair pseudo_costs_t::variable_selection_and_obj_estimat std::vector pseudo_cost_up(num_fractional); std::vector pseudo_cost_down(num_fractional); std::vector score(num_fractional); - f_t estimate = lower_bound; i_t num_initialized_down; i_t num_initialized_up; @@ -298,9 +295,6 @@ std::pair pseudo_costs_t::variable_selection_and_obj_estimat const f_t f_up = std::ceil(solution[j]) - solution[j]; score[k] = std::max(f_down * pseudo_cost_down[k], eps) * std::max(f_up * pseudo_cost_up[k], eps); - - estimate += std::min(std::max(pseudo_cost_down[k] * f_down, eps), - std::max(pseudo_cost_up[k] * f_up, eps)); } i_t branch_var = fractional[0]; @@ -314,13 +308,56 @@ std::pair pseudo_costs_t::variable_selection_and_obj_estimat } } - log.debug("Pseudocost branching on %d. Value %e. Score %e. Obj Estimate %e\n", + log.debug("Pseudocost branching on %d. Value %e. Score %e.\n", branch_var, solution[branch_var], - score[select], - estimate); + score[select]); + + return branch_var; +} + +template +f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, + const std::vector& solution, + f_t lower_bound, + logger_t& log) +{ + std::lock_guard lock(mutex); + + const i_t num_fractional = fractional.size(); + f_t estimate = lower_bound; + + i_t num_initialized_down; + i_t num_initialized_up; + f_t pseudo_cost_down_avg; + f_t pseudo_cost_up_avg; + + initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); + + for (i_t k = 0; k < num_fractional; k++) { + const i_t j = fractional[k]; + f_t pseudo_cost_down = 0; + f_t pseudo_cost_up = 0; + + if (pseudo_cost_num_down[j] != 0) { + pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; + } else { + pseudo_cost_down = pseudo_cost_down_avg; + } + + if (pseudo_cost_num_up[j] != 0) { + pseudo_cost_up = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; + } else { + pseudo_cost_up = pseudo_cost_up_avg; + } + constexpr f_t eps = 1e-6; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + estimate += + std::min(std::max(pseudo_cost_down * f_down, eps), std::max(pseudo_cost_up * f_up, eps)); + } - return {branch_var, estimate}; + return estimate; } template diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 5c34e0296..49a810506 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -43,10 +43,14 @@ class pseudo_costs_t { f_t& pseudo_cost_down_avg, f_t& pseudo_cost_up_avg) const; - std::pair variable_selection_and_obj_estimate(const std::vector& fractional, - const std::vector& solution, - f_t lower_bound, - logger_t& log); + i_t variable_selection(const std::vector& fractional, + const std::vector& solution, + logger_t& log); + + f_t obj_estimate(const std::vector& fractional, + const std::vector& solution, + f_t lower_bound, + logger_t& log); void update_pseudo_costs_from_strong_branching(const std::vector& fractional, const std::vector& root_soln); From a36bf03e64854ee038be29d94ff9f6de19230b0c Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 9 Jan 2026 11:56:51 +0100 Subject: [PATCH 167/366] added log --- cpp/src/dual_simplex/pseudo_costs.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 143b25d24..f3cbb4447 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -357,6 +357,7 @@ f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, std::min(std::max(pseudo_cost_down * f_down, eps), std::max(pseudo_cost_up * f_up, eps)); } + log.debug("pseudocost estimate = %e\n", estimate); return estimate; } From 7c5c9968b1abf2ab50d7251e4db34fe084aae6e2 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 9 Jan 2026 14:29:24 +0100 Subject: [PATCH 168/366] small refactor --- cpp/src/dual_simplex/branch_and_bound.cpp | 31 ++++++++++++----------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index bab8f8685..cbd72e92c 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -845,11 +845,12 @@ void branch_and_bound_t::plunge_with(bnb_worker_t* worker) worker->recompute_basis = !has_children(status); worker->recompute_bounds = !has_children(status); + ++nodes_since_last_log_; ++exploration_stats_.nodes_explored; --exploration_stats_.nodes_unexplored; - ++nodes_since_last_log_; if (status == node_solve_info_t::TIME_LIMIT) { + solver_status_ = mip_exploration_status_t::TIME_LIMIT; break; } else if (has_children(status)) { @@ -1189,15 +1190,25 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut original_lp_, log); + auto down_child = search_tree_.root.get_down_child(); + auto up_child = search_tree_.root.get_up_child(); + node_queue.push(down_child); + node_queue.push(up_child); + settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", settings_.num_threads, settings_.num_bfs_workers, settings_.num_threads - settings_.num_bfs_workers); - auto down_child = search_tree_.root.get_down_child(); - auto up_child = search_tree_.root.get_up_child(); - node_queue.push(down_child); - node_queue.push(up_child); + diving_heuristics_settings_t diving_settings = settings_.diving_settings; + bool is_ramp_up_finished = false; + + std::vector worker_types = {EXPLORATION}; + std::array max_num_workers_per_type; + max_num_workers_per_type.fill(0); + max_num_workers_per_type[EXPLORATION] = settings_.num_threads; + worker_pool_.init(2 * settings_.num_threads, original_lp_, Arow_, var_types_, settings_); + active_workers_per_type.fill(0); f_t lower_bound = get_lower_bound(); f_t abs_gap = upper_bound_ - lower_bound; @@ -1211,16 +1222,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut lower_bound_ceiling_ = inf; min_node_queue_size_ = 2 * settings_.num_threads; - diving_heuristics_settings_t diving_settings = settings_.diving_settings; - bool is_ramp_up_finished = false; - - std::vector worker_types = {EXPLORATION}; - std::array max_num_workers_per_type; - max_num_workers_per_type.fill(0); - max_num_workers_per_type[EXPLORATION] = settings_.num_threads; - worker_pool_.init(2 * settings_.num_threads, original_lp_, Arow_, var_types_, settings_); - active_workers_per_type.fill(0); - settings_.log.printf( " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " "| Time |\n"); From 5753de86aff3441ef080aca80c3f7b0b942071ae Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 9 Jan 2026 15:14:18 +0100 Subject: [PATCH 169/366] code cleanup --- cpp/src/dual_simplex/branch_and_bound.cpp | 39 +++++++++---------- cpp/src/dual_simplex/branch_and_bound.hpp | 9 ++++- .../dual_simplex/simplex_solver_settings.hpp | 2 +- 3 files changed, 27 insertions(+), 23 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index cbd72e92c..fa65d7928 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1189,16 +1189,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut root_vstatus_, original_lp_, log); - - auto down_child = search_tree_.root.get_down_child(); - auto up_child = search_tree_.root.get_up_child(); - node_queue.push(down_child); - node_queue.push(up_child); - - settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", - settings_.num_threads, - settings_.num_bfs_workers, - settings_.num_threads - settings_.num_bfs_workers); + node_queue.push(search_tree_.root.get_down_child()); + node_queue.push(search_tree_.root.get_up_child()); diving_heuristics_settings_t diving_settings = settings_.diving_settings; bool is_ramp_up_finished = false; @@ -1210,11 +1202,10 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut worker_pool_.init(2 * settings_.num_threads, original_lp_, Arow_, var_types_, settings_); active_workers_per_type.fill(0); - f_t lower_bound = get_lower_bound(); - f_t abs_gap = upper_bound_ - lower_bound; - f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); - i_t last_node_depth = 0; - f_t last_log = 0.0; + settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", + settings_.num_threads, + settings_.num_bfs_workers, + settings_.num_threads - settings_.num_bfs_workers); exploration_stats_.nodes_explored = 1; exploration_stats_.nodes_unexplored = 2; @@ -1230,6 +1221,12 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut { #pragma omp master { + f_t lower_bound = get_lower_bound(); + f_t abs_gap = upper_bound_ - lower_bound; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); + i_t last_node_depth = 0; + f_t last_log = 0.0; + while (solver_status_ == mip_exploration_status_t::RUNNING && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && (active_workers_per_type[0] > 0 || node_queue.best_first_queue_size() > 0)) { @@ -1241,8 +1238,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut repair_heuristic_solutions(); if (!is_ramp_up_finished) { - if (node_queue.best_first_queue_size() >= min_node_queue_size_ && - node_queue.bfs_top()->depth >= diving_settings.min_node_depth) { + if (node_queue.best_first_queue_size() >= min_node_queue_size_) { if (!std::isfinite(upper_bound_)) { diving_settings.disable_guided_diving = true; } max_num_workers_per_type = bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); @@ -1338,7 +1334,10 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut std::optional*> start_node = node_queue.pop_diving(); if (!start_node.has_value()) { continue; } - if (upper_bound_ < start_node.value()->lower_bound) { continue; } + if (upper_bound_ < start_node.value()->lower_bound || + start_node.value()->depth < diving_settings.min_node_depth) { + continue; + } bool is_feasible = worker->init_diving(start_node.value(), type, original_lp_, settings_); @@ -1366,8 +1365,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut solver_status_ = mip_exploration_status_t::COMPLETED; } - lower_bound = node_queue.best_first_queue_size() > 0 ? node_queue.get_lower_bound() - : search_tree_.root.lower_bound; + f_t lower_bound = node_queue.best_first_queue_size() > 0 ? node_queue.get_lower_bound() + : search_tree_.root.lower_bound; return set_final_solution(solution, lower_bound); } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 1930b2cb6..e15f7df96 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -163,7 +163,12 @@ class branch_and_bound_t { // Global status of the solver. omp_atomic_t solver_status_; + // Count the number of nodes since the last report. omp_atomic_t nodes_since_last_log_; + + // Minimum number of node in the queue. When the queue size is less than + // than this variable, the nodes are added directly to the queue instead of + // the local stack. This also determines the end of the ramp-up phase. i_t min_node_queue_size_; // In case, a best-first thread encounters a numerical issue when solving a node, @@ -186,12 +191,12 @@ class branch_and_bound_t { // Repairs low-quality solutions from the heuristics, if it is applicable. void repair_heuristic_solutions(); + // Perform a plunge over a subtree using a given worker. void plunge_with(bnb_worker_t* worker); + // Perform a deep dive over a subtree using a given worker. void dive_with(bnb_worker_t* worker); - void master_loop(); - // Solve the LP relaxation of a leaf node and update the tree. node_solve_info_t solve_node(mip_node_t* node_ptr, search_tree_t& search_tree, diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 77e0628ce..c46eda085 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -29,7 +29,7 @@ struct diving_heuristics_settings_t { bool disable_guided_diving = false; bool disable_coefficient_diving = false; - i_t min_node_depth = 5; + i_t min_node_depth = 10; i_t node_limit = 500; f_t iteration_limit_factor = 0.05; i_t backtrack = 5; From 421cbfd84d9e39618a665db3378fbe1442979a53 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 9 Jan 2026 15:31:16 +0100 Subject: [PATCH 170/366] fix reporting frequency --- cpp/src/dual_simplex/branch_and_bound.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index fa65d7928..d42b09e02 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1212,6 +1212,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut solver_status_ = mip_exploration_status_t::RUNNING; lower_bound_ceiling_ = inf; min_node_queue_size_ = 2 * settings_.num_threads; + nodes_since_last_log_ = 0; settings_.log.printf( " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " From 6faeed0f58b749b1d0ad3d246eb1a1e6ef6872eb Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 9 Jan 2026 15:43:49 +0100 Subject: [PATCH 171/366] fix style --- cpp/src/dual_simplex/bnb_worker.cpp | 2 +- cpp/src/dual_simplex/bounds_strengthening.hpp | 2 +- cpp/src/dual_simplex/presolve.cpp | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.cpp b/cpp/src/dual_simplex/bnb_worker.cpp index b4a1d8583..0d8157541 100644 --- a/cpp/src/dual_simplex/bnb_worker.cpp +++ b/cpp/src/dual_simplex/bnb_worker.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/bounds_strengthening.hpp b/cpp/src/dual_simplex/bounds_strengthening.hpp index dfad27005..6a43ef44c 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.hpp +++ b/cpp/src/dual_simplex/bounds_strengthening.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/presolve.cpp b/cpp/src/dual_simplex/presolve.cpp index 56b89f884..6c8c3ae4a 100644 --- a/cpp/src/dual_simplex/presolve.cpp +++ b/cpp/src/dual_simplex/presolve.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ From d7046e31fa25e4fd78f89067141c593af0ccd4f9 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 9 Jan 2026 16:25:53 +0100 Subject: [PATCH 172/366] added missing stl headers. fix incorrect round-robin. --- cpp/src/dual_simplex/bnb_worker.hpp | 11 +++++++---- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- cpp/src/dual_simplex/branch_and_bound.hpp | 2 +- cpp/src/dual_simplex/diving_heuristics.cpp | 1 + cpp/src/dual_simplex/node_queue.hpp | 6 +++++- 5 files changed, 15 insertions(+), 7 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index 00352d50a..121f6a6a3 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -12,7 +12,9 @@ #include #include +#include #include +#include #include namespace cuopt::linear_programming::dual_simplex { @@ -214,10 +216,11 @@ std::array bnb_get_num_workers_round_robin( i_t diving_workers = 2 * settings.num_diving_workers; i_t m = worker_types.size() - 1; - for (size_t i = 1, k = 0; i < bnb_num_worker_types; ++i) { - i_t start = (double)k * diving_workers / m; - i_t end = (double)(k + 1) * diving_workers / m; - max_num_workers[i] = end - start; + + for (size_t i = 1, k = 0; i < worker_types.size(); ++i) { + i_t start = (double)k * diving_workers / m; + i_t end = (double)(k + 1) * diving_workers / m; + max_num_workers[worker_types[i]] = end - start; ++k; } diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d42b09e02..250b7421f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1178,7 +1178,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } // Choose variable to branch on - auto branch_var = pc_.variable_selection(fractional, root_relax_soln_.x, log); + i_t branch_var = pc_.variable_selection(fractional, root_relax_soln_.x, log); search_tree_.root = std::move(mip_node_t(root_objective_, root_vstatus_)); search_tree_.num_nodes = 0; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index e15f7df96..0ee5a82f5 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -167,7 +167,7 @@ class branch_and_bound_t { omp_atomic_t nodes_since_last_log_; // Minimum number of node in the queue. When the queue size is less than - // than this variable, the nodes are added directly to the queue instead of + // this variable, the nodes are added directly to the queue instead of // the local stack. This also determines the end of the ramp-up phase. i_t min_node_queue_size_; diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index ce9460fa9..2d564a815 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -6,6 +6,7 @@ /* clang-format on */ #include +#include namespace cuopt::linear_programming::dual_simplex { diff --git a/cpp/src/dual_simplex/node_queue.hpp b/cpp/src/dual_simplex/node_queue.hpp index 47b31f590..b5c9a9ea7 100644 --- a/cpp/src/dual_simplex/node_queue.hpp +++ b/cpp/src/dual_simplex/node_queue.hpp @@ -6,6 +6,10 @@ #pragma once #include +#include +#include +#include +#include #include #include @@ -36,7 +40,7 @@ class heap_t { template void emplace(Args&&... args) { - buffer.emplace_back(std::forward(args)...); + buffer.emplace_back(std::forward(args)...); std::push_heap(buffer.begin(), buffer.end(), comp); } From eb6bc28ef6900c5813529ef861763e322825f39f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 9 Jan 2026 16:51:22 +0000 Subject: [PATCH 173/366] initial working impl on small problens --- cpp/src/dual_simplex/bb_worker_state.hpp | 289 +++++++-- cpp/src/dual_simplex/bounds_strengthening.cpp | 6 +- cpp/src/dual_simplex/branch_and_bound.cpp | 566 ++++++++++++++---- cpp/src/dual_simplex/branch_and_bound.hpp | 4 - cpp/src/dual_simplex/bsp_debug.hpp | 180 +++++- cpp/src/dual_simplex/mip_node.hpp | 116 +++- cpp/src/dual_simplex/phase2.cpp | 2 - cpp/src/dual_simplex/pseudo_costs.hpp | 57 +- cpp/src/mip/diversity/diversity_manager.cu | 12 +- cpp/src/mip/problem/problem.cu | 4 + cpp/src/mip/utils.cuh | 29 +- cpp/src/utilities/hashing.hpp | 45 ++ 12 files changed, 1069 insertions(+), 241 deletions(-) create mode 100644 cpp/src/utilities/hashing.hpp diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index e759e5bee..a8ea9476e 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -15,19 +15,38 @@ #include #include -#include +#include +#include #include +#include #include namespace cuopt::linear_programming::dual_simplex { +// Queued pseudo-cost update for BSP determinism +// Updates are collected during horizon, then applied in deterministic order at sync +template +struct pseudo_cost_update_t { + i_t variable; + rounding_direction_t direction; + f_t delta; // change_in_obj / frac + double vt; // virtual time when update occurred (for deterministic ordering) + int worker_id; // for tie-breaking in sort +}; + // Per-worker state for BSP (Bulk Synchronous Parallel) branch-and-bound template struct bb_worker_state_t { int worker_id{0}; - // Local node queue - buffer of nodes assigned to this worker for the current horizon - std::deque*> local_queue; + // Type alias for the BSP priority queue + using bsp_queue_t = std::priority_queue*, + std::vector*>, + bsp_node_compare_t>; + + // Local node queue - priority queue ordered by (lower_bound, origin_worker_id, creation_seq) + // Nodes are assigned BSP identity (origin_worker_id, creation_seq) when enqueued + bsp_queue_t local_queue; // Current node being processed (may be paused at horizon boundary) mip_node_t* current_node{nullptr}; @@ -35,6 +54,10 @@ struct bb_worker_state_t { // Worker's virtual time clock (cumulative work units) double clock{0.0}; + // Creation sequence counter - cumulative across horizons for unique identity + // Each node created by this worker gets (worker_id, next_creation_seq++) + int32_t next_creation_seq{0}; + // Events generated during this horizon bb_event_batch_t events; @@ -60,12 +83,49 @@ struct bb_worker_state_t { // Whether basis needs recomputation for next node bool recompute_bounds_and_basis{true}; - // Statistics + // Per-horizon statistics (reset each horizon) i_t nodes_processed_this_horizon{0}; double work_units_this_horizon{0.0}; + // Cumulative statistics (across all horizons) + i_t total_nodes_processed{0}; + i_t total_nodes_pruned{0}; + i_t total_nodes_branched{0}; + i_t total_nodes_infeasible{0}; + i_t total_integer_solutions{0}; + i_t total_nodes_assigned{0}; // via load balancing + double total_work_units{0.0}; + + // Timing statistics (in seconds) + double total_runtime{0.0}; // Total time spent doing actual work + double total_barrier_wait{0.0}; // Total time spent waiting at horizon sync barriers + double horizon_finish_time{ + 0.0}; // Timestamp when worker finished current horizon (for barrier wait calc) + + // Worker-local upper bound for BSP determinism (prevents cross-worker pruning races) + f_t local_upper_bound{std::numeric_limits::infinity()}; + + // Queued integer solutions found during this horizon (merged at sync) + struct queued_integer_solution_t { + f_t objective; + std::vector solution; + i_t depth; + }; + std::vector integer_solutions; + + // Queued pseudo-cost updates (applied in deterministic order at sync) + std::vector> pseudo_cost_updates; + + // Pseudo-cost snapshot for deterministic variable selection + // These are copied from global pseudo-costs at horizon start + std::vector pc_sum_up_snapshot; + std::vector pc_sum_down_snapshot; + std::vector pc_num_up_snapshot; + std::vector pc_num_down_snapshot; + // Constructor - explicit bb_worker_state_t(int id) : worker_id(id), work_context("BB_Worker_" + std::to_string(id)) + explicit bb_worker_state_t(int id) + : worker_id(id), work_context("BB_Worker_" + std::to_string(id)) { } @@ -80,7 +140,7 @@ struct bb_worker_state_t { leaf_problem = std::make_unique>(original_lp); // Initialize basis factors - const i_t m = leaf_problem->num_rows; + const i_t m = leaf_problem->num_rows; basis_factors = std::make_unique>(m, refactor_frequency); // Initialize bounds strengthening @@ -97,35 +157,136 @@ struct bb_worker_state_t { } // Reset for new horizon - void reset_for_horizon(double horizon_start, double horizon_end) + void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) { // Reset clock to horizon_start for consistent VT timestamps across workers clock = horizon_start; events.clear(); - events.horizon_start = horizon_start; - events.horizon_end = horizon_end; - event_sequence = 0; + events.horizon_start = horizon_start; + events.horizon_end = horizon_end; + event_sequence = 0; nodes_processed_this_horizon = 0; - work_units_this_horizon = 0.0; + work_units_this_horizon = 0.0; // Also sync work_context to match clock for consistent tracking work_context.global_work_units_elapsed = horizon_start; + // Note: next_creation_seq is NOT reset - it's cumulative for unique identity + + // Initialize worker-local upper bound from global (for BSP determinism) + local_upper_bound = global_upper_bound; + + // Clear queued updates from previous horizon + integer_solutions.clear(); + pseudo_cost_updates.clear(); } - // Add a node to the local queue - void enqueue_node(mip_node_t* node) { local_queue.push_back(node); } + // Queue a pseudo-cost update (to be applied at sync in deterministic order) + void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) + { + pseudo_cost_updates.push_back({variable, direction, delta, clock, worker_id}); + } - // Get next node to process + // Variable selection using snapshot (for BSP determinism) + // Returns the best variable to branch on based on pseudo-cost scores + i_t variable_selection_from_snapshot(const std::vector& fractional, + const std::vector& solution) const + { + const i_t num_fractional = fractional.size(); + if (num_fractional == 0) return -1; + + // Compute averages from snapshot + i_t num_initialized_down = 0; + i_t num_initialized_up = 0; + f_t pseudo_cost_down_avg = 0; + f_t pseudo_cost_up_avg = 0; + + const i_t n = pc_sum_down_snapshot.size(); + for (i_t j = 0; j < n; ++j) { + if (pc_num_down_snapshot[j] > 0) { + ++num_initialized_down; + if (std::isfinite(pc_sum_down_snapshot[j])) { + pseudo_cost_down_avg += pc_sum_down_snapshot[j] / pc_num_down_snapshot[j]; + } + } + if (pc_num_up_snapshot[j] > 0) { + ++num_initialized_up; + if (std::isfinite(pc_sum_up_snapshot[j])) { + pseudo_cost_up_avg += pc_sum_up_snapshot[j] / pc_num_up_snapshot[j]; + } + } + } + if (num_initialized_down > 0) { + pseudo_cost_down_avg /= num_initialized_down; + } else { + pseudo_cost_down_avg = 1.0; + } + if (num_initialized_up > 0) { + pseudo_cost_up_avg /= num_initialized_up; + } else { + pseudo_cost_up_avg = 1.0; + } + + // Compute scores + std::vector score(num_fractional); + for (i_t k = 0; k < num_fractional; ++k) { + const i_t j = fractional[k]; + f_t pc_down = (pc_num_down_snapshot[j] != 0) + ? pc_sum_down_snapshot[j] / pc_num_down_snapshot[j] + : pseudo_cost_down_avg; + f_t pc_up = (pc_num_up_snapshot[j] != 0) ? pc_sum_up_snapshot[j] / pc_num_up_snapshot[j] + : pseudo_cost_up_avg; + constexpr f_t eps = 1e-6; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + score[k] = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); + } + + // Select variable with maximum score + i_t branch_var = fractional[0]; + f_t max_score = score[0]; + for (i_t k = 1; k < num_fractional; ++k) { + if (score[k] > max_score) { + max_score = score[k]; + branch_var = fractional[k]; + } + } + + return branch_var; + } + + // Add a node to the local queue, assigning BSP identity if not already set + // The tuple (origin_worker_id, creation_seq) uniquely identifies the node + void enqueue_node(mip_node_t* node) + { + // Assign BSP identity if not already set + // Nodes from load balancing keep their original identity + if (!node->has_bsp_identity()) { + node->origin_worker_id = worker_id; + node->creation_seq = next_creation_seq++; + } + local_queue.push(node); + } + + // Add a node that already has BSP identity (from load balancing or initial distribution) + // Does NOT modify the node's identity + void enqueue_node_with_identity(mip_node_t* node) + { + assert(node->has_bsp_identity() && + "Node must have BSP identity for enqueue_node_with_identity"); + local_queue.push(node); + } + + // Get next node to process (highest priority = lowest lower_bound) mip_node_t* dequeue_node() { if (current_node != nullptr) { // Resume paused node mip_node_t* node = current_node; - current_node = nullptr; + current_node = nullptr; return node; } if (local_queue.empty()) { return nullptr; } - mip_node_t* node = local_queue.front(); - local_queue.pop_front(); + mip_node_t* node = local_queue.top(); + local_queue.pop(); return node; } @@ -133,9 +294,37 @@ struct bb_worker_state_t { bool has_work() const { return current_node != nullptr || !local_queue.empty(); } // Get number of nodes in local queue (including paused node) - size_t queue_size() const + size_t queue_size() const { return local_queue.size() + (current_node != nullptr ? 1 : 0); } + + // Extract all nodes from queue (for load balancing) + // Returns nodes in arbitrary order - caller should sort if deterministic order needed + std::vector*> extract_all_nodes() + { + std::vector*> nodes; + nodes.reserve(queue_size()); + + // Include paused node if any + if (current_node != nullptr) { + nodes.push_back(current_node); + current_node = nullptr; + } + + // Extract all nodes from priority queue + while (!local_queue.empty()) { + nodes.push_back(local_queue.top()); + local_queue.pop(); + } + + return nodes; + } + + // Clear the queue without returning nodes (use with caution) + void clear_queue() { - return local_queue.size() + (current_node != nullptr ? 1 : 0); + current_node = nullptr; + while (!local_queue.empty()) { + local_queue.pop(); + } } // Record an event @@ -152,37 +341,37 @@ struct bb_worker_state_t { node->bsp_state = bsp_node_state_t::PAUSED; current_node = node; - record_event(bb_event_t::make_paused(clock, worker_id, node->node_id, 0, accumulated_vt)); + record_event( + bb_event_t::make_paused(clock, worker_id, node->node_id, 0, accumulated_vt)); } // Record node branching event - void record_branched(mip_node_t* node, - i_t down_child_id, - i_t up_child_id, - i_t branch_var, - f_t branch_val) + void record_branched( + mip_node_t* node, i_t down_child_id, i_t up_child_id, i_t branch_var, f_t branch_val) { record_event(bb_event_t::make_branched(clock, - worker_id, - node->node_id, - 0, - down_child_id, - up_child_id, - node->lower_bound, - branch_var, - branch_val)); + worker_id, + node->node_id, + 0, + down_child_id, + up_child_id, + node->lower_bound, + branch_var, + branch_val)); } // Record integer solution found void record_integer_solution(mip_node_t* node, f_t objective) { - record_event(bb_event_t::make_integer_solution(clock, worker_id, node->node_id, 0, objective)); + record_event( + bb_event_t::make_integer_solution(clock, worker_id, node->node_id, 0, objective)); } // Record node fathomed void record_fathomed(mip_node_t* node, f_t lower_bound) { - record_event(bb_event_t::make_fathomed(clock, worker_id, node->node_id, 0, lower_bound)); + record_event( + bb_event_t::make_fathomed(clock, worker_id, node->node_id, 0, lower_bound)); } // Record node infeasible @@ -202,8 +391,31 @@ struct bb_worker_state_t { { clock += work_units; work_units_this_horizon += work_units; + total_work_units += work_units; work_context.record_work(work_units); } + + // Track node processed (called when a node LP solve completes) + void track_node_processed() + { + ++nodes_processed_this_horizon; + ++total_nodes_processed; + } + + // Track node branched + void track_node_branched() { ++total_nodes_branched; } + + // Track node pruned (fathomed due to bound) + void track_node_pruned() { ++total_nodes_pruned; } + + // Track node infeasible + void track_node_infeasible() { ++total_nodes_infeasible; } + + // Track integer solution found + void track_integer_solution() { ++total_integer_solutions; } + + // Track node assigned via load balancing + void track_node_assigned() { ++total_nodes_assigned; } }; // Container for all worker states in BSP B&B @@ -237,10 +449,10 @@ class bb_worker_pool_t { int size() const { return static_cast(workers_.size()); } // Reset all workers for new horizon - void reset_for_horizon(double horizon_start, double horizon_end) + void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) { for (auto& worker : workers_) { - worker.reset_for_horizon(horizon_start, horizon_end); + worker.reset_for_horizon(horizon_start, horizon_end, global_upper_bound); } } @@ -288,4 +500,3 @@ class bb_worker_pool_t { }; } // namespace cuopt::linear_programming::dual_simplex - diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index f1bf52c1e..d857d2f45 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -1,12 +1,14 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ #include +#include + #include #include @@ -98,6 +100,8 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t m = A.m; const i_t n = A.n; + raft::common::nvtx::range fun_scope("bounds_strengthening"); + std::vector constraint_changed(m, true); std::vector variable_changed(n, false); std::vector constraint_changed_next(m, false); diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index e6d71db4b..3d839e7a8 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -19,7 +19,7 @@ #include #include #include -#include +#include #include @@ -232,6 +232,9 @@ branch_and_bound_t::branch_and_bound_t( bsp_debug_settings_(bsp_debug_settings_t::from_environment()) { bsp_debug_settings_.enable_all(); + bsp_debug_settings_.output_dir = "/home/scratch.yboucher_gpu_1/bsp_debug/"; + bsp_debug_settings_.flush_every_horizon = false; + bsp_debug_settings_.disable_all(); exploration_stats_.start_time = tic(); dualize_info_t dualize_info; @@ -676,6 +679,14 @@ mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t::solve(mip_solution_t& solut pc_); } + // Log strong branching results for determinism debugging + { + uint32_t sb_hash = pc_.compute_strong_branch_hash(); + uint32_t pc_hash = pc_.compute_state_hash(); + CUOPT_LOG_DEBUG("Strong branching completed: %zu variables, SB hash=0x%08x, PC hash=0x%08x", + fractional.size(), + sb_hash, + pc_hash); + + // Detailed logging for divergence diagnosis (enabled via environment variable) + const char* log_sb_detail = std::getenv("CUOPT_LOG_STRONG_BRANCHING"); + if (log_sb_detail != nullptr && std::string(log_sb_detail) == "1") { + settings_.log.printf("Strong branching detailed results:\n"); + for (size_t k = 0; k < fractional.size(); ++k) { + i_t var = fractional[k]; + settings_.log.printf(" var[%zu]=%d: down=%+.10e, up=%+.10e\n", + k, + var, + pc_.strong_branch_down[k], + pc_.strong_branch_up[k]); + } + settings_.log.printf("Pseudo-cost state after strong branching:\n"); + i_t non_zero_count = 0; + for (i_t j = 0; j < original_lp_.num_cols; ++j) { + if (pc_.pseudo_cost_num_down[j] > 0 || pc_.pseudo_cost_num_up[j] > 0) { + settings_.log.printf(" pc[%d]: sum_down=%+.10e, num_down=%d, sum_up=%+.10e, num_up=%d\n", + j, + pc_.pseudo_cost_sum_down[j], + pc_.pseudo_cost_num_down[j], + pc_.pseudo_cost_sum_up[j], + pc_.pseudo_cost_num_up[j]); + ++non_zero_count; + } + } + settings_.log.printf("Total %d variables with pseudo-cost data\n", non_zero_count); + } + } + if (toc(exploration_stats_.start_time) > settings_.time_limit) { solver_status_ = mip_exploration_status_t::TIME_LIMIT; return set_final_solution(solution, root_objective_); @@ -1897,10 +1946,11 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tfinal_id = 1; - search_tree_.root.get_up_child()->final_id = 2; - bsp_next_final_id_ = 3; // Next ID to assign + // Set deterministic BSP identity for root children (pre-BSP origin with seq 0 and 1) + search_tree_.root.get_down_child()->origin_worker_id = -1; // Pre-BSP marker + search_tree_.root.get_down_child()->creation_seq = 0; + search_tree_.root.get_up_child()->origin_worker_id = -1; + search_tree_.root.get_up_child()->creation_seq = 1; heap_.push(search_tree_.root.get_down_child()); heap_.push(search_tree_.root.get_up_child()); @@ -1929,8 +1979,17 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_treset_for_horizon(horizon_start, horizon_end); + // Reset workers for new horizon with current global upper bound + // Each worker gets a snapshot of the upper bound for deterministic pruning + bsp_workers_->reset_for_horizon(horizon_start, horizon_end, get_upper_bound()); + + // Snapshot pseudo-costs for deterministic variable selection + for (auto& worker : *bsp_workers_) { + worker.pc_sum_up_snapshot = pc_.pseudo_cost_sum_up; + worker.pc_sum_down_snapshot = pc_.pseudo_cost_sum_down; + worker.pc_num_up_snapshot = pc_.pseudo_cost_num_up; + worker.pc_num_down_snapshot = pc_.pseudo_cost_num_down; + } // PHASE 2: PARALLEL EXECUTION - Workers run until horizon #pragma omp parallel num_threads(num_workers) @@ -1938,34 +1997,64 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t 0) { worker.total_barrier_wait += wait_time; } } // Aggregate worker work into global context for work limit tracking // The global work is the horizon boundary (all workers synchronized to this point) work_unit_context_.global_work_units_elapsed = horizon_end; + raft::common::nvtx::range scope("BB::bsp_coordinator::sync_phase"); + // PHASE 3: SYNCHRONIZATION - The Barrier // Collect and sort all events deterministically - bb_event_batch_t all_events = bsp_workers_->collect_and_sort_events(); + bb_event_batch_t all_events; + { + raft::common::nvtx::range scope("BB::bsp_coordinator::collect_and_sort_events"); + all_events = bsp_workers_->collect_and_sort_events(); + } // Debug: Log sync phase BSP_DEBUG_LOG_SYNC_PHASE_START( bsp_debug_settings_, bsp_debug_logger_, horizon_end, all_events.size()); // Process history and sync - process_history_and_sync(all_events); + { + raft::common::nvtx::range scope("BB::bsp_coordinator::process_history_and_sync"); + process_history_and_sync(all_events); + } - // Debug: Flush final IDs trace and log sync end - BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(bsp_debug_settings_, bsp_debug_logger_); + // Debug: Log sync end (no final_id assignment needed with BSP identity tuples) BSP_DEBUG_LOG_SYNC_PHASE_END(bsp_debug_settings_, bsp_debug_logger_, horizon_end); // Prune paused nodes that are now dominated by new incumbent - prune_worker_nodes_vs_incumbent(); + { + raft::common::nvtx::range scope("BB::bsp_coordinator::prune_worker_nodes_vs_incumbent"); + prune_worker_nodes_vs_incumbent(); + } // Balance worker loads if significant imbalance detected - balance_worker_loads(); + { + raft::common::nvtx::range scope("BB::bsp_coordinator::balance_worker_loads"); + balance_worker_loads(); + } BSP_DEBUG_FLUSH_ASSIGN_TRACE(bsp_debug_settings_, bsp_debug_logger_); // Debug: Log horizon end, emit tree state and JSON state @@ -1974,40 +2063,73 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t state_data; + std::vector state_data; // Global state - state_data.push_back(static_cast(bsp_next_final_id_)); - state_data.push_back(static_cast(exploration_stats_.nodes_explored)); - state_data.push_back(static_cast(exploration_stats_.nodes_unexplored)); + state_data.push_back(static_cast(exploration_stats_.nodes_explored)); + state_data.push_back(static_cast(exploration_stats_.nodes_unexplored)); // Upper/lower bounds (convert to fixed-point for exact comparison) f_t ub = get_upper_bound(); f_t lb = get_lower_bound(); - state_data.push_back(static_cast(ub * 1000000)); // 6 decimal places - state_data.push_back(static_cast(lb * 1000000)); - - // Worker queue contents (sorted by final_id for determinism) - for (const auto& worker : *bsp_workers_) { + state_data.push_back(static_cast(ub * 1000000)); // 6 decimal places + state_data.push_back(static_cast(lb * 1000000)); + + // Worker queue contents using BSP identity tuple (origin_worker_id, creation_seq) + // Each worker's queue is a priority queue - we extract nodes in priority order + // Note: BSP identity is always set for nodes in BSP mode + int nodes_without_identity = 0; + for (auto& worker : *bsp_workers_) { // Hash paused node if any if (worker.current_node != nullptr) { - state_data.push_back(worker.current_node->final_id >= 0 ? worker.current_node->final_id - : worker.current_node->node_id); - } - // Hash local queue - std::vector queue_fids; - for (const auto* node : worker.local_queue) { - queue_fids.push_back(node->final_id >= 0 ? node->final_id : node->node_id); + if (!worker.current_node->has_bsp_identity()) { + ++nodes_without_identity; + CUOPT_LOG_WARN( + "BSP Hash: Worker %d current_node has no BSP identity (node_id=%d, depth=%d)", + worker.worker_id, + worker.current_node->node_id, + worker.current_node->depth); + } + state_data.push_back(worker.current_node->get_bsp_identity_hash()); } - std::sort(queue_fids.begin(), queue_fids.end()); - for (int fid : queue_fids) { - state_data.push_back(fid); + + // Extract queue contents for hashing (preserves priority order) + // We need to temporarily extract to iterate, then restore + std::vector*> queue_nodes; + auto queue_copy = worker.local_queue; // Copy the priority queue + while (!queue_copy.empty()) { + auto* node = queue_copy.top(); + queue_copy.pop(); + if (!node->has_bsp_identity()) { + ++nodes_without_identity; + CUOPT_LOG_WARN( + "BSP Hash: Worker %d queue node has no BSP identity (node_id=%d, depth=%d)", + worker.worker_id, + node->node_id, + node->depth); + } + state_data.push_back(node->get_bsp_identity_hash()); } } + if (nodes_without_identity > 0) { + CUOPT_LOG_WARN( + "BSP Hash at horizon %d: %d nodes without BSP identity - HASH MAY BE " + "NON-DETERMINISTIC!", + bsp_horizon_number_, + nodes_without_identity); + } - uint32_t hash = cuopt::mip::compute_hash(state_data); + // Compute hash from state data + uint32_t hash = 0x811c9dc5u; // FNV-1a initial value + for (uint64_t val : state_data) { + hash ^= static_cast(val & 0xFFFFFFFF); + hash *= 0x01000193u; + hash ^= static_cast(val >> 32); + hash *= 0x01000193u; + } + CUOPT_LOG_DEBUG("BSP Hash at horizon %d: 0x%x", bsp_horizon_number_, hash); BSP_DEBUG_LOG_HORIZON_HASH( bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end, hash); } @@ -2025,7 +2147,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::refill_worker_queues(i_t target_queue_size) } mutex_heap_.unlock(); - // Assign nodes to workers deterministically (round-robin by deterministic ID order) - // Use get_deterministic_id() which returns final_id if set, else node_id - std::sort(nodes_to_assign.begin(), - nodes_to_assign.end(), - [](const mip_node_t* a, const mip_node_t* b) { - return a->get_deterministic_id() < b->get_deterministic_id(); - }); + // Sort by BSP identity for deterministic distribution + // Uses lexicographic order of (origin_worker_id, creation_seq) + auto deterministic_less = [](const mip_node_t* a, const mip_node_t* b) { + // Lexicographic comparison of BSP identity tuple + if (a->origin_worker_id != b->origin_worker_id) { + return a->origin_worker_id < b->origin_worker_id; + } + return a->creation_seq < b->creation_seq; + }; + std::sort(nodes_to_assign.begin(), nodes_to_assign.end(), deterministic_less); for (size_t i = 0; i < nodes_to_assign.size(); ++i) { int worker_id = i % bsp_workers_->size(); auto* node = nodes_to_assign[i]; - (*bsp_workers_)[worker_id].enqueue_node(node); + // Use enqueue_node_with_identity since these nodes already have BSP identity from root setup + (*bsp_workers_)[worker_id].enqueue_node_with_identity(node); + (*bsp_workers_)[worker_id].track_node_assigned(); // Debug: Log node assignment double vt = bsp_current_horizon_ - bsp_horizon_step_; // Start of current horizon @@ -2128,7 +2279,7 @@ void branch_and_bound_t::refill_worker_queues(i_t target_queue_size) vt, worker_id, node->node_id, - node->final_id, + node->origin_worker_id, node->lower_bound); } } @@ -2149,6 +2300,7 @@ void branch_and_bound_t::run_worker_until_horizon(bb_worker_state_tlower_bound >= upper_bound) { worker.record_fathomed(node, node->lower_bound); + worker.track_node_pruned(); search_tree.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; continue; @@ -2186,9 +2338,10 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } if (actual_basic_count != expected_basic_count) { settings_.log.printf( - "ERROR: Node %d (final_id %d) vstatus has %d BASIC entries, expected %d (num_rows)\n", + "ERROR: Node %d (worker %d, seq %d) vstatus has %d BASIC entries, expected %d (num_rows)\n", node_ptr->node_id, - node_ptr->final_id, + node_ptr->origin_worker_id, + node_ptr->creation_seq, actual_basic_count, expected_basic_count); settings_.log.printf(" vstatus.size() = %zu, num_cols = %d\n", @@ -2204,14 +2357,14 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t double clock_at_start = worker.clock; bool is_resumed = (node_ptr->bsp_state == bsp_node_state_t::PAUSED); - // Debug: Log solve start + // Debug: Log solve start (pass origin_worker_id as identifier) double work_limit = current_horizon - worker.clock; BSP_DEBUG_LOG_SOLVE_START(bsp_debug_settings_, bsp_debug_logger_, worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, + node_ptr->origin_worker_id, work_limit, is_resumed); @@ -2235,20 +2388,25 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t // Bounds strengthening simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); - lp_settings.cut_off = get_upper_bound() + settings_.dual_tol; + // Use worker-local upper bound for LP cutoff (deterministic) + lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; lp_settings.inside_mip = 2; lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); // Work limit is the ABSOLUTE VT at which to pause (LP solver compares against absolute elapsed) lp_settings.work_limit = current_horizon; lp_settings.scale_columns = false; - bool feasible = worker.node_presolver->bounds_strengthening( - worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + bool feasible = true; + // TODO: incorporate into work unit estimation + // feasible = worker.node_presolver->bounds_strengthening( + // worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); if (!feasible) { node_ptr->lower_bound = std::numeric_limits::infinity(); search_tree.update(node_ptr, node_status_t::INFEASIBLE); worker.record_infeasible(node_ptr); + worker.track_node_infeasible(); + worker.track_node_processed(); --exploration_stats_.nodes_unexplored; ++exploration_stats_.nodes_explored; worker.recompute_bounds_and_basis = true; @@ -2263,6 +2421,38 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; + // Debug: Log LP input for determinism analysis (enabled via CUOPT_BSP_DEBUG_TRACE=1, + // log_level>=2) + if (bsp_debug_settings_.any_enabled()) { + uint64_t path_hash = node_ptr->compute_path_hash(); + // Compute vstatus hash + uint64_t vstatus_hash = leaf_vstatus.size(); + for (size_t i = 0; i < leaf_vstatus.size(); ++i) { + vstatus_hash ^= (static_cast(leaf_vstatus[i]) << (i % 56)); + vstatus_hash *= 0x100000001b3ULL; + } + // Compute bounds hash + uint64_t bounds_hash = 0; + for (i_t j = 0; j < worker.leaf_problem->num_cols; ++j) { + union { + f_t f; + uint64_t u; + } lb_bits, ub_bits; + lb_bits.f = worker.leaf_problem->lower[j]; + ub_bits.f = worker.leaf_problem->upper[j]; + bounds_hash ^= lb_bits.u + ub_bits.u; + bounds_hash *= 0x100000001b3ULL; + } + BSP_DEBUG_LOG_LP_INPUT(bsp_debug_settings_, + bsp_debug_logger_, + worker.worker_id, + node_ptr->node_id, + path_hash, + node_ptr->depth, + vstatus_hash, + bounds_hash); + } + dual::status_t lp_status = dual_phase2_with_advanced_basis(2, 0, worker.recompute_bounds_and_basis, @@ -2278,6 +2468,42 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t leaf_edge_norms, &worker.work_context); + // Debug: Log LP output for determinism analysis (enabled via CUOPT_BSP_DEBUG_TRACE=1, + // log_level>=2) + if (bsp_debug_settings_.any_enabled()) { + uint64_t path_hash = node_ptr->compute_path_hash(); + // Compute solution hash + uint64_t sol_hash = 0; + for (i_t j = 0; + j < worker.leaf_problem->num_cols && j < static_cast(leaf_solution.x.size()); + ++j) { + union { + f_t f; + uint64_t u; + } val_bits; + val_bits.f = leaf_solution.x[j]; + sol_hash ^= val_bits.u; + sol_hash *= 0x100000001b3ULL; + } + f_t obj = (lp_status == dual::status_t::OPTIMAL) + ? compute_objective(*worker.leaf_problem, leaf_solution.x) + : std::numeric_limits::infinity(); + union { + f_t f; + uint64_t u; + } obj_bits; + obj_bits.f = obj; + BSP_DEBUG_LOG_LP_OUTPUT(bsp_debug_settings_, + bsp_debug_logger_, + worker.worker_id, + node_ptr->node_id, + path_hash, + static_cast(lp_status), + node_iter, + obj_bits.u, + sol_hash); + } + // Validate vstatus after LP solve - check for corruption during simplex { const i_t expected_basic_count = original_lp_.num_rows; @@ -2318,7 +2544,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, + node_ptr->origin_worker_id, "PAUSED", node_ptr->lower_bound); BSP_DEBUG_LOG_PAUSED(bsp_debug_settings_, @@ -2326,7 +2552,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, + node_ptr->origin_worker_id, static_cast(accumulated_vt)); return node_solve_info_t::WORK_LIMIT; } @@ -2341,6 +2567,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t node_ptr->lower_bound = std::numeric_limits::infinity(); search_tree.update(node_ptr, node_status_t::INFEASIBLE); worker.record_infeasible(node_ptr); + worker.track_node_infeasible(); + worker.track_node_processed(); worker.recompute_bounds_and_basis = true; // Debug: Log solve end (infeasible) @@ -2349,7 +2577,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, + node_ptr->origin_worker_id, "INFEASIBLE", node_ptr->lower_bound); BSP_DEBUG_LOG_INFEASIBLE( @@ -2357,9 +2585,12 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t return node_solve_info_t::NO_CHILDREN; } else if (lp_status == dual::status_t::CUTOFF) { - node_ptr->lower_bound = get_upper_bound(); + // Use worker-local upper bound for determinism + node_ptr->lower_bound = worker.local_upper_bound; search_tree.update(node_ptr, node_status_t::FATHOMED); worker.record_fathomed(node_ptr, node_ptr->lower_bound); + worker.track_node_pruned(); + worker.track_node_processed(); worker.recompute_bounds_and_basis = true; // Debug: Log solve end (fathomed - cutoff) @@ -2368,7 +2599,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, + node_ptr->origin_worker_id, "FATHOMED", node_ptr->lower_bound); BSP_DEBUG_LOG_FATHOMED(bsp_debug_settings_, @@ -2386,14 +2617,48 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); node_ptr->lower_bound = leaf_objective; - pc_.update_pseudo_costs(node_ptr, leaf_objective); + + // Queue pseudo-cost update for deterministic application at sync + // (Replicates pc_.update_pseudo_costs logic but defers application to sync phase) + // Note: Original code also sets lower_bound before this, so change_in_obj = 0. + // This matches the original behavior exactly. + if (node_ptr->branch_var >= 0) { + const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; + const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN + ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) + : std::ceil(node_ptr->fractional_val) - node_ptr->fractional_val; + if (frac > 1e-10) { + worker.queue_pseudo_cost_update( + node_ptr->branch_var, node_ptr->branch_dir, change_in_obj / frac); + } + } if (leaf_num_fractional == 0) { - // Integer feasible - add_feasible_solution( - leaf_objective, leaf_solution.x, node_ptr->depth, thread_type_t::EXPLORATION); + // Integer feasible - queue for deterministic processing at sync + if (leaf_objective < worker.local_upper_bound) { + worker.local_upper_bound = leaf_objective; + worker.integer_solutions.push_back({leaf_objective, leaf_solution.x, node_ptr->depth}); + // Log immediately for visibility (global incumbent updated at sync) + i_t nodes_explored = exploration_stats_.nodes_explored.load(); + i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); + f_t user_obj = compute_user_objective(original_lp_, leaf_objective); + f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); + settings_.log.printf( + "%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + feasible_solution_symbol(thread_type_t::EXPLORATION), + nodes_explored, + nodes_unexplored, + user_obj, + user_lower, + node_ptr->depth, + nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0.0, + user_mip_gap(user_obj, user_lower).c_str(), + toc(exploration_stats_.start_time)); + } search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); worker.record_integer_solution(node_ptr, leaf_objective); + worker.track_integer_solution(); + worker.track_node_processed(); worker.recompute_bounds_and_basis = true; // Debug: Log solve end (integer) @@ -2402,7 +2667,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, + node_ptr->origin_worker_id, "INTEGER", leaf_objective); BSP_DEBUG_LOG_INTEGER(bsp_debug_settings_, @@ -2413,11 +2678,14 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t leaf_objective); return node_solve_info_t::NO_CHILDREN; - } else if (leaf_objective <= get_upper_bound() + settings_.absolute_mip_gap_tol / 10) { - // Branch + } else if (leaf_objective <= worker.local_upper_bound + settings_.absolute_mip_gap_tol / 10) { + // Branch - use worker-local upper bound for deterministic pruning decision + // Use pseudo-cost snapshot for deterministic variable selection + const i_t branch_var = + worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); + logger_t log; - log.log = false; - const i_t branch_var = pc_.variable_selection(leaf_fractional, leaf_solution.x, log); + log.log = false; search_tree.branch( node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, *worker.leaf_problem, log); @@ -2427,6 +2695,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t i_t up_child_id = node_ptr->get_up_child()->node_id; worker.record_branched( node_ptr, down_child_id, up_child_id, branch_var, leaf_solution.x[branch_var]); + worker.track_node_branched(); + worker.track_node_processed(); // Debug: Log solve end (branched) and branched event BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, @@ -2434,7 +2704,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, + node_ptr->origin_worker_id, "BRANCH", leaf_objective); BSP_DEBUG_LOG_BRANCHED(bsp_debug_settings_, @@ -2442,6 +2712,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.clock, worker.worker_id, node_ptr->node_id, + node_ptr->origin_worker_id, down_child_id, up_child_id); @@ -2454,7 +2725,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t // others. This is safe because the child's vstatus is a copy of the parent's final vstatus. worker.recompute_bounds_and_basis = true; - // Add children to worker's local queue (deterministic order: down first) + // Add children directly to local queue - they get BSP identity on enqueue + // No need to defer to next horizon since identity is assigned immediately worker.enqueue_node(node_ptr->get_down_child()); worker.enqueue_node(node_ptr->get_up_child()); @@ -2465,6 +2737,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } else { search_tree.update(node_ptr, node_status_t::FATHOMED); worker.record_fathomed(node_ptr, leaf_objective); + worker.track_node_pruned(); + worker.track_node_processed(); worker.recompute_bounds_and_basis = true; // Debug: Log solve end (fathomed by bound) @@ -2473,7 +2747,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.clock, worker.worker_id, node_ptr->node_id, - node_ptr->final_id, + node_ptr->origin_worker_id, "FATHOMED", leaf_objective); BSP_DEBUG_LOG_FATHOMED(bsp_debug_settings_, @@ -2501,6 +2775,10 @@ template void branch_and_bound_t::process_history_and_sync( const bb_event_batch_t& events) { + // With BSP identity tuples (origin_worker_id, creation_seq), we no longer need to assign + // final_ids during sync. Each node gets its identity when created, and it never changes. + // This function now only processes heuristic solutions to update the incumbent. + // Collect queued heuristic solutions std::vector heuristic_solutions; mutex_heuristic_queue_.lock(); @@ -2508,18 +2786,13 @@ void branch_and_bound_t::process_history_and_sync( heuristic_solution_queue_.clear(); mutex_heuristic_queue_.unlock(); - // Sort heuristic solutions by VT + // Sort heuristic solutions by VT for deterministic processing order std::sort(heuristic_solutions.begin(), heuristic_solutions.end(), [](const queued_heuristic_solution_t& a, const queued_heuristic_solution_t& b) { return a.vt_timestamp < b.vt_timestamp; }); - // Build mapping from node_id -> final_id for deterministic node ordering - // node_id is used as the provisional identifier during parallel execution; - // final_id is assigned deterministically during this sync phase based on event order. - std::unordered_map node_id_to_final_id; - // Merge B&B events and heuristic solutions for unified timeline replay // Both are sorted by VT, so we can do a merge-style iteration size_t event_idx = 0; @@ -2558,32 +2831,14 @@ void branch_and_bound_t::process_history_and_sync( break; } - case bb_event_type_t::NODE_BRANCHED: { - // Assign deterministic final IDs to the children - // Events are processed in sorted (deterministic) order, so this assignment is - // deterministic - i_t down_provisional = event.payload.branched.down_child_id; - i_t up_provisional = event.payload.branched.up_child_id; - - i_t down_final_id = bsp_next_final_id_++; - i_t up_final_id = bsp_next_final_id_++; - node_id_to_final_id[down_provisional] = down_final_id; - node_id_to_final_id[up_provisional] = up_final_id; - - // Debug: Log final ID assignments - BSP_DEBUG_LOG_FINAL_ID_ASSIGNED( - bsp_debug_settings_, bsp_debug_logger_, down_provisional, down_final_id); - BSP_DEBUG_LOG_FINAL_ID_ASSIGNED( - bsp_debug_settings_, bsp_debug_logger_, up_provisional, up_final_id); - break; - } - + case bb_event_type_t::NODE_BRANCHED: case bb_event_type_t::NODE_FATHOMED: case bb_event_type_t::NODE_INFEASIBLE: case bb_event_type_t::NODE_NUMERICAL: case bb_event_type_t::NODE_PAUSED: case bb_event_type_t::HEURISTIC_SOLUTION: // These events don't need additional processing during replay + // (BSP identity is already assigned at node creation time) break; } } @@ -2596,7 +2851,6 @@ void branch_and_bound_t::process_history_and_sync( bsp_debug_settings_, bsp_debug_logger_, hsol.vt_timestamp, hsol.objective); // Process heuristic solution at its correct VT position - // FIX: Release mutex before calling get_lower_bound() to avoid deadlock f_t new_upper = std::numeric_limits::infinity(); mutex_upper_.lock(); @@ -2627,25 +2881,68 @@ void branch_and_bound_t::process_history_and_sync( } } - // Apply final IDs to all nodes in worker queues and global heap - // Helper lambda to apply final_id mapping to a node - auto apply_final_id = [&node_id_to_final_id](mip_node_t* node) { - if (node->final_id < 0 && node->node_id > 0) { - auto it = node_id_to_final_id.find(node->node_id); - if (it != node_id_to_final_id.end()) { node->final_id = it->second; } - } + // Merge integer solutions from all workers and update global incumbent + // Sort by (objective, worker_id) for deterministic winner selection + struct worker_solution_t { + f_t objective; + const std::vector* solution; + i_t depth; + int worker_id; }; + std::vector all_integer_solutions; + for (auto& worker : *bsp_workers_) { + for (auto& sol : worker.integer_solutions) { + all_integer_solutions.push_back({sol.objective, &sol.solution, sol.depth, worker.worker_id}); + } + } + + // Sort by objective, then worker_id for deterministic tie-breaking + std::sort(all_integer_solutions.begin(), + all_integer_solutions.end(), + [](const worker_solution_t& a, const worker_solution_t& b) { + if (a.objective != b.objective) return a.objective < b.objective; + return a.worker_id < b.worker_id; + }); + + // Apply the best solution to global incumbent + if (!all_integer_solutions.empty()) { + const auto& best = all_integer_solutions[0]; + mutex_upper_.lock(); + if (best.objective < upper_bound_) { + upper_bound_ = best.objective; + incumbent_.set_incumbent_solution(best.objective, *best.solution); + } + mutex_upper_.unlock(); + } - // Apply to worker local queues + // Merge and apply pseudo-cost updates from all workers in deterministic order + std::vector> all_pc_updates; for (auto& worker : *bsp_workers_) { - for (auto* node : worker.local_queue) { - apply_final_id(node); + for (auto& upd : worker.pseudo_cost_updates) { + all_pc_updates.push_back(upd); + } + } + + // Sort by (vt, worker_id) for deterministic order + std::sort(all_pc_updates.begin(), + all_pc_updates.end(), + [](const pseudo_cost_update_t& a, const pseudo_cost_update_t& b) { + if (a.vt != b.vt) return a.vt < b.vt; + return a.worker_id < b.worker_id; + }); + + // Apply updates in deterministic order + for (const auto& upd : all_pc_updates) { + if (upd.direction == rounding_direction_t::DOWN) { + pc_.pseudo_cost_sum_down[upd.variable] += upd.delta; + pc_.pseudo_cost_num_down[upd.variable]++; + } else { + pc_.pseudo_cost_sum_up[upd.variable] += upd.delta; + pc_.pseudo_cost_num_up[upd.variable]++; } - if (worker.current_node != nullptr) { apply_final_id(worker.current_node); } } - // Workers keep their local queues across horizons - no redistribution here. - // Load balancing is handled separately in balance_worker_loads() if needed. + // No final_id application needed - BSP identity is assigned at node creation time } template @@ -2664,17 +2961,23 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() } } - // Check nodes in local queue - auto it = worker.local_queue.begin(); - while (it != worker.local_queue.end()) { - if ((*it)->lower_bound >= upper_bound) { - search_tree_.update(*it, node_status_t::FATHOMED); + // Check nodes in local queue - need to extract, filter, and rebuild + // since priority_queue doesn't support iteration + std::vector*> surviving_nodes; + while (!worker.local_queue.empty()) { + auto* node = worker.local_queue.top(); + worker.local_queue.pop(); + if (node->lower_bound >= upper_bound) { + search_tree_.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; - it = worker.local_queue.erase(it); } else { - ++it; + surviving_nodes.push_back(node); } } + // Rebuild the queue with surviving nodes + for (auto* node : surviving_nodes) { + worker.local_queue.push(node); + } } } @@ -2692,8 +2995,7 @@ void branch_and_bound_t::balance_worker_loads() for (size_t w = 0; w < num_workers; ++w) { auto& worker = (*bsp_workers_)[w]; - work_counts[w] = worker.local_queue.size(); - if (worker.current_node != nullptr) { work_counts[w]++; } + work_counts[w] = worker.queue_size(); total_work += work_counts[w]; max_work = std::max(max_work, work_counts[w]); min_work = std::min(min_work, work_counts[w]); @@ -2706,12 +3008,13 @@ void branch_and_bound_t::balance_worker_loads() if (!needs_balance) return; - // Collect all redistributable nodes (from local queues only, not current_node which is paused) + // Collect all redistributable nodes from worker queues (excluding paused current_node) std::vector*> all_nodes; for (auto& worker : *bsp_workers_) { + // Extract all nodes from this worker's priority queue (not current_node) while (!worker.local_queue.empty()) { - all_nodes.push_back(worker.local_queue.front()); - worker.local_queue.pop_front(); + all_nodes.push_back(worker.local_queue.top()); + worker.local_queue.pop(); } } @@ -2732,12 +3035,16 @@ void branch_and_bound_t::balance_worker_loads() if (all_nodes.empty()) return; - // Sort by deterministic ID for consistent distribution - std::sort(all_nodes.begin(), - all_nodes.end(), - [](const mip_node_t* a, const mip_node_t* b) { - return a->get_deterministic_id() < b->get_deterministic_id(); - }); + // Sort by BSP identity for deterministic distribution + // Uses lexicographic order of (origin_worker_id, creation_seq) + auto deterministic_less = [](const mip_node_t* a, const mip_node_t* b) { + // Lexicographic comparison of BSP identity tuple + if (a->origin_worker_id != b->origin_worker_id) { + return a->origin_worker_id < b->origin_worker_id; + } + return a->creation_seq < b->creation_seq; + }; + std::sort(all_nodes.begin(), all_nodes.end(), deterministic_less); // Redistribute round-robin, but skip workers that have a paused current_node // (they already have work and will resume that node first) @@ -2750,10 +3057,11 @@ void branch_and_bound_t::balance_worker_loads() if ((*bsp_workers_)[w].current_node != nullptr) { worker_order.push_back(w); } } - // Distribute nodes + // Distribute nodes - use enqueue_node_with_identity to preserve existing identity for (size_t i = 0; i < all_nodes.size(); ++i) { size_t worker_idx = worker_order[i % num_workers]; - (*bsp_workers_)[worker_idx].enqueue_node(all_nodes[i]); + (*bsp_workers_)[worker_idx].enqueue_node_with_identity(all_nodes[i]); + (*bsp_workers_)[worker_idx].track_node_assigned(); // Debug: Log redistribution (happens at horizon END, at the sync point) double vt = bsp_current_horizon_; @@ -2762,7 +3070,7 @@ void branch_and_bound_t::balance_worker_loads() vt, static_cast(worker_idx), all_nodes[i]->node_id, - all_nodes[i]->final_id, + all_nodes[i]->origin_worker_id, all_nodes[i]->lower_bound); } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 537a9cced..b1f640a68 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -374,10 +374,6 @@ class branch_and_bound_t { bool bsp_mode_enabled_{false}; // Whether BSP mode is active int bsp_horizon_number_{0}; // Current horizon number (for debugging) - // Counter for deterministic final_id assignment during sync phase - // Starts at 2 (root's children are 1 and 2), incremented by 2 for each branch - i_t bsp_next_final_id_{3}; - // BSP heuristic solution queue - solutions received from GPU heuristics // Stored with VT timestamp for deterministic ordering struct queued_heuristic_solution_t { diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp index bbab5070e..ff50b0e5d 100644 --- a/cpp/src/dual_simplex/bsp_debug.hpp +++ b/cpp/src/dual_simplex/bsp_debug.hpp @@ -54,6 +54,8 @@ enum class bsp_log_event_t { SYNC_PHASE_START, // Sync phase begins SYNC_PHASE_END, // Sync phase ends WORKER_IDLE, // Worker has no work + LP_INPUT, // LP solver input hashes (for determinism debugging) + LP_OUTPUT, // LP solver output hashes (for determinism debugging) }; inline const char* bsp_log_event_name(bsp_log_event_t event) @@ -78,6 +80,8 @@ inline const char* bsp_log_event_name(bsp_log_event_t event) case bsp_log_event_t::SYNC_PHASE_START: return "SYNC_PHASE_START"; case bsp_log_event_t::SYNC_PHASE_END: return "SYNC_PHASE_END"; case bsp_log_event_t::WORKER_IDLE: return "WORKER_IDLE"; + case bsp_log_event_t::LP_INPUT: return "LP_INPUT"; + case bsp_log_event_t::LP_OUTPUT: return "LP_OUTPUT"; default: return "UNKNOWN"; } } @@ -98,6 +102,7 @@ inline const char* bsp_log_event_name(bsp_log_event_t event) * CUOPT_BSP_DEBUG_ALL=1 Enable all debug output * CUOPT_BSP_DEBUG_DIR=path Output directory (default: ./bsp_debug/) * CUOPT_BSP_DEBUG_LEVEL=N Log level: 0=off, 1=major events, 2=all events + * CUOPT_BSP_DEBUG_FLUSH_EVERY_HORIZON=0 Disable flushing debug output every horizon (default: 1) */ struct bsp_debug_settings_t { bool enable_event_log{false}; @@ -105,6 +110,7 @@ struct bsp_debug_settings_t { bool enable_tree_dot{false}; bool enable_state_json{false}; bool enable_determinism_trace{false}; + bool flush_every_horizon{true}; // Flush debug output at end of each horizon std::string output_dir{"./bsp_debug/"}; int log_level{1}; // 0=off, 1=major events, 2=all events @@ -123,6 +129,15 @@ struct bsp_debug_settings_t { enable_determinism_trace = true; } + void disable_all() + { + enable_event_log = false; + enable_timeline = false; + enable_tree_dot = false; + enable_state_json = false; + enable_determinism_trace = false; + } + /** * @brief Initialize settings from environment variables. * @return A bsp_debug_settings_t populated from environment variables. @@ -167,6 +182,13 @@ struct bsp_debug_settings_t { settings.output_dir = get_env_string("CUOPT_BSP_DEBUG_DIR", "./bsp_debug/"); settings.log_level = get_env_int("CUOPT_BSP_DEBUG_LEVEL", 1); + // Flush every horizon is ON by default; set to 0 to disable + const char* flush_env = std::getenv("CUOPT_BSP_DEBUG_FLUSH_EVERY_HORIZON"); + if (flush_env != nullptr) { + settings.flush_every_horizon = + (std::string(flush_env) == "1" || std::string(flush_env) == "true"); + } + return settings; } }; @@ -353,7 +375,7 @@ class bsp_debug_logger_t { } } - void log_branched(double vt, int worker_id, i_t parent_id, i_t down_id, i_t up_id) + void log_branched(double vt, int worker_id, i_t parent_id, i_t parent_fid, i_t down_id, i_t up_id) { std::lock_guard lock(mutex_); @@ -362,12 +384,13 @@ class bsp_debug_logger_t { worker_id, bsp_log_event_t::NODE_BRANCHED, parent_id, - -1, + parent_fid, "children=" + std::to_string(down_id) + "," + std::to_string(up_id)); } if (settings_.enable_determinism_trace) { - events_trace_ss_ << "BRANCH(" << vt << ",W" << worker_id << "," << parent_id << "->" + // Log parent final_id (deterministic) and children node_ids (non-deterministic until sync) + events_trace_ss_ << "BRANCH(" << vt << ",W" << worker_id << ",F" << parent_fid << "->" << down_id << "," << up_id << "),"; } } @@ -533,6 +556,104 @@ class bsp_debug_logger_t { } } + // ======================================================================== + // LP Determinism Logging (for debugging non-determinism in LP solver) + // ======================================================================== + + void log_lp_input(int worker_id, + i_t node_id, + uint64_t path_hash, + i_t depth, + uint64_t vstatus_hash, + uint64_t bounds_hash) + { + std::lock_guard lock(mutex_); + + // Log to main events file (always when BSP debug is enabled) + if (settings_.enable_event_log) { + char details[320]; + std::snprintf(details, + sizeof(details), + "path=0x%016llx,d=%d,vstatus=0x%016llx,bounds=0x%016llx", + static_cast(path_hash), + depth, + static_cast(vstatus_hash), + static_cast(bounds_hash)); + log_event_unlocked(-1.0, worker_id, bsp_log_event_t::LP_INPUT, node_id, -1, details); + } + + // Also log to determinism trace if enabled + if (settings_.enable_determinism_trace) { + char buf[320]; + std::snprintf(buf, + sizeof(buf), + "LP_IN(W%d,N%d,path=0x%016llx,d=%d,vs=0x%016llx,bnd=0x%016llx)", + worker_id, + node_id, + static_cast(path_hash), + depth, + static_cast(vstatus_hash), + static_cast(bounds_hash)); + events_trace_ss_ << buf << ","; + } + } + + void log_lp_output(int worker_id, + i_t node_id, + uint64_t path_hash, + int status, + i_t iters, + uint64_t obj_bits, + uint64_t sol_hash) + { + std::lock_guard lock(mutex_); + + // Log to main events file (always when BSP debug is enabled) + if (settings_.enable_event_log) { + char details[320]; + std::snprintf(details, + sizeof(details), + "path=0x%016llx,status=%d,iters=%d,obj=0x%016llx,sol=0x%016llx", + static_cast(path_hash), + status, + iters, + static_cast(obj_bits), + static_cast(sol_hash)); + log_event_unlocked(-1.0, worker_id, bsp_log_event_t::LP_OUTPUT, node_id, -1, details); + } + + // Also log to determinism trace if enabled + if (settings_.enable_determinism_trace) { + char buf[320]; + std::snprintf(buf, + sizeof(buf), + "LP_OUT(W%d,N%d,path=0x%016llx,st=%d,it=%d,obj=0x%016llx,sol=0x%016llx)", + worker_id, + node_id, + static_cast(path_hash), + status, + iters, + static_cast(obj_bits), + static_cast(sol_hash)); + events_trace_ss_ << buf << ","; + } + } + + void log_branch_decision(i_t branch_var, uint64_t score_hash, size_t num_ties) + { + if (settings_.enable_determinism_trace && settings_.log_level >= 2) { + std::lock_guard lock(mutex_); + char buf[128]; + std::snprintf(buf, + sizeof(buf), + "BVAR(v=%d,sc=0x%016llx,ties=%zu)", + branch_var, + static_cast(score_hash), + num_ties); + events_trace_ss_ << buf << ","; + } + } + // ======================================================================== // Tree State (DOT format) // ======================================================================== @@ -600,7 +721,8 @@ class bsp_debug_logger_t { file << " \"clock\": " << w.clock << ",\n"; if (w.current_node != nullptr) { file << " \"current_node\": {\"id\": " << w.current_node->node_id - << ", \"final_id\": " << w.current_node->final_id + << ", \"origin_worker\": " << w.current_node->origin_worker_id + << ", \"creation_seq\": " << w.current_node->creation_seq << ", \"acc_vt\": " << w.current_node->accumulated_vt << "},\n"; } else { file << " \"current_node\": null,\n"; @@ -614,9 +736,9 @@ class bsp_debug_logger_t { file << " \"heap\": [\n"; for (size_t i = 0; i < heap_nodes.size(); ++i) { const auto* n = heap_nodes[i]; - file << " {\"id\": " << n->node_id << ", \"final_id\": " << n->final_id - << ", \"lb\": " << n->lower_bound << "}" << (i < heap_nodes.size() - 1 ? "," : "") - << "\n"; + file << " {\"id\": " << n->node_id << ", \"origin_worker\": " << n->origin_worker_id + << ", \"creation_seq\": " << n->creation_seq << ", \"lb\": " << n->lower_bound << "}" + << (i < heap_nodes.size() - 1 ? "," : "") << "\n"; } file << " ],\n"; @@ -836,7 +958,8 @@ class bsp_debug_logger_t { } file << " N" << node->node_id << " [label=\"N" << node->node_id; - if (node->final_id >= 0) file << " (fid=" << node->final_id << ")"; + if (node->has_bsp_identity()) + file << " (w" << node->origin_worker_id << ":" << node->creation_seq << ")"; file << "\\nlb=" << std::fixed << std::setprecision(1) << node->lower_bound; file << "\\n" << status_str; if (node->bsp_state == bsp_node_state_t::PAUSED) { @@ -887,9 +1010,9 @@ class bsp_debug_logger_t { do { \ if ((settings).any_enabled()) (logger).log_node_assigned(vt, w, nid, fid, lb); \ } while (0) -#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) \ - do { \ - if ((settings).any_enabled()) (logger).flush_assign_trace(); \ +#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) \ + do { \ + if ((settings).any_enabled() && (settings).flush_every_horizon) (logger).flush_assign_trace(); \ } while (0) #define BSP_DEBUG_LOG_SOLVE_START(settings, logger, vt, w, nid, fid, wl, resumed) \ do { \ @@ -899,9 +1022,9 @@ class bsp_debug_logger_t { do { \ if ((settings).any_enabled()) (logger).log_solve_end(vt, w, nid, fid, result, lb); \ } while (0) -#define BSP_DEBUG_LOG_BRANCHED(settings, logger, vt, w, pid, did, uid) \ - do { \ - if ((settings).any_enabled()) (logger).log_branched(vt, w, pid, did, uid); \ +#define BSP_DEBUG_LOG_BRANCHED(settings, logger, vt, w, pid, pfid, did, uid) \ + do { \ + if ((settings).any_enabled()) (logger).log_branched(vt, w, pid, pfid, did, uid); \ } while (0) #define BSP_DEBUG_LOG_PAUSED(settings, logger, vt, w, nid, fid, acc) \ do { \ @@ -937,7 +1060,8 @@ class bsp_debug_logger_t { } while (0) #define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(settings, logger) \ do { \ - if ((settings).any_enabled()) (logger).flush_final_ids_trace(); \ + if ((settings).any_enabled() && (settings).flush_every_horizon) \ + (logger).flush_final_ids_trace(); \ } while (0) #define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(settings, logger, vt, obj) \ do { \ @@ -951,14 +1075,27 @@ class bsp_debug_logger_t { do { \ if ((settings).any_enabled()) (logger).log_heap_order(fids); \ } while (0) -#define BSP_DEBUG_EMIT_TREE_STATE(settings, logger, h, root, ub) \ - do { \ - if ((settings).any_enabled()) (logger).emit_tree_state(h, root, ub); \ +#define BSP_DEBUG_LOG_LP_INPUT(settings, logger, w, nid, ph, d, vsh, bh) \ + do { \ + if ((settings).any_enabled()) (logger).log_lp_input(w, nid, ph, d, vsh, bh); \ + } while (0) +#define BSP_DEBUG_LOG_LP_OUTPUT(settings, logger, w, nid, ph, st, it, ob, sh) \ + do { \ + if ((settings).any_enabled()) (logger).log_lp_output(w, nid, ph, st, it, ob, sh); \ + } while (0) +#define BSP_DEBUG_LOG_BRANCH_DECISION(settings, logger, bv, sh, nt) \ + do { \ + if ((settings).any_enabled()) (logger).log_branch_decision(bv, sh, nt); \ + } while (0) +#define BSP_DEBUG_EMIT_TREE_STATE(settings, logger, h, root, ub) \ + do { \ + if ((settings).any_enabled() && (settings).flush_every_horizon) \ + (logger).emit_tree_state(h, root, ub); \ } while (0) #define BSP_DEBUG_EMIT_STATE_JSON( \ settings, logger, h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) \ do { \ - if ((settings).any_enabled()) \ + if ((settings).any_enabled() && (settings).flush_every_horizon) \ (logger).emit_state_json(h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events); \ } while (0) #define BSP_DEBUG_FINALIZE(settings, logger) \ @@ -975,7 +1112,7 @@ class bsp_debug_logger_t { #define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) ((void)0) #define BSP_DEBUG_LOG_SOLVE_START(settings, logger, vt, w, nid, fid, wl, resumed) ((void)0) #define BSP_DEBUG_LOG_SOLVE_END(settings, logger, vt, w, nid, fid, result, lb) ((void)0) -#define BSP_DEBUG_LOG_BRANCHED(settings, logger, vt, w, pid, did, uid) ((void)0) +#define BSP_DEBUG_LOG_BRANCHED(settings, logger, vt, w, pid, pfid, did, uid) ((void)0) #define BSP_DEBUG_LOG_PAUSED(settings, logger, vt, w, nid, fid, acc) ((void)0) #define BSP_DEBUG_LOG_INTEGER(settings, logger, vt, w, nid, obj) ((void)0) #define BSP_DEBUG_LOG_FATHOMED(settings, logger, vt, w, nid, lb) ((void)0) @@ -988,6 +1125,9 @@ class bsp_debug_logger_t { #define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(settings, logger, vt, obj) ((void)0) #define BSP_DEBUG_LOG_INCUMBENT_UPDATE(settings, logger, vt, obj, src) ((void)0) #define BSP_DEBUG_LOG_HEAP_ORDER(settings, logger, fids) ((void)0) +#define BSP_DEBUG_LOG_LP_INPUT(settings, logger, w, nid, ph, d, vsh, bh) ((void)0) +#define BSP_DEBUG_LOG_LP_OUTPUT(settings, logger, w, nid, ph, st, it, ob, sh) ((void)0) +#define BSP_DEBUG_LOG_BRANCH_DECISION(settings, logger, bv, sh, nt) ((void)0) #define BSP_DEBUG_EMIT_TREE_STATE(settings, logger, h, root, ub) ((void)0) #define BSP_DEBUG_EMIT_STATE_JSON( \ settings, logger, h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) \ diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index bfea830e9..eb11f26e6 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -240,9 +240,10 @@ class mip_node_t { copy.fractional_val = fractional_val; copy.node_id = node_id; // Copy BSP fields - copy.accumulated_vt = accumulated_vt; - copy.bsp_state = bsp_state; - copy.final_id = final_id; + copy.accumulated_vt = accumulated_vt; + copy.bsp_state = bsp_state; + copy.origin_worker_id = origin_worker_id; + copy.creation_seq = creation_seq; return copy; } @@ -265,17 +266,54 @@ class mip_node_t { f_t accumulated_vt{0.0}; // Virtual time spent on this node so far bsp_node_state_t bsp_state{bsp_node_state_t::READY}; // BSP processing state - // For deterministic node ID assignment in BSP mode: - // - node_id is assigned non-deterministically during parallel execution (via atomic counter) - // - final_id is assigned deterministically during sync phase based on event order - // - Use final_id for sorting if set (>= 0), otherwise fall back to node_id - i_t final_id{-1}; + // Worker-local identification for deterministic BSP ordering: + // - origin_worker_id: which worker created this node (-1 for pre-BSP/initial nodes) + // - creation_seq: sequence number within that worker (cumulative across horizons) + // The tuple (origin_worker_id, creation_seq) is unique and stable (never changes after + // assignment) This replaces the old final_id approach which required sync-time assignment + int32_t origin_worker_id{-1}; + int32_t creation_seq{-1}; - // Get the ID to use for deterministic ordering - i_t get_deterministic_id() const + // Check if this node has been assigned a BSP identity + bool has_bsp_identity() const { return origin_worker_id >= -1 && creation_seq >= 0; } + + // Get a 64-bit identity value for hashing (combines worker_id and seq) + // Uses origin_worker_id + 1 to handle -1 (pre-BSP nodes) gracefully + uint64_t get_bsp_identity_hash() const + { + return (static_cast(origin_worker_id + 1) << 32) | + static_cast(static_cast(creation_seq)); + } + + // Compute a deterministic path hash based on branching decisions from root + // This uniquely identifies the node regardless of creation order + uint64_t compute_path_hash() const + { + uint64_t hash = 0; + const mip_node_t* node = this; + while (node != nullptr && node->branch_var >= 0) { + // Combine branch_var and branch_dir into hash + uint64_t step = static_cast(node->branch_var) << 1; + step |= (node->branch_dir == rounding_direction_t::UP) ? 1 : 0; + // FNV-1a style mixing + hash ^= step; + hash *= 0x100000001b3ULL; + node = node->parent; + } + return hash; + } + + // Get the branching path as a string for debugging + std::string get_path_string() const { - if (final_id >= 0) return final_id; - return node_id; + std::string path; + const mip_node_t* node = this; + while (node != nullptr && node->branch_var >= 0) { + char dir = (node->branch_dir == rounding_direction_t::UP) ? 'U' : 'D'; + path = std::to_string(node->branch_var) + dir + (path.empty() ? "" : "-") + path; + node = node->parent; + } + return path.empty() ? "root" : path; } }; @@ -289,23 +327,71 @@ void remove_fathomed_nodes(std::vector*>& stack) } } +// Comparator for global heap (used in non-BSP mode and for initial distribution in BSP) +// Uses path_hash for deterministic tie-breaking when BSP identity is not available template class node_compare_t { public: // Comparison for priority queue: returns true if 'a' has lower priority than 'b' // (elements with lower priority are output last from the heap). // Primary: prefer lower bound (best-first search) - // Tie-breaker: use deterministic ID for reproducibility across runs + // Tie-breaker: use deterministic comparison for reproducibility bool operator()(const mip_node_t& a, const mip_node_t& b) const { if (a.lower_bound != b.lower_bound) { return a.lower_bound > b.lower_bound; } - return a.get_deterministic_id() > b.get_deterministic_id(); + return deterministic_compare(a, b); } bool operator()(const mip_node_t* a, const mip_node_t* b) const { if (a->lower_bound != b->lower_bound) { return a->lower_bound > b->lower_bound; } - return a->get_deterministic_id() > b->get_deterministic_id(); + return deterministic_compare(*a, *b); + } + + private: + // Deterministic comparison using BSP identity tuple or path_hash fallback + bool deterministic_compare(const mip_node_t& a, const mip_node_t& b) const + { + assert(a.has_bsp_identity() && b.has_bsp_identity()); + // If both have BSP identity, use lexicographic comparison of (origin_worker_id, creation_seq) + if (a.has_bsp_identity() && b.has_bsp_identity()) { + if (a.origin_worker_id != b.origin_worker_id) { + return a.origin_worker_id > b.origin_worker_id; + } + return a.creation_seq > b.creation_seq; + } + + // If only one has BSP identity, prefer the one with identity (already established) + if (a.has_bsp_identity()) { return false; } // a has priority + if (b.has_bsp_identity()) { return true; } // b has priority + + // Neither has BSP identity - use path_hash for deterministic comparison (non-BSP mode) + uint64_t hash_a = a.compute_path_hash(); + uint64_t hash_b = b.compute_path_hash(); + if (hash_a != hash_b) { return hash_a > hash_b; } + + // Ultimate fallback: compare by depth (shouldn't happen with different paths) + return a.depth > b.depth; + } +}; + +// BSP-specific comparator for worker-local priority queues +// Uses (origin_worker_id, creation_seq) tuple for deterministic ordering within a worker +template +class bsp_node_compare_t { + public: + // Returns true if 'a' has lower priority than 'b' (for max-heap behavior) + bool operator()(const mip_node_t* a, const mip_node_t* b) const + { + // Primary: lower_bound (best-first search - prefer smaller bound) + if (a->lower_bound != b->lower_bound) { return a->lower_bound > b->lower_bound; } + + // Tie-breaker: lexicographic comparison of BSP identity tuple + // This is deterministic regardless of node creation order + if (a->origin_worker_id != b->origin_worker_id) { + return a->origin_worker_id > b->origin_worker_id; + } + return a->creation_seq > b->creation_seq; } }; diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index b6bffacc5..2a8ef0c5f 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -239,8 +239,6 @@ void initial_perturbation(const lp_problem_t& lp, const f_t dual_tol = settings.dual_tol; - std::srand(static_cast(std::time(nullptr))); - objective.resize(n); f_t sum_perturb = 0.0; i_t num_perturb = 0; diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 799cdc3ff..f5bb4badc 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -14,6 +14,8 @@ #include #include +#include +#include namespace cuopt::linear_programming::dual_simplex { @@ -49,6 +51,59 @@ class pseudo_costs_t { void update_pseudo_costs_from_strong_branching(const std::vector& fractional, const std::vector& root_soln); + + // Compute a deterministic hash of the pseudo-cost state for divergence detection + uint32_t compute_state_hash() const + { + uint32_t hash = 0x811c9dc5; // FNV-1a offset basis + auto hash_double = [&hash](double val) { + // Convert to fixed-point representation for exact comparison + int64_t fixed = static_cast(val * 1000000.0); + hash ^= static_cast(fixed & 0xFFFFFFFF); + hash *= 0x01000193; // FNV-1a prime + hash ^= static_cast((fixed >> 32) & 0xFFFFFFFF); + hash *= 0x01000193; + }; + auto hash_int = [&hash](i_t val) { + hash ^= static_cast(val); + hash *= 0x01000193; + }; + + // Hash pseudo-cost sums and counts + for (size_t j = 0; j < pseudo_cost_sum_down.size(); ++j) { + hash_double(pseudo_cost_sum_down[j]); + hash_double(pseudo_cost_sum_up[j]); + hash_int(pseudo_cost_num_down[j]); + hash_int(pseudo_cost_num_up[j]); + } + return hash; + } + + // Compute hash of strong branching results + uint32_t compute_strong_branch_hash() const + { + uint32_t hash = 0x811c9dc5; + auto hash_double = [&hash](double val) { + if (std::isnan(val)) { + hash ^= 0xDEADBEEF; // Special marker for NaN + } else if (std::isinf(val)) { + hash ^= val > 0 ? 0xCAFEBABE : 0xBADCAFE; // Inf markers + } else { + int64_t fixed = static_cast(val * 1000000.0); + hash ^= static_cast(fixed & 0xFFFFFFFF); + hash *= 0x01000193; + hash ^= static_cast((fixed >> 32) & 0xFFFFFFFF); + } + hash *= 0x01000193; + }; + + for (size_t k = 0; k < strong_branch_down.size(); ++k) { + hash_double(strong_branch_down[k]); + hash_double(strong_branch_up[k]); + } + return hash; + } + std::vector pseudo_cost_sum_up; std::vector pseudo_cost_sum_down; std::vector pseudo_cost_num_up; diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index bc6baa1dc..ea1381022 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -189,6 +189,7 @@ bool diversity_manager_t::run_presolve(f_t time_limit) { raft::common::nvtx::range fun_scope("run_presolve"); CUOPT_LOG_INFO("Running presolve!"); + CUOPT_LOG_INFO("Problem fingerprint before DM presolve: 0x%x", problem_ptr->get_fingerprint()); work_limit_timer_t presolve_timer(context.gpu_heur_loop, time_limit); auto term_crit = ls.constraint_prop.bounds_update.solve(*problem_ptr); if (ls.constraint_prop.bounds_update.infeas_constraints_count > 0) { @@ -197,11 +198,17 @@ bool diversity_manager_t::run_presolve(f_t time_limit) } if (termination_criterion_t::NO_UPDATE != term_crit) { ls.constraint_prop.bounds_update.set_updated_bounds(*problem_ptr); + CUOPT_LOG_INFO("Problem fingerprint after cons prop presolve: 0x%x", + problem_ptr->get_fingerprint()); trivial_presolve(*problem_ptr); + CUOPT_LOG_INFO("Problem fingerprint after trivial presolve: 0x%x", + problem_ptr->get_fingerprint()); if (!problem_ptr->empty && !check_bounds_sanity(*problem_ptr)) { return false; } } // May overconstrain if Papilo presolve has been run before - if (!context.settings.presolve) { + // Skip conditional bound strengthening in deterministic mode - the knapsack kernel has + // race conditions where multiple blocks can update the same constraint non-deterministically. + if (!context.settings.presolve && context.settings.determinism_mode != CUOPT_MODE_DETERMINISTIC) { if (!problem_ptr->empty) { // do the resizing no-matter what, bounds presolve might not change the bounds but initial // trivial presolve might have @@ -220,6 +227,7 @@ bool diversity_manager_t::run_presolve(f_t time_limit) problem_ptr->n_constraints, problem_ptr->n_variables, problem_ptr->presolve_data.objective_offset); + CUOPT_LOG_INFO("Problem fingerprint after DM presolve: 0x%x", problem_ptr->get_fingerprint()); return true; } @@ -307,7 +315,7 @@ solution_t diversity_manager_t::run_solver() // Debug: Allow disabling GPU heuristics to test B&B tree determinism in isolation const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); - disable_heuristics_env = "1"; + disable_heuristics_env = "1"; // DO NOT REMOVE! intended debugging line! if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { CUOPT_LOG_INFO("GPU heuristics disabled via CUOPT_DISABLE_GPU_HEURISTICS=1"); // Initialize population minimally and wait for B&B to finish diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index c8ee11176..be6fc8ac6 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1799,6 +1799,10 @@ void problem_t::get_host_user_problem( user_problem.num_range_rows = user_problem.range_rows.size(); std::tie(user_problem.lower, user_problem.upper) = extract_host_bounds(variable_bounds, handle_ptr); + + // Debug: Log bounds fingerprint to detect GPU presolve non-determinism + CUOPT_LOG_INFO("Problem fingerprint after GPU presolve: 0x%x", get_fingerprint()); + user_problem.problem_name = original_problem_ptr->get_problem_name(); if (static_cast(row_names.size()) == m) { user_problem.row_names.resize(m); diff --git a/cpp/src/mip/utils.cuh b/cpp/src/mip/utils.cuh index 2a8de1899..dc9e3f09d 100644 --- a/cpp/src/mip/utils.cuh +++ b/cpp/src/mip/utils.cuh @@ -15,6 +15,7 @@ #include #include #include +#include #include @@ -29,34 +30,6 @@ constexpr int default_int_upper = std::numeric_limits::max(); constexpr int default_int_lower = std::numeric_limits::min(); constexpr double zero_bound = 0.; -template -inline uint32_t compute_hash(std::vector h_contents) -{ - // FNV-1a hash - - uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis - std::vector byte_contents(h_contents.size() * sizeof(i_t)); - std::memcpy(byte_contents.data(), h_contents.data(), h_contents.size() * sizeof(i_t)); - for (size_t i = 0; i < byte_contents.size(); ++i) { - hash ^= byte_contents[i]; - hash *= 16777619u; - } - return hash; -} - -template -HDI uint32_t compute_hash(const i_t val) -{ - uint32_t hash = 2166136261u; - uint8_t byte_contents[sizeof(i_t)]; - std::memcpy(byte_contents, &val, sizeof(i_t)); - for (size_t i = 0; i < sizeof(i_t); ++i) { - hash ^= byte_contents[i]; - hash *= 16777619u; - } - return hash; -} - template inline uint32_t compute_hash(raft::device_span values, rmm::cuda_stream_view stream = rmm::cuda_stream_default) diff --git a/cpp/src/utilities/hashing.hpp b/cpp/src/utilities/hashing.hpp new file mode 100644 index 000000000..a1fa9220f --- /dev/null +++ b/cpp/src/utilities/hashing.hpp @@ -0,0 +1,45 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#pragma once + +#include +#include + +namespace cuopt::linear_programming::detail { + +template +inline uint32_t compute_hash(std::vector h_contents) +{ + // FNV-1a hash + + uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis + std::vector byte_contents(h_contents.size() * sizeof(i_t)); + std::memcpy(byte_contents.data(), h_contents.data(), h_contents.size() * sizeof(i_t)); + for (size_t i = 0; i < byte_contents.size(); ++i) { + hash ^= byte_contents[i]; + hash *= 16777619u; + } + return hash; +} + +template +#ifdef __CUDA_ARCH__ +__device__ +#endif + inline uint32_t + compute_hash(const i_t val) +{ + uint32_t hash = 2166136261u; + uint8_t byte_contents[sizeof(i_t)]; + std::memcpy(byte_contents, &val, sizeof(i_t)); + for (size_t i = 0; i < sizeof(i_t); ++i) { + hash ^= byte_contents[i]; + hash *= 16777619u; + } + return hash; +} + +} // namespace cuopt::linear_programming::detail From d80667c1212378b10278fc7f738b606aebe0957a Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 10 Jan 2026 15:00:33 +0000 Subject: [PATCH 174/366] more instrumentation --- cpp/src/dual_simplex/bounds_strengthening.cpp | 81 ++++++++++++++----- cpp/src/dual_simplex/bounds_strengthening.hpp | 17 ++-- .../dual_simplex/dual_simplex_features.hpp | 59 +++++++++++++- cpp/src/dual_simplex/phase2.cpp | 5 ++ cpp/src/utilities/memory_instrumentation.hpp | 15 ++-- 5 files changed, 143 insertions(+), 34 deletions(-) diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index d857d2f45..c0cfd9646 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -6,6 +6,9 @@ /* clang-format on */ #include +#include +#include +#include #include @@ -97,24 +100,48 @@ bool bounds_strengthening_t::bounds_strengthening( std::vector& upper_bounds, const simplex_solver_settings_t& settings) { - const i_t m = A.m; - const i_t n = A.n; + const i_t m = A.m; + const i_t n = A.n; + const i_t nnz = A.col_start[n]; raft::common::nvtx::range fun_scope("bounds_strengthening"); - - std::vector constraint_changed(m, true); - std::vector variable_changed(n, false); - std::vector constraint_changed_next(m, false); + f_t start_time = tic(); + + // Instrumented vectors for memory tracking (use uint8_t since std::vector is specialized) + cuopt::ins_vector constraint_changed(m, 1); + cuopt::ins_vector variable_changed(n, 0); + cuopt::ins_vector constraint_changed_next(m, 0); + + // Instrumentation manifold for this operation - includes A matrix (CSC and CSR) + cuopt::instrumentation_manifold_t manifold; + manifold.add("constraint_changed", constraint_changed); + manifold.add("variable_changed", variable_changed); + manifold.add("constraint_changed_next", constraint_changed_next); + // A matrix in CSC format (for column access) + manifold.add("A.col_start", A.col_start); + manifold.add("A.i", A.i); + manifold.add("A.x", A.x); + // A matrix in CSR format (for row access) + manifold.add("Arow.row_start", Arow.row_start); + manifold.add("Arow.j", Arow.j); + manifold.add("Arow.x", Arow.x); + // Member vectors for bounds and activity tracking + manifold.add("lower", lower); + manifold.add("upper", upper); + manifold.add("delta_min_activity", delta_min_activity); + manifold.add("delta_max_activity", delta_max_activity); + manifold.add("constraint_lb", constraint_lb); + manifold.add("constraint_ub", constraint_ub); if (!bounds_changed.empty()) { - std::fill(constraint_changed.begin(), constraint_changed.end(), false); + std::fill(constraint_changed.begin(), constraint_changed.end(), static_cast(0)); for (i_t i = 0; i < n; ++i) { if (bounds_changed[i]) { const i_t row_start = A.col_start[i]; const i_t row_end = A.col_start[i + 1]; for (i_t p = row_start; p < row_end; ++p) { const i_t j = A.i[p]; - constraint_changed[j] = true; + constraint_changed[j] = 1; } } } @@ -122,10 +149,11 @@ bool bounds_strengthening_t::bounds_strengthening( lower = lower_bounds; upper = upper_bounds; - print_bounds_stats(lower, upper, settings, "Initial bounds"); + print_bounds_stats(lower.underlying(), upper.underlying(), settings, "Initial bounds"); - i_t iter = 0; - const i_t iter_limit = 10; + i_t iter = 0; + const i_t iter_limit = 10; + i_t total_bounds_changed = 0; while (iter < iter_limit) { for (i_t i = 0; i < m; ++i) { if (!constraint_changed[i]) { continue; } @@ -138,7 +166,7 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t j = Arow.j[p]; const f_t a_ij = Arow.x[p]; - variable_changed[j] = true; + variable_changed[j] = 1; if (a_ij > 0) { min_a += a_ij * lower[j]; max_a += a_ij * upper[j]; @@ -222,28 +250,45 @@ bool bounds_strengthening_t::bounds_strengthening( if (new_lb != old_lb || new_ub != old_ub) { for (i_t p = row_start; p < row_end; ++p) { const i_t i = A.i[p]; - constraint_changed_next[i] = true; + constraint_changed_next[i] = 1; } } lower[k] = std::min(new_lb, new_ub); upper[k] = std::max(new_lb, new_ub); - bool bounds_changed = lb_updated || ub_updated; - if (bounds_changed) { num_bounds_changed++; } + bool bounds_tightened = lb_updated || ub_updated; + if (bounds_tightened) { num_bounds_changed++; } } + total_bounds_changed += num_bounds_changed; if (num_bounds_changed == 0) { break; } std::swap(constraint_changed, constraint_changed_next); - std::fill(constraint_changed_next.begin(), constraint_changed_next.end(), false); - std::fill(variable_changed.begin(), variable_changed.end(), false); + std::fill( + constraint_changed_next.begin(), constraint_changed_next.end(), static_cast(0)); + std::fill(variable_changed.begin(), variable_changed.end(), static_cast(0)); iter++; } // settings.log.printf("Total strengthened variables %d\n", total_strengthened_variables); + // Log bounds strengthening features + { + auto [loads, stores] = manifold.collect_and_flush(); + bounds_strengthening_features_t bs_features; + bs_features.m = m; + bs_features.n = n; + bs_features.nnz = nnz; + bs_features.num_iterations = iter; + bs_features.num_bounds_changed = total_bounds_changed; + bs_features.byte_loads = loads; + bs_features.byte_stores = stores; + bs_features.runtime = toc(start_time); + bs_features.log_single(m, n, nnz); + } + #if DEBUG_BOUND_STRENGTHENING f_t lb_change = 0.0; f_t ub_change = 0.0; @@ -278,7 +323,7 @@ bool bounds_strengthening_t::bounds_strengthening( num_ub_changed, iter); } - print_bounds_stats(lower, upper, settings, "Final bounds"); + print_bounds_stats(lower.underlying(), upper.underlying(), settings, "Final bounds"); #endif lower_bounds = lower; diff --git a/cpp/src/dual_simplex/bounds_strengthening.hpp b/cpp/src/dual_simplex/bounds_strengthening.hpp index e7e218b82..679b732b5 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.hpp +++ b/cpp/src/dual_simplex/bounds_strengthening.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -8,6 +8,7 @@ #pragma once #include +#include namespace cuopt::linear_programming::dual_simplex { @@ -31,12 +32,12 @@ class bounds_strengthening_t { const csr_matrix_t& Arow; const std::vector& var_types; - std::vector lower; - std::vector upper; - - std::vector delta_min_activity; - std::vector delta_max_activity; - std::vector constraint_lb; - std::vector constraint_ub; + // Instrumented vectors for memory tracking + cuopt::ins_vector lower; + cuopt::ins_vector upper; + cuopt::ins_vector delta_min_activity; + cuopt::ins_vector delta_max_activity; + cuopt::ins_vector constraint_lb; + cuopt::ins_vector constraint_ub; }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 73e88970e..49f2171c2 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -155,4 +155,61 @@ struct dual_simplex_features_t { // Feature logging interval (every N iterations) constexpr int FEATURE_LOG_INTERVAL = 100; +// Node bounds strengthening features (for B&B) +template +struct bounds_strengthening_features_t { + i_t m{0}; // number of constraints + i_t n{0}; // number of variables + i_t nnz{0}; // number of nonzeros in constraint matrix + i_t num_iterations{0}; // propagation iterations until fixpoint + i_t num_bounds_changed{0}; // total bounds tightened + size_t byte_loads{0}; + size_t byte_stores{0}; + f_t runtime{0.0}; + + // Interval aggregates (for when bounds strengthening is called multiple times) + i_t call_count{0}; + i_t total_iterations{0}; + i_t total_bounds_changed{0}; + size_t total_byte_loads{0}; + size_t total_byte_stores{0}; + f_t total_runtime{0.0}; + + void accumulate() + { + call_count++; + total_iterations += num_iterations; + total_bounds_changed += num_bounds_changed; + total_byte_loads += byte_loads; + total_byte_stores += byte_stores; + total_runtime += runtime; + } + + void log_single(i_t m_val, i_t n_val, i_t nnz_val) const + { + printf( + "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " + "iterations=%d bounds_changed=%d " + "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", + m_val, + n_val, + nnz_val, + num_iterations, + num_bounds_changed, + byte_loads, + byte_stores, + runtime); + } + + void reset() + { + call_count = 0; + total_iterations = 0; + total_bounds_changed = 0; + total_byte_loads = 0; + total_byte_stores = 0; + total_runtime = 0.0; + } +}; + } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 2a8ef0c5f..4a8144457 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2466,6 +2466,11 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, manifold.add("A_transpose.i", A_transpose.i); manifold.add("A_transpose.x", A_transpose.x); + // Add A matrix for entering column access during basis update + manifold.add("A.col_start", lp.A.col_start); + manifold.add("A.i", lp.A.i); + manifold.add("A.x", lp.A.x); + // Track iteration interval start time for runtime measurement f_t interval_start_time = toc(start_time); i_t last_feature_log_iter = iter; diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index ca8771abf..abf09ae05 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -102,7 +102,8 @@ class instrumentation_manifold_t { // Construct with initializer list of (description, instrumented object) pairs instrumentation_manifold_t( std::initializer_list< - std::pair>> instrumented) + std::pair>> + instrumented) { for (const auto& [name, instr] : instrumented) { instrumented_.insert_or_assign(name, instr); @@ -110,9 +111,9 @@ class instrumentation_manifold_t { } // Add an instrumented object to track with a description - void add(const std::string& description, memory_instrumentation_base_t& instrumented) + void add(const std::string& description, const memory_instrumentation_base_t& instrumented) { - instrumented_.insert_or_assign(description, std::ref(instrumented)); + instrumented_.insert_or_assign(description, std::cref(instrumented)); } // Collect total loads and stores across all instrumented objects @@ -158,7 +159,7 @@ class instrumentation_manifold_t { } private: - std::unordered_map> + std::unordered_map> instrumented_; }; @@ -170,10 +171,10 @@ class instrumentation_manifold_t { instrumentation_manifold_t() = default; instrumentation_manifold_t( std::initializer_list< - std::pair>>) + std::pair>>) { } - void add(const std::string&, memory_instrumentation_base_t&) {} + void add(const std::string&, const memory_instrumentation_base_t&) {} std::pair collect() { return {0, 0}; } std::vector> collect_per_wrapper() { return {}; } std::pair collect_and_flush() { return {0, 0}; } From 14441d15f8898bb819c83df26ca844fb84d65656 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 12 Jan 2026 10:58:32 +0100 Subject: [PATCH 175/366] refactor to eliminate enum --- cpp/src/dual_simplex/CMakeLists.txt | 1 - cpp/src/dual_simplex/bnb_worker.cpp | 81 ------ cpp/src/dual_simplex/bnb_worker.hpp | 85 ++++-- cpp/src/dual_simplex/branch_and_bound.cpp | 325 +++++++++++----------- cpp/src/dual_simplex/branch_and_bound.hpp | 50 ++-- 5 files changed, 247 insertions(+), 295 deletions(-) delete mode 100644 cpp/src/dual_simplex/bnb_worker.cpp diff --git a/cpp/src/dual_simplex/CMakeLists.txt b/cpp/src/dual_simplex/CMakeLists.txt index d59120878..af1415fa9 100644 --- a/cpp/src/dual_simplex/CMakeLists.txt +++ b/cpp/src/dual_simplex/CMakeLists.txt @@ -10,7 +10,6 @@ set(DUAL_SIMPLEX_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/basis_updates.cpp ${CMAKE_CURRENT_SOURCE_DIR}/bound_flipping_ratio_test.cpp ${CMAKE_CURRENT_SOURCE_DIR}/branch_and_bound.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/bnb_worker.cpp ${CMAKE_CURRENT_SOURCE_DIR}/crossover.cpp ${CMAKE_CURRENT_SOURCE_DIR}/folding.cpp ${CMAKE_CURRENT_SOURCE_DIR}/initial_basis.cpp diff --git a/cpp/src/dual_simplex/bnb_worker.cpp b/cpp/src/dual_simplex/bnb_worker.cpp deleted file mode 100644 index 0d8157541..000000000 --- a/cpp/src/dual_simplex/bnb_worker.cpp +++ /dev/null @@ -1,81 +0,0 @@ -/* clang-format off */ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ -/* clang-format on */ - -#include -#include -#include -#include - -namespace cuopt::linear_programming::dual_simplex { - -template -bnb_worker_t::bnb_worker_t(i_t worker_id, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_type, - const simplex_solver_settings_t& settings) - : worker_id(worker_id), - worker_type(EXPLORATION), - is_active(false), - lower_bound(-std::numeric_limits::infinity()), - leaf_problem(original_lp), - basis_factors(original_lp.num_rows, settings.refactor_frequency), - basic_list(original_lp.num_rows), - nonbasic_list(), - node_presolver(leaf_problem, Arow, {}, var_type), - bounds_changed(original_lp.num_cols, false) -{ -} - -template -bool bnb_worker_t::init_diving(mip_node_t* node, - bnb_worker_type_t type, - const lp_problem_t& original_lp, - const simplex_solver_settings_t& settings) -{ - internal_node = node->detach_copy(); - start_node = &internal_node; - - start_lower = original_lp.lower; - start_upper = original_lp.upper; - worker_type = type; - lower_bound = node->lower_bound; - is_active = true; - - std::fill(bounds_changed.begin(), bounds_changed.end(), false); - node->get_variable_bounds(start_lower, start_upper, bounds_changed); - - return node_presolver.bounds_strengthening(start_lower, start_upper, bounds_changed, settings); -} - -template -bool bnb_worker_t::set_lp_variable_bounds_for( - mip_node_t* node_ptr, const simplex_solver_settings_t& settings) -{ - // Reset the bound_changed markers - std::fill(bounds_changed.begin(), bounds_changed.end(), false); - - // Set the correct bounds for the leaf problem - if (recompute_bounds) { - leaf_problem.lower = start_lower; - leaf_problem.upper = start_upper; - node_ptr->get_variable_bounds(leaf_problem.lower, leaf_problem.upper, bounds_changed); - - } else { - node_ptr->update_branched_variable_bounds( - leaf_problem.lower, leaf_problem.upper, bounds_changed); - } - - return node_presolver.bounds_strengthening( - leaf_problem.lower, leaf_problem.upper, bounds_changed, settings); -} - -#ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE -template class bnb_worker_t; -#endif - -} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index 121f6a6a3..e4b12380d 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -36,15 +36,17 @@ enum bnb_worker_type_t : int { template struct bnb_stats_t { - f_t start_time = 0.0; - omp_atomic_t total_lp_solve_time = 0.0; - omp_atomic_t nodes_explored = 0; - omp_atomic_t nodes_unexplored = 0; - omp_atomic_t total_lp_iters = 0; + f_t start_time = 0.0; + omp_atomic_t total_lp_solve_time = 0.0; + omp_atomic_t nodes_explored = 0; + omp_atomic_t nodes_unexplored = 0; + omp_atomic_t total_lp_iters = 0; + omp_atomic_t nodes_since_last_log = 0; + omp_atomic_t last_log = 0.0; }; template -class bnb_worker_t { +class bnb_worker_data_t { public: const i_t worker_id; omp_atomic_t worker_type; @@ -52,6 +54,7 @@ class bnb_worker_t { omp_atomic_t lower_bound; lp_problem_t leaf_problem; + lp_solution_t leaf_solution; basis_update_mpf_t basis_factors; std::vector basic_list; @@ -67,11 +70,24 @@ class bnb_worker_t { bool recompute_basis = true; bool recompute_bounds = true; - bnb_worker_t(i_t worker_id, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_type, - const simplex_solver_settings_t& settings); + bnb_worker_data_t(i_t worker_id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_type, + const simplex_solver_settings_t& settings) + : worker_id(worker_id), + worker_type(EXPLORATION), + is_active(false), + lower_bound(-std::numeric_limits::infinity()), + leaf_problem(original_lp), + leaf_solution(original_lp.num_rows, original_lp.num_cols), + basis_factors(original_lp.num_rows, settings.refactor_frequency), + basic_list(original_lp.num_rows), + nonbasic_list(), + node_presolver(leaf_problem, Arow, {}, var_type), + bounds_changed(original_lp.num_cols, false) + { + } // Set the `start_node` for best-first search. void init_best_first(mip_node_t* node, const lp_problem_t& original_lp) @@ -90,11 +106,44 @@ class bnb_worker_t { bool init_diving(mip_node_t* node, bnb_worker_type_t type, const lp_problem_t& original_lp, - const simplex_solver_settings_t& settings); + const simplex_solver_settings_t& settings) + { + internal_node = node->detach_copy(); + start_node = &internal_node; + + start_lower = original_lp.lower; + start_upper = original_lp.upper; + worker_type = type; + lower_bound = node->lower_bound; + is_active = true; + + std::fill(bounds_changed.begin(), bounds_changed.end(), false); + node->get_variable_bounds(start_lower, start_upper, bounds_changed); + + return node_presolver.bounds_strengthening(start_lower, start_upper, bounds_changed, settings); + } // Set the variables bounds for the LP relaxation of the current node. bool set_lp_variable_bounds_for(mip_node_t* node_ptr, - const simplex_solver_settings_t& settings); + const simplex_solver_settings_t& settings) + { + // Reset the bound_changed markers + std::fill(bounds_changed.begin(), bounds_changed.end(), false); + + // Set the correct bounds for the leaf problem + if (recompute_bounds) { + leaf_problem.lower = start_lower; + leaf_problem.upper = start_upper; + node_ptr->get_variable_bounds(leaf_problem.lower, leaf_problem.upper, bounds_changed); + + } else { + node_ptr->update_branched_variable_bounds( + leaf_problem.lower, leaf_problem.upper, bounds_changed); + } + + return node_presolver.bounds_strengthening( + leaf_problem.lower, leaf_problem.upper, bounds_changed, settings); + } private: // For diving, we need to store the full node instead of @@ -119,12 +168,12 @@ class bnb_worker_pool_t { num_idle_workers_ = num_workers; for (i_t i = 0; i < num_workers; ++i) { workers_[i] = - std::make_unique>(i, original_lp, Arow, var_type, settings); + std::make_unique>(i, original_lp, Arow, var_type, settings); idle_workers_.push_front(i); } } - bnb_worker_t* get_idle_worker() + bnb_worker_data_t* get_idle_worker() { std::lock_guard lock(mutex_); @@ -145,7 +194,7 @@ class bnb_worker_pool_t { } } - bnb_worker_t* get_and_pop_idle_worker() + bnb_worker_data_t* get_and_pop_idle_worker() { std::lock_guard lock(mutex_); @@ -159,7 +208,7 @@ class bnb_worker_pool_t { } } - void return_worker_to_pool(bnb_worker_t* worker) + void return_worker_to_pool(bnb_worker_data_t* worker) { worker->is_active = false; std::lock_guard lock(mutex_); @@ -184,7 +233,7 @@ class bnb_worker_pool_t { private: // Worker pool - std::vector>> workers_; + std::vector>> workers_; omp_mutex_t mutex_; std::deque idle_workers_; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 250b7421f..a3cd256ef 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -206,12 +206,6 @@ inline const char* feasible_solution_symbol(bnb_worker_type_t type) } } -inline bool has_children(node_solve_info_t status) -{ - return status == node_solve_info_t::UP_CHILD_FIRST || - status == node_solve_info_t::DOWN_CHILD_FIRST; -} - } // namespace template @@ -226,7 +220,7 @@ branch_and_bound_t::branch_and_bound_t( root_relax_soln_(1, 1), root_crossover_soln_(1, 1), pc_(1), - solver_status_(mip_exploration_status_t::UNSET) + solver_status_(mip_status_t::UNSET) { exploration_stats_.start_time = tic(); dualize_info_t dualize_info; @@ -240,7 +234,7 @@ template f_t branch_and_bound_t::get_lower_bound() { f_t lower_bound = lower_bound_ceiling_.load(); - f_t heap_lower_bound = node_queue.get_lower_bound(); + f_t heap_lower_bound = node_queue_.get_lower_bound(); lower_bound = std::min(heap_lower_bound, lower_bound); lower_bound = std::min(worker_pool_.get_lower_bounds(), lower_bound); return std::isfinite(lower_bound) ? lower_bound : -inf; @@ -249,7 +243,7 @@ f_t branch_and_bound_t::get_lower_bound() template void branch_and_bound_t::report_heuristic(f_t obj) { - if (solver_status_ == mip_exploration_status_t::RUNNING) { + if (is_running) { f_t user_obj = compute_user_objective(original_lp_, obj); f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); std::string user_gap = user_mip_gap(user_obj, user_lower); @@ -441,23 +435,18 @@ void branch_and_bound_t::repair_heuristic_solutions() } template -mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t& solution, - f_t lower_bound) +void branch_and_bound_t::set_final_solution(mip_solution_t& solution, + f_t lower_bound) { - mip_status_t mip_status = mip_status_t::UNSET; - - if (solver_status_ == mip_exploration_status_t::NUMERICAL) { + if (solver_status_ == mip_status_t::NUMERICAL) { settings_.log.printf("Numerical issue encountered. Stopping the solver...\n"); - mip_status = mip_status_t::NUMERICAL; } - if (solver_status_ == mip_exploration_status_t::TIME_LIMIT) { + if (solver_status_ == mip_status_t::TIME_LIMIT) { settings_.log.printf("Time limit reached. Stopping the solver...\n"); - mip_status = mip_status_t::TIME_LIMIT; } - if (solver_status_ == mip_exploration_status_t::NODE_LIMIT) { + if (solver_status_ == mip_status_t::NODE_LIMIT) { settings_.log.printf("Node limit reached. Stopping the solver...\n"); - mip_status = mip_status_t::NODE_LIMIT; } f_t gap = upper_bound_ - lower_bound; @@ -476,7 +465,7 @@ mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t 0 && gap <= settings_.absolute_mip_gap_tol) { settings_.log.printf("Optimal solution found within absolute MIP gap tolerance (%.1e)\n", settings_.absolute_mip_gap_tol); @@ -491,11 +480,11 @@ mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t 0 && exploration_stats_.nodes_unexplored == 0 && upper_bound_ == inf) { settings_.log.printf("Integer infeasible.\n"); - mip_status = mip_status_t::INFEASIBLE; + solver_status_ = mip_status_t::INFEASIBLE; if (settings_.heuristic_preemption_callback != nullptr) { settings_.heuristic_preemption_callback(); } @@ -510,8 +499,6 @@ mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t @@ -605,17 +592,12 @@ branch_variable_t branch_and_bound_t::variable_selection( } template -node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* node_ptr, - search_tree_t& search_tree, - bnb_worker_type_t thread_type, - bnb_worker_t* worker, +dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* node_ptr, + bnb_worker_data_t* worker_data, bnb_stats_t& stats, logger_t& log) { - const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; - - lp_problem_t& leaf_problem = worker->leaf_problem; - lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); + lp_problem_t& leaf_problem = worker_data->leaf_problem; std::vector& leaf_vstatus = node_ptr->vstatus; assert(leaf_vstatus.size() == leaf_problem.num_cols); @@ -626,12 +608,12 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); lp_settings.scale_columns = false; - if (thread_type != bnb_worker_type_t::EXPLORATION) { + if (worker_data->worker_type != bnb_worker_type_t::EXPLORATION) { i_t bnb_lp_iters = exploration_stats_.total_lp_iters; f_t factor = settings_.diving_settings.iteration_limit_factor; f_t max_iter = factor * bnb_lp_iters; lp_settings.iteration_limit = max_iter - stats.total_lp_iters; - if (lp_settings.iteration_limit <= 0) { return node_solve_info_t::ITERATION_LIMIT; } + if (lp_settings.iteration_limit <= 0) { return dual::status_t::ITERATION_LIMIT; } } #ifdef LOG_NODE_SIMPLEX @@ -656,7 +638,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* node_ptr->vstatus[node_ptr->branch_var]); #endif - bool is_feasible = worker->set_lp_variable_bounds_for(node_ptr, settings_); + bool is_feasible = worker_data->set_lp_variable_bounds_for(node_ptr, settings_); dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; if (is_feasible) { @@ -666,29 +648,30 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* lp_status = dual_phase2_with_advanced_basis(2, 0, - worker->recompute_basis, + worker_data->recompute_basis, lp_start_time, leaf_problem, lp_settings, leaf_vstatus, - worker->basis_factors, - worker->basic_list, - worker->nonbasic_list, - leaf_solution, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, + worker_data->leaf_solution, node_iter, leaf_edge_norms); if (lp_status == dual::status_t::NUMERICAL) { log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); - lp_status_t second_status = solve_linear_program_with_advanced_basis(leaf_problem, - lp_start_time, - lp_settings, - leaf_solution, - worker->basis_factors, - worker->basic_list, - worker->nonbasic_list, - leaf_vstatus, - leaf_edge_norms); + lp_status_t second_status = + solve_linear_program_with_advanced_basis(leaf_problem, + lp_start_time, + lp_settings, + worker_data->leaf_solution, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, + leaf_vstatus, + leaf_edge_norms); lp_status = convert_lp_status_to_dual_status(second_status); } @@ -701,12 +684,27 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* lp_settings.log.printf("\nLP status: %d\n\n", lp_status); #endif + return lp_status; +} +template +std::pair branch_and_bound_t::update_tree( + mip_node_t* node_ptr, + search_tree_t& search_tree, + bnb_worker_data_t* worker_data, + dual::status_t lp_status, + logger_t& log) +{ + const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; + std::vector& leaf_vstatus = node_ptr->vstatus; + lp_problem_t& leaf_problem = worker_data->leaf_problem; + lp_solution_t& leaf_solution = worker_data->leaf_solution; + if (lp_status == dual::status_t::DUAL_UNBOUNDED) { // Node was infeasible. Do not branch node_ptr->lower_bound = inf; search_tree.graphviz_node(log, node_ptr, "infeasible", 0.0); search_tree.update(node_ptr, node_status_t::INFEASIBLE); - return node_solve_info_t::NO_CHILDREN; + return {node_status_t::INFEASIBLE, rounding_direction_t::NONE}; } else if (lp_status == dual::status_t::CUTOFF) { // Node was cut off. Do not branch @@ -714,7 +712,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); search_tree.graphviz_node(log, node_ptr, "cut off", leaf_objective); search_tree.update(node_ptr, node_status_t::FATHOMED); - return node_solve_info_t::NO_CHILDREN; + return {node_status_t::FATHOMED, rounding_direction_t::NONE}; } else if (lp_status == dual::status_t::OPTIMAL) { // LP was feasible @@ -727,7 +725,7 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); pc_.update_pseudo_costs(node_ptr, leaf_objective); - if (thread_type == bnb_worker_type_t::EXPLORATION) { + if (worker_data->worker_type == bnb_worker_type_t::EXPLORATION) { if (settings_.node_processed_callback != nullptr) { std::vector original_x; uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); @@ -737,15 +735,19 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* if (leaf_num_fractional == 0) { // Found a integer feasible solution - add_feasible_solution(leaf_objective, leaf_solution.x, node_ptr->depth, thread_type); + add_feasible_solution( + leaf_objective, leaf_solution.x, node_ptr->depth, worker_data->worker_type); search_tree.graphviz_node(log, node_ptr, "integer feasible", leaf_objective); search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); - return node_solve_info_t::NO_CHILDREN; + return {node_status_t::INTEGER_FEASIBLE, rounding_direction_t::NONE}; } else if (leaf_objective <= upper_bound_ + abs_fathom_tol) { + logger_t select_log; + select_log.log = false; + // Choose fractional variable to branch on auto [branch_var, round_dir] = variable_selection( - node_ptr, leaf_fractional, leaf_solution.x, thread_type, lp_settings.log); + node_ptr, leaf_fractional, leaf_solution.x, worker_data->worker_type, select_log); assert(leaf_vstatus.size() == leaf_problem.num_cols); assert(branch_var >= 0); @@ -754,27 +756,15 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* search_tree.branch( node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, leaf_problem, log); search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); - - if (round_dir == rounding_direction_t::UP) { - return node_solve_info_t::UP_CHILD_FIRST; - } else { - return node_solve_info_t::DOWN_CHILD_FIRST; - } + return {node_status_t::HAS_CHILDREN, round_dir}; } else { search_tree.graphviz_node(log, node_ptr, "fathomed", leaf_objective); search_tree.update(node_ptr, node_status_t::FATHOMED); - return node_solve_info_t::NO_CHILDREN; + return {node_status_t::FATHOMED, rounding_direction_t::NONE}; } - } else if (lp_status == dual::status_t::TIME_LIMIT) { - search_tree.graphviz_node(log, node_ptr, "timeout", 0.0); - return node_solve_info_t::TIME_LIMIT; - - } else if (lp_status == dual::status_t::ITERATION_LIMIT) { - return node_solve_info_t::ITERATION_LIMIT; - } else { - if (thread_type == bnb_worker_type_t::EXPLORATION) { + if (worker_data->worker_type == bnb_worker_type_t::EXPLORATION) { fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); log.printf( "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " @@ -787,20 +777,19 @@ node_solve_info_t branch_and_bound_t::solve_node(mip_node_t* search_tree.graphviz_node(log, node_ptr, "numerical", 0.0); search_tree.update(node_ptr, node_status_t::NUMERICAL); - return node_solve_info_t::NUMERICAL; + return {node_status_t::NUMERICAL, rounding_direction_t::NONE}; } } template -void branch_and_bound_t::plunge_with(bnb_worker_t* worker) +void branch_and_bound_t::plunge_with(bnb_worker_data_t* worker_data) { std::deque*> stack; - stack.push_front(worker->start_node); - - worker->recompute_basis = true; - worker->recompute_bounds = true; + stack.push_front(worker_data->start_node); + worker_data->recompute_basis = true; + worker_data->recompute_bounds = true; - while (stack.size() > 0 && solver_status_ == mip_exploration_status_t::RUNNING) { + while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET) { mip_node_t* node_ptr = stack.front(); stack.pop_front(); @@ -814,46 +803,48 @@ void branch_and_bound_t::plunge_with(bnb_worker_t* worker) // - The current node and its siblings uses the lower bound of the parent before solving the LP // relaxation // - The lower bound of the parent is lower or equal to its children - worker->lower_bound = lower_bound; + worker_data->lower_bound = lower_bound; if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { search_tree_.graphviz_node(settings_.log, node_ptr, "cutoff", node_ptr->lower_bound); search_tree_.update(node_ptr, node_status_t::FATHOMED); - worker->recompute_basis = true; - worker->recompute_bounds = true; + worker_data->recompute_basis = true; + worker_data->recompute_bounds = true; --exploration_stats_.nodes_unexplored; continue; } if (toc(exploration_stats_.start_time) > settings_.time_limit) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; + solver_status_ = mip_status_t::TIME_LIMIT; break; } if (exploration_stats_.nodes_explored >= settings_.node_limit) { - solver_status_ = mip_exploration_status_t::NODE_LIMIT; + solver_status_ = mip_status_t::NODE_LIMIT; break; } - node_solve_info_t status = solve_node(node_ptr, - search_tree_, - bnb_worker_type_t::EXPLORATION, - worker, - exploration_stats_, - settings_.log); + dual::status_t lp_status = + solve_node_lp(node_ptr, worker_data, exploration_stats_, settings_.log); - worker->recompute_basis = !has_children(status); - worker->recompute_bounds = !has_children(status); + if (lp_status == dual::status_t::TIME_LIMIT) { + solver_status_ = mip_status_t::TIME_LIMIT; + break; + } else if (lp_status == dual::status_t::ITERATION_LIMIT) { + break; + } - ++nodes_since_last_log_; + ++exploration_stats_.nodes_since_last_log; ++exploration_stats_.nodes_explored; --exploration_stats_.nodes_unexplored; - if (status == node_solve_info_t::TIME_LIMIT) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; - break; + auto [node_status, round_dir] = + update_tree(node_ptr, search_tree_, worker_data, lp_status, settings_.log); - } else if (has_children(status)) { + worker_data->recompute_basis = node_status != node_status_t::HAS_CHILDREN; + worker_data->recompute_bounds = node_status != node_status_t::HAS_CHILDREN; + + if (node_status == node_status_t::HAS_CHILDREN) { // The stack should only contain the children of the current parent. // If the stack size is greater than 0, // we pop the current node from the stack and place it in the global heap, @@ -861,22 +852,22 @@ void branch_and_bound_t::plunge_with(bnb_worker_t* worker) if (stack.size() > 0) { mip_node_t* node = stack.back(); stack.pop_back(); - node_queue.push(node); + node_queue_.push(node); } exploration_stats_.nodes_unexplored += 2; - if (status == node_solve_info_t::UP_CHILD_FIRST) { - if (node_queue.best_first_queue_size() < min_node_queue_size_) { - node_queue.push(node_ptr->get_down_child()); + if (round_dir == rounding_direction_t::UP) { + if (node_queue_.best_first_queue_size() < min_node_queue_size_) { + node_queue_.push(node_ptr->get_down_child()); } else { stack.push_front(node_ptr->get_down_child()); } stack.push_front(node_ptr->get_up_child()); } else { - if (node_queue.best_first_queue_size() < min_node_queue_size_) { - node_queue.push(node_ptr->get_up_child()); + if (node_queue_.best_first_queue_size() < min_node_queue_size_) { + node_queue_.push(node_ptr->get_up_child()); } else { stack.push_front(node_ptr->get_up_child()); } @@ -886,26 +877,26 @@ void branch_and_bound_t::plunge_with(bnb_worker_t* worker) } } - worker_pool_.return_worker_to_pool(worker); + worker_pool_.return_worker_to_pool(worker_data); active_workers_per_type[EXPLORATION]--; } template -void branch_and_bound_t::dive_with(bnb_worker_t* worker) +void branch_and_bound_t::dive_with(bnb_worker_data_t* worker_data) { logger_t log; log.log = false; - bnb_worker_type_t diving_type = worker->worker_type; + bnb_worker_type_t diving_type = worker_data->worker_type; const i_t node_limit = settings_.diving_settings.node_limit; const i_t backtrack = settings_.diving_settings.backtrack; - worker->recompute_basis = true; - worker->recompute_bounds = true; + worker_data->recompute_basis = true; + worker_data->recompute_bounds = true; - search_tree_t subtree(std::move(*worker->start_node)); + search_tree_t dive_tree(std::move(*worker_data->start_node)); std::deque*> stack; - stack.push_front(&subtree.root); + stack.push_front(&dive_tree.root); bnb_stats_t dive_stats; dive_stats.total_lp_iters = 0; @@ -913,38 +904,42 @@ void branch_and_bound_t::dive_with(bnb_worker_t* worker) dive_stats.nodes_explored = 0; dive_stats.nodes_unexplored = 0; - while (stack.size() > 0 && solver_status_ == mip_exploration_status_t::RUNNING) { + while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET) { mip_node_t* node_ptr = stack.front(); stack.pop_front(); - f_t lower_bound = node_ptr->lower_bound; - f_t upper_bound = upper_bound_; - f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); - worker->lower_bound = lower_bound; + f_t lower_bound = node_ptr->lower_bound; + f_t upper_bound = upper_bound_; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + worker_data->lower_bound = lower_bound; if (node_ptr->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - worker->recompute_basis = true; - worker->recompute_bounds = true; + worker_data->recompute_basis = true; + worker_data->recompute_bounds = true; continue; } if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } if (dive_stats.nodes_explored > node_limit) { break; } - node_solve_info_t status = solve_node(node_ptr, subtree, diving_type, worker, dive_stats, log); - dive_stats.nodes_explored++; - worker->recompute_basis = !has_children(status); - worker->recompute_bounds = !has_children(status); + dual::status_t lp_status = solve_node_lp(node_ptr, worker_data, dive_stats, log); - if (status == node_solve_info_t::TIME_LIMIT) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; + if (lp_status == dual::status_t::TIME_LIMIT) { + solver_status_ = mip_status_t::TIME_LIMIT; break; - - } else if (status == node_solve_info_t::ITERATION_LIMIT) { + } else if (lp_status == dual::status_t::ITERATION_LIMIT) { break; + } + + ++dive_stats.nodes_explored; + + auto [node_status, round_dir] = update_tree(node_ptr, dive_tree, worker_data, lp_status, log); - } else if (has_children(status)) { - if (status == node_solve_info_t::UP_CHILD_FIRST) { + worker_data->recompute_basis = node_status != node_status_t::HAS_CHILDREN; + worker_data->recompute_bounds = node_status != node_status_t::HAS_CHILDREN; + + if (node_status == node_status_t::HAS_CHILDREN) { + if (round_dir == rounding_direction_t::UP) { stack.push_front(node_ptr->get_down_child()); stack.push_front(node_ptr->get_up_child()); } else { @@ -958,7 +953,7 @@ void branch_and_bound_t::dive_with(bnb_worker_t* worker) } } - worker_pool_.return_worker_to_pool(worker); + worker_pool_.return_worker_to_pool(worker_data); active_workers_per_type[diving_type]--; } @@ -1043,7 +1038,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut logger_t log; log.log = false; log.log_prefix = settings_.log.log_prefix; - solver_status_ = mip_exploration_status_t::UNSET; + solver_status_ = mip_status_t::UNSET; + is_running = false; exploration_stats_.nodes_unexplored = 0; exploration_stats_.nodes_explored = 0; original_lp_.A.to_compressed_row(Arow_); @@ -1108,8 +1104,9 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } if (root_status == lp_status_t::TIME_LIMIT) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; - return set_final_solution(solution, -inf); + solver_status_ = mip_status_t::TIME_LIMIT; + set_final_solution(solution, -inf); + return solver_status_; } assert(root_vstatus_.size() == original_lp_.num_cols); @@ -1173,8 +1170,9 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut pc_); if (toc(exploration_stats_.start_time) > settings_.time_limit) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; - return set_final_solution(solution, root_objective_); + solver_status_ = mip_status_t::TIME_LIMIT; + set_final_solution(solution, root_objective_); + return solver_status_; } // Choose variable to branch on @@ -1189,8 +1187,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut root_vstatus_, original_lp_, log); - node_queue.push(search_tree_.root.get_down_child()); - node_queue.push(search_tree_.root.get_up_child()); + node_queue_.push(search_tree_.root.get_down_child()); + node_queue_.push(search_tree_.root.get_up_child()); diving_heuristics_settings_t diving_settings = settings_.diving_settings; bool is_ramp_up_finished = false; @@ -1207,12 +1205,13 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut settings_.num_bfs_workers, settings_.num_threads - settings_.num_bfs_workers); - exploration_stats_.nodes_explored = 1; - exploration_stats_.nodes_unexplored = 2; - solver_status_ = mip_exploration_status_t::RUNNING; - lower_bound_ceiling_ = inf; - min_node_queue_size_ = 2 * settings_.num_threads; - nodes_since_last_log_ = 0; + exploration_stats_.nodes_explored = 1; + exploration_stats_.nodes_unexplored = 2; + exploration_stats_.nodes_since_last_log = 0; + exploration_stats_.last_log = 0.0; + is_running = true; + lower_bound_ceiling_ = inf; + min_node_queue_size_ = 2 * settings_.num_threads; settings_.log.printf( " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " @@ -1226,11 +1225,10 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut f_t abs_gap = upper_bound_ - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); i_t last_node_depth = 0; - f_t last_log = 0.0; - while (solver_status_ == mip_exploration_status_t::RUNNING && - abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && - (active_workers_per_type[0] > 0 || node_queue.best_first_queue_size() > 0)) { + while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && + rel_gap > settings_.relative_mip_gap_tol && + (active_workers_per_type[0] > 0 || node_queue_.best_first_queue_size() > 0)) { bool launched_any_task = false; lower_bound = get_lower_bound(); abs_gap = upper_bound_ - lower_bound; @@ -1239,7 +1237,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut repair_heuristic_solutions(); if (!is_ramp_up_finished) { - if (node_queue.best_first_queue_size() >= min_node_queue_size_) { + if (node_queue_.best_first_queue_size() >= min_node_queue_size_) { if (!std::isfinite(upper_bound_)) { diving_settings.disable_guided_diving = true; } max_num_workers_per_type = bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); @@ -1250,7 +1248,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut settings_.log.debug( "Ramp-up phase is finished. num active workers = %d, heap size = %d\n", active_workers_per_type[EXPLORATION], - node_queue.best_first_queue_size()); + node_queue_.best_first_queue_size()); for (auto type : worker_types) { settings_.log.debug("%s: max num of workers = %d", @@ -1281,21 +1279,23 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } } - f_t now = toc(exploration_stats_.start_time); - f_t time_since_last_log = last_log == 0 ? 1.0 : toc(last_log); + f_t now = toc(exploration_stats_.start_time); + f_t time_since_last_log = + exploration_stats_.last_log == 0 ? 1.0 : toc(exploration_stats_.last_log); + i_t nodes_since_last_log = exploration_stats_.nodes_since_last_log; - if (((nodes_since_last_log_ >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && + if (((nodes_since_last_log >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && time_since_last_log >= 1) || (time_since_last_log > 30) || now > settings_.time_limit) { - i_t depth = - node_queue.best_first_queue_size() > 0 ? node_queue.bfs_top()->depth : last_node_depth; + i_t depth = node_queue_.best_first_queue_size() > 0 ? node_queue_.bfs_top()->depth + : last_node_depth; report(" ", upper_bound_, lower_bound, depth); - last_log = tic(); - nodes_since_last_log_ = 0; + exploration_stats_.last_log = tic(); + exploration_stats_.nodes_since_last_log = 0; } if (now > settings_.time_limit) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; + solver_status_ = mip_status_t::TIME_LIMIT; break; } @@ -1303,12 +1303,12 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut if (active_workers_per_type[type] >= max_num_workers_per_type[type]) { continue; } // Get an idle worker. - bnb_worker_t* worker = worker_pool_.get_idle_worker(); + bnb_worker_data_t* worker = worker_pool_.get_idle_worker(); if (worker == nullptr) { break; } if (type == EXPLORATION) { // If there any node left in the heap, we pop the top node and explore it. - std::optional*> start_node = node_queue.pop_best_first(); + std::optional*> start_node = node_queue_.pop_best_first(); if (!start_node.has_value()) { continue; } if (upper_bound_ < start_node.value()->lower_bound) { @@ -1325,14 +1325,13 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut worker->init_best_first(start_node.value(), original_lp_); last_node_depth = start_node.value()->depth; active_workers_per_type[type]++; - nodes_since_last_log_++; launched_any_task = true; #pragma omp task affinity(worker) plunge_with(worker); } else { - std::optional*> start_node = node_queue.pop_diving(); + std::optional*> start_node = node_queue_.pop_diving(); if (!start_node.has_value()) { continue; } if (upper_bound_ < start_node.value()->lower_bound || @@ -1362,13 +1361,11 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } } - if (solver_status_ == mip_exploration_status_t::RUNNING) { - solver_status_ = mip_exploration_status_t::COMPLETED; - } - - f_t lower_bound = node_queue.best_first_queue_size() > 0 ? node_queue.get_lower_bound() - : search_tree_.root.lower_bound; - return set_final_solution(solution, lower_bound); + is_running = false; + f_t lower_bound = node_queue_.best_first_queue_size() > 0 ? node_queue_.get_lower_bound() + : search_tree_.root.lower_bound; + set_final_solution(solution, lower_bound); + return solver_status_; } #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 0ee5a82f5..18b48311e 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -36,24 +36,6 @@ enum class mip_status_t { UNSET = 6, // The status is not set }; -enum class mip_exploration_status_t { - UNSET = 0, // The status is not set - TIME_LIMIT = 1, // The solver reached a time limit - NODE_LIMIT = 2, // The maximum number of nodes was reached (not implemented) - NUMERICAL = 3, // The solver encountered a numerical error - RUNNING = 4, // The solver is currently exploring the tree - COMPLETED = 5, // The solver finished exploring the tree -}; - -enum class node_solve_info_t { - NO_CHILDREN = 0, // The node does not produced children - UP_CHILD_FIRST = 1, // The up child should be explored first - DOWN_CHILD_FIRST = 2, // The down child should be explored first - TIME_LIMIT = 3, // The solver reached a time limit - ITERATION_LIMIT = 4, // The solver reached a iteration limit - NUMERICAL = 5 // The solver encounter a numerical error when solving the node -}; - template void upper_bound_callback(f_t upper_bound); @@ -148,7 +130,7 @@ class branch_and_bound_t { pseudo_costs_t pc_; // Heap storing the nodes waiting to be explored. - node_queue_t node_queue; + node_queue_t node_queue_; // Search tree search_tree_t search_tree_; @@ -161,10 +143,8 @@ class branch_and_bound_t { bnb_worker_pool_t worker_pool_; // Global status of the solver. - omp_atomic_t solver_status_; - - // Count the number of nodes since the last report. - omp_atomic_t nodes_since_last_log_; + omp_atomic_t solver_status_; + omp_atomic_t is_running{false}; // Minimum number of node in the queue. When the queue size is less than // this variable, the nodes are added directly to the queue instead of @@ -179,7 +159,7 @@ class branch_and_bound_t { void report(std::string symbol, f_t obj, f_t lower_bound, i_t node_depth); // Set the final solution. - mip_status_t set_final_solution(mip_solution_t& solution, f_t lower_bound); + void set_final_solution(mip_solution_t& solution, f_t lower_bound); // Update the incumbent solution with the new feasible solution // found during branch and bound. @@ -192,19 +172,27 @@ class branch_and_bound_t { void repair_heuristic_solutions(); // Perform a plunge over a subtree using a given worker. - void plunge_with(bnb_worker_t* worker); + void plunge_with(bnb_worker_data_t* worker_data); // Perform a deep dive over a subtree using a given worker. - void dive_with(bnb_worker_t* worker); + void dive_with(bnb_worker_data_t* worker_data); - // Solve the LP relaxation of a leaf node and update the tree. - node_solve_info_t solve_node(mip_node_t* node_ptr, - search_tree_t& search_tree, - bnb_worker_type_t thread_type, - bnb_worker_t* worker, + // Solve the LP relaxation of a leaf node + dual::status_t solve_node_lp(mip_node_t* node_ptr, + bnb_worker_data_t* worker_data, bnb_stats_t& stats, logger_t& log); + // Update the tree based on the LP relaxation. Returns the status + // of the node and, if appropriated, the preferred rounding direction + // when visiting the children. + std::pair update_tree( + mip_node_t* node_ptr, + search_tree_t& search_tree, + bnb_worker_data_t* worker_data, + dual::status_t lp_status, + logger_t& log); + // Selects the variable to branch on. branch_variable_t variable_selection(mip_node_t* node_ptr, const std::vector& fractional, From 8ed172a92722e5e3d491dd5da17ee38b60630912 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 12 Jan 2026 05:01:06 -0800 Subject: [PATCH 176/366] keep basis when possible --- cpp/src/dual_simplex/bb_worker_state.hpp | 27 +++- cpp/src/dual_simplex/branch_and_bound.cpp | 152 ++++++++++++++++------ cpp/src/dual_simplex/branch_and_bound.hpp | 3 + 3 files changed, 141 insertions(+), 41 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index a8ea9476e..22ba3d37a 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -51,6 +51,10 @@ struct bb_worker_state_t { // Current node being processed (may be paused at horizon boundary) mip_node_t* current_node{nullptr}; + // Last node that was solved (for basis warm-start detection) + // If next node's parent == last_solved_node, we can reuse basis + mip_node_t* last_solved_node{nullptr}; + // Worker's virtual time clock (cumulative work units) double clock{0.0}; @@ -113,11 +117,15 @@ struct bb_worker_state_t { }; std::vector integer_solutions; - // Queued pseudo-cost updates (applied in deterministic order at sync) + // Queued pseudo-cost updates (applied to global pseudo-costs at sync) std::vector> pseudo_cost_updates; // Pseudo-cost snapshot for deterministic variable selection - // These are copied from global pseudo-costs at horizon start + // Initialized from global pseudo-costs at horizon start, then updated locally + // during the horizon as the worker processes nodes. This allows within-horizon + // learning while maintaining determinism (each worker's updates are sequential). + // At sync, all workers' updates are merged into global pseudo-costs, and new + // snapshots are taken at the next horizon start. std::vector pc_sum_up_snapshot; std::vector pc_sum_down_snapshot; std::vector pc_num_up_snapshot; @@ -179,10 +187,23 @@ struct bb_worker_state_t { pseudo_cost_updates.clear(); } - // Queue a pseudo-cost update (to be applied at sync in deterministic order) + // Queue a pseudo-cost update for global sync AND apply it to local snapshot immediately + // This allows within-horizon learning while maintaining determinism: + // - Local snapshot updates are sequential within each worker (deterministic) + // - Global updates are merged at sync in sorted (VT, worker_id) order (deterministic) void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) { + // Queue for global sync at horizon end pseudo_cost_updates.push_back({variable, direction, delta, clock, worker_id}); + + // Also apply to local snapshot immediately for better variable selection + if (direction == rounding_direction_t::DOWN) { + pc_sum_down_snapshot[variable] += delta; + pc_num_down_snapshot[variable]++; + } else { + pc_sum_up_snapshot[variable] += delta; + pc_num_up_snapshot[variable]++; + } } // Variable selection using snapshot (for BSP determinism) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 3d839e7a8..ef3b6d0b3 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2072,8 +2072,9 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t(exploration_stats_.nodes_unexplored)); // Upper/lower bounds (convert to fixed-point for exact comparison) + // Use compute_bsp_lower_bound() for accurate LB from all worker queues f_t ub = get_upper_bound(); - f_t lb = get_lower_bound(); + f_t lb = compute_bsp_lower_bound(); state_data.push_back(static_cast(ub * 1000000)); // 6 decimal places state_data.push_back(static_cast(lb * 1000000)); @@ -2149,7 +2150,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::run_worker_until_horizon(bb_worker_state_tbsp_state == bsp_node_state_t::PAUSED); + bool is_child = (node->parent == worker.last_solved_node); + bool can_warm_start = is_resumed || is_child; + worker.recompute_bounds_and_basis = !can_warm_start; + // Solve the node (this records events) node_solve_info_t status = solve_node_bsp(worker, node, search_tree, current_horizon); + // Track last solved node for warm-start detection + worker.last_solved_node = node; + // Handle result if (status == node_solve_info_t::TIME_LIMIT) { solver_status_ = mip_exploration_status_t::TIME_LIMIT; @@ -2638,22 +2652,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t if (leaf_objective < worker.local_upper_bound) { worker.local_upper_bound = leaf_objective; worker.integer_solutions.push_back({leaf_objective, leaf_solution.x, node_ptr->depth}); - // Log immediately for visibility (global incumbent updated at sync) - i_t nodes_explored = exploration_stats_.nodes_explored.load(); - i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); - f_t user_obj = compute_user_objective(original_lp_, leaf_objective); - f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); - settings_.log.printf( - "%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - feasible_solution_symbol(thread_type_t::EXPLORATION), - nodes_explored, - nodes_unexplored, - user_obj, - user_lower, - node_ptr->depth, - nodes_explored > 0 ? exploration_stats_.total_lp_iters / nodes_explored : 0.0, - user_mip_gap(user_obj, user_lower).c_str(), - toc(exploration_stats_.start_time)); + // Note: Logging deferred to sync phase for deterministic output } search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); worker.record_integer_solution(node_ptr, leaf_objective); @@ -2718,15 +2717,9 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t exploration_stats_.nodes_unexplored += 2; - // In BSP mode, always recompute basis to ensure basic_list matches the node's vstatus. - // When recompute_bounds_and_basis = false, the simplex reuses basic_list from the previous - // solve, but each node has its own vstatus (copied at branch time). If basic_list and vstatus - // diverge, pivoting corrupts vstatus by adding BASIC entries without properly removing - // others. This is safe because the child's vstatus is a copy of the parent's final vstatus. - worker.recompute_bounds_and_basis = true; - - // Add children directly to local queue - they get BSP identity on enqueue - // No need to defer to next horizon since identity is assigned immediately + // Add children to local queue - they get BSP identity on enqueue + // Note: recompute_bounds_and_basis is set in run_worker_until_horizon based on + // whether we branched (has_children), matching opportunistic mode behavior. worker.enqueue_node(node_ptr->get_down_child()); worker.enqueue_node(node_ptr->get_up_child()); @@ -2779,7 +2772,37 @@ void branch_and_bound_t::process_history_and_sync( // final_ids during sync. Each node gets its identity when created, and it never changes. // This function now only processes heuristic solutions to update the incumbent. - // Collect queued heuristic solutions + // Process repair queue first (for BSP mode) + // Infeasible solutions from GPU heuristics are queued for repair; process them now + { + std::vector> to_repair; + mutex_repair_.lock(); + if (repair_queue_.size() > 0) { + to_repair = repair_queue_; + repair_queue_.clear(); + } + mutex_repair_.unlock(); + + if (to_repair.size() > 0) { + settings_.log.debug("BSP sync: Attempting to repair %ld injected solutions\n", + to_repair.size()); + for (const std::vector& potential_solution : to_repair) { + std::vector repaired_solution; + f_t repaired_obj; + bool success = + repair_solution(edge_norms_, potential_solution, repaired_obj, repaired_solution); + if (success) { + // Queue repaired solution with VT = current horizon for deterministic processing + mutex_heuristic_queue_.lock(); + heuristic_solution_queue_.push_back( + {std::move(repaired_solution), repaired_obj, bsp_current_horizon_}); + mutex_heuristic_queue_.unlock(); + } + } + } + } + + // Collect queued heuristic solutions (including any newly repaired ones) std::vector heuristic_solutions; mutex_heuristic_queue_.lock(); heuristic_solutions = std::move(heuristic_solution_queue_); @@ -2904,15 +2927,39 @@ void branch_and_bound_t::process_history_and_sync( return a.worker_id < b.worker_id; }); - // Apply the best solution to global incumbent - if (!all_integer_solutions.empty()) { - const auto& best = all_integer_solutions[0]; - mutex_upper_.lock(); - if (best.objective < upper_bound_) { - upper_bound_ = best.objective; - incumbent_.set_incumbent_solution(best.objective, *best.solution); + // Apply the best solution to global incumbent and log all improving solutions + // Use compute_bsp_lower_bound() for accurate lower bound in logs + f_t bsp_lower = compute_bsp_lower_bound(); + f_t current_upper = get_upper_bound(); + + for (const auto& sol : all_integer_solutions) { + if (sol.objective < current_upper) { + // Log this improving solution (deterministic: sorted order) + f_t user_obj = compute_user_objective(original_lp_, sol.objective); + f_t user_lower = compute_user_objective(original_lp_, bsp_lower); + i_t nodes_explored = exploration_stats_.nodes_explored.load(); + i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); + settings_.log.printf( + "%s%10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + feasible_solution_symbol(thread_type_t::EXPLORATION), + nodes_explored, + nodes_unexplored, + user_obj, + user_lower, + sol.depth, + nodes_explored > 0 ? exploration_stats_.total_lp_iters.load() / nodes_explored : 0.0, + user_mip_gap(user_obj, user_lower).c_str(), + toc(exploration_stats_.start_time)); + + // Update incumbent + mutex_upper_.lock(); + if (sol.objective < upper_bound_) { + upper_bound_ = sol.objective; + incumbent_.set_incumbent_solution(sol.objective, *sol.solution); + current_upper = sol.objective; + } + mutex_upper_.unlock(); } - mutex_upper_.unlock(); } // Merge and apply pseudo-cost updates from all workers in deterministic order @@ -3075,6 +3122,35 @@ void branch_and_bound_t::balance_worker_loads() } } +template +f_t branch_and_bound_t::compute_bsp_lower_bound() +{ + // Compute accurate lower bound from all BSP sources + // Called during sync phase (single-threaded), so no locking needed for worker queues + const f_t inf = std::numeric_limits::infinity(); + f_t lower_bound = inf; + + // Check global heap (may have nodes not yet distributed) + mutex_heap_.lock(); + if (heap_.size() > 0) { lower_bound = std::min(heap_.top()->lower_bound, lower_bound); } + mutex_heap_.unlock(); + + // Check all worker queues + for (const auto& worker : *bsp_workers_) { + // Check paused node (current_node) + if (worker.current_node != nullptr) { + lower_bound = std::min(worker.current_node->lower_bound, lower_bound); + } + + // Check queue top (min lower bound due to priority queue ordering) + if (!worker.local_queue.empty()) { + lower_bound = std::min(worker.local_queue.top()->lower_bound, lower_bound); + } + } + + return lower_bound; +} + #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE template class branch_and_bound_t; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index b1f640a68..7661ba133 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -366,6 +366,9 @@ class branch_and_bound_t { search_tree_t& search_tree, double current_horizon); + // Compute accurate lower bound from all BSP sources (called during sync phase) + f_t compute_bsp_lower_bound(); + private: // BSP state std::unique_ptr> bsp_workers_; From 89cc6dec98ede67ef8a80f2bb79a4cab9ef37b94 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 12 Jan 2026 16:29:42 +0100 Subject: [PATCH 177/366] fix race condition in guided diving --- cpp/src/dual_simplex/branch_and_bound.cpp | 18 ++++++++++-------- cpp/src/dual_simplex/branch_and_bound.hpp | 3 +-- 2 files changed, 11 insertions(+), 10 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index a3cd256ef..aaed6eb60 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -555,11 +555,13 @@ branch_variable_t branch_and_bound_t::variable_selection( mip_node_t* node_ptr, const std::vector& fractional, const std::vector& solution, - bnb_worker_type_t type, - logger_t& log) + bnb_worker_type_t type) { + logger_t log; + log.log = false; i_t branch_var = -1; rounding_direction_t round_dir = rounding_direction_t::NONE; + std::vector current_incumbent; switch (type) { case bnb_worker_type_t::EXPLORATION: @@ -583,7 +585,10 @@ branch_variable_t branch_and_bound_t::variable_selection( return pseudocost_diving(pc_, fractional, solution, root_relax_soln_.x, log); case bnb_worker_type_t::GUIDED_DIVING: - return guided_diving(pc_, fractional, solution, incumbent_.x, log); + mutex_upper_.lock(); + current_incumbent = incumbent_.x; + mutex_upper_.unlock(); + return guided_diving(pc_, fractional, solution, current_incumbent, log); default: log.debug("Unknown variable selection method: %d\n", type); @@ -742,12 +747,9 @@ std::pair branch_and_bound_t::upd return {node_status_t::INTEGER_FEASIBLE, rounding_direction_t::NONE}; } else if (leaf_objective <= upper_bound_ + abs_fathom_tol) { - logger_t select_log; - select_log.log = false; - // Choose fractional variable to branch on - auto [branch_var, round_dir] = variable_selection( - node_ptr, leaf_fractional, leaf_solution.x, worker_data->worker_type, select_log); + auto [branch_var, round_dir] = + variable_selection(node_ptr, leaf_fractional, leaf_solution.x, worker_data->worker_type); assert(leaf_vstatus.size() == leaf_problem.num_cols); assert(branch_var >= 0); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 18b48311e..564858419 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -197,8 +197,7 @@ class branch_and_bound_t { branch_variable_t variable_selection(mip_node_t* node_ptr, const std::vector& fractional, const std::vector& solution, - bnb_worker_type_t type, - logger_t& log); + bnb_worker_type_t type); }; } // namespace cuopt::linear_programming::dual_simplex From 525f01337bad4493e05e5ca1350b2ea2dbd6b391 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 9 Jan 2026 16:45:53 +0100 Subject: [PATCH 178/366] reliability branching from #599 --- cpp/src/dual_simplex/branch_and_bound.cpp | 41 ++++-- cpp/src/dual_simplex/branch_and_bound.hpp | 5 + cpp/src/dual_simplex/pseudo_costs.cpp | 145 ++++++++++++++++++++++ cpp/src/dual_simplex/pseudo_costs.hpp | 10 ++ 4 files changed, 192 insertions(+), 9 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index aaed6eb60..d72993405 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -553,6 +553,11 @@ rounding_direction_t martin_criteria(f_t val, f_t root_val) template branch_variable_t branch_and_bound_t::variable_selection( mip_node_t* node_ptr, + const lp_problem_t& lp, + const simplex_solver_settings_t& lp_settings, + const std::vector& var_types, + const std::vector& vstatus, + const std::vector& edge_norms, const std::vector& fractional, const std::vector& solution, bnb_worker_type_t type) @@ -565,8 +570,18 @@ branch_variable_t branch_and_bound_t::variable_selection( switch (type) { case bnb_worker_type_t::EXPLORATION: - branch_var = pc_.variable_selection(fractional, solution, log); - round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); + // branch_var = pc_.variable_selection(fractional, solution, log); + branch_var = pc_.reliable_variable_selection(lp, + lp_settings, + var_types_, + vstatus, + edge_norms, + fractional, + solution, + node_ptr->lower_bound, + log); + + round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); // Note that the exploration thread is the only one that can insert new nodes into the heap, // and thus, we only need to calculate the objective estimate here (it is used for @@ -643,13 +658,13 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* node_ptr->vstatus[node_ptr->branch_var]); #endif - bool is_feasible = worker_data->set_lp_variable_bounds_for(node_ptr, settings_); - dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; + bool is_feasible = worker_data->set_lp_variable_bounds_for(node_ptr, settings_); + dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; + std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; if (is_feasible) { - i_t node_iter = 0; - f_t lp_start_time = tic(); - std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; + i_t node_iter = 0; + f_t lp_start_time = tic(); lp_status = dual_phase2_with_advanced_basis(2, 0, @@ -748,8 +763,16 @@ std::pair branch_and_bound_t::upd } else if (leaf_objective <= upper_bound_ + abs_fathom_tol) { // Choose fractional variable to branch on - auto [branch_var, round_dir] = - variable_selection(node_ptr, leaf_fractional, leaf_solution.x, worker_data->worker_type); + auto [branch_var, round_dir] = variable_selection(node_ptr, + leaf_problem, + lp_settings, + var_types_, + leaf_vstatus, + leaf_edge_norms, + leaf_fractional, + leaf_solution.x, + thread_type, + lp_settings.log); assert(leaf_vstatus.size() == leaf_problem.num_cols); assert(branch_var >= 0); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 564858419..cface427f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -195,6 +195,11 @@ class branch_and_bound_t { // Selects the variable to branch on. branch_variable_t variable_selection(mip_node_t* node_ptr, + const lp_problem_t& lp, + const simplex_solver_settings_t& lp_settings, + const std::vector& var_types, + const std::vector& vstatus, + const std::vector& edge_norms, const std::vector& fractional, const std::vector& solution, bnb_worker_type_t type); diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index f3cbb4447..2d6a82fbc 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -133,6 +133,46 @@ void strong_branch_helper(i_t start, } } +template +f_t trial_branching(const lp_problem_t& original_lp, + const simplex_solver_settings_t& settings, + const std::vector& var_types, + const std::vector& root_vstatus, + const std::vector& edge_norms, + i_t branch_var, + f_t branch_var_lower, + f_t branch_var_upper) +{ + lp_problem_t child_problem = original_lp; + child_problem.lower[branch_var] = branch_var_lower; + child_problem.upper[branch_var] = branch_var_upper; + + simplex_solver_settings_t child_settings = settings; + child_settings.set_log(false); + f_t lp_start_time = tic(); + child_settings.iteration_limit = 200; + lp_solution_t solution(original_lp.num_rows, original_lp.num_cols); + i_t iter = 0; + std::vector vstatus = root_vstatus; + std::vector child_edge_norms = edge_norms; + dual::status_t status = dual_phase2( + 2, 0, lp_start_time, child_problem, child_settings, vstatus, solution, iter, child_edge_norms); + printf("Trial branching on variable %d. Lo: %e Up: %e. Iter %d. Status %d. Obj %e\n", + branch_var, + child_problem.lower[branch_var], + child_problem.upper[branch_var], + iter, + status, + compute_objective(child_problem, solution.x)); + + if (status == dual::status_t::OPTIMAL || status == dual::status_t::ITERATION_LIMIT || + status == dual::status_t::CUTOFF) { + return compute_objective(child_problem, solution.x); + } else { + return std::numeric_limits::quiet_NaN(); + } +} + } // namespace template @@ -316,6 +356,111 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio return branch_var; } +template +i_t pseudo_costs_t::reliable_variable_selection( + const lp_problem_t& lp, + const simplex_solver_settings_t& settings, + const std::vector& var_types, + const std::vector& vstatus, + const std::vector& edge_norms, + const std::vector& fractional, + const std::vector& solution, + f_t current_obj, + logger_t& log) +{ + mutex.lock(); + + const i_t num_fractional = fractional.size(); + std::vector pseudo_cost_up(num_fractional); + std::vector pseudo_cost_down(num_fractional); + std::vector score(num_fractional); + + i_t num_initialized_down; + i_t num_initialized_up; + f_t pseudo_cost_down_avg; + f_t pseudo_cost_up_avg; + + initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); + + mutex.unlock(); + + log.printf("PC: num initialized down %d up %d avg down %e up %e\n", + num_initialized_down, + num_initialized_up, + pseudo_cost_down_avg, + pseudo_cost_up_avg); + + const i_t reliable_threshold = 1; + + for (i_t k = 0; k < num_fractional; k++) { + const i_t j = fractional[k]; + mutex.lock(); + bool down_reliable = pseudo_cost_num_down[j] >= reliable_threshold; + mutex.unlock(); + if (down_reliable) { + mutex.lock(); + pseudo_cost_down[k] = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; + mutex.unlock(); + } else { + // Do trial branching on the down branch + f_t obj = trial_branching( + lp, settings, var_types, vstatus, edge_norms, j, lp.lower[j], std::floor(solution[j])); + if (!std::isnan(obj)) { + f_t change_in_obj = obj - current_obj; + f_t change_in_x = solution[j] - std::floor(solution[j]); + mutex.lock(); + pseudo_cost_sum_down[j] += change_in_obj / change_in_x; + pseudo_cost_num_down[j]++; + mutex.unlock(); + pseudo_cost_down[k] = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; + } + } + + mutex.lock(); + bool up_reliable = pseudo_cost_num_up[j] >= reliable_threshold; + mutex.unlock(); + if (up_reliable) { + mutex.lock(); + pseudo_cost_up[k] = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; + mutex.unlock(); + } else { + // Do trial branching on the up branch + f_t obj = trial_branching( + lp, settings, var_types, vstatus, edge_norms, j, std::ceil(solution[j]), lp.upper[j]); + if (!std::isnan(obj)) { + f_t change_in_obj = obj - current_obj; + f_t change_in_x = std::ceil(solution[j]) - solution[j]; + mutex.lock(); + pseudo_cost_sum_up[j] += change_in_obj / change_in_x; + pseudo_cost_num_up[j]++; + pseudo_cost_up[k] = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; + mutex.unlock(); + } + } + constexpr f_t eps = 1e-6; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + score[k] = + std::max(f_down * pseudo_cost_down[k], eps) * std::max(f_up * pseudo_cost_up[k], eps); + } + + i_t branch_var = fractional[0]; + f_t max_score = -1; + i_t select = -1; + for (i_t k = 0; k < num_fractional; k++) { + if (score[k] > max_score) { + max_score = score[k]; + branch_var = fractional[k]; + select = k; + } + } + + log.printf( + "pc branching on %d. Value %e. Score %e\n", branch_var, solution[branch_var], score[select]); + + return branch_var; +} + template f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, const std::vector& solution, diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 49a810506..1569cb1c1 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -47,6 +47,16 @@ class pseudo_costs_t { const std::vector& solution, logger_t& log); + i_t reliable_variable_selection(const lp_problem_t& lp, + const simplex_solver_settings_t& settings, + const std::vector& var_types, + const std::vector& vstatus, + const std::vector& edge_norms, + const std::vector& fractional, + const std::vector& solution, + f_t current_obj, + logger_t& log); + f_t obj_estimate(const std::vector& fractional, const std::vector& solution, f_t lower_bound, From 0fa76f1deae7b384039548d544cc1f488996c29d Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 12 Jan 2026 12:06:17 +0100 Subject: [PATCH 179/366] modfiied reliability branching to reuse basis from node --- cpp/src/dual_simplex/basis_updates.hpp | 3 + cpp/src/dual_simplex/bnb_worker.hpp | 1 + cpp/src/dual_simplex/branch_and_bound.cpp | 53 ++++------ cpp/src/dual_simplex/branch_and_bound.hpp | 8 +- cpp/src/dual_simplex/pseudo_costs.cpp | 123 ++++++++++++++-------- cpp/src/dual_simplex/pseudo_costs.hpp | 5 + 6 files changed, 113 insertions(+), 80 deletions(-) diff --git a/cpp/src/dual_simplex/basis_updates.hpp b/cpp/src/dual_simplex/basis_updates.hpp index afd4f4c9a..038db59b9 100644 --- a/cpp/src/dual_simplex/basis_updates.hpp +++ b/cpp/src/dual_simplex/basis_updates.hpp @@ -223,6 +223,9 @@ class basis_update_mpf_t { reset_stats(); } + basis_update_mpf_t(const basis_update_mpf_t& other) = default; + basis_update_mpf_t& operator=(const basis_update_mpf_t& other) = default; + void print_stats() const { i_t total_L_transpose_calls = total_sparse_L_transpose_ + total_dense_L_transpose_; diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index e4b12380d..e77b64639 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -55,6 +55,7 @@ class bnb_worker_data_t { lp_problem_t leaf_problem; lp_solution_t leaf_solution; + std::vector leaf_edge_norms; basis_update_mpf_t basis_factors; std::vector basic_list; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d72993405..c3fecd35f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -553,32 +553,31 @@ rounding_direction_t martin_criteria(f_t val, f_t root_val) template branch_variable_t branch_and_bound_t::variable_selection( mip_node_t* node_ptr, - const lp_problem_t& lp, - const simplex_solver_settings_t& lp_settings, - const std::vector& var_types, - const std::vector& vstatus, - const std::vector& edge_norms, const std::vector& fractional, - const std::vector& solution, - bnb_worker_type_t type) + bnb_worker_data_t* worker_data) { logger_t log; log.log = false; i_t branch_var = -1; rounding_direction_t round_dir = rounding_direction_t::NONE; std::vector current_incumbent; + std::vector& solution = worker_data->leaf_solution.x; - switch (type) { + switch (worker_data->worker_type) { case bnb_worker_type_t::EXPLORATION: // branch_var = pc_.variable_selection(fractional, solution, log); - branch_var = pc_.reliable_variable_selection(lp, - lp_settings, + branch_var = pc_.reliable_variable_selection(worker_data->leaf_problem, + settings_, var_types_, - vstatus, - edge_norms, + node_ptr->vstatus, + worker_data->leaf_edge_norms, fractional, solution, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, node_ptr->lower_bound, + upper_bound_, log); round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); @@ -606,7 +605,7 @@ branch_variable_t branch_and_bound_t::variable_selection( return guided_diving(pc_, fractional, solution, current_incumbent, log); default: - log.debug("Unknown variable selection method: %d\n", type); + log.debug("Unknown variable selection method: %d\n", worker_data->worker_type); return {-1, rounding_direction_t::NONE}; } } @@ -617,9 +616,8 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* bnb_stats_t& stats, logger_t& log) { - lp_problem_t& leaf_problem = worker_data->leaf_problem; std::vector& leaf_vstatus = node_ptr->vstatus; - assert(leaf_vstatus.size() == leaf_problem.num_cols); + assert(leaf_vstatus.size() == worker_data->leaf_problem.num_cols); simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); @@ -658,9 +656,9 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* node_ptr->vstatus[node_ptr->branch_var]); #endif - bool is_feasible = worker_data->set_lp_variable_bounds_for(node_ptr, settings_); - dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; - std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; + bool is_feasible = worker_data->set_lp_variable_bounds_for(node_ptr, settings_); + dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; + worker_data->leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; if (is_feasible) { i_t node_iter = 0; @@ -670,7 +668,7 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* 0, worker_data->recompute_basis, lp_start_time, - leaf_problem, + worker_data->leaf_problem, lp_settings, leaf_vstatus, worker_data->basis_factors, @@ -678,12 +676,12 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* worker_data->nonbasic_list, worker_data->leaf_solution, node_iter, - leaf_edge_norms); + worker_data->leaf_edge_norms); if (lp_status == dual::status_t::NUMERICAL) { log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); lp_status_t second_status = - solve_linear_program_with_advanced_basis(leaf_problem, + solve_linear_program_with_advanced_basis(worker_data->leaf_problem, lp_start_time, lp_settings, worker_data->leaf_solution, @@ -691,7 +689,7 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* worker_data->basic_list, worker_data->nonbasic_list, leaf_vstatus, - leaf_edge_norms); + worker_data->leaf_edge_norms); lp_status = convert_lp_status_to_dual_status(second_status); } @@ -763,16 +761,7 @@ std::pair branch_and_bound_t::upd } else if (leaf_objective <= upper_bound_ + abs_fathom_tol) { // Choose fractional variable to branch on - auto [branch_var, round_dir] = variable_selection(node_ptr, - leaf_problem, - lp_settings, - var_types_, - leaf_vstatus, - leaf_edge_norms, - leaf_fractional, - leaf_solution.x, - thread_type, - lp_settings.log); + auto [branch_var, round_dir] = variable_selection(node_ptr, leaf_fractional, worker_data); assert(leaf_vstatus.size() == leaf_problem.num_cols); assert(branch_var >= 0); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index cface427f..c4836b356 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -195,14 +195,8 @@ class branch_and_bound_t { // Selects the variable to branch on. branch_variable_t variable_selection(mip_node_t* node_ptr, - const lp_problem_t& lp, - const simplex_solver_settings_t& lp_settings, - const std::vector& var_types, - const std::vector& vstatus, - const std::vector& edge_norms, const std::vector& fractional, - const std::vector& solution, - bnb_worker_type_t type); + bnb_worker_data_t* worker_data); }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 2d6a82fbc..89a539485 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -137,11 +137,15 @@ template f_t trial_branching(const lp_problem_t& original_lp, const simplex_solver_settings_t& settings, const std::vector& var_types, - const std::vector& root_vstatus, + const std::vector& vstatus, const std::vector& edge_norms, + const basis_update_mpf_t& basis_factors, + const std::vector& basic_list, + const std::vector& nonbasic_list, i_t branch_var, f_t branch_var_lower, - f_t branch_var_upper) + f_t branch_var_upper, + f_t upper_bound) { lp_problem_t child_problem = original_lp; child_problem.lower[branch_var] = branch_var_lower; @@ -151,19 +155,38 @@ f_t trial_branching(const lp_problem_t& original_lp, child_settings.set_log(false); f_t lp_start_time = tic(); child_settings.iteration_limit = 200; + child_settings.cut_off = upper_bound + settings.dual_tol; + child_settings.inside_mip = 2; + child_settings.scale_columns = false; + lp_solution_t solution(original_lp.num_rows, original_lp.num_cols); - i_t iter = 0; - std::vector vstatus = root_vstatus; - std::vector child_edge_norms = edge_norms; - dual::status_t status = dual_phase2( - 2, 0, lp_start_time, child_problem, child_settings, vstatus, solution, iter, child_edge_norms); - printf("Trial branching on variable %d. Lo: %e Up: %e. Iter %d. Status %d. Obj %e\n", - branch_var, - child_problem.lower[branch_var], - child_problem.upper[branch_var], - iter, - status, - compute_objective(child_problem, solution.x)); + i_t iter = 0; + std::vector child_vstatus = vstatus; + std::vector child_edge_norms = edge_norms; + std::vector child_basic_list = basic_list; + std::vector child_nonbasic_list = nonbasic_list; + basis_update_mpf_t child_basis_factors = basis_factors; + + dual::status_t status = dual_phase2_with_advanced_basis(2, + 0, + false, + lp_start_time, + child_problem, + child_settings, + child_vstatus, + child_basis_factors, + child_basic_list, + child_nonbasic_list, + solution, + iter, + child_edge_norms); + settings.log.debug("Trial branching on variable %d. Lo: %e Up: %e. Iter %d. Status %d. Obj %e\n", + branch_var, + child_problem.lower[branch_var], + child_problem.upper[branch_var], + iter, + status, + compute_objective(child_problem, solution.x)); if (status == dual::status_t::OPTIMAL || status == dual::status_t::ITERATION_LIMIT || status == dual::status_t::CUTOFF) { @@ -365,16 +388,17 @@ i_t pseudo_costs_t::reliable_variable_selection( const std::vector& edge_norms, const std::vector& fractional, const std::vector& solution, + const basis_update_mpf_t& basis_factors, + const std::vector& basic_list, + const std::vector& nonbasic_list, f_t current_obj, + f_t upper_bound, logger_t& log) { mutex.lock(); - const i_t num_fractional = fractional.size(); - std::vector pseudo_cost_up(num_fractional); - std::vector pseudo_cost_down(num_fractional); - std::vector score(num_fractional); - + i_t branch_var = fractional[0]; + f_t max_score = -1; i_t num_initialized_down; i_t num_initialized_up; f_t pseudo_cost_down_avg; @@ -392,19 +416,32 @@ i_t pseudo_costs_t::reliable_variable_selection( const i_t reliable_threshold = 1; - for (i_t k = 0; k < num_fractional; k++) { - const i_t j = fractional[k]; + for (auto j : fractional) { mutex.lock(); bool down_reliable = pseudo_cost_num_down[j] >= reliable_threshold; mutex.unlock(); + + f_t pseudo_cost_down = 0; + f_t pseudo_cost_up = 0; + if (down_reliable) { mutex.lock(); - pseudo_cost_down[k] = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; + pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; mutex.unlock(); } else { // Do trial branching on the down branch - f_t obj = trial_branching( - lp, settings, var_types, vstatus, edge_norms, j, lp.lower[j], std::floor(solution[j])); + f_t obj = trial_branching(lp, + settings, + var_types, + vstatus, + edge_norms, + basis_factors, + basic_list, + nonbasic_list, + j, + lp.lower[j], + std::floor(solution[j]), + upper_bound); if (!std::isnan(obj)) { f_t change_in_obj = obj - current_obj; f_t change_in_x = solution[j] - std::floor(solution[j]); @@ -412,7 +449,7 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_sum_down[j] += change_in_obj / change_in_x; pseudo_cost_num_down[j]++; mutex.unlock(); - pseudo_cost_down[k] = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; + pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; } } @@ -421,42 +458,46 @@ i_t pseudo_costs_t::reliable_variable_selection( mutex.unlock(); if (up_reliable) { mutex.lock(); - pseudo_cost_up[k] = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; + pseudo_cost_up = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; mutex.unlock(); } else { // Do trial branching on the up branch - f_t obj = trial_branching( - lp, settings, var_types, vstatus, edge_norms, j, std::ceil(solution[j]), lp.upper[j]); + f_t obj = trial_branching(lp, + settings, + var_types, + vstatus, + edge_norms, + basis_factors, + basic_list, + nonbasic_list, + j, + std::ceil(solution[j]), + lp.upper[j], + upper_bound); + if (!std::isnan(obj)) { f_t change_in_obj = obj - current_obj; f_t change_in_x = std::ceil(solution[j]) - solution[j]; mutex.lock(); pseudo_cost_sum_up[j] += change_in_obj / change_in_x; pseudo_cost_num_up[j]++; - pseudo_cost_up[k] = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; + pseudo_cost_up = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; mutex.unlock(); } } constexpr f_t eps = 1e-6; const f_t f_down = solution[j] - std::floor(solution[j]); const f_t f_up = std::ceil(solution[j]) - solution[j]; - score[k] = - std::max(f_down * pseudo_cost_down[k], eps) * std::max(f_up * pseudo_cost_up[k], eps); - } + f_t score = std::max(f_down * pseudo_cost_down, eps) * std::max(f_up * pseudo_cost_up, eps); - i_t branch_var = fractional[0]; - f_t max_score = -1; - i_t select = -1; - for (i_t k = 0; k < num_fractional; k++) { - if (score[k] > max_score) { - max_score = score[k]; - branch_var = fractional[k]; - select = k; + if (score > max_score) { + max_score = score; + branch_var = j; } } log.printf( - "pc branching on %d. Value %e. Score %e\n", branch_var, solution[branch_var], score[select]); + "pc branching on %d. Value %e. Score %e\n", branch_var, solution[branch_var], max_score); return branch_var; } diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 1569cb1c1..8367637b0 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -7,6 +7,7 @@ #pragma once +#include #include #include #include @@ -54,7 +55,11 @@ class pseudo_costs_t { const std::vector& edge_norms, const std::vector& fractional, const std::vector& solution, + const basis_update_mpf_t& basis_factors, + const std::vector& basic_list, + const std::vector& nonbasic_list, f_t current_obj, + f_t upper_bound, logger_t& log); f_t obj_estimate(const std::vector& fractional, From 0926349378876595cd4dac6aa3a407cf36286705 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 12 Jan 2026 12:20:53 +0100 Subject: [PATCH 180/366] replaced mutex with a vector of mutexes --- cpp/src/dual_simplex/diving_heuristics.cpp | 6 ++- cpp/src/dual_simplex/pseudo_costs.cpp | 48 +++++++++++----------- cpp/src/dual_simplex/pseudo_costs.hpp | 9 ++-- 3 files changed, 34 insertions(+), 29 deletions(-) diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 2d564a815..b5e58a097 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -71,7 +71,6 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, const std::vector& root_solution, logger_t& log) { - std::lock_guard lock(pc.mutex); i_t branch_var = -1; f_t max_score = std::numeric_limits::lowest(); rounding_direction_t round_dir = rounding_direction_t::NONE; @@ -89,12 +88,14 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, f_t f_down = solution[j] - std::floor(solution[j]); f_t f_up = std::ceil(solution[j]) - solution[j]; + pc.pseudo_cost_mutex[j].lock(); f_t pc_down = pc.pseudo_cost_num_down[j] != 0 ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] : pseudo_cost_down_avg; f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] : pseudo_cost_up_avg; + pc.pseudo_cost_mutex[j].unlock(); f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); @@ -146,7 +147,6 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, const std::vector& incumbent, logger_t& log) { - std::lock_guard lock(pc.mutex); i_t branch_var = -1; f_t max_score = std::numeric_limits::lowest(); rounding_direction_t round_dir = rounding_direction_t::NONE; @@ -167,12 +167,14 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, rounding_direction_t dir = down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; + pc.pseudo_cost_mutex[j].lock(); f_t pc_down = pc.pseudo_cost_num_down[j] != 0 ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] : pseudo_cost_down_avg; f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] : pseudo_cost_up_avg; + pc.pseudo_cost_mutex[j].unlock(); f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 89a539485..e79284211 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -262,7 +262,7 @@ template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) { - std::lock_guard lock(mutex); + std::lock_guard lock(pseudo_cost_mutex[node_ptr->branch_var]); const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) @@ -280,7 +280,7 @@ template void pseudo_costs_t::initialized(i_t& num_initialized_down, i_t& num_initialized_up, f_t& pseudo_cost_down_avg, - f_t& pseudo_cost_up_avg) const + f_t& pseudo_cost_up_avg) { num_initialized_down = 0; num_initialized_up = 0; @@ -288,6 +288,8 @@ void pseudo_costs_t::initialized(i_t& num_initialized_down, pseudo_cost_up_avg = 0; const i_t n = pseudo_cost_sum_down.size(); for (i_t j = 0; j < n; j++) { + std::lock_guard lock(pseudo_cost_mutex[j]); + if (pseudo_cost_num_down[j] > 0) { num_initialized_down++; if (std::isfinite(pseudo_cost_sum_down[j])) { @@ -320,8 +322,6 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio const std::vector& solution, logger_t& log) { - std::lock_guard lock(mutex); - const i_t num_fractional = fractional.size(); std::vector pseudo_cost_up(num_fractional); std::vector pseudo_cost_down(num_fractional); @@ -342,6 +342,8 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio for (i_t k = 0; k < num_fractional; k++) { const i_t j = fractional[k]; + + pseudo_cost_mutex[j].lock(); if (pseudo_cost_num_down[j] != 0) { pseudo_cost_down[k] = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; } else { @@ -353,6 +355,8 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio } else { pseudo_cost_up[k] = pseudo_cost_up_avg; } + pseudo_cost_mutex[j].unlock(); + constexpr f_t eps = 1e-6; const f_t f_down = solution[j] - std::floor(solution[j]); const f_t f_up = std::ceil(solution[j]) - solution[j]; @@ -395,8 +399,6 @@ i_t pseudo_costs_t::reliable_variable_selection( f_t upper_bound, logger_t& log) { - mutex.lock(); - i_t branch_var = fractional[0]; f_t max_score = -1; i_t num_initialized_down; @@ -406,8 +408,6 @@ i_t pseudo_costs_t::reliable_variable_selection( initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - mutex.unlock(); - log.printf("PC: num initialized down %d up %d avg down %e up %e\n", num_initialized_down, num_initialized_up, @@ -417,17 +417,17 @@ i_t pseudo_costs_t::reliable_variable_selection( const i_t reliable_threshold = 1; for (auto j : fractional) { - mutex.lock(); + pseudo_cost_mutex[j].lock(); bool down_reliable = pseudo_cost_num_down[j] >= reliable_threshold; - mutex.unlock(); + pseudo_cost_mutex[j].unlock(); f_t pseudo_cost_down = 0; f_t pseudo_cost_up = 0; if (down_reliable) { - mutex.lock(); + pseudo_cost_mutex[j].lock(); pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; - mutex.unlock(); + pseudo_cost_mutex[j].unlock(); } else { // Do trial branching on the down branch f_t obj = trial_branching(lp, @@ -445,21 +445,21 @@ i_t pseudo_costs_t::reliable_variable_selection( if (!std::isnan(obj)) { f_t change_in_obj = obj - current_obj; f_t change_in_x = solution[j] - std::floor(solution[j]); - mutex.lock(); + pseudo_cost_mutex[j].lock(); pseudo_cost_sum_down[j] += change_in_obj / change_in_x; pseudo_cost_num_down[j]++; - mutex.unlock(); + pseudo_cost_mutex[j].unlock(); pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; } } - mutex.lock(); + pseudo_cost_mutex[j].lock(); bool up_reliable = pseudo_cost_num_up[j] >= reliable_threshold; - mutex.unlock(); + pseudo_cost_mutex[j].unlock(); if (up_reliable) { - mutex.lock(); + pseudo_cost_mutex[j].lock(); pseudo_cost_up = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; - mutex.unlock(); + pseudo_cost_mutex[j].unlock(); } else { // Do trial branching on the up branch f_t obj = trial_branching(lp, @@ -478,11 +478,11 @@ i_t pseudo_costs_t::reliable_variable_selection( if (!std::isnan(obj)) { f_t change_in_obj = obj - current_obj; f_t change_in_x = std::ceil(solution[j]) - solution[j]; - mutex.lock(); + pseudo_cost_mutex[j].lock(); pseudo_cost_sum_up[j] += change_in_obj / change_in_x; pseudo_cost_num_up[j]++; pseudo_cost_up = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; - mutex.unlock(); + pseudo_cost_mutex[j].unlock(); } } constexpr f_t eps = 1e-6; @@ -508,8 +508,6 @@ f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, f_t lower_bound, logger_t& log) { - std::lock_guard lock(mutex); - const i_t num_fractional = fractional.size(); f_t estimate = lower_bound; @@ -521,10 +519,12 @@ f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); for (i_t k = 0; k < num_fractional; k++) { - const i_t j = fractional[k]; + const i_t j = fractional[k]; + f_t pseudo_cost_down = 0; f_t pseudo_cost_up = 0; + pseudo_cost_mutex[j].lock(); if (pseudo_cost_num_down[j] != 0) { pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; } else { @@ -536,6 +536,8 @@ f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, } else { pseudo_cost_up = pseudo_cost_up_avg; } + pseudo_cost_mutex[j].unlock(); + constexpr f_t eps = 1e-6; const f_t f_down = solution[j] - std::floor(solution[j]); const f_t f_up = std::ceil(solution[j]) - solution[j]; diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 8367637b0..2f2d60f79 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -25,7 +25,8 @@ class pseudo_costs_t { : pseudo_cost_sum_down(num_variables), pseudo_cost_sum_up(num_variables), pseudo_cost_num_down(num_variables), - pseudo_cost_num_up(num_variables) + pseudo_cost_num_up(num_variables), + pseudo_cost_mutex(num_variables) { } @@ -37,12 +38,13 @@ class pseudo_costs_t { pseudo_cost_sum_up.resize(num_variables); pseudo_cost_num_down.resize(num_variables); pseudo_cost_num_up.resize(num_variables); + pseudo_cost_mutex.resize(num_variables); } void initialized(i_t& num_initialized_down, i_t& num_initialized_up, f_t& pseudo_cost_down_avg, - f_t& pseudo_cost_up_avg) const; + f_t& pseudo_cost_up_avg); i_t variable_selection(const std::vector& fractional, const std::vector& solution, @@ -75,8 +77,7 @@ class pseudo_costs_t { std::vector pseudo_cost_num_down; std::vector strong_branch_down; std::vector strong_branch_up; - - omp_mutex_t mutex; + std::vector pseudo_cost_mutex; omp_atomic_t num_strong_branches_completed = 0; }; From 2c069c0de7ce450bf18597932a15e50dc93817d8 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 12 Jan 2026 16:11:01 +0100 Subject: [PATCH 181/366] restrict reliability branching to main thread --- cpp/src/dual_simplex/branch_and_bound.cpp | 33 +++++++++++++---------- 1 file changed, 19 insertions(+), 14 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index c3fecd35f..b72e0bf53 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -565,20 +565,25 @@ branch_variable_t branch_and_bound_t::variable_selection( switch (worker_data->worker_type) { case bnb_worker_type_t::EXPLORATION: - // branch_var = pc_.variable_selection(fractional, solution, log); - branch_var = pc_.reliable_variable_selection(worker_data->leaf_problem, - settings_, - var_types_, - node_ptr->vstatus, - worker_data->leaf_edge_norms, - fractional, - solution, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, - node_ptr->lower_bound, - upper_bound_, - log); + + // RINS/SubMIP path + if (!enable_concurrent_lp_root_solve()) { + branch_var = pc_.variable_selection(fractional, solution, log); + } else { + branch_var = pc_.reliable_variable_selection(worker_data->leaf_problem, + settings_, + var_types_, + node_ptr->vstatus, + worker_data->leaf_edge_norms, + fractional, + solution, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, + node_ptr->lower_bound, + upper_bound_, + log); + } round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); From f043ac90240fd3a7a21ed02f564b3df8f80531a1 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 13 Jan 2026 16:17:16 +0100 Subject: [PATCH 182/366] fixed unintialized pseudocost. added adaptive rule for the number of LP iterations and reliable threshold for reliability branching. --- cpp/src/dual_simplex/branch_and_bound.cpp | 4 +- cpp/src/dual_simplex/pseudo_costs.cpp | 181 ++++++++++++---------- cpp/src/dual_simplex/pseudo_costs.hpp | 11 +- 3 files changed, 111 insertions(+), 85 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index b72e0bf53..d6a95e82e 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -561,7 +561,7 @@ branch_variable_t branch_and_bound_t::variable_selection( i_t branch_var = -1; rounding_direction_t round_dir = rounding_direction_t::NONE; std::vector current_incumbent; - std::vector& solution = worker_data->leaf_solution.x; + std::vector& solution = worker_data->leaf_solution.x; switch (worker_data->worker_type) { case bnb_worker_type_t::EXPLORATION: @@ -582,6 +582,8 @@ branch_variable_t branch_and_bound_t::variable_selection( worker_data->nonbasic_list, node_ptr->lower_bound, upper_bound_, + exploration_stats_.total_lp_iters, + exploration_stats_.nodes_explored, log); } diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index e79284211..de6c3a009 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -145,7 +145,9 @@ f_t trial_branching(const lp_problem_t& original_lp, i_t branch_var, f_t branch_var_lower, f_t branch_var_upper, - f_t upper_bound) + f_t upper_bound, + i_t bnb_lp_iter_per_node, + omp_atomic_t& total_lp_iter) { lp_problem_t child_problem = original_lp; child_problem.lower[branch_var] = branch_var_lower; @@ -154,7 +156,7 @@ f_t trial_branching(const lp_problem_t& original_lp, simplex_solver_settings_t child_settings = settings; child_settings.set_log(false); f_t lp_start_time = tic(); - child_settings.iteration_limit = 200; + child_settings.iteration_limit = std::clamp(bnb_lp_iter_per_node, 10, 100); child_settings.cut_off = upper_bound + settings.dual_tol; child_settings.inside_mip = 2; child_settings.scale_columns = false; @@ -180,16 +182,20 @@ f_t trial_branching(const lp_problem_t& original_lp, solution, iter, child_edge_norms); - settings.log.debug("Trial branching on variable %d. Lo: %e Up: %e. Iter %d. Status %d. Obj %e\n", + total_lp_iter += iter; + settings.log.debug("Trial branching on variable %d. Lo: %e Up: %e. Iter %d. Status %s. Obj %e\n", branch_var, child_problem.lower[branch_var], child_problem.upper[branch_var], iter, - status, + dual::status_to_string(status).c_str(), compute_objective(child_problem, solution.x)); - if (status == dual::status_t::OPTIMAL || status == dual::status_t::ITERATION_LIMIT || - status == dual::status_t::CUTOFF) { + if (status == dual::status_t::DUAL_UNBOUNDED) { + // LP was infeasible + return std::numeric_limits::infinity(); + } else if (status == dual::status_t::OPTIMAL || status == dual::status_t::ITERATION_LIMIT || + status == dual::status_t::CUTOFF) { return compute_objective(child_problem, solution.x); } else { return std::numeric_limits::quiet_NaN(); @@ -211,8 +217,8 @@ void strong_branching(const lp_problem_t& original_lp, pseudo_costs_t& pc) { pc.resize(original_lp.num_cols); - pc.strong_branch_down.resize(fractional.size()); - pc.strong_branch_up.resize(fractional.size()); + pc.strong_branch_down.assign(fractional.size(), 0); + pc.strong_branch_up.assign(fractional.size(), 0); pc.num_strong_branches_completed = 0; settings.log.printf("Strong branching using %d threads and %ld fractional variables\n", @@ -397,6 +403,8 @@ i_t pseudo_costs_t::reliable_variable_selection( const std::vector& nonbasic_list, f_t current_obj, f_t upper_bound, + i_t bnb_lp_iter, + i_t bnb_explored_nodes, logger_t& log) { i_t branch_var = fractional[0]; @@ -414,86 +422,99 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_down_avg, pseudo_cost_up_avg); - const i_t reliable_threshold = 1; - - for (auto j : fractional) { - pseudo_cost_mutex[j].lock(); - bool down_reliable = pseudo_cost_num_down[j] >= reliable_threshold; - pseudo_cost_mutex[j].unlock(); + const i_t max_iter = 0.5 * bnb_lp_iter; + const i_t gamma = (max_iter - total_lp_iter) / (total_lp_iter + 1); + const i_t max_v = 5; + const i_t min_v = 1; + i_t reliable_threshold = 0; // std::clamp((1 - gamma) * min_v + gamma * max_v, min_v, max_v); + // reliable_threshold = total_lp_iter < max_iter ? reliable_threshold : 0; + + settings.log.debug("RB LP iterations = %d, B&B LP iterations = %d reliable_threshold = %d\n", + total_lp_iter.load(), + bnb_lp_iter, + reliable_threshold); + + std::vector pending = fractional; + std::vector conflict; + conflict.reserve(fractional.size()); + + while (!pending.empty()) { + for (auto j : pending) { + bool is_locked = pseudo_cost_mutex[j].try_lock(); + + if (!is_locked) { + conflict.push_back(j); + continue; + } - f_t pseudo_cost_down = 0; - f_t pseudo_cost_up = 0; + if (pseudo_cost_num_down[j] < reliable_threshold) { + // Do trial branching on the down branch + f_t obj = trial_branching(lp, + settings, + var_types, + vstatus, + edge_norms, + basis_factors, + basic_list, + nonbasic_list, + j, + lp.lower[j], + std::floor(solution[j]), + upper_bound, + bnb_lp_iter / bnb_explored_nodes, + total_lp_iter); + if (!std::isnan(obj)) { + f_t change_in_obj = obj - current_obj; + f_t change_in_x = solution[j] - std::floor(solution[j]); + pseudo_cost_sum_down[j] += change_in_obj / change_in_x; + pseudo_cost_num_down[j]++; + } + } - if (down_reliable) { - pseudo_cost_mutex[j].lock(); - pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; - pseudo_cost_mutex[j].unlock(); - } else { - // Do trial branching on the down branch - f_t obj = trial_branching(lp, - settings, - var_types, - vstatus, - edge_norms, - basis_factors, - basic_list, - nonbasic_list, - j, - lp.lower[j], - std::floor(solution[j]), - upper_bound); - if (!std::isnan(obj)) { - f_t change_in_obj = obj - current_obj; - f_t change_in_x = solution[j] - std::floor(solution[j]); - pseudo_cost_mutex[j].lock(); - pseudo_cost_sum_down[j] += change_in_obj / change_in_x; - pseudo_cost_num_down[j]++; - pseudo_cost_mutex[j].unlock(); - pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; + if (pseudo_cost_num_up[j] < reliable_threshold) { + f_t obj = trial_branching(lp, + settings, + var_types, + vstatus, + edge_norms, + basis_factors, + basic_list, + nonbasic_list, + j, + std::ceil(solution[j]), + lp.upper[j], + upper_bound, + bnb_lp_iter / bnb_explored_nodes, + total_lp_iter); + + if (!std::isnan(obj)) { + f_t change_in_obj = obj - current_obj; + f_t change_in_x = std::ceil(solution[j]) - solution[j]; + pseudo_cost_sum_up[j] += change_in_obj / change_in_x; + pseudo_cost_num_up[j]++; + } } - } - pseudo_cost_mutex[j].lock(); - bool up_reliable = pseudo_cost_num_up[j] >= reliable_threshold; - pseudo_cost_mutex[j].unlock(); - if (up_reliable) { - pseudo_cost_mutex[j].lock(); - pseudo_cost_up = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; + f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] + : pseudo_cost_down_avg; + pseudo_cost_mutex[j].unlock(); - } else { - // Do trial branching on the up branch - f_t obj = trial_branching(lp, - settings, - var_types, - vstatus, - edge_norms, - basis_factors, - basic_list, - nonbasic_list, - j, - std::ceil(solution[j]), - lp.upper[j], - upper_bound); - - if (!std::isnan(obj)) { - f_t change_in_obj = obj - current_obj; - f_t change_in_x = std::ceil(solution[j]) - solution[j]; - pseudo_cost_mutex[j].lock(); - pseudo_cost_sum_up[j] += change_in_obj / change_in_x; - pseudo_cost_num_up[j]++; - pseudo_cost_up = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; - pseudo_cost_mutex[j].unlock(); + + constexpr f_t eps = 1e-6; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); + + if (score > max_score) { + max_score = score; + branch_var = j; } } - constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t score = std::max(f_down * pseudo_cost_down, eps) * std::max(f_up * pseudo_cost_up, eps); - if (score > max_score) { - max_score = score; - branch_var = j; - } + std::swap(pending, conflict); + conflict.clear(); } log.printf( diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 2f2d60f79..8e079d0da 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -34,10 +34,10 @@ class pseudo_costs_t { void resize(i_t num_variables) { - pseudo_cost_sum_down.resize(num_variables); - pseudo_cost_sum_up.resize(num_variables); - pseudo_cost_num_down.resize(num_variables); - pseudo_cost_num_up.resize(num_variables); + pseudo_cost_sum_down.assign(num_variables, 0); + pseudo_cost_sum_up.assign(num_variables, 0); + pseudo_cost_num_down.assign(num_variables, 0); + pseudo_cost_num_up.assign(num_variables, 0); pseudo_cost_mutex.resize(num_variables); } @@ -62,6 +62,8 @@ class pseudo_costs_t { const std::vector& nonbasic_list, f_t current_obj, f_t upper_bound, + i_t bnb_lp_iter, + i_t bnb_explored_nodes, logger_t& log); f_t obj_estimate(const std::vector& fractional, @@ -79,6 +81,7 @@ class pseudo_costs_t { std::vector strong_branch_up; std::vector pseudo_cost_mutex; omp_atomic_t num_strong_branches_completed = 0; + omp_atomic_t total_lp_iter = 0; }; template From 24a3838cdcdf929cb757101573763a1141549f0b Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 13 Jan 2026 16:42:13 +0100 Subject: [PATCH 183/366] re-enable reliability branching --- cpp/src/dual_simplex/pseudo_costs.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index de6c3a009..a2c1ef029 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -426,8 +426,8 @@ i_t pseudo_costs_t::reliable_variable_selection( const i_t gamma = (max_iter - total_lp_iter) / (total_lp_iter + 1); const i_t max_v = 5; const i_t min_v = 1; - i_t reliable_threshold = 0; // std::clamp((1 - gamma) * min_v + gamma * max_v, min_v, max_v); - // reliable_threshold = total_lp_iter < max_iter ? reliable_threshold : 0; + i_t reliable_threshold = std::clamp((1 - gamma) * min_v + gamma * max_v, min_v, max_v); + reliable_threshold = total_lp_iter < max_iter ? reliable_threshold : 0; settings.log.debug("RB LP iterations = %d, B&B LP iterations = %d reliable_threshold = %d\n", total_lp_iter.load(), From ccca7febc9f298f17ad122d96756d11b8ea352f6 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 13 Jan 2026 20:00:30 +0100 Subject: [PATCH 184/366] setting reliable_threshold to 1 --- cpp/src/dual_simplex/pseudo_costs.cpp | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index a2c1ef029..e30471f9a 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -422,12 +422,13 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_down_avg, pseudo_cost_up_avg); - const i_t max_iter = 0.5 * bnb_lp_iter; - const i_t gamma = (max_iter - total_lp_iter) / (total_lp_iter + 1); - const i_t max_v = 5; - const i_t min_v = 1; - i_t reliable_threshold = std::clamp((1 - gamma) * min_v + gamma * max_v, min_v, max_v); - reliable_threshold = total_lp_iter < max_iter ? reliable_threshold : 0; + // const i_t max_iter = 0.5 * bnb_lp_iter; + // const i_t gamma = (max_iter - total_lp_iter) / (total_lp_iter + 1); + // const i_t max_v = 5; + // const i_t min_v = 1; + // i_t reliable_threshold = std::clamp((1 - gamma) * min_v + gamma * max_v, min_v, max_v); + // reliable_threshold = total_lp_iter < max_iter ? reliable_threshold : 0; + i_t reliable_threshold = 1; settings.log.debug("RB LP iterations = %d, B&B LP iterations = %d reliable_threshold = %d\n", total_lp_iter.load(), From ef777c6409afa994e610d9f36f041b8d1d1a6d3c Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 14 Jan 2026 12:16:48 +0000 Subject: [PATCH 185/366] more logs, cache cpu clock --- .../dual_simplex/dual_simplex_features.hpp | 28 +++++----- cpp/src/dual_simplex/phase2.cpp | 3 +- cpp/src/utilities/version_info.cpp | 52 +++++++++++-------- 3 files changed, 48 insertions(+), 35 deletions(-) diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 49f2171c2..3b1de9d29 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -11,6 +11,8 @@ #include #include +#include + #include namespace cuopt::linear_programming::dual_simplex { @@ -71,6 +73,8 @@ struct dual_simplex_features_t { bool slack_basis_, bool initialize_basis_) { + raft::common::nvtx::range scope("DualSimplex::init_from_problem"); + num_rows = lp.num_rows; num_cols = lp.num_cols; num_nonzeros = lp.A.col_start[lp.num_cols]; @@ -187,18 +191,18 @@ struct bounds_strengthening_features_t { void log_single(i_t m_val, i_t n_val, i_t nnz_val) const { - printf( - "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " - "iterations=%d bounds_changed=%d " - "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", - m_val, - n_val, - nnz_val, - num_iterations, - num_bounds_changed, - byte_loads, - byte_stores, - runtime); + // printf( + // "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " + // "iterations=%d bounds_changed=%d " + // "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", + // m_val, + // n_val, + // nnz_val, + // num_iterations, + // num_bounds_changed, + // byte_loads, + // byte_stores, + // runtime); } void reset() diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 4a8144457..d98ab876e 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2395,7 +2395,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, f_t obj = compute_objective(lp, sol.x); - raft::common::nvtx::pop_range(); + raft::common::nvtx::pop_range(); // advanced_basis_init const i_t start_iter = iter; @@ -2478,6 +2478,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // Helper to compute scaled work units for a given number of iterations cuopt::work_unit_predictor_t work_predictor{}; auto predict_work_units = [&](i_t num_iters) -> f_t { + raft::common::nvtx::range scope("DualSimplex::predict_work_units"); std::map features_map; features_map["m"] = static_cast(features.num_rows); features_map["n"] = static_cast(features.num_cols); diff --git a/cpp/src/utilities/version_info.cpp b/cpp/src/utilities/version_info.cpp index a14c9fa27..bfcb02ce1 100644 --- a/cpp/src/utilities/version_info.cpp +++ b/cpp/src/utilities/version_info.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -165,32 +165,40 @@ static double get_available_memory_gb() double get_cpu_max_clock_mhz() { - // Try sysfs cpufreq interface first (returns frequency in KHz) - // FIXME: assumes all available CPUs have the same max clock as CPU0 - std::ifstream freq_file("/sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_max_freq"); - if (freq_file.is_open()) { - long khz = 0; - freq_file >> khz; - if (khz > 0) { return khz / 1e3; } - } + // Cache the result - CPU max clock doesn't change during execution + // thread_local to avoid an unecessary sync inserted by the compiler + // due to the standard mandating thread-safe static local variable initialization + // the extra work here is minimal. + thread_local static double cached_mhz = []() { + // Try sysfs cpufreq interface first (returns frequency in KHz) + // FIXME: assumes all available CPUs have the same max clock as CPU0 + std::ifstream freq_file("/sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_max_freq"); + if (freq_file.is_open()) { + long khz = 0; + freq_file >> khz; + if (khz > 0) { return khz / 1e3; } + } - // Fallback: parse /proc/cpuinfo for "cpu MHz" - std::ifstream cpuinfo("/proc/cpuinfo"); - if (!cpuinfo.is_open()) return 0.0; + // Fallback: parse /proc/cpuinfo for "cpu MHz" + std::ifstream cpuinfo("/proc/cpuinfo"); + if (!cpuinfo.is_open()) return 0.0; - std::string line; - double max_mhz = 0.0; - while (std::getline(cpuinfo, line)) { - if (line.find("cpu MHz") != std::string::npos) { - std::size_t colon = line.find(':'); - if (colon != std::string::npos) { - double mhz = std::stod(line.substr(colon + 1)); - if (mhz > max_mhz) { max_mhz = mhz; } + std::string line; + double max_mhz = 0.0; + while (std::getline(cpuinfo, line)) { + if (line.find("cpu MHz") != std::string::npos) { + std::size_t colon = line.find(':'); + if (colon != std::string::npos) { + double mhz = std::stod(line.substr(colon + 1)); + if (mhz > max_mhz) { max_mhz = mhz; } + } } } - } - return max_mhz; + return max_mhz; + }(); + + return cached_mhz; } void print_version_info() From 6dcb0629402971a24133856cedd8bd6607ce9a31 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 14 Jan 2026 13:18:28 +0000 Subject: [PATCH 186/366] plunging for deterministic search --- cpp/src/dual_simplex/bb_worker_state.hpp | 178 ++++++++++++++++++---- cpp/src/dual_simplex/branch_and_bound.cpp | 102 ++++++++----- cpp/src/dual_simplex/bsp_debug.hpp | 3 +- 3 files changed, 214 insertions(+), 69 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 22ba3d37a..0ec0fcc2a 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -39,14 +39,20 @@ template struct bb_worker_state_t { int worker_id{0}; - // Type alias for the BSP priority queue - using bsp_queue_t = std::priority_queue*, - std::vector*>, - bsp_node_compare_t>; + // ========================================================================== + // Plunging data structures (matching explore_subtree strategy) + // ========================================================================== - // Local node queue - priority queue ordered by (lower_bound, origin_worker_id, creation_seq) - // Nodes are assigned BSP identity (origin_worker_id, creation_seq) when enqueued - bsp_queue_t local_queue; + // Plunge stack: depth-first path through the tree + // - Front = next node to process (LIFO) + // - Max size = 2 (current node's sibling only) + // - When branching with sibling on stack, sibling is moved to backlog + std::deque*> plunge_stack; + + // Backlog: nodes "plugged" when branching - candidates for load balancing + // When branching with a sibling on the plunge stack, that sibling moves here. + // At horizon sync, backlog nodes participate in redistribution. + std::vector*> backlog; // Current node being processed (may be paused at horizon boundary) mip_node_t* current_node{nullptr}; @@ -274,50 +280,146 @@ struct bb_worker_state_t { return branch_var; } - // Add a node to the local queue, assigning BSP identity if not already set - // The tuple (origin_worker_id, creation_seq) uniquely identifies the node + // ========================================================================== + // Node enqueueing methods + // ========================================================================== + + // Add a node to the plunge stack, assigning BSP identity if not already set + // Used for initial node assignment and when starting a new plunge from backlog void enqueue_node(mip_node_t* node) { // Assign BSP identity if not already set - // Nodes from load balancing keep their original identity if (!node->has_bsp_identity()) { node->origin_worker_id = worker_id; node->creation_seq = next_creation_seq++; } - local_queue.push(node); + plunge_stack.push_front(node); } // Add a node that already has BSP identity (from load balancing or initial distribution) - // Does NOT modify the node's identity + // Goes to plunge stack front for immediate processing void enqueue_node_with_identity(mip_node_t* node) { assert(node->has_bsp_identity() && "Node must have BSP identity for enqueue_node_with_identity"); - local_queue.push(node); + plunge_stack.push_front(node); + } + + // Add children after branching with proper plunging behavior: + // 1. If plunge stack has a sibling, move it to backlog (plugging) + // 2. Push both children to plunge stack with preferred child on top + // Returns the child that was placed on top (to be explored first) + mip_node_t* enqueue_children_for_plunge(mip_node_t* down_child, + mip_node_t* up_child, + rounding_direction_t preferred_direction) + { + // PLUGGING: If plunge stack has a sibling from previous branch, move it to backlog + if (!plunge_stack.empty()) { + mip_node_t* sibling = plunge_stack.back(); + plunge_stack.pop_back(); + backlog.push_back(sibling); + } + + // Assign BSP identity to children + if (!down_child->has_bsp_identity()) { + down_child->origin_worker_id = worker_id; + down_child->creation_seq = next_creation_seq++; + } + if (!up_child->has_bsp_identity()) { + up_child->origin_worker_id = worker_id; + up_child->creation_seq = next_creation_seq++; + } + + // Push children - preferred child on top (front) for immediate exploration + mip_node_t* first_child; + if (preferred_direction == rounding_direction_t::UP) { + plunge_stack.push_front(down_child); // Second to explore + plunge_stack.push_front(up_child); // First to explore (on top) + first_child = up_child; + } else { + plunge_stack.push_front(up_child); // Second to explore + plunge_stack.push_front(down_child); // First to explore (on top) + first_child = down_child; + } + + return first_child; } - // Get next node to process (highest priority = lowest lower_bound) + // ========================================================================== + // Node dequeueing methods + // ========================================================================== + + // Get next node to process using plunging strategy: + // 1. Resume paused node if any + // 2. Pop from plunge stack (depth-first continuation) + // 3. Fall back to backlog (best-first from plugged nodes) mip_node_t* dequeue_node() { + // 1. Resume paused node if any if (current_node != nullptr) { - // Resume paused node mip_node_t* node = current_node; current_node = nullptr; return node; } - if (local_queue.empty()) { return nullptr; } - mip_node_t* node = local_queue.top(); - local_queue.pop(); - return node; + + // 2. Prefer plunge stack (depth-first continuation) + if (!plunge_stack.empty()) { + mip_node_t* node = plunge_stack.front(); + plunge_stack.pop_front(); + return node; + } + + // 3. Fall back to backlog - select best node (lowest lower_bound) + if (!backlog.empty()) { + auto best_it = + std::min_element(backlog.begin(), + backlog.end(), + [](const mip_node_t* a, const mip_node_t* b) { + // Best-first: prefer lower bound + if (a->lower_bound != b->lower_bound) { + return a->lower_bound < b->lower_bound; + } + // Deterministic tie-breaking by BSP identity + if (a->origin_worker_id != b->origin_worker_id) { + return a->origin_worker_id < b->origin_worker_id; + } + return a->creation_seq < b->creation_seq; + }); + mip_node_t* node = *best_it; + backlog.erase(best_it); + return node; + } + + return nullptr; } + // ========================================================================== + // Queue state queries + // ========================================================================== + // Check if worker has work available - bool has_work() const { return current_node != nullptr || !local_queue.empty(); } + bool has_work() const + { + return current_node != nullptr || !plunge_stack.empty() || !backlog.empty(); + } + + // Get number of nodes in worker's queues (including paused node) + size_t queue_size() const + { + return plunge_stack.size() + backlog.size() + (current_node != nullptr ? 1 : 0); + } - // Get number of nodes in local queue (including paused node) - size_t queue_size() const { return local_queue.size() + (current_node != nullptr ? 1 : 0); } + // Get number of nodes in plunge stack only + size_t plunge_stack_size() const { return plunge_stack.size(); } - // Extract all nodes from queue (for load balancing) + // Get number of nodes in backlog only + size_t backlog_size() const { return backlog.size(); } + + // ========================================================================== + // Load balancing support + // ========================================================================== + + // Extract all nodes from worker (for load balancing) // Returns nodes in arbitrary order - caller should sort if deterministic order needed std::vector*> extract_all_nodes() { @@ -330,22 +432,36 @@ struct bb_worker_state_t { current_node = nullptr; } - // Extract all nodes from priority queue - while (!local_queue.empty()) { - nodes.push_back(local_queue.top()); - local_queue.pop(); + // Extract from plunge stack + for (auto* node : plunge_stack) { + nodes.push_back(node); } + plunge_stack.clear(); + + // Extract from backlog + for (auto* node : backlog) { + nodes.push_back(node); + } + backlog.clear(); return nodes; } - // Clear the queue without returning nodes (use with caution) + // Extract only backlog nodes (for redistribution at horizon sync) + // Plunge stack nodes stay with worker for locality + std::vector*> extract_backlog_nodes() + { + std::vector*> nodes = std::move(backlog); + backlog.clear(); + return nodes; + } + + // Clear all queues without returning nodes (use with caution) void clear_queue() { current_node = nullptr; - while (!local_queue.empty()) { - local_queue.pop(); - } + plunge_stack.clear(); + backlog.clear(); } // Record an event diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 6b05788dc..0926c5cca 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2074,13 +2074,16 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tget_bsp_identity_hash()); } - // Extract queue contents for hashing (preserves priority order) - // We need to temporarily extract to iterate, then restore + // Extract queue contents for hashing + // Include plunge_stack and backlog for complete state std::vector*> queue_nodes; - auto queue_copy = worker.local_queue; // Copy the priority queue - while (!queue_copy.empty()) { - auto* node = queue_copy.top(); - queue_copy.pop(); + for (auto* node : worker.plunge_stack) { + queue_nodes.push_back(node); + } + for (auto* node : worker.backlog) { + queue_nodes.push_back(node); + } + for (auto* node : queue_nodes) { if (!node->has_bsp_identity()) { ++nodes_without_identity; CUOPT_LOG_WARN( @@ -2695,15 +2698,15 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t exploration_stats_.nodes_unexplored += 2; - // Add children to local queue - they get BSP identity on enqueue - // Note: recompute_bounds_and_basis is set in run_worker_until_horizon based on - // whether we branched (has_children), matching opportunistic mode behavior. - worker.enqueue_node(node_ptr->get_down_child()); - worker.enqueue_node(node_ptr->get_up_child()); + // Add children using plunging strategy: + // - If plunge stack has a sibling, it's moved to backlog (plugging) + // - Preferred child goes on top of stack for immediate exploration + rounding_direction_t preferred = child_selection(node_ptr); + worker.enqueue_children_for_plunge( + node_ptr->get_down_child(), node_ptr->get_up_child(), preferred); - return rounding_direction_t::DOWN == child_selection(node_ptr) - ? node_solve_info_t::DOWN_CHILD_FIRST - : node_solve_info_t::UP_CHILD_FIRST; + return preferred == rounding_direction_t::DOWN ? node_solve_info_t::DOWN_CHILD_FIRST + : node_solve_info_t::UP_CHILD_FIRST; } else { search_tree.update(node_ptr, node_status_t::FATHOMED); @@ -2986,22 +2989,32 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() } } - // Check nodes in local queue - need to extract, filter, and rebuild - // since priority_queue doesn't support iteration - std::vector*> surviving_nodes; - while (!worker.local_queue.empty()) { - auto* node = worker.local_queue.top(); - worker.local_queue.pop(); - if (node->lower_bound >= upper_bound) { - search_tree_.update(node, node_status_t::FATHOMED); - --exploration_stats_.nodes_unexplored; - } else { - surviving_nodes.push_back(node); + // Check nodes in plunge stack - filter in place + { + std::deque*> surviving; + for (auto* node : worker.plunge_stack) { + if (node->lower_bound >= upper_bound) { + search_tree_.update(node, node_status_t::FATHOMED); + --exploration_stats_.nodes_unexplored; + } else { + surviving.push_back(node); + } } + worker.plunge_stack = std::move(surviving); } - // Rebuild the queue with surviving nodes - for (auto* node : surviving_nodes) { - worker.local_queue.push(node); + + // Check nodes in backlog - filter in place + { + std::vector*> surviving; + for (auto* node : worker.backlog) { + if (node->lower_bound >= upper_bound) { + search_tree_.update(node, node_status_t::FATHOMED); + --exploration_stats_.nodes_unexplored; + } else { + surviving.push_back(node); + } + } + worker.backlog = std::move(surviving); } } } @@ -3012,7 +3025,7 @@ void branch_and_bound_t::balance_worker_loads() const size_t num_workers = bsp_workers_->size(); if (num_workers <= 1) return; - // Count work for each worker: current_node (if any) + local_queue size + // Count work for each worker: current_node (if any) + plunge_stack + backlog std::vector work_counts(num_workers); size_t total_work = 0; size_t max_work = 0; @@ -3033,14 +3046,24 @@ void branch_and_bound_t::balance_worker_loads() if (!needs_balance) return; - // Collect all redistributable nodes from worker queues (excluding paused current_node) + // Collect all redistributable nodes from worker queues + // With plunging strategy, we redistribute: + // - All backlog nodes (these were "plugged" siblings) + // - Plunge stack nodes (except we keep them with worker for locality if possible) + // For simplicity, redistribute everything for now std::vector*> all_nodes; for (auto& worker : *bsp_workers_) { - // Extract all nodes from this worker's priority queue (not current_node) - while (!worker.local_queue.empty()) { - all_nodes.push_back(worker.local_queue.top()); - worker.local_queue.pop(); + // Extract backlog nodes + for (auto* node : worker.backlog) { + all_nodes.push_back(node); + } + worker.backlog.clear(); + + // Extract plunge stack nodes + for (auto* node : worker.plunge_stack) { + all_nodes.push_back(node); } + worker.plunge_stack.clear(); } // Also pull nodes from global heap if workers need work @@ -3120,9 +3143,14 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() lower_bound = std::min(worker.current_node->lower_bound, lower_bound); } - // Check queue top (min lower bound due to priority queue ordering) - if (!worker.local_queue.empty()) { - lower_bound = std::min(worker.local_queue.top()->lower_bound, lower_bound); + // Check plunge stack nodes + for (auto* node : worker.plunge_stack) { + lower_bound = std::min(node->lower_bound, lower_bound); + } + + // Check backlog nodes + for (auto* node : worker.backlog) { + lower_bound = std::min(node->lower_bound, lower_bound); } } diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp index ff50b0e5d..893327a2d 100644 --- a/cpp/src/dual_simplex/bsp_debug.hpp +++ b/cpp/src/dual_simplex/bsp_debug.hpp @@ -727,7 +727,8 @@ class bsp_debug_logger_t { } else { file << " \"current_node\": null,\n"; } - file << " \"local_queue_size\": " << w.local_queue.size() << "\n"; + file << " \"plunge_stack_size\": " << w.plunge_stack.size() << ",\n"; + file << " \"backlog_size\": " << w.backlog.size() << "\n"; file << " }" << (i < workers.size() - 1 ? "," : "") << "\n"; } file << " ],\n"; From 35e8177ad62a393e57f546ff6dc327b82c5bdbb2 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 15 Jan 2026 09:08:21 +0000 Subject: [PATCH 187/366] move to work_unit_scheduler to allow for mid-node syncs --- cpp/src/dual_simplex/bb_worker_state.hpp | 4 + cpp/src/dual_simplex/branch_and_bound.cpp | 661 +++++++++++----------- cpp/src/dual_simplex/branch_and_bound.hpp | 10 + cpp/src/dual_simplex/bsp_debug.hpp | 13 +- cpp/src/dual_simplex/phase2.cpp | 2 + cpp/src/dual_simplex/pseudo_costs.cpp | 4 +- cpp/src/utilities/work_limit_timer.hpp | 3 +- cpp/src/utilities/work_unit_scheduler.cpp | 47 +- cpp/src/utilities/work_unit_scheduler.hpp | 21 +- 9 files changed, 425 insertions(+), 340 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 0ec0fcc2a..49b9685d9 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -64,6 +64,10 @@ struct bb_worker_state_t { // Worker's virtual time clock (cumulative work units) double clock{0.0}; + // Current horizon boundaries (for BSP sync) + double horizon_start{0.0}; + double horizon_end{0.0}; + // Creation sequence counter - cumulative across horizons for unique identity // Each node created by this worker gets (worker_id, next_creation_seq++) int32_t next_creation_seq{0}; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 0926c5cca..d9739690a 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -31,6 +31,7 @@ #include #include #include +#include #include #include @@ -930,10 +931,6 @@ node_solve_info_t branch_and_bound_t::solve_node( leaf_edge_norms, settings_.deterministic ? &work_unit_context_ : nullptr); } - if (settings_.deterministic && - work_unit_context_.global_work_units_elapsed >= settings_.work_limit) { - lp_status = dual::status_t::WORK_LIMIT; - } if (lp_status == dual::status_t::NUMERICAL) { log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); @@ -1762,44 +1759,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut pc_); } - // Log strong branching results for determinism debugging - { - uint32_t sb_hash = pc_.compute_strong_branch_hash(); - uint32_t pc_hash = pc_.compute_state_hash(); - CUOPT_LOG_DEBUG("Strong branching completed: %zu variables, SB hash=0x%08x, PC hash=0x%08x", - fractional.size(), - sb_hash, - pc_hash); - - // Detailed logging for divergence diagnosis (enabled via environment variable) - const char* log_sb_detail = std::getenv("CUOPT_LOG_STRONG_BRANCHING"); - if (log_sb_detail != nullptr && std::string(log_sb_detail) == "1") { - settings_.log.printf("Strong branching detailed results:\n"); - for (size_t k = 0; k < fractional.size(); ++k) { - i_t var = fractional[k]; - settings_.log.printf(" var[%zu]=%d: down=%+.10e, up=%+.10e\n", - k, - var, - pc_.strong_branch_down[k], - pc_.strong_branch_up[k]); - } - settings_.log.printf("Pseudo-cost state after strong branching:\n"); - i_t non_zero_count = 0; - for (i_t j = 0; j < original_lp_.num_cols; ++j) { - if (pc_.pseudo_cost_num_down[j] > 0 || pc_.pseudo_cost_num_up[j] > 0) { - settings_.log.printf(" pc[%d]: sum_down=%+.10e, num_down=%d, sum_up=%+.10e, num_up=%d\n", - j, - pc_.pseudo_cost_sum_down[j], - pc_.pseudo_cost_num_down[j], - pc_.pseudo_cost_sum_up[j], - pc_.pseudo_cost_num_up[j]); - ++non_zero_count; - } - } - settings_.log.printf("Total %d variables with pseudo-cost data\n", non_zero_count); - } - } - if (toc(exploration_stats_.start_time) > settings_.time_limit) { solver_status_ = mip_exploration_status_t::TIME_LIMIT; return set_final_solution(solution, root_objective_); @@ -1886,7 +1845,21 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut if (has_pending_features_) { flush_pending_features(); } mutex_feature_log_.unlock(); - f_t lower_bound = heap_.size() > 0 ? heap_.top()->lower_bound : search_tree_.root.lower_bound; + // Compute final lower bound + f_t lower_bound; + if (bsp_mode_enabled_) { + // In BSP mode, compute lower bound from all sources (heap + worker queues) + lower_bound = compute_bsp_lower_bound(); + // If no unexplored nodes remain and we have an incumbent, lower bound = upper bound + if (lower_bound == std::numeric_limits::infinity() && incumbent_.has_incumbent) { + lower_bound = get_upper_bound(); + } + } else { + // Non-BSP mode: use heap or fall back to root + lower_bound = heap_.size() > 0 ? heap_.top()->lower_bound : search_tree_.root.lower_bound; + // If heap is empty and we have an incumbent, the tree is fully explored + if (heap_.size() == 0 && incumbent_.has_incumbent) { lower_bound = get_upper_bound(); } + } return set_final_solution(solution, lower_bound); } @@ -1905,6 +1878,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t>(); @@ -1915,6 +1889,15 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t(bsp_horizon_step_); + + // Register all worker contexts with the scheduler + for (auto& worker : *bsp_workers_) { + bsp_scheduler_->register_context(worker.work_context); + } + // Initialize debug logger bsp_debug_logger_.set_settings(bsp_debug_settings_); bsp_debug_logger_.set_num_workers(num_workers); @@ -1933,257 +1916,63 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::infinity(); - f_t lower_bound = get_lower_bound(); - f_t upper_bound = get_upper_bound(); - f_t abs_gap = upper_bound - lower_bound; - f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); - constexpr i_t target_queue_size = 5; // Target nodes per worker // Initial distribution: fill worker queues once at the start refill_worker_queues(target_queue_size); BSP_DEBUG_FLUSH_ASSIGN_TRACE(bsp_debug_settings_, bsp_debug_logger_); - // Main BSP coordinator loop - while (solver_status_ == mip_exploration_status_t::RUNNING && - abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && - (heap_.size() > 0 || bsp_workers_->any_has_work())) { - ++bsp_horizon_number_; - double horizon_start = bsp_current_horizon_ - bsp_horizon_step_; - double horizon_end = bsp_current_horizon_; - - // Debug: Log horizon start - BSP_DEBUG_LOG_HORIZON_START( - bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_start, horizon_end); + // Set sync callback - executed when all workers arrive at barrier + // Returns true to stop the scheduler (and all workers exit cleanly together) + bsp_scheduler_->set_sync_callback([this](double sync_target) -> bool { + bsp_sync_callback(0); + return bsp_terminated_.load(); + }); - // Reset workers for new horizon with current global upper bound - // Each worker gets a snapshot of the upper bound for deterministic pruning - bsp_workers_->reset_for_horizon(horizon_start, horizon_end, get_upper_bound()); - - // Snapshot pseudo-costs for deterministic variable selection - for (auto& worker : *bsp_workers_) { - worker.pc_sum_up_snapshot = pc_.pseudo_cost_sum_up; - worker.pc_sum_down_snapshot = pc_.pseudo_cost_sum_down; - worker.pc_num_up_snapshot = pc_.pseudo_cost_num_up; - worker.pc_num_down_snapshot = pc_.pseudo_cost_num_down; - } + // Set initial upper bound snapshot for all workers + for (auto& worker : *bsp_workers_) { + worker.local_upper_bound = get_upper_bound(); + worker.pc_sum_up_snapshot = pc_.pseudo_cost_sum_up; + worker.pc_sum_down_snapshot = pc_.pseudo_cost_sum_down; + worker.pc_num_up_snapshot = pc_.pseudo_cost_num_up; + worker.pc_num_down_snapshot = pc_.pseudo_cost_num_down; + worker.horizon_start = 0.0; + worker.horizon_end = bsp_horizon_step_; + } - // PHASE 2: PARALLEL EXECUTION - Workers run until horizon + // Main BSP execution - workers run in parallel with scheduler-driven sync #pragma omp parallel num_threads(num_workers) - { - int worker_id = omp_get_thread_num(); - auto& worker = (*bsp_workers_)[worker_id]; - - f_t worker_start_time = tic(); - - // Run worker until horizon - run_worker_until_horizon(worker, search_tree_, bsp_current_horizon_); - - // Record when this worker finished (for barrier wait calculation) - // Store raw timestamp - barrier wait = barrier_end - finish_time - worker.horizon_finish_time = tic(); - worker.total_runtime += toc(worker_start_time); - } - // Implicit OMP barrier here - all workers have finished - f_t barrier_end_time = tic(); - - // Calculate barrier wait time for each worker - // barrier_wait = time from worker finish to barrier completion - for (auto& worker : *bsp_workers_) { - double wait_time = barrier_end_time - worker.horizon_finish_time; - if (wait_time > 0) { worker.total_barrier_wait += wait_time; } - } - - // Aggregate worker work into global context for work limit tracking - // The global work is the horizon boundary (all workers synchronized to this point) - work_unit_context_.global_work_units_elapsed = horizon_end; - - raft::common::nvtx::range scope("BB::bsp_coordinator::sync_phase"); - - // PHASE 3: SYNCHRONIZATION - The Barrier - // Collect and sort all events deterministically - bb_event_batch_t all_events; - { - raft::common::nvtx::range scope("BB::bsp_coordinator::collect_and_sort_events"); - all_events = bsp_workers_->collect_and_sort_events(); - } - - // Debug: Log sync phase - BSP_DEBUG_LOG_SYNC_PHASE_START( - bsp_debug_settings_, bsp_debug_logger_, horizon_end, all_events.size()); - - // Process history and sync - { - raft::common::nvtx::range scope("BB::bsp_coordinator::process_history_and_sync"); - process_history_and_sync(all_events); - } - - // Debug: Log sync end (no final_id assignment needed with BSP identity tuples) - BSP_DEBUG_LOG_SYNC_PHASE_END(bsp_debug_settings_, bsp_debug_logger_, horizon_end); - - // Prune paused nodes that are now dominated by new incumbent - { - raft::common::nvtx::range scope("BB::bsp_coordinator::prune_worker_nodes_vs_incumbent"); - prune_worker_nodes_vs_incumbent(); - } - - // Balance worker loads if significant imbalance detected - { - raft::common::nvtx::range scope("BB::bsp_coordinator::balance_worker_loads"); - balance_worker_loads(); - } - BSP_DEBUG_FLUSH_ASSIGN_TRACE(bsp_debug_settings_, bsp_debug_logger_); - - // Debug: Log horizon end, emit tree state and JSON state - BSP_DEBUG_LOG_HORIZON_END( - bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end); - - // Compute and log determinism fingerprint hash - // This hash captures all state that should be identical across deterministic runs - if (bsp_debug_settings_.any_enabled() || true) { - // Collect all determinism-critical state into a vector for hashing - std::vector state_data; - - // Global state - state_data.push_back(static_cast(exploration_stats_.nodes_explored)); - state_data.push_back(static_cast(exploration_stats_.nodes_unexplored)); - - // Upper/lower bounds (convert to fixed-point for exact comparison) - // Use compute_bsp_lower_bound() for accurate LB from all worker queues - f_t ub = get_upper_bound(); - f_t lb = compute_bsp_lower_bound(); - state_data.push_back(static_cast(ub * 1000000)); // 6 decimal places - state_data.push_back(static_cast(lb * 1000000)); - - // Worker queue contents using BSP identity tuple (origin_worker_id, creation_seq) - // Each worker's queue is a priority queue - we extract nodes in priority order - // Note: BSP identity is always set for nodes in BSP mode - int nodes_without_identity = 0; - for (auto& worker : *bsp_workers_) { - // Hash paused node if any - if (worker.current_node != nullptr) { - if (!worker.current_node->has_bsp_identity()) { - ++nodes_without_identity; - CUOPT_LOG_WARN( - "BSP Hash: Worker %d current_node has no BSP identity (node_id=%d, depth=%d)", - worker.worker_id, - worker.current_node->node_id, - worker.current_node->depth); - } - state_data.push_back(worker.current_node->get_bsp_identity_hash()); - } - - // Extract queue contents for hashing - // Include plunge_stack and backlog for complete state - std::vector*> queue_nodes; - for (auto* node : worker.plunge_stack) { - queue_nodes.push_back(node); - } - for (auto* node : worker.backlog) { - queue_nodes.push_back(node); - } - for (auto* node : queue_nodes) { - if (!node->has_bsp_identity()) { - ++nodes_without_identity; - CUOPT_LOG_WARN( - "BSP Hash: Worker %d queue node has no BSP identity (node_id=%d, depth=%d)", - worker.worker_id, - node->node_id, - node->depth); - } - state_data.push_back(node->get_bsp_identity_hash()); - } - } - if (nodes_without_identity > 0) { - CUOPT_LOG_WARN( - "BSP Hash at horizon %d: %d nodes without BSP identity - HASH MAY BE " - "NON-DETERMINISTIC!", - bsp_horizon_number_, - nodes_without_identity); - } - - // Compute hash from state data - uint32_t hash = 0x811c9dc5u; // FNV-1a initial value - for (uint64_t val : state_data) { - hash ^= static_cast(val & 0xFFFFFFFF); - hash *= 0x01000193u; - hash ^= static_cast(val >> 32); - hash *= 0x01000193u; - } - CUOPT_LOG_DEBUG("BSP Hash at horizon %d: 0x%x", bsp_horizon_number_, hash); - BSP_DEBUG_LOG_HORIZON_HASH( - bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end, hash); - } - - BSP_DEBUG_EMIT_TREE_STATE(bsp_debug_settings_, - bsp_debug_logger_, - bsp_horizon_number_, - search_tree_.root, - get_upper_bound()); - // Collect heap nodes for JSON state (Note: We can't easily iterate the heap, so just log the - // size) - std::vector*> heap_snapshot; - BSP_DEBUG_EMIT_STATE_JSON(bsp_debug_settings_, - bsp_debug_logger_, - bsp_horizon_number_, - horizon_start, - horizon_end, - 0, // No longer tracking next_final_id with BSP identity tuples - get_upper_bound(), - compute_bsp_lower_bound(), - exploration_stats_.nodes_explored, - exploration_stats_.nodes_unexplored, - *bsp_workers_, - heap_snapshot, - all_events); - - // Advance the horizon - bsp_current_horizon_ += bsp_horizon_step_; - - // Update gap info using accurate BSP lower bound from all worker queues - lower_bound = compute_bsp_lower_bound(); - upper_bound = get_upper_bound(); - abs_gap = upper_bound - lower_bound; - rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + { + int worker_id = omp_get_thread_num(); + auto& worker = (*bsp_workers_)[worker_id]; - // Check time/work limits - if (toc(exploration_stats_.start_time) > settings_.time_limit) { - solver_status_ = mip_exploration_status_t::TIME_LIMIT; - } - if (settings_.deterministic && - work_unit_context_.global_work_units_elapsed >= settings_.work_limit) { - solver_status_ = mip_exploration_status_t::WORK_LIMIT; - } + f_t worker_start_time = tic(); - // Progress logging - f_t obj = compute_user_objective(original_lp_, upper_bound); - f_t user_lower = compute_user_objective(original_lp_, lower_bound); - std::string gap_user = user_mip_gap(obj, user_lower); - i_t nodes_explored = exploration_stats_.nodes_explored; - i_t nodes_unexplored = exploration_stats_.nodes_unexplored; + // Run worker loop - scheduler handles sync at horizon boundaries + run_worker_loop(worker, search_tree_); - settings_.log.printf(" %10d %10lu %+13.6e %+10.6e %s %9.2f\n", - nodes_explored, - nodes_unexplored, - obj, - user_lower, - gap_user.c_str(), - toc(exploration_stats_.start_time)); + worker.total_runtime += toc(worker_start_time); + } + // All workers have terminated - deregister contexts + for (auto& worker : *bsp_workers_) { + bsp_scheduler_->deregister_context(worker.work_context); } // Print per-worker statistics settings_.log.printf("\n"); settings_.log.printf("BSP Worker Statistics:\n"); settings_.log.printf( - " Worker | Processed | Branched | Pruned | Infeasible | IntSol | Assigned | Runtime | " - "Wait\n"); + " Worker | Processed | Branched | Pruned | Infeasible | IntSol | Assigned | Runtime | " + "Sync%%\n"); settings_.log.printf( " " - "-------+-----------+----------+--------+------------+--------+----------+-----------+-------" + "-------+-----------+----------+--------+------------+--------+----------+-----------+------" "\n"); for (const auto& worker : *bsp_workers_) { - settings_.log.printf(" %6d | %9d | %8d | %6d | %10d | %6d | %8d | %8.3fs | %5.3fs\n", + double sync_time = worker.work_context.total_sync_time; + double total_time = worker.total_runtime + sync_time; + double sync_percent = (total_time > 0) ? (100.0 * sync_time / total_time) : 0.0; + settings_.log.printf(" %6d | %9d | %8d | %6d | %10d | %6d | %8d | %8.3fs | %5.1f%%\n", worker.worker_id, worker.total_nodes_processed, worker.total_nodes_branched, @@ -2191,8 +1980,8 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::refill_worker_queues(i_t target_queue_size) } } +template +void branch_and_bound_t::run_worker_loop(bb_worker_state_t& worker, + search_tree_t& search_tree) +{ + raft::common::nvtx::range scope("BB::worker_loop"); + + // Workers run continuously until scheduler signals stop (via sync callback) + // The scheduler handles synchronization at horizon boundaries via record_work() + while (!bsp_terminated_.load() && !bsp_scheduler_->is_stopped() && + solver_status_ == mip_exploration_status_t::RUNNING) { + if (worker.has_work()) { + mip_node_t* node = worker.dequeue_node(); + if (node == nullptr) { continue; } + + // Track that this node is being actively processed + worker.current_node = node; + + // Check if node should be pruned (use worker's snapshot for determinism) + f_t upper_bound = worker.local_upper_bound; + if (node->lower_bound >= upper_bound) { + worker.current_node = nullptr; + worker.record_fathomed(node, node->lower_bound); + worker.track_node_pruned(); + search_tree.update(node, node_status_t::FATHOMED); + --exploration_stats_.nodes_unexplored; + continue; + } + + // Check if we can warm-start from the previous solve's basis + bool is_child = (node->parent == worker.last_solved_node); + worker.recompute_bounds_and_basis = !is_child; + + // Solve the node - record_work() inside may block at sync points + // The scheduler's sync callback will execute during barrier waits + node_solve_info_t status = solve_node_bsp(worker, node, search_tree, worker.horizon_end); + + // Track last solved node for warm-start detection + worker.last_solved_node = node; + + // Handle result + if (status == node_solve_info_t::TIME_LIMIT) { + worker.current_node = nullptr; + solver_status_ = mip_exploration_status_t::TIME_LIMIT; + bsp_terminated_.store(true); + break; // Exit loop - scheduler will stop all workers at next sync + } else if (status == node_solve_info_t::WORK_LIMIT) { + worker.current_node = nullptr; + solver_status_ = mip_exploration_status_t::WORK_LIMIT; + bsp_terminated_.store(true); + break; // Exit loop - scheduler will stop all workers at next sync + } else { + // Node completed successfully + worker.current_node = nullptr; + continue; + } + } + + // No work available - advance to next sync point to participate in barrier + // This ensures all workers reach the sync point even if some have no work + cuopt::sync_result_t result = bsp_scheduler_->wait_for_next_sync(worker.work_context); + if (result == cuopt::sync_result_t::STOPPED) { break; } + // After sync, bsp_sync_callback may have redistributed nodes to us + } +} + +template +void branch_and_bound_t::bsp_sync_callback(int worker_id) +{ + // This callback is executed during the barrier by worker 0 + // All other workers are blocked in record_work() at this point + raft::common::nvtx::range scope("BB::bsp_sync_callback"); + + ++bsp_horizon_number_; + double horizon_start = bsp_current_horizon_ - bsp_horizon_step_; + double horizon_end = bsp_current_horizon_; + + // Debug: Log horizon start + BSP_DEBUG_LOG_HORIZON_START( + bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_start, horizon_end); + + // Aggregate worker work into global context + work_unit_context_.global_work_units_elapsed = horizon_end; + + // Collect and sort all events deterministically + bb_event_batch_t all_events = bsp_workers_->collect_and_sort_events(); + + // Debug: Log sync phase + BSP_DEBUG_LOG_SYNC_PHASE_START( + bsp_debug_settings_, bsp_debug_logger_, horizon_end, all_events.size()); + + // Process history and sync (update incumbent, pseudo-costs, etc.) + process_history_and_sync(all_events); + + // Debug: Log sync end + BSP_DEBUG_LOG_SYNC_PHASE_END(bsp_debug_settings_, bsp_debug_logger_, horizon_end); + + // Prune paused nodes that are now dominated by new incumbent + prune_worker_nodes_vs_incumbent(); + + // Balance worker loads if significant imbalance detected + balance_worker_loads(); + BSP_DEBUG_FLUSH_ASSIGN_TRACE(bsp_debug_settings_, bsp_debug_logger_); + + // Debug: Log horizon end and emit state + BSP_DEBUG_LOG_HORIZON_END( + bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end); + + // Compute and log determinism fingerprint hash (always computed for determinism verification) + uint32_t state_hash = 0; + { + std::vector state_data; + state_data.push_back(static_cast(exploration_stats_.nodes_explored)); + state_data.push_back(static_cast(exploration_stats_.nodes_unexplored)); + f_t ub = get_upper_bound(); + f_t lb = compute_bsp_lower_bound(); + state_data.push_back(static_cast(ub * 1000000)); + state_data.push_back(static_cast(lb * 1000000)); + + for (auto& worker : *bsp_workers_) { + if (worker.current_node != nullptr) { + state_data.push_back(worker.current_node->get_bsp_identity_hash()); + } + for (auto* node : worker.plunge_stack) { + state_data.push_back(node->get_bsp_identity_hash()); + } + for (auto* node : worker.backlog) { + state_data.push_back(node->get_bsp_identity_hash()); + } + } + + state_hash = 0x811c9dc5u; // FNV-1a initial value + for (uint64_t val : state_data) { + state_hash ^= static_cast(val & 0xFFFFFFFF); + state_hash *= 0x01000193u; + state_hash ^= static_cast(val >> 32); + state_hash *= 0x01000193u; + } + BSP_DEBUG_LOG_HORIZON_HASH( + bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end, state_hash); + } + + BSP_DEBUG_EMIT_TREE_STATE(bsp_debug_settings_, + bsp_debug_logger_, + bsp_horizon_number_, + search_tree_.root, + get_upper_bound()); + + std::vector*> heap_snapshot; + BSP_DEBUG_EMIT_STATE_JSON(bsp_debug_settings_, + bsp_debug_logger_, + bsp_horizon_number_, + horizon_start, + horizon_end, + 0, + get_upper_bound(), + compute_bsp_lower_bound(), + exploration_stats_.nodes_explored, + exploration_stats_.nodes_unexplored, + *bsp_workers_, + heap_snapshot, + all_events); + + // Advance the horizon for next sync + bsp_current_horizon_ += bsp_horizon_step_; + + // Update worker snapshots for next horizon + for (auto& worker : *bsp_workers_) { + worker.local_upper_bound = get_upper_bound(); + worker.pc_sum_up_snapshot = pc_.pseudo_cost_sum_up; + worker.pc_sum_down_snapshot = pc_.pseudo_cost_sum_down; + worker.pc_num_up_snapshot = pc_.pseudo_cost_num_up; + worker.pc_num_down_snapshot = pc_.pseudo_cost_num_down; + worker.horizon_start = horizon_end; + worker.horizon_end = bsp_current_horizon_; + } + + // Check termination conditions + f_t lower_bound = compute_bsp_lower_bound(); + f_t upper_bound = get_upper_bound(); + f_t abs_gap = upper_bound - lower_bound; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + + bool should_terminate = false; + + // Gap tolerance reached + if (abs_gap <= settings_.absolute_mip_gap_tol || rel_gap <= settings_.relative_mip_gap_tol) { + should_terminate = true; + } + + // No more work + if (heap_.size() == 0 && !bsp_workers_->any_has_work()) { should_terminate = true; } + + // Time limit + if (toc(exploration_stats_.start_time) > settings_.time_limit) { + solver_status_ = mip_exploration_status_t::TIME_LIMIT; + should_terminate = true; + } + + // Work limit (use small tolerance for floating-point comparison since horizon is accumulated) + constexpr double work_limit_tolerance = 1e-9; + if (settings_.deterministic && + work_unit_context_.global_work_units_elapsed + work_limit_tolerance >= settings_.work_limit) { + solver_status_ = mip_exploration_status_t::WORK_LIMIT; + should_terminate = true; + } + + if (should_terminate) { bsp_terminated_.store(true); } + + // Progress logging with horizon number and state hash + f_t obj = compute_user_objective(original_lp_, upper_bound); + f_t user_lower = compute_user_objective(original_lp_, lower_bound); + std::string gap_user = user_mip_gap(obj, user_lower); + settings_.log.printf("S%-4d %8d %8lu %+13.6e %+10.6e %s %8.2f [0x%08x]\n", + bsp_horizon_number_, + exploration_stats_.nodes_explored, + exploration_stats_.nodes_unexplored, + obj, + user_lower, + gap_user.c_str(), + toc(exploration_stats_.start_time), + state_hash); + + // Note: No need to re-queue callback - sync callback is called at every sync point automatically +} + template void branch_and_bound_t::run_worker_until_horizon(bb_worker_state_t& worker, search_tree_t& search_tree, @@ -2291,12 +2305,10 @@ void branch_and_bound_t::run_worker_until_horizon(bb_worker_state_tbsp_state == bsp_node_state_t::PAUSED); + // Check if we can warm-start from the previous solve's basis + // Child nodes inherit their parent's vstatus, so we can reuse the basis bool is_child = (node->parent == worker.last_solved_node); - bool can_warm_start = is_resumed || is_child; - worker.recompute_bounds_and_basis = !can_warm_start; + worker.recompute_bounds_and_basis = !is_child; // Solve the node (this records events) node_solve_info_t status = solve_node_bsp(worker, node, search_tree, current_horizon); @@ -2350,10 +2362,9 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t // Track work units at start (from work_context, which simplex solver updates) double work_units_at_start = worker.work_context.global_work_units_elapsed; double clock_at_start = worker.clock; - bool is_resumed = (node_ptr->bsp_state == bsp_node_state_t::PAUSED); // Debug: Log solve start (pass origin_worker_id as identifier) - double work_limit = current_horizon - worker.clock; + double work_limit = worker.horizon_end - worker.clock; BSP_DEBUG_LOG_SOLVE_START(bsp_debug_settings_, bsp_debug_logger_, worker.clock, @@ -2361,7 +2372,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t node_ptr->node_id, node_ptr->origin_worker_id, work_limit, - is_resumed); + false); // resumed flag no longer used // Setup leaf problem bounds std::fill(worker.node_presolver->bounds_changed.begin(), @@ -2387,8 +2398,10 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; lp_settings.inside_mip = 2; lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); - // Work limit is the ABSOLUTE VT at which to pause (LP solver compares against absolute elapsed) - lp_settings.work_limit = current_horizon; + // Work limit: use GLOBAL work limit + // The check in dual_phase2 compares global_work_units_elapsed against settings.work_limit + // so work_limit must be the GLOBAL limit, not remaining budget + lp_settings.work_limit = settings_.work_limit; lp_settings.scale_columns = false; bool feasible = true; @@ -2527,28 +2540,20 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.work_units_this_horizon += work_performed; // Note: don't call advance_clock() as work_context was already updated by simplex solver - // Check if we hit the horizon mid-solve - if (lp_status == dual::status_t::WORK_LIMIT || worker.clock >= current_horizon) { - // Pause this node - accumulated_vt is the total work spent on this node - double accumulated_vt = (worker.clock - clock_at_start) + node_ptr->accumulated_vt; - worker.pause_current_node(node_ptr, accumulated_vt); - - // Debug: Log solve end (paused) + // Check if we hit the GLOBAL work limit + // In scheduler-based mode, horizon sync happens via record_work() blocking inside the simplex. + // The simplex runs continuously through multiple horizons, so when it completes with a status + // other than WORK_LIMIT, we should process the result normally. + if (lp_status == dual::status_t::WORK_LIMIT) { + // Global work limit reached - solver will terminate BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, bsp_debug_logger_, worker.clock, worker.worker_id, node_ptr->node_id, node_ptr->origin_worker_id, - "PAUSED", + "WORK_LIMIT", node_ptr->lower_bound); - BSP_DEBUG_LOG_PAUSED(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - static_cast(accumulated_vt)); return node_solve_info_t::WORK_LIMIT; } @@ -2560,13 +2565,13 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t // Process LP result if (lp_status == dual::status_t::DUAL_UNBOUNDED) { node_ptr->lower_bound = std::numeric_limits::infinity(); - search_tree.update(node_ptr, node_status_t::INFEASIBLE); + + // Record event and debug logs BEFORE search_tree.update() which may delete the node worker.record_infeasible(node_ptr); worker.track_node_infeasible(); worker.track_node_processed(); worker.recompute_bounds_and_basis = true; - // Debug: Log solve end (infeasible) BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, bsp_debug_logger_, worker.clock, @@ -2577,18 +2582,20 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t node_ptr->lower_bound); BSP_DEBUG_LOG_INFEASIBLE( bsp_debug_settings_, bsp_debug_logger_, worker.clock, worker.worker_id, node_ptr->node_id); + + search_tree.update(node_ptr, node_status_t::INFEASIBLE); return node_solve_info_t::NO_CHILDREN; } else if (lp_status == dual::status_t::CUTOFF) { // Use worker-local upper bound for determinism node_ptr->lower_bound = worker.local_upper_bound; - search_tree.update(node_ptr, node_status_t::FATHOMED); + + // Record event and debug logs BEFORE search_tree.update() which may delete the node worker.record_fathomed(node_ptr, node_ptr->lower_bound); worker.track_node_pruned(); worker.track_node_processed(); worker.recompute_bounds_and_basis = true; - // Debug: Log solve end (fathomed - cutoff) BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, bsp_debug_logger_, worker.clock, @@ -2603,6 +2610,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.worker_id, node_ptr->node_id, node_ptr->lower_bound); + + search_tree.update(node_ptr, node_status_t::FATHOMED); return node_solve_info_t::NO_CHILDREN; } else if (lp_status == dual::status_t::OPTIMAL) { @@ -2635,13 +2644,13 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.integer_solutions.push_back({leaf_objective, leaf_solution.x, node_ptr->depth}); // Note: Logging deferred to sync phase for deterministic output } - search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); + + // Record event and debug logs BEFORE search_tree.update() which may delete the node worker.record_integer_solution(node_ptr, leaf_objective); worker.track_integer_solution(); worker.track_node_processed(); worker.recompute_bounds_and_basis = true; - // Debug: Log solve end (integer) BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, bsp_debug_logger_, worker.clock, @@ -2656,6 +2665,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.worker_id, node_ptr->node_id, leaf_objective); + + search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); return node_solve_info_t::NO_CHILDREN; } else if (leaf_objective <= worker.local_upper_bound + settings_.absolute_mip_gap_tol / 10) { @@ -2709,13 +2720,12 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t : node_solve_info_t::UP_CHILD_FIRST; } else { - search_tree.update(node_ptr, node_status_t::FATHOMED); + // Record event and debug logs BEFORE search_tree.update() which may delete the node worker.record_fathomed(node_ptr, leaf_objective); worker.track_node_pruned(); worker.track_node_processed(); worker.recompute_bounds_and_basis = true; - // Debug: Log solve end (fathomed by bound) BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, bsp_debug_logger_, worker.clock, @@ -2730,6 +2740,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.worker_id, node_ptr->node_id, leaf_objective); + + search_tree.update(node_ptr, node_status_t::FATHOMED); return node_solve_info_t::NO_CHILDREN; } @@ -2737,10 +2749,10 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t return node_solve_info_t::TIME_LIMIT; } else { - // Numerical issue - search_tree.update(node_ptr, node_status_t::NUMERICAL); + // Numerical issue - record BEFORE search_tree.update() which may delete the node worker.record_numerical(node_ptr); worker.recompute_bounds_and_basis = true; + search_tree.update(node_ptr, node_status_t::NUMERICAL); return node_solve_info_t::NUMERICAL; } } @@ -2839,7 +2851,6 @@ void branch_and_bound_t::process_history_and_sync( case bb_event_type_t::NODE_FATHOMED: case bb_event_type_t::NODE_INFEASIBLE: case bb_event_type_t::NODE_NUMERICAL: - case bb_event_type_t::NODE_PAUSED: case bb_event_type_t::HEURISTIC_SOLUTION: // These events don't need additional processing during replay // (BSP identity is already assigned at node creation time) @@ -2979,15 +2990,9 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() f_t upper_bound = get_upper_bound(); for (auto& worker : *bsp_workers_) { - // Check paused node - if (worker.current_node != nullptr) { - if (worker.current_node->lower_bound >= upper_bound) { - // Prune the paused node - search_tree_.update(worker.current_node, node_status_t::FATHOMED); - --exploration_stats_.nodes_unexplored; - worker.current_node = nullptr; - } - } + // NOTE: Do NOT prune worker.current_node here! + // The worker thread has local references (node_ptr, leaf_vstatus) to it. + // The worker will handle cutoff checking when it resumes after sync. // Check nodes in plunge stack - filter in place { @@ -3025,6 +3030,9 @@ void branch_and_bound_t::balance_worker_loads() const size_t num_workers = bsp_workers_->size(); if (num_workers <= 1) return; + // Flag to force rebalancing every sync (reduces idle time at barriers when work is uniform) + constexpr bool force_rebalance_every_sync = true; + // Count work for each worker: current_node (if any) + plunge_stack + backlog std::vector work_counts(num_workers); size_t total_work = 0; @@ -3039,10 +3047,17 @@ void branch_and_bound_t::balance_worker_loads() min_work = std::min(min_work, work_counts[w]); } - // Check if we need to balance: significant imbalance = some worker has 0 work while others have - // 2+ Or max/min ratio is very high - bool needs_balance = - (min_work == 0 && max_work >= 2) || (min_work > 0 && max_work > 4 * min_work); + // Early exit if no work to redistribute + if (total_work == 0) return; + + bool needs_balance; + if (force_rebalance_every_sync) { + // Always rebalance if there's more than one node to distribute + needs_balance = (total_work > 1); + } else { + // Original logic: only rebalance on significant imbalance + needs_balance = (min_work == 0 && max_work >= 2) || (min_work > 0 && max_work > 4 * min_work); + } if (!needs_balance) return; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index a94d0dc51..07cf8ee00 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -24,8 +24,10 @@ #include #include #include +#include #include +#include #include #include @@ -372,9 +374,17 @@ class branch_and_bound_t { // Compute accurate lower bound from all BSP sources (called during sync phase) f_t compute_bsp_lower_bound(); + // BSP worker loop - runs continuously until scheduler signals stop + void run_worker_loop(bb_worker_state_t& worker, search_tree_t& search_tree); + + // BSP sync callback - executed when all workers reach barrier + void bsp_sync_callback(int worker_id); + private: // BSP state std::unique_ptr> bsp_workers_; + std::unique_ptr bsp_scheduler_; + std::atomic bsp_terminated_{false}; double bsp_horizon_step_{5.0}; // Virtual time step per horizon (tunable) double bsp_current_horizon_{0.0}; // Current horizon target bool bsp_mode_enabled_{false}; // Whether BSP mode is active diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp index 893327a2d..edf543bb0 100644 --- a/cpp/src/dual_simplex/bsp_debug.hpp +++ b/cpp/src/dual_simplex/bsp_debug.hpp @@ -333,13 +333,12 @@ class bsp_debug_logger_t { std::lock_guard lock(mutex_); if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked( - vt, - worker_id, - is_resumed ? bsp_log_event_t::NODE_RESUMED : bsp_log_event_t::NODE_SOLVE_START, - node_id, - final_id, - "work_limit=" + std::to_string(work_limit)); + log_event_unlocked(vt, + worker_id, + bsp_log_event_t::NODE_SOLVE_START, + node_id, + final_id, + "work_limit=" + std::to_string(work_limit)); } // Start timeline event diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index d98ab876e..951b5fc37 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -3133,6 +3133,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // prediction, // features.interval_runtime, // prediction - features.interval_runtime); + // printf("Current iter %d\n", iter); work_unit_context->record_work(prediction); } @@ -3168,6 +3169,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, return dual::status_t::CONCURRENT_LIMIT; } } + // printf("NODE SOLVE FINISHED\n"); if (iter >= iter_limit) { status = dual::status_t::ITERATION_LIMIT; } if (phase == 2) { diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 3442c8deb..cdddf0a10 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -159,7 +159,7 @@ void strong_branching(const lp_problem_t& original_lp, settings.num_threads, fractional.size()); -#pragma omp parallel num_threads(settings.num_threads) +#pragma omp parallel num_threads(64) { i_t n = std::min(4 * settings.num_threads, fractional.size()); diff --git a/cpp/src/utilities/work_limit_timer.hpp b/cpp/src/utilities/work_limit_timer.hpp index 862d276b7..08499f55b 100644 --- a/cpp/src/utilities/work_limit_timer.hpp +++ b/cpp/src/utilities/work_limit_timer.hpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -28,6 +28,7 @@ namespace cuopt { struct work_limit_context_t { double global_work_units_elapsed{0.0}; + double total_sync_time{0.0}; // Total time spent waiting at sync barriers (seconds) bool deterministic{false}; work_unit_scheduler_t* scheduler{nullptr}; std::string name; diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp index 0fe5197d3..786118349 100644 --- a/cpp/src/utilities/work_unit_scheduler.cpp +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -58,6 +58,9 @@ void work_unit_scheduler_t::deregister_context(work_limit_context_t& ctx) void work_unit_scheduler_t::on_work_recorded(work_limit_context_t& ctx, double total_work) { + // Early exit if scheduler is stopped + if (stopped_.load()) { return; } + if (verbose) { double sync_target = current_sync_target(); CUOPT_LOG_DEBUG("[%s] Work recorded: %f, sync_target: %f (gen %zu)", @@ -68,11 +71,31 @@ void work_unit_scheduler_t::on_work_recorded(work_limit_context_t& ctx, double t } // Loop to handle large work increments that cross multiple sync points - while (total_work >= current_sync_target()) { + while (!stopped_.load() && total_work >= current_sync_target()) { wait_at_sync_point(ctx, current_sync_target()); } } +void work_unit_scheduler_t::set_sync_callback(sync_callback_t callback) +{ + std::lock_guard lock(mutex_); + sync_callback_ = std::move(callback); +} + +bool work_unit_scheduler_t::is_stopped() const { return stopped_.load(); } + +sync_result_t work_unit_scheduler_t::wait_for_next_sync(work_limit_context_t& ctx) +{ + if (stopped_.load()) { return sync_result_t::STOPPED; } + + // Advance work to next sync point + double next_sync = current_sync_target(); + ctx.global_work_units_elapsed = next_sync; + wait_at_sync_point(ctx, next_sync); + + return stopped_.load() ? sync_result_t::STOPPED : sync_result_t::CONTINUE; +} + void work_unit_scheduler_t::queue_callback(work_limit_context_t& source, work_limit_context_t& destination, callback_t callback) @@ -116,6 +139,13 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double ctx.name.c_str(), barrier_generation_); } + // Execute sync callback if registered (last arrival executes it) + if (sync_callback_) { + lock.unlock(); + bool should_stop = sync_callback_(sync_target); + lock.lock(); + if (should_stop) { stopped_.store(true); } + } cv_.notify_all(); } else { cv_.wait(lock, [&] { return barrier_generation_ != my_generation; }); @@ -150,11 +180,16 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double if (verbose) { CUOPT_LOG_DEBUG("[%s] Woke up from second wait", ctx.name.c_str()); } } + // Track sync time + auto wait_end = std::chrono::high_resolution_clock::now(); + double wait_secs = std::chrono::duration(wait_end - wait_start).count(); + ctx.total_sync_time += wait_secs; + if (verbose) { - auto wait_end = std::chrono::high_resolution_clock::now(); - double wait_ms = std::chrono::duration(wait_end - wait_start).count(); - CUOPT_LOG_DEBUG( - "[%s] Sync complete at %.2f, waited %.2f ms", ctx.name.c_str(), sync_target, wait_ms); + CUOPT_LOG_DEBUG("[%s] Sync complete at %.2f, waited %.2f ms", + ctx.name.c_str(), + sync_target, + wait_secs * 1000.0); } } diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index 57f7d3181..fb6d76502 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -16,6 +16,7 @@ */ #pragma once +#include #include #include #include @@ -27,6 +28,11 @@ namespace cuopt { struct work_limit_context_t; +enum class sync_result_t { + CONTINUE, // Continue processing + STOPPED // Scheduler has been stopped +}; + class work_unit_scheduler_t { public: using callback_t = std::function; @@ -43,6 +49,15 @@ class work_unit_scheduler_t { work_limit_context_t& destination, callback_t callback); + // Sync callback support - callback is executed when all contexts reach sync point + // If callback returns true, scheduler stops and all workers exit cleanly + using sync_callback_t = std::function; + void set_sync_callback(sync_callback_t callback); + bool is_stopped() const; + + // Wait for next sync point (for idle workers with no work) + sync_result_t wait_for_next_sync(work_limit_context_t& ctx); + public: bool verbose{false}; double sync_interval_; @@ -75,6 +90,10 @@ class work_unit_scheduler_t { double current_sync_target_{0}; size_t barrier_generation_{0}; size_t exit_generation_{0}; + + // Sync callback - executed when all contexts reach sync point + sync_callback_t sync_callback_; + std::atomic stopped_{false}; }; } // namespace cuopt From e06af9fa251f96ef90050298a45867fc2cae6f21 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 15 Jan 2026 11:03:13 +0000 Subject: [PATCH 188/366] better debug printouts --- cpp/src/dual_simplex/bb_worker_state.hpp | 1 + cpp/src/dual_simplex/branch_and_bound.cpp | 29 +++++++++++++++++------ cpp/src/dual_simplex/phase2.cpp | 1 + cpp/src/mip/solver.cu | 2 ++ 4 files changed, 26 insertions(+), 7 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 49b9685d9..1714f4260 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -113,6 +113,7 @@ struct bb_worker_state_t { // Timing statistics (in seconds) double total_runtime{0.0}; // Total time spent doing actual work double total_barrier_wait{0.0}; // Total time spent waiting at horizon sync barriers + double total_nowork_time{0.0}; // Total time spent with no nodes to work on double horizon_finish_time{ 0.0}; // Timestamp when worker finished current horizon (for barrier wait calc) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d9739690a..6757a5184 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1962,17 +1962,17 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t 0) ? (100.0 * sync_time / total_time) : 0.0; - settings_.log.printf(" %6d | %9d | %8d | %6d | %10d | %6d | %8d | %8.3fs | %5.1f%%\n", + settings_.log.printf(" %6d | %7d | %8d | %6d | %7d | %6d | %8d | %7.3fs | %4.1f%% | %5.2fs\n", worker.worker_id, worker.total_nodes_processed, worker.total_nodes_branched, @@ -1981,7 +1981,8 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_worker_loop(bb_worker_state_t& // No work available - advance to next sync point to participate in barrier // This ensures all workers reach the sync point even if some have no work + f_t nowork_start = tic(); cuopt::sync_result_t result = bsp_scheduler_->wait_for_next_sync(worker.work_context); + worker.total_nowork_time += toc(nowork_start); if (result == cuopt::sync_result_t::STOPPED) { break; } // After sync, bsp_sync_callback may have redistributed nodes to us } @@ -2267,7 +2270,17 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) f_t obj = compute_user_objective(original_lp_, upper_bound); f_t user_lower = compute_user_objective(original_lp_, lower_bound); std::string gap_user = user_mip_gap(obj, user_lower); - settings_.log.printf("S%-4d %8d %8lu %+13.6e %+10.6e %s %8.2f [0x%08x]\n", + + // Build list of workers that reached sync with no work + std::string idle_workers; + for (const auto& w : *bsp_workers_) { + if (!w.has_work() && w.current_node == nullptr) { + if (!idle_workers.empty()) idle_workers += ","; + idle_workers += "W" + std::to_string(w.worker_id); + } + } + + settings_.log.printf("S%-4d %8d %8lu %+13.6e %+10.6e %s %8.2f [%08x]%s%s\n", bsp_horizon_number_, exploration_stats_.nodes_explored, exploration_stats_.nodes_unexplored, @@ -2275,7 +2288,9 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) user_lower, gap_user.c_str(), toc(exploration_stats_.start_time), - state_hash); + state_hash, + idle_workers.empty() ? "" : " ", + idle_workers.c_str()); // Note: No need to re-queue callback - sync callback is called at every sync point automatically } diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 951b5fc37..13fab4cd0 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2257,6 +2257,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::bound_info(lp, settings); if (initialize_basis) { + raft::common::nvtx::push_range("DualSimplex::init_basis"); std::vector superbasic_list; nonbasic_list.clear(); nonbasic_list.reserve(n - m); diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 9ba8fdbcf..68ef67663 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -197,6 +197,8 @@ solution_t mip_solver_t::run_solver() num_bfs_threads = std::max(1, num_threads); num_diving_threads = 0; // No diving in deterministic mode } + // num_diving_threads = 0; + // num_bfs_threads = std::max(1, num_threads); branch_and_bound_settings.num_bfs_threads = num_bfs_threads; branch_and_bound_settings.num_diving_threads = num_diving_threads; From 99e9ec27787fa6b70f1a8c751fe1adb98051f0cf Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 15 Jan 2026 14:40:35 +0000 Subject: [PATCH 189/366] improve ins_vector coverage; fix case where instrumeted mem accesses would not be accounted for when iterations aren't multiples for LOG_FEATURE_ITERATIONS --- cpp/src/dual_simplex/basis_solves.cpp | 46 +++--- cpp/src/dual_simplex/basis_updates.cpp | 32 ++-- cpp/src/dual_simplex/crossover.cpp | 3 +- cpp/src/dual_simplex/device_sparse_matrix.cu | 26 +--- cpp/src/dual_simplex/phase2.cpp | 145 ++++++++++++++----- cpp/src/dual_simplex/right_looking_lu.cpp | 48 +++--- cpp/src/dual_simplex/right_looking_lu.hpp | 10 +- cpp/src/dual_simplex/singletons.cpp | 56 ++++--- cpp/src/dual_simplex/singletons.hpp | 8 +- cpp/src/dual_simplex/sparse_matrix.cpp | 11 +- cpp/src/dual_simplex/sparse_matrix.hpp | 11 +- cpp/src/dual_simplex/triangle_solve.cpp | 86 +---------- cpp/src/dual_simplex/triangle_solve.hpp | 74 ++++++++-- cpp/src/dual_simplex/vector_math.cpp | 53 +------ cpp/src/dual_simplex/vector_math.hpp | 43 ++++-- 15 files changed, 355 insertions(+), 297 deletions(-) diff --git a/cpp/src/dual_simplex/basis_solves.cpp b/cpp/src/dual_simplex/basis_solves.cpp index a979a9f31..1113bbb57 100644 --- a/cpp/src/dual_simplex/basis_solves.cpp +++ b/cpp/src/dual_simplex/basis_solves.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -12,11 +12,15 @@ #include #include #include +#include #include namespace cuopt::linear_programming::dual_simplex { +// Import instrumented vector type +using cuopt::ins_vector; + template i_t reorder_basic_list(const std::vector& q, std::vector& basic_list) { @@ -59,14 +63,14 @@ void get_basis_from_vstatus(i_t m, namespace { -template +template void write_singleton_info(i_t m, i_t col_singletons, i_t row_singletons, const csc_matrix_t& B, - const std::vector& row_perm, - const std::vector& row_perm_inv, - const std::vector& col_perm) + const VectorI& row_perm, + const VectorI& row_perm_inv, + const VectorI& col_perm) { FILE* file = fopen("singleton_debug.m", "w"); if (file != NULL) { @@ -96,7 +100,7 @@ void write_singleton_info(i_t m, fclose(file); } -template +template void write_factor_info(const char* filename, i_t m, i_t row_singletons, @@ -106,8 +110,8 @@ void write_factor_info(const char* filename, const csc_matrix_t& D, const csc_matrix_t& L, const csc_matrix_t& U, - const std::vector& row_perm, - const std::vector& col_perm) + const VectorI& row_perm, + const VectorI& col_perm) { FILE* file = fopen(filename, "w"); if (file != NULL) { @@ -178,12 +182,12 @@ i_t factorize_basis(const csc_matrix_t& A, f_t fact_start = tic(); csc_matrix_t B(A.m, A.m, 1); form_b(A, basic_list, B); - std::vector row_perm(m); - std::vector col_perm(m); + ins_vector row_perm(m); + ins_vector col_perm(m); i_t row_singletons; i_t col_singletons; find_singletons(B, row_singletons, row_perm, col_singletons, col_perm); - std::vector row_perm_inv(m); + ins_vector row_perm_inv(m); inverse_permutation(row_perm, row_perm_inv); #ifdef PRINT_SINGLETONS @@ -347,12 +351,12 @@ i_t factorize_basis(const csc_matrix_t& A, csc_matrix_t SL(Sdim, Sdim, Snz); csc_matrix_t SU(Sdim, Sdim, Snz); // Factorize S - std::vector S_perm_inv(Sdim); + ins_vector S_perm_inv(Sdim); std::optional> empty = std::nullopt; f_t actual_factor_start = tic(); - std::vector S_col_perm(Sdim); - std::vector identity(Sdim); + ins_vector S_col_perm(Sdim); + ins_vector identity(Sdim); for (i_t h = 0; h < Sdim; ++h) { identity[h] = h; } @@ -376,7 +380,7 @@ i_t factorize_basis(const csc_matrix_t& A, deficient[h - Srank] = col_perm[num_singletons + S_col_perm[h]]; } // Get S_perm - std::vector S_perm(Sdim); + ins_vector S_perm(Sdim); inverse_permutation(S_perm_inv, S_perm); // Get the slacks needed slacks_needed.resize(Sdim - Srank); @@ -388,7 +392,7 @@ i_t factorize_basis(const csc_matrix_t& A, } // Need to permute col_perm[k] according to q - std::vector col_perm_sav(m - num_singletons); + ins_vector col_perm_sav(m - num_singletons); i_t q_j = 0; for (i_t h = num_singletons; h < m; ++h) { col_perm_sav[q_j] = col_perm[h]; @@ -400,7 +404,7 @@ i_t factorize_basis(const csc_matrix_t& A, q_j++; } - std::vector S_perm(m); + ins_vector S_perm(m); inverse_permutation(S_perm_inv, S_perm); actual_factor = toc(actual_factor_start); @@ -475,7 +479,7 @@ i_t factorize_basis(const csc_matrix_t& A, assert(Unz <= Unz_max); U.col_start[m] = Unz; // Finalize U - std::vector last_perm(Sdim); + ins_vector last_perm(Sdim); for (i_t k = 0; k < Sdim; ++k) { last_perm[k] = row_perm[num_singletons + k]; } @@ -628,7 +632,7 @@ i_t basis_repair(const csc_matrix_t& A, assert(nonbasic_list.size() == n - m); // Create slack_map - std::vector slack_map(m); // slack_map[i] = j if column j is e_i + ins_vector slack_map(m); // slack_map[i] = j if column j is e_i i_t slacks_found = 0; for (i_t j = n - 1; j >= n - m; j--) { const i_t col_start = A.col_start[j]; @@ -644,7 +648,7 @@ i_t basis_repair(const csc_matrix_t& A, assert(slacks_found == m); // Create nonbasic_map - std::vector nonbasic_map( + ins_vector nonbasic_map( n, -1); // nonbasic_map[j] = p if nonbasic[p] = j, -1 if j is basic/superbasic const i_t num_nonbasic = nonbasic_list.size(); for (i_t k = 0; k < num_nonbasic; ++k) { @@ -758,6 +762,8 @@ i_t b_transpose_solve(const csc_matrix_t& L, // U'*r = c // L'*w = r + raft::common::nvtx::range scope("LU::b_transpose_solve"); + // Solve for r such that U'*r = c std::vector r = rhs; upper_triangular_transpose_solve(U, r); diff --git a/cpp/src/dual_simplex/basis_updates.cpp b/cpp/src/dual_simplex/basis_updates.cpp index bf00cd9b5..a54ff6c75 100644 --- a/cpp/src/dual_simplex/basis_updates.cpp +++ b/cpp/src/dual_simplex/basis_updates.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -9,6 +9,7 @@ #include #include #include +#include #include @@ -17,6 +18,9 @@ namespace cuopt::linear_programming::dual_simplex { +// Import instrumented vector type +using cuopt::ins_vector; + template i_t basis_update_t::b_solve(const std::vector& rhs, std::vector& solution) const { @@ -552,7 +556,7 @@ i_t basis_update_t::u_solve(std::vector& x) const // 2. Solve for y such that U*y = bprime // 3. Compute Q*y = x const i_t m = U_.m; - std::vector bprime(m); + ins_vector bprime(m); inverse_permute_vector(col_permutation_, x, bprime); #ifdef CHECK_UPPER_SOLVE @@ -603,7 +607,7 @@ i_t basis_update_t::u_transpose_solve(std::vector& x) const // 2. Solve for y such that U'*y = bprime // 3. Compute Q*y = x const i_t m = U_.m; - std::vector bprime(m); + ins_vector bprime(m); inverse_permute_vector(col_permutation_, x, bprime); dual_simplex::upper_triangular_transpose_solve(U_, bprime); permute_vector(col_permutation_, bprime, x); @@ -860,7 +864,7 @@ i_t basis_update_t::update(std::vector& utilde, i_t leaving_index #endif // ubar = Q'*utilde - std::vector ubar(m); + ins_vector ubar(m); inverse_permute_vector(col_permutation_, utilde, ubar); // Find t @@ -871,7 +875,7 @@ i_t basis_update_t::update(std::vector& utilde, i_t leaving_index const f_t delta = u_diagonal(t); // Solve U'*w = delta*et - std::vector w(m); + ins_vector w(m); w[t] = delta; dual_simplex::upper_triangular_transpose_solve(U_, w); #ifdef PARANOID @@ -897,7 +901,7 @@ i_t basis_update_t::update(std::vector& utilde, i_t leaving_index // Set deltabar = w'*ubar const f_t deltabar = update_L ? dot(w, ubar) : ubar[t]; assert(std::abs(deltabar) > 0); - std::vector baru(m); + ins_vector baru(m); for (i_t k = 0; k < t; ++k) { baru[k] = ubar[k]; } @@ -914,11 +918,11 @@ i_t basis_update_t::update(std::vector& utilde, i_t leaving_index } } - std::vector d(m); + ins_vector d(m); d = w; d[t] = 0.0; // dtilde^T = d^T Q^T -> dtilde = Q*d - std::vector dtilde(m); + ins_vector dtilde(m); permute_vector(col_permutation_, d, dtilde); update_upper(baru_ind, baru_val, t); @@ -1028,7 +1032,7 @@ i_t basis_update_t::lower_triangular_multiply(const csc_matrix_t sval; const i_t in_col_start = in.col_start[in_col]; const i_t in_col_end = in.col_start[in_col + 1]; - std::vector sbuffer(m); + ins_vector sbuffer(m); for (i_t p = in_col_start; p < in_col_end; ++p) { sbuffer[inverse_col_permutation_[in.i[p]]] = in.x[p]; } @@ -1062,7 +1066,7 @@ i_t basis_update_t::lower_triangular_multiply(const csc_matrix_t work2(m); + ins_vector work2(m); sind.push_back(r); sval.push_back(-dot); @@ -1081,14 +1085,14 @@ i_t basis_update_t::lower_triangular_multiply(const csc_matrix_t workspace(m); + ins_vector workspace(m); const i_t nx = sind.size(); for (i_t k = 0; k < nx; ++k) { const i_t j = sind[k]; const f_t x = sval[k]; workspace[j] = x; } - std::vector workspace2(m); + ins_vector workspace2(m); matrix_vector_multiply(L0_, 1.0, workspace, 0.0, workspace2); workspace = workspace2; @@ -1981,7 +1985,7 @@ void basis_update_mpf_t::l_multiply(std::vector& inout) const add_sparse_column(S_, u_col, theta, inout); } - std::vector out(m, 0.0); + ins_vector out(m, 0.0); matrix_vector_multiply(L0_, 1.0, inout, 0.0, out); inout = out; } @@ -1990,7 +1994,7 @@ template void basis_update_mpf_t::l_transpose_multiply(std::vector& inout) const { const i_t m = L0_.m; - std::vector out(m, 0.0); + ins_vector out(m, 0.0); matrix_vector_multiply(L0_transpose_, 1.0, inout, 0.0, out); inout = out; diff --git a/cpp/src/dual_simplex/crossover.cpp b/cpp/src/dual_simplex/crossover.cpp index de3c3ef1c..1b12a7353 100644 --- a/cpp/src/dual_simplex/crossover.cpp +++ b/cpp/src/dual_simplex/crossover.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -85,6 +85,7 @@ f_t dual_infeasibility(const lp_problem_t& lp, const std::vector& vstatus, const std::vector& z) { + raft::common::nvtx::range scope("DualSimplex::dual_infeasibility"); const i_t n = lp.num_cols; const i_t m = lp.num_rows; i_t num_infeasible = 0; diff --git a/cpp/src/dual_simplex/device_sparse_matrix.cu b/cpp/src/dual_simplex/device_sparse_matrix.cu index 86ec99c7b..11c3798b8 100644 --- a/cpp/src/dual_simplex/device_sparse_matrix.cu +++ b/cpp/src/dual_simplex/device_sparse_matrix.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -27,29 +27,9 @@ void csc_matrix_t::scale_columns(const std::vector& sc } #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE -template int -matrix_vector_multiply, PinnedHostAllocator>( - const csc_matrix_t& A, - double alpha, - const std::vector>& x, - double beta, - std::vector>& y); -template int -matrix_vector_multiply, std::allocator>( - const csc_matrix_t& A, - double alpha, - const std::vector>& x, - double beta, - std::vector>& y); - -template int -matrix_vector_multiply, PinnedHostAllocator>( - const csc_matrix_t& A, - double alpha, - const std::vector>& x, - double beta, - std::vector>& y); +// NOTE: matrix_vector_multiply is now templated on VectorX and VectorY. +// Since it's defined inline in the header, no explicit instantiation is needed here. template int matrix_transpose_vector_multiply& objective, const std::vector& nonbasic_list, std::vector& z) { + raft::common::nvtx::range scope("DualSimplex::compute_reduced_costs"); + const i_t m = A.m; const i_t n = A.n; // zN = cN - N'*y @@ -416,8 +453,10 @@ void compute_primal_variables(const basis_update_mpf_t& ft, const std::vector& basic_list, const std::vector& nonbasic_list, f_t tight_tol, - std::vector& x) + std::vector& x, + ins_vector& xB_workspace) { + raft::common::nvtx::range scope("DualSimplex::compute_primal_variables"); const i_t m = A.m; const i_t n = A.n; std::vector rhs = lp_rhs; @@ -434,12 +473,12 @@ void compute_primal_variables(const basis_update_mpf_t& ft, } } - std::vector xB(m); - ft.b_solve(rhs, xB); + xB_workspace.resize(m); + ft.b_solve(rhs, xB_workspace); for (i_t k = 0; k < m; ++k) { const i_t j = basic_list[k]; - x[j] = xB[k]; + x[j] = xB_workspace[k]; } } @@ -484,6 +523,8 @@ void compute_dual_residual(const csc_matrix_t& A, const std::vector& z, std::vector& dual_residual) { + raft::common::nvtx::range scope("DualSimplex::compute_dual_residual"); + dual_residual = z; const i_t n = A.n; // r = A'*y + z - c @@ -550,7 +591,7 @@ void compute_dual_solution_from_basis(const lp_problem_t& lp, const i_t n = lp.num_cols; y.resize(m); - std::vector cB(m); + ins_vector cB(m); for (i_t k = 0; k < m; ++k) { const i_t j = basic_list[k]; cB[k] = lp.objective[j]; @@ -589,7 +630,8 @@ i_t compute_primal_solution_from_basis(const lp_problem_t& lp, const std::vector& basic_list, const std::vector& nonbasic_list, const std::vector& vstatus, - std::vector& x) + std::vector& x, + ins_vector& xB_workspace) { const i_t m = lp.num_rows; const i_t n = lp.num_cols; @@ -619,12 +661,12 @@ i_t compute_primal_solution_from_basis(const lp_problem_t& lp, } } - std::vector xB(m); - ft.b_solve(rhs, xB); + xB_workspace.resize(m); + ft.b_solve(rhs, xB_workspace); for (i_t k = 0; k < m; ++k) { const i_t j = basic_list[k]; - x[j] = xB[k]; + x[j] = xB_workspace[k]; } return 0; } @@ -637,6 +679,7 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, ins_vector& squared_infeasibilities, ins_vector& infeasibility_indices) { + raft::common::nvtx::range scope("DualSimplex::compute_initial_primal_infeasibilities"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; squared_infeasibilities.resize(n, 0.0); @@ -1358,9 +1401,9 @@ i_t check_steepest_edge_norms(const simplex_solver_settings_t& setting const i_t m = basic_list.size(); for (i_t k = 0; k < m; ++k) { const i_t j = basic_list[k]; - std::vector ei(m); + ins_vector ei(m); ei[k] = -1.0; - std::vector delta_yi(m); + ins_vector delta_yi(m); ft.b_transpose_solve(ei, delta_yi); const f_t computed_norm = vector_norm2_squared(delta_yi); const f_t updated_norm = delta_y_steepest_edge[j]; @@ -1925,6 +1968,7 @@ void set_primal_variables_on_bounds(const lp_problem_t& lp, std::vector& vstatus, std::vector& x) { + raft::common::nvtx::range scope("DualSimplex::set_primal_variables_on_bounds"); const i_t n = lp.num_cols; for (i_t j = 0; j < n; ++j) { // We set z_j = 0 for basic variables @@ -2244,12 +2288,23 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, y.wrap(sol.y); z.wrap(sol.z); - dual::status_t status = dual::status_t::UNSET; + // Declare instrumented vectors used during initialization (before manifold setup) + ins_vector objective(lp.objective); + ins_vector c_basic(m); + ins_vector xB_workspace(m); - raft::common::nvtx::push_range("DualSimplex::phase2_advanced_init"); + // Create instrumentation manifold early to capture init section memory operations + instrumentation_manifold_t manifold; + manifold.add("x", x); + manifold.add("y", y); + manifold.add("z", z); + manifold.add("objective", objective); + manifold.add("c_basic", c_basic); + manifold.add("xB_workspace", xB_workspace); - // Perturbed objective (instrumented for main loop tracking) - ins_vector objective(lp.objective); + dual::status_t status = dual::status_t::UNSET; + + nvtx_range_guard init_scope("DualSimplex::phase2_advanced_init"); settings.log.printf("Dual Simplex Phase %d\n", phase); std::vector vstatus_old = vstatus; @@ -2257,7 +2312,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::bound_info(lp, settings); if (initialize_basis) { - raft::common::nvtx::push_range("DualSimplex::init_basis"); + raft::common::nvtx::range init_basis_scope("DualSimplex::init_basis"); std::vector superbasic_list; nonbasic_list.clear(); nonbasic_list.reserve(n - m); @@ -2272,7 +2327,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } } - std::vector c_basic(m); + // Populate c_basic after basis is initialized for (i_t k = 0; k < m; ++k) { const i_t j = basic_list[k]; c_basic[k] = objective[j]; @@ -2324,7 +2379,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } phase2::compute_primal_variables( - ft, lp.rhs, lp.A, basic_list, nonbasic_list, settings.tight_tol, sol.x); + ft, lp.rhs, lp.A, basic_list, nonbasic_list, settings.tight_tol, sol.x, xB_workspace); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } if (print_norms) { settings.log.printf("|| x || %e\n", vector_norm2(sol.x)); } @@ -2339,6 +2394,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif if (delta_y_steepest_edge.size() == 0) { + raft::common::nvtx::range scope("DualSimplex::initialize_steepest_edge_norms"); delta_y_steepest_edge.resize(n); if (slack_basis) { phase2::initialize_steepest_edge_norms_from_slack_basis( @@ -2392,11 +2448,16 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif csc_matrix_t A_transpose(1, 1, 0); - lp.A.transpose(A_transpose); - + manifold.add("A_transpose.col_start", A_transpose.col_start); + manifold.add("A_transpose.i", A_transpose.i); + manifold.add("A_transpose.x", A_transpose.x); + { + raft::common::nvtx::range scope("DualSimplex::transpose_A"); + lp.A.transpose(A_transpose); + } f_t obj = compute_objective(lp, sol.x); - raft::common::nvtx::pop_range(); // advanced_basis_init + init_scope.pop(); // End phase2_advanced_init range const i_t start_iter = iter; @@ -2422,8 +2483,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, sparse_vector_t v_sparse(m, 0); // For steepest edge norms sparse_vector_t atilde_sparse(m, 0); // For flip adjustments - // Create instrumentation manifold - instrumentation_manifold_t manifold; + // Add remaining instrumented vectors to manifold (x, y, z, objective, c_basic, xB_workspace added + // earlier) Delta vectors manifold.add("delta_y", delta_y); manifold.add("delta_z", delta_z); manifold.add("delta_x", delta_x); @@ -2439,10 +2500,6 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, manifold.add("squared_infeasibilities", squared_infeasibilities); manifold.add("infeasibility_indices", infeasibility_indices); manifold.add("bounded_variables", bounded_variables); - // Ratio test vectors - // manifold.add("vstatus", vstatus); - // manifold.add("nonbasic_list", nonbasic_list); - manifold.add("z", z); // Add sparse vector internal arrays to manifold manifold.add("delta_y_sparse.i", delta_y_sparse.i); @@ -2508,6 +2565,24 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if (!work_unit_context) return; i_t remaining_iters = iter - last_feature_log_iter; if (remaining_iters <= 0) return; + + auto [total_loads, total_stores] = manifold.collect_and_flush(); + features.byte_loads = total_loads; + features.byte_stores = total_stores; + + f_t now = toc(start_time); + features.interval_runtime = now - interval_start_time; + interval_start_time = now; + + features.iteration = iter; + features.num_refactors = num_refactors; + features.num_basis_updates = ft.num_updates(); + features.sparse_delta_z_count = sparse_delta_z; + features.dense_delta_z_count = dense_delta_z; + features.total_bound_flips = total_bound_flips; + features.num_infeasibilities = infeasibility_indices.size(); + features.delta_y_nz_percentage = delta_y_nz_percentage; + f_t prediction = predict_work_units(remaining_iters); // printf("DualSimplex determ (final): %d iters, predicted %.4f\n", remaining_iters, // prediction); @@ -2523,7 +2598,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, f_t max_val; timers.start_timer(); { - raft::common::nvtx::range scope_pricing("DualSimplex::pricing"); + // raft::common::nvtx::range scope_pricing("DualSimplex::pricing"); if (settings.use_steepest_edge_pricing) { leaving_index = phase2::steepest_edge_pricing_with_infeasibilities(lp, settings, @@ -2568,7 +2643,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_y_sparse.clear(); UTsol_sparse.clear(); { - raft::common::nvtx::range scope_btran("DualSimplex::btran"); + // raft::common::nvtx::range scope_btran("DualSimplex::btran"); phase2::compute_delta_y(ft, basic_leaving_index, direction, delta_y_sparse, UTsol_sparse); } timers.btran_time += timers.stop_timer(); @@ -2643,7 +2718,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, const bool harris_ratio = settings.use_harris_ratio; const bool bound_flip_ratio = settings.use_bound_flip_ratio; { - raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); + // raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); if (harris_ratio) { f_t max_step_length = phase2::first_stage_harris(lp, vstatus, nonbasic_list, z, delta_z); entering_index = phase2::second_stage_harris(lp, @@ -2708,7 +2783,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( - lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); + lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); sol.x = unperturbed_x; primal_infeasibility = phase2::compute_initial_primal_infeasibilities( lp, settings, basic_list, sol.x, squared_infeasibilities, infeasibility_indices); @@ -2745,7 +2820,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } else { std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( - lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); + lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); sol.x = unperturbed_x; primal_infeasibility = phase2::compute_initial_primal_infeasibilities( lp, settings, basic_list, sol.x, squared_infeasibilities, infeasibility_indices); @@ -2883,7 +2958,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, scaled_delta_xB_sparse.clear(); rhs_sparse.from_csc_column(lp.A, entering_index); { - raft::common::nvtx::range scope_ftran("DualSimplex::ftran"); + // raft::common::nvtx::range scope_ftran("DualSimplex::ftran"); if (phase2::compute_delta_x(lp, ft, entering_index, @@ -3029,7 +3104,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); // Refactor or update the basis factorization { - raft::common::nvtx::range scope_update("DualSimplex::basis_update"); + // raft::common::nvtx::range scope_update("DualSimplex::basis_update"); bool should_refactor = ft.num_updates() > settings.refactor_frequency; if (!should_refactor) { i_t recommend_refactor = ft.update(utilde_sparse, UTsol_sparse, basic_leaving_index); @@ -3072,7 +3147,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if (should_recompute_x) { std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( - lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x); + lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); sol.x = unperturbed_x; } phase2::compute_initial_primal_infeasibilities( diff --git a/cpp/src/dual_simplex/right_looking_lu.cpp b/cpp/src/dual_simplex/right_looking_lu.cpp index c4a9bda01..b62e3b0ca 100644 --- a/cpp/src/dual_simplex/right_looking_lu.cpp +++ b/cpp/src/dual_simplex/right_looking_lu.cpp @@ -1,12 +1,13 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ #include #include +#include #include @@ -14,6 +15,8 @@ #include #include +using cuopt::ins_vector; + namespace cuopt::linear_programming::dual_simplex { namespace { @@ -30,9 +33,9 @@ struct element_t { }; constexpr int kNone = -1; -template +template i_t initialize_degree_data(const csc_matrix_t& A, - const std::vector& column_list, + const VectorI& column_list, std::vector& Cdegree, std::vector& Rdegree, std::vector>& col_count, @@ -69,9 +72,9 @@ i_t initialize_degree_data(const csc_matrix_t& A, return Bnz; } -template +template i_t load_elements(const csc_matrix_t& A, - const std::vector& column_list, + const VectorI& column_list, i_t Bnz, std::vector>& elements, std::vector& first_in_row, @@ -569,15 +572,15 @@ void remove_pivot_col(i_t pivot_i, } // namespace -template +template i_t right_looking_lu(const csc_matrix_t& A, const simplex_solver_settings_t& settings, f_t tol, - const std::vector& column_list, - std::vector& q, + const VectorI& column_list, + VectorI& q, csc_matrix_t& L, csc_matrix_t& U, - std::vector& pinv) + VectorI& pinv) { raft::common::nvtx::range scope("LU::right_looking_lu"); const i_t n = column_list.size(); @@ -1146,14 +1149,25 @@ i_t right_looking_lu_row_permutation_only(const csc_matrix_t& A, #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE -template int right_looking_lu(const csc_matrix_t& A, - const simplex_solver_settings_t& settings, - double tol, - const std::vector& column_list, - std::vector& q, - csc_matrix_t& L, - csc_matrix_t& U, - std::vector& pinv); +template int right_looking_lu>( + const csc_matrix_t& A, + const simplex_solver_settings_t& settings, + double tol, + const std::vector& column_list, + std::vector& q, + csc_matrix_t& L, + csc_matrix_t& U, + std::vector& pinv); + +template int right_looking_lu>( + const csc_matrix_t& A, + const simplex_solver_settings_t& settings, + double tol, + const ins_vector& column_list, + ins_vector& q, + csc_matrix_t& L, + csc_matrix_t& U, + ins_vector& pinv); template int right_looking_lu_row_permutation_only( const csc_matrix_t& A, diff --git a/cpp/src/dual_simplex/right_looking_lu.hpp b/cpp/src/dual_simplex/right_looking_lu.hpp index 2f985fb36..179fff01b 100644 --- a/cpp/src/dual_simplex/right_looking_lu.hpp +++ b/cpp/src/dual_simplex/right_looking_lu.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -14,15 +14,15 @@ namespace cuopt::linear_programming::dual_simplex { -template +template i_t right_looking_lu(const csc_matrix_t& A, const simplex_solver_settings_t& settings, f_t tol, - const std::vector& column_list, - std::vector& q, + const VectorI& column_list, + VectorI& q, csc_matrix_t& L, csc_matrix_t& U, - std::vector& pinv); + VectorI& pinv); template i_t right_looking_lu_row_permutation_only(const csc_matrix_t& A, diff --git a/cpp/src/dual_simplex/singletons.cpp b/cpp/src/dual_simplex/singletons.cpp index 71603306c..a3f417c60 100644 --- a/cpp/src/dual_simplex/singletons.cpp +++ b/cpp/src/dual_simplex/singletons.cpp @@ -1,15 +1,18 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ #include #include +#include #include +using cuopt::ins_vector; + namespace cuopt::linear_programming::dual_simplex { // Destroys the queue but prints it @@ -128,8 +131,8 @@ void create_row_representation(const csc_matrix_t& A, } // Complete the permuation -template -i_t complete_permutation(i_t singletons, std::vector& Xdeg, std::vector& Xperm) +template +i_t complete_permutation(i_t singletons, std::vector& Xdeg, VectorI& Xperm) { i_t n = Xdeg.size(); assert(Xperm.size() == n); @@ -154,12 +157,12 @@ i_t complete_permutation(i_t singletons, std::vector& Xdeg, std::vector +template i_t find_singletons(const csc_matrix_t& A, i_t& row_singletons, - std::vector& row_perm, + VectorI& row_perm, i_t& col_singletons, - std::vector& col_perm) + VectorI& col_perm) { i_t n = A.n; i_t m = A.m; @@ -198,12 +201,15 @@ i_t find_singletons(const csc_matrix_t& A, row_form = true; // Find column singletons + // Use .underlying() to get std::vector iterators for row_col_graph_t + auto& col_perm_vec = static_cast&>(col_perm); + auto& row_perm_vec = static_cast&>(row_perm); row_col_graph_t graph{Cdeg.begin(), - col_perm.begin(), + col_perm_vec.begin(), A.col_start.underlying().cbegin(), A.i.underlying().cbegin(), Rdeg.begin(), - row_perm.begin(), + row_perm_vec.begin(), Rp.cbegin(), Rj.cbegin()}; @@ -229,12 +235,15 @@ i_t find_singletons(const csc_matrix_t& A, } // Find row singletons + // Use .underlying() to get std::vector iterators for row_col_graph_t + auto& row_perm_vec2 = static_cast&>(row_perm); + auto& col_perm_vec2 = static_cast&>(col_perm); row_col_graph_t graph{Rdeg.begin(), - row_perm.begin(), + row_perm_vec2.begin(), Rp.cbegin(), Rj.cbegin(), Cdeg.begin(), - col_perm.begin(), + col_perm_vec2.begin(), A.col_start.underlying().cbegin(), A.i.underlying().cbegin()}; #ifdef SINGLETON_DEBUG @@ -280,15 +289,24 @@ template void create_row_representation(const csc_matrix_t& col_index, std::vector& workspace); // Complete the permuation -template int complete_permutation(int singletons, - std::vector& Xdeg, - std::vector& Xperm); - -template int find_singletons(const csc_matrix_t& A, - int& row_singletons, - std::vector& row_perm, - int& col_singleton, - std::vector& col_perm); +template int complete_permutation>(int singletons, + std::vector& Xdeg, + std::vector& Xperm); +template int complete_permutation>(int singletons, + std::vector& Xdeg, + ins_vector& Xperm); + +template int find_singletons>(const csc_matrix_t& A, + int& row_singletons, + std::vector& row_perm, + int& col_singleton, + std::vector& col_perm); + +template int find_singletons>(const csc_matrix_t& A, + int& row_singletons, + ins_vector& row_perm, + int& col_singleton, + ins_vector& col_perm); #endif diff --git a/cpp/src/dual_simplex/singletons.hpp b/cpp/src/dual_simplex/singletons.hpp index b9cfcaa9b..2a1ac3e55 100644 --- a/cpp/src/dual_simplex/singletons.hpp +++ b/cpp/src/dual_simplex/singletons.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -42,11 +42,11 @@ void create_row_representationon(const csc_matrix_t& A, template i_t complete_permutationn(i_t singletons, std::vector& Xdeg, std::vector& Xperm); -template +template i_t find_singletons(const csc_matrix_t& A, i_t& row_singletons, - std::vector& row_perm, + VectorI& row_perm, i_t& col_singleton, - std::vector& col_perm); + VectorI& col_perm); } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/sparse_matrix.cpp b/cpp/src/dual_simplex/sparse_matrix.cpp index 87aa26ab2..080bed53e 100644 --- a/cpp/src/dual_simplex/sparse_matrix.cpp +++ b/cpp/src/dual_simplex/sparse_matrix.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -951,12 +951,9 @@ template double sparse_dot(const std::vector& xind, const csc_matrix_t& Y, int y_col); -template int matrix_vector_multiply, std::allocator>( - const csc_matrix_t& A, - double alpha, - const std::vector>& x, - double beta, - std::vector>& y); +// NOTE: matrix_vector_multiply is now templated on VectorX and VectorY. +// Since it's defined inline in the header, no explicit instantiation is needed here. + template int matrix_transpose_vector_multiply, std::allocator>( const csc_matrix_t& A, diff --git a/cpp/src/dual_simplex/sparse_matrix.hpp b/cpp/src/dual_simplex/sparse_matrix.hpp index 5151432f5..c2d945543 100644 --- a/cpp/src/dual_simplex/sparse_matrix.hpp +++ b/cpp/src/dual_simplex/sparse_matrix.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -280,12 +280,9 @@ i_t matrix_transpose_vector_multiply(const csc_matrix_t& A, } // y <- alpha*A*x + beta*y -template -i_t matrix_vector_multiply(const csc_matrix_t& A, - f_t alpha, - const std::vector& x, - f_t beta, - std::vector& y) +template +i_t matrix_vector_multiply( + const csc_matrix_t& A, f_t alpha, const VectorX& x, f_t beta, VectorY& y) { // y <- alpha*A*x + beta*y i_t m = A.m; diff --git a/cpp/src/dual_simplex/triangle_solve.cpp b/cpp/src/dual_simplex/triangle_solve.cpp index 375784650..2f280295b 100644 --- a/cpp/src/dual_simplex/triangle_solve.cpp +++ b/cpp/src/dual_simplex/triangle_solve.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -11,73 +11,9 @@ namespace cuopt::linear_programming::dual_simplex { -template -i_t lower_triangular_solve(const csc_matrix_t& L, std::vector& x) -{ - i_t n = L.n; - assert(x.size() == n); - for (i_t j = 0; j < n; ++j) { - i_t col_start = L.col_start[j]; - i_t col_end = L.col_start[j + 1]; - if (x[j] != 0.0) { - x[j] /= L.x[col_start]; - for (i_t p = col_start + 1; p < col_end; ++p) { - x[L.i[p]] -= L.x[p] * x[j]; - } - } - } - return 0; -} - -template -i_t lower_triangular_transpose_solve(const csc_matrix_t& L, std::vector& x) -{ - const i_t n = L.n; - assert(x.size() == n); - for (i_t j = n - 1; j >= 0; --j) { - const i_t col_start = L.col_start[j] + 1; - const i_t col_end = L.col_start[j + 1]; - for (i_t p = col_start; p < col_end; ++p) { - x[j] -= L.x[p] * x[L.i[p]]; - } - x[j] /= L.x[L.col_start[j]]; - } - return 0; -} - -template -i_t upper_triangular_solve(const csc_matrix_t& U, std::vector& x) -{ - const i_t n = U.n; - assert(x.size() == n); - for (i_t j = n - 1; j >= 0; --j) { - const i_t col_start = U.col_start[j]; - const i_t col_end = U.col_start[j + 1] - 1; - if (x[j] != 0.0) { - x[j] /= U.x[col_end]; - for (i_t p = col_start; p < col_end; ++p) { - x[U.i[p]] -= U.x[p] * x[j]; - } - } - } - return 0; -} - -template -i_t upper_triangular_transpose_solve(const csc_matrix_t& U, std::vector& x) -{ - const i_t n = U.n; - assert(x.size() == n); - for (i_t j = 0; j < n; ++j) { - const i_t col_start = U.col_start[j]; - const i_t col_end = U.col_start[j + 1] - 1; - for (i_t p = col_start; p < col_end; ++p) { - x[j] -= U.x[p] * x[U.i[p]]; - } - x[j] /= U.x[col_end]; - } - return 0; -} +// NOTE: lower_triangular_solve, lower_triangular_transpose_solve, +// upper_triangular_solve, and upper_triangular_transpose_solve are now +// templated on vector types and defined in the header file. // \brief Reach computes the reach of b in the graph of G // \param[in] b - Sparse vector containing the rhs @@ -204,17 +140,9 @@ i_t sparse_triangle_solve(const sparse_vector_t& b, #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE -template int lower_triangular_solve(const csc_matrix_t& L, - std::vector& x); - -template int lower_triangular_transpose_solve(const csc_matrix_t& L, - std::vector& x); - -template int upper_triangular_solve(const csc_matrix_t& U, - std::vector& x); - -template int upper_triangular_transpose_solve(const csc_matrix_t& U, - std::vector& x); +// NOTE: lower_triangular_solve, lower_triangular_transpose_solve, +// upper_triangular_solve, and upper_triangular_transpose_solve are now +// templated on vector types and defined in the header file, so no explicit instantiation needed. template int reach(const sparse_vector_t& b, const std::optional>& pinv, diff --git a/cpp/src/dual_simplex/triangle_solve.hpp b/cpp/src/dual_simplex/triangle_solve.hpp index 18dbafc85..c940eee48 100644 --- a/cpp/src/dual_simplex/triangle_solve.hpp +++ b/cpp/src/dual_simplex/triangle_solve.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -25,23 +25,79 @@ namespace cuopt::linear_programming::dual_simplex { // Solve L*x = b. On input x contains the right-hand side b, on output the // solution -template -i_t lower_triangular_solve(const csc_matrix_t& L, std::vector& x); +template +i_t lower_triangular_solve(const csc_matrix_t& L, VectorF& x) +{ + i_t n = L.n; + assert(x.size() == n); + for (i_t j = 0; j < n; ++j) { + i_t col_start = L.col_start[j]; + i_t col_end = L.col_start[j + 1]; + if (x[j] != 0.0) { + x[j] /= L.x[col_start]; + for (i_t p = col_start + 1; p < col_end; ++p) { + x[L.i[p]] -= L.x[p] * x[j]; + } + } + } + return 0; +} // Solve L'*x = b. On input x contains the right-hand side b, on output the // solution -template -i_t lower_triangular_transpose_solve(const csc_matrix_t& L, std::vector& x); +template +i_t lower_triangular_transpose_solve(const csc_matrix_t& L, VectorF& x) +{ + const i_t n = L.n; + assert(x.size() == n); + for (i_t j = n - 1; j >= 0; --j) { + const i_t col_start = L.col_start[j] + 1; + const i_t col_end = L.col_start[j + 1]; + for (i_t p = col_start; p < col_end; ++p) { + x[j] -= L.x[p] * x[L.i[p]]; + } + x[j] /= L.x[L.col_start[j]]; + } + return 0; +} // Solve U*x = b. On input x contains the right-hand side b, on output the // solution -template -i_t upper_triangular_solve(const csc_matrix_t& U, std::vector& x); +template +i_t upper_triangular_solve(const csc_matrix_t& U, VectorF& x) +{ + const i_t n = U.n; + assert(x.size() == n); + for (i_t j = n - 1; j >= 0; --j) { + const i_t col_start = U.col_start[j]; + const i_t col_end = U.col_start[j + 1] - 1; + if (x[j] != 0.0) { + x[j] /= U.x[col_end]; + for (i_t p = col_start; p < col_end; ++p) { + x[U.i[p]] -= U.x[p] * x[j]; + } + } + } + return 0; +} // Solve U'*x = b. On input x contains the right-hand side b, on output the // solution -template -i_t upper_triangular_transpose_solve(const csc_matrix_t& U, std::vector& x); +template +i_t upper_triangular_transpose_solve(const csc_matrix_t& U, VectorF& x) +{ + const i_t n = U.n; + assert(x.size() == n); + for (i_t j = 0; j < n; ++j) { + const i_t col_start = U.col_start[j]; + const i_t col_end = U.col_start[j + 1] - 1; + for (i_t p = col_start; p < col_end; ++p) { + x[j] -= U.x[p] * x[U.i[p]]; + } + x[j] /= U.x[col_end]; + } + return 0; +} // \brief Reach computes the reach of b in the graph of G // \param[in] b - sparse vector containing the rhs diff --git a/cpp/src/dual_simplex/vector_math.cpp b/cpp/src/dual_simplex/vector_math.cpp index b935d18e4..f20d17afe 100644 --- a/cpp/src/dual_simplex/vector_math.cpp +++ b/cpp/src/dual_simplex/vector_math.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -122,44 +122,8 @@ f_t sparse_dot(const std::vector& xind, return dot; } -// x = b(p) -template -i_t permute_vector(const std::vector& p, const std::vector& b, std::vector& x) -{ - i_t n = p.size(); - assert(x.size() == n); - assert(b.size() == n); - for (i_t k = 0; k < n; ++k) { - x[k] = b[p[k]]; - } - return 0; -} - -// x(p) = b -template -i_t inverse_permute_vector(const std::vector& p, - const std::vector& b, - std::vector& x) -{ - i_t n = p.size(); - assert(x.size() == n); - assert(b.size() == n); - for (i_t k = 0; k < n; ++k) { - x[p[k]] = b[k]; - } - return 0; -} - -template -i_t inverse_permutation(const std::vector& p, std::vector& pinv) -{ - i_t n = p.size(); - if (pinv.size() != n) { pinv.resize(n); } - for (i_t k = 0; k < n; ++k) { - pinv[p[k]] = k; - } - return 0; -} +// NOTE: permute_vector, inverse_permute_vector, and inverse_permutation are now +// templated on vector types and defined in the header file. #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE @@ -195,15 +159,8 @@ template double sparse_dot(int const* xind, template double sparse_dot( int* xind, double* xval, int nx, int* yind, double* yval, int ny); -template int permute_vector(const std::vector& p, - const std::vector& b, - std::vector& x); - -template int inverse_permute_vector(const std::vector& p, - const std::vector& b, - std::vector& x); - -template int inverse_permutation(const std::vector& p, std::vector& pinv); +// NOTE: permute_vector, inverse_permute_vector, and inverse_permutation are now +// templated on vector types and defined in the header file, so no explicit instantiation needed. #endif } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/vector_math.hpp b/cpp/src/dual_simplex/vector_math.hpp index 44a459935..6bf573e89 100644 --- a/cpp/src/dual_simplex/vector_math.hpp +++ b/cpp/src/dual_simplex/vector_math.hpp @@ -1,12 +1,13 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ #pragma once +#include #include #include @@ -56,17 +57,41 @@ template f_t sparse_dot(i_t* xind, f_t* xval, i_t nx, i_t* yind, f_t* yval, i_t ny); // Computes x = P*b or x=b(p) in MATLAB. -template -i_t permute_vector(const std::vector& p, const std::vector& b, std::vector& x); +template +int permute_vector(const VectorI& p, const VectorF_in& b, VectorF_out& x) +{ + auto n = p.size(); + assert(x.size() == n); + assert(b.size() == n); + for (decltype(n) k = 0; k < n; ++k) { + x[k] = b[p[k]]; + } + return 0; +} // Computes x = P'*b or x(p) = b in MATLAB. -template -i_t inverse_permute_vector(const std::vector& p, - const std::vector& b, - std::vector& x); +template +int inverse_permute_vector(const VectorI& p, const VectorF_in& b, VectorF_out& x) +{ + auto n = p.size(); + assert(x.size() == n); + assert(b.size() == n); + for (decltype(n) k = 0; k < n; ++k) { + x[p[k]] = b[k]; + } + return 0; +} // Computes pinv from p. Or pinv(p) = 1:n in MATLAB -template -i_t inverse_permutation(const std::vector& p, std::vector& pinv); +template +int inverse_permutation(const VectorI_in& p, VectorI_out& pinv) +{ + auto n = p.size(); + if (pinv.size() != n) { pinv.resize(n); } + for (decltype(n) k = 0; k < n; ++k) { + pinv[p[k]] = k; + } + return 0; +} } // namespace cuopt::linear_programming::dual_simplex From 8fdbff8729c9945bf5a86711569ea7366bfcc7d4 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 15 Jan 2026 15:45:52 +0000 Subject: [PATCH 190/366] fix sync bug on termination --- cpp/src/dual_simplex/branch_and_bound.cpp | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 6757a5184..df8c89ac7 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2096,16 +2096,14 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& worker.last_solved_node = node; // Handle result - if (status == node_solve_info_t::TIME_LIMIT) { + if (status == node_solve_info_t::TIME_LIMIT || status == node_solve_info_t::WORK_LIMIT) { worker.current_node = nullptr; - solver_status_ = mip_exploration_status_t::TIME_LIMIT; - bsp_terminated_.store(true); - break; // Exit loop - scheduler will stop all workers at next sync - } else if (status == node_solve_info_t::WORK_LIMIT) { - worker.current_node = nullptr; - solver_status_ = mip_exploration_status_t::WORK_LIMIT; - bsp_terminated_.store(true); - break; // Exit loop - scheduler will stop all workers at next sync + // Don't set solver_status_ or bsp_terminated_ here - that would cause a race + // where other workers see the flag and exit before reaching the sync point. + // The sync callback will check the work/time limit and set termination flags + // when all workers are safely at the barrier. + bsp_scheduler_->wait_for_next_sync(worker.work_context); + break; } else { // Node completed successfully worker.current_node = nullptr; From b6d7ecc020530fbe180859440374f28ad868900b Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 15 Jan 2026 17:25:51 +0000 Subject: [PATCH 191/366] revert disable heuristics --- cpp/src/mip/diversity/diversity_manager.cu | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index ea1381022..f205caa6e 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -315,7 +315,8 @@ solution_t diversity_manager_t::run_solver() // Debug: Allow disabling GPU heuristics to test B&B tree determinism in isolation const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); - disable_heuristics_env = "1"; // DO NOT REMOVE! intended debugging line! + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) + disable_heuristics_env = "1"; // DO NOT REMOVE! intended debugging line! if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { CUOPT_LOG_INFO("GPU heuristics disabled via CUOPT_DISABLE_GPU_HEURISTICS=1"); // Initialize population minimally and wait for B&B to finish From 668391ebedabf297b10eedde6c092f5857d9eef8 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 15 Jan 2026 19:14:46 +0000 Subject: [PATCH 192/366] no presovle when determinsitic --- benchmarks/linear_programming/cuopt/run_mip.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 66435da2b..f746fb71a 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -207,7 +207,7 @@ int run_single_file(std::string file_path, settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; - settings.presolve = false; + if (deterministic) { settings.presolve = false; } cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; From 3b6d532e93978e4d0362eca80a08817fec76fddf Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 16 Jan 2026 09:10:45 +0000 Subject: [PATCH 193/366] restore nondeterminsitc codepath behavior --- cpp/src/dual_simplex/branch_and_bound.cpp | 6 ++++-- cpp/src/dual_simplex/presolve.cpp | 5 +++-- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index df8c89ac7..93603d68e 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2419,8 +2419,10 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t bool feasible = true; // TODO: incorporate into work unit estimation - // feasible = worker.node_presolver->bounds_strengthening( - // worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + if (!settings_.deterministic) { + feasible = worker.node_presolver->bounds_strengthening( + worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + } if (!feasible) { node_ptr->lower_bound = std::numeric_limits::infinity(); diff --git a/cpp/src/dual_simplex/presolve.cpp b/cpp/src/dual_simplex/presolve.cpp index d0211c724..7a2f81c7e 100644 --- a/cpp/src/dual_simplex/presolve.cpp +++ b/cpp/src/dual_simplex/presolve.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -622,7 +622,8 @@ void convert_user_problem(const user_problem_t& user_problem, convert_greater_to_less(user_problem, row_sense, problem, greater_rows, less_rows); } - constexpr bool run_bounds_strengthening = false; + bool run_bounds_strengthening = true; + if (settings.deterministic) { run_bounds_strengthening = false; } if (run_bounds_strengthening) { csr_matrix_t Arow(1, 1, 1); problem.A.to_compressed_row(Arow); From 07cea4d3f79fb82d84109f7ad1ad0ac1b00fcf9f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 16 Jan 2026 10:37:28 +0000 Subject: [PATCH 194/366] cleanup work --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- cpp/src/dual_simplex/pseudo_costs.cpp | 2 +- cpp/src/dual_simplex/singletons.cpp | 2 - cpp/src/linear_programming/pdlp_features.hpp | 58 ++++----- cpp/src/mip/diversity/diversity_manager.cu | 26 ++-- .../recombiners/bound_prop_recombiner.cuh | 18 +-- .../diversity/recombiners/fp_recombiner.cuh | 12 +- .../recombiners/line_segment_recombiner.cuh | 4 +- .../mip/diversity/recombiners/recombiner.cuh | 8 +- .../mip/feasibility_jump/feasibility_jump.cu | 6 +- .../feasibility_jump_kernels.cu | 19 +-- .../feasibility_pump/feasibility_pump.cu | 44 +++---- .../line_segment_search.cu | 3 +- cpp/src/mip/local_search/local_search.cu | 5 +- .../local_search/rounding/bounds_repair.cu | 3 +- .../local_search/rounding/constraint_prop.cu | 59 +++++---- .../local_search/rounding/lb_bounds_repair.cu | 5 +- cpp/src/mip/problem/problem.cu | 4 - cpp/src/mip/relaxed_lp/relaxed_lp.cu | 123 +++++++++--------- cpp/src/mip/solver.cu | 16 +-- 20 files changed, 191 insertions(+), 228 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 93603d68e..06dee7d22 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -379,7 +379,7 @@ void branch_and_bound_t::flush_pending_features() last_features_.node_status); // Single printf call - settings_.log.printf("%s", line_buffer); + // settings_.log.printf("%s", line_buffer); has_pending_features_ = false; } diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index cdddf0a10..5d87a7abb 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -159,7 +159,7 @@ void strong_branching(const lp_problem_t& original_lp, settings.num_threads, fractional.size()); -#pragma omp parallel num_threads(64) +#pragma omp parallel num_threads(settings.num_threads) { i_t n = std::min(4 * settings.num_threads, fractional.size()); diff --git a/cpp/src/dual_simplex/singletons.cpp b/cpp/src/dual_simplex/singletons.cpp index a3f417c60..347604dce 100644 --- a/cpp/src/dual_simplex/singletons.cpp +++ b/cpp/src/dual_simplex/singletons.cpp @@ -201,7 +201,6 @@ i_t find_singletons(const csc_matrix_t& A, row_form = true; // Find column singletons - // Use .underlying() to get std::vector iterators for row_col_graph_t auto& col_perm_vec = static_cast&>(col_perm); auto& row_perm_vec = static_cast&>(row_perm); row_col_graph_t graph{Cdeg.begin(), @@ -235,7 +234,6 @@ i_t find_singletons(const csc_matrix_t& A, } // Find row singletons - // Use .underlying() to get std::vector iterators for row_col_graph_t auto& row_perm_vec2 = static_cast&>(row_perm); auto& col_perm_vec2 = static_cast&>(col_perm); row_col_graph_t graph{Rdeg.begin(), diff --git a/cpp/src/linear_programming/pdlp_features.hpp b/cpp/src/linear_programming/pdlp_features.hpp index 76f016914..6af6b0105 100644 --- a/cpp/src/linear_programming/pdlp_features.hpp +++ b/cpp/src/linear_programming/pdlp_features.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -188,34 +188,34 @@ struct pdlp_features_t { (interval_time_ms / 1000.0) : 0.0; - printf( - "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6e nnz_stddev=%.4f " - "unbalancedness=%.4f interval_iters=%d interval_major=%d interval_restarts=%d " - "spmv_ops=%ld total_iters=%d total_restarts=%d iters_since_restart=%d " - "primal_res=%.6e dual_res=%.6e gap=%.6e kkt=%.6e " - "step_size=%.6e primal_weight=%.6e time_ms=%.2f nnz_per_s=%.2e warm_start=%d", - n_vars, - n_cstrs, - nnz, - sparsity, - nnz_stddev, - unbalancedness, - interval_iters, - interval_major, - interval_restarts, - interval_spmv_ops, - total_iters, - total_restarts, - iters_since_restart, - primal_res, - dual_res, - gap, - kkt_score, - step_size, - primal_weight, - interval_time_ms, - nnz_per_sec, - static_cast(has_warm_start)); + // printf( + // "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6e nnz_stddev=%.4f " + // "unbalancedness=%.4f interval_iters=%d interval_major=%d interval_restarts=%d " + // "spmv_ops=%ld total_iters=%d total_restarts=%d iters_since_restart=%d " + // "primal_res=%.6e dual_res=%.6e gap=%.6e kkt=%.6e " + // "step_size=%.6e primal_weight=%.6e time_ms=%.2f nnz_per_s=%.2e warm_start=%d", + // n_vars, + // n_cstrs, + // nnz, + // sparsity, + // nnz_stddev, + // unbalancedness, + // interval_iters, + // interval_major, + // interval_restarts, + // interval_spmv_ops, + // total_iters, + // total_restarts, + // iters_since_restart, + // primal_res, + // dual_res, + // gap, + // kkt_score, + // step_size, + // primal_weight, + // interval_time_ms, + // nnz_per_sec, + // static_cast(has_warm_start)); } /** diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index f205caa6e..d044bb49d 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -151,8 +151,8 @@ void diversity_manager_t::add_user_given_solutions( rmm::device_uvector init_sol_assignment(*init_sol, sol.handle_ptr->get_stream()); if (problem_ptr->pre_process_assignment(init_sol_assignment)) { relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = std::min(60., timer.remaining_time() / 2); - lp_settings.work_limit = lp_settings.time_limit; + lp_settings.time_limit = std::min(60., timer.remaining_time() / 2); + if (timer.deterministic) { lp_settings.work_limit = lp_settings.time_limit; } lp_settings.tolerance = problem_ptr->tolerances.absolute_tolerance; lp_settings.save_state = false; lp_settings.return_first_feasible = true; @@ -316,7 +316,7 @@ solution_t diversity_manager_t::run_solver() // Debug: Allow disabling GPU heuristics to test B&B tree determinism in isolation const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) - disable_heuristics_env = "1"; // DO NOT REMOVE! intended debugging line! + disable_heuristics_env = "1"; // to cursor: DO NOT REMOVE! intended debugging line! if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { CUOPT_LOG_INFO("GPU heuristics disabled via CUOPT_DISABLE_GPU_HEURISTICS=1"); // Initialize population minimally and wait for B&B to finish @@ -393,13 +393,13 @@ solution_t diversity_manager_t::run_solver() pdlp_settings.tolerances.relative_primal_tolerance = absolute_tolerance / tolerance_divisor; pdlp_settings.tolerances.relative_dual_tolerance = absolute_tolerance / tolerance_divisor; pdlp_settings.time_limit = lp_time_limit; - pdlp_settings.iteration_limit = 100000; - pdlp_settings.first_primal_feasible = false; - pdlp_settings.concurrent_halt = &global_concurrent_halt; - pdlp_settings.method = method_t::Concurrent; - pdlp_settings.inside_mip = true; - pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; - pdlp_settings.num_gpus = context.settings.num_gpus; + if (timer.deterministic) { pdlp_settings.iteration_limit = 100000; } + pdlp_settings.first_primal_feasible = false; + pdlp_settings.concurrent_halt = &global_concurrent_halt; + pdlp_settings.method = method_t::Concurrent; + pdlp_settings.inside_mip = true; + pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; + pdlp_settings.num_gpus = context.settings.num_gpus; timer_t lp_timer(lp_time_limit); auto lp_result = solve_lp_with_method(*problem_ptr, pdlp_settings, lp_timer); @@ -722,8 +722,8 @@ diversity_manager_t::recombine_and_local_search(solution_t& : diversity_config.lp_run_time_if_infeasible; lp_run_time = std::min(lp_run_time, timer.remaining_time()); relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = lp_run_time; - lp_settings.work_limit = lp_settings.time_limit; + lp_settings.time_limit = lp_run_time; + if (timer.deterministic) { lp_settings.work_limit = lp_settings.time_limit; } lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; lp_settings.return_first_feasible = false; lp_settings.save_state = true; @@ -800,7 +800,7 @@ std::pair, bool> diversity_manager_t::recombine( mab_recombiner.set_last_chosen_option(selected_index); recombine_stats.add_attempt((recombiner_enum_t)recombiner); recombine_stats.start_recombiner_time(); - CUOPT_LOG_DEBUG("Recombining sol %x and %x with recombiner %d, weights %x", + CUOPT_LOG_TRACE("Recombining sol %x and %x with recombiner %d, weights %x", a.get_hash(), b.get_hash(), recombiner, diff --git a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh index 3d217a2a5..5efd1d545 100644 --- a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh @@ -164,23 +164,23 @@ class bound_prop_recombiner_t : public recombiner_t { "n_vars_from_guiding %d n_vars_from_other %d", n_vars_from_guiding, n_vars_from_other); // DETERMINISM DEBUG: Log everything that could affect divergence - CUOPT_LOG_DEBUG("BP_DET: sol_a_hash=0x%x sol_b_hash=0x%x offspring_hash=0x%x, seed %x", + CUOPT_LOG_TRACE("BP_DET: sol_a_hash=0x%x sol_b_hash=0x%x offspring_hash=0x%x, seed %x", a.get_hash(), b.get_hash(), offspring.get_hash(), seed); - CUOPT_LOG_DEBUG("BP_DET: n_different_vars=%d n_vars_from_other=%d n_vars_from_guiding=%d", + CUOPT_LOG_TRACE("BP_DET: n_different_vars=%d n_vars_from_other=%d n_vars_from_guiding=%d", n_different_vars, n_vars_from_other, n_vars_from_guiding); - CUOPT_LOG_DEBUG("BP_DET: remaining_indices_hash=0x%x (first %d elements)", + CUOPT_LOG_TRACE("BP_DET: remaining_indices_hash=0x%x (first %d elements)", detail::compute_hash(this->remaining_indices), std::min((i_t)10, n_vars_from_other)); - CUOPT_LOG_DEBUG("BP_DET: guiding_feasible=%d other_feasible=%d expensive_to_fix=%d", + CUOPT_LOG_TRACE("BP_DET: guiding_feasible=%d other_feasible=%d expensive_to_fix=%d", guiding_solution.get_feasible(), other_solution.get_feasible(), a.problem_ptr->expensive_to_fix_vars); - CUOPT_LOG_DEBUG( + CUOPT_LOG_TRACE( "BP_DET: fixed_from_guiding=%d fixed_from_other=%d", fixed_from_guiding, fixed_from_other); // if either all integers are from A(meaning all are common) or all integers are from B(meaning @@ -250,16 +250,16 @@ class bound_prop_recombiner_t : public recombiner_t { } a.handle_ptr->sync_stream(); } else { - CUOPT_LOG_DEBUG("BP_DET: Taking INFEASIBLE path (no variable fixing)"); + CUOPT_LOG_TRACE("BP_DET: Taking INFEASIBLE path (no variable fixing)"); work_limit_timer_t timer(this->context.gpu_heur_loop, bp_recombiner_config_t::bounds_prop_time_limit); get_probing_values_for_infeasible( guiding_solution, other_solution, offspring, probing_values, n_vars_from_other); probing_config.probing_values = host_copy(probing_values, offspring.handle_ptr->get_stream()); - CUOPT_LOG_DEBUG("BP_DET: probing_values_hash=0x%x", detail::compute_hash(probing_values)); + CUOPT_LOG_TRACE("BP_DET: probing_values_hash=0x%x", detail::compute_hash(probing_values)); constraint_prop.apply_round(offspring, lp_run_time_after_feasible, timer, probing_config); } - CUOPT_LOG_DEBUG("BP_DET: After apply_round: offspring_hash=0x%x feasible=%d", + CUOPT_LOG_TRACE("BP_DET: After apply_round: offspring_hash=0x%x feasible=%d", offspring.get_hash(), offspring.get_feasible()); constraint_prop.max_n_failed_repair_iterations = 1; @@ -281,7 +281,7 @@ class bound_prop_recombiner_t : public recombiner_t { bp_recombiner_config_t::decrease_max_n_of_vars_from_other(); } } - CUOPT_LOG_DEBUG( + CUOPT_LOG_TRACE( "BP_DET: Final offspring_hash=0x%x same_as_parents=%d better_cost=%d better_feas=%d", offspring.get_hash(), same_as_parents, diff --git a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh index f7451edfc..7d1ee1dc2 100644 --- a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -50,7 +50,7 @@ class fp_recombiner_t : public recombiner_t { CUOPT_LOG_DEBUG("FP rec: Number of different variables %d MAX_VARS %d", n_different_vars, fp_recombiner_config_t::max_n_of_vars_from_other); - CUOPT_LOG_DEBUG("FP rec: offspring hash 0x%x", offspring.get_hash()); + CUOPT_LOG_TRACE("FP rec: offspring hash 0x%x", offspring.get_hash()); i_t n_vars_from_other = n_different_vars; if (n_vars_from_other > (i_t)fp_recombiner_config_t::max_n_of_vars_from_other) { n_vars_from_other = fp_recombiner_config_t::max_n_of_vars_from_other; @@ -69,20 +69,20 @@ class fp_recombiner_t : public recombiner_t { double work = static_cast(n_vars_from_other); CUOPT_LOG_DEBUG( "n_vars_from_guiding %d n_vars_from_other %d", n_vars_from_guiding, n_vars_from_other); - CUOPT_LOG_DEBUG("FP rec: offspring hash 0x%x, vars to fix 0x%x", + CUOPT_LOG_TRACE("FP rec: offspring hash 0x%x, vars to fix 0x%x", offspring.get_hash(), detail::compute_hash(vars_to_fix)); this->compute_vars_to_fix(offspring, vars_to_fix, n_vars_from_other, n_vars_from_guiding); - CUOPT_LOG_DEBUG("FP rec post computevarstofix: offspring hash 0x%x, vars to fix 0x%x", + CUOPT_LOG_TRACE("FP rec post computevarstofix: offspring hash 0x%x, vars to fix 0x%x", offspring.get_hash(), detail::compute_hash(vars_to_fix)); auto [fixed_problem, fixed_assignment, variable_map] = offspring.fix_variables(vars_to_fix); - CUOPT_LOG_DEBUG("FP rec: fixed_problem hash 0x%x assigned hash 0x%x", + CUOPT_LOG_TRACE("FP rec: fixed_problem hash 0x%x assigned hash 0x%x", fixed_problem.get_fingerprint(), detail::compute_hash(fixed_assignment)); fixed_problem.check_problem_representation(true); if (!guiding_solution.get_feasible() && !other_solution.get_feasible()) { - CUOPT_LOG_DEBUG("FP rec: running LP with infeasibility detection"); + CUOPT_LOG_TRACE("FP rec: running LP with infeasibility detection"); relaxed_lp_settings_t lp_settings; lp_settings.time_limit = fp_recombiner_config_t::infeasibility_detection_time_limit; if (this->context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { diff --git a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh index afa77092d..6ff47e712 100644 --- a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -71,7 +71,7 @@ class line_segment_recombiner_t : public recombiner_t { const weight_t& weights) { raft::common::nvtx::range fun_scope("line_segment_recombiner"); - CUOPT_LOG_DEBUG("LS rec: a %d b %d", a.get_hash(), b.get_hash()); + CUOPT_LOG_TRACE("LS rec: a %d b %d", a.get_hash(), b.get_hash()); auto& guiding_solution = a.get_feasible() ? a : b; auto& other_solution = a.get_feasible() ? b : a; // copy the solution from A diff --git a/cpp/src/mip/diversity/recombiners/recombiner.cuh b/cpp/src/mip/diversity/recombiners/recombiner.cuh index 925d9a7c2..924bc162e 100644 --- a/cpp/src/mip/diversity/recombiners/recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/recombiner.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -97,7 +97,7 @@ class recombiner_t { this->remaining_indices.data(), this->remaining_indices.data() + remaining_variables); - CUOPT_LOG_DEBUG("remaining indices hash 0x%x, size %d", + CUOPT_LOG_TRACE("remaining indices hash 0x%x, size %d", detail::compute_hash(this->remaining_indices), remaining_variables); @@ -181,8 +181,8 @@ class recombiner_t { i_t n_vars_from_guiding) { vars_to_fix.resize(n_vars_from_guiding, offspring.handle_ptr->get_stream()); - CUOPT_LOG_DEBUG("remaining indices hash 0x%x", detail::compute_hash(this->remaining_indices)); - CUOPT_LOG_DEBUG("integer_indices hash 0x%x", + CUOPT_LOG_TRACE("remaining indices hash 0x%x", detail::compute_hash(this->remaining_indices)); + CUOPT_LOG_TRACE("integer_indices hash 0x%x", detail::compute_hash(offspring.problem_ptr->integer_indices)); // set difference needs two sorted arrays thrust::sort(offspring.handle_ptr->get_thrust_policy(), diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 124236647..6ecd86bd3 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -865,7 +865,9 @@ void fj_t::refresh_lhs_and_violation(const rmm::cuda_stream_view& stre thrust::plus()); data.violation_score.set_value_async(violation, stream); data.weighted_violation_score.set_value_async(weighted_violation, stream); - data.violated_constraints.sort(stream); + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + data.violated_constraints.sort(stream); + } #if FJ_SINGLE_STEP CUOPT_LOG_DEBUG("hash assignment %x, hash lhs %x, hash lhscomp %x", detail::compute_hash(data.incumbent_assignment, stream), @@ -1289,7 +1291,7 @@ i_t fj_t::solve(solution_t& solution) bool deterministic = context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC; if (deterministic) { settings.work_limit = settings.time_limit; } // if work_limit is set: compute an estimate of the number of iterations required - if (settings.work_limit != std::numeric_limits::infinity()) { + if (deterministic && settings.work_limit != std::numeric_limits::infinity()) { std::map features_map = get_feature_vector(0); float iter_prediction = std::max( (f_t)0.0, (f_t)ceil(context.work_unit_predictors.fj_predictor.predict_scalar(features_map))); diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index d337527d3..06dc47ca3 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -1224,12 +1224,6 @@ DI thrust::tuple::move_score_t> gridwide_reduc // affected by tabu f_t delta = get_move(var_idx); - - // if (!WeakTabu && !recompute_score) { - // printf("iter[%d] block[%d] considered var %d delta %g\n", *fj.iterations, blockIdx.x, - // var_idx, delta); - // } - if constexpr (WeakTabu) { if ((delta < 0 && *fj.iterations == fj.tabu_lastinc[var_idx] + 1) || (delta > 0 && *fj.iterations == fj.tabu_lastdec[var_idx] + 1)) @@ -1250,12 +1244,6 @@ DI thrust::tuple::move_score_t> gridwide_reduc loc_best_score_info = compute_new_score(fj, var_idx, delta); } - // if (!WeakTabu && !recompute_score) { - // printf("iter[%d] block[%d] found score (%d,%d) for var %d delta %g\n", *fj.iterations, - // blockIdx.x, loc_best_score_info.score.base, loc_best_score_info.score.bonus, var_idx, - // delta); - // } - if (threadIdx.x == 0) { if (loc_best_score_info.score > best_score || (loc_best_score_info.score == best_score && var_idx > best_var)) { @@ -1267,11 +1255,6 @@ DI thrust::tuple::move_score_t> gridwide_reduc } if (threadIdx.x == 0) { - // if (!WeakTabu && !recompute_score) { - // printf("iter[%d] block[%d] var_idx %d score {%d,%d}, delta %g\n", *fj.iterations, - // blockIdx.x, best_var, best_score.base, best_score.bonus, best_delta); - // } - fj.grid_score_buf[blockIdx.x] = best_score; fj.grid_var_buf[blockIdx.x] = best_var; fj.grid_delta_buf[blockIdx.x] = best_delta; diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index ecd93c2e2..f3cd07c62 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -232,7 +232,7 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_tn_variables, solution.handle_ptr->get_stream()); raft::copy(last_projection.data(), solution.assignment.data(), @@ -255,7 +255,7 @@ template bool feasibility_pump_t::round(solution_t& solution) { bool result; - // CUOPT_LOG_DEBUG("Rounding the point"); + CUOPT_LOG_TRACE("Rounding the point"); work_limit_timer_t bounds_prop_timer(context.gpu_heur_loop, std::max(0.05, std::min(0.5, timer.remaining_time() / 10.))); const f_t lp_run_time_after_feasible = 0.; @@ -289,7 +289,7 @@ void feasibility_pump_t::perturbate(solution_t& solution) template bool feasibility_pump_t::run_fj_cycle_escape(solution_t& solution) { - CUOPT_LOG_DEBUG("Running FJ cycle escape"); + CUOPT_LOG_TRACE("Running FJ cycle escape"); bool is_feasible; fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; fj.settings.update_weights = true; @@ -345,7 +345,7 @@ template bool feasibility_pump_t::handle_cycle(solution_t& solution) { raft::common::nvtx::range fun_scope("handle_cycle"); - // CUOPT_LOG_DEBUG("running handle cycle"); + CUOPT_LOG_TRACE("running handle cycle"); bool is_feasible = false; fp_fj_cycle_time_begin = timer.remaining_time(); CUOPT_LOG_DEBUG("Running longer FJ on last rounding"); @@ -422,15 +422,15 @@ bool feasibility_pump_t::check_distance_cycle(solution_t& so std::accumulate(last_distances.begin(), last_distances.end(), 0.0) / last_distances.size(); if (avg_distance - distance_to_last_rounding < config.cycle_distance_reduction_ration * avg_distance) { - // CUOPT_LOG_DEBUG("Distance cycle detected curr %f avg %f for last %d iter", - // distance_to_last_rounding, - // avg_distance, - // last_distances.size()); + CUOPT_LOG_TRACE("Distance cycle detected curr %f avg %f for last %d iter", + distance_to_last_rounding, + avg_distance, + last_distances.size()); is_cycle = true; } last_distances.pop_back(); } else { - // CUOPT_LOG_DEBUG("Distance of projection: %f", distance_to_last_rounding); + CUOPT_LOG_TRACE("Distance of projection: %f", distance_to_last_rounding); } last_distances.push_front(distance_to_last_rounding); return is_cycle; @@ -543,7 +543,7 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s solution.assignment.size(), solution.handle_ptr->get_stream()); - CUOPT_LOG_DEBUG("FP: starting FP descent, sol hash 0x%x", solution.get_hash()); + CUOPT_LOG_TRACE("FP: starting FP descent, sol hash 0x%x", solution.get_hash()); while (true) { fp_iterations++; if (timer.check_time_limit()) { @@ -563,10 +563,7 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s CUOPT_LOG_DEBUG( "FP: after fp projection, iter %d sol hash 0x%x", fp_iterations, solution.get_hash()); i_t n_integers = solution.compute_number_of_integers(); - // CUOPT_LOG_DEBUG("after fp projection n_integers %d total n_integes %d", - // n_integers, - // solution.problem_ptr->n_integer_vars); - bool is_cycle = true; + bool is_cycle = true; // temp comment for presolve run if (config.check_distance_cycle) { // use distance cycle if we are running ii or objective FP @@ -578,7 +575,7 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s bool res = solution.compute_feasibility(); cuopt_assert(res, "Feasibility issue"); f_t time_taken = start_time - timer.remaining_time(); - CUOPT_LOG_INFO( + CUOPT_LOG_TRACE( "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_DISTANCE_CYCLE", fp_iterations, time_taken); @@ -587,12 +584,8 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s cuopt::default_logger().flush(); f_t remaining_time_end_fp = timer.remaining_time(); total_fp_time_until_cycle = fp_fj_cycle_time_begin - remaining_time_end_fp; - // CUOPT_LOG_DEBUG("total_fp_time_until_cycle: %f", total_fp_time_until_cycle); + CUOPT_LOG_TRACE("total_fp_time_until_cycle: %f", total_fp_time_until_cycle); f_t time_taken = start_time - timer.remaining_time(); - // CUOPT_LOG_INFO( - // "FP_RESULT: iterations=%d time_taken=%.6f termination=INFEASIBLE_DISTANCE_CYCLE", - // fp_iterations, - // time_taken); return false; } } @@ -600,11 +593,6 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s if (n_integers == solution.problem_ptr->n_integer_vars) { if (is_feasible) { CUOPT_LOG_DEBUG("Feasible solution found after LP with relative tolerance"); - f_t time_taken = start_time - timer.remaining_time(); - // CUOPT_LOG_INFO( - // "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_LP_PROJECTION", - // fp_iterations, - // time_taken); return true; } // if the solution is almost on polytope @@ -612,8 +600,8 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s // run the LP with full precision to check if it actually is feasible const f_t lp_verify_time_limit = 5.; relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = lp_verify_time_limit; - lp_settings.work_limit = lp_settings.time_limit; + lp_settings.time_limit = lp_verify_time_limit; + if (timer.deterministic) { lp_settings.work_limit = lp_settings.time_limit; } lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; lp_settings.return_first_feasible = true; lp_settings.save_state = true; diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu index 5f6676e77..c700422d7 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -169,7 +169,6 @@ bool line_segment_search_t::search_line_segment( fj.settings.update_weights = false; fj.settings.feasibility_run = is_feasibility_run; fj.settings.time_limit = std::min(0.1, timer.remaining_time()); - fj.settings.work_limit = fj.settings.time_limit; is_feasible = fj.solve(solution); } cuopt_func_call(solution.test_number_all_integer()); diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 693413fe4..5126fb1a9 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -413,11 +413,10 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu const f_t lp_run_time = 2.; relaxed_lp_settings_t lp_settings; lp_settings.time_limit = std::min(lp_run_time, timer.remaining_time()); - lp_settings.work_limit = lp_settings.time_limit; - lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; + if (timer.deterministic) { lp_settings.work_limit = lp_settings.time_limit; } + lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; run_lp_with_vars_fixed( *solution.problem_ptr, solution, solution.problem_ptr->integer_indices, lp_settings); - CUOPT_LOG_DEBUG("FJ ON LP OPT: exited", lp_settings.time_limit); } else { return is_feasible; } diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index d9fbffbae..307711ac4 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -98,7 +98,8 @@ f_t bounds_repair_t::get_ii_violation(problem_t& problem) thrust::make_counting_iterator(0) + problem.n_constraints, [cstr_violations_up = cstr_violations_up.data(), cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) -> f_t { - return max(cstr_violations_up[cstr_idx], cstr_violations_down[cstr_idx]); + auto violation = max(cstr_violations_up[cstr_idx], cstr_violations_down[cstr_idx]); + return violation >= ROUNDOFF_TOLERANCE ? violation : 0.; }, (f_t)0, thrust::plus()); diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index a5ca6dcf0..b463f8d41 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -905,16 +905,16 @@ bool constraint_prop_t::find_integer( set_bounds_on_fixed_vars(sol); } - // CUOPT_LOG_DEBUG("Bounds propagation rounding: unset vars %lu", unset_integer_vars.size()); + CUOPT_LOG_TRACE("Bounds propagation rounding: unset vars %lu", unset_integer_vars.size()); if (unset_integer_vars.size() == 0) { - // CUOPT_LOG_DEBUG("No integer variables provided in the bounds prop rounding"); + CUOPT_LOG_TRACE("No integer variables provided in the bounds prop rounding"); expand_device_copy(orig_sol.assignment, sol.assignment, sol.handle_ptr->get_stream()); cuopt_func_call(orig_sol.test_variable_bounds()); return orig_sol.compute_feasibility(); } // this is needed for the sort inside of the loop bool problem_ii = is_problem_ii(*sol.problem_ptr); - // CUOPT_LOG_DEBUG("is problem ii %d", problem_ii); + CUOPT_LOG_TRACE("is problem ii %d", problem_ii); // if the problem is ii, run the bounds prop in the beginning if (problem_ii) { bool bounds_repaired = @@ -940,7 +940,7 @@ bool constraint_prop_t::find_integer( bool timeout_happened = false; i_t n_failed_repair_iterations = 0; while (set_count < unset_integer_vars.size()) { - CUOPT_LOG_DEBUG("n_set_vars %d vars to set %lu", set_count, unset_integer_vars.size()); + CUOPT_LOG_TRACE("n_set_vars %d vars to set %lu", set_count, unset_integer_vars.size()); CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x", detail::compute_hash(unset_integer_vars)); update_host_assignment(sol); if (max_timer.check_time_limit()) { @@ -978,18 +978,18 @@ bool constraint_prop_t::find_integer( n_vars_to_set, sol.handle_ptr->get_stream()); - CUOPT_LOG_DEBUG("host_vars_to_set hash 0x%x", detail::compute_hash(host_vars_to_set)); + CUOPT_LOG_TRACE("host_vars_to_set hash 0x%x", detail::compute_hash(host_vars_to_set)); auto var_probe_vals = generate_bulk_rounding_vector(sol, orig_sol, host_vars_to_set, probing_config); - CUOPT_LOG_DEBUG("var_probe_vals hash 1 0x%x, hash 2 0x%x, hash 3 0x%x", + CUOPT_LOG_TRACE("var_probe_vals hash 1 0x%x, hash 2 0x%x, hash 3 0x%x", detail::compute_hash(std::get<0>(var_probe_vals)), detail::compute_hash(std::get<1>(var_probe_vals)), detail::compute_hash(std::get<2>(var_probe_vals))); probe( sol, orig_sol.problem_ptr, var_probe_vals, &set_count, unset_integer_vars, probing_config); - CUOPT_LOG_DEBUG("post probe, set count %d, unset var hash 0x%x, size %lu", + CUOPT_LOG_TRACE("post probe, set count %d, unset var hash 0x%x, size %lu", (int)set_count, detail::compute_hash(unset_integer_vars), unset_integer_vars.size()); @@ -1024,7 +1024,7 @@ bool constraint_prop_t::find_integer( make_span(orig_sol.problem_ptr->variable_bounds), make_span(sol.assignment)}); i_t n_fixed_vars = (iter - (unset_vars.begin() + set_count)); - CUOPT_LOG_DEBUG("After repair procedure, number of additional fixed vars %d", n_fixed_vars); + CUOPT_LOG_TRACE("After repair procedure, number of additional fixed vars %d", n_fixed_vars); set_count += n_fixed_vars; } } @@ -1051,10 +1051,10 @@ bool constraint_prop_t::find_integer( // which is the unchanged problem bounds multi_probe.update_host_bounds(sol.handle_ptr, make_span(sol.problem_ptr->variable_bounds)); } - // CUOPT_LOG_DEBUG( - // "Bounds propagation rounding end: ii constraint count first buffer %d, second buffer %d", - // multi_probe.infeas_constraints_count_0, - // multi_probe.infeas_constraints_count_1); + CUOPT_LOG_TRACE( + "Bounds propagation rounding end: ii constraint count first buffer %d, second buffer %d", + multi_probe.infeas_constraints_count_0, + multi_probe.infeas_constraints_count_1); cuopt_assert(sol.test_number_all_integer(), "All integers must be rounded"); expand_device_copy(orig_sol.assignment, sol.assignment, sol.handle_ptr->get_stream()); cuopt_func_call(orig_sol.test_variable_bounds()); @@ -1063,12 +1063,11 @@ bool constraint_prop_t::find_integer( multi_probe.infeas_constraints_count_1 == 0) && !timeout_happened && lp_run_time_after_feasible > 0) { relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = lp_run_time_after_feasible; - // CHANGE + lp_settings.time_limit = lp_run_time_after_feasible; lp_settings.tolerance = orig_sol.problem_ptr->tolerances.absolute_tolerance; lp_settings.save_state = false; lp_settings.return_first_feasible = true; - CUOPT_LOG_DEBUG("bounds repair LP, sol hash 0x%x", orig_sol.get_hash()); + CUOPT_LOG_TRACE("bounds repair LP, sol hash 0x%x", orig_sol.get_hash()); run_lp_with_vars_fixed(*orig_sol.problem_ptr, orig_sol, orig_sol.problem_ptr->integer_indices, @@ -1136,17 +1135,17 @@ bool constraint_prop_t::apply_round( f_t bounds_prop_end_time = max_timer.remaining_time(); repair_stats.total_time_spent_on_bounds_prop += bounds_prop_start_time - bounds_prop_end_time; - // CUOPT_LOG_DEBUG( - // "repair_success %lu repair_attempts %lu intermediate_repair_success %lu total_repair_loops - // %lu " "total_time_spent_on_repair %f total_time_spent_bounds_prop_after_repair %f " - // "total_time_spent_on_bounds_prop %f", - // repair_stats.repair_success, - // repair_stats.repair_attempts, - // repair_stats.intermediate_repair_success, - // repair_stats.total_repair_loops, - // repair_stats.total_time_spent_on_repair, - // repair_stats.total_time_spent_bounds_prop_after_repair, - // repair_stats.total_time_spent_on_bounds_prop); + CUOPT_LOG_TRACE( + "repair_success %lu repair_attempts %lu intermediate_repair_success %lu total_repair_loops" + "%lu total_time_spent_on_repair %f total_time_spent_bounds_prop_after_repair %f " + "total_time_spent_on_bounds_prop %f", + repair_stats.repair_success, + repair_stats.repair_attempts, + repair_stats.intermediate_repair_success, + repair_stats.total_repair_loops, + repair_stats.total_time_spent_on_repair, + repair_stats.total_time_spent_bounds_prop_after_repair, + repair_stats.total_time_spent_on_bounds_prop); // === CONSTRAINT PROP PREDICTOR RESULTS - START === auto cp_end_time = std::chrono::high_resolution_clock::now(); auto cp_elapsed_ms = @@ -1247,10 +1246,10 @@ bool constraint_prop_t::handle_fixed_vars( auto set_count = *set_count_ptr; const f_t int_tol = sol.problem_ptr->tolerances.integrality_tolerance; // which other variables were affected? - CUOPT_LOG_DEBUG("handle_fixed_vars, unset vars hash 0x%x, sol.assignment hash 0x%x", + CUOPT_LOG_TRACE("handle_fixed_vars, unset vars hash 0x%x, sol.assignment hash 0x%x", detail::compute_hash(unset_vars), detail::compute_hash(sol.assignment)); - CUOPT_LOG_DEBUG( + CUOPT_LOG_TRACE( "handle_fixed_vars, original_problem->variable_bounds hash 0x%x, " "sol.problem_ptr->variable_bounds hash 0x%x", detail::compute_hash(original_problem->variable_bounds), @@ -1268,7 +1267,7 @@ bool constraint_prop_t::handle_fixed_vars( cuopt_assert(n_fixed_vars >= std::get<0>(var_probe_vals).size(), "Error in number of vars fixed!"); set_count += n_fixed_vars; - CUOPT_LOG_DEBUG("Set var count increased from %d to %d", *set_count_ptr, set_count); + CUOPT_LOG_TRACE("Set var count increased from %d to %d", *set_count_ptr, set_count); *set_count_ptr = set_count; return multi_probe.infeas_constraints_count_0 == 0 || multi_probe.infeas_constraints_count_1 == 0; } diff --git a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu index 60037b592..6c7921925 100644 --- a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -99,7 +99,8 @@ std::tuple lb_bounds_repair_t::get_ii_violation( thrust::make_counting_iterator(0) + problem.n_constraints, [cstr_violations_up = cstr_violations_up.data(), cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) -> f_t { - return max(cstr_violations_up[cstr_idx], cstr_violations_down[cstr_idx]); + auto violation = max(cstr_violations_up[cstr_idx], cstr_violations_down[cstr_idx]); + return violation >= ROUNDOFF_TOLERANCE ? violation : 0.; }, (f_t)0, thrust::plus()); diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index be6fc8ac6..c8ee11176 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1799,10 +1799,6 @@ void problem_t::get_host_user_problem( user_problem.num_range_rows = user_problem.range_rows.size(); std::tie(user_problem.lower, user_problem.upper) = extract_host_bounds(variable_bounds, handle_ptr); - - // Debug: Log bounds fingerprint to detect GPU presolve non-determinism - CUOPT_LOG_INFO("Problem fingerprint after GPU presolve: 0x%x", get_fingerprint()); - user_problem.problem_name = original_problem_ptr->get_problem_name(); if (static_cast(row_names.size()) == m) { user_problem.row_names.resize(m); diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index 9a0492e66..ffb850e87 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -42,7 +42,7 @@ optimization_problem_solution_t get_relaxed_lp_solution( const relaxed_lp_settings_t& settings) { raft::common::nvtx::range fun_scope("get_relaxed_lp_solution"); - auto function_start_time = std::chrono::high_resolution_clock::now(); + // auto function_start_time = std::chrono::high_resolution_clock::now(); // // === PDLP PREDICTOR FEATURES - START === // CUOPT_LOG_INFO("PDLP_FEATURES: n_variables=%d n_constraints=%d nnz=%lu", @@ -162,69 +162,70 @@ optimization_problem_solution_t get_relaxed_lp_solution( compute_hash(assignment)); } - auto function_end_time = std::chrono::high_resolution_clock::now(); - auto elapsed_ms = - std::chrono::duration_cast(function_end_time - function_start_time) - .count(); - CUOPT_LOG_DEBUG("get_relaxed_lp_solution took %lld ms for %d iterations", - elapsed_ms, - solver_response.get_additional_termination_information().number_of_steps_taken); + // auto function_end_time = std::chrono::high_resolution_clock::now(); + // auto elapsed_ms = + // std::chrono::duration_cast(function_end_time - + // function_start_time) + // .count(); + // CUOPT_LOG_DEBUG("get_relaxed_lp_solution took %lld ms for %d iterations", + // elapsed_ms, + // solver_response.get_additional_termination_information().number_of_steps_taken); - // === PDLP PREDICTOR RESULTS - START === - auto term_info = solver_response.get_additional_termination_information(); - const i_t n_vars = op_problem.n_variables; - const i_t n_cstrs = op_problem.n_constraints; - const int64_t nnz = op_problem.nnz; - const int64_t total_spmv = lp_solver.get_total_spmv_ops(); - const int64_t total_nnz = total_spmv * nnz; - const double nnz_per_sec = - (elapsed_ms > 0) ? static_cast(total_nnz) / (elapsed_ms / 1000.0) : 0.0; - const double nnz_per_iter = (term_info.number_of_steps_taken > 0) - ? static_cast(total_nnz) / term_info.number_of_steps_taken - : 0.0; + // // === PDLP PREDICTOR RESULTS - START === + // auto term_info = solver_response.get_additional_termination_information(); + // const i_t n_vars = op_problem.n_variables; + // const i_t n_cstrs = op_problem.n_constraints; + // const int64_t nnz = op_problem.nnz; + // const int64_t total_spmv = lp_solver.get_total_spmv_ops(); + // const int64_t total_nnz = total_spmv * nnz; + // const double nnz_per_sec = + // (elapsed_ms > 0) ? static_cast(total_nnz) / (elapsed_ms / 1000.0) : 0.0; + // const double nnz_per_iter = (term_info.number_of_steps_taken > 0) + // ? static_cast(total_nnz) / + // term_info.number_of_steps_taken : 0.0; - // Compute sparsity metrics - const double sparsity = (n_cstrs > 0 && n_vars > 0) - ? static_cast(nnz) / (static_cast(n_cstrs) * n_vars) - : 0.0; - double nnz_stddev = 0.0; - [[maybe_unused]] double unbalancedness = 0.0; - if (op_problem.offsets.size() == static_cast(n_cstrs + 1) && n_cstrs > 0) { - std::vector h_offsets(n_cstrs + 1); - raft::copy(h_offsets.data(), - op_problem.offsets.data(), - n_cstrs + 1, - op_problem.handle_ptr->get_stream()); - op_problem.handle_ptr->sync_stream(); + // // Compute sparsity metrics + // const double sparsity = (n_cstrs > 0 && n_vars > 0) + // ? static_cast(nnz) / + // (static_cast(n_cstrs) * n_vars) : 0.0; + // double nnz_stddev = 0.0; + // [[maybe_unused]] double unbalancedness = 0.0; + // if (op_problem.offsets.size() == static_cast(n_cstrs + 1) && n_cstrs > 0) { + // std::vector h_offsets(n_cstrs + 1); + // raft::copy(h_offsets.data(), + // op_problem.offsets.data(), + // n_cstrs + 1, + // op_problem.handle_ptr->get_stream()); + // op_problem.handle_ptr->sync_stream(); - const double mean_nnz = static_cast(nnz) / n_cstrs; - double variance_sum = 0.0; - for (i_t row = 0; row < n_cstrs; ++row) { - const double row_nnz = static_cast(h_offsets[row + 1] - h_offsets[row]); - const double diff = row_nnz - mean_nnz; - variance_sum += diff * diff; - } - const double variance = variance_sum / n_cstrs; - nnz_stddev = std::sqrt(variance); - unbalancedness = (mean_nnz > 0) ? nnz_stddev / mean_nnz : 0.0; - } + // const double mean_nnz = static_cast(nnz) / n_cstrs; + // double variance_sum = 0.0; + // for (i_t row = 0; row < n_cstrs; ++row) { + // const double row_nnz = static_cast(h_offsets[row + 1] - h_offsets[row]); + // const double diff = row_nnz - mean_nnz; + // variance_sum += diff * diff; + // } + // const double variance = variance_sum / n_cstrs; + // nnz_stddev = std::sqrt(variance); + // unbalancedness = (mean_nnz > 0) ? nnz_stddev / mean_nnz : 0.0; + // } - CUOPT_LOG_INFO( - "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f " - "iters=%d time_ms=%lld term=%d spmv_ops=%ld total_nnz=%.2e nnz/s=%.2e nnz/iter=%.2e", - n_vars, - n_cstrs, - nnz, - sparsity, - nnz_stddev, - unbalancedness, - term_info.number_of_steps_taken, - elapsed_ms, - static_cast(solver_response.get_termination_status()), - total_spmv, - static_cast(total_nnz), - nnz_per_sec, - nnz_per_iter); + // CUOPT_LOG_INFO( + // "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f + // " "iters=%d time_ms=%lld term=%d spmv_ops=%ld total_nnz=%.2e nnz/s=%.2e nnz/iter=%.2e", + // n_vars, + // n_cstrs, + // nnz, + // sparsity, + // nnz_stddev, + // unbalancedness, + // term_info.number_of_steps_taken, + // elapsed_ms, + // static_cast(solver_response.get_termination_status()), + // total_spmv, + // static_cast(total_nnz), + // nnz_per_sec, + // nnz_per_iter); // === PDLP PREDICTOR RESULTS - END === return solver_response; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 68ef67663..12e0be333 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -109,7 +109,10 @@ solution_t mip_solver_t::run_solver() diversity_manager_t dm(context); dm.timer = work_limit_timer_t(context.gpu_heur_loop, timer_.get_time_limit()); - bool presolve_success = dm.run_presolve(timer_.get_time_limit()); + f_t time_limit = context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC + ? timer_.get_time_limit() + : timer_.remaining_time(); + bool presolve_success = dm.run_presolve(time_limit); if (!presolve_success) { CUOPT_LOG_INFO("Problem proven infeasible in presolve"); solution_t sol(*context.problem_ptr); @@ -146,8 +149,6 @@ solution_t mip_solver_t::run_solver() context.problem_ptr->post_process_solution(sol); return sol; } - - context.work_unit_scheduler_.verbose = true; context.work_unit_scheduler_.register_context(context.gpu_heur_loop); namespace dual_simplex = cuopt::linear_programming::dual_simplex; @@ -190,15 +191,13 @@ solution_t mip_solver_t::run_solver() i_t num_threads = branch_and_bound_settings.num_threads; i_t num_bfs_threads = std::max(1, num_threads / 4); - i_t num_diving_threads = num_threads - num_bfs_threads; + i_t num_diving_threads = std::max(1, num_threads - num_bfs_threads); // deterministic mode: use BSP coordinator with multiple workers, no diving if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { // BSP mode can use multiple workers deterministically num_bfs_threads = std::max(1, num_threads); num_diving_threads = 0; // No diving in deterministic mode } - // num_diving_threads = 0; - // num_bfs_threads = std::max(1, num_threads); branch_and_bound_settings.num_bfs_threads = num_bfs_threads; branch_and_bound_settings.num_diving_threads = num_diving_threads; @@ -241,10 +240,7 @@ solution_t mip_solver_t::run_solver() branch_and_bound.get(), std::placeholders::_1); } else if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - // In deterministic mode, use VT-aware solution injection - // Use the B&B's current horizon as the VT timestamp - // This ensures heuristic solutions are processed at the END of the current horizon, - // maintaining determinism regardless of when the GPU heuristic actually finds the solution + // TODO context.problem_ptr->branch_and_bound_callback = [bb = branch_and_bound.get()](const std::vector& solution) { double vt = bb->get_current_bsp_horizon(); From 9fb0eddb8a832ddf9167f19641f7a2a4fc7f4d9e Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 16 Jan 2026 11:12:01 +0000 Subject: [PATCH 195/366] more cleanup to test for regressions --- cpp/src/dual_simplex/bounds_strengthening.cpp | 2 + cpp/src/dual_simplex/branch_and_bound.cpp | 286 ++---------------- cpp/src/dual_simplex/branch_and_bound.hpp | 61 ---- cpp/src/utilities/memory_instrumentation.hpp | 2 +- 4 files changed, 22 insertions(+), 329 deletions(-) diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index c0cfd9646..0a01b76cf 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -275,6 +275,7 @@ bool bounds_strengthening_t::bounds_strengthening( // settings.log.printf("Total strengthened variables %d\n", total_strengthened_variables); // Log bounds strengthening features +#if 0 { auto [loads, stores] = manifold.collect_and_flush(); bounds_strengthening_features_t bs_features; @@ -288,6 +289,7 @@ bool bounds_strengthening_t::bounds_strengthening( bs_features.runtime = toc(start_time); bs_features.log_single(m, n, nnz); } +#endif #if DEBUG_BOUND_STRENGTHENING f_t lb_change = 0.0; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 06dee7d22..5c477f395 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -245,165 +245,6 @@ branch_and_bound_t::branch_and_bound_t( mutex_upper_.lock(); upper_bound_ = inf; mutex_upper_.unlock(); - - // Compute static problem features for regression model - compute_static_features(); -} - -template -void branch_and_bound_t::compute_static_features() -{ - const auto& A = original_lp_.A; - static_features_.n_rows = A.m; - static_features_.n_cols = A.n; - static_features_.n_nonzeros = A.col_start[A.n]; - static_features_.density = (f_t)static_features_.n_nonzeros / ((f_t)A.m * A.n); - - // Count variable types - static_features_.n_binary = 0; - static_features_.n_integer = 0; - static_features_.n_continuous = 0; - for (const auto& vt : var_types_) { - if (vt == variable_type_t::BINARY) { - static_features_.n_binary++; - } else if (vt == variable_type_t::INTEGER) { - static_features_.n_integer++; - } else { - static_features_.n_continuous++; - } - } - static_features_.integrality_ratio = - (f_t)(static_features_.n_binary + static_features_.n_integer) / A.n; - - // Compute row statistics (constraint sizes) - std::vector row_nnz(A.m, 0); - for (i_t j = 0; j < A.n; j++) { - for (i_t k = A.col_start[j]; k < A.col_start[j + 1]; k++) { - row_nnz[A.i[k]]++; - } - } - - static_features_.max_row_nnz = 0; - f_t sum_row_nnz = 0; - for (i_t i = 0; i < A.m; i++) { - static_features_.max_row_nnz = std::max(static_features_.max_row_nnz, row_nnz[i]); - sum_row_nnz += row_nnz[i]; - } - static_features_.avg_row_nnz = sum_row_nnz / A.m; - - // Compute row coefficient of variation - f_t row_variance = 0; - for (i_t i = 0; i < A.m; i++) { - f_t diff = row_nnz[i] - static_features_.avg_row_nnz; - row_variance += diff * diff; - } - row_variance /= A.m; - f_t row_std = std::sqrt(row_variance); - static_features_.row_nnz_cv = - static_features_.avg_row_nnz > 0 ? row_std / static_features_.avg_row_nnz : 0.0; - - // Compute column statistics (variable degrees) - static_features_.max_col_nnz = 0; - f_t sum_col_nnz = 0; - for (i_t j = 0; j < A.n; j++) { - i_t col_nnz = A.col_start[j + 1] - A.col_start[j]; - static_features_.max_col_nnz = std::max(static_features_.max_col_nnz, col_nnz); - sum_col_nnz += col_nnz; - } - static_features_.avg_col_nnz = sum_col_nnz / A.n; - - // Compute column coefficient of variation - f_t col_variance = 0; - for (i_t j = 0; j < A.n; j++) { - i_t col_nnz = A.col_start[j + 1] - A.col_start[j]; - f_t diff = col_nnz - static_features_.avg_col_nnz; - col_variance += diff * diff; - } - col_variance /= A.n; - f_t col_std = std::sqrt(col_variance); - static_features_.col_nnz_cv = - static_features_.avg_col_nnz > 0 ? col_std / static_features_.avg_col_nnz : 0.0; -} - -template -void branch_and_bound_t::flush_pending_features() -{ - // Must be called with mutex_feature_log_ already locked - if (!has_pending_features_) return; - - constexpr int LINE_BUFFER_SIZE = 512; - char line_buffer[LINE_BUFFER_SIZE]; - - snprintf(line_buffer, - LINE_BUFFER_SIZE, - "BB_NODE_FEATURES " - "node_id=%d depth=%d time=%.6f " - "n_rows=%d n_cols=%d n_nnz=%d density=%.6f " - "n_bin=%d n_int=%d n_cont=%d int_ratio=%.4f " - "avg_row_nnz=%.2f max_row_nnz=%d row_nnz_cv=%.4f " - "avg_col_nnz=%.2f max_col_nnz=%d col_nnz_cv=%.4f " - "n_bounds_chg=%d cutoff_gap=%.4f basis_from_parent=%d " - "simplex_iters=%d n_refact=%d lp_time=%.6f bound_str_time=%.6f var_sel_time=%.6f " - "n_frac=%d strong_branch=%d n_sb_cand=%d sb_time=%.6f " - "lp_status=%d node_status=%d\n", - last_features_.node_id, - last_features_.node_depth, - last_features_.total_node_time, - last_features_.n_rows, - last_features_.n_cols, - last_features_.n_nonzeros, - last_features_.density, - last_features_.n_binary, - last_features_.n_integer, - last_features_.n_continuous, - last_features_.integrality_ratio, - last_features_.avg_row_nnz, - last_features_.max_row_nnz, - last_features_.row_nnz_cv, - last_features_.avg_col_nnz, - last_features_.max_col_nnz, - last_features_.col_nnz_cv, - last_features_.n_bounds_changed, - last_features_.cutoff_gap_ratio, - last_features_.basis_from_parent ? 1 : 0, - last_features_.simplex_iterations, - last_features_.n_refactorizations, - last_features_.lp_solve_time, - last_features_.bound_str_time, - last_features_.variable_sel_time, - last_features_.n_fractional, - last_features_.strong_branch_performed ? 1 : 0, - last_features_.n_strong_branch_candidates, - last_features_.strong_branch_time, - last_features_.lp_status, - last_features_.node_status); - - // Single printf call - // settings_.log.printf("%s", line_buffer); - - has_pending_features_ = false; -} - -template -void branch_and_bound_t::log_node_features( - const node_solve_features_t& features) -{ - mutex_feature_log_.lock(); - - f_t current_time = toc(exploration_stats_.start_time); - f_t time_since_last_log = current_time - last_feature_log_time_; - - // Always store the latest features - last_features_ = features; - has_pending_features_ = true; - - // Log if enough time has passed (500ms) - if (time_since_last_log >= FEATURE_LOG_INTERVAL) { - flush_pending_features(); - last_feature_log_time_ = current_time; - } - - mutex_feature_log_.unlock(); } template @@ -786,13 +627,6 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, settings_.solution_callback(original_x, upper_bound_); } mutex_upper_.unlock(); - - // Debug: Log incumbent update (after releasing mutex to avoid potential issues) - if (improved_incumbent && bsp_mode_enabled_) { - std::string source = (thread_type == thread_type_t::EXPLORATION) ? "bb_integer" : "diving"; - BSP_DEBUG_LOG_INCUMBENT_UPDATE( - bsp_debug_settings_, bsp_debug_logger_, bsp_current_horizon_, leaf_objective, source); - } } template @@ -831,33 +665,18 @@ node_solve_info_t branch_and_bound_t::solve_node( { raft::common::nvtx::range scope("BB::solve_node"); - // Initialize feature tracking for this node - node_solve_features_t features = static_features_; - f_t node_start_time = tic(); - features.node_id = node_ptr->node_id; - features.node_depth = node_ptr->depth; - const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; - const f_t upper_bound = get_upper_bound(); + const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; + const f_t upper_bound = get_upper_bound(); lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); std::vector& leaf_vstatus = node_ptr->vstatus; assert(leaf_vstatus.size() == leaf_problem.num_cols); - // Track cutoff gap ratio - if (upper_bound < inf) { - features.cutoff_gap_ratio = - (upper_bound - node_ptr->lower_bound) / std::max(std::abs(upper_bound), f_t(1.0)); - } - - // Track if we have parent's basis - features.basis_from_parent = !leaf_vstatus.empty(); - simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); lp_settings.cut_off = upper_bound + settings_.dual_tol; lp_settings.inside_mip = 2; lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); - lp_settings.work_limit = settings_.work_limit; lp_settings.scale_columns = false; #ifdef LOG_NODE_SIMPLEX @@ -900,10 +719,8 @@ node_solve_info_t branch_and_bound_t::solve_node( bool feasible; { raft::common::nvtx::range scope_bs("BB::bound_strengthening"); - f_t bs_start_time = tic(); feasible = node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); - features.bound_str_time = toc(bs_start_time); } dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; @@ -915,21 +732,20 @@ node_solve_info_t branch_and_bound_t::solve_node( { raft::common::nvtx::range scope_lp("BB::node_lp_solve"); - lp_status = - dual_phase2_with_advanced_basis(2, - 0, - recompute_bounds_and_basis, - lp_start_time, - leaf_problem, - lp_settings, - leaf_vstatus, - basis_factors, - basic_list, - nonbasic_list, - leaf_solution, - node_iter, - leaf_edge_norms, - settings_.deterministic ? &work_unit_context_ : nullptr); + lp_status = dual_phase2_with_advanced_basis(2, + 0, + recompute_bounds_and_basis, + lp_start_time, + leaf_problem, + lp_settings, + leaf_vstatus, + basis_factors, + basic_list, + nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms, + nullptr); } if (lp_status == dual::status_t::NUMERICAL) { @@ -947,16 +763,9 @@ node_solve_info_t branch_and_bound_t::solve_node( lp_status = convert_lp_status_to_dual_status(second_status); } - f_t lp_time = toc(lp_start_time); if (thread_type == thread_type_t::EXPLORATION) { - exploration_stats_.total_lp_solve_time += lp_time; + exploration_stats_.total_lp_solve_time += toc(lp_start_time); exploration_stats_.total_lp_iters += node_iter; - - // Track LP solve metrics - features.lp_solve_time = lp_time; - features.simplex_iterations = node_iter; - // Note: We don't directly track refactorizations here, would need instrumentation in - // dual_phase2 } } @@ -969,13 +778,6 @@ node_solve_info_t branch_and_bound_t::solve_node( node_ptr->lower_bound = inf; search_tree.graphviz_node(log, node_ptr, "infeasible", 0.0); search_tree.update(node_ptr, node_status_t::INFEASIBLE); - - // Log features before return - features.lp_status = static_cast(lp_status); - features.node_status = static_cast(node_status_t::INFEASIBLE); - features.total_node_time = toc(node_start_time); - log_node_features(features); - return node_solve_info_t::NO_CHILDREN; } else if (lp_status == dual::status_t::CUTOFF) { @@ -984,13 +786,6 @@ node_solve_info_t branch_and_bound_t::solve_node( f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); search_tree.graphviz_node(log, node_ptr, "cut off", leaf_objective); search_tree.update(node_ptr, node_status_t::FATHOMED); - - // Log features before return - features.lp_status = static_cast(lp_status); - features.node_status = static_cast(node_status_t::FATHOMED); - features.total_node_time = toc(node_start_time); - log_node_features(features); - return node_solve_info_t::NO_CHILDREN; } else if (lp_status == dual::status_t::OPTIMAL) { @@ -999,8 +794,6 @@ node_solve_info_t branch_and_bound_t::solve_node( i_t leaf_num_fractional = fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - features.n_fractional = leaf_num_fractional; - f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); node_ptr->lower_bound = leaf_objective; search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); @@ -1017,33 +810,18 @@ node_solve_info_t branch_and_bound_t::solve_node( add_feasible_solution(leaf_objective, leaf_solution.x, node_ptr->depth, thread_type); search_tree.graphviz_node(log, node_ptr, "integer feasible", leaf_objective); search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); - - // Log features before return - features.lp_status = static_cast(lp_status); - features.node_status = static_cast(node_status_t::INTEGER_FEASIBLE); - features.total_node_time = toc(node_start_time); - log_node_features(features); - return node_solve_info_t::NO_CHILDREN; } else if (leaf_objective <= upper_bound + abs_fathom_tol) { // Choose fractional variable to branch on - f_t var_sel_start = tic(); const i_t branch_var = pc_.variable_selection(leaf_fractional, leaf_solution.x, lp_settings.log); - features.variable_sel_time = toc(var_sel_start); assert(leaf_vstatus.size() == leaf_problem.num_cols); search_tree.branch( node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, leaf_problem, log); search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); - // Log features before return - features.lp_status = static_cast(lp_status); - features.node_status = static_cast(node_status_t::HAS_CHILDREN); - features.total_node_time = toc(node_start_time); - log_node_features(features); - rounding_direction_t round_dir = child_selection(node_ptr); if (round_dir == rounding_direction_t::UP) { @@ -1055,13 +833,6 @@ node_solve_info_t branch_and_bound_t::solve_node( } else { search_tree.graphviz_node(log, node_ptr, "fathomed", leaf_objective); search_tree.update(node_ptr, node_status_t::FATHOMED); - - // Log features before return - features.lp_status = static_cast(lp_status); - features.node_status = static_cast(node_status_t::FATHOMED); - features.total_node_time = toc(node_start_time); - log_node_features(features); - return node_solve_info_t::NO_CHILDREN; } } else if (lp_status == dual::status_t::TIME_LIMIT) { @@ -1085,13 +856,6 @@ node_solve_info_t branch_and_bound_t::solve_node( search_tree.graphviz_node(log, node_ptr, "numerical", 0.0); search_tree.update(node_ptr, node_status_t::NUMERICAL); - - // Log features before return - features.lp_status = static_cast(lp_status); - features.node_status = static_cast(node_status_t::NUMERICAL); - features.total_node_time = toc(node_start_time); - log_node_features(features); - return node_solve_info_t::NUMERICAL; } } @@ -1679,21 +1443,14 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut assert(root_vstatus_.size() == original_lp_.num_cols); - // Validate root_vstatus_ has correct BASIC count - // This catches bugs where the root LP solve produces an invalid vstatus { const i_t expected_basic_count = original_lp_.num_rows; i_t actual_basic_count = 0; for (const auto& status : root_vstatus_) { if (status == variable_status_t::BASIC) { actual_basic_count++; } } - if (actual_basic_count != expected_basic_count) { - settings_.log.printf("ERROR: root_vstatus_ has %d BASIC entries, expected %d (num_rows)\n", - actual_basic_count, - expected_basic_count); - assert(actual_basic_count == expected_basic_count && - "root_vstatus_ BASIC count mismatch - LP solver returned invalid basis"); - } + assert(actual_basic_count == expected_basic_count && + "root_vstatus_ BASIC count mismatch - LP solver returned invalid basis"); } set_uninitialized_steepest_edge_norms(edge_norms_); @@ -1840,11 +1597,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } } - // Flush any pending features - mutex_feature_log_.lock(); - if (has_pending_features_) { flush_pending_features(); } - mutex_feature_log_.unlock(); - // Compute final lower bound f_t lower_bound; if (bsp_mode_enabled_) { diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 07cf8ee00..e00e4176e 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -79,51 +79,6 @@ class bounds_strengthening_t; template void upper_bound_callback(f_t upper_bound); -// Feature tracking for solve_node regression model -template -struct node_solve_features_t { - // Static problem features (compute once) - i_t n_rows{0}; - i_t n_cols{0}; - i_t n_nonzeros{0}; - f_t density{0.0}; - i_t n_binary{0}; - i_t n_integer{0}; - i_t n_continuous{0}; - f_t integrality_ratio{0.0}; - f_t avg_row_nnz{0.0}; - i_t max_row_nnz{0}; - f_t avg_col_nnz{0.0}; - i_t max_col_nnz{0}; - f_t row_nnz_cv{0.0}; - f_t col_nnz_cv{0.0}; - - // Dynamic node state - i_t node_id{0}; - i_t node_depth{0}; - i_t n_bounds_changed{0}; - f_t cutoff_gap_ratio{0.0}; - bool basis_from_parent{false}; - - // LP solve metrics - i_t simplex_iterations{0}; - i_t n_refactorizations{0}; - f_t lp_solve_time{0.0}; - f_t bound_str_time{0.0}; - f_t variable_sel_time{0.0}; - - // Outcome metrics - i_t n_fractional{0}; - bool strong_branch_performed{false}; - i_t n_strong_branch_candidates{0}; - f_t strong_branch_time{0.0}; - i_t lp_status{0}; // Convert dual::status_t to int - i_t node_status{0}; // Convert node_status_t to int - - // Computed at node end - f_t total_node_time{0.0}; -}; - template class branch_and_bound_t { public: @@ -265,22 +220,6 @@ class branch_and_bound_t { // its blocks the progression of the lower bound. omp_atomic_t lower_bound_ceiling_; - // Feature tracking for solve_node regression model - node_solve_features_t static_features_; // Static problem features, computed once - omp_mutex_t mutex_feature_log_; // Protect feature logging - node_solve_features_t last_features_; // Last captured features - f_t last_feature_log_time_{0.0}; // Time of last feature log - bool has_pending_features_{false}; // Whether we have features to log - - // Helper to compute static features once - void compute_static_features(); - - // Helper to log node solve features with time-based throttling - void log_node_features(const node_solve_features_t& features); - - // Helper to flush any pending features at end of solve - void flush_pending_features(); - void report_heuristic(f_t obj); void report(std::string symbol, f_t obj, f_t lower_bound, i_t node_depth); diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index abf09ae05..f4b8a2090 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -35,7 +35,7 @@ #include #include -#define CUOPT_ENABLE_MEMORY_INSTRUMENTATION 1 +#define CUOPT_ENABLE_MEMORY_INSTRUMENTATION 0 #ifdef __NVCC__ #define HDI inline __host__ __device__ From 21b5e29b21389c05bb81571202f47cf23c426c4c Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 16 Jan 2026 14:04:29 +0000 Subject: [PATCH 196/366] move GPU determinism changes to another PR --- cpp/src/linear_programming/pdlp.cu | 114 +- cpp/src/linear_programming/pdlp.cuh | 15 - cpp/src/linear_programming/pdlp_features.hpp | 243 -- .../restart_strategy/pdlp_restart_strategy.cu | 3 - cpp/src/linear_programming/solve.cu | 5 +- .../adaptive_step_size_strategy.cu | 3 +- cpp/src/mip/diversity/diversity_config.hpp | 4 - cpp/src/mip/diversity/diversity_manager.cu | 166 +- cpp/src/mip/diversity/diversity_manager.cuh | 7 +- cpp/src/mip/diversity/multi_armed_bandit.cuh | 14 +- cpp/src/mip/diversity/population.cu | 23 +- cpp/src/mip/diversity/population.cuh | 4 +- .../recombiners/bound_prop_recombiner.cuh | 81 +- .../diversity/recombiners/fp_recombiner.cuh | 38 +- .../recombiners/line_segment_recombiner.cuh | 18 +- .../mip/diversity/recombiners/recombiner.cuh | 20 +- .../recombiners/recombiner_stats.hpp | 5 - cpp/src/mip/diversity/recombiners/sub_mip.cuh | 14 +- cpp/src/mip/diversity/weights.cuh | 7 - .../mip/feasibility_jump/feasibility_jump.cu | 319 +-- .../mip/feasibility_jump/feasibility_jump.cuh | 20 +- .../feasibility_jump_impl_common.cuh | 43 +- .../feasibility_jump_kernels.cu | 200 +- cpp/src/mip/feasibility_jump/fj_cpu.cu | 708 +----- cpp/src/mip/feasibility_jump/fj_cpu.cuh | 124 +- .../mip/feasibility_jump/load_balancing.cuh | 67 +- cpp/src/mip/feasibility_jump/utils.cuh | 18 +- .../feasibility_pump/feasibility_pump.cu | 118 +- .../feasibility_pump/feasibility_pump.cuh | 6 +- .../line_segment_search.cu | 12 +- .../line_segment_search.cuh | 9 +- cpp/src/mip/local_search/local_search.cu | 81 +- cpp/src/mip/local_search/local_search.cuh | 25 +- .../local_search/rounding/bounds_repair.cu | 40 +- .../local_search/rounding/bounds_repair.cuh | 4 +- .../local_search/rounding/constraint_prop.cu | 114 +- .../local_search/rounding/constraint_prop.cuh | 10 +- .../local_search/rounding/lb_bounds_repair.cu | 22 +- .../rounding/lb_bounds_repair.cuh | 4 +- .../rounding/lb_constraint_prop.cu | 4 +- .../rounding/lb_constraint_prop.cuh | 8 +- .../rounding/simple_rounding_kernels.cuh | 2 +- cpp/src/mip/presolve/bounds_presolve.cu | 6 - cpp/src/mip/presolve/bounds_update_data.cu | 29 - cpp/src/mip/presolve/lb_probing_cache.cu | 4 +- cpp/src/mip/presolve/probing_cache.cu | 4 +- cpp/src/mip/presolve/probing_cache.cuh | 3 +- cpp/src/mip/presolve/utils.cuh | 1 - cpp/src/mip/relaxed_lp/relaxed_lp.cu | 155 +- cpp/src/mip/relaxed_lp/relaxed_lp.cuh | 2 - cpp/src/mip/solution/solution.cu | 11 +- cpp/src/mip/solution/solution.cuh | 3 +- cpp/src/mip/solver.cu | 2 +- cpp/src/utilities/embed_models.sh | 89 - cpp/tests/CMakeLists.txt | 44 - cpp/tests/mip/CMakeLists.txt | 12 +- cpp/tests/mip/determinism_utils.cuh | 77 - cpp/tests/mip/diversity_test.cu | 406 --- cpp/tests/mip/feasibility_jump_tests.cu | 239 +- cpp/tests/mip/local_search_test.cu | 263 -- cpp/tests/mip/mip_utils.cuh | 61 - cpp/tests/mip/miplib_test.cu | 14 +- cpp/tests/mip/multi_probe_test.cu | 61 +- cpp/tests/mip/presolve_test.cu | 247 -- cpp/tests/mip/unit_test.cu | 339 ++- cpp/tsan_suppressions.txt | 6 - datasets/mip/download_miplib_test_dataset.sh | 1 - scripts/README_PREDICTOR_WORKFLOW.md | 214 -- scripts/README_REGRESSION.md | 342 --- scripts/determinism_logs_parse.py | 894 ------- scripts/train_regressor.py | 2182 ----------------- 71 files changed, 797 insertions(+), 7656 deletions(-) delete mode 100644 cpp/src/linear_programming/pdlp_features.hpp delete mode 100755 cpp/src/utilities/embed_models.sh delete mode 100644 cpp/tests/mip/determinism_utils.cuh delete mode 100644 cpp/tests/mip/diversity_test.cu delete mode 100644 cpp/tests/mip/local_search_test.cu delete mode 100644 cpp/tsan_suppressions.txt delete mode 100644 scripts/README_PREDICTOR_WORKFLOW.md delete mode 100644 scripts/README_REGRESSION.md delete mode 100755 scripts/determinism_logs_parse.py delete mode 100755 scripts/train_regressor.py diff --git a/cpp/src/linear_programming/pdlp.cu b/cpp/src/linear_programming/pdlp.cu index 02cea44d5..25382ee7f 100644 --- a/cpp/src/linear_programming/pdlp.cu +++ b/cpp/src/linear_programming/pdlp.cu @@ -252,15 +252,7 @@ void pdlp_solver_t::set_initial_dual_solution( initial_dual_.data(), initial_dual_solution.data(), initial_dual_solution.size(), stream_view_); } -static bool time_limit_reached(const timer_t& timer) -{ - bool elapsed = timer.elapsed_time() >= timer.get_time_limit(); - if (elapsed) { - CUOPT_LOG_ERROR("**** PDLP Time limit reached: %f *****", timer.get_time_limit()); - // cuopt_assert(false, "unexpected timer"); - } - return elapsed; -} +static bool time_limit_reached(const timer_t& timer) { return timer.check_time_limit(); } template std::optional> pdlp_solver_t::check_limits( @@ -1182,50 +1174,6 @@ optimization_problem_solution_t pdlp_solver_t::run_solver(co bool warm_start_was_given = settings_.get_pdlp_warm_start_data().last_restart_duality_gap_dual_solution_.size() != 0; - // Reset iteration metrics at the start of the solver run - total_spmv_ops_ = 0; - - // Compute sparsity metrics once (they don't change during solve) - { - const i_t n_vars = problem_ptr->n_variables; - const i_t n_cstrs = problem_ptr->n_constraints; - const int64_t nnz = problem_ptr->nnz; - - cached_sparsity_ = (n_cstrs > 0 && n_vars > 0) - ? static_cast(nnz) / (static_cast(n_cstrs) * n_vars) - : 0.0; - cached_nnz_stddev_ = 0.0; - cached_unbalancedness_ = 0.0; - - if (problem_ptr->offsets.size() == static_cast(n_cstrs + 1) && n_cstrs > 0) { - // Copy offsets to host for efficient computation - std::vector h_offsets(n_cstrs + 1); - raft::copy(h_offsets.data(), problem_ptr->offsets.data(), n_cstrs + 1, stream_view_); - RAFT_CUDA_TRY(cudaStreamSynchronize(stream_view_)); - - const double mean_nnz = static_cast(nnz) / n_cstrs; - double variance_sum = 0.0; - for (i_t row = 0; row < n_cstrs; ++row) { - const double row_nnz = static_cast(h_offsets[row + 1] - h_offsets[row]); - const double diff = row_nnz - mean_nnz; - variance_sum += diff * diff; - } - const double variance = variance_sum / n_cstrs; - cached_nnz_stddev_ = std::sqrt(variance); - cached_unbalancedness_ = (mean_nnz > 0) ? cached_nnz_stddev_ / mean_nnz : 0.0; - } - } - - // Initialize feature tracking for runtime prediction - features_.init_from_problem(problem_ptr->n_variables, - problem_ptr->n_constraints, - problem_ptr->nnz, - static_cast(cached_sparsity_), - static_cast(cached_nnz_stddev_), - static_cast(cached_unbalancedness_), - warm_start_was_given); - interval_start_time_ = std::chrono::high_resolution_clock::now(); - if (!inside_mip_) { CUOPT_LOG_INFO( " Iter Primal Obj. Dual Obj. Gap Primal Res. Dual Res. Time"); @@ -1340,22 +1288,8 @@ optimization_problem_solution_t pdlp_solver_t::run_solver(co average_termination_strategy_.get_convergence_information(), // Needed for KKT restart best_primal_weight_ // Needed for cuPDLP+ restart ); - - // Track restart for feature logging - if (has_restarted) { features_.record_restart(); } } - // Update convergence metrics from termination strategy (for feature logging) - const auto& conv_info = current_termination_strategy_.get_convergence_information(); - const f_t kkt = restart_strategy_.compute_kkt_score(conv_info.get_l2_primal_residual(), - conv_info.get_l2_dual_residual(), - conv_info.get_gap(), - primal_weight_); - features_.update_convergence(conv_info.get_l2_primal_residual().value(stream_view_), - conv_info.get_l2_dual_residual().value(stream_view_), - conv_info.get_gap().value(stream_view_), - kkt); - if (!pdlp_hyper_params::rescale_for_restart) { // We don't need to rescale average because what matters is weighted_average_solution // getting the scaled accumulation @@ -1376,58 +1310,22 @@ optimization_problem_solution_t pdlp_solver_t::run_solver(co #ifdef CUPDLP_DEBUG_MODE printf("Is Major %d\n", (total_pdlp_iterations_ + 1) % pdlp_hyper_params::major_iteration == 0); #endif - // Determine if the NEXT iteration will be a major iteration (for take_step) - const bool next_is_major = - (total_pdlp_iterations_ + 1) % pdlp_hyper_params::major_iteration == 0; - - take_step(total_pdlp_iterations_, next_is_major); - - // Track iteration for features (2 SpMV per regular iteration in Stable3) - features_.record_regular_iteration(); - - // Count SpMV operations for legacy tracking - constexpr int64_t spmv_ops_per_iteration = 2; - total_spmv_ops_ += spmv_ops_per_iteration; + take_step(total_pdlp_iterations_, + (total_pdlp_iterations_ + 1) % pdlp_hyper_params::major_iteration == 0); if (pdlp_hyper_params::use_reflected_primal_dual) { - if (pdlp_hyper_params::use_fixed_point_error && (next_is_major || has_restarted)) { + if (pdlp_hyper_params::use_fixed_point_error && + (total_pdlp_iterations_ + 1) % pdlp_hyper_params::major_iteration == 0 || + has_restarted) compute_fixed_error(has_restarted); // May set has_restarted to false - // compute_fixed_error does 1 additional SpMV - total_spmv_ops_ += 1; - } halpern_update(); } - // Track major iteration for features (after compute_fixed_error adds +1 SpMV) - if (is_major_iteration || is_conditional_major) { features_.record_major_iteration(); } - ++total_pdlp_iterations_; ++internal_solver_iterations_; if (pdlp_hyper_params::never_restart_to_average) restart_strategy_.increment_iteration_since_last_restart(); - - // Update step parameters for feature tracking - features_.update_step_params(step_size_.value(stream_view_), - primal_weight_.value(stream_view_)); - - // Log PDLP features at regular intervals - if (features_.should_log()) { - // Compute elapsed time for this interval - auto now = std::chrono::high_resolution_clock::now(); - auto interval_ms = std::chrono::duration(now - interval_start_time_).count(); - features_.interval_time_ms = interval_ms; - - // Log the features - features_.log_features(); - - // Reset interval counters and timer - features_.reset_interval_counters(); - interval_start_time_ = now; - - // Reset legacy spmv counter for consistency - total_spmv_ops_ = 0; - } } return optimization_problem_solution_t{pdlp_termination_status_t::NumericalError, stream_view_}; diff --git a/cpp/src/linear_programming/pdlp.cuh b/cpp/src/linear_programming/pdlp.cuh index fc68f7bc7..663c617d4 100644 --- a/cpp/src/linear_programming/pdlp.cuh +++ b/cpp/src/linear_programming/pdlp.cuh @@ -13,7 +13,6 @@ #include #include #include -#include #include #include #include @@ -27,7 +26,6 @@ #include #include -#include #include #include "linear_programming/termination_strategy/convergence_information.hpp" @@ -70,7 +68,6 @@ class pdlp_solver_t { f_t get_relative_dual_tolerance_factor() const; f_t get_relative_primal_tolerance_factor() const; detail::pdlp_termination_strategy_t& get_current_termination_strategy(); - int64_t get_total_spmv_ops() const { return total_spmv_ops_; } void set_problem_ptr(problem_t* problem_ptr_); @@ -200,18 +197,6 @@ class pdlp_solver_t { i_t total_pdlp_iterations_{0}; i_t internal_solver_iterations_{0}; - // Iteration metrics for performance tracking - int64_t total_spmv_ops_{0}; // Total SpMV operations performed - - // Feature tracking for runtime prediction (Stable3 mode) - pdlp_features_t features_; - std::chrono::high_resolution_clock::time_point interval_start_time_; - - // Cached sparsity metrics (computed once at start of solve) - double cached_sparsity_{0.0}; - double cached_nnz_stddev_{0.0}; - double cached_unbalancedness_{0.0}; - // Initial solution rmm::device_uvector initial_primal_; rmm::device_uvector initial_dual_; diff --git a/cpp/src/linear_programming/pdlp_features.hpp b/cpp/src/linear_programming/pdlp_features.hpp deleted file mode 100644 index 6af6b0105..000000000 --- a/cpp/src/linear_programming/pdlp_features.hpp +++ /dev/null @@ -1,243 +0,0 @@ -/* clang-format off */ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ -/* clang-format on */ - -#pragma once - -#include -#include -#include - -namespace cuopt::linear_programming::detail { - -// Feature logging interval (every N iterations) -constexpr int PDLP_FEATURE_LOG_INTERVAL = 50; - -/** - * @brief Feature collection structure for PDLP Stable3 runtime prediction. - * - * This structure collects features that can be used to train regression models - * for predicting the runtime of PDLP iterations in Stable3 mode. - * - * Stable3 key characteristics: - * - No adaptive step size retries (deterministic SpMV count) - * - Uses reflected primal/dual (Halpern update) - * - Uses fixed point error computation on major iterations - * - Uses conditional major iterations (frequency increases with iteration count) - * - Never restarts to average (only to current solution) - */ -template -struct pdlp_features_t { - // ========================================================================= - // Problem Features (static, set once at initialization) - // ========================================================================= - i_t n_vars{0}; // Number of variables - i_t n_cstrs{0}; // Number of constraints - int64_t nnz{0}; // Number of nonzeros in constraint matrix - f_t sparsity{0.0}; // nnz / (n_vars * n_cstrs) - f_t nnz_stddev{0.0}; // Standard deviation of row nnz counts - f_t unbalancedness{0.0}; // nnz_stddev / mean_nnz_per_row - - // ========================================================================= - // Interval Counters (reset after each log) - // ========================================================================= - i_t interval_iters{0}; // Iterations in this logging interval - i_t interval_major{0}; // Major iterations in this interval - i_t interval_restarts{0}; // Restarts in this interval - - // ========================================================================= - // SpMV Metrics - // ========================================================================= - // In Stable3: regular iter = 2 SpMV, major iter = 3 SpMV (fixed point error) - int64_t interval_spmv_ops{0}; // SpMV operations in this interval - - // ========================================================================= - // Cumulative Counters (never reset) - // ========================================================================= - i_t total_iters{0}; // Total iterations since solver start - i_t total_restarts{0}; // Total restarts since solver start - i_t iters_since_restart{0}; // Iterations since last restart - - // ========================================================================= - // Convergence Metrics (snapshot at log time, from last major iteration) - // ========================================================================= - f_t primal_res{0.0}; // L2 primal residual - f_t dual_res{0.0}; // L2 dual residual - f_t gap{0.0}; // Duality gap - f_t kkt_score{0.0}; // KKT score (used for restart decisions) - - // ========================================================================= - // Step Parameters (can change on restarts) - // ========================================================================= - f_t step_size{0.0}; // Current step size - f_t primal_weight{0.0}; // Current primal weight - - // ========================================================================= - // Timing - // ========================================================================= - f_t interval_time_ms{0.0}; // Time elapsed in this interval (milliseconds) - - // ========================================================================= - // Warm Start Info - // ========================================================================= - bool has_warm_start{false}; // Whether warm start was provided - - /** - * @brief Initialize static problem features. - * - * Called once at solver initialization. Computes sparsity metrics from - * the problem structure. - */ - void init_from_problem(i_t n_variables, - i_t n_constraints, - int64_t num_nonzeros, - f_t computed_sparsity, - f_t computed_nnz_stddev, - f_t computed_unbalancedness, - bool warm_start) - { - n_vars = n_variables; - n_cstrs = n_constraints; - nnz = num_nonzeros; - sparsity = computed_sparsity; - nnz_stddev = computed_nnz_stddev; - unbalancedness = computed_unbalancedness; - has_warm_start = warm_start; - } - - /** - * @brief Record a regular iteration. - * - * In Stable3, each regular iteration does 2 SpMV operations: - * - compute_At_y(): A^T @ y - * - compute_A_x(): A @ x - */ - void record_regular_iteration() - { - ++interval_iters; - ++total_iters; - ++iters_since_restart; - interval_spmv_ops += 2; - } - - /** - * @brief Record a major iteration. - * - * Major iterations do an additional SpMV for fixed point error computation. - * They also involve termination checks and potential restarts. - */ - void record_major_iteration() - { - ++interval_major; - // Fixed point error computation adds 1 SpMV - interval_spmv_ops += 1; - } - - /** - * @brief Record a restart event. - */ - void record_restart() - { - ++interval_restarts; - ++total_restarts; - iters_since_restart = 0; - } - - /** - * @brief Update convergence metrics from termination strategy. - * - * Called during major iterations when convergence info is computed. - */ - void update_convergence(f_t l2_primal_residual, - f_t l2_dual_residual, - f_t duality_gap, - f_t computed_kkt_score) - { - primal_res = l2_primal_residual; - dual_res = l2_dual_residual; - gap = duality_gap; - kkt_score = computed_kkt_score; - } - - /** - * @brief Update step parameters. - * - * Called when step size or primal weight changes (typically after restarts). - */ - void update_step_params(f_t current_step_size, f_t current_primal_weight) - { - step_size = current_step_size; - primal_weight = current_primal_weight; - } - - /** - * @brief Log all features in key=value format. - * - * Format: PDLP_RESULT: n_vars=N n_cstrs=M nnz=K ... - * - * This format is parsed by determinism_logs_parse.py with --algorithm PDLP - */ - void log_features() const - { - // Compute derived metrics - const int64_t total_nnz_processed = interval_spmv_ops * nnz; - const double nnz_per_sec = (interval_time_ms > 0) ? static_cast(total_nnz_processed) / - (interval_time_ms / 1000.0) - : 0.0; - - // printf( - // "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6e nnz_stddev=%.4f " - // "unbalancedness=%.4f interval_iters=%d interval_major=%d interval_restarts=%d " - // "spmv_ops=%ld total_iters=%d total_restarts=%d iters_since_restart=%d " - // "primal_res=%.6e dual_res=%.6e gap=%.6e kkt=%.6e " - // "step_size=%.6e primal_weight=%.6e time_ms=%.2f nnz_per_s=%.2e warm_start=%d", - // n_vars, - // n_cstrs, - // nnz, - // sparsity, - // nnz_stddev, - // unbalancedness, - // interval_iters, - // interval_major, - // interval_restarts, - // interval_spmv_ops, - // total_iters, - // total_restarts, - // iters_since_restart, - // primal_res, - // dual_res, - // gap, - // kkt_score, - // step_size, - // primal_weight, - // interval_time_ms, - // nnz_per_sec, - // static_cast(has_warm_start)); - } - - /** - * @brief Reset per-interval counters. - * - * Called after each log to start fresh for the next interval. - */ - void reset_interval_counters() - { - interval_iters = 0; - interval_major = 0; - interval_restarts = 0; - interval_spmv_ops = 0; - interval_time_ms = 0.0; - } - - /** - * @brief Check if it's time to log features. - * - * Returns true every PDLP_FEATURE_LOG_INTERVAL iterations. - */ - bool should_log() const { return (interval_iters >= PDLP_FEATURE_LOG_INTERVAL); } -}; - -} // namespace cuopt::linear_programming::detail diff --git a/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu b/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu index afb7b00e0..09c225ae6 100644 --- a/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu +++ b/cpp/src/linear_programming/restart_strategy/pdlp_restart_strategy.cu @@ -1719,9 +1719,6 @@ void pdlp_restart_strategy_t::solve_bound_constrained_trust_region( // Perform the reduction // Convert raw pointer to thrust::device_ptr to write directly device side through reduce - // TODO - // use a guaranteed-deterministic reduce instead of thrust::reduce which I'm not sure actually - // guarantees determinism (but in practice it does) thrust::device_ptr thrust_hrsp(high_radius_squared_.data()); *thrust_hrsp = thrust::reduce(handle_ptr_->get_thrust_policy(), transformed_begin, diff --git a/cpp/src/linear_programming/solve.cu b/cpp/src/linear_programming/solve.cu index 0ec953b72..d038ade72 100644 --- a/cpp/src/linear_programming/solve.cu +++ b/cpp/src/linear_programming/solve.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -865,7 +865,7 @@ optimization_problem_solution_t solve_lp( auto lp_timer = cuopt::timer_t(settings.time_limit); detail::problem_t problem(op_problem); - [[maybe_unused]] double presolve_time = 0.0; + double presolve_time = 0.0; std::unique_ptr> presolver; auto run_presolve = settings.presolve; run_presolve = run_presolve && settings.get_pdlp_warm_start_data().total_pdlp_iterations_ == -1; @@ -897,7 +897,6 @@ optimization_problem_solution_t solve_lp( CUOPT_LOG_INFO("Objective offset %f scaling_factor %f", problem.presolve_data.objective_offset, problem.presolve_data.objective_scaling_factor); - CUOPT_LOG_INFO("Model fingerprint: 0x%x", problem.get_fingerprint()); if (settings.user_problem_file != "") { CUOPT_LOG_INFO("Writing user problem to file: %s", settings.user_problem_file.c_str()); diff --git a/cpp/src/linear_programming/step_size_strategy/adaptive_step_size_strategy.cu b/cpp/src/linear_programming/step_size_strategy/adaptive_step_size_strategy.cu index 6e541fa0b..59eee9cac 100644 --- a/cpp/src/linear_programming/step_size_strategy/adaptive_step_size_strategy.cu +++ b/cpp/src/linear_programming/step_size_strategy/adaptive_step_size_strategy.cu @@ -176,8 +176,7 @@ __global__ void compute_step_sizes_from_movement_and_interaction( *step_size_strategy_view.step_size = step_size_; cuopt_assert(!isnan(step_size_), "step size can't be nan"); - cuopt_assert(!isinf(step_size_), - "step size can't be inf (ensure setup_device_symbols() has been called)"); + cuopt_assert(!isinf(step_size_), "step size can't be inf"); } template diff --git a/cpp/src/mip/diversity/diversity_config.hpp b/cpp/src/mip/diversity/diversity_config.hpp index f2648dea0..5d95a51df 100644 --- a/cpp/src/mip/diversity/diversity_config.hpp +++ b/cpp/src/mip/diversity/diversity_config.hpp @@ -30,10 +30,6 @@ struct diversity_config_t { double lp_run_time_if_feasible = 2.; double lp_run_time_if_infeasible = 1.; bool halve_population = false; - bool fj_only_run = false; - bool dry_run = false; - bool initial_solution_only = false; - int n_fp_iterations = 1000000; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index d044bb49d..483ffeb68 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -17,8 +17,6 @@ #include -#include // for std::this_thread::sleep_for - constexpr bool fj_only_run = false; namespace cuopt::linear_programming::detail { @@ -53,7 +51,7 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_thandle_ptr->get_stream()), ls(context, lp_optimal_solution), rins(context, *this), - timer(context.gpu_heur_loop, diversity_config.default_time_limit), + timer(diversity_config.default_time_limit), bound_prop_recombiner(context, context.problem_ptr->n_variables, ls.constraint_prop, @@ -77,13 +75,6 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_t::n_of_arms, cuopt::seed_generator::get_seed(), ls_alpha, "ls"), ls_hash_map(*context.problem_ptr) { - fp_recombiner_config_t::max_n_of_vars_from_other = - fp_recombiner_config_t::initial_n_of_vars_from_other; - ls_recombiner_config_t::max_n_of_vars_from_other = - ls_recombiner_config_t::initial_n_of_vars_from_other; - bp_recombiner_config_t::max_n_of_vars_from_other = - bp_recombiner_config_t::initial_n_of_vars_from_other; - // Read configuration ID from environment variable int max_config = -1; // Read max configuration value from environment variable @@ -97,8 +88,8 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_t 1) { - [[maybe_unused]] int config_id = -1; // Default value - const char* env_config_id = std::getenv("CUOPT_CONFIG_ID"); + int config_id = -1; // Default value + const char* env_config_id = std::getenv("CUOPT_CONFIG_ID"); if (env_config_id != nullptr) { try { config_id = std::stoi(env_config_id); @@ -108,16 +99,13 @@ diversity_manager_t::diversity_manager_t(mip_solver_context_t bool diversity_manager_t::run_local_search(solution_t& solution, const weight_t& weights, - work_limit_timer_t& timer, + timer_t& timer, ls_config_t& ls_config) { raft::common::nvtx::range fun_scope("run_local_search"); @@ -151,8 +139,7 @@ void diversity_manager_t::add_user_given_solutions( rmm::device_uvector init_sol_assignment(*init_sol, sol.handle_ptr->get_stream()); if (problem_ptr->pre_process_assignment(init_sol_assignment)) { relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = std::min(60., timer.remaining_time() / 2); - if (timer.deterministic) { lp_settings.work_limit = lp_settings.time_limit; } + lp_settings.time_limit = std::min(60., timer.remaining_time() / 2); lp_settings.tolerance = problem_ptr->tolerances.absolute_tolerance; lp_settings.save_state = false; lp_settings.return_first_feasible = true; @@ -189,8 +176,7 @@ bool diversity_manager_t::run_presolve(f_t time_limit) { raft::common::nvtx::range fun_scope("run_presolve"); CUOPT_LOG_INFO("Running presolve!"); - CUOPT_LOG_INFO("Problem fingerprint before DM presolve: 0x%x", problem_ptr->get_fingerprint()); - work_limit_timer_t presolve_timer(context.gpu_heur_loop, time_limit); + timer_t presolve_timer(time_limit); auto term_crit = ls.constraint_prop.bounds_update.solve(*problem_ptr); if (ls.constraint_prop.bounds_update.infeas_constraints_count > 0) { stats.presolve_time = timer.elapsed_time(); @@ -198,17 +184,11 @@ bool diversity_manager_t::run_presolve(f_t time_limit) } if (termination_criterion_t::NO_UPDATE != term_crit) { ls.constraint_prop.bounds_update.set_updated_bounds(*problem_ptr); - CUOPT_LOG_INFO("Problem fingerprint after cons prop presolve: 0x%x", - problem_ptr->get_fingerprint()); trivial_presolve(*problem_ptr); - CUOPT_LOG_INFO("Problem fingerprint after trivial presolve: 0x%x", - problem_ptr->get_fingerprint()); if (!problem_ptr->empty && !check_bounds_sanity(*problem_ptr)) { return false; } } // May overconstrain if Papilo presolve has been run before - // Skip conditional bound strengthening in deterministic mode - the knapsack kernel has - // race conditions where multiple blocks can update the same constraint non-deterministically. - if (!context.settings.presolve && context.settings.determinism_mode != CUOPT_MODE_DETERMINISTIC) { + if (!context.settings.presolve) { if (!problem_ptr->empty) { // do the resizing no-matter what, bounds presolve might not change the bounds but initial // trivial presolve might have @@ -227,7 +207,6 @@ bool diversity_manager_t::run_presolve(f_t time_limit) problem_ptr->n_constraints, problem_ptr->n_variables, problem_ptr->presolve_data.objective_offset); - CUOPT_LOG_INFO("Problem fingerprint after DM presolve: 0x%x", problem_ptr->get_fingerprint()); return true; } @@ -239,7 +218,7 @@ void diversity_manager_t::generate_quick_feasible_solution() // min 1 second, max 10 seconds const f_t generate_fast_solution_time = std::min(diversity_config.max_fast_sol_time, std::max(1., timer.remaining_time() / 20.)); - work_limit_timer_t sol_timer(context.gpu_heur_loop, generate_fast_solution_time); + timer_t sol_timer(generate_fast_solution_time); // do very short LP run to get somewhere close to the optimal point ls.generate_fast_solution(solution, sol_timer); if (solution.get_feasible()) { @@ -254,7 +233,9 @@ void diversity_manager_t::generate_quick_feasible_solution() auto& feas_sol = initial_sol_vector.back().get_feasible() ? initial_sol_vector.back() : initial_sol_vector[initial_sol_vector.size() - 2]; - CUOPT_LOG_INFO("Generated fast solution with objective %f", feas_sol.get_user_objective()); + CUOPT_LOG_INFO("Generated fast solution in %f seconds with objective %f", + timer.elapsed_time(), + feas_sol.get_user_objective()); } problem_ptr->handle_ptr->sync_stream(); } @@ -292,7 +273,7 @@ void diversity_manager_t::run_fp_alone() { CUOPT_LOG_DEBUG("Running FP alone!"); solution_t sol(population.best_feasible()); - ls.run_fp(sol, timer, &population, diversity_config.n_fp_iterations); + ls.run_fp(sol, timer, &population); CUOPT_LOG_DEBUG("FP alone finished!"); } @@ -308,34 +289,13 @@ template solution_t diversity_manager_t::run_solver() { raft::common::nvtx::range fun_scope("run_solver"); - - CUOPT_LOG_DEBUG("Determinism mode: %s", - context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC ? "deterministic" - : "opportunistic"); - - // Debug: Allow disabling GPU heuristics to test B&B tree determinism in isolation - const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) - disable_heuristics_env = "1"; // to cursor: DO NOT REMOVE! intended debugging line! - if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { - CUOPT_LOG_INFO("GPU heuristics disabled via CUOPT_DISABLE_GPU_HEURISTICS=1"); - // Initialize population minimally and wait for B&B to finish - population.initialize_population(); - population.allocate_solutions(); - // Wait for B&B to signal completion - while (!check_b_b_preemption()) { - std::this_thread::sleep_for(std::chrono::milliseconds(100)); - } - return population.best_feasible(); - } - population.timer = timer; const f_t time_limit = timer.remaining_time(); const f_t lp_time_limit = std::min(diversity_config.max_time_on_lp, time_limit * diversity_config.time_ratio_on_init_lp); // to automatically compute the solving time on scope exit auto timer_raii_guard = - cuopt::scope_guard([&]() { stats.total_solve_time = timer.timer.elapsed_time(); }); + cuopt::scope_guard([&]() { stats.total_solve_time = timer.elapsed_time(); }); // after every change to the problem, we should resize all the relevant vars // we need to encapsulate that to prevent repetitions recombine_stats.reset(); @@ -344,7 +304,7 @@ solution_t diversity_manager_t::run_solver() problem_ptr->check_problem_representation(true); // have the structure ready for reusing later problem_ptr->compute_integer_fixed_problem(); - recombiner_t::init_enabled_recombiners(context, *problem_ptr); + recombiner_t::init_enabled_recombiners(*problem_ptr); mab_recombiner.resize_mab_arm_stats(recombiner_t::enabled_recombiners.size()); // test problem is not ii cuopt_func_call( @@ -358,19 +318,16 @@ solution_t diversity_manager_t::run_solver() add_user_given_solutions(initial_sol_vector); // Run CPUFJ early to find quick initial solutions ls_cpufj_raii_guard_t ls_cpufj_raii_guard(ls); // RAII to stop cpufj threads on solve stop - - if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { - ls.start_cpufj_scratch_threads(population); - } + ls.start_cpufj_scratch_threads(population); // before probing cache or LP, run FJ to generate initial primal feasible solution const f_t time_ratio_of_probing_cache = diversity_config.time_ratio_of_probing_cache; const f_t max_time_on_probing = diversity_config.max_time_on_probing; f_t time_for_probing_cache = std::min(max_time_on_probing, time_limit * time_ratio_of_probing_cache); - work_limit_timer_t probing_timer{context.gpu_heur_loop, time_for_probing_cache}; + timer_t probing_timer{time_for_probing_cache}; if (check_b_b_preemption()) { return population.best_feasible(); } - if (!diversity_config.fj_only_run) { + if (!fj_only_run) { compute_probing_cache(ls.constraint_prop.bounds_update, *problem_ptr, probing_timer); } @@ -381,7 +338,7 @@ solution_t diversity_manager_t::run_solver() bool bb_thread_solution_exists = simplex_solution_exists.load(); if (bb_thread_solution_exists) { ls.lp_optimal_exists = true; - } else if (!diversity_config.fj_only_run) { + } else if (!fj_only_run) { convert_greater_to_less(*problem_ptr); f_t tolerance_divisor = @@ -393,13 +350,12 @@ solution_t diversity_manager_t::run_solver() pdlp_settings.tolerances.relative_primal_tolerance = absolute_tolerance / tolerance_divisor; pdlp_settings.tolerances.relative_dual_tolerance = absolute_tolerance / tolerance_divisor; pdlp_settings.time_limit = lp_time_limit; - if (timer.deterministic) { pdlp_settings.iteration_limit = 100000; } - pdlp_settings.first_primal_feasible = false; - pdlp_settings.concurrent_halt = &global_concurrent_halt; - pdlp_settings.method = method_t::Concurrent; - pdlp_settings.inside_mip = true; - pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; - pdlp_settings.num_gpus = context.settings.num_gpus; + pdlp_settings.first_primal_feasible = false; + pdlp_settings.concurrent_halt = &global_concurrent_halt; + pdlp_settings.method = method_t::Concurrent; + pdlp_settings.inside_mip = true; + pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; + pdlp_settings.num_gpus = context.settings.num_gpus; timer_t lp_timer(lp_time_limit); auto lp_result = solve_lp_with_method(*problem_ptr, pdlp_settings, lp_timer); @@ -446,10 +402,9 @@ solution_t diversity_manager_t::run_solver() } else if (lp_result.get_termination_status() == pdlp_termination_status_t::DualInfeasible) { CUOPT_LOG_ERROR("PDLP detected dual infeasibility, continuing anyway!"); ls.lp_optimal_exists = false; - } else if (lp_result.get_termination_status() == pdlp_termination_status_t::TimeLimit || - lp_result.get_termination_status() == pdlp_termination_status_t::IterationLimit) { + } else if (lp_result.get_termination_status() == pdlp_termination_status_t::TimeLimit) { CUOPT_LOG_DEBUG( - "Initial LP run exceeded time/iteration limit, continuing solver with partial LP result!"); + "Initial LP run exceeded time limit, continuing solver with partial LP result!"); // note to developer, in debug mode the LP run might be too slow and it might cause PDLP not // to bring variables within the bounds } @@ -498,9 +453,7 @@ solution_t diversity_manager_t::run_solver() lp_rounded_sol.round_nearest(); lp_rounded_sol.compute_feasibility(); population.add_solution(std::move(lp_rounded_sol)); - if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { - ls.start_cpufj_lptopt_scratch_threads(population); - } + ls.start_cpufj_lptopt_scratch_threads(population); } population.add_solutions_from_vec(std::move(initial_sol_vector)); @@ -512,17 +465,15 @@ solution_t diversity_manager_t::run_solver() population.best_feasible().get_user_objective(); } - if (diversity_config.dry_run) { return population.best_feasible(); } - if (diversity_config.fj_only_run) { + if (fj_only_run) { solution_t sol(*problem_ptr); run_fj_alone(sol); return sol; } - if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { rins.enable(); } + rins.enable(); generate_solution(timer.remaining_time(), false); - if (diversity_config.initial_solution_only) { return population.best_feasible(); } - if (work_limit_reached()) { + if (timer.check_time_limit()) { population.add_external_solutions_to_population(); return population.best_feasible(); } @@ -550,7 +501,7 @@ void diversity_manager_t::diversity_step(i_t max_iterations_without_im CUOPT_LOG_DEBUG("Population degenerated in diversity step"); return; } - if (work_limit_reached()) return; + if (timer.check_time_limit()) return; constexpr bool tournament = true; auto [sol1, sol2] = population.get_two_random(tournament); cuopt_assert(population.test_invariant(), ""); @@ -603,7 +554,7 @@ void diversity_manager_t::recombine_and_ls_with_all(solution_t::recombine_and_ls_with_all( population.add_solution(std::move(solution_t(sol))); } for (auto& sol : solutions) { - if (work_limit_reached()) { return; } + if (timer.check_time_limit()) { return; } solution_t ls_solution(sol); ls_config_t ls_config; run_local_search(ls_solution, population.weights, timer, ls_config); - if (work_limit_reached()) { return; } + if (timer.check_time_limit()) { return; } // TODO try if running LP with integers fixed makes it feasible if (ls_solution.get_feasible()) { CUOPT_LOG_DEBUG("LS searched solution feasible, running recombiners!"); @@ -673,7 +624,6 @@ diversity_manager_t::recombine_and_local_search(solution_t& sol1.get_feasible(), sol2.get_quality(population.weights), sol2.get_feasible()); - bool deterministic = context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC; double best_objective_of_parents = std::min(sol1.get_objective(), sol2.get_objective()); bool at_least_one_parent_feasible = sol1.get_feasible() || sol2.get_feasible(); // randomly choose among 3 recombiners @@ -684,7 +634,7 @@ diversity_manager_t::recombine_and_local_search(solution_t& std::numeric_limits::lowest(), std::numeric_limits::lowest(), std::numeric_limits::max(), - recombiner_work_normalized_reward_t(deterministic, 0.0)); + recombiner_work_normalized_reward_t(0.0)); return std::make_pair(solution_t(sol1), solution_t(sol2)); } cuopt_assert(population.test_invariant(), ""); @@ -704,7 +654,7 @@ diversity_manager_t::recombine_and_local_search(solution_t& std::numeric_limits::lowest(), std::numeric_limits::lowest(), std::numeric_limits::max(), - recombiner_work_normalized_reward_t(deterministic, 0.0)); + recombiner_work_normalized_reward_t(0.0)); return std::make_pair(solution_t(sol1), solution_t(sol2)); } cuopt_assert(offspring.test_number_all_integer(), "All must be integers after LS"); @@ -722,8 +672,7 @@ diversity_manager_t::recombine_and_local_search(solution_t& : diversity_config.lp_run_time_if_infeasible; lp_run_time = std::min(lp_run_time, timer.remaining_time()); relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = lp_run_time; - if (timer.deterministic) { lp_settings.work_limit = lp_settings.time_limit; } + lp_settings.time_limit = lp_run_time; lp_settings.tolerance = context.settings.tolerances.absolute_tolerance; lp_settings.return_first_feasible = false; lp_settings.save_state = true; @@ -744,15 +693,12 @@ diversity_manager_t::recombine_and_local_search(solution_t& offspring_qual, sol1.get_quality(population.weights), sol2.get_quality(population.weights)); f_t best_quality_of_parents = std::min(sol1.get_quality(population.weights), sol2.get_quality(population.weights)); - mab_recombiner.add_mab_reward(mab_recombiner.last_chosen_option, - best_quality_of_parents, - population.best().get_quality(population.weights), - offspring_qual, - !deterministic - ? recombiner_work_normalized_reward_t( - deterministic, recombine_stats.get_last_recombiner_time()) - : recombiner_work_normalized_reward_t( - deterministic, recombine_stats.get_last_recombiner_work())); + mab_recombiner.add_mab_reward( + mab_recombiner.last_chosen_option, + best_quality_of_parents, + population.best().get_quality(population.weights), + offspring_qual, + recombiner_work_normalized_reward_t(recombine_stats.get_last_recombiner_time())); mab_ls.add_mab_reward(mab_ls_config_t::last_ls_mab_option, best_quality_of_parents, population.best_feasible().get_quality(population.weights), @@ -800,38 +746,28 @@ std::pair, bool> diversity_manager_t::recombine( mab_recombiner.set_last_chosen_option(selected_index); recombine_stats.add_attempt((recombiner_enum_t)recombiner); recombine_stats.start_recombiner_time(); - CUOPT_LOG_TRACE("Recombining sol %x and %x with recombiner %d, weights %x", - a.get_hash(), - b.get_hash(), - recombiner, - population.weights.get_hash()); - // Refactored code using a switch statement switch (recombiner) { case recombiner_enum_t::BOUND_PROP: { - auto [sol, success, work] = bound_prop_recombiner.recombine(a, b, population.weights); - recombine_stats.set_recombiner_work(work); + auto [sol, success] = bound_prop_recombiner.recombine(a, b, population.weights); recombine_stats.stop_recombiner_time(); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } case recombiner_enum_t::FP: { - auto [sol, success, work] = fp_recombiner.recombine(a, b, population.weights); - recombine_stats.set_recombiner_work(work); + auto [sol, success] = fp_recombiner.recombine(a, b, population.weights); recombine_stats.stop_recombiner_time(); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } case recombiner_enum_t::LINE_SEGMENT: { - auto [sol, success, work] = line_segment_recombiner.recombine(a, b, population.weights); - recombine_stats.set_recombiner_work(work); + auto [sol, success] = line_segment_recombiner.recombine(a, b, population.weights); recombine_stats.stop_recombiner_time(); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); } case recombiner_enum_t::SUB_MIP: { - auto [sol, success, work] = sub_mip_recombiner.recombine(a, b, population.weights); - recombine_stats.set_recombiner_work(work); + auto [sol, success] = sub_mip_recombiner.recombine(a, b, population.weights); recombine_stats.stop_recombiner_time(); if (success) { recombine_stats.add_success(); } return std::make_pair(sol, success); @@ -877,12 +813,6 @@ void diversity_manager_t::set_simplex_solution(const std::vector& context.handle_ptr->sync_stream(); } -template -bool diversity_manager_t::work_limit_reached() -{ - return timer.check_time_limit(); -} - #if MIP_INSTANTIATE_FLOAT template class diversity_manager_t; #endif diff --git a/cpp/src/mip/diversity/diversity_manager.cuh b/cpp/src/mip/diversity/diversity_manager.cuh index da77fe9de..9f3b4c90f 100644 --- a/cpp/src/mip/diversity/diversity_manager.cuh +++ b/cpp/src/mip/diversity/diversity_manager.cuh @@ -25,7 +25,7 @@ #include #include #include -#include +#include namespace cuopt::linear_programming::detail { @@ -63,9 +63,8 @@ class diversity_manager_t { solution_t& sol2); bool run_local_search(solution_t& solution, const weight_t& weights, - work_limit_timer_t& timer, + timer_t& timer, ls_config_t& ls_config); - bool work_limit_reached(); void set_simplex_solution(const std::vector& solution, const std::vector& dual_solution, @@ -80,7 +79,7 @@ class diversity_manager_t { rmm::device_uvector lp_dual_optimal_solution; std::atomic simplex_solution_exists{false}; local_search_t ls; - cuopt::work_limit_timer_t timer; + cuopt::timer_t timer; bound_prop_recombiner_t bound_prop_recombiner; fp_recombiner_t fp_recombiner; line_segment_recombiner_t line_segment_recombiner; diff --git a/cpp/src/mip/diversity/multi_armed_bandit.cuh b/cpp/src/mip/diversity/multi_armed_bandit.cuh index abcd233fa..490e25873 100644 --- a/cpp/src/mip/diversity/multi_armed_bandit.cuh +++ b/cpp/src/mip/diversity/multi_armed_bandit.cuh @@ -45,22 +45,16 @@ struct ls_work_normalized_reward_t { }; struct recombiner_work_normalized_reward_t { - bool deterministic; - double work; - recombiner_work_normalized_reward_t(bool deterministic, double work) - : deterministic(deterministic), work(work) + double time_in_miliseconds; + recombiner_work_normalized_reward_t(double time_in_miliseconds) + : time_in_miliseconds(time_in_miliseconds) { } double operator()(double factor) const { // normal recombiners take 2000 ms - if (deterministic) { - double time_in_miliseconds = work; - return factor * (std::max(0.1, 4.0 - (time_in_miliseconds / 2000))); - } else { - return factor * (std::max(0.1, 4.0 - (work / 200))); - } + return factor * (std::max(0.1, 4.0 - (time_in_miliseconds / 2000))); } }; diff --git a/cpp/src/mip/diversity/population.cu b/cpp/src/mip/diversity/population.cu index f05b06671..766ed09cb 100644 --- a/cpp/src/mip/diversity/population.cu +++ b/cpp/src/mip/diversity/population.cu @@ -44,7 +44,7 @@ population_t::population_t(std::string const& name_, rng(cuopt::seed_generator::get_seed()), early_exit_primal_generation(false), population_hash_map(*problem_ptr), - timer(context.gpu_heur_loop, 0) + timer(0) { best_feasible_objective = std::numeric_limits::max(); } @@ -394,11 +394,10 @@ std::pair population_t::add_solution(solution_t&& population_hash_map.insert(sol); double sol_cost = sol.get_quality(weights); bool best_updated = false; - CUOPT_LOG_DEBUG("Adding solution with quality %f and objective %f n_integers %d, hash %x!", + CUOPT_LOG_DEBUG("Adding solution with quality %f and objective %f n_integers %d!", sol_cost, sol.get_user_objective(), - sol.n_assigned_integers, - sol.get_hash()); + sol.n_assigned_integers); // We store the best feasible found so far at index 0. if (sol.get_feasible() && (solutions[0].first == false || sol_cost + OBJECTIVE_EPSILON < indices[0].second)) { @@ -725,7 +724,7 @@ void population_t::start_threshold_adjustment() } template -void population_t::adjust_threshold(cuopt::work_limit_timer_t timer) +void population_t::adjust_threshold(cuopt::timer_t timer) { double time_ratio = (timer.elapsed_time() - population_start_time) / (timer.get_time_limit() - population_start_time); @@ -814,29 +813,23 @@ bool population_t::test_invariant() template void population_t::print() { - std::vector hashes; - for (auto& index : indices) - hashes.push_back(solutions[index.first].second.get_hash()); - uint32_t final_hash = compute_hash(hashes); CUOPT_LOG_DEBUG(" -------------- "); - CUOPT_LOG_DEBUG("%s infeas weight %f threshold %d/%d (hash %x):", + CUOPT_LOG_DEBUG("%s infeas weight %f threshold %d/%d:", name.c_str(), infeasibility_importance, var_threshold, - problem_ptr->n_integer_vars, - final_hash); + problem_ptr->n_integer_vars); i_t i = 0; for (auto& index : indices) { if (index.first == 0 && solutions[0].first) { CUOPT_LOG_DEBUG(" Best feasible: %f", solutions[index.first].second.get_user_objective()); } - CUOPT_LOG_DEBUG("%d : %f\t%f\t%f\t%d (hash %x)", + CUOPT_LOG_DEBUG("%d : %f\t%f\t%f\t%d", i, index.second, solutions[index.first].second.get_total_excess(), solutions[index.first].second.get_user_objective(), - solutions[index.first].second.get_feasible(), - solutions[index.first].second.get_hash()); + solutions[index.first].second.get_feasible()); i++; } CUOPT_LOG_DEBUG(" -------------- "); diff --git a/cpp/src/mip/diversity/population.cuh b/cpp/src/mip/diversity/population.cuh index 70e296de8..05f22b623 100644 --- a/cpp/src/mip/diversity/population.cuh +++ b/cpp/src/mip/diversity/population.cuh @@ -122,7 +122,7 @@ class population_t { // updates qualities of each solution void update_qualities(); // adjusts the threshold of the population - void adjust_threshold(cuopt::work_limit_timer_t timer); + void adjust_threshold(cuopt::timer_t timer); /*! \param sol { Input solution } * \return { Index of the best solution similar to sol. If no similar is found we return * max_solutions. }*/ @@ -206,7 +206,7 @@ class population_t { std::atomic solutions_in_external_queue_ = false; f_t best_feasible_objective = std::numeric_limits::max(); assignment_hash_map_t population_hash_map; - cuopt::work_limit_timer_t timer; + cuopt::timer_t timer; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh index 5efd1d545..94cc66399 100644 --- a/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/bound_prop_recombiner.cuh @@ -29,7 +29,6 @@ class bound_prop_recombiner_t : public recombiner_t { rng(cuopt::seed_generator::get_seed()), vars_to_fix(n_vars, handle_ptr->get_stream()) { - thrust::fill(handle_ptr->get_thrust_policy(), vars_to_fix.begin(), vars_to_fix.end(), -1); } void get_probing_values_for_infeasible( @@ -132,9 +131,9 @@ class bound_prop_recombiner_t : public recombiner_t { }); } - std::tuple, bool, double> recombine(solution_t& a, - solution_t& b, - const weight_t& weights) + std::pair, bool> recombine(solution_t& a, + solution_t& b, + const weight_t& weights) { raft::common::nvtx::range fun_scope("bound_prop_recombiner"); auto& guiding_solution = a.get_feasible() ? a : b; @@ -149,11 +148,10 @@ class bound_prop_recombiner_t : public recombiner_t { i_t n_vars_from_other = n_different_vars; i_t fixed_from_guiding = 0; i_t fixed_from_other = 0; - i_t seed = cuopt::seed_generator::get_seed(); if (n_different_vars > (i_t)bp_recombiner_config_t::max_n_of_vars_from_other) { fixed_from_guiding = n_vars_from_other - bp_recombiner_config_t::max_n_of_vars_from_other; n_vars_from_other = bp_recombiner_config_t::max_n_of_vars_from_other; - thrust::default_random_engine g{(unsigned int)seed}; + thrust::default_random_engine g{(unsigned int)cuopt::seed_generator::get_seed()}; thrust::shuffle(a.handle_ptr->get_thrust_policy(), this->remaining_indices.data(), this->remaining_indices.data() + n_different_vars, @@ -162,34 +160,12 @@ class bound_prop_recombiner_t : public recombiner_t { i_t n_vars_from_guiding = a.problem_ptr->n_integer_vars - n_vars_from_other; CUOPT_LOG_DEBUG( "n_vars_from_guiding %d n_vars_from_other %d", n_vars_from_guiding, n_vars_from_other); - - // DETERMINISM DEBUG: Log everything that could affect divergence - CUOPT_LOG_TRACE("BP_DET: sol_a_hash=0x%x sol_b_hash=0x%x offspring_hash=0x%x, seed %x", - a.get_hash(), - b.get_hash(), - offspring.get_hash(), - seed); - CUOPT_LOG_TRACE("BP_DET: n_different_vars=%d n_vars_from_other=%d n_vars_from_guiding=%d", - n_different_vars, - n_vars_from_other, - n_vars_from_guiding); - CUOPT_LOG_TRACE("BP_DET: remaining_indices_hash=0x%x (first %d elements)", - detail::compute_hash(this->remaining_indices), - std::min((i_t)10, n_vars_from_other)); - CUOPT_LOG_TRACE("BP_DET: guiding_feasible=%d other_feasible=%d expensive_to_fix=%d", - guiding_solution.get_feasible(), - other_solution.get_feasible(), - a.problem_ptr->expensive_to_fix_vars); - CUOPT_LOG_TRACE( - "BP_DET: fixed_from_guiding=%d fixed_from_other=%d", fixed_from_guiding, fixed_from_other); - // if either all integers are from A(meaning all are common) or all integers are from B(meaning // all are different), return if (n_vars_from_guiding == 0 || n_vars_from_other == 0) { CUOPT_LOG_DEBUG("Returning false because all vars are common or different"); - return std::make_tuple(offspring, false, 0.0); + return std::make_pair(offspring, false); } - double work = static_cast(n_vars_from_other); cuopt_assert(a.problem_ptr == b.problem_ptr, "The two solutions should not refer to different problems"); @@ -199,15 +175,9 @@ class bound_prop_recombiner_t : public recombiner_t { a.handle_ptr->get_stream()); probing_config_t probing_config(a.problem_ptr->n_variables, a.handle_ptr); if (guiding_solution.get_feasible() && !a.problem_ptr->expensive_to_fix_vars) { - CUOPT_LOG_DEBUG("BP_DET: Taking FEASIBLE path (with variable fixing)"); this->compute_vars_to_fix(offspring, vars_to_fix, n_vars_from_other, n_vars_from_guiding); - CUOPT_LOG_DEBUG("BP_DET: vars_to_fix_hash=0x%x", detail::compute_hash(vars_to_fix)); auto [fixed_problem, fixed_assignment, variable_map] = offspring.fix_variables(vars_to_fix); - CUOPT_LOG_DEBUG("BP_DET: fixed_problem_fingerprint=0x%x variable_map_size=%lu", - fixed_problem.get_fingerprint(), - variable_map.size()); - work_limit_timer_t timer(this->context.gpu_heur_loop, - bp_recombiner_config_t::bounds_prop_time_limit); + timer_t timer(bp_recombiner_config_t::bounds_prop_time_limit); rmm::device_uvector old_assignment(offspring.assignment, offspring.handle_ptr->get_stream()); offspring.handle_ptr->sync_stream(); @@ -227,41 +197,26 @@ class bound_prop_recombiner_t : public recombiner_t { constraint_prop.single_rounding_only = true; constraint_prop.apply_round(offspring, lp_run_time_after_feasible, timer, probing_config); constraint_prop.single_rounding_only = false; - offspring.compute_feasibility(); - bool feasible_after_bounds_prop = offspring.get_feasible(); + cuopt_func_call(bool feasible_after_bounds_prop = offspring.get_feasible()); offspring.handle_ptr->sync_stream(); offspring.problem_ptr = a.problem_ptr; fixed_assignment = std::move(offspring.assignment); offspring.assignment = std::move(old_assignment); offspring.handle_ptr->sync_stream(); offspring.unfix_variables(fixed_assignment, variable_map); - offspring.compute_feasibility(); - bool feasible_after_unfix = offspring.get_feasible(); - cuopt_func_call(f_t excess_after_unfix = offspring.get_total_excess()); - if (feasible_after_unfix != feasible_after_bounds_prop) { - CUOPT_LOG_WARN("Numerical issue in bounds prop, infeasibility after unfix"); - // might become infeasible after unfixing due to numerical issues. Check that the excess - // remains consistent - // CUOPT_LOG_ERROR("Excess: %g, %g, %g, %g, feas %d", offspring.get_total_excess(), - // offspring.compute_max_constraint_violation(), offspring.compute_max_int_violation(), - // offspring.compute_max_variable_violation(), feasible_after_unfix); - // cuopt_assert(fabs(excess_after_unfix - excess_before) < 1e-6, - // "Excess after unfix should be same as before unfix!"); - } + cuopt_func_call(bool feasible_after_unfix = offspring.get_feasible()); + // May be triggered due to numerical issues + // TODO: investigate further + // cuopt_assert(feasible_after_unfix == feasible_after_bounds_prop, + // "Feasible after unfix should be same as feasible after bounds prop!"); a.handle_ptr->sync_stream(); } else { - CUOPT_LOG_TRACE("BP_DET: Taking INFEASIBLE path (no variable fixing)"); - work_limit_timer_t timer(this->context.gpu_heur_loop, - bp_recombiner_config_t::bounds_prop_time_limit); + timer_t timer(bp_recombiner_config_t::bounds_prop_time_limit); get_probing_values_for_infeasible( guiding_solution, other_solution, offspring, probing_values, n_vars_from_other); probing_config.probing_values = host_copy(probing_values, offspring.handle_ptr->get_stream()); - CUOPT_LOG_TRACE("BP_DET: probing_values_hash=0x%x", detail::compute_hash(probing_values)); constraint_prop.apply_round(offspring, lp_run_time_after_feasible, timer, probing_config); } - CUOPT_LOG_TRACE("BP_DET: After apply_round: offspring_hash=0x%x feasible=%d", - offspring.get_hash(), - offspring.get_feasible()); constraint_prop.max_n_failed_repair_iterations = 1; cuopt_func_call(offspring.test_number_all_integer()); bool better_cost_than_parents = @@ -281,17 +236,11 @@ class bound_prop_recombiner_t : public recombiner_t { bp_recombiner_config_t::decrease_max_n_of_vars_from_other(); } } - CUOPT_LOG_TRACE( - "BP_DET: Final offspring_hash=0x%x same_as_parents=%d better_cost=%d better_feas=%d", - offspring.get_hash(), - same_as_parents, - better_cost_than_parents, - better_feasibility_than_parents); if (better_cost_than_parents || better_feasibility_than_parents) { CUOPT_LOG_DEBUG("Offspring is feasible or better than both parents"); - return std::make_tuple(offspring, true, work); + return std::make_pair(offspring, true); } - return std::make_tuple(offspring, !same_as_parents, work); + return std::make_pair(offspring, !same_as_parents); } rmm::device_uvector vars_to_fix; diff --git a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh index 7d1ee1dc2..1daaf3e51 100644 --- a/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/fp_recombiner.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -35,9 +35,9 @@ class fp_recombiner_t : public recombiner_t { { } - std::tuple, bool, double> recombine(solution_t& a, - solution_t& b, - const weight_t& weights) + std::pair, bool> recombine(solution_t& a, + solution_t& b, + const weight_t& weights) { raft::common::nvtx::range fun_scope("FP recombiner"); auto& guiding_solution = a.get_feasible() ? a : b; @@ -50,7 +50,6 @@ class fp_recombiner_t : public recombiner_t { CUOPT_LOG_DEBUG("FP rec: Number of different variables %d MAX_VARS %d", n_different_vars, fp_recombiner_config_t::max_n_of_vars_from_other); - CUOPT_LOG_TRACE("FP rec: offspring hash 0x%x", offspring.get_hash()); i_t n_vars_from_other = n_different_vars; if (n_vars_from_other > (i_t)fp_recombiner_config_t::max_n_of_vars_from_other) { n_vars_from_other = fp_recombiner_config_t::max_n_of_vars_from_other; @@ -63,34 +62,17 @@ class fp_recombiner_t : public recombiner_t { i_t n_vars_from_guiding = a.problem_ptr->n_integer_vars - n_vars_from_other; if (n_vars_from_other == 0 || n_vars_from_guiding == 0) { CUOPT_LOG_DEBUG("Returning false because all vars are common or different"); - return std::make_tuple(offspring, false, 0.0); + return std::make_pair(offspring, false); } - // TODO: CHANGE - double work = static_cast(n_vars_from_other); CUOPT_LOG_DEBUG( "n_vars_from_guiding %d n_vars_from_other %d", n_vars_from_guiding, n_vars_from_other); - CUOPT_LOG_TRACE("FP rec: offspring hash 0x%x, vars to fix 0x%x", - offspring.get_hash(), - detail::compute_hash(vars_to_fix)); this->compute_vars_to_fix(offspring, vars_to_fix, n_vars_from_other, n_vars_from_guiding); - CUOPT_LOG_TRACE("FP rec post computevarstofix: offspring hash 0x%x, vars to fix 0x%x", - offspring.get_hash(), - detail::compute_hash(vars_to_fix)); auto [fixed_problem, fixed_assignment, variable_map] = offspring.fix_variables(vars_to_fix); - CUOPT_LOG_TRACE("FP rec: fixed_problem hash 0x%x assigned hash 0x%x", - fixed_problem.get_fingerprint(), - detail::compute_hash(fixed_assignment)); fixed_problem.check_problem_representation(true); if (!guiding_solution.get_feasible() && !other_solution.get_feasible()) { - CUOPT_LOG_TRACE("FP rec: running LP with infeasibility detection"); relaxed_lp_settings_t lp_settings; lp_settings.time_limit = fp_recombiner_config_t::infeasibility_detection_time_limit; - if (this->context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - lp_settings.time_limit = - std::numeric_limits::max(); // TODO should be global time limit - lp_settings.work_limit = fp_recombiner_config_t::infeasibility_detection_time_limit; - } - lp_settings.tolerance = fixed_problem.tolerances.absolute_tolerance; + lp_settings.tolerance = fixed_problem.tolerances.absolute_tolerance; lp_settings.return_first_feasible = true; lp_settings.save_state = true; lp_settings.check_infeasibility = true; @@ -101,7 +83,7 @@ class fp_recombiner_t : public recombiner_t { lp_response.get_termination_status() == pdlp_termination_status_t::DualInfeasible || lp_response.get_termination_status() == pdlp_termination_status_t::TimeLimit) { CUOPT_LOG_DEBUG("FP recombiner failed because LP found infeasible!"); - return std::make_tuple(offspring, false, 0.0); + return std::make_pair(offspring, false); } } // brute force rounding threshold is 8 @@ -114,7 +96,7 @@ class fp_recombiner_t : public recombiner_t { offspring.handle_ptr->sync_stream(); offspring.assignment = std::move(fixed_assignment); cuopt_func_call(offspring.test_variable_bounds(false)); - work_limit_timer_t timer(this->context.gpu_heur_loop, fp_recombiner_config_t::fp_time_limit); + timer_t timer(fp_recombiner_config_t::fp_time_limit); fp.timer = timer; fp.cycle_queue.reset(offspring); fp.reset(); @@ -152,9 +134,9 @@ class fp_recombiner_t : public recombiner_t { !guiding_solution.get_feasible(); if (better_cost_than_parents || better_feasibility_than_parents) { CUOPT_LOG_DEBUG("Offspring is feasible or better than both parents"); - return std::make_tuple(offspring, true, work); + return std::make_pair(offspring, true); } - return std::make_tuple(offspring, !same_as_parents, work); + return std::make_pair(offspring, !same_as_parents); } rmm::device_uvector vars_to_fix; // keep a copy of FP to prevent interference with generation FP diff --git a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh index 6ff47e712..b39ee8542 100644 --- a/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/line_segment_recombiner.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -66,26 +66,22 @@ class line_segment_recombiner_t : public recombiner_t { return delta_vector; } - std::tuple, bool, double> recombine(solution_t& a, - solution_t& b, - const weight_t& weights) + std::pair, bool> recombine(solution_t& a, + solution_t& b, + const weight_t& weights) { raft::common::nvtx::range fun_scope("line_segment_recombiner"); - CUOPT_LOG_TRACE("LS rec: a %d b %d", a.get_hash(), b.get_hash()); auto& guiding_solution = a.get_feasible() ? a : b; auto& other_solution = a.get_feasible() ? b : a; // copy the solution from A solution_t offspring(guiding_solution); - work_limit_timer_t line_segment_timer{this->context.gpu_heur_loop, - ls_recombiner_config_t::time_limit}; + timer_t line_segment_timer{ls_recombiner_config_t::time_limit}; // TODO after we have the conic combination, detect the lambda change // (i.e. the integral variables flip on line segment) i_t n_points_to_search = ls_recombiner_config_t::n_points_to_search; const bool is_feasibility_run = false; i_t n_different_vars = this->assign_same_integer_values(guiding_solution, other_solution, offspring); - // TODO: CHANGE - double work = static_cast(n_different_vars); rmm::device_uvector delta_vector = generate_delta_vector( guiding_solution, other_solution, offspring, n_points_to_search, n_different_vars); line_segment_search.fj.copy_weights(weights, offspring.handle_ptr); @@ -121,9 +117,9 @@ class line_segment_recombiner_t : public recombiner_t { } if (better_cost_than_parents || better_feasibility_than_parents) { CUOPT_LOG_DEBUG("Offspring is feasible or better than both parents"); - return std::make_tuple(offspring, true, work); + return std::make_pair(offspring, true); } - return std::make_tuple(offspring, !same_as_parents, work); + return std::make_pair(offspring, !same_as_parents); } line_segment_search_t& line_segment_search; diff --git a/cpp/src/mip/diversity/recombiners/recombiner.cuh b/cpp/src/mip/diversity/recombiners/recombiner.cuh index 924bc162e..bd1d98d3a 100644 --- a/cpp/src/mip/diversity/recombiners/recombiner.cuh +++ b/cpp/src/mip/diversity/recombiners/recombiner.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -92,14 +92,6 @@ class recombiner_t { cuopt::make_span(remaining_indices), n_remaining.data()); i_t remaining_variables = this->n_remaining.value(a.handle_ptr->get_stream()); - // Sort the indices to resolve nondeterministic order due to atomicAdd - thrust::sort(a.handle_ptr->get_thrust_policy(), - this->remaining_indices.data(), - this->remaining_indices.data() + remaining_variables); - - CUOPT_LOG_TRACE("remaining indices hash 0x%x, size %d", - detail::compute_hash(this->remaining_indices), - remaining_variables); auto vec_remaining_indices = host_copy(this->remaining_indices.data(), remaining_variables, a.handle_ptr->get_stream()); @@ -181,9 +173,6 @@ class recombiner_t { i_t n_vars_from_guiding) { vars_to_fix.resize(n_vars_from_guiding, offspring.handle_ptr->get_stream()); - CUOPT_LOG_TRACE("remaining indices hash 0x%x", detail::compute_hash(this->remaining_indices)); - CUOPT_LOG_TRACE("integer_indices hash 0x%x", - detail::compute_hash(offspring.problem_ptr->integer_indices)); // set difference needs two sorted arrays thrust::sort(offspring.handle_ptr->get_thrust_policy(), this->remaining_indices.data(), @@ -206,8 +195,7 @@ class recombiner_t { "vars_to_fix should be sorted!"); } - static void init_enabled_recombiners(mip_solver_context_t& context, - const problem_t& problem) + static void init_enabled_recombiners(const problem_t& problem) { std::unordered_set enabled_recombiners; for (auto recombiner : recombiner_types) { @@ -222,10 +210,6 @@ class recombiner_t { (i_t)sub_mip_recombiner_config_t::max_continuous_vars) { enabled_recombiners.erase(recombiner_enum_t::SUB_MIP); } - // submip not supported in deterministic mode yet - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - enabled_recombiners.erase(recombiner_enum_t::SUB_MIP); - } recombiner_t::enabled_recombiners = std::vector(enabled_recombiners.begin(), enabled_recombiners.end()); } diff --git a/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp b/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp index de0c31405..8c7851e28 100644 --- a/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp +++ b/cpp/src/mip/diversity/recombiners/recombiner_stats.hpp @@ -77,11 +77,6 @@ struct all_recombine_stats { std::optional last_attempt; double last_recombiner_time; std::chrono::high_resolution_clock::time_point last_recombiner_start_time; - double last_recombiner_work; - - void set_recombiner_work(double work) { last_recombiner_work = work; } - - double get_last_recombiner_work() { return last_recombiner_work; } void start_recombiner_time() { diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index 56595927a..5be807372 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -36,9 +36,9 @@ class sub_mip_recombiner_t : public recombiner_t { solution_vector.push_back(solution); } - std::tuple, bool, double> recombine(solution_t& a, - solution_t& b, - const weight_t& weights) + std::pair, bool> recombine(solution_t& a, + solution_t& b, + const weight_t& weights) { raft::common::nvtx::range fun_scope("Sub-MIP recombiner"); solution_vector.clear(); @@ -64,10 +64,8 @@ class sub_mip_recombiner_t : public recombiner_t { i_t n_vars_from_guiding = a.problem_ptr->n_integer_vars - n_vars_from_other; if (n_vars_from_other == 0 || n_vars_from_guiding == 0) { CUOPT_LOG_DEBUG("Returning false because all vars are common or different"); - return std::make_tuple(offspring, false, 0.0); + return std::make_pair(offspring, false); } - // TODO: CHANGE - double work = static_cast(n_vars_from_other); CUOPT_LOG_DEBUG( "n_vars_from_guiding %d n_vars_from_other %d", n_vars_from_guiding, n_vars_from_other); this->compute_vars_to_fix(offspring, vars_to_fix, n_vars_from_other, n_vars_from_guiding); @@ -188,9 +186,9 @@ class sub_mip_recombiner_t : public recombiner_t { !guiding_solution.get_feasible(); if (better_cost_than_parents || better_feasibility_than_parents) { CUOPT_LOG_DEBUG("Offspring is feasible or better than both parents"); - return std::make_tuple(offspring, true, work); + return std::make_pair(offspring, true); } - return std::make_tuple(offspring, !std::isnan(branch_and_bound_solution.objective), work); + return std::make_pair(offspring, !std::isnan(branch_and_bound_solution.objective)); } rmm::device_uvector vars_to_fix; mip_solver_context_t& context; diff --git a/cpp/src/mip/diversity/weights.cuh b/cpp/src/mip/diversity/weights.cuh index 38f3975ed..7502ae921 100644 --- a/cpp/src/mip/diversity/weights.cuh +++ b/cpp/src/mip/diversity/weights.cuh @@ -12,8 +12,6 @@ #include #include -#include - namespace cuopt::linear_programming::detail { template @@ -27,11 +25,6 @@ struct weight_t { objective_weight.set_value_async(one, handle_ptr->get_stream()); } - uint32_t get_hash(rmm::cuda_stream_view stream = rmm::cuda_stream_default) const - { - return compute_hash(cstr_weights, stream) ^ compute_hash(objective_weight.value(stream)); - } - rmm::device_uvector cstr_weights; rmm::device_scalar objective_weight; }; diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cu b/cpp/src/mip/feasibility_jump/feasibility_jump.cu index 6ecd86bd3..6dcd768c9 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -29,9 +29,6 @@ #include -#include -#include - #define FJ_LOG_PREFIX "FJ: " namespace cuopt::linear_programming::detail { @@ -432,11 +429,9 @@ void fj_t::climber_init(i_t climber_idx, const rmm::cuda_stream_view& f_t inf = std::numeric_limits::infinity(); climber->best_objective.set_value_async(inf, climber_stream); climber->saved_solution_objective.set_value_async(inf, climber_stream); - refresh_lhs_and_violation(climber_stream); - - // printf("init: Violated constraints hash: %x\n", compute_hash( - // make_span(climber->violated_constraints.contents, 0, - // climber->violated_constraints.set_size.value(climber_stream)), climber_stream)); + climber->violation_score.set_value_to_zero_async(climber_stream); + climber->weighted_violation_score.set_value_to_zero_async(climber_stream); + init_lhs_and_violation<<<256, 256, 0, climber_stream.value()>>>(view); // initialize the best_objective values according to the initial assignment f_t best_obj = compute_objective_from_vec( @@ -652,10 +647,10 @@ void fj_t::run_step_device(const rmm::cuda_stream_view& climber_stream auto [grid_setval, blocks_setval] = setval_launch_dims; auto [grid_update_changed_constraints, blocks_update_changed_constraints] = update_changed_constraints_launch_dims; - auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; - auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; - [[maybe_unused]] auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; - [[maybe_unused]] auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; + auto [grid_resetmoves, blocks_resetmoves] = resetmoves_launch_dims; + auto [grid_resetmoves_bin, blocks_resetmoves_bin] = resetmoves_bin_launch_dims; + auto [grid_update_weights, blocks_update_weights] = update_weights_launch_dims; + auto [grid_lift_move, blocks_lift_move] = lift_move_launch_dims; auto& data = *climbers[climber_idx]; auto v = data.view(); @@ -843,215 +838,9 @@ void fj_t::refresh_lhs_and_violation(const rmm::cuda_stream_view& stre auto v = data.view(); data.violated_constraints.clear(stream); + data.violation_score.set_value_to_zero_async(stream); + data.weighted_violation_score.set_value_to_zero_async(stream); init_lhs_and_violation<<<4096, 256, 0, stream>>>(v); - // both transformreduce could be fused; but oh well hardly a bottleneck - auto violation = - thrust::transform_reduce(rmm::exec_policy(stream), - thrust::make_counting_iterator(0), - thrust::make_counting_iterator(pb_ptr->n_constraints), - cuda::proclaim_return_type([v] __device__(i_t cstr_idx) { - return v.excess_score(cstr_idx, v.incumbent_lhs[cstr_idx]); - }), - (f_t)0, - thrust::plus()); - auto weighted_violation = thrust::transform_reduce( - rmm::exec_policy(stream), - thrust::make_counting_iterator(0), - thrust::make_counting_iterator(pb_ptr->n_constraints), - cuda::proclaim_return_type([v] __device__(i_t cstr_idx) { - return v.excess_score(cstr_idx, v.incumbent_lhs[cstr_idx]) * v.cstr_weights[cstr_idx]; - }), - (f_t)0, - thrust::plus()); - data.violation_score.set_value_async(violation, stream); - data.weighted_violation_score.set_value_async(weighted_violation, stream); - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - data.violated_constraints.sort(stream); - } -#if FJ_SINGLE_STEP - CUOPT_LOG_DEBUG("hash assignment %x, hash lhs %x, hash lhscomp %x", - detail::compute_hash(data.incumbent_assignment, stream), - detail::compute_hash(data.incumbent_lhs, stream), - detail::compute_hash(data.incumbent_lhs_sumcomp, stream)); - CUOPT_LOG_DEBUG("Violated constraints hash post sort: %x, index map %x", - detail::compute_hash(data.violated_constraints.contents, stream), - detail::compute_hash(data.violated_constraints.index_map, stream)); -#endif -} - -template -std::map fj_t::get_feature_vector(i_t climber_idx) const -{ - auto& data = *climbers[climber_idx]; - auto climber_stream = data.stream.view(); - if (climber_idx == 0) climber_stream = handle_ptr->get_stream(); - - std::map features; - - // Basic problem dimensions - features["n_variables"] = (float)pb_ptr->n_variables; - features["n_constraints"] = (float)pb_ptr->n_constraints; - features["nnz"] = (float)pb_ptr->coefficients.size(); - - // Matrix sparsity metrics (already computed) - features["sparsity"] = (float)pb_ptr->sparsity; - features["nnz_stddev"] = (float)pb_ptr->nnz_stddev; - features["unbalancedness"] = (float)pb_ptr->unbalancedness; - - // Algorithm settings - features["time"] = (float)settings.work_limit; - features["n_of_minimums_for_exit"] = (float)settings.n_of_minimums_for_exit; - features["feasibility_run"] = (float)settings.feasibility_run; - - // Variable type metrics - features["n_integer_vars"] = (float)pb_ptr->n_integer_vars; - features["n_binary_vars"] = (float)pb_ptr->n_binary_vars; - features["integer_ratio"] = - pb_ptr->n_variables > 0 ? (float)pb_ptr->n_integer_vars / pb_ptr->n_variables : 0.0f; - features["binary_ratio"] = - pb_ptr->n_variables > 0 ? (float)pb_ptr->n_binary_vars / pb_ptr->n_variables : 0.0f; - - // Initial violation metrics (from current state) - features["initial_violation_count"] = - (float)data.violated_constraints.set_size.value(climber_stream); - // features["initial_violation_score"] = (float)data.violation_score.value(climber_stream); - // features["initial_weighted_violation"] = - // (float)data.weighted_violation_score.value(climber_stream); - - // Load balancing decision - bool use_load_balancing = false; - if (settings.load_balancing_mode == fj_load_balancing_mode_t::ALWAYS_OFF) { - use_load_balancing = false; - } else if (settings.load_balancing_mode == fj_load_balancing_mode_t::ALWAYS_ON) { - use_load_balancing = true; - } else if (settings.load_balancing_mode == fj_load_balancing_mode_t::AUTO) { - use_load_balancing = - pb_ptr->n_variables > settings.parameters.load_balancing_codepath_min_varcount; - } - if (settings.mode == fj_mode_t::ROUNDING) { use_load_balancing = false; } - features["uses_load_balancing"] = (float)use_load_balancing; - - // Related variables metrics (if available) - if (pb_ptr->related_variables_offsets.size() > 0) { - auto h_offsets = cuopt::host_copy(pb_ptr->related_variables_offsets, handle_ptr->get_stream()); - i_t total_related = 0; - i_t max_related = 0; - for (i_t i = 0; i < pb_ptr->n_variables; ++i) { - i_t count = h_offsets[i + 1] - h_offsets[i]; - total_related += count; - max_related = std::max(max_related, count); - } - features["avg_related_vars_per_var"] = - pb_ptr->n_variables > 0 ? (float)total_related / pb_ptr->n_variables : 0.0f; - // features["max_related_vars"] = (float)max_related; - } else { - features["avg_related_vars_per_var"] = 0.0f; - // features["max_related_vars"] = 0.0f; - } - - // Constraint characteristics - auto h_lower = cuopt::host_copy(pb_ptr->constraint_lower_bounds, handle_ptr->get_stream()); - auto h_upper = cuopt::host_copy(pb_ptr->constraint_upper_bounds, handle_ptr->get_stream()); - i_t n_equality = 0; - i_t n_tight = 0; - f_t total_range = 0.0; - i_t n_range_constraints = 0; - - for (i_t i = 0; i < pb_ptr->n_constraints; ++i) { - if (pb_ptr->integer_equal(h_lower[i], h_upper[i])) { - n_equality++; - } else { - f_t range = h_upper[i] - h_lower[i]; - if (std::isfinite(range)) { - total_range += range; - n_range_constraints++; - if (range < 1.0) n_tight++; - } - } - } - features["equality_ratio"] = - pb_ptr->n_constraints > 0 ? (float)n_equality / pb_ptr->n_constraints : 0.0f; - features["avg_constraint_range"] = - n_range_constraints > 0 ? (float)(total_range / n_range_constraints) : 0.0f; - features["tight_constraint_ratio"] = - pb_ptr->n_constraints > 0 ? (float)n_tight / pb_ptr->n_constraints : 0.0f; - - // Variable bound characteristics - auto h_var_bounds = cuopt::host_copy(pb_ptr->variable_bounds, handle_ptr->get_stream()); - i_t n_unbounded = 0; - i_t n_fixed = 0; - f_t total_var_range = 0.0; - i_t n_bounded_vars = 0; - - for (i_t i = 0; i < pb_ptr->n_variables; ++i) { - f_t lower = get_lower(h_var_bounds[i]); - f_t upper = get_upper(h_var_bounds[i]); - - if (!std::isfinite(lower) || !std::isfinite(upper)) { - n_unbounded++; - } else if (pb_ptr->integer_equal(lower, upper)) { - n_fixed++; - } else { - f_t range = upper - lower; - total_var_range += range; - n_bounded_vars++; - } - } - features["unbounded_var_ratio"] = - pb_ptr->n_variables > 0 ? (float)n_unbounded / pb_ptr->n_variables : 0.0f; - features["fixed_var_ratio"] = - pb_ptr->n_variables > 0 ? (float)n_fixed / pb_ptr->n_variables : 0.0f; - features["avg_variable_range"] = - n_bounded_vars > 0 ? (float)(total_var_range / n_bounded_vars) : 0.0f; - - // Objective characteristics - auto h_obj_coeffs = cuopt::host_copy(pb_ptr->objective_coefficients, handle_ptr->get_stream()); - i_t n_obj_vars = 0; - f_t total_obj_magnitude = 0.0; - for (i_t i = 0; i < pb_ptr->n_variables; ++i) { - if (h_obj_coeffs[i] != 0.0) { - n_obj_vars++; - total_obj_magnitude += std::abs(h_obj_coeffs[i]); - } - } - features["obj_var_ratio"] = - pb_ptr->n_variables > 0 ? (float)n_obj_vars / pb_ptr->n_variables : 0.0f; - features["avg_obj_coeff_magnitude"] = - n_obj_vars > 0 ? (float)(total_obj_magnitude / n_obj_vars) : 0.0f; - - // Matrix density patterns - auto h_offsets = cuopt::host_copy(pb_ptr->offsets, handle_ptr->get_stream()); - i_t max_nnz_per_row = 0; - i_t min_nnz_per_row = pb_ptr->n_variables; - f_t sum_sq_dev = 0.0; - f_t mean_nnz = - pb_ptr->n_constraints > 0 ? (f_t)pb_ptr->coefficients.size() / pb_ptr->n_constraints : 0.0f; - - for (i_t i = 0; i < pb_ptr->n_constraints; ++i) { - i_t nnz_row = h_offsets[i + 1] - h_offsets[i]; - max_nnz_per_row = std::max(max_nnz_per_row, nnz_row); - min_nnz_per_row = std::min(min_nnz_per_row, nnz_row); - f_t dev = nnz_row - mean_nnz; - sum_sq_dev += dev * dev; - } - features["max_nnz_per_row"] = (float)max_nnz_per_row; - features["min_nnz_per_row"] = (float)min_nnz_per_row; - features["nnz_variance"] = - pb_ptr->n_constraints > 0 ? (float)(sum_sq_dev / pb_ptr->n_constraints) : 0.0f; - - // Average variable degree (avg constraints per variable) - features["avg_var_degree"] = - pb_ptr->n_variables > 0 ? (float)pb_ptr->coefficients.size() / pb_ptr->n_variables : 0.0f; - - // Derived complexity metrics - features["problem_size_score"] = - (float)(pb_ptr->n_variables * pb_ptr->n_constraints) * (float)pb_ptr->sparsity; - features["structural_complexity"] = - (features["integer_ratio"] + 1.0f) * (float)pb_ptr->unbalancedness; - features["constraint_var_ratio"] = - pb_ptr->n_variables > 0 ? (float)pb_ptr->n_constraints / pb_ptr->n_variables : 0.0f; - - return features; } template @@ -1073,7 +862,7 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) data.incumbent_quality.set_value_async(obj, handle_ptr->get_stream()); - work_limit_timer_t timer(context.gpu_heur_loop, settings.time_limit); + timer_t timer(settings.time_limit); i_t steps; bool limit_reached = false; for (steps = 0; steps < std::numeric_limits::max(); steps += iterations_per_graph) { @@ -1091,7 +880,7 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) CUOPT_LOG_TRACE( "FJ " "step %d viol %.2g [%d], obj %.8g, best %.8g, mins %d, maxw %g, " - "objw %g, sol %x, delta %x, inc %x, lhs %x, lhscomp %x, viol %x, weights %x", + "objw %g", steps, data.violation_score.value(climber_stream), data.violated_constraints.set_size.value(climber_stream), @@ -1099,14 +888,7 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) data.best_objective.value(climber_stream), data.local_minimums_reached.value(climber_stream), max_cstr_weight.value(climber_stream), - objective_weight.value(climber_stream), - solution.get_hash(), - detail::compute_hash(data.jump_move_delta, climber_stream), - detail::compute_hash(data.incumbent_assignment, climber_stream), - detail::compute_hash(data.incumbent_lhs, climber_stream), - detail::compute_hash(data.incumbent_lhs_sumcomp, climber_stream), - detail::compute_hash(data.violated_constraints.contents, climber_stream), - detail::compute_hash(cstr_left_weights, climber_stream)); + objective_weight.value(climber_stream)); } if (!limit_reached) { run_step_device(climber_stream, climber_idx); } @@ -1176,9 +958,6 @@ i_t fj_t::host_loop(solution_t& solution, i_t climber_idx) solution.get_feasible(), data.local_minimums_reached.value(climber_stream)); - // compute total time spent - double elapsed_time = timer.elapsed_time(); - CUOPT_LOG_TRACE("best fractional count %d", data.saved_best_fractional_count.value(climber_stream)); @@ -1268,7 +1047,7 @@ template i_t fj_t::solve(solution_t& solution) { raft::common::nvtx::range scope("fj_solve"); - work_limit_timer_t timer(context.gpu_heur_loop, settings.time_limit); + timer_t timer(settings.time_limit); handle_ptr = const_cast(solution.handle_ptr); pb_ptr = solution.problem_ptr; if (settings.mode != fj_mode_t::ROUNDING) { @@ -1278,30 +1057,6 @@ i_t fj_t::solve(solution_t& solution) pb_ptr->check_problem_representation(true); resize_vectors(solution.handle_ptr); - CUOPT_LOG_DEBUG("FJ: work_limit %f time_limit %f sol hash %x pb hash %x", - settings.work_limit, - settings.time_limit, - solution.get_hash(), - pb_ptr->get_fingerprint()); - CUOPT_LOG_DEBUG("FJ: weights hash %x, left weights hash %x, right weights hash %x", - detail::compute_hash(cstr_weights), - detail::compute_hash(cstr_left_weights), - detail::compute_hash(cstr_right_weights)); - - bool deterministic = context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC; - if (deterministic) { settings.work_limit = settings.time_limit; } - // if work_limit is set: compute an estimate of the number of iterations required - if (deterministic && settings.work_limit != std::numeric_limits::infinity()) { - std::map features_map = get_feature_vector(0); - float iter_prediction = std::max( - (f_t)0.0, (f_t)ceil(context.work_unit_predictors.fj_predictor.predict_scalar(features_map))); - CUOPT_LOG_DEBUG("FJ determ: Estimated number of iterations for %f WU: %f", - settings.work_limit, - iter_prediction); - if (settings.work_limit == 0) iter_prediction = 0; - settings.iteration_limit = std::min(settings.iteration_limit, (i_t)iter_prediction); - } - bool is_initial_feasible = solution.compute_feasibility(); auto initial_solution = solution; // if we're in rounding mode, split the time/iteration limit between the first and second stage @@ -1336,9 +1091,6 @@ i_t fj_t::solve(solution_t& solution) RAFT_CHECK_CUDA(handle_ptr->get_stream()); handle_ptr->sync_stream(); - // Compute and store feature vector for later logging - if (deterministic) { feature_vector = get_feature_vector(0); } - i_t iterations = host_loop(solution); RAFT_CHECK_CUDA(handle_ptr->get_stream()); handle_ptr->sync_stream(); @@ -1386,49 +1138,6 @@ i_t fj_t::solve(solution_t& solution) cuopt_assert(solution.compute_feasibility(), "Reverted solution should be feasible"); } - cuopt_func_call(solution.test_variable_bounds()); - - if (deterministic) { - double work_to_record = settings.work_limit; - - if (iterations < settings.iteration_limit) { - CUOPT_LOG_DEBUG( - "FJ early exit at %d iterations (limit: %d)", iterations, settings.iteration_limit); - // Compute the work unit corresponding to the number of iterations elapsed - // by incrementally guessing work units until the model predicts >= actual iterations - // TODO: awfully ugly, change - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC && iterations > 0) { - double guessed_work = 0.0; - const double work_increment = 0.1; - const double max_work = settings.work_limit * 2.0; // Safety limit - float predicted_iters = 0.0f; - - // Make a copy of the feature vector and modify the time/work_limit field - std::map features_for_prediction = feature_vector; - - while (guessed_work <= max_work) { - features_for_prediction["time"] = (float)guessed_work; - predicted_iters = std::max( - 0.0f, - (float)ceil( - context.work_unit_predictors.fj_predictor.predict_scalar(features_for_prediction))); - - if (predicted_iters >= (float)iterations) { - work_to_record = guessed_work; - break; - } - - guessed_work += work_increment; - } - } - } - - CUOPT_LOG_DEBUG("FJ: recording work %fwu for %d iterations", work_to_record, iterations); - timer.record_work(work_to_record); - } - - CUOPT_LOG_DEBUG("FJ sol hash %x", solution.get_hash()); - return is_new_feasible; } diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh index dabeb7549..36d50af32 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump.cuh +++ b/cpp/src/mip/feasibility_jump/feasibility_jump.cuh @@ -19,9 +19,6 @@ #include -#include -#include - #define FJ_DEBUG_LOAD_BALANCING 0 #define FJ_SINGLE_STEP 0 @@ -102,7 +99,6 @@ struct fj_settings_t { fj_mode_t mode{fj_mode_t::FIRST_FEASIBLE}; fj_candidate_selection_t candidate_selection{fj_candidate_selection_t::WEIGHTED_SCORE}; double time_limit{60.0}; - double work_limit{std::numeric_limits::infinity()}; int iteration_limit{std::numeric_limits::max()}; fj_hyper_parameters_t parameters{}; int n_of_minimums_for_exit = 7000; @@ -133,14 +129,8 @@ struct fj_move_t { // as we dont need them to be floating point per the FJ2 scoring scheme // sizeof(fj_staged_score_t) <= 8 is needed to allow for atomic loads struct fj_staged_score_t { - int32_t base{std::numeric_limits::lowest()}; - int32_t bonus{std::numeric_limits::lowest()}; - - fj_staged_score_t() = default; - fj_staged_score_t(const fj_staged_score_t&) = default; - fj_staged_score_t(fj_staged_score_t&&) = default; - fj_staged_score_t& operator=(const fj_staged_score_t&) = default; - fj_staged_score_t& operator=(fj_staged_score_t&&) = default; + float base{-std::numeric_limits::infinity()}; + float bonus{-std::numeric_limits::infinity()}; HDI bool operator<(fj_staged_score_t other) const noexcept { @@ -158,7 +148,7 @@ struct fj_staged_score_t { HDI static fj_staged_score_t invalid() { - return {std::numeric_limits::lowest(), std::numeric_limits::lowest()}; + return {-std::numeric_limits::infinity(), -std::numeric_limits::infinity()}; } HDI static fj_staged_score_t zero() { return {0, 0}; } @@ -638,10 +628,6 @@ class fj_t { std::vector> climbers; rmm::device_uvector climber_views; fj_settings_t settings; - std::map feature_vector; - - private: - std::map get_feature_vector(i_t climber_idx = 0) const; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh b/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh index 4a3bffb60..fbc5a7b39 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_impl_common.cuh @@ -28,22 +28,20 @@ HDI f_t fj_kahan_babushka_neumaier_sum(Iterator begin, Iterator end) } // Returns the current slack, and the variable delta that would nullify this slack ("tighten" it) -template +template HDI thrust::tuple get_mtm_for_bound( const typename fj_t::climber_data_t::view_t& fj, i_t var_idx, i_t cstr_idx, f_t cstr_coeff, f_t bound, - f_t sign, - const ArrayType& assignment, - const ArrayType& lhs_vector) + f_t sign) { f_t delta_ij = 0; f_t slack = 0; - f_t old_val = assignment[var_idx]; + f_t old_val = fj.incumbent_assignment[var_idx]; - f_t lhs = lhs_vector[cstr_idx] * sign; + f_t lhs = fj.incumbent_lhs[cstr_idx] * sign; f_t rhs = bound * sign; slack = rhs - lhs; // bound might be infinite. let the caller handle this case @@ -52,24 +50,22 @@ HDI thrust::tuple get_mtm_for_bound( return {delta_ij, slack}; } -template +template HDI thrust::tuple get_mtm_for_constraint( const typename fj_t::climber_data_t::view_t& fj, i_t var_idx, i_t cstr_idx, f_t cstr_coeff, f_t c_lb, - f_t c_ub, - const ArrayType& assignment, - const ArrayType& lhs_vector) + f_t c_ub) { f_t sign = -1; f_t delta_ij = 0; f_t slack = 0; - f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx); - f_t old_val = assignment[var_idx]; + f_t old_val = fj.incumbent_assignment[var_idx]; // process each bound as two separate constraints f_t bounds[2] = {c_lb, c_ub}; @@ -81,7 +77,7 @@ HDI thrust::tuple get_mtm_for_constraint( // factor to correct the lhs/rhs to turn a lb <= lhs <= ub constraint into // two virtual constraints lhs <= ub and -lhs <= -lb sign = bound_idx == 0 ? -1 : 1; - f_t lhs = lhs_vector[cstr_idx] * sign; + f_t lhs = fj.incumbent_lhs[cstr_idx] * sign; f_t rhs = bounds[bound_idx] * sign; slack = rhs - lhs; @@ -107,9 +103,7 @@ HDI std::pair feas_score_constraint( f_t cstr_coeff, f_t c_lb, f_t c_ub, - f_t current_lhs, - f_t left_weight, - f_t right_weight) + f_t current_lhs) { cuopt_assert(isfinite(delta), "invalid delta"); cuopt_assert(cstr_coeff != 0 && isfinite(cstr_coeff), "invalid coefficient"); @@ -129,13 +123,14 @@ HDI std::pair feas_score_constraint( // TODO: broadcast left/right weights to a csr_offset-indexed table? local minimums // usually occur on a rarer basis (around 50 iteratiosn to 1 local minimum) // likely unreasonable and overkill however - f_t cstr_weight = bound_idx == 0 ? left_weight : right_weight; - f_t sign = bound_idx == 0 ? -1 : 1; - f_t rhs = bounds[bound_idx] * sign; - f_t old_lhs = current_lhs * sign; - f_t new_lhs = (current_lhs + cstr_coeff * delta) * sign; - f_t old_slack = rhs - old_lhs; - f_t new_slack = rhs - new_lhs; + f_t cstr_weight = + bound_idx == 0 ? fj.cstr_left_weights[cstr_idx] : fj.cstr_right_weights[cstr_idx]; + f_t sign = bound_idx == 0 ? -1 : 1; + f_t rhs = bounds[bound_idx] * sign; + f_t old_lhs = current_lhs * sign; + f_t new_lhs = (current_lhs + cstr_coeff * delta) * sign; + f_t old_slack = rhs - old_lhs; + f_t new_slack = rhs - new_lhs; cuopt_assert(isfinite(cstr_weight), "invalid weight"); cuopt_assert(cstr_weight >= 0, "invalid weight"); @@ -143,7 +138,7 @@ HDI std::pair feas_score_constraint( cuopt_assert(isfinite(new_lhs), ""); cuopt_assert(isfinite(old_slack) && isfinite(new_slack), ""); - f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx); bool old_viol = fj.excess_score(cstr_idx, current_lhs, c_lb, c_ub) < -cstr_tolerance; bool new_viol = diff --git a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu index 06dc47ca3..5e6fef7e1 100644 --- a/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu +++ b/cpp/src/mip/feasibility_jump/feasibility_jump_kernels.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -14,9 +14,6 @@ #include -#include -#include - #include #include "feasibility_jump_impl_common.cuh" @@ -28,22 +25,6 @@ namespace cg = cooperative_groups; namespace cuopt::linear_programming::detail { -template -struct score_with_tiebreaker_comparator { - DI auto operator()(const thrust::pair& a, - const thrust::pair& b) const - { - auto a_score = a.first; - auto a_idx = a.second; - auto b_score = b.first; - auto b_idx = b.second; - - if (a_score > b_score) return a; - if (a_score == b_score && a_idx > b_idx) return a; - return b; - } -}; - template DI thrust::pair move_objective_score( const typename fj_t::climber_data_t::view_t& fj, i_t var_idx, f_t delta) @@ -170,7 +151,10 @@ __global__ void init_lhs_and_violation(typename fj_t::climber_data_t:: fj_kahan_babushka_neumaier_sum(delta_it + offset_begin, delta_it + offset_end); fj.incumbent_lhs_sumcomp[cstr_idx] = 0; - f_t th_violation = fj.excess_score(cstr_idx, fj.incumbent_lhs[cstr_idx]); + f_t th_violation = fj.excess_score(cstr_idx, fj.incumbent_lhs[cstr_idx]); + f_t weighted_violation = th_violation * fj.cstr_weights[cstr_idx]; + atomicAdd(fj.violation_score, th_violation); + atomicAdd(fj.weighted_violation_score, weighted_violation); f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx); if (th_violation < -cstr_tolerance) { fj.violated_constraints.insert(cstr_idx); } } @@ -206,17 +190,8 @@ DI typename fj_t::move_score_info_t compute_new_score( f_t c_lb = fj.pb.constraint_lower_bounds[cstr_idx]; f_t c_ub = fj.pb.constraint_upper_bounds[cstr_idx]; - auto [cstr_base_feas, cstr_bonus_robust] = - feas_score_constraint(fj, - var_idx, - delta, - cstr_idx, - cstr_coeff, - c_lb, - c_ub, - fj.incumbent_lhs[cstr_idx], - fj.cstr_left_weights[cstr_idx], - fj.cstr_right_weights[cstr_idx]); + auto [cstr_base_feas, cstr_bonus_robust] = feas_score_constraint( + fj, var_idx, delta, cstr_idx, cstr_coeff, c_lb, c_ub, fj.incumbent_lhs[cstr_idx]); base_feas += cstr_base_feas; bonus_robust += cstr_bonus_robust; @@ -317,8 +292,8 @@ DI std::pair::move_score_info_t> compute_best_mtm( f_t c_lb = fj.pb.constraint_lower_bounds[cstr_idx]; f_t c_ub = fj.pb.constraint_upper_bounds[cstr_idx]; f_t new_val; - auto [delta_ij, sign, slack, cstr_tolerance] = get_mtm_for_constraint( - fj, var_idx, cstr_idx, cstr_coeff, c_lb, c_ub, fj.incumbent_assignment, fj.incumbent_lhs); + auto [delta_ij, sign, slack, cstr_tolerance] = + get_mtm_for_constraint(fj, var_idx, cstr_idx, cstr_coeff, c_lb, c_ub); if (fj.pb.is_integer_var(var_idx)) { new_val = cstr_coeff * sign > 0 ? floor(old_val + delta_ij + fj.pb.tolerances.integrality_tolerance) @@ -373,7 +348,7 @@ DI std::pair::move_score_info_t> compute_best_mtm( return std::make_pair(best_val, best_score_info); } -template +template DI void update_jump_value(typename fj_t::climber_data_t::view_t fj, i_t var_idx) { cuopt_assert(var_idx >= 0 && var_idx < fj.pb.n_variables, "invalid variable index"); @@ -400,11 +375,12 @@ DI void update_jump_value(typename fj_t::climber_data_t::view_t fj, i_ fj.pb.check_variable_within_bounds(var_idx, fj.incumbent_assignment[var_idx] + delta), "Var not within bounds!"); } - best_score_info = compute_new_score(fj, var_idx, delta); + best_score_info = compute_new_score(fj, var_idx, delta); } else { - auto [best_val, score_info] = compute_best_mtm(fj, var_idx); - delta = best_val - fj.incumbent_assignment[var_idx]; - best_score_info = score_info; + auto [best_val, score_info] = + compute_best_mtm(fj, var_idx); + delta = best_val - fj.incumbent_assignment[var_idx]; + best_score_info = score_info; } } else { delta = round(1.0 - 2 * fj.incumbent_assignment[var_idx]); @@ -600,16 +576,14 @@ __global__ void update_assignment_kernel(typename fj_t::climber_data_t __syncthreads(); - if (threadIdx.x == 0) { - cuopt_assert(isfinite(fj.jump_move_delta[var_idx]), "delta should be finite"); - // Kahan compensated summation - // fj.incumbent_lhs[cstr_idx] = old_lhs + cstr_coeff * fj.jump_move_delta[var_idx]; - f_t y = cstr_coeff * fj.jump_move_delta[var_idx] - fj.incumbent_lhs_sumcomp[cstr_idx]; - f_t t = old_lhs + y; - fj.incumbent_lhs_sumcomp[cstr_idx] = (t - old_lhs) - y; - fj.incumbent_lhs[cstr_idx] = t; - cuopt_assert(isfinite(fj.incumbent_lhs[cstr_idx]), "assignment should be finite"); - } + cuopt_assert(isfinite(fj.jump_move_delta[var_idx]), "delta should be finite"); + // Kahan compensated summation + // fj.incumbent_lhs[cstr_idx] = old_lhs + cstr_coeff * fj.jump_move_delta[var_idx]; + f_t y = cstr_coeff * fj.jump_move_delta[var_idx] - fj.incumbent_lhs_sumcomp[cstr_idx]; + f_t t = old_lhs + y; + fj.incumbent_lhs_sumcomp[cstr_idx] = (t - old_lhs) - y; + fj.incumbent_lhs[cstr_idx] = t; + cuopt_assert(isfinite(fj.incumbent_lhs[cstr_idx]), "assignment should be finite"); } // update the assignment and objective proper @@ -651,8 +625,8 @@ __global__ void update_assignment_kernel(typename fj_t::climber_data_t #if FJ_SINGLE_STEP DEVICE_LOG_DEBUG( - "=---- FJ[%d]: updated %d [%g/%g] :%.4g+{%.4g}=%.4g score {%d,%d}, d_obj %.2g+%.2g=%.2g, " - "err_range %.2g%%, infeas %.2g, total viol %d, obj %x, delta %x, coef %x\n", + "=---- FJ[%d]: updated %d [%g/%g] :%.4g+{%.4g}=%.4g score {%g,%g}, d_obj %.2g+%.2g=%.2g, " + "err_range %.2g%%, infeas %.2g, total viol %d\n", *fj.iterations, var_idx, get_lower(fj.pb.variable_bounds[var_idx]), @@ -667,10 +641,7 @@ __global__ void update_assignment_kernel(typename fj_t::climber_data_t *fj.incumbent_objective + fj.jump_move_delta[var_idx] * fj.pb.objective_coefficients[var_idx], delta_rel_err, fj.jump_move_infeasibility[var_idx], - fj.violated_constraints.size(), - detail::compute_hash(*fj.incumbent_objective), - detail::compute_hash(fj.jump_move_delta[var_idx]), - detail::compute_hash(fj.pb.objective_coefficients[var_idx])); + fj.violated_constraints.size()); #endif // reset the score fj.jump_move_scores[var_idx] = fj_t::move_score_t::invalid(); @@ -759,14 +730,8 @@ DI void update_lift_moves(typename fj_t::climber_data_t::view_t fj) // Process each bound separately, as both are satified and may both be finite // otherwise range constraints aren't correctly handled for (auto [bound, sign] : {std::make_tuple(c_lb, -1), std::make_tuple(c_ub, 1)}) { - auto [delta, slack] = get_mtm_for_bound(fj, - var_idx, - cstr_idx, - cstr_coeff, - bound, - sign, - fj.incumbent_assignment, - fj.incumbent_lhs); + auto [delta, slack] = + get_mtm_for_bound(fj, var_idx, cstr_idx, cstr_coeff, bound, sign); if (cstr_coeff * sign < 0) { if (fj.pb.is_integer_var(var_idx)) delta = ceil(delta); @@ -896,15 +861,6 @@ DI void update_changed_constraints(typename fj_t::climber_data_t::view if (blockIdx.x == 0) { if (threadIdx.x == 0) { - // sort changed constraints to guarantee determinism - // TODO: horribly slow as it is... block-parallelize at least? but not trivial for arbitrary - // sizes w/ CUB - if (fj.settings->work_limit != std::numeric_limits::infinity()) { - thrust::sort(thrust::seq, - fj.constraints_changed.begin(), - fj.constraints_changed.begin() + *fj.constraints_changed_count); - } - for (i_t i = 0; i < *fj.constraints_changed_count; ++i) { i_t idx = fj.constraints_changed[i]; if ((idx & 1) == CONSTRAINT_FLAG_INSERT) { @@ -996,7 +952,7 @@ __global__ void compute_iteration_related_variables_kernel( compute_iteration_related_variables(fj); } -template +template __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_t fj, bool ForceRefresh) { @@ -1008,14 +964,11 @@ __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_ if (*fj.selected_var == std::numeric_limits::max()) full_refresh = true; // always do a full sweep when looking for satisfied mtm moves - i_t split_begin, split_end; - if constexpr (move_type == MTMMoveType::FJ_MTM_SATISFIED) { - full_refresh = true; - split_begin = 0; - split_end = fj.objective_vars.size(); - } + if constexpr (move_type == MTMMoveType::FJ_MTM_SATISFIED) full_refresh = true; + // only update related variables - else if (full_refresh) { + i_t split_begin, split_end; + if (full_refresh) { split_begin = 0; split_end = fj.pb.n_variables; } @@ -1038,15 +991,9 @@ __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_ if (FIRST_THREAD) *fj.relvar_count_last_update = split_end - split_begin; for (i_t i = blockIdx.x + split_begin; i < split_end; i += gridDim.x) { - // if sat MTM mode, go over objective variables only - i_t var_idx; - if constexpr (move_type == MTMMoveType::FJ_MTM_SATISFIED) { - var_idx = fj.objective_vars[i]; - } else { - var_idx = full_refresh ? i - : fj.pb.related_variables.size() == 0 ? i - : fj.pb.related_variables[i]; - } + i_t var_idx = full_refresh ? i + : fj.pb.related_variables.size() == 0 ? i + : fj.pb.related_variables[i]; // skip if we couldnt precompute a related var table and // this variable isnt in the dynamic related variable table @@ -1069,7 +1016,7 @@ __device__ void compute_mtm_moves(typename fj_t::climber_data_t::view_ } cuopt_assert(var_idx >= 0 && var_idx < fj.pb.n_variables, ""); - update_jump_value(fj, var_idx); + update_jump_value(fj, var_idx); } } @@ -1077,7 +1024,7 @@ template __global__ void compute_mtm_moves_kernel(typename fj_t::climber_data_t::view_t fj, bool ForceRefresh) { - compute_mtm_moves(fj, ForceRefresh); + compute_mtm_moves(fj, ForceRefresh); } template @@ -1089,9 +1036,8 @@ __global__ void select_variable_kernel(typename fj_t::climber_data_t:: fj.settings->seed, *fj.iterations * fj.settings->parameters.max_sampled_moves, 0); using move_score_t = typename fj_t::move_score_t; - __shared__ alignas(thrust::pair) char - shmem_storage[raft::WarpSize * sizeof(thrust::pair)]; - auto* const shmem = (thrust::pair*)shmem_storage; + __shared__ alignas(move_score_t) char shmem_storage[2 * raft::WarpSize * sizeof(move_score_t)]; + auto* const shmem = (move_score_t*)shmem_storage; auto th_best_score = fj_t::move_score_t::invalid(); i_t th_selected_var = std::numeric_limits::max(); @@ -1128,11 +1074,8 @@ __global__ void select_variable_kernel(typename fj_t::climber_data_t:: } } // Block level reduction to get the best variable from the sample - // Use deterministic tie-breaking comparator based on var_idx auto [best_score, reduced_selected_var] = - raft::blockReduce(thrust::make_pair(th_best_score, th_selected_var), - (char*)shmem, - score_with_tiebreaker_comparator{}); + raft::blockRankedReduce(th_best_score, shmem, th_selected_var, raft::max_op{}); if (FIRST_THREAD) { // assign it to print the value outside th_best_score = best_score; @@ -1167,9 +1110,9 @@ __global__ void select_variable_kernel(typename fj_t::climber_data_t:: i_t var_range = get_upper(bounds) - get_lower(bounds); double delta_rel_err = fabs(fj.jump_move_delta[selected_var]) / var_range * 100; DEVICE_LOG_INFO( - "=---- FJ: selected %d [%g/%g] %c :%.4g+{%.4g}=%.4g score {%d,%d}, d_obj %.2g+%.2g->%.2g, " + "=---- FJ: selected %d [%g/%g] %c :%.4g+{%.4g}=%.4g score {%g,%g}, d_obj %.2g+%.2g->%.2g, " "delta_rel_err %.2g%%, " - "infeas %.2g, total viol %d, out of %d, obj %x\n", + "infeas %.2g, total viol %d, out of %d\n", selected_var, get_lower(bounds), get_upper(bounds), @@ -1186,18 +1129,9 @@ __global__ void select_variable_kernel(typename fj_t::climber_data_t:: delta_rel_err, fj.jump_move_infeasibility[selected_var], fj.violated_constraints.size(), - good_var_count, - detail::compute_hash(*fj.incumbent_objective)); + good_var_count); #endif cuopt_assert(fj.jump_move_scores[selected_var].valid(), ""); - } else { -#if FJ_SINGLE_STEP - DEVICE_LOG_INFO("=[%d]---- FJ: no var selected, obj is %g, viol %d, out of %d\n", - *fj.iterations, - *fj.incumbent_objective, - fj.violated_constraints.size(), - good_var_count); -#endif } } } @@ -1267,32 +1201,27 @@ DI thrust::tuple::move_score_t> gridwide_reduc if (blockIdx.x == 0) { using move_score_t = typename fj_t::move_score_t; - __shared__ alignas(thrust::pair) char - shmem_storage[2 * raft::WarpSize * sizeof(thrust::pair)]; - auto* const shmem = (thrust::pair*)shmem_storage; + __shared__ alignas(move_score_t) char shmem_storage[2 * raft::WarpSize * sizeof(move_score_t)]; + auto* const shmem = (move_score_t*)shmem_storage; auto th_best_score = fj_t::move_score_t::invalid(); i_t th_best_block = 0; - i_t th_best_var = -1; for (i_t i = threadIdx.x; i < gridDim.x; i += blockDim.x) { auto var_idx = fj.grid_var_buf[i]; auto move_score = fj.grid_score_buf[i]; - if (move_score > th_best_score || (move_score == th_best_score && var_idx > th_best_var)) { + if (move_score > th_best_score || + (move_score == th_best_score && var_idx > fj.grid_var_buf[th_best_block])) { th_best_score = move_score; th_best_block = i; - th_best_var = var_idx; } } // Block level reduction to get the best variable from all blocks - auto [reduced_best_score_pair, reduced_best_block] = - raft::blockRankedReduce(thrust::make_pair(th_best_score, th_best_var), - shmem, - th_best_block, - score_with_tiebreaker_comparator{}); - - if (reduced_best_score_pair.first.valid() && threadIdx.x == 0) { - cuopt_assert(reduced_best_block < gridDim.x, ""); + auto [reduced_best_score, reduced_best_block] = + raft::blockRankedReduce(th_best_score, shmem, th_best_block, raft::max_op{}); + + if (reduced_best_score.valid() && threadIdx.x == 0) { + cuopt_assert(th_best_block < gridDim.x, ""); best_var = fj.grid_var_buf[reduced_best_block]; best_delta = fj.grid_delta_buf[reduced_best_block]; best_score = fj.grid_score_buf[reduced_best_block]; @@ -1314,9 +1243,6 @@ DI thrust::tuple::move_score_t> best_random_mt raft::random::PCGenerator rng(fj.settings->seed + *fj.iterations, 0, 0); i_t cstr_idx = fj.violated_constraints.contents[rng.next_u32() % fj.violated_constraints.size()]; - cuopt_assert(fj.excess_score(cstr_idx, fj.incumbent_lhs[cstr_idx]) < 0, - "constraint isn't violated"); - auto [offset_begin, offset_end] = fj.pb.range_for_constraint(cstr_idx); return gridwide_reduce_best_move( @@ -1331,9 +1257,7 @@ DI thrust::tuple::move_score_t> best_sat_cstr_ typename fj_t::climber_data_t::view_t fj) { // compute all MTM moves within satisfied constraints - compute_mtm_moves(fj, true); - // NOTE: grid sync not required since each block only reduces over variables that it updated in - // compute_mtm_moves + compute_mtm_moves(fj, true); return gridwide_reduce_best_move( fj, fj.objective_vars.begin(), fj.objective_vars.end(), [fj] __device__(i_t var_idx) { return fj.jump_move_delta[var_idx]; @@ -1488,10 +1412,9 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat if (sat_best_score.base > 0 && sat_best_score > best_score) { if (FIRST_THREAD) { - best_score = sat_best_score; - best_var = sat_best_var; - best_delta = sat_best_delta; - best_movetype = 'S'; + best_score = sat_best_score; + best_var = sat_best_var; + best_delta = sat_best_delta; } } } @@ -1503,15 +1426,6 @@ __global__ void handle_local_minimum_kernel(typename fj_t::climber_dat best_var, fj.incumbent_assignment[best_var] + best_delta), "assignment not within bounds"); fj.jump_move_delta[best_var] = best_delta; -#if FJ_SINGLE_STEP - DEVICE_LOG_DEBUG("FJ[%d] selected_var: %d, delta %g, score {%d %d}, type %c\n", - *fj.iterations, - best_var, - best_delta, - best_score.base, - best_score.bonus, - best_movetype); -#endif } } } diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 2d9857d67..8d534dfff 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -14,11 +14,7 @@ #include #include -#include -#include #include -#include -#include #include #include @@ -66,306 +62,43 @@ static void print_timing_stats(fj_cpu_climber_t& fj_cpu) auto [apply_avg, apply_total] = compute_avg_and_total(fj_cpu.apply_move_times); auto [weights_avg, weights_total] = compute_avg_and_total(fj_cpu.update_weights_times); auto [compute_score_avg, compute_score_total] = compute_avg_and_total(fj_cpu.compute_score_times); - CUOPT_LOG_TRACE("=== Timing Statistics (Iteration %d) ===", fj_cpu.iterations); - CUOPT_LOG_TRACE("find_lift_move: avg=%.6f ms, total=%.6f ms, calls=%zu", + CUOPT_LOG_TRACE("=== Timing Statistics (Iteration %d) ===\n", fj_cpu.iterations); + CUOPT_LOG_TRACE("find_lift_move: avg=%.6f ms, total=%.6f ms, calls=%zu\n", lift_avg * 1000.0, lift_total * 1000.0, fj_cpu.find_lift_move_times.size()); - CUOPT_LOG_TRACE("find_mtm_move_viol: avg=%.6f ms, total=%.6f ms, calls=%zu", + CUOPT_LOG_TRACE("find_mtm_move_viol: avg=%.6f ms, total=%.6f ms, calls=%zu\n", viol_avg * 1000.0, viol_total * 1000.0, fj_cpu.find_mtm_move_viol_times.size()); - CUOPT_LOG_TRACE("find_mtm_move_sat: avg=%.6f ms, total=%.6f ms, calls=%zu", + CUOPT_LOG_TRACE("find_mtm_move_sat: avg=%.6f ms, total=%.6f ms, calls=%zu\n", sat_avg * 1000.0, sat_total * 1000.0, fj_cpu.find_mtm_move_sat_times.size()); - CUOPT_LOG_TRACE("apply_move: avg=%.6f ms, total=%.6f ms, calls=%zu", + CUOPT_LOG_TRACE("apply_move: avg=%.6f ms, total=%.6f ms, calls=%zu\n", apply_avg * 1000.0, apply_total * 1000.0, fj_cpu.apply_move_times.size()); - CUOPT_LOG_TRACE("update_weights: avg=%.6f ms, total=%.6f ms, calls=%zu", + CUOPT_LOG_TRACE("update_weights: avg=%.6f ms, total=%.6f ms, calls=%zu\n", weights_avg * 1000.0, weights_total * 1000.0, fj_cpu.update_weights_times.size()); - CUOPT_LOG_TRACE("compute_score: avg=%.6f ms, total=%.6f ms, calls=%zu", + CUOPT_LOG_TRACE("compute_score: avg=%.6f ms, total=%.6f ms, calls=%zu\n", compute_score_avg * 1000.0, compute_score_total * 1000.0, fj_cpu.compute_score_times.size()); - CUOPT_LOG_TRACE("cache hit percentage: %.2f%%", + CUOPT_LOG_TRACE("cache hit percentage: %.2f%%\n", (double)fj_cpu.hit_count / (fj_cpu.hit_count + fj_cpu.miss_count) * 100.0); - CUOPT_LOG_TRACE("bin candidate move hit percentage: %.2f%%", + CUOPT_LOG_TRACE("bin candidate move hit percentage: %.2f%%\n", (double)fj_cpu.candidate_move_hits[0] / (fj_cpu.candidate_move_hits[0] + fj_cpu.candidate_move_misses[0]) * 100.0); - CUOPT_LOG_TRACE("int candidate move hit percentage: %.2f%%", + CUOPT_LOG_TRACE("int candidate move hit percentage: %.2f%%\n", (double)fj_cpu.candidate_move_hits[1] / (fj_cpu.candidate_move_hits[1] + fj_cpu.candidate_move_misses[1]) * 100.0); - CUOPT_LOG_TRACE("cont candidate move hit percentage: %.2f%%", + CUOPT_LOG_TRACE("cont candidate move hit percentage: %.2f%%\n", (double)fj_cpu.candidate_move_hits[2] / (fj_cpu.candidate_move_hits[2] + fj_cpu.candidate_move_misses[2]) * 100.0); - CUOPT_LOG_TRACE("========================================"); -} - -template -static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) -{ - // Count variable types - use host vectors - fj_cpu.n_binary_vars = 0; - fj_cpu.n_integer_vars = 0; - // MEMORY OPS: Loop over all variables (n_vars iterations) - for (i_t i = 0; i < (i_t)fj_cpu.h_is_binary_variable.size(); i++) { - // ARRAY READ: h_is_binary_variable[i] - 1 read per iteration - if (fj_cpu.h_is_binary_variable[i]) { - fj_cpu.n_binary_vars++; - } else if (fj_cpu.h_var_types[i] == var_t::INTEGER) { - // ARRAY READ: h_var_types[i] - 1 read per iteration (conditional) - fj_cpu.n_integer_vars++; - } - // Total per iteration: 2 array reads - } - - i_t total_nnz = fj_cpu.h_reverse_offsets.back(); - i_t n_vars = fj_cpu.h_reverse_offsets.size() - 1; - i_t n_cstrs = fj_cpu.h_offsets.size() - 1; - - fj_cpu.avg_var_degree = (double)total_nnz / n_vars; - - // Compute variable degree statistics (max, cv) - fj_cpu.max_var_degree = 0; - std::vector var_degrees(n_vars); - // MEMORY OPS: Loop over all variables (n_vars iterations) - for (i_t i = 0; i < n_vars; i++) { - // ARRAY READ: h_reverse_offsets[i] and h_reverse_offsets[i+1] - 2 reads per iteration - i_t degree = fj_cpu.h_reverse_offsets[i + 1] - fj_cpu.h_reverse_offsets[i]; - // ARRAY WRITE: var_degrees[i] - 1 write per iteration - var_degrees[i] = degree; - fj_cpu.max_var_degree = std::max(fj_cpu.max_var_degree, degree); - // Total per iteration: 2 reads + 1 write = 3 memory ops - } - - // Compute variable degree coefficient of variation - double var_deg_variance = 0.0; - // MEMORY OPS: Loop over all variables (n_vars iterations) - for (i_t i = 0; i < n_vars; i++) { - // ARRAY READ: var_degrees[i] - 1 read per iteration - double diff = var_degrees[i] - fj_cpu.avg_var_degree; - var_deg_variance += diff * diff; - // Total per iteration: 1 read - } - var_deg_variance /= n_vars; - double var_degree_std = std::sqrt(var_deg_variance); - fj_cpu.var_degree_cv = fj_cpu.avg_var_degree > 0 ? var_degree_std / fj_cpu.avg_var_degree : 0.0; - - fj_cpu.avg_cstr_degree = (double)total_nnz / n_cstrs; - - // Compute constraint degree statistics (max, cv) - fj_cpu.max_cstr_degree = 0; - std::vector cstr_degrees(n_cstrs); - // MEMORY OPS: Loop over all constraints (n_cstrs iterations) - for (i_t i = 0; i < n_cstrs; i++) { - // ARRAY READ: h_offsets[i] and h_offsets[i+1] - 2 reads per iteration - i_t degree = fj_cpu.h_offsets[i + 1] - fj_cpu.h_offsets[i]; - // ARRAY WRITE: cstr_degrees[i] - 1 write per iteration - cstr_degrees[i] = degree; - fj_cpu.max_cstr_degree = std::max(fj_cpu.max_cstr_degree, degree); - // Total per iteration: 2 reads + 1 write = 3 memory ops - } - - // Compute constraint degree coefficient of variation - double cstr_deg_variance = 0.0; - // MEMORY OPS: Loop over all constraints (n_cstrs iterations) - for (i_t i = 0; i < n_cstrs; i++) { - // ARRAY READ: cstr_degrees[i] - 1 read per iteration - double diff = cstr_degrees[i] - fj_cpu.avg_cstr_degree; - cstr_deg_variance += diff * diff; - // Total per iteration: 1 read - } - cstr_deg_variance /= n_cstrs; - double cstr_degree_std = std::sqrt(cstr_deg_variance); - fj_cpu.cstr_degree_cv = - fj_cpu.avg_cstr_degree > 0 ? cstr_degree_std / fj_cpu.avg_cstr_degree : 0.0; - - fj_cpu.problem_density = (double)total_nnz / ((double)n_vars * n_cstrs); -} - -template -static void log_regression_features(fj_cpu_climber_t& fj_cpu, - double time_window_ms, - double total_time_ms, - size_t mem_loads_bytes, - size_t mem_stores_bytes) -{ - i_t total_nnz = fj_cpu.h_reverse_offsets.back(); - i_t n_vars = fj_cpu.h_reverse_offsets.size() - 1; - i_t n_cstrs = fj_cpu.h_offsets.size() - 1; - - // Dynamic runtime features - double violated_ratio = (double)fj_cpu.violated_constraints.size() / n_cstrs; - - // Compute per-iteration metrics - double nnz_per_move = 0.0; - i_t total_moves = - fj_cpu.n_lift_moves_window + fj_cpu.n_mtm_viol_moves_window + fj_cpu.n_mtm_sat_moves_window; - if (total_moves > 0) { nnz_per_move = (double)fj_cpu.nnz_processed_window / total_moves; } - - double eval_intensity = (double)fj_cpu.nnz_processed_window / 1000.0; - - // Cache and locality metrics - i_t cache_hits_window = fj_cpu.hit_count - fj_cpu.hit_count_window_start; - i_t cache_misses_window = fj_cpu.miss_count - fj_cpu.miss_count_window_start; - i_t total_cache_accesses = cache_hits_window + cache_misses_window; - double cache_hit_rate = - total_cache_accesses > 0 ? (double)cache_hits_window / total_cache_accesses : 0.0; - - i_t unique_cstrs = fj_cpu.unique_cstrs_accessed_window.size(); - i_t unique_vars = fj_cpu.unique_vars_accessed_window.size(); - - // Reuse ratios: how many times each constraint/variable was accessed on average - double cstr_reuse_ratio = - unique_cstrs > 0 ? (double)fj_cpu.nnz_processed_window / unique_cstrs : 0.0; - double var_reuse_ratio = - unique_vars > 0 ? (double)fj_cpu.n_variable_updates_window / unique_vars : 0.0; - - // Working set size estimation (KB) - // Each constraint: lhs (f_t) + 2 bounds (f_t) + sumcomp (f_t) = 4 * sizeof(f_t) - // Each variable: assignment (f_t) = 1 * sizeof(f_t) - i_t working_set_bytes = unique_cstrs * 4 * sizeof(f_t) + unique_vars * sizeof(f_t); - double working_set_kb = working_set_bytes / 1024.0; - - // Coverage: what fraction of problem is actively touched - double cstr_coverage = (double)unique_cstrs / n_cstrs; - double var_coverage = (double)unique_vars / n_vars; - - double loads_per_iter = 0.0; - double stores_per_iter = 0.0; - double l1_miss = -1.0; - double l3_miss = -1.0; - - // Compute memory statistics - double mem_loads_mb = mem_loads_bytes / 1e6; - double mem_stores_mb = mem_stores_bytes / 1e6; - double mem_total_mb = (mem_loads_bytes + mem_stores_bytes) / 1e6; - double mem_bandwidth_gb_per_sec = (mem_total_mb / 1000.0) / (time_window_ms / 1000.0); - - // Build per-wrapper memory statistics string - std::stringstream wrapper_stats; - auto per_wrapper_stats = fj_cpu.memory_manifold.collect_per_wrapper(); - for (const auto& [name, loads, stores] : per_wrapper_stats) { - wrapper_stats << " " << name << "_loads=" << loads << " " << name << "_stores=" << stores; - } - - fj_cpu.memory_manifold.flush(); - - // Print everything on a single line using precomputed features - CUOPT_LOG_DEBUG( - "%sCPUFJ_FEATURES iter=%d time_window=%.2f " - "n_vars=%d n_cstrs=%d n_bin=%d n_int=%d total_nnz=%d " - "avg_var_deg=%.2f max_var_deg=%d var_deg_cv=%.4f " - "avg_cstr_deg=%.2f max_cstr_deg=%d cstr_deg_cv=%.4f " - "density=%.6f " - "total_viol=%.4f obj_weight=%.4f max_weight=%.4f " - "n_locmin=%d iter_since_best=%d feas_found=%d " - "nnz_proc=%d n_lift=%d n_mtm_viol=%d n_mtm_sat=%d n_var_updates=%d " - "cache_hit_rate=%.4f unique_cstrs=%d unique_vars=%d " - "cstr_reuse=%.2f var_reuse=%.2f working_set_kb=%.1f " - "cstr_coverage=%.4f var_coverage=%.4f " - "L1_miss=%.2f L3_miss=%.2f loads_per_iter=%.0f stores_per_iter=%.0f " - "viol_ratio=%.4f nnz_per_move=%.2f eval_intensity=%.2f " - "mem_loads_mb=%.3f mem_stores_mb=%.3f mem_total_mb=%.3f mem_bandwidth_gb_s=%.3f%s", - fj_cpu.log_prefix.c_str(), - fj_cpu.iterations, - time_window_ms, - n_vars, - n_cstrs, - fj_cpu.n_binary_vars, - fj_cpu.n_integer_vars, - total_nnz, - fj_cpu.avg_var_degree, - fj_cpu.max_var_degree, - fj_cpu.var_degree_cv, - fj_cpu.avg_cstr_degree, - fj_cpu.max_cstr_degree, - fj_cpu.cstr_degree_cv, - fj_cpu.problem_density, - fj_cpu.total_violations, - fj_cpu.h_objective_weight, - fj_cpu.max_weight, - fj_cpu.n_local_minima_window, - fj_cpu.iterations_since_best, - fj_cpu.feasible_found ? 1 : 0, - fj_cpu.nnz_processed_window, - fj_cpu.n_lift_moves_window, - fj_cpu.n_mtm_viol_moves_window, - fj_cpu.n_mtm_sat_moves_window, - fj_cpu.n_variable_updates_window, - cache_hit_rate, - unique_cstrs, - unique_vars, - cstr_reuse_ratio, - var_reuse_ratio, - working_set_kb, - cstr_coverage, - var_coverage, - l1_miss, - l3_miss, - loads_per_iter, - stores_per_iter, - violated_ratio, - nnz_per_move, - eval_intensity, - mem_loads_mb, - mem_stores_mb, - mem_total_mb, - mem_bandwidth_gb_per_sec, - wrapper_stats.str().c_str()); - - // Reset window counters - fj_cpu.nnz_processed_window = 0; - fj_cpu.n_lift_moves_window = 0; - fj_cpu.n_mtm_viol_moves_window = 0; - fj_cpu.n_mtm_sat_moves_window = 0; - fj_cpu.n_variable_updates_window = 0; - fj_cpu.n_local_minima_window = 0; - fj_cpu.prev_best_objective = fj_cpu.h_best_objective; - - // Reset cache and locality tracking - fj_cpu.hit_count_window_start = fj_cpu.hit_count; - fj_cpu.miss_count_window_start = fj_cpu.miss_count; - fj_cpu.unique_cstrs_accessed_window.clear(); - fj_cpu.unique_vars_accessed_window.clear(); -} - -// Local implementations that use instrumented vectors -template -static inline std::pair reverse_range_for_var(fj_cpu_climber_t& fj_cpu, - i_t var_idx) -{ - cuopt_assert(var_idx >= 0 && var_idx < fj_cpu.view.pb.n_variables, - "Variable should be within the range"); - return std::make_pair(fj_cpu.h_reverse_offsets[var_idx], fj_cpu.h_reverse_offsets[var_idx + 1]); -} - -template -static inline std::pair range_for_constraint(fj_cpu_climber_t& fj_cpu, - i_t cstr_idx) -{ - return std::make_pair(fj_cpu.h_offsets[cstr_idx], fj_cpu.h_offsets[cstr_idx + 1]); -} - -template -static inline bool check_variable_within_bounds(fj_cpu_climber_t& fj_cpu, - i_t var_idx, - f_t val) -{ - const f_t int_tol = fj_cpu.view.pb.tolerances.integrality_tolerance; - auto bounds = fj_cpu.h_var_bounds[var_idx].get(); - bool within_bounds = val <= (get_upper(bounds) + int_tol) && val >= (get_lower(bounds) - int_tol); - return within_bounds; -} - -template -static inline bool is_integer_var(fj_cpu_climber_t& fj_cpu, i_t var_idx) -{ - return var_t::INTEGER == fj_cpu.h_var_types[var_idx]; + CUOPT_LOG_TRACE("========================================\n"); } template @@ -384,16 +117,16 @@ static inline bool tabu_check(fj_cpu_climber_t& fj_cpu, } template -static bool check_variable_feasibility(fj_cpu_climber_t& fj_cpu, +static bool check_variable_feasibility(const typename fj_t::climber_data_t::view_t& fj, bool check_integer = true) { - for (i_t var_idx = 0; var_idx < fj_cpu.view.pb.n_variables; var_idx += 1) { - auto val = fj_cpu.h_assignment[var_idx]; - bool feasible = check_variable_within_bounds(fj_cpu, var_idx, val); + for (i_t var_idx = 0; var_idx < fj.pb.n_variables; var_idx += 1) { + auto val = fj.incumbent_assignment[var_idx]; + bool feasible = fj.pb.check_variable_within_bounds(var_idx, val); if (!feasible) return false; - if (check_integer && is_integer_var(fj_cpu, var_idx) && - !fj_cpu.view.pb.is_integer(fj_cpu.h_assignment[var_idx])) + if (check_integer && fj.pb.is_integer_var(var_idx) && + !fj.pb.is_integer(fj.incumbent_assignment[var_idx])) return false; } return true; @@ -406,7 +139,6 @@ static inline std::pair compute_score(fj_cpu_climber_t timer(fj_cpu.compute_score_times); - // ARRAY READ: h_obj_coeffs[var_idx] - 1 read f_t obj_diff = fj_cpu.h_obj_coeffs[var_idx] * delta; cuopt_assert(isfinite(delta), ""); @@ -416,41 +148,19 @@ static inline std::pair compute_score(fj_cpu_climber_t(fj_cpu, var_idx); - fj_cpu.nnz_processed_window += (offset_end - offset_begin); - - // MEMORY OPS: Loop over all constraints involving this variable (avg_var_degree iterations) - // This is one of the HOTTEST loops in the code - called for every candidate move evaluation + auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); for (i_t i = offset_begin; i < offset_end; i++) { - // ARRAY READ: h_reverse_constraints[i] - 1 read per iteration - auto cstr_idx = fj_cpu.h_reverse_constraints[i]; - fj_cpu.unique_cstrs_accessed_window.insert(cstr_idx); - // ARRAY READ: h_reverse_coefficients[i] - 1 read per iteration - auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; - // ARRAY READ: cached_cstr_bounds[i] - 1 read per iteration (reads 2 f_t values) - auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i].get(); + auto cstr_idx = fj_cpu.h_reverse_constraints[i]; + auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; + auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i]; cuopt_assert(c_lb <= c_ub, "invalid bounds"); - // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (indirect indexing) - // feas_score_constraint also reads from h_cstr_left_weights[cstr_idx] and - // h_cstr_right_weights[cstr_idx] - auto [cstr_base_feas, cstr_bonus_robust] = - feas_score_constraint(fj_cpu.view, - var_idx, - delta, - cstr_idx, - cstr_coeff, - c_lb, - c_ub, - fj_cpu.h_lhs[cstr_idx], - fj_cpu.h_cstr_left_weights[cstr_idx], - fj_cpu.h_cstr_right_weights[cstr_idx]); + auto [cstr_base_feas, cstr_bonus_robust] = feas_score_constraint( + fj_cpu.view, var_idx, delta, cstr_idx, cstr_coeff, c_lb, c_ub, fj_cpu.h_lhs[cstr_idx]); base_feas_sum += cstr_base_feas; bonus_robust_sum += cstr_bonus_robust; - // Total per iteration: ~6-7 array reads (h_reverse_constraints, h_reverse_coefficients, - // cached_cstr_bounds (2 values), h_lhs, h_cstr_left_weights, h_cstr_right_weights) } f_t base_obj = 0; @@ -478,21 +188,15 @@ static inline std::pair compute_score(fj_cpu_climber_t static void smooth_weights(fj_cpu_climber_t& fj_cpu) { - // MEMORY OPS: Loop over all constraints (n_cstrs iterations) for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; cstr_idx++) { // consider only satisfied constraints if (fj_cpu.violated_constraints.count(cstr_idx)) continue; - // ARRAY READ: h_cstr_left_weights[cstr_idx] - 1 read per iteration (if not violated) f_t weight_l = max((f_t)0, fj_cpu.h_cstr_left_weights[cstr_idx] - 1); - // ARRAY READ: h_cstr_right_weights[cstr_idx] - 1 read per iteration (if not violated) f_t weight_r = max((f_t)0, fj_cpu.h_cstr_right_weights[cstr_idx] - 1); - // ARRAY WRITE: h_cstr_left_weights[cstr_idx] - 1 write per iteration (if not violated) - fj_cpu.h_cstr_left_weights[cstr_idx] = weight_l; - // ARRAY WRITE: h_cstr_right_weights[cstr_idx] - 1 write per iteration (if not violated) + fj_cpu.h_cstr_left_weights[cstr_idx] = weight_l; fj_cpu.h_cstr_right_weights[cstr_idx] = weight_r; - // Total per iteration (for satisfied constraints): 2 reads + 2 writes = 4 memory ops } if (fj_cpu.h_objective_weight > 0 && fj_cpu.h_incumbent_objective >= fj_cpu.h_best_objective) { @@ -513,24 +217,18 @@ static void update_weights(fj_cpu_climber_t& fj_cpu) return; } - // MEMORY OPS: Loop over violated constraints (typically small: <100 iterations) for (auto cstr_idx : fj_cpu.violated_constraints) { - // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration f_t curr_incumbent_lhs = fj_cpu.h_lhs[cstr_idx]; - // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per iteration f_t curr_lower_excess = fj_cpu.view.lower_excess_score(cstr_idx, curr_incumbent_lhs, fj_cpu.h_cstr_lb[cstr_idx]); - // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per iteration f_t curr_upper_excess = fj_cpu.view.upper_excess_score(cstr_idx, curr_incumbent_lhs, fj_cpu.h_cstr_ub[cstr_idx]); f_t curr_excess_score = curr_lower_excess + curr_upper_excess; f_t old_weight; if (curr_lower_excess < 0.) { - // ARRAY READ: h_cstr_left_weights[cstr_idx] - 1 read per iteration (conditional) old_weight = fj_cpu.h_cstr_left_weights[cstr_idx]; } else { - // ARRAY READ: h_cstr_right_weights[cstr_idx] - 1 read per iteration (conditional) old_weight = fj_cpu.h_cstr_right_weights[cstr_idx]; } @@ -543,26 +241,18 @@ static void update_weights(fj_cpu_climber_t& fj_cpu) new_weight = round(new_weight); if (curr_lower_excess < 0.) { - // ARRAY WRITE: h_cstr_left_weights[cstr_idx] - 1 write per iteration (conditional) fj_cpu.h_cstr_left_weights[cstr_idx] = new_weight; fj_cpu.max_weight = max(fj_cpu.max_weight, new_weight); } else { - // ARRAY WRITE: h_cstr_right_weights[cstr_idx] - 1 write per iteration (conditional) fj_cpu.h_cstr_right_weights[cstr_idx] = new_weight; fj_cpu.max_weight = max(fj_cpu.max_weight, new_weight); } // Invalidate related cached move scores - auto [relvar_offset_begin, relvar_offset_end] = - range_for_constraint(fj_cpu, cstr_idx); - // MEMORY OPS: Inner loop over variables in this constraint (avg_cstr_degree iterations per - // outer iteration) + auto [relvar_offset_begin, relvar_offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); for (auto i = relvar_offset_begin; i < relvar_offset_end; i++) { - // ARRAY WRITE: cached_mtm_moves[i].first - 1 write per inner iteration fj_cpu.cached_mtm_moves[i].first = 0; - // Total per inner iteration: 1 write } - // Total per outer iteration: 4 reads + 1 write + (avg_cstr_degree writes in inner loop) } if (fj_cpu.violated_constraints.empty()) { fj_cpu.h_objective_weight += 1; } @@ -580,47 +270,30 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, cuopt_assert(var_idx < fj_cpu.view.pb.n_variables, "variable index out of bounds"); // Update the LHSs of all involved constraints. - auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); - - // Track work metrics for regression model - fj_cpu.nnz_processed_window += (offset_end - offset_begin); - fj_cpu.n_variable_updates_window++; - fj_cpu.unique_vars_accessed_window.insert(var_idx); + auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); i_t previous_viol = fj_cpu.violated_constraints.size(); - // MEMORY OPS: CRITICAL LOOP - Loop over all constraints involving this variable (avg_var_degree - // iterations) This loop is called ONCE PER ITERATION and updates constraint LHS values for (auto i = offset_begin; i < offset_end; i++) { cuopt_assert(i < (i_t)fj_cpu.h_reverse_constraints.size(), ""); - // ARRAY READ: cached_cstr_bounds[i] - 1 read per iteration (reads 2 f_t values) - auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i].get(); + auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i]; - // ARRAY READ: h_reverse_constraints[i] - 1 read per iteration - auto cstr_idx = fj_cpu.h_reverse_constraints[i]; - fj_cpu.unique_cstrs_accessed_window.insert(cstr_idx); - // ARRAY READ: h_reverse_coefficients[i] - 1 read per iteration + auto cstr_idx = fj_cpu.h_reverse_constraints[i]; auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; - // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (indirect indexing) f_t old_lhs = fj_cpu.h_lhs[cstr_idx]; // Kahan compensated summation - // ARRAY READ: h_lhs_sumcomp[cstr_idx] - 1 read per iteration - f_t y = cstr_coeff * delta - fj_cpu.h_lhs_sumcomp[cstr_idx]; - f_t t = old_lhs + y; - // ARRAY WRITE: h_lhs_sumcomp[cstr_idx] - 1 write per iteration + f_t y = cstr_coeff * delta - fj_cpu.h_lhs_sumcomp[cstr_idx]; + f_t t = old_lhs + y; fj_cpu.h_lhs_sumcomp[cstr_idx] = (t - old_lhs) - y; - // ARRAY WRITE: h_lhs[cstr_idx] - 1 write per iteration - fj_cpu.h_lhs[cstr_idx] = t; - // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (just written) - f_t new_lhs = fj_cpu.h_lhs[cstr_idx]; - f_t old_cost = fj_cpu.view.excess_score(cstr_idx, old_lhs, c_lb, c_ub); - f_t new_cost = fj_cpu.view.excess_score(cstr_idx, new_lhs, c_lb, c_ub); - f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + fj_cpu.h_lhs[cstr_idx] = t; + f_t new_lhs = fj_cpu.h_lhs[cstr_idx]; + f_t old_cost = fj_cpu.view.excess_score(cstr_idx, old_lhs, c_lb, c_ub); + f_t new_cost = fj_cpu.view.excess_score(cstr_idx, new_lhs, c_lb, c_ub); + f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); // trigger early lhs recomputation if the sumcomp term gets too large // to avoid large numerical errors - // ARRAY READ: h_lhs_sumcomp[cstr_idx] - 1 read per iteration if (fabs(fj_cpu.h_lhs_sumcomp[cstr_idx]) > BIGVAL_THRESHOLD) fj_cpu.trigger_early_lhs_recomputation = true; @@ -638,16 +311,10 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, cuopt_assert(isfinite(fj_cpu.h_lhs[cstr_idx]), "assignment should be finite"); // Invalidate related cached move scores - auto [relvar_offset_begin, relvar_offset_end] = - range_for_constraint(fj_cpu, cstr_idx); - // MEMORY OPS: Inner loop over variables in this constraint (avg_cstr_degree iterations per - // outer iteration) + auto [relvar_offset_begin, relvar_offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); for (auto i = relvar_offset_begin; i < relvar_offset_end; i++) { - // ARRAY WRITE: cached_mtm_moves[i].first - 1 write per inner iteration fj_cpu.cached_mtm_moves[i].first = 0; - // Total per inner iteration: 1 write } - // Total per outer iteration: 7 reads + 3 writes + (avg_cstr_degree writes in inner loop) } if (previous_viol > 0 && fj_cpu.violated_constraints.empty()) { @@ -655,34 +322,29 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, } // update the assignment and objective proper - // ARRAY READ: h_assignment[var_idx] - 1 read f_t new_val = fj_cpu.h_assignment[var_idx] + delta; - if (is_integer_var(fj_cpu, var_idx)) { + if (fj_cpu.view.pb.is_integer_var(var_idx)) { cuopt_assert(fj_cpu.view.pb.integer_equal(new_val, round(new_val)), "new_val is not integer"); new_val = round(new_val); } - // ARRAY WRITE: h_assignment[var_idx] - 1 write fj_cpu.h_assignment[var_idx] = new_val; - cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), + cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), "assignment not within bounds"); cuopt_assert(isfinite(new_val), "assignment is not finite"); - // ARRAY READ: h_obj_coeffs[var_idx] - 1 read fj_cpu.h_incumbent_objective += fj_cpu.h_obj_coeffs[var_idx] * delta; if (fj_cpu.h_incumbent_objective < fj_cpu.h_best_objective && fj_cpu.violated_constraints.empty()) { // recompute the LHS values to cancel out accumulation errors, then check if feasibility remains recompute_lhs(fj_cpu); - if (fj_cpu.violated_constraints.empty() && check_variable_feasibility(fj_cpu)) { + if (fj_cpu.violated_constraints.empty() && check_variable_feasibility(fj_cpu.view)) { cuopt_assert(fj_cpu.satisfied_constraints.size() == fj_cpu.view.pb.n_constraints, ""); fj_cpu.h_best_objective = fj_cpu.h_incumbent_objective - fj_cpu.settings.parameters.breakthrough_move_epsilon; - // ARRAY WRITE: h_best_assignment = h_assignment - n_vars writes (vector copy) - fj_cpu.h_best_assignment = fj_cpu.h_assignment; - fj_cpu.iterations_since_best = 0; // Reset counter on improvement - CUOPT_LOG_TRACE("%sCPUFJ: new best objective: %g", + fj_cpu.h_best_assignment = fj_cpu.h_assignment; + CUOPT_LOG_TRACE("%sCPUFJ: new best objective: %g\n", fj_cpu.log_prefix.c_str(), fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective)); if (fj_cpu.improvement_callback) { @@ -696,25 +358,18 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, rng.next_u32() % (fj_cpu.settings.parameters.tabu_tenure_max - fj_cpu.settings.parameters.tabu_tenure_min); if (delta > 0) { - // ARRAY WRITE: h_tabu_lastinc[var_idx] - 1 write - fj_cpu.h_tabu_lastinc[var_idx] = fj_cpu.iterations; - // ARRAY WRITE: h_tabu_nodec_until[var_idx] - 1 write + fj_cpu.h_tabu_lastinc[var_idx] = fj_cpu.iterations; fj_cpu.h_tabu_nodec_until[var_idx] = fj_cpu.iterations + tabu_tenure; - // ARRAY WRITE: h_tabu_noinc_until[var_idx] - 1 write fj_cpu.h_tabu_noinc_until[var_idx] = fj_cpu.iterations + tabu_tenure / 2; - // CUOPT_LOG_TRACE("CPU: tabu nodec_until: %d", fj_cpu.h_tabu_nodec_until[var_idx]); + // CUOPT_LOG_TRACE("CPU: tabu nodec_until: %d\n", fj_cpu.h_tabu_nodec_until[var_idx]); } else { - // ARRAY WRITE: h_tabu_lastdec[var_idx] - 1 write - fj_cpu.h_tabu_lastdec[var_idx] = fj_cpu.iterations; - // ARRAY WRITE: h_tabu_noinc_until[var_idx] - 1 write + fj_cpu.h_tabu_lastdec[var_idx] = fj_cpu.iterations; fj_cpu.h_tabu_noinc_until[var_idx] = fj_cpu.iterations + tabu_tenure; - // ARRAY WRITE: h_tabu_nodec_until[var_idx] - 1 write fj_cpu.h_tabu_nodec_until[var_idx] = fj_cpu.iterations + tabu_tenure / 2; - // CUOPT_LOG_TRACE("CPU: tabu noinc_until: %d", fj_cpu.h_tabu_nodec_until[var_idx]); + // CUOPT_LOG_TRACE("CPU: tabu noinc_until: %d\n", fj_cpu.h_tabu_noinc_until[var_idx]); } - // ARRAY WRITE: flip_move_computed - n_vars writes (fill with false) + std::fill(fj_cpu.flip_move_computed.begin(), fj_cpu.flip_move_computed.end(), false); - // ARRAY WRITE: var_bitmap - n_vars writes (fill with false) std::fill(fj_cpu.var_bitmap.begin(), fj_cpu.var_bitmap.end(), false); fj_cpu.iter_mtm_vars.clear(); } @@ -731,58 +386,44 @@ static thrust::tuple find_mtm_move( fj_staged_score_t best_score = fj_staged_score_t::invalid(); // collect all the variables that are involved in the target constraints - // MEMORY OPS: Outer loop over target constraints (sample_size iterations, typically 15-100) for (size_t cstr_idx : target_cstrs) { - auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); - // MEMORY OPS: Inner loop over variables in each constraint (avg_cstr_degree per outer - // iteration) + auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); for (auto i = offset_begin; i < offset_end; i++) { - // ARRAY READ: h_variables[i] - 1 read per iteration i_t var_idx = fj_cpu.h_variables[i]; - // ARRAY READ: var_bitmap[var_idx] - 1 read per iteration if (fj_cpu.var_bitmap[var_idx]) continue; fj_cpu.iter_mtm_vars.push_back(var_idx); - // ARRAY WRITE: var_bitmap[var_idx] - 1 write per iteration (if not already set) fj_cpu.var_bitmap[var_idx] = true; - // Total per inner iteration: 2 reads + 1 write (conditional) } } // estimate the amount of nnzs to consider i_t nnz_sum = 0; for (auto var_idx : fj_cpu.iter_mtm_vars) { - auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); + auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); nnz_sum += offset_end - offset_begin; } f_t nnz_pick_probability = 1; if (nnz_sum > fj_cpu.nnz_samples) nnz_pick_probability = (f_t)fj_cpu.nnz_samples / nnz_sum; - // MEMORY OPS: HOTTEST LOOP - Outer loop over target constraints (sample_size iterations) for (size_t cstr_idx : target_cstrs) { - auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); - f_t cstr_tol = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + f_t cstr_tol = fj_cpu.view.get_corrected_tolerance(cstr_idx); cuopt_assert(cstr_idx < fj_cpu.h_cstr_lb.size(), "cstr_idx is out of bounds"); - auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); - // MEMORY OPS: Inner loop over variables (avg_cstr_degree per outer iteration) + auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); for (auto i = offset_begin; i < offset_end; i++) { // early cached check - // ARRAY READ: cached_mtm_moves[i] - 1 read per iteration if (auto& cached_move = fj_cpu.cached_mtm_moves[i]; cached_move.first != 0) { if (best_score < cached_move.second) { - // ARRAY READ: h_variables[i] - 1 read per iteration (cache hit) auto var_idx = fj_cpu.h_variables[i]; - // ARRAY READ: h_assignment[var_idx] - 1 read per iteration (cache hit) - if (check_variable_within_bounds( - fj_cpu, var_idx, fj_cpu.h_assignment[var_idx] + cached_move.first)) { + if (fj_cpu.view.pb.check_variable_within_bounds( + var_idx, fj_cpu.h_assignment[var_idx] + cached_move.first)) { best_score = cached_move.second; best_move = fj_move_t{var_idx, cached_move.first}; } - // cuopt_assert(check_variable_within_bounds(fj_cpu, var_idx, + // cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, // fj_cpu.h_assignment[var_idx] + cached_move.first), "best move not within bounds"); } fj_cpu.hit_count++; - // Total per cache hit: 3 reads continue; } @@ -790,40 +431,25 @@ static thrust::tuple find_mtm_move( if (nnz_pick_probability < 1) if (rng.next_float() > nnz_pick_probability) continue; - // ARRAY READ: h_variables[i] - 1 read per iteration (cache miss) auto var_idx = fj_cpu.h_variables[i]; - // ARRAY READ: h_assignment[var_idx] - 1 read per iteration (cache miss) f_t val = fj_cpu.h_assignment[var_idx]; f_t new_val = val; f_t delta = 0; // Special case for binary variables - // ARRAY READ: h_is_binary_variable[var_idx] - 1 read per iteration if (fj_cpu.h_is_binary_variable[var_idx]) { - // ARRAY READ: flip_move_computed[var_idx] - 1 read per iteration (conditional) if (fj_cpu.flip_move_computed[var_idx]) continue; - // ARRAY WRITE: flip_move_computed[var_idx] - 1 write per iteration (conditional) fj_cpu.flip_move_computed[var_idx] = true; new_val = 1 - val; } else { - // ARRAY READ: h_coefficients[i] - 1 read per iteration (non-binary) auto cstr_coeff = fj_cpu.h_coefficients[i]; - // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per iteration (non-binary) - f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; - // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per iteration (non-binary) - f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; - auto [delta, sign, slack, cstr_tolerance] = - get_mtm_for_constraint(fj_cpu.view, - var_idx, - cstr_idx, - cstr_coeff, - c_lb, - c_ub, - fj_cpu.h_assignment, - fj_cpu.h_lhs); - if (is_integer_var(fj_cpu, var_idx)) { + f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; + f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; + auto [delta, sign, slack, cstr_tolerance] = get_mtm_for_constraint( + fj_cpu.view, var_idx, cstr_idx, cstr_coeff, c_lb, c_ub); + if (fj_cpu.view.pb.is_integer_var(var_idx)) { new_val = cstr_coeff * sign > 0 ? floor(val + delta + fj_cpu.view.pb.tolerances.integrality_tolerance) : ceil(val + delta - fj_cpu.view.pb.tolerances.integrality_tolerance); @@ -831,30 +457,26 @@ static thrust::tuple find_mtm_move( new_val = val + delta; } // fallback - // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration (non-binary, conditional) - if (new_val < get_lower(fj_cpu.h_var_bounds[var_idx].get()) || - new_val > get_upper(fj_cpu.h_var_bounds[var_idx].get())) { - new_val = cstr_coeff * sign > 0 ? get_lower(fj_cpu.h_var_bounds[var_idx].get()) - : get_upper(fj_cpu.h_var_bounds[var_idx].get()); + if (new_val < get_lower(fj_cpu.h_var_bounds[var_idx]) || + new_val > get_upper(fj_cpu.h_var_bounds[var_idx])) { + new_val = cstr_coeff * sign > 0 ? get_lower(fj_cpu.h_var_bounds[var_idx]) + : get_upper(fj_cpu.h_var_bounds[var_idx]); } } if (!isfinite(new_val)) continue; - cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), + cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), "new_val is not within bounds"); delta = new_val - val; // more permissive tabu in the case of local minima - if (tabu_check(fj_cpu, var_idx, delta, localmin)) continue; + if (tabu_check(fj_cpu, var_idx, delta, localmin)) continue; if (fabs(delta) < cstr_tol) continue; auto move = fj_move_t{var_idx, delta}; cuopt_assert(move.var_idx < fj_cpu.h_assignment.size(), "move.var_idx is out of bounds"); cuopt_assert(move.var_idx >= 0, "move.var_idx is not positive"); - // CRITICAL: compute_score() does ~6-7 array reads per constraint (see compute_score - // annotations) auto [score, infeasibility] = compute_score(fj_cpu, var_idx, delta); - // ARRAY WRITE: cached_mtm_moves[i] - 1 write per iteration (cache miss) - fj_cpu.cached_mtm_moves[i] = std::make_pair(delta, score); + fj_cpu.cached_mtm_moves[i] = std::make_pair(delta, score); fj_cpu.miss_count++; // reject this move if it would increase the target variable to a numerically unstable value if (fj_cpu.view.move_numerically_stable( @@ -864,7 +486,6 @@ static thrust::tuple find_mtm_move( best_move = move; } } - // Total per cache miss: ~8-11 reads + 1 write + compute_score overhead } } @@ -873,9 +494,7 @@ static thrust::tuple find_mtm_move( fj_cpu.h_best_objective < std::numeric_limits::infinity() && fj_cpu.h_incumbent_objective >= fj_cpu.h_best_objective + fj_cpu.settings.parameters.breakthrough_move_epsilon) { - // MEMORY OPS: Loop over objective variables (num_obj_vars iterations, typically small) for (auto var_idx : fj_cpu.h_objective_vars) { - // ARRAY READ: h_assignment[var_idx] - 1 read per iteration f_t old_val = fj_cpu.h_assignment[var_idx]; f_t new_val = get_breakthrough_move(fj_cpu.view, var_idx); @@ -888,12 +507,11 @@ static thrust::tuple find_mtm_move( cuopt_assert(move.var_idx < fj_cpu.h_assignment.size(), "move.var_idx is out of bounds"); cuopt_assert(move.var_idx >= 0, "move.var_idx is not positive"); - if (tabu_check(fj_cpu, var_idx, delta)) continue; + if (tabu_check(fj_cpu, var_idx, delta)) continue; - // CRITICAL: compute_score() does ~6-7 array reads per constraint involved auto [score, infeasibility] = compute_score(fj_cpu, var_idx, delta); - cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), ""); + cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), ""); cuopt_assert(isfinite(delta), ""); if (fj_cpu.view.move_numerically_stable( @@ -903,7 +521,6 @@ static thrust::tuple find_mtm_move( best_move = move; } } - // Total per iteration: 1 read + compute_score overhead } } @@ -952,44 +569,29 @@ static void recompute_lhs(fj_cpu_climber_t& fj_cpu) fj_cpu.violated_constraints.clear(); fj_cpu.satisfied_constraints.clear(); fj_cpu.total_violations = 0; - // MEMORY OPS: CRITICAL LOOP - Loop over all constraints (n_cstrs iterations) - // This is called periodically to recompute all LHS values from scratch for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; ++cstr_idx) { - auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); - auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); - // MEMORY OPS: For each constraint, reads avg_cstr_degree elements from: - // - h_coefficients[offset_begin:offset_end] - avg_cstr_degree reads - // - h_variables[offset_begin:offset_end] - avg_cstr_degree reads (indirect indexing) - // - h_assignment[h_variables[i]] - avg_cstr_degree reads (indirect indexing) + auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); auto delta_it = - thrust::make_transform_iterator(thrust::make_counting_iterator(0), [&fj_cpu](i_t j) { - return fj_cpu.h_coefficients[j] * fj_cpu.h_assignment[fj_cpu.h_variables[j]]; + thrust::make_transform_iterator(thrust::make_counting_iterator(0), [fj = fj_cpu.view](i_t j) { + return fj.pb.coefficients[j] * fj.incumbent_assignment[fj.pb.variables[j]]; }); - // ARRAY WRITE: h_lhs[cstr_idx] - 1 write per constraint fj_cpu.h_lhs[cstr_idx] = fj_kahan_babushka_neumaier_sum(delta_it + offset_begin, delta_it + offset_end); - // ARRAY WRITE: h_lhs_sumcomp[cstr_idx] - 1 write per constraint fj_cpu.h_lhs_sumcomp[cstr_idx] = 0; - f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); - // ARRAY READ: h_lhs[cstr_idx] - 1 read per constraint - f_t new_cost = fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); + f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx); + f_t new_cost = fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); if (new_cost < -cstr_tolerance) { fj_cpu.violated_constraints.insert(cstr_idx); fj_cpu.total_violations += new_cost; } else { fj_cpu.satisfied_constraints.insert(cstr_idx); } - // Total per constraint: (3 * avg_cstr_degree) reads + 2 writes + 1 read = (3 * avg_cstr_degree - // + 1) reads + 2 writes } // compute incumbent objective - // MEMORY OPS: Reads all n_vars elements from h_assignment and h_obj_coeffs - // ARRAY READ: h_assignment (n_vars reads) and h_obj_coeffs (n_vars reads) fj_cpu.h_incumbent_objective = thrust::inner_product( fj_cpu.h_assignment.begin(), fj_cpu.h_assignment.end(), fj_cpu.h_obj_coeffs.begin(), 0.); - // Total: 2 * n_vars reads } template @@ -1001,20 +603,15 @@ static thrust::tuple find_lift_move( fj_move_t best_move = fj_move_t{-1, 0}; fj_staged_score_t best_score = fj_staged_score_t::zero(); - // MEMORY OPS: Loop over objective variables (num_obj_vars iterations) - // This is called when in the feasible region to find improving moves for (auto var_idx : fj_cpu.h_objective_vars) { cuopt_assert(var_idx < fj_cpu.h_obj_coeffs.size(), "var_idx is out of bounds"); cuopt_assert(var_idx >= 0, "var_idx is out of bounds"); - // ARRAY READ: h_obj_coeffs[var_idx] - 1 read per iteration f_t obj_coeff = fj_cpu.h_obj_coeffs[var_idx]; f_t delta = -std::numeric_limits::infinity(); - // ARRAY READ: h_assignment[var_idx] - 1 read per iteration - f_t val = fj_cpu.h_assignment[var_idx]; + f_t val = fj_cpu.h_assignment[var_idx]; // special path for binary variables - // ARRAY READ: h_is_binary_variable[var_idx] - 1 read per iteration if (fj_cpu.h_is_binary_variable[var_idx]) { cuopt_assert(fj_cpu.view.pb.is_integer(val), "binary variable is not integer"); cuopt_assert(fj_cpu.view.pb.integer_equal(val, 0) || fj_cpu.view.pb.integer_equal(val, 1), @@ -1023,43 +620,29 @@ static thrust::tuple find_lift_move( // flip move wouldn't improve if (delta * obj_coeff >= 0) continue; } else { - // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration (non-binary) - f_t lfd_lb = get_lower(fj_cpu.h_var_bounds[var_idx].get()) - val; - f_t lfd_ub = get_upper(fj_cpu.h_var_bounds[var_idx].get()) - val; - auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); - // MEMORY OPS: Inner loop over constraints involving this variable (avg_var_degree iterations - // per outer iteration) + f_t lfd_lb = get_lower(fj_cpu.h_var_bounds[var_idx]) - val; + f_t lfd_ub = get_upper(fj_cpu.h_var_bounds[var_idx]) - val; + auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); for (i_t j = offset_begin; j < offset_end; j += 1) { - // ARRAY READ: h_reverse_constraints[j] - 1 read per inner iteration - auto cstr_idx = fj_cpu.h_reverse_constraints[j]; - // ARRAY READ: h_reverse_coefficients[j] - 1 read per inner iteration - auto cstr_coeff = fj_cpu.h_reverse_coefficients[j]; - // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per inner iteration (indirect) - f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; - // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per inner iteration (indirect) - f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; + auto cstr_idx = fj_cpu.view.pb.reverse_constraints[j]; + auto cstr_coeff = fj_cpu.view.pb.reverse_coefficients[j]; + f_t c_lb = fj_cpu.view.pb.constraint_lower_bounds[cstr_idx]; + f_t c_ub = fj_cpu.view.pb.constraint_upper_bounds[cstr_idx]; f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); cuopt_assert(c_lb <= c_ub, "invalid bounds"); - // ARRAY READ: h_lhs[cstr_idx] - 1 read per inner iteration cuopt_assert(fj_cpu.view.cstr_satisfied(cstr_idx, fj_cpu.h_lhs[cstr_idx]), "cstr should be satisfied"); // Process each bound separately, as both are satified and may both be finite // otherwise range constraints aren't correctly handled for (auto [bound, sign] : {std::make_tuple(c_lb, -1), std::make_tuple(c_ub, 1)}) { - auto [delta, slack] = get_mtm_for_bound(fj_cpu.view, - var_idx, - cstr_idx, - cstr_coeff, - bound, - sign, - fj_cpu.h_assignment, - fj_cpu.h_lhs); + auto [delta, slack] = + get_mtm_for_bound(fj_cpu.view, var_idx, cstr_idx, cstr_coeff, bound, sign); if (cstr_coeff * sign < 0) { - if (is_integer_var(fj_cpu, var_idx)) delta = ceil(delta); + if (fj_cpu.view.pb.is_integer_var(var_idx)) delta = ceil(delta); } else { - if (is_integer_var(fj_cpu, var_idx)) delta = floor(delta); + if (fj_cpu.view.pb.is_integer_var(var_idx)) delta = floor(delta); } // skip this variable if there is no slack @@ -1069,7 +652,7 @@ static thrust::tuple find_lift_move( } else { lfd_lb = 0; } - } else if (!check_variable_within_bounds(fj_cpu, var_idx, val + delta)) { + } else if (!fj_cpu.view.pb.check_variable_within_bounds(var_idx, val + delta)) { continue; } else { if (cstr_coeff * sign < 0) { @@ -1080,15 +663,13 @@ static thrust::tuple find_lift_move( } } if (lfd_lb >= lfd_ub) break; - // Total per inner iteration: 5 reads (h_reverse_constraints, h_reverse_coefficients, - // h_cstr_lb, h_cstr_ub, h_lhs) } // invalid crossing bounds if (lfd_lb >= lfd_ub) { lfd_lb = lfd_ub = 0; } - if (!check_variable_within_bounds(fj_cpu, var_idx, val + lfd_lb)) { lfd_lb = 0; } - if (!check_variable_within_bounds(fj_cpu, var_idx, val + lfd_ub)) { lfd_ub = 0; } + if (!fj_cpu.view.pb.check_variable_within_bounds(var_idx, val + lfd_lb)) { lfd_lb = 0; } + if (!fj_cpu.view.pb.check_variable_within_bounds(var_idx, val + lfd_ub)) { lfd_ub = 0; } // Now that the life move domain is computed, compute the correct lift move cuopt_assert(isfinite(val), "invalid assignment value"); @@ -1097,7 +678,7 @@ static thrust::tuple find_lift_move( if (!isfinite(delta)) delta = 0; if (fj_cpu.view.pb.integer_equal(delta, (f_t)0)) continue; - if (tabu_check(fj_cpu, var_idx, delta)) continue; + if (tabu_check(fj_cpu, var_idx, delta)) continue; cuopt_assert(delta * obj_coeff < 0, "lift move doesn't improve the objective!"); @@ -1111,7 +692,6 @@ static thrust::tuple find_lift_move( best_score = score; best_move = move; } - // Total per outer iteration: 3 reads + (5 * avg_var_degree reads in inner loop for non-binary) } return thrust::make_tuple(best_move, best_score); @@ -1129,24 +709,20 @@ static void perturb(fj_cpu_climber_t& fj_cpu) std::mt19937(fj_cpu.settings.seed + fj_cpu.iterations)); raft::random::PCGenerator rng(fj_cpu.settings.seed + fj_cpu.iterations, 0, 0); - // MEMORY OPS: Loop over sampled variables (2 iterations typically) for (auto var_idx : sampled_vars) { - // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration - f_t lb = std::max(get_lower(fj_cpu.h_var_bounds[var_idx].get()), -1e7); - f_t ub = std::min(get_upper(fj_cpu.h_var_bounds[var_idx].get()), 1e7); + f_t lb = std::max(get_lower(fj_cpu.h_var_bounds[var_idx]), -1e7); + f_t ub = std::min(get_upper(fj_cpu.h_var_bounds[var_idx]), 1e7); f_t val = lb + (ub - lb) * rng.next_double(); - if (is_integer_var(fj_cpu, var_idx)) { + if (fj_cpu.view.pb.is_integer_var(var_idx)) { lb = std::ceil(lb); ub = std::floor(ub); val = std::round(val); val = std::min(std::max(val, lb), ub); } - cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, val)), + cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, val), "value is out of bounds"); - // ARRAY WRITE: h_assignment[var_idx] - 1 write per iteration fj_cpu.h_assignment[var_idx] = val; - // Total per iteration: 1 read + 1 write } recompute_lhs(fj_cpu); @@ -1267,20 +843,12 @@ static void init_fj_cpu(fj_cpu_climber_t& fj_cpu, std::make_pair(0, fj_staged_score_t::zero())); fj_cpu.cached_cstr_bounds.resize(fj_cpu.h_reverse_coefficients.size()); - // MEMORY OPS: INITIALIZATION - Loop over all variables (n_vars iterations) for (i_t var_idx = 0; var_idx < (i_t)fj_cpu.view.pb.n_variables; ++var_idx) { - auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); - // MEMORY OPS: Inner loop over constraints per variable (avg_var_degree iterations per outer - // iteration) + auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); for (i_t i = offset_begin; i < offset_end; ++i) { - // ARRAY READ: h_reverse_constraints[i] - 1 read per inner iteration - // ARRAY READ: h_cstr_lb[h_reverse_constraints[i]] - 1 read per inner iteration (indirect) - // ARRAY READ: h_cstr_ub[h_reverse_constraints[i]] - 1 read per inner iteration (indirect) - // ARRAY WRITE: cached_cstr_bounds[i] - 1 write per inner iteration (2 f_t values) fj_cpu.cached_cstr_bounds[i] = std::make_pair(fj_cpu.h_cstr_lb[fj_cpu.h_reverse_constraints[i]], fj_cpu.h_cstr_ub[fj_cpu.h_reverse_constraints[i]]); - // Total per inner iteration: 3 reads + 1 write (2 values) } } @@ -1289,9 +857,6 @@ static void init_fj_cpu(fj_cpu_climber_t& fj_cpu, fj_cpu.iter_mtm_vars.reserve(fj_cpu.view.pb.n_variables); recompute_lhs(fj_cpu); - - // Precompute static problem features for regression model - precompute_problem_features(fj_cpu); } template @@ -1377,30 +942,17 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l auto loop_start = std::chrono::high_resolution_clock::now(); auto time_limit = std::chrono::milliseconds((int)(in_time_limit * 1000)); auto loop_time_start = std::chrono::high_resolution_clock::now(); - - // Initialize feature tracking - fj_cpu.last_feature_log_time = loop_start; - fj_cpu.prev_best_objective = fj_cpu.h_best_objective; - fj_cpu.iterations_since_best = 0; - while (!fj_cpu.halted && !fj_cpu.preemption_flag.load()) { // Check if 5 seconds have passed auto now = std::chrono::high_resolution_clock::now(); if (in_time_limit < std::numeric_limits::infinity() && now - loop_time_start > time_limit) { - CUOPT_LOG_TRACE("%sTime limit of %.4f seconds reached, breaking loop at iteration %d", + CUOPT_LOG_TRACE("%sTime limit of %.4f seconds reached, breaking loop at iteration %d\n", fj_cpu.log_prefix.c_str(), time_limit.count() / 1000.f, fj_cpu.iterations); break; } - if (fj_cpu.iterations >= fj_cpu.settings.iteration_limit) { - CUOPT_LOG_TRACE("%sIteration limit of %d reached, breaking loop at iteration %d", - fj_cpu.log_prefix.c_str(), - fj_cpu.settings.iteration_limit, - fj_cpu.iterations); - break; - } // periodically recompute the LHS and violation scores // to correct any accumulated numerical errors @@ -1414,24 +966,15 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l fj_move_t move = fj_move_t{-1, 0}; fj_staged_score_t score = fj_staged_score_t::invalid(); - bool is_lift = false; - bool is_mtm_viol = false; - bool is_mtm_sat = false; - // Perform lift moves - if (fj_cpu.violated_constraints.empty()) { - thrust::tie(move, score) = find_lift_move(fj_cpu); - if (score > fj_staged_score_t::zero()) is_lift = true; - } + if (fj_cpu.violated_constraints.empty()) { thrust::tie(move, score) = find_lift_move(fj_cpu); } // Regular MTM if (!(score > fj_staged_score_t::zero())) { thrust::tie(move, score) = find_mtm_move_viol(fj_cpu, fj_cpu.mtm_viol_samples); - if (score > fj_staged_score_t::zero()) is_mtm_viol = true; } // try with MTM in satisfied constraints if (fj_cpu.feasible_found && !(score > fj_staged_score_t::zero())) { thrust::tie(move, score) = find_mtm_move_sat(fj_cpu, fj_cpu.mtm_sat_samples); - if (score > fj_staged_score_t::zero()) is_mtm_sat = true; } // if we're in the feasible region but haven't found improvements in the last n iterations, // perturb @@ -1444,17 +987,13 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l if (score > fj_staged_score_t::zero() && !should_perturb) { apply_move(fj_cpu, move.var_idx, move.value, false); - // Track move types - if (is_lift) fj_cpu.n_lift_moves_window++; - if (is_mtm_viol) fj_cpu.n_mtm_viol_moves_window++; - if (is_mtm_sat) fj_cpu.n_mtm_sat_moves_window++; } else { // Local Min update_weights(fj_cpu); if (should_perturb) { perturb(fj_cpu); - for (size_t i = 0; i < fj_cpu.cached_mtm_moves.size(); i++) - fj_cpu.cached_mtm_moves[i].first = 0; + for (auto& cached_move : fj_cpu.cached_mtm_moves) + cached_move.first = 0; } thrust::tie(move, score) = find_mtm_move_viol(fj_cpu, 1, true); // pick a single random violated constraint @@ -1462,28 +1001,20 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l f_t delta = move.var_idx >= 0 ? move.value : 0; apply_move(fj_cpu, var_idx, delta, true); ++local_mins; - ++fj_cpu.n_local_minima_window; } // number of violated constraints is usually small (<100). recomputing from all LHSs is cheap // and more numerically precise than just adding to the accumulator in apply_move fj_cpu.total_violations = 0; - // MEMORY OPS: Loop over violated constraints (typically <100 iterations per main iteration) for (auto cstr_idx : fj_cpu.violated_constraints) { - // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration fj_cpu.total_violations += fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); - // Total per iteration: 1 read } if (fj_cpu.iterations % fj_cpu.log_interval == 0) { CUOPT_LOG_TRACE( - "%sCPUFJ iteration: %d/%d, local mins: %d, best_objective: %g, viol: %zu, obj weight %g, " - "maxw " - "%g", + "%sCPUFJ iteration: %d, local mins: %d, best_objective: %g, viol: %zu, obj weight %g, maxw " + "%g\n", fj_cpu.log_prefix.c_str(), fj_cpu.iterations, - fj_cpu.settings.iteration_limit != std::numeric_limits::max() - ? fj_cpu.settings.iteration_limit - : -1, local_mins, fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective), fj_cpu.violated_constraints.size(), @@ -1503,55 +1034,20 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l print_timing_stats(fj_cpu); } #endif - - // Collect and print PAPI metrics and regression features every 1000 iterations - if (fj_cpu.iterations % 1000 == 0 && fj_cpu.iterations > 0) { - auto now = std::chrono::high_resolution_clock::now(); - double time_window_ms = std::chrono::duration_cast>( - now - fj_cpu.last_feature_log_time) - .count() * - 1000.0; - double total_time_ms = - std::chrono::duration_cast>(now - loop_start).count() * - 1000.0; - - // Collect memory statistics - auto [loads, stores] = fj_cpu.memory_manifold.collect(); - - // Log all features including memory statistics - // log_regression_features(fj_cpu, time_window_ms, total_time_ms, loads, stores); - - fj_cpu.last_feature_log_time = now; - - std::map features_map; - features_map["n_vars"] = (float)fj_cpu.h_reverse_offsets.size() - 1; - features_map["n_cstrs"] = (float)fj_cpu.h_offsets.size() - 1; - features_map["total_nnz"] = (float)fj_cpu.h_reverse_offsets.back(); - features_map["mem_total_mb"] = (float)(loads + stores) / 1e6; - float time_prediction = std::max( - (f_t)0.0, - (f_t)ceil(context.work_unit_predictors.cpufj_predictor.predict_scalar(features_map))); - // CUOPT_LOG_DEBUG("FJ determ: Estimated time for 1000 iters: %f, actual time: %f, error %f", - // time_prediction, - // time_window_ms, - // time_prediction - time_window_ms); - } - cuopt_func_call(sanity_checks(fj_cpu)); fj_cpu.iterations++; - fj_cpu.iterations_since_best++; } auto loop_end = std::chrono::high_resolution_clock::now(); double total_time = std::chrono::duration_cast>(loop_end - loop_start).count(); double avg_time_per_iter = total_time / fj_cpu.iterations; - CUOPT_LOG_TRACE("%sCPUFJ Average time per iteration: %.8fms", + CUOPT_LOG_TRACE("%sCPUFJ Average time per iteration: %.8fms\n", fj_cpu.log_prefix.c_str(), avg_time_per_iter * 1000.0); #if CPUFJ_TIMING_TRACE // Print final timing statistics - CUOPT_LOG_TRACE("=== Final Timing Statistics ==="); + CUOPT_LOG_TRACE("\n=== Final Timing Statistics ===\n"); print_timing_stats(fj_cpu); #endif diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cuh b/cpp/src/mip/feasibility_jump/fj_cpu.cuh index 4a15bb720..4b9cfc0cc 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cuh +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cuh @@ -18,7 +18,6 @@ #include #include -#include namespace cuopt::linear_programming::detail { @@ -26,41 +25,7 @@ namespace cuopt::linear_programming::detail { // Maintaining a single source of truth for all members would be nice template struct fj_cpu_climber_t { - fj_cpu_climber_t(std::atomic& preemption_flag) : preemption_flag(preemption_flag) - { -#define ADD_INSTRUMENTED(var) \ - std::make_pair(#var, std::ref(static_cast(var))) - - // Initialize memory manifold with all ins_vector members - memory_manifold = instrumentation_manifold_t{ADD_INSTRUMENTED(h_reverse_coefficients), - ADD_INSTRUMENTED(h_reverse_constraints), - ADD_INSTRUMENTED(h_reverse_offsets), - ADD_INSTRUMENTED(h_coefficients), - ADD_INSTRUMENTED(h_offsets), - ADD_INSTRUMENTED(h_variables), - ADD_INSTRUMENTED(h_obj_coeffs), - ADD_INSTRUMENTED(h_var_bounds), - ADD_INSTRUMENTED(h_cstr_lb), - ADD_INSTRUMENTED(h_cstr_ub), - ADD_INSTRUMENTED(h_var_types), - ADD_INSTRUMENTED(h_is_binary_variable), - ADD_INSTRUMENTED(h_objective_vars), - ADD_INSTRUMENTED(h_binary_indices), - ADD_INSTRUMENTED(h_tabu_nodec_until), - ADD_INSTRUMENTED(h_tabu_noinc_until), - ADD_INSTRUMENTED(h_tabu_lastdec), - ADD_INSTRUMENTED(h_tabu_lastinc), - ADD_INSTRUMENTED(h_lhs), - ADD_INSTRUMENTED(h_lhs_sumcomp), - ADD_INSTRUMENTED(h_cstr_left_weights), - ADD_INSTRUMENTED(h_cstr_right_weights), - ADD_INSTRUMENTED(h_assignment), - ADD_INSTRUMENTED(h_best_assignment), - ADD_INSTRUMENTED(cached_cstr_bounds), - ADD_INSTRUMENTED(iter_mtm_vars)}; - -#undef ADD_INSTRUMENTED - } + fj_cpu_climber_t(std::atomic& preemption_flag) : preemption_flag(preemption_flag) {} fj_cpu_climber_t(const fj_cpu_climber_t& other) = delete; fj_cpu_climber_t& operator=(const fj_cpu_climber_t& other) = delete; @@ -71,33 +36,33 @@ struct fj_cpu_climber_t { fj_settings_t settings; typename fj_t::climber_data_t::view_t view; // Host copies of device data as struct members - ins_vector h_reverse_coefficients; - ins_vector h_reverse_constraints; - ins_vector h_reverse_offsets; - ins_vector h_coefficients; - ins_vector h_offsets; - ins_vector h_variables; - ins_vector h_obj_coeffs; - ins_vector::type> h_var_bounds; - ins_vector h_cstr_lb; - ins_vector h_cstr_ub; - ins_vector h_var_types; - ins_vector h_is_binary_variable; - ins_vector h_objective_vars; - ins_vector h_binary_indices; - - ins_vector h_tabu_nodec_until; - ins_vector h_tabu_noinc_until; - ins_vector h_tabu_lastdec; - ins_vector h_tabu_lastinc; - - ins_vector h_lhs; - ins_vector h_lhs_sumcomp; - ins_vector h_cstr_left_weights; - ins_vector h_cstr_right_weights; + std::vector h_reverse_coefficients; + std::vector h_reverse_constraints; + std::vector h_reverse_offsets; + std::vector h_coefficients; + std::vector h_offsets; + std::vector h_variables; + std::vector h_obj_coeffs; + std::vector::type> h_var_bounds; + std::vector h_cstr_lb; + std::vector h_cstr_ub; + std::vector h_var_types; + std::vector h_is_binary_variable; + std::vector h_objective_vars; + std::vector h_binary_indices; + + std::vector h_tabu_nodec_until; + std::vector h_tabu_noinc_until; + std::vector h_tabu_lastdec; + std::vector h_tabu_lastinc; + + std::vector h_lhs; + std::vector h_lhs_sumcomp; + std::vector h_cstr_left_weights; + std::vector h_cstr_right_weights; f_t max_weight; - ins_vector h_assignment; - ins_vector h_best_assignment; + std::vector h_assignment; + std::vector h_best_assignment; f_t h_objective_weight; f_t h_incumbent_objective; f_t h_best_objective; @@ -131,10 +96,10 @@ struct fj_cpu_climber_t { // CSC (transposed!) nnz-offset-indexed constraint bounds (lb, ub) // std::pair better compile down to 16 bytes!! GCC do your job! - ins_vector> cached_cstr_bounds; + std::vector> cached_cstr_bounds; std::vector var_bitmap; - ins_vector iter_mtm_vars; + std::vector iter_mtm_vars; i_t mtm_viol_samples{25}; i_t mtm_sat_samples{15}; @@ -150,37 +115,6 @@ struct fj_cpu_climber_t { std::string log_prefix{""}; std::atomic halted{false}; - - // Feature tracking for regression model (last 1000 iterations) - i_t nnz_processed_window{0}; - i_t n_lift_moves_window{0}; - i_t n_mtm_viol_moves_window{0}; - i_t n_mtm_sat_moves_window{0}; - i_t n_variable_updates_window{0}; - i_t n_local_minima_window{0}; - std::chrono::high_resolution_clock::time_point last_feature_log_time; - f_t prev_best_objective{std::numeric_limits::infinity()}; - i_t iterations_since_best{0}; - - // Cache and locality tracking - i_t hit_count_window_start{0}; - i_t miss_count_window_start{0}; - std::unordered_set unique_cstrs_accessed_window; - std::unordered_set unique_vars_accessed_window; - - // Precomputed static problem features - i_t n_binary_vars{0}; - i_t n_integer_vars{0}; - i_t max_var_degree{0}; - i_t max_cstr_degree{0}; - double avg_var_degree{0.0}; - double avg_cstr_degree{0.0}; - double var_degree_cv{0.0}; - double cstr_degree_cv{0.0}; - double problem_density{0.0}; - - // Memory instrumentation manifold - instrumentation_manifold_t memory_manifold; // TODO atomic ref? c++20 std::atomic& preemption_flag; }; diff --git a/cpp/src/mip/feasibility_jump/load_balancing.cuh b/cpp/src/mip/feasibility_jump/load_balancing.cuh index 379fdbde6..dfc9b3c88 100644 --- a/cpp/src/mip/feasibility_jump/load_balancing.cuh +++ b/cpp/src/mip/feasibility_jump/load_balancing.cuh @@ -124,14 +124,12 @@ __global__ void load_balancing_prepare_iteration(const __grid_constant__ // alternate codepath in the case of a small related_var/total_var ratio if (!full_refresh && fj.pb.related_variables.size() > 0 && fj.pb.n_variables / fj.work_ids_for_related_vars[*fj.selected_var] >= - fj.settings->parameters.old_codepath_total_var_to_relvar_ratio_threshold && - fj.settings->load_balancing_mode != fj_load_balancing_mode_t::ALWAYS_ON) { + fj.settings->parameters.old_codepath_total_var_to_relvar_ratio_threshold) { auto range = fj.pb.range_for_related_vars(*fj.selected_var); for (i_t i = blockIdx.x + range.first; i < range.second; i += gridDim.x) { i_t var_idx = fj.pb.related_variables[i]; - update_jump_value(fj, - var_idx); + update_jump_value(fj, var_idx); } if (FIRST_THREAD) *fj.load_balancing_skip = true; @@ -336,17 +334,8 @@ __global__ void load_balancing_compute_scores_binary( auto c_lb = fj.constraint_lower_bounds_csr[csr_offset]; auto c_ub = fj.constraint_upper_bounds_csr[csr_offset]; - auto [cstr_base_feas, cstr_bonus_robust] = - feas_score_constraint(fj, - var_idx, - delta, - cstr_idx, - cstr_coeff, - c_lb, - c_ub, - fj.incumbent_lhs[cstr_idx], - fj.cstr_left_weights[cstr_idx], - fj.cstr_right_weights[cstr_idx]); + auto [cstr_base_feas, cstr_bonus_robust] = feas_score_constraint( + fj, var_idx, delta, cstr_idx, cstr_coeff, c_lb, c_ub, fj.incumbent_lhs[cstr_idx]); base_feas += cstr_base_feas; bonus_robust += cstr_bonus_robust; @@ -537,8 +526,8 @@ __launch_bounds__(TPB_loadbalance, 16) __global__ auto& score_info = candidate.score; - int32_t base_feas = 0; - int32_t bonus_robust = 0; + f_t base_feas = 0; + f_t bonus_robust = 0; // same as for the binary var kernel, compute each score compoenent per thread // and merge then via a wapr reduce @@ -546,17 +535,8 @@ __launch_bounds__(TPB_loadbalance, 16) __global__ cuopt_assert(c_lb == fj.pb.constraint_lower_bounds[cstr_idx], "bound sanity check failed"); cuopt_assert(c_ub == fj.pb.constraint_upper_bounds[cstr_idx], "bound sanity check failed"); - auto [cstr_base_feas, cstr_bonus_robust] = - feas_score_constraint(fj, - var_idx, - delta, - cstr_idx, - cstr_coeff, - c_lb, - c_ub, - fj.incumbent_lhs[cstr_idx], - fj.cstr_left_weights[cstr_idx], - fj.cstr_right_weights[cstr_idx]); + auto [cstr_base_feas, cstr_bonus_robust] = feas_score_constraint( + fj, var_idx, delta, cstr_idx, cstr_coeff, c_lb, c_ub, fj.incumbent_lhs[cstr_idx]); base_feas += cstr_base_feas; bonus_robust += cstr_bonus_robust; @@ -585,29 +565,24 @@ __launch_bounds__(TPB_loadbalance, 16) __global__ best_score_ref{fj.jump_move_scores[var_idx]}; auto best_score = best_score_ref.load(cuda::memory_order_relaxed); - cuda::atomic_ref best_delta_ref{ - fj.jump_move_delta[var_idx]}; - auto best_delta = best_delta_ref.load(cuda::memory_order_relaxed); - if (best_score < candidate.score || - (best_score == candidate.score && candidate.delta < best_delta)) { + (best_score == candidate.score && candidate.delta < fj.jump_move_delta[var_idx])) { // update the best move delta acquire_lock(&fj.jump_locks[var_idx]); // reject this move if it would increase the target variable to a numerically unstable // value - // only skip updating, don't invalidate existing valid moves - if (fj.move_numerically_stable(fj.incumbent_assignment[var_idx], - fj.incumbent_assignment[var_idx] + delta, - base_feas, - *fj.violation_score)) { - if (fj.jump_move_scores[var_idx] < candidate.score - // determinism for ease of debugging - || (fj.jump_move_scores[var_idx] == candidate.score && - candidate.delta < fj.jump_move_delta[var_idx])) { - fj.jump_move_delta[var_idx] = candidate.delta; - fj.jump_move_scores[var_idx] = candidate.score; - } + if (!fj.move_numerically_stable(fj.incumbent_assignment[var_idx], + fj.incumbent_assignment[var_idx] + delta, + base_feas, + *fj.violation_score)) { + fj.jump_move_scores[var_idx] = fj_t::move_score_t::invalid(); + } else if (fj.jump_move_scores[var_idx] < candidate.score + // determinism for ease of debugging + || (fj.jump_move_scores[var_idx] == candidate.score && + candidate.delta < fj.jump_move_delta[var_idx])) { + fj.jump_move_delta[var_idx] = candidate.delta; + fj.jump_move_scores[var_idx] = candidate.score; } release_lock(&fj.jump_locks[var_idx]); } @@ -669,7 +644,7 @@ __global__ void load_balancing_sanity_checks(const __grid_constant__ if (!(score_1 == score_1.invalid() && score_2 == score_2.invalid()) && !(v.pb.integer_equal(score_1.base, score_2.base) && v.pb.integer_equal(score_1.bonus, score_2.bonus))) { - printf("(iter %d) [%d, int:%d]: delta %g/%g was %d/%d, is %d/%d\n", + printf("(iter %d) [%d, int:%d]: delta %g/%g was %f/%f, is %f/%f\n", *v.iterations, var_idx, v.pb.is_integer_var(var_idx), diff --git a/cpp/src/mip/feasibility_jump/utils.cuh b/cpp/src/mip/feasibility_jump/utils.cuh index b779e56a2..d98686bcc 100644 --- a/cpp/src/mip/feasibility_jump/utils.cuh +++ b/cpp/src/mip/feasibility_jump/utils.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -10,7 +10,6 @@ #include "feasibility_jump.cuh" #include -#include #include #include #include @@ -134,21 +133,6 @@ struct contiguous_set_t { validity_bitmap.resize(size, stream); } - void sort(const rmm::cuda_stream_view& stream) - { - thrust::sort( - rmm::exec_policy(stream), contents.begin(), contents.begin() + set_size.value(stream)); - thrust::fill(rmm::exec_policy(stream), index_map.begin(), index_map.end(), -1); - thrust::for_each(rmm::exec_policy(stream), - thrust::make_counting_iterator(0), - thrust::make_counting_iterator(set_size.value(stream)), - [v = view()] __device__(i_t idx) { v.index_map[v.contents[idx]] = idx; }); - - // TODO: remove, only useful for debugging and ensuring the same hashes - thrust::fill( - rmm::exec_policy(stream), contents.begin() + set_size.value(stream), contents.end(), 0); - } - struct view_t { i_t* set_size; i_t* lock; diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu index f3cd07c62..76d930b45 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -51,12 +51,8 @@ feasibility_pump_t::feasibility_pump_t( context.problem_ptr->handle_ptr->get_stream()), lp_optimal_solution(lp_optimal_solution_), rng(cuopt::seed_generator::get_seed()), - timer(context.gpu_heur_loop, 20.) + timer(20.) { - thrust::fill(context.problem_ptr->handle_ptr->get_thrust_policy(), - last_projection.begin(), - last_projection.end(), - (f_t)0); } template @@ -221,18 +217,12 @@ bool feasibility_pump_t::linear_project_onto_polytope(solution_t::infinity(); - lp_settings.work_limit = time_limit; - } - auto solver_response = get_relaxed_lp_solution(temp_p, solution, lp_settings); + auto solver_response = get_relaxed_lp_solution(temp_p, solution, lp_settings); cuopt_func_call(solution.test_variable_bounds(false)); last_lp_time = old_remaining - timer.remaining_time(); lp_time += last_lp_time; n_calls++; - CUOPT_LOG_TRACE("lp_time %f average lp_time %f", last_lp_time, lp_time / n_calls); + CUOPT_LOG_DEBUG("lp_time %f average lp_time %f", last_lp_time, lp_time / n_calls); solution.assignment.resize(solution.problem_ptr->n_variables, solution.handle_ptr->get_stream()); raft::copy(last_projection.data(), solution.assignment.data(), @@ -255,9 +245,8 @@ template bool feasibility_pump_t::round(solution_t& solution) { bool result; - CUOPT_LOG_TRACE("Rounding the point"); - work_limit_timer_t bounds_prop_timer(context.gpu_heur_loop, - std::max(0.05, std::min(0.5, timer.remaining_time() / 10.))); + CUOPT_LOG_DEBUG("Rounding the point"); + timer_t bounds_prop_timer(std::max(0.05, std::min(0.5, timer.remaining_time() / 10.))); const f_t lp_run_time_after_feasible = 0.; bool old_var = constraint_prop.round_all_vars; f_t old_time = constraint_prop.max_time_for_bounds_prop; @@ -289,7 +278,6 @@ void feasibility_pump_t::perturbate(solution_t& solution) template bool feasibility_pump_t::run_fj_cycle_escape(solution_t& solution) { - CUOPT_LOG_TRACE("Running FJ cycle escape"); bool is_feasible; fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; fj.settings.update_weights = true; @@ -311,17 +299,13 @@ bool feasibility_pump_t::run_fj_cycle_escape(solution_t& sol template bool feasibility_pump_t::test_fj_feasible(solution_t& solution, f_t time_limit) { - CUOPT_LOG_DEBUG("Running 20%% FJ, remaining %fwu", timer.remaining_time()); + CUOPT_LOG_DEBUG("Running 20%% with %f time limit", time_limit); bool is_feasible; - fj.settings = fj_settings_t{}; fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; fj.settings.update_weights = true; fj.settings.feasibility_run = true; fj.settings.n_of_minimums_for_exit = 5000; fj.settings.time_limit = std::min(time_limit, timer.remaining_time()); - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - fj.settings.work_limit = fj.settings.time_limit; - } cuopt_func_call(solution.test_variable_bounds(true)); is_feasible = fj.solve(solution); cuopt_func_call(solution.test_variable_bounds(true)); @@ -335,9 +319,6 @@ bool feasibility_pump_t::test_fj_feasible(solution_t& soluti } else { CUOPT_LOG_DEBUG("20%% FJ run found feasible!"); } - CUOPT_LOG_DEBUG("20%% FJ run finished, elapsed %fs remaining %fwu", - fj.settings.time_limit, - timer.remaining_time()); return is_feasible; } @@ -345,7 +326,7 @@ template bool feasibility_pump_t::handle_cycle(solution_t& solution) { raft::common::nvtx::range fun_scope("handle_cycle"); - CUOPT_LOG_TRACE("running handle cycle"); + CUOPT_LOG_DEBUG("running handle cycle"); bool is_feasible = false; fp_fj_cycle_time_begin = timer.remaining_time(); CUOPT_LOG_DEBUG("Running longer FJ on last rounding"); @@ -422,7 +403,7 @@ bool feasibility_pump_t::check_distance_cycle(solution_t& so std::accumulate(last_distances.begin(), last_distances.end(), 0.0) / last_distances.size(); if (avg_distance - distance_to_last_rounding < config.cycle_distance_reduction_ration * avg_distance) { - CUOPT_LOG_TRACE("Distance cycle detected curr %f avg %f for last %d iter", + CUOPT_LOG_DEBUG("Distance cycle detected curr %f avg %f for last %d iter", distance_to_last_rounding, avg_distance, last_distances.size()); @@ -430,7 +411,7 @@ bool feasibility_pump_t::check_distance_cycle(solution_t& so } last_distances.pop_back(); } else { - CUOPT_LOG_TRACE("Distance of projection: %f", distance_to_last_rounding); + CUOPT_LOG_DEBUG("Distance of projection: %f", distance_to_last_rounding); } last_distances.push_front(distance_to_last_rounding); return is_cycle; @@ -489,69 +470,15 @@ template bool feasibility_pump_t::run_single_fp_descent(solution_t& solution) { raft::common::nvtx::range fun_scope("run_single_fp_descent"); - - // === FP PREDICTOR FEATURES - START === - f_t start_time = timer.remaining_time(); - i_t fp_iterations = 0; - - // // Problem structure features - // CUOPT_LOG_INFO("FP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d - // n_binary_vars=%d", - // solution.problem_ptr->n_variables, - // solution.problem_ptr->n_constraints, - // solution.problem_ptr->n_integer_vars, - // solution.problem_ptr->n_binary_vars); - - // CUOPT_LOG_INFO("FP_FEATURES: nnz=%lu sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f", - // solution.problem_ptr->coefficients.size(), - // solution.problem_ptr->sparsity, - // solution.problem_ptr->nnz_stddev, - // solution.problem_ptr->unbalancedness); - - // Initial solution features - solution.compute_feasibility(); - i_t initial_n_integers = solution.compute_number_of_integers(); - f_t initial_ratio_of_integers = solution.problem_ptr->n_integer_vars > 0 - ? (f_t)initial_n_integers / solution.problem_ptr->n_integer_vars - : 0.0; - - // CUOPT_LOG_INFO("FP_FEATURES: initial_feasibility=%d initial_excess=%.6f - // initial_objective=%.6f", - // solution.get_feasible(), - // solution.get_total_excess(), - // solution.get_objective()); - - // CUOPT_LOG_INFO("FP_FEATURES: initial_ratio_of_integers=%.6f initial_n_integers=%d", - // initial_ratio_of_integers, - // initial_n_integers); - - // // Algorithm configuration features - // CUOPT_LOG_INFO("FP_FEATURES: alpha=%.6f check_distance_cycle=%d cycle_detection_length=%d", - // config.alpha, - // config.check_distance_cycle, - // cycle_queue.cycle_detection_length); - - // CUOPT_LOG_INFO("FP_FEATURES: has_cutting_plane=%d time_budget=%.6f", - // solution.problem_ptr->cutting_plane_added, - // timer.remaining_time()); - // === FP PREDICTOR FEATURES - END === - // start by doing nearest rounding solution.round_nearest(); raft::copy(last_rounding.data(), solution.assignment.data(), solution.assignment.size(), solution.handle_ptr->get_stream()); - - CUOPT_LOG_TRACE("FP: starting FP descent, sol hash 0x%x", solution.get_hash()); while (true) { - fp_iterations++; if (timer.check_time_limit()) { CUOPT_LOG_DEBUG("FP time limit reached!"); - f_t time_taken = start_time - timer.remaining_time(); - // CUOPT_LOG_INFO("FP_RESULT: iterations=%d time_taken=%.6f termination=TIME_LIMIT", - // fp_iterations, - // time_taken); round(solution); return false; } @@ -560,10 +487,11 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s f_t ratio_of_assigned_integers = f_t(solution.n_assigned_integers) / solution.problem_ptr->n_integer_vars; bool is_feasible = linear_project_onto_polytope(solution, ratio_of_assigned_integers); - CUOPT_LOG_DEBUG( - "FP: after fp projection, iter %d sol hash 0x%x", fp_iterations, solution.get_hash()); - i_t n_integers = solution.compute_number_of_integers(); - bool is_cycle = true; + i_t n_integers = solution.compute_number_of_integers(); + CUOPT_LOG_DEBUG("after fp projection n_integers %d total n_integes %d", + n_integers, + solution.problem_ptr->n_integer_vars); + bool is_cycle = true; // temp comment for presolve run if (config.check_distance_cycle) { // use distance cycle if we are running ii or objective FP @@ -574,18 +502,12 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s if (is_feasible) { bool res = solution.compute_feasibility(); cuopt_assert(res, "Feasibility issue"); - f_t time_taken = start_time - timer.remaining_time(); - CUOPT_LOG_TRACE( - "FP_RESULT: iterations=%d time_taken=%.6f termination=FEASIBLE_DISTANCE_CYCLE", - fp_iterations, - time_taken); return true; } cuopt::default_logger().flush(); f_t remaining_time_end_fp = timer.remaining_time(); total_fp_time_until_cycle = fp_fj_cycle_time_begin - remaining_time_end_fp; - CUOPT_LOG_TRACE("total_fp_time_until_cycle: %f", total_fp_time_until_cycle); - f_t time_taken = start_time - timer.remaining_time(); + CUOPT_LOG_DEBUG("total_fp_time_until_cycle: %f", total_fp_time_until_cycle); return false; } } @@ -600,12 +522,10 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s // run the LP with full precision to check if it actually is feasible const f_t lp_verify_time_limit = 5.; relaxed_lp_settings_t lp_settings; - lp_settings.time_limit = lp_verify_time_limit; - if (timer.deterministic) { lp_settings.work_limit = lp_settings.time_limit; } + lp_settings.time_limit = lp_verify_time_limit; lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; lp_settings.return_first_feasible = true; lp_settings.save_state = true; - CUOPT_LOG_DEBUG("FP LP verify, sol hash 0x%x", solution.get_hash()); run_lp_with_vars_fixed(*solution.problem_ptr, solution, solution.problem_ptr->integer_indices, @@ -622,8 +542,8 @@ bool feasibility_pump_t::run_single_fp_descent(solution_t& s cuopt_func_call(solution.test_variable_bounds(false)); is_feasible = round(solution); cuopt_func_call(solution.test_variable_bounds(true)); - proj_and_round_time = timer.remaining_time(); - if (!is_feasible && proj_and_round_time > 0) { + proj_and_round_time = proj_begin - timer.remaining_time(); + if (!is_feasible) { const f_t time_ratio = 0.2; is_feasible = test_fj_feasible(solution, time_ratio * proj_and_round_time); } diff --git a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh index 9f7599120..1a771da3f 100644 --- a/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh +++ b/cpp/src/mip/local_search/feasibility_pump/feasibility_pump.cuh @@ -105,6 +105,7 @@ class feasibility_pump_t { feasibility_pump_t() = delete; feasibility_pump_t(mip_solver_context_t& context, fj_t& fj, + // fj_tree_t& fj_tree_, constraint_prop_t& constraint_prop_, line_segment_search_t& line_segment_search_, rmm::device_uvector& lp_optimal_solution_); @@ -126,7 +127,7 @@ class feasibility_pump_t { bool check_distance_cycle(solution_t& solution); void reset(); void resize_vectors(problem_t& problem, const raft::handle_t* handle_ptr); - bool random_round_with_fj(solution_t& solution, work_limit_timer_t& round_timer); + bool random_round_with_fj(solution_t& solution, timer_t& round_timer); bool round_multiple_points(solution_t& solution); void relax_general_integers(solution_t& solution); void revert_relaxation(solution_t& solution); @@ -135,6 +136,7 @@ class feasibility_pump_t { mip_solver_context_t& context; // keep a reference from upstream local search fj_t& fj; + // fj_tree_t& fj_tree; line_segment_search_t& line_segment_search; cycle_queue_t cycle_queue; constraint_prop_t& constraint_prop; @@ -153,7 +155,7 @@ class feasibility_pump_t { f_t proj_begin; i_t n_fj_single_descents; i_t max_n_of_integers = 0; - cuopt::work_limit_timer_t timer; + cuopt::timer_t timer; }; } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu index c700422d7..a1477276a 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -17,10 +17,8 @@ namespace cuopt::linear_programming::detail { template line_segment_search_t::line_segment_search_t( - mip_solver_context_t& context_, - fj_t& fj_, - constraint_prop_t& constraint_prop_) - : context(context_), fj(fj_), constraint_prop(constraint_prop_) + fj_t& fj_, constraint_prop_t& constraint_prop_) + : fj(fj_), constraint_prop(constraint_prop_) { } @@ -130,7 +128,7 @@ bool line_segment_search_t::search_line_segment( const rmm::device_uvector& point_2, const rmm::device_uvector& delta_vector, bool is_feasibility_run, - cuopt::work_limit_timer_t& timer) + cuopt::timer_t& timer) { CUOPT_LOG_DEBUG("Running line segment search with a given delta vector"); cuopt_assert(point_1.size() == point_2.size(), "size mismatch"); @@ -265,7 +263,7 @@ bool line_segment_search_t::search_line_segment(solution_t& const rmm::device_uvector& point_1, const rmm::device_uvector& point_2, bool is_feasibility_run, - cuopt::work_limit_timer_t& timer) + cuopt::timer_t& timer) { CUOPT_LOG_DEBUG("Running line segment search"); cuopt_assert(point_1.size() == point_2.size(), "size mismatch"); diff --git a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh index e260c9a00..6e81ef938 100644 --- a/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh +++ b/cpp/src/mip/local_search/line_segment_search/line_segment_search.cuh @@ -26,21 +26,19 @@ template class line_segment_search_t { public: line_segment_search_t() = delete; - line_segment_search_t(mip_solver_context_t& context, - fj_t& fj, - constraint_prop_t& constraint_prop); + line_segment_search_t(fj_t& fj, constraint_prop_t& constraint_prop); bool search_line_segment(solution_t& solution, const rmm::device_uvector& point_1, const rmm::device_uvector& point_2, bool is_feasibility_run, - cuopt::work_limit_timer_t& timer); + cuopt::timer_t& timer); bool search_line_segment(solution_t& solution, const rmm::device_uvector& point_1, const rmm::device_uvector& point_2, const rmm::device_uvector& delta_vector, bool is_feasibility_run, - cuopt::work_limit_timer_t& timer); + cuopt::timer_t& timer); void save_solution_if_better(solution_t& solution, const rmm::device_uvector& point_1, @@ -51,7 +49,6 @@ class line_segment_search_t { f_t& best_feasible_cost, f_t curr_cost); - mip_solver_context_t& context; fj_t& fj; constraint_prop_t& constraint_prop; line_segment_settings_t settings; diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 5126fb1a9..ecd277065 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -14,7 +14,7 @@ #include #include #include -#include +#include #include @@ -34,7 +34,7 @@ local_search_t::local_search_t(mip_solver_context_t& context fj(context), // fj_tree(fj), constraint_prop(context), - line_segment_search(context, fj, constraint_prop), + line_segment_search(fj, constraint_prop), fp(context, fj, // fj_tree, @@ -150,7 +150,7 @@ bool local_search_t::do_fj_solve(solution_t& solution, { if (time_limit == 0.) return solution.get_feasible(); - work_limit_timer_t timer(context.gpu_heur_loop, time_limit); + timer_t timer(time_limit); auto h_weights = cuopt::host_copy(in_fj.cstr_weights, solution.handle_ptr->get_stream()); auto h_objective_weight = in_fj.objective_weight.value(solution.handle_ptr->get_stream()); @@ -167,10 +167,8 @@ bool local_search_t::do_fj_solve(solution_t& solution, auto solution_copy = solution; // Start CPU solver in background thread - if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { - for (auto& cpu_fj : ls_cpu_fj) { - cpu_fj.start_cpu_solver(); - } + for (auto& cpu_fj : ls_cpu_fj) { + cpu_fj.start_cpu_solver(); } // Run GPU solver and measure execution time @@ -179,10 +177,8 @@ bool local_search_t::do_fj_solve(solution_t& solution, in_fj.solve(solution); // Stop CPU solver - if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { - for (auto& cpu_fj : ls_cpu_fj) { - cpu_fj.stop_cpu_solver(); - } + for (auto& cpu_fj : ls_cpu_fj) { + cpu_fj.stop_cpu_solver(); } auto gpu_fj_end = std::chrono::high_resolution_clock::now(); @@ -192,16 +188,14 @@ bool local_search_t::do_fj_solve(solution_t& solution, f_t best_cpu_obj = std::numeric_limits::max(); // // Wait for CPU solver to finish - if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { - for (auto& cpu_fj : ls_cpu_fj) { - bool cpu_sol_found = cpu_fj.wait_for_cpu_solver(); - if (cpu_sol_found) { - f_t cpu_obj = cpu_fj.fj_cpu->h_best_objective; - if (cpu_obj < best_cpu_obj) { - best_cpu_obj = cpu_obj; - solution_cpu.copy_new_assignment(cpu_fj.fj_cpu->h_best_assignment); - solution_cpu.compute_feasibility(); - } + for (auto& cpu_fj : ls_cpu_fj) { + bool cpu_sol_found = cpu_fj.wait_for_cpu_solver(); + if (cpu_sol_found) { + f_t cpu_obj = cpu_fj.fj_cpu->h_best_objective; + if (cpu_obj < best_cpu_obj) { + best_cpu_obj = cpu_obj; + solution_cpu.copy_new_assignment(cpu_fj.fj_cpu->h_best_assignment); + solution_cpu.compute_feasibility(); } } } @@ -239,10 +233,8 @@ bool local_search_t::do_fj_solve(solution_t& solution, } template -void local_search_t::generate_fast_solution(solution_t& solution, - work_limit_timer_t timer) +void local_search_t::generate_fast_solution(solution_t& solution, timer_t timer) { - CUOPT_LOG_DEBUG("Running FJ fast sol"); thrust::fill(solution.handle_ptr->get_thrust_policy(), solution.assignment.begin(), solution.assignment.end(), @@ -254,8 +246,7 @@ void local_search_t::generate_fast_solution(solution_t& solu fj.settings.feasibility_run = true; fj.settings.time_limit = std::min(30., timer.remaining_time()); while (!timer.check_time_limit()) { - work_limit_timer_t constr_prop_timer = - work_limit_timer_t(context.gpu_heur_loop, std::min(timer.remaining_time(), 2.)); + timer_t constr_prop_timer = timer_t(std::min(timer.remaining_time(), 2.)); // do constraint prop on lp optimal solution constraint_prop.apply_round(solution, 1., constr_prop_timer); if (solution.compute_feasibility()) { return; } @@ -272,7 +263,7 @@ void local_search_t::generate_fast_solution(solution_t& solu template bool local_search_t::run_local_search(solution_t& solution, const weight_t& weights, - work_limit_timer_t timer, + timer_t timer, const ls_config_t& ls_config) { raft::common::nvtx::range fun_scope("local search"); @@ -282,10 +273,10 @@ bool local_search_t::run_local_search(solution_t& solution, if (!solution.get_feasible()) { if (ls_config.at_least_one_parent_feasible) { fj_settings.time_limit = 0.5; - timer = work_limit_timer_t(context.gpu_heur_loop, fj_settings.time_limit); + timer = timer_t(fj_settings.time_limit); } else { fj_settings.time_limit = 0.25; - timer = work_limit_timer_t(context.gpu_heur_loop, fj_settings.time_limit); + timer = timer_t(fj_settings.time_limit); } } else { fj_settings.time_limit = std::min(1., timer.remaining_time()); @@ -316,9 +307,8 @@ bool local_search_t::run_local_search(solution_t& solution, template bool local_search_t::run_fj_until_timer(solution_t& solution, const weight_t& weights, - work_limit_timer_t timer) + timer_t timer) { - CUOPT_LOG_DEBUG("Running FJ until timer"); bool is_feasible; fj.settings.n_of_minimums_for_exit = 1e6; fj.settings.mode = fj_mode_t::EXIT_NON_IMPROVING; @@ -335,7 +325,7 @@ bool local_search_t::run_fj_until_timer(solution_t& solution template bool local_search_t::run_fj_annealing(solution_t& solution, - work_limit_timer_t timer, + timer_t timer, const ls_config_t& ls_config) { raft::common::nvtx::range fun_scope("run_fj_annealing"); @@ -365,7 +355,7 @@ bool local_search_t::run_fj_annealing(solution_t& solution, template bool local_search_t::run_fj_line_segment(solution_t& solution, - work_limit_timer_t timer, + timer_t timer, const ls_config_t& ls_config) { raft::common::nvtx::range fun_scope("run_fj_line_segment"); @@ -388,7 +378,7 @@ bool local_search_t::run_fj_line_segment(solution_t& solutio template bool local_search_t::check_fj_on_lp_optimal(solution_t& solution, bool perturb, - work_limit_timer_t timer) + timer_t timer) { raft::common::nvtx::range fun_scope("check_fj_on_lp_optimal"); if (lp_optimal_exists) { @@ -405,16 +395,14 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu } cuopt_func_call(solution.test_variable_bounds(false)); f_t lp_run_time_after_feasible = std::min(1., timer.remaining_time()); - work_limit_timer_t bounds_prop_timer = - work_limit_timer_t(context.gpu_heur_loop, std::min(timer.remaining_time(), 10.)); + timer_t bounds_prop_timer = timer_t(std::min(timer.remaining_time(), 10.)); bool is_feasible = constraint_prop.apply_round(solution, lp_run_time_after_feasible, bounds_prop_timer); if (!is_feasible) { const f_t lp_run_time = 2.; relaxed_lp_settings_t lp_settings; lp_settings.time_limit = std::min(lp_run_time, timer.remaining_time()); - if (timer.deterministic) { lp_settings.work_limit = lp_settings.time_limit; } - lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; + lp_settings.tolerance = solution.problem_ptr->tolerances.absolute_tolerance; run_lp_with_vars_fixed( *solution.problem_ptr, solution, solution.problem_ptr->integer_indices, lp_settings); } else { @@ -431,8 +419,7 @@ bool local_search_t::check_fj_on_lp_optimal(solution_t& solu } template -bool local_search_t::run_fj_on_zero(solution_t& solution, - work_limit_timer_t timer) +bool local_search_t::run_fj_on_zero(solution_t& solution, timer_t timer) { raft::common::nvtx::range fun_scope("run_fj_on_zero"); thrust::fill(solution.handle_ptr->get_thrust_policy(), @@ -451,7 +438,7 @@ bool local_search_t::run_fj_on_zero(solution_t& solution, template bool local_search_t::run_staged_fp(solution_t& solution, - work_limit_timer_t timer, + timer_t timer, population_t* population_ptr) { raft::common::nvtx::range fun_scope("run_staged_fp"); @@ -479,7 +466,7 @@ bool local_search_t::run_staged_fp(solution_t& solution, } CUOPT_LOG_DEBUG("Running staged FP from beginning it %d", i); fp.relax_general_integers(solution); - work_limit_timer_t binary_timer(context.gpu_heur_loop, timer.remaining_time() / 3); + timer_t binary_timer(timer.remaining_time() / 3); i_t binary_it_counter = 0; for (; binary_it_counter < 100; ++binary_it_counter) { population_ptr->add_external_solutions_to_population(); @@ -644,19 +631,19 @@ void local_search_t::reset_alpha_and_run_recombiners( template bool local_search_t::run_fp(solution_t& solution, - work_limit_timer_t timer, - population_t* population_ptr, - i_t n_fp_iterations) + timer_t timer, + population_t* population_ptr) { raft::common::nvtx::range fun_scope("run_fp"); cuopt_assert(population_ptr != nullptr, "Population pointer must not be null"); + const i_t n_fp_iterations = 1000000; bool is_feasible = solution.compute_feasibility(); cutting_plane_added_for_active_run = is_feasible; double best_objective = is_feasible ? solution.get_objective() : std::numeric_limits::max(); rmm::device_uvector best_solution(solution.assignment, solution.handle_ptr->get_stream()); problem_t* old_problem_ptr = solution.problem_ptr; - fp.timer = work_limit_timer_t(context.gpu_heur_loop, timer.remaining_time()); + fp.timer = timer_t(timer.remaining_time()); // if it has not been initialized yet, create a new problem and move it to the cut problem if (!problem_with_objective_cut.cutting_plane_added) { problem_with_objective_cut = std::move(problem_t(*old_problem_ptr)); @@ -757,7 +744,7 @@ bool local_search_t::generate_solution(solution_t& solution, { raft::common::nvtx::range fun_scope("generate_solution"); cuopt_assert(population_ptr != nullptr, "Population pointer must not be null"); - work_limit_timer_t timer(context.gpu_heur_loop, time_limit); + timer_t timer(time_limit); auto n_vars = solution.problem_ptr->n_variables; auto n_binary_vars = solution.problem_ptr->get_n_binary_variables(); auto n_integer_vars = solution.problem_ptr->n_integer_vars; diff --git a/cpp/src/mip/local_search/local_search.cuh b/cpp/src/mip/local_search/local_search.cuh index 3316160ab..6fdf4ac72 100644 --- a/cpp/src/mip/local_search/local_search.cuh +++ b/cpp/src/mip/local_search/local_search.cuh @@ -13,7 +13,7 @@ #include #include #include -#include +#include #include #include @@ -53,35 +53,32 @@ class local_search_t { void start_cpufj_scratch_threads(population_t& population); void start_cpufj_lptopt_scratch_threads(population_t& population); void stop_cpufj_scratch_threads(); - void generate_fast_solution(solution_t& solution, work_limit_timer_t timer); + void generate_fast_solution(solution_t& solution, timer_t timer); bool generate_solution(solution_t& solution, bool perturb, population_t* population_ptr, f_t time_limit = 300.); bool run_fj_until_timer(solution_t& solution, const weight_t& weights, - work_limit_timer_t timer); + timer_t timer); bool run_local_search(solution_t& solution, const weight_t& weights, - work_limit_timer_t timer, + timer_t timer, const ls_config_t& ls_config); bool run_fj_annealing(solution_t& solution, - work_limit_timer_t timer, + timer_t timer, const ls_config_t& ls_config); bool run_fj_line_segment(solution_t& solution, - work_limit_timer_t timer, + timer_t timer, const ls_config_t& ls_config); - bool run_fj_on_zero(solution_t& solution, work_limit_timer_t timer); - bool check_fj_on_lp_optimal(solution_t& solution, - bool perturb, - work_limit_timer_t timer); + bool run_fj_on_zero(solution_t& solution, timer_t timer); + bool check_fj_on_lp_optimal(solution_t& solution, bool perturb, timer_t timer); bool run_staged_fp(solution_t& solution, - work_limit_timer_t timer, + timer_t timer, population_t* population_ptr); bool run_fp(solution_t& solution, - work_limit_timer_t timer, - population_t* population_ptr = nullptr, - i_t n_fp_iterations = 1000000); + timer_t timer, + population_t* population_ptr = nullptr); void resize_vectors(problem_t& problem, const raft::handle_t* handle_ptr); bool do_fj_solve(solution_t& solution, diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cu b/cpp/src/mip/local_search/rounding/bounds_repair.cu index 307711ac4..4679bc2a5 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -68,7 +68,8 @@ f_t bounds_repair_t::get_ii_violation(problem_t& problem) min_act = bound_presolve.upd.min_activity.data(), max_act = bound_presolve.upd.max_activity.data(), cstr_violations_up = cstr_violations_up.data(), - cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) { + cstr_violations_down = cstr_violations_down.data(), + total_vio = total_vio.data()] __device__(i_t cstr_idx) { f_t cnst_lb = pb_v.constraint_lower_bounds[cstr_idx]; f_t cnst_ub = pb_v.constraint_upper_bounds[cstr_idx]; f_t eps = get_cstr_tolerance( @@ -78,31 +79,21 @@ f_t bounds_repair_t::get_ii_violation(problem_t& problem) f_t violation = max(curr_cstr_violation_up, curr_cstr_violation_down); if (violation >= ROUNDOFF_TOLERANCE) { violated_cstr_map[cstr_idx] = 1; + atomicAdd(total_vio, violation); } else { violated_cstr_map[cstr_idx] = 0; } cstr_violations_up[cstr_idx] = curr_cstr_violation_up; cstr_violations_down[cstr_idx] = curr_cstr_violation_down; }); - auto iter = thrust::copy_if(handle_ptr->get_thrust_policy(), + auto iter = thrust::copy_if(handle_ptr->get_thrust_policy(), thrust::make_counting_iterator(0), thrust::make_counting_iterator(0) + problem.n_constraints, violated_cstr_map.data(), violated_constraints.data(), cuda::std::identity{}); - h_n_violated_cstr = iter - violated_constraints.data(); - // Use deterministic reduction instead of non-deterministic atomicAdd - f_t total_violation = thrust::transform_reduce( - handle_ptr->get_thrust_policy(), - thrust::make_counting_iterator(0), - thrust::make_counting_iterator(0) + problem.n_constraints, - [cstr_violations_up = cstr_violations_up.data(), - cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) -> f_t { - auto violation = max(cstr_violations_up[cstr_idx], cstr_violations_down[cstr_idx]); - return violation >= ROUNDOFF_TOLERANCE ? violation : 0.; - }, - (f_t)0, - thrust::plus()); + h_n_violated_cstr = iter - violated_constraints.data(); + f_t total_violation = total_vio.value(handle_ptr->get_stream()); CUOPT_LOG_TRACE( "Repair: n_violated_cstr %d total_violation %f", h_n_violated_cstr, total_violation); return total_violation; @@ -199,15 +190,7 @@ i_t bounds_repair_t::compute_best_shift(problem_t& problem, } }); handle_ptr->sync_stream(); - i_t n_candidates = candidates.n_candidates.value(handle_ptr->get_stream()); - - // Sort by variable index to ensure deterministic ordering - thrust::sort_by_key(handle_ptr->get_thrust_policy(), - candidates.variable_index.begin(), - candidates.variable_index.begin() + n_candidates, - candidates.bound_shift.begin()); - - return n_candidates; + return candidates.n_candidates.value(handle_ptr->get_stream()); } template @@ -394,7 +377,7 @@ void bounds_repair_t::apply_move(problem_t& problem, template bool bounds_repair_t::repair_problem(problem_t& problem, problem_t& original_problem, - work_limit_timer_t timer_, + timer_t timer_, const raft::handle_t* handle_ptr_) { CUOPT_LOG_DEBUG("Running bounds repair"); @@ -406,10 +389,7 @@ bool bounds_repair_t::repair_problem(problem_t& problem, curr_violation = best_violation; best_bounds.update_from(problem, handle_ptr); i_t no_candidate_in_a_row = 0; - // TODO: do this better - i_t iter_limit = std::numeric_limits::max(); - if (problem.deterministic) { iter_limit = 20; } - while (h_n_violated_cstr > 0 && iter_limit-- > 0) { + while (h_n_violated_cstr > 0) { CUOPT_LOG_TRACE("Bounds repair loop: n_violated %d best_violation %f curr_violation %f", h_n_violated_cstr, best_violation, diff --git a/cpp/src/mip/local_search/rounding/bounds_repair.cuh b/cpp/src/mip/local_search/rounding/bounds_repair.cuh index ca08c030f..b5d39ee1e 100644 --- a/cpp/src/mip/local_search/rounding/bounds_repair.cuh +++ b/cpp/src/mip/local_search/rounding/bounds_repair.cuh @@ -120,7 +120,7 @@ class bounds_repair_t { void compute_damages(problem_t& problem, i_t n_candidates); bool repair_problem(problem_t& problem, problem_t& original_problem, - work_limit_timer_t timer_, + timer_t timer_, const raft::handle_t* handle_ptr_); void apply_move(problem_t& problem, problem_t& original_problem, @@ -144,7 +144,7 @@ class bounds_repair_t { i_t h_n_violated_cstr; const raft::handle_t* handle_ptr; std::mt19937 gen; - work_limit_timer_t timer; + timer_t timer{0.}; std::vector cycle_vector; i_t cycle_write_pos = 0; }; diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cu b/cpp/src/mip/local_search/rounding/constraint_prop.cu index b463f8d41..99dc3c71d 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -755,11 +755,9 @@ void constraint_prop_t::restore_original_bounds_on_unfixed( template bool constraint_prop_t::run_repair_procedure(problem_t& problem, problem_t& original_problem, - work_limit_timer_t& timer, + timer_t& timer, const raft::handle_t* handle_ptr) { - CUOPT_LOG_DEBUG("Running repair procedure"); - // select the first probing value i_t select = 0; multi_probe.set_updated_bounds(problem, select, handle_ptr); @@ -767,15 +765,9 @@ bool constraint_prop_t::run_repair_procedure(problem_t& prob repair_stats.repair_attempts++; f_t repair_start_time = timer.remaining_time(); i_t n_of_repairs_needed_for_feasible = 0; - // TODO: do this better - i_t iter_limit = std::numeric_limits::max(); - if (this->context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - timer = work_limit_timer_t(context.gpu_heur_loop, std::numeric_limits::infinity()); - iter_limit = 100; - } do { n_of_repairs_needed_for_feasible++; - if (timer.check_time_limit() || iter_limit-- <= 0) { + if (timer.check_time_limit()) { CUOPT_LOG_DEBUG("Time limit is reached in repair loop!"); f_t repair_end_time = timer.remaining_time(); repair_stats.total_time_spent_on_repair += repair_start_time - repair_end_time; @@ -840,7 +832,6 @@ bool constraint_prop_t::is_problem_ii(problem_t& problem) { bounds_update.calculate_activity_on_problem_bounds(problem); bounds_update.calculate_infeasible_redundant_constraints(problem); - multi_probe.calculate_activity(problem, problem.handle_ptr); bool problem_ii = bounds_update.infeas_constraints_count > 0; return problem_ii; } @@ -850,7 +841,7 @@ bool constraint_prop_t::find_integer( solution_t& sol, solution_t& orig_sol, f_t lp_run_time_after_feasible, - work_limit_timer_t& timer, + timer_t& timer, std::optional>> probing_config) { using crit_t = termination_criterion_t; @@ -880,7 +871,6 @@ bool constraint_prop_t::find_integer( sol.problem_ptr->integer_indices.data(), sol.problem_ptr->n_integer_vars, sol.handle_ptr->get_stream()); - CUOPT_LOG_DEBUG("sol hash 0x%x", sol.get_hash()); } else { find_unset_integer_vars(sol, unset_integer_vars); sort_by_frac(sol, make_span(unset_integer_vars)); @@ -905,17 +895,16 @@ bool constraint_prop_t::find_integer( set_bounds_on_fixed_vars(sol); } - CUOPT_LOG_TRACE("Bounds propagation rounding: unset vars %lu", unset_integer_vars.size()); + CUOPT_LOG_DEBUG("Bounds propagation rounding: unset vars %lu", unset_integer_vars.size()); if (unset_integer_vars.size() == 0) { - CUOPT_LOG_TRACE("No integer variables provided in the bounds prop rounding"); + CUOPT_LOG_DEBUG("No integer variables provided in the bounds prop rounding"); expand_device_copy(orig_sol.assignment, sol.assignment, sol.handle_ptr->get_stream()); cuopt_func_call(orig_sol.test_variable_bounds()); return orig_sol.compute_feasibility(); } // this is needed for the sort inside of the loop bool problem_ii = is_problem_ii(*sol.problem_ptr); - CUOPT_LOG_TRACE("is problem ii %d", problem_ii); - // if the problem is ii, run the bounds prop in the beginning + // if the problem is ii, run the bounds prop in the beginning if (problem_ii) { bool bounds_repaired = bounds_repair.repair_problem(*sol.problem_ptr, *orig_sol.problem_ptr, timer, sol.handle_ptr); @@ -941,7 +930,6 @@ bool constraint_prop_t::find_integer( i_t n_failed_repair_iterations = 0; while (set_count < unset_integer_vars.size()) { CUOPT_LOG_TRACE("n_set_vars %d vars to set %lu", set_count, unset_integer_vars.size()); - CUOPT_LOG_DEBUG("hash unset_integer_vars 0x%x", detail::compute_hash(unset_integer_vars)); update_host_assignment(sol); if (max_timer.check_time_limit()) { CUOPT_LOG_DEBUG("Second time limit is reached returning nearest rounding!"); @@ -977,26 +965,13 @@ bool constraint_prop_t::find_integer( unset_integer_vars.data() + set_count, n_vars_to_set, sol.handle_ptr->get_stream()); - - CUOPT_LOG_TRACE("host_vars_to_set hash 0x%x", detail::compute_hash(host_vars_to_set)); - auto var_probe_vals = generate_bulk_rounding_vector(sol, orig_sol, host_vars_to_set, probing_config); - - CUOPT_LOG_TRACE("var_probe_vals hash 1 0x%x, hash 2 0x%x, hash 3 0x%x", - detail::compute_hash(std::get<0>(var_probe_vals)), - detail::compute_hash(std::get<1>(var_probe_vals)), - detail::compute_hash(std::get<2>(var_probe_vals))); probe( sol, orig_sol.problem_ptr, var_probe_vals, &set_count, unset_integer_vars, probing_config); - CUOPT_LOG_TRACE("post probe, set count %d, unset var hash 0x%x, size %lu", - (int)set_count, - detail::compute_hash(unset_integer_vars), - unset_integer_vars.size()); if (!(n_failed_repair_iterations >= max_n_failed_repair_iterations) && rounding_ii && !timeout_happened) { - // timer_t repair_timer{std::min(timer.remaining_time() / 5, timer.elapsed_time() / 3)}; - work_limit_timer_t repair_timer(context.gpu_heur_loop, timer.remaining_time() / 5); + timer_t repair_timer{std::min(timer.remaining_time() / 5, timer.elapsed_time() / 3)}; save_bounds(sol); // update bounds and run repair procedure bool bounds_repaired = @@ -1051,7 +1026,7 @@ bool constraint_prop_t::find_integer( // which is the unchanged problem bounds multi_probe.update_host_bounds(sol.handle_ptr, make_span(sol.problem_ptr->variable_bounds)); } - CUOPT_LOG_TRACE( + CUOPT_LOG_DEBUG( "Bounds propagation rounding end: ii constraint count first buffer %d, second buffer %d", multi_probe.infeas_constraints_count_0, multi_probe.infeas_constraints_count_1); @@ -1067,7 +1042,6 @@ bool constraint_prop_t::find_integer( lp_settings.tolerance = orig_sol.problem_ptr->tolerances.absolute_tolerance; lp_settings.save_state = false; lp_settings.return_first_feasible = true; - CUOPT_LOG_TRACE("bounds repair LP, sol hash 0x%x", orig_sol.get_hash()); run_lp_with_vars_fixed(*orig_sol.problem_ptr, orig_sol, orig_sol.problem_ptr->integer_indices, @@ -1083,45 +1057,12 @@ template bool constraint_prop_t::apply_round( solution_t& sol, f_t lp_run_time_after_feasible, - work_limit_timer_t& timer, + timer_t& timer, std::optional>> probing_config) { raft::common::nvtx::range fun_scope("constraint prop round"); - - // === CONSTRAINT PROP PREDICTOR FEATURES - START === - auto cp_start_time = std::chrono::high_resolution_clock::now(); - - // CUOPT_LOG_INFO("CP_FEATURES: n_variables=%d n_constraints=%d n_integer_vars=%d", - // sol.problem_ptr->n_variables, - // sol.problem_ptr->n_constraints, - // sol.problem_ptr->n_integer_vars); - - // CUOPT_LOG_INFO("CP_FEATURES: nnz=%lu sparsity=%.6f", - // sol.problem_ptr->coefficients.size(), - // sol.problem_ptr->sparsity); - - sol.compute_feasibility(); - i_t n_unset_integers = sol.problem_ptr->n_integer_vars - sol.compute_number_of_integers(); - - // CUOPT_LOG_INFO("CP_FEATURES: n_unset_vars=%d initial_excess=%.6f time_budget=%.6f", - // n_unset_integers, - // sol.get_total_excess(), - // max_time_for_bounds_prop); - - // CUOPT_LOG_INFO("CP_FEATURES: round_all_vars=%d lp_run_time_after_feasible=%.6f", - // round_all_vars, - // lp_run_time_after_feasible); - // === CONSTRAINT PROP PREDICTOR FEATURES - END === - - max_timer = work_limit_timer_t{context.gpu_heur_loop, max_time_for_bounds_prop}; - if (check_brute_force_rounding(sol)) { - auto cp_end_time = std::chrono::high_resolution_clock::now(); - auto cp_elapsed_ms = - std::chrono::duration_cast(cp_end_time - cp_start_time).count(); - // CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=BRUTE_FORCE_SUCCESS iterations=0", - // cp_elapsed_ms); - return true; - } + max_timer = timer_t{max_time_for_bounds_prop}; + if (check_brute_force_rounding(sol)) { return true; } recovery_mode = false; rounding_ii = false; n_iter_in_recovery = 0; @@ -1135,9 +1076,9 @@ bool constraint_prop_t::apply_round( f_t bounds_prop_end_time = max_timer.remaining_time(); repair_stats.total_time_spent_on_bounds_prop += bounds_prop_start_time - bounds_prop_end_time; - CUOPT_LOG_TRACE( - "repair_success %lu repair_attempts %lu intermediate_repair_success %lu total_repair_loops" - "%lu total_time_spent_on_repair %f total_time_spent_bounds_prop_after_repair %f " + CUOPT_LOG_DEBUG( + "repair_success %lu repair_attempts %lu intermediate_repair_success %lu total_repair_loops %lu " + "total_time_spent_on_repair %f total_time_spent_bounds_prop_after_repair %f " "total_time_spent_on_bounds_prop %f", repair_stats.repair_success, repair_stats.repair_attempts, @@ -1146,25 +1087,11 @@ bool constraint_prop_t::apply_round( repair_stats.total_time_spent_on_repair, repair_stats.total_time_spent_bounds_prop_after_repair, repair_stats.total_time_spent_on_bounds_prop); - // === CONSTRAINT PROP PREDICTOR RESULTS - START === - auto cp_end_time = std::chrono::high_resolution_clock::now(); - auto cp_elapsed_ms = - std::chrono::duration_cast(cp_end_time - cp_start_time).count(); - // === CONSTRAINT PROP PREDICTOR RESULTS - END === - if (!sol_found) { sol.compute_feasibility(); - // CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=FAILED iterations=%d", - // cp_elapsed_ms, - // 0); // TODO: track actual iterations return false; } - bool result = sol.compute_feasibility(); - // CUOPT_LOG_INFO("CP_RESULT: time_ms=%lld termination=%s iterations=%d", - // cp_elapsed_ms, - // result ? "SUCCESS" : "FAILED", - // 0); // TODO: track actual iterations - return result; + return sol.compute_feasibility(); } template @@ -1246,15 +1173,6 @@ bool constraint_prop_t::handle_fixed_vars( auto set_count = *set_count_ptr; const f_t int_tol = sol.problem_ptr->tolerances.integrality_tolerance; // which other variables were affected? - CUOPT_LOG_TRACE("handle_fixed_vars, unset vars hash 0x%x, sol.assignment hash 0x%x", - detail::compute_hash(unset_vars), - detail::compute_hash(sol.assignment)); - CUOPT_LOG_TRACE( - "handle_fixed_vars, original_problem->variable_bounds hash 0x%x, " - "sol.problem_ptr->variable_bounds hash 0x%x", - detail::compute_hash(original_problem->variable_bounds), - detail::compute_hash(sol.problem_ptr->variable_bounds)); - auto iter = thrust::stable_partition(sol.handle_ptr->get_thrust_policy(), unset_vars.begin() + set_count, unset_vars.end(), diff --git a/cpp/src/mip/local_search/rounding/constraint_prop.cuh b/cpp/src/mip/local_search/rounding/constraint_prop.cuh index 9df8e1518..a300a8576 100644 --- a/cpp/src/mip/local_search/rounding/constraint_prop.cuh +++ b/cpp/src/mip/local_search/rounding/constraint_prop.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -43,7 +43,7 @@ struct constraint_prop_t { constraint_prop_t(mip_solver_context_t& context); bool apply_round(solution_t& sol, f_t lp_run_time_after_feasible, - work_limit_timer_t& timer, + timer_t& timer, std::optional>> probing_config = std::nullopt); void sort_by_implied_slack_consumption(solution_t& sol, @@ -56,7 +56,7 @@ struct constraint_prop_t { bool find_integer(solution_t& sol, solution_t& orig_sol, f_t lp_run_time_after_feasible, - work_limit_timer_t& timer, + timer_t& timer, std::optional>> probing_config = std::nullopt); void find_set_integer_vars(solution_t& sol, rmm::device_uvector& set_vars); @@ -121,7 +121,7 @@ struct constraint_prop_t { const raft::handle_t* handle_ptr); bool run_repair_procedure(problem_t& problem, problem_t& original_problem, - work_limit_timer_t& timer, + timer_t& timer, const raft::handle_t* handle_ptr); bool handle_fixed_vars( solution_t& sol, @@ -149,7 +149,7 @@ struct constraint_prop_t { i_t bounds_prop_interval = 1; i_t n_iter_in_recovery = 0; i_t max_n_failed_repair_iterations = 1; - work_limit_timer_t max_timer; + timer_t max_timer{0.}; bool use_probing_cache = true; static repair_stats_t repair_stats; bool single_rounding_only = false; diff --git a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu index 6c7921925..04ae200b9 100644 --- a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu +++ b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -68,7 +68,8 @@ std::tuple lb_bounds_repair_t::get_ii_violation( constraint_upper_bounds = problem.constraint_upper_bounds, cnst_slack = make_span_2(lb_bound_presolve.cnst_slack), cstr_violations_up = cstr_violations_up.data(), - cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) { + cstr_violations_down = cstr_violations_down.data(), + total_vio = total_vio.data()] __device__(i_t cstr_idx) { f_t cnst_lb = constraint_lower_bounds[cstr_idx]; f_t cnst_ub = constraint_upper_bounds[cstr_idx]; f_t2 slack = cnst_slack[cstr_idx]; @@ -79,6 +80,7 @@ std::tuple lb_bounds_repair_t::get_ii_violation( f_t violation = max(curr_cstr_violation_up, curr_cstr_violation_down); if (violation >= ROUNDOFF_TOLERANCE) { violated_cstr_map[cstr_idx] = 1; + atomicAdd(total_vio, violation); } else { violated_cstr_map[cstr_idx] = 0; } @@ -92,18 +94,7 @@ std::tuple lb_bounds_repair_t::get_ii_violation( violated_constraints.data(), cuda::std::identity{}); i_t n_violated_cstr = iter - violated_constraints.data(); - // Use deterministic reduction instead of non-deterministic atomicAdd - f_t total_violation = thrust::transform_reduce( - handle_ptr->get_thrust_policy(), - thrust::make_counting_iterator(0), - thrust::make_counting_iterator(0) + problem.n_constraints, - [cstr_violations_up = cstr_violations_up.data(), - cstr_violations_down = cstr_violations_down.data()] __device__(i_t cstr_idx) -> f_t { - auto violation = max(cstr_violations_up[cstr_idx], cstr_violations_down[cstr_idx]); - return violation >= ROUNDOFF_TOLERANCE ? violation : 0.; - }, - (f_t)0, - thrust::plus()); + f_t total_violation = total_vio.value(handle_ptr->get_stream()); CUOPT_LOG_TRACE( "Repair: n_violated_cstr %d total_violation %f", n_violated_cstr, total_violation); return std::make_tuple(total_violation, n_violated_cstr); @@ -409,8 +400,7 @@ bool lb_bounds_repair_t::repair_problem( timer_t timer_, const raft::handle_t* handle_ptr_) { - nvtx::range fun_scope("LB repair_problem"); - CUOPT_LOG_DEBUG("LB Running bounds repair"); + CUOPT_LOG_DEBUG("Running bounds repair"); handle_ptr = handle_ptr_; timer = timer_; resize(*problem); diff --git a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cuh b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cuh index 5b6cd54ac..0f980d6e6 100644 --- a/cpp/src/mip/local_search/rounding/lb_bounds_repair.cuh +++ b/cpp/src/mip/local_search/rounding/lb_bounds_repair.cuh @@ -58,7 +58,7 @@ class lb_bounds_repair_t { bool repair_problem(load_balanced_problem_t* problem, load_balanced_bounds_presolve_t& lb_bound_presolve, problem_t& original_problem, - work_limit_timer_t timer_, + timer_t timer_, const raft::handle_t* handle_ptr_); void apply_move(load_balanced_problem_t* problem, problem_t& original_problem, @@ -82,7 +82,7 @@ class lb_bounds_repair_t { i_t h_n_violated_cstr; const raft::handle_t* handle_ptr; std::mt19937 gen; - work_limit_timer_t timer; + timer_t timer{0.}; std::vector cycle_vector; i_t cycle_write_pos = 0; }; diff --git a/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu b/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu index 87f6eae43..5789ade26 100644 --- a/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu +++ b/cpp/src/mip/local_search/rounding/lb_constraint_prop.cu @@ -700,14 +700,14 @@ template bool lb_constraint_prop_t::apply_round( solution_t& sol, f_t lp_run_time_after_feasible, - work_limit_timer_t& timer, + timer_t& timer, std::optional>> probing_candidates) { raft::common::nvtx::range fun_scope("constraint prop round"); // this is second timer that can continue but without recovery mode const f_t max_time_for_bounds_prop = 5.; - max_timer = work_limit_timer_t{context.gpu_heur_loop, max_time_for_bounds_prop}; + max_timer = timer_t{max_time_for_bounds_prop}; if (check_brute_force_rounding(sol)) { return true; } recovery_mode = false; rounding_ii = false; diff --git a/cpp/src/mip/local_search/rounding/lb_constraint_prop.cuh b/cpp/src/mip/local_search/rounding/lb_constraint_prop.cuh index d09a41c14..8230df984 100644 --- a/cpp/src/mip/local_search/rounding/lb_constraint_prop.cuh +++ b/cpp/src/mip/local_search/rounding/lb_constraint_prop.cuh @@ -23,7 +23,7 @@ struct lb_constraint_prop_t { bool apply_round( solution_t& sol, f_t lp_run_time_after_feasible, - work_limit_timer_t& timer, + timer_t& timer, std::optional>> probing_candidates = std::nullopt); void sort_by_implied_slack_consumption( problem_t& original_problem, @@ -40,7 +40,7 @@ struct lb_constraint_prop_t { load_balanced_bounds_presolve_t& lb_bounds_update, solution_t& orig_sol, f_t lp_run_time_after_feasible, - work_limit_timer_t& timer, + timer_t& timer, std::optional>> probing_candidates); std::tuple probing_values( load_balanced_bounds_presolve_t& lb_bounds_update, @@ -83,7 +83,7 @@ struct lb_constraint_prop_t { bool run_repair_procedure(load_balanced_problem_t* problem, load_balanced_bounds_presolve_t& lb_bounds_update, problem_t& original_problem, - work_limit_timer_t& timer, + timer_t& timer, const raft::handle_t* handle_ptr); mip_solver_context_t& context; @@ -100,7 +100,7 @@ struct lb_constraint_prop_t { bool rounding_ii = false; i_t bounds_prop_interval = 1; i_t n_iter_in_recovery = 0; - work_limit_timer_t max_timer; + timer_t max_timer{0.}; bool use_probing_cache = true; size_t repair_attempts = 0; diff --git a/cpp/src/mip/local_search/rounding/simple_rounding_kernels.cuh b/cpp/src/mip/local_search/rounding/simple_rounding_kernels.cuh index 30736839d..2906e648f 100644 --- a/cpp/src/mip/local_search/rounding/simple_rounding_kernels.cuh +++ b/cpp/src/mip/local_search/rounding/simple_rounding_kernels.cuh @@ -130,7 +130,7 @@ __global__ void brute_force_check_kernel(typename solution_t::view_t s __shared__ i_t shbuf[raft::WarpSize]; i_t total_feasible = raft::blockReduce(th_feasible_count, (char*)shbuf); if (threadIdx.x == 0) { - if (total_feasible == solution.problem.n_constraints) { atomicMin(best_config, config); } + if (total_feasible == solution.problem.n_constraints) { atomicExch(best_config, config); } } } diff --git a/cpp/src/mip/presolve/bounds_presolve.cu b/cpp/src/mip/presolve/bounds_presolve.cu index 06c924430..05cc26bca 100644 --- a/cpp/src/mip/presolve/bounds_presolve.cu +++ b/cpp/src/mip/presolve/bounds_presolve.cu @@ -171,12 +171,6 @@ termination_criterion_t bound_presolve_t::bound_update_loop(problem_t< { termination_criterion_t criteria = termination_criterion_t::ITERATION_LIMIT; - // CHANGE once we have a work predictor - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - timer = timer_t(std::numeric_limits::infinity()); - settings.iteration_limit = std::min(settings.iteration_limit, 50); - } - i_t iter; upd.init_changed_constraints(pb.handle_ptr); for (iter = 0; iter < settings.iteration_limit; ++iter) { diff --git a/cpp/src/mip/presolve/bounds_update_data.cu b/cpp/src/mip/presolve/bounds_update_data.cu index a06f2b7f5..bad8de83f 100644 --- a/cpp/src/mip/presolve/bounds_update_data.cu +++ b/cpp/src/mip/presolve/bounds_update_data.cu @@ -35,35 +35,6 @@ void bounds_update_data_t::resize(problem_t& problem) changed_constraints.resize(problem.n_constraints, problem.handle_ptr->get_stream()); next_changed_constraints.resize(problem.n_constraints, problem.handle_ptr->get_stream()); changed_variables.resize(problem.n_variables, problem.handle_ptr->get_stream()); - - thrust::fill(problem.handle_ptr->get_thrust_policy(), - min_activity.begin(), - min_activity.end(), - std::numeric_limits::signaling_NaN()); - thrust::fill(problem.handle_ptr->get_thrust_policy(), - max_activity.begin(), - max_activity.end(), - std::numeric_limits::signaling_NaN()); - thrust::fill(problem.handle_ptr->get_thrust_policy(), - lb.begin(), - lb.end(), - std::numeric_limits::signaling_NaN()); - thrust::fill(problem.handle_ptr->get_thrust_policy(), - ub.begin(), - ub.end(), - std::numeric_limits::signaling_NaN()); - thrust::fill(problem.handle_ptr->get_thrust_policy(), - changed_constraints.begin(), - changed_constraints.end(), - -1); - thrust::fill(problem.handle_ptr->get_thrust_policy(), - next_changed_constraints.begin(), - next_changed_constraints.end(), - -1); - thrust::fill(problem.handle_ptr->get_thrust_policy(), - changed_variables.begin(), - changed_variables.end(), - -1); } template diff --git a/cpp/src/mip/presolve/lb_probing_cache.cu b/cpp/src/mip/presolve/lb_probing_cache.cu index b655260f4..790ed32e4 100644 --- a/cpp/src/mip/presolve/lb_probing_cache.cu +++ b/cpp/src/mip/presolve/lb_probing_cache.cu @@ -309,7 +309,7 @@ inline std::vector compute_prioritized_integer_indices( template void compute_probing_cache(load_balanced_bounds_presolve_t& bound_presolve, load_balanced_problem_t& problem, - work_limit_timer_t timer) + timer_t timer) { // we dont want to compute the probing cache for all variables for time and computation resources auto priority_indices = compute_prioritized_integer_indices(bound_presolve, problem); @@ -400,7 +400,7 @@ void compute_probing_cache(load_balanced_bounds_presolve_t& bound_pres template void compute_probing_cache( \ load_balanced_bounds_presolve_t & bound_presolve, \ load_balanced_problem_t & problem, \ - work_limit_timer_t timer); \ + timer_t timer); \ template class lb_probing_cache_t; #if MIP_INSTANTIATE_FLOAT diff --git a/cpp/src/mip/presolve/probing_cache.cu b/cpp/src/mip/presolve/probing_cache.cu index 99a1cd0a3..e191cdde9 100644 --- a/cpp/src/mip/presolve/probing_cache.cu +++ b/cpp/src/mip/presolve/probing_cache.cu @@ -455,7 +455,7 @@ void compute_cache_for_var(i_t var_idx, template void compute_probing_cache(bound_presolve_t& bound_presolve, problem_t& problem, - work_limit_timer_t timer) + timer_t timer) { raft::common::nvtx::range fun_scope("compute_probing_cache"); // we dont want to compute the probing cache for all variables for time and computation resources @@ -521,7 +521,7 @@ void compute_probing_cache(bound_presolve_t& bound_presolve, #define INSTANTIATE(F_TYPE) \ template void compute_probing_cache(bound_presolve_t & bound_presolve, \ problem_t & problem, \ - work_limit_timer_t timer); \ + timer_t timer); \ template class probing_cache_t; #if MIP_INSTANTIATE_FLOAT diff --git a/cpp/src/mip/presolve/probing_cache.cuh b/cpp/src/mip/presolve/probing_cache.cuh index e35aa6069..908ec1de9 100644 --- a/cpp/src/mip/presolve/probing_cache.cuh +++ b/cpp/src/mip/presolve/probing_cache.cuh @@ -12,7 +12,6 @@ #include #include -#include namespace cuopt::linear_programming::detail { @@ -118,6 +117,6 @@ class lb_probing_cache_t { template void compute_probing_cache(bound_presolve_t& bound_presolve, problem_t& problem, - work_limit_timer_t timer); + timer_t timer); } // namespace cuopt::linear_programming::detail diff --git a/cpp/src/mip/presolve/utils.cuh b/cpp/src/mip/presolve/utils.cuh index 2d354504d..4870b3180 100644 --- a/cpp/src/mip/presolve/utils.cuh +++ b/cpp/src/mip/presolve/utils.cuh @@ -12,7 +12,6 @@ namespace cuopt::linear_programming::detail { enum class termination_criterion_t { TIME_LIMIT, ITERATION_LIMIT, - WORK_LIMIT, CONVERGENCE, INFEASIBLE, NO_UPDATE diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cu b/cpp/src/mip/relaxed_lp/relaxed_lp.cu index ffb850e87..4842a986a 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cu +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -13,8 +13,6 @@ #include #include -#include - #include #include @@ -42,31 +40,6 @@ optimization_problem_solution_t get_relaxed_lp_solution( const relaxed_lp_settings_t& settings) { raft::common::nvtx::range fun_scope("get_relaxed_lp_solution"); - // auto function_start_time = std::chrono::high_resolution_clock::now(); - - // // === PDLP PREDICTOR FEATURES - START === - // CUOPT_LOG_INFO("PDLP_FEATURES: n_variables=%d n_constraints=%d nnz=%lu", - // op_problem.n_variables, - // op_problem.n_constraints, - // op_problem.coefficients.size()); - - // CUOPT_LOG_INFO("PDLP_FEATURES: sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f", - // op_problem.sparsity, - // op_problem.nnz_stddev, - // op_problem.unbalancedness); - - // CUOPT_LOG_INFO("PDLP_FEATURES: has_warm_start=%d time_limit=%.6f iteration_limit=%d", - // settings.has_initial_primal, - // settings.time_limit, - // settings.iteration_limit); - - // CUOPT_LOG_INFO("PDLP_FEATURES: tolerance=%.10f check_infeasibility=%d - // return_first_feasible=%d", - // settings.tolerance, - // settings.check_infeasibility, - // settings.return_first_feasible); - // // === PDLP PREDICTOR FEATURES - END === - pdlp_solver_settings_t pdlp_settings{}; pdlp_settings.detect_infeasibility = settings.check_infeasibility; pdlp_settings.set_optimality_tolerance(settings.tolerance); @@ -76,42 +49,21 @@ optimization_problem_solution_t get_relaxed_lp_solution( pdlp_settings.tolerances.relative_primal_tolerance = settings.tolerance / tolerance_divisor; pdlp_settings.tolerances.relative_dual_tolerance = settings.tolerance / tolerance_divisor; pdlp_settings.time_limit = settings.time_limit; - pdlp_settings.iteration_limit = settings.iteration_limit; - - // CHANGE - i_t work_limit = settings.work_limit; - bool determinism_mode = work_limit != std::numeric_limits::infinity(); - pdlp_settings.concurrent_halt = settings.concurrent_halt; - pdlp_settings.per_constraint_residual = settings.per_constraint_residual; - pdlp_settings.first_primal_feasible = settings.return_first_feasible; - pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; - if (determinism_mode) { - // try to estimate the iteration count based on the requested work limit - int estim_iters = 100; - do { - // TODO: use an actual predictor model here - double estim_ms = 313 + 200 * op_problem.n_variables - 400 * op_problem.n_constraints + - 600 * op_problem.coefficients.size() + 7100 * estim_iters; - estim_ms = std::max(0.0, estim_ms); - if (estim_ms > work_limit * 1000) { break; } - estim_iters += 100; - } while (true); - CUOPT_LOG_DEBUG("estimated iterations %d for work limit %f", estim_iters, settings.work_limit); - pdlp_settings.iteration_limit = estim_iters; - pdlp_settings.time_limit = std::numeric_limits::infinity(); - pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable3; - } + pdlp_settings.concurrent_halt = settings.concurrent_halt; + pdlp_settings.per_constraint_residual = settings.per_constraint_residual; + pdlp_settings.first_primal_feasible = settings.return_first_feasible; + pdlp_settings.pdlp_solver_mode = pdlp_solver_mode_t::Stable2; set_pdlp_solver_mode(pdlp_settings); // TODO: set Stable3 here? pdlp_solver_t lp_solver(op_problem, pdlp_settings); if (settings.has_initial_primal) { i_t prev_size = lp_state.prev_dual.size(); - // CUOPT_LOG_DEBUG( - // "setting initial primal solution of size %d dual size %d problem vars %d cstrs %d", - // assignment.size(), - // lp_state.prev_dual.size(), - // op_problem.n_variables, - // op_problem.n_constraints); + CUOPT_LOG_DEBUG( + "setting initial primal solution of size %d dual size %d problem vars %d cstrs %d", + assignment.size(), + lp_state.prev_dual.size(), + op_problem.n_variables, + op_problem.n_constraints); lp_state.resize(op_problem, op_problem.handle_ptr->get_stream()); clamp_within_var_bounds(assignment, &op_problem, op_problem.handle_ptr); // The previous dual sometimes contain invalid values w.r.t current problem @@ -127,22 +79,18 @@ optimization_problem_solution_t get_relaxed_lp_solution( lp_solver.set_initial_primal_solution(assignment); lp_solver.set_initial_dual_solution(lp_state.prev_dual); } - // CUOPT_LOG_DEBUG( - // "running LP with n_vars %d n_cstr %d", op_problem.n_variables, op_problem.n_constraints); + CUOPT_LOG_DEBUG( + "running LP with n_vars %d n_cstr %d", op_problem.n_variables, op_problem.n_constraints); // before LP flush the logs as it takes quite some time cuopt::default_logger().flush(); // temporarily add timer auto start_time = timer_t(pdlp_settings.time_limit); lp_solver.set_inside_mip(true); - CUOPT_LOG_DEBUG("prev primal hash 0x%x", detail::compute_hash(assignment)); - CUOPT_LOG_DEBUG("prev dual hash 0x%x", detail::compute_hash(lp_state.prev_dual)); auto solver_response = lp_solver.run_solver(start_time); - CUOPT_LOG_DEBUG("post LP primal hash 0x%x", - detail::compute_hash(solver_response.get_primal_solution())); if (solver_response.get_primal_solution().size() != 0 && solver_response.get_dual_solution().size() != 0 && settings.save_state) { - // CUOPT_LOG_DEBUG("saving initial primal solution of size %d", lp_state.prev_primal.size()); + CUOPT_LOG_DEBUG("saving initial primal solution of size %d", lp_state.prev_primal.size()); lp_state.set_state(solver_response.get_primal_solution(), solver_response.get_dual_solution()); } if (solver_response.get_primal_solution().size() != 0) { @@ -153,81 +101,12 @@ optimization_problem_solution_t get_relaxed_lp_solution( op_problem.handle_ptr->get_stream()); } if (solver_response.get_termination_status() == pdlp_termination_status_t::Optimal) { - // CUOPT_LOG_DEBUG("feasible solution found with LP objective %f", - // solver_response.get_objective_value()); + CUOPT_LOG_DEBUG("feasible solution found with LP objective %f", + solver_response.get_objective_value()); } else { - CUOPT_LOG_DEBUG("LP returned with reason %d, %d iterations, sol hash 0x%x", - solver_response.get_termination_status(), - solver_response.get_additional_termination_information().number_of_steps_taken, - compute_hash(assignment)); + CUOPT_LOG_DEBUG("LP returned with reason %d", solver_response.get_termination_status()); } - // auto function_end_time = std::chrono::high_resolution_clock::now(); - // auto elapsed_ms = - // std::chrono::duration_cast(function_end_time - - // function_start_time) - // .count(); - // CUOPT_LOG_DEBUG("get_relaxed_lp_solution took %lld ms for %d iterations", - // elapsed_ms, - // solver_response.get_additional_termination_information().number_of_steps_taken); - - // // === PDLP PREDICTOR RESULTS - START === - // auto term_info = solver_response.get_additional_termination_information(); - // const i_t n_vars = op_problem.n_variables; - // const i_t n_cstrs = op_problem.n_constraints; - // const int64_t nnz = op_problem.nnz; - // const int64_t total_spmv = lp_solver.get_total_spmv_ops(); - // const int64_t total_nnz = total_spmv * nnz; - // const double nnz_per_sec = - // (elapsed_ms > 0) ? static_cast(total_nnz) / (elapsed_ms / 1000.0) : 0.0; - // const double nnz_per_iter = (term_info.number_of_steps_taken > 0) - // ? static_cast(total_nnz) / - // term_info.number_of_steps_taken : 0.0; - - // // Compute sparsity metrics - // const double sparsity = (n_cstrs > 0 && n_vars > 0) - // ? static_cast(nnz) / - // (static_cast(n_cstrs) * n_vars) : 0.0; - // double nnz_stddev = 0.0; - // [[maybe_unused]] double unbalancedness = 0.0; - // if (op_problem.offsets.size() == static_cast(n_cstrs + 1) && n_cstrs > 0) { - // std::vector h_offsets(n_cstrs + 1); - // raft::copy(h_offsets.data(), - // op_problem.offsets.data(), - // n_cstrs + 1, - // op_problem.handle_ptr->get_stream()); - // op_problem.handle_ptr->sync_stream(); - - // const double mean_nnz = static_cast(nnz) / n_cstrs; - // double variance_sum = 0.0; - // for (i_t row = 0; row < n_cstrs; ++row) { - // const double row_nnz = static_cast(h_offsets[row + 1] - h_offsets[row]); - // const double diff = row_nnz - mean_nnz; - // variance_sum += diff * diff; - // } - // const double variance = variance_sum / n_cstrs; - // nnz_stddev = std::sqrt(variance); - // unbalancedness = (mean_nnz > 0) ? nnz_stddev / mean_nnz : 0.0; - // } - - // CUOPT_LOG_INFO( - // "PDLP_RESULT: n_vars=%d n_cstrs=%d nnz=%ld sparsity=%.6f nnz_stddev=%.6f unbalancedness=%.6f - // " "iters=%d time_ms=%lld term=%d spmv_ops=%ld total_nnz=%.2e nnz/s=%.2e nnz/iter=%.2e", - // n_vars, - // n_cstrs, - // nnz, - // sparsity, - // nnz_stddev, - // unbalancedness, - // term_info.number_of_steps_taken, - // elapsed_ms, - // static_cast(solver_response.get_termination_status()), - // total_spmv, - // static_cast(total_nnz), - // nnz_per_sec, - // nnz_per_iter); - // === PDLP PREDICTOR RESULTS - END === - return solver_response; } diff --git a/cpp/src/mip/relaxed_lp/relaxed_lp.cuh b/cpp/src/mip/relaxed_lp/relaxed_lp.cuh index af136e3e1..0094f5982 100644 --- a/cpp/src/mip/relaxed_lp/relaxed_lp.cuh +++ b/cpp/src/mip/relaxed_lp/relaxed_lp.cuh @@ -19,8 +19,6 @@ namespace cuopt::linear_programming::detail { struct relaxed_lp_settings_t { double tolerance = 1e-4; double time_limit = 1.0; - int iteration_limit = std::numeric_limits::max(); - double work_limit = std::numeric_limits::infinity(); bool check_infeasibility = true; bool return_first_feasible = false; bool save_state = true; diff --git a/cpp/src/mip/solution/solution.cu b/cpp/src/mip/solution/solution.cu index c89c027b8..9e9a2d75f 100644 --- a/cpp/src/mip/solution/solution.cu +++ b/cpp/src/mip/solution/solution.cu @@ -623,7 +623,7 @@ mip_solution_t solution_t::get_solution(bool output_feasible "Solution objective: %f , relative_mip_gap %f solution_bound %f presolve_time %f " "total_solve_time %f " "max constraint violation %f max int violation %f max var bounds violation %f " - "nodes %d simplex_iterations %d hash %x", + "nodes %d simplex_iterations %d", h_user_obj, rel_mip_gap, solution_bound, @@ -633,8 +633,7 @@ mip_solution_t solution_t::get_solution(bool output_feasible max_int_violation, max_variable_bound_violation, num_nodes, - num_simplex_iterations, - get_hash()); + num_simplex_iterations); } const bool not_optimal = rel_mip_gap > problem_ptr->tolerances.relative_mip_gap && abs_mip_gap > problem_ptr->tolerances.absolute_mip_gap; @@ -658,12 +657,6 @@ mip_solution_t solution_t::get_solution(bool output_feasible } } -template -uint32_t solution_t::get_hash() const -{ - return compute_hash(assignment); -} - #if MIP_INSTANTIATE_FLOAT template class solution_t; #endif diff --git a/cpp/src/mip/solution/solution.cuh b/cpp/src/mip/solution/solution.cuh index 7a007c70f..342827b1a 100644 --- a/cpp/src/mip/solution/solution.cuh +++ b/cpp/src/mip/solution/solution.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -99,7 +99,6 @@ class solution_t { f_t compute_max_constraint_violation(); f_t compute_max_int_violation(); f_t compute_max_variable_violation(); - uint32_t get_hash() const; struct view_t { // let's not bloat the class for every simple getter and setters diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 12e0be333..0d5b980bd 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -108,7 +108,7 @@ solution_t mip_solver_t::run_solver() } diversity_manager_t dm(context); - dm.timer = work_limit_timer_t(context.gpu_heur_loop, timer_.get_time_limit()); + dm.timer = timer_; f_t time_limit = context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC ? timer_.get_time_limit() : timer_.remaining_time(); diff --git a/cpp/src/utilities/embed_models.sh b/cpp/src/utilities/embed_models.sh deleted file mode 100755 index f402c411c..000000000 --- a/cpp/src/utilities/embed_models.sh +++ /dev/null @@ -1,89 +0,0 @@ -#!/bin/sh -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -set -e - -if [ $# -lt 2 ]; then - echo "Usage: $0 [model2.ubj.gz ...]" - exit 1 -fi - -OUTPUT_HEADER="$1" -shift - -# Initialize the header file -echo "// Auto-generated model arrays" > "$OUTPUT_HEADER" -echo "" >> "$OUTPUT_HEADER" - -# Collect model names for the mapping array -MODEL_NAMES="" - -# Process each model file -for MODEL_FILE in "$@"; do - # Extract base name without .ubj.gz extension - MODEL_BASENAME=$(basename "$MODEL_FILE" .ubj.gz) - - echo "Processing: $MODEL_BASENAME from $MODEL_FILE" - - # Create temporary file with proper name so xxd uses it - TEMP_DIR=$(mktemp -d) - TEMP_FILE="$TEMP_DIR/${MODEL_BASENAME}.ubj" - - # Decompress the model - gzip -cd "$MODEL_FILE" > "$TEMP_FILE" - - # Convert to C array with xxd and append to header - # xxd generates variable names like: _tmp_tmp_xyz_model_ubj - # We need to replace the entire variable name, not just add to it - # Add const to place arrays in .rodata section - xxd -i "$TEMP_FILE" | \ - sed "s/unsigned char [_a-zA-Z0-9]*/const unsigned char ${MODEL_BASENAME}_ubj/" | \ - sed "s/unsigned int [_a-zA-Z0-9]*/const unsigned int ${MODEL_BASENAME}_ubj_len/" >> "$OUTPUT_HEADER" - - # Clean up - rm -rf "$TEMP_DIR" - - # Add to list for mapping array - if [ -z "$MODEL_NAMES" ]; then - MODEL_NAMES="$MODEL_BASENAME" - else - MODEL_NAMES="$MODEL_NAMES $MODEL_BASENAME" - fi -done - -# Add model mapping structure -echo "" >> "$OUTPUT_HEADER" -echo "// Model mapping structure" >> "$OUTPUT_HEADER" -echo "struct xgboost_model_info {" >> "$OUTPUT_HEADER" -echo " const char* name;" >> "$OUTPUT_HEADER" -echo " const unsigned char* data;" >> "$OUTPUT_HEADER" -echo " unsigned int length;" >> "$OUTPUT_HEADER" -echo "};" >> "$OUTPUT_HEADER" -echo "" >> "$OUTPUT_HEADER" - -# Generate the mapping array -echo "// Array of all available models" >> "$OUTPUT_HEADER" -echo "static const struct xgboost_model_info xgboost_models[] = {" >> "$OUTPUT_HEADER" - -for MODEL_NAME in $MODEL_NAMES; do - echo " {\"$MODEL_NAME\", ${MODEL_NAME}_ubj, ${MODEL_NAME}_ubj_len}," >> "$OUTPUT_HEADER" -done - -echo "};" >> "$OUTPUT_HEADER" -echo "" >> "$OUTPUT_HEADER" -echo "static const unsigned int xgboost_models_count = sizeof(xgboost_models) / sizeof(xgboost_models[0]);" >> "$OUTPUT_HEADER" - -echo "Successfully embedded models into $OUTPUT_HEADER" diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index 4199e8af9..fb8f0e26b 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -33,40 +33,6 @@ endif() set(CUOPT_TEST_DIR ${CMAKE_CURRENT_SOURCE_DIR}) -if (EXISTS "${CUDAToolkit_LIBRARY_ROOT}/extras/CUPTI/lib64") - # NVIDIA installer layout: - set(cuopt_cupti_root "${CUDAToolkit_LIBRARY_ROOT}/extras/CUPTI") -else() - # Ubuntu package layout: - set(cuopt_cupti_root "${CUDAToolkit_LIBRARY_ROOT}") -endif() -message(STATUS "cuopt_cupti_root = ${cuopt_cupti_root}") - -# The CUPTI targets in FindCUDAToolkit are broken: -# - The dll locations are not specified -# - Dependent libraries nvperf_* are not linked. -# So we create our own targets: -function(cuopt_add_cupti_dep dep_name) - string(TOLOWER ${dep_name} dep_name_lower) - string(TOUPPER ${dep_name} dep_name_upper) - - add_library(cuopt::${dep_name_lower} SHARED IMPORTED) - - find_library(CUOPT_${dep_name_upper}_LIBRARY ${dep_name_lower} REQUIRED - DOC "The full path to lib${dep_name_lower}.so from the CUDA Toolkit." - HINTS "${cuopt_cupti_root}/lib64" "${cuopt_cupti_root}/lib" - ) - mark_as_advanced(CUOPT_${dep_name_upper}_LIBRARY) - - set_target_properties(cuopt::${dep_name_lower} PROPERTIES - IMPORTED_LOCATION "${CUOPT_${dep_name_upper}_LIBRARY}" - ) -endfunction() - -#cuopt_add_cupti_dep(nvperf_target) -#cuopt_add_cupti_dep(nvperf_host) -#cuopt_add_cupti_dep(cupti) - # ################################################################ ------------------------------------------------------------------ function(ConfigureTest CMAKE_TEST_NAME) add_executable(${CMAKE_TEST_NAME} ${ARGN}) @@ -93,16 +59,6 @@ function(ConfigureTest CMAKE_TEST_NAME) target_link_options(${CMAKE_TEST_NAME} PRIVATE -Wl,--enable-new-dtags) endif() - target_link_libraries(${CMAKE_TEST_NAME} PRIVATE - #cuopt::nvperf_target - #cuopt::nvperf_host - #cuopt::cupti - cuda - ) - target_include_directories(${CMAKE_TEST_NAME} PRIVATE - "${cuopt_cupti_root}/include" - ) - add_test(NAME ${CMAKE_TEST_NAME} COMMAND ${CMAKE_TEST_NAME}) install( diff --git a/cpp/tests/mip/CMakeLists.txt b/cpp/tests/mip/CMakeLists.txt index 6c98e361e..ce47f3144 100644 --- a/cpp/tests/mip/CMakeLists.txt +++ b/cpp/tests/mip/CMakeLists.txt @@ -37,15 +37,9 @@ ConfigureTest(PRESOLVE_TEST ${CMAKE_CURRENT_SOURCE_DIR}/presolve_test.cu ) # Disable for now -ConfigureTest(FEASIBILITY_JUMP_TEST - ${CMAKE_CURRENT_SOURCE_DIR}/feasibility_jump_tests.cu -) +# ConfigureTest(FEASIBILITY_JUMP_TEST +# ${CMAKE_CURRENT_SOURCE_DIR}/feasibility_jump_tests.cu +# ) ConfigureTest(MIP_TERMINATION_STATUS_TEST ${CMAKE_CURRENT_SOURCE_DIR}/termination_test.cu ) -ConfigureTest(LOCAL_SEARCH_TEST - ${CMAKE_CURRENT_SOURCE_DIR}/local_search_test.cu -) -ConfigureTest(DIVERSITY_TEST - ${CMAKE_CURRENT_SOURCE_DIR}/diversity_test.cu -) diff --git a/cpp/tests/mip/determinism_utils.cuh b/cpp/tests/mip/determinism_utils.cuh deleted file mode 100644 index 28e8c4b51..000000000 --- a/cpp/tests/mip/determinism_utils.cuh +++ /dev/null @@ -1,77 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include - -#include - -#include -#include - -#include - -namespace cuopt::linear_programming::test { - -static __global__ void spin_kernel(int* flag, unsigned long long timeout_clocks = 10000000) -{ - cuda::atomic_ref flag_ref(*flag); - - long long int start_clock, sample_clock; - start_clock = clock64(); - - while (flag_ref.load() == 0) { - sample_clock = clock64(); - - if (sample_clock - start_clock > timeout_clocks) { break; } - } -} - -static void launch_spin_kernel_stream_thread(rmm::cuda_stream_view stream_view, int* flag) -{ - while (true) { - int blocks = rand() % 64 + 1; - int threads = rand() % 1024 + 1; - spin_kernel<<>>(flag); - cudaStreamSynchronize(stream_view); - if (host_copy(flag, 1, stream_view)[0] != 0) { break; } - std::this_thread::sleep_for(std::chrono::milliseconds(rand() % 1000 + 1)); - } -} - -class spin_stream_raii_t { - public: - spin_stream_raii_t() - : flag(0, stream), spin_thread(launch_spin_kernel_stream_thread, stream.view(), flag.data()) - { - } - - ~spin_stream_raii_t() - { - int one = 1; - flag.set_value_async(one, stream); - spin_thread.join(); - } - - private: - rmm::cuda_stream stream; - rmm::device_scalar flag; - std::thread spin_thread; -}; - -} // namespace cuopt::linear_programming::test \ No newline at end of file diff --git a/cpp/tests/mip/diversity_test.cu b/cpp/tests/mip/diversity_test.cu deleted file mode 100644 index ddbb3286a..000000000 --- a/cpp/tests/mip/diversity_test.cu +++ /dev/null @@ -1,406 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "../linear_programming/utilities/pdlp_test_utilities.cuh" -#include "determinism_utils.cuh" -#include "mip_utils.cuh" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include -#include -#include - -#include - -#include -#include -#include - -#include -#include -#include -#include -#include - -namespace cuopt::linear_programming::test { - -void init_handler(const raft::handle_t* handle_ptr) -{ - // Init cuBlas / cuSparse context here to avoid having it during solving time - RAFT_CUBLAS_TRY(raft::linalg::detail::cublassetpointermode( - handle_ptr->get_cublas_handle(), CUBLAS_POINTER_MODE_DEVICE, handle_ptr->get_stream())); - RAFT_CUSPARSE_TRY(raft::sparse::detail::cusparsesetpointermode( - handle_ptr->get_cusparse_handle(), CUSPARSE_POINTER_MODE_DEVICE, handle_ptr->get_stream())); -} - -static void setup_device_symbols(rmm::cuda_stream_view stream_view) -{ - raft::common::nvtx::range fun_scope("Setting device symbol"); - detail::set_adaptive_step_size_hyper_parameters(stream_view); - detail::set_restart_hyper_parameters(stream_view); - detail::set_pdlp_hyper_parameters(stream_view); -} - -static uint32_t test_full_run_determinism(std::string path, - unsigned long seed = std::random_device{}()) -{ - const raft::handle_t handle_{}; - - cuopt::mps_parser::mps_data_model_t mps_problem = - cuopt::mps_parser::parse_mps(path, false); - handle_.sync_stream(); - auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); - problem_checking_t::check_problem_representation(op_problem); - - init_handler(op_problem.get_handle_ptr()); - // run the problem constructor of MIP, so that we do bounds standardization - detail::problem_t problem(op_problem); - problem.preprocess_problem(); - - setup_device_symbols(op_problem.get_handle_ptr()->get_stream()); - - detail::pdlp_initial_scaling_strategy_t scaling(&handle_, - problem, - 10, - 1.0, - problem.reverse_coefficients, - problem.reverse_offsets, - problem.reverse_constraints, - nullptr, - true); - - auto settings = mip_solver_settings_t{}; - settings.time_limit = 3000.; - settings.work_limit = 10; // about 10 seconds of runtime - settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; - settings.heuristics_only = true; - auto timer = cuopt::timer_t(3000); - detail::mip_solver_t solver(problem, settings, scaling, timer); - problem.tolerances = settings.get_tolerances(); - - detail::diversity_manager_t diversity_manager(solver.context); - work_limit_context_t work_limit_context("DiversityManager"); - diversity_manager.timer = work_limit_timer_t(work_limit_context, 60000); - diversity_manager.run_solver(); - - std::vector hashes; - auto pop = diversity_manager.get_population_pointer(); - for (const auto& sol : pop->population_to_vector()) { - hashes.push_back(sol.get_hash()); - } - - uint32_t final_hash = detail::compute_hash(hashes); - printf("%s: final hash: 0x%x, pop size %d\n", - path.c_str(), - final_hash, - (int)pop->population_to_vector().size()); - return final_hash; -} - -static uint32_t test_initial_solution_determinism(std::string path, - unsigned long seed = std::random_device{}()) -{ - const raft::handle_t handle_{}; - - cuopt::mps_parser::mps_data_model_t mps_problem = - cuopt::mps_parser::parse_mps(path, false); - handle_.sync_stream(); - auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); - problem_checking_t::check_problem_representation(op_problem); - - init_handler(op_problem.get_handle_ptr()); - // run the problem constructor of MIP, so that we do bounds standardization - detail::problem_t problem(op_problem); - problem.preprocess_problem(); - - setup_device_symbols(op_problem.get_handle_ptr()->get_stream()); - - detail::pdlp_initial_scaling_strategy_t scaling(&handle_, - problem, - 10, - 1.0, - problem.reverse_coefficients, - problem.reverse_offsets, - problem.reverse_constraints, - nullptr, - true); - - auto settings = mip_solver_settings_t{}; - settings.time_limit = 3000.; - settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; - settings.heuristics_only = true; - auto timer = cuopt::timer_t(3000); - detail::mip_solver_t solver(problem, settings, scaling, timer); - problem.tolerances = settings.get_tolerances(); - - detail::diversity_manager_t diversity_manager(solver.context); - work_limit_context_t work_limit_context("DiversityManager"); - diversity_manager.timer = work_limit_timer_t(work_limit_context, 60000); - diversity_manager.diversity_config.initial_solution_only = true; - diversity_manager.run_solver(); - - std::vector hashes; - auto pop = diversity_manager.get_population_pointer(); - for (const auto& sol : pop->population_to_vector()) { - hashes.push_back(sol.get_hash()); - } - - uint32_t final_hash = detail::compute_hash(hashes); - printf("%s: final hash: 0x%x, pop size %d\n", - path.c_str(), - final_hash, - (int)pop->population_to_vector().size()); - return final_hash; -} - -static uint32_t test_recombiners_determinism(std::string path, - unsigned long seed = std::random_device{}()) -{ - const raft::handle_t handle_{}; - - cuopt::mps_parser::mps_data_model_t mps_problem = - cuopt::mps_parser::parse_mps(path, false); - handle_.sync_stream(); - auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); - problem_checking_t::check_problem_representation(op_problem); - - init_handler(op_problem.get_handle_ptr()); - // run the problem constructor of MIP, so that we do bounds standardization - detail::problem_t problem(op_problem); - problem.preprocess_problem(); - - setup_device_symbols(op_problem.get_handle_ptr()->get_stream()); - - detail::pdlp_initial_scaling_strategy_t scaling(&handle_, - problem, - 10, - 1.0, - problem.reverse_coefficients, - problem.reverse_offsets, - problem.reverse_constraints, - nullptr, - true); - - auto settings = mip_solver_settings_t{}; - settings.time_limit = 3000.; - settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; - settings.heuristics_only = true; - auto timer = cuopt::timer_t(3000); - detail::mip_solver_t solver(problem, settings, scaling, timer); - problem.tolerances = settings.get_tolerances(); - - detail::diversity_manager_t diversity_manager(solver.context); - work_limit_context_t work_limit_context("DiversityManager"); - diversity_manager.timer = work_limit_timer_t(work_limit_context, 60000); - diversity_manager.diversity_config.dry_run = true; - diversity_manager.run_solver(); - - // Generate a population by running FJ on random starting points - // recombine a few solutions, observe the output - for (int i = diversity_manager.population.current_size(); i < 3; ++i) { - detail::solution_t random_initial_solution(problem); - random_initial_solution.assign_random_within_bounds(); - detail::fj_settings_t fj_settings; - fj_settings.feasibility_run = false; - fj_settings.iteration_limit = 1000 + i * 100; - fj_settings.seed = seed + i; - auto solution = - run_fj(problem, fj_settings, fj_tweaks_t{}, random_initial_solution.get_host_assignment()) - .solution; - printf("population %d hash: 0x%x\n", i, solution.get_hash()); - diversity_manager.population.add_solution(std::move(solution)); - } - - auto pop_vector = diversity_manager.get_population_pointer()->population_to_vector(); - - std::vector hashes; - - static std::map, uint32_t> hash_map; - - for (auto recombiner : {detail::recombiner_enum_t::LINE_SEGMENT, - detail::recombiner_enum_t::BOUND_PROP, - detail::recombiner_enum_t::FP}) { - for (int i = 1; i < (int)pop_vector.size(); i++) { - for (int j = i + 1; j < (int)pop_vector.size(); j++) { - printf("recombining %d and %d w/ recombiner %s\n", - i, - j, - detail::all_recombine_stats::recombiner_labels[(int)recombiner]); - auto [offspring, success] = - diversity_manager.recombine(pop_vector[i], pop_vector[j], recombiner); - auto offspring_hash = offspring.get_hash(); - printf("for %d,%d: offspring hash: 0x%x, parent 1 hash: 0x%x, parent 2 hash: 0x%x\n", - i, - j, - offspring_hash, - pop_vector[i].get_hash(), - pop_vector[j].get_hash()); - if (hash_map.find(std::make_tuple(path, i, j, recombiner)) == hash_map.end()) { - hash_map[std::make_tuple(path, i, j, recombiner)] = offspring_hash; - } else { - if (hash_map[std::make_tuple(path, i, j, recombiner)] != offspring_hash) { - printf("%s: hash mismatch for %d,%d: %d != %d\n", - path.c_str(), - i, - j, - hash_map[std::make_tuple(path, i, j, recombiner)], - offspring_hash); - exit(1); - } - } - hashes.push_back(offspring_hash); - } - } - } - return detail::compute_hash(hashes); - - auto pop = diversity_manager.get_population_pointer(); - for (const auto& sol : pop->population_to_vector()) { - hashes.push_back(sol.get_hash()); - } - - uint32_t final_hash = detail::compute_hash(hashes); - printf("%s: final hash: 0x%x, pop size %d\n", - path.c_str(), - final_hash, - (int)pop->population_to_vector().size()); - return final_hash; -} - -class DiversityTestParams : public testing::TestWithParam> {}; - -// TEST_P(DiversityTestParams, recombiners_deterministic) -// { -// cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); - -// spin_stream_raii_t spin_stream_1; -// spin_stream_raii_t spin_stream_2; - -// auto test_instance = std::get<0>(GetParam()); -// std::cout << "Running: " << test_instance << std::endl; -// int seed = -// std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); -// std::cerr << "Tested with seed " << seed << "\n"; -// auto path = make_path_absolute(test_instance); -// test_instance = std::getenv("CUOPT_INSTANCE") ? std::getenv("CUOPT_INSTANCE") : test_instance; -// path = "/home/scratch.yboucher_gpu_1/collection/" + test_instance; -// uint32_t gold_hash = 0; -// for (int i = 0; i < 2; ++i) { -// cuopt::seed_generator::set_seed(seed); -// std::cout << "Running " << test_instance << " " << i << std::endl; -// std::cout << "-------------------------------------------------------------\n"; -// auto hash = test_recombiners_determinism(path, seed); -// if (i == 0) { -// gold_hash = hash; -// std::cout << "Gold hash: " << gold_hash << std::endl; -// } else { -// ASSERT_EQ(hash, gold_hash); -// } -// } -// } - -// TEST_P(DiversityTestParams, initial_solution_deterministic) -// { -// cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); - -// spin_stream_raii_t spin_stream_1; -// spin_stream_raii_t spin_stream_2; - -// auto test_instance = std::get<0>(GetParam()); -// std::cout << "Running: " << test_instance << std::endl; -// int seed = -// std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); -// std::cerr << "Tested with seed " << seed << "\n"; -// auto path = make_path_absolute(test_instance); -// test_instance = std::getenv("CUOPT_INSTANCE") ? std::getenv("CUOPT_INSTANCE") : test_instance; -// path = "/home/scratch.yboucher_gpu_1/collection/" + test_instance; -// uint32_t gold_hash = 0; -// for (int i = 0; i < 2; ++i) { -// cuopt::seed_generator::set_seed(seed); -// std::cout << "Running " << test_instance << " " << i << std::endl; -// std::cout << "-------------------------------------------------------------\n"; -// auto hash = test_initial_solution_determinism(path, seed); -// if (i == 0) { -// gold_hash = hash; -// std::cout << "Gold hash: " << gold_hash << std::endl; -// } else { -// ASSERT_EQ(hash, gold_hash); -// } -// } -// } - -TEST_P(DiversityTestParams, full_run_deterministic) -{ - cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); - - spin_stream_raii_t spin_stream_1; - spin_stream_raii_t spin_stream_2; - - auto test_instance = std::get<0>(GetParam()); - std::cout << "Running: " << test_instance << std::endl; - int seed = - std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); - std::cerr << "Tested with seed " << seed << "\n"; - auto path = make_path_absolute(test_instance); - test_instance = std::getenv("CUOPT_INSTANCE") ? std::getenv("CUOPT_INSTANCE") : test_instance; - path = "/home/scratch.yboucher_gpu_1/collection/" + test_instance; - uint32_t gold_hash = 0; - for (int i = 0; i < 2; ++i) { - cuopt::seed_generator::set_seed(seed); - std::cout << "Running " << test_instance << " " << i << std::endl; - std::cout << "-------------------------------------------------------------\n"; - auto hash = test_full_run_determinism(path, seed); - if (i == 0) { - gold_hash = hash; - std::cout << "Gold hash: " << gold_hash << std::endl; - } else { - ASSERT_EQ(hash, gold_hash); - } - } -} - -INSTANTIATE_TEST_SUITE_P(DiversityTest, - DiversityTestParams, - testing::Values( // std::make_tuple("gen-ip054.mps"), - // std::make_tuple("pk1.mps") - std::make_tuple("uccase9.mps"), - // std::make_tuple("mip/sct2.mps") - // std::make_tuple("mip/thor50dday.mps") - // std::make_tuple("uccase9.mps"), - // std::make_tuple("mip/neos5.mps") - std::make_tuple("50v-10.mps") - // std::make_tuple("rmatr200-p5.mps") - )); - -} // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/feasibility_jump_tests.cu b/cpp/tests/mip/feasibility_jump_tests.cu index 19390e568..36410599c 100644 --- a/cpp/tests/mip/feasibility_jump_tests.cu +++ b/cpp/tests/mip/feasibility_jump_tests.cu @@ -17,7 +17,6 @@ #include #include #include -#include #include #include @@ -46,22 +45,28 @@ void init_handler(const raft::handle_t* handle_ptr) handle_ptr->get_cusparse_handle(), CUSPARSE_POINTER_MODE_DEVICE, handle_ptr->get_stream())); } +struct fj_tweaks_t { + double objective_weight = 0; +}; + +struct fj_state_t { + detail::solution_t solution; + std::vector solution_vector; + int minimums; + double incumbent_objective; + double incumbent_violation; +}; + // Helper function to setup MIP solver and run FJ with given settings and initial solution -static fj_state_t run_fj_instance(std::string test_instance, - const detail::fj_settings_t& fj_settings, - fj_tweaks_t tweaks = {}, - std::vector initial_solution = {}) +static fj_state_t run_fj(std::string test_instance, + const detail::fj_settings_t& fj_settings, + fj_tweaks_t tweaks = {}, + std::vector initial_solution = {}) { const raft::handle_t handle_{}; std::cout << "Running: " << test_instance << std::endl; auto path = cuopt::test::get_rapids_dataset_root_dir() + ("/mip/" + test_instance); - - if (std::getenv("CUOPT_INSTANCE")) { - path = std::string("/home/scratch.yboucher_gpu_1/collection/") + std::getenv("CUOPT_INSTANCE"); - std::cout << "Using instance from CUOPT_INSTANCE: " << path << std::endl; - } - cuopt::mps_parser::mps_data_model_t mps_problem = cuopt::mps_parser::parse_mps(path, false); handle_.sync_stream(); @@ -72,8 +77,46 @@ static fj_state_t run_fj_instance(std::string test_instance, // run the problem constructor of MIP, so that we do bounds standardization detail::problem_t problem(op_problem); problem.preprocess_problem(); + detail::pdhg_solver_t pdhg_solver(problem.handle_ptr, problem); + detail::pdlp_initial_scaling_strategy_t scaling(&handle_, + problem, + 10, + 1.0, + pdhg_solver, + problem.reverse_coefficients, + problem.reverse_offsets, + problem.reverse_constraints, + true); + + auto settings = mip_solver_settings_t{}; + settings.time_limit = 30.; + auto timer = cuopt::timer_t(30); + detail::mip_solver_t solver(problem, settings, scaling, timer); + + detail::solution_t solution(*solver.context.problem_ptr); + if (initial_solution.size() > 0) { + expand_device_copy(solution.assignment, initial_solution, solution.handle_ptr->get_stream()); + } else { + thrust::fill(solution.handle_ptr->get_thrust_policy(), + solution.assignment.begin(), + solution.assignment.end(), + 0.0); + } + solution.clamp_within_bounds(); + + detail::fj_t fj(solver.context, fj_settings); + fj.reset_weights(solution.handle_ptr->get_stream(), 1.); + fj.objective_weight.set_value_async(tweaks.objective_weight, solution.handle_ptr->get_stream()); + solution.handle_ptr->sync_stream(); - return run_fj(problem, fj_settings, tweaks, initial_solution); + fj.solve(solution); + auto solution_vector = host_copy(solution.assignment, solution.handle_ptr->get_stream()); + + return {solution, + solution_vector, + fj.climbers[0]->local_minimums_reached.value(solution.handle_ptr->get_stream()), + fj.climbers[0]->incumbent_objective.value(solution.handle_ptr->get_stream()), + fj.climbers[0]->violation_score.value(solution.handle_ptr->get_stream())}; } // FJ had a bug causing objective/violation values to explode in magnitude in certain scenarios. @@ -83,12 +126,12 @@ static bool run_fj_check_no_obj_runoff(std::string test_instance) detail::fj_settings_t fj_settings; fj_settings.time_limit = 30.; fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 5000; + fj_settings.n_of_minimums_for_exit = 20000 * 1000; fj_settings.update_weights = true; fj_settings.feasibility_run = false; fj_settings.iteration_limit = 20000; - auto state = run_fj_instance(test_instance, fj_settings); + auto state = run_fj(test_instance, fj_settings); // ensure that the objective and the violation in the FJ state are not too large (<1e60) EXPECT_LE(state.incumbent_violation, 1e60) << "FJ violation too large"; @@ -105,12 +148,12 @@ static bool run_fj_check_objective(std::string test_instance, int iter_limit, do detail::fj_settings_t fj_settings; fj_settings.time_limit = 30.; fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 5000; + fj_settings.n_of_minimums_for_exit = 20000 * 1000; fj_settings.update_weights = true; fj_settings.feasibility_run = obj_target == +std::numeric_limits::infinity(); fj_settings.iteration_limit = iter_limit; - auto state = run_fj_instance(test_instance, fj_settings); + auto state = run_fj(test_instance, fj_settings); auto& solution = state.solution; CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", @@ -132,12 +175,12 @@ static bool run_fj_check_feasible(std::string test_instance) detail::fj_settings_t fj_settings; fj_settings.time_limit = 30.; fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 5000; + fj_settings.n_of_minimums_for_exit = 20000 * 1000; fj_settings.update_weights = true; fj_settings.feasibility_run = false; fj_settings.iteration_limit = 25000; - auto state = run_fj_instance(test_instance, fj_settings); + auto state = run_fj(test_instance, fj_settings); auto& solution = state.solution; bool previous_feasible = solution.get_feasible(); @@ -148,8 +191,8 @@ static bool run_fj_check_feasible(std::string test_instance) // again but with very large obj weight to force FJ into the infeasible region fj_tweaks_t tweaks; tweaks.objective_weight = 1e6; - auto new_state = run_fj_instance(test_instance, fj_settings, tweaks, state.solution_vector); - auto& new_solution = new_state.solution; + auto new_state = run_fj(test_instance, fj_settings, tweaks, state.solution_vector); + auto& new_solution = new_state.solution; CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", test_instance.c_str(), @@ -164,117 +207,63 @@ static bool run_fj_check_feasible(std::string test_instance) return true; } -static bool run_fj_check_determinism(std::string test_instance, int iter_limit) -{ - detail::fj_settings_t fj_settings; - fj_settings.time_limit = std::numeric_limits::max(); - fj_settings.mode = detail::fj_mode_t::EXIT_NON_IMPROVING; - fj_settings.n_of_minimums_for_exit = 5000 * 1000; - fj_settings.work_limit = 0.5; // run for 0.5wu (~0.5s) - fj_settings.update_weights = true; - fj_settings.feasibility_run = false; - // fj_settings.iteration_limit = iter_limit; - fj_settings.load_balancing_mode = detail::fj_load_balancing_mode_t::ALWAYS_ON; - fj_settings.seed = cuopt::seed_generator::get_seed(); +class MIPSolveParametricTest : public testing::TestWithParam> { +}; - auto state = run_fj_instance(test_instance, fj_settings); - auto& solution = state.solution; - - CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", - test_instance.c_str(), - solution.get_feasible(), - solution.get_user_objective(), - solution.get_objective()); +TEST_P(MIPSolveParametricTest, feasibility_jump_obj_test) +{ + auto [instance, obj_target, iter_limit] = GetParam(); + EXPECT_TRUE(run_fj_check_objective(instance, iter_limit, obj_target)); +} - static std::unordered_map first_val_map; - if (first_val_map.count(test_instance) == 0) { - first_val_map[test_instance] = solution.get_user_objective(); +INSTANTIATE_TEST_SUITE_P( + MIPSolveTest, + MIPSolveParametricTest, + testing::Values( + // Bug: https://github.com/NVIDIA/cuopt/issues/214 + // std::make_tuple("50v-10.mps", 7800, 100000), + // std::make_tuple("fiball.mps", 140, 25000), + // std::make_tuple("rmatr200-p5.mps", 7000, 10000), + std::make_tuple("gen-ip054.mps", 7500, 20000), + std::make_tuple("sct2.mps", 100, 50000), + std::make_tuple("uccase9.mps", 4000000, 50000), + // unstable, prone to failure on slight weight changes + // std::make_tuple("drayage-25-23.mps", 300000, 50000), + std::make_tuple("tr12-30.mps", 300000, 50000), + std::make_tuple("neos-3004026-krka.mps", + +std::numeric_limits::infinity(), + 35000), // feasibility + // std::make_tuple("nursesched-medium-hint03.mps", 12000, 50000), // too large + std::make_tuple("ns1208400.mps", 2, 60000), + std::make_tuple("gmu-35-50.mps", -2300000, 25000), + std::make_tuple("n2seq36q.mps", 158800, 25000), + std::make_tuple("seymour1.mps", 440, 50000), + std::make_tuple("cvs16r128-89.mps", -50, 10000) +// TEMPORARY: occasional cusparse transpose issues on ARM in CI +#ifndef __aarch64__ + , + std::make_tuple("thor50dday.mps", 250000, 1000) +#endif + )); + +TEST(mip_solve, feasibility_jump_feas_test) +{ + for (const auto& instance : {"tr12-30.mps", + "sct2.mps" +#ifndef __aarch64__ + , + "thor50dday.mps" +#endif + }) { + run_fj_check_feasible(instance); } - if (std::abs(solution.get_user_objective() - first_val_map[test_instance]) > 1) exit(0); - - return true; } -// class MIPSolveParametricTest : public testing::TestWithParam> { -// }; - -// TEST_P(MIPSolveParametricTest, feasibility_jump_obj_test) -// { -// auto [instance, obj_target, iter_limit] = GetParam(); -// EXPECT_TRUE(run_fj_check_objective(instance, iter_limit, obj_target)); -// } - -// INSTANTIATE_TEST_SUITE_P( -// MIPSolveTest, -// MIPSolveParametricTest, -// testing::Values( -// // Bug: https://github.com/NVIDIA/cuopt/issues/214 -// // std::make_tuple("50v-10.mps", 7800, 100000), -// // std::make_tuple("fiball.mps", 140, 25000), -// // std::make_tuple("rmatr200-p5.mps", 7000, 10000), -// std::make_tuple("gen-ip054.mps", 7500, 20000), -// std::make_tuple("sct2.mps", 100, 50000), -// std::make_tuple("uccase9.mps", 4000000, 50000), -// // unstable, prone to failure on slight weight changes -// // std::make_tuple("drayage-25-23.mps", 300000, 50000), -// std::make_tuple("tr12-30.mps", 300000, 50000), -// std::make_tuple("neos-3004026-krka.mps", -// +std::numeric_limits::infinity(), -// 35000), // feasibility -// // std::make_tuple("nursesched-medium-hint03.mps", 12000, 50000), // too large -// std::make_tuple("ns1208400.mps", 2, 60000), -// std::make_tuple("gmu-35-50.mps", -2300000, 25000), -// std::make_tuple("n2seq36q.mps", 158800, 25000), -// std::make_tuple("seymour1.mps", 440, 50000), -// std::make_tuple("cvs16r128-89.mps", -50, 10000) -// // TEMPORARY: occasional cusparse transpose issues on ARM in CI -// #ifndef __aarch64__ -// , -// std::make_tuple("thor50dday.mps", 250000, 1000) -// #endif -// )); - -// TEST(mip_solve, feasibility_jump_feas_test) -// { -// for (const auto& instance : {"tr12-30.mps", -// "sct2.mps" -// #ifndef __aarch64__ -// , -// "thor50dday.mps" -// #endif -// }) { -// run_fj_check_feasible(instance); -// } -// } - -// TEST(mip_solve, feasibility_jump_obj_runoff_test) -// { -// for (const auto& instance : {"minrep_inf.mps", "sct2.mps", "uccase9.mps", -// /*"buildingenergy.mps"*/}) { -// run_fj_check_no_obj_runoff(instance); -// } -// } - -TEST(mip_solve, feasibility_jump_determinism) +TEST(mip_solve, feasibility_jump_obj_runoff_test) { - int seed = - std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); - - for (const auto& [instance, iter_limit] : {std::make_pair("thor50dday.mps", 1000), - std::make_pair("gen-ip054.mps", 1000), - std::make_pair("50v-10.mps", 1000), - std::make_pair("seymour1.mps", 1000), - std::make_pair("rmatr200-p5.mps", 1000), - std::make_pair("tr12-30.mps", 1000), - std::make_pair("sct2.mps", 1000), - std::make_pair("uccase9.mps", 1000), - std::make_pair("supportcase42.mps", 25000)}) { - for (int i = 0; i < 10; i++) { - // while (true) { - cuopt::seed_generator::set_seed(seed); - run_fj_check_determinism(instance, iter_limit); - } + for (const auto& instance : {"minrep_inf.mps", "sct2.mps", "uccase9.mps", + /*"buildingenergy.mps"*/}) { + run_fj_check_no_obj_runoff(instance); } } diff --git a/cpp/tests/mip/local_search_test.cu b/cpp/tests/mip/local_search_test.cu deleted file mode 100644 index 8b541958b..000000000 --- a/cpp/tests/mip/local_search_test.cu +++ /dev/null @@ -1,263 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "../linear_programming/utilities/pdlp_test_utilities.cuh" -#include "determinism_utils.cuh" -#include "mip_utils.cuh" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include -#include -#include - -#include - -#include -#include -#include - -#include -#include -#include -#include -#include - -namespace cuopt::linear_programming::test { - -void init_handler(const raft::handle_t* handle_ptr) -{ - // Init cuBlas / cuSparse context here to avoid having it during solving time - RAFT_CUBLAS_TRY(raft::linalg::detail::cublassetpointermode( - handle_ptr->get_cublas_handle(), CUBLAS_POINTER_MODE_DEVICE, handle_ptr->get_stream())); - RAFT_CUSPARSE_TRY(raft::sparse::detail::cusparsesetpointermode( - handle_ptr->get_cusparse_handle(), CUSPARSE_POINTER_MODE_DEVICE, handle_ptr->get_stream())); -} - -static void setup_device_symbols(rmm::cuda_stream_view stream_view) -{ - raft::common::nvtx::range fun_scope("Setting device symbol"); - detail::set_adaptive_step_size_hyper_parameters(stream_view); - detail::set_restart_hyper_parameters(stream_view); - detail::set_pdlp_hyper_parameters(stream_view); -} - -enum local_search_mode_t { - FP = 0, - STAGED_FP, - FJ_LINE_SEGMENT, - FJ_ON_ZERO, - FJ_ANNEALING, -}; - -// Helper function to setup MIP solver and run FJ with given settings and initial solution -static uint32_t run_fp(std::string test_instance, local_search_mode_t mode) -{ - const raft::handle_t handle_{}; - std::cout << "Running: " << test_instance << std::endl; - - auto path = cuopt::test::get_rapids_dataset_root_dir() + ("/mip/" + test_instance); - cuopt::mps_parser::mps_data_model_t mps_problem = - cuopt::mps_parser::parse_mps(path, false); - handle_.sync_stream(); - auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); - problem_checking_t::check_problem_representation(op_problem); - - init_handler(op_problem.get_handle_ptr()); - // run the problem constructor of MIP, so that we do bounds standardization - detail::problem_t problem(op_problem); - problem.preprocess_problem(); - - setup_device_symbols(op_problem.get_handle_ptr()->get_stream()); - - detail::pdlp_initial_scaling_strategy_t scaling(&handle_, - problem, - 10, - 1.0, - problem.reverse_coefficients, - problem.reverse_offsets, - problem.reverse_constraints, - nullptr, - true); - - auto settings = mip_solver_settings_t{}; - settings.time_limit = 30.; - settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; - auto timer = cuopt::timer_t(30); - detail::mip_solver_t solver(problem, settings, scaling, timer); - problem.tolerances = settings.get_tolerances(); - - rmm::device_uvector lp_optimal_solution(problem.n_variables, - problem.handle_ptr->get_stream()); - thrust::fill(problem.handle_ptr->get_thrust_policy(), - lp_optimal_solution.begin(), - lp_optimal_solution.end(), - 0.0); - detail::lp_state_t& lp_state = problem.lp_state; - // resize because some constructor might be called before the presolve - lp_state.resize(problem, problem.handle_ptr->get_stream()); - detail::relaxed_lp_settings_t lp_settings{}; - lp_settings.time_limit = std::numeric_limits::max(); - lp_settings.tolerance = 1e-6; - lp_settings.return_first_feasible = false; - lp_settings.save_state = false; - // lp_settings.iteration_limit = 5; - auto lp_result = - detail::get_relaxed_lp_solution(problem, lp_optimal_solution, lp_state, lp_settings); - EXPECT_EQ(lp_result.get_termination_status(), pdlp_termination_status_t::Optimal); - clamp_within_var_bounds(lp_optimal_solution, &problem, problem.handle_ptr); - - // return detail::compute_hash(lp_optimal_solution); - - detail::local_search_t local_search(solver.context, lp_optimal_solution); - - detail::solution_t solution(problem); - solution.assign_random_within_bounds(); - solution.compute_feasibility(); - - printf("Model fingerprint: 0x%x\n", problem.get_fingerprint()); - printf("LP optimal hash: 0x%x\n", detail::compute_hash(lp_optimal_solution)); - printf("running mode: %d\n", mode); - - work_limit_context_t work_limit_context("LocalSearch"); - local_search.fp.timer = work_limit_timer_t(work_limit_context, 6000); - - detail::ls_config_t ls_config{}; - - if (mode == local_search_mode_t::FP) { - bool is_feasible = false; - int iterations = 0; - while (true) { - is_feasible = local_search.fp.run_single_fp_descent(solution); - printf("fp_loop it %d, is_feasible %d\n", iterations, is_feasible); - // if feasible return true - if (is_feasible) { - break; - } - // if not feasible, it means it is a cycle - else { - is_feasible = local_search.fp.restart_fp(solution); - if (is_feasible) { break; } - } - iterations++; - } - } else if (mode == local_search_mode_t::FJ_LINE_SEGMENT) { - local_search.run_fj_line_segment( - solution, work_limit_timer_t(work_limit_context, 6000), ls_config); - } else if (mode == local_search_mode_t::FJ_ON_ZERO) { - local_search.run_fj_on_zero(solution, work_limit_timer_t(work_limit_context, 6000)); - } else if (mode == local_search_mode_t::FJ_ANNEALING) { - local_search.run_fj_annealing( - solution, work_limit_timer_t(work_limit_context, 6000), ls_config); - } - - std::vector hashes; - hashes.push_back(detail::compute_hash(solution.get_host_assignment())); - printf("hashes: 0x%x, hash of the hash: 0x%x\n", hashes[0], detail::compute_hash(hashes)); - - return detail::compute_hash(hashes); - // return {host_copy(solution_vector, problem.handle_ptr->get_stream()), iterations}; -} - -static uint32_t run_fp_check_determinism(std::string test_instance, local_search_mode_t mode) -{ - int seed = - std::getenv("CUOPT_SEED") ? std::stoi(std::getenv("CUOPT_SEED")) : std::random_device{}(); - cuopt::seed_generator::set_seed(seed); - - return run_fp(test_instance, mode); - - // auto state = run_fp(test_instance, fj_settings); - // auto& solution = state.solution; - - // CUOPT_LOG_DEBUG("%s: Solution generated with FJ: is_feasible %d, objective %g (raw %g)", - // test_instance.c_str(), - // solution.get_feasible(), - // solution.get_user_objective(), - // solution.get_objective()); - - // static auto first_val = solution.get_user_objective(); - - // if (abs(solution.get_user_objective() - first_val) > 1) exit(0); -} - -class LocalSearchTestParams : public testing::TestWithParam> {}; - -TEST_P(LocalSearchTestParams, local_search_operator_determinism) -{ - cuopt::default_logger().set_pattern("[%n] [%-6l] %v"); - - spin_stream_raii_t spin_stream_1; - spin_stream_raii_t spin_stream_2; - - auto mode = std::get<0>(GetParam()); - - for (const auto& instance : { - //"thor50dday.mps", - "gen-ip054.mps", - "50v-10.mps", - //"seymour1.mps", - "rmatr200-p5.mps", - //"tr12-30.mps", - //"sct2.mps", - //"uccase9.mps" - }) { - // for (int i = 0; i < 10; i++) - // while (true) { - // run_fp_check_determinism(instance, 1000); - // } - - unsigned long seed = std::random_device{}(); - std::cerr << "Tested with seed " << seed << "\n"; - uint32_t gold_hash = 0; - for (int i = 0; i < 5; ++i) { - uint32_t hash = run_fp_check_determinism(instance, mode); - if (i == 0) { - gold_hash = hash; - printf("Gold hash: 0x%x\n", gold_hash); - } else { - ASSERT_EQ(hash, gold_hash); - printf("Hash: 0x%x\n", hash); - } - } - } -} - -INSTANTIATE_TEST_SUITE_P(LocalSearchTests, - LocalSearchTestParams, - testing::Values( // std::make_tuple(local_search_mode_t::FP), - std::make_tuple(local_search_mode_t::FJ_LINE_SEGMENT), - std::make_tuple(local_search_mode_t::FJ_ON_ZERO), - std::make_tuple(local_search_mode_t::FJ_ANNEALING))); - -} // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/mip_utils.cuh b/cpp/tests/mip/mip_utils.cuh index 4fb96fb80..19c44b2fd 100644 --- a/cpp/tests/mip/mip_utils.cuh +++ b/cpp/tests/mip/mip_utils.cuh @@ -8,10 +8,7 @@ #include #include #include -#include #include -#include -#include #include #include @@ -126,62 +123,4 @@ static std::tuple test_mps_file( solution.get_solution_bound()); } -struct fj_tweaks_t { - double objective_weight = 0; -}; - -struct fj_state_t { - detail::solution_t solution; - std::vector solution_vector; - int minimums; - double incumbent_objective; - double incumbent_violation; -}; - -static fj_state_t run_fj(detail::problem_t& problem, - const detail::fj_settings_t& fj_settings, - fj_tweaks_t tweaks = {}, - std::vector initial_solution = {}) -{ - detail::pdlp_initial_scaling_strategy_t scaling(problem.handle_ptr, - problem, - 10, - 1.0, - problem.reverse_coefficients, - problem.reverse_offsets, - problem.reverse_constraints, - nullptr, - true); - - auto settings = mip_solver_settings_t{}; - settings.time_limit = 30.; - auto timer = timer_t(30); - detail::mip_solver_t solver(problem, settings, scaling, timer); - - detail::solution_t solution(*solver.context.problem_ptr); - if (initial_solution.size() > 0) { - expand_device_copy(solution.assignment, initial_solution, solution.handle_ptr->get_stream()); - } else { - thrust::fill(solution.handle_ptr->get_thrust_policy(), - solution.assignment.begin(), - solution.assignment.end(), - 0.0); - } - solution.clamp_within_bounds(); - - detail::fj_t fj(solver.context, fj_settings); - fj.reset_weights(solution.handle_ptr->get_stream(), 1.); - fj.objective_weight.set_value_async(tweaks.objective_weight, solution.handle_ptr->get_stream()); - solution.handle_ptr->sync_stream(); - - fj.solve(solution); - auto solution_vector = host_copy(solution.assignment, solution.handle_ptr->get_stream()); - - return {solution, - solution_vector, - fj.climbers[0]->local_minimums_reached.value(solution.handle_ptr->get_stream()), - fj.climbers[0]->incumbent_objective.value(solution.handle_ptr->get_stream()), - fj.climbers[0]->violation_score.value(solution.handle_ptr->get_stream())}; -} - } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/miplib_test.cu b/cpp/tests/mip/miplib_test.cu index 5a8266958..f72874d49 100644 --- a/cpp/tests/mip/miplib_test.cu +++ b/cpp/tests/mip/miplib_test.cu @@ -33,9 +33,7 @@ struct result_map_t { double cost; }; -void test_miplib_file(result_map_t test_instance, - mip_solver_settings_t settings, - bool heuristic = false) +void test_miplib_file(result_map_t test_instance, mip_solver_settings_t settings) { const raft::handle_t handle_{}; @@ -51,7 +49,6 @@ void test_miplib_file(result_map_t test_instance, #endif settings.time_limit = test_time_limit; - settings.heuristics_only = heuristic; mip_solution_t solution = solve_mip(&handle_, problem, settings); bool is_feasible = solution.get_termination_status() == mip_termination_status_t::FeasibleFound || solution.get_termination_status() == mip_termination_status_t::Optimal; @@ -73,13 +70,4 @@ TEST(mip_solve, run_small_tests) } } -// TEST(mip_solve, run_small_tests_determinism) -// { -// std::vector test_instances = { -// {"mip/50v-10.mps", 11311031.}, {"mip/neos5.mps", 15.}, {"mip/swath1.mps", 1300.}}; -// for (const auto& test_instance : test_instances) { -// test_miplib_file(test_instance, true); -// } -// } - } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/multi_probe_test.cu b/cpp/tests/mip/multi_probe_test.cu index f5a6e1a21..9a933c054 100644 --- a/cpp/tests/mip/multi_probe_test.cu +++ b/cpp/tests/mip/multi_probe_test.cu @@ -6,7 +6,6 @@ /* clang-format on */ #include "../linear_programming/utilities/pdlp_test_utilities.cuh" -#include "determinism_utils.cuh" #include "mip_utils.cuh" #include @@ -14,7 +13,6 @@ #include #include #include -#include #include #include #include @@ -45,10 +43,9 @@ void init_handler(const raft::handle_t* handle_ptr) } std::tuple, std::vector, std::vector> select_k_random( - detail::problem_t& problem, - int sample_size, - unsigned long seed = std::random_device{}()) + detail::problem_t& problem, int sample_size) { + auto seed = std::random_device{}(); std::cerr << "Tested with seed " << seed << "\n"; problem.compute_n_integer_vars(); auto [v_lb, v_ub] = extract_host_bounds(problem.variable_bounds, problem.handle_ptr); @@ -141,7 +138,7 @@ multi_probe_results( std::move(h_lb_0), std::move(h_ub_0), std::move(h_lb_1), std::move(h_ub_1)); } -uint32_t test_multi_probe(std::string path, unsigned long seed = std::random_device{}()) +void test_multi_probe(std::string path) { auto memory_resource = make_async(); rmm::mr::set_current_device_resource(memory_resource.get()); @@ -162,12 +159,12 @@ uint32_t test_multi_probe(std::string path, unsigned long seed = std::random_dev problem.reverse_constraints, nullptr, true); - detail::mip_solver_t solver(problem, default_settings, scaling, timer_t(0)); + detail::mip_solver_t solver(problem, default_settings, scaling, cuopt::timer_t(0)); detail::bound_presolve_t bnd_prb_0(solver.context); detail::bound_presolve_t bnd_prb_1(solver.context); detail::multi_probe_t multi_probe_prs(solver.context); - auto probe_tuple = select_k_random(problem, 100, seed); + auto probe_tuple = select_k_random(problem, 100); auto bounds_probe_vals = convert_probe_tuple(probe_tuple); auto [bnd_lb_0, bnd_ub_0, bnd_lb_1, bnd_ub_1] = @@ -186,16 +183,6 @@ uint32_t test_multi_probe(std::string path, unsigned long seed = std::random_dev auto mlp_min_act_1 = host_copy(multi_probe_prs.upd_1.min_activity, stream); auto mlp_max_act_1 = host_copy(multi_probe_prs.upd_1.max_activity, stream); - std::vector hashes; - hashes.push_back(detail::compute_hash(bnd_min_act_0)); - hashes.push_back(detail::compute_hash(bnd_min_act_1)); - hashes.push_back(detail::compute_hash(bnd_max_act_0)); - hashes.push_back(detail::compute_hash(bnd_max_act_1)); - hashes.push_back(detail::compute_hash(bnd_lb_0)); - hashes.push_back(detail::compute_hash(bnd_ub_0)); - hashes.push_back(detail::compute_hash(bnd_lb_1)); - hashes.push_back(detail::compute_hash(bnd_ub_1)); - for (int i = 0; i < (int)bnd_min_act_0.size(); ++i) { EXPECT_DOUBLE_EQ(bnd_min_act_0[i], mlp_min_act_0[i]); EXPECT_DOUBLE_EQ(bnd_max_act_0[i], mlp_max_act_0[i]); @@ -209,45 +196,17 @@ uint32_t test_multi_probe(std::string path, unsigned long seed = std::random_dev EXPECT_DOUBLE_EQ(bnd_lb_1[i], m_lb_1[i]); EXPECT_DOUBLE_EQ(bnd_ub_1[i], m_ub_1[i]); } - - // return a composite hash of all the hashes to check for determinism - return detail::compute_hash(hashes); } -// TEST(presolve, multi_probe) -// { -// std::vector test_instances = { -// "mip/50v-10-free-bound.mps", "mip/neos5-free-bound.mps", "mip/neos5.mps"}; -// for (const auto& test_instance : test_instances) { -// std::cout << "Running: " << test_instance << std::endl; -// auto path = make_path_absolute(test_instance); -// test_multi_probe(path); -// } -// } - -TEST(presolve, multi_probe_deterministic) +TEST(presolve, multi_probe) { - spin_stream_raii_t spin_stream_1; - std::vector test_instances = { - "mip/50v-10-free-bound.mps", - "mip/neos5-free-bound.mps", - "mip/neos5.mps", - "mip/50v-10.mps", - }; + "mip/50v-10-free-bound.mps", "mip/neos5-free-bound.mps", "mip/neos5.mps"}; for (const auto& test_instance : test_instances) { std::cout << "Running: " << test_instance << std::endl; - unsigned long seed = std::random_device{}(); - auto path = make_path_absolute(test_instance); - uint32_t gold_hash = 0; - for (int i = 0; i < 10; ++i) { - auto hash = test_multi_probe(path, seed); - if (i == 0) { - gold_hash = hash; - } else { - EXPECT_EQ(hash, gold_hash); - } - } + auto path = make_path_absolute(test_instance); + test_multi_probe(path); } } + } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/presolve_test.cu b/cpp/tests/mip/presolve_test.cu index 57cd93fa2..893602e20 100644 --- a/cpp/tests/mip/presolve_test.cu +++ b/cpp/tests/mip/presolve_test.cu @@ -6,20 +6,11 @@ /* clang-format on */ #include "../linear_programming/utilities/pdlp_test_utilities.cuh" -#include "determinism_utils.cuh" -#include "mip_utils.cuh" -#include #include -#include -#include #include -#include -#include #include -#include #include -#include #include #include #include @@ -29,8 +20,6 @@ #include #include -#include - #include #include @@ -40,183 +29,6 @@ namespace cuopt::linear_programming::test { -inline auto make_async() { return std::make_shared(); } - -void init_handler(const raft::handle_t* handle_ptr) -{ - // Init cuBlas / cuSparse context here to avoid having it during solving time - RAFT_CUBLAS_TRY(raft::linalg::detail::cublassetpointermode( - handle_ptr->get_cublas_handle(), CUBLAS_POINTER_MODE_DEVICE, handle_ptr->get_stream())); - RAFT_CUSPARSE_TRY(raft::sparse::detail::cusparsesetpointermode( - handle_ptr->get_cusparse_handle(), CUSPARSE_POINTER_MODE_DEVICE, handle_ptr->get_stream())); -} - -std::tuple, std::vector, std::vector> select_k_random( - detail::problem_t& problem, - int sample_size, - unsigned long seed = std::random_device{}()) -{ - std::cerr << "Tested with seed " << seed << "\n"; - problem.compute_n_integer_vars(); - auto [v_lb, v_ub] = extract_host_bounds(problem.variable_bounds, problem.handle_ptr); - auto int_var_id = host_copy(problem.integer_indices, problem.handle_ptr->get_stream()); - int_var_id.erase( - std::remove_if(int_var_id.begin(), - int_var_id.end(), - [v_lb_sp = v_lb, v_ub_sp = v_ub](auto id) { - return !(std::isfinite(v_lb_sp[id]) && std::isfinite(v_ub_sp[id])); - }), - int_var_id.end()); - sample_size = std::min(sample_size, static_cast(int_var_id.size())); - std::vector random_int_vars; - std::mt19937 m{seed}; - std::sample( - int_var_id.begin(), int_var_id.end(), std::back_inserter(random_int_vars), sample_size, m); - std::vector probe_0(sample_size); - std::vector probe_1(sample_size); - for (int i = 0; i < static_cast(random_int_vars.size()); ++i) { - if (i % 2) { - probe_0[i] = v_lb[random_int_vars[i]]; - probe_1[i] = v_ub[random_int_vars[i]]; - } else { - probe_1[i] = v_lb[random_int_vars[i]]; - probe_0[i] = v_ub[random_int_vars[i]]; - } - } - return std::make_tuple(std::move(random_int_vars), std::move(probe_0), std::move(probe_1)); -} - -std::pair>, std::vector>> -convert_probe_tuple(std::tuple, std::vector, std::vector>& probe) -{ - std::vector> probe_first; - std::vector> probe_second; - for (size_t i = 0; i < std::get<0>(probe).size(); ++i) { - probe_first.emplace_back(thrust::make_pair(std::get<0>(probe)[i], std::get<1>(probe)[i])); - probe_second.emplace_back(thrust::make_pair(std::get<0>(probe)[i], std::get<2>(probe)[i])); - } - return std::make_pair(std::move(probe_first), std::move(probe_second)); -} - -uint32_t test_probing_cache_determinism(std::string path, - unsigned long seed = std::random_device{}()) -{ - auto memory_resource = make_async(); - rmm::mr::set_current_device_resource(memory_resource.get()); - const raft::handle_t handle_{}; - cuopt::mps_parser::mps_data_model_t mps_problem = - cuopt::mps_parser::parse_mps(path, false); - handle_.sync_stream(); - auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); - problem_checking_t::check_problem_representation(op_problem); - detail::problem_t problem(op_problem); - mip_solver_settings_t default_settings{}; - default_settings.mip_scaling = false; // we're not checking scaling determinism here - detail::pdlp_initial_scaling_strategy_t scaling(&handle_, - problem, - 10, - 1.0, - problem.reverse_coefficients, - problem.reverse_offsets, - problem.reverse_constraints, - nullptr, - true); - detail::mip_solver_t solver(problem, default_settings, scaling, cuopt::timer_t(0)); - detail::bound_presolve_t bnd_prb(solver.context); - - work_limit_context_t work_limit_context("ProbingCache"); - // rely on the iteration limit - compute_probing_cache( - bnd_prb, problem, work_limit_timer_t(work_limit_context, std::numeric_limits::max())); - std::vector, 2>>> cached_values( - bnd_prb.probing_cache.probing_cache.begin(), bnd_prb.probing_cache.probing_cache.end()); - std::sort(cached_values.begin(), cached_values.end(), [](const auto& a, const auto& b) { - return a.first < b.first; - }); - - std::vector probed_indices; - std::vector intervals; - std::vector interval_types; - - std::vector var_to_cached_bound_keys; - std::vector var_to_cached_bound_lb; - std::vector var_to_cached_bound_ub; - for (const auto& a : cached_values) { - probed_indices.push_back(a.first); - intervals.push_back(a.second[0].val_interval.val); - intervals.push_back(a.second[1].val_interval.val); - interval_types.push_back(a.second[0].val_interval.interval_type); - interval_types.push_back(a.second[1].val_interval.interval_type); - - auto sorted_map = std::map>( - a.second[0].var_to_cached_bound_map.begin(), a.second[0].var_to_cached_bound_map.end()); - for (const auto& [var_id, cached_bound] : sorted_map) { - var_to_cached_bound_keys.push_back(var_id); - var_to_cached_bound_lb.push_back(cached_bound.lb); - var_to_cached_bound_ub.push_back(cached_bound.ub); - } - } - - std::vector hashes; - hashes.push_back(detail::compute_hash(probed_indices)); - hashes.push_back(detail::compute_hash(intervals)); - hashes.push_back(detail::compute_hash(interval_types)); - hashes.push_back(detail::compute_hash(var_to_cached_bound_keys)); - hashes.push_back(detail::compute_hash(var_to_cached_bound_lb)); - hashes.push_back(detail::compute_hash(var_to_cached_bound_ub)); - - // return a composite hash of all the hashes to check for determinism - return detail::compute_hash(hashes); -} - -uint32_t test_scaling_determinism(std::string path, unsigned long seed = std::random_device{}()) -{ - auto memory_resource = make_async(); - rmm::mr::set_current_device_resource(memory_resource.get()); - const raft::handle_t handle_{}; - cuopt::mps_parser::mps_data_model_t mps_problem = - cuopt::mps_parser::parse_mps(path, false); - handle_.sync_stream(); - auto op_problem = mps_data_model_to_optimization_problem(&handle_, mps_problem); - problem_checking_t::check_problem_representation(op_problem); - detail::problem_t problem(op_problem); - - pdlp_hyper_params::update_primal_weight_on_initial_solution = false; - pdlp_hyper_params::update_step_size_on_initial_solution = true; - // problem contains unpreprocessed data - detail::problem_t scaled_problem(problem); - - detail::pdlp_initial_scaling_strategy_t scaling( - scaled_problem.handle_ptr, - scaled_problem, - pdlp_hyper_params::default_l_inf_ruiz_iterations, - (double)pdlp_hyper_params::default_alpha_pock_chambolle_rescaling, - scaled_problem.reverse_coefficients, - scaled_problem.reverse_offsets, - scaled_problem.reverse_constraints, - nullptr, - true); - - scaling.scale_problem(); - - // generate a random initial solution in order to ensure scaling of solution vectors is - // deterministic as well as the initial step size - std::vector initial_solution(scaled_problem.n_variables); - std::mt19937 m{seed}; - std::generate(initial_solution.begin(), initial_solution.end(), [&m]() { return m(); }); - auto d_initial_solution = device_copy(initial_solution, handle_.get_stream()); - scaling.scale_primal(d_initial_solution); - - scaled_problem.preprocess_problem(); - - detail::trivial_presolve(scaled_problem); - - std::vector hashes; - hashes.push_back(detail::compute_hash(d_initial_solution, handle_.get_stream())); - hashes.push_back(scaled_problem.get_fingerprint()); - return detail::compute_hash(hashes); -} - TEST(problem, find_implied_integers) { const raft::handle_t handle_{}; @@ -244,63 +56,4 @@ TEST(problem, find_implied_integers) ((int)detail::problem_t::var_flags_t::VAR_IMPLIED_INTEGER)); } -TEST(presolve, probing_cache_deterministic) -{ - spin_stream_raii_t spin_stream_1; - - std::vector test_instances = {"mip/50v-10-free-bound.mps", - "mip/neos5-free-bound.mps", - "mip/neos5.mps", - "mip/50v-10.mps", - "mip/gen-ip054.mps", - "mip/rmatr200-p5.mps"}; - for (const auto& test_instance : test_instances) { - std::cout << "Running: " << test_instance << std::endl; - unsigned long seed = std::random_device{}(); - std::cerr << "Tested with seed " << seed << "\n"; - auto path = make_path_absolute(test_instance); - uint32_t gold_hash = 0; - for (int i = 0; i < 10; ++i) { - auto hash = test_probing_cache_determinism(path, seed); - if (i == 0) { - gold_hash = hash; - std::cout << "Gold hash: " << gold_hash << std::endl; - } else { - EXPECT_EQ(hash, gold_hash); - } - } - } -} - -// TEST(presolve, mip_scaling_deterministic) -// { -// spin_stream_raii_t spin_stream_1; -// spin_stream_raii_t spin_stream_2; - -// std::vector test_instances = {"mip/sct2.mps", -// "mip/thor50dday.mps", -// "mip/uccase9.mps", -// "mip/neos5-free-bound.mps", -// "mip/neos5.mps", -// "mip/50v-10.mps", -// "mip/gen-ip054.mps", -// "mip/rmatr200-p5.mps"}; -// for (const auto& test_instance : test_instances) { -// std::cout << "Running: " << test_instance << std::endl; -// unsigned long seed = std::random_device{}(); -// std::cerr << "Tested with seed " << seed << "\n"; -// auto path = make_path_absolute(test_instance); -// uint32_t gold_hash = 0; -// for (int i = 0; i < 10; ++i) { -// auto hash = test_scaling_determinism(path, seed); -// if (i == 0) { -// gold_hash = hash; -// std::cout << "Gold hash: " << gold_hash << std::endl; -// } else { -// EXPECT_EQ(hash, gold_hash); -// } -// } -// } -// } - } // namespace cuopt::linear_programming::test diff --git a/cpp/tests/mip/unit_test.cu b/cpp/tests/mip/unit_test.cu index 45eb08ccb..f9d76611d 100644 --- a/cpp/tests/mip/unit_test.cu +++ b/cpp/tests/mip/unit_test.cu @@ -123,185 +123,164 @@ mps_parser::mps_data_model_t create_single_var_milp_problem(bool ma return problem; } -// TEST(LPTest, TestSampleLP2) -// { -// raft::handle_t handle; - -// // Construct a simple LP problem: -// // Minimize: x -// // Subject to: x <= 1 -// // x <= 1 -// // x >= 0 - -// // One variable, two constraints (both x <= 1) -// std::vector A_values = {1.0, 1.0}; -// std::vector A_indices = {0, 0}; -// std::vector A_offsets = {0, 1, 2}; // CSR: 2 constraints, 1 variable - -// std::vector b = {1.0, 1.0}; // RHS for both constraints -// std::vector b_lower = {-std::numeric_limits::infinity(), -// -std::numeric_limits::infinity()}; - -// std::vector c = {1.0}; // Objective: Minimize x - -// std::vector row_types = {'L', 'L'}; // Both constraints are <= - -// // Build the problem -// mps_parser::mps_data_model_t problem; -// problem.set_csr_constraint_matrix(A_values.data(), -// A_values.size(), -// A_indices.data(), -// A_indices.size(), -// A_offsets.data(), -// A_offsets.size()); -// problem.set_constraint_upper_bounds(b.data(), b.size()); -// problem.set_constraint_lower_bounds(b_lower.data(), b_lower.size()); - -// // Set variable bounds (x >= 0) -// std::vector var_lower = {0.0}; -// std::vector var_upper = {std::numeric_limits::infinity()}; -// problem.set_variable_lower_bounds(var_lower.data(), var_lower.size()); -// problem.set_variable_upper_bounds(var_upper.data(), var_upper.size()); - -// problem.set_objective_coefficients(c.data(), c.size()); -// problem.set_maximize(false); -// // Set up solver settings -// cuopt::linear_programming::pdlp_solver_settings_t settings{}; -// settings.set_optimality_tolerance(1e-2); -// settings.method = cuopt::linear_programming::method_t::PDLP; -// settings.time_limit = 5; - -// // Solve -// auto result = cuopt::linear_programming::solve_lp(&handle, problem, settings); - -// // Check results -// EXPECT_EQ(result.get_termination_status(), -// cuopt::linear_programming::pdlp_termination_status_t::Optimal); -// ASSERT_EQ(result.get_primal_solution().size(), 1); - -// // Copy solution to host to access values -// auto primal_host = cuopt::host_copy(result.get_primal_solution(), handle.get_stream()); -// EXPECT_NEAR(primal_host[0], 0.0, 1e-6); - -// EXPECT_NEAR(result.get_additional_termination_information().primal_objective, 0.0, 1e-6); -// EXPECT_NEAR(result.get_additional_termination_information().dual_objective, 0.0, 1e-6); -// } - -// TEST(LPTest, TestSampleLP) -// { -// raft::handle_t handle; -// auto problem = create_std_lp_problem(); - -// cuopt::linear_programming::pdlp_solver_settings_t settings{}; -// settings.set_optimality_tolerance(1e-4); -// settings.time_limit = 5; -// settings.presolve = false; - -// auto result = cuopt::linear_programming::solve_lp(&handle, problem, settings); - -// EXPECT_EQ(result.get_termination_status(), -// cuopt::linear_programming::pdlp_termination_status_t::Optimal); -// } - -// TEST(ErrorTest, TestError) -// { -// raft::handle_t handle; -// auto problem = create_std_milp_problem(false); - -// cuopt::linear_programming::mip_solver_settings_t settings{}; -// settings.time_limit = 5; -// settings.presolve = false; - -// // Set constraint bounds -// std::vector lower_bounds = {1.0}; -// std::vector upper_bounds = {1.0, 1.0}; -// problem.set_constraint_lower_bounds(lower_bounds.data(), lower_bounds.size()); -// problem.set_constraint_upper_bounds(upper_bounds.data(), upper_bounds.size()); - -// auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); - -// EXPECT_EQ(result.get_termination_status(), -// cuopt::linear_programming::mip_termination_status_t::NoTermination); -// } - -// class MILPTestParams -// : public testing::TestWithParam< -// std::tuple> {}; - -// TEST_P(MILPTestParams, TestSampleMILP) -// { -// bool maximize = std::get<0>(GetParam()); -// bool scaling = std::get<1>(GetParam()); -// bool heuristics_only = std::get<2>(GetParam()); -// auto expected_termination_status = std::get<3>(GetParam()); - -// raft::handle_t handle; -// auto problem = create_std_milp_problem(maximize); - -// cuopt::linear_programming::mip_solver_settings_t settings{}; -// settings.time_limit = 5; -// settings.mip_scaling = scaling; -// settings.heuristics_only = heuristics_only; -// settings.presolve = false; - -// auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); - -// EXPECT_EQ(result.get_termination_status(), expected_termination_status); -// } - -// TEST_P(MILPTestParams, TestSingleVarMILP) -// { -// bool maximize = std::get<0>(GetParam()); -// bool scaling = std::get<1>(GetParam()); -// bool heuristics_only = std::get<2>(GetParam()); -// auto expected_termination_status = std::get<3>(GetParam()); - -// raft::handle_t handle; -// auto problem = create_single_var_milp_problem(maximize); - -// cuopt::linear_programming::mip_solver_settings_t settings{}; -// settings.time_limit = 5; -// settings.mip_scaling = scaling; -// settings.heuristics_only = heuristics_only; -// settings.presolve = false; - -// auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); - -// EXPECT_EQ(result.get_termination_status(), -// cuopt::linear_programming::mip_termination_status_t::Optimal); -// } - -// INSTANTIATE_TEST_SUITE_P( -// MILPTests, -// MILPTestParams, -// testing::Values( -// std::make_tuple(true, true, true, -// cuopt::linear_programming::mip_termination_status_t::Optimal), std::make_tuple( -// false, true, false, cuopt::linear_programming::mip_termination_status_t::Optimal), -// std::make_tuple( -// true, false, true, cuopt::linear_programming::mip_termination_status_t::Optimal), -// std::make_tuple( -// false, false, false, cuopt::linear_programming::mip_termination_status_t::Optimal))); - -// TEST_P(MILPTestParams, TestDeterminism) -// { -// bool maximize = std::get<0>(GetParam()); -// bool scaling = std::get<1>(GetParam()); -// bool heuristics_only = std::get<2>(GetParam()); -// auto expected_termination_status = std::get<3>(GetParam()); - -// raft::handle_t handle; -// auto problem = create_std_milp_problem(maximize); - -// cuopt::linear_programming::mip_solver_settings_t settings{}; -// settings.mip_scaling = true; -// settings.heuristics_only = true; -// settings.presolve = true; -// settings.deterministic = true; - -// auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); - -// EXPECT_EQ(result.get_termination_status(), expected_termination_status); -// } +TEST(LPTest, TestSampleLP2) +{ + raft::handle_t handle; + + // Construct a simple LP problem: + // Minimize: x + // Subject to: x <= 1 + // x <= 1 + // x >= 0 + + // One variable, two constraints (both x <= 1) + std::vector A_values = {1.0, 1.0}; + std::vector A_indices = {0, 0}; + std::vector A_offsets = {0, 1, 2}; // CSR: 2 constraints, 1 variable + + std::vector b = {1.0, 1.0}; // RHS for both constraints + std::vector b_lower = {-std::numeric_limits::infinity(), + -std::numeric_limits::infinity()}; + + std::vector c = {1.0}; // Objective: Minimize x + + std::vector row_types = {'L', 'L'}; // Both constraints are <= + + // Build the problem + mps_parser::mps_data_model_t problem; + problem.set_csr_constraint_matrix(A_values.data(), + A_values.size(), + A_indices.data(), + A_indices.size(), + A_offsets.data(), + A_offsets.size()); + problem.set_constraint_upper_bounds(b.data(), b.size()); + problem.set_constraint_lower_bounds(b_lower.data(), b_lower.size()); + + // Set variable bounds (x >= 0) + std::vector var_lower = {0.0}; + std::vector var_upper = {std::numeric_limits::infinity()}; + problem.set_variable_lower_bounds(var_lower.data(), var_lower.size()); + problem.set_variable_upper_bounds(var_upper.data(), var_upper.size()); + + problem.set_objective_coefficients(c.data(), c.size()); + problem.set_maximize(false); + // Set up solver settings + cuopt::linear_programming::pdlp_solver_settings_t settings{}; + settings.set_optimality_tolerance(1e-2); + settings.method = cuopt::linear_programming::method_t::PDLP; + settings.time_limit = 5; + + // Solve + auto result = cuopt::linear_programming::solve_lp(&handle, problem, settings); + + // Check results + EXPECT_EQ(result.get_termination_status(), + cuopt::linear_programming::pdlp_termination_status_t::Optimal); + ASSERT_EQ(result.get_primal_solution().size(), 1); + + // Copy solution to host to access values + auto primal_host = cuopt::host_copy(result.get_primal_solution(), handle.get_stream()); + EXPECT_NEAR(primal_host[0], 0.0, 1e-6); + + EXPECT_NEAR(result.get_additional_termination_information().primal_objective, 0.0, 1e-6); + EXPECT_NEAR(result.get_additional_termination_information().dual_objective, 0.0, 1e-6); +} + +TEST(LPTest, TestSampleLP) +{ + raft::handle_t handle; + auto problem = create_std_lp_problem(); + + cuopt::linear_programming::pdlp_solver_settings_t settings{}; + settings.set_optimality_tolerance(1e-4); + settings.time_limit = 5; + settings.presolve = false; + + auto result = cuopt::linear_programming::solve_lp(&handle, problem, settings); + + EXPECT_EQ(result.get_termination_status(), + cuopt::linear_programming::pdlp_termination_status_t::Optimal); +} + +TEST(ErrorTest, TestError) +{ + raft::handle_t handle; + auto problem = create_std_milp_problem(false); + + cuopt::linear_programming::mip_solver_settings_t settings{}; + settings.time_limit = 5; + settings.presolve = false; + + // Set constraint bounds + std::vector lower_bounds = {1.0}; + std::vector upper_bounds = {1.0, 1.0}; + problem.set_constraint_lower_bounds(lower_bounds.data(), lower_bounds.size()); + problem.set_constraint_upper_bounds(upper_bounds.data(), upper_bounds.size()); + + auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); + + EXPECT_EQ(result.get_termination_status(), + cuopt::linear_programming::mip_termination_status_t::NoTermination); +} + +class MILPTestParams + : public testing::TestWithParam< + std::tuple> {}; + +TEST_P(MILPTestParams, TestSampleMILP) +{ + bool maximize = std::get<0>(GetParam()); + bool scaling = std::get<1>(GetParam()); + bool heuristics_only = std::get<2>(GetParam()); + auto expected_termination_status = std::get<3>(GetParam()); + + raft::handle_t handle; + auto problem = create_std_milp_problem(maximize); + + cuopt::linear_programming::mip_solver_settings_t settings{}; + settings.time_limit = 5; + settings.mip_scaling = scaling; + settings.heuristics_only = heuristics_only; + settings.presolve = false; + + auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); + + EXPECT_EQ(result.get_termination_status(), expected_termination_status); +} + +TEST_P(MILPTestParams, TestSingleVarMILP) +{ + bool maximize = std::get<0>(GetParam()); + bool scaling = std::get<1>(GetParam()); + bool heuristics_only = std::get<2>(GetParam()); + auto expected_termination_status = std::get<3>(GetParam()); + + raft::handle_t handle; + auto problem = create_single_var_milp_problem(maximize); + + cuopt::linear_programming::mip_solver_settings_t settings{}; + settings.time_limit = 5; + settings.mip_scaling = scaling; + settings.heuristics_only = heuristics_only; + settings.presolve = false; + + auto result = cuopt::linear_programming::solve_mip(&handle, problem, settings); + + EXPECT_EQ(result.get_termination_status(), + cuopt::linear_programming::mip_termination_status_t::Optimal); +} + +INSTANTIATE_TEST_SUITE_P( + MILPTests, + MILPTestParams, + testing::Values( + std::make_tuple(true, true, true, cuopt::linear_programming::mip_termination_status_t::Optimal), + std::make_tuple( + false, true, false, cuopt::linear_programming::mip_termination_status_t::Optimal), + std::make_tuple( + true, false, true, cuopt::linear_programming::mip_termination_status_t::Optimal), + std::make_tuple( + false, false, false, cuopt::linear_programming::mip_termination_status_t::Optimal))); } // namespace cuopt::linear_programming::test diff --git a/cpp/tsan_suppressions.txt b/cpp/tsan_suppressions.txt deleted file mode 100644 index b6f413e37..000000000 --- a/cpp/tsan_suppressions.txt +++ /dev/null @@ -1,6 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. -# SPDX-License-Identifier: Apache-2.0 - -# Ignore races in external header-only libraries -race:tbb -race:Papilo diff --git a/datasets/mip/download_miplib_test_dataset.sh b/datasets/mip/download_miplib_test_dataset.sh index 07040276f..dc2dd7966 100755 --- a/datasets/mip/download_miplib_test_dataset.sh +++ b/datasets/mip/download_miplib_test_dataset.sh @@ -24,7 +24,6 @@ INSTANCES=( "enlight_hard" "enlight11" "supportcase22" - "supportcase42" ) BASE_URL="https://miplib.zib.de/WebData/instances" diff --git a/scripts/README_PREDICTOR_WORKFLOW.md b/scripts/README_PREDICTOR_WORKFLOW.md deleted file mode 100644 index 7cc1fbc8a..000000000 --- a/scripts/README_PREDICTOR_WORKFLOW.md +++ /dev/null @@ -1,214 +0,0 @@ -# Predictor Training Workflow - -Quick reference for training iteration predictors from solver logs. - -## Prerequisites - -```bash -pip install pandas scikit-learn xgboost lightgbm treelite tl2cgen joblib -``` - -## Workflow - -### 1. Collect Logs - -Run solver with `INFO` log level to capture feature logs: - -```bash -export CUOPT_LOG_LEVEL=INFO -./solver problem.mps > logs/problem.log 2>&1 -``` - -### 2. Parse Logs - -Extract training data for specific algorithm: - -```bash -# Parse Feasibility Pump logs -python scripts/determinism_logs_parse.py logs/ --algorithm FP -o fp_data.pkl - -# Parse PDLP (LP solver) logs -python scripts/determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.pkl - -# Parse Constraint Propagation logs -python scripts/determinism_logs_parse.py logs/ --algorithm CP -o cp_data.pkl - -# Parse Feasibility Jump legacy logs -python scripts/determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.pkl -``` - -**The parser shows real-time progress:** -- File scanning progress -- Grep execution on N files -- Line processing progress (updates every 10,000 lines) -- Pairing progress (updates every 10 files) -- Final statistics and file size - -**IMPORTANT - Log Filtering:** -The parser uses grep with EXACT pattern matching to be highly efficient: -- Pattern: `FP_FEATURES:` and `FP_RESULT:` (with colon suffix) -- Only matches predictor log lines, ignores all other FP-related logs -- Example: A log with 100,000 lines might have 10,000 lines with "FP" but only 200 predictor lines -- Grep filters these down BEFORE Python processing, making it extremely fast even with noisy logs - -### 3. Inspect Features (Optional) - -See what features are available: - -```bash -python scripts/train_regressor.py fp_data.pkl --regressor xgboost --list-features -``` - -### 4. Train Model - -Train with XGBoost or LightGBM: - -```bash -# XGBoost with early stopping and C++ code generation -python scripts/train_regressor.py fp_data.pkl \ - --regressor xgboost \ - --seed 42 \ - --early-stopping 20 \ - --treelite-compile 8 - -# LightGBM with stratified split -python scripts/train_regressor.py pdlp_data.pkl \ - --regressor lightgbm \ - --seed 42 \ - --stratify-split \ - --early-stopping 20 \ - --treelite-compile 8 -``` - -### 5. Use Generated Code - -The trained model will be exported to C++ code in `./models/_c_code/`: - -- `header.h` - Class declaration -- `main.cpp` - Implementation -- `quantize.cpp` - Quantization helpers - -Copy to `cpp/src/utilities/models/_predictor/` and integrate into solver. - -## Log Format Reference - -### FP (Feasibility Pump) -``` -FP_FEATURES: n_variables=100 n_constraints=50 n_integer_vars=80 ... -FP_RESULT: iterations=142 time_taken=5.234 termination=FEASIBLE_LP_PROJECTION -``` - -### PDLP (LP Solver) -``` -PDLP_FEATURES: n_variables=100 n_constraints=50 nnz=450 ... -PDLP_RESULT: iterations=237 time_ms=1234 termination=2 -``` - -### CP (Constraint Propagation) -``` -CP_FEATURES: n_variables=100 n_constraints=50 n_unset_vars=25 ... -CP_RESULT: time_ms=567 termination=SUCCESS iterations=0 -``` - -## Common Options - -### Training Script - -- `--regressor {xgboost,lightgbm,linear,poly2,...}` - Model type -- `--seed N` - Random seed for reproducibility -- `--test-size 0.2` - Test set proportion (default 20%) -- `--stratify-split` - Balance train/test by target distribution -- `--early-stopping N` - Early stopping patience (prevents overfitting) -- `--treelite-compile N` - Generate C++ code with N threads -- `--list-features` - Show available features and exit -- `--tune` - Use tuned hyperparameters - -### Parsing Script - -- `--algorithm {FP,PDLP,CP,FJ}` - Which algorithm to parse -- `-o FILE` - Output pickle file (default: `_data.pkl`) -- `--verbose` - Show warnings and detailed output - -## Tips - -1. **Collect diverse problems**: Train on variety of problem types/sizes -2. **Check train/test split**: Use `--stratify-split` if targets are imbalanced -3. **Prevent overfitting**: Use `--early-stopping` with tree models -4. **Feature selection**: Edit `FEATURES_TO_EXCLUDE` in `train_regressor.py` -5. **Reproducibility**: Always set `--seed` for consistent results - -## Troubleshooting - -**No entries found for algorithm X** -- Check log level is set to INFO or DEBUG -- Verify solver is executing the algorithm -- Look for `X_FEATURES` and `X_RESULT` lines in logs - -**Poor model performance** -- Collect more training data -- Try different regressor types -- Use `--list-features` to identify important features -- Enable `--stratify-split` for balanced splits - -**C++ code generation fails** -- Install: `pip install treelite tl2cgen` -- Only works with XGBoost and LightGBM -- Check model trained successfully first - -## Example Output - -```bash -$ python scripts/determinism_logs_parse.py logs/ --algorithm FP -o fp_data.pkl - -Scanning logs/ for .log files... -Found 42 log files - -Parsing FP (Feasibility Pump) logs... - Running grep on 42 files... - Processing 3046 matching lines... - Processed 3046 lines from 42 files - Pairing features with results... - Found 1523 complete entries from 42 files - - Total entries: 1523 - Unique files: 42 - Avg entries per file: 36.26 - Iterations (target): min=1, max=847, avg=142.35 - -Saving 1523 entries to fp_data.pkl... - -====================================================================== -✓ Success! Saved 1523 entries to fp_data.pkl - File size: 2.34 MB -====================================================================== - -$ python scripts/train_regressor.py fp_data.pkl --regressor xgboost --seed 42 - -Loading data from: fp_data.pkl -Loaded 1523 entries with 29 columns - -Data Split: - Total entries: 1523 - Train entries: 1218 (34 files) - Test entries: 305 (8 files) - -Training xgboost regressor... - Training complete! - -Test Set Metrics: - MSE: 1234.56 - RMSE: 35.14 - MAE: 22.67 - R²: 0.8542 - -Feature Importance: - 1. n_variables : 0.245123 - 2. n_constraints : 0.187456 - 3. initial_ratio_of_integers : 0.156234 - ... - -C source code generated to: ./models/fp_data_c_code/ - Contains optimized model source code (branch-annotated, quantized) - -Success! Saved 1523 entries to fp_data.pkl -``` diff --git a/scripts/README_REGRESSION.md b/scripts/README_REGRESSION.md deleted file mode 100644 index ca6cba0ca..000000000 --- a/scripts/README_REGRESSION.md +++ /dev/null @@ -1,342 +0,0 @@ -# Regression Model Training Scripts - -This directory contains scripts for parsing algorithm log files and training regression models to predict iteration counts. - -## Overview - -The workflow consists of two steps: -1. **Parse log files** → Extract key-value pairs into a pickle file -2. **Train models** → Learn to predict iterations from features - -## Installation - -Install required dependencies: - -```bash -pip install -r requirements.txt -``` - -## Usage - -### Step 1: Parse Log Files - -Extract features from log files containing `FJ:` entries: - -```bash -python determinism_logs_parse.py /path/to/logs/directory -o parsed_data.pkl -``` - -**Arguments:** -- `input_dir`: Directory containing `.log` files -- `-o, --output`: Output pickle file (default: `output.pkl`) - -**Output:** -- Pickle file containing list of dictionaries with all key-value pairs -- Each entry includes `file=` field - -### Step 2: Train Regression Model - -Train a model to predict `iter` values from other features: - -```bash -python train_regressor.py parsed_data.pkl --regressor xgboost --seed 42 -``` - -**Arguments:** -- `input_pkl`: Input pickle file from step 1 -- `--regressor, -r`: Type of regressor (required) - - `linear` - Linear Regression - - `poly2`, `poly3`, `poly4` - Polynomial Regression (degree 2, 3, 4) - - `xgboost` - XGBoost Regressor - - `lightgbm` - LightGBM Regressor - - `random_forest` - Random Forest Regressor - - `gradient_boosting` - Gradient Boosting Regressor -- `--output-dir, -o`: Directory to save models (default: `./models`) -- `--seed, -s`: Random seed for reproducibility (optional) -- `--tune`: Enable hyperparameter tuning -- `--cv-folds`: Number of cross-validation folds (default: 5) -- `--test-size`: Test set proportion (default: 0.2) -- `--no-progress`: Disable training progress output -- `--list-features`: List all available features in the dataset and exit -- `--stratify-split`: Stratify train/test split by target distribution -- `--early-stopping N`: Early stopping patience in rounds (default: 20, use 0 to disable) -- `--treelite-compile N`: Export XGBoost/LightGBM as optimized C source code with TL2cgen (N threads, default: 1, includes branch annotation and quantization) - -## Features - -### Data Splitting -- **File-based split**: Ensures entries from the same file go exclusively to train OR test -- **Prevents data leakage**: Improves generalization and reduces overfitting -- **Default**: 20% of files for testing - -### Preprocessing -- **Automatic scaling**: Applied to linear/polynomial models (not tree-based) -- **Polynomial features**: All numeric features expanded for polynomial regression -- **Clean data assumption**: Script expects valid pickle data - -### Feature Selection -- **Manual feature selection**: Edit `FEATURES_TO_EXCLUDE` or `FEATURES_TO_INCLUDE_ONLY` directly in the script -- **Exclude specific features**: Add feature names to `FEATURES_TO_EXCLUDE` list -- **Include only specific features**: Add feature names to `FEATURES_TO_INCLUDE_ONLY` list (overrides exclusion) -- **List available features**: Run with `--list-features` to see all features in your dataset -- **No command-line config**: Intentionally not exposed as CLI args for cleaner configuration file management - -### Model Evaluation -- **Cross-validation**: K-fold CV on training set with progress output -- **Comprehensive metrics**: MSE, RMSE, MAE, R² -- **Feature importance**: All features ranked by importance (top 50 for polynomial models) -- **Sample predictions**: 20 random test predictions with errors - -### Training Progress -- **Progress indicators**: Tree-based models (XGBoost, LightGBM, Random Forest, Gradient Boosting) show real-time training progress -- **Polynomial feature tracking**: Shows number of polynomial features being generated -- **CV progress**: Cross-validation shows progress for each fold -- **Disable option**: Use `--no-progress` flag to suppress all progress output - -### Overfitting Prevention -- **Early stopping**: Enabled by default for XGBoost and LightGBM (20 rounds patience) to prevent overfitting -- **Regularization**: XGBoost and LightGBM include L1/L2 regularization, subsampling, and minimum child weight -- **Stratified splitting**: Use `--stratify-split` to ensure balanced target distributions -- **Disable early stopping**: Use `--early-stopping 0` if you want full training without early stopping - -### Model Persistence -- **XGBoost**: Saved as `.ubj.gz` (UBJ format with gzip compression) -- **LightGBM**: Saved as `.txt` (text format, human-readable) -- **Sklearn models**: Saved as `.joblib` (efficient for numpy arrays) -- **Metadata**: Feature names and preprocessing info saved separately -- **Scaler**: Saved for models requiring normalization - -### TL2cgen Source Export (Optional) -- **C source code export**: Export XGBoost/LightGBM models as optimized C source code using TL2cgen -- **Portable and fast**: Compile the source on any platform for 10-100x faster predictions -- **Enabled by default**: Automatically exports C source with 1 thread (use `--treelite-compile N` for more threads) -- **Requires**: `treelite>=4.0` and `tl2cgen` packages (optional dependencies) -- **Output**: Optimized C source files in dedicated directory -- **Note**: Treelite 4.0+ moved C compilation to TL2cgen ([migration guide](https://tl2cgen.readthedocs.io/en/latest/treelite-migration.html)) - -### Built-in TL2cgen Optimizations -The following optimizations are **automatically applied** when using TL2cgen: - -- **Branch annotation**: - - Analyzes which branches are taken during training - - Optimizes C code with branch prediction hints - - Improves inference speed by 10-30% - - Saves annotation file for inspection -- **Quantization**: - - Reduces model memory footprint by ~75% - - Uses 8-bit integers instead of 32-bit floats where possible - - Minimal accuracy loss (typically <0.1% R²) - - Faster inference on memory-constrained systems - -**Combined effect**: 1.2-1.5x faster inference with 75% less memory - -## Example Workflow - -```bash -# 1. Parse logs -python determinism_logs_parse.py /data/algorithm_logs -o data.pkl - -# 2. List available features -python train_regressor.py data.pkl --regressor xgboost --list-features - -# 3. Train XGBoost model -python train_regressor.py data.pkl --regressor xgboost --seed 42 -o ./models - -# 4. Train polynomial model with tuning -python train_regressor.py data.pkl --regressor poly3 --tune --seed 42 - -# 5. Compare different models -for model in linear poly2 poly3 xgboost lightgbm random_forest gradient_boosting; do - echo "Training $model..." - python train_regressor.py data.pkl --regressor $model --seed 42 -done - -# 6. Train LightGBM model -python train_regressor.py data.pkl --regressor lightgbm --seed 42 - -# 7. Export XGBoost model as C source for production deployment (enabled by default) -python train_regressor.py data.pkl --regressor xgboost --seed 42 - -# Or specify more threads for compilation -python train_regressor.py data.pkl --regressor xgboost --treelite-compile 8 --seed 42 -``` - -## TL2cgen Source Export Example - -For production deployments requiring fast inference, models are **automatically exported as optimized C source code** (if `treelite` and `tl2cgen` are installed): - -```bash -# Install treelite and tl2cgen (optional) -pip install treelite tl2cgen - -# Train model - optimized C source is automatically exported with branch annotation and quantization -python train_regressor.py data.pkl --regressor xgboost -o ./models - -# Use more threads for faster parallel compilation -python train_regressor.py data.pkl --regressor xgboost --treelite-compile 8 -o ./models - -# The C source files will be in: models/xgboost_c_code/ -# Contains optimized C source code with branch annotation and quantization ready for compilation - -# Same process works for LightGBM -python train_regressor.py data.pkl --regressor lightgbm --treelite-compile 8 -o ./models -# Output: models/lightgbm_c_code/ -``` - -### Optimization Impact - -All TL2cgen exports include the following optimizations automatically: - -1. **Branch Annotation**: Uses training data statistics to add branch prediction hints -2. **Quantization**: Reduces memory footprint by converting floating-point to integers -3. **Missing Data Removal**: Removes unnecessary missing data checks (assumes all features provided) - -| Configuration | Speed | Memory | Accuracy | -|---------------|-------|--------|----------| -| Standard XGBoost/LightGBM | 1x | 100% | 100% | -| **TL2cgen optimized (default)** | **1.2-1.5x** | **25%** | **>99.9%** | - -**Note:** Treelite 4.0+ moved C code generation to TL2cgen. See the [migration guide](https://tl2cgen.readthedocs.io/en/latest/treelite-migration.html) for details. - -### Output Files - -When TL2cgen is enabled, the following files are automatically created: - -``` -models/ -├── xgboost_model.ubj.gz # Standard XGBoost model -├── xgboost_metadata.pkl # Feature names and config -├── xgboost_annotation.json # Branch statistics (automatic) -└── xgboost_c_code/ # TL2cgen generated optimized C++ source - ├── header.h # Header with feature names declaration - ├── main.cpp # Implementation with feature names array - └── *.cpp / *.h # Other C++ source files (quantized + annotated) -``` - -**Class Wrapping**: All generated files are automatically wrapped in a C++ class with the model name (derived from the input pickle file basename) to avoid naming conflicts when using multiple models in the same project. For example, if the input is `my_dataset.pkl`: -- All functions and data are in `class my_dataset { public: ... };` -- All class members are `static` - no instantiation required -- Access functions as `my_dataset::predict()`, `my_dataset::get_num_features()`, etc. -- All `.c` files are renamed to `.cpp` for C++ compilation -- Header includes `#pragma once` for include guards - -The generated `header.h` includes: -- `#pragma once` at the top -- `#include` statements (outside the class) -- `class { public: ... };` wrapping all declarations -- `static constexpr int NUM_FEATURES` - Number of features -- `static const char* feature_names[]` - Feature names declaration -- Function declarations (e.g., `predict()`, `get_num_features()`) as public static members - -The generated `main.cpp` includes: -- `#include` statements at the top -- Macro definitions (`LIKELY`, `UNLIKELY`, `N_TARGET`, `MAX_N_CLASS`) - moved from header for implementation-only use -- Function implementations with `::function_name` qualification -- `const char* ::feature_names[]` - Feature names array definition (at the end of file) - -### Example Generated Code Structure - -**header.h:** - -```cpp -#pragma once - -#include - -class my_dataset { -public: - static float predict(float* data, int pred_margin); - static int get_num_feature(); - // ... other function declarations ... - - static constexpr int NUM_FEATURES = 42; - static const char* feature_names[NUM_FEATURES]; -}; -``` - -**main.cpp:** - -```cpp -#include "header.h" - -#define LIKELY(x) __builtin_expect(!!(x), 1) -#define N_TARGET 1 - -float my_dataset::predict(float* data, int pred_margin) { - // implementation -} - -int my_dataset::get_num_feature() { - return NUM_FEATURES; -} - -// Feature names array -const char* my_dataset::feature_names[my_dataset::NUM_FEATURES] = { - "n_variables", - "n_constraints", - // ... -}; -``` - -**Usage:** - -```cpp -#include "xgboost_c_code/header.h" - -// Call static methods directly - no instantiation needed -float result = my_dataset::predict(features, 0); -int num = my_dataset::get_num_feature(); -``` - -## Feature Selection Examples - -To perform feature selection, edit the configuration section at the top of `train_regressor.py`: - -### Example 1: Exclude specific features - -```python -FEATURES_TO_EXCLUDE = [ - 'time', # Exclude time as it may not be available at prediction time - 'avg_constraint_range', - 'binary_ratio', -] - -FEATURES_TO_INCLUDE_ONLY = [] -``` - -### Example 2: Use only specific features - -```python -FEATURES_TO_EXCLUDE = [] - -FEATURES_TO_INCLUDE_ONLY = [ - 'n_variables', - 'n_constraints', - 'sparsity', - 'structural_complexity', -] -``` - -**Note:** If `FEATURES_TO_INCLUDE_ONLY` is non-empty, it overrides `FEATURES_TO_EXCLUDE`. - -## Output Structure - -After training, the output directory contains: - -``` -models/ -├── xgboost_model.ubj.gz # Compressed XGBoost model -├── xgboost_metadata.pkl # Feature names and config -├── linear_model.joblib # Linear regression model -├── linear_scaler.pkl # StandardScaler for linear model -├── linear_metadata.pkl # Metadata -└── ... -``` - -## Notes - -- The train/test split is based on **unique files**, not individual entries -- Models requiring scaling (linear, polynomial) automatically apply `StandardScaler` -- Tree-based models (XGBoost, Random Forest, Gradient Boosting) don't use scaling -- Feature importance shows the most predictive features for iteration count -- Use `--seed` for reproducible results across runs diff --git a/scripts/determinism_logs_parse.py b/scripts/determinism_logs_parse.py deleted file mode 100755 index 503165641..000000000 --- a/scripts/determinism_logs_parse.py +++ /dev/null @@ -1,894 +0,0 @@ -#!/usr/bin/env python3 -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. -# SPDX-License-Identifier: Apache-2.0 -# All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Parse log files containing algorithm feature logs and export to pickle format for training. - -Supports parsing of: -- FP (Feasibility Pump): FP_FEATURES and FP_RESULT logs -- PDLP (LP Solver): PDLP_RESULT single-line logs -- CP (Constraint Propagation): CP_FEATURES and CP_RESULT logs -- FJ (Feasibility Jump): Legacy FJ: format -- CPUFJ (CPU Feasibility Jump): CPUFJ_FEATURES single-line logs -- BB (Branch and Bound): BB_NODE_FEATURES single-line logs -- DS (Dual Simplex): DS_FEATURES single-line logs - -IMPORTANT - Grep Specificity: -The parser uses EXACT pattern matching with grep to filter logs efficiently. -For example, when parsing FP logs: -- Grep pattern: 'FP_FEATURES:' and 'FP_RESULT:' (with colon) -- Matches ONLY predictor log lines, NOT general FP debug/info lines -- A log with 10,000 lines containing "FP" might have only 100 predictor lines -- Grep filters down to only the relevant lines before Python processing - -Performance optimizations for very large log files: -- Single grep call per algorithm (instead of separate calls for features/results) -- Uses grep's -n flag to get line numbers for efficient pairing -- Minimal Python string processing (split instead of regex) -- Single-pass parsing with dictionary accumulation -- Avoids redundant string operations -- DRY refactoring: Generic parser eliminates duplicate code (~105 lines removed) -- Real-time progress indicators (every 10K lines, every 10 files) - -Usage: - python determinism_logs_parse.py --algorithm FP [-o output.feather] - python determinism_logs_parse.py --algorithm PDLP [-o output.feather] - python determinism_logs_parse.py --algorithm CP [-o output.feather] - python determinism_logs_parse.py --algorithm FJ [-o output.feather] - python determinism_logs_parse.py --algorithm CPUFJ [-o output.feather] - python determinism_logs_parse.py --algorithm BB [-o output.feather] - python determinism_logs_parse.py --algorithm DS [-o output.feather] -""" - -import argparse -import subprocess -import os -import glob -import numpy as np -import pandas as pd -from typing import List, Dict, Any, Optional - - -SUPPORTED_ALGORITHMS = ["FP", "PDLP", "CP", "FJ", "CPUFJ", "BB", "DS"] - - -def parse_value(value_str: str) -> Any: - """Convert string value to appropriate type (int, float, or str).""" - try: - # Handle special float values first (nan, inf, -inf) - value_lower = value_str.lower() - if value_lower in ("nan", "inf", "-inf", "+inf"): - return float(value_str) - - # Try to parse as float if it contains a decimal point or scientific notation - if "." in value_str or "e" in value_str.lower(): - return float(value_str) - else: - return int(value_str) - except ValueError: - # Keep as string if conversion fails - return value_str - - -def parse_key_value_line(line: str, prefix: str) -> Dict[str, Any]: - """ - Parse a line containing key=value pairs after removing prefix. - - Example - ------- - "FP_FEATURES: n_variables=100 n_constraints=50" - -> {'n_variables': 100, 'n_constraints': 50} - """ - entry = {} - - # Remove prefix - if prefix in line: - line = line.split(prefix, 1)[1].strip() - - # Parse key=value pairs - # Handle both space-separated and comma-separated - for kv_pair in line.split(): - if "=" in kv_pair: - key, value = kv_pair.split("=", 1) - # Remove trailing commas - value = value.rstrip(",") - entry[key] = parse_value(value) - - return entry - - -def parse_generic_algorithm_logs( - log_files: List[str], algorithm: str, algorithm_name: str -) -> List[Dict[str, Any]]: - """ - Generic parser for algorithm feature and result logs. - - Matches _FEATURES lines with subsequent _RESULT lines. - Uses grep efficiently to minimize Python-side processing. - - Args: - log_files: List of log file paths to parse - algorithm: Algorithm prefix (e.g., 'FP', 'PDLP', 'CP') - algorithm_name: Full name for display (e.g., 'Feasibility Pump') - - Returns - ------- - List of dictionaries with combined features and results - """ - print(f"\nParsing {algorithm} ({algorithm_name}) logs...") - print(f" Running grep on {len(log_files)} files...") - - # Construct grep patterns with EXACT match requirements - # The colon at the end ensures we ONLY match the feature/result log lines - # and ignore all other lines containing the algorithm name - features_pattern = f"{algorithm}_FEATURES:" - result_pattern = f"{algorithm}_RESULT:" - - # Use grep with: - # -H: Always print filename (even with single file) - # -n: Print line numbers for correct pairing - # -e: Multiple patterns to match - # This ensures we ONLY get the specific predictor log lines, not debug/info lines - cmd = [ - "grep", - "-Hn", - "-e", - features_pattern, - "-e", - result_pattern, - ] + log_files - - result = subprocess.run(cmd, capture_output=True, text=True) - - if not result.stdout: - print(f" No {algorithm} logs found") - return [] - - # Count lines for progress indication - total_lines = result.stdout.count("\n") - print(f" Processing {total_lines} matching lines...") - - # Process grep output efficiently - # Format: filename:linenum:_FEATURES: key1=value1 ... - entries_by_file = {} - lines_processed = 0 - files_seen = set() - - for line in result.stdout.split("\n"): - if not line: - continue - - lines_processed += 1 - - # Progress update every 10000 lines - if lines_processed % 10000 == 0: - pct = ( - (lines_processed / total_lines * 100) if total_lines > 0 else 0 - ) - print( - f" Progress: {pct:.1f}% ({lines_processed}/{total_lines} lines, {len(files_seen)} files)", - end="\r", - ) - - # Split on first two colons to get filename, linenum, and content - # The rest of the line after the second colon is the log content - parts = line.split(":", 2) - if len(parts) < 3: - continue - - filename = os.path.basename(parts[0]) - linenum = int(parts[1]) - content = parts[2] # This includes everything after linenum - - if filename not in entries_by_file: - entries_by_file[filename] = {"features": [], "results": []} - files_seen.add(filename) - - # Double-check pattern match (grep already filtered, but be extra safe) - # This ensures we ONLY process lines with the exact patterns we want - if features_pattern in content: - # Parse features - only if pattern is present - features = parse_key_value_line(content, features_pattern) - if features: # Only add if parsing succeeded - entries_by_file[filename]["features"].append( - (linenum, features) - ) - elif result_pattern in content: - # Parse results - only if pattern is present - results = parse_key_value_line(content, result_pattern) - if results: # Only add if parsing succeeded - entries_by_file[filename]["results"].append((linenum, results)) - - # Clear progress line - if lines_processed > 0: - print( - f" Processed {lines_processed} lines from {len(files_seen)} files " - ) - - # Match features with results - # IMPORTANT: Multiple FEATURES lines followed by multiple RESULT lines form ONE complete entry - # We need to merge consecutive lines of the same type, then combine them - print(" Merging features and results...") - entries = [] - files_processed = 0 - total_files = len(entries_by_file) - - for filename, data in entries_by_file.items(): - files_processed += 1 - - # Progress update every 10 files - if files_processed % 10 == 0 or files_processed == total_files: - print( - f" Merging: {files_processed}/{total_files} files, {len(entries)} entries found", - end="\r", - ) - - # Combine features and results by line number - # Group consecutive FEATURES lines and consecutive RESULT lines - all_items = [] - for linenum, features in data["features"]: - all_items.append((linenum, "features", features)) - for linenum, results in data["results"]: - all_items.append((linenum, "results", results)) - - # Sort by line number - all_items.sort(key=lambda x: x[0]) - - # Merge consecutive items of the same type - current_features = {} - current_results = {} - last_type = None - - for linenum, item_type, content in all_items: - # If we transition from RESULT back to FEATURES, save the previous entry - if item_type == "features" and last_type == "results": - if current_features and current_results: - # Create combined entry - entry = {"file": filename} - entry.update(current_features) - entry.update(current_results) - - # Rename 'iterations' to 'iter' for consistency - if "iterations" in entry: - entry["iter"] = entry.pop("iterations") - - entries.append(entry) - - # Reset for next entry - current_features = {} - current_results = {} - - if item_type == "features": - # Accumulate features - current_features.update(content) - else: # results - # Accumulate results - current_results.update(content) - - last_type = item_type - - # Don't forget the last entry in the file - if current_features and current_results: - entry = {"file": filename} - entry.update(current_features) - entry.update(current_results) - - if "iterations" in entry: - entry["iter"] = entry.pop("iterations") - - entries.append(entry) - - # Clear progress line and show final count - if total_files > 0: - print( - f" Found {len(entries)} complete entries from {total_files} files " - ) - - return entries - - -# Algorithm-specific wrappers for the generic parser -# These provide a clean API and eliminate code duplication - - -def parse_fp_logs(log_files: List[str]) -> List[Dict[str, Any]]: - """Parse Feasibility Pump feature and result logs.""" - return parse_generic_algorithm_logs(log_files, "FP", "Feasibility Pump") - - -def parse_pdlp_logs(log_files: List[str]) -> List[Dict[str, Any]]: - """Parse PDLP (LP Solver) result logs.""" - return parse_single_line_logs( - log_files, "PDLP_RESULT:", "PDLP (LP Solver)", "PDLP_RESULT:" - ) - - -def parse_cp_logs(log_files: List[str]) -> List[Dict[str, Any]]: - """Parse Constraint Propagation feature and result logs.""" - return parse_generic_algorithm_logs( - log_files, "CP", "Constraint Propagation" - ) - - -def parse_single_line_logs( - log_files: List[str], - pattern: str, - algorithm_name: str, - prefix_to_remove: Optional[str] = None, -) -> List[Dict[str, Any]]: - """ - Generic parser for single-line logs with key=value pairs. - - Used for legacy formats that don't have separate FEATURES/RESULT lines. - - Args: - log_files: List of log file paths to parse - pattern: Grep pattern to match (e.g., 'FJ:', 'CPUFJ_FEATURES') - algorithm_name: Full name for display - prefix_to_remove: Optional prefix to strip from content (e.g., 'FJ:') - - Returns - ------- - List of dictionaries with parsed key-value pairs - """ - print(f"\nParsing {algorithm_name} logs...") - print(f" Running grep on {len(log_files)} files...") - - # Use grep to efficiently extract ONLY lines with the exact pattern - cmd = ["grep", "-H", pattern] + log_files - result = subprocess.run(cmd, capture_output=True, text=True) - - if not result.stdout: - print(f" No {algorithm_name} logs found") - return [] - - # Count lines for progress indication - total_lines = result.stdout.count("\n") - print(f" Processing {total_lines} matching lines...") - - # Parse grep output efficiently - entries = [] - lines_processed = 0 - - for line in result.stdout.split("\n"): - if not line: - continue - - lines_processed += 1 - - # Progress update every 10000 lines - if lines_processed % 10000 == 0: - pct = ( - (lines_processed / total_lines * 100) if total_lines > 0 else 0 - ) - print( - f" Progress: {pct:.1f}% ({lines_processed}/{total_lines} lines, {len(entries)} entries)", - end="\r", - ) - - # Grep output format: filename:content - parts = line.split(":", 2) - if len(parts) < 3: - continue - - filename = os.path.basename(parts[0]) - content = parts[2] - - # Remove prefix if specified - if prefix_to_remove and content.startswith(prefix_to_remove): - content = content[len(prefix_to_remove) :].strip() - - # Parse key-value pairs - entry = {"file": filename} - for kv_pair in content.split(): - if "=" in kv_pair: - key, value = kv_pair.split("=", 1) - # Remove trailing commas - value = value.rstrip(",") - entry[key] = parse_value(value) - - # Only add entry if it has more than just the filename - if len(entry) > 1: - entries.append(entry) - - # Clear progress line - if lines_processed > 0: - print( - f" Found {len(entries)} entries from {total_lines} lines " - ) - - return entries - - -def parse_fj_logs(log_files: List[str]) -> List[Dict[str, Any]]: - """Parse legacy Feasibility Jump logs (original format).""" - return parse_single_line_logs( - log_files, "FJ:", "FJ (Feasibility Jump)", "FJ:" - ) - - -def parse_cpufj_logs(log_files: List[str]) -> List[Dict[str, Any]]: - """Parse CPU Feasibility Jump feature logs.""" - return parse_single_line_logs( - log_files, "CPUFJ_FEATURES", "CPUFJ (CPU Feasibility Jump)" - ) - - -def parse_bb_logs(log_files: List[str]) -> List[Dict[str, Any]]: - """Parse Branch and Bound node feature logs.""" - return parse_single_line_logs( - log_files, "BB_NODE_FEATURES", "BB (Branch and Bound)" - ) - - -def parse_ds_logs(log_files: List[str]) -> List[Dict[str, Any]]: - """Parse Dual Simplex feature logs.""" - return parse_single_line_logs( - log_files, "DS_FEATURES:", "DS (Dual Simplex)", "DS_FEATURES:" - ) - - -def print_statistics(entries: List[Dict[str, Any]], algorithm: str) -> None: - """Print statistics about parsed entries.""" - if not entries: - print(f"\n No entries found for {algorithm}") - return - - unique_files = set(entry["file"] for entry in entries) - avg_entries_per_file = ( - len(entries) / len(unique_files) if unique_files else 0 - ) - - # Check if 'iter' field exists - has_iter = all("iter" in entry for entry in entries) - - if has_iter: - iter_values = [entry["iter"] for entry in entries] - min_iter = min(iter_values) - max_iter = max(iter_values) - avg_iter = sum(iter_values) / len(iter_values) - - print(f"\n Total entries: {len(entries)}") - print(f" Unique files: {len(unique_files)}") - print(f" Avg entries per file: {avg_entries_per_file:.2f}") - print( - f" Iterations (target): min={min_iter}, max={max_iter}, avg={avg_iter:.2f}" - ) - else: - print(f"\n Total entries: {len(entries)}") - print(f" Unique files: {len(unique_files)}") - print(f" Avg entries per file: {avg_entries_per_file:.2f}") - - # Show sample entry - if entries: - print("\n Sample entry (first):") - sample = entries[0] - for key, value in sorted(sample.items()): - print(f" {key}: {value}") - - -def main(): - parser = argparse.ArgumentParser( - description="Parse algorithm feature logs and export to Feather format for training", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Supported Algorithms: - FP - Feasibility Pump (parses FP_FEATURES and FP_RESULT logs) - PDLP - LP Solver (parses PDLP_RESULT single-line logs) - CP - Constraint Propagation (parses CP_FEATURES and CP_RESULT logs) - FJ - Feasibility Jump (parses legacy FJ: format) - CPUFJ - CPU Feasibility Jump (parses CPUFJ_FEATURES single-line logs) - BB - Branch and Bound (parses BB_NODE_FEATURES single-line logs) - DS - Dual Simplex (parses DS_FEATURES single-line logs) - -Examples: - python determinism_logs_parse.py logs/ --algorithm FP -o fp_data.feather - python determinism_logs_parse.py logs/ --algorithm PDLP -o pdlp_data.feather - python determinism_logs_parse.py logs/ --algorithm CP -o cp_data.feather - python determinism_logs_parse.py logs/ --algorithm FJ -o fj_data.feather - python determinism_logs_parse.py logs/ --algorithm CPUFJ -o cpufj_data.feather - python determinism_logs_parse.py logs/ --algorithm BB -o bb_data.feather - python determinism_logs_parse.py logs/ --algorithm DS -o ds_data.feather - - # Limit to first 10 files for testing - python determinism_logs_parse.py logs/ --algorithm FP --max-files 10 - """, - ) - - parser.add_argument( - "input_dir", help="Directory containing .log files to parse" - ) - parser.add_argument( - "--algorithm", - "-a", - required=True, - choices=SUPPORTED_ALGORITHMS, - help="Algorithm to parse logs for", - ) - parser.add_argument( - "-o", - "--output", - default=None, - help="Output Feather file path (default: _data.feather)", - ) - parser.add_argument( - "--verbose", - "-v", - action="store_true", - help="Print verbose output including warnings", - ) - parser.add_argument( - "--max-files", - type=int, - default=None, - help="Limit number of log files to process (useful for testing)", - ) - - args = parser.parse_args() - - # Set default output filename based on algorithm - if args.output is None: - args.output = f"{args.algorithm.lower()}_data.feather" - - # Find all .log files in the input directory - print(f"\nScanning {args.input_dir} for .log files...") - log_files = glob.glob(os.path.join(args.input_dir, "*.log")) - - if not log_files: - print(f"Error: No .log files found in {args.input_dir}") - return 1 - - print(f"Found {len(log_files)} log files") - - # Apply max-files limit if specified - if args.max_files is not None and args.max_files > 0: - if args.max_files < len(log_files): - log_files = log_files[: args.max_files] - print(f"Limiting to first {args.max_files} files (--max-files)") - else: - print( - f"Note: --max-files={args.max_files} is >= total files, using all files" - ) - - # Parse logs based on algorithm - if args.algorithm == "FP": - entries = parse_fp_logs(log_files) - elif args.algorithm == "PDLP": - entries = parse_pdlp_logs(log_files) - elif args.algorithm == "CP": - entries = parse_cp_logs(log_files) - elif args.algorithm == "FJ": - entries = parse_fj_logs(log_files) - elif args.algorithm == "CPUFJ": - entries = parse_cpufj_logs(log_files) - elif args.algorithm == "BB": - entries = parse_bb_logs(log_files) - elif args.algorithm == "DS": - entries = parse_ds_logs(log_files) - else: - print(f"Error: Unsupported algorithm: {args.algorithm}") - return 1 - - if not entries: - print(f"\nError: No entries found for {args.algorithm}") - if args.algorithm in ["FP", "CP"]: - print( - f"Make sure your logs contain {args.algorithm}_FEATURES and {args.algorithm}_RESULT lines" - ) - elif args.algorithm == "PDLP": - print( - "Make sure your logs contain PDLP_RESULT: lines with key=value pairs" - ) - elif args.algorithm == "FJ": - print("Make sure your logs contain FJ: lines with key=value pairs") - elif args.algorithm == "CPUFJ": - print( - "Make sure your logs contain CPUFJ_FEATURES lines with key=value pairs" - ) - elif args.algorithm == "BB": - print( - "Make sure your logs contain BB_NODE_FEATURES lines with key=value pairs" - ) - elif args.algorithm == "DS": - print( - "Make sure your logs contain DS_FEATURES: lines with key=value pairs" - ) - return 1 - - # Print statistics - print_statistics(entries, args.algorithm) - - # Convert to DataFrame - df = pd.DataFrame(entries) - - # Convert all non-string columns to numeric types FIRST - # This ensures proper type inference before validation - print("\nConverting column types...") - for col in df.columns: - if col not in ["file"]: # Keep 'file' as string - # Try to convert to numeric, coercing errors to NaN - df[col] = pd.to_numeric(df[col], errors="coerce") - print( - f" ✓ Converted {len([c for c in df.columns if c != 'file'])} columns to numeric types" - ) - - # Validate: Check for NaN and infinite values - print("\nValidating data integrity...") - - # Check for NaN - nan_counts = df.isna().sum() - columns_with_nan = nan_counts[nan_counts > 0] - - # Check for infinite values in numeric columns - numeric_cols = df.select_dtypes(include=[np.number]).columns - inf_counts = {} - for col in numeric_cols: - inf_count = np.isinf(df[col]).sum() - if inf_count > 0: - inf_counts[col] = inf_count - - # Collect all problematic columns - problematic_columns = set() - problematic_columns.update(columns_with_nan.index) - problematic_columns.update(inf_counts.keys()) - - if problematic_columns: - print(f"\n{'=' * 70}") - print("⚠️ WARNING: Invalid data detected (NaN/inf values)!") - print(f"{'=' * 70}") - print("\nColumns with invalid values (will be removed):") - for col in sorted(problematic_columns): - nan_count = ( - nan_counts.get(col, 0) if col in columns_with_nan.index else 0 - ) - inf_count = inf_counts.get(col, 0) - total_invalid = nan_count + inf_count - pct = (total_invalid / len(df)) * 100 - - issues = [] - if nan_count > 0: - issues.append(f"{nan_count} NaN") - if inf_count > 0: - issues.append(f"{inf_count} inf") - - print(f" ❌ {col}: {', '.join(issues)} ({pct:.1f}%)") - - # Show which log files have the issues - rows_with_issues_mask = df.isna().any(axis=1) - for col in inf_counts.keys(): - if col in df.columns: - rows_with_issues_mask |= np.isinf(df[col]) - rows_with_issues = df[rows_with_issues_mask] - problematic_files = rows_with_issues["file"].unique() - print( - f"\nAffected log files: {len(problematic_files)} files, {len(rows_with_issues)} entries" - ) - if len(problematic_files) <= 10: - for filename in sorted(problematic_files): - count_in_file = len( - rows_with_issues[rows_with_issues["file"] == filename] - ) - print( - f" - {filename}: {count_in_file} entries with invalid values" - ) - else: - print(f" (Showing first 10 of {len(problematic_files)} files)") - for i, filename in enumerate(sorted(problematic_files)[:10], 1): - count_in_file = len( - rows_with_issues[rows_with_issues["file"] == filename] - ) - print(f" {i}. {filename}: {count_in_file} entries") - - # Remove problematic columns - original_column_count = len(df.columns) - df = df.drop(columns=list(problematic_columns)) - removed_count = len(problematic_columns) - remaining_count = len(df.columns) - - print(f"\n✓ Removed {removed_count} problematic column(s)") - print(f" Original columns: {original_column_count}") - print(f" Remaining columns: {remaining_count}") - print(f" Kept all {len(df)} entries") - print(f"{'=' * 70}") - - # Check if we have any meaningful columns left (excluding metadata) - feature_cols_remaining = [ - col - for col in df.columns - if col not in ["file", "iter", "iterations"] - ] - if len(feature_cols_remaining) < 5: - print( - f"\n⚠️ WARNING: Very few features remaining ({len(feature_cols_remaining)})!" - ) - print( - " This may not be sufficient for training a regression model." - ) - print(" Consider fixing the underlying issues in your logs.") - else: - print(" ✅ No invalid values detected") - - # Filter out negative iterations (invalid data) - if "iter" in df.columns: - negative_mask = df["iter"] < 0 - negative_count = negative_mask.sum() - - if negative_count > 0: - print( - f"\n⚠️ Found {negative_count} entries with negative iterations ({negative_count / len(df) * 100:.2f}%)" - ) - - # Show which files have negative iterations - negative_entries = df[negative_mask] - problematic_files = negative_entries["file"].unique() - if len(problematic_files) <= 10: - print( - f" Affected files: {', '.join(sorted(problematic_files))}" - ) - else: - print(f" Affected files: {len(problematic_files)} files") - - # Drop negative iterations - df = df[~negative_mask].reset_index(drop=True) - print( - f" → Dropped negative entries, remaining: {len(df)} entries" - ) - - # Check if we have any data left - if len(df) == 0: - print( - "\n❌ Error: No valid entries remaining after filtering!" - ) - print(" All entries had negative iterations.") - return 1 - - # Print obtained features (all columns) - print(f"\nObtained Features ({len(df.columns)} total):") - print(f"{'=' * 70}") - - # Separate metadata from actual features - metadata_cols = ["file"] - target_cols = ["iter", "iterations"] # Target variable (if present) - - feature_cols = [ - col - for col in df.columns - if col not in metadata_cols and col not in target_cols - ] - - # Print in categories - if metadata_cols: - meta_present = [col for col in metadata_cols if col in df.columns] - if meta_present: - print(f" Metadata: {', '.join(meta_present)}") - - target_present = [col for col in target_cols if col in df.columns] - if target_present: - print(f" Target: {', '.join(target_present)}") - - if feature_cols: - print(f" Features ({len(feature_cols)}):") - for i, col in enumerate(sorted(feature_cols), 1): - # Print 3 features per line for readability - if i % 3 == 1: - print(" ", end="") - print(f"{col:30s}", end="") - if i % 3 == 0: - print() # Newline after 3 features - if len(feature_cols) % 3 != 0: - print() # Final newline if needed - - print(f"{'=' * 70}") - - # Final validation: Ensure NO NaN values remain in the DataFrame - print("\nFinal validation before saving...") - remaining_nans = df.isna().sum() - columns_with_remaining_nans = remaining_nans[remaining_nans > 0] - - if len(columns_with_remaining_nans) > 0: - print(f"\n{'=' * 70}") - print("❌ CRITICAL ERROR: NaN values still present after cleaning!") - print(f"{'=' * 70}") - print("\nColumns with remaining NaN values:") - for col, count in columns_with_remaining_nans.items(): - pct = (count / len(df)) * 100 - print(f" - {col}: {count} NaN values ({pct:.1f}%)") - - print("\nRemoving these columns to ensure clean output...") - df = df.drop(columns=list(columns_with_remaining_nans.index)) - print( - f" ✓ Removed {len(columns_with_remaining_nans)} additional column(s)" - ) - print(f" Remaining columns: {len(df.columns)}") - - # Check if we have any meaningful columns left - feature_cols_remaining = [ - col - for col in df.columns - if col not in ["file", "iter", "iterations"] - ] - if len(feature_cols_remaining) == 0: - print( - "\n❌ FATAL ERROR: No feature columns remaining after NaN removal!" - ) - print(" All columns contained NaN values.") - print(" Cannot proceed with saving - no valid data to save.") - return 1 - - print(f"{'=' * 70}") - - # Double-check: verify no NaN or inf values exist - total_nans = df.isna().sum().sum() - if total_nans > 0: - print( - f"\n❌ FATAL ERROR: {total_nans} NaN values still present despite cleaning!" - ) - print(" This should not happen - please report this as a bug.") - return 1 - - # Check for infinite values - numeric_cols = df.select_dtypes(include=[np.number]).columns - total_infs = 0 - for col in numeric_cols: - total_infs += np.isinf(df[col]).sum() - - if total_infs > 0: - print( - f"\n❌ FATAL ERROR: {total_infs} infinite values still present despite cleaning!" - ) - print(" This should not happen - please report this as a bug.") - # Show which columns still have inf - for col in numeric_cols: - inf_count = np.isinf(df[col]).sum() - if inf_count > 0: - print(f" - {col}: {inf_count} inf values") - return 1 - - print(" ✅ No NaN or infinite values detected - data is clean") - - # Save to Feather file (Apache Arrow format for fast I/O) - print(f"\nSaving {len(df)} entries to {args.output}...") - df.to_feather(args.output, compression="lz4") - - # Get file size - file_size_bytes = os.path.getsize(args.output) - file_size_mb = file_size_bytes / (1024 * 1024) - - print(f"\n{'=' * 70}") - print(f"✓ Success! Saved {len(df)} entries to {args.output}") - print(f" File size: {file_size_mb:.2f} MB") - print(f"{'=' * 70}") - print("\nNext steps:") - print(" 1. View available features:") - print( - f" python scripts/train_regressor.py {args.output} --regressor xgboost --list-features" - ) - print(" 2. Train a model:") - print( - f" python scripts/train_regressor.py {args.output} --regressor xgboost --seed 42" - ) - print(" 3. Train with early stopping and C++ export:") - print( - f" python scripts/train_regressor.py {args.output} --regressor xgboost --seed 42 --early-stopping 20 --treelite-compile 8" - ) - - return 0 - - -if __name__ == "__main__": - exit(main()) diff --git a/scripts/train_regressor.py b/scripts/train_regressor.py deleted file mode 100755 index 9c17e9e76..000000000 --- a/scripts/train_regressor.py +++ /dev/null @@ -1,2182 +0,0 @@ -#!/usr/bin/env python3 -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. -# SPDX-License-Identifier: Apache-2.0 -# All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Train regression models to predict algorithm iterations from log features. - -Usage: - python train_regressor.py --regressor [options] -""" - -import argparse -import pickle -import numpy as np -import pandas as pd -from sklearn.model_selection import train_test_split, cross_val_score, KFold -from sklearn.preprocessing import StandardScaler, PolynomialFeatures -from sklearn.linear_model import LinearRegression, Ridge -from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor -from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score -from sklearn.pipeline import Pipeline -import joblib -import os -from typing import List, Dict, Any, Tuple -import warnings - -warnings.filterwarnings("ignore", category=UserWarning) - - -AVAILABLE_REGRESSORS = [ - "linear", - "poly2", - "poly3", - "poly4", - "xgboost", - "lightgbm", - "random_forest", - "gradient_boosting", -] - -# ============================================================================ -# FEATURE SELECTION CONFIGURATION -# Edit this list to exclude specific features from training -# Leave empty to use all features (except 'file' and 'iter') -# ============================================================================ -FEATURES_TO_EXCLUDE = [ - # Example usage (uncomment to exclude): - # 'time', - # 'avg_constraint_range', - # 'binary_ratio', - "avg_obj_coeff_magnitude", - "n_of_minimums_for_exit", - "feasibility_run", - "fixed_var_ratio", - "unbounded_var_ratio", - "obj_var_ratio", - "avg_related_vars_per_var", - "avg_constraint_range", - "nnz_variance", - "avg_variable_range", - "min_nnz_per_row", - "constraint_var_ratio", - "avg_var_degree", - "equality_ratio", - "integer_ratio", - "binary_ratio", - "max_related_vars", - "problem_size_score", - "structural_complexity", - "tight_constraint_ratio", - "tolerance", - "time_limit", - "tolerance", - "primal_objective", - "dual_objective", - "gap", - "l2_primal_residual", - "l2_dual_residual", - "detect_infeasibility", - "iteration_limit", - "termination", - "check_infeasibility", - "iter_since_best", - "tid", - "curr_obj", - # "obj_weight", - "L3_miss", - "L2_miss", - "L1_miss", - "stores_per_iter", - "loads_per_iter", - # "is_feas", - # "feas_found", - # "viol_ratio", - # "eval_intensity", - # "nnz_per_move", - "iter", - # "max_weight", - "avg_cstr_deg", - "avg_var_deg", - "lp_time", - "node_id", - "var_sel_time", - "bound_str_time", - "sb_time", - "lp_status", - "node_status", - "depth", - "cutoff_gap", - "mem_bandwidth_gb_s", - "max_cstr_deg", - "viol_ratio", - "nnz_per_move", - "h_cstr_right_weights_loads", - "h_cstr_left_weights_loads", - "h_cstr_right_weights_stores", - "h_cstr_left_weights_stores", - "total_viol", - "feas_found", - "obj_weight", - "h_tabu_nodec_until_stores", - "h_tabu_nodec_until_loads", - "h_tabu_noinc_until_stores", - "h_tabu_noinc_until_loads", - "h_tabu_lastdec_stores", - "h_tabu_lastdec_loads", - "h_tabu_lastinc_stores", - "h_tabu_lastinc_loads", - "max_weight", - "fixed", - "phase", - "iters", - "nnz/s", - "nnz/iter", -] - -# Alternatively, specify ONLY the features you want to use -# If non-empty, only these features will be used (overrides FEATURES_TO_EXCLUDE) -FEATURES_TO_INCLUDE_ONLY = [ - # Example usage (uncomment to use only specific features): - # 'n_variables', - # 'n_constraints', - #'sparsity', - # "n_vars", - # "n_cstrs", - # #"total_nnz", - # "mem_total_mb", - # #"cache_hit_rate", - # #"cstr_deg_cv" - # "mem_stores_mb", - # "mem_loads_mb", -] -# ============================================================================ - - -def load_data(data_path: str, target_col: str = "iter") -> pd.DataFrame: - """Load data file (supports .feather and legacy .pkl formats).""" - ext = os.path.splitext(data_path)[1].lower() - - if ext == ".feather": - # Fast Apache Arrow format - df = pd.read_feather(data_path) - elif ext == ".pkl": - # Legacy pickle support - with open(data_path, "rb") as f: - data = pickle.load(f) - - if not isinstance(data, list): - raise ValueError( - f"Expected list of dictionaries, got {type(data)}" - ) - - if len(data) == 0: - raise ValueError("Empty dataset") - - df = pd.DataFrame(data) - else: - raise ValueError( - f"Unsupported file format: {ext}. Use .feather (recommended) or .pkl" - ) - - # Validate required columns - if "file" not in df.columns: - raise ValueError("Missing required 'file' column in data") - if target_col not in df.columns: - raise ValueError( - f"Missing target column '{target_col}' in data. Available columns: {list(df.columns)}" - ) - - return df - - -def split_by_files( - df: pd.DataFrame, - test_size: float = 0.2, - random_state: int = None, - stratify_by: str = None, -) -> Tuple[pd.DataFrame, pd.DataFrame]: - """ - Split data into train/test sets based on unique files. - Ensures all entries from a file go to either train or test, not both. - - Args: - ---- - stratify_by: Optional column name to stratify split (e.g., 'iter' for balanced target distribution) - """ - unique_files = df["file"].unique() - - # Optionally stratify by target distribution - if stratify_by: - # Create stratification labels based on quantiles of the specified column - file_stats = df.groupby("file")[stratify_by].median() - stratify_labels = pd.qcut( - file_stats, - q=min(5, len(unique_files)), - labels=False, - duplicates="drop", - ) - train_files, test_files = train_test_split( - unique_files, - test_size=test_size, - random_state=random_state, - stratify=stratify_labels, - ) - else: - train_files, test_files = train_test_split( - unique_files, test_size=test_size, random_state=random_state - ) - - train_df = df[df["file"].isin(train_files)].copy() - test_df = df[df["file"].isin(test_files)].copy() - - # Validate no data leakage: ensure no overlap between train and test files - train_files_set = set(train_files) - test_files_set = set(test_files) - overlap = train_files_set.intersection(test_files_set) - - if overlap: - raise ValueError( - f"Data leakage detected! {len(overlap)} file(s) appear in both train and test sets:\n" - f" {list(overlap)[:10]}{'...' if len(overlap) > 10 else ''}" - ) - - # Verify the actual dataframes have no file overlap - train_files_in_df = set(train_df["file"].unique()) - test_files_in_df = set(test_df["file"].unique()) - actual_overlap = train_files_in_df.intersection(test_files_in_df) - - if actual_overlap: - raise ValueError( - f"Data leakage detected in dataframes! {len(actual_overlap)} file(s) appear in both:\n" - f" {list(actual_overlap)[:10]}{'...' if len(actual_overlap) > 10 else ''}" - ) - - print("\nData Split:") - print(f" Total entries: {len(df)}") - print(f" Train entries: {len(train_df)} ({len(train_files)} files)") - print(f" Test entries: {len(test_df)} ({len(test_files)} files)") - print(" ✓ Verified: Zero file overlap between train and test sets") - - # Check distribution similarity (use stratify_by column if provided, otherwise first numeric column) - target_col = ( - stratify_by - if stratify_by - else df.select_dtypes(include=[np.number]).columns[0] - ) - if target_col in train_df.columns and target_col in test_df.columns: - train_target_mean = train_df[target_col].mean() - test_target_mean = test_df[target_col].mean() - train_target_std = train_df[target_col].std() - test_target_std = test_df[target_col].std() - - print(f"\nTarget ('{target_col}') Distribution:") - print( - f" Train: mean={train_target_mean:.2f}, std={train_target_std:.2f}" - ) - print( - f" Test: mean={test_target_mean:.2f}, std={test_target_std:.2f}" - ) - - mean_diff_pct = ( - abs(train_target_mean - test_target_mean) / train_target_mean * 100 - if train_target_mean != 0 - else 0 - ) - if mean_diff_pct > 10: - print( - f" ⚠️ Warning: Train/test target means differ by {mean_diff_pct:.1f}%" - ) - print( - " Consider using stratified split or different random seed" - ) - - return train_df, test_df - - -def list_available_features( - df: pd.DataFrame, target_col: str = "iter" -) -> List[str]: - """ - List all available numeric features in the dataset. - Helper function to see what features can be selected/excluded. - """ - # Drop target and metadata columns - cols_to_drop = [ - target_col, - "file", - "iter", - "iterations", - ] # Drop common target column names - X = df.drop( - columns=[c for c in cols_to_drop if c in df.columns], errors="ignore" - ) - X = X.select_dtypes(include=[np.number]) - return sorted(X.columns.tolist()) - - -def validate_data_quality( - df: pd.DataFrame, target_col: str = "iter", verbose: bool = True -) -> Tuple[bool, Dict[str, Any]]: - """ - Validate data quality and report issues. - - Returns - ------- - (is_valid, report_dict) - """ - report = { - "target_issues": [], - "feature_issues": [], - "rows_with_issues": [], - "is_valid": True, - } - - # Check target variable - if target_col not in df.columns: - report["is_valid"] = False - report["target_issues"].append(f"Missing '{target_col}' column") - return False, report - - y = df[target_col] - - # Check for NaN in target - nan_count = y.isna().sum() - if nan_count > 0: - report["is_valid"] = False - report["target_issues"].append( - f"Target has {nan_count} NaN values ({nan_count / len(y) * 100:.1f}%)" - ) - report["rows_with_issues"].extend(df[y.isna()].index.tolist()) - - # Check for inf in target - inf_count = np.isinf(y).sum() - if inf_count > 0: - report["is_valid"] = False - report["target_issues"].append( - f"Target has {inf_count} infinite values ({inf_count / len(y) * 100:.1f}%)" - ) - report["rows_with_issues"].extend(df[np.isinf(y)].index.tolist()) - - # Check for extreme values in target - if not y.isna().all() and not np.isinf(y).all(): - y_clean = y[~(y.isna() | np.isinf(y))] - if len(y_clean) > 0: - y_max = y_clean.max() - y_min = y_clean.min() - if y_max > 1e10: - report["target_issues"].append( - f"Target has very large values (max={y_max:.2e})" - ) - if y_min < -1e10: - report["target_issues"].append( - f"Target has very large negative values (min={y_min:.2e})" - ) - - # Check features - X = df.drop(columns=[target_col, "file"], errors="ignore") - X_numeric = X.select_dtypes(include=[np.number]) - - for col in X_numeric.columns: - col_data = X_numeric[col] - - # Check for NaN - nan_count = col_data.isna().sum() - if nan_count > 0: - pct = nan_count / len(col_data) * 100 - if pct > 10: # Only report if > 10% are NaN - report["feature_issues"].append( - f"{col}: {nan_count} NaN ({pct:.1f}%)" - ) - - # Check for inf - inf_count = np.isinf(col_data).sum() - if inf_count > 0: - report["is_valid"] = False - pct = inf_count / len(col_data) * 100 - report["feature_issues"].append( - f"{col}: {inf_count} infinite ({pct:.1f}%)" - ) - report["rows_with_issues"].extend( - df[np.isinf(col_data)].index.tolist() - ) - - # Deduplicate row indices - report["rows_with_issues"] = sorted(list(set(report["rows_with_issues"]))) - - # Print report if verbose - if verbose and (report["target_issues"] or report["feature_issues"]): - print("\n" + "=" * 70) - print("DATA QUALITY ISSUES DETECTED") - print("=" * 70) - - if report["target_issues"]: - print("\nTarget Variable Issues:") - for issue in report["target_issues"]: - print(f" ❌ {issue}") - - if report["feature_issues"]: - print( - f"\nFeature Issues ({len(report['feature_issues'])} features affected):" - ) - for issue in report["feature_issues"][:10]: # Show first 10 - print(f" ⚠️ {issue}") - if len(report["feature_issues"]) > 10: - print( - f" ... and {len(report['feature_issues']) - 10} more features" - ) - - if report["rows_with_issues"]: - print(f"\nAffected Rows: {len(report['rows_with_issues'])} total") - if len(report["rows_with_issues"]) <= 10: - print(f" Row indices: {report['rows_with_issues']}") - else: - print(f" First 10: {report['rows_with_issues'][:10]}") - - # Show sample problematic rows with filenames - if "file" in df.columns: - print("\n Sample problematic entries:") - sample_indices = report["rows_with_issues"][:5] - for idx in sample_indices: - if idx < len(df): - filename = df.iloc[idx].get("file", "unknown") - iter_val = df.iloc[idx].get("iter", "N/A") - print( - f" Row {idx}: file={filename}, iter={iter_val}" - ) - - print("\nSuggested Actions:") - if not report["is_valid"]: - print(" 1. Remove rows with invalid data: --drop-invalid-rows") - print(" 2. Check your log files for data collection issues") - print(" 3. Verify algorithm didn't produce invalid results") - else: - print( - " Data is valid but has some NaN values in features (will be handled)" - ) - - print("=" * 70 + "\n") - - return report["is_valid"], report - - -def prepare_features( - df: pd.DataFrame, target_col: str = "iter" -) -> Tuple[pd.DataFrame, pd.Series, List[str]]: - """ - Prepare features and target from DataFrame. - Excludes 'file' and target column from features. - Applies feature selection based on FEATURES_TO_EXCLUDE and FEATURES_TO_INCLUDE_ONLY. - """ - # Separate target - y = df[target_col].copy() - - # Drop non-feature columns - X = df.drop(columns=[target_col, "file"]) - - # Ensure all features are numeric - non_numeric = X.select_dtypes(exclude=[np.number]).columns.tolist() - if non_numeric: - print(f"Warning: Dropping non-numeric columns: {non_numeric}") - X = X.select_dtypes(include=[np.number]) - - # Apply feature selection - original_feature_count = len(X.columns) - - if FEATURES_TO_INCLUDE_ONLY: - # Use only specified features - available_features = [ - f for f in FEATURES_TO_INCLUDE_ONLY if f in X.columns - ] - missing_features = [ - f for f in FEATURES_TO_INCLUDE_ONLY if f not in X.columns - ] - - if missing_features: - print( - f"Warning: Requested features not found in data: {missing_features}" - ) - - X = X[available_features] - print( - f"Feature selection: Using only {len(available_features)} specified features" - ) - - elif FEATURES_TO_EXCLUDE: - # Exclude specified features - features_to_drop = [f for f in FEATURES_TO_EXCLUDE if f in X.columns] - if features_to_drop: - X = X.drop(columns=features_to_drop) - print( - f"Feature selection: Excluded {len(features_to_drop)} features: {features_to_drop}" - ) - - feature_names = X.columns.tolist() - - if len(feature_names) == 0: - raise ValueError( - "No features remaining after feature selection! " - "Check FEATURES_TO_EXCLUDE and FEATURES_TO_INCLUDE_ONLY settings." - ) - - if len(feature_names) != original_feature_count: - print( - f" Using {len(feature_names)} of {original_feature_count} available features" - ) - - return X, y, feature_names - - -def create_regressor( - regressor_type: str, - random_state: int = None, - tune_hyperparams: bool = False, - verbose: bool = True, -): - """ - Create a regression model with optional preprocessing pipeline. - - Returns: (model, needs_scaling) - """ - if regressor_type == "linear": - model = LinearRegression() - needs_scaling = True - - elif regressor_type.startswith("poly"): - degree = int(regressor_type[-1]) - model = Pipeline( - [ - ( - "poly", - PolynomialFeatures(degree=degree, include_bias=False), - ), - ( - "regressor", - Ridge(alpha=1.0), - ), # Ridge to handle multicollinearity - ] - ) - needs_scaling = True - - elif regressor_type == "xgboost": - try: - import xgboost as xgb - except ImportError: - raise ImportError( - "XGBoost not installed. Install with: pip install xgboost" - ) - - params = { - "objective": "reg:squarederror", - "random_state": random_state, - "n_estimators": 100, - "max_depth": 6, - "tree_method": "hist", - "learning_rate": 0.1, - "verbosity": 1 if verbose else 0, - # Regularization to prevent overfitting - "min_child_weight": 3, # Minimum sum of weights in a leaf - "gamma": 0.1, # Minimum loss reduction for split - "subsample": 0.8, # Fraction of samples per tree - "colsample_bytree": 0.8, # Fraction of features per tree - "reg_alpha": 0.1, # L1 regularization - "reg_lambda": 1.0, # L2 regularization - } - - if tune_hyperparams: - # Stronger regularization for tuned version - params.update( - { - "n_estimators": 200, - "max_depth": 5, # Shallower trees - "learning_rate": 0.05, # Lower learning rate - "min_child_weight": 5, # Higher minimum weight - "gamma": 0.2, # More conservative splits - "subsample": 0.7, # More aggressive subsampling - "colsample_bytree": 0.7, - "reg_alpha": 0.5, # Stronger L1 - "reg_lambda": 2.0, # Stronger L2 - } - ) - - model = xgb.XGBRegressor(**params) - needs_scaling = True - - elif regressor_type == "lightgbm": - try: - import lightgbm as lgb - except ImportError: - raise ImportError( - "LightGBM not installed. Install with: pip install lightgbm" - ) - - params = { - "objective": "regression", - "random_state": random_state, - "n_estimators": 150, - "max_depth": 6, - "learning_rate": 0.1, - "verbosity": 1 if verbose else -1, - # Regularization to prevent overfitting - "min_child_weight": 3, - "min_split_gain": 0.1, - "subsample": 0.8, - "colsample_bytree": 0.8, - "reg_alpha": 0.1, - "reg_lambda": 1.0, - } - - if tune_hyperparams: - # Stronger regularization for tuned version - params.update( - { - "n_estimators": 200, - "max_depth": 5, - "learning_rate": 0.05, - "min_child_weight": 5, - "min_split_gain": 0.2, - "subsample": 0.7, - "colsample_bytree": 0.7, - "reg_alpha": 0.5, - "reg_lambda": 2.0, - } - ) - - model = lgb.LGBMRegressor(**params) - needs_scaling = True - - elif regressor_type == "random_forest": - params = { - "random_state": random_state, - "n_estimators": 100, - "max_depth": None, - "min_samples_split": 2, - "verbose": 1 if verbose else 0, - } - - if tune_hyperparams: - params.update( - {"n_estimators": 200, "max_depth": 20, "min_samples_split": 5} - ) - - model = RandomForestRegressor(**params) - needs_scaling = False - - elif regressor_type == "gradient_boosting": - params = { - "random_state": random_state, - "n_estimators": 100, - "max_depth": 5, - "learning_rate": 0.1, - "verbose": 1 if verbose else 0, - } - - if tune_hyperparams: - params.update( - {"n_estimators": 200, "max_depth": 7, "learning_rate": 0.05} - ) - - model = GradientBoostingRegressor(**params) - needs_scaling = False - - else: - raise ValueError(f"Unknown regressor type: {regressor_type}") - - return model, needs_scaling - - -def get_feature_importance( - model, feature_names: List[str], regressor_type: str -) -> None: - """Extract and print feature importance if available.""" - print("\nFeature Importance:") - - try: - if regressor_type in [ - "xgboost", - "lightgbm", - "random_forest", - "gradient_boosting", - ]: - # Tree-based models have feature_importances_ - importances = model.feature_importances_ - indices = np.argsort(importances)[::-1] - - for i, idx in enumerate(indices, 1): - print( - f" {i:3d}. {feature_names[idx]:40s}: {importances[idx]:.6f}" - ) - - elif regressor_type == "linear": - # Linear regression coefficients and intercept - print(f"\nIntercept: {model.intercept_:.6f}\n") - print("Coefficients:") - - # Print each feature with its coefficient - for i, (feature_name, coef) in enumerate( - zip(feature_names, model.coef_), 1 - ): - print(f" {i:3d}. {feature_name:40s}: {coef:.6f}") - - # Also show sorted by absolute value for importance ranking - print("\nRanked by absolute magnitude:") - coefs_abs = np.abs(model.coef_) - indices = np.argsort(coefs_abs)[::-1] - for i, idx in enumerate(indices, 1): - print( - f" {i:3d}. {feature_names[idx]:40s}: {model.coef_[idx]:.6f} (|{coefs_abs[idx]:.6f}|)" - ) - - elif regressor_type.startswith("poly"): - # For polynomial, get feature names and coefficients from the Ridge step - poly_features = model.named_steps["poly"].get_feature_names_out( - feature_names - ) - coefs = np.abs(model.named_steps["regressor"].coef_) - indices = np.argsort(coefs)[::-1] - - print(f" (Showing top 50 of {len(indices)} polynomial features)") - for i, idx in enumerate(indices[:50], 1): - feat_name = poly_features[idx] - # Truncate very long polynomial feature names - if len(feat_name) > 60: - feat_name = feat_name[:57] + "..." - print(f" {i:3d}. {feat_name:60s}: {coefs[idx]:.6f}") - else: - print(" Feature importance not available for this model type") - - except Exception as e: - print(f" Could not extract feature importance: {e}") - - -def evaluate_model( - model, - X_train, - y_train, - X_test, - y_test, - feature_names: List[str], - regressor_type: str, - cv_folds: int = 5, - verbose: int = 0, - skip_cv: bool = False, - X_test_original: pd.DataFrame = None, - test_df: pd.DataFrame = None, - log_transform: bool = False, - y_train_original: pd.Series = None, - y_test_original: pd.Series = None, -) -> Tuple[float, float]: - """Evaluate model and print metrics. Returns (train_r2, test_r2). - - Args: - ---- - X_test_original: Unscaled X_test for displaying feature values - test_df: Original test dataframe with 'file' column - log_transform: If True, predictions are in log-space and need inverse transform - y_train_original: Original (non-log) training targets (if log_transform=True) - y_test_original: Original (non-log) test targets (if log_transform=True) - """ - # Cross-validation on training set (skip if using early stopping) - if not skip_cv: - print(f"\nCross-Validation on Training Set ({cv_folds}-fold):") - try: - # Compute RMSE and R² in log-space - cv_scores_mse = cross_val_score( - model, - X_train, - y_train, - cv=cv_folds, - scoring="neg_mean_squared_error", - n_jobs=-1, - verbose=verbose, - ) - cv_scores_r2 = cross_val_score( - model, - X_train, - y_train, - cv=cv_folds, - scoring="r2", - n_jobs=-1, - verbose=verbose, - ) - cv_rmse = np.sqrt(-cv_scores_mse) - - if log_transform: - print(" Log-space metrics:") - print( - f" CV RMSE: {cv_rmse.mean():.4f} (+/- {cv_rmse.std():.4f})" - ) - print( - f" CV R²: {cv_scores_r2.mean():.4f} (+/- {cv_scores_r2.std():.4f})" - ) - - # Also compute metrics in original space - kfold = KFold(n_splits=cv_folds, shuffle=True, random_state=42) - - cv_mape_scores = [] - cv_r2_original_scores = [] - - for train_idx, val_idx in kfold.split(X_train): - X_train_fold = ( - X_train.iloc[train_idx] - if hasattr(X_train, "iloc") - else X_train[train_idx] - ) - y_train_fold = ( - y_train.iloc[train_idx] - if hasattr(y_train, "iloc") - else y_train[train_idx] - ) - X_val_fold = ( - X_train.iloc[val_idx] - if hasattr(X_train, "iloc") - else X_train[val_idx] - ) - y_val_original_fold = ( - y_train_original.iloc[val_idx] - if hasattr(y_train_original, "iloc") - else y_train_original[val_idx] - ) - - # Train on fold - model_fold = type(model)(**model.get_params()) - model_fold.fit(X_train_fold, y_train_fold) - - # Predict and transform back - y_pred_log = model_fold.predict(X_val_fold) - y_pred_original = np.exp(y_pred_log) - - # Compute metrics in original space - mape = ( - np.mean( - np.abs( - (y_val_original_fold - y_pred_original) - / y_val_original_fold - ) - ) - * 100 - ) - r2_original = r2_score( - y_val_original_fold, y_pred_original - ) - - cv_mape_scores.append(mape) - cv_r2_original_scores.append(r2_original) - - cv_mape = np.array(cv_mape_scores) - cv_r2_original = np.array(cv_r2_original_scores) - - print(" Original-space metrics:") - print( - f" CV MAPE: {cv_mape.mean():.2f}% (+/- {cv_mape.std():.2f}%)" - ) - print( - f" CV R²: {cv_r2_original.mean():.4f} (+/- {cv_r2_original.std():.4f})" - ) - else: - print( - f" CV RMSE: {cv_rmse.mean():.4f} (+/- {cv_rmse.std():.4f})" - ) - print( - f" CV R²: {cv_scores_r2.mean():.4f} (+/- {cv_scores_r2.std():.4f})" - ) - - except Exception as e: - print( - f" CV failed (likely due to early stopping): {str(e)[:100]}" - ) - print(" Skipping cross-validation...") - else: - print("\nSkipping cross-validation (incompatible with early stopping)") - - # Training set metrics - y_train_pred = model.predict(X_train) - - # If log-transformed, also compute metrics in original space - if log_transform: - # Inverse transform predictions - y_train_pred_original = np.exp(y_train_pred) - y_test_pred_log = model.predict(X_test) - y_test_pred_original = np.exp(y_test_pred_log) - - # Metrics in log-space - train_mse_log = mean_squared_error(y_train, y_train_pred) - train_rmse_log = np.sqrt(train_mse_log) - train_r2_log = r2_score(y_train, y_train_pred) - - # Metrics in original space - train_mse = mean_squared_error(y_train_original, y_train_pred_original) - train_rmse = np.sqrt(train_mse) - train_mae = mean_absolute_error( - y_train_original, y_train_pred_original - ) - train_r2 = r2_score(y_train_original, y_train_pred_original) - train_mape = ( - np.mean( - np.abs( - (y_train_original - y_train_pred_original) - / y_train_original - ) - ) - * 100 - ) - - print("\nTraining Set Metrics (Original Space):") - print(f" MSE: {train_mse:.4f}") - print(f" RMSE: {train_rmse:.4f}") - print(f" MAE: {train_mae:.4f}") - print(f" MAPE: {train_mape:.2f}% (optimized metric)") - print(f" R²: {train_r2:.4f}") - print("\nTraining Set Metrics (Log Space):") - print(f" RMSE: {train_rmse_log:.4f}") - print(f" R²: {train_r2_log:.4f}") - else: - train_mse = mean_squared_error(y_train, y_train_pred) - train_rmse = np.sqrt(train_mse) - train_mae = mean_absolute_error(y_train, y_train_pred) - train_r2 = r2_score(y_train, y_train_pred) - - print("\nTraining Set Metrics:") - print(f" MSE: {train_mse:.4f}") - print(f" RMSE: {train_rmse:.4f}") - print(f" MAE: {train_mae:.4f}") - print(f" R²: {train_r2:.4f}") - - # Test set metrics - if log_transform: - # Already computed above - test_mse_log = mean_squared_error(y_test, y_test_pred_log) - test_rmse_log = np.sqrt(test_mse_log) - test_r2_log = r2_score(y_test, y_test_pred_log) - - # Metrics in original space - test_mse = mean_squared_error(y_test_original, y_test_pred_original) - test_rmse = np.sqrt(test_mse) - test_mae = mean_absolute_error(y_test_original, y_test_pred_original) - test_r2 = r2_score(y_test_original, y_test_pred_original) - test_mape = ( - np.mean( - np.abs( - (y_test_original - y_test_pred_original) / y_test_original - ) - ) - * 100 - ) - - print("\nTest Set Metrics (Original Space):") - print(f" MSE: {test_mse:.4f}") - print(f" RMSE: {test_rmse:.4f}") - print(f" MAE: {test_mae:.4f}") - print(f" MAPE: {test_mape:.2f}% (optimized metric)") - print(f" R²: {test_r2:.4f}") - print("\nTest Set Metrics (Log Space):") - print(f" RMSE: {test_rmse_log:.4f}") - print(f" R²: {test_r2_log:.4f}") - - # Use original space predictions for sample display - y_test_pred = y_test_pred_original - y_test = y_test_original - else: - y_test_pred = model.predict(X_test) - test_mse = mean_squared_error(y_test, y_test_pred) - test_rmse = np.sqrt(test_mse) - test_mae = mean_absolute_error(y_test, y_test_pred) - test_r2 = r2_score(y_test, y_test_pred) - - print("\nTest Set Metrics:") - print(f" MSE: {test_mse:.4f}") - print(f" RMSE: {test_rmse:.4f}") - print(f" MAE: {test_mae:.4f}") - print(f" R²: {test_r2:.4f}") - - # If R² is negative, show baseline comparison for debugging - if test_r2 < 0: - y_test_mean = np.mean(y_test) - baseline_pred = np.full_like(y_test_pred, y_test_mean) - baseline_mse = mean_squared_error(y_test, baseline_pred) - baseline_rmse = np.sqrt(baseline_mse) - print("\n WARNING: Negative R² detected!") - print( - f" This means the model is worse than predicting the mean: {y_test_mean:.4f}" - ) - print(f" Baseline (mean) RMSE: {baseline_rmse:.4f}") - print(f" Model RMSE: {test_rmse:.4f}") - print( - f" Model is {test_rmse / baseline_rmse:.2f}x worse than baseline" - ) - - # Feature importance - get_feature_importance(model, feature_names, regressor_type) - - # Sample predictions - print("\n20 Sample Predictions from Test Set:") - print( - f" {'Actual':>10s} {'Predicted':>10s} {'Error':>10s} {'Error %':>10s}" - ) - print(f" {'-' * 10} {'-' * 10} {'-' * 10} {'-' * 10}") - - sample_indices = np.random.choice( - len(y_test), min(20, len(y_test)), replace=False - ) - for idx in sample_indices: - actual = y_test.iloc[idx] - predicted = y_test_pred[idx] - error = actual - predicted - error_pct = (error / actual * 100) if actual != 0 else 0 - print( - f" {actual:10.2f} {predicted:10.2f} {error:10.2f} {error_pct:9.2f}%" - ) - - # Show worst predictions with feature values - print("\n5 Worst Predictions (Largest Absolute Error):") - abs_errors = np.abs(y_test_pred - y_test.values) - worst_indices = np.argsort(abs_errors)[-5:][::-1] - - # Use original (unscaled) features if available, otherwise use X_test - X_display = X_test_original if X_test_original is not None else X_test - - for rank, idx in enumerate(worst_indices, 1): - actual = y_test.iloc[idx] - predicted = y_test_pred[idx] - error = actual - predicted - error_pct = (error / actual * 100) if actual != 0 else 0 - - # Get filename if available - filename = "" - if test_df is not None and "file" in test_df.columns: - filename = f" (file: {test_df.iloc[idx]['file']})" - - print( - f"\n #{rank} - Actual: {actual:.2f}, Predicted: {predicted:.2f}, " - f"Error: {error:.2f} ({error_pct:.1f}%){filename}" - ) - - # Get feature values (handle both DataFrame and array) - if isinstance(X_display, pd.DataFrame): - feature_values = X_display.iloc[idx].values - elif isinstance(X_display, np.ndarray): - feature_values = X_display[idx] - else: - feature_values = X_display[idx] - - # Display features compactly (5 per line) - print(" Features:", end="") - for i, (feat_name, feat_val) in enumerate( - zip(feature_names, feature_values) - ): - if i % 5 == 0: - print("\n ", end="") - # Format feature value - if isinstance(feat_val, (int, np.integer)): - print(f"{feat_name}={feat_val}", end=" ") - else: - print(f"{feat_name}={feat_val:.3g}", end=" ") - print() # Final newline - - return train_r2, test_r2 - - -def compile_model_treelite( - model, - regressor_type: str, - output_dir: str, - num_threads: int, - X_train=None, - annotate: bool = False, - quantize: bool = False, - feature_names: List[str] = None, - model_name: str = None, - log_transform: bool = False, -) -> None: - """Compile XGBoost/LightGBM model to C source files using TL2cgen. - - Args: - ---- - model: Trained model - regressor_type: Type of regressor - output_dir: Output directory - num_threads: Number of parallel compilation threads - X_train: Training data for branch annotation (optional) - annotate: Whether to annotate branches for optimization - quantize: Whether to use quantization in code generation - feature_names: List of feature names in expected order (optional) - model_name: Name prefix for functions (optional, derived from training file) - log_transform: Whether model predicts in log-space (will add exp() wrapper) - """ - if regressor_type not in ["xgboost", "lightgbm"]: - print( - f"Warning: TL2cgen compilation only supported for XGBoost and LightGBM, skipping for {regressor_type}" - ) - return - - try: - import treelite - import tl2cgen - except ImportError: - missing = [] - try: - import treelite - except ImportError: - missing.append("treelite") - try: - import tl2cgen - except ImportError: - missing.append("tl2cgen") - - print( - f"Warning: {', '.join(missing)} not installed. Install with: pip install {' '.join(missing)}" - ) - print("Skipping C code generation.") - return - - optimization_info = [] - if annotate: - optimization_info.append("branch annotation") - if quantize: - optimization_info.append("quantization") - - opt_str = ( - f" with {', '.join(optimization_info)}" if optimization_info else "" - ) - print( - f"\nGenerating C source code with TL2cgen (threads={num_threads}){opt_str}..." - ) - - # Convert model to treelite format using frontend API - try: - if regressor_type == "xgboost": - tl_model = treelite.frontend.from_xgboost(model.get_booster()) - elif regressor_type == "lightgbm": - tl_model = treelite.frontend.from_lightgbm(model.booster_) - except Exception as e: - print( - f"Warning: Failed to convert {regressor_type} model to treelite: {e}" - ) - return - - # Annotate branches if requested and training data is available - annotation_path = None - if annotate and X_train is not None: - try: - print(" Annotating branches with training data...") - # Convert to numpy array if it's a DataFrame - if hasattr(X_train, "values"): - X_train_array = X_train.values.astype(np.float32) - else: - X_train_array = np.asarray(X_train, dtype=np.float32) - - dmat = tl2cgen.DMatrix(X_train_array, dtype="float32") - annotation_path = os.path.join( - output_dir, f"{regressor_type}_annotation.json" - ) - tl2cgen.annotate_branch( - tl_model, dmat=dmat, path=annotation_path, verbose=False - ) - print(f" Branch annotations saved to: {annotation_path}") - except Exception as e: - print(f" Warning: Branch annotation failed: {e}") - print(" Continuing without branch annotation") - annotation_path = None - elif annotate and X_train is None: - print( - " Warning: Branch annotation requested but no training data available" - ) - print(" Skipping branch annotation") - - # Generate C source files using TL2cgen - source_dir = os.path.join(output_dir, f"{regressor_type}_c_code") - - try: - # params = {'parallel_comp': num_threads} - params = {} - - # Add quantization parameter if requested - if quantize: - params["quantize"] = 1 # Enable quantization in code generation - - # Add annotation file if available - if annotation_path: - params["annotate_in"] = annotation_path - - tl2cgen.generate_c_code( - tl_model, dirpath=source_dir, params=params, verbose=False - ) - - # Post-process generated files - header_path = os.path.join(source_dir, "header.h") - main_path = os.path.join(source_dir, "main.c") - quantize_path = os.path.join(source_dir, "quantize.c") - recipe_path = os.path.join(source_dir, "recipe.json") - - # Rename all .c files to .cpp and wrap in class - if model_name: - try: - import glob - - c_files = glob.glob(os.path.join(source_dir, "*.c")) - - for c_file in c_files: - cpp_file = c_file[:-2] + ".cpp" - - # Read content - with open(c_file, "r") as f: - content = f.read() - - # Split content into includes and rest - lines = content.split("\n") - include_lines = [] - code_lines = [] - in_includes = True - - for line in lines: - if in_includes and ( - line.strip().startswith("#include") - or line.strip().startswith("#") - or line.strip() == "" - ): - include_lines.append(line) - else: - in_includes = False - code_lines.append(line) - - # Prefix function definitions with ClassName:: (for .cpp files, not class wrapping) - import re - - processed_lines = [] - for line in code_lines: - # Detect function definitions (return_type function_name(...)) - if ( - line - and not line.strip().startswith("//") - and not line.strip().startswith("/*") - ): - # Check if it's a function definition - # Pattern: type name(...) or type* name(...) etc. - func_pattern = r"^(\s*)((?:const\s+)?(?:unsigned\s+)?(?:struct\s+)?[\w_]+(?:\s*\*)*\s+)([\w_]+)(\s*\()" - match = re.match(func_pattern, line) - if ( - match and "::" not in line - ): # Don't add if already qualified - indent = match.group(1) - return_type = match.group(2) - func_name = match.group(3) - rest = line[match.end(3) :] - # Prefix function name with class name - line = f"{indent}{return_type}{model_name}::{func_name}{rest}" - processed_lines.append(line) - code_lines = processed_lines - - # Don't wrap in class for .cpp files - just output the definitions - includes_str = "\n".join(include_lines) - code_str = "\n".join(code_lines) - - # For .cpp files, no class wrapper needed - cpp_content = f"{includes_str}\n\n{code_str}\n" - - # Write to .cpp file - with open(cpp_file, "w") as f: - f.write(cpp_content) - - # Remove original .c file - os.remove(c_file) - - # Update paths for further processing - main_path = main_path[:-2] + ".cpp" - quantize_path = quantize_path[:-2] + ".cpp" - - print(f" Renamed {len(c_files)} .c files to .cpp") - except Exception as e: - print(f" Warning: Failed to rename .c files: {e}") - - # Optimize main.cpp by removing unnecessary missing data checks - # Since all features are always provided, replace !(data[X].missing != -1) with false - if os.path.exists(main_path): - try: - with open(main_path, "r") as f: - content = f.read() - - # Replace pattern !(data[N].missing != -1) with false - import re - - original_content = content - content = re.sub( - r"!\(data\[\d+\]\.missing != -1\)", "false", content - ) - - # If log_transform is used, wrap return values with exp() - if log_transform: - # Add include if not present - if ( - "#include " not in content - and "#include" not in content - ): - # Find the last #include and add after it - include_match = None - for match in re.finditer( - r'#include\s*[<"].*?[>"]', content - ): - include_match = match - if include_match: - insert_pos = include_match.end() - content = ( - content[:insert_pos] - + "\n#include " - + content[insert_pos:] - ) - - # Replace "return sum;" with "return std::exp(sum);" - # Match various return patterns in the predict function - content = re.sub( - r"(\s+)return\s+(sum|result|pred)\s*;", - r"\1return std::exp(\2);", - content, - ) - - # Also handle single-line returns like "return value;" - content = re.sub( - r"(\s+)return\s+([\w\.]+)\s*;", - lambda m: f"{m.group(1)}return std::exp({m.group(2)});" - if m.group(2) not in ["true", "false", "0", "1"] - else m.group(0), - content, - ) - - print( - " Added exp() transformation to convert log-space predictions to original space" - ) - - if content != original_content: - with open(main_path, "w") as f: - f.write(content) - if not log_transform: - print( - " Optimized main.cpp by removing unnecessary missing data checks" - ) - except Exception as e: - print(f" Warning: Failed to optimize main.cpp: {e}") - - # Wrap header.h content in class with #pragma once - defines_to_move = [] - if model_name and os.path.exists(header_path): - try: - with open(header_path, "r") as f: - content = f.read() - - # Split content into includes, defines to move, and rest - lines = content.split("\n") - include_lines = [] - code_lines = [] - in_includes = True - i = 0 - - while i < len(lines): - line = lines[i] - - if in_includes and ( - line.strip().startswith("#include") - or line.strip() == "" - ): - include_lines.append(line) - i += 1 - # Detect macros to move to main.cpp - elif ( - line.strip().startswith("#if defined(__clang__)") - or line.strip().startswith("#define N_TARGET") - or line.strip().startswith("#define MAX_N_CLASS") - ): - in_includes = False - # Capture the entire #if block or single #define - if line.strip().startswith("#if defined(__clang__)"): - # Capture the entire #if...#endif block - macro_block = [] - macro_block.append(line) - i += 1 - while i < len(lines) and not lines[ - i - ].strip().startswith("#endif"): - macro_block.append(lines[i]) - i += 1 - if i < len(lines): - macro_block.append(lines[i]) # Include #endif - i += 1 - defines_to_move.append("\n".join(macro_block)) - else: - # Single #define line - defines_to_move.append(line) - i += 1 - else: - in_includes = False - code_lines.append(line) - i += 1 - - # Add static keyword to function declarations - import re - - processed_lines = [] - for line in code_lines: - # Detect function declarations/definitions (return_type function_name(...)) - # Match lines that look like function declarations but don't already have static - if ( - line - and not line.strip().startswith("//") - and not line.strip().startswith("/*") - ): - # Check if it's a function declaration/definition - # Pattern: type name(...) or type* name(...) or type name[...](...) etc. - func_pattern = r"^(\s*)((?:const\s+)?(?:unsigned\s+)?(?:struct\s+)?[\w_]+(?:\s*\*)*\s+)([\w_]+)\s*\(" - match = re.match(func_pattern, line) - if match and "static" not in line: - indent = match.group(1) - return_type = match.group(2) - # Add static keyword - line = f"{indent}static {return_type}{line[len(indent) + len(return_type) :]}" - processed_lines.append(line) - code_lines = processed_lines - - # Wrap code in class declaration - includes_str = "\n".join(include_lines) - code_str = "\n".join(code_lines) - - wrapped_content = f"#pragma once\n\n{includes_str}\n\nclass {model_name} {{\npublic:\n{code_str}\n}}; // class {model_name}\n" - - with open(header_path, "w") as f: - f.write(wrapped_content) - - print( - f" Wrapped header.h in class '{model_name}' with #pragma once" - ) - except Exception as e: - print(f" Warning: Failed to wrap header.h: {e}") - - # Add defines to main.cpp (moved from header.h) - if defines_to_move and os.path.exists(main_path): - try: - with open(main_path, "r") as f: - content = f.read() - - # Insert defines after includes (look for where code starts - typically after blank line after includes) - defines_str = "\n".join(defines_to_move) - - # Find the first non-include, non-blank line to insert before - lines = content.split("\n") - insert_pos = 0 - for i, line in enumerate(lines): - if line.strip() and not line.strip().startswith( - "#include" - ): - insert_pos = i - break - - # Insert defines at the position - lines.insert(insert_pos, defines_str) - lines.insert( - insert_pos + 1, "" - ) # Add blank line after defines - - content = "\n".join(lines) - with open(main_path, "w") as f: - f.write(content) - print( - f" Moved {len(defines_to_move)} macro definition(s) from header.h to main.cpp" - ) - except Exception as e: - print(f" Warning: Failed to add defines to main.cpp: {e}") - - # Add feature names to header and implementation - if ( - feature_names - and os.path.exists(header_path) - and os.path.exists(main_path) - ): - try: - # Append to header.h (inside class) - with open(header_path, "r") as f: - content = f.read() - - # Insert before closing class - insertion = f"\n // Feature names\n static constexpr int NUM_FEATURES = {len(feature_names)};\n static const char* feature_names[NUM_FEATURES];\n" - content = content.replace( - f"}}; // class {model_name}\n", - f"{insertion}}}; // class {model_name}\n", - ) - - with open(header_path, "w") as f: - f.write(content) - - # Append to main.cpp (at the end of the file, outside any class) - with open(main_path, "r") as f: - content = f.read() - - # Append feature array definition at the end of the file - feature_array = f"\n// Feature names array\nconst char* {model_name}::feature_names[{model_name}::NUM_FEATURES] = {{\n" - for i, name in enumerate(feature_names): - comma = "," if i < len(feature_names) - 1 else "" - feature_array += f' "{name}"{comma}\n' - feature_array += "};\n" - - # Append to end of file - content = content.rstrip() + "\n" + feature_array - - with open(main_path, "w") as f: - f.write(content) - - print( - f" Added {len(feature_names)} feature names to header.h and main.cpp" - ) - except Exception as e: - print(f" Warning: Failed to add feature names: {e}") - - # Remove recipe.json if it exists - if os.path.exists(recipe_path): - try: - os.remove(recipe_path) - print(" Removed recipe.json") - except Exception as e: - print(f" Warning: Failed to remove recipe.json: {e}") - - opt_msg = [] - if annotation_path: - opt_msg.append("branch-annotated") - if quantize: - opt_msg.append("quantized") - opt_suffix = f" ({', '.join(opt_msg)})" if opt_msg else "" - - print(f"C source code generated to: {source_dir}/") - print( - f" Contains optimized model source code{opt_suffix} ready for compilation" - ) - except Exception as e: - print(f"Warning: TL2cgen code generation failed: {e}") - print(" Model saved in standard format only.") - - -def save_model( - model, - scaler, - regressor_type: str, - output_dir: str, - feature_names: List[str], - log_transform: bool = False, -) -> None: - """Save trained model and preprocessing components to disk.""" - os.makedirs(output_dir, exist_ok=True) - - # Save metadata - metadata = { - "regressor_type": regressor_type, - "feature_names": feature_names, - "has_scaler": scaler is not None, - "log_transform": log_transform, - } - - metadata_path = os.path.join(output_dir, f"{regressor_type}_metadata.pkl") - with open(metadata_path, "wb") as f: - pickle.dump(metadata, f) - print(f"\nSaved metadata to: {metadata_path}") - - # Save scaler if exists - if scaler is not None: - scaler_path = os.path.join(output_dir, f"{regressor_type}_scaler.pkl") - joblib.dump(scaler, scaler_path) - print(f"Saved scaler to: {scaler_path}") - - # Save model - if regressor_type == "xgboost": - # Save as UBJ with gzip compression - model_path = os.path.join(output_dir, f"{regressor_type}_model.ubj") - model.save_model(model_path) - - # Gzip the file - import gzip - import shutil - - with open(model_path, "rb") as f_in: - with gzip.open(model_path + ".gz", "wb") as f_out: - shutil.copyfileobj(f_in, f_out) - os.remove(model_path) # Remove uncompressed version - print(f"Saved model to: {model_path}.gz") - elif regressor_type == "lightgbm": - # Save LightGBM model as text file - model_path = os.path.join(output_dir, f"{regressor_type}_model.txt") - model.booster_.save_model(model_path) - print(f"Saved model to: {model_path}") - else: - # Save sklearn models as joblib (more efficient than pickle for large arrays) - model_path = os.path.join(output_dir, f"{regressor_type}_model.joblib") - joblib.dump(model, model_path) - print(f"Saved model to: {model_path}") - - -def main(): - parser = argparse.ArgumentParser( - description="Train regression models to predict algorithm iterations", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Available regressors: - linear - Linear Regression - poly2, poly3, poly4 - Polynomial Regression (degree 2, 3, 4) - xgboost - XGBoost Regressor - lightgbm - LightGBM Regressor - random_forest - Random Forest Regressor - gradient_boosting - Gradient Boosting Regressor - -Examples: - # Train a model (default target: iter) - python train_regressor.py data.feather --regressor xgboost --seed 42 - python train_regressor.py data.feather --regressor lightgbm --seed 42 - - # Train to predict time instead of iterations - python train_regressor.py data.feather --regressor xgboost --target time_ms --seed 42 - - # Check data quality before training - python train_regressor.py data.feather --regressor xgboost --check-data - - # Train with automatic removal of invalid rows - python train_regressor.py data.feather --regressor xgboost --drop-invalid-rows --seed 42 - - # Stratify train/test split by target column (ensures balanced distribution) - python train_regressor.py data.feather --regressor xgboost --stratify-split --seed 42 - - # Stratify split by a specific column (e.g., time_ms) - python train_regressor.py data.feather --regressor xgboost --stratify-split time_ms --seed 42 - - # Optimize for relative error (recommended for targets spanning multiple orders of magnitude) - python train_regressor.py data.feather --regressor xgboost --log-transform --seed 42 - - # Legacy pickle format - python train_regressor.py data.pkl --regressor xgboost --seed 42 - """, - ) - - parser.add_argument( - "input_pkl", help="Input data file (.feather or .pkl) with log data" - ) - parser.add_argument( - "--regressor", - "-r", - required=True, - choices=AVAILABLE_REGRESSORS, - help="Type of regressor to train", - ) - parser.add_argument( - "--output-dir", - "-o", - default="./models", - help="Output directory for saved models (default: ./models)", - ) - parser.add_argument( - "--seed", - "-s", - type=int, - default=None, - help="Random seed for reproducibility (optional)", - ) - parser.add_argument( - "--tune", - action="store_true", - help="Enable hyperparameter tuning (uses predefined tuned parameters)", - ) - parser.add_argument( - "--cv-folds", - type=int, - default=5, - help="Number of cross-validation folds (default: 5)", - ) - parser.add_argument( - "--test-size", - type=float, - default=0.2, - help="Proportion of files to use for testing (default: 0.2)", - ) - parser.add_argument( - "--no-progress", - action="store_true", - help="Disable training progress output", - ) - parser.add_argument( - "--list-features", - action="store_true", - help="List all available features in the dataset and exit", - ) - parser.add_argument( - "--stratify-split", - type=str, - nargs="?", - const="__target__", - default=None, - metavar="COLUMN", - help="Stratify train/test split by specified column distribution. If no column specified, uses target column. Example: --stratify-split time_ms or just --stratify-split for target column", - ) - parser.add_argument( - "--early-stopping", - type=int, - default=0, - metavar="N", - help="Enable early stopping for tree models (default: 20 rounds, use 0 to disable)", - ) - parser.add_argument( - "--treelite-compile", - type=int, - default=1, - metavar="THREADS", - help="Export XGBoost/LightGBM model as optimized C source code with TL2cgen (includes branch annotation and quantization)", - ) - parser.add_argument( - "--drop-invalid-rows", - action="store_true", - help="Drop rows with NaN or infinite values in target variable (instead of failing)", - ) - parser.add_argument( - "--check-data", - action="store_true", - help="Run data quality checks and exit (no training)", - ) - parser.add_argument( - "--target", - type=str, - default="iter", - help="Target column to predict (default: iter). Examples: iter, time_ms, iterations", - ) - parser.add_argument( - "--log-transform", - action="store_true", - help="Use log-transform on target variable to optimize for relative error instead of absolute error. Recommended when target values span multiple orders of magnitude.", - ) - - args = parser.parse_args() - - # Set random seed if provided - if args.seed is not None: - np.random.seed(args.seed) - print(f"Random seed set to: {args.seed}") - - # Load data - print(f"\nLoading data from: {args.input_pkl}") - print(f"Target column: '{args.target}'") - df = load_data(args.input_pkl, target_col=args.target) - print(f"Loaded {len(df)} entries with {len(df.columns)} columns") - - # Report file format - ext = os.path.splitext(args.input_pkl)[1].lower() - if ext == ".feather": - print(" Format: Apache Arrow/Feather (fast I/O)") - elif ext == ".pkl": - print(" Format: Pickle (legacy)") - - # Extract model name from input file (for prefixing generated C functions) - model_name = os.path.splitext(os.path.basename(args.input_pkl))[0] - - # Validate data quality - print("\nValidating data quality...") - is_valid, report = validate_data_quality( - df, target_col=args.target, verbose=True - ) - - # If just checking data, exit here - if args.check_data: - if is_valid: - print("\n✅ Data quality check passed! Ready for training.") - return 0 - else: - print( - "\n❌ Data quality check failed! Fix issues before training." - ) - return 1 - - # Handle invalid data - if not is_valid: - if args.drop_invalid_rows: - print( - f"\nDropping {len(report['rows_with_issues'])} rows with invalid data..." - ) - df_clean = df.drop(index=report["rows_with_issues"]) - df = df_clean.reset_index(drop=True) - print(f" Remaining: {len(df)} entries") - - # Re-validate - is_valid_after, _ = validate_data_quality(df, verbose=False) - if not is_valid_after: - print( - "❌ Error: Data still invalid after dropping rows. Check your data." - ) - return 1 - print(" ✅ Data is now valid") - else: - print("\n❌ Training aborted due to invalid data.") - print( - " Use --drop-invalid-rows to automatically remove invalid rows, or" - ) - print( - " Use --check-data to just run validation without training" - ) - return 1 - - # If listing features, do that and exit - if args.list_features: - features = list_available_features(df, target_col=args.target) - - # Also list potential target columns - numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() - potential_targets = [ - col for col in numeric_cols if col not in features - ] - - print(f"\n{'=' * 70}") - print(f"Available features in dataset ({len(features)} total):") - print(f"{'=' * 70}") - for i, feat in enumerate(features, 1): - print(f" {i:3d}. {feat}") - - if potential_targets: - print(f"\n{'=' * 70}") - print( - f"Potential target columns ({len(potential_targets)} total):" - ) - print(f"{'=' * 70}") - for i, col in enumerate(potential_targets, 1): - marker = " (current)" if col == args.target else "" - print(f" {i:3d}. {col}{marker}") - - print("\nTo exclude features, edit FEATURES_TO_EXCLUDE in the script:") - print(f" {__file__}") - print("\nTo use only specific features, edit FEATURES_TO_INCLUDE_ONLY") - print("\nTo change target column, use: --target ") - return - - # Split data by files - # Handle stratify-split argument - if args.stratify_split is None: - stratify_by = None - elif args.stratify_split == "__target__": - stratify_by = args.target - print(f"Stratifying split by target column: '{args.target}'") - else: - stratify_by = args.stratify_split - if stratify_by not in df.columns: - print( - f"\n❌ Error: Stratify column '{stratify_by}' not found in dataset" - ) - print(f"Available columns: {list(df.columns)}") - return 1 - print(f"Stratifying split by column: '{stratify_by}'") - - train_df, test_df = split_by_files( - df, - test_size=args.test_size, - random_state=args.seed, - stratify_by=stratify_by, - ) - - # Prepare features - X_train, y_train, feature_names = prepare_features( - train_df, target_col=args.target - ) - X_test, y_test, _ = prepare_features(test_df, target_col=args.target) - - print(f"\nFeatures: {len(feature_names)}") - print(f"Target: {args.target} (prediction target)") - - # Apply log transform if requested (for relative error optimization) - if args.log_transform: - # Check for non-positive values before log transform - if np.any(y_train <= 0) or np.any(y_test <= 0): - n_nonpositive_train = np.sum(y_train <= 0) - n_nonpositive_test = np.sum(y_test <= 0) - print( - "\n❌ Error: Cannot apply log-transform with non-positive target values!" - ) - print(f" Train set: {n_nonpositive_train} non-positive values") - print(f" Test set: {n_nonpositive_test} non-positive values") - print( - f" Target range: [{np.min(y_train):.2f}, {np.max(y_train):.2f}]" - ) - print( - "\nSuggestion: Add a small constant (e.g., +1) to all target values before log" - ) - return 1 - - print( - "\nApplying log-transform to target variable (optimizes for relative error)" - ) - y_train_original = y_train.copy() - y_test_original = y_test.copy() - - y_train = np.log(y_train) - y_test = np.log(y_test) - - print( - f" Original target range: [{np.min(y_train_original):.2f}, {np.max(y_train_original):.2f}]" - ) - print( - f" Log-space target range: [{np.min(y_train):.4f}, {np.max(y_train):.4f}]" - ) - else: - y_train_original = None - y_test_original = None - - # Enhanced diagnostics for XGBoost compatibility (only show if problems found) - X_train_array = X_train.values if hasattr(X_train, "values") else X_train - - # XGBoost internal limits (approximate) - xgb_max_safe = 1e38 # XGBoost uses float32 internally - - problematic_features = [] - problem_details = [] - - for i, col_name in enumerate(feature_names): - col_data = X_train_array[:, i] - - n_nan = np.sum(np.isnan(col_data)) - n_inf = np.sum(np.isinf(col_data)) - n_posinf = np.sum(np.isposinf(col_data)) - n_neginf = np.sum(np.isneginf(col_data)) - - # Get statistics on finite values - finite_mask = np.isfinite(col_data) - if np.any(finite_mask): - finite_data = col_data[finite_mask] - col_min = np.min(finite_data) - col_max = np.max(finite_data) - col_mean = np.mean(finite_data) - col_std = np.std(finite_data) - abs_max = max(abs(col_min), abs(col_max)) - else: - col_min = col_max = col_mean = col_std = abs_max = np.nan - - # Check if values are too large for XGBoost - is_problematic = ( - n_nan > 0 - or n_inf > 0 - or (not np.isnan(abs_max) and abs_max > xgb_max_safe) - ) - - if is_problematic: - problematic_features.append(col_name) - detail = f"\n⚠️ '{col_name}':" - if n_nan > 0: - detail += f"\n NaN: {n_nan:8d} ({100 * n_nan / len(col_data):6.2f}%)" - if n_posinf > 0: - detail += f"\n +Inf: {n_posinf:8d} ({100 * n_posinf / len(col_data):6.2f}%)" - if n_neginf > 0: - detail += f"\n -Inf: {n_neginf:8d} ({100 * n_neginf / len(col_data):6.2f}%)" - if not np.isnan(abs_max): - detail += f"\n Range: [{col_min:.6e}, {col_max:.6e}]" - detail += f"\n Max abs: {abs_max:.6e}" - if abs_max > xgb_max_safe: - detail += f"\n ❌ TOO LARGE! Exceeds XGBoost safe limit (~{xgb_max_safe:.2e})" - detail += f"\n Mean: {col_mean:.6e}" - detail += f"\n Std: {col_std:.6e}" - problem_details.append(detail) - - # Check target variable - n_nan_target = np.sum(np.isnan(y_train)) - n_inf_target = np.sum(np.isinf(y_train)) - if n_nan_target > 0 or n_inf_target > 0: - problematic_features.append(f"TARGET[{args.target}]") - detail = f"\n⚠️ Target '{args.target}':" - if n_nan_target > 0: - detail += f"\n NaN: {n_nan_target} ({100 * n_nan_target / len(y_train):.2f}%)" - if n_inf_target > 0: - detail += f"\n Inf: {n_inf_target} ({100 * n_inf_target / len(y_train):.2f}%)" - problem_details.append(detail) - - # Only print if problems found - if len(problematic_features) > 0: - print("\n" + "=" * 70) - print("⚠️ FEATURE VALUE PROBLEMS DETECTED") - print("=" * 70) - for detail in problem_details: - print(detail) - print("\n" + "=" * 70) - print(f"❌ Found {len(problematic_features)} problematic feature(s):") - for feat in problematic_features: - print(f" - {feat}") - print("\nTo fix:") - print( - " 1. Add these features to FEATURES_TO_EXCLUDE at top of script" - ) - print( - " 2. Or investigate why these features have extreme/invalid values" - ) - print("=" * 70 + "\n") - - # Create model - print(f"\nTraining {args.regressor} regressor...") - model, needs_scaling = create_regressor( - args.regressor, - random_state=args.seed, - tune_hyperparams=args.tune, - verbose=not args.no_progress, - ) - - # Apply scaling if needed - scaler = None - needs_scaling = False - X_test_original = X_test.copy() # Keep unscaled version for display - if needs_scaling: - print(" Applying StandardScaler to features...") - scaler = StandardScaler() - X_train_scaled = scaler.fit_transform(X_train) - X_test_scaled = scaler.transform(X_test) - - # Sanity check: verify scaling worked correctly - print( - f" Scaled features - mean: {np.mean(X_train_scaled):.6f}, std: {np.std(X_train_scaled):.6f}" - ) - if np.any(np.isnan(X_train_scaled)) or np.any( - np.isinf(X_train_scaled) - ): - print(" WARNING: NaN or Inf detected in scaled training data!") - if np.any(np.isnan(X_test_scaled)) or np.any(np.isinf(X_test_scaled)): - print(" WARNING: NaN or Inf detected in scaled test data!") - else: - X_train_scaled = X_train - X_test_scaled = X_test - - # Train model - if args.regressor.startswith("poly"): - degree = int(args.regressor[-1]) - n_features = X_train_scaled.shape[1] - from math import comb - - n_poly_features = sum( - comb(n_features + d - 1, d) for d in range(1, degree + 1) - ) - print( - f" Generating {n_poly_features} polynomial features (degree {degree})..." - ) - - # Use early stopping for tree-based models if requested - if ( - args.early_stopping - and args.early_stopping > 0 - and args.regressor in ["xgboost", "lightgbm", "gradient_boosting"] - ): - if args.regressor == "xgboost": - print( - f" Using early stopping (patience={args.early_stopping} rounds)..." - ) - # Set early stopping parameter - model.set_params(early_stopping_rounds=args.early_stopping) - model.fit( - X_train_scaled, - y_train, - eval_set=[(X_test_scaled, y_test)], - verbose=False, - ) - # Report best iteration - best_iteration = ( - model.best_iteration - if hasattr(model, "best_iteration") - else model.n_estimators - ) - print( - f" Best iteration: {best_iteration} (out of {model.n_estimators} max)" - ) - elif args.regressor == "lightgbm": - print( - f" Using early stopping (patience={args.early_stopping} rounds)..." - ) - model.fit( - X_train_scaled, - y_train, - eval_set=[(X_test_scaled, y_test)], - callbacks=[ - __import__("lightgbm").early_stopping( - stopping_rounds=args.early_stopping, verbose=False - ) - ], - ) - # Report best iteration - best_iteration = ( - model.best_iteration_ - if hasattr(model, "best_iteration_") - else model.n_estimators - ) - print( - f" Best iteration: {best_iteration} (out of {model.n_estimators} max)" - ) - else: # gradient_boosting - # Gradient Boosting uses n_iter_no_change parameter - print( - " Note: Use --tune with gradient_boosting for early stopping" - ) - model.fit(X_train_scaled, y_train) - else: - model.fit(X_train_scaled, y_train) - - print(" Training complete!") - - # Evaluate model - skip_cv = args.early_stopping is not None and args.early_stopping > 0 - train_r2, test_r2 = evaluate_model( - model, - X_train_scaled, - y_train, - X_test_scaled, - y_test, - feature_names, - args.regressor, - cv_folds=args.cv_folds, - verbose=2 if not args.no_progress else 0, - skip_cv=skip_cv, - X_test_original=X_test_original, - test_df=test_df, - log_transform=args.log_transform, - y_train_original=y_train_original, - y_test_original=y_test_original, - ) - - # Save model - save_model( - model, - scaler, - args.regressor, - args.output_dir, - feature_names, - log_transform=args.log_transform, - ) - - # Compile with TL2cgen if requested (with optimizations enabled by default) - if args.treelite_compile is not None: - # Use unscaled training data for branch annotation when scaling is not applied - # Note: All models now use scaling for consistency - X_train_for_annotation = X_train if not needs_scaling else None - - compile_model_treelite( - model, - args.regressor, - args.output_dir, - args.treelite_compile, - X_train=X_train_for_annotation, - annotate=True, # Always enable branch annotation - quantize=True, # Always enable quantization - feature_names=feature_names, - model_name=model_name, - log_transform=args.log_transform, - ) - - print("\n" + "=" * 70) - print("Training completed successfully!") - print("=" * 70) - print("\nFinal R² Scores:") - print(f" Train R²: {train_r2:.4f}") - print(f" Test R²: {test_r2:.4f}") - - -if __name__ == "__main__": - main() From 8ed90acca32b7d8dce1fa11b8441d806e8d487d1 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 16 Jan 2026 14:07:47 +0000 Subject: [PATCH 197/366] restore run_bounds_strengthening=false in DS presolve --- cpp/src/dual_simplex/presolve.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/presolve.cpp b/cpp/src/dual_simplex/presolve.cpp index 7a2f81c7e..8d915bd2f 100644 --- a/cpp/src/dual_simplex/presolve.cpp +++ b/cpp/src/dual_simplex/presolve.cpp @@ -622,8 +622,7 @@ void convert_user_problem(const user_problem_t& user_problem, convert_greater_to_less(user_problem, row_sense, problem, greater_rows, less_rows); } - bool run_bounds_strengthening = true; - if (settings.deterministic) { run_bounds_strengthening = false; } + constexpr bool run_bounds_strengthening = false; if (run_bounds_strengthening) { csr_matrix_t Arow(1, 1, 1); problem.A.to_compressed_row(Arow); From cdcd1010636b0e201e0287e28bf29a5af63a12e4 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 16 Jan 2026 15:39:10 +0000 Subject: [PATCH 198/366] spring cleaning --- benchmarks/linear_programming/cuopt/run_mip.cpp | 3 --- cpp/CMakeLists.txt | 14 -------------- cpp/src/dual_simplex/bsp_debug.hpp | 2 +- cpp/src/utilities/seed_generator.cuh | 10 ---------- cpp/src/utilities/timer.hpp | 17 +---------------- 5 files changed, 2 insertions(+), 44 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index f746fb71a..fdaae26bb 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -206,9 +206,6 @@ int run_single_file(std::string file_path, settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; - - if (deterministic) { settings.presolve = false; } - cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; auto start_run_solver = std::chrono::high_resolution_clock::now(); diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 9cef17cdb..fac00c66a 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -62,9 +62,6 @@ message(VERBOSE "cuOpt: fatbin: ${WRITE_FATBIN}") # CUDA runtime rapids_cuda_init_runtime(USE_STATIC ON) -message(STATUS "HEY") -find_package(CUDAToolkit) -message(STATUS "HEY") rapids_find_package(CUDAToolkit REQUIRED BUILD_EXPORT_SET cuopt-exports @@ -350,17 +347,6 @@ target_link_libraries(cuopt ${CUOPT_PRIVATE_CUDA_LIBS} ) -# find_path(PAPI_INCLUDE_DIR papi.h) -# find_library(PAPI_LIBRARY papi) - -# if (PAPI_INCLUDE_DIR AND PAPI_LIBRARY) -# message(STATUS "Found PAPI in ${PAPI_INCLUDE_DIR}") -# target_include_directories(cuopt PRIVATE ${PAPI_INCLUDE_DIR}) -# target_link_libraries(cuopt PRIVATE ${PAPI_LIBRARY}) -# else() -# message(FATAL_ERROR "Could not find PAPI") -# endif() - # ################################################################################################## # - generate tests -------------------------------------------------------------------------------- diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp index edf543bb0..d4c13181d 100644 --- a/cpp/src/dual_simplex/bsp_debug.hpp +++ b/cpp/src/dual_simplex/bsp_debug.hpp @@ -10,7 +10,7 @@ // Enable BSP debug macros. The actual logging is controlled at runtime via // environment variables (CUOPT_BSP_DEBUG_*). This define enables the macro // infrastructure; without it, all BSP_DEBUG_* macros become complete no-ops. -#define BSP_DEBUG_ENABLED +// #define BSP_DEBUG_ENABLED #include #include diff --git a/cpp/src/utilities/seed_generator.cuh b/cpp/src/utilities/seed_generator.cuh index 7692ff467..dd5e79d84 100644 --- a/cpp/src/utilities/seed_generator.cuh +++ b/cpp/src/utilities/seed_generator.cuh @@ -31,17 +31,7 @@ class seed_generator { set_seed(seed1 + ((seed0 + seed1) * (seed0 + seed1 + 1) / 2), seeds...); } -#if SEED_GENERATOR_DEBUG - static int64_t get_seed(const char* caller = __builtin_FUNCTION(), - const char* file = __builtin_FILE(), - int line = __builtin_LINE()) - { - printf("&&&&&&& SEED CALLED BY %s:%d: %s() ***\n", file, line, caller); - return seed_++; - } -#else static int64_t get_seed() { return seed_++; } -#endif public: seed_generator(seed_generator const&) = delete; diff --git a/cpp/src/utilities/timer.hpp b/cpp/src/utilities/timer.hpp index 8e53d1a4c..1d1a4881e 100644 --- a/cpp/src/utilities/timer.hpp +++ b/cpp/src/utilities/timer.hpp @@ -34,22 +34,7 @@ class timer_t { elapsed_time()); } - bool check_time_limit(const char* caller = __builtin_FUNCTION(), - const char* file = __builtin_FILE(), - int line = __builtin_LINE()) const noexcept - { - bool elapsed = elapsed_time() >= time_limit; - if (elapsed) { - printf("************ TIME LIMIT (%.2gs) REACHED BY %s:%d: %s() ***\n", - time_limit, - file, - line, - caller); - // assert(false && "unexpected timer"); - //__builtin_trap(); - } - return elapsed; - } + bool check_time_limit() const noexcept { return elapsed_time() >= time_limit; } bool check_half_time() const noexcept { return elapsed_time() >= time_limit / 2; } From cd5d073ffa6a934d2389601d6df1e7308f6de6bd Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 16 Jan 2026 15:52:55 +0000 Subject: [PATCH 199/366] cleaning --- cpp/src/dual_simplex/phase2.cpp | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 497eb3d8a..8e0c661a8 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2183,20 +2183,20 @@ class phase2_timers_t { delta_z_time + lu_update_time + se_norms_time + se_entering_time + perturb_time + vector_time + objective_time + update_infeasibility_time; // clang-format off - printf("BFRT time %.2fs %4.1f%\n", bfrt_time, 100.0 * bfrt_time / total_time); - printf("Pricing time %.2fs %4.1f%\n", pricing_time, 100.0 * pricing_time / total_time); - printf("BTran time %.2fs %4.1f%\n", btran_time, 100.0 * btran_time / total_time); - printf("FTran time %.2fs %4.1f%\n", ftran_time, 100.0 * ftran_time / total_time); - printf("Flip time %.2fs %4.1f%\n", flip_time, 100.0 * flip_time / total_time); - printf("Delta_z time %.2fs %4.1f%\n", delta_z_time, 100.0 * delta_z_time / total_time); - printf("LU update time %.2fs %4.1f%\n", lu_update_time, 100.0 * lu_update_time / total_time); - printf("SE norms time %.2fs %4.1f%\n", se_norms_time, 100.0 * se_norms_time / total_time); - printf("SE enter time %.2fs %4.1f%\n", se_entering_time, 100.0 * se_entering_time / total_time); - printf("Perturb time %.2fs %4.1f%\n", perturb_time, 100.0 * perturb_time / total_time); - printf("Vector time %.2fs %4.1f%\n", vector_time, 100.0 * vector_time / total_time); - printf("Objective time %.2fs %4.1f%\n", objective_time, 100.0 * objective_time / total_time); - printf("Inf update time %.2fs %4.1f%\n", update_infeasibility_time, 100.0 * update_infeasibility_time / total_time); - printf("Sum %.2fs\n", total_time); + settings.log.printf("BFRT time %.2fs %4.1f%\n", bfrt_time, 100.0 * bfrt_time / total_time); + settings.log.printf("Pricing time %.2fs %4.1f%\n", pricing_time, 100.0 * pricing_time / total_time); + settings.log.printf("BTran time %.2fs %4.1f%\n", btran_time, 100.0 * btran_time / total_time); + settings.log.printf("FTran time %.2fs %4.1f%\n", ftran_time, 100.0 * ftran_time / total_time); + settings.log.printf("Flip time %.2fs %4.1f%\n", flip_time, 100.0 * flip_time / total_time); + settings.log.printf("Delta_z time %.2fs %4.1f%\n", delta_z_time, 100.0 * delta_z_time / total_time); + settings.log.printf("LU update time %.2fs %4.1f%\n", lu_update_time, 100.0 * lu_update_time / total_time); + settings.log.printf("SE norms time %.2fs %4.1f%\n", se_norms_time, 100.0 * se_norms_time / total_time); + settings.log.printf("SE enter time %.2fs %4.1f%\n", se_entering_time, 100.0 * se_entering_time / total_time); + settings.log.printf("Perturb time %.2fs %4.1f%\n", perturb_time, 100.0 * perturb_time / total_time); + settings.log.printf("Vector time %.2fs %4.1f%\n", vector_time, 100.0 * vector_time / total_time); + settings.log.printf("Objective time %.2fs %4.1f%\n", objective_time, 100.0 * objective_time / total_time); + settings.log.printf("Inf update time %.2fs %4.1f%\n", update_infeasibility_time, 100.0 * update_infeasibility_time / total_time); + settings.log.printf("Sum %.2fs\n", total_time); // clang-format on } f_t bfrt_time; @@ -2673,7 +2673,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_y_nz_percentage = delta_y_nz0 / static_cast(m) * 100.0; const bool use_transpose = delta_y_nz_percentage <= 30.0; { - raft::common::nvtx::range scope_compute_delta_z("DualSimplex::delta_z"); + // raft::common::nvtx::range scope_compute_delta_z("DualSimplex::delta_z"); if (use_transpose) { sparse_delta_z++; phase2::compute_delta_z(A_transpose, @@ -3183,7 +3183,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, f_t now = toc(start_time); // Feature logging for regression training (every FEATURE_LOG_INTERVAL iterations) - if ((iter % FEATURE_LOG_INTERVAL) == 0) { + if ((iter % FEATURE_LOG_INTERVAL) == 0 && work_unit_context) { i_t iters_elapsed = iter - last_feature_log_iter; auto [total_loads, total_stores] = manifold.collect_and_flush(); From 14d3911e923f1b0593c17a964ebeb5d31a9c5ee2 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 16 Jan 2026 16:50:25 +0000 Subject: [PATCH 200/366] no bs changes --- cpp/src/dual_simplex/bounds_strengthening.cpp | 89 ++-------- cpp/src/dual_simplex/bounds_strengthening.hpp | 17 +- cpp/src/mip/solve.cu | 6 +- cpp/src/utilities/memory_instrumentation.hpp | 162 +++++++++++++++++- 4 files changed, 188 insertions(+), 86 deletions(-) diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index 0a01b76cf..f1bf52c1e 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -1,16 +1,11 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ #include -#include -#include -#include - -#include #include #include @@ -100,48 +95,22 @@ bool bounds_strengthening_t::bounds_strengthening( std::vector& upper_bounds, const simplex_solver_settings_t& settings) { - const i_t m = A.m; - const i_t n = A.n; - const i_t nnz = A.col_start[n]; - - raft::common::nvtx::range fun_scope("bounds_strengthening"); - f_t start_time = tic(); - - // Instrumented vectors for memory tracking (use uint8_t since std::vector is specialized) - cuopt::ins_vector constraint_changed(m, 1); - cuopt::ins_vector variable_changed(n, 0); - cuopt::ins_vector constraint_changed_next(m, 0); - - // Instrumentation manifold for this operation - includes A matrix (CSC and CSR) - cuopt::instrumentation_manifold_t manifold; - manifold.add("constraint_changed", constraint_changed); - manifold.add("variable_changed", variable_changed); - manifold.add("constraint_changed_next", constraint_changed_next); - // A matrix in CSC format (for column access) - manifold.add("A.col_start", A.col_start); - manifold.add("A.i", A.i); - manifold.add("A.x", A.x); - // A matrix in CSR format (for row access) - manifold.add("Arow.row_start", Arow.row_start); - manifold.add("Arow.j", Arow.j); - manifold.add("Arow.x", Arow.x); - // Member vectors for bounds and activity tracking - manifold.add("lower", lower); - manifold.add("upper", upper); - manifold.add("delta_min_activity", delta_min_activity); - manifold.add("delta_max_activity", delta_max_activity); - manifold.add("constraint_lb", constraint_lb); - manifold.add("constraint_ub", constraint_ub); + const i_t m = A.m; + const i_t n = A.n; + + std::vector constraint_changed(m, true); + std::vector variable_changed(n, false); + std::vector constraint_changed_next(m, false); if (!bounds_changed.empty()) { - std::fill(constraint_changed.begin(), constraint_changed.end(), static_cast(0)); + std::fill(constraint_changed.begin(), constraint_changed.end(), false); for (i_t i = 0; i < n; ++i) { if (bounds_changed[i]) { const i_t row_start = A.col_start[i]; const i_t row_end = A.col_start[i + 1]; for (i_t p = row_start; p < row_end; ++p) { const i_t j = A.i[p]; - constraint_changed[j] = 1; + constraint_changed[j] = true; } } } @@ -149,11 +118,10 @@ bool bounds_strengthening_t::bounds_strengthening( lower = lower_bounds; upper = upper_bounds; - print_bounds_stats(lower.underlying(), upper.underlying(), settings, "Initial bounds"); + print_bounds_stats(lower, upper, settings, "Initial bounds"); - i_t iter = 0; - const i_t iter_limit = 10; - i_t total_bounds_changed = 0; + i_t iter = 0; + const i_t iter_limit = 10; while (iter < iter_limit) { for (i_t i = 0; i < m; ++i) { if (!constraint_changed[i]) { continue; } @@ -166,7 +134,7 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t j = Arow.j[p]; const f_t a_ij = Arow.x[p]; - variable_changed[j] = 1; + variable_changed[j] = true; if (a_ij > 0) { min_a += a_ij * lower[j]; max_a += a_ij * upper[j]; @@ -250,47 +218,28 @@ bool bounds_strengthening_t::bounds_strengthening( if (new_lb != old_lb || new_ub != old_ub) { for (i_t p = row_start; p < row_end; ++p) { const i_t i = A.i[p]; - constraint_changed_next[i] = 1; + constraint_changed_next[i] = true; } } lower[k] = std::min(new_lb, new_ub); upper[k] = std::max(new_lb, new_ub); - bool bounds_tightened = lb_updated || ub_updated; - if (bounds_tightened) { num_bounds_changed++; } + bool bounds_changed = lb_updated || ub_updated; + if (bounds_changed) { num_bounds_changed++; } } - total_bounds_changed += num_bounds_changed; if (num_bounds_changed == 0) { break; } std::swap(constraint_changed, constraint_changed_next); - std::fill( - constraint_changed_next.begin(), constraint_changed_next.end(), static_cast(0)); - std::fill(variable_changed.begin(), variable_changed.end(), static_cast(0)); + std::fill(constraint_changed_next.begin(), constraint_changed_next.end(), false); + std::fill(variable_changed.begin(), variable_changed.end(), false); iter++; } // settings.log.printf("Total strengthened variables %d\n", total_strengthened_variables); - // Log bounds strengthening features -#if 0 - { - auto [loads, stores] = manifold.collect_and_flush(); - bounds_strengthening_features_t bs_features; - bs_features.m = m; - bs_features.n = n; - bs_features.nnz = nnz; - bs_features.num_iterations = iter; - bs_features.num_bounds_changed = total_bounds_changed; - bs_features.byte_loads = loads; - bs_features.byte_stores = stores; - bs_features.runtime = toc(start_time); - bs_features.log_single(m, n, nnz); - } -#endif - #if DEBUG_BOUND_STRENGTHENING f_t lb_change = 0.0; f_t ub_change = 0.0; @@ -325,7 +274,7 @@ bool bounds_strengthening_t::bounds_strengthening( num_ub_changed, iter); } - print_bounds_stats(lower.underlying(), upper.underlying(), settings, "Final bounds"); + print_bounds_stats(lower, upper, settings, "Final bounds"); #endif lower_bounds = lower; diff --git a/cpp/src/dual_simplex/bounds_strengthening.hpp b/cpp/src/dual_simplex/bounds_strengthening.hpp index 679b732b5..e7e218b82 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.hpp +++ b/cpp/src/dual_simplex/bounds_strengthening.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -8,7 +8,6 @@ #pragma once #include -#include namespace cuopt::linear_programming::dual_simplex { @@ -32,12 +31,12 @@ class bounds_strengthening_t { const csr_matrix_t& Arow; const std::vector& var_types; - // Instrumented vectors for memory tracking - cuopt::ins_vector lower; - cuopt::ins_vector upper; - cuopt::ins_vector delta_min_activity; - cuopt::ins_vector delta_max_activity; - cuopt::ins_vector constraint_lb; - cuopt::ins_vector constraint_ub; + std::vector lower; + std::vector upper; + + std::vector delta_min_activity; + std::vector delta_max_activity; + std::vector constraint_lb; + std::vector constraint_ub; }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index ebd0242f2..14f894b21 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -140,9 +140,9 @@ mip_solution_t run_mip(detail::problem_t& problem, auto sol = scaled_sol.get_solution( is_feasible_before_scaling || is_feasible_after_unscaling, solver.get_solver_stats(), false); - // TODO: RESTORE THIS - - // detail::print_solution(scaled_problem.handle_ptr, sol.get_solution()); + int hidesol = + std::getenv("CUOPT_MIP_PRINT_SOLUTION") ? atoi(std::getenv("CUOPT_MIP_PRINT_SOLUTION")) : 0; + if (!hidesol) { detail::print_solution(scaled_problem.handle_ptr, sol.get_solution()); } return sol; } diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index f4b8a2090..945b8953f 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -45,12 +45,12 @@ namespace cuopt { -// Define CUOPT_ENABLE_MEMORY_INSTRUMENTATION to enable memory tracking -// If undefined, instrumentation becomes a zero-overhead passthrough +// Define CUOPT_ENABLE_MEMORY_INSTRUMENTATION to 1 to enable memory tracking +// When 0, instrumentation becomes a zero-overhead passthrough // Base class for memory operation instrumentation struct memory_instrumentation_base_t { -#ifdef CUOPT_ENABLE_MEMORY_INSTRUMENTATION +#if CUOPT_ENABLE_MEMORY_INSTRUMENTATION HDI void reset_counters() const { byte_loads = byte_stores = 0; } template @@ -92,7 +92,7 @@ struct memory_instrumentation_base_t { #endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION }; -#ifdef CUOPT_ENABLE_MEMORY_INSTRUMENTATION +#if CUOPT_ENABLE_MEMORY_INSTRUMENTATION // Manifold class to collect statistics from multiple instrumented objects class instrumentation_manifold_t { @@ -266,6 +266,8 @@ struct has_back().back())>> : std::true_ } // namespace type_traits_utils +#if CUOPT_ENABLE_MEMORY_INSTRUMENTATION + // Memory operation instrumentation wrapper for container-like types template struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { @@ -404,7 +406,9 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { auto operator*() const { if constexpr (IsConst) { +#ifdef CUOPT_ENABLE_MEMORY_INSTRUMENTATION wrapper_->byte_loads += sizeof(value_type); +#endif return *iter_; } else { return element_proxy_t(*iter_, *wrapper_); @@ -792,6 +796,156 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { value_type* data_ptr{nullptr}; }; +#else // !CUOPT_ENABLE_MEMORY_INSTRUMENTATION + +// Zero-overhead passthrough wrapper when instrumentation is disabled +// Provides the same interface as the instrumented version but just forwards to the underlying +// container +template +struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { + using value_type = typename T::value_type; + using size_type = typename T::size_type; + using difference_type = typename T::difference_type; + using reference = typename T::reference; + using const_reference = typename T::const_reference; + using pointer = typename T::pointer; + using const_pointer = typename T::const_pointer; + using iterator = typename T::iterator; + using const_iterator = typename T::const_iterator; + using reverse_iterator = typename T::reverse_iterator; + using const_reverse_iterator = typename T::const_reverse_iterator; + + // Constructors - forward everything to the underlying container + memop_instrumentation_wrapper_t() = default; + memop_instrumentation_wrapper_t(const T& arr) : array_(arr), wrapped_ptr_(nullptr) {} + memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)), wrapped_ptr_(nullptr) {} + + template , memop_instrumentation_wrapper_t> && + !std::is_same_v, T> && + (sizeof...(Args) > 0 || !std::is_convertible_v)>> + explicit memop_instrumentation_wrapper_t(Arg&& arg, Args&&... args) + : array_(std::forward(arg), std::forward(args)...), wrapped_ptr_(nullptr) + { + } + + // Copy constructor - always copy the data, never share wrapped pointer + memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) + : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_), wrapped_ptr_(nullptr) + { + } + + // Move constructor - take ownership of array, never share wrapped pointer + memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept + : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_)), + wrapped_ptr_(nullptr) + { + } + + // Copy assignment - always copy the data + memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) + { + if (this != &other) { + if (wrapped_ptr_) { + *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; + } else { + array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; + } + } + return *this; + } + + // Move assignment - take the data + memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept + { + if (this != &other) { + if (wrapped_ptr_) { + *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); + } else { + array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); + } + } + return *this; + } + + // Element access - direct passthrough + reference operator[](size_type index) { return underlying()[index]; } + const_reference operator[](size_type index) const { return underlying()[index]; } + + reference front() { return underlying().front(); } + const_reference front() const { return underlying().front(); } + + reference back() { return underlying().back(); } + const_reference back() const { return underlying().back(); } + + pointer data() noexcept { return underlying().data(); } + const_pointer data() const noexcept { return underlying().data(); } + + // Iterators - use underlying container's iterators directly + iterator begin() noexcept { return underlying().begin(); } + const_iterator begin() const noexcept { return underlying().begin(); } + const_iterator cbegin() const noexcept { return underlying().cbegin(); } + + iterator end() noexcept { return underlying().end(); } + const_iterator end() const noexcept { return underlying().end(); } + const_iterator cend() const noexcept { return underlying().cend(); } + + reverse_iterator rbegin() noexcept { return underlying().rbegin(); } + const_reverse_iterator rbegin() const noexcept { return underlying().rbegin(); } + const_reverse_iterator crbegin() const noexcept { return underlying().crbegin(); } + + reverse_iterator rend() noexcept { return underlying().rend(); } + const_reverse_iterator rend() const noexcept { return underlying().rend(); } + const_reverse_iterator crend() const noexcept { return underlying().crend(); } + + // Capacity + bool empty() const noexcept { return underlying().empty(); } + size_type size() const noexcept { return underlying().size(); } + size_type max_size() const noexcept { return underlying().max_size(); } + size_type capacity() const noexcept { return underlying().capacity(); } + + void reserve(size_type new_cap) { underlying().reserve(new_cap); } + void shrink_to_fit() { underlying().shrink_to_fit(); } + + // Modifiers + void clear() noexcept { underlying().clear(); } + void push_back(const value_type& value) { underlying().push_back(value); } + void push_back(value_type&& value) { underlying().push_back(std::move(value)); } + + template + void emplace_back(Args&&... args) + { + underlying().emplace_back(std::forward(args)...); + } + + void pop_back() { underlying().pop_back(); } + void resize(size_type count) { underlying().resize(count); } + void resize(size_type count, const value_type& value) { underlying().resize(count, value); } + + // Conversion operators + operator T&() { return underlying(); } + operator const T&() const { return underlying(); } + + T&& release_array() { return std::move(array_); } + + // Wrap/unwrap interface (for compatibility, but wrap is essentially a no-op for perf) + void wrap(T& external_array) { wrapped_ptr_ = &external_array; } + void unwrap() { wrapped_ptr_ = nullptr; } + bool is_wrapping() const { return wrapped_ptr_ != nullptr; } + + // Get the underlying container + T& underlying() { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } + const T& underlying() const { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } + + private: + T array_; + T* wrapped_ptr_{nullptr}; +}; + +#endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION + // Convenience alias for instrumented std::vector template using ins_vector = memop_instrumentation_wrapper_t>; From 9289be4d157b34523e673a521a019f6996333853 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 16 Jan 2026 17:02:46 +0000 Subject: [PATCH 201/366] memins on --- cpp/src/utilities/memory_instrumentation.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 945b8953f..1c899ef1f 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -35,7 +35,7 @@ #include #include -#define CUOPT_ENABLE_MEMORY_INSTRUMENTATION 0 +#define CUOPT_ENABLE_MEMORY_INSTRUMENTATION 1 #ifdef __NVCC__ #define HDI inline __host__ __device__ From c1526b816670b680560434e970492b7fb3da78cf Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 16 Jan 2026 18:54:34 +0000 Subject: [PATCH 202/366] fix memins bug --- cpp/src/utilities/memory_instrumentation.hpp | 37 ++++++++++++++------ 1 file changed, 27 insertions(+), 10 deletions(-) diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 1c899ef1f..75aea76e3 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -541,9 +541,11 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } - // Copy constructor - must update data_ptr to point to our own array + // Copy constructor - copy from wrapped array if wrapping, never share wrapped pointer memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : memory_instrumentation_base_t(other), array_(other.array_) + : memory_instrumentation_base_t(other), + array_(other.wrapped_ptr ? *other.wrapped_ptr : other.array_), + wrapped_ptr(nullptr) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -552,9 +554,12 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } - // Move constructor - must update data_ptr to point to our own array + // Move constructor - copy from wrapped array if wrapping (can't move wrapped), never share + // pointer memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : memory_instrumentation_base_t(std::move(other)), array_(std::move(other.array_)) + : memory_instrumentation_base_t(std::move(other)), + array_(other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_)), + wrapped_ptr(nullptr) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -563,14 +568,21 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } - // Copy assignment - must update data_ptr to point to our own array + // Copy assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) { if (this != &other) { memory_instrumentation_base_t::operator=(other); - array_ = other.array_; + // Get source data (from wrapped or owned array) + const T& source = other.wrapped_ptr ? *other.wrapped_ptr : other.array_; + // Write to destination (wrapped or owned array) + if (wrapped_ptr) { + *wrapped_ptr = source; + } else { + array_ = source; + } if constexpr (type_traits_utils::has_data::value) { - data_ptr = array_.data(); + data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); } else { data_ptr = nullptr; } @@ -578,14 +590,19 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { return *this; } - // Move assignment - must update data_ptr to point to our own array + // Move assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept { if (this != &other) { memory_instrumentation_base_t::operator=(std::move(other)); - array_ = std::move(other.array_); + // Get source data (copy from wrapped, move from owned) + if (wrapped_ptr) { + *wrapped_ptr = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); + } else { + array_ = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); + } if constexpr (type_traits_utils::has_data::value) { - data_ptr = array_.data(); + data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); } else { data_ptr = nullptr; } From a6e18d788f54eaf01ce5a8c209e9a6fe237e2eff Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 18 Jan 2026 09:46:15 +0000 Subject: [PATCH 203/366] no ins_wrapper for bound strengthening to allow autovectorization (compiler is a bit dumb) --- cpp/src/dual_simplex/bounds_strengthening.cpp | 22 ++- cpp/src/dual_simplex/phase2.cpp | 139 ++++++++++-------- 2 files changed, 90 insertions(+), 71 deletions(-) diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index f1bf52c1e..8d0511404 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -102,6 +102,14 @@ bool bounds_strengthening_t::bounds_strengthening( std::vector variable_changed(n, false); std::vector constraint_changed_next(m, false); + // ins_wrapper prevetns compiler autovectorization + // fine since for now bounds_strengthening doesn't get a work unit estimator + // but should be easy enough to estimate manually + auto& A_i = A.i.underlying(); + auto& A_x = A.x.underlying(); + auto& Arow_j = Arow.j.underlying(); + auto& Arow_x = Arow.x.underlying(); + if (!bounds_changed.empty()) { std::fill(constraint_changed.begin(), constraint_changed.end(), false); for (i_t i = 0; i < n; ++i) { @@ -109,7 +117,7 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t row_start = A.col_start[i]; const i_t row_end = A.col_start[i + 1]; for (i_t p = row_start; p < row_end; ++p) { - const i_t j = A.i[p]; + const i_t j = A_i[p]; constraint_changed[j] = true; } } @@ -131,8 +139,8 @@ bool bounds_strengthening_t::bounds_strengthening( f_t min_a = 0.0; f_t max_a = 0.0; for (i_t p = row_start; p < row_end; ++p) { - const i_t j = Arow.j[p]; - const f_t a_ij = Arow.x[p]; + const i_t j = Arow_j[p]; + const f_t a_ij = Arow_x[p]; variable_changed[j] = true; if (a_ij > 0) { @@ -182,10 +190,10 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t row_start = A.col_start[k]; const i_t row_end = A.col_start[k + 1]; for (i_t p = row_start; p < row_end; ++p) { - const i_t i = A.i[p]; + const i_t i = A_i[p]; if (!constraint_changed[i]) { continue; } - const f_t a_ik = A.x[p]; + const f_t a_ik = A_x[p]; f_t delta_min_act = delta_min_activity[i]; f_t delta_max_act = delta_max_activity[i]; @@ -217,7 +225,7 @@ bool bounds_strengthening_t::bounds_strengthening( } if (new_lb != old_lb || new_ub != old_ub) { for (i_t p = row_start; p < row_end; ++p) { - const i_t i = A.i[p]; + const i_t i = A_i[p]; constraint_changed_next[i] = true; } } diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 8e0c661a8..9575bc02a 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -807,7 +807,7 @@ void clean_up_infeasibilities(ins_vector& squared_infeasibilities, template i_t steepest_edge_pricing_with_infeasibilities(const lp_problem_t& lp, const simplex_solver_settings_t& settings, - const ins_vector& x, + const std::vector& x, const std::vector& dy_steepest_edge, const ins_vector& basic_mark, ins_vector& squared_infeasibilities, @@ -909,7 +909,7 @@ i_t steepest_edge_pricing(const lp_problem_t& lp, template i_t phase2_pricing(const lp_problem_t& lp, const simplex_solver_settings_t& settings, - const ins_vector& x, + const std::vector& x, const std::vector& basic_list, i_t& direction, i_t& basic_leaving, @@ -954,7 +954,7 @@ template f_t first_stage_harris(const lp_problem_t& lp, const std::vector& vstatus, const std::vector& nonbasic_list, - ins_vector& z, + std::vector& z, ins_vector& delta_z) { const i_t n = lp.num_cols; @@ -988,7 +988,7 @@ template i_t second_stage_harris(const lp_problem_t& lp, const std::vector& vstatus, const std::vector& nonbasic_list, - const ins_vector& z, + const std::vector& z, const ins_vector& delta_z, f_t max_step_length, f_t& step_length, @@ -1031,7 +1031,7 @@ i_t phase2_ratio_test(const lp_problem_t& lp, const simplex_solver_settings_t& settings, std::vector& vstatus, std::vector& nonbasic_list, - ins_vector& z, + std::vector& z, ins_vector& delta_z, f_t& step_length, i_t& nonbasic_entering) @@ -1084,7 +1084,7 @@ i_t flip_bounds(const lp_problem_t& lp, const simplex_solver_settings_t& settings, const ins_vector& bounded_variables, const ins_vector& objective, - const ins_vector& z, + const std::vector& z, const ins_vector& delta_z_indices, const std::vector& nonbasic_list, i_t entering_index, @@ -1420,7 +1420,7 @@ template i_t compute_perturbation(const lp_problem_t& lp, const simplex_solver_settings_t& settings, const ins_vector& delta_z_indices, - ins_vector& z, + std::vector& z, ins_vector& objective, f_t& sum_perturb) { @@ -1541,8 +1541,8 @@ i_t update_dual_variables(const sparse_vector_t& delta_y_sparse, const ins_vector& delta_z, f_t step_length, i_t leaving_index, - ins_vector& y, - ins_vector& z) + std::vector& y, + std::vector& z) { // Update dual variables // y <- y + steplength * delta_y @@ -1571,7 +1571,7 @@ void adjust_for_flips(const basis_update_mpf_t& ft, sparse_vector_t& atilde_sparse, sparse_vector_t& delta_xB_0_sparse, ins_vector& delta_x_flip, - ins_vector& x) + std::vector& x) { const i_t atilde_nz = atilde_index.size(); // B*delta_xB_0 = atilde @@ -1616,7 +1616,7 @@ i_t compute_delta_x(const lp_problem_t& lp, const std::vector& basic_list, const ins_vector& delta_x_flip, const sparse_vector_t& rhs_sparse, - const ins_vector& x, + const std::vector& x, sparse_vector_t& utilde_sparse, sparse_vector_t& scaled_delta_xB_sparse, ins_vector& delta_x) @@ -1679,7 +1679,7 @@ void update_primal_variables(const sparse_vector_t& scaled_delta_xB_sp const std::vector& basic_list, const ins_vector& delta_x, i_t entering_index, - ins_vector& x) + std::vector& x) { // x <- x + delta_x const i_t scaled_delta_xB_nz = scaled_delta_xB_sparse.i.size(); @@ -2052,7 +2052,7 @@ void set_primal_variables_on_bounds(const lp_problem_t& lp, } template -f_t compute_perturbed_objective(const ins_vector& objective, const ins_vector& x) +f_t compute_perturbed_objective(const ins_vector& objective, const std::vector& x) { const size_t n = objective.size(); f_t obj_val = 0.0; @@ -2085,9 +2085,9 @@ void prepare_optimality(const lp_problem_t& lp, f_t start_time, f_t max_val, i_t iter, - const ins_vector& x, - ins_vector& y, - ins_vector& z, + const std::vector& x, + std::vector& y, + std::vector& z, lp_solution_t& sol) { const i_t m = lp.num_rows; @@ -2283,10 +2283,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(lp.rhs.size() == m); // Create instrumented wrappers around sol vectors (no ownership transfer) - ins_vector x, y, z; - x.wrap(sol.x); - y.wrap(sol.y); - z.wrap(sol.z); + // ins_vector x, y, z; + // x.wrap(sol.x); + // y.wrap(sol.y); + // z.wrap(sol.z); // Declare instrumented vectors used during initialization (before manifold setup) ins_vector objective(lp.objective); @@ -2295,9 +2295,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // Create instrumentation manifold early to capture init section memory operations instrumentation_manifold_t manifold; - manifold.add("x", x); - manifold.add("y", y); - manifold.add("z", z); + // manifold.add("x", x); + // manifold.add("y", y); + // manifold.add("z", z); manifold.add("objective", objective); manifold.add("c_basic", c_basic); manifold.add("xB_workspace", xB_workspace); @@ -2308,7 +2308,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings.log.printf("Dual Simplex Phase %d\n", phase); std::vector vstatus_old = vstatus; - std::vector z_old = z; + std::vector z_old = sol.z; phase2::bound_info(lp, settings); if (initialize_basis) { @@ -2338,17 +2338,20 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } constexpr bool print_norms = false; if constexpr (print_norms) { - settings.log.printf( - "|| y || %e || cB || %e\n", vector_norm_inf(y), vector_norm_inf(c_basic)); + settings.log.printf("|| y || %e || cB || %e\n", + vector_norm_inf(sol.y), + vector_norm_inf(c_basic)); } phase2::compute_reduced_costs( objective.underlying(), lp.A, sol.y, basic_list, nonbasic_list, sol.z); - if constexpr (print_norms) { settings.log.printf("|| z || %e\n", vector_norm_inf(z)); } + if constexpr (print_norms) { + settings.log.printf("|| z || %e\n", vector_norm_inf(sol.z)); + } #ifdef COMPUTE_DUAL_RESIDUAL std::vector dual_res1; - compute_dual_residual(lp.A, objective, y, z, dual_res1); + compute_dual_residual(lp.A, objective, sol.y, sol.z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.tight_tol) { settings.log.printf("|| A'*y + z - c || %e\n", dual_res_norm); @@ -2361,7 +2364,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #ifdef PRINT_VSTATUS_CHANGES i_t num_vstatus_changes; i_t num_z_changes; - phase2::vstatus_changes(vstatus, vstatus_old, z, z_old, num_vstatus_changes, num_z_changes); + phase2::vstatus_changes(vstatus, vstatus_old, sol.z, z_old, num_vstatus_changes, num_z_changes); settings.log.printf("Number of vstatus changes %d\n", num_vstatus_changes); settings.log.printf("Number of z changes %d\n", num_z_changes); #endif @@ -2386,7 +2389,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #ifdef COMPUTE_PRIMAL_RESIDUAL std::vector residual = lp.rhs; - matrix_vector_multiply(lp.A, 1.0, x, -1.0, residual); + matrix_vector_multiply(lp.A, 1.0, sol.x, -1.0, residual); f_t primal_residual = vector_norm_inf(residual); if (primal_residual > settings.primal_tol) { settings.log.printf("|| A*x - b || %e\n", primal_residual); @@ -2602,7 +2605,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if (settings.use_steepest_edge_pricing) { leaving_index = phase2::steepest_edge_pricing_with_infeasibilities(lp, settings, - x, + sol.x, delta_y_steepest_edge, basic_mark, squared_infeasibilities, @@ -2613,7 +2616,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } else { // Max infeasibility pricing leaving_index = phase2::phase2_pricing( - lp, settings, x, basic_list, direction, basic_leaving_index, primal_infeasibility); + lp, settings, sol.x, basic_list, direction, basic_leaving_index, primal_infeasibility); } } timers.pricing_time += timers.stop_timer(); @@ -2629,9 +2632,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - x, - y, - z, + sol.x, + sol.y, + sol.z, sol); status = dual::status_t::OPTIMAL; break; @@ -2720,19 +2723,20 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, { // raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); if (harris_ratio) { - f_t max_step_length = phase2::first_stage_harris(lp, vstatus, nonbasic_list, z, delta_z); - entering_index = phase2::second_stage_harris(lp, + f_t max_step_length = + phase2::first_stage_harris(lp, vstatus, nonbasic_list, sol.z, delta_z); + entering_index = phase2::second_stage_harris(lp, vstatus, nonbasic_list, - z, + sol.z, delta_z, max_step_length, step_length, nonbasic_entering_index); } else if (bound_flip_ratio) { timers.start_timer(); - f_t slope = direction == 1 ? (lp.lower[leaving_index] - x[leaving_index]) - : (x[leaving_index] - lp.upper[leaving_index]); + f_t slope = direction == 1 ? (lp.lower[leaving_index] - sol.x[leaving_index]) + : (sol.x[leaving_index] - lp.upper[leaving_index]); bound_flipping_ratio_test_t bfrt(settings, start_time, m, @@ -2754,8 +2758,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } timers.bfrt_time += timers.stop_timer(); } else { - entering_index = phase2::phase2_ratio_test( - lp, settings, vstatus, nonbasic_list, z, delta_z, step_length, nonbasic_entering_index); + entering_index = phase2::phase2_ratio_test(lp, + settings, + vstatus, + nonbasic_list, + sol.z, + delta_z, + step_length, + nonbasic_entering_index); } } if (entering_index == -2) { return dual::status_t::TIME_LIMIT; } @@ -2777,8 +2787,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings.log.printf("Dual infeasibility after removing perturbation %e\n", dual_infeas); if (dual_infeas <= settings.dual_tol) { settings.log.printf("Removed perturbation of %.2e.\n", perturbation); - z = unperturbed_z; - y = unperturbed_y; + sol.z = unperturbed_z; + sol.y = unperturbed_y; perturbation = 0.0; std::vector unperturbed_x(n); @@ -2791,7 +2801,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, objective = lp.objective; // Need to reset the objective value, since we have recomputed x - obj = phase2::compute_perturbed_objective(objective, x); + obj = phase2::compute_perturbed_objective(objective, sol.x); if (dual_infeas <= settings.dual_tol && primal_infeasibility <= settings.primal_tol) { phase2::prepare_optimality(lp, settings, @@ -2804,9 +2814,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - x, - y, - z, + sol.x, + sol.y, + sol.z, sol); status = dual::status_t::OPTIMAL; break; @@ -2841,9 +2851,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - x, - y, - z, + sol.x, + sol.y, + sol.z, sol); status = dual::status_t::OPTIMAL; break; @@ -2864,13 +2874,13 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_y_sparse.to_dense(my_delta_y); // TODO(CMM): Do I use the perturbed or unperturbed objective? - const f_t obj_val = phase2::compute_perturbed_objective(objective, x); + const f_t obj_val = phase2::compute_perturbed_objective(objective, sol.x); phase2::compute_farkas_certificate(lp, settings, vstatus, - x, - y, - z, + sol.x, + sol.y, + sol.z, my_delta_y, delta_z, direction, @@ -2903,7 +2913,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // y <- y + steplength * delta_y // z <- z + steplength * delta_z i_t update_dual_variables_status = phase2::update_dual_variables( - delta_y_sparse, delta_z_indices, delta_z, step_length, leaving_index, y, z); + delta_y_sparse, delta_z_indices, delta_z, step_length, leaving_index, sol.y, sol.z); if (update_dual_variables_status == -1) { settings.log.printf("Numerical issues encountered in update_dual_variables.\n"); return dual::status_t::NUMERICAL; @@ -2911,7 +2921,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.vector_time += timers.stop_timer(); #ifdef COMPUTE_DUAL_RESIDUAL - phase2::compute_dual_residual(lp.A, objective, y, z, dual_res1); + phase2::compute_dual_residual(lp.A, objective, sol.y, sol.z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.dual_tol) { settings.log.printf("|| A'*y + z - c || %e steplength %e\n", dual_res_norm, step_length); @@ -2924,7 +2934,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings, bounded_variables, objective, - z, + sol.z, delta_z_indices, nonbasic_list, entering_index, @@ -2949,7 +2959,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, atilde_sparse, delta_xB_0_sparse, delta_x_flip, - x); + sol.x); timers.ftran_time += timers.stop_timer(); } @@ -2968,7 +2978,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, basic_list, delta_x_flip, rhs_sparse, - x, + sol.x, utilde_sparse, scaled_delta_xB_sparse, delta_x) == -1) { @@ -3010,12 +3020,13 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); // x <- x + delta_x - phase2::update_primal_variables(scaled_delta_xB_sparse, basic_list, delta_x, entering_index, x); + phase2::update_primal_variables( + scaled_delta_xB_sparse, basic_list, delta_x, entering_index, sol.x); timers.vector_time += timers.stop_timer(); #ifdef COMPUTE_PRIMAL_RESIDUAL residual = lp.rhs; - matrix_vector_multiply(lp.A, 1.0, x, -1.0, residual); + matrix_vector_multiply(lp.A, 1.0, sol.x, -1.0, residual); primal_residual = vector_norm_inf(residual); if (iter % 100 == 0 && primal_residual > 10 * settings.primal_tol) { settings.log.printf("|| A*x - b || %e\n", primal_residual); @@ -3071,7 +3082,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #if CHECK_PRIMAL_INFEASIBILITIES phase2::check_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + lp, settings, basic_list, sol.x, squared_infeasibilities, infeasibility_indices); #endif timers.update_infeasibility_time += timers.stop_timer(); @@ -3080,7 +3091,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); f_t sum_perturb = 0.0; - phase2::compute_perturbation(lp, settings, delta_z_indices, z, objective, sum_perturb); + phase2::compute_perturbation(lp, settings, delta_z_indices, sol.z, objective, sum_perturb); timers.perturb_time += timers.stop_timer(); // Update basis information From ac57402d26d33608b3a6242180fd2c3c59d22d0d Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 18 Jan 2026 09:50:59 +0000 Subject: [PATCH 204/366] stripped down ins_wrapper --- cpp/src/utilities/memory_instrumentation.hpp | 831 +++++-------------- 1 file changed, 223 insertions(+), 608 deletions(-) diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 75aea76e3..4b0143b05 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -40,7 +40,7 @@ #ifdef __NVCC__ #define HDI inline __host__ __device__ #else -#define HDI inline +#define HDI inline __attribute__((always_inline)) #endif namespace cuopt { @@ -48,72 +48,20 @@ namespace cuopt { // Define CUOPT_ENABLE_MEMORY_INSTRUMENTATION to 1 to enable memory tracking // When 0, instrumentation becomes a zero-overhead passthrough -// Base class for memory operation instrumentation -struct memory_instrumentation_base_t { -#if CUOPT_ENABLE_MEMORY_INSTRUMENTATION - HDI void reset_counters() const { byte_loads = byte_stores = 0; } - - template - HDI void record_load() const - { - byte_loads += sizeof(T); - } - - template - HDI void record_store() const - { - byte_stores += sizeof(T); - } - - template - HDI void record_rmw() const - { - byte_loads += sizeof(T); - byte_stores += sizeof(T); - } - - mutable size_t byte_loads{0}; - mutable size_t byte_stores{0}; -#else - // No-op methods when instrumentation is disabled - these inline away to zero overhead - HDI void reset_counters() const {} - template - HDI void record_load() const - { - } - template - HDI void record_store() const - { - } - template - HDI void record_rmw() const - { - } -#endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION -}; - #if CUOPT_ENABLE_MEMORY_INSTRUMENTATION // Manifold class to collect statistics from multiple instrumented objects +// Stores raw pointers to counters - works with any instrumented type class instrumentation_manifold_t { public: instrumentation_manifold_t() = default; - // Construct with initializer list of (description, instrumented object) pairs - instrumentation_manifold_t( - std::initializer_list< - std::pair>> - instrumented) - { - for (const auto& [name, instr] : instrumented) { - instrumented_.insert_or_assign(name, instr); - } - } - - // Add an instrumented object to track with a description - void add(const std::string& description, const memory_instrumentation_base_t& instrumented) + // Template add - works with any type that has byte_loads/byte_stores members + template + void add(const std::string& description, const Instrumented& instrumented) { - instrumented_.insert_or_assign(description, std::cref(instrumented)); + instrumented_.insert_or_assign( + description, std::make_pair(&instrumented.byte_loads, &instrumented.byte_stores)); } // Collect total loads and stores across all instrumented objects @@ -122,9 +70,9 @@ class instrumentation_manifold_t { size_t total_loads = 0; size_t total_stores = 0; - for (auto& [name, instr] : instrumented_) { - total_loads += instr.get().byte_loads; - total_stores += instr.get().byte_stores; + for (const auto& [name, counters] : instrumented_) { + total_loads += *counters.first; + total_stores += *counters.second; } return {total_loads, total_stores}; @@ -136,8 +84,8 @@ class instrumentation_manifold_t { std::vector> results; results.reserve(instrumented_.size()); - for (auto& [name, instr] : instrumented_) { - results.emplace_back(name, instr.get().byte_loads, instr.get().byte_stores); + for (const auto& [name, counters] : instrumented_) { + results.emplace_back(name, *counters.first, *counters.second); } return results; @@ -153,14 +101,14 @@ class instrumentation_manifold_t { void flush() { - for (auto& [name, instr] : instrumented_) { - instr.get().reset_counters(); + for (const auto& [name, counters] : instrumented_) { + *const_cast(counters.first) = 0; + *const_cast(counters.second) = 0; } } private: - std::unordered_map> - instrumented_; + std::unordered_map> instrumented_; }; #else @@ -169,12 +117,10 @@ class instrumentation_manifold_t { class instrumentation_manifold_t { public: instrumentation_manifold_t() = default; - instrumentation_manifold_t( - std::initializer_list< - std::pair>>) + template + void add(const std::string&, const Instrumented&) { } - void add(const std::string&, const memory_instrumentation_base_t&) {} std::pair collect() { return {0, 0}; } std::vector> collect_per_wrapper() { return {}; } std::pair collect_and_flush() { return {0, 0}; } @@ -269,262 +215,60 @@ struct has_back().back())>> : std::true_ #if CUOPT_ENABLE_MEMORY_INSTRUMENTATION // Memory operation instrumentation wrapper for container-like types +// No inheritance - counters embedded directly for compiler optimization template -struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { - // Standard container type traits - using value_type = std::remove_reference_t()[0])>; - using size_type = std::size_t; - using difference_type = std::ptrdiff_t; - using reference = value_type&; - using const_reference = const value_type&; - using pointer = value_type*; - using const_pointer = const value_type*; - - static_assert(std::is_standard_layout_v, - "value_type must have standard layout for memory instrumentation"); - static constexpr size_t type_size = sizeof(value_type); - - // Proxy class to track reads and writes for a single element - class element_proxy_t { - public: - element_proxy_t(value_type& ref, memop_instrumentation_wrapper_t& wrapper) - : ref_(ref), wrapper_(wrapper) - { - } - - element_proxy_t& operator=(const value_type& value) - { - wrapper_.template record_store(); - ref_ = value; - return *this; - } - element_proxy_t& operator=(const element_proxy_t& other) - { - wrapper_.template record_store(); - other.wrapper_.template record_load(); - ref_ = other.ref_; - return *this; - } - - operator value_type() const - { - wrapper_.template record_load(); - return ref_; - } - - // // Allow implicit conversion to reference for functions expecting references - // operator value_type&() { return ref_; } - - // operator const value_type&() const { return ref_; } - - // // Member access operator for structured types (e.g., type_2) - // value_type* operator->() { return &ref_; } - - // const value_type* operator->() const { return &ref_; } - - // Get underlying element reference (records a load) - value_type& get() - { - wrapper_.template record_load(); - return ref_; - } - - const value_type& get() const - { - wrapper_.template record_load(); - return ref_; - } - - element_proxy_t& operator+=(const value_type& value) - { - wrapper_.template record_rmw(); - ref_ += value; - return *this; - } - element_proxy_t& operator-=(const value_type& value) - { - wrapper_.template record_rmw(); - ref_ -= value; - return *this; - } - element_proxy_t& operator*=(const value_type& value) - { - wrapper_.template record_rmw(); - ref_ *= value; - return *this; - } - element_proxy_t& operator/=(const value_type& value) - { - wrapper_.template record_rmw(); - ref_ /= value; - return *this; - } - element_proxy_t& operator++() - { - wrapper_.template record_rmw(); - ++ref_; - return *this; - } - element_proxy_t& operator--() - { - wrapper_.template record_rmw(); - --ref_; - return *this; - } - - value_type operator++(int) - { - wrapper_.template record_rmw(); - return ref_++; - } - value_type operator--(int) - { - wrapper_.template record_rmw(); - return ref_--; - } - - value_type& ref_; - memop_instrumentation_wrapper_t& wrapper_; - }; - - // Instrumented iterator that tracks memory accesses - template - class instrumented_iterator_t { - public: - using iterator_category = std::random_access_iterator_tag; - using value_type = memop_instrumentation_wrapper_t::value_type; - using difference_type = std::ptrdiff_t; - using pointer = std::conditional_t; - using reference = std::conditional_t; - using wrapper_ptr = std::conditional_t; - - instrumented_iterator_t(IterT iter, wrapper_ptr wrapper) : iter_(iter), wrapper_(wrapper) {} - - // Dereference - returns proxy for non-const, tracks load for const - auto operator*() const - { - if constexpr (IsConst) { -#ifdef CUOPT_ENABLE_MEMORY_INSTRUMENTATION - wrapper_->byte_loads += sizeof(value_type); -#endif - return *iter_; - } else { - return element_proxy_t(*iter_, *wrapper_); - } - } - - auto operator->() const { return &(*iter_); } - - instrumented_iterator_t& operator++() - { - ++iter_; - return *this; - } - - instrumented_iterator_t operator++(int) - { - auto tmp = *this; - ++iter_; - return tmp; - } - - instrumented_iterator_t& operator--() - { - --iter_; - return *this; - } - - instrumented_iterator_t operator--(int) - { - auto tmp = *this; - --iter_; - return tmp; - } - - instrumented_iterator_t& operator+=(difference_type n) - { - iter_ += n; - return *this; - } - - instrumented_iterator_t& operator-=(difference_type n) - { - iter_ -= n; - return *this; - } - - instrumented_iterator_t operator+(difference_type n) const - { - return instrumented_iterator_t(iter_ + n, wrapper_); - } - - instrumented_iterator_t operator-(difference_type n) const - { - return instrumented_iterator_t(iter_ - n, wrapper_); - } - - difference_type operator-(const instrumented_iterator_t& other) const - { - return iter_ - other.iter_; - } - - auto operator[](difference_type n) const { return *(*this + n); } - - bool operator==(const instrumented_iterator_t& other) const { return iter_ == other.iter_; } - bool operator!=(const instrumented_iterator_t& other) const { return iter_ != other.iter_; } - bool operator<(const instrumented_iterator_t& other) const { return iter_ < other.iter_; } - bool operator<=(const instrumented_iterator_t& other) const { return iter_ <= other.iter_; } - bool operator>(const instrumented_iterator_t& other) const { return iter_ > other.iter_; } - bool operator>=(const instrumented_iterator_t& other) const { return iter_ >= other.iter_; } - - IterT base() const { return iter_; } - - // Allow iterator_traits to access the underlying iterator - friend struct std::iterator_traits; - - private: - IterT iter_; - wrapper_ptr wrapper_; - }; +struct memop_instrumentation_wrapper_t { + // Instrumentation counters - embedded directly, no base class + mutable size_t byte_loads{0}; + mutable size_t byte_stores{0}; - // Iterator type definitions (must come after instrumented_iterator_t) - using iterator = instrumented_iterator_t().begin()), false>; - using const_iterator = instrumented_iterator_t().begin()), true>; - using reverse_iterator = std::reverse_iterator; - using const_reverse_iterator = std::reverse_iterator; + HDI void reset_counters() const { byte_loads = byte_stores = 0; } - // Constructors - memop_instrumentation_wrapper_t() : array_(), wrapped_ptr(nullptr) + template + HDI void record_load() const { - if constexpr (type_traits_utils::has_data::value) { - data_ptr = array_.data(); - } else { - data_ptr = nullptr; - } + byte_loads += sizeof(U); } - // Copy/move from underlying type - memop_instrumentation_wrapper_t(const T& arr) : array_(arr) + template + HDI void record_store() const { - if constexpr (type_traits_utils::has_data::value) { - data_ptr = const_cast(array_.data()); - } else { - data_ptr = nullptr; - } + byte_stores += sizeof(U); } - memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)) + + template + HDI void record_rmw() const { - if constexpr (type_traits_utils::has_data::value) { - data_ptr = array_.data(); - } else { - data_ptr = nullptr; - } + byte_loads += sizeof(U); + byte_stores += sizeof(U); } + // Standard container type traits + using value_type = typename T::value_type; + using size_type = typename T::size_type; + using difference_type = typename T::difference_type; + using reference = typename T::reference; + using const_reference = typename T::const_reference; + using pointer = typename T::pointer; + using const_pointer = typename T::const_pointer; + + static_assert(std::is_trivially_copyable_v, + "value_type must be trivially copyable for memory instrumentation"); + static constexpr size_t type_size = sizeof(value_type); + + // Use native iterators - no instrumentation overhead on iteration + // Memory access counting is done via operator[] and batch methods + using iterator = typename T::iterator; + using const_iterator = typename T::const_iterator; + using reverse_iterator = typename T::reverse_iterator; + using const_reverse_iterator = typename T::const_reverse_iterator; + + // Constructors - cache data pointer for device access + memop_instrumentation_wrapper_t() : array_(), data_ptr_(array_.data()) {} + memop_instrumentation_wrapper_t(const T& arr) : array_(arr), data_ptr_(array_.data()) {} + memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)), data_ptr_(array_.data()) {} + // Forwarding constructor for underlying container initialization - // Only enabled for types that aren't the wrapper itself or the underlying type template , T> && (sizeof...(Args) > 0 || !std::is_convertible_v)>> explicit memop_instrumentation_wrapper_t(Arg&& arg, Args&&... args) - : array_(std::forward(arg), std::forward(args)...) + : array_(std::forward(arg), std::forward(args)...), data_ptr_(array_.data()) { - if constexpr (type_traits_utils::has_data::value) { - data_ptr = array_.data(); - } else { - data_ptr = nullptr; - } } - // Copy constructor - copy from wrapped array if wrapping, never share wrapped pointer + // Copy/move - update data pointer cache, reset counters for new instance memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : memory_instrumentation_base_t(other), - array_(other.wrapped_ptr ? *other.wrapped_ptr : other.array_), - wrapped_ptr(nullptr) - { - if constexpr (type_traits_utils::has_data::value) { - data_ptr = array_.data(); - } else { - data_ptr = nullptr; - } + : byte_loads(0), byte_stores(0), array_(other.array_), data_ptr_(array_.data()) + { } - - // Move constructor - copy from wrapped array if wrapping (can't move wrapped), never share - // pointer memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : memory_instrumentation_base_t(std::move(other)), - array_(other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_)), - wrapped_ptr(nullptr) - { - if constexpr (type_traits_utils::has_data::value) { - data_ptr = array_.data(); - } else { - data_ptr = nullptr; - } + : byte_loads(0), byte_stores(0), array_(std::move(other.array_)), data_ptr_(array_.data()) + { } - - // Copy assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) { if (this != &other) { - memory_instrumentation_base_t::operator=(other); - // Get source data (from wrapped or owned array) - const T& source = other.wrapped_ptr ? *other.wrapped_ptr : other.array_; - // Write to destination (wrapped or owned array) - if (wrapped_ptr) { - *wrapped_ptr = source; - } else { - array_ = source; - } - if constexpr (type_traits_utils::has_data::value) { - data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); - } else { - data_ptr = nullptr; - } + array_ = other.array_; + data_ptr_ = array_.data(); + // Don't copy counters - each instance tracks its own accesses } return *this; } - - // Move assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept { if (this != &other) { - memory_instrumentation_base_t::operator=(std::move(other)); - // Get source data (copy from wrapped, move from owned) - if (wrapped_ptr) { - *wrapped_ptr = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); - } else { - array_ = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); - } - if constexpr (type_traits_utils::has_data::value) { - data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); - } else { - data_ptr = nullptr; - } + array_ = std::move(other.array_); + data_ptr_ = array_.data(); + // Don't move counters - each instance tracks its own accesses } return *this; } - element_proxy_t operator[](size_type index) + // Element access - return reference directly, count optimistically + // Use cached data_ptr_ to avoid calling host-only std::vector methods from device code + HDI reference operator[](size_type index) { - return element_proxy_t(underlying()[index], *this); + record_store(); + return data_ptr_[index]; } HDI value_type operator[](size_type index) const { - this->template record_load(); - // really ugly hack because otherwise nvcc complains about vector operator[] being __host__ only - if constexpr (type_traits_utils::has_data::value) { - return data_ptr[index]; - } else { - return underlying()[index]; - } + record_load(); + return data_ptr_[index]; } - template - std::enable_if_t::value, element_proxy_t> front() + reference front() { - return element_proxy_t(underlying().front(), *this); + record_store(); + return array_.front(); } - - template - std::enable_if_t::value, value_type> front() const + value_type front() const { - this->template record_load(); - return underlying().front(); + record_load(); + return array_.front(); } - template - std::enable_if_t::value, element_proxy_t> back() + reference back() { - return element_proxy_t(underlying().back(), *this); + record_store(); + return array_.back(); } - - template - std::enable_if_t::value, value_type> back() const + value_type back() const { - this->template record_load(); - return underlying().back(); + record_load(); + return array_.back(); } - // Iterators - iterator begin() noexcept { return iterator(std::begin(underlying()), this); } - const_iterator begin() const noexcept { return const_iterator(std::begin(underlying()), this); } - const_iterator cbegin() const noexcept { return const_iterator(std::begin(underlying()), this); } + // Raw pointer access - bypasses instrumentation for hot loops + pointer data() noexcept { return array_.data(); } + const_pointer data() const noexcept { return array_.data(); } - iterator end() noexcept { return iterator(std::end(underlying()), this); } - const_iterator end() const noexcept { return const_iterator(std::end(underlying()), this); } - const_iterator cend() const noexcept { return const_iterator(std::end(underlying()), this); } + // Iterators - native iterators, no instrumentation overhead + iterator begin() noexcept { return array_.begin(); } + const_iterator begin() const noexcept { return array_.begin(); } + const_iterator cbegin() const noexcept { return array_.cbegin(); } - reverse_iterator rbegin() noexcept { return reverse_iterator(end()); } - const_reverse_iterator rbegin() const noexcept - { - return const_reverse_iterator(std::end(underlying())); - } - const_reverse_iterator crbegin() const noexcept { return const_reverse_iterator(cend()); } + iterator end() noexcept { return array_.end(); } + const_iterator end() const noexcept { return array_.end(); } + const_iterator cend() const noexcept { return array_.cend(); } - reverse_iterator rend() noexcept { return reverse_iterator(begin()); } - const_reverse_iterator rend() const noexcept { return const_reverse_iterator(begin()); } - const_reverse_iterator crend() const noexcept { return const_reverse_iterator(cbegin()); } + reverse_iterator rbegin() noexcept { return array_.rbegin(); } + const_reverse_iterator rbegin() const noexcept { return array_.rbegin(); } + const_reverse_iterator crbegin() const noexcept { return array_.crbegin(); } - // Capacity - bool empty() const noexcept { return std::begin(underlying()) == std::end(underlying()); } - size_type size() const noexcept - { - return std::distance(std::begin(underlying()), std::end(underlying())); - } + reverse_iterator rend() noexcept { return array_.rend(); } + const_reverse_iterator rend() const noexcept { return array_.rend(); } + const_reverse_iterator crend() const noexcept { return array_.crend(); } - // Conditional methods - only available if underlying type supports them - template - std::enable_if_t::value, size_type> max_size() const noexcept - { - return underlying().max_size(); - } - - template - std::enable_if_t::value, size_type> capacity() const noexcept - { - return underlying().capacity(); - } + // Capacity - direct forwarding + bool empty() const noexcept { return array_.empty(); } + size_type size() const noexcept { return array_.size(); } + size_type max_size() const noexcept { return array_.max_size(); } + size_type capacity() const noexcept { return array_.capacity(); } - template - std::enable_if_t::value> reserve(size_type new_cap) + void reserve(size_type new_cap) { - underlying().reserve(new_cap); - if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } + array_.reserve(new_cap); + data_ptr_ = array_.data(); } - - template - std::enable_if_t::value> shrink_to_fit() + void shrink_to_fit() { - underlying().shrink_to_fit(); - if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } + array_.shrink_to_fit(); + data_ptr_ = array_.data(); } - template - std::enable_if_t::value> clear() noexcept - { - underlying().clear(); - if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } - } - - template - std::enable_if_t::value> push_back(const value_type& value) + // Modifiers + void clear() noexcept { - // we should probably take into account possible copies done by std::vector. oh well. - // hot loops shouldn't be doing such operations anyway - this->template record_store(); - underlying().push_back(value); - if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } + array_.clear(); + data_ptr_ = array_.data(); } - template - std::enable_if_t::value> push_back(value_type&& value) + void push_back(const value_type& value) { - this->template record_store(); - underlying().push_back(std::move(value)); - if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } + record_store(); + array_.push_back(value); + data_ptr_ = array_.data(); } - template - std::enable_if_t::value> emplace_back(Args&&... args) + void push_back(value_type&& value) { - this->template record_store(); - underlying().emplace_back(std::forward(args)...); - if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } + record_store(); + array_.push_back(std::move(value)); + data_ptr_ = array_.data(); } - template - std::enable_if_t::value> pop_back() + template + void emplace_back(Args&&... args) { - this->template record_load(); // Reading the element before removal - underlying().pop_back(); - if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } + record_store(); + array_.emplace_back(std::forward(args)...); + data_ptr_ = array_.data(); } - template - std::enable_if_t::value> resize(size_type count) + void pop_back() { - size_type old_size = underlying().size(); - underlying().resize(count); - if (count > old_size) { - this->byte_stores += (count - old_size) * type_size; // New elements initialized - } - if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } + record_load(); + array_.pop_back(); + // data_ptr_ unchanged - pop_back doesn't reallocate } - template - std::enable_if_t::value> resize(size_type count, - const value_type& value) + void resize(size_type count) { - size_type old_size = underlying().size(); - underlying().resize(count, value); - if (count > old_size) { this->byte_stores += (count - old_size) * type_size; } - if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } + size_type old_size = array_.size(); + array_.resize(count); + data_ptr_ = array_.data(); + if (count > old_size) { byte_stores += (count - old_size) * type_size; } } - template - std::enable_if_t::value, value_type*> data() noexcept + void resize(size_type count, const value_type& value) { - return underlying().data(); + size_type old_size = array_.size(); + array_.resize(count, value); + data_ptr_ = array_.data(); + if (count > old_size) { byte_stores += (count - old_size) * type_size; } } - template - std::enable_if_t::value, const value_type*> data() const noexcept - { - return underlying().data(); - } + // Batch counting for manual instrumentation after raw pointer use + void record_loads(size_t count) const { byte_loads += count * type_size; } + void record_stores(size_t count) { byte_stores += count * type_size; } - // Access to underlying array - operator T&() { return underlying(); } - operator const T&() const { return underlying(); } + // Conversion operators + operator T&() { return array_; } + operator const T&() const { return array_; } T&& release_array() { return std::move(array_); } - // Wrap an external vector without taking ownership - void wrap(T& external_array) - { - wrapped_ptr = &external_array; - if constexpr (type_traits_utils::has_data::value) { data_ptr = external_array.data(); } - } - - // Stop wrapping and return to using the owned array - void unwrap() - { - wrapped_ptr = nullptr; - if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); } - } - - // Check if currently wrapping an external array - bool is_wrapping() const { return wrapped_ptr != nullptr; } - - // Get the underlying container (wrapped or owned) - T& underlying() { return wrapped_ptr ? *wrapped_ptr : array_; } - const T& underlying() const { return wrapped_ptr ? *wrapped_ptr : array_; } + T& underlying() { return array_; } + const T& underlying() const { return array_; } private: T array_; - T* wrapped_ptr{nullptr}; - value_type* data_ptr{nullptr}; + pointer __restrict__ data_ptr_{nullptr}; // Cached for device access }; #else // !CUOPT_ENABLE_MEMORY_INSTRUMENTATION // Zero-overhead passthrough wrapper when instrumentation is disabled -// Provides the same interface as the instrumented version but just forwards to the underlying -// container template -struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { +struct memop_instrumentation_wrapper_t { + // No-op instrumentation methods for API compatibility + HDI void reset_counters() const {} + template + HDI void record_load() const + { + } + template + HDI void record_store() const + { + } + template + HDI void record_rmw() const + { + } using value_type = typename T::value_type; using size_type = typename T::size_type; using difference_type = typename T::difference_type; @@ -832,10 +483,10 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { using reverse_iterator = typename T::reverse_iterator; using const_reverse_iterator = typename T::const_reverse_iterator; - // Constructors - forward everything to the underlying container + // Constructors memop_instrumentation_wrapper_t() = default; - memop_instrumentation_wrapper_t(const T& arr) : array_(arr), wrapped_ptr_(nullptr) {} - memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)), wrapped_ptr_(nullptr) {} + memop_instrumentation_wrapper_t(const T& arr) : array_(arr) {} + memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)) {} template , T> && (sizeof...(Args) > 0 || !std::is_convertible_v)>> explicit memop_instrumentation_wrapper_t(Arg&& arg, Args&&... args) - : array_(std::forward(arg), std::forward(args)...), wrapped_ptr_(nullptr) - { - } - - // Copy constructor - always copy the data, never share wrapped pointer - memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_), wrapped_ptr_(nullptr) - { - } - - // Move constructor - take ownership of array, never share wrapped pointer - memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_)), - wrapped_ptr_(nullptr) - { - } - - // Copy assignment - always copy the data - memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) + : array_(std::forward(arg), std::forward(args)...) { - if (this != &other) { - if (wrapped_ptr_) { - *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; - } else { - array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; - } - } - return *this; } - // Move assignment - take the data - memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept - { - if (this != &other) { - if (wrapped_ptr_) { - *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); - } else { - array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); - } - } - return *this; - } + // Default copy/move + memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t&) = default; + memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&&) noexcept = default; + memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t&) = default; + memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&&) noexcept = default; // Element access - direct passthrough - reference operator[](size_type index) { return underlying()[index]; } - const_reference operator[](size_type index) const { return underlying()[index]; } + reference operator[](size_type index) { return array_[index]; } + const_reference operator[](size_type index) const { return array_[index]; } - reference front() { return underlying().front(); } - const_reference front() const { return underlying().front(); } + reference front() { return array_.front(); } + const_reference front() const { return array_.front(); } - reference back() { return underlying().back(); } - const_reference back() const { return underlying().back(); } + reference back() { return array_.back(); } + const_reference back() const { return array_.back(); } - pointer data() noexcept { return underlying().data(); } - const_pointer data() const noexcept { return underlying().data(); } + pointer data() noexcept { return array_.data(); } + const_pointer data() const noexcept { return array_.data(); } - // Iterators - use underlying container's iterators directly - iterator begin() noexcept { return underlying().begin(); } - const_iterator begin() const noexcept { return underlying().begin(); } - const_iterator cbegin() const noexcept { return underlying().cbegin(); } + // Iterators + iterator begin() noexcept { return array_.begin(); } + const_iterator begin() const noexcept { return array_.begin(); } + const_iterator cbegin() const noexcept { return array_.cbegin(); } - iterator end() noexcept { return underlying().end(); } - const_iterator end() const noexcept { return underlying().end(); } - const_iterator cend() const noexcept { return underlying().cend(); } + iterator end() noexcept { return array_.end(); } + const_iterator end() const noexcept { return array_.end(); } + const_iterator cend() const noexcept { return array_.cend(); } - reverse_iterator rbegin() noexcept { return underlying().rbegin(); } - const_reverse_iterator rbegin() const noexcept { return underlying().rbegin(); } - const_reverse_iterator crbegin() const noexcept { return underlying().crbegin(); } + reverse_iterator rbegin() noexcept { return array_.rbegin(); } + const_reverse_iterator rbegin() const noexcept { return array_.rbegin(); } + const_reverse_iterator crbegin() const noexcept { return array_.crbegin(); } - reverse_iterator rend() noexcept { return underlying().rend(); } - const_reverse_iterator rend() const noexcept { return underlying().rend(); } - const_reverse_iterator crend() const noexcept { return underlying().crend(); } + reverse_iterator rend() noexcept { return array_.rend(); } + const_reverse_iterator rend() const noexcept { return array_.rend(); } + const_reverse_iterator crend() const noexcept { return array_.crend(); } // Capacity - bool empty() const noexcept { return underlying().empty(); } - size_type size() const noexcept { return underlying().size(); } - size_type max_size() const noexcept { return underlying().max_size(); } - size_type capacity() const noexcept { return underlying().capacity(); } + bool empty() const noexcept { return array_.empty(); } + size_type size() const noexcept { return array_.size(); } + size_type max_size() const noexcept { return array_.max_size(); } + size_type capacity() const noexcept { return array_.capacity(); } - void reserve(size_type new_cap) { underlying().reserve(new_cap); } - void shrink_to_fit() { underlying().shrink_to_fit(); } + void reserve(size_type new_cap) { array_.reserve(new_cap); } + void shrink_to_fit() { array_.shrink_to_fit(); } // Modifiers - void clear() noexcept { underlying().clear(); } - void push_back(const value_type& value) { underlying().push_back(value); } - void push_back(value_type&& value) { underlying().push_back(std::move(value)); } + void clear() noexcept { array_.clear(); } + void push_back(const value_type& value) { array_.push_back(value); } + void push_back(value_type&& value) { array_.push_back(std::move(value)); } template void emplace_back(Args&&... args) { - underlying().emplace_back(std::forward(args)...); + array_.emplace_back(std::forward(args)...); } - void pop_back() { underlying().pop_back(); } - void resize(size_type count) { underlying().resize(count); } - void resize(size_type count, const value_type& value) { underlying().resize(count, value); } + void pop_back() { array_.pop_back(); } + void resize(size_type count) { array_.resize(count); } + void resize(size_type count, const value_type& value) { array_.resize(count, value); } // Conversion operators - operator T&() { return underlying(); } - operator const T&() const { return underlying(); } + operator T&() { return array_; } + operator const T&() const { return array_; } T&& release_array() { return std::move(array_); } - // Wrap/unwrap interface (for compatibility, but wrap is essentially a no-op for perf) - void wrap(T& external_array) { wrapped_ptr_ = &external_array; } - void unwrap() { wrapped_ptr_ = nullptr; } - bool is_wrapping() const { return wrapped_ptr_ != nullptr; } + T& underlying() { return array_; } + const T& underlying() const { return array_; } - // Get the underlying container - T& underlying() { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } - const T& underlying() const { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } + // No-op batch counting stubs for API compatibility + void record_loads(size_t) const {} + void record_stores(size_t) {} private: T array_; - T* wrapped_ptr_{nullptr}; }; #endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION From 512a960f18fe0835d597b74b677b0298c882d338 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 18 Jan 2026 12:16:48 +0000 Subject: [PATCH 205/366] Revert "stripped down ins_wrapper" This reverts commit ac57402d26d33608b3a6242180fd2c3c59d22d0d. --- cpp/src/utilities/memory_instrumentation.hpp | 831 ++++++++++++++----- 1 file changed, 608 insertions(+), 223 deletions(-) diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 4b0143b05..75aea76e3 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -40,7 +40,7 @@ #ifdef __NVCC__ #define HDI inline __host__ __device__ #else -#define HDI inline __attribute__((always_inline)) +#define HDI inline #endif namespace cuopt { @@ -48,20 +48,72 @@ namespace cuopt { // Define CUOPT_ENABLE_MEMORY_INSTRUMENTATION to 1 to enable memory tracking // When 0, instrumentation becomes a zero-overhead passthrough +// Base class for memory operation instrumentation +struct memory_instrumentation_base_t { +#if CUOPT_ENABLE_MEMORY_INSTRUMENTATION + HDI void reset_counters() const { byte_loads = byte_stores = 0; } + + template + HDI void record_load() const + { + byte_loads += sizeof(T); + } + + template + HDI void record_store() const + { + byte_stores += sizeof(T); + } + + template + HDI void record_rmw() const + { + byte_loads += sizeof(T); + byte_stores += sizeof(T); + } + + mutable size_t byte_loads{0}; + mutable size_t byte_stores{0}; +#else + // No-op methods when instrumentation is disabled - these inline away to zero overhead + HDI void reset_counters() const {} + template + HDI void record_load() const + { + } + template + HDI void record_store() const + { + } + template + HDI void record_rmw() const + { + } +#endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION +}; + #if CUOPT_ENABLE_MEMORY_INSTRUMENTATION // Manifold class to collect statistics from multiple instrumented objects -// Stores raw pointers to counters - works with any instrumented type class instrumentation_manifold_t { public: instrumentation_manifold_t() = default; - // Template add - works with any type that has byte_loads/byte_stores members - template - void add(const std::string& description, const Instrumented& instrumented) + // Construct with initializer list of (description, instrumented object) pairs + instrumentation_manifold_t( + std::initializer_list< + std::pair>> + instrumented) + { + for (const auto& [name, instr] : instrumented) { + instrumented_.insert_or_assign(name, instr); + } + } + + // Add an instrumented object to track with a description + void add(const std::string& description, const memory_instrumentation_base_t& instrumented) { - instrumented_.insert_or_assign( - description, std::make_pair(&instrumented.byte_loads, &instrumented.byte_stores)); + instrumented_.insert_or_assign(description, std::cref(instrumented)); } // Collect total loads and stores across all instrumented objects @@ -70,9 +122,9 @@ class instrumentation_manifold_t { size_t total_loads = 0; size_t total_stores = 0; - for (const auto& [name, counters] : instrumented_) { - total_loads += *counters.first; - total_stores += *counters.second; + for (auto& [name, instr] : instrumented_) { + total_loads += instr.get().byte_loads; + total_stores += instr.get().byte_stores; } return {total_loads, total_stores}; @@ -84,8 +136,8 @@ class instrumentation_manifold_t { std::vector> results; results.reserve(instrumented_.size()); - for (const auto& [name, counters] : instrumented_) { - results.emplace_back(name, *counters.first, *counters.second); + for (auto& [name, instr] : instrumented_) { + results.emplace_back(name, instr.get().byte_loads, instr.get().byte_stores); } return results; @@ -101,14 +153,14 @@ class instrumentation_manifold_t { void flush() { - for (const auto& [name, counters] : instrumented_) { - *const_cast(counters.first) = 0; - *const_cast(counters.second) = 0; + for (auto& [name, instr] : instrumented_) { + instr.get().reset_counters(); } } private: - std::unordered_map> instrumented_; + std::unordered_map> + instrumented_; }; #else @@ -117,10 +169,12 @@ class instrumentation_manifold_t { class instrumentation_manifold_t { public: instrumentation_manifold_t() = default; - template - void add(const std::string&, const Instrumented&) + instrumentation_manifold_t( + std::initializer_list< + std::pair>>) { } + void add(const std::string&, const memory_instrumentation_base_t&) {} std::pair collect() { return {0, 0}; } std::vector> collect_per_wrapper() { return {}; } std::pair collect_and_flush() { return {0, 0}; } @@ -215,60 +269,262 @@ struct has_back().back())>> : std::true_ #if CUOPT_ENABLE_MEMORY_INSTRUMENTATION // Memory operation instrumentation wrapper for container-like types -// No inheritance - counters embedded directly for compiler optimization template -struct memop_instrumentation_wrapper_t { - // Instrumentation counters - embedded directly, no base class - mutable size_t byte_loads{0}; - mutable size_t byte_stores{0}; +struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { + // Standard container type traits + using value_type = std::remove_reference_t()[0])>; + using size_type = std::size_t; + using difference_type = std::ptrdiff_t; + using reference = value_type&; + using const_reference = const value_type&; + using pointer = value_type*; + using const_pointer = const value_type*; + + static_assert(std::is_standard_layout_v, + "value_type must have standard layout for memory instrumentation"); + static constexpr size_t type_size = sizeof(value_type); - HDI void reset_counters() const { byte_loads = byte_stores = 0; } + // Proxy class to track reads and writes for a single element + class element_proxy_t { + public: + element_proxy_t(value_type& ref, memop_instrumentation_wrapper_t& wrapper) + : ref_(ref), wrapper_(wrapper) + { + } - template - HDI void record_load() const + element_proxy_t& operator=(const value_type& value) + { + wrapper_.template record_store(); + ref_ = value; + return *this; + } + element_proxy_t& operator=(const element_proxy_t& other) + { + wrapper_.template record_store(); + other.wrapper_.template record_load(); + ref_ = other.ref_; + return *this; + } + + operator value_type() const + { + wrapper_.template record_load(); + return ref_; + } + + // // Allow implicit conversion to reference for functions expecting references + // operator value_type&() { return ref_; } + + // operator const value_type&() const { return ref_; } + + // // Member access operator for structured types (e.g., type_2) + // value_type* operator->() { return &ref_; } + + // const value_type* operator->() const { return &ref_; } + + // Get underlying element reference (records a load) + value_type& get() + { + wrapper_.template record_load(); + return ref_; + } + + const value_type& get() const + { + wrapper_.template record_load(); + return ref_; + } + + element_proxy_t& operator+=(const value_type& value) + { + wrapper_.template record_rmw(); + ref_ += value; + return *this; + } + element_proxy_t& operator-=(const value_type& value) + { + wrapper_.template record_rmw(); + ref_ -= value; + return *this; + } + element_proxy_t& operator*=(const value_type& value) + { + wrapper_.template record_rmw(); + ref_ *= value; + return *this; + } + element_proxy_t& operator/=(const value_type& value) + { + wrapper_.template record_rmw(); + ref_ /= value; + return *this; + } + element_proxy_t& operator++() + { + wrapper_.template record_rmw(); + ++ref_; + return *this; + } + element_proxy_t& operator--() + { + wrapper_.template record_rmw(); + --ref_; + return *this; + } + + value_type operator++(int) + { + wrapper_.template record_rmw(); + return ref_++; + } + value_type operator--(int) + { + wrapper_.template record_rmw(); + return ref_--; + } + + value_type& ref_; + memop_instrumentation_wrapper_t& wrapper_; + }; + + // Instrumented iterator that tracks memory accesses + template + class instrumented_iterator_t { + public: + using iterator_category = std::random_access_iterator_tag; + using value_type = memop_instrumentation_wrapper_t::value_type; + using difference_type = std::ptrdiff_t; + using pointer = std::conditional_t; + using reference = std::conditional_t; + using wrapper_ptr = std::conditional_t; + + instrumented_iterator_t(IterT iter, wrapper_ptr wrapper) : iter_(iter), wrapper_(wrapper) {} + + // Dereference - returns proxy for non-const, tracks load for const + auto operator*() const + { + if constexpr (IsConst) { +#ifdef CUOPT_ENABLE_MEMORY_INSTRUMENTATION + wrapper_->byte_loads += sizeof(value_type); +#endif + return *iter_; + } else { + return element_proxy_t(*iter_, *wrapper_); + } + } + + auto operator->() const { return &(*iter_); } + + instrumented_iterator_t& operator++() + { + ++iter_; + return *this; + } + + instrumented_iterator_t operator++(int) + { + auto tmp = *this; + ++iter_; + return tmp; + } + + instrumented_iterator_t& operator--() + { + --iter_; + return *this; + } + + instrumented_iterator_t operator--(int) + { + auto tmp = *this; + --iter_; + return tmp; + } + + instrumented_iterator_t& operator+=(difference_type n) + { + iter_ += n; + return *this; + } + + instrumented_iterator_t& operator-=(difference_type n) + { + iter_ -= n; + return *this; + } + + instrumented_iterator_t operator+(difference_type n) const + { + return instrumented_iterator_t(iter_ + n, wrapper_); + } + + instrumented_iterator_t operator-(difference_type n) const + { + return instrumented_iterator_t(iter_ - n, wrapper_); + } + + difference_type operator-(const instrumented_iterator_t& other) const + { + return iter_ - other.iter_; + } + + auto operator[](difference_type n) const { return *(*this + n); } + + bool operator==(const instrumented_iterator_t& other) const { return iter_ == other.iter_; } + bool operator!=(const instrumented_iterator_t& other) const { return iter_ != other.iter_; } + bool operator<(const instrumented_iterator_t& other) const { return iter_ < other.iter_; } + bool operator<=(const instrumented_iterator_t& other) const { return iter_ <= other.iter_; } + bool operator>(const instrumented_iterator_t& other) const { return iter_ > other.iter_; } + bool operator>=(const instrumented_iterator_t& other) const { return iter_ >= other.iter_; } + + IterT base() const { return iter_; } + + // Allow iterator_traits to access the underlying iterator + friend struct std::iterator_traits; + + private: + IterT iter_; + wrapper_ptr wrapper_; + }; + + // Iterator type definitions (must come after instrumented_iterator_t) + using iterator = instrumented_iterator_t().begin()), false>; + using const_iterator = instrumented_iterator_t().begin()), true>; + using reverse_iterator = std::reverse_iterator; + using const_reverse_iterator = std::reverse_iterator; + + // Constructors + memop_instrumentation_wrapper_t() : array_(), wrapped_ptr(nullptr) { - byte_loads += sizeof(U); + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array_.data(); + } else { + data_ptr = nullptr; + } } - template - HDI void record_store() const + // Copy/move from underlying type + memop_instrumentation_wrapper_t(const T& arr) : array_(arr) { - byte_stores += sizeof(U); + if constexpr (type_traits_utils::has_data::value) { + data_ptr = const_cast(array_.data()); + } else { + data_ptr = nullptr; + } } - - template - HDI void record_rmw() const + memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)) { - byte_loads += sizeof(U); - byte_stores += sizeof(U); + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array_.data(); + } else { + data_ptr = nullptr; + } } - // Standard container type traits - using value_type = typename T::value_type; - using size_type = typename T::size_type; - using difference_type = typename T::difference_type; - using reference = typename T::reference; - using const_reference = typename T::const_reference; - using pointer = typename T::pointer; - using const_pointer = typename T::const_pointer; - - static_assert(std::is_trivially_copyable_v, - "value_type must be trivially copyable for memory instrumentation"); - static constexpr size_t type_size = sizeof(value_type); - - // Use native iterators - no instrumentation overhead on iteration - // Memory access counting is done via operator[] and batch methods - using iterator = typename T::iterator; - using const_iterator = typename T::const_iterator; - using reverse_iterator = typename T::reverse_iterator; - using const_reverse_iterator = typename T::const_reverse_iterator; - - // Constructors - cache data pointer for device access - memop_instrumentation_wrapper_t() : array_(), data_ptr_(array_.data()) {} - memop_instrumentation_wrapper_t(const T& arr) : array_(arr), data_ptr_(array_.data()) {} - memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)), data_ptr_(array_.data()) {} - // Forwarding constructor for underlying container initialization + // Only enabled for types that aren't the wrapper itself or the underlying type template , T> && (sizeof...(Args) > 0 || !std::is_convertible_v)>> explicit memop_instrumentation_wrapper_t(Arg&& arg, Args&&... args) - : array_(std::forward(arg), std::forward(args)...), data_ptr_(array_.data()) + : array_(std::forward(arg), std::forward(args)...) { + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array_.data(); + } else { + data_ptr = nullptr; + } } - // Copy/move - update data pointer cache, reset counters for new instance + // Copy constructor - copy from wrapped array if wrapping, never share wrapped pointer memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : byte_loads(0), byte_stores(0), array_(other.array_), data_ptr_(array_.data()) - { + : memory_instrumentation_base_t(other), + array_(other.wrapped_ptr ? *other.wrapped_ptr : other.array_), + wrapped_ptr(nullptr) + { + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array_.data(); + } else { + data_ptr = nullptr; + } } + + // Move constructor - copy from wrapped array if wrapping (can't move wrapped), never share + // pointer memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : byte_loads(0), byte_stores(0), array_(std::move(other.array_)), data_ptr_(array_.data()) - { + : memory_instrumentation_base_t(std::move(other)), + array_(other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_)), + wrapped_ptr(nullptr) + { + if constexpr (type_traits_utils::has_data::value) { + data_ptr = array_.data(); + } else { + data_ptr = nullptr; + } } + + // Copy assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) { if (this != &other) { - array_ = other.array_; - data_ptr_ = array_.data(); - // Don't copy counters - each instance tracks its own accesses + memory_instrumentation_base_t::operator=(other); + // Get source data (from wrapped or owned array) + const T& source = other.wrapped_ptr ? *other.wrapped_ptr : other.array_; + // Write to destination (wrapped or owned array) + if (wrapped_ptr) { + *wrapped_ptr = source; + } else { + array_ = source; + } + if constexpr (type_traits_utils::has_data::value) { + data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); + } else { + data_ptr = nullptr; + } } return *this; } + + // Move assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept { if (this != &other) { - array_ = std::move(other.array_); - data_ptr_ = array_.data(); - // Don't move counters - each instance tracks its own accesses + memory_instrumentation_base_t::operator=(std::move(other)); + // Get source data (copy from wrapped, move from owned) + if (wrapped_ptr) { + *wrapped_ptr = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); + } else { + array_ = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); + } + if constexpr (type_traits_utils::has_data::value) { + data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); + } else { + data_ptr = nullptr; + } } return *this; } - // Element access - return reference directly, count optimistically - // Use cached data_ptr_ to avoid calling host-only std::vector methods from device code - HDI reference operator[](size_type index) + element_proxy_t operator[](size_type index) { - record_store(); - return data_ptr_[index]; + return element_proxy_t(underlying()[index], *this); } HDI value_type operator[](size_type index) const { - record_load(); - return data_ptr_[index]; + this->template record_load(); + // really ugly hack because otherwise nvcc complains about vector operator[] being __host__ only + if constexpr (type_traits_utils::has_data::value) { + return data_ptr[index]; + } else { + return underlying()[index]; + } } - reference front() + template + std::enable_if_t::value, element_proxy_t> front() { - record_store(); - return array_.front(); + return element_proxy_t(underlying().front(), *this); } - value_type front() const + + template + std::enable_if_t::value, value_type> front() const { - record_load(); - return array_.front(); + this->template record_load(); + return underlying().front(); } - reference back() + template + std::enable_if_t::value, element_proxy_t> back() { - record_store(); - return array_.back(); + return element_proxy_t(underlying().back(), *this); } - value_type back() const + + template + std::enable_if_t::value, value_type> back() const { - record_load(); - return array_.back(); + this->template record_load(); + return underlying().back(); } - // Raw pointer access - bypasses instrumentation for hot loops - pointer data() noexcept { return array_.data(); } - const_pointer data() const noexcept { return array_.data(); } + // Iterators + iterator begin() noexcept { return iterator(std::begin(underlying()), this); } + const_iterator begin() const noexcept { return const_iterator(std::begin(underlying()), this); } + const_iterator cbegin() const noexcept { return const_iterator(std::begin(underlying()), this); } - // Iterators - native iterators, no instrumentation overhead - iterator begin() noexcept { return array_.begin(); } - const_iterator begin() const noexcept { return array_.begin(); } - const_iterator cbegin() const noexcept { return array_.cbegin(); } + iterator end() noexcept { return iterator(std::end(underlying()), this); } + const_iterator end() const noexcept { return const_iterator(std::end(underlying()), this); } + const_iterator cend() const noexcept { return const_iterator(std::end(underlying()), this); } - iterator end() noexcept { return array_.end(); } - const_iterator end() const noexcept { return array_.end(); } - const_iterator cend() const noexcept { return array_.cend(); } + reverse_iterator rbegin() noexcept { return reverse_iterator(end()); } + const_reverse_iterator rbegin() const noexcept + { + return const_reverse_iterator(std::end(underlying())); + } + const_reverse_iterator crbegin() const noexcept { return const_reverse_iterator(cend()); } - reverse_iterator rbegin() noexcept { return array_.rbegin(); } - const_reverse_iterator rbegin() const noexcept { return array_.rbegin(); } - const_reverse_iterator crbegin() const noexcept { return array_.crbegin(); } + reverse_iterator rend() noexcept { return reverse_iterator(begin()); } + const_reverse_iterator rend() const noexcept { return const_reverse_iterator(begin()); } + const_reverse_iterator crend() const noexcept { return const_reverse_iterator(cbegin()); } - reverse_iterator rend() noexcept { return array_.rend(); } - const_reverse_iterator rend() const noexcept { return array_.rend(); } - const_reverse_iterator crend() const noexcept { return array_.crend(); } + // Capacity + bool empty() const noexcept { return std::begin(underlying()) == std::end(underlying()); } + size_type size() const noexcept + { + return std::distance(std::begin(underlying()), std::end(underlying())); + } - // Capacity - direct forwarding - bool empty() const noexcept { return array_.empty(); } - size_type size() const noexcept { return array_.size(); } - size_type max_size() const noexcept { return array_.max_size(); } - size_type capacity() const noexcept { return array_.capacity(); } + // Conditional methods - only available if underlying type supports them + template + std::enable_if_t::value, size_type> max_size() const noexcept + { + return underlying().max_size(); + } - void reserve(size_type new_cap) + template + std::enable_if_t::value, size_type> capacity() const noexcept { - array_.reserve(new_cap); - data_ptr_ = array_.data(); + return underlying().capacity(); } - void shrink_to_fit() + + template + std::enable_if_t::value> reserve(size_type new_cap) { - array_.shrink_to_fit(); - data_ptr_ = array_.data(); + underlying().reserve(new_cap); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } - // Modifiers - void clear() noexcept + template + std::enable_if_t::value> shrink_to_fit() { - array_.clear(); - data_ptr_ = array_.data(); + underlying().shrink_to_fit(); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } - void push_back(const value_type& value) + template + std::enable_if_t::value> clear() noexcept { - record_store(); - array_.push_back(value); - data_ptr_ = array_.data(); + underlying().clear(); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } - void push_back(value_type&& value) + template + std::enable_if_t::value> push_back(const value_type& value) { - record_store(); - array_.push_back(std::move(value)); - data_ptr_ = array_.data(); + // we should probably take into account possible copies done by std::vector. oh well. + // hot loops shouldn't be doing such operations anyway + this->template record_store(); + underlying().push_back(value); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } - template - void emplace_back(Args&&... args) + template + std::enable_if_t::value> push_back(value_type&& value) { - record_store(); - array_.emplace_back(std::forward(args)...); - data_ptr_ = array_.data(); + this->template record_store(); + underlying().push_back(std::move(value)); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } - void pop_back() + template + std::enable_if_t::value> emplace_back(Args&&... args) { - record_load(); - array_.pop_back(); - // data_ptr_ unchanged - pop_back doesn't reallocate + this->template record_store(); + underlying().emplace_back(std::forward(args)...); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } - void resize(size_type count) + template + std::enable_if_t::value> pop_back() { - size_type old_size = array_.size(); - array_.resize(count); - data_ptr_ = array_.data(); - if (count > old_size) { byte_stores += (count - old_size) * type_size; } + this->template record_load(); // Reading the element before removal + underlying().pop_back(); + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } - void resize(size_type count, const value_type& value) + template + std::enable_if_t::value> resize(size_type count) { - size_type old_size = array_.size(); - array_.resize(count, value); - data_ptr_ = array_.data(); - if (count > old_size) { byte_stores += (count - old_size) * type_size; } + size_type old_size = underlying().size(); + underlying().resize(count); + if (count > old_size) { + this->byte_stores += (count - old_size) * type_size; // New elements initialized + } + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } } - // Batch counting for manual instrumentation after raw pointer use - void record_loads(size_t count) const { byte_loads += count * type_size; } - void record_stores(size_t count) { byte_stores += count * type_size; } + template + std::enable_if_t::value> resize(size_type count, + const value_type& value) + { + size_type old_size = underlying().size(); + underlying().resize(count, value); + if (count > old_size) { this->byte_stores += (count - old_size) * type_size; } + if constexpr (type_traits_utils::has_data::value) { data_ptr = underlying().data(); } + } - // Conversion operators - operator T&() { return array_; } - operator const T&() const { return array_; } + template + std::enable_if_t::value, value_type*> data() noexcept + { + return underlying().data(); + } + + template + std::enable_if_t::value, const value_type*> data() const noexcept + { + return underlying().data(); + } + + // Access to underlying array + operator T&() { return underlying(); } + operator const T&() const { return underlying(); } T&& release_array() { return std::move(array_); } - T& underlying() { return array_; } - const T& underlying() const { return array_; } + // Wrap an external vector without taking ownership + void wrap(T& external_array) + { + wrapped_ptr = &external_array; + if constexpr (type_traits_utils::has_data::value) { data_ptr = external_array.data(); } + } + + // Stop wrapping and return to using the owned array + void unwrap() + { + wrapped_ptr = nullptr; + if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); } + } + + // Check if currently wrapping an external array + bool is_wrapping() const { return wrapped_ptr != nullptr; } + + // Get the underlying container (wrapped or owned) + T& underlying() { return wrapped_ptr ? *wrapped_ptr : array_; } + const T& underlying() const { return wrapped_ptr ? *wrapped_ptr : array_; } private: T array_; - pointer __restrict__ data_ptr_{nullptr}; // Cached for device access + T* wrapped_ptr{nullptr}; + value_type* data_ptr{nullptr}; }; #else // !CUOPT_ENABLE_MEMORY_INSTRUMENTATION // Zero-overhead passthrough wrapper when instrumentation is disabled +// Provides the same interface as the instrumented version but just forwards to the underlying +// container template -struct memop_instrumentation_wrapper_t { - // No-op instrumentation methods for API compatibility - HDI void reset_counters() const {} - template - HDI void record_load() const - { - } - template - HDI void record_store() const - { - } - template - HDI void record_rmw() const - { - } +struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { using value_type = typename T::value_type; using size_type = typename T::size_type; using difference_type = typename T::difference_type; @@ -483,10 +832,10 @@ struct memop_instrumentation_wrapper_t { using reverse_iterator = typename T::reverse_iterator; using const_reverse_iterator = typename T::const_reverse_iterator; - // Constructors + // Constructors - forward everything to the underlying container memop_instrumentation_wrapper_t() = default; - memop_instrumentation_wrapper_t(const T& arr) : array_(arr) {} - memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)) {} + memop_instrumentation_wrapper_t(const T& arr) : array_(arr), wrapped_ptr_(nullptr) {} + memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)), wrapped_ptr_(nullptr) {} template , T> && (sizeof...(Args) > 0 || !std::is_convertible_v)>> explicit memop_instrumentation_wrapper_t(Arg&& arg, Args&&... args) - : array_(std::forward(arg), std::forward(args)...) + : array_(std::forward(arg), std::forward(args)...), wrapped_ptr_(nullptr) + { + } + + // Copy constructor - always copy the data, never share wrapped pointer + memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) + : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_), wrapped_ptr_(nullptr) { } - // Default copy/move - memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t&) = default; - memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&&) noexcept = default; - memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t&) = default; - memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&&) noexcept = default; + // Move constructor - take ownership of array, never share wrapped pointer + memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept + : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_)), + wrapped_ptr_(nullptr) + { + } + + // Copy assignment - always copy the data + memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) + { + if (this != &other) { + if (wrapped_ptr_) { + *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; + } else { + array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; + } + } + return *this; + } + + // Move assignment - take the data + memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept + { + if (this != &other) { + if (wrapped_ptr_) { + *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); + } else { + array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); + } + } + return *this; + } // Element access - direct passthrough - reference operator[](size_type index) { return array_[index]; } - const_reference operator[](size_type index) const { return array_[index]; } + reference operator[](size_type index) { return underlying()[index]; } + const_reference operator[](size_type index) const { return underlying()[index]; } - reference front() { return array_.front(); } - const_reference front() const { return array_.front(); } + reference front() { return underlying().front(); } + const_reference front() const { return underlying().front(); } - reference back() { return array_.back(); } - const_reference back() const { return array_.back(); } + reference back() { return underlying().back(); } + const_reference back() const { return underlying().back(); } - pointer data() noexcept { return array_.data(); } - const_pointer data() const noexcept { return array_.data(); } + pointer data() noexcept { return underlying().data(); } + const_pointer data() const noexcept { return underlying().data(); } - // Iterators - iterator begin() noexcept { return array_.begin(); } - const_iterator begin() const noexcept { return array_.begin(); } - const_iterator cbegin() const noexcept { return array_.cbegin(); } + // Iterators - use underlying container's iterators directly + iterator begin() noexcept { return underlying().begin(); } + const_iterator begin() const noexcept { return underlying().begin(); } + const_iterator cbegin() const noexcept { return underlying().cbegin(); } - iterator end() noexcept { return array_.end(); } - const_iterator end() const noexcept { return array_.end(); } - const_iterator cend() const noexcept { return array_.cend(); } + iterator end() noexcept { return underlying().end(); } + const_iterator end() const noexcept { return underlying().end(); } + const_iterator cend() const noexcept { return underlying().cend(); } - reverse_iterator rbegin() noexcept { return array_.rbegin(); } - const_reverse_iterator rbegin() const noexcept { return array_.rbegin(); } - const_reverse_iterator crbegin() const noexcept { return array_.crbegin(); } + reverse_iterator rbegin() noexcept { return underlying().rbegin(); } + const_reverse_iterator rbegin() const noexcept { return underlying().rbegin(); } + const_reverse_iterator crbegin() const noexcept { return underlying().crbegin(); } - reverse_iterator rend() noexcept { return array_.rend(); } - const_reverse_iterator rend() const noexcept { return array_.rend(); } - const_reverse_iterator crend() const noexcept { return array_.crend(); } + reverse_iterator rend() noexcept { return underlying().rend(); } + const_reverse_iterator rend() const noexcept { return underlying().rend(); } + const_reverse_iterator crend() const noexcept { return underlying().crend(); } // Capacity - bool empty() const noexcept { return array_.empty(); } - size_type size() const noexcept { return array_.size(); } - size_type max_size() const noexcept { return array_.max_size(); } - size_type capacity() const noexcept { return array_.capacity(); } + bool empty() const noexcept { return underlying().empty(); } + size_type size() const noexcept { return underlying().size(); } + size_type max_size() const noexcept { return underlying().max_size(); } + size_type capacity() const noexcept { return underlying().capacity(); } - void reserve(size_type new_cap) { array_.reserve(new_cap); } - void shrink_to_fit() { array_.shrink_to_fit(); } + void reserve(size_type new_cap) { underlying().reserve(new_cap); } + void shrink_to_fit() { underlying().shrink_to_fit(); } // Modifiers - void clear() noexcept { array_.clear(); } - void push_back(const value_type& value) { array_.push_back(value); } - void push_back(value_type&& value) { array_.push_back(std::move(value)); } + void clear() noexcept { underlying().clear(); } + void push_back(const value_type& value) { underlying().push_back(value); } + void push_back(value_type&& value) { underlying().push_back(std::move(value)); } template void emplace_back(Args&&... args) { - array_.emplace_back(std::forward(args)...); + underlying().emplace_back(std::forward(args)...); } - void pop_back() { array_.pop_back(); } - void resize(size_type count) { array_.resize(count); } - void resize(size_type count, const value_type& value) { array_.resize(count, value); } + void pop_back() { underlying().pop_back(); } + void resize(size_type count) { underlying().resize(count); } + void resize(size_type count, const value_type& value) { underlying().resize(count, value); } // Conversion operators - operator T&() { return array_; } - operator const T&() const { return array_; } + operator T&() { return underlying(); } + operator const T&() const { return underlying(); } T&& release_array() { return std::move(array_); } - T& underlying() { return array_; } - const T& underlying() const { return array_; } + // Wrap/unwrap interface (for compatibility, but wrap is essentially a no-op for perf) + void wrap(T& external_array) { wrapped_ptr_ = &external_array; } + void unwrap() { wrapped_ptr_ = nullptr; } + bool is_wrapping() const { return wrapped_ptr_ != nullptr; } - // No-op batch counting stubs for API compatibility - void record_loads(size_t) const {} - void record_stores(size_t) {} + // Get the underlying container + T& underlying() { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } + const T& underlying() const { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } private: T array_; + T* wrapped_ptr_{nullptr}; }; #endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION From 61c15637acd13da780cdcc49389433b7eaf72ea2 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 18 Jan 2026 17:37:38 +0000 Subject: [PATCH 206/366] initial detemrinistic diving impl --- cpp/src/dual_simplex/bb_worker_state.hpp | 384 ++++++++++++++ cpp/src/dual_simplex/branch_and_bound.cpp | 577 ++++++++++++++++++++- cpp/src/dual_simplex/branch_and_bound.hpp | 52 +- cpp/src/dual_simplex/diving_heuristics.hpp | 12 + cpp/src/dual_simplex/node_queue.hpp | 3 + cpp/src/mip/solver.cu | 6 +- 6 files changed, 996 insertions(+), 38 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 1714f4260..cf43ba80a 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -10,12 +10,16 @@ #include #include #include +#include #include #include #include #include +#include + #include +#include #include #include #include @@ -560,6 +564,386 @@ struct bb_worker_state_t { void track_node_assigned() { ++total_nodes_assigned; } }; +// ============================================================================= +// BSP Diving Worker State +// ============================================================================= + +// Per-worker state for BSP deterministic diving +// Diving workers operate on detached copies of nodes and don't modify the main tree +template +struct bsp_diving_worker_state_t { + int worker_id{0}; + bnb_worker_type_t diving_type{bnb_worker_type_t::PSEUDOCOST_DIVING}; + + // Worker's virtual time clock (cumulative work units) + double clock{0.0}; + + // Current horizon boundaries + double horizon_start{0.0}; + double horizon_end{0.0}; + + // Work context for horizon sync + work_limit_context_t work_context; + + // LP problem copy for this worker + std::unique_ptr> leaf_problem; + + // Basis factorization state + std::unique_ptr> basis_factors; + + // Bounds strengthening + std::unique_ptr> node_presolver; + + // Working vectors for basis + std::vector basic_list; + std::vector nonbasic_list; + + // Whether basis needs recomputation for next node + bool recompute_bounds_and_basis{true}; + + // ========================================================================== + // Snapshots for determinism (taken at horizon start) + // ========================================================================== + + f_t local_upper_bound{std::numeric_limits::infinity()}; + + // Incumbent snapshot for guided diving + std::vector incumbent_snapshot; + + // Pseudo-cost snapshots + std::vector pc_sum_up_snapshot; + std::vector pc_sum_down_snapshot; + std::vector pc_num_up_snapshot; + std::vector pc_num_down_snapshot; + + // Root relaxation solution (for line search diving) + const std::vector* root_solution{nullptr}; + + // ========================================================================== + // Diving-specific state + // ========================================================================== + + // Queue of starting nodes for dives (detached copies assigned at sync) + // Worker processes these until queue empty or horizon exhausted + std::deque> dive_queue; + + // Current lower/upper bounds for the dive (initialized from starting node) + std::vector dive_lower; + std::vector dive_upper; + + // ========================================================================== + // Queued results (merged at sync) + // ========================================================================== + + struct queued_integer_solution_t { + f_t objective; + std::vector solution; + i_t depth; + }; + std::vector integer_solutions; + + // ========================================================================== + // Statistics + // ========================================================================== + + i_t nodes_explored_this_horizon{0}; + i_t total_nodes_explored{0}; + i_t total_integer_solutions{0}; + i_t total_dives{0}; + double total_runtime{0.0}; + double total_nowork_time{0.0}; + + // ========================================================================== + // Constructor and initialization + // ========================================================================== + + explicit bsp_diving_worker_state_t(int id, bnb_worker_type_t type) + : worker_id(id), diving_type(type), work_context("Diving_Worker_" + std::to_string(id)) + { + } + + void initialize(const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + i_t refactor_frequency, + bool deterministic) + { + leaf_problem = std::make_unique>(original_lp); + + const i_t m = leaf_problem->num_rows; + basis_factors = std::make_unique>(m, refactor_frequency); + + std::vector row_sense; + node_presolver = + std::make_unique>(*leaf_problem, Arow, row_sense, var_types); + + basic_list.resize(m); + nonbasic_list.clear(); + + dive_lower = original_lp.lower; + dive_upper = original_lp.upper; + + work_context.deterministic = deterministic; + } + + void reset_for_horizon(double start, double end, f_t upper_bound) + { + clock = start; + horizon_start = start; + horizon_end = end; + work_context.global_work_units_elapsed = start; + + local_upper_bound = upper_bound; + nodes_explored_this_horizon = 0; + // Note: Don't clear dive_queue here - workers may still have nodes to process + integer_solutions.clear(); + recompute_bounds_and_basis = true; + } + + void set_snapshots(const std::vector& pc_sum_up, + const std::vector& pc_sum_down, + const std::vector& pc_num_up, + const std::vector& pc_num_down, + const std::vector& incumbent, + const std::vector* root_sol) + { + pc_sum_up_snapshot = pc_sum_up; + pc_sum_down_snapshot = pc_sum_down; + pc_num_up_snapshot = pc_num_up; + pc_num_down_snapshot = pc_num_down; + incumbent_snapshot = incumbent; + root_solution = root_sol; + } + + void enqueue_dive_node(mip_node_t* node) { dive_queue.push_back(node->detach_copy()); } + + std::optional> dequeue_dive_node() + { + if (dive_queue.empty()) return std::nullopt; + auto node = std::move(dive_queue.front()); + dive_queue.pop_front(); + ++total_dives; + return node; + } + + bool has_work() const { return !dive_queue.empty(); } + + size_t dive_queue_size() const { return dive_queue.size(); } + + void queue_integer_solution(f_t objective, const std::vector& solution, i_t depth) + { + integer_solutions.push_back({objective, solution, depth}); + ++total_integer_solutions; + } + + // Variable selection using snapshot pseudo-costs (for pseudocost diving) + branch_variable_t variable_selection_from_snapshot(const std::vector& fractional, + const std::vector& solution) const + { + const i_t num_fractional = fractional.size(); + if (num_fractional == 0) return {-1, rounding_direction_t::NONE}; + + i_t num_initialized_down = 0; + i_t num_initialized_up = 0; + f_t pseudo_cost_down_avg = 0; + f_t pseudo_cost_up_avg = 0; + + const i_t n = pc_sum_down_snapshot.size(); + for (i_t j = 0; j < n; ++j) { + if (pc_num_down_snapshot[j] > 0) { + ++num_initialized_down; + if (std::isfinite(pc_sum_down_snapshot[j])) { + pseudo_cost_down_avg += pc_sum_down_snapshot[j] / pc_num_down_snapshot[j]; + } + } + if (pc_num_up_snapshot[j] > 0) { + ++num_initialized_up; + if (std::isfinite(pc_sum_up_snapshot[j])) { + pseudo_cost_up_avg += pc_sum_up_snapshot[j] / pc_num_up_snapshot[j]; + } + } + } + pseudo_cost_down_avg = + (num_initialized_down > 0) ? pseudo_cost_down_avg / num_initialized_down : 1.0; + pseudo_cost_up_avg = (num_initialized_up > 0) ? pseudo_cost_up_avg / num_initialized_up : 1.0; + + i_t branch_var = fractional[0]; + f_t max_score = std::numeric_limits::lowest(); + rounding_direction_t round_dir = rounding_direction_t::DOWN; + constexpr f_t eps = 1e-6; + + for (i_t j : fractional) { + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + + f_t pc_down = pc_num_down_snapshot[j] != 0 ? pc_sum_down_snapshot[j] / pc_num_down_snapshot[j] + : pseudo_cost_down_avg; + f_t pc_up = pc_num_up_snapshot[j] != 0 ? pc_sum_up_snapshot[j] / pc_num_up_snapshot[j] + : pseudo_cost_up_avg; + + f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); + f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); + + f_t score = 0; + rounding_direction_t dir = rounding_direction_t::DOWN; + + f_t root_val = (root_solution && j < static_cast(root_solution->size())) + ? (*root_solution)[j] + : solution[j]; + + if (solution[j] < root_val - 0.4) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else if (solution[j] > root_val + 0.4) { + score = score_up; + dir = rounding_direction_t::UP; + } else if (f_down < 0.3) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else if (f_down > 0.7) { + score = score_up; + dir = rounding_direction_t::UP; + } else if (pc_down < pc_up + eps) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else { + score = score_up; + dir = rounding_direction_t::UP; + } + + if (score > max_score) { + max_score = score; + branch_var = j; + round_dir = dir; + } + } + + return {branch_var, round_dir}; + } + + // Guided diving variable selection using incumbent snapshot + branch_variable_t guided_variable_selection(const std::vector& fractional, + const std::vector& solution) const + { + if (incumbent_snapshot.empty()) { + return variable_selection_from_snapshot(fractional, solution); + } + + const i_t num_fractional = fractional.size(); + if (num_fractional == 0) return {-1, rounding_direction_t::NONE}; + + i_t num_initialized_down = 0; + i_t num_initialized_up = 0; + f_t pseudo_cost_down_avg = 0; + f_t pseudo_cost_up_avg = 0; + + const i_t n = pc_sum_down_snapshot.size(); + for (i_t j = 0; j < n; ++j) { + if (pc_num_down_snapshot[j] > 0) { + ++num_initialized_down; + if (std::isfinite(pc_sum_down_snapshot[j])) { + pseudo_cost_down_avg += pc_sum_down_snapshot[j] / pc_num_down_snapshot[j]; + } + } + if (pc_num_up_snapshot[j] > 0) { + ++num_initialized_up; + if (std::isfinite(pc_sum_up_snapshot[j])) { + pseudo_cost_up_avg += pc_sum_up_snapshot[j] / pc_num_up_snapshot[j]; + } + } + } + pseudo_cost_down_avg = + (num_initialized_down > 0) ? pseudo_cost_down_avg / num_initialized_down : 1.0; + pseudo_cost_up_avg = (num_initialized_up > 0) ? pseudo_cost_up_avg / num_initialized_up : 1.0; + + i_t branch_var = fractional[0]; + f_t max_score = std::numeric_limits::lowest(); + rounding_direction_t round_dir = rounding_direction_t::DOWN; + constexpr f_t eps = 1e-6; + + for (i_t j : fractional) { + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t down_dist = std::abs(incumbent_snapshot[j] - std::floor(solution[j])); + f_t up_dist = std::abs(std::ceil(solution[j]) - incumbent_snapshot[j]); + rounding_direction_t dir = + down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; + + f_t pc_down = pc_num_down_snapshot[j] != 0 ? pc_sum_down_snapshot[j] / pc_num_down_snapshot[j] + : pseudo_cost_down_avg; + f_t pc_up = pc_num_up_snapshot[j] != 0 ? pc_sum_up_snapshot[j] / pc_num_up_snapshot[j] + : pseudo_cost_up_avg; + + f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; + f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; + f_t score = (score1 + score2) / 6; + + if (score > max_score) { + max_score = score; + branch_var = j; + round_dir = dir; + } + } + + return {branch_var, round_dir}; + } +}; + +// Container for all diving worker states +template +class bsp_diving_worker_pool_t { + public: + bsp_diving_worker_pool_t() = default; + + void initialize(int num_workers, + const std::vector& diving_types, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + i_t refactor_frequency, + bool deterministic) + { + workers_.clear(); + workers_.reserve(num_workers); + for (int i = 0; i < num_workers; ++i) { + bnb_worker_type_t type = diving_types[i % diving_types.size()]; + workers_.emplace_back(i, type); + workers_.back().initialize(original_lp, Arow, var_types, refactor_frequency, deterministic); + } + } + + bsp_diving_worker_state_t& operator[](int worker_id) { return workers_[worker_id]; } + const bsp_diving_worker_state_t& operator[](int worker_id) const + { + return workers_[worker_id]; + } + + int size() const { return static_cast(workers_.size()); } + + void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) + { + for (auto& worker : workers_) { + worker.reset_for_horizon(horizon_start, horizon_end, global_upper_bound); + } + } + + bool any_has_work() const + { + for (const auto& worker : workers_) { + if (worker.has_work()) return true; + } + return false; + } + + auto begin() { return workers_.begin(); } + auto end() { return workers_.end(); } + auto begin() const { return workers_.begin(); } + auto end() const { return workers_.end(); } + + private: + std::vector> workers_; +}; + // Container for all worker states in BSP B&B template class bb_worker_pool_t { diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 50cb366b6..54153863b 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1739,37 +1739,71 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t>(); - bsp_workers_->initialize(num_workers, + bsp_workers_->initialize(num_bfs_workers, original_lp_, Arow, var_types_, settings_.refactor_frequency, settings_.deterministic); + // Initialize diving worker pool if we have diving workers + if (num_diving_workers > 0) { + std::vector diving_types = {bnb_worker_type_t::PSEUDOCOST_DIVING, + bnb_worker_type_t::LINE_SEARCH_DIVING, + bnb_worker_type_t::GUIDED_DIVING, + bnb_worker_type_t::COEFFICIENT_DIVING}; + bsp_diving_workers_ = std::make_unique>(); + bsp_diving_workers_->initialize(num_diving_workers, + diving_types, + original_lp_, + Arow, + var_types_, + settings_.refactor_frequency, + settings_.deterministic); + + // Calculate variable locks for coefficient diving + calculate_variable_locks(original_lp_, var_up_locks_, var_down_locks_); + } + // Initialize scheduler for automatic sync at horizon boundaries // Workers will block in record_work() when they cross sync points bsp_scheduler_ = std::make_unique(bsp_horizon_step_); - // Register all worker contexts with the scheduler + // Register all BFS worker contexts with the scheduler for (auto& worker : *bsp_workers_) { bsp_scheduler_->register_context(worker.work_context); } + // Register all diving worker contexts with the scheduler + if (bsp_diving_workers_) { + for (auto& worker : *bsp_diving_workers_) { + bsp_scheduler_->register_context(worker.work_context); + } + } + // Initialize debug logger bsp_debug_logger_.set_settings(bsp_debug_settings_); - bsp_debug_logger_.set_num_workers(num_workers); + bsp_debug_logger_.set_num_workers(num_bfs_workers); bsp_debug_logger_.set_horizon_step(bsp_horizon_step_); settings_.log.printf( - "BSP Mode: %d workers, horizon step = %.2f work units\n", num_workers, bsp_horizon_step_); + "BSP Mode: %d BFS workers + %d diving workers, horizon step = %.2f work " + "units\n", + num_bfs_workers, + num_diving_workers, + bsp_horizon_step_); // Push initial children to the global heap // Set deterministic BSP identity for root children (pre-BSP origin with seq 0 and 1) @@ -1794,7 +1828,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t incumbent_snapshot; + if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } - f_t worker_start_time = tic(); + for (auto& worker : *bsp_diving_workers_) { + worker.set_snapshots(pc_.pseudo_cost_sum_up, + pc_.pseudo_cost_sum_down, + pc_.pseudo_cost_num_up, + pc_.pseudo_cost_num_down, + incumbent_snapshot, + &root_relax_soln_.x); + worker.local_upper_bound = upper_bound_.load(); + worker.horizon_start = 0.0; + worker.horizon_end = bsp_horizon_step_; + } + } - // Run worker loop - scheduler handles sync at horizon boundaries - run_worker_loop(worker, search_tree_); + const int total_thread_count = num_bfs_workers + num_diving_workers; - worker.total_runtime += toc(worker_start_time); + // Main BSP execution - workers run in parallel with scheduler-driven sync +#pragma omp parallel num_threads(total_thread_count) + { + int thread_id = omp_get_thread_num(); + + if (thread_id < num_bfs_workers) { + // BFS worker + auto& worker = (*bsp_workers_)[thread_id]; + f_t worker_start_time = tic(); + run_worker_loop(worker, search_tree_); + worker.total_runtime += toc(worker_start_time); + } else { + // Diving worker + int diving_id = thread_id - num_bfs_workers; + auto& worker = (*bsp_diving_workers_)[diving_id]; + f_t worker_start_time = tic(); + run_diving_worker_loop(worker); + worker.total_runtime += toc(worker_start_time); + } } + // All workers have terminated - deregister contexts for (auto& worker : *bsp_workers_) { bsp_scheduler_->deregister_context(worker.work_context); } + if (bsp_diving_workers_) { + for (auto& worker : *bsp_diving_workers_) { + bsp_scheduler_->deregister_context(worker.work_context); + } + } // Print per-worker statistics settings_.log.printf("\n"); - settings_.log.printf("BSP Worker Statistics:\n"); + settings_.log.printf("BSP BFS Worker Statistics:\n"); settings_.log.printf( " Worker | Nodes | Branched | Pruned | Infeas. | IntSol | Assigned | Clock | " "Sync%% | NoWork\n"); @@ -1849,6 +1916,31 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tsize() > 0) { + settings_.log.printf("\nBSP Diving Worker Statistics:\n"); + settings_.log.printf(" Worker | Type | Dives | Nodes | IntSol | Clock | NoWork\n"); + settings_.log.printf(" -------+--------+---------+--------+--------+----------+-------\n"); + for (const auto& worker : *bsp_diving_workers_) { + const char* type_str = "???"; + switch (worker.diving_type) { + case bnb_worker_type_t::PSEUDOCOST_DIVING: type_str = "PC"; break; + case bnb_worker_type_t::LINE_SEARCH_DIVING: type_str = "LS"; break; + case bnb_worker_type_t::GUIDED_DIVING: type_str = "GD"; break; + case bnb_worker_type_t::COEFFICIENT_DIVING: type_str = "CD"; break; + default: break; + } + settings_.log.printf(" %6d | %6s | %7d | %6d | %6d | %7.3fs | %5.2fs\n", + worker.worker_id, + type_str, + worker.total_dives, + worker.total_nodes_explored, + worker.total_integer_solutions, + worker.total_runtime, + worker.total_nowork_time); + } + } settings_.log.printf("\n"); // Finalize debug logger @@ -2015,7 +2107,18 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) // Prune paused nodes that are now dominated by new incumbent prune_worker_nodes_vs_incumbent(); + // === Diving worker sync operations === + // 1. Merge diving solutions found this horizon + merge_diving_solutions(); + + // 2. Populate diving heap from BFS backlogs BEFORE load balancing clears them + populate_diving_heap_at_sync(); + + // 3. Assign new starting nodes to diving workers + assign_diving_nodes(); + // Balance worker loads if significant imbalance detected + // Note: This must happen AFTER diving heap population since it clears backlogs balance_worker_loads(); BSP_DEBUG_FLUSH_ASSIGN_TRACE(bsp_debug_settings_, bsp_debug_logger_); @@ -2092,6 +2195,24 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) worker.horizon_end = bsp_current_horizon_; } + // Update diving worker snapshots for next horizon + if (bsp_diving_workers_) { + std::vector incumbent_snapshot; + if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } + + for (auto& worker : *bsp_diving_workers_) { + worker.set_snapshots(pc_.pseudo_cost_sum_up, + pc_.pseudo_cost_sum_down, + pc_.pseudo_cost_num_up, + pc_.pseudo_cost_num_down, + incumbent_snapshot, + &root_relax_soln_.x); + worker.local_upper_bound = upper_bound_.load(); + worker.horizon_start = horizon_end; + worker.horizon_end = bsp_current_horizon_; + } + } + // Check termination conditions f_t lower_bound = compute_bsp_lower_bound(); f_t upper_bound = upper_bound_.load(); @@ -2105,8 +2226,11 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) should_terminate = true; } - // No more work - if (heap_.size() == 0 && !bsp_workers_->any_has_work()) { should_terminate = true; } + // No more work (for both BFS and diving) + bool diving_has_work = bsp_diving_workers_ && bsp_diving_workers_->any_has_work(); + if (heap_.size() == 0 && !bsp_workers_->any_has_work() && !diving_has_work) { + should_terminate = true; + } // Time limit if (toc(exploration_stats_.start_time) > settings_.time_limit) { @@ -3027,7 +3151,7 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() if (heap_.size() > 0) { lower_bound = std::min(heap_.top()->lower_bound, lower_bound); } mutex_heap_.unlock(); - // Check all worker queues + // Check all BFS worker queues for (const auto& worker : *bsp_workers_) { // Check paused node (current_node) if (worker.current_node != nullptr) { @@ -3045,9 +3169,416 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() } } + // Note: diving worker queues contain copies of nodes that remain in backlogs/heap + // (speculative diving model), so no need to check them separately. + return lower_bound; } +// ============================================================================ +// BSP Diving Implementation +// ============================================================================ + +template +void branch_and_bound_t::populate_diving_heap_at_sync() +{ + // Clear diving heap from previous horizon + diving_heap_.clear(); + + if (!bsp_diving_workers_ || bsp_diving_workers_->size() == 0) return; + + const int num_diving = bsp_diving_workers_->size(); + constexpr int target_nodes_per_worker = 10; + const int target_total = num_diving * target_nodes_per_worker; + f_t upper_bound = upper_bound_.load(); + + // Collect candidate nodes from backlogs with their scores. + // Following the opportunistic diving model: we DON'T remove nodes from their + // original locations. Diving is speculative - original nodes stay in backlogs/heap + // for BFS processing and correct lower bound computation. Diving workers get + // copies via detach_copy() in enqueue_dive_node(). + std::vector*, f_t>> candidates; + + for (auto& worker : *bsp_workers_) { + for (auto* node : worker.backlog) { + if (node->lower_bound < upper_bound) { + f_t score = node->objective_estimate; + if (!std::isfinite(score)) { score = node->lower_bound; } + candidates.push_back({node, score}); + } + } + } + + // If backlogs don't have enough nodes, also consider global heap nodes. + // Use data() to iterate without popping - nodes stay in heap. + if ((int)candidates.size() < target_total) { + mutex_heap_.lock(); + for (auto* node : heap_.data()) { + if (node->lower_bound < upper_bound) { + f_t score = node->objective_estimate; + if (!std::isfinite(score)) { score = node->lower_bound; } + candidates.push_back({node, score}); + if ((int)candidates.size() >= target_total) break; + } + } + mutex_heap_.unlock(); + } + + if (candidates.empty()) return; + + // Sort candidates by score (lower is better for diving - closer to optimum) + std::sort(candidates.begin(), candidates.end(), [](const auto& a, const auto& b) { + return a.second < b.second; + }); + + // Take enough nodes for diving workers' queues + int nodes_to_take = std::min(target_total, (int)candidates.size()); + + for (int i = 0; i < nodes_to_take; ++i) { + diving_heap_.push({candidates[i].first, candidates[i].second}); + } + + // Original nodes stay in backlogs/heap - no removal needed. + // Diving workers will get copies via detach_copy() in assign_diving_nodes(). +} + +template +void branch_and_bound_t::assign_diving_nodes() +{ + if (!bsp_diving_workers_ || bsp_diving_workers_->size() == 0) { + // No diving workers - just clear the diving heap. + // Original nodes remain in backlogs/heap (speculative diving model). + diving_heap_.clear(); + return; + } + + // Assign multiple nodes per diving worker from the diving heap. + // Diving workers get copies via detach_copy() in enqueue_dive_node(). + constexpr int target_nodes_per_worker = 10; + + // Round-robin assignment to balance load across workers + int worker_idx = 0; + const int num_workers = bsp_diving_workers_->size(); + + while (!diving_heap_.empty()) { + auto& worker = (*bsp_diving_workers_)[worker_idx]; + + // Skip workers that already have enough nodes + if ((int)worker.dive_queue_size() >= target_nodes_per_worker) { + worker_idx = (worker_idx + 1) % num_workers; + // Check if all workers are full + bool all_full = true; + for (auto& w : *bsp_diving_workers_) { + if ((int)w.dive_queue_size() < target_nodes_per_worker) { + all_full = false; + break; + } + } + if (all_full) break; + continue; + } + + auto entry = diving_heap_.pop(); + if (entry.has_value()) { worker.enqueue_dive_node(entry.value().node); } + + worker_idx = (worker_idx + 1) % num_workers; + } + + // Any remaining nodes in diving_heap_ can be discarded - originals remain in + // backlogs/heap per the speculative diving model. + diving_heap_.clear(); +} + +template +void branch_and_bound_t::merge_diving_solutions() +{ + if (!bsp_diving_workers_) return; + + // Collect all integer solutions from diving workers + std::vector::queued_integer_solution_t*> + all_solutions; + + for (auto& worker : *bsp_diving_workers_) { + for (auto& sol : worker.integer_solutions) { + all_solutions.push_back(&sol); + } + } + + // Sort by objective for deterministic processing + std::sort(all_solutions.begin(), all_solutions.end(), [](const auto* a, const auto* b) { + return a->objective < b->objective; + }); + + // Apply improving solutions to incumbent + f_t current_upper = upper_bound_.load(); + for (const auto* sol : all_solutions) { + if (sol->objective < current_upper) { + f_t user_obj = compute_user_objective(original_lp_, sol->objective); + f_t bsp_lower = compute_bsp_lower_bound(); + f_t user_lower = compute_user_objective(original_lp_, bsp_lower); + i_t nodes_explored = exploration_stats_.nodes_explored.load(); + i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); + + settings_.log.printf("D %10d %10d %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + nodes_explored, + nodes_unexplored, + user_obj, + user_lower, + sol->depth, + nodes_explored > 0 + ? (double)exploration_stats_.total_lp_iters.load() / nodes_explored + : 0.0, + user_mip_gap(user_obj, user_lower).c_str(), + toc(exploration_stats_.start_time)); + + mutex_upper_.lock(); + if (sol->objective < upper_bound_) { + upper_bound_ = sol->objective; + incumbent_.set_incumbent_solution(sol->objective, sol->solution); + current_upper = sol->objective; + } + mutex_upper_.unlock(); + } + } + + // Clear solution queues + for (auto& worker : *bsp_diving_workers_) { + worker.integer_solutions.clear(); + } +} + +template +void branch_and_bound_t::run_diving_worker_loop( + bsp_diving_worker_state_t& worker) +{ + raft::common::nvtx::range scope("BB::diving_worker_loop"); + + while (!bsp_terminated_.load() && !bsp_scheduler_->is_stopped() && + solver_status_ == mip_status_t::UNSET) { + // Process dives from queue until empty or horizon exhausted + auto node_opt = worker.dequeue_dive_node(); + if (node_opt.has_value()) { + dive_from_bsp(worker, std::move(node_opt.value())); + continue; + } + + // Queue empty - wait for next sync point where we'll be assigned new nodes + f_t nowork_start = tic(); + cuopt::sync_result_t result = bsp_scheduler_->wait_for_next_sync(worker.work_context); + worker.total_nowork_time += toc(nowork_start); + if (result == cuopt::sync_result_t::STOPPED) { break; } + } +} + +template +void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t& worker, + mip_node_t starting_node) +{ + raft::common::nvtx::range scope("BB::dive_from_bsp"); + + // Create local search tree for the dive + search_tree_t dive_tree(std::move(starting_node)); + std::deque*> stack; + stack.push_front(&dive_tree.root); + + // Initialize bounds from root node + worker.dive_lower = original_lp_.lower; + worker.dive_upper = original_lp_.upper; + dive_tree.root.get_variable_bounds( + worker.dive_lower, worker.dive_upper, worker.node_presolver->bounds_changed); + + const i_t max_nodes_per_dive = 100; + const i_t max_backtrack_depth = 5; + i_t nodes_this_dive = 0; + worker.recompute_bounds_and_basis = true; + + while (!stack.empty() && solver_status_ == mip_status_t::UNSET && !bsp_terminated_.load() && + nodes_this_dive < max_nodes_per_dive) { + // Check horizon budget + if (worker.work_context.global_work_units_elapsed >= worker.horizon_end) { + bsp_scheduler_->wait_for_next_sync(worker.work_context); + if (bsp_terminated_.load()) break; + } + + mip_node_t* node_ptr = stack.front(); + stack.pop_front(); + + // Prune check using snapshot upper bound + if (node_ptr->lower_bound >= worker.local_upper_bound) { + worker.recompute_bounds_and_basis = true; + continue; + } + + // Setup bounds for this node + std::fill(worker.node_presolver->bounds_changed.begin(), + worker.node_presolver->bounds_changed.end(), + false); + + if (worker.recompute_bounds_and_basis) { + worker.leaf_problem->lower = worker.dive_lower; + worker.leaf_problem->upper = worker.dive_upper; + node_ptr->get_variable_bounds(worker.leaf_problem->lower, + worker.leaf_problem->upper, + worker.node_presolver->bounds_changed); + } else { + node_ptr->update_branched_variable_bounds(worker.leaf_problem->lower, + worker.leaf_problem->upper, + worker.node_presolver->bounds_changed); + } + + // Setup LP settings + simplex_solver_settings_t lp_settings = settings_; + lp_settings.set_log(false); + lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; + lp_settings.inside_mip = 2; + lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); + lp_settings.work_limit = settings_.work_limit; + lp_settings.scale_columns = false; + + // Solve LP relaxation + lp_solution_t leaf_solution(worker.leaf_problem->num_rows, + worker.leaf_problem->num_cols); + std::vector& leaf_vstatus = node_ptr->vstatus; + i_t node_iter = 0; + f_t lp_start_time = tic(); + std::vector leaf_edge_norms = edge_norms_; + + dual::status_t lp_status = dual_phase2_with_advanced_basis(2, + 0, + worker.recompute_bounds_and_basis, + lp_start_time, + *worker.leaf_problem, + lp_settings, + leaf_vstatus, + *worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms, + &worker.work_context); + + ++nodes_this_dive; + ++worker.nodes_explored_this_horizon; + ++worker.total_nodes_explored; + + // Update worker clock from work context + worker.clock = worker.work_context.global_work_units_elapsed; + + if (lp_status == dual::status_t::TIME_LIMIT) { + solver_status_ = mip_status_t::TIME_LIMIT; + break; + } + if (lp_status == dual::status_t::WORK_LIMIT) { break; } + + if (lp_status == dual::status_t::DUAL_UNBOUNDED || lp_status == dual::status_t::CUTOFF) { + worker.recompute_bounds_and_basis = true; + continue; + } + + if (lp_status == dual::status_t::OPTIMAL) { + std::vector leaf_fractional; + fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); + + f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); + node_ptr->lower_bound = leaf_objective; + + if (leaf_fractional.empty()) { + // Integer feasible solution found! + if (leaf_objective < worker.local_upper_bound) { + worker.queue_integer_solution(leaf_objective, leaf_solution.x, node_ptr->depth); + } + worker.recompute_bounds_and_basis = true; + continue; + } + + if (leaf_objective <= worker.local_upper_bound + settings_.absolute_mip_gap_tol / 10) { + // Branch - select variable using diving-type-specific strategy + branch_variable_t branch_result; + + switch (worker.diving_type) { + case bnb_worker_type_t::PSEUDOCOST_DIVING: + branch_result = + worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); + break; + + case bnb_worker_type_t::LINE_SEARCH_DIVING: + if (worker.root_solution) { + logger_t log; + log.log = false; + branch_result = line_search_diving( + leaf_fractional, leaf_solution.x, *worker.root_solution, log); + } else { + branch_result = + worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); + } + break; + + case bnb_worker_type_t::GUIDED_DIVING: + branch_result = worker.guided_variable_selection(leaf_fractional, leaf_solution.x); + break; + + case bnb_worker_type_t::COEFFICIENT_DIVING: { + logger_t log; + log.log = false; + branch_result = coefficient_diving(*worker.leaf_problem, + leaf_fractional, + leaf_solution.x, + var_up_locks_, + var_down_locks_, + log); + } break; + + default: + branch_result = + worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); + break; + } + + i_t branch_var = branch_result.variable; + rounding_direction_t round_dir = branch_result.direction; + + if (branch_var < 0) { + worker.recompute_bounds_and_basis = true; + continue; + } + + // Create children + logger_t log; + log.log = false; + dive_tree.branch(node_ptr, + branch_var, + leaf_solution.x[branch_var], + leaf_vstatus, + *worker.leaf_problem, + log); + + // Add children to stack (preferred direction first) + if (round_dir == rounding_direction_t::UP) { + stack.push_front(node_ptr->get_down_child()); + stack.push_front(node_ptr->get_up_child()); + } else { + stack.push_front(node_ptr->get_up_child()); + stack.push_front(node_ptr->get_down_child()); + } + + // Limit backtracking depth + if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > max_backtrack_depth) { + stack.pop_back(); + } + + worker.recompute_bounds_and_basis = false; + } else { + // Fathomed by bound + worker.recompute_bounds_and_basis = true; + } + } else { + // Numerical or other error + worker.recompute_bounds_and_basis = true; + } + } +} + #ifdef DUAL_SIMPLEX_INSTANTIATE_DOUBLE template class branch_and_bound_t; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index a02a0ef65..eec93c4ff 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -64,18 +64,6 @@ enum class node_solve_info_t { WORK_LIMIT = 6, // The solver reached a deterministic work limit }; -// Indicate the search and variable selection algorithms used by the thread (See [1]). -// -// [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, -// Berlin, 2007. doi: 10.14279/depositonce-1634. -enum class bnb_worker_type_t { - BEST_FIRST = 0, // Best-First + Plunging. - PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) - LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) - GUIDED_DIVING = 3, // Guided diving (9.2.3). If no incumbent is found yet, use pseudocost diving. - COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) -}; - template class bounds_strengthening_t; @@ -345,6 +333,26 @@ class branch_and_bound_t { // BSP sync callback - executed when all workers reach barrier void bsp_sync_callback(int worker_id); + // ============================================================================ + // BSP Diving methods + // ============================================================================ + + // Run diving worker loop + void run_diving_worker_loop(bsp_diving_worker_state_t& worker); + + // Perform a deterministic dive from the given starting node + void dive_from_bsp(bsp_diving_worker_state_t& worker, + mip_node_t starting_node); + + // Populate diving heap from BFS worker backlogs at sync + void populate_diving_heap_at_sync(); + + // Assign starting nodes to diving workers from diving heap + void assign_diving_nodes(); + + // Collect and merge diving solutions at sync + void merge_diving_solutions(); + private: // BSP state std::unique_ptr> bsp_workers_; @@ -379,6 +387,26 @@ class branch_and_bound_t { bsp_debug_settings_t bsp_debug_settings_; bsp_debug_logger_t bsp_debug_logger_; + // ============================================================================ + // BSP Diving state + // ============================================================================ + + // Diving worker pool + std::unique_ptr> bsp_diving_workers_; + + // Diving heap - nodes available for diving, sorted by objective estimate + struct diving_entry_t { + mip_node_t* node; + f_t score; // objective_estimate for diving priority + }; + struct diving_score_comp { + bool operator()(const diving_entry_t& a, const diving_entry_t& b) const + { + return a.score > b.score; // Min-heap by score (lower is better) + } + }; + heap_t diving_heap_; + public: // Accessor for GPU heuristics to get the current BSP horizon double get_current_bsp_horizon() const { return bsp_current_horizon_; } diff --git a/cpp/src/dual_simplex/diving_heuristics.hpp b/cpp/src/dual_simplex/diving_heuristics.hpp index 3c6d77c04..81dd9abf7 100644 --- a/cpp/src/dual_simplex/diving_heuristics.hpp +++ b/cpp/src/dual_simplex/diving_heuristics.hpp @@ -14,6 +14,18 @@ namespace cuopt::linear_programming::dual_simplex { +// Indicate the search and variable selection algorithms used by the worker (See [1]). +// +// [1] T. Achterberg, "Constraint Integer Programming," PhD, Technischen Universität Berlin, +// Berlin, 2007. doi: 10.14279/depositonce-1634. +enum class bnb_worker_type_t { + BEST_FIRST = 0, // Best-First + Plunging. + PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) + LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) + GUIDED_DIVING = 3, // Guided diving (9.2.3). If no incumbent is found yet, use pseudocost diving. + COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) +}; + template struct branch_variable_t { i_t variable; diff --git a/cpp/src/dual_simplex/node_queue.hpp b/cpp/src/dual_simplex/node_queue.hpp index 28072795a..d5463a8b6 100644 --- a/cpp/src/dual_simplex/node_queue.hpp +++ b/cpp/src/dual_simplex/node_queue.hpp @@ -59,6 +59,9 @@ class heap_t { void clear() { buffer.clear(); } bool empty() const { return buffer.empty(); } + // Read-only access to underlying buffer for iteration without modification + const std::vector& data() const { return buffer; } + private: std::vector buffer; Comp comp; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 7bd959ef4..fcb395968 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -191,11 +191,11 @@ solution_t mip_solver_t::run_solver() i_t num_threads = branch_and_bound_settings.num_threads; i_t num_bfs_workers = std::max(1, num_threads / 4); i_t num_diving_workers = std::max(1, num_threads - num_bfs_workers); - // deterministic mode: use BSP coordinator with multiple workers, no diving + // deterministic mode: use BSP coordinator which internally allocates workers + // The BSP coordinator splits num_bfs_workers 50/50 between BFS and deterministic diving if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - // BSP mode can use multiple workers deterministically num_bfs_workers = std::max(1, num_threads); - num_diving_workers = 0; // No diving in deterministic mode + num_diving_workers = 0; // Opportunistic diving disabled; BSP diving handled internally } branch_and_bound_settings.num_bfs_workers = num_bfs_workers; branch_and_bound_settings.diving_settings.num_diving_workers = num_diving_workers; From bfb6611ce06a631e3a90baed51c12a6d9ed7b4d4 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 18 Jan 2026 19:28:30 +0000 Subject: [PATCH 207/366] separate time limit and work unit parameters --- .../linear_programming/cuopt/run_mip.cpp | 14 ++++- cpp/src/dual_simplex/branch_and_bound.cpp | 54 +++++++++++++------ cpp/src/dual_simplex/phase2.cpp | 12 ++--- cpp/src/mip/solver.cu | 24 ++++++--- cpp/src/utilities/work_unit_scheduler.cpp | 16 +++++- cpp/src/utilities/work_unit_scheduler.hpp | 3 ++ 6 files changed, 91 insertions(+), 32 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 87dd93c25..28feab130 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -148,6 +148,7 @@ int run_single_file(std::string file_path, bool write_log_file, bool log_to_console, double time_limit, + double work_limit, bool deterministic) { const raft::handle_t handle_{}; @@ -199,6 +200,7 @@ int run_single_file(std::string file_path, } settings.time_limit = time_limit; + settings.work_limit = work_limit; settings.heuristics_only = heuristics_only; settings.num_cpu_threads = num_cpu_threads; settings.log_to_console = log_to_console; @@ -259,6 +261,7 @@ void run_single_file_mp(std::string file_path, bool write_log_file, bool log_to_console, double time_limit, + double work_limit, bool deterministic) { std::cout << "running file " << file_path << " on gpu : " << device << std::endl; @@ -275,6 +278,7 @@ void run_single_file_mp(std::string file_path, write_log_file, log_to_console, time_limit, + work_limit, deterministic); // this is a bad design to communicate the result but better than adding complexity of IPC or // pipes @@ -345,7 +349,12 @@ int main(int argc, char* argv[]) .default_value(std::string("t")); program.add_argument("--time-limit") - .help("time limit") + .help("time limit in seconds") + .scan<'g', double>() + .default_value(std::numeric_limits::infinity()); + + program.add_argument("--work-limit") + .help("work unit limit (for deterministic mode)") .scan<'g', double>() .default_value(std::numeric_limits::infinity()); @@ -377,6 +386,7 @@ int main(int argc, char* argv[]) std::string run_dir_arg = program.get("--run-dir"); bool run_dir = run_dir_arg[0] == 't'; double time_limit = program.get("--time-limit"); + double work_limit = program.get("--work-limit"); bool run_selected = program.get("--run-selected")[0] == 't'; int n_gpus = program.get("--n-gpus"); @@ -480,6 +490,7 @@ int main(int argc, char* argv[]) write_log_file, log_to_console, time_limit, + work_limit, deterministic); } else if (sys_pid < 0) { std::cerr << "Fork failed!" << std::endl; @@ -521,6 +532,7 @@ int main(int argc, char* argv[]) write_log_file, log_to_console, time_limit, + work_limit, deterministic); } diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 54153863b..2659bfc62 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2018,6 +2018,14 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& // The scheduler handles synchronization at horizon boundaries via record_work() while (!bsp_terminated_.load() && !bsp_scheduler_->is_stopped() && solver_status_ == mip_status_t::UNSET) { + // Check time limit directly - don't wait for sync if time is up + if (toc(exploration_stats_.start_time) > settings_.time_limit) { + solver_status_ = mip_status_t::TIME_LIMIT; + bsp_terminated_.store(true); + bsp_scheduler_->stop(); // Wake up workers waiting at barrier + break; + } + if (worker.has_work()) { mip_node_t* node = worker.dequeue_node(); if (node == nullptr) { continue; } @@ -2048,19 +2056,12 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& worker.last_solved_node = node; // Handle result + worker.current_node = nullptr; if (status == node_solve_info_t::TIME_LIMIT || status == node_solve_info_t::WORK_LIMIT) { - worker.current_node = nullptr; - // Don't set solver_status_ or bsp_terminated_ here - that would cause a race - // where other workers see the flag and exit before reaching the sync point. - // The sync callback will check the work/time limit and set termination flags - // when all workers are safely at the barrier. - bsp_scheduler_->wait_for_next_sync(worker.work_context); - break; - } else { - // Node completed successfully - worker.current_node = nullptr; + // Time/work limit hit - the loop head will detect this and terminate properly continue; } + // Node completed successfully - continue to next iteration } // No work available - advance to next sync point to participate in barrier @@ -2388,13 +2389,17 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.node_presolver->bounds_changed); } + // Check if time limit already exceeded - return immediately if so + double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); + if (remaining_time <= 0) { return node_solve_info_t::TIME_LIMIT; } + // Bounds strengthening simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); // Use worker-local upper bound for LP cutoff (deterministic) lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; lp_settings.inside_mip = 2; - lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); + lp_settings.time_limit = remaining_time; // Work limit: use GLOBAL work limit // The check in dual_phase2 compares global_work_units_elapsed against settings.work_limit // so work_limit must be the GLOBAL limit, not remaining budget @@ -3355,6 +3360,14 @@ void branch_and_bound_t::run_diving_worker_loop( while (!bsp_terminated_.load() && !bsp_scheduler_->is_stopped() && solver_status_ == mip_status_t::UNSET) { + // Check time limit directly - don't wait for sync if time is up + if (toc(exploration_stats_.start_time) > settings_.time_limit) { + solver_status_ = mip_status_t::TIME_LIMIT; + bsp_terminated_.store(true); + bsp_scheduler_->stop(); // Wake up workers waiting at barrier + break; + } + // Process dives from queue until empty or horizon exhausted auto node_opt = worker.dequeue_dive_node(); if (node_opt.has_value()) { @@ -3394,6 +3407,14 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t settings_.time_limit) { + solver_status_ = mip_status_t::TIME_LIMIT; + bsp_terminated_.store(true); + bsp_scheduler_->stop(); // Wake up workers waiting at barrier + break; + } + // Check horizon budget if (worker.work_context.global_work_units_elapsed >= worker.horizon_end) { bsp_scheduler_->wait_for_next_sync(worker.work_context); @@ -3426,12 +3447,16 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tbounds_changed); } + // Check if time limit already exceeded - just break, outer loop will handle termination + double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); + if (remaining_time <= 0) { break; } + // Setup LP settings simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; lp_settings.inside_mip = 2; - lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); + lp_settings.time_limit = remaining_time; lp_settings.work_limit = settings_.work_limit; lp_settings.scale_columns = false; @@ -3465,11 +3490,10 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t(m) * 100.0; const bool use_transpose = delta_y_nz_percentage <= 30.0; { - // raft::common::nvtx::range scope_compute_delta_z("DualSimplex::delta_z"); + raft::common::nvtx::range scope_compute_delta_z("DualSimplex::delta_z"); if (use_transpose) { sparse_delta_z++; phase2::compute_delta_z(A_transpose, @@ -2733,7 +2733,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, const bool harris_ratio = settings.use_harris_ratio; const bool bound_flip_ratio = settings.use_bound_flip_ratio; { - // raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); + raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); if (harris_ratio) { f_t max_step_length = phase2::first_stage_harris(lp, vstatus, nonbasic_list, sol.z, delta_z); @@ -2992,7 +2992,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, scaled_delta_xB_sparse.clear(); rhs_sparse.from_csc_column(lp.A, entering_index); { - // raft::common::nvtx::range scope_ftran("DualSimplex::ftran"); + raft::common::nvtx::range scope_ftran("DualSimplex::ftran"); if (phase2::compute_delta_x(lp, ft, entering_index, @@ -3139,7 +3139,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); // Refactor or update the basis factorization { - // raft::common::nvtx::range scope_update("DualSimplex::basis_update"); + raft::common::nvtx::range scope_update("DualSimplex::basis_update"); bool should_refactor = ft.num_updates() > settings.refactor_frequency; if (!should_refactor) { i_t recommend_refactor = ft.update(utilde_sparse, UTsol_sparse, basic_leaving_index); diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index fcb395968..d109d44bd 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -23,6 +23,7 @@ #include #include +#include #include #include #include @@ -167,20 +168,27 @@ solution_t mip_solver_t::run_solver() branch_and_bound_solution.resize(branch_and_bound_problem.num_cols); // Fill in the settings for branch and bound - branch_and_bound_settings.time_limit = timer_.remaining_time(); - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - branch_and_bound_settings.time_limit = std::numeric_limits::infinity(); - } + // Time limit applies in both modes + branch_and_bound_settings.time_limit = timer_.remaining_time(); branch_and_bound_settings.print_presolve_stats = false; branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; branch_and_bound_settings.deterministic = context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC; - branch_and_bound_settings.work_limit = - context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC - ? context.settings.time_limit - : std::numeric_limits::infinity(); + + // Work limit: use user-specified work_limit, with backward compatibility + // (if work_limit is infinity in deterministic mode and time_limit is finite, + // fall back to time_limit as work units for backward compatibility) + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + if (std::isinf(context.settings.work_limit) && !std::isinf(context.settings.time_limit)) { + branch_and_bound_settings.work_limit = context.settings.time_limit; + } else { + branch_and_bound_settings.work_limit = context.settings.work_limit; + } + } else { + branch_and_bound_settings.work_limit = std::numeric_limits::infinity(); + } if (context.settings.num_cpu_threads < 0) { branch_and_bound_settings.num_threads = omp_get_max_threads() - 1; diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp index 786118349..9c14a3a1e 100644 --- a/cpp/src/utilities/work_unit_scheduler.cpp +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -84,6 +84,12 @@ void work_unit_scheduler_t::set_sync_callback(sync_callback_t callback) bool work_unit_scheduler_t::is_stopped() const { return stopped_.load(); } +void work_unit_scheduler_t::stop() +{ + stopped_.store(true); + cv_.notify_all(); +} + sync_result_t work_unit_scheduler_t::wait_for_next_sync(work_limit_context_t& ctx) { if (stopped_.load()) { return sync_result_t::STOPPED; } @@ -148,8 +154,14 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double } cv_.notify_all(); } else { - cv_.wait(lock, [&] { return barrier_generation_ != my_generation; }); + cv_.wait(lock, [&] { return barrier_generation_ != my_generation || stopped_.load(); }); if (verbose) { CUOPT_LOG_DEBUG("[%s] Woke up from first wait", ctx.name.c_str()); } + if (stopped_.load()) { + // Decrement barrier count before returning to avoid leaving others stuck + contexts_at_barrier_--; + cv_.notify_all(); + return; + } } size_t my_exit_generation = exit_generation_; @@ -176,7 +188,7 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double ctx.name.c_str(), contexts_at_barrier_); } - cv_.wait(lock, [&] { return exit_generation_ != my_exit_generation; }); + cv_.wait(lock, [&] { return exit_generation_ != my_exit_generation || stopped_.load(); }); if (verbose) { CUOPT_LOG_DEBUG("[%s] Woke up from second wait", ctx.name.c_str()); } } diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index fb6d76502..fef361f6e 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -55,6 +55,9 @@ class work_unit_scheduler_t { void set_sync_callback(sync_callback_t callback); bool is_stopped() const; + // Stop the scheduler immediately and wake up all waiting workers + void stop(); + // Wait for next sync point (for idle workers with no work) sync_result_t wait_for_next_sync(work_limit_context_t& ctx); From 2f673e78c156e8aa3d01061af16362024edddc76 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 18 Jan 2026 20:34:12 +0000 Subject: [PATCH 208/366] fix issue on presolved to optimality instances --- cpp/src/mip/diversity/diversity_manager.cu | 1 + cpp/src/mip/problem/problem_helpers.cuh | 3 +++ 2 files changed, 4 insertions(+) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 0f52b1f79..8833ad023 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -311,6 +311,7 @@ solution_t diversity_manager_t::run_solver() const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) disable_heuristics_env = "1"; // to cursor: DO NOT REMOVE! intended debugging line! + disable_heuristics_env = "1"; if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { CUOPT_LOG_INFO("GPU heuristics disabled via CUOPT_DISABLE_GPU_HEURISTICS=1"); // Initialize population minimally and wait for B&B to finish diff --git a/cpp/src/mip/problem/problem_helpers.cuh b/cpp/src/mip/problem/problem_helpers.cuh index a317d47a3..00ea274e4 100644 --- a/cpp/src/mip/problem/problem_helpers.cuh +++ b/cpp/src/mip/problem/problem_helpers.cuh @@ -400,6 +400,9 @@ static void csrsort_cusparse(rmm::device_uvector& values, i_t cols, const raft::handle_t* handle_ptr) { + // skip if the matrix is empty + if (values.size() == 0) { return; } + auto stream = offsets.stream(); cusparseHandle_t handle; cusparseCreate(&handle); From cfbb99b0305ce623a736f47aa988d36ac2cb8d67 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 18 Jan 2026 20:37:10 +0000 Subject: [PATCH 209/366] restore gpu heurs --- cpp/src/mip/diversity/diversity_manager.cu | 1 - 1 file changed, 1 deletion(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 8833ad023..0f52b1f79 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -311,7 +311,6 @@ solution_t diversity_manager_t::run_solver() const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) disable_heuristics_env = "1"; // to cursor: DO NOT REMOVE! intended debugging line! - disable_heuristics_env = "1"; if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { CUOPT_LOG_INFO("GPU heuristics disabled via CUOPT_DISABLE_GPU_HEURISTICS=1"); // Initialize population minimally and wait for B&B to finish From 01747f4d61610f1b0c65e91f562e2b80a67dedf0 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 19 Jan 2026 10:58:38 +0000 Subject: [PATCH 210/366] fix BSP b&b getting starved too early --- cpp/src/dual_simplex/branch_and_bound.cpp | 80 +++++++---------------- cpp/src/dual_simplex/phase2.cpp | 38 +++++------ 2 files changed, 41 insertions(+), 77 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 2659bfc62..ec1955a14 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1710,7 +1710,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut // Compute final lower bound f_t lower_bound; if (bsp_mode_enabled_) { - // In BSP mode, compute lower bound from all sources (heap + worker queues) + // In BSP mode, compute lower bound from worker queues only (no global heap) lower_bound = compute_bsp_lower_bound(); // If no unexplored nodes remain and we have an incumbent, lower bound = upper bound if (lower_bound == std::numeric_limits::infinity() && incumbent_.has_incumbent) { @@ -1807,18 +1807,17 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_torigin_worker_id = -1; // Pre-BSP marker search_tree_.root.get_down_child()->creation_seq = 0; search_tree_.root.get_up_child()->origin_worker_id = -1; search_tree_.root.get_up_child()->creation_seq = 1; - heap_.push(search_tree_.root.get_down_child()); - heap_.push(search_tree_.root.get_up_child()); - - constexpr i_t target_queue_size = 5; // Target nodes per worker - - // Initial distribution: fill worker queues once at the start - refill_worker_queues(target_queue_size); + // Distribute root children directly to BFS workers (no global heap in BSP mode) + (*bsp_workers_)[0].enqueue_node_with_identity(search_tree_.root.get_down_child()); + (*bsp_workers_)[0].track_node_assigned(); + (*bsp_workers_)[1 % num_bfs_workers].enqueue_node_with_identity(search_tree_.root.get_up_child()); + (*bsp_workers_)[1 % num_bfs_workers].track_node_assigned(); BSP_DEBUG_FLUSH_ASSIGN_TRACE(bsp_debug_settings_, bsp_debug_logger_); // Set sync callback - executed when all workers arrive at barrier @@ -1902,7 +1901,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t 0) ? (100.0 * sync_time / total_time) : 0.0; settings_.log.printf(" %6d | %7d | %8d | %6d | %7d | %6d | %8d | %7.3fs | %4.1f%% | %5.2fs\n", worker.worker_id, @@ -2061,7 +2060,8 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& // Time/work limit hit - the loop head will detect this and terminate properly continue; } - // Node completed successfully - continue to next iteration + // Node completed successfully - loop back to process children + continue; } // No work available - advance to next sync point to participate in barrier @@ -2227,11 +2227,9 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) should_terminate = true; } - // No more work (for both BFS and diving) + // No more work (for both BFS and diving) - all nodes live in worker local structures bool diving_has_work = bsp_diving_workers_ && bsp_diving_workers_->any_has_work(); - if (heap_.size() == 0 && !bsp_workers_->any_has_work() && !diving_has_work) { - should_terminate = true; - } + if (!bsp_workers_->any_has_work() && !diving_has_work) { should_terminate = true; } // Time limit if (toc(exploration_stats_.start_time) > settings_.time_limit) { @@ -3086,21 +3084,7 @@ void branch_and_bound_t::balance_worker_loads() worker.plunge_stack.clear(); } - // Also pull nodes from global heap if workers need work - mutex_heap_.lock(); - f_t upper_bound = upper_bound_.load(); - while (!heap_.empty() && all_nodes.size() < num_workers * 5) { - mip_node_t* node = heap_.top(); - heap_.pop(); - if (node->lower_bound < upper_bound) { - all_nodes.push_back(node); - } else { - search_tree_.update(node, node_status_t::FATHOMED); - --exploration_stats_.nodes_unexplored; - } - } - mutex_heap_.unlock(); - + // In BSP mode, all nodes live in worker local structures - no global heap if (all_nodes.empty()) return; // Sort by BSP identity for deterministic distribution @@ -3146,16 +3130,12 @@ void branch_and_bound_t::balance_worker_loads() template f_t branch_and_bound_t::compute_bsp_lower_bound() { - // Compute accurate lower bound from all BSP sources - // Called during sync phase (single-threaded), so no locking needed for worker queues + // Compute lower bound from BFS worker local structures only + // In BSP mode, all nodes live in worker queues - no global heap + // Called during sync phase (single-threaded), so no locking needed const f_t inf = std::numeric_limits::infinity(); f_t lower_bound = inf; - // Check global heap (may have nodes not yet distributed) - mutex_heap_.lock(); - if (heap_.size() > 0) { lower_bound = std::min(heap_.top()->lower_bound, lower_bound); } - mutex_heap_.unlock(); - // Check all BFS worker queues for (const auto& worker : *bsp_workers_) { // Check paused node (current_node) @@ -3174,7 +3154,7 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() } } - // Note: diving worker queues contain copies of nodes that remain in backlogs/heap + // Note: diving worker queues contain copies of nodes that remain in backlogs // (speculative diving model), so no need to check them separately. return lower_bound; @@ -3197,11 +3177,10 @@ void branch_and_bound_t::populate_diving_heap_at_sync() const int target_total = num_diving * target_nodes_per_worker; f_t upper_bound = upper_bound_.load(); - // Collect candidate nodes from backlogs with their scores. - // Following the opportunistic diving model: we DON'T remove nodes from their - // original locations. Diving is speculative - original nodes stay in backlogs/heap - // for BFS processing and correct lower bound computation. Diving workers get - // copies via detach_copy() in enqueue_dive_node(). + // Collect candidate nodes from BFS worker backlogs only (no global heap in BSP mode). + // Following the speculative diving model: we DON'T remove nodes from backlogs. + // Original nodes stay in backlogs for BFS processing and correct lower bound computation. + // Diving workers get copies via detach_copy() in enqueue_dive_node(). std::vector*, f_t>> candidates; for (auto& worker : *bsp_workers_) { @@ -3214,21 +3193,6 @@ void branch_and_bound_t::populate_diving_heap_at_sync() } } - // If backlogs don't have enough nodes, also consider global heap nodes. - // Use data() to iterate without popping - nodes stay in heap. - if ((int)candidates.size() < target_total) { - mutex_heap_.lock(); - for (auto* node : heap_.data()) { - if (node->lower_bound < upper_bound) { - f_t score = node->objective_estimate; - if (!std::isfinite(score)) { score = node->lower_bound; } - candidates.push_back({node, score}); - if ((int)candidates.size() >= target_total) break; - } - } - mutex_heap_.unlock(); - } - if (candidates.empty()) return; // Sort candidates by score (lower is better for diving - closer to optimum) @@ -3243,7 +3207,7 @@ void branch_and_bound_t::populate_diving_heap_at_sync() diving_heap_.push({candidates[i].first, candidates[i].second}); } - // Original nodes stay in backlogs/heap - no removal needed. + // Original nodes stay in backlogs - no removal needed. // Diving workers will get copies via detach_copy() in assign_diving_nodes(). } diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index a4facf8ff..ae3c987a9 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -421,7 +421,7 @@ void compute_reduced_costs(const std::vector& objective, const std::vector& nonbasic_list, std::vector& z) { - raft::common::nvtx::range scope("DualSimplex::compute_reduced_costs"); + // raft::common::nvtx::range scope("DualSimplex::compute_reduced_costs"); const i_t m = A.m; const i_t n = A.n; @@ -456,7 +456,7 @@ void compute_primal_variables(const basis_update_mpf_t& ft, std::vector& x, ins_vector& xB_workspace) { - raft::common::nvtx::range scope("DualSimplex::compute_primal_variables"); + // raft::common::nvtx::range scope("DualSimplex::compute_primal_variables"); const i_t m = A.m; const i_t n = A.n; std::vector rhs = lp_rhs; @@ -523,7 +523,7 @@ void compute_dual_residual(const csc_matrix_t& A, const std::vector& z, std::vector& dual_residual) { - raft::common::nvtx::range scope("DualSimplex::compute_dual_residual"); + // raft::common::nvtx::range scope("DualSimplex::compute_dual_residual"); dual_residual = z; const i_t n = A.n; @@ -680,7 +680,7 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, ins_vector& infeasibility_indices, f_t& primal_inf) { - raft::common::nvtx::range scope("DualSimplex::compute_initial_primal_infeasibilities"); + // raft::common::nvtx::range scope("DualSimplex::compute_initial_primal_infeasibilities"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; squared_infeasibilities.resize(n); @@ -1972,7 +1972,7 @@ void set_primal_variables_on_bounds(const lp_problem_t& lp, std::vector& vstatus, std::vector& x) { - raft::common::nvtx::range scope("DualSimplex::set_primal_variables_on_bounds"); + // raft::common::nvtx::range scope("DualSimplex::set_primal_variables_on_bounds"); const i_t n = lp.num_cols; for (i_t j = 0; j < n; ++j) { // We set z_j = 0 for basic variables @@ -2235,7 +2235,7 @@ dual::status_t dual_phase2(i_t phase, i_t& iter, std::vector& delta_y_steepest_edge) { - raft::common::nvtx::range scope("DualSimplex::phase2"); + // raft::common::nvtx::range scope("DualSimplex::phase2"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; std::vector basic_list(m); @@ -2274,7 +2274,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector& delta_y_steepest_edge, work_limit_context_t* work_unit_context) { - raft::common::nvtx::range scope("DualSimplex::phase2_advanced"); + // raft::common::nvtx::range scope("DualSimplex::phase2_advanced"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; assert(m <= n); @@ -2316,7 +2316,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::bound_info(lp, settings); if (initialize_basis) { - raft::common::nvtx::range init_basis_scope("DualSimplex::init_basis"); + // raft::common::nvtx::range init_basis_scope("DualSimplex::init_basis"); std::vector superbasic_list; nonbasic_list.clear(); nonbasic_list.reserve(n - m); @@ -2402,7 +2402,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif if (delta_y_steepest_edge.size() == 0) { - raft::common::nvtx::range scope("DualSimplex::initialize_steepest_edge_norms"); + // raft::common::nvtx::range scope("DualSimplex::initialize_steepest_edge_norms"); delta_y_steepest_edge.resize(n); if (slack_basis) { phase2::initialize_steepest_edge_norms_from_slack_basis( @@ -2467,7 +2467,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, manifold.add("A_transpose.i", A_transpose.i); manifold.add("A_transpose.x", A_transpose.x); { - raft::common::nvtx::range scope("DualSimplex::transpose_A"); + // raft::common::nvtx::range scope("DualSimplex::transpose_A"); lp.A.transpose(A_transpose); } f_t obj = compute_objective(lp, sol.x); @@ -2551,7 +2551,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // Helper to compute scaled work units for a given number of iterations cuopt::work_unit_predictor_t work_predictor{}; auto predict_work_units = [&](i_t num_iters) -> f_t { - raft::common::nvtx::range scope("DualSimplex::predict_work_units"); + // raft::common::nvtx::range scope("DualSimplex::predict_work_units"); std::map features_map; features_map["m"] = static_cast(features.num_rows); features_map["n"] = static_cast(features.num_cols); @@ -2605,7 +2605,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, }); while (iter < iter_limit) { - raft::common::nvtx::range scope_main("DualSimplex::phase2_main_loop"); + // raft::common::nvtx::range scope_main("DualSimplex::phase2_main_loop"); // Pricing i_t direction = 0; i_t basic_leaving_index = -1; @@ -2613,7 +2613,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, f_t max_val; timers.start_timer(); { - raft::common::nvtx::range scope_pricing("DualSimplex::pricing"); + // raft::common::nvtx::range scope_pricing("DualSimplex::pricing"); if (settings.use_steepest_edge_pricing) { leaving_index = phase2::steepest_edge_pricing_with_infeasibilities(lp, settings, @@ -2658,7 +2658,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_y_sparse.clear(); UTsol_sparse.clear(); { - raft::common::nvtx::range scope_btran("DualSimplex::btran"); + // raft::common::nvtx::range scope_btran("DualSimplex::btran"); phase2::compute_delta_y(ft, basic_leaving_index, direction, delta_y_sparse, UTsol_sparse); } timers.btran_time += timers.stop_timer(); @@ -2688,7 +2688,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_y_nz_percentage = delta_y_nz0 / static_cast(m) * 100.0; const bool use_transpose = delta_y_nz_percentage <= 30.0; { - raft::common::nvtx::range scope_compute_delta_z("DualSimplex::delta_z"); + // raft::common::nvtx::range scope_compute_delta_z("DualSimplex::delta_z"); if (use_transpose) { sparse_delta_z++; phase2::compute_delta_z(A_transpose, @@ -2733,7 +2733,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, const bool harris_ratio = settings.use_harris_ratio; const bool bound_flip_ratio = settings.use_bound_flip_ratio; { - raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); + // raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); if (harris_ratio) { f_t max_step_length = phase2::first_stage_harris(lp, vstatus, nonbasic_list, sol.z, delta_z); @@ -2992,7 +2992,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, scaled_delta_xB_sparse.clear(); rhs_sparse.from_csc_column(lp.A, entering_index); { - raft::common::nvtx::range scope_ftran("DualSimplex::ftran"); + // raft::common::nvtx::range scope_ftran("DualSimplex::ftran"); if (phase2::compute_delta_x(lp, ft, entering_index, @@ -3139,7 +3139,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); // Refactor or update the basis factorization { - raft::common::nvtx::range scope_update("DualSimplex::basis_update"); + // raft::common::nvtx::range scope_update("DualSimplex::basis_update"); bool should_refactor = ft.num_updates() > settings.refactor_frequency; if (!should_refactor) { i_t recommend_refactor = ft.update(utilde_sparse, UTsol_sparse, basic_leaving_index); @@ -3153,7 +3153,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 6); #endif if (should_refactor) { - raft::common::nvtx::range scope_refactor("DualSimplex::refactorization"); + // raft::common::nvtx::range scope_refactor("DualSimplex::refactorization"); num_refactors++; bool should_recompute_x = false; if (ft.refactor_basis( From e68915418551446e2044368e6ec60a75052f57ac Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 19 Jan 2026 14:57:14 +0000 Subject: [PATCH 211/366] same diving ratio as base solver --- cpp/src/dual_simplex/branch_and_bound.cpp | 4 ++-- cpp/src/dual_simplex/triangle_solve.hpp | 6 ++++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index ec1955a14..8675c3990 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1739,9 +1739,9 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t& L, VectorF& x) i_t col_end = L.col_start[j + 1]; if (x[j] != 0.0) { x[j] /= L.x[col_start]; + auto x_j = x[j]; for (i_t p = col_start + 1; p < col_end; ++p) { - x[L.i[p]] -= L.x[p] * x[j]; + x[L.i[p]] -= L.x[p] * x_j; } } } @@ -73,8 +74,9 @@ i_t upper_triangular_solve(const csc_matrix_t& U, VectorF& x) const i_t col_end = U.col_start[j + 1] - 1; if (x[j] != 0.0) { x[j] /= U.x[col_end]; + auto x_j = x[j]; for (i_t p = col_start; p < col_end; ++p) { - x[U.i[p]] -= U.x[p] * x[j]; + x[U.i[p]] -= U.x[p] * x_j; } } } From 14eb9a7d4179c44856612c184bc782e86a9cfbff Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 19 Jan 2026 16:29:22 +0000 Subject: [PATCH 212/366] cleanup work --- cpp/src/dual_simplex/bb_event.hpp | 117 +++---- cpp/src/dual_simplex/bb_worker_state.hpp | 52 ++- cpp/src/dual_simplex/branch_and_bound.cpp | 321 ++++-------------- cpp/src/dual_simplex/branch_and_bound.hpp | 6 - cpp/src/dual_simplex/bsp_debug.hpp | 8 - cpp/src/dual_simplex/diving_heuristics.hpp | 12 - .../dual_simplex/dual_simplex_features.hpp | 54 +-- cpp/src/dual_simplex/mip_node.hpp | 43 +-- cpp/src/mip/solver.cu | 17 +- cpp/src/utilities/work_unit_scheduler.hpp | 30 ++ 10 files changed, 221 insertions(+), 439 deletions(-) diff --git a/cpp/src/dual_simplex/bb_event.hpp b/cpp/src/dual_simplex/bb_event.hpp index 280863f6a..1a41c5324 100644 --- a/cpp/src/dual_simplex/bb_event.hpp +++ b/cpp/src/dual_simplex/bb_event.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -15,18 +15,17 @@ namespace cuopt::linear_programming::dual_simplex { -// Event types generated by B&B workers during BSP execution +// Event types generated by B&B workers during deterministic execution enum class bb_event_type_t : int8_t { - NODE_BRANCHED = 0, // Node was solved and branched into two children - NODE_FATHOMED = 1, // Node was fathomed (cutoff or infeasible) - NODE_INTEGER = 2, // Node has an integer feasible solution - NODE_PAUSED = 3, // Node processing paused at horizon boundary - NODE_INFEASIBLE = 4, // Node LP relaxation is infeasible - NODE_NUMERICAL = 5, // Numerical issue encountered - HEURISTIC_SOLUTION = 6 // Solution received from heuristics + NODE_BRANCHED = 0, + NODE_FATHOMED = 1, + NODE_INTEGER = 2, + NODE_PAUSED = 3, + NODE_INFEASIBLE = 4, + NODE_NUMERICAL = 5, + HEURISTIC_SOLUTION = 6 }; -// Payload for NODE_BRANCHED events template struct branched_payload_t { i_t down_child_id; @@ -36,42 +35,35 @@ struct branched_payload_t { f_t branch_value; }; -// Payload for NODE_INTEGER events (integer feasible solution found) template struct integer_solution_payload_t { f_t objective_value; - // Note: the full solution is stored separately, not in the event }; -// Payload for NODE_FATHOMED events template struct fathomed_payload_t { - f_t lower_bound; // Bound at which node was fathomed + f_t lower_bound; }; -// Payload for NODE_PAUSED events template struct paused_payload_t { - f_t accumulated_vt; // How much virtual time spent on this node so far + f_t accumulated_vt; }; -// Payload for HEURISTIC_SOLUTION events template struct heuristic_solution_payload_t { f_t objective_value; - size_t solution_index; // Index into a solution storage vector + size_t solution_index; }; -// A single event generated during BSP execution template struct bb_event_t { bb_event_type_t type; - double vt_timestamp; // Virtual time when this event occurred - int worker_id; // Which worker generated this event - i_t node_id; // Node that generated this event - int event_sequence; // Sequence number within worker for tie-breaking + double vt_timestamp; + int worker_id; + i_t node_id; + int event_sequence; - // Payload union - only one is valid based on type union { branched_payload_t branched; integer_solution_payload_t integer_solution; @@ -80,7 +72,6 @@ struct bb_event_t { heuristic_solution_payload_t heuristic; } payload; - // Default constructor bb_event_t() : type(bb_event_type_t::NODE_FATHOMED), vt_timestamp(0.0), @@ -91,15 +82,12 @@ struct bb_event_t { payload.fathomed = {0.0}; } - // Comparison for deterministic sorting: (vt_timestamp, worker_id, event_sequence) bool operator<(const bb_event_t& other) const { return std::tie(vt_timestamp, worker_id, event_sequence) < std::tie(other.vt_timestamp, other.worker_id, other.event_sequence); } - // Factory methods for creating events - static bb_event_t make_branched(double vt, int worker, i_t node, @@ -111,11 +99,11 @@ struct bb_event_t { f_t branch_val) { bb_event_t e; - e.type = bb_event_type_t::NODE_BRANCHED; - e.vt_timestamp = vt; - e.worker_id = worker; - e.node_id = node; - e.event_sequence = seq; + e.type = bb_event_type_t::NODE_BRANCHED; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; e.payload.branched = {down_id, up_id, lower_bound, branch_var, branch_val}; return e; } @@ -123,11 +111,11 @@ struct bb_event_t { static bb_event_t make_integer_solution(double vt, int worker, i_t node, int seq, f_t objective) { bb_event_t e; - e.type = bb_event_type_t::NODE_INTEGER; - e.vt_timestamp = vt; - e.worker_id = worker; - e.node_id = node; - e.event_sequence = seq; + e.type = bb_event_type_t::NODE_INTEGER; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; e.payload.integer_solution = {objective}; return e; } @@ -135,11 +123,11 @@ struct bb_event_t { static bb_event_t make_fathomed(double vt, int worker, i_t node, int seq, f_t lower_bound) { bb_event_t e; - e.type = bb_event_type_t::NODE_FATHOMED; - e.vt_timestamp = vt; - e.worker_id = worker; - e.node_id = node; - e.event_sequence = seq; + e.type = bb_event_type_t::NODE_FATHOMED; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; e.payload.fathomed = {lower_bound}; return e; } @@ -147,11 +135,11 @@ struct bb_event_t { static bb_event_t make_infeasible(double vt, int worker, i_t node, int seq) { bb_event_t e; - e.type = bb_event_type_t::NODE_INFEASIBLE; - e.vt_timestamp = vt; - e.worker_id = worker; - e.node_id = node; - e.event_sequence = seq; + e.type = bb_event_type_t::NODE_INFEASIBLE; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; e.payload.fathomed = {std::numeric_limits::infinity()}; return e; } @@ -171,33 +159,29 @@ struct bb_event_t { static bb_event_t make_numerical(double vt, int worker, i_t node, int seq) { bb_event_t e; - e.type = bb_event_type_t::NODE_NUMERICAL; - e.vt_timestamp = vt; - e.worker_id = worker; - e.node_id = node; - e.event_sequence = seq; + e.type = bb_event_type_t::NODE_NUMERICAL; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = node; + e.event_sequence = seq; e.payload.fathomed = {std::numeric_limits::infinity()}; return e; } - static bb_event_t make_heuristic_solution(double vt, - int worker, - int seq, - f_t objective, - size_t sol_idx) + static bb_event_t make_heuristic_solution( + double vt, int worker, int seq, f_t objective, size_t sol_idx) { bb_event_t e; - e.type = bb_event_type_t::HEURISTIC_SOLUTION; - e.vt_timestamp = vt; - e.worker_id = worker; - e.node_id = -1; // Not associated with a B&B node - e.event_sequence = seq; + e.type = bb_event_type_t::HEURISTIC_SOLUTION; + e.vt_timestamp = vt; + e.worker_id = worker; + e.node_id = -1; + e.event_sequence = seq; e.payload.heuristic = {objective, sol_idx}; return e; } }; -// Batch of events from a single horizon period template struct bb_event_batch_t { std::vector> events; @@ -221,8 +205,3 @@ struct bb_event_batch_t { }; } // namespace cuopt::linear_programming::dual_simplex - - - - - diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index cf43ba80a..58f6b4677 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -27,6 +27,19 @@ namespace cuopt::linear_programming::dual_simplex { +// Indicate the search and variable selection algorithms used by each thread +// in B&B (See [1]). +// +// [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, +// Berlin, 2007. doi: 10.14279/depositonce-1634. +enum class bnb_worker_type_t { + BEST_FIRST = 0, // Best-First + Plunging. + PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) + LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) + GUIDED_DIVING = 3, // Guided diving (9.2.3). If no incumbent is found yet, use pseudocost diving. + COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) +}; + // Queued pseudo-cost update for BSP determinism // Updates are collected during horizon, then applied in deterministic order at sync template @@ -202,6 +215,24 @@ struct bb_worker_state_t { pseudo_cost_updates.clear(); } + // Update snapshots from global state at horizon boundary + void set_snapshots(f_t global_upper_bound, + const std::vector& pc_sum_up, + const std::vector& pc_sum_down, + const std::vector& pc_num_up, + const std::vector& pc_num_down, + double new_horizon_start, + double new_horizon_end) + { + local_upper_bound = global_upper_bound; + pc_sum_up_snapshot = pc_sum_up; + pc_sum_down_snapshot = pc_sum_down; + pc_num_up_snapshot = pc_num_up; + pc_num_down_snapshot = pc_num_down; + horizon_start = new_horizon_start; + horizon_end = new_horizon_end; + } + // Queue a pseudo-cost update for global sync AND apply it to local snapshot immediately // This allows within-horizon learning while maintaining determinism: // - Local snapshot updates are sequential within each worker (deterministic) @@ -480,17 +511,6 @@ struct bb_worker_state_t { events.add(std::move(event)); } - // Pause current node processing at horizon boundary - void pause_current_node(mip_node_t* node, double accumulated_vt) - { - node->accumulated_vt = accumulated_vt; - node->bsp_state = bsp_node_state_t::PAUSED; - current_node = node; - - record_event( - bb_event_t::make_paused(clock, worker_id, node->node_id, 0, accumulated_vt)); - } - // Record node branching event void record_branched( mip_node_t* node, i_t down_child_id, i_t up_child_id, i_t branch_var, f_t branch_val) @@ -700,19 +720,25 @@ struct bsp_diving_worker_state_t { recompute_bounds_and_basis = true; } - void set_snapshots(const std::vector& pc_sum_up, + void set_snapshots(f_t global_upper_bound, + const std::vector& pc_sum_up, const std::vector& pc_sum_down, const std::vector& pc_num_up, const std::vector& pc_num_down, const std::vector& incumbent, - const std::vector* root_sol) + const std::vector* root_sol, + double new_horizon_start, + double new_horizon_end) { + local_upper_bound = global_upper_bound; pc_sum_up_snapshot = pc_sum_up; pc_sum_down_snapshot = pc_sum_down; pc_num_up_snapshot = pc_num_up; pc_num_down_snapshot = pc_num_down; incumbent_snapshot = incumbent; root_solution = root_sol; + horizon_start = new_horizon_start; + horizon_end = new_horizon_end; } void enqueue_dive_node(mip_node_t* node) { dive_queue.push_back(node->detach_copy()); } diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 8675c3990..cd95b7aef 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1657,7 +1657,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut active_subtrees_ = 0; is_running = true; lower_bound_ceiling_ = inf; - work_unit_context_.deterministic = settings_.deterministic; should_report_ = true; settings_.log.printf( @@ -1666,8 +1665,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut // Choose between BSP coordinator (deterministic) and opportunistic exploration if (settings_.deterministic && settings_.num_bfs_workers > 0) { - // Use deterministic BSP coordinator for parallel execution - settings_.log.printf("Using BSP coordinator for deterministic parallel B&B\n"); run_bsp_coordinator(Arow_); } else { // Use traditional opportunistic parallel exploration @@ -1712,7 +1709,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut if (bsp_mode_enabled_) { // In BSP mode, compute lower bound from worker queues only (no global heap) lower_bound = compute_bsp_lower_bound(); - // If no unexplored nodes remain and we have an incumbent, lower bound = upper bound if (lower_bound == std::numeric_limits::infinity() && incumbent_.has_incumbent) { lower_bound = upper_bound_.load(); } @@ -1729,7 +1725,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } // ============================================================================ -// BSP (Bulk Synchronous Parallel) Implementation +// BSP (Bulk Synchronous Parallel) Deterministic implementation // ============================================================================ template @@ -1739,10 +1735,8 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t(bsp_horizon_step_); - // Register all BFS worker contexts with the scheduler + scoped_context_registrations_t context_registrations(*bsp_scheduler_); for (auto& worker : *bsp_workers_) { - bsp_scheduler_->register_context(worker.work_context); + context_registrations.add(worker.work_context); } - - // Register all diving worker contexts with the scheduler if (bsp_diving_workers_) { for (auto& worker : *bsp_diving_workers_) { - bsp_scheduler_->register_context(worker.work_context); + context_registrations.add(worker.work_context); } } - // Initialize debug logger bsp_debug_logger_.set_settings(bsp_debug_settings_); bsp_debug_logger_.set_num_workers(num_bfs_workers); bsp_debug_logger_.set_horizon_step(bsp_horizon_step_); @@ -1805,15 +1795,12 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_torigin_worker_id = -1; // Pre-BSP marker search_tree_.root.get_down_child()->creation_seq = 0; search_tree_.root.get_up_child()->origin_worker_id = -1; search_tree_.root.get_up_child()->creation_seq = 1; - // Distribute root children directly to BFS workers (no global heap in BSP mode) (*bsp_workers_)[0].enqueue_node_with_identity(search_tree_.root.get_down_child()); (*bsp_workers_)[0].track_node_assigned(); (*bsp_workers_)[1 % num_bfs_workers].enqueue_node_with_identity(search_tree_.root.get_up_child()); @@ -1827,32 +1814,31 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t incumbent_snapshot; if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } for (auto& worker : *bsp_diving_workers_) { - worker.set_snapshots(pc_.pseudo_cost_sum_up, + worker.set_snapshots(upper_bound_.load(), + pc_.pseudo_cost_sum_up, pc_.pseudo_cost_sum_down, pc_.pseudo_cost_num_up, pc_.pseudo_cost_num_down, incumbent_snapshot, - &root_relax_soln_.x); - worker.local_upper_bound = upper_bound_.load(); - worker.horizon_start = 0.0; - worker.horizon_end = bsp_horizon_step_; + &root_relax_soln_.x, + 0.0, + bsp_horizon_step_); } } @@ -1879,16 +1865,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tderegister_context(worker.work_context); - } - if (bsp_diving_workers_) { - for (auto& worker : *bsp_diving_workers_) { - bsp_scheduler_->deregister_context(worker.work_context); - } - } - // Print per-worker statistics settings_.log.printf("\n"); settings_.log.printf("BSP BFS Worker Statistics:\n"); @@ -2077,57 +2053,40 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& template void branch_and_bound_t::bsp_sync_callback(int worker_id) { - // This callback is executed during the barrier by worker 0 - // All other workers are blocked in record_work() at this point raft::common::nvtx::range scope("BB::bsp_sync_callback"); ++bsp_horizon_number_; double horizon_start = bsp_current_horizon_ - bsp_horizon_step_; double horizon_end = bsp_current_horizon_; - // Debug: Log horizon start + work_unit_context_.global_work_units_elapsed = horizon_end; + BSP_DEBUG_LOG_HORIZON_START( bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_start, horizon_end); - // Aggregate worker work into global context - work_unit_context_.global_work_units_elapsed = horizon_end; - - // Collect and sort all events deterministically bb_event_batch_t all_events = bsp_workers_->collect_and_sort_events(); - // Debug: Log sync phase BSP_DEBUG_LOG_SYNC_PHASE_START( bsp_debug_settings_, bsp_debug_logger_, horizon_end, all_events.size()); - // Process history and sync (update incumbent, pseudo-costs, etc.) process_history_and_sync(all_events); - // Debug: Log sync end BSP_DEBUG_LOG_SYNC_PHASE_END(bsp_debug_settings_, bsp_debug_logger_, horizon_end); - // Prune paused nodes that are now dominated by new incumbent prune_worker_nodes_vs_incumbent(); - // === Diving worker sync operations === - // 1. Merge diving solutions found this horizon merge_diving_solutions(); - // 2. Populate diving heap from BFS backlogs BEFORE load balancing clears them populate_diving_heap_at_sync(); - // 3. Assign new starting nodes to diving workers assign_diving_nodes(); - // Balance worker loads if significant imbalance detected - // Note: This must happen AFTER diving heap population since it clears backlogs balance_worker_loads(); BSP_DEBUG_FLUSH_ASSIGN_TRACE(bsp_debug_settings_, bsp_debug_logger_); - // Debug: Log horizon end and emit state BSP_DEBUG_LOG_HORIZON_END( bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end); - // Compute and log determinism fingerprint hash (always computed for determinism verification) uint32_t state_hash = 0; { std::vector state_data; @@ -2139,14 +2098,12 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) state_data.push_back(static_cast(lb * 1000000)); for (auto& worker : *bsp_workers_) { - if (worker.current_node != nullptr) { - state_data.push_back(worker.current_node->get_bsp_identity_hash()); - } + if (worker.current_node != nullptr) { state_data.push_back(worker.current_node->get_hash()); } for (auto* node : worker.plunge_stack) { - state_data.push_back(node->get_bsp_identity_hash()); + state_data.push_back(node->get_hash()); } for (auto* node : worker.backlog) { - state_data.push_back(node->get_bsp_identity_hash()); + state_data.push_back(node->get_hash()); } } @@ -2187,13 +2144,13 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) // Update worker snapshots for next horizon for (auto& worker : *bsp_workers_) { - worker.local_upper_bound = upper_bound_.load(); - worker.pc_sum_up_snapshot = pc_.pseudo_cost_sum_up; - worker.pc_sum_down_snapshot = pc_.pseudo_cost_sum_down; - worker.pc_num_up_snapshot = pc_.pseudo_cost_num_up; - worker.pc_num_down_snapshot = pc_.pseudo_cost_num_down; - worker.horizon_start = horizon_end; - worker.horizon_end = bsp_current_horizon_; + worker.set_snapshots(upper_bound_.load(), + pc_.pseudo_cost_sum_up, + pc_.pseudo_cost_sum_down, + pc_.pseudo_cost_num_up, + pc_.pseudo_cost_num_down, + horizon_end, + bsp_current_horizon_); } // Update diving worker snapshots for next horizon @@ -2202,15 +2159,15 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } for (auto& worker : *bsp_diving_workers_) { - worker.set_snapshots(pc_.pseudo_cost_sum_up, + worker.set_snapshots(upper_bound_.load(), + pc_.pseudo_cost_sum_up, pc_.pseudo_cost_sum_down, pc_.pseudo_cost_num_up, pc_.pseudo_cost_num_down, incumbent_snapshot, - &root_relax_soln_.x); - worker.local_upper_bound = upper_bound_.load(); - worker.horizon_start = horizon_end; - worker.horizon_end = bsp_current_horizon_; + &root_relax_soln_.x, + horizon_end, + bsp_current_horizon_); } } @@ -2227,20 +2184,15 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) should_terminate = true; } - // No more work (for both BFS and diving) - all nodes live in worker local structures bool diving_has_work = bsp_diving_workers_ && bsp_diving_workers_->any_has_work(); if (!bsp_workers_->any_has_work() && !diving_has_work) { should_terminate = true; } - // Time limit if (toc(exploration_stats_.start_time) > settings_.time_limit) { solver_status_ = mip_status_t::TIME_LIMIT; should_terminate = true; } - // Work limit (use small tolerance for floating-point comparison since horizon is accumulated) - constexpr double work_limit_tolerance = 1e-9; - if (settings_.deterministic && - work_unit_context_.global_work_units_elapsed + work_limit_tolerance >= settings_.work_limit) { + if (work_unit_context_.global_work_units_elapsed >= settings_.work_limit) { solver_status_ = mip_status_t::WORK_LIMIT; should_terminate = true; } @@ -2272,8 +2224,6 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) state_hash, idle_workers.empty() ? "" : " ", idle_workers.c_str()); - - // Note: No need to re-queue callback - sync callback is called at every sync point automatically } template @@ -2295,29 +2245,21 @@ void branch_and_bound_t::run_worker_until_horizon(bb_worker_state_tparent == worker.last_solved_node); worker.recompute_bounds_and_basis = !is_child; - // Solve the node (this records events) node_solve_info_t status = solve_node_bsp(worker, node, search_tree, current_horizon); + worker.last_solved_node = node; - // Track last solved node for warm-start detection - worker.last_solved_node = node; - - // Handle result if (status == node_solve_info_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; break; } else if (status == node_solve_info_t::WORK_LIMIT) { - // Node paused at horizon - already handled in solve_node_bsp + solver_status_ = mip_status_t::WORK_LIMIT; break; } } @@ -2331,8 +2273,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t { raft::common::nvtx::range scope("BB::solve_node_bsp"); - // Validate vstatus has correct BASIC count before processing - // This helps diagnose heap overflow bugs where vstatus has too many BASIC entries { const i_t expected_basic_count = original_lp_.num_rows; i_t actual_basic_count = 0; @@ -2355,11 +2295,9 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } } - // Track work units at start (from work_context, which simplex solver updates) double work_units_at_start = worker.work_context.global_work_units_elapsed; double clock_at_start = worker.clock; - // Debug: Log solve start (pass origin_worker_id as identifier) double work_limit = worker.horizon_end - worker.clock; BSP_DEBUG_LOG_SOLVE_START(bsp_debug_settings_, bsp_debug_logger_, @@ -2368,9 +2306,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t node_ptr->node_id, node_ptr->origin_worker_id, work_limit, - false); // resumed flag no longer used + false); - // Setup leaf problem bounds std::fill(worker.node_presolver->bounds_changed.begin(), worker.node_presolver->bounds_changed.end(), false); @@ -2387,21 +2324,16 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.node_presolver->bounds_changed); } - // Check if time limit already exceeded - return immediately if so double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); if (remaining_time <= 0) { return node_solve_info_t::TIME_LIMIT; } // Bounds strengthening simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); - // Use worker-local upper bound for LP cutoff (deterministic) - lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; - lp_settings.inside_mip = 2; - lp_settings.time_limit = remaining_time; - // Work limit: use GLOBAL work limit - // The check in dual_phase2 compares global_work_units_elapsed against settings.work_limit - // so work_limit must be the GLOBAL limit, not remaining budget - lp_settings.work_limit = settings_.work_limit; + + lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; + lp_settings.inside_mip = 2; + lp_settings.time_limit = remaining_time; lp_settings.scale_columns = false; bool feasible = true; @@ -2431,8 +2363,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; - // Debug: Log LP input for determinism analysis (enabled via CUOPT_BSP_DEBUG_TRACE=1, - // log_level>=2) + // Debug: Log LP input for determinism analysis if (bsp_debug_settings_.any_enabled()) { uint64_t path_hash = node_ptr->compute_path_hash(); // Compute vstatus hash @@ -2478,8 +2409,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t leaf_edge_norms, &worker.work_context); - // Debug: Log LP output for determinism analysis (enabled via CUOPT_BSP_DEBUG_TRACE=1, - // log_level>=2) if (bsp_debug_settings_.any_enabled()) { uint64_t path_hash = node_ptr->compute_path_hash(); // Compute solution hash @@ -2534,30 +2463,9 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } } - // Update worker clock with work performed - // The simplex solver recorded work to work_context.global_work_units_elapsed - // Compute the delta and advance the worker clock (but don't double-record to work_context) double work_performed = worker.work_context.global_work_units_elapsed - work_units_at_start; worker.clock += work_performed; worker.work_units_this_horizon += work_performed; - // Note: don't call advance_clock() as work_context was already updated by simplex solver - - // Check if we hit the GLOBAL work limit - // In scheduler-based mode, horizon sync happens via record_work() blocking inside the simplex. - // The simplex runs continuously through multiple horizons, so when it completes with a status - // other than WORK_LIMIT, we should process the result normally. - if (lp_status == dual::status_t::WORK_LIMIT) { - // Global work limit reached - solver will terminate - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "WORK_LIMIT", - node_ptr->lower_bound); - return node_solve_info_t::WORK_LIMIT; - } exploration_stats_.total_lp_solve_time += toc(lp_start_time); exploration_stats_.total_lp_iters += node_iter; @@ -2568,7 +2476,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t if (lp_status == dual::status_t::DUAL_UNBOUNDED) { node_ptr->lower_bound = std::numeric_limits::infinity(); - // Record event and debug logs BEFORE search_tree.update() which may delete the node worker.record_infeasible(node_ptr); worker.track_node_infeasible(); worker.track_node_processed(); @@ -2589,10 +2496,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t return node_solve_info_t::NO_CHILDREN; } else if (lp_status == dual::status_t::CUTOFF) { - // Use worker-local upper bound for determinism node_ptr->lower_bound = worker.local_upper_bound; - // Record event and debug logs BEFORE search_tree.update() which may delete the node worker.record_fathomed(node_ptr, node_ptr->lower_bound); worker.track_node_pruned(); worker.track_node_processed(); @@ -2625,9 +2530,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t node_ptr->lower_bound = leaf_objective; // Queue pseudo-cost update for deterministic application at sync - // (Replicates pc_.update_pseudo_costs logic but defers application to sync phase) - // Note: Original code also sets lower_bound before this, so change_in_obj = 0. - // This matches the original behavior exactly. if (node_ptr->branch_var >= 0) { const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN @@ -2644,10 +2546,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t if (leaf_objective < worker.local_upper_bound) { worker.local_upper_bound = leaf_objective; worker.integer_solutions.push_back({leaf_objective, leaf_solution.x, node_ptr->depth}); - // Note: Logging deferred to sync phase for deterministic output } - // Record event and debug logs BEFORE search_tree.update() which may delete the node worker.record_integer_solution(node_ptr, leaf_objective); worker.track_integer_solution(); worker.track_node_processed(); @@ -2673,7 +2573,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } else if (leaf_objective <= worker.local_upper_bound + settings_.absolute_mip_gap_tol / 10) { // Branch - use worker-local upper bound for deterministic pruning decision - // Use pseudo-cost snapshot for deterministic variable selection + // Use pseudo-cost snapshot for variable selection const i_t branch_var = worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); @@ -2691,7 +2591,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.track_node_branched(); worker.track_node_processed(); - // Debug: Log solve end (branched) and branched event BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, bsp_debug_logger_, worker.clock, @@ -2711,9 +2610,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t exploration_stats_.nodes_unexplored += 2; - // Add children using plunging strategy: - // - If plunge stack has a sibling, it's moved to backlog (plugging) - // - Preferred child goes on top of stack for immediate exploration rounding_direction_t preferred = martin_criteria(leaf_solution.x[branch_var], root_relax_soln_.x[branch_var]); worker.enqueue_children_for_plunge( @@ -2752,7 +2648,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t return node_solve_info_t::TIME_LIMIT; } else { - // Numerical issue - record BEFORE search_tree.update() which may delete the node worker.record_numerical(node_ptr); worker.recompute_bounds_and_basis = true; search_tree.update(node_ptr, node_status_t::NUMERICAL); @@ -2764,11 +2659,6 @@ template void branch_and_bound_t::process_history_and_sync( const bb_event_batch_t& events) { - // With BSP identity tuples (origin_worker_id, creation_seq), we no longer need to assign - // final_ids during sync. Each node gets its identity when created, and it never changes. - // This function now only processes heuristic solutions to update the incumbent. - - // Process repair queue first (for BSP mode) // Infeasible solutions from GPU heuristics are queued for repair; process them now { std::vector> to_repair; @@ -2788,7 +2678,7 @@ void branch_and_bound_t::process_history_and_sync( bool success = repair_solution(edge_norms_, potential_solution, repaired_obj, repaired_solution); if (success) { - // Queue repaired solution with VT = current horizon for deterministic processing + // Queue repaired solution with work unit timestamp (...workstamp?) mutex_heuristic_queue_.lock(); heuristic_solution_queue_.push_back( {std::move(repaired_solution), repaired_obj, bsp_current_horizon_}); @@ -2798,22 +2688,22 @@ void branch_and_bound_t::process_history_and_sync( } } - // Collect queued heuristic solutions (including any newly repaired ones) std::vector heuristic_solutions; mutex_heuristic_queue_.lock(); heuristic_solutions = std::move(heuristic_solution_queue_); heuristic_solution_queue_.clear(); mutex_heuristic_queue_.unlock(); - // Sort heuristic solutions by VT for deterministic processing order + // sort by work unit timestamp, with objective and solution values as tie-breakers std::sort(heuristic_solutions.begin(), heuristic_solutions.end(), [](const queued_heuristic_solution_t& a, const queued_heuristic_solution_t& b) { - return a.vt_timestamp < b.vt_timestamp; + if (a.vt_timestamp != b.vt_timestamp) { return a.vt_timestamp < b.vt_timestamp; } + if (a.objective != b.objective) { return a.objective < b.objective; } + return a.solution < b.solution; // edge-case - lexicographical comparison }); // Merge B&B events and heuristic solutions for unified timeline replay - // Both are sorted by VT, so we can do a merge-style iteration size_t event_idx = 0; size_t heuristic_idx = 0; @@ -2826,7 +2716,7 @@ void branch_and_bound_t::process_history_and_sync( } else if (heuristic_idx >= heuristic_solutions.size()) { process_event = true; } else { - // Both have items - pick the one with smaller VT + // Both have items - pick the one with smaller WT if (events.events[event_idx].vt_timestamp <= heuristic_solutions[heuristic_idx].vt_timestamp) { process_event = true; @@ -2838,26 +2728,12 @@ void branch_and_bound_t::process_history_and_sync( if (process_event) { const auto& event = events.events[event_idx++]; switch (event.type) { - case bb_event_type_t::NODE_INTEGER: { - // Check if this solution beats the incumbent KNOWN AT THAT VIRTUAL TIME - f_t obj = event.payload.integer_solution.objective_value; - mutex_upper_.lock(); - if (obj < upper_bound_) { - // Note: The actual solution was already stored during solve_node_bsp - // Here we just acknowledge the event order - } - mutex_upper_.unlock(); - break; - } - + case bb_event_type_t::NODE_INTEGER: case bb_event_type_t::NODE_BRANCHED: case bb_event_type_t::NODE_FATHOMED: case bb_event_type_t::NODE_INFEASIBLE: case bb_event_type_t::NODE_NUMERICAL: - case bb_event_type_t::HEURISTIC_SOLUTION: - // These events don't need additional processing during replay - // (BSP identity is already assigned at node creation time) - break; + case bb_event_type_t::HEURISTIC_SOLUTION: break; } } @@ -2868,7 +2744,7 @@ void branch_and_bound_t::process_history_and_sync( BSP_DEBUG_LOG_HEURISTIC_RECEIVED( bsp_debug_settings_, bsp_debug_logger_, hsol.vt_timestamp, hsol.objective); - // Process heuristic solution at its correct VT position + // Process heuristic solution at its correct work unit timestamp position f_t new_upper = std::numeric_limits::infinity(); mutex_upper_.lock(); @@ -2883,7 +2759,6 @@ void branch_and_bound_t::process_history_and_sync( } mutex_upper_.unlock(); - // Log after releasing mutex to avoid holding mutex while calling get_lower_bound() if (new_upper < std::numeric_limits::infinity()) { f_t user_obj = compute_user_objective(original_lp_, new_upper); f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); @@ -2901,6 +2776,7 @@ void branch_and_bound_t::process_history_and_sync( // Merge integer solutions from all workers and update global incumbent // Sort by (objective, worker_id) for deterministic winner selection + // lexicographical sort as a fallback struct worker_solution_t { f_t objective; const std::vector* solution; @@ -2914,22 +2790,12 @@ void branch_and_bound_t::process_history_and_sync( } } - // Sort by objective, then worker_id for deterministic tie-breaking - std::sort(all_integer_solutions.begin(), - all_integer_solutions.end(), - [](const worker_solution_t& a, const worker_solution_t& b) { - if (a.objective != b.objective) return a.objective < b.objective; - return a.worker_id < b.worker_id; - }); - - // Apply the best solution to global incumbent and log all improving solutions - // Use compute_bsp_lower_bound() for accurate lower bound in logs f_t bsp_lower = compute_bsp_lower_bound(); f_t current_upper = upper_bound_.load(); for (const auto& sol : all_integer_solutions) { + // improving solution found, log it if (sol.objective < current_upper) { - // Log this improving solution (deterministic: sorted order) f_t user_obj = compute_user_objective(original_lp_, sol.objective); f_t user_lower = compute_user_objective(original_lp_, bsp_lower); i_t nodes_explored = exploration_stats_.nodes_explored.load(); @@ -2965,15 +2831,6 @@ void branch_and_bound_t::process_history_and_sync( } } - // Sort by (vt, worker_id) for deterministic order - std::sort(all_pc_updates.begin(), - all_pc_updates.end(), - [](const pseudo_cost_update_t& a, const pseudo_cost_update_t& b) { - if (a.vt != b.vt) return a.vt < b.vt; - return a.worker_id < b.worker_id; - }); - - // Apply updates in deterministic order for (const auto& upd : all_pc_updates) { if (upd.direction == rounding_direction_t::DOWN) { pc_.pseudo_cost_sum_down[upd.variable] += upd.delta; @@ -2983,8 +2840,6 @@ void branch_and_bound_t::process_history_and_sync( pc_.pseudo_cost_num_up[upd.variable]++; } } - - // No final_id application needed - BSP identity is assigned at node creation time } template @@ -2993,10 +2848,6 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() f_t upper_bound = upper_bound_.load(); for (auto& worker : *bsp_workers_) { - // NOTE: Do NOT prune worker.current_node here! - // The worker thread has local references (node_ptr, leaf_vstatus) to it. - // The worker will handle cutoff checking when it resumes after sync. - // Check nodes in plunge stack - filter in place { std::deque*> surviving; @@ -3033,7 +2884,6 @@ void branch_and_bound_t::balance_worker_loads() const size_t num_workers = bsp_workers_->size(); if (num_workers <= 1) return; - // Flag to force rebalancing every sync (reduces idle time at barriers when work is uniform) constexpr bool force_rebalance_every_sync = true; // Count work for each worker: current_node (if any) + plunge_stack + backlog @@ -3049,8 +2899,6 @@ void branch_and_bound_t::balance_worker_loads() max_work = std::max(max_work, work_counts[w]); min_work = std::min(min_work, work_counts[w]); } - - // Early exit if no work to redistribute if (total_work == 0) return; bool needs_balance; @@ -3058,17 +2906,12 @@ void branch_and_bound_t::balance_worker_loads() // Always rebalance if there's more than one node to distribute needs_balance = (total_work > 1); } else { - // Original logic: only rebalance on significant imbalance needs_balance = (min_work == 0 && max_work >= 2) || (min_work > 0 && max_work > 4 * min_work); } if (!needs_balance) return; // Collect all redistributable nodes from worker queues - // With plunging strategy, we redistribute: - // - All backlog nodes (these were "plugged" siblings) - // - Plunge stack nodes (except we keep them with worker for locality if possible) - // For simplicity, redistribute everything for now std::vector*> all_nodes; for (auto& worker : *bsp_workers_) { // Extract backlog nodes @@ -3084,13 +2927,11 @@ void branch_and_bound_t::balance_worker_loads() worker.plunge_stack.clear(); } - // In BSP mode, all nodes live in worker local structures - no global heap if (all_nodes.empty()) return; // Sort by BSP identity for deterministic distribution // Uses lexicographic order of (origin_worker_id, creation_seq) auto deterministic_less = [](const mip_node_t* a, const mip_node_t* b) { - // Lexicographic comparison of BSP identity tuple if (a->origin_worker_id != b->origin_worker_id) { return a->origin_worker_id < b->origin_worker_id; } @@ -3098,15 +2939,10 @@ void branch_and_bound_t::balance_worker_loads() }; std::sort(all_nodes.begin(), all_nodes.end(), deterministic_less); - // Redistribute round-robin, but skip workers that have a paused current_node - // (they already have work and will resume that node first) + // Redistribute round-robin std::vector worker_order; for (size_t w = 0; w < num_workers; ++w) { - // Prioritize workers without a paused node - if ((*bsp_workers_)[w].current_node == nullptr) { worker_order.push_back(w); } - } - for (size_t w = 0; w < num_workers; ++w) { - if ((*bsp_workers_)[w].current_node != nullptr) { worker_order.push_back(w); } + worker_order.push_back(w); } // Distribute nodes - use enqueue_node_with_identity to preserve existing identity @@ -3115,7 +2951,6 @@ void branch_and_bound_t::balance_worker_loads() (*bsp_workers_)[worker_idx].enqueue_node_with_identity(all_nodes[i]); (*bsp_workers_)[worker_idx].track_node_assigned(); - // Debug: Log redistribution (happens at horizon END, at the sync point) double vt = bsp_current_horizon_; BSP_DEBUG_LOG_NODE_ASSIGNED(bsp_debug_settings_, bsp_debug_logger_, @@ -3131,8 +2966,6 @@ template f_t branch_and_bound_t::compute_bsp_lower_bound() { // Compute lower bound from BFS worker local structures only - // In BSP mode, all nodes live in worker queues - no global heap - // Called during sync phase (single-threaded), so no locking needed const f_t inf = std::numeric_limits::infinity(); f_t lower_bound = inf; @@ -3154,14 +2987,11 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() } } - // Note: diving worker queues contain copies of nodes that remain in backlogs - // (speculative diving model), so no need to check them separately. - return lower_bound; } // ============================================================================ -// BSP Diving Implementation +// BSP Diving // ============================================================================ template @@ -3177,10 +3007,7 @@ void branch_and_bound_t::populate_diving_heap_at_sync() const int target_total = num_diving * target_nodes_per_worker; f_t upper_bound = upper_bound_.load(); - // Collect candidate nodes from BFS worker backlogs only (no global heap in BSP mode). - // Following the speculative diving model: we DON'T remove nodes from backlogs. - // Original nodes stay in backlogs for BFS processing and correct lower bound computation. - // Diving workers get copies via detach_copy() in enqueue_dive_node(). + // Collect candidate nodes from BFS worker backlogs std::vector*, f_t>> candidates; for (auto& worker : *bsp_workers_) { @@ -3200,29 +3027,21 @@ void branch_and_bound_t::populate_diving_heap_at_sync() return a.second < b.second; }); - // Take enough nodes for diving workers' queues int nodes_to_take = std::min(target_total, (int)candidates.size()); for (int i = 0; i < nodes_to_take; ++i) { diving_heap_.push({candidates[i].first, candidates[i].second}); } - - // Original nodes stay in backlogs - no removal needed. - // Diving workers will get copies via detach_copy() in assign_diving_nodes(). } template void branch_and_bound_t::assign_diving_nodes() { if (!bsp_diving_workers_ || bsp_diving_workers_->size() == 0) { - // No diving workers - just clear the diving heap. - // Original nodes remain in backlogs/heap (speculative diving model). diving_heap_.clear(); return; } - // Assign multiple nodes per diving worker from the diving heap. - // Diving workers get copies via detach_copy() in enqueue_dive_node(). constexpr int target_nodes_per_worker = 10; // Round-robin assignment to balance load across workers @@ -3253,8 +3072,6 @@ void branch_and_bound_t::assign_diving_nodes() worker_idx = (worker_idx + 1) % num_workers; } - // Any remaining nodes in diving_heap_ can be discarded - originals remain in - // backlogs/heap per the speculative diving model. diving_heap_.clear(); } @@ -3273,11 +3090,6 @@ void branch_and_bound_t::merge_diving_solutions() } } - // Sort by objective for deterministic processing - std::sort(all_solutions.begin(), all_solutions.end(), [](const auto* a, const auto* b) { - return a->objective < b->objective; - }); - // Apply improving solutions to incumbent f_t current_upper = upper_bound_.load(); for (const auto* sol : all_solutions) { @@ -3361,6 +3173,9 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tbounds_changed.begin(), + worker.node_presolver->bounds_changed.end(), + false); dive_tree.root.get_variable_bounds( worker.dive_lower, worker.dive_upper, worker.node_presolver->bounds_changed); @@ -3411,7 +3226,6 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tbounds_changed); } - // Check if time limit already exceeded - just break, outer loop will handle termination double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); if (remaining_time <= 0) { break; } @@ -3421,7 +3235,6 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t::dive_from_bsp(bsp_diving_worker_state_t& original_problem_; const simplex_solver_settings_t settings_; - // Work unit contexts for each worker - // TODO: only one for now, sequential B&B for now work_limit_context_t work_unit_context_{"B&B"}; // Initial guess. @@ -406,10 +404,6 @@ class branch_and_bound_t { } }; heap_t diving_heap_; - - public: - // Accessor for GPU heuristics to get the current BSP horizon - double get_current_bsp_horizon() const { return bsp_current_horizon_; } }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp index d4c13181d..316907242 100644 --- a/cpp/src/dual_simplex/bsp_debug.hpp +++ b/cpp/src/dual_simplex/bsp_debug.hpp @@ -952,19 +952,11 @@ class bsp_debug_logger_t { break; } - if (node->bsp_state == bsp_node_state_t::PAUSED) { - color = "yellow"; - status_str = "PAUSED"; - } - file << " N" << node->node_id << " [label=\"N" << node->node_id; if (node->has_bsp_identity()) file << " (w" << node->origin_worker_id << ":" << node->creation_seq << ")"; file << "\\nlb=" << std::fixed << std::setprecision(1) << node->lower_bound; file << "\\n" << status_str; - if (node->bsp_state == bsp_node_state_t::PAUSED) { - file << "\\nacc_vt=" << node->accumulated_vt; - } file << "\" style=filled fillcolor=" << color << "];\n"; // Emit edges to children diff --git a/cpp/src/dual_simplex/diving_heuristics.hpp b/cpp/src/dual_simplex/diving_heuristics.hpp index 81dd9abf7..3c6d77c04 100644 --- a/cpp/src/dual_simplex/diving_heuristics.hpp +++ b/cpp/src/dual_simplex/diving_heuristics.hpp @@ -14,18 +14,6 @@ namespace cuopt::linear_programming::dual_simplex { -// Indicate the search and variable selection algorithms used by the worker (See [1]). -// -// [1] T. Achterberg, "Constraint Integer Programming," PhD, Technischen Universität Berlin, -// Berlin, 2007. doi: 10.14279/depositonce-1634. -enum class bnb_worker_type_t { - BEST_FIRST = 0, // Best-First + Plunging. - PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) - LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) - GUIDED_DIVING = 3, // Guided diving (9.2.3). If no incumbent is found yet, use pseudocost diving. - COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) -}; - template struct branch_variable_t { i_t variable; diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 3b1de9d29..0ad68185d 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -116,33 +116,33 @@ struct dual_simplex_features_t { */ void log_features(const simplex_solver_settings_t& settings) const { - printf( - "DS_FEATURES: iter=%d m=%d n=%d nnz=%d density=%.6e avg_nnz_col=%.2f avg_nnz_row=%.2f " - "bounded=%d free=%d fixed=%d phase=%d refact_freq=%d num_refacts=%d num_updates=%d " - "sparse_dz=%d dense_dz=%d bound_flips=%d num_infeas=%d dy_nz_pct=%.2f " - "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", - iteration, - num_rows, - num_cols, - num_nonzeros, - matrix_density, - avg_nnz_per_col, - avg_nnz_per_row, - num_bounded_vars, - num_free_vars, - num_fixed_vars, - phase, - refactor_frequency, - num_refactors, - num_basis_updates, - sparse_delta_z_count, - dense_delta_z_count, - total_bound_flips, - num_infeasibilities, - delta_y_nz_percentage, - byte_loads, - byte_stores, - interval_runtime); + // printf( + // "DS_FEATURES: iter=%d m=%d n=%d nnz=%d density=%.6e avg_nnz_col=%.2f avg_nnz_row=%.2f " + // "bounded=%d free=%d fixed=%d phase=%d refact_freq=%d num_refacts=%d num_updates=%d " + // "sparse_dz=%d dense_dz=%d bound_flips=%d num_infeas=%d dy_nz_pct=%.2f " + // "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", + // iteration, + // num_rows, + // num_cols, + // num_nonzeros, + // matrix_density, + // avg_nnz_per_col, + // avg_nnz_per_row, + // num_bounded_vars, + // num_free_vars, + // num_fixed_vars, + // phase, + // refactor_frequency, + // num_refactors, + // num_basis_updates, + // sparse_delta_z_count, + // dense_delta_z_count, + // total_bound_flips, + // num_infeasibilities, + // delta_y_nz_percentage, + // byte_loads, + // byte_stores, + // interval_runtime); } /** diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index e65fae6a8..0e0b74967 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -29,12 +29,6 @@ enum class node_status_t : int { enum class rounding_direction_t : int8_t { NONE = -1, DOWN = 0, UP = 1 }; -// BSP state for deterministic parallel B&B -enum class bsp_node_state_t : int8_t { - READY = 0, // Node is ready to be processed - PAUSED = 1 // Node processing was paused at horizon boundary -}; - bool inactive_status(node_status_t status); template @@ -245,7 +239,6 @@ class mip_node_t { copy.node_id = node_id; // Copy BSP fields copy.accumulated_vt = accumulated_vt; - copy.bsp_state = bsp_state; copy.origin_worker_id = origin_worker_id; copy.creation_seq = creation_seq; return copy; @@ -268,8 +261,7 @@ class mip_node_t { std::vector vstatus; // BSP fields for deterministic parallel B&B - f_t accumulated_vt{0.0}; // Virtual time spent on this node so far - bsp_node_state_t bsp_state{bsp_node_state_t::READY}; // BSP processing state + f_t accumulated_vt{0.0}; // Virtual time spent on this node so far // Worker-local identification for deterministic BSP ordering: // - origin_worker_id: which worker created this node (-1 for pre-BSP/initial nodes) @@ -284,7 +276,7 @@ class mip_node_t { // Get a 64-bit identity value for hashing (combines worker_id and seq) // Uses origin_worker_id + 1 to handle -1 (pre-BSP nodes) gracefully - uint64_t get_bsp_identity_hash() const + uint64_t get_hash() const { return (static_cast(origin_worker_id + 1) << 32) | static_cast(static_cast(creation_seq)); @@ -307,19 +299,6 @@ class mip_node_t { } return hash; } - - // Get the branching path as a string for debugging - std::string get_path_string() const - { - std::string path; - const mip_node_t* node = this; - while (node != nullptr && node->branch_var >= 0) { - char dir = (node->branch_dir == rounding_direction_t::UP) ? 'U' : 'D'; - path = std::to_string(node->branch_var) + dir + (path.empty() ? "" : "-") + path; - node = node->parent; - } - return path.empty() ? "root" : path; - } }; template @@ -358,26 +337,16 @@ class node_compare_t { bool deterministic_compare(const mip_node_t& a, const mip_node_t& b) const { // non-BSP case - if (!a.has_bsp_identity() && !b.has_bsp_identity()) { return a.depth > b.depth; } + if (!a.has_bsp_identity() || !b.has_bsp_identity()) { + return a.depth > b.depth; + } // If both have BSP identity, use lexicographic comparison of (origin_worker_id, creation_seq) - if (a.has_bsp_identity() && b.has_bsp_identity()) { + else { if (a.origin_worker_id != b.origin_worker_id) { return a.origin_worker_id > b.origin_worker_id; } return a.creation_seq > b.creation_seq; } - - // If only one has BSP identity, prefer the one with identity (already established) - if (a.has_bsp_identity()) { return false; } // a has priority - if (b.has_bsp_identity()) { return true; } // b has priority - - // Neither has BSP identity - use path_hash for deterministic comparison (non-BSP mode) - uint64_t hash_a = a.compute_path_hash(); - uint64_t hash_b = b.compute_path_hash(); - if (hash_a != hash_b) { return hash_a > hash_b; } - - // Ultimate fallback: compare by depth (shouldn't happen with different paths) - return a.depth > b.depth; } }; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index d109d44bd..0e4a4b415 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -196,16 +196,10 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.num_threads = std::max(1, context.settings.num_cpu_threads); } - i_t num_threads = branch_and_bound_settings.num_threads; - i_t num_bfs_workers = std::max(1, num_threads / 4); - i_t num_diving_workers = std::max(1, num_threads - num_bfs_workers); - // deterministic mode: use BSP coordinator which internally allocates workers - // The BSP coordinator splits num_bfs_workers 50/50 between BFS and deterministic diving - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - num_bfs_workers = std::max(1, num_threads); - num_diving_workers = 0; // Opportunistic diving disabled; BSP diving handled internally - } - branch_and_bound_settings.num_bfs_workers = num_bfs_workers; + i_t num_threads = branch_and_bound_settings.num_threads; + i_t num_bfs_workers = std::max(1, num_threads / 4); + i_t num_diving_workers = std::max(1, num_threads - num_bfs_workers); + branch_and_bound_settings.num_bfs_workers = num_bfs_workers; branch_and_bound_settings.diving_settings.num_diving_workers = num_diving_workers; // Set the branch and bound -> primal heuristics callback @@ -250,8 +244,7 @@ solution_t mip_solver_t::run_solver() // TODO context.problem_ptr->branch_and_bound_callback = [bb = branch_and_bound.get()](const std::vector& solution) { - double vt = bb->get_current_bsp_horizon(); - bb->set_new_solution_deterministic(solution, vt); + bb->set_new_solution_deterministic(solution, 0.0); }; } diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index fef361f6e..12b03929c 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -99,4 +99,34 @@ class work_unit_scheduler_t { std::atomic stopped_{false}; }; +// RAII helper for registering multiple contexts with automatic cleanup +class scoped_context_registrations_t { + public: + explicit scoped_context_registrations_t(work_unit_scheduler_t& scheduler) : scheduler_(scheduler) + { + } + + ~scoped_context_registrations_t() + { + for (auto* ctx : contexts_) { + scheduler_.deregister_context(*ctx); + } + } + + void add(work_limit_context_t& ctx) + { + scheduler_.register_context(ctx); + contexts_.push_back(&ctx); + } + + scoped_context_registrations_t(const scoped_context_registrations_t&) = delete; + scoped_context_registrations_t& operator=(const scoped_context_registrations_t&) = delete; + scoped_context_registrations_t(scoped_context_registrations_t&&) = delete; + scoped_context_registrations_t& operator=(scoped_context_registrations_t&&) = delete; + + private: + work_unit_scheduler_t& scheduler_; + std::vector contexts_; +}; + } // namespace cuopt From 7755bd7e28541d9e9fedbda114fd3639f89c406b Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 19 Jan 2026 17:18:56 +0000 Subject: [PATCH 213/366] further cleanup work --- cpp/src/dual_simplex/bb_worker_state.hpp | 30 ++----- cpp/src/dual_simplex/branch_and_bound.cpp | 68 ++++----------- cpp/src/dual_simplex/bsp_debug.hpp | 3 +- cpp/src/dual_simplex/mip_node.hpp | 51 +++-------- cpp/src/dual_simplex/phase2.cpp | 3 - cpp/src/dual_simplex/presolve.cpp | 2 - cpp/src/dual_simplex/primal.cpp | 3 - cpp/src/dual_simplex/pseudo_costs.hpp | 47 +--------- cpp/src/dual_simplex/sparse_matrix.cpp | 99 +++++++--------------- cpp/src/dual_simplex/sparse_matrix.hpp | 26 ++---- cpp/src/dual_simplex/sparse_vector.cpp | 3 +- cpp/src/dual_simplex/sparse_vector.hpp | 3 +- cpp/src/mip/diversity/diversity_manager.cu | 9 +- cpp/src/mip/local_search/local_search.cu | 2 - cpp/src/mip/problem/problem.cu | 10 +-- cpp/src/mip/problem/problem_helpers.cuh | 44 ---------- cpp/src/mip/solver.cu | 20 ++--- cpp/src/mip/solver.cuh | 2 +- cpp/src/mip/solver_context.cuh | 3 +- cpp/src/utilities/version_info.cpp | 1 - 20 files changed, 96 insertions(+), 333 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 58f6b4677..13c34d690 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -324,26 +324,16 @@ struct bb_worker_state_t { // Node enqueueing methods // ========================================================================== - // Add a node to the plunge stack, assigning BSP identity if not already set - // Used for initial node assignment and when starting a new plunge from backlog + // Add a node to the plunge stack, assigning BSP identity void enqueue_node(mip_node_t* node) { - // Assign BSP identity if not already set - if (!node->has_bsp_identity()) { - node->origin_worker_id = worker_id; - node->creation_seq = next_creation_seq++; - } + node->origin_worker_id = worker_id; + node->creation_seq = next_creation_seq++; plunge_stack.push_front(node); } // Add a node that already has BSP identity (from load balancing or initial distribution) - // Goes to plunge stack front for immediate processing - void enqueue_node_with_identity(mip_node_t* node) - { - assert(node->has_bsp_identity() && - "Node must have BSP identity for enqueue_node_with_identity"); - plunge_stack.push_front(node); - } + void enqueue_node_with_identity(mip_node_t* node) { plunge_stack.push_front(node); } // Add children after branching with proper plunging behavior: // 1. If plunge stack has a sibling, move it to backlog (plugging) @@ -361,14 +351,10 @@ struct bb_worker_state_t { } // Assign BSP identity to children - if (!down_child->has_bsp_identity()) { - down_child->origin_worker_id = worker_id; - down_child->creation_seq = next_creation_seq++; - } - if (!up_child->has_bsp_identity()) { - up_child->origin_worker_id = worker_id; - up_child->creation_seq = next_creation_seq++; - } + down_child->origin_worker_id = worker_id; + down_child->creation_seq = next_creation_seq++; + up_child->origin_worker_id = worker_id; + up_child->creation_seq = next_creation_seq++; // Push children - preferred child on top (front) for immediate exploration mip_node_t* first_child; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index cd95b7aef..aa424fe0a 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -19,6 +19,7 @@ #include #include #include +#include #include @@ -2098,22 +2099,18 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) state_data.push_back(static_cast(lb * 1000000)); for (auto& worker : *bsp_workers_) { - if (worker.current_node != nullptr) { state_data.push_back(worker.current_node->get_hash()); } + if (worker.current_node != nullptr) { + state_data.push_back(worker.current_node->get_id_packed()); + } for (auto* node : worker.plunge_stack) { - state_data.push_back(node->get_hash()); + state_data.push_back(node->get_id_packed()); } for (auto* node : worker.backlog) { - state_data.push_back(node->get_hash()); + state_data.push_back(node->get_id_packed()); } } - state_hash = 0x811c9dc5u; // FNV-1a initial value - for (uint64_t val : state_data) { - state_hash ^= static_cast(val & 0xFFFFFFFF); - state_hash *= 0x01000193u; - state_hash ^= static_cast(val >> 32); - state_hash *= 0x01000193u; - } + state_hash = detail::compute_hash(state_data); BSP_DEBUG_LOG_HORIZON_HASH( bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end, state_hash); } @@ -2365,25 +2362,10 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t // Debug: Log LP input for determinism analysis if (bsp_debug_settings_.any_enabled()) { - uint64_t path_hash = node_ptr->compute_path_hash(); - // Compute vstatus hash - uint64_t vstatus_hash = leaf_vstatus.size(); - for (size_t i = 0; i < leaf_vstatus.size(); ++i) { - vstatus_hash ^= (static_cast(leaf_vstatus[i]) << (i % 56)); - vstatus_hash *= 0x100000001b3ULL; - } - // Compute bounds hash - uint64_t bounds_hash = 0; - for (i_t j = 0; j < worker.leaf_problem->num_cols; ++j) { - union { - f_t f; - uint64_t u; - } lb_bits, ub_bits; - lb_bits.f = worker.leaf_problem->lower[j]; - ub_bits.f = worker.leaf_problem->upper[j]; - bounds_hash ^= lb_bits.u + ub_bits.u; - bounds_hash *= 0x100000001b3ULL; - } + uint64_t path_hash = node_ptr->compute_path_hash(); + uint64_t vstatus_hash = detail::compute_hash(leaf_vstatus); + uint64_t bounds_hash = detail::compute_hash(worker.leaf_problem->lower) ^ + detail::compute_hash(worker.leaf_problem->upper); BSP_DEBUG_LOG_LP_INPUT(bsp_debug_settings_, bsp_debug_logger_, worker.worker_id, @@ -2411,27 +2393,11 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t if (bsp_debug_settings_.any_enabled()) { uint64_t path_hash = node_ptr->compute_path_hash(); - // Compute solution hash - uint64_t sol_hash = 0; - for (i_t j = 0; - j < worker.leaf_problem->num_cols && j < static_cast(leaf_solution.x.size()); - ++j) { - union { - f_t f; - uint64_t u; - } val_bits; - val_bits.f = leaf_solution.x[j]; - sol_hash ^= val_bits.u; - sol_hash *= 0x100000001b3ULL; - } - f_t obj = (lp_status == dual::status_t::OPTIMAL) - ? compute_objective(*worker.leaf_problem, leaf_solution.x) - : std::numeric_limits::infinity(); - union { - f_t f; - uint64_t u; - } obj_bits; - obj_bits.f = obj; + uint64_t sol_hash = detail::compute_hash(leaf_solution.x); + f_t obj = (lp_status == dual::status_t::OPTIMAL) + ? compute_objective(*worker.leaf_problem, leaf_solution.x) + : std::numeric_limits::infinity(); + uint64_t obj_hash = detail::compute_hash(obj); BSP_DEBUG_LOG_LP_OUTPUT(bsp_debug_settings_, bsp_debug_logger_, worker.worker_id, @@ -2439,7 +2405,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t path_hash, static_cast(lp_status), node_iter, - obj_bits.u, + obj_hash, sol_hash); } diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp index 316907242..9e92096d3 100644 --- a/cpp/src/dual_simplex/bsp_debug.hpp +++ b/cpp/src/dual_simplex/bsp_debug.hpp @@ -953,8 +953,7 @@ class bsp_debug_logger_t { } file << " N" << node->node_id << " [label=\"N" << node->node_id; - if (node->has_bsp_identity()) - file << " (w" << node->origin_worker_id << ":" << node->creation_seq << ")"; + file << " (w" << node->origin_worker_id << ":" << node->creation_seq << ")"; file << "\\nlb=" << std::fixed << std::setprecision(1) << node->lower_bound; file << "\\n" << status_str; file << "\" style=filled fillcolor=" << color << "];\n"; diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index 0e0b74967..428e79586 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -9,6 +9,7 @@ #include #include +#include #include #include @@ -264,40 +265,29 @@ class mip_node_t { f_t accumulated_vt{0.0}; // Virtual time spent on this node so far // Worker-local identification for deterministic BSP ordering: - // - origin_worker_id: which worker created this node (-1 for pre-BSP/initial nodes) + // - origin_worker_id: which worker created this node // - creation_seq: sequence number within that worker (cumulative across horizons) - // The tuple (origin_worker_id, creation_seq) is unique and stable (never changes after - // assignment) This replaces the old final_id approach which required sync-time assignment + // The tuple (origin_worker_id, creation_seq) is unique and stable int32_t origin_worker_id{-1}; int32_t creation_seq{-1}; - // Check if this node has been assigned a BSP identity - bool has_bsp_identity() const { return origin_worker_id >= -1 && creation_seq >= 0; } - - // Get a 64-bit identity value for hashing (combines worker_id and seq) - // Uses origin_worker_id + 1 to handle -1 (pre-BSP nodes) gracefully - uint64_t get_hash() const + uint64_t get_id_packed() const { return (static_cast(origin_worker_id + 1) << 32) | static_cast(static_cast(creation_seq)); } - // Compute a deterministic path hash based on branching decisions from root - // This uniquely identifies the node regardless of creation order uint64_t compute_path_hash() const { - uint64_t hash = 0; + std::vector path_steps; const mip_node_t* node = this; while (node != nullptr && node->branch_var >= 0) { - // Combine branch_var and branch_dir into hash uint64_t step = static_cast(node->branch_var) << 1; step |= (node->branch_dir == rounding_direction_t::UP) ? 1 : 0; - // FNV-1a style mixing - hash ^= step; - hash *= 0x100000001b3ULL; + path_steps.push_back(step); node = node->parent; } - return hash; + return detail::compute_hash(path_steps); } }; @@ -311,42 +301,23 @@ void remove_fathomed_nodes(std::vector*>& stack) } } -// Comparator for global heap (used in non-BSP mode and for initial distribution in BSP) -// Uses path_hash for deterministic tie-breaking when BSP identity is not available +// Comparator for global heap (used in non-BSP mode) template class node_compare_t { public: // Comparison for priority queue: returns true if 'a' has lower priority than 'b' - // (elements with lower priority are output last from the heap). // Primary: prefer lower bound (best-first search) - // Tie-breaker: use deterministic comparison for reproducibility + // Tie-breaker: prefer deeper nodes bool operator()(const mip_node_t& a, const mip_node_t& b) const { if (a.lower_bound != b.lower_bound) { return a.lower_bound > b.lower_bound; } - return deterministic_compare(a, b); + return a.depth > b.depth; } bool operator()(const mip_node_t* a, const mip_node_t* b) const { if (a->lower_bound != b->lower_bound) { return a->lower_bound > b->lower_bound; } - return deterministic_compare(*a, *b); - } - - private: - // Deterministic comparison using BSP identity tuple or path_hash fallback - bool deterministic_compare(const mip_node_t& a, const mip_node_t& b) const - { - // non-BSP case - if (!a.has_bsp_identity() || !b.has_bsp_identity()) { - return a.depth > b.depth; - } - // If both have BSP identity, use lexicographic comparison of (origin_worker_id, creation_seq) - else { - if (a.origin_worker_id != b.origin_worker_id) { - return a.origin_worker_id > b.origin_worker_id; - } - return a.creation_seq > b.creation_seq; - } + return a->depth > b->depth; } }; diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index ae3c987a9..b19c2c956 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -3287,7 +3287,6 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, return dual::status_t::CONCURRENT_LIMIT; } } - // printf("NODE SOLVE FINISHED\n"); if (iter >= iter_limit) { status = dual::status_t::ITERATION_LIMIT; } if (phase == 2) { @@ -3307,8 +3306,6 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, *settings.concurrent_halt = 1; } } - - // printf("Iterations: %d, time taken: %f\n", iter, toc(start_time)); return status; } diff --git a/cpp/src/dual_simplex/presolve.cpp b/cpp/src/dual_simplex/presolve.cpp index 315f3429f..bbfe18d61 100644 --- a/cpp/src/dual_simplex/presolve.cpp +++ b/cpp/src/dual_simplex/presolve.cpp @@ -13,8 +13,6 @@ #include #include -#include - #include #include #include diff --git a/cpp/src/dual_simplex/primal.cpp b/cpp/src/dual_simplex/primal.cpp index bb3196597..69f15ba18 100644 --- a/cpp/src/dual_simplex/primal.cpp +++ b/cpp/src/dual_simplex/primal.cpp @@ -14,8 +14,6 @@ #include #include -#include - namespace cuopt::linear_programming::dual_simplex { namespace { @@ -256,7 +254,6 @@ primal::status_t primal_phase2(i_t phase, lp_solution_t& sol, i_t& iter) { - raft::common::nvtx::range scope("PrimalSimplex::phase2"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; assert(m <= n); diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index dffe894b3..5cbddfdfd 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -57,56 +57,15 @@ class pseudo_costs_t { void update_pseudo_costs_from_strong_branching(const std::vector& fractional, const std::vector& root_soln); - // Compute a deterministic hash of the pseudo-cost state for divergence detection uint32_t compute_state_hash() const { - uint32_t hash = 0x811c9dc5; // FNV-1a offset basis - auto hash_double = [&hash](double val) { - // Convert to fixed-point representation for exact comparison - int64_t fixed = static_cast(val * 1000000.0); - hash ^= static_cast(fixed & 0xFFFFFFFF); - hash *= 0x01000193; // FNV-1a prime - hash ^= static_cast((fixed >> 32) & 0xFFFFFFFF); - hash *= 0x01000193; - }; - auto hash_int = [&hash](i_t val) { - hash ^= static_cast(val); - hash *= 0x01000193; - }; - - // Hash pseudo-cost sums and counts - for (size_t j = 0; j < pseudo_cost_sum_down.size(); ++j) { - hash_double(pseudo_cost_sum_down[j]); - hash_double(pseudo_cost_sum_up[j]); - hash_int(pseudo_cost_num_down[j]); - hash_int(pseudo_cost_num_up[j]); - } - return hash; + return detail::compute_hash(pseudo_cost_sum_down) ^ detail::compute_hash(pseudo_cost_sum_up) ^ + detail::compute_hash(pseudo_cost_num_down) ^ detail::compute_hash(pseudo_cost_num_up); } - // Compute hash of strong branching results uint32_t compute_strong_branch_hash() const { - uint32_t hash = 0x811c9dc5; - auto hash_double = [&hash](double val) { - if (std::isnan(val)) { - hash ^= 0xDEADBEEF; // Special marker for NaN - } else if (std::isinf(val)) { - hash ^= val > 0 ? 0xCAFEBABE : 0xBADCAFE; // Inf markers - } else { - int64_t fixed = static_cast(val * 1000000.0); - hash ^= static_cast(fixed & 0xFFFFFFFF); - hash *= 0x01000193; - hash ^= static_cast((fixed >> 32) & 0xFFFFFFFF); - } - hash *= 0x01000193; - }; - - for (size_t k = 0; k < strong_branch_down.size(); ++k) { - hash_double(strong_branch_down[k]); - hash_double(strong_branch_up[k]); - } - return hash; + return detail::compute_hash(strong_branch_down) ^ detail::compute_hash(strong_branch_up); } std::vector pseudo_cost_sum_up; diff --git a/cpp/src/dual_simplex/sparse_matrix.cpp b/cpp/src/dual_simplex/sparse_matrix.cpp index 080bed53e..7d510a849 100644 --- a/cpp/src/dual_simplex/sparse_matrix.cpp +++ b/cpp/src/dual_simplex/sparse_matrix.cpp @@ -579,43 +579,8 @@ void csr_matrix_t::check_matrix(std::string matrix_name) const } // x <- x + alpha * A(:, j) -template -void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, std::vector& x) -{ - const i_t col_start = A.col_start[j]; - const i_t col_end = A.col_start[j + 1]; - for (i_t p = col_start; p < col_end; ++p) { - const i_t i = A.i[p]; - const f_t ax = A.x[p]; - x[i] += alpha * ax; - } -} - -// x <- x + alpha * A(:, j) -template -void scatter_dense(const csc_matrix_t& A, - i_t j, - f_t alpha, - std::vector& x, - std::vector& mark, - std::vector& indices) -{ - const i_t col_start = A.col_start[j]; - const i_t col_end = A.col_start[j + 1]; - for (i_t p = col_start; p < col_end; ++p) { - const i_t i = A.i[p]; - const f_t ax = A.x[p]; - x[i] += alpha * ax; - if (!mark[i]) { - mark[i] = 1; - indices.push_back(i); - } - } -} - -// Instrumented vector overload: x <- x + alpha * A(:, j) -template -void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, ins_vector& x) +template +void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, VectorF& x) { const i_t col_start = A.col_start[j]; const i_t col_end = A.col_start[j + 1]; @@ -626,14 +591,10 @@ void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, ins_vector } } -// Instrumented vector overload: x <- x + alpha * A(:, j) with mark/indices tracking -template -void scatter_dense(const csc_matrix_t& A, - i_t j, - f_t alpha, - ins_vector& x, - ins_vector& mark, - ins_vector& indices) +// x <- x + alpha * A(:, j) with mark/indices tracking +template +void scatter_dense( + const csc_matrix_t& A, i_t j, f_t alpha, VectorF& x, VectorI& mark, VectorI& indices) { const i_t col_start = A.col_start[j]; const i_t col_end = A.col_start[j + 1]; @@ -912,29 +873,31 @@ template int scatter(const csc_matrix_t& A, csc_matrix_t& C, int nz); -template void scatter_dense(const csc_matrix_t& A, - int j, - double alpha, - std::vector& x); - -template void scatter_dense(const csc_matrix_t& A, - int j, - double alpha, - std::vector& x, - std::vector& mark, - std::vector& indices); - -template void scatter_dense(const csc_matrix_t& A, - int j, - double alpha, - ins_vector& x); - -template void scatter_dense(const csc_matrix_t& A, - int j, - double alpha, - ins_vector& x, - ins_vector& mark, - ins_vector& indices); +template void scatter_dense>(const csc_matrix_t& A, + int j, + double alpha, + std::vector& x); + +template void scatter_dense, std::vector>( + const csc_matrix_t& A, + int j, + double alpha, + std::vector& x, + std::vector& mark, + std::vector& indices); + +template void scatter_dense>(const csc_matrix_t& A, + int j, + double alpha, + ins_vector& x); + +template void scatter_dense, ins_vector>( + const csc_matrix_t& A, + int j, + double alpha, + ins_vector& x, + ins_vector& mark, + ins_vector& indices); template int multiply(const csc_matrix_t& A, const csc_matrix_t& B, diff --git a/cpp/src/dual_simplex/sparse_matrix.hpp b/cpp/src/dual_simplex/sparse_matrix.hpp index c2d945543..48548a08a 100644 --- a/cpp/src/dual_simplex/sparse_matrix.hpp +++ b/cpp/src/dual_simplex/sparse_matrix.hpp @@ -201,28 +201,12 @@ i_t scatter(const csc_matrix_t& A, i_t nz); // x <- x + alpha * A(:, j) -template -void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, std::vector& x); +template +void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, VectorF& x); -template -void scatter_dense(const csc_matrix_t& A, - i_t j, - f_t alpha, - std::vector& x, - std::vector& mark, - std::vector& indices); - -// Instrumented vector overloads -template -void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, ins_vector& x); - -template -void scatter_dense(const csc_matrix_t& A, - i_t j, - f_t alpha, - ins_vector& x, - ins_vector& mark, - ins_vector& indices); +template +void scatter_dense( + const csc_matrix_t& A, i_t j, f_t alpha, VectorF& x, VectorI& mark, VectorI& indices); // Compute C = A*B where C is m x n, A is m x k, and B = k x n // Do this by computing C(:, j) = A*B(:, j) = sum (i=1 to k) A(:, k)*B(i, j) diff --git a/cpp/src/dual_simplex/sparse_vector.cpp b/cpp/src/dual_simplex/sparse_vector.cpp index 79d323d31..bfcb03774 100644 --- a/cpp/src/dual_simplex/sparse_vector.cpp +++ b/cpp/src/dual_simplex/sparse_vector.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -200,7 +200,6 @@ void sparse_vector_t::sort() for (i_t k = 0; k < nz; ++k) { perm[k] = k; } - // Need to capture the underlying array for the lambda auto& iunsorted = i; std::sort( perm.begin(), perm.end(), [&iunsorted](i_t a, i_t b) { return iunsorted[a] < iunsorted[b]; }); diff --git a/cpp/src/dual_simplex/sparse_vector.hpp b/cpp/src/dual_simplex/sparse_vector.hpp index f3d6769ab..4b61cb8e3 100644 --- a/cpp/src/dual_simplex/sparse_vector.hpp +++ b/cpp/src/dual_simplex/sparse_vector.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -55,7 +55,6 @@ class sparse_vector_t { void negate(); f_t find_coefficient(i_t index) const; - // Clear the sparse vector (removes all entries, keeps capacity) void clear() { i.clear(); diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 0f52b1f79..00efb15b9 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -309,14 +309,15 @@ solution_t diversity_manager_t::run_solver() // Debug: Allow disabling GPU heuristics to test B&B tree determinism in isolation const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) - disable_heuristics_env = "1"; // to cursor: DO NOT REMOVE! intended debugging line! + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + CUOPT_LOG_INFO("Determinism is only supported for the B&B search. Disabling GPU heuristics"); + disable_heuristics_env = "1"; + } if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { CUOPT_LOG_INFO("GPU heuristics disabled via CUOPT_DISABLE_GPU_HEURISTICS=1"); - // Initialize population minimally and wait for B&B to finish population.initialize_population(); population.allocate_solutions(); - // Wait for B&B to signal completion + while (!check_b_b_preemption()) { std::this_thread::sleep_for(std::chrono::milliseconds(100)); } diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 6b9f5ea24..3bd4bac7b 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -59,7 +59,6 @@ static population_t* pop_ptr = nullptr; template void local_search_t::start_cpufj_scratch_threads(population_t& population) { - // CPUFJ is non-deterministic, skip in deterministic mode if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { return; } pop_ptr = &population; @@ -107,7 +106,6 @@ template void local_search_t::start_cpufj_lptopt_scratch_threads( population_t& population) { - // CPUFJ is non-deterministic, skip in deterministic mode if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { return; } pop_ptr = &population; diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 200bd2134..529354417 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1635,11 +1635,11 @@ problem_t problem_t::get_problem_after_fixing_vars( [[maybe_unused]] static int total_calls = 0; total_time_taken += time_taken; total_calls++; - // CUOPT_LOG_DEBUG( - // "Time taken to fix variables: %f milliseconds, average: %f milliseconds total time: %f", - // time_taken, - // total_time_taken / total_calls, - // total_time_taken); + CUOPT_LOG_TRACE( + "Time taken to fix variables: %f milliseconds, average: %f milliseconds total time: %f", + time_taken, + total_time_taken / total_calls, + total_time_taken); // if the fixing is greater than 150, mark this as expensive. // this way we can avoid frequent fixings for this problem constexpr double expensive_time_threshold = 150; diff --git a/cpp/src/mip/problem/problem_helpers.cuh b/cpp/src/mip/problem/problem_helpers.cuh index 00ea274e4..fa8a5000d 100644 --- a/cpp/src/mip/problem/problem_helpers.cuh +++ b/cpp/src/mip/problem/problem_helpers.cuh @@ -23,50 +23,6 @@ #include namespace cuopt::linear_programming::detail { - -template -struct cusparse_data_type { - static_assert(sizeof(T) == 0, "cusparse_data_type mapping not defined for this type"); -}; -template -struct cusparse_index_type { - static_assert(sizeof(T) == 0, "cusparse_index_type mapping not defined for this type"); -}; - -template <> -struct cusparse_data_type<__half> { - static constexpr cudaDataType value = CUDA_R_16F; -}; -template <> -struct cusparse_data_type<__nv_bfloat16> { - static constexpr cudaDataType value = CUDA_R_16BF; -}; -template <> -struct cusparse_data_type { - static constexpr cudaDataType value = CUDA_R_32F; -}; -template <> -struct cusparse_data_type { - static constexpr cudaDataType value = CUDA_R_64F; -}; -template <> -struct cusparse_data_type { - static constexpr cudaDataType value = CUDA_R_8I; -}; -template <> -struct cusparse_data_type { - static constexpr cudaDataType value = CUDA_R_32I; -}; - -template <> -struct cusparse_index_type { - static constexpr cusparseIndexType_t value = CUSPARSE_INDEX_32I; -}; -template <> -struct cusparse_index_type { - static constexpr cusparseIndexType_t value = CUSPARSE_INDEX_64I; -}; - template struct transform_bounds_functor { __device__ thrust::tuple operator()(const thrust::tuple& input) const diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 0e4a4b415..6e04a9fa8 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -177,15 +177,8 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.deterministic = context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC; - // Work limit: use user-specified work_limit, with backward compatibility - // (if work_limit is infinity in deterministic mode and time_limit is finite, - // fall back to time_limit as work units for backward compatibility) if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - if (std::isinf(context.settings.work_limit) && !std::isinf(context.settings.time_limit)) { - branch_and_bound_settings.work_limit = context.settings.time_limit; - } else { - branch_and_bound_settings.work_limit = context.settings.work_limit; - } + branch_and_bound_settings.work_limit = context.settings.work_limit; } else { branch_and_bound_settings.work_limit = std::numeric_limits::infinity(); } @@ -207,13 +200,12 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.heuristic_preemption_callback = std::bind( &branch_and_bound_solution_helper_t::preempt_heuristic_solver, &solution_helper); + branch_and_bound_settings.solution_callback = + std::bind(&branch_and_bound_solution_helper_t::solution_callback, + &solution_helper, + std::placeholders::_1, + std::placeholders::_2); if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { - branch_and_bound_settings.solution_callback = - std::bind(&branch_and_bound_solution_helper_t::solution_callback, - &solution_helper, - std::placeholders::_1, - std::placeholders::_2); - branch_and_bound_settings.set_simplex_solution_callback = std::bind(&branch_and_bound_solution_helper_t::set_simplex_solution, &solution_helper, diff --git a/cpp/src/mip/solver.cuh b/cpp/src/mip/solver.cuh index 262bdd80c..b6d16fa2d 100644 --- a/cpp/src/mip/solver.cuh +++ b/cpp/src/mip/solver.cuh @@ -10,7 +10,7 @@ #include #include #include -#include +#include #pragma once namespace cuopt::linear_programming::detail { diff --git a/cpp/src/mip/solver_context.cuh b/cpp/src/mip/solver_context.cuh index a473e0577..055ad165f 100644 --- a/cpp/src/mip/solver_context.cuh +++ b/cpp/src/mip/solver_context.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -59,7 +59,6 @@ struct mip_solver_context_t { const mip_solver_settings_t settings; pdlp_initial_scaling_strategy_t& scaling; solver_stats_t stats; - // TODO: ensure thread local (or use locks...?) mip_solver_work_unit_predictors_t work_unit_predictors; // Work limit context for tracking work units in deterministic mode (shared across all timers in // GPU heuristic loop) diff --git a/cpp/src/utilities/version_info.cpp b/cpp/src/utilities/version_info.cpp index bfcb02ce1..6b2192afd 100644 --- a/cpp/src/utilities/version_info.cpp +++ b/cpp/src/utilities/version_info.cpp @@ -165,7 +165,6 @@ static double get_available_memory_gb() double get_cpu_max_clock_mhz() { - // Cache the result - CPU max clock doesn't change during execution // thread_local to avoid an unecessary sync inserted by the compiler // due to the standard mandating thread-safe static local variable initialization // the extra work here is minimal. From a63f0326c54047567bfa8298a1681c4a89a4ec22 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 19 Jan 2026 18:12:41 +0000 Subject: [PATCH 214/366] incorporating cpufj into the deterministic framework --- cpp/src/dual_simplex/branch_and_bound.cpp | 19 +- cpp/src/dual_simplex/branch_and_bound.hpp | 8 + cpp/src/mip/diversity/diversity_manager.cu | 19 +- cpp/src/mip/feasibility_jump/fj_cpu.cu | 889 ++++++++++++++++++--- cpp/src/mip/feasibility_jump/fj_cpu.cuh | 136 +++- cpp/src/mip/local_search/local_search.cu | 83 +- cpp/src/mip/local_search/local_search.cuh | 12 +- cpp/src/utilities/producer_sync.hpp | 143 ++++ 8 files changed, 1156 insertions(+), 153 deletions(-) create mode 100644 cpp/src/utilities/producer_sync.hpp diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index aa424fe0a..fce46f886 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2060,6 +2060,10 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) double horizon_start = bsp_current_horizon_ - bsp_horizon_step_; double horizon_end = bsp_current_horizon_; + // Wait for external producers (CPUFJ) to reach horizon_start before processing + // This ensures we don't process B&B events before producers have caught up + producer_sync_.wait_for_producers(horizon_start); + work_unit_context_.global_work_units_elapsed = horizon_end; BSP_DEBUG_LOG_HORIZON_START( @@ -2654,10 +2658,21 @@ void branch_and_bound_t::process_history_and_sync( } } + // Extract heuristic solutions, keeping future solutions for next horizon + // Use bsp_current_horizon_ as the upper bound (horizon_end) std::vector heuristic_solutions; mutex_heuristic_queue_.lock(); - heuristic_solutions = std::move(heuristic_solution_queue_); - heuristic_solution_queue_.clear(); + { + std::vector future_solutions; + for (auto& sol : heuristic_solution_queue_) { + if (sol.vt_timestamp < bsp_current_horizon_) { + heuristic_solutions.push_back(std::move(sol)); + } else { + future_solutions.push_back(std::move(sol)); + } + } + heuristic_solution_queue_ = std::move(future_solutions); + } mutex_heuristic_queue_.unlock(); // sort by work unit timestamp, with objective and solution values as tie-breakers diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 16cd258a3..ba92b11c5 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -23,6 +23,7 @@ #include #include +#include #include #include #include @@ -136,6 +137,9 @@ class branch_and_bound_t { work_limit_context_t& get_work_unit_context() { return work_unit_context_; } + // Get producer sync for external heuristics (e.g., CPUFJ) to register + producer_sync_t& get_producer_sync() { return producer_sync_; } + private: const user_problem_t& original_problem_; const simplex_solver_settings_t settings_; @@ -361,6 +365,10 @@ class branch_and_bound_t { bool bsp_mode_enabled_{false}; // Whether BSP mode is active int bsp_horizon_number_{0}; // Current horizon number (for debugging) + // Producer synchronization for external heuristics (CPUFJ) + // B&B waits for registered producers at each horizon sync + producer_sync_t producer_sync_; + // BSP node heap - priority queue for unexplored nodes (ordered by lower bound) struct node_ptr_lower_bound_comp { bool operator()(const mip_node_t* a, const mip_node_t* b) const diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 00efb15b9..208bfc2e5 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -310,8 +310,23 @@ solution_t diversity_manager_t::run_solver() // Debug: Allow disabling GPU heuristics to test B&B tree determinism in isolation const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - CUOPT_LOG_INFO("Determinism is only supported for the B&B search. Disabling GPU heuristics"); - disable_heuristics_env = "1"; + CUOPT_LOG_INFO("Running deterministic mode with CPUFJ heuristic"); + population.initialize_population(); + population.allocate_solutions(); + + // Start CPUFJ in deterministic mode with B&B integration + if (context.branch_and_bound_ptr != nullptr) { + ls.start_cpufj_deterministic(*context.branch_and_bound_ptr); + } + + while (!check_b_b_preemption()) { + std::this_thread::sleep_for(std::chrono::milliseconds(100)); + } + + // Stop CPUFJ when B&B is done + ls.stop_cpufj_deterministic(); + + return population.best_feasible(); } if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { CUOPT_LOG_INFO("GPU heuristics disabled via CUOPT_DISABLE_GPU_HEURISTICS=1"); diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 8d534dfff..f82b4d785 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -14,7 +14,11 @@ #include #include +#include +#include #include +#include +#include #include #include @@ -22,6 +26,170 @@ namespace cuopt::linear_programming::detail { +template +HDI thrust::tuple get_mtm_for_bound( + const typename fj_t::climber_data_t::view_t& fj, + i_t var_idx, + i_t cstr_idx, + f_t cstr_coeff, + f_t bound, + f_t sign, + const ArrayType& assignment, + const ArrayType& lhs_vector) +{ + f_t delta_ij = 0; + f_t slack = 0; + f_t old_val = assignment[var_idx]; + + f_t lhs = lhs_vector[cstr_idx] * sign; + f_t rhs = bound * sign; + slack = rhs - lhs; // bound might be infinite. let the caller handle this case + + delta_ij = slack / (cstr_coeff * sign); + + return {delta_ij, slack}; +} + +template +HDI thrust::tuple get_mtm_for_constraint( + const typename fj_t::climber_data_t::view_t& fj, + i_t var_idx, + i_t cstr_idx, + f_t cstr_coeff, + f_t c_lb, + f_t c_ub, + const ArrayType& assignment, + const ArrayType& lhs_vector) +{ + f_t sign = -1; + f_t delta_ij = 0; + f_t slack = 0; + + f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + + f_t old_val = assignment[var_idx]; + + // process each bound as two separate constraints + f_t bounds[2] = {c_lb, c_ub}; + cuopt_assert(isfinite(bounds[0]) || isfinite(bounds[1]), "bounds are not finite"); + + for (i_t bound_idx = 0; bound_idx < 2; ++bound_idx) { + if (!isfinite(bounds[bound_idx])) continue; + + // factor to correct the lhs/rhs to turn a lb <= lhs <= ub constraint into + // two virtual constraints lhs <= ub and -lhs <= -lb + sign = bound_idx == 0 ? -1 : 1; + f_t lhs = lhs_vector[cstr_idx] * sign; + f_t rhs = bounds[bound_idx] * sign; + slack = rhs - lhs; + + // skip constraints that are violated/satisfied based on the MTM move type + bool violated = slack < -cstr_tolerance; + if (move_type == MTMMoveType::FJ_MTM_VIOLATED ? !violated : violated) continue; + + f_t new_val = old_val; + + delta_ij = slack / (cstr_coeff * sign); + break; + } + + return {delta_ij, sign, slack, cstr_tolerance}; +} + +template +HDI std::pair feas_score_constraint( + const typename fj_t::climber_data_t::view_t& fj, + i_t var_idx, + f_t delta, + i_t cstr_idx, + f_t cstr_coeff, + f_t c_lb, + f_t c_ub, + f_t current_lhs, + f_t left_weight, + f_t right_weight) +{ + cuopt_assert(isfinite(delta), "invalid delta"); + cuopt_assert(cstr_coeff != 0 && isfinite(cstr_coeff), "invalid coefficient"); + + f_t base_feas = 0; + f_t bonus_robust = 0; + + f_t bounds[2] = {c_lb, c_ub}; + cuopt_assert(isfinite(c_lb) || isfinite(c_ub), "no range"); + for (i_t bound_idx = 0; bound_idx < 2; ++bound_idx) { + if (!isfinite(bounds[bound_idx])) continue; + + // factor to correct the lhs/rhs to turn a lb <= lhs <= ub constraint into + // two virtual leq constraints "lhs <= ub" and "-lhs <= -lb" in order to match + // the convention of the paper + + // TODO: broadcast left/right weights to a csr_offset-indexed table? local minimums + // usually occur on a rarer basis (around 50 iteratiosn to 1 local minimum) + // likely unreasonable and overkill however + f_t cstr_weight = bound_idx == 0 ? left_weight : right_weight; + f_t sign = bound_idx == 0 ? -1 : 1; + f_t rhs = bounds[bound_idx] * sign; + f_t old_lhs = current_lhs * sign; + f_t new_lhs = (current_lhs + cstr_coeff * delta) * sign; + f_t old_slack = rhs - old_lhs; + f_t new_slack = rhs - new_lhs; + + cuopt_assert(isfinite(cstr_weight), "invalid weight"); + cuopt_assert(cstr_weight >= 0, "invalid weight"); + cuopt_assert(isfinite(old_lhs), ""); + cuopt_assert(isfinite(new_lhs), ""); + cuopt_assert(isfinite(old_slack) && isfinite(new_slack), ""); + + f_t cstr_tolerance = fj.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + + bool old_viol = fj.excess_score(cstr_idx, current_lhs, c_lb, c_ub) < -cstr_tolerance; + bool new_viol = + fj.excess_score(cstr_idx, current_lhs + cstr_coeff * delta, c_lb, c_ub) < -cstr_tolerance; + + bool old_sat = old_lhs < rhs + cstr_tolerance; + bool new_sat = new_lhs < rhs + cstr_tolerance; + + // equality + if (fj.pb.integer_equal(c_lb, c_ub)) { + if (!old_viol) cuopt_assert(old_sat == !old_viol, ""); + if (!new_viol) cuopt_assert(new_sat == !new_viol, ""); + } + + // if it would feasibilize this constraint + if (!old_sat && new_sat) { + cuopt_assert(old_viol, ""); + base_feas += cstr_weight; + } + // would cause this constraint to be violated + else if (old_sat && !new_sat) { + cuopt_assert(new_viol, ""); + base_feas -= cstr_weight; + } + // simple improvement + else if (!old_sat && !new_sat && old_lhs > new_lhs) { + cuopt_assert(old_viol && new_viol, ""); + base_feas += (i_t)(cstr_weight * fj.settings->parameters.excess_improvement_weight); + } + // simple worsening + else if (!old_sat && !new_sat && old_lhs <= new_lhs) { + cuopt_assert(old_viol && new_viol, ""); + base_feas -= (i_t)(cstr_weight * fj.settings->parameters.excess_improvement_weight); + } + + // robustness score bonus if this would leave some strick slack + bool old_stable = old_lhs < rhs - cstr_tolerance; + bool new_stable = new_lhs < rhs - cstr_tolerance; + if (!old_stable && new_stable) { + bonus_robust += cstr_weight; + } else if (old_stable && !new_stable) { + bonus_robust -= cstr_weight; + } + } + + return {base_feas, bonus_robust}; +} + static constexpr double BIGVAL_THRESHOLD = 1e20; template @@ -62,43 +230,306 @@ static void print_timing_stats(fj_cpu_climber_t& fj_cpu) auto [apply_avg, apply_total] = compute_avg_and_total(fj_cpu.apply_move_times); auto [weights_avg, weights_total] = compute_avg_and_total(fj_cpu.update_weights_times); auto [compute_score_avg, compute_score_total] = compute_avg_and_total(fj_cpu.compute_score_times); - CUOPT_LOG_TRACE("=== Timing Statistics (Iteration %d) ===\n", fj_cpu.iterations); - CUOPT_LOG_TRACE("find_lift_move: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("=== Timing Statistics (Iteration %d) ===", fj_cpu.iterations); + CUOPT_LOG_TRACE("find_lift_move: avg=%.6f ms, total=%.6f ms, calls=%zu", lift_avg * 1000.0, lift_total * 1000.0, fj_cpu.find_lift_move_times.size()); - CUOPT_LOG_TRACE("find_mtm_move_viol: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("find_mtm_move_viol: avg=%.6f ms, total=%.6f ms, calls=%zu", viol_avg * 1000.0, viol_total * 1000.0, fj_cpu.find_mtm_move_viol_times.size()); - CUOPT_LOG_TRACE("find_mtm_move_sat: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("find_mtm_move_sat: avg=%.6f ms, total=%.6f ms, calls=%zu", sat_avg * 1000.0, sat_total * 1000.0, fj_cpu.find_mtm_move_sat_times.size()); - CUOPT_LOG_TRACE("apply_move: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("apply_move: avg=%.6f ms, total=%.6f ms, calls=%zu", apply_avg * 1000.0, apply_total * 1000.0, fj_cpu.apply_move_times.size()); - CUOPT_LOG_TRACE("update_weights: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("update_weights: avg=%.6f ms, total=%.6f ms, calls=%zu", weights_avg * 1000.0, weights_total * 1000.0, fj_cpu.update_weights_times.size()); - CUOPT_LOG_TRACE("compute_score: avg=%.6f ms, total=%.6f ms, calls=%zu\n", + CUOPT_LOG_TRACE("compute_score: avg=%.6f ms, total=%.6f ms, calls=%zu", compute_score_avg * 1000.0, compute_score_total * 1000.0, fj_cpu.compute_score_times.size()); - CUOPT_LOG_TRACE("cache hit percentage: %.2f%%\n", + CUOPT_LOG_TRACE("cache hit percentage: %.2f%%", (double)fj_cpu.hit_count / (fj_cpu.hit_count + fj_cpu.miss_count) * 100.0); - CUOPT_LOG_TRACE("bin candidate move hit percentage: %.2f%%\n", + CUOPT_LOG_TRACE("bin candidate move hit percentage: %.2f%%", (double)fj_cpu.candidate_move_hits[0] / (fj_cpu.candidate_move_hits[0] + fj_cpu.candidate_move_misses[0]) * 100.0); - CUOPT_LOG_TRACE("int candidate move hit percentage: %.2f%%\n", + CUOPT_LOG_TRACE("int candidate move hit percentage: %.2f%%", (double)fj_cpu.candidate_move_hits[1] / (fj_cpu.candidate_move_hits[1] + fj_cpu.candidate_move_misses[1]) * 100.0); - CUOPT_LOG_TRACE("cont candidate move hit percentage: %.2f%%\n", + CUOPT_LOG_TRACE("cont candidate move hit percentage: %.2f%%", (double)fj_cpu.candidate_move_hits[2] / (fj_cpu.candidate_move_hits[2] + fj_cpu.candidate_move_misses[2]) * 100.0); - CUOPT_LOG_TRACE("========================================\n"); + CUOPT_LOG_TRACE("========================================"); +} + +template +static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) +{ + // Count variable types - use host vectors + fj_cpu.n_binary_vars = 0; + fj_cpu.n_integer_vars = 0; + // MEMORY OPS: Loop over all variables (n_vars iterations) + for (i_t i = 0; i < (i_t)fj_cpu.h_is_binary_variable.size(); i++) { + // ARRAY READ: h_is_binary_variable[i] - 1 read per iteration + if (fj_cpu.h_is_binary_variable[i]) { + fj_cpu.n_binary_vars++; + } else if (fj_cpu.h_var_types[i] == var_t::INTEGER) { + // ARRAY READ: h_var_types[i] - 1 read per iteration (conditional) + fj_cpu.n_integer_vars++; + } + // Total per iteration: 2 array reads + } + + i_t total_nnz = fj_cpu.h_reverse_offsets.back(); + i_t n_vars = fj_cpu.h_reverse_offsets.size() - 1; + i_t n_cstrs = fj_cpu.h_offsets.size() - 1; + + fj_cpu.avg_var_degree = (double)total_nnz / n_vars; + + // Compute variable degree statistics (max, cv) + fj_cpu.max_var_degree = 0; + std::vector var_degrees(n_vars); + // MEMORY OPS: Loop over all variables (n_vars iterations) + for (i_t i = 0; i < n_vars; i++) { + // ARRAY READ: h_reverse_offsets[i] and h_reverse_offsets[i+1] - 2 reads per iteration + i_t degree = fj_cpu.h_reverse_offsets[i + 1] - fj_cpu.h_reverse_offsets[i]; + // ARRAY WRITE: var_degrees[i] - 1 write per iteration + var_degrees[i] = degree; + fj_cpu.max_var_degree = std::max(fj_cpu.max_var_degree, degree); + // Total per iteration: 2 reads + 1 write = 3 memory ops + } + + // Compute variable degree coefficient of variation + double var_deg_variance = 0.0; + // MEMORY OPS: Loop over all variables (n_vars iterations) + for (i_t i = 0; i < n_vars; i++) { + // ARRAY READ: var_degrees[i] - 1 read per iteration + double diff = var_degrees[i] - fj_cpu.avg_var_degree; + var_deg_variance += diff * diff; + // Total per iteration: 1 read + } + var_deg_variance /= n_vars; + double var_degree_std = std::sqrt(var_deg_variance); + fj_cpu.var_degree_cv = fj_cpu.avg_var_degree > 0 ? var_degree_std / fj_cpu.avg_var_degree : 0.0; + + fj_cpu.avg_cstr_degree = (double)total_nnz / n_cstrs; + + // Compute constraint degree statistics (max, cv) + fj_cpu.max_cstr_degree = 0; + std::vector cstr_degrees(n_cstrs); + // MEMORY OPS: Loop over all constraints (n_cstrs iterations) + for (i_t i = 0; i < n_cstrs; i++) { + // ARRAY READ: h_offsets[i] and h_offsets[i+1] - 2 reads per iteration + i_t degree = fj_cpu.h_offsets[i + 1] - fj_cpu.h_offsets[i]; + // ARRAY WRITE: cstr_degrees[i] - 1 write per iteration + cstr_degrees[i] = degree; + fj_cpu.max_cstr_degree = std::max(fj_cpu.max_cstr_degree, degree); + // Total per iteration: 2 reads + 1 write = 3 memory ops + } + + // Compute constraint degree coefficient of variation + double cstr_deg_variance = 0.0; + // MEMORY OPS: Loop over all constraints (n_cstrs iterations) + for (i_t i = 0; i < n_cstrs; i++) { + // ARRAY READ: cstr_degrees[i] - 1 read per iteration + double diff = cstr_degrees[i] - fj_cpu.avg_cstr_degree; + cstr_deg_variance += diff * diff; + // Total per iteration: 1 read + } + cstr_deg_variance /= n_cstrs; + double cstr_degree_std = std::sqrt(cstr_deg_variance); + fj_cpu.cstr_degree_cv = + fj_cpu.avg_cstr_degree > 0 ? cstr_degree_std / fj_cpu.avg_cstr_degree : 0.0; + + fj_cpu.problem_density = (double)total_nnz / ((double)n_vars * n_cstrs); +} + +template +static void log_regression_features(fj_cpu_climber_t& fj_cpu, + double time_window_ms, + double total_time_ms, + size_t mem_loads_bytes, + size_t mem_stores_bytes) +{ + i_t total_nnz = fj_cpu.h_reverse_offsets.back(); + i_t n_vars = fj_cpu.h_reverse_offsets.size() - 1; + i_t n_cstrs = fj_cpu.h_offsets.size() - 1; + + // Dynamic runtime features + double violated_ratio = (double)fj_cpu.violated_constraints.size() / n_cstrs; + + // Compute per-iteration metrics + double nnz_per_move = 0.0; + i_t total_moves = + fj_cpu.n_lift_moves_window + fj_cpu.n_mtm_viol_moves_window + fj_cpu.n_mtm_sat_moves_window; + if (total_moves > 0) { nnz_per_move = (double)fj_cpu.nnz_processed_window / total_moves; } + + double eval_intensity = (double)fj_cpu.nnz_processed_window / 1000.0; + + // Cache and locality metrics + i_t cache_hits_window = fj_cpu.hit_count - fj_cpu.hit_count_window_start; + i_t cache_misses_window = fj_cpu.miss_count - fj_cpu.miss_count_window_start; + i_t total_cache_accesses = cache_hits_window + cache_misses_window; + double cache_hit_rate = + total_cache_accesses > 0 ? (double)cache_hits_window / total_cache_accesses : 0.0; + + i_t unique_cstrs = fj_cpu.unique_cstrs_accessed_window.size(); + i_t unique_vars = fj_cpu.unique_vars_accessed_window.size(); + + // Reuse ratios: how many times each constraint/variable was accessed on average + double cstr_reuse_ratio = + unique_cstrs > 0 ? (double)fj_cpu.nnz_processed_window / unique_cstrs : 0.0; + double var_reuse_ratio = + unique_vars > 0 ? (double)fj_cpu.n_variable_updates_window / unique_vars : 0.0; + + // Working set size estimation (KB) + // Each constraint: lhs (f_t) + 2 bounds (f_t) + sumcomp (f_t) = 4 * sizeof(f_t) + // Each variable: assignment (f_t) = 1 * sizeof(f_t) + i_t working_set_bytes = unique_cstrs * 4 * sizeof(f_t) + unique_vars * sizeof(f_t); + double working_set_kb = working_set_bytes / 1024.0; + + // Coverage: what fraction of problem is actively touched + double cstr_coverage = (double)unique_cstrs / n_cstrs; + double var_coverage = (double)unique_vars / n_vars; + + double loads_per_iter = 0.0; + double stores_per_iter = 0.0; + double l1_miss = -1.0; + double l3_miss = -1.0; + + // Compute memory statistics + double mem_loads_mb = mem_loads_bytes / 1e6; + double mem_stores_mb = mem_stores_bytes / 1e6; + double mem_total_mb = (mem_loads_bytes + mem_stores_bytes) / 1e6; + double mem_bandwidth_gb_per_sec = (mem_total_mb / 1000.0) / (time_window_ms / 1000.0); + + // Build per-wrapper memory statistics string + std::stringstream wrapper_stats; + auto per_wrapper_stats = fj_cpu.memory_manifold.collect_per_wrapper(); + for (const auto& [name, loads, stores] : per_wrapper_stats) { + wrapper_stats << " " << name << "_loads=" << loads << " " << name << "_stores=" << stores; + } + + fj_cpu.memory_manifold.flush(); + + // Print everything on a single line using precomputed features + CUOPT_LOG_DEBUG( + "%sCPUFJ_FEATURES iter=%d time_window=%.2f " + "n_vars=%d n_cstrs=%d n_bin=%d n_int=%d total_nnz=%d " + "avg_var_deg=%.2f max_var_deg=%d var_deg_cv=%.4f " + "avg_cstr_deg=%.2f max_cstr_deg=%d cstr_deg_cv=%.4f " + "density=%.6f " + "total_viol=%.4f obj_weight=%.4f max_weight=%.4f " + "n_locmin=%d iter_since_best=%d feas_found=%d " + "nnz_proc=%d n_lift=%d n_mtm_viol=%d n_mtm_sat=%d n_var_updates=%d " + "cache_hit_rate=%.4f unique_cstrs=%d unique_vars=%d " + "cstr_reuse=%.2f var_reuse=%.2f working_set_kb=%.1f " + "cstr_coverage=%.4f var_coverage=%.4f " + "L1_miss=%.2f L3_miss=%.2f loads_per_iter=%.0f stores_per_iter=%.0f " + "viol_ratio=%.4f nnz_per_move=%.2f eval_intensity=%.2f " + "mem_loads_mb=%.3f mem_stores_mb=%.3f mem_total_mb=%.3f mem_bandwidth_gb_s=%.3f%s", + fj_cpu.log_prefix.c_str(), + fj_cpu.iterations, + time_window_ms, + n_vars, + n_cstrs, + fj_cpu.n_binary_vars, + fj_cpu.n_integer_vars, + total_nnz, + fj_cpu.avg_var_degree, + fj_cpu.max_var_degree, + fj_cpu.var_degree_cv, + fj_cpu.avg_cstr_degree, + fj_cpu.max_cstr_degree, + fj_cpu.cstr_degree_cv, + fj_cpu.problem_density, + fj_cpu.total_violations, + fj_cpu.h_objective_weight, + fj_cpu.max_weight, + fj_cpu.n_local_minima_window, + fj_cpu.iterations_since_best, + fj_cpu.feasible_found ? 1 : 0, + fj_cpu.nnz_processed_window, + fj_cpu.n_lift_moves_window, + fj_cpu.n_mtm_viol_moves_window, + fj_cpu.n_mtm_sat_moves_window, + fj_cpu.n_variable_updates_window, + cache_hit_rate, + unique_cstrs, + unique_vars, + cstr_reuse_ratio, + var_reuse_ratio, + working_set_kb, + cstr_coverage, + var_coverage, + l1_miss, + l3_miss, + loads_per_iter, + stores_per_iter, + violated_ratio, + nnz_per_move, + eval_intensity, + mem_loads_mb, + mem_stores_mb, + mem_total_mb, + mem_bandwidth_gb_per_sec, + wrapper_stats.str().c_str()); + + // Reset window counters + fj_cpu.nnz_processed_window = 0; + fj_cpu.n_lift_moves_window = 0; + fj_cpu.n_mtm_viol_moves_window = 0; + fj_cpu.n_mtm_sat_moves_window = 0; + fj_cpu.n_variable_updates_window = 0; + fj_cpu.n_local_minima_window = 0; + fj_cpu.prev_best_objective = fj_cpu.h_best_objective; + + // Reset cache and locality tracking + fj_cpu.hit_count_window_start = fj_cpu.hit_count; + fj_cpu.miss_count_window_start = fj_cpu.miss_count; + fj_cpu.unique_cstrs_accessed_window.clear(); + fj_cpu.unique_vars_accessed_window.clear(); +} + +// Local implementations that use instrumented vectors +template +static inline std::pair reverse_range_for_var(fj_cpu_climber_t& fj_cpu, + i_t var_idx) +{ + cuopt_assert(var_idx >= 0 && var_idx < fj_cpu.view.pb.n_variables, + "Variable should be within the range"); + return std::make_pair(fj_cpu.h_reverse_offsets[var_idx], fj_cpu.h_reverse_offsets[var_idx + 1]); +} + +template +static inline std::pair range_for_constraint(fj_cpu_climber_t& fj_cpu, + i_t cstr_idx) +{ + return std::make_pair(fj_cpu.h_offsets[cstr_idx], fj_cpu.h_offsets[cstr_idx + 1]); +} + +template +static inline bool check_variable_within_bounds(fj_cpu_climber_t& fj_cpu, + i_t var_idx, + f_t val) +{ + const f_t int_tol = fj_cpu.view.pb.tolerances.integrality_tolerance; + auto bounds = fj_cpu.h_var_bounds[var_idx].get(); + bool within_bounds = val <= (get_upper(bounds) + int_tol) && val >= (get_lower(bounds) - int_tol); + return within_bounds; +} + +template +static inline bool is_integer_var(fj_cpu_climber_t& fj_cpu, i_t var_idx) +{ + return var_t::INTEGER == fj_cpu.h_var_types[var_idx]; } template @@ -117,16 +548,16 @@ static inline bool tabu_check(fj_cpu_climber_t& fj_cpu, } template -static bool check_variable_feasibility(const typename fj_t::climber_data_t::view_t& fj, +static bool check_variable_feasibility(fj_cpu_climber_t& fj_cpu, bool check_integer = true) { - for (i_t var_idx = 0; var_idx < fj.pb.n_variables; var_idx += 1) { - auto val = fj.incumbent_assignment[var_idx]; - bool feasible = fj.pb.check_variable_within_bounds(var_idx, val); + for (i_t var_idx = 0; var_idx < fj_cpu.view.pb.n_variables; var_idx += 1) { + auto val = fj_cpu.h_assignment[var_idx]; + bool feasible = check_variable_within_bounds(fj_cpu, var_idx, val); if (!feasible) return false; - if (check_integer && fj.pb.is_integer_var(var_idx) && - !fj.pb.is_integer(fj.incumbent_assignment[var_idx])) + if (check_integer && is_integer_var(fj_cpu, var_idx) && + !fj_cpu.view.pb.is_integer(fj_cpu.h_assignment[var_idx])) return false; } return true; @@ -139,6 +570,7 @@ static inline std::pair compute_score(fj_cpu_climber_t timer(fj_cpu.compute_score_times); + // ARRAY READ: h_obj_coeffs[var_idx] - 1 read f_t obj_diff = fj_cpu.h_obj_coeffs[var_idx] * delta; cuopt_assert(isfinite(delta), ""); @@ -148,19 +580,41 @@ static inline std::pair compute_score(fj_cpu_climber_t(fj_cpu, var_idx); + fj_cpu.nnz_processed_window += (offset_end - offset_begin); + + // MEMORY OPS: Loop over all constraints involving this variable (avg_var_degree iterations) + // This is one of the HOTTEST loops in the code - called for every candidate move evaluation for (i_t i = offset_begin; i < offset_end; i++) { - auto cstr_idx = fj_cpu.h_reverse_constraints[i]; - auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; - auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i]; + // ARRAY READ: h_reverse_constraints[i] - 1 read per iteration + auto cstr_idx = fj_cpu.h_reverse_constraints[i]; + fj_cpu.unique_cstrs_accessed_window.insert(cstr_idx); + // ARRAY READ: h_reverse_coefficients[i] - 1 read per iteration + auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; + // ARRAY READ: cached_cstr_bounds[i] - 1 read per iteration (reads 2 f_t values) + auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i].get(); cuopt_assert(c_lb <= c_ub, "invalid bounds"); - auto [cstr_base_feas, cstr_bonus_robust] = feas_score_constraint( - fj_cpu.view, var_idx, delta, cstr_idx, cstr_coeff, c_lb, c_ub, fj_cpu.h_lhs[cstr_idx]); + // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (indirect indexing) + // feas_score_constraint also reads from h_cstr_left_weights[cstr_idx] and + // h_cstr_right_weights[cstr_idx] + auto [cstr_base_feas, cstr_bonus_robust] = + feas_score_constraint(fj_cpu.view, + var_idx, + delta, + cstr_idx, + cstr_coeff, + c_lb, + c_ub, + fj_cpu.h_lhs[cstr_idx], + fj_cpu.h_cstr_left_weights[cstr_idx], + fj_cpu.h_cstr_right_weights[cstr_idx]); base_feas_sum += cstr_base_feas; bonus_robust_sum += cstr_bonus_robust; + // Total per iteration: ~6-7 array reads (h_reverse_constraints, h_reverse_coefficients, + // cached_cstr_bounds (2 values), h_lhs, h_cstr_left_weights, h_cstr_right_weights) } f_t base_obj = 0; @@ -188,15 +642,21 @@ static inline std::pair compute_score(fj_cpu_climber_t static void smooth_weights(fj_cpu_climber_t& fj_cpu) { + // MEMORY OPS: Loop over all constraints (n_cstrs iterations) for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; cstr_idx++) { // consider only satisfied constraints if (fj_cpu.violated_constraints.count(cstr_idx)) continue; + // ARRAY READ: h_cstr_left_weights[cstr_idx] - 1 read per iteration (if not violated) f_t weight_l = max((f_t)0, fj_cpu.h_cstr_left_weights[cstr_idx] - 1); + // ARRAY READ: h_cstr_right_weights[cstr_idx] - 1 read per iteration (if not violated) f_t weight_r = max((f_t)0, fj_cpu.h_cstr_right_weights[cstr_idx] - 1); - fj_cpu.h_cstr_left_weights[cstr_idx] = weight_l; + // ARRAY WRITE: h_cstr_left_weights[cstr_idx] - 1 write per iteration (if not violated) + fj_cpu.h_cstr_left_weights[cstr_idx] = weight_l; + // ARRAY WRITE: h_cstr_right_weights[cstr_idx] - 1 write per iteration (if not violated) fj_cpu.h_cstr_right_weights[cstr_idx] = weight_r; + // Total per iteration (for satisfied constraints): 2 reads + 2 writes = 4 memory ops } if (fj_cpu.h_objective_weight > 0 && fj_cpu.h_incumbent_objective >= fj_cpu.h_best_objective) { @@ -217,18 +677,24 @@ static void update_weights(fj_cpu_climber_t& fj_cpu) return; } + // MEMORY OPS: Loop over violated constraints (typically small: <100 iterations) for (auto cstr_idx : fj_cpu.violated_constraints) { + // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration f_t curr_incumbent_lhs = fj_cpu.h_lhs[cstr_idx]; + // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per iteration f_t curr_lower_excess = fj_cpu.view.lower_excess_score(cstr_idx, curr_incumbent_lhs, fj_cpu.h_cstr_lb[cstr_idx]); + // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per iteration f_t curr_upper_excess = fj_cpu.view.upper_excess_score(cstr_idx, curr_incumbent_lhs, fj_cpu.h_cstr_ub[cstr_idx]); f_t curr_excess_score = curr_lower_excess + curr_upper_excess; f_t old_weight; if (curr_lower_excess < 0.) { + // ARRAY READ: h_cstr_left_weights[cstr_idx] - 1 read per iteration (conditional) old_weight = fj_cpu.h_cstr_left_weights[cstr_idx]; } else { + // ARRAY READ: h_cstr_right_weights[cstr_idx] - 1 read per iteration (conditional) old_weight = fj_cpu.h_cstr_right_weights[cstr_idx]; } @@ -241,18 +707,26 @@ static void update_weights(fj_cpu_climber_t& fj_cpu) new_weight = round(new_weight); if (curr_lower_excess < 0.) { + // ARRAY WRITE: h_cstr_left_weights[cstr_idx] - 1 write per iteration (conditional) fj_cpu.h_cstr_left_weights[cstr_idx] = new_weight; fj_cpu.max_weight = max(fj_cpu.max_weight, new_weight); } else { + // ARRAY WRITE: h_cstr_right_weights[cstr_idx] - 1 write per iteration (conditional) fj_cpu.h_cstr_right_weights[cstr_idx] = new_weight; fj_cpu.max_weight = max(fj_cpu.max_weight, new_weight); } // Invalidate related cached move scores - auto [relvar_offset_begin, relvar_offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [relvar_offset_begin, relvar_offset_end] = + range_for_constraint(fj_cpu, cstr_idx); + // MEMORY OPS: Inner loop over variables in this constraint (avg_cstr_degree iterations per + // outer iteration) for (auto i = relvar_offset_begin; i < relvar_offset_end; i++) { + // ARRAY WRITE: cached_mtm_moves[i].first - 1 write per inner iteration fj_cpu.cached_mtm_moves[i].first = 0; + // Total per inner iteration: 1 write } + // Total per outer iteration: 4 reads + 1 write + (avg_cstr_degree writes in inner loop) } if (fj_cpu.violated_constraints.empty()) { fj_cpu.h_objective_weight += 1; } @@ -270,30 +744,47 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, cuopt_assert(var_idx < fj_cpu.view.pb.n_variables, "variable index out of bounds"); // Update the LHSs of all involved constraints. - auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); + + // Track work metrics for regression model + fj_cpu.nnz_processed_window += (offset_end - offset_begin); + fj_cpu.n_variable_updates_window++; + fj_cpu.unique_vars_accessed_window.insert(var_idx); i_t previous_viol = fj_cpu.violated_constraints.size(); + // MEMORY OPS: CRITICAL LOOP - Loop over all constraints involving this variable (avg_var_degree + // iterations) This loop is called ONCE PER ITERATION and updates constraint LHS values for (auto i = offset_begin; i < offset_end; i++) { cuopt_assert(i < (i_t)fj_cpu.h_reverse_constraints.size(), ""); - auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i]; + // ARRAY READ: cached_cstr_bounds[i] - 1 read per iteration (reads 2 f_t values) + auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i].get(); - auto cstr_idx = fj_cpu.h_reverse_constraints[i]; + // ARRAY READ: h_reverse_constraints[i] - 1 read per iteration + auto cstr_idx = fj_cpu.h_reverse_constraints[i]; + fj_cpu.unique_cstrs_accessed_window.insert(cstr_idx); + // ARRAY READ: h_reverse_coefficients[i] - 1 read per iteration auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; + // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (indirect indexing) f_t old_lhs = fj_cpu.h_lhs[cstr_idx]; // Kahan compensated summation - f_t y = cstr_coeff * delta - fj_cpu.h_lhs_sumcomp[cstr_idx]; - f_t t = old_lhs + y; + // ARRAY READ: h_lhs_sumcomp[cstr_idx] - 1 read per iteration + f_t y = cstr_coeff * delta - fj_cpu.h_lhs_sumcomp[cstr_idx]; + f_t t = old_lhs + y; + // ARRAY WRITE: h_lhs_sumcomp[cstr_idx] - 1 write per iteration fj_cpu.h_lhs_sumcomp[cstr_idx] = (t - old_lhs) - y; - fj_cpu.h_lhs[cstr_idx] = t; - f_t new_lhs = fj_cpu.h_lhs[cstr_idx]; - f_t old_cost = fj_cpu.view.excess_score(cstr_idx, old_lhs, c_lb, c_ub); - f_t new_cost = fj_cpu.view.excess_score(cstr_idx, new_lhs, c_lb, c_ub); - f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + // ARRAY WRITE: h_lhs[cstr_idx] - 1 write per iteration + fj_cpu.h_lhs[cstr_idx] = t; + // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (just written) + f_t new_lhs = fj_cpu.h_lhs[cstr_idx]; + f_t old_cost = fj_cpu.view.excess_score(cstr_idx, old_lhs, c_lb, c_ub); + f_t new_cost = fj_cpu.view.excess_score(cstr_idx, new_lhs, c_lb, c_ub); + f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); // trigger early lhs recomputation if the sumcomp term gets too large // to avoid large numerical errors + // ARRAY READ: h_lhs_sumcomp[cstr_idx] - 1 read per iteration if (fabs(fj_cpu.h_lhs_sumcomp[cstr_idx]) > BIGVAL_THRESHOLD) fj_cpu.trigger_early_lhs_recomputation = true; @@ -311,10 +802,16 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, cuopt_assert(isfinite(fj_cpu.h_lhs[cstr_idx]), "assignment should be finite"); // Invalidate related cached move scores - auto [relvar_offset_begin, relvar_offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [relvar_offset_begin, relvar_offset_end] = + range_for_constraint(fj_cpu, cstr_idx); + // MEMORY OPS: Inner loop over variables in this constraint (avg_cstr_degree iterations per + // outer iteration) for (auto i = relvar_offset_begin; i < relvar_offset_end; i++) { + // ARRAY WRITE: cached_mtm_moves[i].first - 1 write per inner iteration fj_cpu.cached_mtm_moves[i].first = 0; + // Total per inner iteration: 1 write } + // Total per outer iteration: 7 reads + 3 writes + (avg_cstr_degree writes in inner loop) } if (previous_viol > 0 && fj_cpu.violated_constraints.empty()) { @@ -322,33 +819,40 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, } // update the assignment and objective proper + // ARRAY READ: h_assignment[var_idx] - 1 read f_t new_val = fj_cpu.h_assignment[var_idx] + delta; - if (fj_cpu.view.pb.is_integer_var(var_idx)) { + if (is_integer_var(fj_cpu, var_idx)) { cuopt_assert(fj_cpu.view.pb.integer_equal(new_val, round(new_val)), "new_val is not integer"); new_val = round(new_val); } + // ARRAY WRITE: h_assignment[var_idx] - 1 write fj_cpu.h_assignment[var_idx] = new_val; - cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), + cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), "assignment not within bounds"); cuopt_assert(isfinite(new_val), "assignment is not finite"); + // ARRAY READ: h_obj_coeffs[var_idx] - 1 read fj_cpu.h_incumbent_objective += fj_cpu.h_obj_coeffs[var_idx] * delta; if (fj_cpu.h_incumbent_objective < fj_cpu.h_best_objective && fj_cpu.violated_constraints.empty()) { // recompute the LHS values to cancel out accumulation errors, then check if feasibility remains recompute_lhs(fj_cpu); - if (fj_cpu.violated_constraints.empty() && check_variable_feasibility(fj_cpu.view)) { + if (fj_cpu.violated_constraints.empty() && check_variable_feasibility(fj_cpu)) { cuopt_assert(fj_cpu.satisfied_constraints.size() == fj_cpu.view.pb.n_constraints, ""); fj_cpu.h_best_objective = fj_cpu.h_incumbent_objective - fj_cpu.settings.parameters.breakthrough_move_epsilon; - fj_cpu.h_best_assignment = fj_cpu.h_assignment; - CUOPT_LOG_TRACE("%sCPUFJ: new best objective: %g\n", + // ARRAY WRITE: h_best_assignment = h_assignment - n_vars writes (vector copy) + fj_cpu.h_best_assignment = fj_cpu.h_assignment; + fj_cpu.iterations_since_best = 0; // Reset counter on improvement + CUOPT_LOG_TRACE("%sCPUFJ: new best objective: %g", fj_cpu.log_prefix.c_str(), fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective)); if (fj_cpu.improvement_callback) { - fj_cpu.improvement_callback(fj_cpu.h_best_objective, fj_cpu.h_assignment); + double current_work_units = fj_cpu.work_units_elapsed.load(std::memory_order_acquire); + fj_cpu.improvement_callback( + fj_cpu.h_best_objective, fj_cpu.h_assignment, current_work_units); } fj_cpu.feasible_found = true; } @@ -358,18 +862,25 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, rng.next_u32() % (fj_cpu.settings.parameters.tabu_tenure_max - fj_cpu.settings.parameters.tabu_tenure_min); if (delta > 0) { - fj_cpu.h_tabu_lastinc[var_idx] = fj_cpu.iterations; + // ARRAY WRITE: h_tabu_lastinc[var_idx] - 1 write + fj_cpu.h_tabu_lastinc[var_idx] = fj_cpu.iterations; + // ARRAY WRITE: h_tabu_nodec_until[var_idx] - 1 write fj_cpu.h_tabu_nodec_until[var_idx] = fj_cpu.iterations + tabu_tenure; + // ARRAY WRITE: h_tabu_noinc_until[var_idx] - 1 write fj_cpu.h_tabu_noinc_until[var_idx] = fj_cpu.iterations + tabu_tenure / 2; - // CUOPT_LOG_TRACE("CPU: tabu nodec_until: %d\n", fj_cpu.h_tabu_nodec_until[var_idx]); + // CUOPT_LOG_TRACE("CPU: tabu nodec_until: %d", fj_cpu.h_tabu_nodec_until[var_idx]); } else { - fj_cpu.h_tabu_lastdec[var_idx] = fj_cpu.iterations; + // ARRAY WRITE: h_tabu_lastdec[var_idx] - 1 write + fj_cpu.h_tabu_lastdec[var_idx] = fj_cpu.iterations; + // ARRAY WRITE: h_tabu_noinc_until[var_idx] - 1 write fj_cpu.h_tabu_noinc_until[var_idx] = fj_cpu.iterations + tabu_tenure; + // ARRAY WRITE: h_tabu_nodec_until[var_idx] - 1 write fj_cpu.h_tabu_nodec_until[var_idx] = fj_cpu.iterations + tabu_tenure / 2; - // CUOPT_LOG_TRACE("CPU: tabu noinc_until: %d\n", fj_cpu.h_tabu_noinc_until[var_idx]); + // CUOPT_LOG_TRACE("CPU: tabu noinc_until: %d", fj_cpu.h_tabu_nodec_until[var_idx]); } - + // ARRAY WRITE: flip_move_computed - n_vars writes (fill with false) std::fill(fj_cpu.flip_move_computed.begin(), fj_cpu.flip_move_computed.end(), false); + // ARRAY WRITE: var_bitmap - n_vars writes (fill with false) std::fill(fj_cpu.var_bitmap.begin(), fj_cpu.var_bitmap.end(), false); fj_cpu.iter_mtm_vars.clear(); } @@ -386,44 +897,58 @@ static thrust::tuple find_mtm_move( fj_staged_score_t best_score = fj_staged_score_t::invalid(); // collect all the variables that are involved in the target constraints + // MEMORY OPS: Outer loop over target constraints (sample_size iterations, typically 15-100) for (size_t cstr_idx : target_cstrs) { - auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); + // MEMORY OPS: Inner loop over variables in each constraint (avg_cstr_degree per outer + // iteration) for (auto i = offset_begin; i < offset_end; i++) { + // ARRAY READ: h_variables[i] - 1 read per iteration i_t var_idx = fj_cpu.h_variables[i]; + // ARRAY READ: var_bitmap[var_idx] - 1 read per iteration if (fj_cpu.var_bitmap[var_idx]) continue; fj_cpu.iter_mtm_vars.push_back(var_idx); + // ARRAY WRITE: var_bitmap[var_idx] - 1 write per iteration (if not already set) fj_cpu.var_bitmap[var_idx] = true; + // Total per inner iteration: 2 reads + 1 write (conditional) } } // estimate the amount of nnzs to consider i_t nnz_sum = 0; for (auto var_idx : fj_cpu.iter_mtm_vars) { - auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); nnz_sum += offset_end - offset_begin; } f_t nnz_pick_probability = 1; if (nnz_sum > fj_cpu.nnz_samples) nnz_pick_probability = (f_t)fj_cpu.nnz_samples / nnz_sum; + // MEMORY OPS: HOTTEST LOOP - Outer loop over target constraints (sample_size iterations) for (size_t cstr_idx : target_cstrs) { - f_t cstr_tol = fj_cpu.view.get_corrected_tolerance(cstr_idx); + auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); + f_t cstr_tol = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); cuopt_assert(cstr_idx < fj_cpu.h_cstr_lb.size(), "cstr_idx is out of bounds"); - auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); + // MEMORY OPS: Inner loop over variables (avg_cstr_degree per outer iteration) for (auto i = offset_begin; i < offset_end; i++) { // early cached check + // ARRAY READ: cached_mtm_moves[i] - 1 read per iteration if (auto& cached_move = fj_cpu.cached_mtm_moves[i]; cached_move.first != 0) { if (best_score < cached_move.second) { + // ARRAY READ: h_variables[i] - 1 read per iteration (cache hit) auto var_idx = fj_cpu.h_variables[i]; - if (fj_cpu.view.pb.check_variable_within_bounds( - var_idx, fj_cpu.h_assignment[var_idx] + cached_move.first)) { + // ARRAY READ: h_assignment[var_idx] - 1 read per iteration (cache hit) + if (check_variable_within_bounds( + fj_cpu, var_idx, fj_cpu.h_assignment[var_idx] + cached_move.first)) { best_score = cached_move.second; best_move = fj_move_t{var_idx, cached_move.first}; } - // cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, + // cuopt_assert(check_variable_within_bounds(fj_cpu, var_idx, // fj_cpu.h_assignment[var_idx] + cached_move.first), "best move not within bounds"); } fj_cpu.hit_count++; + // Total per cache hit: 3 reads continue; } @@ -431,25 +956,40 @@ static thrust::tuple find_mtm_move( if (nnz_pick_probability < 1) if (rng.next_float() > nnz_pick_probability) continue; + // ARRAY READ: h_variables[i] - 1 read per iteration (cache miss) auto var_idx = fj_cpu.h_variables[i]; + // ARRAY READ: h_assignment[var_idx] - 1 read per iteration (cache miss) f_t val = fj_cpu.h_assignment[var_idx]; f_t new_val = val; f_t delta = 0; // Special case for binary variables + // ARRAY READ: h_is_binary_variable[var_idx] - 1 read per iteration if (fj_cpu.h_is_binary_variable[var_idx]) { + // ARRAY READ: flip_move_computed[var_idx] - 1 read per iteration (conditional) if (fj_cpu.flip_move_computed[var_idx]) continue; + // ARRAY WRITE: flip_move_computed[var_idx] - 1 write per iteration (conditional) fj_cpu.flip_move_computed[var_idx] = true; new_val = 1 - val; } else { + // ARRAY READ: h_coefficients[i] - 1 read per iteration (non-binary) auto cstr_coeff = fj_cpu.h_coefficients[i]; - f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; - f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; - auto [delta, sign, slack, cstr_tolerance] = get_mtm_for_constraint( - fj_cpu.view, var_idx, cstr_idx, cstr_coeff, c_lb, c_ub); - if (fj_cpu.view.pb.is_integer_var(var_idx)) { + // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per iteration (non-binary) + f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; + // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per iteration (non-binary) + f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; + auto [delta, sign, slack, cstr_tolerance] = + get_mtm_for_constraint(fj_cpu.view, + var_idx, + cstr_idx, + cstr_coeff, + c_lb, + c_ub, + fj_cpu.h_assignment, + fj_cpu.h_lhs); + if (is_integer_var(fj_cpu, var_idx)) { new_val = cstr_coeff * sign > 0 ? floor(val + delta + fj_cpu.view.pb.tolerances.integrality_tolerance) : ceil(val + delta - fj_cpu.view.pb.tolerances.integrality_tolerance); @@ -457,26 +997,30 @@ static thrust::tuple find_mtm_move( new_val = val + delta; } // fallback - if (new_val < get_lower(fj_cpu.h_var_bounds[var_idx]) || - new_val > get_upper(fj_cpu.h_var_bounds[var_idx])) { - new_val = cstr_coeff * sign > 0 ? get_lower(fj_cpu.h_var_bounds[var_idx]) - : get_upper(fj_cpu.h_var_bounds[var_idx]); + // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration (non-binary, conditional) + if (new_val < get_lower(fj_cpu.h_var_bounds[var_idx].get()) || + new_val > get_upper(fj_cpu.h_var_bounds[var_idx].get())) { + new_val = cstr_coeff * sign > 0 ? get_lower(fj_cpu.h_var_bounds[var_idx].get()) + : get_upper(fj_cpu.h_var_bounds[var_idx].get()); } } if (!isfinite(new_val)) continue; - cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), + cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), "new_val is not within bounds"); delta = new_val - val; // more permissive tabu in the case of local minima - if (tabu_check(fj_cpu, var_idx, delta, localmin)) continue; + if (tabu_check(fj_cpu, var_idx, delta, localmin)) continue; if (fabs(delta) < cstr_tol) continue; auto move = fj_move_t{var_idx, delta}; cuopt_assert(move.var_idx < fj_cpu.h_assignment.size(), "move.var_idx is out of bounds"); cuopt_assert(move.var_idx >= 0, "move.var_idx is not positive"); + // CRITICAL: compute_score() does ~6-7 array reads per constraint (see compute_score + // annotations) auto [score, infeasibility] = compute_score(fj_cpu, var_idx, delta); - fj_cpu.cached_mtm_moves[i] = std::make_pair(delta, score); + // ARRAY WRITE: cached_mtm_moves[i] - 1 write per iteration (cache miss) + fj_cpu.cached_mtm_moves[i] = std::make_pair(delta, score); fj_cpu.miss_count++; // reject this move if it would increase the target variable to a numerically unstable value if (fj_cpu.view.move_numerically_stable( @@ -486,6 +1030,7 @@ static thrust::tuple find_mtm_move( best_move = move; } } + // Total per cache miss: ~8-11 reads + 1 write + compute_score overhead } } @@ -494,7 +1039,9 @@ static thrust::tuple find_mtm_move( fj_cpu.h_best_objective < std::numeric_limits::infinity() && fj_cpu.h_incumbent_objective >= fj_cpu.h_best_objective + fj_cpu.settings.parameters.breakthrough_move_epsilon) { + // MEMORY OPS: Loop over objective variables (num_obj_vars iterations, typically small) for (auto var_idx : fj_cpu.h_objective_vars) { + // ARRAY READ: h_assignment[var_idx] - 1 read per iteration f_t old_val = fj_cpu.h_assignment[var_idx]; f_t new_val = get_breakthrough_move(fj_cpu.view, var_idx); @@ -507,11 +1054,12 @@ static thrust::tuple find_mtm_move( cuopt_assert(move.var_idx < fj_cpu.h_assignment.size(), "move.var_idx is out of bounds"); cuopt_assert(move.var_idx >= 0, "move.var_idx is not positive"); - if (tabu_check(fj_cpu, var_idx, delta)) continue; + if (tabu_check(fj_cpu, var_idx, delta)) continue; + // CRITICAL: compute_score() does ~6-7 array reads per constraint involved auto [score, infeasibility] = compute_score(fj_cpu, var_idx, delta); - cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, new_val), ""); + cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), ""); cuopt_assert(isfinite(delta), ""); if (fj_cpu.view.move_numerically_stable( @@ -521,6 +1069,7 @@ static thrust::tuple find_mtm_move( best_move = move; } } + // Total per iteration: 1 read + compute_score overhead } } @@ -569,29 +1118,44 @@ static void recompute_lhs(fj_cpu_climber_t& fj_cpu) fj_cpu.violated_constraints.clear(); fj_cpu.satisfied_constraints.clear(); fj_cpu.total_violations = 0; + // MEMORY OPS: CRITICAL LOOP - Loop over all constraints (n_cstrs iterations) + // This is called periodically to recompute all LHS values from scratch for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; ++cstr_idx) { - auto [offset_begin, offset_end] = fj_cpu.view.pb.range_for_constraint(cstr_idx); + auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); + auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); + // MEMORY OPS: For each constraint, reads avg_cstr_degree elements from: + // - h_coefficients[offset_begin:offset_end] - avg_cstr_degree reads + // - h_variables[offset_begin:offset_end] - avg_cstr_degree reads (indirect indexing) + // - h_assignment[h_variables[i]] - avg_cstr_degree reads (indirect indexing) auto delta_it = - thrust::make_transform_iterator(thrust::make_counting_iterator(0), [fj = fj_cpu.view](i_t j) { - return fj.pb.coefficients[j] * fj.incumbent_assignment[fj.pb.variables[j]]; + thrust::make_transform_iterator(thrust::make_counting_iterator(0), [&fj_cpu](i_t j) { + return fj_cpu.h_coefficients[j] * fj_cpu.h_assignment[fj_cpu.h_variables[j]]; }); + // ARRAY WRITE: h_lhs[cstr_idx] - 1 write per constraint fj_cpu.h_lhs[cstr_idx] = fj_kahan_babushka_neumaier_sum(delta_it + offset_begin, delta_it + offset_end); + // ARRAY WRITE: h_lhs_sumcomp[cstr_idx] - 1 write per constraint fj_cpu.h_lhs_sumcomp[cstr_idx] = 0; - f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx); - f_t new_cost = fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); + f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + // ARRAY READ: h_lhs[cstr_idx] - 1 read per constraint + f_t new_cost = fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); if (new_cost < -cstr_tolerance) { fj_cpu.violated_constraints.insert(cstr_idx); fj_cpu.total_violations += new_cost; } else { fj_cpu.satisfied_constraints.insert(cstr_idx); } + // Total per constraint: (3 * avg_cstr_degree) reads + 2 writes + 1 read = (3 * avg_cstr_degree + // + 1) reads + 2 writes } // compute incumbent objective + // MEMORY OPS: Reads all n_vars elements from h_assignment and h_obj_coeffs + // ARRAY READ: h_assignment (n_vars reads) and h_obj_coeffs (n_vars reads) fj_cpu.h_incumbent_objective = thrust::inner_product( fj_cpu.h_assignment.begin(), fj_cpu.h_assignment.end(), fj_cpu.h_obj_coeffs.begin(), 0.); + // Total: 2 * n_vars reads } template @@ -603,15 +1167,20 @@ static thrust::tuple find_lift_move( fj_move_t best_move = fj_move_t{-1, 0}; fj_staged_score_t best_score = fj_staged_score_t::zero(); + // MEMORY OPS: Loop over objective variables (num_obj_vars iterations) + // This is called when in the feasible region to find improving moves for (auto var_idx : fj_cpu.h_objective_vars) { cuopt_assert(var_idx < fj_cpu.h_obj_coeffs.size(), "var_idx is out of bounds"); cuopt_assert(var_idx >= 0, "var_idx is out of bounds"); + // ARRAY READ: h_obj_coeffs[var_idx] - 1 read per iteration f_t obj_coeff = fj_cpu.h_obj_coeffs[var_idx]; f_t delta = -std::numeric_limits::infinity(); - f_t val = fj_cpu.h_assignment[var_idx]; + // ARRAY READ: h_assignment[var_idx] - 1 read per iteration + f_t val = fj_cpu.h_assignment[var_idx]; // special path for binary variables + // ARRAY READ: h_is_binary_variable[var_idx] - 1 read per iteration if (fj_cpu.h_is_binary_variable[var_idx]) { cuopt_assert(fj_cpu.view.pb.is_integer(val), "binary variable is not integer"); cuopt_assert(fj_cpu.view.pb.integer_equal(val, 0) || fj_cpu.view.pb.integer_equal(val, 1), @@ -620,29 +1189,43 @@ static thrust::tuple find_lift_move( // flip move wouldn't improve if (delta * obj_coeff >= 0) continue; } else { - f_t lfd_lb = get_lower(fj_cpu.h_var_bounds[var_idx]) - val; - f_t lfd_ub = get_upper(fj_cpu.h_var_bounds[var_idx]) - val; - auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration (non-binary) + f_t lfd_lb = get_lower(fj_cpu.h_var_bounds[var_idx].get()) - val; + f_t lfd_ub = get_upper(fj_cpu.h_var_bounds[var_idx].get()) - val; + auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); + // MEMORY OPS: Inner loop over constraints involving this variable (avg_var_degree iterations + // per outer iteration) for (i_t j = offset_begin; j < offset_end; j += 1) { - auto cstr_idx = fj_cpu.view.pb.reverse_constraints[j]; - auto cstr_coeff = fj_cpu.view.pb.reverse_coefficients[j]; - f_t c_lb = fj_cpu.view.pb.constraint_lower_bounds[cstr_idx]; - f_t c_ub = fj_cpu.view.pb.constraint_upper_bounds[cstr_idx]; + // ARRAY READ: h_reverse_constraints[j] - 1 read per inner iteration + auto cstr_idx = fj_cpu.h_reverse_constraints[j]; + // ARRAY READ: h_reverse_coefficients[j] - 1 read per inner iteration + auto cstr_coeff = fj_cpu.h_reverse_coefficients[j]; + // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per inner iteration (indirect) + f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; + // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per inner iteration (indirect) + f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); cuopt_assert(c_lb <= c_ub, "invalid bounds"); + // ARRAY READ: h_lhs[cstr_idx] - 1 read per inner iteration cuopt_assert(fj_cpu.view.cstr_satisfied(cstr_idx, fj_cpu.h_lhs[cstr_idx]), "cstr should be satisfied"); // Process each bound separately, as both are satified and may both be finite // otherwise range constraints aren't correctly handled for (auto [bound, sign] : {std::make_tuple(c_lb, -1), std::make_tuple(c_ub, 1)}) { - auto [delta, slack] = - get_mtm_for_bound(fj_cpu.view, var_idx, cstr_idx, cstr_coeff, bound, sign); + auto [delta, slack] = get_mtm_for_bound(fj_cpu.view, + var_idx, + cstr_idx, + cstr_coeff, + bound, + sign, + fj_cpu.h_assignment, + fj_cpu.h_lhs); if (cstr_coeff * sign < 0) { - if (fj_cpu.view.pb.is_integer_var(var_idx)) delta = ceil(delta); + if (is_integer_var(fj_cpu, var_idx)) delta = ceil(delta); } else { - if (fj_cpu.view.pb.is_integer_var(var_idx)) delta = floor(delta); + if (is_integer_var(fj_cpu, var_idx)) delta = floor(delta); } // skip this variable if there is no slack @@ -652,7 +1235,7 @@ static thrust::tuple find_lift_move( } else { lfd_lb = 0; } - } else if (!fj_cpu.view.pb.check_variable_within_bounds(var_idx, val + delta)) { + } else if (!check_variable_within_bounds(fj_cpu, var_idx, val + delta)) { continue; } else { if (cstr_coeff * sign < 0) { @@ -663,13 +1246,15 @@ static thrust::tuple find_lift_move( } } if (lfd_lb >= lfd_ub) break; + // Total per inner iteration: 5 reads (h_reverse_constraints, h_reverse_coefficients, + // h_cstr_lb, h_cstr_ub, h_lhs) } // invalid crossing bounds if (lfd_lb >= lfd_ub) { lfd_lb = lfd_ub = 0; } - if (!fj_cpu.view.pb.check_variable_within_bounds(var_idx, val + lfd_lb)) { lfd_lb = 0; } - if (!fj_cpu.view.pb.check_variable_within_bounds(var_idx, val + lfd_ub)) { lfd_ub = 0; } + if (!check_variable_within_bounds(fj_cpu, var_idx, val + lfd_lb)) { lfd_lb = 0; } + if (!check_variable_within_bounds(fj_cpu, var_idx, val + lfd_ub)) { lfd_ub = 0; } // Now that the life move domain is computed, compute the correct lift move cuopt_assert(isfinite(val), "invalid assignment value"); @@ -678,7 +1263,7 @@ static thrust::tuple find_lift_move( if (!isfinite(delta)) delta = 0; if (fj_cpu.view.pb.integer_equal(delta, (f_t)0)) continue; - if (tabu_check(fj_cpu, var_idx, delta)) continue; + if (tabu_check(fj_cpu, var_idx, delta)) continue; cuopt_assert(delta * obj_coeff < 0, "lift move doesn't improve the objective!"); @@ -692,6 +1277,7 @@ static thrust::tuple find_lift_move( best_score = score; best_move = move; } + // Total per outer iteration: 3 reads + (5 * avg_var_degree reads in inner loop for non-binary) } return thrust::make_tuple(best_move, best_score); @@ -709,20 +1295,24 @@ static void perturb(fj_cpu_climber_t& fj_cpu) std::mt19937(fj_cpu.settings.seed + fj_cpu.iterations)); raft::random::PCGenerator rng(fj_cpu.settings.seed + fj_cpu.iterations, 0, 0); + // MEMORY OPS: Loop over sampled variables (2 iterations typically) for (auto var_idx : sampled_vars) { - f_t lb = std::max(get_lower(fj_cpu.h_var_bounds[var_idx]), -1e7); - f_t ub = std::min(get_upper(fj_cpu.h_var_bounds[var_idx]), 1e7); + // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration + f_t lb = std::max(get_lower(fj_cpu.h_var_bounds[var_idx].get()), -1e7); + f_t ub = std::min(get_upper(fj_cpu.h_var_bounds[var_idx].get()), 1e7); f_t val = lb + (ub - lb) * rng.next_double(); - if (fj_cpu.view.pb.is_integer_var(var_idx)) { + if (is_integer_var(fj_cpu, var_idx)) { lb = std::ceil(lb); ub = std::floor(ub); val = std::round(val); val = std::min(std::max(val, lb), ub); } - cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, val), + cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, val)), "value is out of bounds"); + // ARRAY WRITE: h_assignment[var_idx] - 1 write per iteration fj_cpu.h_assignment[var_idx] = val; + // Total per iteration: 1 read + 1 write } recompute_lhs(fj_cpu); @@ -843,12 +1433,20 @@ static void init_fj_cpu(fj_cpu_climber_t& fj_cpu, std::make_pair(0, fj_staged_score_t::zero())); fj_cpu.cached_cstr_bounds.resize(fj_cpu.h_reverse_coefficients.size()); + // MEMORY OPS: INITIALIZATION - Loop over all variables (n_vars iterations) for (i_t var_idx = 0; var_idx < (i_t)fj_cpu.view.pb.n_variables; ++var_idx) { - auto [offset_begin, offset_end] = fj_cpu.view.pb.reverse_range_for_var(var_idx); + auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); + // MEMORY OPS: Inner loop over constraints per variable (avg_var_degree iterations per outer + // iteration) for (i_t i = offset_begin; i < offset_end; ++i) { + // ARRAY READ: h_reverse_constraints[i] - 1 read per inner iteration + // ARRAY READ: h_cstr_lb[h_reverse_constraints[i]] - 1 read per inner iteration (indirect) + // ARRAY READ: h_cstr_ub[h_reverse_constraints[i]] - 1 read per inner iteration (indirect) + // ARRAY WRITE: cached_cstr_bounds[i] - 1 write per inner iteration (2 f_t values) fj_cpu.cached_cstr_bounds[i] = std::make_pair(fj_cpu.h_cstr_lb[fj_cpu.h_reverse_constraints[i]], fj_cpu.h_cstr_ub[fj_cpu.h_reverse_constraints[i]]); + // Total per inner iteration: 3 reads + 1 write (2 values) } } @@ -857,6 +1455,9 @@ static void init_fj_cpu(fj_cpu_climber_t& fj_cpu, fj_cpu.iter_mtm_vars.reserve(fj_cpu.view.pb.n_variables); recompute_lhs(fj_cpu); + + // Precompute static problem features for regression model + precompute_problem_features(fj_cpu); } template @@ -942,17 +1543,30 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l auto loop_start = std::chrono::high_resolution_clock::now(); auto time_limit = std::chrono::milliseconds((int)(in_time_limit * 1000)); auto loop_time_start = std::chrono::high_resolution_clock::now(); + + // Initialize feature tracking + fj_cpu.last_feature_log_time = loop_start; + fj_cpu.prev_best_objective = fj_cpu.h_best_objective; + fj_cpu.iterations_since_best = 0; + while (!fj_cpu.halted && !fj_cpu.preemption_flag.load()) { // Check if 5 seconds have passed auto now = std::chrono::high_resolution_clock::now(); if (in_time_limit < std::numeric_limits::infinity() && now - loop_time_start > time_limit) { - CUOPT_LOG_TRACE("%sTime limit of %.4f seconds reached, breaking loop at iteration %d\n", + CUOPT_LOG_TRACE("%sTime limit of %.4f seconds reached, breaking loop at iteration %d", fj_cpu.log_prefix.c_str(), time_limit.count() / 1000.f, fj_cpu.iterations); break; } + if (fj_cpu.iterations >= fj_cpu.settings.iteration_limit) { + CUOPT_LOG_TRACE("%sIteration limit of %d reached, breaking loop at iteration %d", + fj_cpu.log_prefix.c_str(), + fj_cpu.settings.iteration_limit, + fj_cpu.iterations); + break; + } // periodically recompute the LHS and violation scores // to correct any accumulated numerical errors @@ -966,15 +1580,24 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l fj_move_t move = fj_move_t{-1, 0}; fj_staged_score_t score = fj_staged_score_t::invalid(); + bool is_lift = false; + bool is_mtm_viol = false; + bool is_mtm_sat = false; + // Perform lift moves - if (fj_cpu.violated_constraints.empty()) { thrust::tie(move, score) = find_lift_move(fj_cpu); } + if (fj_cpu.violated_constraints.empty()) { + thrust::tie(move, score) = find_lift_move(fj_cpu); + if (score > fj_staged_score_t::zero()) is_lift = true; + } // Regular MTM if (!(score > fj_staged_score_t::zero())) { thrust::tie(move, score) = find_mtm_move_viol(fj_cpu, fj_cpu.mtm_viol_samples); + if (score > fj_staged_score_t::zero()) is_mtm_viol = true; } // try with MTM in satisfied constraints if (fj_cpu.feasible_found && !(score > fj_staged_score_t::zero())) { thrust::tie(move, score) = find_mtm_move_sat(fj_cpu, fj_cpu.mtm_sat_samples); + if (score > fj_staged_score_t::zero()) is_mtm_sat = true; } // if we're in the feasible region but haven't found improvements in the last n iterations, // perturb @@ -987,13 +1610,17 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l if (score > fj_staged_score_t::zero() && !should_perturb) { apply_move(fj_cpu, move.var_idx, move.value, false); + // Track move types + if (is_lift) fj_cpu.n_lift_moves_window++; + if (is_mtm_viol) fj_cpu.n_mtm_viol_moves_window++; + if (is_mtm_sat) fj_cpu.n_mtm_sat_moves_window++; } else { // Local Min update_weights(fj_cpu); if (should_perturb) { perturb(fj_cpu); - for (auto& cached_move : fj_cpu.cached_mtm_moves) - cached_move.first = 0; + for (size_t i = 0; i < fj_cpu.cached_mtm_moves.size(); i++) + fj_cpu.cached_mtm_moves[i].first = 0; } thrust::tie(move, score) = find_mtm_move_viol(fj_cpu, 1, true); // pick a single random violated constraint @@ -1001,20 +1628,28 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l f_t delta = move.var_idx >= 0 ? move.value : 0; apply_move(fj_cpu, var_idx, delta, true); ++local_mins; + ++fj_cpu.n_local_minima_window; } // number of violated constraints is usually small (<100). recomputing from all LHSs is cheap // and more numerically precise than just adding to the accumulator in apply_move fj_cpu.total_violations = 0; + // MEMORY OPS: Loop over violated constraints (typically <100 iterations per main iteration) for (auto cstr_idx : fj_cpu.violated_constraints) { + // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration fj_cpu.total_violations += fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); + // Total per iteration: 1 read } if (fj_cpu.iterations % fj_cpu.log_interval == 0) { CUOPT_LOG_TRACE( - "%sCPUFJ iteration: %d, local mins: %d, best_objective: %g, viol: %zu, obj weight %g, maxw " - "%g\n", + "%sCPUFJ iteration: %d/%d, local mins: %d, best_objective: %g, viol: %zu, obj weight %g, " + "maxw " + "%g", fj_cpu.log_prefix.c_str(), fj_cpu.iterations, + fj_cpu.settings.iteration_limit != std::numeric_limits::max() + ? fj_cpu.settings.iteration_limit + : -1, local_mins, fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective), fj_cpu.violated_constraints.size(), @@ -1034,20 +1669,66 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l print_timing_stats(fj_cpu); } #endif + + // Collect and print PAPI metrics and regression features every 1000 iterations + if (fj_cpu.iterations % 1000 == 0 && fj_cpu.iterations > 0) { + auto now = std::chrono::high_resolution_clock::now(); + double time_window_ms = std::chrono::duration_cast>( + now - fj_cpu.last_feature_log_time) + .count() * + 1000.0; + double total_time_ms = + std::chrono::duration_cast>(now - loop_start).count() * + 1000.0; + + // Collect memory statistics + auto [loads, stores] = fj_cpu.memory_manifold.collect(); + + // Log all features including memory statistics + // log_regression_features(fj_cpu, time_window_ms, total_time_ms, loads, stores); + + fj_cpu.last_feature_log_time = now; + + std::map features_map; + features_map["n_vars"] = (float)fj_cpu.h_reverse_offsets.size() - 1; + features_map["n_cstrs"] = (float)fj_cpu.h_offsets.size() - 1; + features_map["total_nnz"] = (float)fj_cpu.h_reverse_offsets.back(); + features_map["mem_total_mb"] = (float)(loads + stores) / 1e6; + float time_prediction = + std::max( + (f_t)0.0, + (f_t)ceil(context.work_unit_predictors.cpufj_predictor.predict_scalar(features_map))) / + 1000.0; + + // Record work units with bias applied (bias < 1.0 makes CPUFJ appear faster) + double biased_work = time_prediction * fj_cpu.work_unit_bias; + double new_work_units = + fj_cpu.work_units_elapsed.load(std::memory_order_relaxed) + biased_work; + fj_cpu.work_units_elapsed.store(new_work_units, std::memory_order_release); + + // Notify producer_sync if registered + if (fj_cpu.producer_sync != nullptr) { fj_cpu.producer_sync->notify_progress(); } + + CUOPT_LOG_DEBUG("CPUFJ work units: %f incumbent %g", + fj_cpu.work_units_elapsed.load(std::memory_order_relaxed), + fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective)); + } + cuopt_func_call(sanity_checks(fj_cpu)); fj_cpu.iterations++; + fj_cpu.iterations_since_best++; } auto loop_end = std::chrono::high_resolution_clock::now(); double total_time = std::chrono::duration_cast>(loop_end - loop_start).count(); double avg_time_per_iter = total_time / fj_cpu.iterations; - CUOPT_LOG_TRACE("%sCPUFJ Average time per iteration: %.8fms\n", + CUOPT_LOG_TRACE("%sCPUFJ Average time per iteration: %.8fms", fj_cpu.log_prefix.c_str(), avg_time_per_iter * 1000.0); #if CPUFJ_TIMING_TRACE // Print final timing statistics - CUOPT_LOG_TRACE("\n=== Final Timing Statistics ===\n"); + CUOPT_LOG_TRACE("=== Final Timing Statistics ==="); print_timing_stats(fj_cpu); #endif diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cuh b/cpp/src/mip/feasibility_jump/fj_cpu.cuh index 4b9cfc0cc..a1ec24054 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cuh +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -18,6 +18,8 @@ #include #include +#include +#include namespace cuopt::linear_programming::detail { @@ -25,7 +27,41 @@ namespace cuopt::linear_programming::detail { // Maintaining a single source of truth for all members would be nice template struct fj_cpu_climber_t { - fj_cpu_climber_t(std::atomic& preemption_flag) : preemption_flag(preemption_flag) {} + fj_cpu_climber_t(std::atomic& preemption_flag) : preemption_flag(preemption_flag) + { +#define ADD_INSTRUMENTED(var) \ + std::make_pair(#var, std::ref(static_cast(var))) + + // Initialize memory manifold with all ins_vector members + memory_manifold = instrumentation_manifold_t{ADD_INSTRUMENTED(h_reverse_coefficients), + ADD_INSTRUMENTED(h_reverse_constraints), + ADD_INSTRUMENTED(h_reverse_offsets), + ADD_INSTRUMENTED(h_coefficients), + ADD_INSTRUMENTED(h_offsets), + ADD_INSTRUMENTED(h_variables), + ADD_INSTRUMENTED(h_obj_coeffs), + ADD_INSTRUMENTED(h_var_bounds), + ADD_INSTRUMENTED(h_cstr_lb), + ADD_INSTRUMENTED(h_cstr_ub), + ADD_INSTRUMENTED(h_var_types), + ADD_INSTRUMENTED(h_is_binary_variable), + ADD_INSTRUMENTED(h_objective_vars), + ADD_INSTRUMENTED(h_binary_indices), + ADD_INSTRUMENTED(h_tabu_nodec_until), + ADD_INSTRUMENTED(h_tabu_noinc_until), + ADD_INSTRUMENTED(h_tabu_lastdec), + ADD_INSTRUMENTED(h_tabu_lastinc), + ADD_INSTRUMENTED(h_lhs), + ADD_INSTRUMENTED(h_lhs_sumcomp), + ADD_INSTRUMENTED(h_cstr_left_weights), + ADD_INSTRUMENTED(h_cstr_right_weights), + ADD_INSTRUMENTED(h_assignment), + ADD_INSTRUMENTED(h_best_assignment), + ADD_INSTRUMENTED(cached_cstr_bounds), + ADD_INSTRUMENTED(iter_mtm_vars)}; + +#undef ADD_INSTRUMENTED + } fj_cpu_climber_t(const fj_cpu_climber_t& other) = delete; fj_cpu_climber_t& operator=(const fj_cpu_climber_t& other) = delete; @@ -36,33 +72,33 @@ struct fj_cpu_climber_t { fj_settings_t settings; typename fj_t::climber_data_t::view_t view; // Host copies of device data as struct members - std::vector h_reverse_coefficients; - std::vector h_reverse_constraints; - std::vector h_reverse_offsets; - std::vector h_coefficients; - std::vector h_offsets; - std::vector h_variables; - std::vector h_obj_coeffs; - std::vector::type> h_var_bounds; - std::vector h_cstr_lb; - std::vector h_cstr_ub; - std::vector h_var_types; - std::vector h_is_binary_variable; - std::vector h_objective_vars; - std::vector h_binary_indices; - - std::vector h_tabu_nodec_until; - std::vector h_tabu_noinc_until; - std::vector h_tabu_lastdec; - std::vector h_tabu_lastinc; - - std::vector h_lhs; - std::vector h_lhs_sumcomp; - std::vector h_cstr_left_weights; - std::vector h_cstr_right_weights; + ins_vector h_reverse_coefficients; + ins_vector h_reverse_constraints; + ins_vector h_reverse_offsets; + ins_vector h_coefficients; + ins_vector h_offsets; + ins_vector h_variables; + ins_vector h_obj_coeffs; + ins_vector::type> h_var_bounds; + ins_vector h_cstr_lb; + ins_vector h_cstr_ub; + ins_vector h_var_types; + ins_vector h_is_binary_variable; + ins_vector h_objective_vars; + ins_vector h_binary_indices; + + ins_vector h_tabu_nodec_until; + ins_vector h_tabu_noinc_until; + ins_vector h_tabu_lastdec; + ins_vector h_tabu_lastinc; + + ins_vector h_lhs; + ins_vector h_lhs_sumcomp; + ins_vector h_cstr_left_weights; + ins_vector h_cstr_right_weights; f_t max_weight; - std::vector h_assignment; - std::vector h_best_assignment; + ins_vector h_assignment; + ins_vector h_best_assignment; f_t h_objective_weight; f_t h_incumbent_objective; f_t h_best_objective; @@ -96,10 +132,10 @@ struct fj_cpu_climber_t { // CSC (transposed!) nnz-offset-indexed constraint bounds (lb, ub) // std::pair better compile down to 16 bytes!! GCC do your job! - std::vector> cached_cstr_bounds; + ins_vector> cached_cstr_bounds; std::vector var_bitmap; - std::vector iter_mtm_vars; + ins_vector iter_mtm_vars; i_t mtm_viol_samples{25}; i_t mtm_sat_samples{15}; @@ -110,11 +146,49 @@ struct fj_cpu_climber_t { i_t diversity_callback_interval{3000}; i_t timing_stats_interval{5000}; - std::function&)> improvement_callback{nullptr}; + // Callback with work unit timestamp for deterministic mode + // Parameters: objective, solution, work_units + std::function&, double)> improvement_callback{nullptr}; std::function&)> diversity_callback{nullptr}; std::string log_prefix{""}; + // Work unit tracking for deterministic synchronization + std::atomic work_units_elapsed{0.0}; + double work_unit_bias{0.8}; // Bias factor to keep CPUFJ ahead of B&B (< 1.0 means faster) + producer_sync_t* producer_sync{nullptr}; // Optional sync utility for notifying progress + std::atomic halted{false}; + + // Feature tracking for regression model (last 1000 iterations) + i_t nnz_processed_window{0}; + i_t n_lift_moves_window{0}; + i_t n_mtm_viol_moves_window{0}; + i_t n_mtm_sat_moves_window{0}; + i_t n_variable_updates_window{0}; + i_t n_local_minima_window{0}; + std::chrono::high_resolution_clock::time_point last_feature_log_time; + f_t prev_best_objective{std::numeric_limits::infinity()}; + i_t iterations_since_best{0}; + + // Cache and locality tracking + i_t hit_count_window_start{0}; + i_t miss_count_window_start{0}; + std::unordered_set unique_cstrs_accessed_window; + std::unordered_set unique_vars_accessed_window; + + // Precomputed static problem features + i_t n_binary_vars{0}; + i_t n_integer_vars{0}; + i_t max_var_degree{0}; + i_t max_cstr_degree{0}; + double avg_var_degree{0.0}; + double avg_cstr_degree{0.0}; + double var_degree_cv{0.0}; + double cstr_degree_cv{0.0}; + double problem_density{0.0}; + + // Memory instrumentation manifold + instrumentation_manifold_t memory_manifold; // TODO atomic ref? c++20 std::atomic& preemption_flag; }; diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 3bd4bac7b..0207df346 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -10,6 +10,7 @@ #include +#include #include #include #include @@ -82,18 +83,19 @@ void local_search_t::start_cpufj_scratch_threads(population_t 0); - cpu_fj.fj_cpu->log_prefix = "******* scratch " + std::to_string(counter) + ": "; - cpu_fj.fj_cpu->improvement_callback = [&population](f_t obj, const std::vector& h_vec) { - population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); - if (obj < local_search_best_obj) { - CUOPT_LOG_TRACE("******* New local search best obj %g, best overall %g", - context.problem_ptr->get_user_obj_from_solver_obj(obj), - context.problem_ptr->get_user_obj_from_solver_obj( - population.is_feasible() ? population.best_feasible().get_objective() - : std::numeric_limits::max())); - local_search_best_obj = obj; - } - }; + cpu_fj.fj_cpu->log_prefix = "******* scratch " + std::to_string(counter) + ": "; + cpu_fj.fj_cpu->improvement_callback = + [&population](f_t obj, const std::vector& h_vec, double /*work_units*/) { + population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); + if (obj < local_search_best_obj) { + CUOPT_LOG_TRACE("******* New local search best obj %g, best overall %g", + context.problem_ptr->get_user_obj_from_solver_obj(obj), + context.problem_ptr->get_user_obj_from_solver_obj( + population.is_feasible() ? population.best_feasible().get_objective() + : std::numeric_limits::max())); + local_search_best_obj = obj; + } + }; counter++; }; @@ -120,7 +122,7 @@ void local_search_t::start_cpufj_lptopt_scratch_threads( solution_lp, default_weights, default_weights, 0., context.preempt_heuristic_solver_); scratch_cpu_fj_on_lp_opt.fj_cpu->log_prefix = "******* scratch on LP optimal: "; scratch_cpu_fj_on_lp_opt.fj_cpu->improvement_callback = - [this, &population](f_t obj, const std::vector& h_vec) { + [this, &population](f_t obj, const std::vector& h_vec, double /*work_units*/) { population.add_external_solution(h_vec, obj, solution_origin_t::CPUFJ); if (obj < local_search_best_obj) { CUOPT_LOG_DEBUG("******* New local search best obj %g, best overall %g", @@ -146,6 +148,61 @@ void local_search_t::stop_cpufj_scratch_threads() scratch_cpu_fj_on_lp_opt.request_termination(); } +template +void local_search_t::start_cpufj_deterministic( + dual_simplex::branch_and_bound_t& bb) +{ + std::vector default_weights(context.problem_ptr->n_constraints, 1.); + + solution_t solution(*context.problem_ptr); + thrust::fill(solution.handle_ptr->get_thrust_policy(), + solution.assignment.begin(), + solution.assignment.end(), + 0.0); + solution.clamp_within_bounds(); + + deterministic_cpu_fj.fj_ptr = &fj; + deterministic_cpu_fj.fj_cpu = fj.create_cpu_climber(solution, + default_weights, + default_weights, + 0., + context.preempt_heuristic_solver_, + fj_settings_t{}, + /*randomize=*/true); + + deterministic_cpu_fj.fj_cpu->log_prefix = "******* deterministic CPUFJ: "; + + // Register with producer_sync for B&B synchronization + producer_sync_t& producer_sync = bb.get_producer_sync(); + deterministic_cpu_fj.fj_cpu->producer_sync = &producer_sync; + producer_sync.register_producer(&deterministic_cpu_fj.fj_cpu->work_units_elapsed); + + // Set up callback to send solutions to B&B with work unit timestamps + deterministic_cpu_fj.fj_cpu->improvement_callback = + [&bb](f_t obj, const std::vector& h_vec, double work_units) { + bb.set_new_solution_deterministic(h_vec, work_units); + }; + + // Start the CPUFJ thread + deterministic_cpu_fj.start_cpu_solver(); + + // Signal that registration is complete - B&B can now wait on producers + producer_sync.registration_complete(); +} + +template +void local_search_t::stop_cpufj_deterministic() +{ + if (deterministic_cpu_fj.fj_cpu) { + // Deregister from producer_sync before stopping + if (deterministic_cpu_fj.fj_cpu->producer_sync) { + deterministic_cpu_fj.fj_cpu->producer_sync->deregister_producer( + &deterministic_cpu_fj.fj_cpu->work_units_elapsed); + } + deterministic_cpu_fj.request_termination(); + } +} + template bool local_search_t::do_fj_solve(solution_t& solution, fj_t& in_fj, diff --git a/cpp/src/mip/local_search/local_search.cuh b/cpp/src/mip/local_search/local_search.cuh index 6fdf4ac72..8bf914270 100644 --- a/cpp/src/mip/local_search/local_search.cuh +++ b/cpp/src/mip/local_search/local_search.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -21,6 +21,11 @@ #include #include +namespace cuopt::linear_programming::dual_simplex { +template +class branch_and_bound_t; +} + namespace cuopt::linear_programming::detail { // make sure RANDOM is always the last @@ -87,6 +92,10 @@ class local_search_t { const std::string& source); i_t ls_threads() const { return ls_cpu_fj.size() + scratch_cpu_fj.size(); } + + // Start CPUFJ thread for deterministic mode with B&B integration + void start_cpufj_deterministic(dual_simplex::branch_and_bound_t& bb); + void stop_cpufj_deterministic(); void save_solution_and_add_cutting_plane(solution_t& solution, rmm::device_uvector& best_solution, f_t& best_objective); @@ -120,6 +129,7 @@ class local_search_t { std::array, 8> ls_cpu_fj; std::array, 1> scratch_cpu_fj; cpu_fj_thread_t scratch_cpu_fj_on_lp_opt; + cpu_fj_thread_t deterministic_cpu_fj; // CPUFJ for deterministic mode problem_t problem_with_objective_cut; bool cutting_plane_added_for_active_run{false}; }; diff --git a/cpp/src/utilities/producer_sync.hpp b/cpp/src/utilities/producer_sync.hpp new file mode 100644 index 000000000..13c9bde38 --- /dev/null +++ b/cpp/src/utilities/producer_sync.hpp @@ -0,0 +1,143 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include +#include +#include +#include + +namespace cuopt { + +/** + * One-way synchronization utility for producer threads. + * + * Producers (e.g., CPUFJ) register their work unit progress atomics and advance independently. + * The consumer (e.g., B&B BSP coordinator) can wait until all producers have reached a + * target work unit threshold before proceeding. + * + * Key invariant: Producers must not fall behind the consumer's horizon. The consumer + * waits at sync points until all producers have caught up. + */ +class producer_sync_t { + public: + producer_sync_t() = default; + + /** + * Register a producer's work unit progress atomic. + * Must be called before registration_complete(). + */ + void register_producer(std::atomic* progress_ptr) + { + std::lock_guard lock(mutex_); + producers_.push_back(progress_ptr); + cv_.notify_all(); + } + + /** + * Deregister a producer (e.g., when it terminates). + */ + void deregister_producer(std::atomic* progress_ptr) + { + std::lock_guard lock(mutex_); + auto it = std::find(producers_.begin(), producers_.end(), progress_ptr); + if (it != producers_.end()) { producers_.erase(it); } + cv_.notify_all(); + } + + /** + * Signal that all expected producers have been registered. + * Must be called before the consumer can proceed with wait_for_producers(). + */ + void registration_complete() + { + std::lock_guard lock(mutex_); + registration_complete_ = true; + cv_.notify_all(); + } + + /** + * Check if registration is complete (non-blocking). + */ + bool is_registration_complete() const + { + std::lock_guard lock(mutex_); + return registration_complete_; + } + + /** + * Wait until: + * 1. registration_complete() has been called, AND + * 2. All registered producers have work units >= target_work_units + * + * Returns immediately if no producers are registered (after registration_complete). + */ + void wait_for_producers(double target_work_units) + { + std::unique_lock lock(mutex_); + cv_.wait(lock, [this, target_work_units] { + if (!registration_complete_) { return false; } + return all_producers_at_or_ahead(target_work_units); + }); + } + + /** + * Wake up any waiting consumer. Call this when a producer advances its work units. + */ + void notify_progress() { cv_.notify_all(); } + + /** + * Get the minimum work units among all registered producers. + * Returns infinity if no producers are registered. + */ + double get_min_producer_progress() const + { + std::lock_guard lock(mutex_); + if (producers_.empty()) { return std::numeric_limits::infinity(); } + + double min_progress = std::numeric_limits::infinity(); + for (const auto* progress_ptr : producers_) { + min_progress = std::min(min_progress, progress_ptr->load(std::memory_order_acquire)); + } + return min_progress; + } + + /** + * Get the number of registered producers. + */ + size_t num_producers() const + { + std::lock_guard lock(mutex_); + return producers_.size(); + } + + private: + bool all_producers_at_or_ahead(double target) const + { + for (const auto* progress_ptr : producers_) { + if (progress_ptr->load(std::memory_order_acquire) < target) { return false; } + } + return true; + } + + mutable std::mutex mutex_; + std::condition_variable cv_; + std::vector*> producers_; + bool registration_complete_{false}; +}; + +} // namespace cuopt From bc3bfde6e19033d9ea935a4f8ca1283bc9c196f1 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 09:01:13 +0000 Subject: [PATCH 215/366] fjcpu cleanup --- cpp/src/mip/feasibility_jump/fj_cpu.cu | 207 +++---------------------- 1 file changed, 25 insertions(+), 182 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index f82b4d785..b1d8e2664 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -272,19 +272,14 @@ static void print_timing_stats(fj_cpu_climber_t& fj_cpu) template static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) { - // Count variable types - use host vectors fj_cpu.n_binary_vars = 0; fj_cpu.n_integer_vars = 0; - // MEMORY OPS: Loop over all variables (n_vars iterations) for (i_t i = 0; i < (i_t)fj_cpu.h_is_binary_variable.size(); i++) { - // ARRAY READ: h_is_binary_variable[i] - 1 read per iteration if (fj_cpu.h_is_binary_variable[i]) { fj_cpu.n_binary_vars++; } else if (fj_cpu.h_var_types[i] == var_t::INTEGER) { - // ARRAY READ: h_var_types[i] - 1 read per iteration (conditional) fj_cpu.n_integer_vars++; } - // Total per iteration: 2 array reads } i_t total_nnz = fj_cpu.h_reverse_offsets.back(); @@ -293,27 +288,18 @@ static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) fj_cpu.avg_var_degree = (double)total_nnz / n_vars; - // Compute variable degree statistics (max, cv) fj_cpu.max_var_degree = 0; std::vector var_degrees(n_vars); - // MEMORY OPS: Loop over all variables (n_vars iterations) for (i_t i = 0; i < n_vars; i++) { - // ARRAY READ: h_reverse_offsets[i] and h_reverse_offsets[i+1] - 2 reads per iteration - i_t degree = fj_cpu.h_reverse_offsets[i + 1] - fj_cpu.h_reverse_offsets[i]; - // ARRAY WRITE: var_degrees[i] - 1 write per iteration + i_t degree = fj_cpu.h_reverse_offsets[i + 1] - fj_cpu.h_reverse_offsets[i]; var_degrees[i] = degree; fj_cpu.max_var_degree = std::max(fj_cpu.max_var_degree, degree); - // Total per iteration: 2 reads + 1 write = 3 memory ops } - // Compute variable degree coefficient of variation double var_deg_variance = 0.0; - // MEMORY OPS: Loop over all variables (n_vars iterations) for (i_t i = 0; i < n_vars; i++) { - // ARRAY READ: var_degrees[i] - 1 read per iteration double diff = var_degrees[i] - fj_cpu.avg_var_degree; var_deg_variance += diff * diff; - // Total per iteration: 1 read } var_deg_variance /= n_vars; double var_degree_std = std::sqrt(var_deg_variance); @@ -321,27 +307,18 @@ static void precompute_problem_features(fj_cpu_climber_t& fj_cpu) fj_cpu.avg_cstr_degree = (double)total_nnz / n_cstrs; - // Compute constraint degree statistics (max, cv) fj_cpu.max_cstr_degree = 0; std::vector cstr_degrees(n_cstrs); - // MEMORY OPS: Loop over all constraints (n_cstrs iterations) for (i_t i = 0; i < n_cstrs; i++) { - // ARRAY READ: h_offsets[i] and h_offsets[i+1] - 2 reads per iteration - i_t degree = fj_cpu.h_offsets[i + 1] - fj_cpu.h_offsets[i]; - // ARRAY WRITE: cstr_degrees[i] - 1 write per iteration + i_t degree = fj_cpu.h_offsets[i + 1] - fj_cpu.h_offsets[i]; cstr_degrees[i] = degree; fj_cpu.max_cstr_degree = std::max(fj_cpu.max_cstr_degree, degree); - // Total per iteration: 2 reads + 1 write = 3 memory ops } - // Compute constraint degree coefficient of variation double cstr_deg_variance = 0.0; - // MEMORY OPS: Loop over all constraints (n_cstrs iterations) for (i_t i = 0; i < n_cstrs; i++) { - // ARRAY READ: cstr_degrees[i] - 1 read per iteration double diff = cstr_degrees[i] - fj_cpu.avg_cstr_degree; cstr_deg_variance += diff * diff; - // Total per iteration: 1 read } cstr_deg_variance /= n_cstrs; double cstr_degree_std = std::sqrt(cstr_deg_variance); @@ -498,7 +475,6 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, fj_cpu.unique_vars_accessed_window.clear(); } -// Local implementations that use instrumented vectors template static inline std::pair reverse_range_for_var(fj_cpu_climber_t& fj_cpu, i_t var_idx) @@ -570,7 +546,6 @@ static inline std::pair compute_score(fj_cpu_climber_t timer(fj_cpu.compute_score_times); - // ARRAY READ: h_obj_coeffs[var_idx] - 1 read f_t obj_diff = fj_cpu.h_obj_coeffs[var_idx] * delta; cuopt_assert(isfinite(delta), ""); @@ -583,22 +558,14 @@ static inline std::pair compute_score(fj_cpu_climber_t(fj_cpu, var_idx); fj_cpu.nnz_processed_window += (offset_end - offset_begin); - // MEMORY OPS: Loop over all constraints involving this variable (avg_var_degree iterations) - // This is one of the HOTTEST loops in the code - called for every candidate move evaluation for (i_t i = offset_begin; i < offset_end; i++) { - // ARRAY READ: h_reverse_constraints[i] - 1 read per iteration auto cstr_idx = fj_cpu.h_reverse_constraints[i]; fj_cpu.unique_cstrs_accessed_window.insert(cstr_idx); - // ARRAY READ: h_reverse_coefficients[i] - 1 read per iteration - auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; - // ARRAY READ: cached_cstr_bounds[i] - 1 read per iteration (reads 2 f_t values) + auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i].get(); cuopt_assert(c_lb <= c_ub, "invalid bounds"); - // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (indirect indexing) - // feas_score_constraint also reads from h_cstr_left_weights[cstr_idx] and - // h_cstr_right_weights[cstr_idx] auto [cstr_base_feas, cstr_bonus_robust] = feas_score_constraint(fj_cpu.view, var_idx, @@ -613,8 +580,6 @@ static inline std::pair compute_score(fj_cpu_climber_t compute_score(fj_cpu_climber_t static void smooth_weights(fj_cpu_climber_t& fj_cpu) { - // MEMORY OPS: Loop over all constraints (n_cstrs iterations) for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; cstr_idx++) { // consider only satisfied constraints if (fj_cpu.violated_constraints.count(cstr_idx)) continue; - // ARRAY READ: h_cstr_left_weights[cstr_idx] - 1 read per iteration (if not violated) f_t weight_l = max((f_t)0, fj_cpu.h_cstr_left_weights[cstr_idx] - 1); - // ARRAY READ: h_cstr_right_weights[cstr_idx] - 1 read per iteration (if not violated) f_t weight_r = max((f_t)0, fj_cpu.h_cstr_right_weights[cstr_idx] - 1); - // ARRAY WRITE: h_cstr_left_weights[cstr_idx] - 1 write per iteration (if not violated) - fj_cpu.h_cstr_left_weights[cstr_idx] = weight_l; - // ARRAY WRITE: h_cstr_right_weights[cstr_idx] - 1 write per iteration (if not violated) + fj_cpu.h_cstr_left_weights[cstr_idx] = weight_l; fj_cpu.h_cstr_right_weights[cstr_idx] = weight_r; - // Total per iteration (for satisfied constraints): 2 reads + 2 writes = 4 memory ops } if (fj_cpu.h_objective_weight > 0 && fj_cpu.h_incumbent_objective >= fj_cpu.h_best_objective) { @@ -677,24 +636,18 @@ static void update_weights(fj_cpu_climber_t& fj_cpu) return; } - // MEMORY OPS: Loop over violated constraints (typically small: <100 iterations) for (auto cstr_idx : fj_cpu.violated_constraints) { - // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration f_t curr_incumbent_lhs = fj_cpu.h_lhs[cstr_idx]; - // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per iteration f_t curr_lower_excess = fj_cpu.view.lower_excess_score(cstr_idx, curr_incumbent_lhs, fj_cpu.h_cstr_lb[cstr_idx]); - // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per iteration f_t curr_upper_excess = fj_cpu.view.upper_excess_score(cstr_idx, curr_incumbent_lhs, fj_cpu.h_cstr_ub[cstr_idx]); f_t curr_excess_score = curr_lower_excess + curr_upper_excess; f_t old_weight; if (curr_lower_excess < 0.) { - // ARRAY READ: h_cstr_left_weights[cstr_idx] - 1 read per iteration (conditional) old_weight = fj_cpu.h_cstr_left_weights[cstr_idx]; } else { - // ARRAY READ: h_cstr_right_weights[cstr_idx] - 1 read per iteration (conditional) old_weight = fj_cpu.h_cstr_right_weights[cstr_idx]; } @@ -707,11 +660,9 @@ static void update_weights(fj_cpu_climber_t& fj_cpu) new_weight = round(new_weight); if (curr_lower_excess < 0.) { - // ARRAY WRITE: h_cstr_left_weights[cstr_idx] - 1 write per iteration (conditional) fj_cpu.h_cstr_left_weights[cstr_idx] = new_weight; fj_cpu.max_weight = max(fj_cpu.max_weight, new_weight); } else { - // ARRAY WRITE: h_cstr_right_weights[cstr_idx] - 1 write per iteration (conditional) fj_cpu.h_cstr_right_weights[cstr_idx] = new_weight; fj_cpu.max_weight = max(fj_cpu.max_weight, new_weight); } @@ -719,14 +670,9 @@ static void update_weights(fj_cpu_climber_t& fj_cpu) // Invalidate related cached move scores auto [relvar_offset_begin, relvar_offset_end] = range_for_constraint(fj_cpu, cstr_idx); - // MEMORY OPS: Inner loop over variables in this constraint (avg_cstr_degree iterations per - // outer iteration) for (auto i = relvar_offset_begin; i < relvar_offset_end; i++) { - // ARRAY WRITE: cached_mtm_moves[i].first - 1 write per inner iteration fj_cpu.cached_mtm_moves[i].first = 0; - // Total per inner iteration: 1 write } - // Total per outer iteration: 4 reads + 1 write + (avg_cstr_degree writes in inner loop) } if (fj_cpu.violated_constraints.empty()) { fj_cpu.h_objective_weight += 1; } @@ -746,45 +692,33 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, // Update the LHSs of all involved constraints. auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); - // Track work metrics for regression model fj_cpu.nnz_processed_window += (offset_end - offset_begin); fj_cpu.n_variable_updates_window++; fj_cpu.unique_vars_accessed_window.insert(var_idx); i_t previous_viol = fj_cpu.violated_constraints.size(); - // MEMORY OPS: CRITICAL LOOP - Loop over all constraints involving this variable (avg_var_degree - // iterations) This loop is called ONCE PER ITERATION and updates constraint LHS values for (auto i = offset_begin; i < offset_end; i++) { cuopt_assert(i < (i_t)fj_cpu.h_reverse_constraints.size(), ""); - // ARRAY READ: cached_cstr_bounds[i] - 1 read per iteration (reads 2 f_t values) auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[i].get(); - // ARRAY READ: h_reverse_constraints[i] - 1 read per iteration auto cstr_idx = fj_cpu.h_reverse_constraints[i]; fj_cpu.unique_cstrs_accessed_window.insert(cstr_idx); - // ARRAY READ: h_reverse_coefficients[i] - 1 read per iteration auto cstr_coeff = fj_cpu.h_reverse_coefficients[i]; - // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (indirect indexing) f_t old_lhs = fj_cpu.h_lhs[cstr_idx]; // Kahan compensated summation - // ARRAY READ: h_lhs_sumcomp[cstr_idx] - 1 read per iteration - f_t y = cstr_coeff * delta - fj_cpu.h_lhs_sumcomp[cstr_idx]; - f_t t = old_lhs + y; - // ARRAY WRITE: h_lhs_sumcomp[cstr_idx] - 1 write per iteration + f_t y = cstr_coeff * delta - fj_cpu.h_lhs_sumcomp[cstr_idx]; + f_t t = old_lhs + y; fj_cpu.h_lhs_sumcomp[cstr_idx] = (t - old_lhs) - y; - // ARRAY WRITE: h_lhs[cstr_idx] - 1 write per iteration - fj_cpu.h_lhs[cstr_idx] = t; - // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration (just written) - f_t new_lhs = fj_cpu.h_lhs[cstr_idx]; - f_t old_cost = fj_cpu.view.excess_score(cstr_idx, old_lhs, c_lb, c_ub); - f_t new_cost = fj_cpu.view.excess_score(cstr_idx, new_lhs, c_lb, c_ub); - f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + fj_cpu.h_lhs[cstr_idx] = t; + f_t new_lhs = fj_cpu.h_lhs[cstr_idx]; + f_t old_cost = fj_cpu.view.excess_score(cstr_idx, old_lhs, c_lb, c_ub); + f_t new_cost = fj_cpu.view.excess_score(cstr_idx, new_lhs, c_lb, c_ub); + f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); // trigger early lhs recomputation if the sumcomp term gets too large // to avoid large numerical errors - // ARRAY READ: h_lhs_sumcomp[cstr_idx] - 1 read per iteration if (fabs(fj_cpu.h_lhs_sumcomp[cstr_idx]) > BIGVAL_THRESHOLD) fj_cpu.trigger_early_lhs_recomputation = true; @@ -804,14 +738,9 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, // Invalidate related cached move scores auto [relvar_offset_begin, relvar_offset_end] = range_for_constraint(fj_cpu, cstr_idx); - // MEMORY OPS: Inner loop over variables in this constraint (avg_cstr_degree iterations per - // outer iteration) for (auto i = relvar_offset_begin; i < relvar_offset_end; i++) { - // ARRAY WRITE: cached_mtm_moves[i].first - 1 write per inner iteration fj_cpu.cached_mtm_moves[i].first = 0; - // Total per inner iteration: 1 write } - // Total per outer iteration: 7 reads + 3 writes + (avg_cstr_degree writes in inner loop) } if (previous_viol > 0 && fj_cpu.violated_constraints.empty()) { @@ -819,20 +748,17 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, } // update the assignment and objective proper - // ARRAY READ: h_assignment[var_idx] - 1 read f_t new_val = fj_cpu.h_assignment[var_idx] + delta; if (is_integer_var(fj_cpu, var_idx)) { cuopt_assert(fj_cpu.view.pb.integer_equal(new_val, round(new_val)), "new_val is not integer"); new_val = round(new_val); } - // ARRAY WRITE: h_assignment[var_idx] - 1 write fj_cpu.h_assignment[var_idx] = new_val; cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), "assignment not within bounds"); cuopt_assert(isfinite(new_val), "assignment is not finite"); - // ARRAY READ: h_obj_coeffs[var_idx] - 1 read fj_cpu.h_incumbent_objective += fj_cpu.h_obj_coeffs[var_idx] * delta; if (fj_cpu.h_incumbent_objective < fj_cpu.h_best_objective && fj_cpu.violated_constraints.empty()) { @@ -843,9 +769,8 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, cuopt_assert(fj_cpu.satisfied_constraints.size() == fj_cpu.view.pb.n_constraints, ""); fj_cpu.h_best_objective = fj_cpu.h_incumbent_objective - fj_cpu.settings.parameters.breakthrough_move_epsilon; - // ARRAY WRITE: h_best_assignment = h_assignment - n_vars writes (vector copy) fj_cpu.h_best_assignment = fj_cpu.h_assignment; - fj_cpu.iterations_since_best = 0; // Reset counter on improvement + fj_cpu.iterations_since_best = 0; CUOPT_LOG_TRACE("%sCPUFJ: new best objective: %g", fj_cpu.log_prefix.c_str(), fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective)); @@ -862,25 +787,18 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, rng.next_u32() % (fj_cpu.settings.parameters.tabu_tenure_max - fj_cpu.settings.parameters.tabu_tenure_min); if (delta > 0) { - // ARRAY WRITE: h_tabu_lastinc[var_idx] - 1 write - fj_cpu.h_tabu_lastinc[var_idx] = fj_cpu.iterations; - // ARRAY WRITE: h_tabu_nodec_until[var_idx] - 1 write + fj_cpu.h_tabu_lastinc[var_idx] = fj_cpu.iterations; fj_cpu.h_tabu_nodec_until[var_idx] = fj_cpu.iterations + tabu_tenure; - // ARRAY WRITE: h_tabu_noinc_until[var_idx] - 1 write fj_cpu.h_tabu_noinc_until[var_idx] = fj_cpu.iterations + tabu_tenure / 2; - // CUOPT_LOG_TRACE("CPU: tabu nodec_until: %d", fj_cpu.h_tabu_nodec_until[var_idx]); + // CUOPT_LOG_TRACE("CPU: tabu nodec_until: %d\n", fj_cpu.h_tabu_nodec_until[var_idx]); } else { - // ARRAY WRITE: h_tabu_lastdec[var_idx] - 1 write - fj_cpu.h_tabu_lastdec[var_idx] = fj_cpu.iterations; - // ARRAY WRITE: h_tabu_noinc_until[var_idx] - 1 write + fj_cpu.h_tabu_lastdec[var_idx] = fj_cpu.iterations; fj_cpu.h_tabu_noinc_until[var_idx] = fj_cpu.iterations + tabu_tenure; - // ARRAY WRITE: h_tabu_nodec_until[var_idx] - 1 write fj_cpu.h_tabu_nodec_until[var_idx] = fj_cpu.iterations + tabu_tenure / 2; - // CUOPT_LOG_TRACE("CPU: tabu noinc_until: %d", fj_cpu.h_tabu_nodec_until[var_idx]); + // CUOPT_LOG_TRACE("CPU: tabu noinc_until: %d\n", fj_cpu.h_tabu_noinc_until[var_idx]); } - // ARRAY WRITE: flip_move_computed - n_vars writes (fill with false) + std::fill(fj_cpu.flip_move_computed.begin(), fj_cpu.flip_move_computed.end(), false); - // ARRAY WRITE: var_bitmap - n_vars writes (fill with false) std::fill(fj_cpu.var_bitmap.begin(), fj_cpu.var_bitmap.end(), false); fj_cpu.iter_mtm_vars.clear(); } @@ -897,20 +815,13 @@ static thrust::tuple find_mtm_move( fj_staged_score_t best_score = fj_staged_score_t::invalid(); // collect all the variables that are involved in the target constraints - // MEMORY OPS: Outer loop over target constraints (sample_size iterations, typically 15-100) for (size_t cstr_idx : target_cstrs) { auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); - // MEMORY OPS: Inner loop over variables in each constraint (avg_cstr_degree per outer - // iteration) for (auto i = offset_begin; i < offset_end; i++) { - // ARRAY READ: h_variables[i] - 1 read per iteration i_t var_idx = fj_cpu.h_variables[i]; - // ARRAY READ: var_bitmap[var_idx] - 1 read per iteration if (fj_cpu.var_bitmap[var_idx]) continue; fj_cpu.iter_mtm_vars.push_back(var_idx); - // ARRAY WRITE: var_bitmap[var_idx] - 1 write per iteration (if not already set) fj_cpu.var_bitmap[var_idx] = true; - // Total per inner iteration: 2 reads + 1 write (conditional) } } // estimate the amount of nnzs to consider @@ -923,32 +834,26 @@ static thrust::tuple find_mtm_move( f_t nnz_pick_probability = 1; if (nnz_sum > fj_cpu.nnz_samples) nnz_pick_probability = (f_t)fj_cpu.nnz_samples / nnz_sum; - // MEMORY OPS: HOTTEST LOOP - Outer loop over target constraints (sample_size iterations) for (size_t cstr_idx : target_cstrs) { auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); f_t cstr_tol = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); cuopt_assert(cstr_idx < fj_cpu.h_cstr_lb.size(), "cstr_idx is out of bounds"); auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); - // MEMORY OPS: Inner loop over variables (avg_cstr_degree per outer iteration) for (auto i = offset_begin; i < offset_end; i++) { // early cached check - // ARRAY READ: cached_mtm_moves[i] - 1 read per iteration if (auto& cached_move = fj_cpu.cached_mtm_moves[i]; cached_move.first != 0) { if (best_score < cached_move.second) { - // ARRAY READ: h_variables[i] - 1 read per iteration (cache hit) auto var_idx = fj_cpu.h_variables[i]; - // ARRAY READ: h_assignment[var_idx] - 1 read per iteration (cache hit) if (check_variable_within_bounds( fj_cpu, var_idx, fj_cpu.h_assignment[var_idx] + cached_move.first)) { best_score = cached_move.second; best_move = fj_move_t{var_idx, cached_move.first}; } - // cuopt_assert(check_variable_within_bounds(fj_cpu, var_idx, + // cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, // fj_cpu.h_assignment[var_idx] + cached_move.first), "best move not within bounds"); } fj_cpu.hit_count++; - // Total per cache hit: 3 reads continue; } @@ -956,29 +861,21 @@ static thrust::tuple find_mtm_move( if (nnz_pick_probability < 1) if (rng.next_float() > nnz_pick_probability) continue; - // ARRAY READ: h_variables[i] - 1 read per iteration (cache miss) auto var_idx = fj_cpu.h_variables[i]; - // ARRAY READ: h_assignment[var_idx] - 1 read per iteration (cache miss) f_t val = fj_cpu.h_assignment[var_idx]; f_t new_val = val; f_t delta = 0; // Special case for binary variables - // ARRAY READ: h_is_binary_variable[var_idx] - 1 read per iteration if (fj_cpu.h_is_binary_variable[var_idx]) { - // ARRAY READ: flip_move_computed[var_idx] - 1 read per iteration (conditional) if (fj_cpu.flip_move_computed[var_idx]) continue; - // ARRAY WRITE: flip_move_computed[var_idx] - 1 write per iteration (conditional) fj_cpu.flip_move_computed[var_idx] = true; new_val = 1 - val; } else { - // ARRAY READ: h_coefficients[i] - 1 read per iteration (non-binary) auto cstr_coeff = fj_cpu.h_coefficients[i]; - // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per iteration (non-binary) f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; - // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per iteration (non-binary) f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; auto [delta, sign, slack, cstr_tolerance] = get_mtm_for_constraint(fj_cpu.view, @@ -997,7 +894,6 @@ static thrust::tuple find_mtm_move( new_val = val + delta; } // fallback - // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration (non-binary, conditional) if (new_val < get_lower(fj_cpu.h_var_bounds[var_idx].get()) || new_val > get_upper(fj_cpu.h_var_bounds[var_idx].get())) { new_val = cstr_coeff * sign > 0 ? get_lower(fj_cpu.h_var_bounds[var_idx].get()) @@ -1016,11 +912,8 @@ static thrust::tuple find_mtm_move( cuopt_assert(move.var_idx < fj_cpu.h_assignment.size(), "move.var_idx is out of bounds"); cuopt_assert(move.var_idx >= 0, "move.var_idx is not positive"); - // CRITICAL: compute_score() does ~6-7 array reads per constraint (see compute_score - // annotations) auto [score, infeasibility] = compute_score(fj_cpu, var_idx, delta); - // ARRAY WRITE: cached_mtm_moves[i] - 1 write per iteration (cache miss) - fj_cpu.cached_mtm_moves[i] = std::make_pair(delta, score); + fj_cpu.cached_mtm_moves[i] = std::make_pair(delta, score); fj_cpu.miss_count++; // reject this move if it would increase the target variable to a numerically unstable value if (fj_cpu.view.move_numerically_stable( @@ -1030,7 +923,6 @@ static thrust::tuple find_mtm_move( best_move = move; } } - // Total per cache miss: ~8-11 reads + 1 write + compute_score overhead } } @@ -1039,9 +931,7 @@ static thrust::tuple find_mtm_move( fj_cpu.h_best_objective < std::numeric_limits::infinity() && fj_cpu.h_incumbent_objective >= fj_cpu.h_best_objective + fj_cpu.settings.parameters.breakthrough_move_epsilon) { - // MEMORY OPS: Loop over objective variables (num_obj_vars iterations, typically small) for (auto var_idx : fj_cpu.h_objective_vars) { - // ARRAY READ: h_assignment[var_idx] - 1 read per iteration f_t old_val = fj_cpu.h_assignment[var_idx]; f_t new_val = get_breakthrough_move(fj_cpu.view, var_idx); @@ -1056,7 +946,6 @@ static thrust::tuple find_mtm_move( if (tabu_check(fj_cpu, var_idx, delta)) continue; - // CRITICAL: compute_score() does ~6-7 array reads per constraint involved auto [score, infeasibility] = compute_score(fj_cpu, var_idx, delta); cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, new_val)), ""); @@ -1069,7 +958,6 @@ static thrust::tuple find_mtm_move( best_move = move; } } - // Total per iteration: 1 read + compute_score overhead } } @@ -1118,44 +1006,30 @@ static void recompute_lhs(fj_cpu_climber_t& fj_cpu) fj_cpu.violated_constraints.clear(); fj_cpu.satisfied_constraints.clear(); fj_cpu.total_violations = 0; - // MEMORY OPS: CRITICAL LOOP - Loop over all constraints (n_cstrs iterations) - // This is called periodically to recompute all LHS values from scratch for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; ++cstr_idx) { auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); - // MEMORY OPS: For each constraint, reads avg_cstr_degree elements from: - // - h_coefficients[offset_begin:offset_end] - avg_cstr_degree reads - // - h_variables[offset_begin:offset_end] - avg_cstr_degree reads (indirect indexing) - // - h_assignment[h_variables[i]] - avg_cstr_degree reads (indirect indexing) auto delta_it = thrust::make_transform_iterator(thrust::make_counting_iterator(0), [&fj_cpu](i_t j) { return fj_cpu.h_coefficients[j] * fj_cpu.h_assignment[fj_cpu.h_variables[j]]; }); - // ARRAY WRITE: h_lhs[cstr_idx] - 1 write per constraint fj_cpu.h_lhs[cstr_idx] = fj_kahan_babushka_neumaier_sum(delta_it + offset_begin, delta_it + offset_end); - // ARRAY WRITE: h_lhs_sumcomp[cstr_idx] - 1 write per constraint fj_cpu.h_lhs_sumcomp[cstr_idx] = 0; f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); - // ARRAY READ: h_lhs[cstr_idx] - 1 read per constraint - f_t new_cost = fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); + f_t new_cost = fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); if (new_cost < -cstr_tolerance) { fj_cpu.violated_constraints.insert(cstr_idx); fj_cpu.total_violations += new_cost; } else { fj_cpu.satisfied_constraints.insert(cstr_idx); } - // Total per constraint: (3 * avg_cstr_degree) reads + 2 writes + 1 read = (3 * avg_cstr_degree - // + 1) reads + 2 writes } // compute incumbent objective - // MEMORY OPS: Reads all n_vars elements from h_assignment and h_obj_coeffs - // ARRAY READ: h_assignment (n_vars reads) and h_obj_coeffs (n_vars reads) fj_cpu.h_incumbent_objective = thrust::inner_product( fj_cpu.h_assignment.begin(), fj_cpu.h_assignment.end(), fj_cpu.h_obj_coeffs.begin(), 0.); - // Total: 2 * n_vars reads } template @@ -1167,20 +1041,15 @@ static thrust::tuple find_lift_move( fj_move_t best_move = fj_move_t{-1, 0}; fj_staged_score_t best_score = fj_staged_score_t::zero(); - // MEMORY OPS: Loop over objective variables (num_obj_vars iterations) - // This is called when in the feasible region to find improving moves for (auto var_idx : fj_cpu.h_objective_vars) { cuopt_assert(var_idx < fj_cpu.h_obj_coeffs.size(), "var_idx is out of bounds"); cuopt_assert(var_idx >= 0, "var_idx is out of bounds"); - // ARRAY READ: h_obj_coeffs[var_idx] - 1 read per iteration f_t obj_coeff = fj_cpu.h_obj_coeffs[var_idx]; f_t delta = -std::numeric_limits::infinity(); - // ARRAY READ: h_assignment[var_idx] - 1 read per iteration - f_t val = fj_cpu.h_assignment[var_idx]; + f_t val = fj_cpu.h_assignment[var_idx]; // special path for binary variables - // ARRAY READ: h_is_binary_variable[var_idx] - 1 read per iteration if (fj_cpu.h_is_binary_variable[var_idx]) { cuopt_assert(fj_cpu.view.pb.is_integer(val), "binary variable is not integer"); cuopt_assert(fj_cpu.view.pb.integer_equal(val, 0) || fj_cpu.view.pb.integer_equal(val, 1), @@ -1189,24 +1058,16 @@ static thrust::tuple find_lift_move( // flip move wouldn't improve if (delta * obj_coeff >= 0) continue; } else { - // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration (non-binary) f_t lfd_lb = get_lower(fj_cpu.h_var_bounds[var_idx].get()) - val; f_t lfd_ub = get_upper(fj_cpu.h_var_bounds[var_idx].get()) - val; auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); - // MEMORY OPS: Inner loop over constraints involving this variable (avg_var_degree iterations - // per outer iteration) for (i_t j = offset_begin; j < offset_end; j += 1) { - // ARRAY READ: h_reverse_constraints[j] - 1 read per inner iteration - auto cstr_idx = fj_cpu.h_reverse_constraints[j]; - // ARRAY READ: h_reverse_coefficients[j] - 1 read per inner iteration - auto cstr_coeff = fj_cpu.h_reverse_coefficients[j]; - // ARRAY READ: h_cstr_lb[cstr_idx] - 1 read per inner iteration (indirect) - f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; - // ARRAY READ: h_cstr_ub[cstr_idx] - 1 read per inner iteration (indirect) + auto cstr_idx = fj_cpu.h_reverse_constraints[j]; + auto cstr_coeff = fj_cpu.h_reverse_coefficients[j]; + f_t c_lb = fj_cpu.h_cstr_lb[cstr_idx]; f_t c_ub = fj_cpu.h_cstr_ub[cstr_idx]; f_t cstr_tolerance = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); cuopt_assert(c_lb <= c_ub, "invalid bounds"); - // ARRAY READ: h_lhs[cstr_idx] - 1 read per inner iteration cuopt_assert(fj_cpu.view.cstr_satisfied(cstr_idx, fj_cpu.h_lhs[cstr_idx]), "cstr should be satisfied"); @@ -1246,8 +1107,6 @@ static thrust::tuple find_lift_move( } } if (lfd_lb >= lfd_ub) break; - // Total per inner iteration: 5 reads (h_reverse_constraints, h_reverse_coefficients, - // h_cstr_lb, h_cstr_ub, h_lhs) } // invalid crossing bounds @@ -1256,7 +1115,7 @@ static thrust::tuple find_lift_move( if (!check_variable_within_bounds(fj_cpu, var_idx, val + lfd_lb)) { lfd_lb = 0; } if (!check_variable_within_bounds(fj_cpu, var_idx, val + lfd_ub)) { lfd_ub = 0; } - // Now that the life move domain is computed, compute the correct lift move + // Now that the lift move domain is computed, compute the correct lift move cuopt_assert(isfinite(val), "invalid assignment value"); delta = obj_coeff < 0 ? lfd_ub : lfd_lb; } @@ -1277,7 +1136,6 @@ static thrust::tuple find_lift_move( best_score = score; best_move = move; } - // Total per outer iteration: 3 reads + (5 * avg_var_degree reads in inner loop for non-binary) } return thrust::make_tuple(best_move, best_score); @@ -1295,9 +1153,7 @@ static void perturb(fj_cpu_climber_t& fj_cpu) std::mt19937(fj_cpu.settings.seed + fj_cpu.iterations)); raft::random::PCGenerator rng(fj_cpu.settings.seed + fj_cpu.iterations, 0, 0); - // MEMORY OPS: Loop over sampled variables (2 iterations typically) for (auto var_idx : sampled_vars) { - // ARRAY READ: h_var_bounds[var_idx] - 1 read per iteration f_t lb = std::max(get_lower(fj_cpu.h_var_bounds[var_idx].get()), -1e7); f_t ub = std::min(get_upper(fj_cpu.h_var_bounds[var_idx].get()), 1e7); f_t val = lb + (ub - lb) * rng.next_double(); @@ -1310,9 +1166,7 @@ static void perturb(fj_cpu_climber_t& fj_cpu) cuopt_assert((check_variable_within_bounds(fj_cpu, var_idx, val)), "value is out of bounds"); - // ARRAY WRITE: h_assignment[var_idx] - 1 write per iteration fj_cpu.h_assignment[var_idx] = val; - // Total per iteration: 1 read + 1 write } recompute_lhs(fj_cpu); @@ -1433,20 +1287,12 @@ static void init_fj_cpu(fj_cpu_climber_t& fj_cpu, std::make_pair(0, fj_staged_score_t::zero())); fj_cpu.cached_cstr_bounds.resize(fj_cpu.h_reverse_coefficients.size()); - // MEMORY OPS: INITIALIZATION - Loop over all variables (n_vars iterations) for (i_t var_idx = 0; var_idx < (i_t)fj_cpu.view.pb.n_variables; ++var_idx) { auto [offset_begin, offset_end] = reverse_range_for_var(fj_cpu, var_idx); - // MEMORY OPS: Inner loop over constraints per variable (avg_var_degree iterations per outer - // iteration) for (i_t i = offset_begin; i < offset_end; ++i) { - // ARRAY READ: h_reverse_constraints[i] - 1 read per inner iteration - // ARRAY READ: h_cstr_lb[h_reverse_constraints[i]] - 1 read per inner iteration (indirect) - // ARRAY READ: h_cstr_ub[h_reverse_constraints[i]] - 1 read per inner iteration (indirect) - // ARRAY WRITE: cached_cstr_bounds[i] - 1 write per inner iteration (2 f_t values) fj_cpu.cached_cstr_bounds[i] = std::make_pair(fj_cpu.h_cstr_lb[fj_cpu.h_reverse_constraints[i]], fj_cpu.h_cstr_ub[fj_cpu.h_reverse_constraints[i]]); - // Total per inner iteration: 3 reads + 1 write (2 values) } } @@ -1634,11 +1480,8 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l // number of violated constraints is usually small (<100). recomputing from all LHSs is cheap // and more numerically precise than just adding to the accumulator in apply_move fj_cpu.total_violations = 0; - // MEMORY OPS: Loop over violated constraints (typically <100 iterations per main iteration) for (auto cstr_idx : fj_cpu.violated_constraints) { - // ARRAY READ: h_lhs[cstr_idx] - 1 read per iteration fj_cpu.total_violations += fj_cpu.view.excess_score(cstr_idx, fj_cpu.h_lhs[cstr_idx]); - // Total per iteration: 1 read } if (fj_cpu.iterations % fj_cpu.log_interval == 0) { CUOPT_LOG_TRACE( From 50c574ee4fdb133dda01349df88718473ddb4be9 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 09:19:24 +0000 Subject: [PATCH 216/366] update terminology --- cpp/src/dual_simplex/bb_event.hpp | 46 ++- cpp/src/dual_simplex/bb_worker_state.hpp | 6 +- cpp/src/dual_simplex/branch_and_bound.cpp | 29 +- cpp/src/dual_simplex/branch_and_bound.hpp | 10 +- cpp/src/dual_simplex/bsp_debug.hpp | 324 ++++++++++++---------- cpp/src/dual_simplex/mip_node.hpp | 4 +- 6 files changed, 218 insertions(+), 201 deletions(-) diff --git a/cpp/src/dual_simplex/bb_event.hpp b/cpp/src/dual_simplex/bb_event.hpp index 1a41c5324..6c3f189fe 100644 --- a/cpp/src/dual_simplex/bb_event.hpp +++ b/cpp/src/dual_simplex/bb_event.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -47,7 +47,7 @@ struct fathomed_payload_t { template struct paused_payload_t { - f_t accumulated_vt; + f_t accumulated_wut; }; template @@ -59,7 +59,7 @@ struct heuristic_solution_payload_t { template struct bb_event_t { bb_event_type_t type; - double vt_timestamp; + double wut; int worker_id; i_t node_id; int event_sequence; @@ -73,22 +73,18 @@ struct bb_event_t { } payload; bb_event_t() - : type(bb_event_type_t::NODE_FATHOMED), - vt_timestamp(0.0), - worker_id(0), - node_id(0), - event_sequence(0) + : type(bb_event_type_t::NODE_FATHOMED), wut(0.0), worker_id(0), node_id(0), event_sequence(0) { payload.fathomed = {0.0}; } bool operator<(const bb_event_t& other) const { - return std::tie(vt_timestamp, worker_id, event_sequence) < - std::tie(other.vt_timestamp, other.worker_id, other.event_sequence); + return std::tie(wut, worker_id, event_sequence) < + std::tie(other.wut, other.worker_id, other.event_sequence); } - static bb_event_t make_branched(double vt, + static bb_event_t make_branched(double work_unit_ts, int worker, i_t node, int seq, @@ -100,7 +96,7 @@ struct bb_event_t { { bb_event_t e; e.type = bb_event_type_t::NODE_BRANCHED; - e.vt_timestamp = vt; + e.wut = work_unit_ts; e.worker_id = worker; e.node_id = node; e.event_sequence = seq; @@ -108,11 +104,12 @@ struct bb_event_t { return e; } - static bb_event_t make_integer_solution(double vt, int worker, i_t node, int seq, f_t objective) + static bb_event_t make_integer_solution( + double work_unit_ts, int worker, i_t node, int seq, f_t objective) { bb_event_t e; e.type = bb_event_type_t::NODE_INTEGER; - e.vt_timestamp = vt; + e.wut = work_unit_ts; e.worker_id = worker; e.node_id = node; e.event_sequence = seq; @@ -120,11 +117,12 @@ struct bb_event_t { return e; } - static bb_event_t make_fathomed(double vt, int worker, i_t node, int seq, f_t lower_bound) + static bb_event_t make_fathomed( + double work_unit_ts, int worker, i_t node, int seq, f_t lower_bound) { bb_event_t e; e.type = bb_event_type_t::NODE_FATHOMED; - e.vt_timestamp = vt; + e.wut = work_unit_ts; e.worker_id = worker; e.node_id = node; e.event_sequence = seq; @@ -132,11 +130,11 @@ struct bb_event_t { return e; } - static bb_event_t make_infeasible(double vt, int worker, i_t node, int seq) + static bb_event_t make_infeasible(double work_unit_ts, int worker, i_t node, int seq) { bb_event_t e; e.type = bb_event_type_t::NODE_INFEASIBLE; - e.vt_timestamp = vt; + e.wut = work_unit_ts; e.worker_id = worker; e.node_id = node; e.event_sequence = seq; @@ -144,11 +142,11 @@ struct bb_event_t { return e; } - static bb_event_t make_paused(double vt, int worker, i_t node, int seq, f_t accumulated) + static bb_event_t make_paused(double work_unit_ts, int worker, i_t node, int seq, f_t accumulated) { bb_event_t e; e.type = bb_event_type_t::NODE_PAUSED; - e.vt_timestamp = vt; + e.wut = work_unit_ts; e.worker_id = worker; e.node_id = node; e.event_sequence = seq; @@ -156,11 +154,11 @@ struct bb_event_t { return e; } - static bb_event_t make_numerical(double vt, int worker, i_t node, int seq) + static bb_event_t make_numerical(double work_unit_ts, int worker, i_t node, int seq) { bb_event_t e; e.type = bb_event_type_t::NODE_NUMERICAL; - e.vt_timestamp = vt; + e.wut = work_unit_ts; e.worker_id = worker; e.node_id = node; e.event_sequence = seq; @@ -169,11 +167,11 @@ struct bb_event_t { } static bb_event_t make_heuristic_solution( - double vt, int worker, int seq, f_t objective, size_t sol_idx) + double work_unit_ts, int worker, int seq, f_t objective, size_t sol_idx) { bb_event_t e; e.type = bb_event_type_t::HEURISTIC_SOLUTION; - e.vt_timestamp = vt; + e.wut = work_unit_ts; e.worker_id = worker; e.node_id = -1; e.event_sequence = seq; diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 13c34d690..2afc787b4 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -47,7 +47,7 @@ struct pseudo_cost_update_t { i_t variable; rounding_direction_t direction; f_t delta; // change_in_obj / frac - double vt; // virtual time when update occurred (for deterministic ordering) + double wut; // work unit timestamp when update occurred (for deterministic ordering) int worker_id; // for tie-breaking in sort }; @@ -78,7 +78,7 @@ struct bb_worker_state_t { // If next node's parent == last_solved_node, we can reuse basis mip_node_t* last_solved_node{nullptr}; - // Worker's virtual time clock (cumulative work units) + // Worker's work unit clock (cumulative) double clock{0.0}; // Current horizon boundaries (for BSP sync) @@ -581,7 +581,7 @@ struct bsp_diving_worker_state_t { int worker_id{0}; bnb_worker_type_t diving_type{bnb_worker_type_t::PSEUDOCOST_DIVING}; - // Worker's virtual time clock (cumulative work units) + // Worker's work unit clock (cumulative) double clock{0.0}; // Current horizon boundaries diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index fce46f886..f8ecdcf17 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -357,9 +357,9 @@ void branch_and_bound_t::set_new_solution(const std::vector& solu template void branch_and_bound_t::set_new_solution_deterministic(const std::vector& solution, - double vt_timestamp) + double work_unit_ts) { - // In BSP mode, queue the solution to be processed at the correct virtual time + // In BSP mode, queue the solution to be processed at the correct work unit timestamp // This ensures deterministic ordering of solution events if (solution.size() != original_problem_.num_cols) { @@ -388,9 +388,9 @@ void branch_and_bound_t::set_new_solution_deterministic(const std::vec return; } - // Queue the solution with its VT timestamp + // Queue the solution with its work unit timestamp mutex_heuristic_queue_.lock(); - heuristic_solution_queue_.push_back({std::move(crushed_solution), obj, vt_timestamp}); + heuristic_solution_queue_.push_back({std::move(crushed_solution), obj, work_unit_ts}); mutex_heuristic_queue_.unlock(); } @@ -1973,10 +1973,10 @@ void branch_and_bound_t::refill_worker_queues(i_t target_queue_size) (*bsp_workers_)[worker_id].track_node_assigned(); // Debug: Log node assignment - double vt = bsp_current_horizon_ - bsp_horizon_step_; // Start of current horizon + double wut = bsp_current_horizon_ - bsp_horizon_step_; // Start of current horizon BSP_DEBUG_LOG_NODE_ASSIGNED(bsp_debug_settings_, bsp_debug_logger_, - vt, + wut, worker_id, node->node_id, node->origin_worker_id, @@ -2665,7 +2665,7 @@ void branch_and_bound_t::process_history_and_sync( { std::vector future_solutions; for (auto& sol : heuristic_solution_queue_) { - if (sol.vt_timestamp < bsp_current_horizon_) { + if (sol.wut < bsp_current_horizon_) { heuristic_solutions.push_back(std::move(sol)); } else { future_solutions.push_back(std::move(sol)); @@ -2679,7 +2679,7 @@ void branch_and_bound_t::process_history_and_sync( std::sort(heuristic_solutions.begin(), heuristic_solutions.end(), [](const queued_heuristic_solution_t& a, const queued_heuristic_solution_t& b) { - if (a.vt_timestamp != b.vt_timestamp) { return a.vt_timestamp < b.vt_timestamp; } + if (a.wut != b.wut) { return a.wut < b.wut; } if (a.objective != b.objective) { return a.objective < b.objective; } return a.solution < b.solution; // edge-case - lexicographical comparison }); @@ -2697,9 +2697,8 @@ void branch_and_bound_t::process_history_and_sync( } else if (heuristic_idx >= heuristic_solutions.size()) { process_event = true; } else { - // Both have items - pick the one with smaller WT - if (events.events[event_idx].vt_timestamp <= - heuristic_solutions[heuristic_idx].vt_timestamp) { + // Both have items - pick the one with smaller WUT + if (events.events[event_idx].wut <= heuristic_solutions[heuristic_idx].wut) { process_event = true; } else { process_heuristic = true; @@ -2723,7 +2722,7 @@ void branch_and_bound_t::process_history_and_sync( // Debug: Log heuristic received BSP_DEBUG_LOG_HEURISTIC_RECEIVED( - bsp_debug_settings_, bsp_debug_logger_, hsol.vt_timestamp, hsol.objective); + bsp_debug_settings_, bsp_debug_logger_, hsol.wut, hsol.objective); // Process heuristic solution at its correct work unit timestamp position f_t new_upper = std::numeric_limits::infinity(); @@ -2736,7 +2735,7 @@ void branch_and_bound_t::process_history_and_sync( // Debug: Log incumbent update BSP_DEBUG_LOG_INCUMBENT_UPDATE( - bsp_debug_settings_, bsp_debug_logger_, hsol.vt_timestamp, hsol.objective, "heuristic"); + bsp_debug_settings_, bsp_debug_logger_, hsol.wut, hsol.objective, "heuristic"); } mutex_upper_.unlock(); @@ -2932,10 +2931,10 @@ void branch_and_bound_t::balance_worker_loads() (*bsp_workers_)[worker_idx].enqueue_node_with_identity(all_nodes[i]); (*bsp_workers_)[worker_idx].track_node_assigned(); - double vt = bsp_current_horizon_; + double wut = bsp_current_horizon_; BSP_DEBUG_LOG_NODE_ASSIGNED(bsp_debug_settings_, bsp_debug_logger_, - vt, + wut, static_cast(worker_idx), all_nodes[i]->node_id, all_nodes[i]->origin_worker_id, diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index ba92b11c5..a4cd67b3d 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -115,8 +115,8 @@ class branch_and_bound_t { void set_new_solution(const std::vector& solution); // BSP-aware solution injection for deterministic mode - // This queues the solution to be processed at the correct virtual time - void set_new_solution_deterministic(const std::vector& solution, double vt_timestamp); + // This queues the solution to be processed at the correct work unit timestamp + void set_new_solution_deterministic(const std::vector& solution, double work_unit_ts); void set_concurrent_lp_root_solve(bool enable) { enable_concurrent_lp_root_solve_ = enable; } @@ -360,7 +360,7 @@ class branch_and_bound_t { std::unique_ptr> bsp_workers_; std::unique_ptr bsp_scheduler_; std::atomic bsp_terminated_{false}; - double bsp_horizon_step_{5.0}; // Virtual time step per horizon (tunable) + double bsp_horizon_step_{5.0}; // Work unit step per horizon (tunable) double bsp_current_horizon_{0.0}; // Current horizon target bool bsp_mode_enabled_{false}; // Whether BSP mode is active int bsp_horizon_number_{0}; // Current horizon number (for debugging) @@ -380,11 +380,11 @@ class branch_and_bound_t { omp_mutex_t mutex_heap_; // BSP heuristic solution queue - solutions received from GPU heuristics - // Stored with VT timestamp for deterministic ordering + // Stored with work unit timestamp for deterministic ordering struct queued_heuristic_solution_t { std::vector solution; f_t objective; - double vt_timestamp; + double wut; }; omp_mutex_t mutex_heuristic_queue_; std::vector heuristic_solution_queue_; diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp index 9e92096d3..0ee34d711 100644 --- a/cpp/src/dual_simplex/bsp_debug.hpp +++ b/cpp/src/dual_simplex/bsp_debug.hpp @@ -198,8 +198,8 @@ struct bsp_debug_settings_t { // ============================================================================ struct timeline_event_t { - double vt_start; - double vt_end; + double wut_start; + double wut_end; int worker_id; int node_id; int final_id; @@ -252,46 +252,48 @@ class bsp_debug_logger_t { // Event Logging // ======================================================================== - void log_horizon_start(int horizon_num, double vt_start, double vt_end) + void log_horizon_start(int horizon_num, double wut_start, double wut_end) { - current_horizon_ = horizon_num; - horizon_vt_start_ = vt_start; - horizon_vt_end_ = vt_end; + current_horizon_ = horizon_num; + horizon_wut_start_ = wut_start; + horizon_wut_end_ = wut_end; if (settings_.enable_event_log) { - log_event(vt_start, + log_event(wut_start, -1, bsp_log_event_t::HORIZON_START, -1, -1, - "horizon=[" + std::to_string(vt_start) + "," + std::to_string(vt_end) + ")"); + "horizon=[" + std::to_string(wut_start) + "," + std::to_string(wut_end) + ")"); } if (settings_.enable_determinism_trace) { - trace_ss_ << "H" << horizon_num << ":START:vt=[" << vt_start << "," << vt_end << ")\n"; + trace_ss_ << "H" << horizon_num << ":START:wut=[" << wut_start << "," << wut_end << ")\n"; } // Clear timeline events for new horizon timeline_events_.clear(); } - void log_horizon_end(int horizon_num, double vt) + void log_horizon_end(int horizon_num, double work_unit_ts) { - if (settings_.enable_event_log) { log_event(vt, -1, bsp_log_event_t::HORIZON_END, -1, -1, ""); } + if (settings_.enable_event_log) { + log_event(work_unit_ts, -1, bsp_log_event_t::HORIZON_END, -1, -1, ""); + } if (settings_.enable_timeline) { emit_timeline_for_horizon(horizon_num); } } // Log determinism fingerprint hash for the horizon // This hash captures all state that should be identical across deterministic runs - void log_horizon_hash(int horizon_num, double vt, uint32_t hash) + void log_horizon_hash(int horizon_num, double work_unit_ts, uint32_t hash) { std::lock_guard lock(mutex_); if (settings_.enable_event_log) { std::stringstream ss; ss << "hash=0x" << std::hex << std::setfill('0') << std::setw(8) << hash; - log_event_unlocked(vt, -1, bsp_log_event_t::HORIZON_HASH, -1, -1, ss.str()); + log_event_unlocked(work_unit_ts, -1, bsp_log_event_t::HORIZON_HASH, -1, -1, ss.str()); } if (settings_.enable_determinism_trace) { @@ -300,10 +302,11 @@ class bsp_debug_logger_t { } } - void log_node_assigned(double vt, int worker_id, i_t node_id, i_t final_id, f_t lower_bound) + void log_node_assigned( + double work_unit_ts, int worker_id, i_t node_id, i_t final_id, f_t lower_bound) { if (settings_.enable_event_log) { - log_event(vt, + log_event(work_unit_ts, worker_id, bsp_log_event_t::NODE_ASSIGNED, node_id, @@ -327,13 +330,17 @@ class bsp_debug_logger_t { } } - void log_solve_start( - double vt, int worker_id, i_t node_id, i_t final_id, double work_limit, bool is_resumed) + void log_solve_start(double work_unit_ts, + int worker_id, + i_t node_id, + i_t final_id, + double work_limit, + bool is_resumed) { std::lock_guard lock(mutex_); if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked(vt, + log_event_unlocked(work_unit_ts, worker_id, bsp_log_event_t::NODE_SOLVE_START, node_id, @@ -342,18 +349,22 @@ class bsp_debug_logger_t { } // Start timeline event - current_solve_start_[worker_id] = vt; + current_solve_start_[worker_id] = work_unit_ts; current_solve_node_[worker_id] = node_id; current_solve_fid_[worker_id] = final_id; } - void log_solve_end( - double vt, int worker_id, i_t node_id, i_t final_id, const std::string& result, f_t lower_bound) + void log_solve_end(double work_unit_ts, + int worker_id, + i_t node_id, + i_t final_id, + const std::string& result, + f_t lower_bound) { std::lock_guard lock(mutex_); if (settings_.enable_event_log) { - log_event_unlocked(vt, + log_event_unlocked(work_unit_ts, worker_id, bsp_log_event_t::NODE_SOLVE_END, node_id, @@ -364,8 +375,8 @@ class bsp_debug_logger_t { // Complete timeline event if (settings_.enable_timeline) { timeline_event_t te; - te.vt_start = current_solve_start_[worker_id]; - te.vt_end = vt; + te.wut_start = current_solve_start_[worker_id]; + te.wut_end = work_unit_ts; te.worker_id = worker_id; te.node_id = node_id; te.final_id = final_id; @@ -374,12 +385,13 @@ class bsp_debug_logger_t { } } - void log_branched(double vt, int worker_id, i_t parent_id, i_t parent_fid, i_t down_id, i_t up_id) + void log_branched( + double work_unit_ts, int worker_id, i_t parent_id, i_t parent_fid, i_t down_id, i_t up_id) { std::lock_guard lock(mutex_); if (settings_.enable_event_log) { - log_event_unlocked(vt, + log_event_unlocked(work_unit_ts, worker_id, bsp_log_event_t::NODE_BRANCHED, parent_id, @@ -389,35 +401,36 @@ class bsp_debug_logger_t { if (settings_.enable_determinism_trace) { // Log parent final_id (deterministic) and children node_ids (non-deterministic until sync) - events_trace_ss_ << "BRANCH(" << vt << ",W" << worker_id << ",F" << parent_fid << "->" - << down_id << "," << up_id << "),"; + events_trace_ss_ << "BRANCH(" << work_unit_ts << ",W" << worker_id << ",F" << parent_fid + << "->" << down_id << "," << up_id << "),"; } } - void log_paused(double vt, int worker_id, i_t node_id, i_t final_id, f_t accumulated_vt) + void log_paused( + double work_unit_ts, int worker_id, i_t node_id, i_t final_id, f_t accumulated_wut) { std::lock_guard lock(mutex_); if (settings_.enable_event_log) { - log_event_unlocked(vt, + log_event_unlocked(work_unit_ts, worker_id, bsp_log_event_t::NODE_PAUSED, node_id, final_id, - "acc_vt=" + std::to_string(accumulated_vt)); + "acc_wut=" + std::to_string(accumulated_wut)); } if (settings_.enable_determinism_trace) { - events_trace_ss_ << "PAUSE(" << vt << ",W" << worker_id << "," << node_id << "),"; + events_trace_ss_ << "PAUSE(" << work_unit_ts << ",W" << worker_id << "," << node_id << "),"; } } - void log_integer(double vt, int worker_id, i_t node_id, f_t objective) + void log_integer(double work_unit_ts, int worker_id, i_t node_id, f_t objective) { std::lock_guard lock(mutex_); if (settings_.enable_event_log) { - log_event_unlocked(vt, + log_event_unlocked(work_unit_ts, worker_id, bsp_log_event_t::NODE_INTEGER, node_id, @@ -426,16 +439,16 @@ class bsp_debug_logger_t { } if (settings_.enable_determinism_trace) { - events_trace_ss_ << "INT(" << vt << ",W" << worker_id << "," << objective << "),"; + events_trace_ss_ << "INT(" << work_unit_ts << ",W" << worker_id << "," << objective << "),"; } } - void log_fathomed(double vt, int worker_id, i_t node_id, f_t lower_bound) + void log_fathomed(double work_unit_ts, int worker_id, i_t node_id, f_t lower_bound) { std::lock_guard lock(mutex_); if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked(vt, + log_event_unlocked(work_unit_ts, worker_id, bsp_log_event_t::NODE_FATHOMED, node_id, @@ -444,22 +457,23 @@ class bsp_debug_logger_t { } } - void log_infeasible(double vt, int worker_id, i_t node_id) + void log_infeasible(double work_unit_ts, int worker_id, i_t node_id) { std::lock_guard lock(mutex_); if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked(vt, worker_id, bsp_log_event_t::NODE_INFEASIBLE, node_id, -1, ""); + log_event_unlocked( + work_unit_ts, worker_id, bsp_log_event_t::NODE_INFEASIBLE, node_id, -1, ""); } } - void log_pruned(double vt, i_t node_id, i_t final_id, f_t lower_bound, f_t upper_bound) + void log_pruned(double work_unit_ts, i_t node_id, i_t final_id, f_t lower_bound, f_t upper_bound) { std::lock_guard lock(mutex_); if (settings_.enable_event_log && settings_.log_level >= 2) { log_event_unlocked( - vt, + work_unit_ts, -1, bsp_log_event_t::NODE_PRUNED, node_id, @@ -468,10 +482,10 @@ class bsp_debug_logger_t { } } - void log_sync_phase_start(double vt, size_t num_events) + void log_sync_phase_start(double work_unit_ts, size_t num_events) { if (settings_.enable_event_log) { - log_event(vt, + log_event(work_unit_ts, -1, bsp_log_event_t::SYNC_PHASE_START, -1, @@ -480,10 +494,10 @@ class bsp_debug_logger_t { } } - void log_sync_phase_end(double vt) + void log_sync_phase_end(double work_unit_ts) { if (settings_.enable_event_log) { - log_event(vt, -1, bsp_log_event_t::SYNC_PHASE_END, -1, -1, ""); + log_event(work_unit_ts, -1, bsp_log_event_t::SYNC_PHASE_END, -1, -1, ""); } // Flush event trace @@ -514,22 +528,27 @@ class bsp_debug_logger_t { } } - void log_heuristic_received(double vt, f_t objective) + void log_heuristic_received(double work_unit_ts, f_t objective) { if (settings_.enable_event_log) { - log_event( - vt, -1, bsp_log_event_t::HEURISTIC_RECEIVED, -1, -1, "obj=" + std::to_string(objective)); + log_event(work_unit_ts, + -1, + bsp_log_event_t::HEURISTIC_RECEIVED, + -1, + -1, + "obj=" + std::to_string(objective)); } if (settings_.enable_determinism_trace) { - trace_ss_ << "H" << current_horizon_ << ":HEURISTIC:" << objective << "@" << vt << "\n"; + trace_ss_ << "H" << current_horizon_ << ":HEURISTIC:" << objective << "@" << work_unit_ts + << "\n"; } } - void log_incumbent_update(double vt, f_t objective, const std::string& source) + void log_incumbent_update(double work_unit_ts, f_t objective, const std::string& source) { if (settings_.enable_event_log) { - log_event(vt, + log_event(work_unit_ts, -1, bsp_log_event_t::INCUMBENT_UPDATE, -1, @@ -538,8 +557,8 @@ class bsp_debug_logger_t { } if (settings_.enable_determinism_trace) { - trace_ss_ << "H" << current_horizon_ << ":INCUMBENT:" << objective << "@" << vt << "(" - << source << ")\n"; + trace_ss_ << "H" << current_horizon_ << ":INCUMBENT:" << objective << "@" << work_unit_ts + << "(" << source << ")\n"; } } @@ -683,8 +702,8 @@ class bsp_debug_logger_t { template void emit_state_json(int horizon_num, - double vt_start, - double vt_end, + double wut_start, + double wut_end, i_t next_final_id, f_t upper_bound, f_t lower_bound, @@ -703,7 +722,7 @@ class bsp_debug_logger_t { file << "{\n"; file << " \"horizon\": " << horizon_num << ",\n"; - file << " \"vt_range\": [" << vt_start << ", " << vt_end << "],\n"; + file << " \"wut_range\": [" << wut_start << ", " << wut_end << "],\n"; file << " \"bsp_next_final_id\": " << next_final_id << ",\n"; file << " \"upper_bound\": " << (upper_bound >= 1e30 ? "\"inf\"" : std::to_string(upper_bound)) << ",\n"; @@ -722,7 +741,7 @@ class bsp_debug_logger_t { file << " \"current_node\": {\"id\": " << w.current_node->node_id << ", \"origin_worker\": " << w.current_node->origin_worker_id << ", \"creation_seq\": " << w.current_node->creation_seq - << ", \"acc_vt\": " << w.current_node->accumulated_vt << "},\n"; + << ", \"acc_wut\": " << w.current_node->accumulated_wut << "},\n"; } else { file << " \"current_node\": null,\n"; } @@ -766,8 +785,8 @@ class bsp_debug_logger_t { int num_workers_{0}; double horizon_step_{5.0}; int current_horizon_{0}; - double horizon_vt_start_{0.0}; - double horizon_vt_end_{0.0}; + double horizon_wut_start_{0.0}; + double horizon_wut_end_{0.0}; std::mutex mutex_; std::stringstream log_ss_; @@ -788,7 +807,7 @@ class bsp_debug_logger_t { return std::chrono::duration(now - start_time_).count(); } - void log_event(double vt, + void log_event(double work_unit_ts, int worker_id, bsp_log_event_t event, i_t node_id, @@ -796,17 +815,17 @@ class bsp_debug_logger_t { const std::string& details) { std::lock_guard lock(mutex_); - log_event_unlocked(vt, worker_id, event, node_id, final_id, details); + log_event_unlocked(work_unit_ts, worker_id, event, node_id, final_id, details); } - void log_event_unlocked(double vt, + void log_event_unlocked(double work_unit_ts, int worker_id, bsp_log_event_t event, i_t node_id, i_t final_id, const std::string& details) { - log_ss_ << std::fixed << std::setprecision(3) << vt << "\t" << get_real_time() << "\t" + log_ss_ << std::fixed << std::setprecision(3) << work_unit_ts << "\t" << get_real_time() << "\t" << current_horizon_ << "\t" << (worker_id < 0 ? "COORD" : std::to_string(worker_id)) << "\t" << bsp_log_event_name(event) << "\t" << (node_id < 0 ? "-" : std::to_string(node_id)) << "\t" @@ -842,27 +861,27 @@ class bsp_debug_logger_t { std::ofstream file(filename, std::ios::app); if (!file.is_open()) return; - const int width = 80; - const double vt_min = horizon_vt_start_; - const double vt_max = horizon_vt_end_; - const double vt_range = vt_max - vt_min; + const int width = 80; + const double wut_min = horizon_wut_start_; + const double wut_max = horizon_wut_end_; + const double wut_range = wut_max - wut_min; // Avoid division by zero - if (vt_range <= 0.0) { - file << "\nHorizon " << horizon_num << ": Empty or invalid VT range\n"; + if (wut_range <= 0.0) { + file << "\nHorizon " << horizon_num << ": Empty or invalid WUT range\n"; return; } file << "\n"; file << std::string(width, '=') << "\n"; - file << "Horizon " << horizon_num << ": VT [" << vt_min << ", " << vt_max << ")\n"; + file << "Horizon " << horizon_num << ": WUT [" << wut_min << ", " << wut_max << ")\n"; file << std::string(width, '-') << "\n"; - // VT scale - file << " VT: "; + // WUT scale + file << " WUT: "; for (int i = 0; i <= 5; ++i) { - double vt = vt_min + (vt_range * i / 5.0); - file << std::setw(10) << std::fixed << std::setprecision(1) << vt; + double wut = wut_min + (wut_range * i / 5.0); + file << std::setw(10) << std::fixed << std::setprecision(1) << wut; } file << "\n"; @@ -875,8 +894,8 @@ class bsp_debug_logger_t { for (const auto& te : timeline_events_) { if (te.worker_id != w) continue; - int start_col = static_cast((te.vt_start - vt_min) / vt_range * 60); - int end_col = static_cast((te.vt_end - vt_min) / vt_range * 60); + int start_col = static_cast((te.wut_start - wut_min) / wut_range * 60); + int end_col = static_cast((te.wut_end - wut_min) / wut_range * 60); start_col = std::max(0, std::min(59, start_col)); end_col = std::max(0, std::min(59, end_col)); @@ -900,8 +919,9 @@ class bsp_debug_logger_t { std::string labels(60, ' '); for (const auto& te : timeline_events_) { if (te.worker_id != w) continue; - int mid_col = static_cast(((te.vt_start + te.vt_end) / 2 - vt_min) / vt_range * 60); - mid_col = std::max(0, std::min(55, mid_col)); + int mid_col = + static_cast(((te.wut_start + te.wut_end) / 2 - wut_min) / wut_range * 60); + mid_col = std::max(0, std::min(55, mid_col)); std::string label = "N" + std::to_string(te.final_id); for (size_t i = 0; i < label.size() && mid_col + i < 60; ++i) { @@ -985,65 +1005,65 @@ class bsp_debug_logger_t { #ifdef BSP_DEBUG_ENABLED -#define BSP_DEBUG_LOG_HORIZON_START(settings, logger, h, vs, ve) \ +#define BSP_DEBUG_LOG_HORIZON_START(settings, logger, h, ws, we) \ do { \ - if ((settings).any_enabled()) (logger).log_horizon_start(h, vs, ve); \ + if ((settings).any_enabled()) (logger).log_horizon_start(h, ws, we); \ } while (0) -#define BSP_DEBUG_LOG_HORIZON_END(settings, logger, h, vt) \ - do { \ - if ((settings).any_enabled()) (logger).log_horizon_end(h, vt); \ +#define BSP_DEBUG_LOG_HORIZON_END(settings, logger, h, wut) \ + do { \ + if ((settings).any_enabled()) (logger).log_horizon_end(h, wut); \ } while (0) -#define BSP_DEBUG_LOG_HORIZON_HASH(settings, logger, h, vt, hash) \ - do { \ - if ((settings).any_enabled()) (logger).log_horizon_hash(h, vt, hash); \ +#define BSP_DEBUG_LOG_HORIZON_HASH(settings, logger, h, wut, hash) \ + do { \ + if ((settings).any_enabled()) (logger).log_horizon_hash(h, wut, hash); \ } while (0) -#define BSP_DEBUG_LOG_NODE_ASSIGNED(settings, logger, vt, w, nid, fid, lb) \ - do { \ - if ((settings).any_enabled()) (logger).log_node_assigned(vt, w, nid, fid, lb); \ +#define BSP_DEBUG_LOG_NODE_ASSIGNED(settings, logger, wut, w, nid, fid, lb) \ + do { \ + if ((settings).any_enabled()) (logger).log_node_assigned(wut, w, nid, fid, lb); \ } while (0) #define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) \ do { \ if ((settings).any_enabled() && (settings).flush_every_horizon) (logger).flush_assign_trace(); \ } while (0) -#define BSP_DEBUG_LOG_SOLVE_START(settings, logger, vt, w, nid, fid, wl, resumed) \ - do { \ - if ((settings).any_enabled()) (logger).log_solve_start(vt, w, nid, fid, wl, resumed); \ +#define BSP_DEBUG_LOG_SOLVE_START(settings, logger, wut, w, nid, fid, wl, resumed) \ + do { \ + if ((settings).any_enabled()) (logger).log_solve_start(wut, w, nid, fid, wl, resumed); \ } while (0) -#define BSP_DEBUG_LOG_SOLVE_END(settings, logger, vt, w, nid, fid, result, lb) \ - do { \ - if ((settings).any_enabled()) (logger).log_solve_end(vt, w, nid, fid, result, lb); \ +#define BSP_DEBUG_LOG_SOLVE_END(settings, logger, wut, w, nid, fid, result, lb) \ + do { \ + if ((settings).any_enabled()) (logger).log_solve_end(wut, w, nid, fid, result, lb); \ } while (0) -#define BSP_DEBUG_LOG_BRANCHED(settings, logger, vt, w, pid, pfid, did, uid) \ - do { \ - if ((settings).any_enabled()) (logger).log_branched(vt, w, pid, pfid, did, uid); \ +#define BSP_DEBUG_LOG_BRANCHED(settings, logger, wut, w, pid, pfid, did, uid) \ + do { \ + if ((settings).any_enabled()) (logger).log_branched(wut, w, pid, pfid, did, uid); \ } while (0) -#define BSP_DEBUG_LOG_PAUSED(settings, logger, vt, w, nid, fid, acc) \ - do { \ - if ((settings).any_enabled()) (logger).log_paused(vt, w, nid, fid, acc); \ +#define BSP_DEBUG_LOG_PAUSED(settings, logger, wut, w, nid, fid, acc) \ + do { \ + if ((settings).any_enabled()) (logger).log_paused(wut, w, nid, fid, acc); \ } while (0) -#define BSP_DEBUG_LOG_INTEGER(settings, logger, vt, w, nid, obj) \ - do { \ - if ((settings).any_enabled()) (logger).log_integer(vt, w, nid, obj); \ +#define BSP_DEBUG_LOG_INTEGER(settings, logger, wut, w, nid, obj) \ + do { \ + if ((settings).any_enabled()) (logger).log_integer(wut, w, nid, obj); \ } while (0) -#define BSP_DEBUG_LOG_FATHOMED(settings, logger, vt, w, nid, lb) \ - do { \ - if ((settings).any_enabled()) (logger).log_fathomed(vt, w, nid, lb); \ +#define BSP_DEBUG_LOG_FATHOMED(settings, logger, wut, w, nid, lb) \ + do { \ + if ((settings).any_enabled()) (logger).log_fathomed(wut, w, nid, lb); \ } while (0) -#define BSP_DEBUG_LOG_INFEASIBLE(settings, logger, vt, w, nid) \ - do { \ - if ((settings).any_enabled()) (logger).log_infeasible(vt, w, nid); \ +#define BSP_DEBUG_LOG_INFEASIBLE(settings, logger, wut, w, nid) \ + do { \ + if ((settings).any_enabled()) (logger).log_infeasible(wut, w, nid); \ } while (0) -#define BSP_DEBUG_LOG_PRUNED(settings, logger, vt, nid, fid, lb, ub) \ - do { \ - if ((settings).any_enabled()) (logger).log_pruned(vt, nid, fid, lb, ub); \ +#define BSP_DEBUG_LOG_PRUNED(settings, logger, wut, nid, fid, lb, ub) \ + do { \ + if ((settings).any_enabled()) (logger).log_pruned(wut, nid, fid, lb, ub); \ } while (0) -#define BSP_DEBUG_LOG_SYNC_PHASE_START(settings, logger, vt, ne) \ - do { \ - if ((settings).any_enabled()) (logger).log_sync_phase_start(vt, ne); \ +#define BSP_DEBUG_LOG_SYNC_PHASE_START(settings, logger, wut, ne) \ + do { \ + if ((settings).any_enabled()) (logger).log_sync_phase_start(wut, ne); \ } while (0) -#define BSP_DEBUG_LOG_SYNC_PHASE_END(settings, logger, vt) \ - do { \ - if ((settings).any_enabled()) (logger).log_sync_phase_end(vt); \ +#define BSP_DEBUG_LOG_SYNC_PHASE_END(settings, logger, wut) \ + do { \ + if ((settings).any_enabled()) (logger).log_sync_phase_end(wut); \ } while (0) #define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(settings, logger, pid, fid) \ do { \ @@ -1054,13 +1074,13 @@ class bsp_debug_logger_t { if ((settings).any_enabled() && (settings).flush_every_horizon) \ (logger).flush_final_ids_trace(); \ } while (0) -#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(settings, logger, vt, obj) \ - do { \ - if ((settings).any_enabled()) (logger).log_heuristic_received(vt, obj); \ +#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(settings, logger, wut, obj) \ + do { \ + if ((settings).any_enabled()) (logger).log_heuristic_received(wut, obj); \ } while (0) -#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(settings, logger, vt, obj, src) \ - do { \ - if ((settings).any_enabled()) (logger).log_incumbent_update(vt, obj, src); \ +#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(settings, logger, wut, obj, src) \ + do { \ + if ((settings).any_enabled()) (logger).log_incumbent_update(wut, obj, src); \ } while (0) #define BSP_DEBUG_LOG_HEAP_ORDER(settings, logger, fids) \ do { \ @@ -1084,10 +1104,10 @@ class bsp_debug_logger_t { (logger).emit_tree_state(h, root, ub); \ } while (0) #define BSP_DEBUG_EMIT_STATE_JSON( \ - settings, logger, h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) \ + settings, logger, h, ws, we, nfid, ub, lb, ne, nu, workers, heap, events) \ do { \ if ((settings).any_enabled() && (settings).flush_every_horizon) \ - (logger).emit_state_json(h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events); \ + (logger).emit_state_json(h, ws, we, nfid, ub, lb, ne, nu, workers, heap, events); \ } while (0) #define BSP_DEBUG_FINALIZE(settings, logger) \ do { \ @@ -1096,32 +1116,32 @@ class bsp_debug_logger_t { #else -#define BSP_DEBUG_LOG_HORIZON_START(settings, logger, h, vs, ve) ((void)0) -#define BSP_DEBUG_LOG_HORIZON_END(settings, logger, h, vt) ((void)0) -#define BSP_DEBUG_LOG_HORIZON_HASH(settings, logger, h, vt, hash) ((void)0) -#define BSP_DEBUG_LOG_NODE_ASSIGNED(settings, logger, vt, w, nid, fid, lb) ((void)0) -#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) ((void)0) -#define BSP_DEBUG_LOG_SOLVE_START(settings, logger, vt, w, nid, fid, wl, resumed) ((void)0) -#define BSP_DEBUG_LOG_SOLVE_END(settings, logger, vt, w, nid, fid, result, lb) ((void)0) -#define BSP_DEBUG_LOG_BRANCHED(settings, logger, vt, w, pid, pfid, did, uid) ((void)0) -#define BSP_DEBUG_LOG_PAUSED(settings, logger, vt, w, nid, fid, acc) ((void)0) -#define BSP_DEBUG_LOG_INTEGER(settings, logger, vt, w, nid, obj) ((void)0) -#define BSP_DEBUG_LOG_FATHOMED(settings, logger, vt, w, nid, lb) ((void)0) -#define BSP_DEBUG_LOG_INFEASIBLE(settings, logger, vt, w, nid) ((void)0) -#define BSP_DEBUG_LOG_PRUNED(settings, logger, vt, nid, fid, lb, ub) ((void)0) -#define BSP_DEBUG_LOG_SYNC_PHASE_START(settings, logger, vt, ne) ((void)0) -#define BSP_DEBUG_LOG_SYNC_PHASE_END(settings, logger, vt) ((void)0) -#define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(settings, logger, pid, fid) ((void)0) -#define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(settings, logger) ((void)0) -#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(settings, logger, vt, obj) ((void)0) -#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(settings, logger, vt, obj, src) ((void)0) -#define BSP_DEBUG_LOG_HEAP_ORDER(settings, logger, fids) ((void)0) -#define BSP_DEBUG_LOG_LP_INPUT(settings, logger, w, nid, ph, d, vsh, bh) ((void)0) -#define BSP_DEBUG_LOG_LP_OUTPUT(settings, logger, w, nid, ph, st, it, ob, sh) ((void)0) -#define BSP_DEBUG_LOG_BRANCH_DECISION(settings, logger, bv, sh, nt) ((void)0) -#define BSP_DEBUG_EMIT_TREE_STATE(settings, logger, h, root, ub) ((void)0) +#define BSP_DEBUG_LOG_HORIZON_START(settings, logger, h, ws, we) ((void)0) +#define BSP_DEBUG_LOG_HORIZON_END(settings, logger, h, wut) ((void)0) +#define BSP_DEBUG_LOG_HORIZON_HASH(settings, logger, h, wut, hash) ((void)0) +#define BSP_DEBUG_LOG_NODE_ASSIGNED(settings, logger, wut, w, nid, fid, lb) ((void)0) +#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) ((void)0) +#define BSP_DEBUG_LOG_SOLVE_START(settings, logger, wut, w, nid, fid, wl, resumed) ((void)0) +#define BSP_DEBUG_LOG_SOLVE_END(settings, logger, wut, w, nid, fid, result, lb) ((void)0) +#define BSP_DEBUG_LOG_BRANCHED(settings, logger, wut, w, pid, pfid, did, uid) ((void)0) +#define BSP_DEBUG_LOG_PAUSED(settings, logger, wut, w, nid, fid, acc) ((void)0) +#define BSP_DEBUG_LOG_INTEGER(settings, logger, wut, w, nid, obj) ((void)0) +#define BSP_DEBUG_LOG_FATHOMED(settings, logger, wut, w, nid, lb) ((void)0) +#define BSP_DEBUG_LOG_INFEASIBLE(settings, logger, wut, w, nid) ((void)0) +#define BSP_DEBUG_LOG_PRUNED(settings, logger, wut, nid, fid, lb, ub) ((void)0) +#define BSP_DEBUG_LOG_SYNC_PHASE_START(settings, logger, wut, ne) ((void)0) +#define BSP_DEBUG_LOG_SYNC_PHASE_END(settings, logger, wut) ((void)0) +#define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(settings, logger, pid, fid) ((void)0) +#define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(settings, logger) ((void)0) +#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(settings, logger, wut, obj) ((void)0) +#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(settings, logger, wut, obj, src) ((void)0) +#define BSP_DEBUG_LOG_HEAP_ORDER(settings, logger, fids) ((void)0) +#define BSP_DEBUG_LOG_LP_INPUT(settings, logger, w, nid, ph, d, vsh, bh) ((void)0) +#define BSP_DEBUG_LOG_LP_OUTPUT(settings, logger, w, nid, ph, st, it, ob, sh) ((void)0) +#define BSP_DEBUG_LOG_BRANCH_DECISION(settings, logger, bv, sh, nt) ((void)0) +#define BSP_DEBUG_EMIT_TREE_STATE(settings, logger, h, root, ub) ((void)0) #define BSP_DEBUG_EMIT_STATE_JSON( \ - settings, logger, h, vs, ve, nfid, ub, lb, ne, nu, workers, heap, events) \ + settings, logger, h, ws, we, nfid, ub, lb, ne, nu, workers, heap, events) \ ((void)0) #define BSP_DEBUG_FINALIZE(settings, logger) ((void)0) diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index 428e79586..033c01a43 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -239,7 +239,7 @@ class mip_node_t { copy.objective_estimate = objective_estimate; copy.node_id = node_id; // Copy BSP fields - copy.accumulated_vt = accumulated_vt; + copy.accumulated_wut = accumulated_wut; copy.origin_worker_id = origin_worker_id; copy.creation_seq = creation_seq; return copy; @@ -262,7 +262,7 @@ class mip_node_t { std::vector vstatus; // BSP fields for deterministic parallel B&B - f_t accumulated_vt{0.0}; // Virtual time spent on this node so far + f_t accumulated_wut{0.0}; // Work units accumulated on this node so far // Worker-local identification for deterministic BSP ordering: // - origin_worker_id: which worker created this node From 2947a232c14243a19475c97fc326f6d5a32e313b Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 09:54:03 +0000 Subject: [PATCH 217/366] unify pseudocost computations --- cpp/src/dual_simplex/bb_worker_state.hpp | 318 +++------------------ cpp/src/dual_simplex/branch_and_bound.cpp | 3 +- cpp/src/dual_simplex/diving_heuristics.cpp | 149 +++------- cpp/src/dual_simplex/diving_heuristics.hpp | 120 ++++++++ cpp/src/dual_simplex/pseudo_costs.cpp | 147 ++++------ cpp/src/dual_simplex/pseudo_costs.hpp | 79 +++++ 6 files changed, 328 insertions(+), 488 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 2afc787b4..df026ad72 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -51,6 +51,14 @@ struct pseudo_cost_update_t { int worker_id; // for tie-breaking in sort }; +// Queued integer solution found during a horizon (merged at sync) +template +struct queued_integer_solution_t { + f_t objective; + std::vector solution; + i_t depth; +}; + // Per-worker state for BSP (Bulk Synchronous Parallel) branch-and-bound template struct bb_worker_state_t { @@ -128,22 +136,14 @@ struct bb_worker_state_t { double total_work_units{0.0}; // Timing statistics (in seconds) - double total_runtime{0.0}; // Total time spent doing actual work - double total_barrier_wait{0.0}; // Total time spent waiting at horizon sync barriers - double total_nowork_time{0.0}; // Total time spent with no nodes to work on - double horizon_finish_time{ - 0.0}; // Timestamp when worker finished current horizon (for barrier wait calc) + double total_runtime{0.0}; // Total time spent doing actual work + double total_nowork_time{0.0}; // Total time spent with no nodes to work on // Worker-local upper bound for BSP determinism (prevents cross-worker pruning races) f_t local_upper_bound{std::numeric_limits::infinity()}; // Queued integer solutions found during this horizon (merged at sync) - struct queued_integer_solution_t { - f_t objective; - std::vector solution; - i_t depth; - }; - std::vector integer_solutions; + std::vector> integer_solutions; // Queued pseudo-cost updates (applied to global pseudo-costs at sync) std::vector> pseudo_cost_updates; @@ -195,7 +195,6 @@ struct bb_worker_state_t { // Reset for new horizon void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) { - // Reset clock to horizon_start for consistent VT timestamps across workers clock = horizon_start; events.clear(); events.horizon_start = horizon_start; @@ -233,10 +232,9 @@ struct bb_worker_state_t { horizon_end = new_horizon_end; } - // Queue a pseudo-cost update for global sync AND apply it to local snapshot immediately - // This allows within-horizon learning while maintaining determinism: - // - Local snapshot updates are sequential within each worker (deterministic) - // - Global updates are merged at sync in sorted (VT, worker_id) order (deterministic) + // Queue a pseudo-cost update for global sync AND apply it to local snapshot immediately. + // Local snapshot updates are sequential within each worker (deterministic). + // Global updates are merged at sync in sorted (wut, worker_id) order (deterministic). void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) { // Queue for global sync at horizon end @@ -257,81 +255,19 @@ struct bb_worker_state_t { i_t variable_selection_from_snapshot(const std::vector& fractional, const std::vector& solution) const { - const i_t num_fractional = fractional.size(); - if (num_fractional == 0) return -1; - - // Compute averages from snapshot - i_t num_initialized_down = 0; - i_t num_initialized_up = 0; - f_t pseudo_cost_down_avg = 0; - f_t pseudo_cost_up_avg = 0; - - const i_t n = pc_sum_down_snapshot.size(); - for (i_t j = 0; j < n; ++j) { - if (pc_num_down_snapshot[j] > 0) { - ++num_initialized_down; - if (std::isfinite(pc_sum_down_snapshot[j])) { - pseudo_cost_down_avg += pc_sum_down_snapshot[j] / pc_num_down_snapshot[j]; - } - } - if (pc_num_up_snapshot[j] > 0) { - ++num_initialized_up; - if (std::isfinite(pc_sum_up_snapshot[j])) { - pseudo_cost_up_avg += pc_sum_up_snapshot[j] / pc_num_up_snapshot[j]; - } - } - } - if (num_initialized_down > 0) { - pseudo_cost_down_avg /= num_initialized_down; - } else { - pseudo_cost_down_avg = 1.0; - } - if (num_initialized_up > 0) { - pseudo_cost_up_avg /= num_initialized_up; - } else { - pseudo_cost_up_avg = 1.0; - } - - // Compute scores - std::vector score(num_fractional); - for (i_t k = 0; k < num_fractional; ++k) { - const i_t j = fractional[k]; - f_t pc_down = (pc_num_down_snapshot[j] != 0) - ? pc_sum_down_snapshot[j] / pc_num_down_snapshot[j] - : pseudo_cost_down_avg; - f_t pc_up = (pc_num_up_snapshot[j] != 0) ? pc_sum_up_snapshot[j] / pc_num_up_snapshot[j] - : pseudo_cost_up_avg; - constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - score[k] = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); - } - - // Select variable with maximum score - i_t branch_var = fractional[0]; - f_t max_score = score[0]; - for (i_t k = 1; k < num_fractional; ++k) { - if (score[k] > max_score) { - max_score = score[k]; - branch_var = fractional[k]; - } - } - - return branch_var; + return variable_selection_from_pseudo_costs(pc_sum_down_snapshot.data(), + pc_sum_up_snapshot.data(), + pc_num_down_snapshot.data(), + pc_num_up_snapshot.data(), + (i_t)pc_sum_down_snapshot.size(), + fractional, + solution); } // ========================================================================== // Node enqueueing methods // ========================================================================== - // Add a node to the plunge stack, assigning BSP identity - void enqueue_node(mip_node_t* node) - { - node->origin_worker_id = worker_id; - node->creation_seq = next_creation_seq++; - plunge_stack.push_front(node); - } - // Add a node that already has BSP identity (from load balancing or initial distribution) void enqueue_node_with_identity(mip_node_t* node) { plunge_stack.push_front(node); } @@ -435,44 +371,6 @@ struct bb_worker_state_t { return plunge_stack.size() + backlog.size() + (current_node != nullptr ? 1 : 0); } - // Get number of nodes in plunge stack only - size_t plunge_stack_size() const { return plunge_stack.size(); } - - // Get number of nodes in backlog only - size_t backlog_size() const { return backlog.size(); } - - // ========================================================================== - // Load balancing support - // ========================================================================== - - // Extract all nodes from worker (for load balancing) - // Returns nodes in arbitrary order - caller should sort if deterministic order needed - std::vector*> extract_all_nodes() - { - std::vector*> nodes; - nodes.reserve(queue_size()); - - // Include paused node if any - if (current_node != nullptr) { - nodes.push_back(current_node); - current_node = nullptr; - } - - // Extract from plunge stack - for (auto* node : plunge_stack) { - nodes.push_back(node); - } - plunge_stack.clear(); - - // Extract from backlog - for (auto* node : backlog) { - nodes.push_back(node); - } - backlog.clear(); - - return nodes; - } - // Extract only backlog nodes (for redistribution at horizon sync) // Plunge stack nodes stay with worker for locality std::vector*> extract_backlog_nodes() @@ -482,14 +380,6 @@ struct bb_worker_state_t { return nodes; } - // Clear all queues without returning nodes (use with caution) - void clear_queue() - { - current_node = nullptr; - plunge_stack.clear(); - backlog.clear(); - } - // Record an event void record_event(bb_event_t event) { @@ -637,16 +527,8 @@ struct bsp_diving_worker_state_t { std::vector dive_lower; std::vector dive_upper; - // ========================================================================== - // Queued results (merged at sync) - // ========================================================================== - - struct queued_integer_solution_t { - f_t objective; - std::vector solution; - i_t depth; - }; - std::vector integer_solutions; + // Queued integer solutions found during this horizon (merged at sync) + std::vector> integer_solutions; // ========================================================================== // Statistics @@ -752,85 +634,16 @@ struct bsp_diving_worker_state_t { branch_variable_t variable_selection_from_snapshot(const std::vector& fractional, const std::vector& solution) const { - const i_t num_fractional = fractional.size(); - if (num_fractional == 0) return {-1, rounding_direction_t::NONE}; - - i_t num_initialized_down = 0; - i_t num_initialized_up = 0; - f_t pseudo_cost_down_avg = 0; - f_t pseudo_cost_up_avg = 0; - - const i_t n = pc_sum_down_snapshot.size(); - for (i_t j = 0; j < n; ++j) { - if (pc_num_down_snapshot[j] > 0) { - ++num_initialized_down; - if (std::isfinite(pc_sum_down_snapshot[j])) { - pseudo_cost_down_avg += pc_sum_down_snapshot[j] / pc_num_down_snapshot[j]; - } - } - if (pc_num_up_snapshot[j] > 0) { - ++num_initialized_up; - if (std::isfinite(pc_sum_up_snapshot[j])) { - pseudo_cost_up_avg += pc_sum_up_snapshot[j] / pc_num_up_snapshot[j]; - } - } - } - pseudo_cost_down_avg = - (num_initialized_down > 0) ? pseudo_cost_down_avg / num_initialized_down : 1.0; - pseudo_cost_up_avg = (num_initialized_up > 0) ? pseudo_cost_up_avg / num_initialized_up : 1.0; - - i_t branch_var = fractional[0]; - f_t max_score = std::numeric_limits::lowest(); - rounding_direction_t round_dir = rounding_direction_t::DOWN; - constexpr f_t eps = 1e-6; - - for (i_t j : fractional) { - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - - f_t pc_down = pc_num_down_snapshot[j] != 0 ? pc_sum_down_snapshot[j] / pc_num_down_snapshot[j] - : pseudo_cost_down_avg; - f_t pc_up = pc_num_up_snapshot[j] != 0 ? pc_sum_up_snapshot[j] / pc_num_up_snapshot[j] - : pseudo_cost_up_avg; - - f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); - f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); - - f_t score = 0; - rounding_direction_t dir = rounding_direction_t::DOWN; - - f_t root_val = (root_solution && j < static_cast(root_solution->size())) - ? (*root_solution)[j] - : solution[j]; - - if (solution[j] < root_val - 0.4) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else if (solution[j] > root_val + 0.4) { - score = score_up; - dir = rounding_direction_t::UP; - } else if (f_down < 0.3) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else if (f_down > 0.7) { - score = score_up; - dir = rounding_direction_t::UP; - } else if (pc_down < pc_up + eps) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else { - score = score_up; - dir = rounding_direction_t::UP; - } - - if (score > max_score) { - max_score = score; - branch_var = j; - round_dir = dir; - } - } - - return {branch_var, round_dir}; + // Use root_solution if available, otherwise use solution as fallback + const std::vector& root_sol = (root_solution != nullptr) ? *root_solution : solution; + return pseudocost_diving_from_arrays(pc_sum_down_snapshot.data(), + pc_sum_up_snapshot.data(), + pc_num_down_snapshot.data(), + pc_num_up_snapshot.data(), + (i_t)pc_sum_down_snapshot.size(), + fractional, + solution, + root_sol); } // Guided diving variable selection using incumbent snapshot @@ -841,63 +654,14 @@ struct bsp_diving_worker_state_t { return variable_selection_from_snapshot(fractional, solution); } - const i_t num_fractional = fractional.size(); - if (num_fractional == 0) return {-1, rounding_direction_t::NONE}; - - i_t num_initialized_down = 0; - i_t num_initialized_up = 0; - f_t pseudo_cost_down_avg = 0; - f_t pseudo_cost_up_avg = 0; - - const i_t n = pc_sum_down_snapshot.size(); - for (i_t j = 0; j < n; ++j) { - if (pc_num_down_snapshot[j] > 0) { - ++num_initialized_down; - if (std::isfinite(pc_sum_down_snapshot[j])) { - pseudo_cost_down_avg += pc_sum_down_snapshot[j] / pc_num_down_snapshot[j]; - } - } - if (pc_num_up_snapshot[j] > 0) { - ++num_initialized_up; - if (std::isfinite(pc_sum_up_snapshot[j])) { - pseudo_cost_up_avg += pc_sum_up_snapshot[j] / pc_num_up_snapshot[j]; - } - } - } - pseudo_cost_down_avg = - (num_initialized_down > 0) ? pseudo_cost_down_avg / num_initialized_down : 1.0; - pseudo_cost_up_avg = (num_initialized_up > 0) ? pseudo_cost_up_avg / num_initialized_up : 1.0; - - i_t branch_var = fractional[0]; - f_t max_score = std::numeric_limits::lowest(); - rounding_direction_t round_dir = rounding_direction_t::DOWN; - constexpr f_t eps = 1e-6; - - for (i_t j : fractional) { - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t down_dist = std::abs(incumbent_snapshot[j] - std::floor(solution[j])); - f_t up_dist = std::abs(std::ceil(solution[j]) - incumbent_snapshot[j]); - rounding_direction_t dir = - down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; - - f_t pc_down = pc_num_down_snapshot[j] != 0 ? pc_sum_down_snapshot[j] / pc_num_down_snapshot[j] - : pseudo_cost_down_avg; - f_t pc_up = pc_num_up_snapshot[j] != 0 ? pc_sum_up_snapshot[j] / pc_num_up_snapshot[j] - : pseudo_cost_up_avg; - - f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; - f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; - f_t score = (score1 + score2) / 6; - - if (score > max_score) { - max_score = score; - branch_var = j; - round_dir = dir; - } - } - - return {branch_var, round_dir}; + return guided_diving_from_arrays(pc_sum_down_snapshot.data(), + pc_sum_up_snapshot.data(), + pc_num_down_snapshot.data(), + pc_num_up_snapshot.data(), + (i_t)pc_sum_down_snapshot.size(), + fractional, + solution, + incumbent_snapshot); } }; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index f8ecdcf17..5f6452fa4 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -3061,8 +3061,7 @@ void branch_and_bound_t::merge_diving_solutions() if (!bsp_diving_workers_) return; // Collect all integer solutions from diving workers - std::vector::queued_integer_solution_t*> - all_solutions; + std::vector*> all_solutions; for (auto& worker : *bsp_diving_workers_) { for (auto& sol : worker.integer_solutions) { diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index a56b4cce3..ca006bd9d 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -72,71 +72,26 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, logger_t& log) { std::lock_guard lock(pc.mutex); - i_t branch_var = -1; - f_t max_score = std::numeric_limits::lowest(); - rounding_direction_t round_dir = rounding_direction_t::NONE; - constexpr f_t eps = 1e-6; - - i_t num_initialized_down; - i_t num_initialized_up; - f_t pseudo_cost_down_avg; - f_t pseudo_cost_up_avg; - pc.initialized( - num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - - for (i_t j : fractional) { - rounding_direction_t dir = rounding_direction_t::NONE; - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - - f_t pc_down = pc.pseudo_cost_num_down[j] != 0 - ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - - f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - - f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); - f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); - f_t score = 0; - - if (solution[j] < root_solution[j] - 0.4) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else if (solution[j] > root_solution[j] + 0.4) { - score = score_up; - dir = rounding_direction_t::UP; - } else if (f_down < 0.3) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else if (f_down > 0.7) { - score = score_up; - dir = rounding_direction_t::UP; - } else if (pc_down < pc_up + eps) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else { - score = score_up; - dir = rounding_direction_t::UP; - } - - if (score > max_score) { - max_score = score; - branch_var = j; - round_dir = dir; - } - } - assert(round_dir != rounding_direction_t::NONE); - assert(branch_var >= 0); - - log.debug("Pseudocost diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - max_score); - - return {branch_var, round_dir}; + const i_t n = pc.pseudo_cost_sum_down.size(); + auto result = pseudocost_diving_from_arrays(pc.pseudo_cost_sum_down.data(), + pc.pseudo_cost_sum_up.data(), + pc.pseudo_cost_num_down.data(), + pc.pseudo_cost_num_up.data(), + n, + fractional, + solution, + root_solution); + + assert(result.direction != rounding_direction_t::NONE); + assert(result.variable >= 0); + + log.debug("Pseudocost diving: selected var %d with val = %e, round dir = %d\n", + result.variable, + solution[result.variable], + result.direction); + + return result; } template @@ -147,54 +102,26 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, logger_t& log) { std::lock_guard lock(pc.mutex); - i_t branch_var = -1; - f_t max_score = std::numeric_limits::lowest(); - rounding_direction_t round_dir = rounding_direction_t::NONE; - constexpr f_t eps = 1e-6; - - i_t num_initialized_down; - i_t num_initialized_up; - f_t pseudo_cost_down_avg; - f_t pseudo_cost_up_avg; - pc.initialized( - num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - - for (i_t j : fractional) { - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t down_dist = std::abs(incumbent[j] - std::floor(solution[j])); - f_t up_dist = std::abs(std::ceil(solution[j]) - incumbent[j]); - rounding_direction_t dir = - down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; - - f_t pc_down = pc.pseudo_cost_num_down[j] != 0 - ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - - f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - - f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; - f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; - f_t score = (score1 + score2) / 6; - if (score > max_score) { - max_score = score; - branch_var = j; - round_dir = dir; - } - } - - assert(round_dir != rounding_direction_t::NONE); - assert(branch_var >= 0); - - log.debug("Guided diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - max_score); - - return {branch_var, round_dir}; + const i_t n = pc.pseudo_cost_sum_down.size(); + auto result = guided_diving_from_arrays(pc.pseudo_cost_sum_down.data(), + pc.pseudo_cost_sum_up.data(), + pc.pseudo_cost_num_down.data(), + pc.pseudo_cost_num_up.data(), + n, + fractional, + solution, + incumbent); + + assert(result.direction != rounding_direction_t::NONE); + assert(result.variable >= 0); + + log.debug("Guided diving: selected var %d with val = %e, round dir = %d\n", + result.variable, + solution[result.variable], + result.direction); + + return result; } template diff --git a/cpp/src/dual_simplex/diving_heuristics.hpp b/cpp/src/dual_simplex/diving_heuristics.hpp index 3c6d77c04..8279cc47e 100644 --- a/cpp/src/dual_simplex/diving_heuristics.hpp +++ b/cpp/src/dual_simplex/diving_heuristics.hpp @@ -20,6 +20,126 @@ struct branch_variable_t { rounding_direction_t direction; }; +// ============================================================================= +// Lock-free diving implementations that work on raw pseudo-cost arrays +// These can be called directly with snapshot data or through the wrapper functions +// ============================================================================= + +// Pseudocost diving variable selection (lock-free implementation) +template +branch_variable_t pseudocost_diving_from_arrays(const f_t* pc_sum_down, + const f_t* pc_sum_up, + const i_t* pc_num_down, + const i_t* pc_num_up, + i_t n_vars, + const std::vector& fractional, + const std::vector& solution, + const std::vector& root_solution) +{ + const i_t num_fractional = fractional.size(); + if (num_fractional == 0) return {-1, rounding_direction_t::NONE}; + + auto avgs = compute_pseudo_cost_averages(pc_sum_down, pc_sum_up, pc_num_down, pc_num_up, n_vars); + + i_t branch_var = fractional[0]; + f_t max_score = std::numeric_limits::lowest(); + rounding_direction_t round_dir = rounding_direction_t::DOWN; + constexpr f_t eps = f_t(1e-6); + + for (i_t j : fractional) { + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t pc_down = pc_num_down[j] != 0 ? pc_sum_down[j] / pc_num_down[j] : avgs.down_avg; + f_t pc_up = pc_num_up[j] != 0 ? pc_sum_up[j] / pc_num_up[j] : avgs.up_avg; + + f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); + f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); + + f_t score = 0; + rounding_direction_t dir = rounding_direction_t::DOWN; + + f_t root_val = (j < static_cast(root_solution.size())) ? root_solution[j] : solution[j]; + + if (solution[j] < root_val - f_t(0.4)) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else if (solution[j] > root_val + f_t(0.4)) { + score = score_up; + dir = rounding_direction_t::UP; + } else if (f_down < f_t(0.3)) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else if (f_down > f_t(0.7)) { + score = score_up; + dir = rounding_direction_t::UP; + } else if (pc_down < pc_up + eps) { + score = score_down; + dir = rounding_direction_t::DOWN; + } else { + score = score_up; + dir = rounding_direction_t::UP; + } + + if (score > max_score) { + max_score = score; + branch_var = j; + round_dir = dir; + } + } + + return {branch_var, round_dir}; +} + +// Guided diving variable selection (lock-free implementation) +template +branch_variable_t guided_diving_from_arrays(const f_t* pc_sum_down, + const f_t* pc_sum_up, + const i_t* pc_num_down, + const i_t* pc_num_up, + i_t n_vars, + const std::vector& fractional, + const std::vector& solution, + const std::vector& incumbent) +{ + const i_t num_fractional = fractional.size(); + if (num_fractional == 0) return {-1, rounding_direction_t::NONE}; + + auto avgs = compute_pseudo_cost_averages(pc_sum_down, pc_sum_up, pc_num_down, pc_num_up, n_vars); + + i_t branch_var = fractional[0]; + f_t max_score = std::numeric_limits::lowest(); + rounding_direction_t round_dir = rounding_direction_t::DOWN; + constexpr f_t eps = f_t(1e-6); + + for (i_t j : fractional) { + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t down_dist = std::abs(incumbent[j] - std::floor(solution[j])); + f_t up_dist = std::abs(std::ceil(solution[j]) - incumbent[j]); + rounding_direction_t dir = + down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; + + f_t pc_down = pc_num_down[j] != 0 ? pc_sum_down[j] / pc_num_down[j] : avgs.down_avg; + f_t pc_up = pc_num_up[j] != 0 ? pc_sum_up[j] / pc_num_up[j] : avgs.up_avg; + + f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; + f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; + f_t score = (score1 + score2) / 6; + + if (score > max_score) { + max_score = score; + branch_var = j; + round_dir = dir; + } + } + + return {branch_var, round_dir}; +} + +// ============================================================================= +// Wrapper functions that acquire locks and call the impl versions +// ============================================================================= + template branch_variable_t line_search_diving(const std::vector& fractional, const std::vector& solution, diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index c896ef6aa..206e45807 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -222,37 +222,22 @@ void pseudo_costs_t::initialized(i_t& num_initialized_down, f_t& pseudo_cost_down_avg, f_t& pseudo_cost_up_avg) const { + // Count initialized variables while computing averages num_initialized_down = 0; num_initialized_up = 0; - pseudo_cost_down_avg = 0; - pseudo_cost_up_avg = 0; const i_t n = pseudo_cost_sum_down.size(); for (i_t j = 0; j < n; j++) { - if (pseudo_cost_num_down[j] > 0) { - num_initialized_down++; - if (std::isfinite(pseudo_cost_sum_down[j])) { - pseudo_cost_down_avg += pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; - } - } - - if (pseudo_cost_num_up[j] > 0) { - num_initialized_up++; - - if (std::isfinite(pseudo_cost_sum_up[j])) { - pseudo_cost_up_avg += pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; - } - } - } - if (num_initialized_down > 0) { - pseudo_cost_down_avg /= num_initialized_down; - } else { - pseudo_cost_down_avg = 1.0; - } - if (num_initialized_up > 0) { - pseudo_cost_up_avg /= num_initialized_up; - } else { - pseudo_cost_up_avg = 1.0; + if (pseudo_cost_num_down[j] > 0) { num_initialized_down++; } + if (pseudo_cost_num_up[j] > 0) { num_initialized_up++; } } + + auto avgs = compute_pseudo_cost_averages(pseudo_cost_sum_down.data(), + pseudo_cost_sum_up.data(), + pseudo_cost_num_down.data(), + pseudo_cost_num_up.data(), + n); + pseudo_cost_down_avg = avgs.down_avg; + pseudo_cost_up_avg = avgs.up_avg; } template @@ -262,57 +247,34 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio { std::lock_guard lock(mutex); - const i_t num_fractional = fractional.size(); - std::vector pseudo_cost_up(num_fractional); - std::vector pseudo_cost_down(num_fractional); - std::vector score(num_fractional); - - i_t num_initialized_down; - i_t num_initialized_up; - f_t pseudo_cost_down_avg; - f_t pseudo_cost_up_avg; - - initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - - log.printf("PC: num initialized down %d up %d avg down %e up %e\n", - num_initialized_down, - num_initialized_up, - pseudo_cost_down_avg, - pseudo_cost_up_avg); + const i_t n = pseudo_cost_sum_down.size(); - for (i_t k = 0; k < num_fractional; k++) { - const i_t j = fractional[k]; - if (pseudo_cost_num_down[j] != 0) { - pseudo_cost_down[k] = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; - } else { - pseudo_cost_down[k] = pseudo_cost_down_avg; - } - - if (pseudo_cost_num_up[j] != 0) { - pseudo_cost_up[k] = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; - } else { - pseudo_cost_up[k] = pseudo_cost_up_avg; - } - constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - score[k] = - std::max(f_down * pseudo_cost_down[k], eps) * std::max(f_up * pseudo_cost_up[k], eps); + // Log averages for debugging + auto avgs = compute_pseudo_cost_averages(pseudo_cost_sum_down.data(), + pseudo_cost_sum_up.data(), + pseudo_cost_num_down.data(), + pseudo_cost_num_up.data(), + n); + i_t num_down = 0, num_up = 0; + for (i_t j = 0; j < n; j++) { + if (pseudo_cost_num_down[j] > 0) num_down++; + if (pseudo_cost_num_up[j] > 0) num_up++; } + log.printf("PC: num initialized down %d up %d avg down %e up %e\n", + num_down, + num_up, + avgs.down_avg, + avgs.up_avg); - i_t branch_var = fractional[0]; - f_t max_score = -1; - i_t select = -1; - for (i_t k = 0; k < num_fractional; k++) { - if (score[k] > max_score) { - max_score = score[k]; - branch_var = fractional[k]; - select = k; - } - } + i_t branch_var = variable_selection_from_pseudo_costs(pseudo_cost_sum_down.data(), + pseudo_cost_sum_up.data(), + pseudo_cost_num_down.data(), + pseudo_cost_num_up.data(), + n, + fractional, + solution); - log.printf( - "pc branching on %d. Value %e. Score %e\n", branch_var, solution[branch_var], score[select]); + log.printf("pc branching on %d. Value %e.\n", branch_var, solution[branch_var]); return branch_var; } @@ -325,36 +287,25 @@ f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, { std::lock_guard lock(mutex); + const i_t n = pseudo_cost_sum_down.size(); const i_t num_fractional = fractional.size(); f_t estimate = lower_bound; - i_t num_initialized_down; - i_t num_initialized_up; - f_t pseudo_cost_down_avg; - f_t pseudo_cost_up_avg; - - initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); + auto avgs = compute_pseudo_cost_averages(pseudo_cost_sum_down.data(), + pseudo_cost_sum_up.data(), + pseudo_cost_num_down.data(), + pseudo_cost_num_up.data(), + n); for (i_t k = 0; k < num_fractional; k++) { - const i_t j = fractional[k]; - f_t pseudo_cost_down = 0; - f_t pseudo_cost_up = 0; - - if (pseudo_cost_num_down[j] != 0) { - pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; - } else { - pseudo_cost_down = pseudo_cost_down_avg; - } - - if (pseudo_cost_num_up[j] != 0) { - pseudo_cost_up = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; - } else { - pseudo_cost_up = pseudo_cost_up_avg; - } - constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - estimate += std::min(pseudo_cost_down * f_down, pseudo_cost_up * f_up); + const i_t j = fractional[k]; + f_t pc_down = pseudo_cost_num_down[j] != 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] + : avgs.down_avg; + f_t pc_up = + pseudo_cost_num_up[j] != 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] : avgs.up_avg; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + estimate += std::min(pc_down * f_down, pc_up * f_up); } log.printf("pseudocost estimate = %e\n", estimate); diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 5cbddfdfd..a5832aef3 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -19,6 +19,85 @@ namespace cuopt::linear_programming::dual_simplex { +// ============================================================================= +// Pseudo-cost utility functions (lock-free implementations) +// These can be called directly with snapshot data or through pseudo_costs_t methods +// ============================================================================= + +template +struct pseudo_cost_averages_t { + f_t down_avg; + f_t up_avg; +}; + +// Compute average pseudo-costs from arrays +// Works with either pseudo_costs_t members or snapshot arrays +template +pseudo_cost_averages_t compute_pseudo_cost_averages( + const f_t* pc_sum_down, const f_t* pc_sum_up, const i_t* pc_num_down, const i_t* pc_num_up, i_t n) +{ + i_t num_initialized_down = 0; + i_t num_initialized_up = 0; + f_t pseudo_cost_down_avg = 0; + f_t pseudo_cost_up_avg = 0; + + for (i_t j = 0; j < n; ++j) { + if (pc_num_down[j] > 0) { + ++num_initialized_down; + if (std::isfinite(pc_sum_down[j])) { + pseudo_cost_down_avg += pc_sum_down[j] / pc_num_down[j]; + } + } + if (pc_num_up[j] > 0) { + ++num_initialized_up; + if (std::isfinite(pc_sum_up[j])) { pseudo_cost_up_avg += pc_sum_up[j] / pc_num_up[j]; } + } + } + + pseudo_cost_down_avg = + (num_initialized_down > 0) ? pseudo_cost_down_avg / num_initialized_down : f_t(1.0); + pseudo_cost_up_avg = + (num_initialized_up > 0) ? pseudo_cost_up_avg / num_initialized_up : f_t(1.0); + + return {pseudo_cost_down_avg, pseudo_cost_up_avg}; +} + +// Variable selection using pseudo-cost product scoring +// Returns the best variable to branch on +template +i_t variable_selection_from_pseudo_costs(const f_t* pc_sum_down, + const f_t* pc_sum_up, + const i_t* pc_num_down, + const i_t* pc_num_up, + i_t n_vars, + const std::vector& fractional, + const std::vector& solution) +{ + const i_t num_fractional = fractional.size(); + if (num_fractional == 0) return -1; + + auto [pc_down_avg, pc_up_avg] = + compute_pseudo_cost_averages(pc_sum_down, pc_sum_up, pc_num_down, pc_num_up, n_vars); + + i_t branch_var = fractional[0]; + f_t max_score = std::numeric_limits::lowest(); + constexpr f_t eps = f_t(1e-6); + + for (i_t j : fractional) { + f_t pc_down = pc_num_down[j] != 0 ? pc_sum_down[j] / pc_num_down[j] : pc_down_avg; + f_t pc_up = pc_num_up[j] != 0 ? pc_sum_up[j] / pc_num_up[j] : pc_up_avg; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); + if (score > max_score) { + max_score = score; + branch_var = j; + } + } + + return branch_var; +} + template class pseudo_costs_t { public: From 5f58f6de0ec677d14371b3eda0716796b062166a Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 20 Jan 2026 14:34:06 +0100 Subject: [PATCH 218/366] parallelized the trial branching --- cpp/src/dual_simplex/pseudo_costs.cpp | 48 ++++++++++++++++++++++----- 1 file changed, 39 insertions(+), 9 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index e30471f9a..a2d0d1de4 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -435,16 +435,45 @@ i_t pseudo_costs_t::reliable_variable_selection( bnb_lp_iter, reliable_threshold); - std::vector pending = fractional; - std::vector conflict; - conflict.reserve(fractional.size()); + std::vector pending(fractional.size(), -1); + std::vector next(fractional.size(), -1); + omp_atomic_t num_pending = 0; + omp_atomic_t num_next = 0; + omp_mutex_t score_mutex; - while (!pending.empty()) { - for (auto j : pending) { + for (auto j : fractional) { + std::lock_guard lock(pseudo_cost_mutex[j]); + + if (pseudo_cost_num_down[j] < reliable_threshold || + pseudo_cost_num_up[j] < reliable_threshold) { + pending[num_pending++] = j; + continue; + } + + f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] + : pseudo_cost_down_avg; + + constexpr f_t eps = 1e-6; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); + + if (score > max_score) { + max_score = score; + branch_var = j; + } + } + + while (num_pending != 0) { +#pragma omp taskloop priority(20) + for (i_t i = 0; i < num_pending; ++i) { + const i_t j = pending[i]; bool is_locked = pseudo_cost_mutex[j].try_lock(); if (!is_locked) { - conflict.push_back(j); + next[num_next++] = j; continue; } @@ -508,14 +537,15 @@ i_t pseudo_costs_t::reliable_variable_selection( const f_t f_up = std::ceil(solution[j]) - solution[j]; f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); - if (score > max_score) { + if (std::lock_guard lock(score_mutex); score > max_score) { max_score = score; branch_var = j; } } - std::swap(pending, conflict); - conflict.clear(); + std::swap(pending, next); + num_pending = num_next; + num_next = 0; } log.printf( From fbc17c9fb23c8259704b985c005cefc972a04a42 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 20 Jan 2026 15:08:45 +0100 Subject: [PATCH 219/366] added debug log --- cpp/src/dual_simplex/pseudo_costs.cpp | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index a2d0d1de4..76dd8e125 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -430,11 +430,6 @@ i_t pseudo_costs_t::reliable_variable_selection( // reliable_threshold = total_lp_iter < max_iter ? reliable_threshold : 0; i_t reliable_threshold = 1; - settings.log.debug("RB LP iterations = %d, B&B LP iterations = %d reliable_threshold = %d\n", - total_lp_iter.load(), - bnb_lp_iter, - reliable_threshold); - std::vector pending(fractional.size(), -1); std::vector next(fractional.size(), -1); omp_atomic_t num_pending = 0; @@ -466,8 +461,18 @@ i_t pseudo_costs_t::reliable_variable_selection( } } + if (num_pending != 0) { + settings.log.debug( + "RB LP iterations = %d, B&B LP iterations = %d reliable_threshold = %d, num strong branches " + "= %d\n", + total_lp_iter.load(), + bnb_lp_iter, + reliable_threshold, + num_pending.load()); + } + while (num_pending != 0) { -#pragma omp taskloop priority(20) +#pragma omp taskloop priority(20) grainsize(2) if (num_pending > 2) for (i_t i = 0; i < num_pending; ++i) { const i_t j = pending[i]; bool is_locked = pseudo_cost_mutex[j].try_lock(); From a1eb6b89f7da4511174783ed2cd724ddc9dcad37 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 20 Jan 2026 15:27:19 +0100 Subject: [PATCH 220/366] solved early termination in CMS750_4. fixed hard coded number of thread in cuopt_cli --- cpp/src/mip/diversity/diversity_manager.cu | 14 ++++++-------- cpp/src/mip/presolve/probing_cache.cu | 22 +++++++++++----------- cpp/src/mip/problem/problem.cu | 7 +++++++ cpp/src/mip/problem/problem.cuh | 1 + 4 files changed, 25 insertions(+), 19 deletions(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index cfe9876de..f3158316f 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -434,15 +434,13 @@ solution_t diversity_manager_t::run_solver() problem_ptr->handle_ptr->get_stream()); problem_ptr->handle_ptr->sync_stream(); - auto user_obj = problem_ptr->get_user_obj_from_solver_obj(lp_result.get_objective_value()); + // PDLP returns user-space objective (it applies objective_scaling_factor internally) + auto user_obj = lp_result.get_objective_value(); + auto solver_obj = problem_ptr->get_solver_obj_from_user_obj(user_obj); auto iterations = lp_result.get_additional_termination_information().number_of_steps_taken; - // Set for the B&B - problem_ptr->set_root_relaxation_solution_callback(host_primal, - host_dual, - host_reduced_costs, - lp_result.get_objective_value(), - user_obj, - iterations); + // Set for the B&B (param4 expects solver space, param5 expects user space) + problem_ptr->set_root_relaxation_solution_callback( + host_primal, host_dual, host_reduced_costs, solver_obj, user_obj, iterations); } // in case the pdlp returned var boudns that are out of bounds diff --git a/cpp/src/mip/presolve/probing_cache.cu b/cpp/src/mip/presolve/probing_cache.cu index fc2d974e3..0bc2e6733 100644 --- a/cpp/src/mip/presolve/probing_cache.cu +++ b/cpp/src/mip/presolve/probing_cache.cu @@ -856,17 +856,15 @@ bool compute_probing_cache(bound_presolve_t& bound_presolve, bound_presolve.settings.iteration_limit = 50; bound_presolve.settings.time_limit = timer.remaining_time(); - // Set the number of threads - const size_t max_threads = 8; - omp_set_num_threads(max_threads); + const int num_threads = 8; // Create a vector of multi_probe_t objects std::vector> multi_probe_presolve_pool; - std::vector>> modification_vector_pool(max_threads); - std::vector>> substitution_vector_pool(max_threads); + std::vector>> modification_vector_pool(num_threads); + std::vector>> substitution_vector_pool(num_threads); // Initialize multi_probe_presolve_pool - for (size_t i = 0; i < max_threads; i++) { + for (size_t i = 0; i < num_threads; i++) { multi_probe_presolve_pool.emplace_back(bound_presolve.context); multi_probe_presolve_pool[i].resize(problem); multi_probe_presolve_pool[i].compute_stats = true; @@ -879,12 +877,14 @@ bool compute_probing_cache(bound_presolve_t& bound_presolve, size_t last_it_implied_singletons = 0; bool early_exit = false; const size_t step_size = min((size_t)2048, priority_indices.size()); - for (size_t step_start = 0; step_start < priority_indices.size(); step_start += step_size) { - if (timer.check_time_limit() || early_exit || problem_is_infeasible.load()) { break; } - size_t step_end = std::min(step_start + step_size, priority_indices.size()); + // Main parallel loop -#pragma omp parallel - { +#pragma omp parallel num_threads(num_threads) + { + for (size_t step_start = 0; step_start < priority_indices.size(); step_start += step_size) { + if (timer.check_time_limit() || early_exit || problem_is_infeasible.load()) { break; } + size_t step_end = std::min(step_start + step_size, priority_indices.size()); + #pragma omp for schedule(static, 4) for (size_t i = step_start; i < step_end; ++i) { auto var_idx = priority_indices[i]; diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 9e9b74a2e..8feaee523 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1988,6 +1988,13 @@ void problem_t::get_host_user_problem( : cuopt::linear_programming::dual_simplex::variable_type_t::INTEGER; } } + +template +f_t problem_t::get_solver_obj_from_user_obj(f_t user_obj) const +{ + return (user_obj / presolve_data.objective_scaling_factor) - presolve_data.objective_offset; +} + template f_t problem_t::get_user_obj_from_solver_obj(f_t solver_obj) const { diff --git a/cpp/src/mip/problem/problem.cuh b/cpp/src/mip/problem/problem.cuh index 9719f0b54..910079916 100644 --- a/cpp/src/mip/problem/problem.cuh +++ b/cpp/src/mip/problem/problem.cuh @@ -91,6 +91,7 @@ class problem_t { void post_process_solution(solution_t& solution); void compute_transpose_of_problem(); f_t get_user_obj_from_solver_obj(f_t solver_obj) const; + f_t get_solver_obj_from_user_obj(f_t user_obj) const; bool is_objective_integral() const { return objective_is_integral; } void compute_integer_fixed_problem(); void fill_integer_fixed_problem(rmm::device_uvector& assignment, From 8c5e9f6cf0ed56366c5e4ce973fc8919710494a2 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 10:44:17 +0000 Subject: [PATCH 221/366] policy system for solve_lp_ --- .../cuopt/linear_programming/constants.h | 4 +- cpp/src/dual_simplex/bb_solve_policy.hpp | 844 +++++++++++++ cpp/src/dual_simplex/branch_and_bound.cpp | 1111 +++++++---------- cpp/src/dual_simplex/branch_and_bound.hpp | 26 +- cpp/src/dual_simplex/phase2.cpp | 153 ++- cpp/src/dual_simplex/sparse_matrix.cpp | 2 +- cpp/src/dual_simplex/triangle_solve.hpp | 5 +- cpp/src/math_optimization/solver_settings.cu | 4 +- cpp/src/mip/local_search/local_search.cu | 1 - cpp/src/utilities/memory_instrumentation.hpp | 100 +- cpp/src/utilities/producer_sync.hpp | 31 +- cpp/src/utilities/work_unit_predictor.cpp | 21 +- cpp/src/utilities/work_unit_scheduler.cpp | 48 +- cpp/src/utilities/work_unit_scheduler.hpp | 6 +- 14 files changed, 1423 insertions(+), 933 deletions(-) create mode 100644 cpp/src/dual_simplex/bb_solve_policy.hpp diff --git a/cpp/include/cuopt/linear_programming/constants.h b/cpp/include/cuopt/linear_programming/constants.h index 6929b6720..eb32fa1fb 100644 --- a/cpp/include/cuopt/linear_programming/constants.h +++ b/cpp/include/cuopt/linear_programming/constants.h @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -49,7 +49,7 @@ #define CUOPT_CUDSS_DETERMINISTIC "cudss_deterministic" #define CUOPT_PRESOLVE "presolve" #define CUOPT_DUAL_POSTSOLVE "dual_postsolve" -#define CUOPT_MIP_DETERMINISTIC "mip_deterministic" +#define CUOPT_MIP_DETERMINISM_MODE "mip_determinism_mode" #define CUOPT_MIP_ABSOLUTE_TOLERANCE "mip_absolute_tolerance" #define CUOPT_MIP_RELATIVE_TOLERANCE "mip_relative_tolerance" #define CUOPT_MIP_INTEGRALITY_TOLERANCE "mip_integrality_tolerance" diff --git a/cpp/src/dual_simplex/bb_solve_policy.hpp b/cpp/src/dual_simplex/bb_solve_policy.hpp new file mode 100644 index 000000000..c16fe9a1a --- /dev/null +++ b/cpp/src/dual_simplex/bb_solve_policy.hpp @@ -0,0 +1,844 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +#include + +namespace cuopt::linear_programming::dual_simplex { + +// Import types from parent namespace +using cuopt::omp_atomic_t; + +template +struct bnb_stats_t; + +// Martin's criteria for the preferred rounding direction +// Reference: A. Martin, "Integer Programs with Block Structure," +// Technische Universitat Berlin, Berlin, 1999. +// https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/391 +template +inline rounding_direction_t martin_criteria(f_t val, f_t root_val) +{ + const f_t down_val = std::floor(root_val); + const f_t up_val = std::ceil(root_val); + const f_t down_dist = val - down_val; + const f_t up_dist = up_val - val; + constexpr f_t eps = 1e-6; + + if (down_dist < up_dist + eps) { + return rounding_direction_t::DOWN; + } else { + return rounding_direction_t::UP; + } +} + +// Result of solving a node LP and processing the outcome +template +struct node_solve_result_t { + node_status_t status; + rounding_direction_t preferred_direction; + bool needs_recompute_basis; +}; + +// ============================================================================= +// Standard (non-deterministic) policy implementation +// ============================================================================= + +template +class standard_solve_policy_t { + public: + // Callback types for optional hooks + using node_processed_callback_t = std::function&, f_t)>; + using objective_estimate_fn_t = + std::function&, const std::vector&, f_t)>; + + standard_solve_policy_t( + omp_atomic_t& upper_bound, + f_t fathom_tolerance, + const std::vector& root_lower, + const std::vector& root_upper, + const std::vector& edge_norms, + const std::vector& root_solution, + pseudo_costs_t& pc, + bnb_stats_t& stats, + logger_t& log, + std::function&, i_t, bnb_worker_type_t)> add_solution_fn, + node_processed_callback_t node_processed_callback = nullptr, + objective_estimate_fn_t objective_estimate_fn = nullptr) + : upper_bound_(upper_bound), + fathom_tolerance_(fathom_tolerance), + root_lower_(root_lower), + root_upper_(root_upper), + edge_norms_(edge_norms), + root_solution_(root_solution), + pc_(pc), + stats_(stats), + log_(log), + add_solution_fn_(add_solution_fn), + node_processed_callback_(node_processed_callback), + objective_estimate_fn_(objective_estimate_fn) + { + } + + f_t get_upper_bound() const { return upper_bound_.load(); } + f_t get_fathom_tolerance() const { return fathom_tolerance_; } + bool should_run_bounds_strengthening() const { return true; } + work_limit_context_t* get_work_context() { return nullptr; } + const std::vector& get_root_lower() const { return root_lower_; } + const std::vector& get_root_upper() const { return root_upper_; } + const std::vector& get_edge_norms() const { return edge_norms_; } + const std::vector& get_root_solution() const { return root_solution_; } + + void on_solve_start(mip_node_t* node) {} + + void on_lp_input(mip_node_t* node, + const lp_problem_t& problem, + const std::vector& vstatus) + { + } + + void on_lp_output(mip_node_t* node, + dual::status_t status, + i_t iterations, + const lp_solution_t& solution, + const lp_problem_t& problem) + { + if (status == dual::status_t::OPTIMAL) { + f_t objective = compute_objective(problem, solution.x); + log_.graphviz_node(node, "lower bound", objective); + if (node_processed_callback_) { node_processed_callback_(solution.x, objective); } + } + } + + void on_lp_solve_complete(mip_node_t* node, + i_t iterations, + f_t solve_time, + dual::status_t status) + { + stats_.total_lp_solve_time += solve_time; + stats_.total_lp_iters += iterations; + } + + void on_infeasible(mip_node_t* node, search_tree_t& tree) + { + node->lower_bound = std::numeric_limits::infinity(); + tree.graphviz_node(log_, node, "infeasible", 0.0); + tree.update(node, node_status_t::INFEASIBLE); + } + + void on_fathomed(mip_node_t* node, f_t objective, search_tree_t& tree) + { + tree.graphviz_node(log_, node, "fathomed", objective); + tree.update(node, node_status_t::FATHOMED); + } + + void on_integer_solution(mip_node_t* node, + f_t objective, + const std::vector& solution, + i_t depth, + search_tree_t& tree) + { + add_solution_fn_(objective, solution, depth, bnb_worker_type_t::BEST_FIRST); + tree.graphviz_node(log_, node, "integer feasible", objective); + tree.update(node, node_status_t::INTEGER_FEASIBLE); + } + + void on_branched(mip_node_t* node, + i_t branch_var, + f_t branch_val, + i_t down_child_id, + i_t up_child_id, + rounding_direction_t preferred, + search_tree_t& tree, + const std::vector& fractional, + const std::vector& solution) + { + if (objective_estimate_fn_) { + node->objective_estimate = objective_estimate_fn_(fractional, solution, node->lower_bound); + } + tree.update(node, node_status_t::HAS_CHILDREN); + } + + void on_numerical(mip_node_t* node, search_tree_t& tree) + { + tree.graphviz_node(log_, node, "numerical", 0.0); + tree.update(node, node_status_t::NUMERICAL); + } + + void update_pseudo_costs(mip_node_t* node, f_t objective) + { + pc_.update_pseudo_costs(node, objective); + } + + i_t select_branch_variable(const std::vector& fractional, const std::vector& solution) + { + logger_t pc_log; + pc_log.log = false; + return pc_.variable_selection(fractional, solution, pc_log); + } + + rounding_direction_t get_preferred_direction(i_t branch_var, f_t branch_val) + { + f_t root_val = + branch_var < (i_t)root_solution_.size() ? root_solution_[branch_var] : branch_val; + return martin_criteria(branch_val, root_val); + } + + void track_node_explored() { ++stats_.nodes_explored; } + + void track_node_unexplored_delta(i_t delta) { stats_.nodes_unexplored += delta; } + + private: + omp_atomic_t& upper_bound_; + f_t fathom_tolerance_; + const std::vector& root_lower_; + const std::vector& root_upper_; + const std::vector& edge_norms_; + const std::vector& root_solution_; + pseudo_costs_t& pc_; + bnb_stats_t& stats_; + logger_t& log_; + std::function&, i_t, bnb_worker_type_t)> add_solution_fn_; + node_processed_callback_t node_processed_callback_; + objective_estimate_fn_t objective_estimate_fn_; +}; + +// ============================================================================= +// BSP (deterministic) policy implementation +// ============================================================================= + +template +class bsp_solve_policy_t { + public: + bsp_solve_policy_t(bb_worker_state_t& worker, + f_t fathom_tolerance, + const std::vector& root_lower, + const std::vector& root_upper, + const std::vector& edge_norms, + const std::vector& root_solution, + bnb_stats_t& stats, + bsp_debug_settings_t& debug_settings, + bsp_debug_logger_t& debug_logger) + : worker_(worker), + fathom_tolerance_(fathom_tolerance), + root_lower_(root_lower), + root_upper_(root_upper), + edge_norms_(edge_norms), + root_solution_(root_solution), + stats_(stats), + debug_settings_(debug_settings), + debug_logger_(debug_logger) + { + } + + f_t get_upper_bound() const { return worker_.local_upper_bound; } + f_t get_fathom_tolerance() const { return fathom_tolerance_; } + bool should_run_bounds_strengthening() const { return false; } + work_limit_context_t* get_work_context() { return &worker_.work_context; } + const std::vector& get_root_lower() const { return root_lower_; } + const std::vector& get_root_upper() const { return root_upper_; } + const std::vector& get_edge_norms() const { return edge_norms_; } + const std::vector& get_root_solution() const { return root_solution_; } + + void on_solve_start(mip_node_t* node) + { + work_units_at_start_ = worker_.work_context.global_work_units_elapsed; + double work_limit = worker_.horizon_end - worker_.clock; + BSP_DEBUG_LOG_SOLVE_START(debug_settings_, + debug_logger_, + worker_.clock, + worker_.worker_id, + node->node_id, + node->origin_worker_id, + work_limit, + false); + } + + void on_lp_input(mip_node_t* node, + const lp_problem_t& problem, + const std::vector& vstatus) + { + if (debug_settings_.any_enabled()) { + uint64_t path_hash = node->compute_path_hash(); + uint64_t vstatus_hash = detail::compute_hash(vstatus); + uint64_t bounds_hash = + detail::compute_hash(problem.lower) ^ detail::compute_hash(problem.upper); + BSP_DEBUG_LOG_LP_INPUT(debug_settings_, + debug_logger_, + worker_.worker_id, + node->node_id, + path_hash, + node->depth, + vstatus_hash, + bounds_hash); + } + } + + void on_lp_output(mip_node_t* node, + dual::status_t status, + i_t iterations, + const lp_solution_t& solution, + const lp_problem_t& problem) + { + if (debug_settings_.any_enabled()) { + uint64_t path_hash = node->compute_path_hash(); + uint64_t sol_hash = detail::compute_hash(solution.x); + f_t obj = (status == dual::status_t::OPTIMAL) ? compute_objective(problem, solution.x) + : std::numeric_limits::infinity(); + uint64_t obj_hash = detail::compute_hash(obj); + BSP_DEBUG_LOG_LP_OUTPUT(debug_settings_, + debug_logger_, + worker_.worker_id, + node->node_id, + path_hash, + static_cast(status), + iterations, + obj_hash, + sol_hash); + } + } + + void on_lp_solve_complete(mip_node_t* node, + i_t iterations, + f_t solve_time, + dual::status_t status) + { + stats_.total_lp_solve_time += solve_time; + stats_.total_lp_iters += iterations; + + double work_performed = worker_.work_context.global_work_units_elapsed - work_units_at_start_; + worker_.clock += work_performed; + worker_.work_units_this_horizon += work_performed; + } + + void on_infeasible(mip_node_t* node, search_tree_t& tree) + { + node->lower_bound = std::numeric_limits::infinity(); + worker_.record_infeasible(node); + worker_.track_node_infeasible(); + worker_.track_node_processed(); + + BSP_DEBUG_LOG_SOLVE_END(debug_settings_, + debug_logger_, + worker_.clock, + worker_.worker_id, + node->node_id, + node->origin_worker_id, + "INFEASIBLE", + node->lower_bound); + BSP_DEBUG_LOG_INFEASIBLE( + debug_settings_, debug_logger_, worker_.clock, worker_.worker_id, node->node_id); + + tree.update(node, node_status_t::INFEASIBLE); + } + + void on_fathomed(mip_node_t* node, f_t objective, search_tree_t& tree) + { + worker_.record_fathomed(node, objective); + worker_.track_node_pruned(); + worker_.track_node_processed(); + worker_.recompute_bounds_and_basis = true; + + BSP_DEBUG_LOG_SOLVE_END(debug_settings_, + debug_logger_, + worker_.clock, + worker_.worker_id, + node->node_id, + node->origin_worker_id, + "FATHOMED", + objective); + BSP_DEBUG_LOG_FATHOMED( + debug_settings_, debug_logger_, worker_.clock, worker_.worker_id, node->node_id, objective); + + tree.update(node, node_status_t::FATHOMED); + } + + void on_integer_solution(mip_node_t* node, + f_t objective, + const std::vector& solution, + i_t depth, + search_tree_t& tree) + { + if (objective < worker_.local_upper_bound) { + worker_.local_upper_bound = objective; + worker_.integer_solutions.push_back({objective, solution, depth}); + } + + worker_.record_integer_solution(node, objective); + worker_.track_integer_solution(); + worker_.track_node_processed(); + worker_.recompute_bounds_and_basis = true; + + BSP_DEBUG_LOG_SOLVE_END(debug_settings_, + debug_logger_, + worker_.clock, + worker_.worker_id, + node->node_id, + node->origin_worker_id, + "INTEGER", + objective); + BSP_DEBUG_LOG_INTEGER( + debug_settings_, debug_logger_, worker_.clock, worker_.worker_id, node->node_id, objective); + + tree.update(node, node_status_t::INTEGER_FEASIBLE); + } + + void on_branched(mip_node_t* node, + i_t branch_var, + f_t branch_val, + i_t down_child_id, + i_t up_child_id, + rounding_direction_t preferred, + search_tree_t& tree, + const std::vector& /*fractional*/, + const std::vector& /*solution*/) + { + tree.update(node, node_status_t::HAS_CHILDREN); + + worker_.record_branched(node, down_child_id, up_child_id, branch_var, branch_val); + worker_.track_node_branched(); + worker_.track_node_processed(); + + BSP_DEBUG_LOG_SOLVE_END(debug_settings_, + debug_logger_, + worker_.clock, + worker_.worker_id, + node->node_id, + node->origin_worker_id, + "BRANCH", + node->lower_bound); + BSP_DEBUG_LOG_BRANCHED(debug_settings_, + debug_logger_, + worker_.clock, + worker_.worker_id, + node->node_id, + node->origin_worker_id, + down_child_id, + up_child_id); + + worker_.enqueue_children_for_plunge(node->get_down_child(), node->get_up_child(), preferred); + } + + void on_numerical(mip_node_t* node, search_tree_t& tree) + { + worker_.record_numerical(node); + worker_.recompute_bounds_and_basis = true; + tree.update(node, node_status_t::NUMERICAL); + } + + void update_pseudo_costs(mip_node_t* node, f_t objective) + { + if (node->branch_var >= 0) { + const f_t change_in_obj = objective - node->lower_bound; + const f_t frac = node->branch_dir == rounding_direction_t::DOWN + ? node->fractional_val - std::floor(node->fractional_val) + : std::ceil(node->fractional_val) - node->fractional_val; + if (frac > 1e-10) { + worker_.queue_pseudo_cost_update(node->branch_var, node->branch_dir, change_in_obj / frac); + } + } + } + + i_t select_branch_variable(const std::vector& fractional, const std::vector& solution) + { + return worker_.variable_selection_from_snapshot(fractional, solution); + } + + rounding_direction_t get_preferred_direction(i_t branch_var, f_t branch_val) + { + f_t root_val = + branch_var < (i_t)root_solution_.size() ? root_solution_[branch_var] : branch_val; + f_t frac = branch_val - std::floor(branch_val); + if (branch_val < root_val - 0.4 || frac < 0.3) { + return rounding_direction_t::DOWN; + } else if (branch_val > root_val + 0.4 || frac > 0.7) { + return rounding_direction_t::UP; + } + return rounding_direction_t::DOWN; + } + + void track_node_explored() { ++stats_.nodes_explored; } + + void track_node_unexplored_delta(i_t delta) { stats_.nodes_unexplored += delta; } + + void set_work_units_at_start(double work_units) { work_units_at_start_ = work_units; } + + private: + bb_worker_state_t& worker_; + f_t fathom_tolerance_; + const std::vector& root_lower_; + const std::vector& root_upper_; + const std::vector& edge_norms_; + const std::vector& root_solution_; + bnb_stats_t& stats_; + bsp_debug_settings_t& debug_settings_; + bsp_debug_logger_t& debug_logger_; + double work_units_at_start_{0.0}; +}; + +// ============================================================================= +// Standard diving policy implementation (non-deterministic) +// ============================================================================= + +template +class standard_diving_solve_policy_t { + public: + standard_diving_solve_policy_t( + omp_atomic_t& upper_bound, + f_t fathom_tolerance, + const std::vector& dive_lower, + const std::vector& dive_upper, + const std::vector& edge_norms, + const std::vector& root_solution, + pseudo_costs_t& pc, + bnb_stats_t& stats, + std::function&, i_t, bnb_worker_type_t)> add_solution_fn, + bnb_worker_type_t diving_type, + const lp_problem_t* lp_problem, + const std::vector* up_locks, + const std::vector* down_locks) + : upper_bound_(upper_bound), + fathom_tolerance_(fathom_tolerance), + dive_lower_(dive_lower), + dive_upper_(dive_upper), + edge_norms_(edge_norms), + root_solution_(root_solution), + pc_(pc), + stats_(stats), + add_solution_fn_(add_solution_fn), + diving_type_(diving_type), + lp_problem_(lp_problem), + up_locks_(up_locks), + down_locks_(down_locks) + { + log_.log = false; + } + + f_t get_upper_bound() const { return upper_bound_.load(); } + f_t get_fathom_tolerance() const { return fathom_tolerance_; } + bool should_run_bounds_strengthening() const { return true; } + work_limit_context_t* get_work_context() { return nullptr; } + const std::vector& get_root_lower() const { return dive_lower_; } + const std::vector& get_root_upper() const { return dive_upper_; } + const std::vector& get_edge_norms() const { return edge_norms_; } + const std::vector& get_root_solution() const { return root_solution_; } + + void on_solve_start(mip_node_t* node) {} + + void on_lp_input(mip_node_t* node, + const lp_problem_t& problem, + const std::vector& vstatus) + { + } + + void on_lp_output(mip_node_t* node, + dual::status_t status, + i_t iterations, + const lp_solution_t& solution, + const lp_problem_t& problem) + { + } + + void on_lp_solve_complete(mip_node_t* node, + i_t iterations, + f_t solve_time, + dual::status_t status) + { + stats_.total_lp_solve_time += solve_time; + stats_.total_lp_iters += iterations; + } + + void on_infeasible(mip_node_t* node, search_tree_t& tree) + { + node->lower_bound = std::numeric_limits::infinity(); + tree.update(node, node_status_t::INFEASIBLE); + } + + void on_fathomed(mip_node_t* node, f_t objective, search_tree_t& tree) + { + tree.update(node, node_status_t::FATHOMED); + } + + void on_integer_solution(mip_node_t* node, + f_t objective, + const std::vector& solution, + i_t depth, + search_tree_t& tree) + { + add_solution_fn_(objective, solution, depth, diving_type_); + tree.update(node, node_status_t::INTEGER_FEASIBLE); + } + + void on_branched(mip_node_t* node, + i_t branch_var, + f_t branch_val, + i_t down_child_id, + i_t up_child_id, + rounding_direction_t preferred, + search_tree_t& tree, + const std::vector& /*fractional*/, + const std::vector& /*solution*/) + { + tree.update(node, node_status_t::HAS_CHILDREN); + } + + void on_numerical(mip_node_t* node, search_tree_t& tree) + { + tree.update(node, node_status_t::NUMERICAL); + } + + void update_pseudo_costs(mip_node_t* node, f_t objective) + { + pc_.update_pseudo_costs(node, objective); + } + + i_t select_branch_variable(const std::vector& fractional, const std::vector& solution) + { + branch_variable_t result; + switch (diving_type_) { + case bnb_worker_type_t::PSEUDOCOST_DIVING: + result = pseudocost_diving(pc_, fractional, solution, root_solution_, log_); + break; + case bnb_worker_type_t::GUIDED_DIVING: + if (!incumbent_.empty()) { + result = guided_diving(pc_, fractional, solution, incumbent_, log_); + } else { + result = pseudocost_diving(pc_, fractional, solution, root_solution_, log_); + } + break; + case bnb_worker_type_t::LINE_SEARCH_DIVING: + result = line_search_diving(fractional, solution, root_solution_, log_); + break; + case bnb_worker_type_t::COEFFICIENT_DIVING: + if (lp_problem_ && up_locks_ && down_locks_) { + result = + coefficient_diving(*lp_problem_, fractional, solution, *up_locks_, *down_locks_, log_); + } else { + result = pseudocost_diving(pc_, fractional, solution, root_solution_, log_); + } + break; + default: result = pseudocost_diving(pc_, fractional, solution, root_solution_, log_); break; + } + selected_direction_ = result.direction; + return result.variable; + } + + rounding_direction_t get_preferred_direction(i_t branch_var, f_t branch_val) + { + return selected_direction_; + } + + void track_node_explored() { ++stats_.nodes_explored; } + + void track_node_unexplored_delta(i_t delta) {} + + void set_incumbent(const std::vector& incumbent) { incumbent_ = incumbent; } + + private: + omp_atomic_t& upper_bound_; + f_t fathom_tolerance_; + const std::vector& dive_lower_; + const std::vector& dive_upper_; + const std::vector& edge_norms_; + const std::vector& root_solution_; + pseudo_costs_t& pc_; + bnb_stats_t& stats_; + std::function&, i_t, bnb_worker_type_t)> add_solution_fn_; + bnb_worker_type_t diving_type_; + const lp_problem_t* lp_problem_; + const std::vector* up_locks_; + const std::vector* down_locks_; + logger_t log_; + std::vector incumbent_; + rounding_direction_t selected_direction_{rounding_direction_t::DOWN}; +}; + +// ============================================================================= +// BSP diving policy implementation (deterministic) +// ============================================================================= + +template +class bsp_diving_solve_policy_t { + public: + bsp_diving_solve_policy_t(bsp_diving_worker_state_t& worker, + f_t fathom_tolerance, + const std::vector& dive_lower, + const std::vector& dive_upper, + const std::vector& edge_norms, + const std::vector& root_solution, + bnb_stats_t& stats, + const lp_problem_t* lp_problem, + const std::vector* up_locks, + const std::vector* down_locks) + : worker_(worker), + fathom_tolerance_(fathom_tolerance), + dive_lower_(dive_lower), + dive_upper_(dive_upper), + edge_norms_(edge_norms), + root_solution_(root_solution), + stats_(stats), + lp_problem_(lp_problem), + up_locks_(up_locks), + down_locks_(down_locks) + { + log_.log = false; + } + + f_t get_upper_bound() const { return worker_.local_upper_bound; } + f_t get_fathom_tolerance() const { return fathom_tolerance_; } + bool should_run_bounds_strengthening() const { return true; } + work_limit_context_t* get_work_context() { return &worker_.work_context; } + const std::vector& get_root_lower() const { return dive_lower_; } + const std::vector& get_root_upper() const { return dive_upper_; } + const std::vector& get_edge_norms() const { return edge_norms_; } + const std::vector& get_root_solution() const { return root_solution_; } + + void on_solve_start(mip_node_t* node) {} + + void on_lp_input(mip_node_t* node, + const lp_problem_t& problem, + const std::vector& vstatus) + { + } + + void on_lp_output(mip_node_t* node, + dual::status_t status, + i_t iterations, + const lp_solution_t& solution, + const lp_problem_t& problem) + { + } + + void on_lp_solve_complete(mip_node_t* node, + i_t iterations, + f_t solve_time, + dual::status_t status) + { + stats_.total_lp_solve_time += solve_time; + stats_.total_lp_iters += iterations; + worker_.clock = worker_.work_context.global_work_units_elapsed; + } + + void on_infeasible(mip_node_t* node, search_tree_t& tree) + { + node->lower_bound = std::numeric_limits::infinity(); + tree.update(node, node_status_t::INFEASIBLE); + } + + void on_fathomed(mip_node_t* node, f_t objective, search_tree_t& tree) + { + tree.update(node, node_status_t::FATHOMED); + } + + void on_integer_solution(mip_node_t* node, + f_t objective, + const std::vector& solution, + i_t depth, + search_tree_t& tree) + { + if (objective < worker_.local_upper_bound) { + worker_.queue_integer_solution(objective, solution, depth); + } + tree.update(node, node_status_t::INTEGER_FEASIBLE); + } + + void on_branched(mip_node_t* node, + i_t branch_var, + f_t branch_val, + i_t down_child_id, + i_t up_child_id, + rounding_direction_t preferred, + search_tree_t& tree, + const std::vector& /*fractional*/, + const std::vector& /*solution*/) + { + tree.update(node, node_status_t::HAS_CHILDREN); + } + + void on_numerical(mip_node_t* node, search_tree_t& tree) + { + tree.update(node, node_status_t::NUMERICAL); + } + + void update_pseudo_costs(mip_node_t* node, f_t objective) {} + + i_t select_branch_variable(const std::vector& fractional, const std::vector& solution) + { + branch_variable_t result; + switch (worker_.diving_type) { + case bnb_worker_type_t::PSEUDOCOST_DIVING: + result = worker_.variable_selection_from_snapshot(fractional, solution); + break; + case bnb_worker_type_t::GUIDED_DIVING: + result = worker_.guided_variable_selection(fractional, solution); + break; + case bnb_worker_type_t::LINE_SEARCH_DIVING: + if (worker_.root_solution) { + result = line_search_diving(fractional, solution, *worker_.root_solution, log_); + } else { + result = worker_.variable_selection_from_snapshot(fractional, solution); + } + break; + case bnb_worker_type_t::COEFFICIENT_DIVING: + if (lp_problem_ && up_locks_ && down_locks_) { + result = + coefficient_diving(*lp_problem_, fractional, solution, *up_locks_, *down_locks_, log_); + } else { + result = worker_.variable_selection_from_snapshot(fractional, solution); + } + break; + default: result = worker_.variable_selection_from_snapshot(fractional, solution); break; + } + selected_direction_ = result.direction; + return result.variable; + } + + rounding_direction_t get_preferred_direction(i_t branch_var, f_t branch_val) + { + return selected_direction_; + } + + void track_node_explored() + { + ++worker_.nodes_explored_this_horizon; + ++worker_.total_nodes_explored; + } + + void track_node_unexplored_delta(i_t delta) {} + + private: + bsp_diving_worker_state_t& worker_; + f_t fathom_tolerance_; + const std::vector& dive_lower_; + const std::vector& dive_upper_; + const std::vector& edge_norms_; + const std::vector& root_solution_; + bnb_stats_t& stats_; + const lp_problem_t* lp_problem_; + const std::vector* up_locks_; + const std::vector* down_locks_; + logger_t log_; + rounding_direction_t selected_direction_{rounding_direction_t::DOWN}; +}; + +} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 5f6452fa4..a8ba4ff17 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -609,7 +609,6 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, mutex_upper_.unlock(); } -// Martin's criteria for the preferred rounding direction (see [1]) // [1] A. Martin, “Integer Programs with Block Structure,” // Technische Universit¨at Berlin, Berlin, 1999. Accessed: Aug. 08, 2025. // [Online]. Available: https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/391 @@ -807,111 +806,202 @@ dual::status_t branch_and_bound_t::solve_node_lp( return lp_status; } +// ============================================================================= +// Unified node solving with policy-based callbacks +// ============================================================================= + template -std::pair branch_and_bound_t::update_tree( +template +node_solve_result_t branch_and_bound_t::solve_node_with_policy( mip_node_t* node_ptr, - search_tree_t& search_tree, lp_problem_t& leaf_problem, lp_solution_t& leaf_solution, - bnb_worker_type_t thread_type, - dual::status_t lp_status, - logger_t& log) + basis_update_mpf_t& basis_factors, + std::vector& basic_list, + std::vector& nonbasic_list, + bounds_strengthening_t& node_presolver, + bool recompute_bounds_and_basis, + search_tree_t& search_tree, + Policy& policy) { - const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; + raft::common::nvtx::range scope("BB::solve_node_with_policy"); + + node_solve_result_t result; + result.status = node_status_t::PENDING; + result.preferred_direction = rounding_direction_t::NONE; + result.needs_recompute_basis = true; + std::vector& leaf_vstatus = node_ptr->vstatus; + // Notify policy that solve is starting + policy.on_solve_start(node_ptr); + + // Reset bounds_changed markers + std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); + + // Set up bounds from node path + const std::vector& root_lower = policy.get_root_lower(); + const std::vector& root_upper = policy.get_root_upper(); + + if (recompute_bounds_and_basis) { + leaf_problem.lower = root_lower; + leaf_problem.upper = root_upper; + node_ptr->get_variable_bounds( + leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); + } else { + node_ptr->update_branched_variable_bounds( + leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); + } + + // Check time limit + double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); + if (remaining_time <= 0) { + result.status = node_status_t::PENDING; + return result; + } + + // LP settings + simplex_solver_settings_t lp_settings = settings_; + lp_settings.set_log(false); + lp_settings.cut_off = policy.get_upper_bound() + settings_.dual_tol; + lp_settings.inside_mip = 2; + lp_settings.time_limit = remaining_time; + lp_settings.scale_columns = false; + + // Bounds strengthening (policy controls whether to run) + bool feasible = true; + if (policy.should_run_bounds_strengthening()) { + feasible = + node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); + } + + if (!feasible) { + policy.on_infeasible(node_ptr, search_tree); + policy.track_node_explored(); + policy.track_node_unexplored_delta(-1); + result.status = node_status_t::INFEASIBLE; + result.needs_recompute_basis = true; + return result; + } + + // Solve LP relaxation + i_t node_iter = 0; + f_t lp_start_time = tic(); + std::vector leaf_edge_norms = policy.get_edge_norms(); + + // Debug hook: log LP input + policy.on_lp_input(node_ptr, leaf_problem, leaf_vstatus); + + dual::status_t lp_status = dual_phase2_with_advanced_basis(2, + 0, + recompute_bounds_and_basis, + lp_start_time, + leaf_problem, + lp_settings, + leaf_vstatus, + basis_factors, + basic_list, + nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms, + policy.get_work_context()); + + // Debug hook: log LP output + policy.on_lp_output(node_ptr, lp_status, node_iter, leaf_solution, leaf_problem); + + f_t solve_time = toc(lp_start_time); + policy.on_lp_solve_complete(node_ptr, node_iter, solve_time, lp_status); + policy.track_node_explored(); + policy.track_node_unexplored_delta(-1); + + // Process LP result + const f_t abs_fathom_tol = policy.get_fathom_tolerance(); + if (lp_status == dual::status_t::DUAL_UNBOUNDED) { - // Node was infeasible. Do not branch - node_ptr->lower_bound = inf; - search_tree.graphviz_node(log, node_ptr, "infeasible", 0.0); - search_tree.update(node_ptr, node_status_t::INFEASIBLE); - return {node_status_t::INFEASIBLE, rounding_direction_t::NONE}; + node_ptr->lower_bound = std::numeric_limits::infinity(); + policy.on_infeasible(node_ptr, search_tree); + result.status = node_status_t::INFEASIBLE; + result.needs_recompute_basis = true; } else if (lp_status == dual::status_t::CUTOFF) { - // Node was cut off. Do not branch - node_ptr->lower_bound = upper_bound_; - f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); - search_tree.graphviz_node(log, node_ptr, "cut off", leaf_objective); - search_tree.update(node_ptr, node_status_t::FATHOMED); - return {node_status_t::FATHOMED, rounding_direction_t::NONE}; + node_ptr->lower_bound = policy.get_upper_bound(); + policy.on_fathomed(node_ptr, node_ptr->lower_bound, search_tree); + result.status = node_status_t::FATHOMED; + result.needs_recompute_basis = true; } else if (lp_status == dual::status_t::OPTIMAL) { - // LP was feasible std::vector leaf_fractional; i_t leaf_num_fractional = fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); node_ptr->lower_bound = leaf_objective; - search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); - pc_.update_pseudo_costs(node_ptr, leaf_objective); - - if (thread_type == bnb_worker_type_t::BEST_FIRST) { - if (settings_.node_processed_callback != nullptr) { - std::vector original_x; - uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); - settings_.node_processed_callback(original_x, leaf_objective); - } - } + + // Update pseudo-costs via policy + policy.update_pseudo_costs(node_ptr, leaf_objective); if (leaf_num_fractional == 0) { - // Found a integer feasible solution - add_feasible_solution(leaf_objective, leaf_solution.x, node_ptr->depth, thread_type); - search_tree.graphviz_node(log, node_ptr, "integer feasible", leaf_objective); - search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); - return {node_status_t::INTEGER_FEASIBLE, rounding_direction_t::NONE}; - - } else if (leaf_objective <= upper_bound_ + abs_fathom_tol) { - // Choose fractional variable to branch on - auto [branch_var, round_dir] = - variable_selection(node_ptr, leaf_fractional, leaf_solution.x, thread_type); - - assert(leaf_vstatus.size() == leaf_problem.num_cols); - assert(branch_var >= 0); - assert(round_dir != rounding_direction_t::NONE); - - // Note that the exploration thread is the only one that can insert new nodes into the heap, - // and thus, we only need to calculate the objective estimate here (it is used for - // sorting the nodes for diving). - if (thread_type == bnb_worker_type_t::BEST_FIRST) { - logger_t pc_log; - pc_log.log = false; - node_ptr->objective_estimate = - pc_.obj_estimate(leaf_fractional, leaf_solution.x, node_ptr->lower_bound, pc_log); + // Integer feasible solution + policy.on_integer_solution( + node_ptr, leaf_objective, leaf_solution.x, node_ptr->depth, search_tree); + result.status = node_status_t::INTEGER_FEASIBLE; + result.needs_recompute_basis = true; + + } else if (leaf_objective <= policy.get_upper_bound() + abs_fathom_tol) { + // Branch + i_t branch_var = policy.select_branch_variable(leaf_fractional, leaf_solution.x); + + if (branch_var < 0) { + result.status = node_status_t::NUMERICAL; + result.needs_recompute_basis = true; + return result; } - search_tree.branch( - node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, leaf_problem, log); - search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); - return {node_status_t::HAS_CHILDREN, round_dir}; + f_t branch_val = leaf_solution.x[branch_var]; + + logger_t log; + log.log = false; + search_tree.branch(node_ptr, branch_var, branch_val, leaf_vstatus, leaf_problem, log); + + i_t down_child_id = node_ptr->get_down_child()->node_id; + i_t up_child_id = node_ptr->get_up_child()->node_id; + + rounding_direction_t preferred = policy.get_preferred_direction(branch_var, branch_val); + policy.on_branched(node_ptr, + branch_var, + branch_val, + down_child_id, + up_child_id, + preferred, + search_tree, + leaf_fractional, + leaf_solution.x); + policy.track_node_unexplored_delta(2); + + result.status = node_status_t::HAS_CHILDREN; + result.preferred_direction = preferred; + result.needs_recompute_basis = false; } else { - search_tree.graphviz_node(log, node_ptr, "fathomed", leaf_objective); - search_tree.update(node_ptr, node_status_t::FATHOMED); - return {node_status_t::FATHOMED, rounding_direction_t::NONE}; - } - } else if (lp_status == dual::status_t::TIME_LIMIT) { - search_tree.graphviz_node(log, node_ptr, "timeout", 0.0); - return {node_status_t::PENDING, rounding_direction_t::NONE}; - } else if (lp_status == dual::status_t::WORK_LIMIT) { - search_tree.graphviz_node(log, node_ptr, "work limit", 0.0); - return {node_status_t::PENDING, rounding_direction_t::NONE}; - } else { - if (thread_type == bnb_worker_type_t::BEST_FIRST) { - fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); - log.printf( - "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " - "to " - "%+10.6e.\n", - lp_status, - node_ptr->node_id, - compute_user_objective(original_lp_, lower_bound_ceiling_.load())); + // Fathomed by bound + policy.on_fathomed(node_ptr, leaf_objective, search_tree); + result.status = node_status_t::FATHOMED; + result.needs_recompute_basis = true; } - search_tree.graphviz_node(log, node_ptr, "numerical", 0.0); - search_tree.update(node_ptr, node_status_t::NUMERICAL); - return {node_status_t::NUMERICAL, rounding_direction_t::NONE}; + } else if (lp_status == dual::status_t::TIME_LIMIT || lp_status == dual::status_t::WORK_LIMIT) { + result.status = node_status_t::PENDING; + result.needs_recompute_basis = true; + + } else { + // Numerical error + policy.on_numerical(node_ptr, search_tree); + result.status = node_status_t::NUMERICAL; + result.needs_recompute_basis = true; } + + return result; } template @@ -971,39 +1061,60 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod std::vector nonbasic_list; lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); - dual::status_t lp_status = solve_node_lp(node, - leaf_problem, - leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, - bnb_worker_type_t::BEST_FIRST, - true, - original_lp_.lower, - original_lp_.upper, - exploration_stats_, - settings_.log); - if (lp_status == dual::status_t::TIME_LIMIT) { + + // Set up callbacks for the policy + auto add_solution_fn = + [this](f_t obj, const std::vector& sol, i_t depth, bnb_worker_type_t type) { + add_feasible_solution(obj, sol, depth, type); + }; + + auto node_processed_callback = [this](const std::vector& sol, f_t objective) { + if (settings_.node_processed_callback != nullptr) { + std::vector original_x; + uncrush_primal_solution(original_problem_, original_lp_, sol, original_x); + settings_.node_processed_callback(original_x, objective); + } + }; + + auto objective_estimate_fn = + [this](const std::vector& fractional, const std::vector& solution, f_t lower_bound) { + logger_t pc_log; + pc_log.log = false; + return pc_.obj_estimate(fractional, solution, lower_bound, pc_log); + }; + + standard_solve_policy_t solve_policy(upper_bound_, + settings_.absolute_mip_gap_tol / 10, + original_lp_.lower, + original_lp_.upper, + edge_norms_, + root_relax_soln_.x, + pc_, + exploration_stats_, + settings_.log, + add_solution_fn, + node_processed_callback, + objective_estimate_fn); + + node_solve_result_t result = solve_node_with_policy(node, + leaf_problem, + leaf_solution, + basis_factors, + basic_list, + nonbasic_list, + node_presolver, + true, + search_tree_, + solve_policy); + + if (result.status == node_status_t::PENDING) { solver_status_ = mip_status_t::TIME_LIMIT; return; } ++exploration_stats_.nodes_since_last_log; - ++exploration_stats_.nodes_explored; - --exploration_stats_.nodes_unexplored; - - auto [node_status, round_dir] = update_tree(node, - search_tree_, - leaf_problem, - leaf_solution, - bnb_worker_type_t::BEST_FIRST, - lp_status, - settings_.log); - - if (node_status == node_status_t::HAS_CHILDREN) { - exploration_stats_.nodes_unexplored += 2; + if (result.status == node_status_t::HAS_CHILDREN) { // If we haven't generated enough nodes to keep the threads busy, continue the ramp up phase if (exploration_stats_.nodes_unexplored < initial_heap_size) { #pragma omp task @@ -1034,6 +1145,41 @@ void branch_and_bound_t::plunge_from(i_t task_id, std::deque*> stack; stack.push_front(start_node); + auto add_solution_fn = + [this](f_t obj, const std::vector& sol, i_t depth, bnb_worker_type_t type) { + add_feasible_solution(obj, sol, depth, type); + }; + + auto node_processed_callback = [this](const std::vector& sol, f_t objective) { + if (settings_.node_processed_callback != nullptr) { + std::vector original_x; + uncrush_primal_solution(original_problem_, original_lp_, sol, original_x); + settings_.node_processed_callback(original_x, objective); + } + }; + + auto objective_estimate_fn = + [this](const std::vector& fractional, const std::vector& solution, f_t lower_bound) { + logger_t pc_log; + pc_log.log = false; + return pc_.obj_estimate(fractional, solution, lower_bound, pc_log); + }; + + standard_solve_policy_t solve_policy(upper_bound_, + settings_.absolute_mip_gap_tol / 10, + original_lp_.lower, + original_lp_.upper, + edge_norms_, + root_relax_soln_.x, + pc_, + exploration_stats_, + settings_.log, + add_solution_fn, + node_processed_callback, + objective_estimate_fn); + + lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); + while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET && is_running) { if (task_id == 0) { repair_heuristic_solutions(); } @@ -1045,13 +1191,7 @@ void branch_and_bound_t::plunge_from(i_t task_id, f_t abs_gap = upper_bound - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); - // This is based on three assumptions: - // - The stack only contains sibling nodes, i.e., the current node and it sibling (if - // applicable) - // - The current node and its siblings uses the lower bound of the parent before solving the LP - // relaxation - // - The lower bound of the parent is lower or equal to its children - assert(task_id < local_lower_bounds_.size()); + assert(task_id < (i_t)local_lower_bounds_.size()); local_lower_bounds_[task_id] = lower_bound; if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { @@ -1087,56 +1227,34 @@ void branch_and_bound_t::plunge_from(i_t task_id, break; } - lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); - dual::status_t lp_status = solve_node_lp(node_ptr, - leaf_problem, - leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, - bnb_worker_type_t::BEST_FIRST, - recompute_bounds_and_basis, - original_lp_.lower, - original_lp_.upper, - exploration_stats_, - settings_.log); - - if (lp_status == dual::status_t::TIME_LIMIT) { + node_solve_result_t result = solve_node_with_policy(node_ptr, + leaf_problem, + leaf_solution, + basis_factors, + basic_list, + nonbasic_list, + node_presolver, + recompute_bounds_and_basis, + search_tree_, + solve_policy); + + if (result.status == node_status_t::PENDING) { solver_status_ = mip_status_t::TIME_LIMIT; break; - } else if (lp_status == dual::status_t::ITERATION_LIMIT) { - break; } ++exploration_stats_.nodes_since_last_log; - ++exploration_stats_.nodes_explored; - --exploration_stats_.nodes_unexplored; - auto [node_status, round_dir] = update_tree(node_ptr, - search_tree_, - leaf_problem, - leaf_solution, - bnb_worker_type_t::BEST_FIRST, - lp_status, - settings_.log); - - recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; - - if (node_status == node_status_t::HAS_CHILDREN) { - // The stack should only contain the children of the current parent. - // If the stack size is greater than 0, - // we pop the current node from the stack and place it in the global heap, - // since we are about to add the two children to the stack + recompute_bounds_and_basis = result.needs_recompute_basis; + + if (result.status == node_status_t::HAS_CHILDREN) { if (stack.size() > 0) { - mip_node_t* node = stack.back(); + mip_node_t* sibling = stack.back(); stack.pop_back(); - node_queue_.push(node); + node_queue_.push(sibling); } - exploration_stats_.nodes_unexplored += 2; - - if (round_dir == rounding_direction_t::UP) { + if (result.preferred_direction == rounding_direction_t::UP) { stack.push_front(node_ptr->get_down_child()); stack.push_front(node_ptr->get_up_child()); } else { @@ -1228,8 +1346,6 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, bnb_worker_type_t diving_type) { raft::common::nvtx::range scope("BB::diving_thread"); - logger_t log; - log.log = false; const i_t diving_node_limit = settings_.diving_settings.node_limit; const i_t diving_backtrack_limit = settings_.diving_settings.backtrack_limit; @@ -1244,13 +1360,37 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, dive_stats.nodes_explored = 0; dive_stats.nodes_unexplored = 1; + auto add_solution_fn = + [this](f_t obj, const std::vector& sol, i_t depth, bnb_worker_type_t type) { + add_feasible_solution(obj, sol, depth, type); + }; + + standard_diving_solve_policy_t policy(upper_bound_, + settings_.absolute_mip_gap_tol / 10, + start_lower, + start_upper, + edge_norms_, + root_relax_soln_.x, + pc_, + dive_stats, + add_solution_fn, + diving_type, + &leaf_problem, + &var_up_locks_, + &var_down_locks_); + + if (diving_type == bnb_worker_type_t::GUIDED_DIVING && incumbent_.has_incumbent) { + policy.set_incumbent(incumbent_.x); + } + + lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); + while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET && is_running) { mip_node_t* node_ptr = stack.front(); stack.pop_front(); - f_t lower_bound = node_ptr->lower_bound; f_t upper_bound = upper_bound_; - f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node_ptr->lower_bound); if (node_ptr->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { recompute_bounds_and_basis = true; @@ -1260,36 +1400,26 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } if (dive_stats.nodes_explored > diving_node_limit) { break; } - lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); - dual::status_t lp_status = solve_node_lp(node_ptr, - leaf_problem, - leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, - diving_type, - recompute_bounds_and_basis, - start_lower, - start_upper, - dive_stats, - log); - - if (lp_status == dual::status_t::TIME_LIMIT) { + node_solve_result_t result = solve_node_with_policy(node_ptr, + leaf_problem, + leaf_solution, + basis_factors, + basic_list, + nonbasic_list, + node_presolver, + recompute_bounds_and_basis, + dive_tree, + policy); + + if (result.status == node_status_t::PENDING) { solver_status_ = mip_status_t::TIME_LIMIT; break; - } else if (lp_status == dual::status_t::ITERATION_LIMIT) { - break; } - ++dive_stats.nodes_explored; - - auto [node_status, round_dir] = - update_tree(node_ptr, dive_tree, leaf_problem, leaf_solution, diving_type, lp_status, log); - recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; + recompute_bounds_and_basis = result.needs_recompute_basis; - if (node_status == node_status_t::HAS_CHILDREN) { - if (round_dir == rounding_direction_t::UP) { + if (result.status == node_status_t::HAS_CHILDREN) { + if (result.preferred_direction == rounding_direction_t::UP) { stack.push_front(node_ptr->get_down_child()); stack.push_front(node_ptr->get_up_child()); } else { @@ -1298,8 +1428,6 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, } } - // Remove nodes that we no longer can backtrack to (i.e., from the current node, we can only - // backtrack to a node that is has a depth of at most 5 levels lower than the current node). if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > diving_backtrack_limit) { stack.pop_back(); } @@ -2274,6 +2402,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t { raft::common::nvtx::range scope("BB::solve_node_bsp"); + // Validate vstatus before solving - check for corruption { const i_t expected_basic_count = original_lp_.num_rows; i_t actual_basic_count = 0; @@ -2296,332 +2425,52 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } } - double work_units_at_start = worker.work_context.global_work_units_elapsed; - double clock_at_start = worker.clock; - - double work_limit = worker.horizon_end - worker.clock; - BSP_DEBUG_LOG_SOLVE_START(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - work_limit, - false); - - std::fill(worker.node_presolver->bounds_changed.begin(), - worker.node_presolver->bounds_changed.end(), - false); - - if (worker.recompute_bounds_and_basis) { - worker.leaf_problem->lower = original_lp_.lower; - worker.leaf_problem->upper = original_lp_.upper; - node_ptr->get_variable_bounds(worker.leaf_problem->lower, - worker.leaf_problem->upper, - worker.node_presolver->bounds_changed); - } else { - node_ptr->update_branched_variable_bounds(worker.leaf_problem->lower, - worker.leaf_problem->upper, - worker.node_presolver->bounds_changed); - } - - double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); - if (remaining_time <= 0) { return node_solve_info_t::TIME_LIMIT; } - - // Bounds strengthening - simplex_solver_settings_t lp_settings = settings_; - lp_settings.set_log(false); - - lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; - lp_settings.inside_mip = 2; - lp_settings.time_limit = remaining_time; - lp_settings.scale_columns = false; - - bool feasible = true; - // TODO: incorporate into work unit estimation - if (!settings_.deterministic) { - feasible = worker.node_presolver->bounds_strengthening( - worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); - } - - if (!feasible) { - node_ptr->lower_bound = std::numeric_limits::infinity(); - search_tree.update(node_ptr, node_status_t::INFEASIBLE); - worker.record_infeasible(node_ptr); - worker.track_node_infeasible(); - worker.track_node_processed(); - --exploration_stats_.nodes_unexplored; - ++exploration_stats_.nodes_explored; - worker.recompute_bounds_and_basis = true; - return node_solve_info_t::NO_CHILDREN; - } + // Create BSP policy + bsp_solve_policy_t policy(worker, + settings_.absolute_mip_gap_tol / 10, + original_lp_.lower, + original_lp_.upper, + edge_norms_, + root_relax_soln_.x, + exploration_stats_, + bsp_debug_settings_, + bsp_debug_logger_); - // Solve LP relaxation + // Allocate solution buffer lp_solution_t leaf_solution(worker.leaf_problem->num_rows, worker.leaf_problem->num_cols); - std::vector& leaf_vstatus = node_ptr->vstatus; - i_t node_iter = 0; - f_t lp_start_time = tic(); - std::vector leaf_edge_norms = edge_norms_; - - // Debug: Log LP input for determinism analysis - if (bsp_debug_settings_.any_enabled()) { - uint64_t path_hash = node_ptr->compute_path_hash(); - uint64_t vstatus_hash = detail::compute_hash(leaf_vstatus); - uint64_t bounds_hash = detail::compute_hash(worker.leaf_problem->lower) ^ - detail::compute_hash(worker.leaf_problem->upper); - BSP_DEBUG_LOG_LP_INPUT(bsp_debug_settings_, - bsp_debug_logger_, - worker.worker_id, - node_ptr->node_id, - path_hash, - node_ptr->depth, - vstatus_hash, - bounds_hash); - } - - dual::status_t lp_status = dual_phase2_with_advanced_basis(2, - 0, - worker.recompute_bounds_and_basis, - lp_start_time, - *worker.leaf_problem, - lp_settings, - leaf_vstatus, - *worker.basis_factors, - worker.basic_list, - worker.nonbasic_list, - leaf_solution, - node_iter, - leaf_edge_norms, - &worker.work_context); - - if (bsp_debug_settings_.any_enabled()) { - uint64_t path_hash = node_ptr->compute_path_hash(); - uint64_t sol_hash = detail::compute_hash(leaf_solution.x); - f_t obj = (lp_status == dual::status_t::OPTIMAL) - ? compute_objective(*worker.leaf_problem, leaf_solution.x) - : std::numeric_limits::infinity(); - uint64_t obj_hash = detail::compute_hash(obj); - BSP_DEBUG_LOG_LP_OUTPUT(bsp_debug_settings_, - bsp_debug_logger_, - worker.worker_id, - node_ptr->node_id, - path_hash, - static_cast(lp_status), - node_iter, - obj_hash, - sol_hash); - } - - // Validate vstatus after LP solve - check for corruption during simplex - { - const i_t expected_basic_count = original_lp_.num_rows; - i_t actual_basic_count = 0; - for (const auto& status : leaf_vstatus) { - if (status == variable_status_t::BASIC) { actual_basic_count++; } - } - if (actual_basic_count != expected_basic_count) { - settings_.log.printf( - "ERROR: After LP solve, node %d vstatus has %d BASIC entries, expected %d\n", - node_ptr->node_id, - actual_basic_count, - expected_basic_count); - settings_.log.printf(" lp_status = %d, recompute_basis = %d\n", - static_cast(lp_status), - worker.recompute_bounds_and_basis ? 1 : 0); - assert(actual_basic_count == expected_basic_count && "vstatus corrupted during LP solve"); - } - } - - double work_performed = worker.work_context.global_work_units_elapsed - work_units_at_start; - worker.clock += work_performed; - worker.work_units_this_horizon += work_performed; - - exploration_stats_.total_lp_solve_time += toc(lp_start_time); - exploration_stats_.total_lp_iters += node_iter; - ++exploration_stats_.nodes_explored; - --exploration_stats_.nodes_unexplored; - - // Process LP result - if (lp_status == dual::status_t::DUAL_UNBOUNDED) { - node_ptr->lower_bound = std::numeric_limits::infinity(); - - worker.record_infeasible(node_ptr); - worker.track_node_infeasible(); - worker.track_node_processed(); - worker.recompute_bounds_and_basis = true; - - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "INFEASIBLE", - node_ptr->lower_bound); - BSP_DEBUG_LOG_INFEASIBLE( - bsp_debug_settings_, bsp_debug_logger_, worker.clock, worker.worker_id, node_ptr->node_id); - - search_tree.update(node_ptr, node_status_t::INFEASIBLE); - return node_solve_info_t::NO_CHILDREN; - - } else if (lp_status == dual::status_t::CUTOFF) { - node_ptr->lower_bound = worker.local_upper_bound; - - worker.record_fathomed(node_ptr, node_ptr->lower_bound); - worker.track_node_pruned(); - worker.track_node_processed(); - worker.recompute_bounds_and_basis = true; - - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "FATHOMED", - node_ptr->lower_bound); - BSP_DEBUG_LOG_FATHOMED(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->lower_bound); - - search_tree.update(node_ptr, node_status_t::FATHOMED); - return node_solve_info_t::NO_CHILDREN; - - } else if (lp_status == dual::status_t::OPTIMAL) { - std::vector leaf_fractional; - i_t leaf_num_fractional = - fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - - f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); - node_ptr->lower_bound = leaf_objective; - - // Queue pseudo-cost update for deterministic application at sync - if (node_ptr->branch_var >= 0) { - const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; - const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN - ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) - : std::ceil(node_ptr->fractional_val) - node_ptr->fractional_val; - if (frac > 1e-10) { - worker.queue_pseudo_cost_update( - node_ptr->branch_var, node_ptr->branch_dir, change_in_obj / frac); - } - } - - if (leaf_num_fractional == 0) { - // Integer feasible - queue for deterministic processing at sync - if (leaf_objective < worker.local_upper_bound) { - worker.local_upper_bound = leaf_objective; - worker.integer_solutions.push_back({leaf_objective, leaf_solution.x, node_ptr->depth}); - } - - worker.record_integer_solution(node_ptr, leaf_objective); - worker.track_integer_solution(); - worker.track_node_processed(); - worker.recompute_bounds_and_basis = true; - - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "INTEGER", - leaf_objective); - BSP_DEBUG_LOG_INTEGER(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - leaf_objective); - - search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); - return node_solve_info_t::NO_CHILDREN; - - } else if (leaf_objective <= worker.local_upper_bound + settings_.absolute_mip_gap_tol / 10) { - // Branch - use worker-local upper bound for deterministic pruning decision - // Use pseudo-cost snapshot for variable selection - const i_t branch_var = - worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); - - logger_t log; - log.log = false; - search_tree.branch( - node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, *worker.leaf_problem, log); - search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); + // Solve using unified function + node_solve_result_t result = solve_node_with_policy(node_ptr, + *worker.leaf_problem, + leaf_solution, + *worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + *worker.node_presolver, + worker.recompute_bounds_and_basis, + search_tree, + policy); - i_t down_child_id = node_ptr->get_down_child()->node_id; - i_t up_child_id = node_ptr->get_up_child()->node_id; - worker.record_branched( - node_ptr, down_child_id, up_child_id, branch_var, leaf_solution.x[branch_var]); - worker.track_node_branched(); - worker.track_node_processed(); - - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "BRANCH", - leaf_objective); - BSP_DEBUG_LOG_BRANCHED(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - down_child_id, - up_child_id); - - exploration_stats_.nodes_unexplored += 2; - - rounding_direction_t preferred = - martin_criteria(leaf_solution.x[branch_var], root_relax_soln_.x[branch_var]); - worker.enqueue_children_for_plunge( - node_ptr->get_down_child(), node_ptr->get_up_child(), preferred); - - return preferred == rounding_direction_t::DOWN ? node_solve_info_t::DOWN_CHILD_FIRST - : node_solve_info_t::UP_CHILD_FIRST; + // Update worker state + worker.recompute_bounds_and_basis = result.needs_recompute_basis; - } else { - // Record event and debug logs BEFORE search_tree.update() which may delete the node - worker.record_fathomed(node_ptr, leaf_objective); - worker.track_node_pruned(); - worker.track_node_processed(); - worker.recompute_bounds_and_basis = true; + // Convert result to node_solve_info_t + switch (result.status) { + case node_status_t::INFEASIBLE: + case node_status_t::INTEGER_FEASIBLE: + case node_status_t::FATHOMED: return node_solve_info_t::NO_CHILDREN; - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "FATHOMED", - leaf_objective); - BSP_DEBUG_LOG_FATHOMED(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - leaf_objective); + case node_status_t::HAS_CHILDREN: + return result.preferred_direction == rounding_direction_t::DOWN + ? node_solve_info_t::DOWN_CHILD_FIRST + : node_solve_info_t::UP_CHILD_FIRST; - search_tree.update(node_ptr, node_status_t::FATHOMED); - return node_solve_info_t::NO_CHILDREN; - } + case node_status_t::PENDING: return node_solve_info_t::TIME_LIMIT; - } else if (lp_status == dual::status_t::TIME_LIMIT) { - return node_solve_info_t::TIME_LIMIT; + case node_status_t::NUMERICAL: return node_solve_info_t::NUMERICAL; - } else { - worker.record_numerical(node_ptr); - worker.recompute_bounds_and_basis = true; - search_tree.update(node_ptr, node_status_t::NUMERICAL); - return node_solve_info_t::NUMERICAL; + default: return node_solve_info_t::NUMERICAL; } } @@ -3144,12 +2993,10 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t dive_tree(std::move(starting_node)); std::deque*> stack; stack.push_front(&dive_tree.root); - // Initialize bounds from root node worker.dive_lower = original_lp_.lower; worker.dive_upper = original_lp_.upper; std::fill(worker.node_presolver->bounds_changed.begin(), @@ -3158,22 +3005,39 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tbounds_changed); - const i_t max_nodes_per_dive = 100; - const i_t max_backtrack_depth = 5; - i_t nodes_this_dive = 0; - worker.recompute_bounds_and_basis = true; + const i_t max_nodes_per_dive = 100; + const i_t max_backtrack_depth = 5; + i_t nodes_this_dive = 0; + + bnb_stats_t dive_stats; + dive_stats.total_lp_iters = 0; + dive_stats.total_lp_solve_time = 0; + dive_stats.nodes_explored = 0; + dive_stats.nodes_unexplored = 1; + + bsp_diving_solve_policy_t policy(worker, + settings_.absolute_mip_gap_tol / 10, + worker.dive_lower, + worker.dive_upper, + edge_norms_, + root_relax_soln_.x, + dive_stats, + worker.leaf_problem.get(), + &var_up_locks_, + &var_down_locks_); + + lp_solution_t leaf_solution(worker.leaf_problem->num_rows, + worker.leaf_problem->num_cols); while (!stack.empty() && solver_status_ == mip_status_t::UNSET && !bsp_terminated_.load() && nodes_this_dive < max_nodes_per_dive) { - // Check time limit directly if (toc(exploration_stats_.start_time) > settings_.time_limit) { solver_status_ = mip_status_t::TIME_LIMIT; bsp_terminated_.store(true); - bsp_scheduler_->stop(); // Wake up workers waiting at barrier + bsp_scheduler_->stop(); break; } - // Check horizon budget if (worker.work_context.global_work_units_elapsed >= worker.horizon_end) { bsp_scheduler_->wait_for_next_sync(worker.work_context); if (bsp_terminated_.load()) break; @@ -3182,177 +3046,40 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t* node_ptr = stack.front(); stack.pop_front(); - // Prune check using snapshot upper bound if (node_ptr->lower_bound >= worker.local_upper_bound) { worker.recompute_bounds_and_basis = true; continue; } - // Setup bounds for this node - std::fill(worker.node_presolver->bounds_changed.begin(), - worker.node_presolver->bounds_changed.end(), - false); - - if (worker.recompute_bounds_and_basis) { - worker.leaf_problem->lower = worker.dive_lower; - worker.leaf_problem->upper = worker.dive_upper; - node_ptr->get_variable_bounds(worker.leaf_problem->lower, - worker.leaf_problem->upper, - worker.node_presolver->bounds_changed); - } else { - node_ptr->update_branched_variable_bounds(worker.leaf_problem->lower, - worker.leaf_problem->upper, - worker.node_presolver->bounds_changed); - } - - double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); - if (remaining_time <= 0) { break; } - - // Setup LP settings - simplex_solver_settings_t lp_settings = settings_; - lp_settings.set_log(false); - lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; - lp_settings.inside_mip = 2; - lp_settings.time_limit = remaining_time; - lp_settings.scale_columns = false; - - // Solve LP relaxation - lp_solution_t leaf_solution(worker.leaf_problem->num_rows, - worker.leaf_problem->num_cols); - std::vector& leaf_vstatus = node_ptr->vstatus; - i_t node_iter = 0; - f_t lp_start_time = tic(); - std::vector leaf_edge_norms = edge_norms_; - - dual::status_t lp_status = dual_phase2_with_advanced_basis(2, - 0, - worker.recompute_bounds_and_basis, - lp_start_time, - *worker.leaf_problem, - lp_settings, - leaf_vstatus, - *worker.basis_factors, - worker.basic_list, - worker.nonbasic_list, - leaf_solution, - node_iter, - leaf_edge_norms, - &worker.work_context); + node_solve_result_t result = solve_node_with_policy(node_ptr, + *worker.leaf_problem, + leaf_solution, + *worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + *worker.node_presolver, + worker.recompute_bounds_and_basis, + dive_tree, + policy); ++nodes_this_dive; - ++worker.nodes_explored_this_horizon; - ++worker.total_nodes_explored; - worker.clock = worker.work_context.global_work_units_elapsed; + if (result.status == node_status_t::PENDING) { break; } - if (lp_status == dual::status_t::TIME_LIMIT || lp_status == dual::status_t::WORK_LIMIT) { - break; - } - - if (lp_status == dual::status_t::DUAL_UNBOUNDED || lp_status == dual::status_t::CUTOFF) { - worker.recompute_bounds_and_basis = true; - continue; - } - - if (lp_status == dual::status_t::OPTIMAL) { - std::vector leaf_fractional; - fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - - f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); - node_ptr->lower_bound = leaf_objective; + worker.recompute_bounds_and_basis = result.needs_recompute_basis; - if (leaf_fractional.empty()) { - // Integer feasible solution found! - if (leaf_objective < worker.local_upper_bound) { - worker.queue_integer_solution(leaf_objective, leaf_solution.x, node_ptr->depth); - } - worker.recompute_bounds_and_basis = true; - continue; + if (result.status == node_status_t::HAS_CHILDREN) { + if (result.preferred_direction == rounding_direction_t::UP) { + stack.push_front(node_ptr->get_down_child()); + stack.push_front(node_ptr->get_up_child()); + } else { + stack.push_front(node_ptr->get_up_child()); + stack.push_front(node_ptr->get_down_child()); } - if (leaf_objective <= worker.local_upper_bound + settings_.absolute_mip_gap_tol / 10) { - // Branch - select variable using diving-type-specific strategy - branch_variable_t branch_result; - - switch (worker.diving_type) { - case bnb_worker_type_t::PSEUDOCOST_DIVING: - branch_result = - worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); - break; - - case bnb_worker_type_t::LINE_SEARCH_DIVING: - if (worker.root_solution) { - logger_t log; - log.log = false; - branch_result = line_search_diving( - leaf_fractional, leaf_solution.x, *worker.root_solution, log); - } else { - branch_result = - worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); - } - break; - - case bnb_worker_type_t::GUIDED_DIVING: - branch_result = worker.guided_variable_selection(leaf_fractional, leaf_solution.x); - break; - - case bnb_worker_type_t::COEFFICIENT_DIVING: { - logger_t log; - log.log = false; - branch_result = coefficient_diving(*worker.leaf_problem, - leaf_fractional, - leaf_solution.x, - var_up_locks_, - var_down_locks_, - log); - } break; - - default: - branch_result = - worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); - break; - } - - i_t branch_var = branch_result.variable; - rounding_direction_t round_dir = branch_result.direction; - - if (branch_var < 0) { - worker.recompute_bounds_and_basis = true; - continue; - } - - // Create children - logger_t log; - log.log = false; - dive_tree.branch(node_ptr, - branch_var, - leaf_solution.x[branch_var], - leaf_vstatus, - *worker.leaf_problem, - log); - - // Add children to stack (preferred direction first) - if (round_dir == rounding_direction_t::UP) { - stack.push_front(node_ptr->get_down_child()); - stack.push_front(node_ptr->get_up_child()); - } else { - stack.push_front(node_ptr->get_up_child()); - stack.push_front(node_ptr->get_down_child()); - } - - // Limit backtracking depth - if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > max_backtrack_depth) { - stack.pop_back(); - } - - worker.recompute_bounds_and_basis = false; - } else { - // Fathomed by bound - worker.recompute_bounds_and_basis = true; + if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > max_backtrack_depth) { + stack.pop_back(); } - } else { - // Numerical or other error - worker.recompute_bounds_and_basis = true; } } } @@ -3361,6 +3088,56 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t; +// Explicit instantiations for solve_node_with_policy with both policy types +template node_solve_result_t branch_and_bound_t::solve_node_with_policy< + bsp_solve_policy_t>(mip_node_t*, + lp_problem_t&, + lp_solution_t&, + basis_update_mpf_t&, + std::vector&, + std::vector&, + bounds_strengthening_t&, + bool, + search_tree_t&, + bsp_solve_policy_t&); + +template node_solve_result_t branch_and_bound_t::solve_node_with_policy< + standard_solve_policy_t>(mip_node_t*, + lp_problem_t&, + lp_solution_t&, + basis_update_mpf_t&, + std::vector&, + std::vector&, + bounds_strengthening_t&, + bool, + search_tree_t&, + standard_solve_policy_t&); + +// Explicit instantiations for diving policies +template node_solve_result_t branch_and_bound_t::solve_node_with_policy< + standard_diving_solve_policy_t>(mip_node_t*, + lp_problem_t&, + lp_solution_t&, + basis_update_mpf_t&, + std::vector&, + std::vector&, + bounds_strengthening_t&, + bool, + search_tree_t&, + standard_diving_solve_policy_t&); + +template node_solve_result_t branch_and_bound_t::solve_node_with_policy< + bsp_diving_solve_policy_t>(mip_node_t*, + lp_problem_t&, + lp_solution_t&, + basis_update_mpf_t&, + std::vector&, + std::vector&, + bounds_strengthening_t&, + bool, + search_tree_t&, + bsp_diving_solve_policy_t&); + #endif } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index a4cd67b3d..120ba00bb 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -8,6 +8,7 @@ #pragma once #include +#include #include #include #include @@ -279,17 +280,6 @@ class branch_and_bound_t { bnb_stats_t& stats, logger_t& log); - // Update the tree based on the LP relaxation. Returns the status - // of the node and, if appropriated, the preferred rounding direction - // when visiting the children. - std::pair update_tree(mip_node_t* node_ptr, - search_tree_t& search_tree, - lp_problem_t& leaf_problem, - lp_solution_t& leaf_solution, - bnb_worker_type_t thread_type, - dual::status_t lp_status, - logger_t& log); - // Selects the variable to branch on. branch_variable_t variable_selection(mip_node_t* node_ptr, const std::vector& fractional, @@ -326,6 +316,20 @@ class branch_and_bound_t { search_tree_t& search_tree, double current_horizon); + // Unified node solving with policy-based callbacks (compile-time polymorphism) + template + node_solve_result_t solve_node_with_policy( + mip_node_t* node_ptr, + lp_problem_t& leaf_problem, + lp_solution_t& leaf_solution, + basis_update_mpf_t& basis_factors, + std::vector& basic_list, + std::vector& nonbasic_list, + bounds_strengthening_t& node_presolver, + bool recompute_bounds_and_basis, + search_tree_t& search_tree, + Policy& policy); + // Compute accurate lower bound from all BSP sources (called during sync phase) f_t compute_bsp_lower_bound(); diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index b19c2c956..5102b4326 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2097,7 +2097,7 @@ void prepare_optimality(const lp_problem_t& lp, const i_t m = lp.num_rows; const i_t n = lp.num_cols; - sol.objective = compute_objective(lp, sol.x); + sol.objective = compute_objective(lp, x); sol.user_objective = compute_user_objective(lp, sol.objective); f_t perturbation = phase2::amount_of_perturbation(lp, objective); if (perturbation > 1e-6 && phase == 2) { @@ -2122,8 +2122,8 @@ void prepare_optimality(const lp_problem_t& lp, sol.l2_primal_residual = l2_primal_residual(lp, sol); sol.l2_dual_residual = l2_dual_residual(lp, sol); - const f_t dual_infeas = phase2::dual_infeasibility(lp, settings, vstatus, sol.z, 0.0, 0.0); - const f_t primal_infeas = phase2::primal_infeasibility(lp, settings, vstatus, sol.x); + const f_t dual_infeas = phase2::dual_infeasibility(lp, settings, vstatus, z, 0.0, 0.0); + const f_t primal_infeas = phase2::primal_infeasibility(lp, settings, vstatus, x); if (phase == 1 && iter > 0) { settings.log.printf("Dual phase I complete. Iterations %d. Time %.2f\n", iter, toc(start_time)); } @@ -2286,13 +2286,12 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(lp.upper.size() == n); assert(lp.rhs.size() == m); - // Create instrumented wrappers around sol vectors (no ownership transfer) - // ins_vector x, y, z; - // x.wrap(sol.x); - // y.wrap(sol.y); - // z.wrap(sol.z); + std::vector& x = sol.x; + std::vector& y = sol.y; + std::vector& z = sol.z; // Declare instrumented vectors used during initialization (before manifold setup) + // Perturbed objective ins_vector objective(lp.objective); ins_vector c_basic(m); ins_vector xB_workspace(m); @@ -2312,7 +2311,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings.log.printf("Dual Simplex Phase %d\n", phase); std::vector vstatus_old = vstatus; - std::vector z_old = sol.z; + std::vector z_old = z; phase2::bound_info(lp, settings); if (initialize_basis) { @@ -2339,24 +2338,20 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } // Solve B'*y = cB - ft.b_transpose_solve(c_basic, sol.y); + ft.b_transpose_solve(c_basic, y); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } constexpr bool print_norms = false; if constexpr (print_norms) { - settings.log.printf("|| y || %e || cB || %e\n", - vector_norm_inf(sol.y), - vector_norm_inf(c_basic)); + settings.log.printf( + "|| y || %e || cB || %e\n", vector_norm_inf(y), vector_norm_inf(c_basic)); } - phase2::compute_reduced_costs( - objective.underlying(), lp.A, sol.y, basic_list, nonbasic_list, sol.z); - if constexpr (print_norms) { - settings.log.printf("|| z || %e\n", vector_norm_inf(sol.z)); - } + phase2::compute_reduced_costs(objective.underlying(), lp.A, y, basic_list, nonbasic_list, z); + if constexpr (print_norms) { settings.log.printf("|| z || %e\n", vector_norm_inf(z)); } #ifdef COMPUTE_DUAL_RESIDUAL std::vector dual_res1; - phase2::compute_dual_residual(lp.A, objective, sol.y, sol.z, dual_res1); + phase2::compute_dual_residual(lp.A, objective, y, z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.tight_tol) { settings.log.printf("|| A'*y + z - c || %e\n", dual_res_norm); @@ -2364,18 +2359,18 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(dual_res_norm < 1e-3); #endif - phase2::set_primal_variables_on_bounds(lp, settings, sol.z, vstatus, sol.x); + phase2::set_primal_variables_on_bounds(lp, settings, z, vstatus, x); #ifdef PRINT_VSTATUS_CHANGES i_t num_vstatus_changes; i_t num_z_changes; - phase2::vstatus_changes(vstatus, vstatus_old, sol.z, z_old, num_vstatus_changes, num_z_changes); + phase2::vstatus_changes(vstatus, vstatus_old, z, z_old, num_vstatus_changes, num_z_changes); settings.log.printf("Number of vstatus changes %d\n", num_vstatus_changes); settings.log.printf("Number of z changes %d\n", num_z_changes); #endif const f_t init_dual_inf = - phase2::dual_infeasibility(lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); + phase2::dual_infeasibility(lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); if (init_dual_inf > settings.dual_tol) { settings.log.printf("Initial dual infeasibility %e\n", init_dual_inf); } @@ -2387,14 +2382,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } phase2::compute_primal_variables( - ft, lp.rhs, lp.A, basic_list, nonbasic_list, settings.tight_tol, sol.x, xB_workspace); + ft, lp.rhs, lp.A, basic_list, nonbasic_list, settings.tight_tol, x, xB_workspace); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } - if (print_norms) { settings.log.printf("|| x || %e\n", vector_norm2(sol.x)); } + if (print_norms) { settings.log.printf("|| x || %e\n", vector_norm2(x)); } #ifdef COMPUTE_PRIMAL_RESIDUAL std::vector residual = lp.rhs; - matrix_vector_multiply(lp.A, 1.0, sol.x, -1.0, residual); + matrix_vector_multiply(lp.A, 1.0, x, -1.0, residual); f_t primal_residual = vector_norm_inf(residual); if (primal_residual > settings.primal_tol) { settings.log.printf("|| A*x - b || %e\n", primal_residual); @@ -2453,7 +2448,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); @@ -2470,7 +2465,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // raft::common::nvtx::range scope("DualSimplex::transpose_A"); lp.A.transpose(A_transpose); } - f_t obj = compute_objective(lp, sol.x); + f_t obj = compute_objective(lp, x); init_scope.pop(); // End phase2_advanced_init range @@ -2617,7 +2612,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if (settings.use_steepest_edge_pricing) { leaving_index = phase2::steepest_edge_pricing_with_infeasibilities(lp, settings, - sol.x, + x, delta_y_steepest_edge, basic_mark, squared_infeasibilities, @@ -2628,7 +2623,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } else { // Max infeasibility pricing leaving_index = phase2::phase2_pricing( - lp, settings, sol.x, basic_list, direction, basic_leaving_index, primal_infeasibility); + lp, settings, x, basic_list, direction, basic_leaving_index, primal_infeasibility); } } timers.pricing_time += timers.stop_timer(); @@ -2644,9 +2639,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - sol.x, - sol.y, - sol.z, + x, + y, + z, sol); status = dual::status_t::OPTIMAL; break; @@ -2735,20 +2730,19 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, { // raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); if (harris_ratio) { - f_t max_step_length = - phase2::first_stage_harris(lp, vstatus, nonbasic_list, sol.z, delta_z); - entering_index = phase2::second_stage_harris(lp, + f_t max_step_length = phase2::first_stage_harris(lp, vstatus, nonbasic_list, z, delta_z); + entering_index = phase2::second_stage_harris(lp, vstatus, nonbasic_list, - sol.z, + z, delta_z, max_step_length, step_length, nonbasic_entering_index); } else if (bound_flip_ratio) { timers.start_timer(); - f_t slope = direction == 1 ? (lp.lower[leaving_index] - sol.x[leaving_index]) - : (sol.x[leaving_index] - lp.upper[leaving_index]); + f_t slope = direction == 1 ? (lp.lower[leaving_index] - x[leaving_index]) + : (x[leaving_index] - lp.upper[leaving_index]); bound_flipping_ratio_test_t bfrt(settings, start_time, m, @@ -2759,7 +2753,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, bounded_variables.underlying(), vstatus, nonbasic_list, - sol.z, + z, delta_z.underlying(), delta_z_indices.underlying(), nonbasic_mark); @@ -2770,14 +2764,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } timers.bfrt_time += timers.stop_timer(); } else { - entering_index = phase2::phase2_ratio_test(lp, - settings, - vstatus, - nonbasic_list, - sol.z, - delta_z, - step_length, - nonbasic_entering_index); + entering_index = phase2::phase2_ratio_test( + lp, settings, vstatus, nonbasic_list, z, delta_z, step_length, nonbasic_entering_index); } } if (entering_index == -2) { return dual::status_t::TIME_LIMIT; } @@ -2799,19 +2787,19 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings.log.printf("Dual infeasibility after removing perturbation %e\n", dual_infeas); if (dual_infeas <= settings.dual_tol) { settings.log.printf("Removed perturbation of %.2e.\n", perturbation); - sol.z = unperturbed_z; - sol.y = unperturbed_y; + z = unperturbed_z; + y = unperturbed_y; perturbation = 0.0; std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); - sol.x = unperturbed_x; + x = unperturbed_x; primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); @@ -2819,7 +2807,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, objective = lp.objective; // Need to reset the objective value, since we have recomputed x - obj = phase2::compute_perturbed_objective(objective, sol.x); + obj = phase2::compute_perturbed_objective(objective, x); if (dual_infeas <= settings.dual_tol && primal_infeasibility <= settings.primal_tol) { phase2::prepare_optimality(lp, settings, @@ -2832,9 +2820,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - sol.x, - sol.y, - sol.z, + x, + y, + z, sol); status = dual::status_t::OPTIMAL; break; @@ -2849,18 +2837,18 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); - sol.x = unperturbed_x; + x = unperturbed_x; primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); const f_t orig_dual_infeas = phase2::dual_infeasibility( - lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); + lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); if (primal_infeasibility <= settings.primal_tol && orig_dual_infeas <= settings.dual_tol) { @@ -2875,9 +2863,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - sol.x, - sol.y, - sol.z, + x, + y, + z, sol); status = dual::status_t::OPTIMAL; break; @@ -2898,13 +2886,13 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_y_sparse.to_dense(my_delta_y); // TODO(CMM): Do I use the perturbed or unperturbed objective? - const f_t obj_val = phase2::compute_perturbed_objective(objective, sol.x); + const f_t obj_val = phase2::compute_perturbed_objective(objective, x); phase2::compute_farkas_certificate(lp, settings, vstatus, - sol.x, - sol.y, - sol.z, + x, + y, + z, my_delta_y, delta_z, direction, @@ -2917,10 +2905,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } } - const f_t dual_infeas = phase2::dual_infeasibility( - lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); + const f_t dual_infeas = + phase2::dual_infeasibility(lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); settings.log.printf("Dual infeasibility %e\n", dual_infeas); - const f_t primal_inf = phase2::primal_infeasibility(lp, settings, vstatus, sol.x); + const f_t primal_inf = phase2::primal_infeasibility(lp, settings, vstatus, x); settings.log.printf("Primal infeasibility %e\n", primal_inf); settings.log.printf("Updates %d\n", ft.num_updates()); settings.log.printf("Steepest edge %e\n", max_val); @@ -2937,7 +2925,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // y <- y + steplength * delta_y // z <- z + steplength * delta_z i_t update_dual_variables_status = phase2::update_dual_variables( - delta_y_sparse, delta_z_indices, delta_z, step_length, leaving_index, sol.y, sol.z); + delta_y_sparse, delta_z_indices, delta_z, step_length, leaving_index, y, z); if (update_dual_variables_status == -1) { settings.log.printf("Numerical issues encountered in update_dual_variables.\n"); return dual::status_t::NUMERICAL; @@ -2945,7 +2933,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.vector_time += timers.stop_timer(); #ifdef COMPUTE_DUAL_RESIDUAL - phase2::compute_dual_residual(lp.A, objective, sol.y, sol.z, dual_res1); + phase2::compute_dual_residual(lp.A, objective, y, z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.dual_tol) { settings.log.printf("|| A'*y + z - c || %e steplength %e\n", dual_res_norm, step_length); @@ -2958,7 +2946,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings, bounded_variables, objective, - sol.z, + z, delta_z_indices, nonbasic_list, entering_index, @@ -2983,7 +2971,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, atilde_sparse, delta_xB_0_sparse, delta_x_flip, - sol.x); + x); timers.ftran_time += timers.stop_timer(); } @@ -3002,7 +2990,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, basic_list, delta_x_flip, rhs_sparse, - sol.x, + x, utilde_sparse, scaled_delta_xB_sparse, delta_x) == -1) { @@ -3044,13 +3032,12 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); // x <- x + delta_x - phase2::update_primal_variables( - scaled_delta_xB_sparse, basic_list, delta_x, entering_index, sol.x); + phase2::update_primal_variables(scaled_delta_xB_sparse, basic_list, delta_x, entering_index, x); timers.vector_time += timers.stop_timer(); #ifdef COMPUTE_PRIMAL_RESIDUAL residual = lp.rhs; - matrix_vector_multiply(lp.A, 1.0, sol.x, -1.0, residual); + matrix_vector_multiply(lp.A, 1.0, x, -1.0, residual); primal_residual = vector_norm_inf(residual); if (iter % 100 == 0 && primal_residual > 10 * settings.primal_tol) { settings.log.printf("|| A*x - b || %e\n", primal_residual); @@ -3073,7 +3060,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::update_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, entering_index, leaving_index, delta_xB_0_sparse.i.underlying(), @@ -3085,7 +3072,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::update_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, entering_index, leaving_index, scaled_delta_xB_sparse.i.underlying(), @@ -3095,7 +3082,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // Update the entering variable phase2::update_single_primal_infeasibility(lp.lower, lp.upper, - sol.x, + x, settings.primal_tol, squared_infeasibilities.underlying(), infeasibility_indices.underlying(), @@ -3106,7 +3093,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #if CHECK_PRIMAL_INFEASIBILITIES phase2::check_primal_infeasibilities( - lp, settings, basic_list, sol.x, squared_infeasibilities, infeasibility_indices); + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); #endif timers.update_infeasibility_time += timers.stop_timer(); @@ -3115,7 +3102,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); f_t sum_perturb = 0.0; - phase2::compute_perturbation(lp, settings, delta_z_indices, sol.z, objective, sum_perturb); + phase2::compute_perturbation(lp, settings, delta_z_indices, z, objective, sum_perturb); timers.perturb_time += timers.stop_timer(); // Update basis information @@ -3184,13 +3171,13 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); - sol.x = unperturbed_x; + x = unperturbed_x; } primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); diff --git a/cpp/src/dual_simplex/sparse_matrix.cpp b/cpp/src/dual_simplex/sparse_matrix.cpp index 7d510a849..d6eab1c15 100644 --- a/cpp/src/dual_simplex/sparse_matrix.cpp +++ b/cpp/src/dual_simplex/sparse_matrix.cpp @@ -591,7 +591,7 @@ void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, VectorF& x } } -// x <- x + alpha * A(:, j) with mark/indices tracking +// x <- x + alpha * A(:, j) template void scatter_dense( const csc_matrix_t& A, i_t j, f_t alpha, VectorF& x, VectorI& mark, VectorI& indices) diff --git a/cpp/src/dual_simplex/triangle_solve.hpp b/cpp/src/dual_simplex/triangle_solve.hpp index cb1ce5a79..f7fc7744d 100644 --- a/cpp/src/dual_simplex/triangle_solve.hpp +++ b/cpp/src/dual_simplex/triangle_solve.hpp @@ -35,7 +35,8 @@ i_t lower_triangular_solve(const csc_matrix_t& L, VectorF& x) i_t col_end = L.col_start[j + 1]; if (x[j] != 0.0) { x[j] /= L.x[col_start]; - auto x_j = x[j]; + auto x_j = x[j]; // hoist this load out of the loop + // as the compiler cannot guess that x[j] never aliases to x[L.i[p]] for (i_t p = col_start + 1; p < col_end; ++p) { x[L.i[p]] -= L.x[p] * x_j; } @@ -74,7 +75,7 @@ i_t upper_triangular_solve(const csc_matrix_t& U, VectorF& x) const i_t col_end = U.col_start[j + 1] - 1; if (x[j] != 0.0) { x[j] /= U.x[col_end]; - auto x_j = x[j]; + auto x_j = x[j]; // same x_j load hoisting for (i_t p = col_start; p < col_end; ++p) { x[U.i[p]] -= U.x[p] * x_j; } diff --git a/cpp/src/math_optimization/solver_settings.cu b/cpp/src/math_optimization/solver_settings.cu index e16b4be46..1b5efeb0d 100644 --- a/cpp/src/math_optimization/solver_settings.cu +++ b/cpp/src/math_optimization/solver_settings.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -89,7 +89,7 @@ solver_settings_t::solver_settings_t() : pdlp_settings(), mip_settings {CUOPT_BARRIER_DUAL_INITIAL_POINT, &pdlp_settings.barrier_dual_initial_point, -1, 1, -1}, {CUOPT_NUM_GPUS, &pdlp_settings.num_gpus, 1, 2, 1}, {CUOPT_NUM_GPUS, &mip_settings.num_gpus, 1, 2, 1}, - {CUOPT_MIP_DETERMINISTIC, &mip_settings.determinism_mode, CUOPT_MODE_OPPORTUNISTIC, CUOPT_MODE_DETERMINISTIC, CUOPT_MODE_OPPORTUNISTIC} + {CUOPT_MIP_DETERMINISM_MODE, &mip_settings.determinism_mode, CUOPT_MODE_OPPORTUNISTIC, CUOPT_MODE_DETERMINISTIC, CUOPT_MODE_OPPORTUNISTIC} }; // Bool parameters diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 0207df346..3f66a3c37 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -194,7 +194,6 @@ template void local_search_t::stop_cpufj_deterministic() { if (deterministic_cpu_fj.fj_cpu) { - // Deregister from producer_sync before stopping if (deterministic_cpu_fj.fj_cpu->producer_sync) { deterministic_cpu_fj.fj_cpu->producer_sync->deregister_producer( &deterministic_cpu_fj.fj_cpu->work_units_elapsed); diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 75aea76e3..3839b1aa8 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -496,7 +496,7 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { using const_reverse_iterator = std::reverse_iterator; // Constructors - memop_instrumentation_wrapper_t() : array_(), wrapped_ptr(nullptr) + memop_instrumentation_wrapper_t() : array_() { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -541,11 +541,8 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } - // Copy constructor - copy from wrapped array if wrapping, never share wrapped pointer memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : memory_instrumentation_base_t(other), - array_(other.wrapped_ptr ? *other.wrapped_ptr : other.array_), - wrapped_ptr(nullptr) + : memory_instrumentation_base_t(other), array_(other.array_) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -554,12 +551,8 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } - // Move constructor - copy from wrapped array if wrapping (can't move wrapped), never share - // pointer memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : memory_instrumentation_base_t(std::move(other)), - array_(other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_)), - wrapped_ptr(nullptr) + : memory_instrumentation_base_t(std::move(other)), array_(std::move(other.array_)) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -568,21 +561,13 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } - // Copy assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) { if (this != &other) { memory_instrumentation_base_t::operator=(other); - // Get source data (from wrapped or owned array) - const T& source = other.wrapped_ptr ? *other.wrapped_ptr : other.array_; - // Write to destination (wrapped or owned array) - if (wrapped_ptr) { - *wrapped_ptr = source; - } else { - array_ = source; - } + array_ = other.array_; if constexpr (type_traits_utils::has_data::value) { - data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } @@ -590,19 +575,13 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { return *this; } - // Move assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept { if (this != &other) { memory_instrumentation_base_t::operator=(std::move(other)); - // Get source data (copy from wrapped, move from owned) - if (wrapped_ptr) { - *wrapped_ptr = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); - } else { - array_ = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); - } + array_ = std::move(other.array_); if constexpr (type_traits_utils::has_data::value) { - data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } @@ -786,30 +765,11 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { T&& release_array() { return std::move(array_); } - // Wrap an external vector without taking ownership - void wrap(T& external_array) - { - wrapped_ptr = &external_array; - if constexpr (type_traits_utils::has_data::value) { data_ptr = external_array.data(); } - } - - // Stop wrapping and return to using the owned array - void unwrap() - { - wrapped_ptr = nullptr; - if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); } - } - - // Check if currently wrapping an external array - bool is_wrapping() const { return wrapped_ptr != nullptr; } - - // Get the underlying container (wrapped or owned) - T& underlying() { return wrapped_ptr ? *wrapped_ptr : array_; } - const T& underlying() const { return wrapped_ptr ? *wrapped_ptr : array_; } + T& underlying() { return array_; } + const T& underlying() const { return array_; } private: T array_; - T* wrapped_ptr{nullptr}; value_type* data_ptr{nullptr}; }; @@ -834,8 +794,8 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { // Constructors - forward everything to the underlying container memop_instrumentation_wrapper_t() = default; - memop_instrumentation_wrapper_t(const T& arr) : array_(arr), wrapped_ptr_(nullptr) {} - memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)), wrapped_ptr_(nullptr) {} + memop_instrumentation_wrapper_t(const T& arr) : array_(arr) {} + memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)) {} template , T> && (sizeof...(Args) > 0 || !std::is_convertible_v)>> explicit memop_instrumentation_wrapper_t(Arg&& arg, Args&&... args) - : array_(std::forward(arg), std::forward(args)...), wrapped_ptr_(nullptr) + : array_(std::forward(arg), std::forward(args)...) { } - // Copy constructor - always copy the data, never share wrapped pointer memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_), wrapped_ptr_(nullptr) + : array_(other.array_) { } - // Move constructor - take ownership of array, never share wrapped pointer memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_)), - wrapped_ptr_(nullptr) + : array_(std::move(other.array_)) { } - // Copy assignment - always copy the data memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) { - if (this != &other) { - if (wrapped_ptr_) { - *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; - } else { - array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; - } - } + if (this != &other) { array_ = other.array_; } return *this; } - // Move assignment - take the data memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept { - if (this != &other) { - if (wrapped_ptr_) { - *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); - } else { - array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); - } - } + if (this != &other) { array_ = std::move(other.array_); } return *this; } @@ -947,18 +890,11 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { T&& release_array() { return std::move(array_); } - // Wrap/unwrap interface (for compatibility, but wrap is essentially a no-op for perf) - void wrap(T& external_array) { wrapped_ptr_ = &external_array; } - void unwrap() { wrapped_ptr_ = nullptr; } - bool is_wrapping() const { return wrapped_ptr_ != nullptr; } - - // Get the underlying container - T& underlying() { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } - const T& underlying() const { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } + T& underlying() { return array_; } + const T& underlying() const { return array_; } private: T array_; - T* wrapped_ptr_{nullptr}; }; #endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION diff --git a/cpp/src/utilities/producer_sync.hpp b/cpp/src/utilities/producer_sync.hpp index 13c9bde38..df410fca8 100644 --- a/cpp/src/utilities/producer_sync.hpp +++ b/cpp/src/utilities/producer_sync.hpp @@ -27,7 +27,7 @@ namespace cuopt { * One-way synchronization utility for producer threads. * * Producers (e.g., CPUFJ) register their work unit progress atomics and advance independently. - * The consumer (e.g., B&B BSP coordinator) can wait until all producers have reached a + * The consumer (e.g., B&B coordinator) can wait until all producers have reached a * target work unit threshold before proceeding. * * Key invariant: Producers must not fall behind the consumer's horizon. The consumer @@ -37,10 +37,6 @@ class producer_sync_t { public: producer_sync_t() = default; - /** - * Register a producer's work unit progress atomic. - * Must be called before registration_complete(). - */ void register_producer(std::atomic* progress_ptr) { std::lock_guard lock(mutex_); @@ -48,9 +44,6 @@ class producer_sync_t { cv_.notify_all(); } - /** - * Deregister a producer (e.g., when it terminates). - */ void deregister_producer(std::atomic* progress_ptr) { std::lock_guard lock(mutex_); @@ -70,9 +63,6 @@ class producer_sync_t { cv_.notify_all(); } - /** - * Check if registration is complete (non-blocking). - */ bool is_registration_complete() const { std::lock_guard lock(mutex_); @@ -100,25 +90,6 @@ class producer_sync_t { */ void notify_progress() { cv_.notify_all(); } - /** - * Get the minimum work units among all registered producers. - * Returns infinity if no producers are registered. - */ - double get_min_producer_progress() const - { - std::lock_guard lock(mutex_); - if (producers_.empty()) { return std::numeric_limits::infinity(); } - - double min_progress = std::numeric_limits::infinity(); - for (const auto* progress_ptr : producers_) { - min_progress = std::min(min_progress, progress_ptr->load(std::memory_order_acquire)); - } - return min_progress; - } - - /** - * Get the number of registered producers. - */ size_t num_producers() const { std::lock_guard lock(mutex_); diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp index 0aebf953d..949d84b2c 100644 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -30,22 +30,9 @@ #include "models/fj_predictor/header.h" #include "models/pdlp_predictor/header.h" -namespace cuopt { - -template -static inline uint32_t compute_hash(std::vector h_contents) -{ - // FNV-1a hash +#include "hashing.hpp" - uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis - std::vector byte_contents(h_contents.size() * sizeof(i_t)); - std::memcpy(byte_contents.data(), h_contents.data(), h_contents.size() * sizeof(i_t)); - for (size_t i = 0; i < byte_contents.size(); ++i) { - hash ^= byte_contents[i]; - hash *= 16777619u; - } - return hash; -} +namespace cuopt { template float work_unit_predictor_t::predict_scalar( @@ -69,7 +56,7 @@ float work_unit_predictor_t::predict_scalar( cache_vec.push_back(data[i].missing != -1 ? data[i].fvalue : std::numeric_limits::quiet_NaN()); } - uint32_t key = compute_hash(cache_vec); + uint32_t key = cuopt::linear_programming::detail::compute_hash(cache_vec); auto cached_it = prediction_cache.find(key); if (cached_it != prediction_cache.end()) { return cached_it->second; } diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp index 9c14a3a1e..746d5e6dd 100644 --- a/cpp/src/utilities/work_unit_scheduler.cpp +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -102,15 +102,15 @@ sync_result_t work_unit_scheduler_t::wait_for_next_sync(work_limit_context_t& ct return stopped_.load() ? sync_result_t::STOPPED : sync_result_t::CONTINUE; } -void work_unit_scheduler_t::queue_callback(work_limit_context_t& source, - work_limit_context_t& destination, - callback_t callback) -{ - std::lock_guard lock(mutex_); - double tag = source.global_work_units_elapsed; - auto it = callback_queues_.find(&destination); - if (it != callback_queues_.end()) { it->second.push({tag, std::move(callback)}); } -} +// void work_unit_scheduler_t::queue_callback(work_limit_context_t& source, +// work_limit_context_t& destination, +// callback_t callback) +// { +// std::lock_guard lock(mutex_); +// double tag = source.global_work_units_elapsed; +// auto it = callback_queues_.find(&destination); +// if (it != callback_queues_.end()) { it->second.push({tag, std::move(callback)}); } +// } double work_unit_scheduler_t::current_sync_target() const { @@ -167,9 +167,9 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double size_t my_exit_generation = exit_generation_; if (verbose) { CUOPT_LOG_DEBUG("[%s] Processing callbacks", ctx.name.c_str()); } - lock.unlock(); - process_callbacks_for_context(ctx, sync_target); - lock.lock(); + // lock.unlock(); + // process_callbacks_for_context(ctx, sync_target); + // lock.lock(); if (verbose) { CUOPT_LOG_DEBUG("[%s] Done processing callbacks", ctx.name.c_str()); } contexts_at_barrier_--; @@ -205,26 +205,10 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double } } -void work_unit_scheduler_t::process_callbacks_for_context(work_limit_context_t& ctx, - double up_to_work_units) -{ - std::vector to_execute; - - { - std::lock_guard lock(mutex_); - auto it = callback_queues_.find(&ctx); - if (it == callback_queues_.end()) return; +// void work_unit_scheduler_t::process_callbacks_for_context(work_limit_context_t& ctx, +// double up_to_work_units) +// { - auto& queue = it->second; - while (!queue.empty() && queue.top().work_unit_tag <= up_to_work_units) { - to_execute.push_back(std::move(const_cast(queue.top()).callback)); - queue.pop(); - } - } - - for (auto& cb : to_execute) { - cb(); - } -} +// } } // namespace cuopt diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index 12b03929c..59b7fbffe 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -45,9 +45,9 @@ class work_unit_scheduler_t { void register_context(work_limit_context_t& ctx); void deregister_context(work_limit_context_t& ctx); void on_work_recorded(work_limit_context_t& ctx, double total_work); - void queue_callback(work_limit_context_t& source, - work_limit_context_t& destination, - callback_t callback); + // void queue_callback(work_limit_context_t& source, + // work_limit_context_t& destination, + // callback_t callback); // Sync callback support - callback is executed when all contexts reach sync point // If callback returns true, scheduler stops and all workers exit cleanly From c2bab577bf1845f0a7cc35f322e0cd92f344f224 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 14:35:19 +0000 Subject: [PATCH 222/366] Revert policy system to move it to a later PR This reverts commit 8c5e9f6cf0ed56366c5e4ce973fc8919710494a2. --- .../cuopt/linear_programming/constants.h | 4 +- cpp/src/dual_simplex/bb_solve_policy.hpp | 844 ------------- cpp/src/dual_simplex/branch_and_bound.cpp | 1111 ++++++++++------- cpp/src/dual_simplex/branch_and_bound.hpp | 26 +- cpp/src/dual_simplex/phase2.cpp | 153 +-- cpp/src/dual_simplex/sparse_matrix.cpp | 2 +- cpp/src/dual_simplex/triangle_solve.hpp | 5 +- cpp/src/math_optimization/solver_settings.cu | 4 +- cpp/src/mip/local_search/local_search.cu | 1 + cpp/src/utilities/memory_instrumentation.hpp | 100 +- cpp/src/utilities/producer_sync.hpp | 31 +- cpp/src/utilities/work_unit_predictor.cpp | 21 +- cpp/src/utilities/work_unit_scheduler.cpp | 48 +- cpp/src/utilities/work_unit_scheduler.hpp | 6 +- 14 files changed, 933 insertions(+), 1423 deletions(-) delete mode 100644 cpp/src/dual_simplex/bb_solve_policy.hpp diff --git a/cpp/include/cuopt/linear_programming/constants.h b/cpp/include/cuopt/linear_programming/constants.h index eb32fa1fb..6929b6720 100644 --- a/cpp/include/cuopt/linear_programming/constants.h +++ b/cpp/include/cuopt/linear_programming/constants.h @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -49,7 +49,7 @@ #define CUOPT_CUDSS_DETERMINISTIC "cudss_deterministic" #define CUOPT_PRESOLVE "presolve" #define CUOPT_DUAL_POSTSOLVE "dual_postsolve" -#define CUOPT_MIP_DETERMINISM_MODE "mip_determinism_mode" +#define CUOPT_MIP_DETERMINISTIC "mip_deterministic" #define CUOPT_MIP_ABSOLUTE_TOLERANCE "mip_absolute_tolerance" #define CUOPT_MIP_RELATIVE_TOLERANCE "mip_relative_tolerance" #define CUOPT_MIP_INTEGRALITY_TOLERANCE "mip_integrality_tolerance" diff --git a/cpp/src/dual_simplex/bb_solve_policy.hpp b/cpp/src/dual_simplex/bb_solve_policy.hpp deleted file mode 100644 index c16fe9a1a..000000000 --- a/cpp/src/dual_simplex/bb_solve_policy.hpp +++ /dev/null @@ -1,844 +0,0 @@ -/* clang-format off */ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ -/* clang-format on */ - -#pragma once - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include - -#include - -namespace cuopt::linear_programming::dual_simplex { - -// Import types from parent namespace -using cuopt::omp_atomic_t; - -template -struct bnb_stats_t; - -// Martin's criteria for the preferred rounding direction -// Reference: A. Martin, "Integer Programs with Block Structure," -// Technische Universitat Berlin, Berlin, 1999. -// https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/391 -template -inline rounding_direction_t martin_criteria(f_t val, f_t root_val) -{ - const f_t down_val = std::floor(root_val); - const f_t up_val = std::ceil(root_val); - const f_t down_dist = val - down_val; - const f_t up_dist = up_val - val; - constexpr f_t eps = 1e-6; - - if (down_dist < up_dist + eps) { - return rounding_direction_t::DOWN; - } else { - return rounding_direction_t::UP; - } -} - -// Result of solving a node LP and processing the outcome -template -struct node_solve_result_t { - node_status_t status; - rounding_direction_t preferred_direction; - bool needs_recompute_basis; -}; - -// ============================================================================= -// Standard (non-deterministic) policy implementation -// ============================================================================= - -template -class standard_solve_policy_t { - public: - // Callback types for optional hooks - using node_processed_callback_t = std::function&, f_t)>; - using objective_estimate_fn_t = - std::function&, const std::vector&, f_t)>; - - standard_solve_policy_t( - omp_atomic_t& upper_bound, - f_t fathom_tolerance, - const std::vector& root_lower, - const std::vector& root_upper, - const std::vector& edge_norms, - const std::vector& root_solution, - pseudo_costs_t& pc, - bnb_stats_t& stats, - logger_t& log, - std::function&, i_t, bnb_worker_type_t)> add_solution_fn, - node_processed_callback_t node_processed_callback = nullptr, - objective_estimate_fn_t objective_estimate_fn = nullptr) - : upper_bound_(upper_bound), - fathom_tolerance_(fathom_tolerance), - root_lower_(root_lower), - root_upper_(root_upper), - edge_norms_(edge_norms), - root_solution_(root_solution), - pc_(pc), - stats_(stats), - log_(log), - add_solution_fn_(add_solution_fn), - node_processed_callback_(node_processed_callback), - objective_estimate_fn_(objective_estimate_fn) - { - } - - f_t get_upper_bound() const { return upper_bound_.load(); } - f_t get_fathom_tolerance() const { return fathom_tolerance_; } - bool should_run_bounds_strengthening() const { return true; } - work_limit_context_t* get_work_context() { return nullptr; } - const std::vector& get_root_lower() const { return root_lower_; } - const std::vector& get_root_upper() const { return root_upper_; } - const std::vector& get_edge_norms() const { return edge_norms_; } - const std::vector& get_root_solution() const { return root_solution_; } - - void on_solve_start(mip_node_t* node) {} - - void on_lp_input(mip_node_t* node, - const lp_problem_t& problem, - const std::vector& vstatus) - { - } - - void on_lp_output(mip_node_t* node, - dual::status_t status, - i_t iterations, - const lp_solution_t& solution, - const lp_problem_t& problem) - { - if (status == dual::status_t::OPTIMAL) { - f_t objective = compute_objective(problem, solution.x); - log_.graphviz_node(node, "lower bound", objective); - if (node_processed_callback_) { node_processed_callback_(solution.x, objective); } - } - } - - void on_lp_solve_complete(mip_node_t* node, - i_t iterations, - f_t solve_time, - dual::status_t status) - { - stats_.total_lp_solve_time += solve_time; - stats_.total_lp_iters += iterations; - } - - void on_infeasible(mip_node_t* node, search_tree_t& tree) - { - node->lower_bound = std::numeric_limits::infinity(); - tree.graphviz_node(log_, node, "infeasible", 0.0); - tree.update(node, node_status_t::INFEASIBLE); - } - - void on_fathomed(mip_node_t* node, f_t objective, search_tree_t& tree) - { - tree.graphviz_node(log_, node, "fathomed", objective); - tree.update(node, node_status_t::FATHOMED); - } - - void on_integer_solution(mip_node_t* node, - f_t objective, - const std::vector& solution, - i_t depth, - search_tree_t& tree) - { - add_solution_fn_(objective, solution, depth, bnb_worker_type_t::BEST_FIRST); - tree.graphviz_node(log_, node, "integer feasible", objective); - tree.update(node, node_status_t::INTEGER_FEASIBLE); - } - - void on_branched(mip_node_t* node, - i_t branch_var, - f_t branch_val, - i_t down_child_id, - i_t up_child_id, - rounding_direction_t preferred, - search_tree_t& tree, - const std::vector& fractional, - const std::vector& solution) - { - if (objective_estimate_fn_) { - node->objective_estimate = objective_estimate_fn_(fractional, solution, node->lower_bound); - } - tree.update(node, node_status_t::HAS_CHILDREN); - } - - void on_numerical(mip_node_t* node, search_tree_t& tree) - { - tree.graphviz_node(log_, node, "numerical", 0.0); - tree.update(node, node_status_t::NUMERICAL); - } - - void update_pseudo_costs(mip_node_t* node, f_t objective) - { - pc_.update_pseudo_costs(node, objective); - } - - i_t select_branch_variable(const std::vector& fractional, const std::vector& solution) - { - logger_t pc_log; - pc_log.log = false; - return pc_.variable_selection(fractional, solution, pc_log); - } - - rounding_direction_t get_preferred_direction(i_t branch_var, f_t branch_val) - { - f_t root_val = - branch_var < (i_t)root_solution_.size() ? root_solution_[branch_var] : branch_val; - return martin_criteria(branch_val, root_val); - } - - void track_node_explored() { ++stats_.nodes_explored; } - - void track_node_unexplored_delta(i_t delta) { stats_.nodes_unexplored += delta; } - - private: - omp_atomic_t& upper_bound_; - f_t fathom_tolerance_; - const std::vector& root_lower_; - const std::vector& root_upper_; - const std::vector& edge_norms_; - const std::vector& root_solution_; - pseudo_costs_t& pc_; - bnb_stats_t& stats_; - logger_t& log_; - std::function&, i_t, bnb_worker_type_t)> add_solution_fn_; - node_processed_callback_t node_processed_callback_; - objective_estimate_fn_t objective_estimate_fn_; -}; - -// ============================================================================= -// BSP (deterministic) policy implementation -// ============================================================================= - -template -class bsp_solve_policy_t { - public: - bsp_solve_policy_t(bb_worker_state_t& worker, - f_t fathom_tolerance, - const std::vector& root_lower, - const std::vector& root_upper, - const std::vector& edge_norms, - const std::vector& root_solution, - bnb_stats_t& stats, - bsp_debug_settings_t& debug_settings, - bsp_debug_logger_t& debug_logger) - : worker_(worker), - fathom_tolerance_(fathom_tolerance), - root_lower_(root_lower), - root_upper_(root_upper), - edge_norms_(edge_norms), - root_solution_(root_solution), - stats_(stats), - debug_settings_(debug_settings), - debug_logger_(debug_logger) - { - } - - f_t get_upper_bound() const { return worker_.local_upper_bound; } - f_t get_fathom_tolerance() const { return fathom_tolerance_; } - bool should_run_bounds_strengthening() const { return false; } - work_limit_context_t* get_work_context() { return &worker_.work_context; } - const std::vector& get_root_lower() const { return root_lower_; } - const std::vector& get_root_upper() const { return root_upper_; } - const std::vector& get_edge_norms() const { return edge_norms_; } - const std::vector& get_root_solution() const { return root_solution_; } - - void on_solve_start(mip_node_t* node) - { - work_units_at_start_ = worker_.work_context.global_work_units_elapsed; - double work_limit = worker_.horizon_end - worker_.clock; - BSP_DEBUG_LOG_SOLVE_START(debug_settings_, - debug_logger_, - worker_.clock, - worker_.worker_id, - node->node_id, - node->origin_worker_id, - work_limit, - false); - } - - void on_lp_input(mip_node_t* node, - const lp_problem_t& problem, - const std::vector& vstatus) - { - if (debug_settings_.any_enabled()) { - uint64_t path_hash = node->compute_path_hash(); - uint64_t vstatus_hash = detail::compute_hash(vstatus); - uint64_t bounds_hash = - detail::compute_hash(problem.lower) ^ detail::compute_hash(problem.upper); - BSP_DEBUG_LOG_LP_INPUT(debug_settings_, - debug_logger_, - worker_.worker_id, - node->node_id, - path_hash, - node->depth, - vstatus_hash, - bounds_hash); - } - } - - void on_lp_output(mip_node_t* node, - dual::status_t status, - i_t iterations, - const lp_solution_t& solution, - const lp_problem_t& problem) - { - if (debug_settings_.any_enabled()) { - uint64_t path_hash = node->compute_path_hash(); - uint64_t sol_hash = detail::compute_hash(solution.x); - f_t obj = (status == dual::status_t::OPTIMAL) ? compute_objective(problem, solution.x) - : std::numeric_limits::infinity(); - uint64_t obj_hash = detail::compute_hash(obj); - BSP_DEBUG_LOG_LP_OUTPUT(debug_settings_, - debug_logger_, - worker_.worker_id, - node->node_id, - path_hash, - static_cast(status), - iterations, - obj_hash, - sol_hash); - } - } - - void on_lp_solve_complete(mip_node_t* node, - i_t iterations, - f_t solve_time, - dual::status_t status) - { - stats_.total_lp_solve_time += solve_time; - stats_.total_lp_iters += iterations; - - double work_performed = worker_.work_context.global_work_units_elapsed - work_units_at_start_; - worker_.clock += work_performed; - worker_.work_units_this_horizon += work_performed; - } - - void on_infeasible(mip_node_t* node, search_tree_t& tree) - { - node->lower_bound = std::numeric_limits::infinity(); - worker_.record_infeasible(node); - worker_.track_node_infeasible(); - worker_.track_node_processed(); - - BSP_DEBUG_LOG_SOLVE_END(debug_settings_, - debug_logger_, - worker_.clock, - worker_.worker_id, - node->node_id, - node->origin_worker_id, - "INFEASIBLE", - node->lower_bound); - BSP_DEBUG_LOG_INFEASIBLE( - debug_settings_, debug_logger_, worker_.clock, worker_.worker_id, node->node_id); - - tree.update(node, node_status_t::INFEASIBLE); - } - - void on_fathomed(mip_node_t* node, f_t objective, search_tree_t& tree) - { - worker_.record_fathomed(node, objective); - worker_.track_node_pruned(); - worker_.track_node_processed(); - worker_.recompute_bounds_and_basis = true; - - BSP_DEBUG_LOG_SOLVE_END(debug_settings_, - debug_logger_, - worker_.clock, - worker_.worker_id, - node->node_id, - node->origin_worker_id, - "FATHOMED", - objective); - BSP_DEBUG_LOG_FATHOMED( - debug_settings_, debug_logger_, worker_.clock, worker_.worker_id, node->node_id, objective); - - tree.update(node, node_status_t::FATHOMED); - } - - void on_integer_solution(mip_node_t* node, - f_t objective, - const std::vector& solution, - i_t depth, - search_tree_t& tree) - { - if (objective < worker_.local_upper_bound) { - worker_.local_upper_bound = objective; - worker_.integer_solutions.push_back({objective, solution, depth}); - } - - worker_.record_integer_solution(node, objective); - worker_.track_integer_solution(); - worker_.track_node_processed(); - worker_.recompute_bounds_and_basis = true; - - BSP_DEBUG_LOG_SOLVE_END(debug_settings_, - debug_logger_, - worker_.clock, - worker_.worker_id, - node->node_id, - node->origin_worker_id, - "INTEGER", - objective); - BSP_DEBUG_LOG_INTEGER( - debug_settings_, debug_logger_, worker_.clock, worker_.worker_id, node->node_id, objective); - - tree.update(node, node_status_t::INTEGER_FEASIBLE); - } - - void on_branched(mip_node_t* node, - i_t branch_var, - f_t branch_val, - i_t down_child_id, - i_t up_child_id, - rounding_direction_t preferred, - search_tree_t& tree, - const std::vector& /*fractional*/, - const std::vector& /*solution*/) - { - tree.update(node, node_status_t::HAS_CHILDREN); - - worker_.record_branched(node, down_child_id, up_child_id, branch_var, branch_val); - worker_.track_node_branched(); - worker_.track_node_processed(); - - BSP_DEBUG_LOG_SOLVE_END(debug_settings_, - debug_logger_, - worker_.clock, - worker_.worker_id, - node->node_id, - node->origin_worker_id, - "BRANCH", - node->lower_bound); - BSP_DEBUG_LOG_BRANCHED(debug_settings_, - debug_logger_, - worker_.clock, - worker_.worker_id, - node->node_id, - node->origin_worker_id, - down_child_id, - up_child_id); - - worker_.enqueue_children_for_plunge(node->get_down_child(), node->get_up_child(), preferred); - } - - void on_numerical(mip_node_t* node, search_tree_t& tree) - { - worker_.record_numerical(node); - worker_.recompute_bounds_and_basis = true; - tree.update(node, node_status_t::NUMERICAL); - } - - void update_pseudo_costs(mip_node_t* node, f_t objective) - { - if (node->branch_var >= 0) { - const f_t change_in_obj = objective - node->lower_bound; - const f_t frac = node->branch_dir == rounding_direction_t::DOWN - ? node->fractional_val - std::floor(node->fractional_val) - : std::ceil(node->fractional_val) - node->fractional_val; - if (frac > 1e-10) { - worker_.queue_pseudo_cost_update(node->branch_var, node->branch_dir, change_in_obj / frac); - } - } - } - - i_t select_branch_variable(const std::vector& fractional, const std::vector& solution) - { - return worker_.variable_selection_from_snapshot(fractional, solution); - } - - rounding_direction_t get_preferred_direction(i_t branch_var, f_t branch_val) - { - f_t root_val = - branch_var < (i_t)root_solution_.size() ? root_solution_[branch_var] : branch_val; - f_t frac = branch_val - std::floor(branch_val); - if (branch_val < root_val - 0.4 || frac < 0.3) { - return rounding_direction_t::DOWN; - } else if (branch_val > root_val + 0.4 || frac > 0.7) { - return rounding_direction_t::UP; - } - return rounding_direction_t::DOWN; - } - - void track_node_explored() { ++stats_.nodes_explored; } - - void track_node_unexplored_delta(i_t delta) { stats_.nodes_unexplored += delta; } - - void set_work_units_at_start(double work_units) { work_units_at_start_ = work_units; } - - private: - bb_worker_state_t& worker_; - f_t fathom_tolerance_; - const std::vector& root_lower_; - const std::vector& root_upper_; - const std::vector& edge_norms_; - const std::vector& root_solution_; - bnb_stats_t& stats_; - bsp_debug_settings_t& debug_settings_; - bsp_debug_logger_t& debug_logger_; - double work_units_at_start_{0.0}; -}; - -// ============================================================================= -// Standard diving policy implementation (non-deterministic) -// ============================================================================= - -template -class standard_diving_solve_policy_t { - public: - standard_diving_solve_policy_t( - omp_atomic_t& upper_bound, - f_t fathom_tolerance, - const std::vector& dive_lower, - const std::vector& dive_upper, - const std::vector& edge_norms, - const std::vector& root_solution, - pseudo_costs_t& pc, - bnb_stats_t& stats, - std::function&, i_t, bnb_worker_type_t)> add_solution_fn, - bnb_worker_type_t diving_type, - const lp_problem_t* lp_problem, - const std::vector* up_locks, - const std::vector* down_locks) - : upper_bound_(upper_bound), - fathom_tolerance_(fathom_tolerance), - dive_lower_(dive_lower), - dive_upper_(dive_upper), - edge_norms_(edge_norms), - root_solution_(root_solution), - pc_(pc), - stats_(stats), - add_solution_fn_(add_solution_fn), - diving_type_(diving_type), - lp_problem_(lp_problem), - up_locks_(up_locks), - down_locks_(down_locks) - { - log_.log = false; - } - - f_t get_upper_bound() const { return upper_bound_.load(); } - f_t get_fathom_tolerance() const { return fathom_tolerance_; } - bool should_run_bounds_strengthening() const { return true; } - work_limit_context_t* get_work_context() { return nullptr; } - const std::vector& get_root_lower() const { return dive_lower_; } - const std::vector& get_root_upper() const { return dive_upper_; } - const std::vector& get_edge_norms() const { return edge_norms_; } - const std::vector& get_root_solution() const { return root_solution_; } - - void on_solve_start(mip_node_t* node) {} - - void on_lp_input(mip_node_t* node, - const lp_problem_t& problem, - const std::vector& vstatus) - { - } - - void on_lp_output(mip_node_t* node, - dual::status_t status, - i_t iterations, - const lp_solution_t& solution, - const lp_problem_t& problem) - { - } - - void on_lp_solve_complete(mip_node_t* node, - i_t iterations, - f_t solve_time, - dual::status_t status) - { - stats_.total_lp_solve_time += solve_time; - stats_.total_lp_iters += iterations; - } - - void on_infeasible(mip_node_t* node, search_tree_t& tree) - { - node->lower_bound = std::numeric_limits::infinity(); - tree.update(node, node_status_t::INFEASIBLE); - } - - void on_fathomed(mip_node_t* node, f_t objective, search_tree_t& tree) - { - tree.update(node, node_status_t::FATHOMED); - } - - void on_integer_solution(mip_node_t* node, - f_t objective, - const std::vector& solution, - i_t depth, - search_tree_t& tree) - { - add_solution_fn_(objective, solution, depth, diving_type_); - tree.update(node, node_status_t::INTEGER_FEASIBLE); - } - - void on_branched(mip_node_t* node, - i_t branch_var, - f_t branch_val, - i_t down_child_id, - i_t up_child_id, - rounding_direction_t preferred, - search_tree_t& tree, - const std::vector& /*fractional*/, - const std::vector& /*solution*/) - { - tree.update(node, node_status_t::HAS_CHILDREN); - } - - void on_numerical(mip_node_t* node, search_tree_t& tree) - { - tree.update(node, node_status_t::NUMERICAL); - } - - void update_pseudo_costs(mip_node_t* node, f_t objective) - { - pc_.update_pseudo_costs(node, objective); - } - - i_t select_branch_variable(const std::vector& fractional, const std::vector& solution) - { - branch_variable_t result; - switch (diving_type_) { - case bnb_worker_type_t::PSEUDOCOST_DIVING: - result = pseudocost_diving(pc_, fractional, solution, root_solution_, log_); - break; - case bnb_worker_type_t::GUIDED_DIVING: - if (!incumbent_.empty()) { - result = guided_diving(pc_, fractional, solution, incumbent_, log_); - } else { - result = pseudocost_diving(pc_, fractional, solution, root_solution_, log_); - } - break; - case bnb_worker_type_t::LINE_SEARCH_DIVING: - result = line_search_diving(fractional, solution, root_solution_, log_); - break; - case bnb_worker_type_t::COEFFICIENT_DIVING: - if (lp_problem_ && up_locks_ && down_locks_) { - result = - coefficient_diving(*lp_problem_, fractional, solution, *up_locks_, *down_locks_, log_); - } else { - result = pseudocost_diving(pc_, fractional, solution, root_solution_, log_); - } - break; - default: result = pseudocost_diving(pc_, fractional, solution, root_solution_, log_); break; - } - selected_direction_ = result.direction; - return result.variable; - } - - rounding_direction_t get_preferred_direction(i_t branch_var, f_t branch_val) - { - return selected_direction_; - } - - void track_node_explored() { ++stats_.nodes_explored; } - - void track_node_unexplored_delta(i_t delta) {} - - void set_incumbent(const std::vector& incumbent) { incumbent_ = incumbent; } - - private: - omp_atomic_t& upper_bound_; - f_t fathom_tolerance_; - const std::vector& dive_lower_; - const std::vector& dive_upper_; - const std::vector& edge_norms_; - const std::vector& root_solution_; - pseudo_costs_t& pc_; - bnb_stats_t& stats_; - std::function&, i_t, bnb_worker_type_t)> add_solution_fn_; - bnb_worker_type_t diving_type_; - const lp_problem_t* lp_problem_; - const std::vector* up_locks_; - const std::vector* down_locks_; - logger_t log_; - std::vector incumbent_; - rounding_direction_t selected_direction_{rounding_direction_t::DOWN}; -}; - -// ============================================================================= -// BSP diving policy implementation (deterministic) -// ============================================================================= - -template -class bsp_diving_solve_policy_t { - public: - bsp_diving_solve_policy_t(bsp_diving_worker_state_t& worker, - f_t fathom_tolerance, - const std::vector& dive_lower, - const std::vector& dive_upper, - const std::vector& edge_norms, - const std::vector& root_solution, - bnb_stats_t& stats, - const lp_problem_t* lp_problem, - const std::vector* up_locks, - const std::vector* down_locks) - : worker_(worker), - fathom_tolerance_(fathom_tolerance), - dive_lower_(dive_lower), - dive_upper_(dive_upper), - edge_norms_(edge_norms), - root_solution_(root_solution), - stats_(stats), - lp_problem_(lp_problem), - up_locks_(up_locks), - down_locks_(down_locks) - { - log_.log = false; - } - - f_t get_upper_bound() const { return worker_.local_upper_bound; } - f_t get_fathom_tolerance() const { return fathom_tolerance_; } - bool should_run_bounds_strengthening() const { return true; } - work_limit_context_t* get_work_context() { return &worker_.work_context; } - const std::vector& get_root_lower() const { return dive_lower_; } - const std::vector& get_root_upper() const { return dive_upper_; } - const std::vector& get_edge_norms() const { return edge_norms_; } - const std::vector& get_root_solution() const { return root_solution_; } - - void on_solve_start(mip_node_t* node) {} - - void on_lp_input(mip_node_t* node, - const lp_problem_t& problem, - const std::vector& vstatus) - { - } - - void on_lp_output(mip_node_t* node, - dual::status_t status, - i_t iterations, - const lp_solution_t& solution, - const lp_problem_t& problem) - { - } - - void on_lp_solve_complete(mip_node_t* node, - i_t iterations, - f_t solve_time, - dual::status_t status) - { - stats_.total_lp_solve_time += solve_time; - stats_.total_lp_iters += iterations; - worker_.clock = worker_.work_context.global_work_units_elapsed; - } - - void on_infeasible(mip_node_t* node, search_tree_t& tree) - { - node->lower_bound = std::numeric_limits::infinity(); - tree.update(node, node_status_t::INFEASIBLE); - } - - void on_fathomed(mip_node_t* node, f_t objective, search_tree_t& tree) - { - tree.update(node, node_status_t::FATHOMED); - } - - void on_integer_solution(mip_node_t* node, - f_t objective, - const std::vector& solution, - i_t depth, - search_tree_t& tree) - { - if (objective < worker_.local_upper_bound) { - worker_.queue_integer_solution(objective, solution, depth); - } - tree.update(node, node_status_t::INTEGER_FEASIBLE); - } - - void on_branched(mip_node_t* node, - i_t branch_var, - f_t branch_val, - i_t down_child_id, - i_t up_child_id, - rounding_direction_t preferred, - search_tree_t& tree, - const std::vector& /*fractional*/, - const std::vector& /*solution*/) - { - tree.update(node, node_status_t::HAS_CHILDREN); - } - - void on_numerical(mip_node_t* node, search_tree_t& tree) - { - tree.update(node, node_status_t::NUMERICAL); - } - - void update_pseudo_costs(mip_node_t* node, f_t objective) {} - - i_t select_branch_variable(const std::vector& fractional, const std::vector& solution) - { - branch_variable_t result; - switch (worker_.diving_type) { - case bnb_worker_type_t::PSEUDOCOST_DIVING: - result = worker_.variable_selection_from_snapshot(fractional, solution); - break; - case bnb_worker_type_t::GUIDED_DIVING: - result = worker_.guided_variable_selection(fractional, solution); - break; - case bnb_worker_type_t::LINE_SEARCH_DIVING: - if (worker_.root_solution) { - result = line_search_diving(fractional, solution, *worker_.root_solution, log_); - } else { - result = worker_.variable_selection_from_snapshot(fractional, solution); - } - break; - case bnb_worker_type_t::COEFFICIENT_DIVING: - if (lp_problem_ && up_locks_ && down_locks_) { - result = - coefficient_diving(*lp_problem_, fractional, solution, *up_locks_, *down_locks_, log_); - } else { - result = worker_.variable_selection_from_snapshot(fractional, solution); - } - break; - default: result = worker_.variable_selection_from_snapshot(fractional, solution); break; - } - selected_direction_ = result.direction; - return result.variable; - } - - rounding_direction_t get_preferred_direction(i_t branch_var, f_t branch_val) - { - return selected_direction_; - } - - void track_node_explored() - { - ++worker_.nodes_explored_this_horizon; - ++worker_.total_nodes_explored; - } - - void track_node_unexplored_delta(i_t delta) {} - - private: - bsp_diving_worker_state_t& worker_; - f_t fathom_tolerance_; - const std::vector& dive_lower_; - const std::vector& dive_upper_; - const std::vector& edge_norms_; - const std::vector& root_solution_; - bnb_stats_t& stats_; - const lp_problem_t* lp_problem_; - const std::vector* up_locks_; - const std::vector* down_locks_; - logger_t log_; - rounding_direction_t selected_direction_{rounding_direction_t::DOWN}; -}; - -} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index a8ba4ff17..5f6452fa4 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -609,6 +609,7 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, mutex_upper_.unlock(); } +// Martin's criteria for the preferred rounding direction (see [1]) // [1] A. Martin, “Integer Programs with Block Structure,” // Technische Universit¨at Berlin, Berlin, 1999. Accessed: Aug. 08, 2025. // [Online]. Available: https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/391 @@ -806,202 +807,111 @@ dual::status_t branch_and_bound_t::solve_node_lp( return lp_status; } -// ============================================================================= -// Unified node solving with policy-based callbacks -// ============================================================================= - template -template -node_solve_result_t branch_and_bound_t::solve_node_with_policy( +std::pair branch_and_bound_t::update_tree( mip_node_t* node_ptr, + search_tree_t& search_tree, lp_problem_t& leaf_problem, lp_solution_t& leaf_solution, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list, - bounds_strengthening_t& node_presolver, - bool recompute_bounds_and_basis, - search_tree_t& search_tree, - Policy& policy) + bnb_worker_type_t thread_type, + dual::status_t lp_status, + logger_t& log) { - raft::common::nvtx::range scope("BB::solve_node_with_policy"); - - node_solve_result_t result; - result.status = node_status_t::PENDING; - result.preferred_direction = rounding_direction_t::NONE; - result.needs_recompute_basis = true; - + const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; std::vector& leaf_vstatus = node_ptr->vstatus; - // Notify policy that solve is starting - policy.on_solve_start(node_ptr); - - // Reset bounds_changed markers - std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); - - // Set up bounds from node path - const std::vector& root_lower = policy.get_root_lower(); - const std::vector& root_upper = policy.get_root_upper(); - - if (recompute_bounds_and_basis) { - leaf_problem.lower = root_lower; - leaf_problem.upper = root_upper; - node_ptr->get_variable_bounds( - leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); - } else { - node_ptr->update_branched_variable_bounds( - leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); - } - - // Check time limit - double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); - if (remaining_time <= 0) { - result.status = node_status_t::PENDING; - return result; - } - - // LP settings - simplex_solver_settings_t lp_settings = settings_; - lp_settings.set_log(false); - lp_settings.cut_off = policy.get_upper_bound() + settings_.dual_tol; - lp_settings.inside_mip = 2; - lp_settings.time_limit = remaining_time; - lp_settings.scale_columns = false; - - // Bounds strengthening (policy controls whether to run) - bool feasible = true; - if (policy.should_run_bounds_strengthening()) { - feasible = - node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); - } - - if (!feasible) { - policy.on_infeasible(node_ptr, search_tree); - policy.track_node_explored(); - policy.track_node_unexplored_delta(-1); - result.status = node_status_t::INFEASIBLE; - result.needs_recompute_basis = true; - return result; - } - - // Solve LP relaxation - i_t node_iter = 0; - f_t lp_start_time = tic(); - std::vector leaf_edge_norms = policy.get_edge_norms(); - - // Debug hook: log LP input - policy.on_lp_input(node_ptr, leaf_problem, leaf_vstatus); - - dual::status_t lp_status = dual_phase2_with_advanced_basis(2, - 0, - recompute_bounds_and_basis, - lp_start_time, - leaf_problem, - lp_settings, - leaf_vstatus, - basis_factors, - basic_list, - nonbasic_list, - leaf_solution, - node_iter, - leaf_edge_norms, - policy.get_work_context()); - - // Debug hook: log LP output - policy.on_lp_output(node_ptr, lp_status, node_iter, leaf_solution, leaf_problem); - - f_t solve_time = toc(lp_start_time); - policy.on_lp_solve_complete(node_ptr, node_iter, solve_time, lp_status); - policy.track_node_explored(); - policy.track_node_unexplored_delta(-1); - - // Process LP result - const f_t abs_fathom_tol = policy.get_fathom_tolerance(); - if (lp_status == dual::status_t::DUAL_UNBOUNDED) { - node_ptr->lower_bound = std::numeric_limits::infinity(); - policy.on_infeasible(node_ptr, search_tree); - result.status = node_status_t::INFEASIBLE; - result.needs_recompute_basis = true; + // Node was infeasible. Do not branch + node_ptr->lower_bound = inf; + search_tree.graphviz_node(log, node_ptr, "infeasible", 0.0); + search_tree.update(node_ptr, node_status_t::INFEASIBLE); + return {node_status_t::INFEASIBLE, rounding_direction_t::NONE}; } else if (lp_status == dual::status_t::CUTOFF) { - node_ptr->lower_bound = policy.get_upper_bound(); - policy.on_fathomed(node_ptr, node_ptr->lower_bound, search_tree); - result.status = node_status_t::FATHOMED; - result.needs_recompute_basis = true; + // Node was cut off. Do not branch + node_ptr->lower_bound = upper_bound_; + f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); + search_tree.graphviz_node(log, node_ptr, "cut off", leaf_objective); + search_tree.update(node_ptr, node_status_t::FATHOMED); + return {node_status_t::FATHOMED, rounding_direction_t::NONE}; } else if (lp_status == dual::status_t::OPTIMAL) { + // LP was feasible std::vector leaf_fractional; i_t leaf_num_fractional = fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); node_ptr->lower_bound = leaf_objective; - - // Update pseudo-costs via policy - policy.update_pseudo_costs(node_ptr, leaf_objective); + search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); + pc_.update_pseudo_costs(node_ptr, leaf_objective); + + if (thread_type == bnb_worker_type_t::BEST_FIRST) { + if (settings_.node_processed_callback != nullptr) { + std::vector original_x; + uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); + settings_.node_processed_callback(original_x, leaf_objective); + } + } if (leaf_num_fractional == 0) { - // Integer feasible solution - policy.on_integer_solution( - node_ptr, leaf_objective, leaf_solution.x, node_ptr->depth, search_tree); - result.status = node_status_t::INTEGER_FEASIBLE; - result.needs_recompute_basis = true; - - } else if (leaf_objective <= policy.get_upper_bound() + abs_fathom_tol) { - // Branch - i_t branch_var = policy.select_branch_variable(leaf_fractional, leaf_solution.x); - - if (branch_var < 0) { - result.status = node_status_t::NUMERICAL; - result.needs_recompute_basis = true; - return result; + // Found a integer feasible solution + add_feasible_solution(leaf_objective, leaf_solution.x, node_ptr->depth, thread_type); + search_tree.graphviz_node(log, node_ptr, "integer feasible", leaf_objective); + search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); + return {node_status_t::INTEGER_FEASIBLE, rounding_direction_t::NONE}; + + } else if (leaf_objective <= upper_bound_ + abs_fathom_tol) { + // Choose fractional variable to branch on + auto [branch_var, round_dir] = + variable_selection(node_ptr, leaf_fractional, leaf_solution.x, thread_type); + + assert(leaf_vstatus.size() == leaf_problem.num_cols); + assert(branch_var >= 0); + assert(round_dir != rounding_direction_t::NONE); + + // Note that the exploration thread is the only one that can insert new nodes into the heap, + // and thus, we only need to calculate the objective estimate here (it is used for + // sorting the nodes for diving). + if (thread_type == bnb_worker_type_t::BEST_FIRST) { + logger_t pc_log; + pc_log.log = false; + node_ptr->objective_estimate = + pc_.obj_estimate(leaf_fractional, leaf_solution.x, node_ptr->lower_bound, pc_log); } - f_t branch_val = leaf_solution.x[branch_var]; - - logger_t log; - log.log = false; - search_tree.branch(node_ptr, branch_var, branch_val, leaf_vstatus, leaf_problem, log); - - i_t down_child_id = node_ptr->get_down_child()->node_id; - i_t up_child_id = node_ptr->get_up_child()->node_id; - - rounding_direction_t preferred = policy.get_preferred_direction(branch_var, branch_val); - policy.on_branched(node_ptr, - branch_var, - branch_val, - down_child_id, - up_child_id, - preferred, - search_tree, - leaf_fractional, - leaf_solution.x); - policy.track_node_unexplored_delta(2); - - result.status = node_status_t::HAS_CHILDREN; - result.preferred_direction = preferred; - result.needs_recompute_basis = false; + search_tree.branch( + node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, leaf_problem, log); + search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); + return {node_status_t::HAS_CHILDREN, round_dir}; } else { - // Fathomed by bound - policy.on_fathomed(node_ptr, leaf_objective, search_tree); - result.status = node_status_t::FATHOMED; - result.needs_recompute_basis = true; + search_tree.graphviz_node(log, node_ptr, "fathomed", leaf_objective); + search_tree.update(node_ptr, node_status_t::FATHOMED); + return {node_status_t::FATHOMED, rounding_direction_t::NONE}; + } + } else if (lp_status == dual::status_t::TIME_LIMIT) { + search_tree.graphviz_node(log, node_ptr, "timeout", 0.0); + return {node_status_t::PENDING, rounding_direction_t::NONE}; + } else if (lp_status == dual::status_t::WORK_LIMIT) { + search_tree.graphviz_node(log, node_ptr, "work limit", 0.0); + return {node_status_t::PENDING, rounding_direction_t::NONE}; + } else { + if (thread_type == bnb_worker_type_t::BEST_FIRST) { + fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); + log.printf( + "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " + "to " + "%+10.6e.\n", + lp_status, + node_ptr->node_id, + compute_user_objective(original_lp_, lower_bound_ceiling_.load())); } - } else if (lp_status == dual::status_t::TIME_LIMIT || lp_status == dual::status_t::WORK_LIMIT) { - result.status = node_status_t::PENDING; - result.needs_recompute_basis = true; - - } else { - // Numerical error - policy.on_numerical(node_ptr, search_tree); - result.status = node_status_t::NUMERICAL; - result.needs_recompute_basis = true; + search_tree.graphviz_node(log, node_ptr, "numerical", 0.0); + search_tree.update(node_ptr, node_status_t::NUMERICAL); + return {node_status_t::NUMERICAL, rounding_direction_t::NONE}; } - - return result; } template @@ -1061,60 +971,39 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod std::vector nonbasic_list; lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); - - // Set up callbacks for the policy - auto add_solution_fn = - [this](f_t obj, const std::vector& sol, i_t depth, bnb_worker_type_t type) { - add_feasible_solution(obj, sol, depth, type); - }; - - auto node_processed_callback = [this](const std::vector& sol, f_t objective) { - if (settings_.node_processed_callback != nullptr) { - std::vector original_x; - uncrush_primal_solution(original_problem_, original_lp_, sol, original_x); - settings_.node_processed_callback(original_x, objective); - } - }; - - auto objective_estimate_fn = - [this](const std::vector& fractional, const std::vector& solution, f_t lower_bound) { - logger_t pc_log; - pc_log.log = false; - return pc_.obj_estimate(fractional, solution, lower_bound, pc_log); - }; - - standard_solve_policy_t solve_policy(upper_bound_, - settings_.absolute_mip_gap_tol / 10, - original_lp_.lower, - original_lp_.upper, - edge_norms_, - root_relax_soln_.x, - pc_, - exploration_stats_, - settings_.log, - add_solution_fn, - node_processed_callback, - objective_estimate_fn); - - node_solve_result_t result = solve_node_with_policy(node, - leaf_problem, - leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, - true, - search_tree_, - solve_policy); - - if (result.status == node_status_t::PENDING) { + dual::status_t lp_status = solve_node_lp(node, + leaf_problem, + leaf_solution, + basis_factors, + basic_list, + nonbasic_list, + node_presolver, + bnb_worker_type_t::BEST_FIRST, + true, + original_lp_.lower, + original_lp_.upper, + exploration_stats_, + settings_.log); + if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; return; } ++exploration_stats_.nodes_since_last_log; + ++exploration_stats_.nodes_explored; + --exploration_stats_.nodes_unexplored; + + auto [node_status, round_dir] = update_tree(node, + search_tree_, + leaf_problem, + leaf_solution, + bnb_worker_type_t::BEST_FIRST, + lp_status, + settings_.log); + + if (node_status == node_status_t::HAS_CHILDREN) { + exploration_stats_.nodes_unexplored += 2; - if (result.status == node_status_t::HAS_CHILDREN) { // If we haven't generated enough nodes to keep the threads busy, continue the ramp up phase if (exploration_stats_.nodes_unexplored < initial_heap_size) { #pragma omp task @@ -1145,41 +1034,6 @@ void branch_and_bound_t::plunge_from(i_t task_id, std::deque*> stack; stack.push_front(start_node); - auto add_solution_fn = - [this](f_t obj, const std::vector& sol, i_t depth, bnb_worker_type_t type) { - add_feasible_solution(obj, sol, depth, type); - }; - - auto node_processed_callback = [this](const std::vector& sol, f_t objective) { - if (settings_.node_processed_callback != nullptr) { - std::vector original_x; - uncrush_primal_solution(original_problem_, original_lp_, sol, original_x); - settings_.node_processed_callback(original_x, objective); - } - }; - - auto objective_estimate_fn = - [this](const std::vector& fractional, const std::vector& solution, f_t lower_bound) { - logger_t pc_log; - pc_log.log = false; - return pc_.obj_estimate(fractional, solution, lower_bound, pc_log); - }; - - standard_solve_policy_t solve_policy(upper_bound_, - settings_.absolute_mip_gap_tol / 10, - original_lp_.lower, - original_lp_.upper, - edge_norms_, - root_relax_soln_.x, - pc_, - exploration_stats_, - settings_.log, - add_solution_fn, - node_processed_callback, - objective_estimate_fn); - - lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); - while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET && is_running) { if (task_id == 0) { repair_heuristic_solutions(); } @@ -1191,7 +1045,13 @@ void branch_and_bound_t::plunge_from(i_t task_id, f_t abs_gap = upper_bound - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); - assert(task_id < (i_t)local_lower_bounds_.size()); + // This is based on three assumptions: + // - The stack only contains sibling nodes, i.e., the current node and it sibling (if + // applicable) + // - The current node and its siblings uses the lower bound of the parent before solving the LP + // relaxation + // - The lower bound of the parent is lower or equal to its children + assert(task_id < local_lower_bounds_.size()); local_lower_bounds_[task_id] = lower_bound; if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { @@ -1227,34 +1087,56 @@ void branch_and_bound_t::plunge_from(i_t task_id, break; } - node_solve_result_t result = solve_node_with_policy(node_ptr, - leaf_problem, - leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, - recompute_bounds_and_basis, - search_tree_, - solve_policy); - - if (result.status == node_status_t::PENDING) { + lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); + dual::status_t lp_status = solve_node_lp(node_ptr, + leaf_problem, + leaf_solution, + basis_factors, + basic_list, + nonbasic_list, + node_presolver, + bnb_worker_type_t::BEST_FIRST, + recompute_bounds_and_basis, + original_lp_.lower, + original_lp_.upper, + exploration_stats_, + settings_.log); + + if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; break; + } else if (lp_status == dual::status_t::ITERATION_LIMIT) { + break; } ++exploration_stats_.nodes_since_last_log; + ++exploration_stats_.nodes_explored; + --exploration_stats_.nodes_unexplored; - recompute_bounds_and_basis = result.needs_recompute_basis; - - if (result.status == node_status_t::HAS_CHILDREN) { + auto [node_status, round_dir] = update_tree(node_ptr, + search_tree_, + leaf_problem, + leaf_solution, + bnb_worker_type_t::BEST_FIRST, + lp_status, + settings_.log); + + recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; + + if (node_status == node_status_t::HAS_CHILDREN) { + // The stack should only contain the children of the current parent. + // If the stack size is greater than 0, + // we pop the current node from the stack and place it in the global heap, + // since we are about to add the two children to the stack if (stack.size() > 0) { - mip_node_t* sibling = stack.back(); + mip_node_t* node = stack.back(); stack.pop_back(); - node_queue_.push(sibling); + node_queue_.push(node); } - if (result.preferred_direction == rounding_direction_t::UP) { + exploration_stats_.nodes_unexplored += 2; + + if (round_dir == rounding_direction_t::UP) { stack.push_front(node_ptr->get_down_child()); stack.push_front(node_ptr->get_up_child()); } else { @@ -1346,6 +1228,8 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, bnb_worker_type_t diving_type) { raft::common::nvtx::range scope("BB::diving_thread"); + logger_t log; + log.log = false; const i_t diving_node_limit = settings_.diving_settings.node_limit; const i_t diving_backtrack_limit = settings_.diving_settings.backtrack_limit; @@ -1360,37 +1244,13 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, dive_stats.nodes_explored = 0; dive_stats.nodes_unexplored = 1; - auto add_solution_fn = - [this](f_t obj, const std::vector& sol, i_t depth, bnb_worker_type_t type) { - add_feasible_solution(obj, sol, depth, type); - }; - - standard_diving_solve_policy_t policy(upper_bound_, - settings_.absolute_mip_gap_tol / 10, - start_lower, - start_upper, - edge_norms_, - root_relax_soln_.x, - pc_, - dive_stats, - add_solution_fn, - diving_type, - &leaf_problem, - &var_up_locks_, - &var_down_locks_); - - if (diving_type == bnb_worker_type_t::GUIDED_DIVING && incumbent_.has_incumbent) { - policy.set_incumbent(incumbent_.x); - } - - lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); - while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET && is_running) { mip_node_t* node_ptr = stack.front(); stack.pop_front(); + f_t lower_bound = node_ptr->lower_bound; f_t upper_bound = upper_bound_; - f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node_ptr->lower_bound); + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); if (node_ptr->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { recompute_bounds_and_basis = true; @@ -1400,26 +1260,36 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } if (dive_stats.nodes_explored > diving_node_limit) { break; } - node_solve_result_t result = solve_node_with_policy(node_ptr, - leaf_problem, - leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, - recompute_bounds_and_basis, - dive_tree, - policy); - - if (result.status == node_status_t::PENDING) { + lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); + dual::status_t lp_status = solve_node_lp(node_ptr, + leaf_problem, + leaf_solution, + basis_factors, + basic_list, + nonbasic_list, + node_presolver, + diving_type, + recompute_bounds_and_basis, + start_lower, + start_upper, + dive_stats, + log); + + if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; break; + } else if (lp_status == dual::status_t::ITERATION_LIMIT) { + break; } - recompute_bounds_and_basis = result.needs_recompute_basis; + ++dive_stats.nodes_explored; + + auto [node_status, round_dir] = + update_tree(node_ptr, dive_tree, leaf_problem, leaf_solution, diving_type, lp_status, log); + recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; - if (result.status == node_status_t::HAS_CHILDREN) { - if (result.preferred_direction == rounding_direction_t::UP) { + if (node_status == node_status_t::HAS_CHILDREN) { + if (round_dir == rounding_direction_t::UP) { stack.push_front(node_ptr->get_down_child()); stack.push_front(node_ptr->get_up_child()); } else { @@ -1428,6 +1298,8 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, } } + // Remove nodes that we no longer can backtrack to (i.e., from the current node, we can only + // backtrack to a node that is has a depth of at most 5 levels lower than the current node). if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > diving_backtrack_limit) { stack.pop_back(); } @@ -2402,7 +2274,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t { raft::common::nvtx::range scope("BB::solve_node_bsp"); - // Validate vstatus before solving - check for corruption { const i_t expected_basic_count = original_lp_.num_rows; i_t actual_basic_count = 0; @@ -2425,52 +2296,332 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } } - // Create BSP policy - bsp_solve_policy_t policy(worker, - settings_.absolute_mip_gap_tol / 10, - original_lp_.lower, - original_lp_.upper, - edge_norms_, - root_relax_soln_.x, - exploration_stats_, - bsp_debug_settings_, - bsp_debug_logger_); + double work_units_at_start = worker.work_context.global_work_units_elapsed; + double clock_at_start = worker.clock; + + double work_limit = worker.horizon_end - worker.clock; + BSP_DEBUG_LOG_SOLVE_START(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->origin_worker_id, + work_limit, + false); + + std::fill(worker.node_presolver->bounds_changed.begin(), + worker.node_presolver->bounds_changed.end(), + false); + + if (worker.recompute_bounds_and_basis) { + worker.leaf_problem->lower = original_lp_.lower; + worker.leaf_problem->upper = original_lp_.upper; + node_ptr->get_variable_bounds(worker.leaf_problem->lower, + worker.leaf_problem->upper, + worker.node_presolver->bounds_changed); + } else { + node_ptr->update_branched_variable_bounds(worker.leaf_problem->lower, + worker.leaf_problem->upper, + worker.node_presolver->bounds_changed); + } + + double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); + if (remaining_time <= 0) { return node_solve_info_t::TIME_LIMIT; } + + // Bounds strengthening + simplex_solver_settings_t lp_settings = settings_; + lp_settings.set_log(false); + + lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; + lp_settings.inside_mip = 2; + lp_settings.time_limit = remaining_time; + lp_settings.scale_columns = false; + + bool feasible = true; + // TODO: incorporate into work unit estimation + if (!settings_.deterministic) { + feasible = worker.node_presolver->bounds_strengthening( + worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + } + + if (!feasible) { + node_ptr->lower_bound = std::numeric_limits::infinity(); + search_tree.update(node_ptr, node_status_t::INFEASIBLE); + worker.record_infeasible(node_ptr); + worker.track_node_infeasible(); + worker.track_node_processed(); + --exploration_stats_.nodes_unexplored; + ++exploration_stats_.nodes_explored; + worker.recompute_bounds_and_basis = true; + return node_solve_info_t::NO_CHILDREN; + } - // Allocate solution buffer + // Solve LP relaxation lp_solution_t leaf_solution(worker.leaf_problem->num_rows, worker.leaf_problem->num_cols); + std::vector& leaf_vstatus = node_ptr->vstatus; + i_t node_iter = 0; + f_t lp_start_time = tic(); + std::vector leaf_edge_norms = edge_norms_; + + // Debug: Log LP input for determinism analysis + if (bsp_debug_settings_.any_enabled()) { + uint64_t path_hash = node_ptr->compute_path_hash(); + uint64_t vstatus_hash = detail::compute_hash(leaf_vstatus); + uint64_t bounds_hash = detail::compute_hash(worker.leaf_problem->lower) ^ + detail::compute_hash(worker.leaf_problem->upper); + BSP_DEBUG_LOG_LP_INPUT(bsp_debug_settings_, + bsp_debug_logger_, + worker.worker_id, + node_ptr->node_id, + path_hash, + node_ptr->depth, + vstatus_hash, + bounds_hash); + } + + dual::status_t lp_status = dual_phase2_with_advanced_basis(2, + 0, + worker.recompute_bounds_and_basis, + lp_start_time, + *worker.leaf_problem, + lp_settings, + leaf_vstatus, + *worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms, + &worker.work_context); + + if (bsp_debug_settings_.any_enabled()) { + uint64_t path_hash = node_ptr->compute_path_hash(); + uint64_t sol_hash = detail::compute_hash(leaf_solution.x); + f_t obj = (lp_status == dual::status_t::OPTIMAL) + ? compute_objective(*worker.leaf_problem, leaf_solution.x) + : std::numeric_limits::infinity(); + uint64_t obj_hash = detail::compute_hash(obj); + BSP_DEBUG_LOG_LP_OUTPUT(bsp_debug_settings_, + bsp_debug_logger_, + worker.worker_id, + node_ptr->node_id, + path_hash, + static_cast(lp_status), + node_iter, + obj_hash, + sol_hash); + } + + // Validate vstatus after LP solve - check for corruption during simplex + { + const i_t expected_basic_count = original_lp_.num_rows; + i_t actual_basic_count = 0; + for (const auto& status : leaf_vstatus) { + if (status == variable_status_t::BASIC) { actual_basic_count++; } + } + if (actual_basic_count != expected_basic_count) { + settings_.log.printf( + "ERROR: After LP solve, node %d vstatus has %d BASIC entries, expected %d\n", + node_ptr->node_id, + actual_basic_count, + expected_basic_count); + settings_.log.printf(" lp_status = %d, recompute_basis = %d\n", + static_cast(lp_status), + worker.recompute_bounds_and_basis ? 1 : 0); + assert(actual_basic_count == expected_basic_count && "vstatus corrupted during LP solve"); + } + } + + double work_performed = worker.work_context.global_work_units_elapsed - work_units_at_start; + worker.clock += work_performed; + worker.work_units_this_horizon += work_performed; + + exploration_stats_.total_lp_solve_time += toc(lp_start_time); + exploration_stats_.total_lp_iters += node_iter; + ++exploration_stats_.nodes_explored; + --exploration_stats_.nodes_unexplored; + + // Process LP result + if (lp_status == dual::status_t::DUAL_UNBOUNDED) { + node_ptr->lower_bound = std::numeric_limits::infinity(); + + worker.record_infeasible(node_ptr); + worker.track_node_infeasible(); + worker.track_node_processed(); + worker.recompute_bounds_and_basis = true; + + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->origin_worker_id, + "INFEASIBLE", + node_ptr->lower_bound); + BSP_DEBUG_LOG_INFEASIBLE( + bsp_debug_settings_, bsp_debug_logger_, worker.clock, worker.worker_id, node_ptr->node_id); + + search_tree.update(node_ptr, node_status_t::INFEASIBLE); + return node_solve_info_t::NO_CHILDREN; + + } else if (lp_status == dual::status_t::CUTOFF) { + node_ptr->lower_bound = worker.local_upper_bound; + + worker.record_fathomed(node_ptr, node_ptr->lower_bound); + worker.track_node_pruned(); + worker.track_node_processed(); + worker.recompute_bounds_and_basis = true; + + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->origin_worker_id, + "FATHOMED", + node_ptr->lower_bound); + BSP_DEBUG_LOG_FATHOMED(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->lower_bound); + + search_tree.update(node_ptr, node_status_t::FATHOMED); + return node_solve_info_t::NO_CHILDREN; + + } else if (lp_status == dual::status_t::OPTIMAL) { + std::vector leaf_fractional; + i_t leaf_num_fractional = + fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); + + f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); + node_ptr->lower_bound = leaf_objective; + + // Queue pseudo-cost update for deterministic application at sync + if (node_ptr->branch_var >= 0) { + const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; + const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN + ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) + : std::ceil(node_ptr->fractional_val) - node_ptr->fractional_val; + if (frac > 1e-10) { + worker.queue_pseudo_cost_update( + node_ptr->branch_var, node_ptr->branch_dir, change_in_obj / frac); + } + } + + if (leaf_num_fractional == 0) { + // Integer feasible - queue for deterministic processing at sync + if (leaf_objective < worker.local_upper_bound) { + worker.local_upper_bound = leaf_objective; + worker.integer_solutions.push_back({leaf_objective, leaf_solution.x, node_ptr->depth}); + } + + worker.record_integer_solution(node_ptr, leaf_objective); + worker.track_integer_solution(); + worker.track_node_processed(); + worker.recompute_bounds_and_basis = true; + + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->origin_worker_id, + "INTEGER", + leaf_objective); + BSP_DEBUG_LOG_INTEGER(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + leaf_objective); + + search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); + return node_solve_info_t::NO_CHILDREN; + + } else if (leaf_objective <= worker.local_upper_bound + settings_.absolute_mip_gap_tol / 10) { + // Branch - use worker-local upper bound for deterministic pruning decision + // Use pseudo-cost snapshot for variable selection + const i_t branch_var = + worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); + + logger_t log; + log.log = false; - // Solve using unified function - node_solve_result_t result = solve_node_with_policy(node_ptr, - *worker.leaf_problem, - leaf_solution, - *worker.basis_factors, - worker.basic_list, - worker.nonbasic_list, - *worker.node_presolver, - worker.recompute_bounds_and_basis, - search_tree, - policy); + search_tree.branch( + node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, *worker.leaf_problem, log); + search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); - // Update worker state - worker.recompute_bounds_and_basis = result.needs_recompute_basis; + i_t down_child_id = node_ptr->get_down_child()->node_id; + i_t up_child_id = node_ptr->get_up_child()->node_id; + worker.record_branched( + node_ptr, down_child_id, up_child_id, branch_var, leaf_solution.x[branch_var]); + worker.track_node_branched(); + worker.track_node_processed(); + + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->origin_worker_id, + "BRANCH", + leaf_objective); + BSP_DEBUG_LOG_BRANCHED(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->origin_worker_id, + down_child_id, + up_child_id); + + exploration_stats_.nodes_unexplored += 2; + + rounding_direction_t preferred = + martin_criteria(leaf_solution.x[branch_var], root_relax_soln_.x[branch_var]); + worker.enqueue_children_for_plunge( + node_ptr->get_down_child(), node_ptr->get_up_child(), preferred); + + return preferred == rounding_direction_t::DOWN ? node_solve_info_t::DOWN_CHILD_FIRST + : node_solve_info_t::UP_CHILD_FIRST; - // Convert result to node_solve_info_t - switch (result.status) { - case node_status_t::INFEASIBLE: - case node_status_t::INTEGER_FEASIBLE: - case node_status_t::FATHOMED: return node_solve_info_t::NO_CHILDREN; + } else { + // Record event and debug logs BEFORE search_tree.update() which may delete the node + worker.record_fathomed(node_ptr, leaf_objective); + worker.track_node_pruned(); + worker.track_node_processed(); + worker.recompute_bounds_and_basis = true; - case node_status_t::HAS_CHILDREN: - return result.preferred_direction == rounding_direction_t::DOWN - ? node_solve_info_t::DOWN_CHILD_FIRST - : node_solve_info_t::UP_CHILD_FIRST; + BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + node_ptr->origin_worker_id, + "FATHOMED", + leaf_objective); + BSP_DEBUG_LOG_FATHOMED(bsp_debug_settings_, + bsp_debug_logger_, + worker.clock, + worker.worker_id, + node_ptr->node_id, + leaf_objective); - case node_status_t::PENDING: return node_solve_info_t::TIME_LIMIT; + search_tree.update(node_ptr, node_status_t::FATHOMED); + return node_solve_info_t::NO_CHILDREN; + } - case node_status_t::NUMERICAL: return node_solve_info_t::NUMERICAL; + } else if (lp_status == dual::status_t::TIME_LIMIT) { + return node_solve_info_t::TIME_LIMIT; - default: return node_solve_info_t::NUMERICAL; + } else { + worker.record_numerical(node_ptr); + worker.recompute_bounds_and_basis = true; + search_tree.update(node_ptr, node_status_t::NUMERICAL); + return node_solve_info_t::NUMERICAL; } } @@ -2993,10 +3144,12 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t dive_tree(std::move(starting_node)); std::deque*> stack; stack.push_front(&dive_tree.root); + // Initialize bounds from root node worker.dive_lower = original_lp_.lower; worker.dive_upper = original_lp_.upper; std::fill(worker.node_presolver->bounds_changed.begin(), @@ -3005,39 +3158,22 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tbounds_changed); - const i_t max_nodes_per_dive = 100; - const i_t max_backtrack_depth = 5; - i_t nodes_this_dive = 0; - - bnb_stats_t dive_stats; - dive_stats.total_lp_iters = 0; - dive_stats.total_lp_solve_time = 0; - dive_stats.nodes_explored = 0; - dive_stats.nodes_unexplored = 1; - - bsp_diving_solve_policy_t policy(worker, - settings_.absolute_mip_gap_tol / 10, - worker.dive_lower, - worker.dive_upper, - edge_norms_, - root_relax_soln_.x, - dive_stats, - worker.leaf_problem.get(), - &var_up_locks_, - &var_down_locks_); - - lp_solution_t leaf_solution(worker.leaf_problem->num_rows, - worker.leaf_problem->num_cols); + const i_t max_nodes_per_dive = 100; + const i_t max_backtrack_depth = 5; + i_t nodes_this_dive = 0; + worker.recompute_bounds_and_basis = true; while (!stack.empty() && solver_status_ == mip_status_t::UNSET && !bsp_terminated_.load() && nodes_this_dive < max_nodes_per_dive) { + // Check time limit directly if (toc(exploration_stats_.start_time) > settings_.time_limit) { solver_status_ = mip_status_t::TIME_LIMIT; bsp_terminated_.store(true); - bsp_scheduler_->stop(); + bsp_scheduler_->stop(); // Wake up workers waiting at barrier break; } + // Check horizon budget if (worker.work_context.global_work_units_elapsed >= worker.horizon_end) { bsp_scheduler_->wait_for_next_sync(worker.work_context); if (bsp_terminated_.load()) break; @@ -3046,40 +3182,177 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t* node_ptr = stack.front(); stack.pop_front(); + // Prune check using snapshot upper bound if (node_ptr->lower_bound >= worker.local_upper_bound) { worker.recompute_bounds_and_basis = true; continue; } - node_solve_result_t result = solve_node_with_policy(node_ptr, - *worker.leaf_problem, - leaf_solution, - *worker.basis_factors, - worker.basic_list, - worker.nonbasic_list, - *worker.node_presolver, - worker.recompute_bounds_and_basis, - dive_tree, - policy); + // Setup bounds for this node + std::fill(worker.node_presolver->bounds_changed.begin(), + worker.node_presolver->bounds_changed.end(), + false); + + if (worker.recompute_bounds_and_basis) { + worker.leaf_problem->lower = worker.dive_lower; + worker.leaf_problem->upper = worker.dive_upper; + node_ptr->get_variable_bounds(worker.leaf_problem->lower, + worker.leaf_problem->upper, + worker.node_presolver->bounds_changed); + } else { + node_ptr->update_branched_variable_bounds(worker.leaf_problem->lower, + worker.leaf_problem->upper, + worker.node_presolver->bounds_changed); + } + + double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); + if (remaining_time <= 0) { break; } + + // Setup LP settings + simplex_solver_settings_t lp_settings = settings_; + lp_settings.set_log(false); + lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; + lp_settings.inside_mip = 2; + lp_settings.time_limit = remaining_time; + lp_settings.scale_columns = false; + + // Solve LP relaxation + lp_solution_t leaf_solution(worker.leaf_problem->num_rows, + worker.leaf_problem->num_cols); + std::vector& leaf_vstatus = node_ptr->vstatus; + i_t node_iter = 0; + f_t lp_start_time = tic(); + std::vector leaf_edge_norms = edge_norms_; + + dual::status_t lp_status = dual_phase2_with_advanced_basis(2, + 0, + worker.recompute_bounds_and_basis, + lp_start_time, + *worker.leaf_problem, + lp_settings, + leaf_vstatus, + *worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms, + &worker.work_context); ++nodes_this_dive; + ++worker.nodes_explored_this_horizon; + ++worker.total_nodes_explored; - if (result.status == node_status_t::PENDING) { break; } + worker.clock = worker.work_context.global_work_units_elapsed; - worker.recompute_bounds_and_basis = result.needs_recompute_basis; + if (lp_status == dual::status_t::TIME_LIMIT || lp_status == dual::status_t::WORK_LIMIT) { + break; + } - if (result.status == node_status_t::HAS_CHILDREN) { - if (result.preferred_direction == rounding_direction_t::UP) { - stack.push_front(node_ptr->get_down_child()); - stack.push_front(node_ptr->get_up_child()); - } else { - stack.push_front(node_ptr->get_up_child()); - stack.push_front(node_ptr->get_down_child()); + if (lp_status == dual::status_t::DUAL_UNBOUNDED || lp_status == dual::status_t::CUTOFF) { + worker.recompute_bounds_and_basis = true; + continue; + } + + if (lp_status == dual::status_t::OPTIMAL) { + std::vector leaf_fractional; + fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); + + f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); + node_ptr->lower_bound = leaf_objective; + + if (leaf_fractional.empty()) { + // Integer feasible solution found! + if (leaf_objective < worker.local_upper_bound) { + worker.queue_integer_solution(leaf_objective, leaf_solution.x, node_ptr->depth); + } + worker.recompute_bounds_and_basis = true; + continue; } - if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > max_backtrack_depth) { - stack.pop_back(); + if (leaf_objective <= worker.local_upper_bound + settings_.absolute_mip_gap_tol / 10) { + // Branch - select variable using diving-type-specific strategy + branch_variable_t branch_result; + + switch (worker.diving_type) { + case bnb_worker_type_t::PSEUDOCOST_DIVING: + branch_result = + worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); + break; + + case bnb_worker_type_t::LINE_SEARCH_DIVING: + if (worker.root_solution) { + logger_t log; + log.log = false; + branch_result = line_search_diving( + leaf_fractional, leaf_solution.x, *worker.root_solution, log); + } else { + branch_result = + worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); + } + break; + + case bnb_worker_type_t::GUIDED_DIVING: + branch_result = worker.guided_variable_selection(leaf_fractional, leaf_solution.x); + break; + + case bnb_worker_type_t::COEFFICIENT_DIVING: { + logger_t log; + log.log = false; + branch_result = coefficient_diving(*worker.leaf_problem, + leaf_fractional, + leaf_solution.x, + var_up_locks_, + var_down_locks_, + log); + } break; + + default: + branch_result = + worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); + break; + } + + i_t branch_var = branch_result.variable; + rounding_direction_t round_dir = branch_result.direction; + + if (branch_var < 0) { + worker.recompute_bounds_and_basis = true; + continue; + } + + // Create children + logger_t log; + log.log = false; + dive_tree.branch(node_ptr, + branch_var, + leaf_solution.x[branch_var], + leaf_vstatus, + *worker.leaf_problem, + log); + + // Add children to stack (preferred direction first) + if (round_dir == rounding_direction_t::UP) { + stack.push_front(node_ptr->get_down_child()); + stack.push_front(node_ptr->get_up_child()); + } else { + stack.push_front(node_ptr->get_up_child()); + stack.push_front(node_ptr->get_down_child()); + } + + // Limit backtracking depth + if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > max_backtrack_depth) { + stack.pop_back(); + } + + worker.recompute_bounds_and_basis = false; + } else { + // Fathomed by bound + worker.recompute_bounds_and_basis = true; } + } else { + // Numerical or other error + worker.recompute_bounds_and_basis = true; } } } @@ -3088,56 +3361,6 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t; -// Explicit instantiations for solve_node_with_policy with both policy types -template node_solve_result_t branch_and_bound_t::solve_node_with_policy< - bsp_solve_policy_t>(mip_node_t*, - lp_problem_t&, - lp_solution_t&, - basis_update_mpf_t&, - std::vector&, - std::vector&, - bounds_strengthening_t&, - bool, - search_tree_t&, - bsp_solve_policy_t&); - -template node_solve_result_t branch_and_bound_t::solve_node_with_policy< - standard_solve_policy_t>(mip_node_t*, - lp_problem_t&, - lp_solution_t&, - basis_update_mpf_t&, - std::vector&, - std::vector&, - bounds_strengthening_t&, - bool, - search_tree_t&, - standard_solve_policy_t&); - -// Explicit instantiations for diving policies -template node_solve_result_t branch_and_bound_t::solve_node_with_policy< - standard_diving_solve_policy_t>(mip_node_t*, - lp_problem_t&, - lp_solution_t&, - basis_update_mpf_t&, - std::vector&, - std::vector&, - bounds_strengthening_t&, - bool, - search_tree_t&, - standard_diving_solve_policy_t&); - -template node_solve_result_t branch_and_bound_t::solve_node_with_policy< - bsp_diving_solve_policy_t>(mip_node_t*, - lp_problem_t&, - lp_solution_t&, - basis_update_mpf_t&, - std::vector&, - std::vector&, - bounds_strengthening_t&, - bool, - search_tree_t&, - bsp_diving_solve_policy_t&); - #endif } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 120ba00bb..a4cd67b3d 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -8,7 +8,6 @@ #pragma once #include -#include #include #include #include @@ -280,6 +279,17 @@ class branch_and_bound_t { bnb_stats_t& stats, logger_t& log); + // Update the tree based on the LP relaxation. Returns the status + // of the node and, if appropriated, the preferred rounding direction + // when visiting the children. + std::pair update_tree(mip_node_t* node_ptr, + search_tree_t& search_tree, + lp_problem_t& leaf_problem, + lp_solution_t& leaf_solution, + bnb_worker_type_t thread_type, + dual::status_t lp_status, + logger_t& log); + // Selects the variable to branch on. branch_variable_t variable_selection(mip_node_t* node_ptr, const std::vector& fractional, @@ -316,20 +326,6 @@ class branch_and_bound_t { search_tree_t& search_tree, double current_horizon); - // Unified node solving with policy-based callbacks (compile-time polymorphism) - template - node_solve_result_t solve_node_with_policy( - mip_node_t* node_ptr, - lp_problem_t& leaf_problem, - lp_solution_t& leaf_solution, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list, - bounds_strengthening_t& node_presolver, - bool recompute_bounds_and_basis, - search_tree_t& search_tree, - Policy& policy); - // Compute accurate lower bound from all BSP sources (called during sync phase) f_t compute_bsp_lower_bound(); diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 5102b4326..b19c2c956 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2097,7 +2097,7 @@ void prepare_optimality(const lp_problem_t& lp, const i_t m = lp.num_rows; const i_t n = lp.num_cols; - sol.objective = compute_objective(lp, x); + sol.objective = compute_objective(lp, sol.x); sol.user_objective = compute_user_objective(lp, sol.objective); f_t perturbation = phase2::amount_of_perturbation(lp, objective); if (perturbation > 1e-6 && phase == 2) { @@ -2122,8 +2122,8 @@ void prepare_optimality(const lp_problem_t& lp, sol.l2_primal_residual = l2_primal_residual(lp, sol); sol.l2_dual_residual = l2_dual_residual(lp, sol); - const f_t dual_infeas = phase2::dual_infeasibility(lp, settings, vstatus, z, 0.0, 0.0); - const f_t primal_infeas = phase2::primal_infeasibility(lp, settings, vstatus, x); + const f_t dual_infeas = phase2::dual_infeasibility(lp, settings, vstatus, sol.z, 0.0, 0.0); + const f_t primal_infeas = phase2::primal_infeasibility(lp, settings, vstatus, sol.x); if (phase == 1 && iter > 0) { settings.log.printf("Dual phase I complete. Iterations %d. Time %.2f\n", iter, toc(start_time)); } @@ -2286,12 +2286,13 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(lp.upper.size() == n); assert(lp.rhs.size() == m); - std::vector& x = sol.x; - std::vector& y = sol.y; - std::vector& z = sol.z; + // Create instrumented wrappers around sol vectors (no ownership transfer) + // ins_vector x, y, z; + // x.wrap(sol.x); + // y.wrap(sol.y); + // z.wrap(sol.z); // Declare instrumented vectors used during initialization (before manifold setup) - // Perturbed objective ins_vector objective(lp.objective); ins_vector c_basic(m); ins_vector xB_workspace(m); @@ -2311,7 +2312,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings.log.printf("Dual Simplex Phase %d\n", phase); std::vector vstatus_old = vstatus; - std::vector z_old = z; + std::vector z_old = sol.z; phase2::bound_info(lp, settings); if (initialize_basis) { @@ -2338,20 +2339,24 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } // Solve B'*y = cB - ft.b_transpose_solve(c_basic, y); + ft.b_transpose_solve(c_basic, sol.y); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } constexpr bool print_norms = false; if constexpr (print_norms) { - settings.log.printf( - "|| y || %e || cB || %e\n", vector_norm_inf(y), vector_norm_inf(c_basic)); + settings.log.printf("|| y || %e || cB || %e\n", + vector_norm_inf(sol.y), + vector_norm_inf(c_basic)); } - phase2::compute_reduced_costs(objective.underlying(), lp.A, y, basic_list, nonbasic_list, z); - if constexpr (print_norms) { settings.log.printf("|| z || %e\n", vector_norm_inf(z)); } + phase2::compute_reduced_costs( + objective.underlying(), lp.A, sol.y, basic_list, nonbasic_list, sol.z); + if constexpr (print_norms) { + settings.log.printf("|| z || %e\n", vector_norm_inf(sol.z)); + } #ifdef COMPUTE_DUAL_RESIDUAL std::vector dual_res1; - phase2::compute_dual_residual(lp.A, objective, y, z, dual_res1); + phase2::compute_dual_residual(lp.A, objective, sol.y, sol.z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.tight_tol) { settings.log.printf("|| A'*y + z - c || %e\n", dual_res_norm); @@ -2359,18 +2364,18 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(dual_res_norm < 1e-3); #endif - phase2::set_primal_variables_on_bounds(lp, settings, z, vstatus, x); + phase2::set_primal_variables_on_bounds(lp, settings, sol.z, vstatus, sol.x); #ifdef PRINT_VSTATUS_CHANGES i_t num_vstatus_changes; i_t num_z_changes; - phase2::vstatus_changes(vstatus, vstatus_old, z, z_old, num_vstatus_changes, num_z_changes); + phase2::vstatus_changes(vstatus, vstatus_old, sol.z, z_old, num_vstatus_changes, num_z_changes); settings.log.printf("Number of vstatus changes %d\n", num_vstatus_changes); settings.log.printf("Number of z changes %d\n", num_z_changes); #endif const f_t init_dual_inf = - phase2::dual_infeasibility(lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); + phase2::dual_infeasibility(lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); if (init_dual_inf > settings.dual_tol) { settings.log.printf("Initial dual infeasibility %e\n", init_dual_inf); } @@ -2382,14 +2387,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } phase2::compute_primal_variables( - ft, lp.rhs, lp.A, basic_list, nonbasic_list, settings.tight_tol, x, xB_workspace); + ft, lp.rhs, lp.A, basic_list, nonbasic_list, settings.tight_tol, sol.x, xB_workspace); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } - if (print_norms) { settings.log.printf("|| x || %e\n", vector_norm2(x)); } + if (print_norms) { settings.log.printf("|| x || %e\n", vector_norm2(sol.x)); } #ifdef COMPUTE_PRIMAL_RESIDUAL std::vector residual = lp.rhs; - matrix_vector_multiply(lp.A, 1.0, x, -1.0, residual); + matrix_vector_multiply(lp.A, 1.0, sol.x, -1.0, residual); f_t primal_residual = vector_norm_inf(residual); if (primal_residual > settings.primal_tol) { settings.log.printf("|| A*x - b || %e\n", primal_residual); @@ -2448,7 +2453,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - x, + sol.x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); @@ -2465,7 +2470,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // raft::common::nvtx::range scope("DualSimplex::transpose_A"); lp.A.transpose(A_transpose); } - f_t obj = compute_objective(lp, x); + f_t obj = compute_objective(lp, sol.x); init_scope.pop(); // End phase2_advanced_init range @@ -2612,7 +2617,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if (settings.use_steepest_edge_pricing) { leaving_index = phase2::steepest_edge_pricing_with_infeasibilities(lp, settings, - x, + sol.x, delta_y_steepest_edge, basic_mark, squared_infeasibilities, @@ -2623,7 +2628,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } else { // Max infeasibility pricing leaving_index = phase2::phase2_pricing( - lp, settings, x, basic_list, direction, basic_leaving_index, primal_infeasibility); + lp, settings, sol.x, basic_list, direction, basic_leaving_index, primal_infeasibility); } } timers.pricing_time += timers.stop_timer(); @@ -2639,9 +2644,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - x, - y, - z, + sol.x, + sol.y, + sol.z, sol); status = dual::status_t::OPTIMAL; break; @@ -2730,19 +2735,20 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, { // raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); if (harris_ratio) { - f_t max_step_length = phase2::first_stage_harris(lp, vstatus, nonbasic_list, z, delta_z); - entering_index = phase2::second_stage_harris(lp, + f_t max_step_length = + phase2::first_stage_harris(lp, vstatus, nonbasic_list, sol.z, delta_z); + entering_index = phase2::second_stage_harris(lp, vstatus, nonbasic_list, - z, + sol.z, delta_z, max_step_length, step_length, nonbasic_entering_index); } else if (bound_flip_ratio) { timers.start_timer(); - f_t slope = direction == 1 ? (lp.lower[leaving_index] - x[leaving_index]) - : (x[leaving_index] - lp.upper[leaving_index]); + f_t slope = direction == 1 ? (lp.lower[leaving_index] - sol.x[leaving_index]) + : (sol.x[leaving_index] - lp.upper[leaving_index]); bound_flipping_ratio_test_t bfrt(settings, start_time, m, @@ -2753,7 +2759,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, bounded_variables.underlying(), vstatus, nonbasic_list, - z, + sol.z, delta_z.underlying(), delta_z_indices.underlying(), nonbasic_mark); @@ -2764,8 +2770,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } timers.bfrt_time += timers.stop_timer(); } else { - entering_index = phase2::phase2_ratio_test( - lp, settings, vstatus, nonbasic_list, z, delta_z, step_length, nonbasic_entering_index); + entering_index = phase2::phase2_ratio_test(lp, + settings, + vstatus, + nonbasic_list, + sol.z, + delta_z, + step_length, + nonbasic_entering_index); } } if (entering_index == -2) { return dual::status_t::TIME_LIMIT; } @@ -2787,19 +2799,19 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings.log.printf("Dual infeasibility after removing perturbation %e\n", dual_infeas); if (dual_infeas <= settings.dual_tol) { settings.log.printf("Removed perturbation of %.2e.\n", perturbation); - z = unperturbed_z; - y = unperturbed_y; + sol.z = unperturbed_z; + sol.y = unperturbed_y; perturbation = 0.0; std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); - x = unperturbed_x; + sol.x = unperturbed_x; primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - x, + sol.x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); @@ -2807,7 +2819,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, objective = lp.objective; // Need to reset the objective value, since we have recomputed x - obj = phase2::compute_perturbed_objective(objective, x); + obj = phase2::compute_perturbed_objective(objective, sol.x); if (dual_infeas <= settings.dual_tol && primal_infeasibility <= settings.primal_tol) { phase2::prepare_optimality(lp, settings, @@ -2820,9 +2832,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - x, - y, - z, + sol.x, + sol.y, + sol.z, sol); status = dual::status_t::OPTIMAL; break; @@ -2837,18 +2849,18 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); - x = unperturbed_x; + sol.x = unperturbed_x; primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - x, + sol.x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); const f_t orig_dual_infeas = phase2::dual_infeasibility( - lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); + lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); if (primal_infeasibility <= settings.primal_tol && orig_dual_infeas <= settings.dual_tol) { @@ -2863,9 +2875,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - x, - y, - z, + sol.x, + sol.y, + sol.z, sol); status = dual::status_t::OPTIMAL; break; @@ -2886,13 +2898,13 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_y_sparse.to_dense(my_delta_y); // TODO(CMM): Do I use the perturbed or unperturbed objective? - const f_t obj_val = phase2::compute_perturbed_objective(objective, x); + const f_t obj_val = phase2::compute_perturbed_objective(objective, sol.x); phase2::compute_farkas_certificate(lp, settings, vstatus, - x, - y, - z, + sol.x, + sol.y, + sol.z, my_delta_y, delta_z, direction, @@ -2905,10 +2917,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } } - const f_t dual_infeas = - phase2::dual_infeasibility(lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); + const f_t dual_infeas = phase2::dual_infeasibility( + lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); settings.log.printf("Dual infeasibility %e\n", dual_infeas); - const f_t primal_inf = phase2::primal_infeasibility(lp, settings, vstatus, x); + const f_t primal_inf = phase2::primal_infeasibility(lp, settings, vstatus, sol.x); settings.log.printf("Primal infeasibility %e\n", primal_inf); settings.log.printf("Updates %d\n", ft.num_updates()); settings.log.printf("Steepest edge %e\n", max_val); @@ -2925,7 +2937,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // y <- y + steplength * delta_y // z <- z + steplength * delta_z i_t update_dual_variables_status = phase2::update_dual_variables( - delta_y_sparse, delta_z_indices, delta_z, step_length, leaving_index, y, z); + delta_y_sparse, delta_z_indices, delta_z, step_length, leaving_index, sol.y, sol.z); if (update_dual_variables_status == -1) { settings.log.printf("Numerical issues encountered in update_dual_variables.\n"); return dual::status_t::NUMERICAL; @@ -2933,7 +2945,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.vector_time += timers.stop_timer(); #ifdef COMPUTE_DUAL_RESIDUAL - phase2::compute_dual_residual(lp.A, objective, y, z, dual_res1); + phase2::compute_dual_residual(lp.A, objective, sol.y, sol.z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.dual_tol) { settings.log.printf("|| A'*y + z - c || %e steplength %e\n", dual_res_norm, step_length); @@ -2946,7 +2958,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings, bounded_variables, objective, - z, + sol.z, delta_z_indices, nonbasic_list, entering_index, @@ -2971,7 +2983,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, atilde_sparse, delta_xB_0_sparse, delta_x_flip, - x); + sol.x); timers.ftran_time += timers.stop_timer(); } @@ -2990,7 +3002,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, basic_list, delta_x_flip, rhs_sparse, - x, + sol.x, utilde_sparse, scaled_delta_xB_sparse, delta_x) == -1) { @@ -3032,12 +3044,13 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); // x <- x + delta_x - phase2::update_primal_variables(scaled_delta_xB_sparse, basic_list, delta_x, entering_index, x); + phase2::update_primal_variables( + scaled_delta_xB_sparse, basic_list, delta_x, entering_index, sol.x); timers.vector_time += timers.stop_timer(); #ifdef COMPUTE_PRIMAL_RESIDUAL residual = lp.rhs; - matrix_vector_multiply(lp.A, 1.0, x, -1.0, residual); + matrix_vector_multiply(lp.A, 1.0, sol.x, -1.0, residual); primal_residual = vector_norm_inf(residual); if (iter % 100 == 0 && primal_residual > 10 * settings.primal_tol) { settings.log.printf("|| A*x - b || %e\n", primal_residual); @@ -3060,7 +3073,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::update_primal_infeasibilities(lp, settings, basic_list, - x, + sol.x, entering_index, leaving_index, delta_xB_0_sparse.i.underlying(), @@ -3072,7 +3085,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::update_primal_infeasibilities(lp, settings, basic_list, - x, + sol.x, entering_index, leaving_index, scaled_delta_xB_sparse.i.underlying(), @@ -3082,7 +3095,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // Update the entering variable phase2::update_single_primal_infeasibility(lp.lower, lp.upper, - x, + sol.x, settings.primal_tol, squared_infeasibilities.underlying(), infeasibility_indices.underlying(), @@ -3093,7 +3106,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #if CHECK_PRIMAL_INFEASIBILITIES phase2::check_primal_infeasibilities( - lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); + lp, settings, basic_list, sol.x, squared_infeasibilities, infeasibility_indices); #endif timers.update_infeasibility_time += timers.stop_timer(); @@ -3102,7 +3115,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); f_t sum_perturb = 0.0; - phase2::compute_perturbation(lp, settings, delta_z_indices, z, objective, sum_perturb); + phase2::compute_perturbation(lp, settings, delta_z_indices, sol.z, objective, sum_perturb); timers.perturb_time += timers.stop_timer(); // Update basis information @@ -3171,13 +3184,13 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); - x = unperturbed_x; + sol.x = unperturbed_x; } primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - x, + sol.x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); diff --git a/cpp/src/dual_simplex/sparse_matrix.cpp b/cpp/src/dual_simplex/sparse_matrix.cpp index d6eab1c15..7d510a849 100644 --- a/cpp/src/dual_simplex/sparse_matrix.cpp +++ b/cpp/src/dual_simplex/sparse_matrix.cpp @@ -591,7 +591,7 @@ void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, VectorF& x } } -// x <- x + alpha * A(:, j) +// x <- x + alpha * A(:, j) with mark/indices tracking template void scatter_dense( const csc_matrix_t& A, i_t j, f_t alpha, VectorF& x, VectorI& mark, VectorI& indices) diff --git a/cpp/src/dual_simplex/triangle_solve.hpp b/cpp/src/dual_simplex/triangle_solve.hpp index f7fc7744d..cb1ce5a79 100644 --- a/cpp/src/dual_simplex/triangle_solve.hpp +++ b/cpp/src/dual_simplex/triangle_solve.hpp @@ -35,8 +35,7 @@ i_t lower_triangular_solve(const csc_matrix_t& L, VectorF& x) i_t col_end = L.col_start[j + 1]; if (x[j] != 0.0) { x[j] /= L.x[col_start]; - auto x_j = x[j]; // hoist this load out of the loop - // as the compiler cannot guess that x[j] never aliases to x[L.i[p]] + auto x_j = x[j]; for (i_t p = col_start + 1; p < col_end; ++p) { x[L.i[p]] -= L.x[p] * x_j; } @@ -75,7 +74,7 @@ i_t upper_triangular_solve(const csc_matrix_t& U, VectorF& x) const i_t col_end = U.col_start[j + 1] - 1; if (x[j] != 0.0) { x[j] /= U.x[col_end]; - auto x_j = x[j]; // same x_j load hoisting + auto x_j = x[j]; for (i_t p = col_start; p < col_end; ++p) { x[U.i[p]] -= U.x[p] * x_j; } diff --git a/cpp/src/math_optimization/solver_settings.cu b/cpp/src/math_optimization/solver_settings.cu index 1b5efeb0d..e16b4be46 100644 --- a/cpp/src/math_optimization/solver_settings.cu +++ b/cpp/src/math_optimization/solver_settings.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -89,7 +89,7 @@ solver_settings_t::solver_settings_t() : pdlp_settings(), mip_settings {CUOPT_BARRIER_DUAL_INITIAL_POINT, &pdlp_settings.barrier_dual_initial_point, -1, 1, -1}, {CUOPT_NUM_GPUS, &pdlp_settings.num_gpus, 1, 2, 1}, {CUOPT_NUM_GPUS, &mip_settings.num_gpus, 1, 2, 1}, - {CUOPT_MIP_DETERMINISM_MODE, &mip_settings.determinism_mode, CUOPT_MODE_OPPORTUNISTIC, CUOPT_MODE_DETERMINISTIC, CUOPT_MODE_OPPORTUNISTIC} + {CUOPT_MIP_DETERMINISTIC, &mip_settings.determinism_mode, CUOPT_MODE_OPPORTUNISTIC, CUOPT_MODE_DETERMINISTIC, CUOPT_MODE_OPPORTUNISTIC} }; // Bool parameters diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 3f66a3c37..0207df346 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -194,6 +194,7 @@ template void local_search_t::stop_cpufj_deterministic() { if (deterministic_cpu_fj.fj_cpu) { + // Deregister from producer_sync before stopping if (deterministic_cpu_fj.fj_cpu->producer_sync) { deterministic_cpu_fj.fj_cpu->producer_sync->deregister_producer( &deterministic_cpu_fj.fj_cpu->work_units_elapsed); diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 3839b1aa8..75aea76e3 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -496,7 +496,7 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { using const_reverse_iterator = std::reverse_iterator; // Constructors - memop_instrumentation_wrapper_t() : array_() + memop_instrumentation_wrapper_t() : array_(), wrapped_ptr(nullptr) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -541,8 +541,11 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } + // Copy constructor - copy from wrapped array if wrapping, never share wrapped pointer memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : memory_instrumentation_base_t(other), array_(other.array_) + : memory_instrumentation_base_t(other), + array_(other.wrapped_ptr ? *other.wrapped_ptr : other.array_), + wrapped_ptr(nullptr) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -551,8 +554,12 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } + // Move constructor - copy from wrapped array if wrapping (can't move wrapped), never share + // pointer memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : memory_instrumentation_base_t(std::move(other)), array_(std::move(other.array_)) + : memory_instrumentation_base_t(std::move(other)), + array_(other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_)), + wrapped_ptr(nullptr) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -561,13 +568,21 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } + // Copy assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) { if (this != &other) { memory_instrumentation_base_t::operator=(other); - array_ = other.array_; + // Get source data (from wrapped or owned array) + const T& source = other.wrapped_ptr ? *other.wrapped_ptr : other.array_; + // Write to destination (wrapped or owned array) + if (wrapped_ptr) { + *wrapped_ptr = source; + } else { + array_ = source; + } if constexpr (type_traits_utils::has_data::value) { - data_ptr = array_.data(); + data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); } else { data_ptr = nullptr; } @@ -575,13 +590,19 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { return *this; } + // Move assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept { if (this != &other) { memory_instrumentation_base_t::operator=(std::move(other)); - array_ = std::move(other.array_); + // Get source data (copy from wrapped, move from owned) + if (wrapped_ptr) { + *wrapped_ptr = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); + } else { + array_ = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); + } if constexpr (type_traits_utils::has_data::value) { - data_ptr = array_.data(); + data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); } else { data_ptr = nullptr; } @@ -765,11 +786,30 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { T&& release_array() { return std::move(array_); } - T& underlying() { return array_; } - const T& underlying() const { return array_; } + // Wrap an external vector without taking ownership + void wrap(T& external_array) + { + wrapped_ptr = &external_array; + if constexpr (type_traits_utils::has_data::value) { data_ptr = external_array.data(); } + } + + // Stop wrapping and return to using the owned array + void unwrap() + { + wrapped_ptr = nullptr; + if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); } + } + + // Check if currently wrapping an external array + bool is_wrapping() const { return wrapped_ptr != nullptr; } + + // Get the underlying container (wrapped or owned) + T& underlying() { return wrapped_ptr ? *wrapped_ptr : array_; } + const T& underlying() const { return wrapped_ptr ? *wrapped_ptr : array_; } private: T array_; + T* wrapped_ptr{nullptr}; value_type* data_ptr{nullptr}; }; @@ -794,8 +834,8 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { // Constructors - forward everything to the underlying container memop_instrumentation_wrapper_t() = default; - memop_instrumentation_wrapper_t(const T& arr) : array_(arr) {} - memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)) {} + memop_instrumentation_wrapper_t(const T& arr) : array_(arr), wrapped_ptr_(nullptr) {} + memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)), wrapped_ptr_(nullptr) {} template , T> && (sizeof...(Args) > 0 || !std::is_convertible_v)>> explicit memop_instrumentation_wrapper_t(Arg&& arg, Args&&... args) - : array_(std::forward(arg), std::forward(args)...) + : array_(std::forward(arg), std::forward(args)...), wrapped_ptr_(nullptr) { } + // Copy constructor - always copy the data, never share wrapped pointer memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : array_(other.array_) + : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_), wrapped_ptr_(nullptr) { } + // Move constructor - take ownership of array, never share wrapped pointer memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : array_(std::move(other.array_)) + : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_)), + wrapped_ptr_(nullptr) { } + // Copy assignment - always copy the data memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) { - if (this != &other) { array_ = other.array_; } + if (this != &other) { + if (wrapped_ptr_) { + *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; + } else { + array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; + } + } return *this; } + // Move assignment - take the data memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept { - if (this != &other) { array_ = std::move(other.array_); } + if (this != &other) { + if (wrapped_ptr_) { + *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); + } else { + array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); + } + } return *this; } @@ -890,11 +947,18 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { T&& release_array() { return std::move(array_); } - T& underlying() { return array_; } - const T& underlying() const { return array_; } + // Wrap/unwrap interface (for compatibility, but wrap is essentially a no-op for perf) + void wrap(T& external_array) { wrapped_ptr_ = &external_array; } + void unwrap() { wrapped_ptr_ = nullptr; } + bool is_wrapping() const { return wrapped_ptr_ != nullptr; } + + // Get the underlying container + T& underlying() { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } + const T& underlying() const { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } private: T array_; + T* wrapped_ptr_{nullptr}; }; #endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION diff --git a/cpp/src/utilities/producer_sync.hpp b/cpp/src/utilities/producer_sync.hpp index df410fca8..13c9bde38 100644 --- a/cpp/src/utilities/producer_sync.hpp +++ b/cpp/src/utilities/producer_sync.hpp @@ -27,7 +27,7 @@ namespace cuopt { * One-way synchronization utility for producer threads. * * Producers (e.g., CPUFJ) register their work unit progress atomics and advance independently. - * The consumer (e.g., B&B coordinator) can wait until all producers have reached a + * The consumer (e.g., B&B BSP coordinator) can wait until all producers have reached a * target work unit threshold before proceeding. * * Key invariant: Producers must not fall behind the consumer's horizon. The consumer @@ -37,6 +37,10 @@ class producer_sync_t { public: producer_sync_t() = default; + /** + * Register a producer's work unit progress atomic. + * Must be called before registration_complete(). + */ void register_producer(std::atomic* progress_ptr) { std::lock_guard lock(mutex_); @@ -44,6 +48,9 @@ class producer_sync_t { cv_.notify_all(); } + /** + * Deregister a producer (e.g., when it terminates). + */ void deregister_producer(std::atomic* progress_ptr) { std::lock_guard lock(mutex_); @@ -63,6 +70,9 @@ class producer_sync_t { cv_.notify_all(); } + /** + * Check if registration is complete (non-blocking). + */ bool is_registration_complete() const { std::lock_guard lock(mutex_); @@ -90,6 +100,25 @@ class producer_sync_t { */ void notify_progress() { cv_.notify_all(); } + /** + * Get the minimum work units among all registered producers. + * Returns infinity if no producers are registered. + */ + double get_min_producer_progress() const + { + std::lock_guard lock(mutex_); + if (producers_.empty()) { return std::numeric_limits::infinity(); } + + double min_progress = std::numeric_limits::infinity(); + for (const auto* progress_ptr : producers_) { + min_progress = std::min(min_progress, progress_ptr->load(std::memory_order_acquire)); + } + return min_progress; + } + + /** + * Get the number of registered producers. + */ size_t num_producers() const { std::lock_guard lock(mutex_); diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp index 949d84b2c..0aebf953d 100644 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -30,10 +30,23 @@ #include "models/fj_predictor/header.h" #include "models/pdlp_predictor/header.h" -#include "hashing.hpp" - namespace cuopt { +template +static inline uint32_t compute_hash(std::vector h_contents) +{ + // FNV-1a hash + + uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis + std::vector byte_contents(h_contents.size() * sizeof(i_t)); + std::memcpy(byte_contents.data(), h_contents.data(), h_contents.size() * sizeof(i_t)); + for (size_t i = 0; i < byte_contents.size(); ++i) { + hash ^= byte_contents[i]; + hash *= 16777619u; + } + return hash; +} + template float work_unit_predictor_t::predict_scalar( const std::map& features) const @@ -56,7 +69,7 @@ float work_unit_predictor_t::predict_scalar( cache_vec.push_back(data[i].missing != -1 ? data[i].fvalue : std::numeric_limits::quiet_NaN()); } - uint32_t key = cuopt::linear_programming::detail::compute_hash(cache_vec); + uint32_t key = compute_hash(cache_vec); auto cached_it = prediction_cache.find(key); if (cached_it != prediction_cache.end()) { return cached_it->second; } diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp index 746d5e6dd..9c14a3a1e 100644 --- a/cpp/src/utilities/work_unit_scheduler.cpp +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -102,15 +102,15 @@ sync_result_t work_unit_scheduler_t::wait_for_next_sync(work_limit_context_t& ct return stopped_.load() ? sync_result_t::STOPPED : sync_result_t::CONTINUE; } -// void work_unit_scheduler_t::queue_callback(work_limit_context_t& source, -// work_limit_context_t& destination, -// callback_t callback) -// { -// std::lock_guard lock(mutex_); -// double tag = source.global_work_units_elapsed; -// auto it = callback_queues_.find(&destination); -// if (it != callback_queues_.end()) { it->second.push({tag, std::move(callback)}); } -// } +void work_unit_scheduler_t::queue_callback(work_limit_context_t& source, + work_limit_context_t& destination, + callback_t callback) +{ + std::lock_guard lock(mutex_); + double tag = source.global_work_units_elapsed; + auto it = callback_queues_.find(&destination); + if (it != callback_queues_.end()) { it->second.push({tag, std::move(callback)}); } +} double work_unit_scheduler_t::current_sync_target() const { @@ -167,9 +167,9 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double size_t my_exit_generation = exit_generation_; if (verbose) { CUOPT_LOG_DEBUG("[%s] Processing callbacks", ctx.name.c_str()); } - // lock.unlock(); - // process_callbacks_for_context(ctx, sync_target); - // lock.lock(); + lock.unlock(); + process_callbacks_for_context(ctx, sync_target); + lock.lock(); if (verbose) { CUOPT_LOG_DEBUG("[%s] Done processing callbacks", ctx.name.c_str()); } contexts_at_barrier_--; @@ -205,10 +205,26 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double } } -// void work_unit_scheduler_t::process_callbacks_for_context(work_limit_context_t& ctx, -// double up_to_work_units) -// { +void work_unit_scheduler_t::process_callbacks_for_context(work_limit_context_t& ctx, + double up_to_work_units) +{ + std::vector to_execute; + + { + std::lock_guard lock(mutex_); + auto it = callback_queues_.find(&ctx); + if (it == callback_queues_.end()) return; -// } + auto& queue = it->second; + while (!queue.empty() && queue.top().work_unit_tag <= up_to_work_units) { + to_execute.push_back(std::move(const_cast(queue.top()).callback)); + queue.pop(); + } + } + + for (auto& cb : to_execute) { + cb(); + } +} } // namespace cuopt diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index 59b7fbffe..12b03929c 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -45,9 +45,9 @@ class work_unit_scheduler_t { void register_context(work_limit_context_t& ctx); void deregister_context(work_limit_context_t& ctx); void on_work_recorded(work_limit_context_t& ctx, double total_work); - // void queue_callback(work_limit_context_t& source, - // work_limit_context_t& destination, - // callback_t callback); + void queue_callback(work_limit_context_t& source, + work_limit_context_t& destination, + callback_t callback); // Sync callback support - callback is executed when all contexts reach sync point // If callback returns true, scheduler stops and all workers exit cleanly From f16db0044cbc52ccf137976eeb6d866a325608cd Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 14:51:38 +0000 Subject: [PATCH 223/366] restore fixes --- .../cuopt/linear_programming/constants.h | 4 +- cpp/src/dual_simplex/phase2.cpp | 153 ++++++++---------- cpp/src/dual_simplex/sparse_matrix.cpp | 2 +- cpp/src/dual_simplex/triangle_solve.hpp | 5 +- cpp/src/math_optimization/solver_settings.cu | 4 +- cpp/src/mip/local_search/local_search.cu | 1 - cpp/src/mip/solver.cu | 2 +- cpp/src/utilities/memory_instrumentation.hpp | 100 +++--------- cpp/src/utilities/producer_sync.hpp | 31 +--- cpp/src/utilities/work_unit_predictor.cpp | 21 +-- cpp/src/utilities/work_unit_scheduler.cpp | 48 ++---- cpp/src/utilities/work_unit_scheduler.hpp | 6 +- 12 files changed, 121 insertions(+), 256 deletions(-) diff --git a/cpp/include/cuopt/linear_programming/constants.h b/cpp/include/cuopt/linear_programming/constants.h index 6929b6720..eb32fa1fb 100644 --- a/cpp/include/cuopt/linear_programming/constants.h +++ b/cpp/include/cuopt/linear_programming/constants.h @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -49,7 +49,7 @@ #define CUOPT_CUDSS_DETERMINISTIC "cudss_deterministic" #define CUOPT_PRESOLVE "presolve" #define CUOPT_DUAL_POSTSOLVE "dual_postsolve" -#define CUOPT_MIP_DETERMINISTIC "mip_deterministic" +#define CUOPT_MIP_DETERMINISM_MODE "mip_determinism_mode" #define CUOPT_MIP_ABSOLUTE_TOLERANCE "mip_absolute_tolerance" #define CUOPT_MIP_RELATIVE_TOLERANCE "mip_relative_tolerance" #define CUOPT_MIP_INTEGRALITY_TOLERANCE "mip_integrality_tolerance" diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index b19c2c956..5102b4326 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2097,7 +2097,7 @@ void prepare_optimality(const lp_problem_t& lp, const i_t m = lp.num_rows; const i_t n = lp.num_cols; - sol.objective = compute_objective(lp, sol.x); + sol.objective = compute_objective(lp, x); sol.user_objective = compute_user_objective(lp, sol.objective); f_t perturbation = phase2::amount_of_perturbation(lp, objective); if (perturbation > 1e-6 && phase == 2) { @@ -2122,8 +2122,8 @@ void prepare_optimality(const lp_problem_t& lp, sol.l2_primal_residual = l2_primal_residual(lp, sol); sol.l2_dual_residual = l2_dual_residual(lp, sol); - const f_t dual_infeas = phase2::dual_infeasibility(lp, settings, vstatus, sol.z, 0.0, 0.0); - const f_t primal_infeas = phase2::primal_infeasibility(lp, settings, vstatus, sol.x); + const f_t dual_infeas = phase2::dual_infeasibility(lp, settings, vstatus, z, 0.0, 0.0); + const f_t primal_infeas = phase2::primal_infeasibility(lp, settings, vstatus, x); if (phase == 1 && iter > 0) { settings.log.printf("Dual phase I complete. Iterations %d. Time %.2f\n", iter, toc(start_time)); } @@ -2286,13 +2286,12 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(lp.upper.size() == n); assert(lp.rhs.size() == m); - // Create instrumented wrappers around sol vectors (no ownership transfer) - // ins_vector x, y, z; - // x.wrap(sol.x); - // y.wrap(sol.y); - // z.wrap(sol.z); + std::vector& x = sol.x; + std::vector& y = sol.y; + std::vector& z = sol.z; // Declare instrumented vectors used during initialization (before manifold setup) + // Perturbed objective ins_vector objective(lp.objective); ins_vector c_basic(m); ins_vector xB_workspace(m); @@ -2312,7 +2311,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings.log.printf("Dual Simplex Phase %d\n", phase); std::vector vstatus_old = vstatus; - std::vector z_old = sol.z; + std::vector z_old = z; phase2::bound_info(lp, settings); if (initialize_basis) { @@ -2339,24 +2338,20 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } // Solve B'*y = cB - ft.b_transpose_solve(c_basic, sol.y); + ft.b_transpose_solve(c_basic, y); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } constexpr bool print_norms = false; if constexpr (print_norms) { - settings.log.printf("|| y || %e || cB || %e\n", - vector_norm_inf(sol.y), - vector_norm_inf(c_basic)); + settings.log.printf( + "|| y || %e || cB || %e\n", vector_norm_inf(y), vector_norm_inf(c_basic)); } - phase2::compute_reduced_costs( - objective.underlying(), lp.A, sol.y, basic_list, nonbasic_list, sol.z); - if constexpr (print_norms) { - settings.log.printf("|| z || %e\n", vector_norm_inf(sol.z)); - } + phase2::compute_reduced_costs(objective.underlying(), lp.A, y, basic_list, nonbasic_list, z); + if constexpr (print_norms) { settings.log.printf("|| z || %e\n", vector_norm_inf(z)); } #ifdef COMPUTE_DUAL_RESIDUAL std::vector dual_res1; - phase2::compute_dual_residual(lp.A, objective, sol.y, sol.z, dual_res1); + phase2::compute_dual_residual(lp.A, objective, y, z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.tight_tol) { settings.log.printf("|| A'*y + z - c || %e\n", dual_res_norm); @@ -2364,18 +2359,18 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, assert(dual_res_norm < 1e-3); #endif - phase2::set_primal_variables_on_bounds(lp, settings, sol.z, vstatus, sol.x); + phase2::set_primal_variables_on_bounds(lp, settings, z, vstatus, x); #ifdef PRINT_VSTATUS_CHANGES i_t num_vstatus_changes; i_t num_z_changes; - phase2::vstatus_changes(vstatus, vstatus_old, sol.z, z_old, num_vstatus_changes, num_z_changes); + phase2::vstatus_changes(vstatus, vstatus_old, z, z_old, num_vstatus_changes, num_z_changes); settings.log.printf("Number of vstatus changes %d\n", num_vstatus_changes); settings.log.printf("Number of z changes %d\n", num_z_changes); #endif const f_t init_dual_inf = - phase2::dual_infeasibility(lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); + phase2::dual_infeasibility(lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); if (init_dual_inf > settings.dual_tol) { settings.log.printf("Initial dual infeasibility %e\n", init_dual_inf); } @@ -2387,14 +2382,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } phase2::compute_primal_variables( - ft, lp.rhs, lp.A, basic_list, nonbasic_list, settings.tight_tol, sol.x, xB_workspace); + ft, lp.rhs, lp.A, basic_list, nonbasic_list, settings.tight_tol, x, xB_workspace); if (toc(start_time) > settings.time_limit) { return dual::status_t::TIME_LIMIT; } - if (print_norms) { settings.log.printf("|| x || %e\n", vector_norm2(sol.x)); } + if (print_norms) { settings.log.printf("|| x || %e\n", vector_norm2(x)); } #ifdef COMPUTE_PRIMAL_RESIDUAL std::vector residual = lp.rhs; - matrix_vector_multiply(lp.A, 1.0, sol.x, -1.0, residual); + matrix_vector_multiply(lp.A, 1.0, x, -1.0, residual); f_t primal_residual = vector_norm_inf(residual); if (primal_residual > settings.primal_tol) { settings.log.printf("|| A*x - b || %e\n", primal_residual); @@ -2453,7 +2448,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); @@ -2470,7 +2465,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // raft::common::nvtx::range scope("DualSimplex::transpose_A"); lp.A.transpose(A_transpose); } - f_t obj = compute_objective(lp, sol.x); + f_t obj = compute_objective(lp, x); init_scope.pop(); // End phase2_advanced_init range @@ -2617,7 +2612,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if (settings.use_steepest_edge_pricing) { leaving_index = phase2::steepest_edge_pricing_with_infeasibilities(lp, settings, - sol.x, + x, delta_y_steepest_edge, basic_mark, squared_infeasibilities, @@ -2628,7 +2623,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } else { // Max infeasibility pricing leaving_index = phase2::phase2_pricing( - lp, settings, sol.x, basic_list, direction, basic_leaving_index, primal_infeasibility); + lp, settings, x, basic_list, direction, basic_leaving_index, primal_infeasibility); } } timers.pricing_time += timers.stop_timer(); @@ -2644,9 +2639,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - sol.x, - sol.y, - sol.z, + x, + y, + z, sol); status = dual::status_t::OPTIMAL; break; @@ -2735,20 +2730,19 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, { // raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); if (harris_ratio) { - f_t max_step_length = - phase2::first_stage_harris(lp, vstatus, nonbasic_list, sol.z, delta_z); - entering_index = phase2::second_stage_harris(lp, + f_t max_step_length = phase2::first_stage_harris(lp, vstatus, nonbasic_list, z, delta_z); + entering_index = phase2::second_stage_harris(lp, vstatus, nonbasic_list, - sol.z, + z, delta_z, max_step_length, step_length, nonbasic_entering_index); } else if (bound_flip_ratio) { timers.start_timer(); - f_t slope = direction == 1 ? (lp.lower[leaving_index] - sol.x[leaving_index]) - : (sol.x[leaving_index] - lp.upper[leaving_index]); + f_t slope = direction == 1 ? (lp.lower[leaving_index] - x[leaving_index]) + : (x[leaving_index] - lp.upper[leaving_index]); bound_flipping_ratio_test_t bfrt(settings, start_time, m, @@ -2759,7 +2753,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, bounded_variables.underlying(), vstatus, nonbasic_list, - sol.z, + z, delta_z.underlying(), delta_z_indices.underlying(), nonbasic_mark); @@ -2770,14 +2764,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } timers.bfrt_time += timers.stop_timer(); } else { - entering_index = phase2::phase2_ratio_test(lp, - settings, - vstatus, - nonbasic_list, - sol.z, - delta_z, - step_length, - nonbasic_entering_index); + entering_index = phase2::phase2_ratio_test( + lp, settings, vstatus, nonbasic_list, z, delta_z, step_length, nonbasic_entering_index); } } if (entering_index == -2) { return dual::status_t::TIME_LIMIT; } @@ -2799,19 +2787,19 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings.log.printf("Dual infeasibility after removing perturbation %e\n", dual_infeas); if (dual_infeas <= settings.dual_tol) { settings.log.printf("Removed perturbation of %.2e.\n", perturbation); - sol.z = unperturbed_z; - sol.y = unperturbed_y; + z = unperturbed_z; + y = unperturbed_y; perturbation = 0.0; std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); - sol.x = unperturbed_x; + x = unperturbed_x; primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); @@ -2819,7 +2807,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, objective = lp.objective; // Need to reset the objective value, since we have recomputed x - obj = phase2::compute_perturbed_objective(objective, sol.x); + obj = phase2::compute_perturbed_objective(objective, x); if (dual_infeas <= settings.dual_tol && primal_infeasibility <= settings.primal_tol) { phase2::prepare_optimality(lp, settings, @@ -2832,9 +2820,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - sol.x, - sol.y, - sol.z, + x, + y, + z, sol); status = dual::status_t::OPTIMAL; break; @@ -2849,18 +2837,18 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); - sol.x = unperturbed_x; + x = unperturbed_x; primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); const f_t orig_dual_infeas = phase2::dual_infeasibility( - lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); + lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); if (primal_infeasibility <= settings.primal_tol && orig_dual_infeas <= settings.dual_tol) { @@ -2875,9 +2863,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, start_time, max_val, iter, - sol.x, - sol.y, - sol.z, + x, + y, + z, sol); status = dual::status_t::OPTIMAL; break; @@ -2898,13 +2886,13 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_y_sparse.to_dense(my_delta_y); // TODO(CMM): Do I use the perturbed or unperturbed objective? - const f_t obj_val = phase2::compute_perturbed_objective(objective, sol.x); + const f_t obj_val = phase2::compute_perturbed_objective(objective, x); phase2::compute_farkas_certificate(lp, settings, vstatus, - sol.x, - sol.y, - sol.z, + x, + y, + z, my_delta_y, delta_z, direction, @@ -2917,10 +2905,10 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, } } - const f_t dual_infeas = phase2::dual_infeasibility( - lp, settings, vstatus, sol.z, settings.tight_tol, settings.dual_tol); + const f_t dual_infeas = + phase2::dual_infeasibility(lp, settings, vstatus, z, settings.tight_tol, settings.dual_tol); settings.log.printf("Dual infeasibility %e\n", dual_infeas); - const f_t primal_inf = phase2::primal_infeasibility(lp, settings, vstatus, sol.x); + const f_t primal_inf = phase2::primal_infeasibility(lp, settings, vstatus, x); settings.log.printf("Primal infeasibility %e\n", primal_inf); settings.log.printf("Updates %d\n", ft.num_updates()); settings.log.printf("Steepest edge %e\n", max_val); @@ -2937,7 +2925,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // y <- y + steplength * delta_y // z <- z + steplength * delta_z i_t update_dual_variables_status = phase2::update_dual_variables( - delta_y_sparse, delta_z_indices, delta_z, step_length, leaving_index, sol.y, sol.z); + delta_y_sparse, delta_z_indices, delta_z, step_length, leaving_index, y, z); if (update_dual_variables_status == -1) { settings.log.printf("Numerical issues encountered in update_dual_variables.\n"); return dual::status_t::NUMERICAL; @@ -2945,7 +2933,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.vector_time += timers.stop_timer(); #ifdef COMPUTE_DUAL_RESIDUAL - phase2::compute_dual_residual(lp.A, objective, sol.y, sol.z, dual_res1); + phase2::compute_dual_residual(lp.A, objective, y, z, dual_res1); f_t dual_res_norm = vector_norm_inf(dual_res1); if (dual_res_norm > settings.dual_tol) { settings.log.printf("|| A'*y + z - c || %e steplength %e\n", dual_res_norm, step_length); @@ -2958,7 +2946,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, settings, bounded_variables, objective, - sol.z, + z, delta_z_indices, nonbasic_list, entering_index, @@ -2983,7 +2971,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, atilde_sparse, delta_xB_0_sparse, delta_x_flip, - sol.x); + x); timers.ftran_time += timers.stop_timer(); } @@ -3002,7 +2990,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, basic_list, delta_x_flip, rhs_sparse, - sol.x, + x, utilde_sparse, scaled_delta_xB_sparse, delta_x) == -1) { @@ -3044,13 +3032,12 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); // x <- x + delta_x - phase2::update_primal_variables( - scaled_delta_xB_sparse, basic_list, delta_x, entering_index, sol.x); + phase2::update_primal_variables(scaled_delta_xB_sparse, basic_list, delta_x, entering_index, x); timers.vector_time += timers.stop_timer(); #ifdef COMPUTE_PRIMAL_RESIDUAL residual = lp.rhs; - matrix_vector_multiply(lp.A, 1.0, sol.x, -1.0, residual); + matrix_vector_multiply(lp.A, 1.0, x, -1.0, residual); primal_residual = vector_norm_inf(residual); if (iter % 100 == 0 && primal_residual > 10 * settings.primal_tol) { settings.log.printf("|| A*x - b || %e\n", primal_residual); @@ -3073,7 +3060,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::update_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, entering_index, leaving_index, delta_xB_0_sparse.i.underlying(), @@ -3085,7 +3072,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::update_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, entering_index, leaving_index, scaled_delta_xB_sparse.i.underlying(), @@ -3095,7 +3082,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // Update the entering variable phase2::update_single_primal_infeasibility(lp.lower, lp.upper, - sol.x, + x, settings.primal_tol, squared_infeasibilities.underlying(), infeasibility_indices.underlying(), @@ -3106,7 +3093,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #if CHECK_PRIMAL_INFEASIBILITIES phase2::check_primal_infeasibilities( - lp, settings, basic_list, sol.x, squared_infeasibilities, infeasibility_indices); + lp, settings, basic_list, x, squared_infeasibilities, infeasibility_indices); #endif timers.update_infeasibility_time += timers.stop_timer(); @@ -3115,7 +3102,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); f_t sum_perturb = 0.0; - phase2::compute_perturbation(lp, settings, delta_z_indices, sol.z, objective, sum_perturb); + phase2::compute_perturbation(lp, settings, delta_z_indices, z, objective, sum_perturb); timers.perturb_time += timers.stop_timer(); // Update basis information @@ -3184,13 +3171,13 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector unperturbed_x(n); phase2::compute_primal_solution_from_basis( lp, ft, basic_list, nonbasic_list, vstatus, unperturbed_x, xB_workspace); - sol.x = unperturbed_x; + x = unperturbed_x; } primal_infeasibility_squared = phase2::compute_initial_primal_infeasibilities(lp, settings, basic_list, - sol.x, + x, squared_infeasibilities, infeasibility_indices, primal_infeasibility); diff --git a/cpp/src/dual_simplex/sparse_matrix.cpp b/cpp/src/dual_simplex/sparse_matrix.cpp index 7d510a849..d6eab1c15 100644 --- a/cpp/src/dual_simplex/sparse_matrix.cpp +++ b/cpp/src/dual_simplex/sparse_matrix.cpp @@ -591,7 +591,7 @@ void scatter_dense(const csc_matrix_t& A, i_t j, f_t alpha, VectorF& x } } -// x <- x + alpha * A(:, j) with mark/indices tracking +// x <- x + alpha * A(:, j) template void scatter_dense( const csc_matrix_t& A, i_t j, f_t alpha, VectorF& x, VectorI& mark, VectorI& indices) diff --git a/cpp/src/dual_simplex/triangle_solve.hpp b/cpp/src/dual_simplex/triangle_solve.hpp index cb1ce5a79..f7fc7744d 100644 --- a/cpp/src/dual_simplex/triangle_solve.hpp +++ b/cpp/src/dual_simplex/triangle_solve.hpp @@ -35,7 +35,8 @@ i_t lower_triangular_solve(const csc_matrix_t& L, VectorF& x) i_t col_end = L.col_start[j + 1]; if (x[j] != 0.0) { x[j] /= L.x[col_start]; - auto x_j = x[j]; + auto x_j = x[j]; // hoist this load out of the loop + // as the compiler cannot guess that x[j] never aliases to x[L.i[p]] for (i_t p = col_start + 1; p < col_end; ++p) { x[L.i[p]] -= L.x[p] * x_j; } @@ -74,7 +75,7 @@ i_t upper_triangular_solve(const csc_matrix_t& U, VectorF& x) const i_t col_end = U.col_start[j + 1] - 1; if (x[j] != 0.0) { x[j] /= U.x[col_end]; - auto x_j = x[j]; + auto x_j = x[j]; // same x_j load hoisting for (i_t p = col_start; p < col_end; ++p) { x[U.i[p]] -= U.x[p] * x_j; } diff --git a/cpp/src/math_optimization/solver_settings.cu b/cpp/src/math_optimization/solver_settings.cu index e16b4be46..1b5efeb0d 100644 --- a/cpp/src/math_optimization/solver_settings.cu +++ b/cpp/src/math_optimization/solver_settings.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -89,7 +89,7 @@ solver_settings_t::solver_settings_t() : pdlp_settings(), mip_settings {CUOPT_BARRIER_DUAL_INITIAL_POINT, &pdlp_settings.barrier_dual_initial_point, -1, 1, -1}, {CUOPT_NUM_GPUS, &pdlp_settings.num_gpus, 1, 2, 1}, {CUOPT_NUM_GPUS, &mip_settings.num_gpus, 1, 2, 1}, - {CUOPT_MIP_DETERMINISTIC, &mip_settings.determinism_mode, CUOPT_MODE_OPPORTUNISTIC, CUOPT_MODE_DETERMINISTIC, CUOPT_MODE_OPPORTUNISTIC} + {CUOPT_MIP_DETERMINISM_MODE, &mip_settings.determinism_mode, CUOPT_MODE_OPPORTUNISTIC, CUOPT_MODE_DETERMINISTIC, CUOPT_MODE_OPPORTUNISTIC} }; // Bool parameters diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 0207df346..3f66a3c37 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -194,7 +194,6 @@ template void local_search_t::stop_cpufj_deterministic() { if (deterministic_cpu_fj.fj_cpu) { - // Deregister from producer_sync before stopping if (deterministic_cpu_fj.fj_cpu->producer_sync) { deterministic_cpu_fj.fj_cpu->producer_sync->deregister_producer( &deterministic_cpu_fj.fj_cpu->work_units_elapsed); diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 6e04a9fa8..90b4cac5c 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -233,7 +233,7 @@ solution_t mip_solver_t::run_solver() branch_and_bound.get(), std::placeholders::_1); } else if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - // TODO + // TODO once deterministic GPU heuristics are integrated context.problem_ptr->branch_and_bound_callback = [bb = branch_and_bound.get()](const std::vector& solution) { bb->set_new_solution_deterministic(solution, 0.0); diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 75aea76e3..3839b1aa8 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -496,7 +496,7 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { using const_reverse_iterator = std::reverse_iterator; // Constructors - memop_instrumentation_wrapper_t() : array_(), wrapped_ptr(nullptr) + memop_instrumentation_wrapper_t() : array_() { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -541,11 +541,8 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } - // Copy constructor - copy from wrapped array if wrapping, never share wrapped pointer memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : memory_instrumentation_base_t(other), - array_(other.wrapped_ptr ? *other.wrapped_ptr : other.array_), - wrapped_ptr(nullptr) + : memory_instrumentation_base_t(other), array_(other.array_) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -554,12 +551,8 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } - // Move constructor - copy from wrapped array if wrapping (can't move wrapped), never share - // pointer memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : memory_instrumentation_base_t(std::move(other)), - array_(other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_)), - wrapped_ptr(nullptr) + : memory_instrumentation_base_t(std::move(other)), array_(std::move(other.array_)) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -568,21 +561,13 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } } - // Copy assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) { if (this != &other) { memory_instrumentation_base_t::operator=(other); - // Get source data (from wrapped or owned array) - const T& source = other.wrapped_ptr ? *other.wrapped_ptr : other.array_; - // Write to destination (wrapped or owned array) - if (wrapped_ptr) { - *wrapped_ptr = source; - } else { - array_ = source; - } + array_ = other.array_; if constexpr (type_traits_utils::has_data::value) { - data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } @@ -590,19 +575,13 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { return *this; } - // Move assignment - handle both source and destination wrapping cases memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept { if (this != &other) { memory_instrumentation_base_t::operator=(std::move(other)); - // Get source data (copy from wrapped, move from owned) - if (wrapped_ptr) { - *wrapped_ptr = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); - } else { - array_ = other.wrapped_ptr ? *other.wrapped_ptr : std::move(other.array_); - } + array_ = std::move(other.array_); if constexpr (type_traits_utils::has_data::value) { - data_ptr = wrapped_ptr ? wrapped_ptr->data() : array_.data(); + data_ptr = array_.data(); } else { data_ptr = nullptr; } @@ -786,30 +765,11 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { T&& release_array() { return std::move(array_); } - // Wrap an external vector without taking ownership - void wrap(T& external_array) - { - wrapped_ptr = &external_array; - if constexpr (type_traits_utils::has_data::value) { data_ptr = external_array.data(); } - } - - // Stop wrapping and return to using the owned array - void unwrap() - { - wrapped_ptr = nullptr; - if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); } - } - - // Check if currently wrapping an external array - bool is_wrapping() const { return wrapped_ptr != nullptr; } - - // Get the underlying container (wrapped or owned) - T& underlying() { return wrapped_ptr ? *wrapped_ptr : array_; } - const T& underlying() const { return wrapped_ptr ? *wrapped_ptr : array_; } + T& underlying() { return array_; } + const T& underlying() const { return array_; } private: T array_; - T* wrapped_ptr{nullptr}; value_type* data_ptr{nullptr}; }; @@ -834,8 +794,8 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { // Constructors - forward everything to the underlying container memop_instrumentation_wrapper_t() = default; - memop_instrumentation_wrapper_t(const T& arr) : array_(arr), wrapped_ptr_(nullptr) {} - memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)), wrapped_ptr_(nullptr) {} + memop_instrumentation_wrapper_t(const T& arr) : array_(arr) {} + memop_instrumentation_wrapper_t(T&& arr) : array_(std::move(arr)) {} template , T> && (sizeof...(Args) > 0 || !std::is_convertible_v)>> explicit memop_instrumentation_wrapper_t(Arg&& arg, Args&&... args) - : array_(std::forward(arg), std::forward(args)...), wrapped_ptr_(nullptr) + : array_(std::forward(arg), std::forward(args)...) { } - // Copy constructor - always copy the data, never share wrapped pointer memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_), wrapped_ptr_(nullptr) + : array_(other.array_) { } - // Move constructor - take ownership of array, never share wrapped pointer memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : array_(other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_)), - wrapped_ptr_(nullptr) + : array_(std::move(other.array_)) { } - // Copy assignment - always copy the data memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) { - if (this != &other) { - if (wrapped_ptr_) { - *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; - } else { - array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : other.array_; - } - } + if (this != &other) { array_ = other.array_; } return *this; } - // Move assignment - take the data memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept { - if (this != &other) { - if (wrapped_ptr_) { - *wrapped_ptr_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); - } else { - array_ = other.wrapped_ptr_ ? *other.wrapped_ptr_ : std::move(other.array_); - } - } + if (this != &other) { array_ = std::move(other.array_); } return *this; } @@ -947,18 +890,11 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { T&& release_array() { return std::move(array_); } - // Wrap/unwrap interface (for compatibility, but wrap is essentially a no-op for perf) - void wrap(T& external_array) { wrapped_ptr_ = &external_array; } - void unwrap() { wrapped_ptr_ = nullptr; } - bool is_wrapping() const { return wrapped_ptr_ != nullptr; } - - // Get the underlying container - T& underlying() { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } - const T& underlying() const { return wrapped_ptr_ ? *wrapped_ptr_ : array_; } + T& underlying() { return array_; } + const T& underlying() const { return array_; } private: T array_; - T* wrapped_ptr_{nullptr}; }; #endif // CUOPT_ENABLE_MEMORY_INSTRUMENTATION diff --git a/cpp/src/utilities/producer_sync.hpp b/cpp/src/utilities/producer_sync.hpp index 13c9bde38..df410fca8 100644 --- a/cpp/src/utilities/producer_sync.hpp +++ b/cpp/src/utilities/producer_sync.hpp @@ -27,7 +27,7 @@ namespace cuopt { * One-way synchronization utility for producer threads. * * Producers (e.g., CPUFJ) register their work unit progress atomics and advance independently. - * The consumer (e.g., B&B BSP coordinator) can wait until all producers have reached a + * The consumer (e.g., B&B coordinator) can wait until all producers have reached a * target work unit threshold before proceeding. * * Key invariant: Producers must not fall behind the consumer's horizon. The consumer @@ -37,10 +37,6 @@ class producer_sync_t { public: producer_sync_t() = default; - /** - * Register a producer's work unit progress atomic. - * Must be called before registration_complete(). - */ void register_producer(std::atomic* progress_ptr) { std::lock_guard lock(mutex_); @@ -48,9 +44,6 @@ class producer_sync_t { cv_.notify_all(); } - /** - * Deregister a producer (e.g., when it terminates). - */ void deregister_producer(std::atomic* progress_ptr) { std::lock_guard lock(mutex_); @@ -70,9 +63,6 @@ class producer_sync_t { cv_.notify_all(); } - /** - * Check if registration is complete (non-blocking). - */ bool is_registration_complete() const { std::lock_guard lock(mutex_); @@ -100,25 +90,6 @@ class producer_sync_t { */ void notify_progress() { cv_.notify_all(); } - /** - * Get the minimum work units among all registered producers. - * Returns infinity if no producers are registered. - */ - double get_min_producer_progress() const - { - std::lock_guard lock(mutex_); - if (producers_.empty()) { return std::numeric_limits::infinity(); } - - double min_progress = std::numeric_limits::infinity(); - for (const auto* progress_ptr : producers_) { - min_progress = std::min(min_progress, progress_ptr->load(std::memory_order_acquire)); - } - return min_progress; - } - - /** - * Get the number of registered producers. - */ size_t num_producers() const { std::lock_guard lock(mutex_); diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp index 0aebf953d..949d84b2c 100644 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -30,22 +30,9 @@ #include "models/fj_predictor/header.h" #include "models/pdlp_predictor/header.h" -namespace cuopt { - -template -static inline uint32_t compute_hash(std::vector h_contents) -{ - // FNV-1a hash +#include "hashing.hpp" - uint32_t hash = 2166136261u; // FNV-1a 32-bit offset basis - std::vector byte_contents(h_contents.size() * sizeof(i_t)); - std::memcpy(byte_contents.data(), h_contents.data(), h_contents.size() * sizeof(i_t)); - for (size_t i = 0; i < byte_contents.size(); ++i) { - hash ^= byte_contents[i]; - hash *= 16777619u; - } - return hash; -} +namespace cuopt { template float work_unit_predictor_t::predict_scalar( @@ -69,7 +56,7 @@ float work_unit_predictor_t::predict_scalar( cache_vec.push_back(data[i].missing != -1 ? data[i].fvalue : std::numeric_limits::quiet_NaN()); } - uint32_t key = compute_hash(cache_vec); + uint32_t key = cuopt::linear_programming::detail::compute_hash(cache_vec); auto cached_it = prediction_cache.find(key); if (cached_it != prediction_cache.end()) { return cached_it->second; } diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp index 9c14a3a1e..746d5e6dd 100644 --- a/cpp/src/utilities/work_unit_scheduler.cpp +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -102,15 +102,15 @@ sync_result_t work_unit_scheduler_t::wait_for_next_sync(work_limit_context_t& ct return stopped_.load() ? sync_result_t::STOPPED : sync_result_t::CONTINUE; } -void work_unit_scheduler_t::queue_callback(work_limit_context_t& source, - work_limit_context_t& destination, - callback_t callback) -{ - std::lock_guard lock(mutex_); - double tag = source.global_work_units_elapsed; - auto it = callback_queues_.find(&destination); - if (it != callback_queues_.end()) { it->second.push({tag, std::move(callback)}); } -} +// void work_unit_scheduler_t::queue_callback(work_limit_context_t& source, +// work_limit_context_t& destination, +// callback_t callback) +// { +// std::lock_guard lock(mutex_); +// double tag = source.global_work_units_elapsed; +// auto it = callback_queues_.find(&destination); +// if (it != callback_queues_.end()) { it->second.push({tag, std::move(callback)}); } +// } double work_unit_scheduler_t::current_sync_target() const { @@ -167,9 +167,9 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double size_t my_exit_generation = exit_generation_; if (verbose) { CUOPT_LOG_DEBUG("[%s] Processing callbacks", ctx.name.c_str()); } - lock.unlock(); - process_callbacks_for_context(ctx, sync_target); - lock.lock(); + // lock.unlock(); + // process_callbacks_for_context(ctx, sync_target); + // lock.lock(); if (verbose) { CUOPT_LOG_DEBUG("[%s] Done processing callbacks", ctx.name.c_str()); } contexts_at_barrier_--; @@ -205,26 +205,10 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double } } -void work_unit_scheduler_t::process_callbacks_for_context(work_limit_context_t& ctx, - double up_to_work_units) -{ - std::vector to_execute; - - { - std::lock_guard lock(mutex_); - auto it = callback_queues_.find(&ctx); - if (it == callback_queues_.end()) return; +// void work_unit_scheduler_t::process_callbacks_for_context(work_limit_context_t& ctx, +// double up_to_work_units) +// { - auto& queue = it->second; - while (!queue.empty() && queue.top().work_unit_tag <= up_to_work_units) { - to_execute.push_back(std::move(const_cast(queue.top()).callback)); - queue.pop(); - } - } - - for (auto& cb : to_execute) { - cb(); - } -} +// } } // namespace cuopt diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index 12b03929c..59b7fbffe 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -45,9 +45,9 @@ class work_unit_scheduler_t { void register_context(work_limit_context_t& ctx); void deregister_context(work_limit_context_t& ctx); void on_work_recorded(work_limit_context_t& ctx, double total_work); - void queue_callback(work_limit_context_t& source, - work_limit_context_t& destination, - callback_t callback); + // void queue_callback(work_limit_context_t& source, + // work_limit_context_t& destination, + // callback_t callback); // Sync callback support - callback is executed when all contexts reach sync point // If callback returns true, scheduler stops and all workers exit cleanly From 2b7859e97f1515938f92a9771927e9cff5582ddf Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 15:49:07 +0000 Subject: [PATCH 224/366] log ds features and bounds strenghtening --- cpp/src/dual_simplex/bounds_strengthening.cpp | 10 ++- cpp/src/dual_simplex/bounds_strengthening.hpp | 3 +- cpp/src/dual_simplex/branch_and_bound.cpp | 9 ++ cpp/src/dual_simplex/branch_and_bound.hpp | 4 + .../dual_simplex/dual_simplex_features.hpp | 88 +++++++++---------- cpp/src/dual_simplex/phase2.cpp | 13 +-- 6 files changed, 71 insertions(+), 56 deletions(-) diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index 8adc1de8f..4fbe1525b 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -102,14 +102,13 @@ bool bounds_strengthening_t::bounds_strengthening( std::vector variable_changed(n, false); std::vector constraint_changed_next(m, false); - // ins_wrapper prevetns compiler autovectorization - // fine since for now bounds_strengthening doesn't get a work unit estimator - // but should be easy enough to estimate manually auto& A_i = A.i.underlying(); auto& A_x = A.x.underlying(); auto& Arow_j = Arow.j.underlying(); auto& Arow_x = Arow.x.underlying(); + size_t nnz_processed = 0; + if (!bounds_changed.empty()) { std::fill(constraint_changed.begin(), constraint_changed.end(), false); for (i_t i = 0; i < n; ++i) { @@ -135,6 +134,7 @@ bool bounds_strengthening_t::bounds_strengthening( if (!constraint_changed[i]) { continue; } const i_t row_start = Arow.row_start[i]; const i_t row_end = Arow.row_start[i + 1]; + nnz_processed += (row_end - row_start); f_t min_a = 0.0; f_t max_a = 0.0; @@ -170,6 +170,7 @@ bool bounds_strengthening_t::bounds_strengthening( cnst_ub, min_a, max_a); + last_nnz_processed = nnz_processed; return false; } @@ -189,6 +190,7 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t row_start = A.col_start[k]; const i_t row_end = A.col_start[k + 1]; + nnz_processed += (row_end - row_start); for (i_t p = row_start; p < row_end; ++p) { const i_t i = A_i[p]; @@ -221,6 +223,7 @@ bool bounds_strengthening_t::bounds_strengthening( if (new_lb > new_ub + 1e-6) { settings.log.debug( "Iter:: %d, Infeasible variable after update %d, %e > %e\n", iter, k, new_lb, new_ub); + last_nnz_processed = nnz_processed; return false; } if (new_lb != old_lb || new_ub != old_ub) { @@ -288,6 +291,7 @@ bool bounds_strengthening_t::bounds_strengthening( lower_bounds = lower; upper_bounds = upper; + last_nnz_processed = nnz_processed; return true; } diff --git a/cpp/src/dual_simplex/bounds_strengthening.hpp b/cpp/src/dual_simplex/bounds_strengthening.hpp index e7e218b82..bb069a067 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.hpp +++ b/cpp/src/dual_simplex/bounds_strengthening.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -25,6 +25,7 @@ class bounds_strengthening_t { const simplex_solver_settings_t& settings); std::vector bounds_changed; + size_t last_nnz_processed{0}; private: const csc_matrix_t& A; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 5f6452fa4..a616df33e 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -752,8 +752,17 @@ dual::status_t branch_and_bound_t::solve_node_lp( bool feasible; { raft::common::nvtx::range scope_bs("BB::bound_strengthening"); + f_t bs_start = tic(); feasible = node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); + f_t bs_runtime = toc(bs_start); + + bs_features_.m = leaf_problem.num_rows; + bs_features_.n = leaf_problem.num_cols; + bs_features_.nnz = leaf_problem.A.col_start[leaf_problem.num_cols]; + bs_features_.nnz_processed = node_presolver.last_nnz_processed; + bs_features_.runtime = bs_runtime; + bs_features_.log_single(bs_features_.m, bs_features_.n, bs_features_.nnz); } dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index a4cd67b3d..b8dbe561f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -11,6 +11,7 @@ #include #include #include +#include #include #include #include @@ -176,6 +177,9 @@ class branch_and_bound_t { // Structure with the general info of the solver. bnb_stats_t exploration_stats_; + // Bounds strengthening feature tracking (non-deterministic path) + bounds_strengthening_features_t bs_features_; + // Mutex for repair omp_mutex_t mutex_repair_; std::vector> repair_queue_; diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 0ad68185d..9a80c9f4b 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -116,33 +116,33 @@ struct dual_simplex_features_t { */ void log_features(const simplex_solver_settings_t& settings) const { - // printf( - // "DS_FEATURES: iter=%d m=%d n=%d nnz=%d density=%.6e avg_nnz_col=%.2f avg_nnz_row=%.2f " - // "bounded=%d free=%d fixed=%d phase=%d refact_freq=%d num_refacts=%d num_updates=%d " - // "sparse_dz=%d dense_dz=%d bound_flips=%d num_infeas=%d dy_nz_pct=%.2f " - // "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", - // iteration, - // num_rows, - // num_cols, - // num_nonzeros, - // matrix_density, - // avg_nnz_per_col, - // avg_nnz_per_row, - // num_bounded_vars, - // num_free_vars, - // num_fixed_vars, - // phase, - // refactor_frequency, - // num_refactors, - // num_basis_updates, - // sparse_delta_z_count, - // dense_delta_z_count, - // total_bound_flips, - // num_infeasibilities, - // delta_y_nz_percentage, - // byte_loads, - // byte_stores, - // interval_runtime); + printf( + "DS_FEATURES: iter=%d m=%d n=%d nnz=%d density=%.6e avg_nnz_col=%.2f avg_nnz_row=%.2f " + "bounded=%d free=%d fixed=%d phase=%d refact_freq=%d num_refacts=%d num_updates=%d " + "sparse_dz=%d dense_dz=%d bound_flips=%d num_infeas=%d dy_nz_pct=%.2f " + "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", + iteration, + num_rows, + num_cols, + num_nonzeros, + matrix_density, + avg_nnz_per_col, + avg_nnz_per_row, + num_bounded_vars, + num_free_vars, + num_fixed_vars, + phase, + refactor_frequency, + num_refactors, + num_basis_updates, + sparse_delta_z_count, + dense_delta_z_count, + total_bound_flips, + num_infeasibilities, + delta_y_nz_percentage, + byte_loads, + byte_stores, + interval_runtime); } /** @@ -167,16 +167,14 @@ struct bounds_strengthening_features_t { i_t nnz{0}; // number of nonzeros in constraint matrix i_t num_iterations{0}; // propagation iterations until fixpoint i_t num_bounds_changed{0}; // total bounds tightened - size_t byte_loads{0}; - size_t byte_stores{0}; + size_t nnz_processed{0}; // non-zeros traversed (work metric) f_t runtime{0.0}; // Interval aggregates (for when bounds strengthening is called multiple times) i_t call_count{0}; i_t total_iterations{0}; i_t total_bounds_changed{0}; - size_t total_byte_loads{0}; - size_t total_byte_stores{0}; + size_t total_nnz_processed{0}; f_t total_runtime{0.0}; void accumulate() @@ -184,25 +182,22 @@ struct bounds_strengthening_features_t { call_count++; total_iterations += num_iterations; total_bounds_changed += num_bounds_changed; - total_byte_loads += byte_loads; - total_byte_stores += byte_stores; + total_nnz_processed += nnz_processed; total_runtime += runtime; } void log_single(i_t m_val, i_t n_val, i_t nnz_val) const { - // printf( - // "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " - // "iterations=%d bounds_changed=%d " - // "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", - // m_val, - // n_val, - // nnz_val, - // num_iterations, - // num_bounds_changed, - // byte_loads, - // byte_stores, - // runtime); + printf( + "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " + "iterations=%d bounds_changed=%d nnz_processed=%zu runtime=%.6f\n", + m_val, + n_val, + nnz_val, + num_iterations, + num_bounds_changed, + nnz_processed, + runtime); } void reset() @@ -210,8 +205,7 @@ struct bounds_strengthening_features_t { call_count = 0; total_iterations = 0; total_bounds_changed = 0; - total_byte_loads = 0; - total_byte_stores = 0; + total_nnz_processed = 0; total_runtime = 0.0; } }; diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 5102b4326..8711de567 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2572,7 +2572,6 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, }; cuopt::scope_guard work_unit_guard([&]() { - if (!work_unit_context) return; i_t remaining_iters = iter - last_feature_log_iter; if (remaining_iters <= 0) return; @@ -2592,11 +2591,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, features.total_bound_flips = total_bound_flips; features.num_infeasibilities = infeasibility_indices.size(); features.delta_y_nz_percentage = delta_y_nz_percentage; + features.log_features(settings); - f_t prediction = predict_work_units(remaining_iters); - // printf("DualSimplex determ (final): %d iters, predicted %.4f\n", remaining_iters, - // prediction); - work_unit_context->record_work(prediction); + if (work_unit_context) { + f_t prediction = predict_work_units(remaining_iters); + // printf("DualSimplex determ (final): %d iters, predicted %.4f\n", remaining_iters, + // prediction); + work_unit_context->record_work(prediction); + } }); while (iter < iter_limit) { @@ -3230,6 +3232,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, features.total_bound_flips = total_bound_flips; features.num_infeasibilities = infeasibility_indices.size(); features.delta_y_nz_percentage = delta_y_nz_percentage; + features.log_features(settings); if (work_unit_context) { f_t prediction = predict_work_units(iters_elapsed); From af31388c1425034c2c5c4c0c21b9f7eed1d797b7 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 17:09:53 +0000 Subject: [PATCH 225/366] fix logs --- cpp/src/dual_simplex/dual_simplex_features.hpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 9a80c9f4b..2eaf4ef4e 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -116,7 +116,7 @@ struct dual_simplex_features_t { */ void log_features(const simplex_solver_settings_t& settings) const { - printf( + CUOPT_LOG_DEBUG( "DS_FEATURES: iter=%d m=%d n=%d nnz=%d density=%.6e avg_nnz_col=%.2f avg_nnz_row=%.2f " "bounded=%d free=%d fixed=%d phase=%d refact_freq=%d num_refacts=%d num_updates=%d " "sparse_dz=%d dense_dz=%d bound_flips=%d num_infeas=%d dy_nz_pct=%.2f " @@ -188,7 +188,7 @@ struct bounds_strengthening_features_t { void log_single(i_t m_val, i_t n_val, i_t nnz_val) const { - printf( + CUOPT_LOG_DEBUG( "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " "iterations=%d bounds_changed=%d nnz_processed=%zu runtime=%.6f\n", m_val, From f1021391280c691e0359dc4606119a6769457035 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 19:31:25 +0000 Subject: [PATCH 226/366] timing stuff --- .../dual_simplex/dual_simplex_features.hpp | 74 +++++++++---------- cpp/src/dual_simplex/phase2.cpp | 36 +++++++-- cpp/src/utilities/timing_utils.hpp | 71 ++++++++++++++++++ 3 files changed, 136 insertions(+), 45 deletions(-) create mode 100644 cpp/src/utilities/timing_utils.hpp diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 2eaf4ef4e..e715f23b6 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -116,33 +116,33 @@ struct dual_simplex_features_t { */ void log_features(const simplex_solver_settings_t& settings) const { - CUOPT_LOG_DEBUG( - "DS_FEATURES: iter=%d m=%d n=%d nnz=%d density=%.6e avg_nnz_col=%.2f avg_nnz_row=%.2f " - "bounded=%d free=%d fixed=%d phase=%d refact_freq=%d num_refacts=%d num_updates=%d " - "sparse_dz=%d dense_dz=%d bound_flips=%d num_infeas=%d dy_nz_pct=%.2f " - "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", - iteration, - num_rows, - num_cols, - num_nonzeros, - matrix_density, - avg_nnz_per_col, - avg_nnz_per_row, - num_bounded_vars, - num_free_vars, - num_fixed_vars, - phase, - refactor_frequency, - num_refactors, - num_basis_updates, - sparse_delta_z_count, - dense_delta_z_count, - total_bound_flips, - num_infeasibilities, - delta_y_nz_percentage, - byte_loads, - byte_stores, - interval_runtime); + // printf( + // "DS_FEATURES: iter=%d m=%d n=%d nnz=%d density=%.6e avg_nnz_col=%.2f avg_nnz_row=%.2f " + // "bounded=%d free=%d fixed=%d phase=%d refact_freq=%d num_refacts=%d num_updates=%d " + // "sparse_dz=%d dense_dz=%d bound_flips=%d num_infeas=%d dy_nz_pct=%.2f " + // "byte_loads=%zu byte_stores=%zu runtime=%.6f\n", + // iteration, + // num_rows, + // num_cols, + // num_nonzeros, + // matrix_density, + // avg_nnz_per_col, + // avg_nnz_per_row, + // num_bounded_vars, + // num_free_vars, + // num_fixed_vars, + // phase, + // refactor_frequency, + // num_refactors, + // num_basis_updates, + // sparse_delta_z_count, + // dense_delta_z_count, + // total_bound_flips, + // num_infeasibilities, + // delta_y_nz_percentage, + // byte_loads, + // byte_stores, + // interval_runtime); } /** @@ -188,16 +188,16 @@ struct bounds_strengthening_features_t { void log_single(i_t m_val, i_t n_val, i_t nnz_val) const { - CUOPT_LOG_DEBUG( - "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " - "iterations=%d bounds_changed=%d nnz_processed=%zu runtime=%.6f\n", - m_val, - n_val, - nnz_val, - num_iterations, - num_bounds_changed, - nnz_processed, - runtime); + // printf( + // "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " + // "iterations=%d bounds_changed=%d nnz_processed=%zu runtime=%.6f\n", + // m_val, + // n_val, + // nnz_val, + // num_iterations, + // num_bounds_changed, + // nnz_processed, + // runtime); } void reset() diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 8711de567..0a4dda5a6 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -19,6 +19,7 @@ #include #include +#include #include #include @@ -26,6 +27,8 @@ #include +#include +#include #include #include #include @@ -326,28 +329,45 @@ void compute_reduced_cost_update(const lp_problem_t& lp, const i_t m = lp.num_rows; const i_t n = lp.num_cols; + const f_t* __restrict__ delta_y_ptr = delta_y.data(); + const f_t* __restrict__ Ax = lp.A.x.data(); + const i_t* __restrict__ Ai = lp.A.i.data(); + const i_t* __restrict__ ptr_col_start = lp.A.col_start.data(); + f_t* __restrict__ delta_z_ptr = delta_z.data(); + + size_t nnzs_processed = 0; + // delta_zB = sigma*ei for (i_t k = 0; k < m; k++) { - const i_t j = basic_list[k]; - delta_z[j] = 0; + const i_t j = basic_list[k]; + delta_z_ptr[j] = 0; } - delta_z[leaving_index] = direction; + delta_z_ptr[leaving_index] = direction; // delta_zN = -N'*delta_y - for (i_t k = 0; k < n - m; k++) { + const i_t num_nonbasic = n - m; + for (i_t k = 0; k < num_nonbasic; k++) { const i_t j = nonbasic_list[k]; // z_j <- -A(:, j)'*delta_y - const i_t col_start = lp.A.col_start[j]; - const i_t col_end = lp.A.col_start[j + 1]; + const i_t col_start = ptr_col_start[j]; + const i_t col_end = ptr_col_start[j + 1]; f_t dot = 0.0; for (i_t p = col_start; p < col_end; ++p) { - dot += lp.A.x[p] * delta_y[lp.A.i[p]]; + dot += Ax[p] * delta_y_ptr[Ai[p]]; } - delta_z[j] = -dot; + nnzs_processed += col_end - col_start; + + delta_z_ptr[j] = -dot; if (dot != 0.0) { delta_z_indices.push_back(j); // Note delta_z_indices has n elements reserved delta_z_mark[j] = 1; } } + + lp.A.x.byte_loads += nnzs_processed * sizeof(f_t); + lp.A.i.byte_loads += nnzs_processed * sizeof(i_t); + delta_y.byte_loads += nnzs_processed * sizeof(f_t); + lp.A.col_start.byte_loads += 2 * num_nonbasic * sizeof(i_t); + delta_z.byte_stores += (m + 1 + num_nonbasic) * sizeof(f_t); } template diff --git a/cpp/src/utilities/timing_utils.hpp b/cpp/src/utilities/timing_utils.hpp new file mode 100644 index 000000000..569630a86 --- /dev/null +++ b/cpp/src/utilities/timing_utils.hpp @@ -0,0 +1,71 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#if defined(__x86_64__) || defined(_M_X64) || defined(__i386__) || defined(_M_IX86) +#define CUOPT_HAS_RDTSC 1 +#else +#define CUOPT_HAS_RDTSC 0 +#endif + +#if CUOPT_HAS_RDTSC + +#include +#include +#include +#include + +namespace cuopt { + +inline uint64_t rdtsc() +{ + uint32_t lo, hi; + __asm__ volatile("rdtsc" : "=a"(lo), "=d"(hi)); + return ((uint64_t)hi << 32) | lo; +} + +} // namespace cuopt + +// clang-format off +#define CYCLE_TIMING_PROLOGUE(name) \ + static constexpr size_t timing_buffer_size_##name = 1024; \ + static thread_local std::array timing_buffer_##name; \ + static thread_local size_t timing_idx_##name = 0; \ + uint64_t t_start_##name = cuopt::rdtsc(); + +#define CYCLE_TIMING_EPILOGUE(name) \ + do { \ + uint64_t t_end_##name = cuopt::rdtsc(); \ + timing_buffer_##name[timing_idx_##name++] = t_end_##name - t_start_##name; \ + if (timing_idx_##name == timing_buffer_size_##name) { \ + uint64_t sum_##name = 0; \ + for (size_t i = 0; i < timing_buffer_size_##name; ++i) \ + sum_##name += timing_buffer_##name[i]; \ + uint64_t avg_##name = sum_##name / timing_buffer_size_##name; \ + std::array sorted_##name = timing_buffer_##name; \ + std::nth_element(sorted_##name.begin(), \ + sorted_##name.begin() + timing_buffer_size_##name / 2, \ + sorted_##name.end()); \ + uint64_t median_##name = sorted_##name[timing_buffer_size_##name / 2]; \ + printf(#name ": avg=%lu cycles, median=%lu cycles (n=%zu)\n", \ + avg_##name, median_##name, timing_buffer_size_##name); \ + timing_idx_##name = 0; \ + } \ + } while (0) +// clang-format on + +#else // !CUOPT_HAS_RDTSC + +#define CYCLE_TIMING_PROLOGUE(name) \ + do { \ + } while (0) +#define CYCLE_TIMING_EPILOGUE(name) \ + do { \ + } while (0) + +#endif // CUOPT_HAS_RDTSC From 5a623931d98d3ee7b95d4f368d181794fa5ea934 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 19:33:08 +0000 Subject: [PATCH 227/366] bump 1 --- cpp/src/utilities/timing_utils.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/utilities/timing_utils.hpp b/cpp/src/utilities/timing_utils.hpp index 569630a86..dcf9cf748 100644 --- a/cpp/src/utilities/timing_utils.hpp +++ b/cpp/src/utilities/timing_utils.hpp @@ -19,7 +19,7 @@ #include #include #include - +// A namespace cuopt { inline uint64_t rdtsc() From fc82a41493ac716934e57b6040824ce6981164bd Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 20 Jan 2026 19:33:28 +0000 Subject: [PATCH 228/366] bump 2 --- cpp/src/utilities/timing_utils.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/utilities/timing_utils.hpp b/cpp/src/utilities/timing_utils.hpp index dcf9cf748..569630a86 100644 --- a/cpp/src/utilities/timing_utils.hpp +++ b/cpp/src/utilities/timing_utils.hpp @@ -19,7 +19,7 @@ #include #include #include -// A + namespace cuopt { inline uint64_t rdtsc() From 115a0b17db2988352c9b44a32da705a881130dda Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 21 Jan 2026 13:16:52 +0100 Subject: [PATCH 229/366] silenced logs from the concurrent mode when running inside MIP. ignored concurrent solution when the B&B is already running. moved number of threads in the probing cache to the setting struct. --- cpp/src/dual_simplex/branch_and_bound.hpp | 18 +++--- cpp/src/linear_programming/solve.cu | 73 +++++++++++++---------- cpp/src/mip/presolve/bounds_presolve.cuh | 5 +- cpp/src/mip/presolve/probing_cache.cu | 4 +- 4 files changed, 56 insertions(+), 44 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index dac1ab393..c22d3ef28 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -84,14 +84,16 @@ class branch_and_bound_t { f_t user_objective, i_t iterations) { - root_crossover_soln_.x = primal; - root_crossover_soln_.y = dual; - root_crossover_soln_.z = reduced_costs; - root_objective_ = objective; - root_crossover_soln_.objective = objective; - root_crossover_soln_.user_objective = user_objective; - root_crossover_soln_.iterations = iterations; - root_crossover_solution_set_.store(true, std::memory_order_release); + if (!is_running) { + root_crossover_soln_.x = primal; + root_crossover_soln_.y = dual; + root_crossover_soln_.z = reduced_costs; + root_objective_ = objective; + root_crossover_soln_.objective = objective; + root_crossover_soln_.user_objective = user_objective; + root_crossover_soln_.iterations = iterations; + root_crossover_solution_set_.store(true, std::memory_order_release); + } } // Set a solution based on the user problem during the course of the solve diff --git a/cpp/src/linear_programming/solve.cu b/cpp/src/linear_programming/solve.cu index d038ade72..9982924ea 100644 --- a/cpp/src/linear_programming/solve.cu +++ b/cpp/src/linear_programming/solve.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -42,6 +42,9 @@ #include // For std::thread +#define CUOPT_LP_SOLVER_LOG_INFO(...) \ + if (!settings.inside_mip) { CUOPT_LOG_INFO(__VA_ARGS__); } + namespace cuopt::linear_programming { // This serves as both a warm up but also a mandatory initial call to setup cuSparse and cuBLAS @@ -417,7 +420,7 @@ run_barrier(dual_simplex::user_problem_t& user_problem, auto status = dual_simplex::solve_linear_program_with_barrier( user_problem, barrier_settings, solution); - CUOPT_LOG_INFO("Barrier finished in %.2f seconds", timer.elapsed_time()); + CUOPT_LP_SOLVER_LOG_INFO("Barrier finished in %.2f seconds", timer.elapsed_time()); if (settings.concurrent_halt != nullptr && (status == dual_simplex::lp_status_t::OPTIMAL || status == dual_simplex::lp_status_t::UNBOUNDED || @@ -489,9 +492,9 @@ run_dual_simplex(dual_simplex::user_problem_t& user_problem, auto status = dual_simplex::solve_linear_program(user_problem, dual_simplex_settings, solution); - CUOPT_LOG_INFO("Dual simplex finished in %.2f seconds, total time %.2f", - timer_dual_simplex.elapsed_time(), - timer.elapsed_time()); + CUOPT_LP_SOLVER_LOG_INFO("Dual simplex finished in %.2f seconds, total time %.2f", + timer_dual_simplex.elapsed_time(), + timer.elapsed_time()); if (settings.concurrent_halt != nullptr && (status == dual_simplex::lp_status_t::OPTIMAL || status == dual_simplex::lp_status_t::UNBOUNDED || @@ -530,7 +533,8 @@ static optimization_problem_solution_t run_pdlp_solver( bool is_batch_mode) { if (problem.n_constraints == 0) { - CUOPT_LOG_INFO("No constraints in the problem: PDLP can't be run, use Dual Simplex instead."); + CUOPT_LP_SOLVER_LOG_INFO( + "No constraints in the problem: PDLP can't be run, use Dual Simplex instead."); return optimization_problem_solution_t{pdlp_termination_status_t::NumericalError, problem.handle_ptr->get_stream()}; } @@ -551,14 +555,15 @@ optimization_problem_solution_t run_pdlp(detail::problem_t& auto sol = run_pdlp_solver(problem, settings, timer, is_batch_mode); auto pdlp_solve_time = timer_pdlp.elapsed_time(); sol.set_solve_time(timer.elapsed_time()); - CUOPT_LOG_INFO("PDLP finished"); + CUOPT_LP_SOLVER_LOG_INFO("PDLP finished"); if (sol.get_termination_status() != pdlp_termination_status_t::ConcurrentLimit) { - CUOPT_LOG_INFO("Status: %s Objective: %.8e Iterations: %d Time: %.3fs, Total time %.3fs", - sol.get_termination_status_string().c_str(), - sol.get_objective_value(), - sol.get_additional_termination_information().number_of_steps_taken, - pdlp_solve_time, - sol.get_solve_time()); + CUOPT_LP_SOLVER_LOG_INFO( + "Status: %s Objective: %.8e Iterations: %d Time: %.3fs, Total time %.3fs", + sol.get_termination_status_string().c_str(), + sol.get_objective_value(), + sol.get_additional_termination_information().number_of_steps_taken, + pdlp_solve_time, + sol.get_solve_time()); } const bool do_crossover = settings.crossover; @@ -620,12 +625,12 @@ optimization_problem_solution_t run_pdlp(detail::problem_t& info, termination_status); sol.copy_from(problem.handle_ptr, sol_crossover); - CUOPT_LOG_INFO("Crossover status %s", sol.get_termination_status_string().c_str()); + CUOPT_LP_SOLVER_LOG_INFO("Crossover status %s", sol.get_termination_status_string().c_str()); } if (settings.method == method_t::Concurrent && settings.concurrent_halt != nullptr && crossover_info == 0 && sol.get_termination_status() == pdlp_termination_status_t::Optimal) { // We finished. Tell dual simplex to stop if it is still running. - CUOPT_LOG_INFO("PDLP finished. Telling others to stop"); + CUOPT_LP_SOLVER_LOG_INFO("PDLP finished. Telling others to stop"); *settings.concurrent_halt = 1; } return sol; @@ -653,7 +658,7 @@ optimization_problem_solution_t run_concurrent( const timer_t& timer, bool is_batch_mode) { - CUOPT_LOG_INFO("Running concurrent\n"); + CUOPT_LP_SOLVER_LOG_INFO("Running concurrent\n"); timer_t timer_concurrent(timer.remaining_time()); // Copy the settings so that we can set the concurrent halt pointer @@ -668,7 +673,7 @@ optimization_problem_solution_t run_concurrent( if (settings.num_gpus > 1) { int device_count = raft::device_setter::get_device_count(); - CUOPT_LOG_INFO("Running PDLP and Barrier on %d GPUs", device_count); + CUOPT_LP_SOLVER_LOG_INFO("Running PDLP and Barrier on %d GPUs", device_count); cuopt_expects( device_count > 1, error_type_t::RuntimeError, "Multi-GPU mode requires at least 2 GPUs"); } @@ -752,41 +757,43 @@ optimization_problem_solution_t run_concurrent( 1); f_t end_time = timer.elapsed_time(); - CUOPT_LOG_INFO( + CUOPT_LP_SOLVER_LOG_INFO( "Concurrent time: %.3fs, total time %.3fs", timer_concurrent.elapsed_time(), end_time); // Check status to see if we should return the pdlp solution or the dual simplex solution if (!settings.inside_mip && (sol_dual_simplex.get_termination_status() == pdlp_termination_status_t::Optimal || sol_dual_simplex.get_termination_status() == pdlp_termination_status_t::PrimalInfeasible || sol_dual_simplex.get_termination_status() == pdlp_termination_status_t::DualInfeasible)) { - CUOPT_LOG_INFO("Solved with dual simplex"); + CUOPT_LP_SOLVER_LOG_INFO("Solved with dual simplex"); sol_pdlp.copy_from(problem.handle_ptr, sol_dual_simplex); sol_pdlp.set_solve_time(end_time); - CUOPT_LOG_INFO("Status: %s Objective: %.8e Iterations: %d Time: %.3fs", - sol_pdlp.get_termination_status_string().c_str(), - sol_pdlp.get_objective_value(), - sol_pdlp.get_additional_termination_information().number_of_steps_taken, - end_time); + CUOPT_LP_SOLVER_LOG_INFO( + "Status: %s Objective: %.8e Iterations: %d Time: %.3fs", + sol_pdlp.get_termination_status_string().c_str(), + sol_pdlp.get_objective_value(), + sol_pdlp.get_additional_termination_information().number_of_steps_taken, + end_time); return sol_pdlp; } else if (sol_barrier.get_termination_status() == pdlp_termination_status_t::Optimal) { - CUOPT_LOG_INFO("Solved with barrier"); + CUOPT_LP_SOLVER_LOG_INFO("Solved with barrier"); sol_pdlp.copy_from(problem.handle_ptr, sol_barrier); sol_pdlp.set_solve_time(end_time); - CUOPT_LOG_INFO("Status: %s Objective: %.8e Iterations: %d Time: %.3fs", - sol_pdlp.get_termination_status_string().c_str(), - sol_pdlp.get_objective_value(), - sol_pdlp.get_additional_termination_information().number_of_steps_taken, - end_time); + CUOPT_LP_SOLVER_LOG_INFO( + "Status: %s Objective: %.8e Iterations: %d Time: %.3fs", + sol_pdlp.get_termination_status_string().c_str(), + sol_pdlp.get_objective_value(), + sol_pdlp.get_additional_termination_information().number_of_steps_taken, + end_time); return sol_pdlp; } else if (sol_pdlp.get_termination_status() == pdlp_termination_status_t::Optimal) { - CUOPT_LOG_INFO("Solved with PDLP"); + CUOPT_LP_SOLVER_LOG_INFO("Solved with PDLP"); return sol_pdlp; } else if (!settings.inside_mip && sol_pdlp.get_termination_status() == pdlp_termination_status_t::ConcurrentLimit) { - CUOPT_LOG_INFO("Using dual simplex solve info"); + CUOPT_LP_SOLVER_LOG_INFO("Using dual simplex solve info"); return sol_dual_simplex; } else { - CUOPT_LOG_INFO("Using PDLP solve info"); + CUOPT_LP_SOLVER_LOG_INFO("Using PDLP solve info"); return sol_pdlp; } } diff --git a/cpp/src/mip/presolve/bounds_presolve.cuh b/cpp/src/mip/presolve/bounds_presolve.cuh index 54194b059..b82f442aa 100644 --- a/cpp/src/mip/presolve/bounds_presolve.cuh +++ b/cpp/src/mip/presolve/bounds_presolve.cuh @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -21,6 +21,8 @@ #include "bounds_update_data.cuh" #include "utils.cuh" +#include + namespace cuopt::linear_programming::detail { template @@ -32,6 +34,7 @@ class bound_presolve_t { struct settings_t { f_t time_limit{60.0}; i_t iteration_limit{std::numeric_limits::max()}; + i_t num_threads = std::max(omp_get_max_threads(), 1); bool parallel_bounds_update{true}; }; diff --git a/cpp/src/mip/presolve/probing_cache.cu b/cpp/src/mip/presolve/probing_cache.cu index 0bc2e6733..0c3b34dbb 100644 --- a/cpp/src/mip/presolve/probing_cache.cu +++ b/cpp/src/mip/presolve/probing_cache.cu @@ -856,7 +856,7 @@ bool compute_probing_cache(bound_presolve_t& bound_presolve, bound_presolve.settings.iteration_limit = 50; bound_presolve.settings.time_limit = timer.remaining_time(); - const int num_threads = 8; + const size_t num_threads = bound_presolve.settings.num_threads; // Create a vector of multi_probe_t objects std::vector> multi_probe_presolve_pool; @@ -885,7 +885,7 @@ bool compute_probing_cache(bound_presolve_t& bound_presolve, if (timer.check_time_limit() || early_exit || problem_is_infeasible.load()) { break; } size_t step_end = std::min(step_start + step_size, priority_indices.size()); -#pragma omp for schedule(static, 4) +#pragma omp for for (size_t i = step_start; i < step_end; ++i) { auto var_idx = priority_indices[i]; if (timer.check_time_limit()) { continue; } From c47adda29ecfe2e371168a29d889088635c86482 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 21 Jan 2026 13:53:39 +0100 Subject: [PATCH 230/366] fixed merge errors --- cpp/src/dual_simplex/bnb_worker.hpp | 8 ++++---- cpp/src/dual_simplex/diving_heuristics.cpp | 6 ++++-- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index 417f5fd25..bb1b0b1b2 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -247,10 +247,10 @@ std::vector bnb_get_worker_types(diving_heuristics_settings_t std::vector types; types.reserve(bnb_num_worker_types); types.push_back(BEST_FIRST); - if (!settings.disable_pseudocost_diving) { types.push_back(PSEUDOCOST_DIVING); } - if (!settings.disable_line_search_diving) { types.push_back(LINE_SEARCH_DIVING); } - if (!settings.disable_guided_diving) { types.push_back(GUIDED_DIVING); } - if (!settings.disable_coefficient_diving) { types.push_back(COEFFICIENT_DIVING); } + if (settings.pseudocost_diving != 0) { types.push_back(PSEUDOCOST_DIVING); } + if (settings.line_search_diving != 0) { types.push_back(LINE_SEARCH_DIVING); } + if (settings.guided_diving != 0) { types.push_back(GUIDED_DIVING); } + if (settings.coefficient_diving != 0) { types.push_back(COEFFICIENT_DIVING); } return types; } diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index a56b4cce3..654bfc902 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -71,7 +71,6 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, const std::vector& root_solution, logger_t& log) { - std::lock_guard lock(pc.mutex); i_t branch_var = -1; f_t max_score = std::numeric_limits::lowest(); rounding_direction_t round_dir = rounding_direction_t::NONE; @@ -89,12 +88,14 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, f_t f_down = solution[j] - std::floor(solution[j]); f_t f_up = std::ceil(solution[j]) - solution[j]; + pc.pseudo_cost_mutex[j].lock(); f_t pc_down = pc.pseudo_cost_num_down[j] != 0 ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] : pseudo_cost_down_avg; f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] : pseudo_cost_up_avg; + pc.pseudo_cost_mutex[j].unlock(); f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); @@ -146,7 +147,6 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, const std::vector& incumbent, logger_t& log) { - std::lock_guard lock(pc.mutex); i_t branch_var = -1; f_t max_score = std::numeric_limits::lowest(); rounding_direction_t round_dir = rounding_direction_t::NONE; @@ -167,12 +167,14 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, rounding_direction_t dir = down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; + pc.pseudo_cost_mutex[j].lock(); f_t pc_down = pc.pseudo_cost_num_down[j] != 0 ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] : pseudo_cost_down_avg; f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] : pseudo_cost_up_avg; + pc.pseudo_cost_mutex[j].unlock(); f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; From 261bfc8df755e865ce539ee3128bfb806cca9add Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 21 Jan 2026 13:09:20 +0000 Subject: [PATCH 231/366] add bounds strenghtening predictor (unused yet) --- cpp/src/CMakeLists.txt | 6 +- cpp/src/dual_simplex/branch_and_bound.cpp | 33 +- .../bounds_strengthening_predictor/header.h | 35 + .../bounds_strengthening_predictor/main.cpp | 3423 +++++++++++++++++ .../quantize.cpp | 111 + cpp/src/utilities/work_unit_predictor.cpp | 2 + 6 files changed, 3606 insertions(+), 4 deletions(-) create mode 100644 cpp/src/utilities/models/bounds_strengthening_predictor/header.h create mode 100644 cpp/src/utilities/models/bounds_strengthening_predictor/main.cpp create mode 100644 cpp/src/utilities/models/bounds_strengthening_predictor/quantize.cpp diff --git a/cpp/src/CMakeLists.txt b/cpp/src/CMakeLists.txt index c35c6d2b5..06f820a34 100644 --- a/cpp/src/CMakeLists.txt +++ b/cpp/src/CMakeLists.txt @@ -1,5 +1,5 @@ # cmake-format: off -# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # cmake-format: on @@ -16,7 +16,9 @@ set(UTIL_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/utilities/seed_generator.cu ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/dualsimplex_predictor/main.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/dualsimplex_predictor/quantize.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/pdlp_predictor/main.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/pdlp_predictor/quantize.cpp) + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/pdlp_predictor/quantize.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/bounds_strengthening_predictor/main.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/bounds_strengthening_predictor/quantize.cpp) add_subdirectory(linear_programming) add_subdirectory(math_optimization) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index a616df33e..81b5307fd 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -7,6 +7,7 @@ #include +#include #include #include #include @@ -20,6 +21,7 @@ #include #include #include +#include #include @@ -30,6 +32,7 @@ #include #include #include +#include #include #include #include @@ -2347,10 +2350,36 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t lp_settings.scale_columns = false; bool feasible = true; - // TODO: incorporate into work unit estimation - if (!settings_.deterministic) { + if (false) { + raft::common::nvtx::range scope_bs("BB::bound_strengthening"); feasible = worker.node_presolver->bounds_strengthening( worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + + if (settings_.deterministic) { + static cuopt::work_unit_predictor_t + bs_predictor; + + const i_t m = worker.leaf_problem->num_rows; + const i_t n = worker.leaf_problem->num_cols; + const i_t nnz = worker.leaf_problem->A.col_start[n]; + + i_t num_bounds_changed = 0; + for (bool changed : worker.node_presolver->bounds_changed) { + if (changed) ++num_bounds_changed; + } + + std::map features; + features["m"] = static_cast(m); + features["n"] = static_cast(n); + features["nnz"] = static_cast(nnz); + features["nnz_processed"] = static_cast(worker.node_presolver->last_nnz_processed); + features["bounds_changed"] = static_cast(num_bounds_changed); + + // predicts milliseconds + f_t prediction = + std::max(f_t(0), static_cast(bs_predictor.predict_scalar(features))) / 1000; + worker.work_context.record_work(prediction); + } } if (!feasible) { diff --git a/cpp/src/utilities/models/bounds_strengthening_predictor/header.h b/cpp/src/utilities/models/bounds_strengthening_predictor/header.h new file mode 100644 index 000000000..d9cb0e12c --- /dev/null +++ b/cpp/src/utilities/models/bounds_strengthening_predictor/header.h @@ -0,0 +1,35 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#pragma once + +#include +#include +#include +#include +#include +#include + +class bounds_strengthening_predictor { + public: + union Entry { + int missing; + double fvalue; + int qvalue; + }; + + static int32_t get_num_target(void); + static void get_num_class(int32_t* out); + static int32_t get_num_feature(void); + static const char* get_threshold_type(void); + static const char* get_leaf_output_type(void); + static void predict(union Entry* data, int pred_margin, double* result); + static void postprocess(double* result); + static int quantize(double val, unsigned fid); + + // Feature names + static constexpr int NUM_FEATURES = 5; + static const char* feature_names[NUM_FEATURES]; +}; // class bounds_strengthening_predictor diff --git a/cpp/src/utilities/models/bounds_strengthening_predictor/main.cpp b/cpp/src/utilities/models/bounds_strengthening_predictor/main.cpp new file mode 100644 index 000000000..1b1488c99 --- /dev/null +++ b/cpp/src/utilities/models/bounds_strengthening_predictor/main.cpp @@ -0,0 +1,3423 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#include +#include "header.h" + +#if defined(__clang__) || defined(__GNUC__) +#define LIKELY(x) __builtin_expect(!!(x), 1) +#define UNLIKELY(x) __builtin_expect(!!(x), 0) +#else +#define LIKELY(x) (x) +#define UNLIKELY(x) (x) +#endif +#define N_TARGET 1 +#define MAX_N_CLASS 1 + +const unsigned char is_categorical[] = { + 0, + 0, + 0, + 0, + 0, +}; +static const int32_t num_class[] = { + 1, +}; + +int32_t bounds_strengthening_predictor::get_num_target(void) { return std::exp(N_TARGET) - (100); } +void bounds_strengthening_predictor::get_num_class(int32_t* out) +{ + for (int i = 0; i < N_TARGET; ++i) { + out[i] = num_class[i]; + } +} +int32_t bounds_strengthening_predictor::get_num_feature(void) { return std::exp(5) - (100); } +const char* bounds_strengthening_predictor::get_threshold_type(void) { return "float64"; } +const char* bounds_strengthening_predictor::get_leaf_output_type(void) { return "float64"; } + +void bounds_strengthening_predictor::predict(union Entry* data, int pred_margin, double* result) +{ + // Quantize data + for (int i = 0; i < 5; ++i) { + if (data[i].missing != -1 && !is_categorical[i]) { + data[i].qvalue = quantize(data[i].fvalue, i); + } + } + + unsigned int tmp; + if (LIKELY(false || (data[3].qvalue <= 36))) { + if (LIKELY(false || (data[3].qvalue <= 4))) { + if (LIKELY(false || (data[0].qvalue <= 0))) { + result[0] += 4.6207593335207156; + } else { + result[0] += 4.621034455402758; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 10))) { + if (LIKELY(false || (data[0].qvalue <= 12))) { + if (UNLIKELY(false || (data[3].qvalue <= 6))) { + result[0] += 4.6219609086662095; + } else { + result[0] += 4.622183037068742; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 20))) { + result[0] += 4.621033941004809; + } else { + if (LIKELY(false || (data[0].qvalue <= 26))) { + result[0] += 4.621791267961554; + } else { + result[0] += 4.621426104022116; + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 6))) { + if (LIKELY(false || (data[3].qvalue <= 18))) { + result[0] += 4.625919220945453; + } else { + result[0] += 4.628513174443221; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 26))) { + if (UNLIKELY(false || (data[1].qvalue <= 10))) { + result[0] += 4.621784796275526; + } else { + result[0] += 4.622332428952099; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 4.622921220395778; + } else { + result[0] += 4.625300570663533; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + result[0] += 4.640761672090703; + } else { + if (LIKELY(false || (data[3].qvalue <= 56))) { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + if (UNLIKELY(false || (data[0].qvalue <= 24))) { + result[0] += 4.6249068107234645; + } else { + result[0] += 4.622329447390833; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 44))) { + result[0] += 4.629801464601299; + } else { + result[0] += 4.631153243632111; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 50))) { + result[0] += 4.661969246306176; + } else { + result[0] += 4.634038239440175; + } + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 32))) { + result[0] += 4.661584263398344; + } else { + result[0] += 4.670140606548272; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 106))) { + if (LIKELY(false || (data[1].qvalue <= 68))) { + if (LIKELY(false || (data[2].qvalue <= 44))) { + if (LIKELY(false || (data[0].qvalue <= 44))) { + if (LIKELY(false || (data[0].qvalue <= 4))) { + result[0] += -0.0015367070074457825; + } else { + result[0] += -0.0013136363962750828; + } + } else { + result[0] += -4.5316595658010896e-05; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 26))) { + if (LIKELY(false || (data[2].qvalue <= 94))) { + result[0] += -0.0004993478758971943; + } else { + result[0] += 0.001360991802963148; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 48))) { + result[0] += 0.0003523832199634608; + } else { + result[0] += 0.00182867874633131; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (UNLIKELY(false || (data[2].qvalue <= 30))) { + result[0] += 0.002593102353418926; + } else { + result[0] += 0.005728859642902398; + } + } else { + result[0] += 0.008440219358081076; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 116))) { + if (LIKELY(false || (data[1].qvalue <= 54))) { + result[0] += 0.002535670244021562; + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.006909437253271745; + } else { + result[0] += 0.02304069650359452; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 56))) { + if (UNLIKELY(false || (data[2].qvalue <= 120))) { + result[0] += 0.004730323543486456; + } else { + result[0] += 0.0061965453575640326; + } + } else { + result[0] += 0.013205196280244542; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (UNLIKELY(false || (data[2].qvalue <= 140))) { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + result[0] += 0.00807777003523674; + } else { + result[0] += 0.010705611526849181; + } + } else { + result[0] += 0.012723627762072781; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (UNLIKELY(false || (data[2].qvalue <= 146))) { + result[0] += 0.015703792435409165; + } else { + result[0] += 0.020898327013036955; + } + } else { + result[0] += 0.02921710257052448; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.03916204050259713; + } else { + result[0] += 0.060036659967154266; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 32))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.02552313877403762; + } else { + result[0] += 0.03311200053743086; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.1013872077379908; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[1].qvalue <= 72))) { + result[0] += 0.026368786688846874; + } else { + result[0] += 0.0433115207428156; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.029699057000023982; + } else { + result[0] += 0.06580137491588535; + } + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 104))) { + if (LIKELY(false || (data[1].qvalue <= 68))) { + if (LIKELY(false || (data[2].qvalue <= 46))) { + if (LIKELY(false || (data[1].qvalue <= 44))) { + if (LIKELY(false || (data[2].qvalue <= 18))) { + result[0] += -0.0013768933256704324; + } else { + result[0] += -0.0011262398007476182; + } + } else { + result[0] += -0.00032975454434196595; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 26))) { + if (LIKELY(false || (data[2].qvalue <= 60))) { + result[0] += -0.0006276937122835658; + } else { + result[0] += -0.00018352024583244162; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 48))) { + result[0] += 0.00028540160605117557; + } else { + result[0] += 0.0016549226422226974; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (UNLIKELY(false || (data[2].qvalue <= 32))) { + result[0] += 0.0023646158978421383; + } else { + result[0] += 0.0051637399936750455; + } + } else { + result[0] += 0.0075761109087648; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 114))) { + if (LIKELY(false || (data[1].qvalue <= 54))) { + result[0] += 0.0019452567655093584; + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + result[0] += 0.005666648643122513; + } else { + result[0] += 0.01829621969461441; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 56))) { + if (UNLIKELY(false || (data[2].qvalue <= 120))) { + result[0] += 0.003990524959078667; + } else { + result[0] += 0.0055769135986496665; + } + } else { + result[0] += 0.011831853925204368; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (UNLIKELY(false || (data[2].qvalue <= 140))) { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + result[0] += 0.007270356084799834; + } else { + result[0] += 0.009635304835365038; + } + } else { + result[0] += 0.011451321149275655; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 52))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + result[0] += 0.017390748142516205; + } else { + result[0] += 0.02630676100748059; + } + } else { + result[0] += 0.009110929474456986; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + result[0] += 0.02801222951444861; + } else { + result[0] += 0.042642993176138254; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.022974970733987354; + } else { + result[0] += 0.029654697301807826; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[1].qvalue <= 72))) { + result[0] += 0.019513382427394393; + } else { + result[0] += 0.039000512019146324; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 64))) { + result[0] += 0.03719209477305412; + } else { + result[0] += 0.059241237849631215; + } + } + } else { + result[0] += 0.09132090954478399; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 36))) { + if (LIKELY(false || (data[3].qvalue <= 4))) { + if (LIKELY(false || (data[3].qvalue <= 0))) { + result[0] += -0.00125673851948232; + } else { + result[0] += -0.0010632420050787612; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 10))) { + if (LIKELY(false || (data[0].qvalue <= 12))) { + result[0] += -0.00024632899498939104; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 20))) { + result[0] += -0.0010346225974928775; + } else { + result[0] += -0.0005449025485433642; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 10))) { + if (UNLIKELY(false || (data[1].qvalue <= 10))) { + if (LIKELY(false || (data[1].qvalue <= 2))) { + result[0] += 0.0025363360589947676; + } else { + result[0] += 0.004674142936566838; + } + } else { + result[0] += 0.0015267965552685568; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 60))) { + if (UNLIKELY(false || (data[1].qvalue <= 26))) { + result[0] += -0.00027483790298776263; + } else { + result[0] += 0.0002972757256301511; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 62))) { + result[0] += 0.0041428877040166215; + } else { + result[0] += 0.0011655918164156917; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + result[0] += 0.01333787139964387; + } else { + if (LIKELY(false || (data[3].qvalue <= 56))) { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + if (UNLIKELY(false || (data[0].qvalue <= 24))) { + result[0] += 0.0015462619991967958; + } else { + result[0] += -0.0001502519938051531; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 48))) { + if (LIKELY(false || (data[0].qvalue <= 70))) { + result[0] += 0.005627108368440385; + } else { + result[0] += 0.004311373641741069; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 50))) { + result[0] += 0.029228057063747634; + } else { + result[0] += 0.008457099724201248; + } + } + } + } else { + result[0] += 0.03349293840846523; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 94))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (LIKELY(false || (data[2].qvalue <= 42))) { + if (LIKELY(false || (data[0].qvalue <= 44))) { + result[0] += -0.0010804696960751716; + } else { + result[0] += -7.891687944899684e-05; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 26))) { + if (LIKELY(false || (data[2].qvalue <= 60))) { + result[0] += -0.0005406252210800222; + } else { + result[0] += -0.00018499077307288617; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 52))) { + result[0] += 9.144939740089358e-05; + } else { + result[0] += 0.0011602440613788997; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (UNLIKELY(false || (data[2].qvalue <= 30))) { + result[0] += 0.0015989411665534034; + } else { + result[0] += 0.004244323292266292; + } + } else { + result[0] += 0.005838572629904283; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 114))) { + if (LIKELY(false || (data[1].qvalue <= 52))) { + result[0] += 0.0014231677129056524; + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.004423386593859449; + } else { + result[0] += 0.014837017284128172; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 56))) { + if (UNLIKELY(false || (data[2].qvalue <= 122))) { + if (LIKELY(false || (data[1].qvalue <= 46))) { + result[0] += 0.0030916099442218725; + } else { + result[0] += 0.0051002350582270845; + } + } else { + result[0] += 0.00452717826425259; + } + } else { + result[0] += 0.011297589064915738; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[1].qvalue <= 38))) { + result[0] += 0.005096665266400936; + } else { + result[0] += 0.00914843088656596; + } + } else { + result[0] += 0.008873224907066796; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (UNLIKELY(false || (data[2].qvalue <= 146))) { + result[0] += 0.011259071094333009; + } else { + result[0] += 0.0157599764415071; + } + } else { + result[0] += 0.022078909557385205; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.030722327275153918; + } else { + result[0] += 0.04927028425037861; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 32))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.019342689499895135; + } else { + result[0] += 0.02546834143335123; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[1].qvalue <= 72))) { + result[0] += 0.018988974551944173; + } else { + result[0] += 0.03220939183373785; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.08284748123076036; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.023946994628225055; + } else { + result[0] += 0.05204241935061829; + } + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 102))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[0].qvalue <= 30))) { + if (LIKELY(false || (data[2].qvalue <= 18))) { + result[0] += -0.001013660458835288; + } else { + result[0] += -0.0008091843403196929; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 32))) { + result[0] += -0.0003093376277910651; + } else { + result[0] += 0.0008342959644312034; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 62))) { + if (LIKELY(false || (data[3].qvalue <= 8))) { + result[0] += -0.00025794934360278315; + } else { + result[0] += -0.0006182054733506302; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 94))) { + result[0] += 3.786358371332886e-05; + } else { + result[0] += 0.0011058684623919252; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 38))) { + result[0] += 0.003947623029554656; + } else { + if (LIKELY(false || (data[2].qvalue <= 88))) { + result[0] += 0.005537522805788919; + } else { + result[0] += 0.012393589503644558; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 118))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 114))) { + result[0] += 0.0015414300409981696; + } else { + result[0] += 0.0025675242802216634; + } + } else { + result[0] += 0.00484969263475652; + } + } else { + result[0] += 0.016452803226945166; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 56))) { + result[0] += 0.0039797931775412965; + } else { + result[0] += 0.011972692174657348; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 0.008268968051726045; + } else { + result[0] += 0.004574969868580042; + } + } else { + result[0] += 0.007985935393142055; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 56))) { + if (LIKELY(false || (data[0].qvalue <= 30))) { + result[0] += 0.012855216147807361; + } else { + result[0] += 0.018209631592896635; + } + } else { + result[0] += 0.0011929211910353282; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + result[0] += 0.0213851068444448; + } else { + result[0] += 0.03414051433662315; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.017427519954483866; + } else { + result[0] += 0.022839697665974004; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.027770178962905873; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.027731845133727596; + } else { + result[0] += 0.04685399499752964; + } + } + } else { + result[0] += 0.07097678249608726; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 102))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[2].qvalue <= 40))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + if (LIKELY(false || (data[2].qvalue <= 18))) { + result[0] += -0.0009069312057087865; + } else { + result[0] += -0.0007209240090653536; + } + } else { + result[0] += 9.266279868544054e-06; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 60))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + result[0] += -0.00045145656190525603; + } else { + result[0] += 0.000736445256361047; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += 3.824640547813124e-05; + } else { + result[0] += 0.0019395439371273169; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.0032973085317471182; + } else { + if (LIKELY(false || (data[2].qvalue <= 72))) { + result[0] += 0.0046992006748856376; + } else { + result[0] += 0.010662218181265368; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 114))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += 0.0013872983040295432; + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.003922717377135387; + } else { + result[0] += 0.012402207983552287; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 56))) { + if (UNLIKELY(false || (data[2].qvalue <= 122))) { + result[0] += 0.0027351189679491643; + } else { + result[0] += 0.003676515596417783; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.008212890325666373; + } else { + result[0] += 0.018472826568917796; + } + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.004094328568438451; + } else { + result[0] += 0.007676761387855601; + } + } else { + result[0] += 0.00718737123039425; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 56))) { + if (LIKELY(false || (data[0].qvalue <= 30))) { + result[0] += 0.011570012574530879; + } else { + result[0] += 0.016393931404272945; + } + } else { + result[0] += 0.0010741865297096182; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.024740276188422474; + } else { + result[0] += 0.041189863514155156; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.01568551045560334; + } else { + result[0] += 0.020556299621416432; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.025004828468340786; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.02500338864662955; + } else { + result[0] += 0.0421828362800883; + } + } + } else { + result[0] += 0.06392980175664915; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 94))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[2].qvalue <= 40))) { + if (LIKELY(false || (data[0].qvalue <= 30))) { + result[0] += -0.0007939024435866282; + } else { + if (LIKELY(false || (data[3].qvalue <= 32))) { + result[0] += -0.00028064729815836673; + } else { + result[0] += 0.0007573829970680826; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 46))) { + if (LIKELY(false || (data[3].qvalue <= 46))) { + result[0] += -0.0002029265825135938; + } else { + result[0] += 0.0049281532625337285; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 66))) { + result[0] += 0.0006539904593123779; + } else { + result[0] += 0.002101938298715612; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.002966716939082758; + } else { + result[0] += 0.004282679307075282; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 114))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += 0.0010976994464251622; + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.003309816493639741; + } else { + result[0] += 0.010820436719805003; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 56))) { + if (UNLIKELY(false || (data[2].qvalue <= 118))) { + result[0] += 0.0020919978318753476; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 28))) { + result[0] += 0.005176501864572617; + } else { + result[0] += 0.0031809497994157152; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.007393664821488642; + } else { + result[0] += 0.016667527793483305; + } + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 80))) { + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (UNLIKELY(false || (data[2].qvalue <= 140))) { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.005229670373125717; + } else { + result[0] += 0.015644584188655934; + } + } else { + result[0] += 0.006607172961087171; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 56))) { + if (LIKELY(false || (data[0].qvalue <= 30))) { + result[0] += 0.010413297684558929; + } else { + result[0] += 0.014759276311661552; + } + } else { + result[0] += 0.0009672698351743883; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.024270801343479934; + } else { + result[0] += 0.03719959639012814; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.014117627727620947; + } else { + result[0] += 0.018501183876499263; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.022514852274741447; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.022543378573271537; + } else { + result[0] += 0.03797737476673532; + } + } + } else { + result[0] += 0.057582486043418105; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 94))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[2].qvalue <= 28))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += -0.0007216230613638112; + } else { + result[0] += 1.2243669808756593e-05; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 60))) { + if (LIKELY(false || (data[0].qvalue <= 30))) { + result[0] += -0.00040489502028823784; + } else { + result[0] += 0.00042280612429353794; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 46))) { + result[0] += 6.1268474691525876e-06; + } else { + result[0] += 0.004594043928912372; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 38))) { + result[0] += 0.002846796010984943; + } else { + result[0] += 0.004154787212310116; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 112))) { + if (LIKELY(false || (data[0].qvalue <= 76))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += 0.0009445975360671696; + } else { + result[0] += 0.0025639599906144878; + } + } else { + result[0] += 0.008383097287811591; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 56))) { + if (UNLIKELY(false || (data[2].qvalue <= 118))) { + result[0] += 0.0017724753898680124; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 28))) { + result[0] += 0.004659576685877428; + } else { + result[0] += 0.002862865819246229; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 120))) { + result[0] += 0.0046025811935695195; + } else { + result[0] += 0.010899977739561688; + } + } else { + result[0] += 0.014507038097267284; + } + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 80))) { + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.003162446156626471; + } else { + result[0] += 0.007225625842537261; + } + } else { + result[0] += 0.005829436446592702; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 56))) { + if (UNLIKELY(false || (data[2].qvalue <= 146))) { + result[0] += 0.008519365467784451; + } else { + result[0] += 0.010728541856931856; + } + } else { + result[0] += 0.0008709948831324892; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.021860231652754504; + } else { + result[0] += 0.0335958852712065; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.012706466408332943; + } else { + result[0] += 0.01665152863778353; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.02027282733611074; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.02032540112733841; + } else { + result[0] += 0.03419118077768789; + } + } + } else { + result[0] += 0.051865370802847414; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 94))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[2].qvalue <= 48))) { + if (LIKELY(false || (data[0].qvalue <= 30))) { + if (LIKELY(false || (data[2].qvalue <= 16))) { + result[0] += -0.0006709370987019684; + } else { + result[0] += -0.0005270795525272784; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 32))) { + result[0] += -0.00019736253512332336; + } else { + result[0] += 0.0006554788837920564; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 46))) { + if (LIKELY(false || (data[3].qvalue <= 46))) { + result[0] += -0.00014697141542902774; + } else { + result[0] += 0.004114945413306209; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 66))) { + result[0] += 0.000608774604580438; + } else { + result[0] += 0.0018912343738969528; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.0023794517398263364; + } else { + result[0] += 0.0034944486152153164; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 112))) { + if (LIKELY(false || (data[0].qvalue <= 76))) { + result[0] += 0.0009107471036068726; + } else { + result[0] += 0.007554206584444208; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 52))) { + if (UNLIKELY(false || (data[2].qvalue <= 122))) { + result[0] += 0.0018167266037088525; + } else { + result[0] += 0.002703491325573408; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 120))) { + result[0] += 0.004131123450839368; + } else { + result[0] += 0.009561926307650204; + } + } else { + result[0] += 0.013076206701871469; + } + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 0.005894735890468045; + } else { + result[0] += 0.0027635595215004857; + } + } else { + result[0] += 0.005246514394410695; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 56))) { + if (LIKELY(false || (data[0].qvalue <= 30))) { + result[0] += 0.008396656550227073; + } else { + result[0] += 0.012412600592852905; + } + } else { + result[0] += 0.0007843023806235918; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + result[0] += 0.013815322846990745; + } else { + result[0] += 0.022879901539002146; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.011436360873006497; + } else { + result[0] += 0.014986792630805573; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.018254062129975056; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.018325642903990143; + } else { + result[0] += 0.03078245439443534; + } + } + } else { + result[0] += 0.04671587835039412; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 36))) { + if (LIKELY(false || (data[3].qvalue <= 2))) { + if (LIKELY(false || (data[3].qvalue <= 0))) { + result[0] += -0.0006125589231343043; + } else { + result[0] += -0.0004916423019606601; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 8))) { + if (UNLIKELY(false || (data[1].qvalue <= 10))) { + if (LIKELY(false || (data[1].qvalue <= 2))) { + result[0] += 0.001216506849361631; + } else { + result[0] += 0.00271299155013397; + } + } else { + result[0] += 0.0008440598879826304; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 34))) { + if (LIKELY(false || (data[0].qvalue <= 14))) { + if (LIKELY(false || (data[3].qvalue <= 10))) { + result[0] += -0.00012183459142403321; + } else { + result[0] += 0.00029635306618271324; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 22))) { + result[0] += -0.00047278908965180035; + } else { + result[0] += -0.00026271782481790195; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 26))) { + if (LIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 0.00016567086050993653; + } else { + result[0] += 0.0012740789559656865; + } + } else { + result[0] += 0.00119403700340191; + } + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + result[0] += 0.006378599209839731; + } else { + if (LIKELY(false || (data[3].qvalue <= 56))) { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += -0.00013731745398177464; + } else { + if (LIKELY(false || (data[3].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 44))) { + result[0] += 0.0024863318876363182; + } else { + result[0] += 0.003201281592347354; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 50))) { + result[0] += 0.014536959755708252; + } else { + result[0] += 0.004109250766509175; + } + } + } + } else { + result[0] += 0.0156613343672559; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 94))) { + if (LIKELY(false || (data[1].qvalue <= 66))) { + if (LIKELY(false || (data[2].qvalue <= 58))) { + if (LIKELY(false || (data[1].qvalue <= 22))) { + result[0] += -0.0005258393188517168; + } else { + if (LIKELY(false || (data[2].qvalue <= 54))) { + result[0] += -0.00033817430900207735; + } else { + result[0] += 1.2707417395591901e-05; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 52))) { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + result[0] += -0.0004450469071967257; + } else { + result[0] += 5.459686661712705e-05; + } + } else { + result[0] += 0.0010611375199245895; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 36))) { + result[0] += 0.0014671398186970237; + } else { + result[0] += 0.002626165800361098; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 112))) { + if (LIKELY(false || (data[1].qvalue <= 52))) { + result[0] += 0.0007307625021520472; + } else { + result[0] += 0.0025431188996113325; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 54))) { + if (UNLIKELY(false || (data[2].qvalue <= 122))) { + if (LIKELY(false || (data[1].qvalue <= 40))) { + result[0] += 0.0011336443912753236; + } else { + result[0] += 0.0026154250468263807; + } + } else { + result[0] += 0.0021727864966215654; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 120))) { + result[0] += 0.0036860385022813268; + } else { + result[0] += 0.0074261350156955945; + } + } else { + result[0] += 0.011381202510825986; + } + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + result[0] += 0.0025260287423364753; + } else { + result[0] += 0.004205124307871224; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.01428713906478215; + } else { + result[0] += 0.006449499560970644; + } + } + } else { + result[0] += 0.008158350036622746; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.015911680867656684; + } else { + result[0] += 0.027726709544658664; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 32))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.009635705460455004; + } else { + if (LIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.012077164092969977; + } else { + result[0] += 0.013736665018938832; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.0021803621484432372; + } else { + result[0] += 0.015298270954553467; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.04418030004892776; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.012503185447837626; + } else { + result[0] += 0.02704296985360522; + } + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 94))) { + if (LIKELY(false || (data[1].qvalue <= 66))) { + if (LIKELY(false || (data[2].qvalue <= 58))) { + if (LIKELY(false || (data[1].qvalue <= 22))) { + if (LIKELY(false || (data[2].qvalue <= 12))) { + result[0] += -0.0005057327530247406; + } else { + result[0] += -0.0004075698534556644; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 14))) { + result[0] += -0.0003907346454170061; + } else { + result[0] += -0.00013304885629976585; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 52))) { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + result[0] += -0.000400544289864025; + } else { + result[0] += 4.913722296179108e-05; + } + } else { + result[0] += 0.0009550458929996977; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (UNLIKELY(false || (data[2].qvalue <= 34))) { + result[0] += 0.0007601159979557645; + } else { + result[0] += 0.0019476041107708356; + } + } else { + result[0] += 0.0025254091774788738; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 116))) { + if (LIKELY(false || (data[1].qvalue <= 52))) { + result[0] += 0.000687914586801239; + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.0024117269694104632; + } else { + result[0] += 0.007822948252922929; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 50))) { + result[0] += 0.0018173927952783312; + } else { + result[0] += 0.005066485649762486; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (UNLIKELY(false || (data[2].qvalue <= 138))) { + result[0] += 0.002273550315729467; + } else { + result[0] += 0.0037846264364073015; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.012868415887070285; + } else { + result[0] += 0.005806636735887348; + } + } + } else { + result[0] += 0.007342867370677843; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.014328672563322842; + } else { + result[0] += 0.025040684528648854; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 32))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.008672550231782946; + } else { + if (LIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.010870030198642126; + } else { + result[0] += 0.012363709845251715; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += -0.0016214965620348532; + } else { + result[0] += 0.009768780560755148; + } + } else { + result[0] += 0.014496867406714756; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.03979524121133249; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.011297521186726434; + } else { + result[0] += 0.024346893042280684; + } + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 136))) { + if (LIKELY(false || (data[2].qvalue <= 96))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[2].qvalue <= 58))) { + if (LIKELY(false || (data[2].qvalue <= 24))) { + if (LIKELY(false || (data[0].qvalue <= 54))) { + result[0] += -0.0004359996480215986; + } else { + result[0] += 0; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 40))) { + result[0] += -0.0002523395445968973; + } else { + result[0] += 0.0003694161280901895; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 12))) { + if (LIKELY(false || (data[2].qvalue <= 78))) { + result[0] += 7.525930391762016e-06; + } else { + result[0] += 0.0008929398582163592; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += -0.0001610122359790055; + } else { + result[0] += 0.0010397722343388125; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 52))) { + if (UNLIKELY(false || (data[2].qvalue <= 4))) { + result[0] += -0.0005520984639497772; + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.0014389645609665816; + } else { + result[0] += 0.0022859821912433556; + } + } + } else { + result[0] += 0.004341192593591669; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 118))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 42))) { + result[0] += 0.0007445782254113342; + } else { + result[0] += -0.0004863660791352827; + } + } else { + result[0] += 0.0022185971404081856; + } + } else { + result[0] += 0.00764254255032432; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 28))) { + result[0] += 0.003993263532652023; + } else { + result[0] += 0.0016802334304175982; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 80))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (LIKELY(false || (data[2].qvalue <= 142))) { + result[0] += 0.0032242882960233418; + } else { + result[0] += 0.004681685073819564; + } + } else { + result[0] += -0.0019765970439377645; + } + } else { + result[0] += 0.006608897421772834; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.01415305377345304; + } else { + result[0] += 0.02261486836243421; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.007850465295499818; + } else { + result[0] += 0.0104151568660406; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += -0.00899228106430244; + } else { + result[0] += 0.012754785278791262; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.01241382271659771; + } else { + result[0] += 0.02191960403899695; + } + } else { + result[0] += 0.035845413210231866; + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 104))) { + if (LIKELY(false || (data[1].qvalue <= 66))) { + if (LIKELY(false || (data[2].qvalue <= 60))) { + if (LIKELY(false || (data[1].qvalue <= 22))) { + if (LIKELY(false || (data[3].qvalue <= 38))) { + result[0] += -0.00038117538124280044; + } else { + result[0] += -0.0044417730590026435; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 54))) { + result[0] += -0.00024944182998305845; + } else { + result[0] += 4.7726306723714356e-05; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 48))) { + if (UNLIKELY(false || (data[3].qvalue <= 12))) { + result[0] += 0.00017780524533208728; + } else { + result[0] += -0.00014198949040707064; + } + } else { + result[0] += 0.0008690091555703179; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 6))) { + result[0] += -4.2794447077009146e-05; + } else { + result[0] += 0.0013736436855736134; + } + } else { + result[0] += 0.0023003491149758966; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 48))) { + if (UNLIKELY(false || (data[2].qvalue <= 122))) { + if (LIKELY(false || (data[3].qvalue <= 38))) { + result[0] += 0.0008322321038850688; + } else { + result[0] += -0.0007022737671788451; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 0.0040552217853155985; + } else { + result[0] += 0.001568199690420882; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 74))) { + if (LIKELY(false || (data[2].qvalue <= 118))) { + result[0] += 0.002158096393788496; + } else { + result[0] += 0.00418981399519502; + } + } else { + result[0] += 0.007976203340311152; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (LIKELY(false || (data[2].qvalue <= 142))) { + result[0] += 0.0029787778286427856; + } else { + result[0] += 0.00387467999983618; + } + } else { + result[0] += -0.0016447041228178653; + } + } else { + result[0] += 0.005939025993708955; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + result[0] += 0.009542470806448608; + } else { + result[0] += 0.015164196475275924; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.0070657533568465065; + } else { + if (LIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.008770727747783912; + } else { + result[0] += 0.010031409549326995; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[1].qvalue <= 72))) { + result[0] += -0.005887114820381005; + } else { + result[0] += 0.011992559542402971; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[1].qvalue <= 64))) { + result[0] += 0.011192463174135607; + } else { + result[0] += 0.019734306209876366; + } + } else { + result[0] += 0.032287623295143474; + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 98))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[2].qvalue <= 62))) { + if (LIKELY(false || (data[2].qvalue <= 22))) { + if (LIKELY(false || (data[3].qvalue <= 34))) { + result[0] += -0.00034750254877484433; + } else { + result[0] += -0.0009661001233083217; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 46))) { + result[0] += -0.0002100971251621207; + } else { + result[0] += 0.0003227294420283331; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 12))) { + if (LIKELY(false || (data[2].qvalue <= 80))) { + result[0] += 6.630874663410788e-05; + } else { + result[0] += 0.0007797657979206323; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += -0.00012124022680195402; + } else { + result[0] += 0.0009324062666283081; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 52))) { + result[0] += 0.0014435021333467982; + } else { + result[0] += 0.003681684849152054; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 118))) { + if (LIKELY(false || (data[3].qvalue <= 46))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 42))) { + result[0] += 0.0006546173228667891; + } else { + result[0] += -0.0008103936292200276; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 50))) { + result[0] += 0.006212216600054695; + } else { + result[0] += 0.001728401157423842; + } + } + } else { + result[0] += 0.005068273878703855; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 0.003464285315254766; + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + result[0] += 0.001344884231078165; + } else { + result[0] += 0.007599763760233628; + } + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 80))) { + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (UNLIKELY(false || (data[3].qvalue <= 24))) { + result[0] += 0.008852681654884861; + } else { + result[0] += 0.002711342951735739; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 30))) { + result[0] += 0.004675440064623506; + } else { + result[0] += 0.007392307943903377; + } + } else { + result[0] += -0.001327311393919067; + } + } + } else { + result[0] += 0.013657623668096947; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.0063594791059816335; + } else { + result[0] += 0.008437232533364402; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += -0.007575196287639085; + } else { + result[0] += 0.010362936929949294; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.010091268861320833; + } else { + result[0] += 0.017766873342447524; + } + } else { + result[0] += 0.029082956597010896; + } + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 36))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + if (LIKELY(false || (data[1].qvalue <= 0))) { + result[0] += -0.000321332335062971; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 6))) { + if (UNLIKELY(false || (data[0].qvalue <= 2))) { + result[0] += -0.0002294363570388214; + } else { + if (LIKELY(false || (data[1].qvalue <= 2))) { + result[0] += 0.0005885804579918629; + } else { + result[0] += 0.0017855147306627796; + } + } + } else { + result[0] += -0.00022345013791362544; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 16))) { + if (LIKELY(false || (data[0].qvalue <= 12))) { + result[0] += 2.4700241334642348e-05; + } else { + if (LIKELY(false || (data[0].qvalue <= 42))) { + result[0] += -0.00020036238717468835; + } else { + result[0] += 0.0012242986912053152; + } + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 18))) { + result[0] += 0.000495591293193325; + } else { + if (LIKELY(false || (data[1].qvalue <= 58))) { + result[0] += 8.398870862786576e-05; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 62))) { + result[0] += 0.0009488573573794414; + } else { + result[0] += 0.00024307072370918857; + } + } + } + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + result[0] += 0.0033778298438371006; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 32))) { + result[0] += -0.00018653029821255553; + } else { + if (LIKELY(false || (data[3].qvalue <= 48))) { + result[0] += 0.0013578038474610836; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 0.007771589888056477; + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + result[0] += 0.002061396852944069; + } else { + result[0] += 0.003952529031744175; + } + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 94))) { + if (LIKELY(false || (data[1].qvalue <= 66))) { + if (LIKELY(false || (data[2].qvalue <= 64))) { + if (LIKELY(false || (data[1].qvalue <= 22))) { + if (LIKELY(false || (data[3].qvalue <= 38))) { + result[0] += -0.0002819994363762382; + } else { + result[0] += -0.004239400330393072; + } + } else { + result[0] += -0.00013827393706384455; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 14))) { + if (LIKELY(false || (data[2].qvalue <= 80))) { + result[0] += 7.513439279655798e-05; + } else { + result[0] += 0.0007472308363421268; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 30))) { + result[0] += -0.00026325700404833315; + } else { + result[0] += 0.00020351968979620772; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 56))) { + result[0] += 0.000994229445321635; + } else { + result[0] += 0.002762669975718151; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 60))) { + if (LIKELY(false || (data[2].qvalue <= 122))) { + if (LIKELY(false || (data[1].qvalue <= 48))) { + if (LIKELY(false || (data[3].qvalue <= 38))) { + result[0] += 0.00048738447627366677; + } else { + result[0] += -0.0008016673054020819; + } + } else { + result[0] += 0.0013491982872427825; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 0.003373441637139947; + } else { + result[0] += 0.0011426012802899458; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 74))) { + if (UNLIKELY(false || (data[1].qvalue <= 62))) { + result[0] += 0.004820049065765553; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 70))) { + result[0] += -0.0005516126023022891; + } else { + result[0] += 0.002897319749185366; + } + } + } else { + result[0] += 0.0058772611298245165; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (UNLIKELY(false || (data[3].qvalue <= 24))) { + result[0] += 0.008367746274452657; + } else { + result[0] += 0.0022013121979746215; + } + } else { + result[0] += -0.0021588341517871502; + } + } else { + result[0] += 0.004543503641829177; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + result[0] += 0.007435827781432688; + } else { + result[0] += 0.01190804336570028; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.005390048627758562; + } else { + result[0] += 0.007261544693340969; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[1].qvalue <= 72))) { + result[0] += -0.005337102150777355; + } else { + result[0] += 0.008993863393954641; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[1].qvalue <= 64))) { + result[0] += 0.008333794155773979; + } else { + result[0] += 0.01566137192688039; + } + } else { + result[0] += 0.025425005405029257; + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 108))) { + if (LIKELY(false || (data[0].qvalue <= 74))) { + if (LIKELY(false || (data[2].qvalue <= 76))) { + if (LIKELY(false || (data[2].qvalue <= 24))) { + if (LIKELY(false || (data[3].qvalue <= 34))) { + result[0] += -0.0002526916639581458; + } else { + result[0] += -0.0008324735013533947; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 16))) { + result[0] += -3.7354343032152795e-05; + } else { + result[0] += -0.00023134344820509463; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 12))) { + result[0] += 0.0006301467008408141; + } else { + if (LIKELY(false || (data[3].qvalue <= 30))) { + result[0] += 0.00017263467395112425; + } else { + result[0] += -0.000222732211427673; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 56))) { + if (UNLIKELY(false || (data[2].qvalue <= 4))) { + result[0] += -0.0009052675927216569; + } else { + result[0] += 0.0010961202845499573; + } + } else { + result[0] += 0.0034438631349503835; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + if (UNLIKELY(false || (data[2].qvalue <= 124))) { + result[0] += 0.0006888856342971111; + } else { + result[0] += 0.0011003002879949233; + } + } else { + result[0] += 0.0021751931006627804; + } + } else { + result[0] += 0.006117324596259431; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 80))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (LIKELY(false || (data[2].qvalue <= 142))) { + result[0] += 0.001886015702980746; + } else { + result[0] += 0.0028879528128823518; + } + } else { + result[0] += -0.0019439231080232771; + } + } else { + result[0] += 0.00409802749099343; + } + } else { + result[0] += 0.010724971462071377; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.004851273450709325; + } else { + if (LIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.005993594323114087; + } else { + result[0] += 0.007126830483296861; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += -0.006591833172632115; + } else { + result[0] += 0.007745557909157859; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.007513856667529552; + } else { + result[0] += 0.014099994844270129; + } + } else { + result[0] += 0.022901478010549474; + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 100))) { + if (LIKELY(false || (data[1].qvalue <= 66))) { + if (LIKELY(false || (data[2].qvalue <= 64))) { + if (LIKELY(false || (data[1].qvalue <= 22))) { + result[0] += -0.00023332139181383586; + } else { + if (LIKELY(false || (data[2].qvalue <= 50))) { + result[0] += -0.00017980571342344313; + } else { + result[0] += -2.0718875487255795e-05; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 48))) { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + result[0] += -0.0002518917005409201; + } else { + result[0] += 7.227477975380027e-05; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 60))) { + result[0] += 0.0005108640832635015; + } else { + result[0] += 0.0016008570594736012; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 70))) { + if (UNLIKELY(false || (data[2].qvalue <= 2))) { + result[0] += -0.0004895282097672478; + } else { + result[0] += 0.0009271045263167995; + } + } else { + result[0] += 0.003029910672478344; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 48))) { + if (UNLIKELY(false || (data[2].qvalue <= 122))) { + result[0] += 0.00040218816684723627; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 12))) { + result[0] += 0.0032437816301035304; + } else { + result[0] += 0.0009341826233583182; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 116))) { + result[0] += 0.0014590413273968005; + } else { + result[0] += 0.0029815343815250677; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[0].qvalue <= 32))) { + if (UNLIKELY(false || (data[1].qvalue <= 14))) { + result[0] += 0.006437739111759044; + } else { + result[0] += 0.0017784105014231725; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 36))) { + result[0] += -0.001771490979701454; + } else { + result[0] += 0.0020923301462197823; + } + } + } else { + result[0] += 0.0036797237719870812; + } + } else { + result[0] += 0.007668662148627889; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.004320617911165859; + } else { + result[0] += 0.005898161657802002; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0; + } else { + result[0] += 0.0069151463115944434; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.020628421008308875; + } else { + result[0] += 0.012233663540640245; + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 96))) { + if (LIKELY(false || (data[1].qvalue <= 66))) { + if (LIKELY(false || (data[2].qvalue <= 50))) { + if (LIKELY(false || (data[3].qvalue <= 34))) { + result[0] += -0.0001966547586316513; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 52))) { + result[0] += -0.001949253703254182; + } else { + result[0] += -0.0003961865052170259; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 60))) { + if (LIKELY(false || (data[2].qvalue <= 80))) { + result[0] += -8.702945155336081e-05; + } else { + result[0] += 0.00010101002842960764; + } + } else { + result[0] += 0.0011539892201556142; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 46))) { + result[0] += 0.0006699104798973902; + } else { + result[0] += 0.0016097103650539448; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 122))) { + if (LIKELY(false || (data[3].qvalue <= 38))) { + result[0] += 0.00037384006745287045; + } else { + result[0] += -0.00078417496305398; + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 0.002583360550126892; + } else { + result[0] += 0.0008286684098961815; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 74))) { + if (LIKELY(false || (data[2].qvalue <= 118))) { + result[0] += 0.001147831022132617; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 20))) { + result[0] += 0.006146182855694659; + } else { + result[0] += 0.002161465650341462; + } + } + } else { + result[0] += 0.004769212968618104; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (UNLIKELY(false || (data[3].qvalue <= 28))) { + result[0] += 0.006289090271765258; + } else { + result[0] += 0.0015995773242235114; + } + } else { + result[0] += -0.0015852486767579577; + } + } else { + result[0] += 0.0033119101068960115; + } + } else { + result[0] += 0.006903666361705358; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + if (LIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.004350684674853294; + } else { + result[0] += 0.0058160004156852805; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[1].qvalue <= 72))) { + result[0] += -0.005029844201946011; + } else { + result[0] += 0.006639408804302992; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[1].qvalue <= 64))) { + result[0] += 0.00619428725665315; + } else { + result[0] += 0.011474633151106254; + } + } else { + result[0] += 0.01858097359565879; + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 108))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + if (LIKELY(false || (data[2].qvalue <= 76))) { + if (LIKELY(false || (data[2].qvalue <= 20))) { + if (LIKELY(false || (data[3].qvalue <= 38))) { + result[0] += -0.00019940358387546183; + } else { + result[0] += -0.000703113116348102; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 16))) { + result[0] += -4.638458464186633e-05; + } else { + result[0] += -0.0002294784137776043; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 12))) { + result[0] += 0.0005471125373971178; + } else { + if (LIKELY(false || (data[3].qvalue <= 30))) { + result[0] += 9.670414976687889e-05; + } else { + result[0] += -0.00021767801684133135; + } + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 8))) { + result[0] += -3.166344582237319e-05; + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (UNLIKELY(false || (data[0].qvalue <= 58))) { + result[0] += 0.0008152326933001966; + } else { + result[0] += 0.00025174464498736135; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 80))) { + result[0] += 0.0013971405413898312; + } else { + result[0] += -0.0006916749685131233; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[0].qvalue <= 42))) { + result[0] += 0.0006645427754437797; + } else { + result[0] += 0.0013310551905353592; + } + } else { + result[0] += 0.004798737931736182; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 150))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[0].qvalue <= 80))) { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (LIKELY(false || (data[2].qvalue <= 142))) { + result[0] += 0.0013586733582895156; + } else { + result[0] += 0.002186283295362004; + } + } else { + result[0] += -0.0014274378502657555; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.006650116111876395; + } else { + result[0] += 0.013644770354148933; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + result[0] += 0.0032603013506928773; + } else { + result[0] += -0.010240866610706277; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (LIKELY(false || (data[0].qvalue <= 68))) { + if (LIKELY(false || (data[3].qvalue <= 48))) { + result[0] += 0.004779958775547548; + } else { + result[0] += 0.00686445672987769; + } + } else { + result[0] += 0.010596439063715636; + } + } else { + result[0] += 0.016736742650200403; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 104))) { + if (LIKELY(false || (data[1].qvalue <= 66))) { + if (LIKELY(false || (data[2].qvalue <= 74))) { + if (LIKELY(false || (data[1].qvalue <= 22))) { + result[0] += -0.00017265302736832624; + } else { + result[0] += -6.590425058702988e-05; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 8))) { + result[0] += -0.00021500489047340408; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 28))) { + result[0] += 0.0003260276251636524; + } else { + result[0] += 1.1618765633385006e-05; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 56))) { + result[0] += 0.0006091551148605127; + } else { + result[0] += 0.002041106193822838; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 48))) { + if (UNLIKELY(false || (data[2].qvalue <= 124))) { + result[0] += 0.0003264125370269558; + } else { + result[0] += 0.0007305621801641551; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 74))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += 0.0015308893471321235; + } else { + result[0] += -0.0011053771592844164; + } + } else { + result[0] += 0.004019341238603161; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 150))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + result[0] += 0.0012825109063898839; + } else { + result[0] += 0.005737490966667883; + } + } else { + result[0] += 0.002875416523570694; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + result[0] += 0.0043220277108669625; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.01467062094307446; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.00487359498506815; + } else { + result[0] += 0.009006784051361535; + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[2].qvalue <= 108))) { + if (LIKELY(false || (data[1].qvalue <= 60))) { + if (LIKELY(false || (data[2].qvalue <= 82))) { + if (LIKELY(false || (data[2].qvalue <= 12))) { + result[0] += -0.0001702686007892128; + } else { + if (LIKELY(false || (data[0].qvalue <= 16))) { + result[0] += -4.754655862563027e-05; + } else { + result[0] += -0.00017474323658447127; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 18))) { + result[0] += -0.00018950144326743167; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 24))) { + result[0] += 0.0005661111931274635; + } else { + result[0] += 0.00014359699750018804; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 44))) { + if (UNLIKELY(false || (data[2].qvalue <= 4))) { + result[0] += -9.242813453442713e-05; + } else { + result[0] += 0.0004318537730927437; + } + } else { + result[0] += 0.000996025787596676; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (UNLIKELY(false || (data[1].qvalue <= 10))) { + result[0] += 0.0013570211830916067; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 34))) { + result[0] += 6.900395934849614e-05; + } else { + result[0] += 0.000526718399212838; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 74))) { + result[0] += 0.0012271596277449244; + } else { + result[0] += 0.0038837618064354986; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (LIKELY(false || (data[2].qvalue <= 146))) { + result[0] += 0.0011542642646145598; + } else { + result[0] += 0.0023728018844858226; + } + } else { + result[0] += 0.00520880423871823; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + if (LIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.003135293929630502; + } else { + result[0] += 0.004343381199101189; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + if (UNLIKELY(false || (data[0].qvalue <= 38))) { + result[0] += 0.0005685181748121977; + } else { + result[0] += 0.004925780552866418; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.013619444400285816; + } else { + result[0] += 0.008108635586280942; + } + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 36))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + result[0] += -0.00012650171567985005; + } else { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += -4.938551105130385e-07; + } else { + result[0] += 0.00023777414317281933; + } + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + result[0] += 0.0014209088284525466; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 32))) { + result[0] += -0.00013475701157859363; + } else { + if (LIKELY(false || (data[3].qvalue <= 48))) { + result[0] += 0.0005692054876058483; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 72))) { + result[0] += 0.002924335912848483; + } else { + result[0] += 0.0011432533753282545; + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 142))) { + if (LIKELY(false || (data[2].qvalue <= 108))) { + if (LIKELY(false || (data[2].qvalue <= 26))) { + result[0] += -0.00012932196425586976; + } else { + if (LIKELY(false || (data[0].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 80))) { + result[0] += -5.748038407974522e-05; + } else { + result[0] += 9.26994872808481e-05; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 58))) { + result[0] += 0.0008936605731170518; + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + result[0] += -1.2440258612188177e-05; + } else { + result[0] += 0.0005585540355690144; + } + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (UNLIKELY(false || (data[2].qvalue <= 132))) { + result[0] += 0.00040803348778467856; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 0.0025997388488009357; + } else { + if (LIKELY(false || (data[3].qvalue <= 54))) { + result[0] += 0.0007607395473800554; + } else { + result[0] += -0.006367553819648243; + } + } + } + } else { + result[0] += 0.004347370506359142; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 152))) { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[0].qvalue <= 80))) { + if (UNLIKELY(false || (data[3].qvalue <= 26))) { + result[0] += 0.008218266539471714; + } else { + result[0] += 0.001689778047040225; + } + } else { + result[0] += 0.010197310490906239; + } + } else { + result[0] += 0.002771954438342974; + } + } else { + result[0] += -0.00263078958160482; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 32))) { + result[0] += 0.003762501649809371; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.011977412732067837; + } else { + result[0] += 0.007180131322773255; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 142))) { + if (LIKELY(false || (data[2].qvalue <= 110))) { + if (LIKELY(false || (data[1].qvalue <= 60))) { + if (LIKELY(false || (data[2].qvalue <= 84))) { + if (LIKELY(false || (data[3].qvalue <= 38))) { + result[0] += -9.548885583609136e-05; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 40))) { + result[0] += -0.003540081420843614; + } else { + result[0] += -0.00024649854685375276; + } + } + } else { + if (UNLIKELY(false || (data[3].qvalue <= 20))) { + result[0] += 0.0003053226725768677; + } else { + result[0] += -6.602264761802435e-05; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 68))) { + if (UNLIKELY(false || (data[2].qvalue <= 4))) { + result[0] += -9.762664695776116e-05; + } else { + result[0] += 0.00038670152054575205; + } + } else { + result[0] += 0.0014758023283282069; + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 76))) { + if (LIKELY(false || (data[2].qvalue <= 138))) { + if (LIKELY(false || (data[1].qvalue <= 42))) { + if (LIKELY(false || (data[3].qvalue <= 38))) { + result[0] += 0.0003950345531674714; + } else { + result[0] += -0.0006780454492008814; + } + } else { + result[0] += 0.0011275851879113837; + } + } else { + result[0] += 0.000772236033582265; + } + } else { + result[0] += 0.004143313796252665; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 150))) { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[3].qvalue <= 26))) { + result[0] += 0.009037786461412907; + } else { + if (LIKELY(false || (data[1].qvalue <= 78))) { + result[0] += 0.0016797847044042091; + } else { + result[0] += 0.009218368388153613; + } + } + } else { + result[0] += -0.002368890210880294; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (LIKELY(false || (data[1].qvalue <= 64))) { + result[0] += 0.0030468957829948803; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.003570656648731198; + } else { + result[0] += 0.006838003122049602; + } + } + } else { + result[0] += 0.010788609192028134; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (LIKELY(false || (data[2].qvalue <= 116))) { + if (LIKELY(false || (data[1].qvalue <= 48))) { + if (LIKELY(false || (data[2].qvalue <= 50))) { + result[0] += -0.00010835034231308543; + } else { + result[0] += -3.592209291896241e-06; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 10))) { + result[0] += -6.758697818328856e-05; + } else { + if (LIKELY(false || (data[2].qvalue <= 68))) { + result[0] += 0.0002567234765659431; + } else { + result[0] += 0.0006684446514199459; + } + } + } + } else { + if (LIKELY(false || (data[1].qvalue <= 38))) { + if (UNLIKELY(false || (data[1].qvalue <= 4))) { + result[0] += 0.0019020060332526339; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 126))) { + result[0] += 0.00014095668063188527; + } else { + result[0] += 0.0006037159852907501; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 74))) { + result[0] += 0.001560769119473446; + } else { + result[0] += 0.004308113069032753; + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 152))) { + if (LIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.001609181337370336; + } else { + result[0] += 0.0025188474001257414; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 32))) { + result[0] += 0.0030819144903457287; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.009717799607497545; + } else { + result[0] += 0.005811008536776794; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 142))) { + if (LIKELY(false || (data[2].qvalue <= 108))) { + if (LIKELY(false || (data[2].qvalue <= 26))) { + result[0] += -9.633149362070105e-05; + } else { + if (LIKELY(false || (data[3].qvalue <= 46))) { + if (LIKELY(false || (data[3].qvalue <= 42))) { + result[0] += -2.3786842856587217e-06; + } else { + result[0] += -0.0008784412376800542; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 52))) { + result[0] += 0.0005592856904334903; + } else { + result[0] += -0.0010997740778614094; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 134))) { + result[0] += 0.0003003870717462951; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 0.0021186501027940312; + } else { + result[0] += 0.0005632598779634045; + } + } + } else { + result[0] += 0.003155110609239992; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 152))) { + if (LIKELY(false || (data[3].qvalue <= 58))) { + if (UNLIKELY(false || (data[3].qvalue <= 26))) { + if (LIKELY(false || (data[3].qvalue <= 22))) { + result[0] += 0.002531461699501328; + } else { + result[0] += 0.013085974014940716; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 148))) { + result[0] += 0.0012521484883360095; + } else { + result[0] += 0.002029939821163165; + } + } + } else { + result[0] += -0.002291005669175251; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + result[0] += 0.0027415007075521983; + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + result[0] += 0.00517479080318933; + } else { + result[0] += 0.008753272564108693; + } + } + } + } + if (LIKELY(false || (data[3].qvalue <= 36))) { + if (LIKELY(false || (data[1].qvalue <= 20))) { + result[0] += -7.805033787009575e-05; + } else { + result[0] += 2.3801570604239546e-05; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + result[0] += 0.0008308640887867767; + } else { + if (LIKELY(false || (data[1].qvalue <= 36))) { + if (UNLIKELY(false || (data[1].qvalue <= 32))) { + result[0] += -0.0001255589210486739; + } else { + result[0] += 0.0002974589212792894; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 66))) { + result[0] += 0.001434875231823905; + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + result[0] += 0.00015393892861622098; + } else { + result[0] += 0.0008355777768832701; + } + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (LIKELY(false || (data[2].qvalue <= 100))) { + if (LIKELY(false || (data[0].qvalue <= 46))) { + if (LIKELY(false || (data[2].qvalue <= 80))) { + result[0] += -7.09997632022635e-05; + } else { + if (LIKELY(false || (data[2].qvalue <= 92))) { + if (LIKELY(false || (data[2].qvalue <= 90))) { + result[0] += 6.700198529498124e-05; + } else { + result[0] += 0.0006739111265931478; + } + } else { + result[0] += -7.873394714685852e-05; + } + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 0))) { + result[0] += -0.00043781627567509543; + } else { + result[0] += 0.00020885997574915155; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 80))) { + if (UNLIKELY(false || (data[2].qvalue <= 130))) { + result[0] += 0.00021999474610715451; + } else { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 0.001993992760088811; + } else { + result[0] += 0.00044991030401214167; + } + } + } else { + result[0] += 0.004000181025335582; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 152))) { + if (LIKELY(false || (data[3].qvalue <= 58))) { + result[0] += 0.0015042104367088394; + } else { + result[0] += -0.0022578272852008534; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 32))) { + result[0] += 0.002410829276957461; + } else { + result[0] += 0.0055512269901312646; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[2].qvalue <= 104))) { + if (UNLIKELY(false || (data[2].qvalue <= 12))) { + result[0] += -8.496817920340536e-05; + } else { + if (LIKELY(false || (data[0].qvalue <= 48))) { + result[0] += -1.7540259968379833e-05; + } else { + result[0] += 0.00025745579920166244; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 138))) { + result[0] += 0.00023710498818372454; + } else { + result[0] += 0.0005008600118271386; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.0021586178965176087; + } else { + result[0] += 0.008461107069451827; + } + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + if (LIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.0012443975867339444; + } else { + result[0] += 0.0019477678730867058; + } + } else { + if (UNLIKELY(false || (data[2].qvalue <= 152))) { + result[0] += 0.0019342118146449738; + } else { + result[0] += 0.004997239694835386; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 32))) { + if (LIKELY(false || (data[1].qvalue <= 16))) { + result[0] += -6.499770451690952e-05; + } else { + result[0] += 9.93532909532843e-06; + } + } else { + if (UNLIKELY(false || (data[1].qvalue <= 28))) { + result[0] += 0.0005901691687912098; + } else { + if (LIKELY(false || (data[1].qvalue <= 70))) { + result[0] += 0.00015807135019769365; + } else { + result[0] += 0.0005244958255941764; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[2].qvalue <= 86))) { + result[0] += -4.676198214734394e-05; + } else { + if (LIKELY(false || (data[1].qvalue <= 60))) { + if (UNLIKELY(false || (data[1].qvalue <= 14))) { + if (LIKELY(false || (data[2].qvalue <= 118))) { + result[0] += -0.00010600660656932565; + } else { + result[0] += 0.0015161007267231429; + } + } else { + result[0] += 0.0002612619805398477; + } + } else { + result[0] += 0.0015693112213150122; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 44))) { + result[0] += 0.0013664188809999066; + } else { + if (UNLIKELY(false || (data[2].qvalue <= 150))) { + result[0] += -0.010022143385034394; + } else { + result[0] += 0.0037975534515937988; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 142))) { + if (LIKELY(false || (data[2].qvalue <= 86))) { + result[0] += -4.208578851825266e-05; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 14))) { + if (LIKELY(false || (data[2].qvalue <= 116))) { + result[0] += -0.00011403525794098995; + } else { + result[0] += 0.0009114640165412386; + } + } else { + if (LIKELY(false || (data[1].qvalue <= 60))) { + result[0] += 0.00022356508848759622; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 62))) { + result[0] += 0.0027995720407202294; + } else { + if (UNLIKELY(false || (data[1].qvalue <= 70))) { + result[0] += -0.0008798782950704978; + } else { + result[0] += 0.0014448416755765952; + } + } + } + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 152))) { + if (LIKELY(false || (data[1].qvalue <= 70))) { + result[0] += 0.0008384216911964453; + } else { + result[0] += 0.002447881422746419; + } + } else { + if (LIKELY(false || (data[0].qvalue <= 32))) { + result[0] += 0.0017733230820123455; + } else { + result[0] += 0.004110382169902663; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[2].qvalue <= 86))) { + result[0] += -3.787721484857798e-05; + } else { + if (LIKELY(false || (data[1].qvalue <= 70))) { + if (UNLIKELY(false || (data[1].qvalue <= 14))) { + if (LIKELY(false || (data[2].qvalue <= 118))) { + result[0] += -8.689697470917008e-05; + } else { + result[0] += 0.0012740214207030438; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 54))) { + result[0] += 0.00021910612133028753; + } else { + result[0] += -0.0019394310314736379; + } + } + } else { + result[0] += 0.001605288614427337; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 152))) { + if (LIKELY(false || (data[3].qvalue <= 52))) { + result[0] += 0.0010209367212090006; + } else { + result[0] += -0.0070652788154780865; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + result[0] += 0.001794208969053978; + } else { + result[0] += 0.00589450452114028; + } + } + } + if (LIKELY(false || (data[2].qvalue <= 142))) { + if (LIKELY(false || (data[2].qvalue <= 86))) { + result[0] += -3.4089497281904435e-05; + } else { + if (LIKELY(false || (data[1].qvalue <= 48))) { + result[0] += 0.00011859360145590661; + } else { + result[0] += 0.0005882149306744061; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 150))) { + result[0] += 0.0006289461581674275; + } else { + if (LIKELY(false || (data[0].qvalue <= 24))) { + result[0] += 0.0012857444628638962; + } else { + if (UNLIKELY(false || (data[0].qvalue <= 36))) { + result[0] += 0.0053522964003919205; + } else { + result[0] += 0.002180858513372534; + } + } + } + } + if (LIKELY(false || (data[2].qvalue <= 142))) { + if (LIKELY(false || (data[2].qvalue <= 122))) { + if (LIKELY(false || (data[1].qvalue <= 48))) { + result[0] += -3.231127347736361e-05; + } else { + result[0] += 0.0001305453669476947; + } + } else { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (UNLIKELY(false || (data[3].qvalue <= 36))) { + result[0] += 0.0015725449376088275; + } else { + result[0] += 0.0002082949912808661; + } + } else { + result[0] += -0.006547446409612894; + } + } + } else { + if (LIKELY(false || (data[2].qvalue <= 150))) { + if (UNLIKELY(false || (data[3].qvalue <= 26))) { + result[0] += 0.0065892055427092455; + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + result[0] += 0.000599515916925125; + } else { + result[0] += -0.001937681221394829; + } + } + } else { + if (LIKELY(false || (data[3].qvalue <= 58))) { + result[0] += 0.00127534830686723; + } else { + result[0] += 0.004778217595251305; + } + } + } + if (LIKELY(false || (data[3].qvalue <= 38))) { + result[0] += -1.2646257755117516e-05; + } else { + result[0] += 0.0002454812344733204; + } + if (LIKELY(false || (data[3].qvalue <= 32))) { + result[0] += -1.5786346014169378e-05; + } else { + result[0] += 0.00016700826916388908; + } + if (LIKELY(false || (data[2].qvalue <= 146))) { + if (LIKELY(false || (data[2].qvalue <= 80))) { + result[0] += -2.887770604298617e-05; + } else { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[0].qvalue <= 64))) { + result[0] += 0.00010275584396454631; + } else { + result[0] += -0.001987768939441655; + } + } else { + result[0] += 0.0019405763097977533; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 0.0008150039520986579; + } else { + result[0] += 0.003428131746816993; + } + } + if (LIKELY(false || (data[3].qvalue <= 38))) { + result[0] += -9.428440320689368e-06; + } else { + result[0] += 0.00018303284915371275; + } + if (LIKELY(false || (data[2].qvalue <= 148))) { + if (LIKELY(false || (data[2].qvalue <= 126))) { + result[0] += -1.6525789323637374e-05; + } else { + result[0] += 0.00019819706642161615; + } + } else { + result[0] += 0.0009033266907285568; + } + if (LIKELY(false || (data[2].qvalue <= 142))) { + if (LIKELY(false || (data[2].qvalue <= 80))) { + result[0] += -2.3731074344715385e-05; + } else { + if (LIKELY(false || (data[3].qvalue <= 54))) { + if (LIKELY(false || (data[0].qvalue <= 48))) { + if (UNLIKELY(false || (data[3].qvalue <= 12))) { + result[0] += 0.0003259490041081464; + } else { + result[0] += 3.657792922172123e-05; + } + } else { + if (UNLIKELY(false || (data[0].qvalue <= 50))) { + result[0] += 0.00406008107199644; + } else { + result[0] += 0.0005606708031635196; + } + } + } else { + result[0] += -0.005101178498007357; + } + } + } else { + if (LIKELY(false || (data[0].qvalue <= 60))) { + result[0] += 0.0005373762500503928; + } else { + result[0] += 0.0027015011546189507; + } + } + if (LIKELY(false || (data[2].qvalue <= 150))) { + if (LIKELY(false || (data[2].qvalue <= 128))) { + result[0] += -1.3661286266953386e-05; + } else { + result[0] += 0.00018261991840147; + } + } else { + result[0] += 0.000931821730518591; + } + if (LIKELY(false || (data[2].qvalue <= 150))) { + if (LIKELY(false || (data[2].qvalue <= 126))) { + result[0] += -1.2529314401332627e-05; + } else { + result[0] += 0.00016018169881967258; + } + } else { + result[0] += 0.0008386591836191829; + } + if (LIKELY(false || (data[2].qvalue <= 144))) { + if (LIKELY(false || (data[0].qvalue <= 78))) { + if (LIKELY(false || (data[2].qvalue <= 64))) { + result[0] += -2.4302315497459032e-05; + } else { + result[0] += 5.194281681207711e-05; + } + } else { + if (LIKELY(false || (data[2].qvalue <= 140))) { + result[0] += 0.00045022060778704474; + } else { + result[0] += 0.006445361257938202; + } + } + } else { + result[0] += 0.0004861295980061241; + } + if (LIKELY(false || (data[2].qvalue <= 150))) { + if (LIKELY(false || (data[2].qvalue <= 86))) { + result[0] += -1.559693370405162e-05; + } else { + result[0] += 8.018493624717838e-05; + } + } else { + result[0] += 0.0007062082614175126; + } + if (LIKELY(false || (data[2].qvalue <= 148))) { + result[0] += -3.500609763214595e-06; + } else { + result[0] += 0.0005238382736763112; + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + result[0] += -7.872314087503873e-06; + } else { + if (LIKELY(false || (data[1].qvalue <= 40))) { + result[0] += 0.00016982337133831138; + } else { + result[0] += 0.0015582625369628602; + } + } + if (LIKELY(false || (data[2].qvalue <= 152))) { + result[0] += -1.7980922930840279e-06; + } else { + result[0] += 0.0008003180445416104; + } + if (LIKELY(false || (data[2].qvalue <= 138))) { + result[0] += -6.905273724778028e-06; + } else { + result[0] += 0.00018194518281833516; + } + if (LIKELY(false || (data[1].qvalue <= 48))) { + result[0] += -7.5322283086623e-06; + } else { + result[0] += 0.00013574551159436845; + } + if (LIKELY(false || (data[2].qvalue <= 152))) { + result[0] += -1.5802465245244315e-06; + } else { + result[0] += 0.0007009422698652187; + } + if (LIKELY(false || (data[2].qvalue <= 142))) { + result[0] += -3.6413942554064128e-06; + } else { + result[0] += 0.0002746418585991201; + } + if (UNLIKELY(false || (data[2].qvalue <= 12))) { + result[0] += -3.379293117509959e-05; + } else { + result[0] += 2.828160884444577e-05; + } + + // Apply base_scores + result[0] += 0; + + // Apply postprocessor + if (!pred_margin) { postprocess(result); } +} + +void bounds_strengthening_predictor::postprocess(double* result) +{ + // Do nothing +} + +// Feature names array +const char* + bounds_strengthening_predictor::feature_names[bounds_strengthening_predictor::NUM_FEATURES] = { + "m", "n", "nnz_processed", "nnz", "bounds_changed"}; diff --git a/cpp/src/utilities/models/bounds_strengthening_predictor/quantize.cpp b/cpp/src/utilities/models/bounds_strengthening_predictor/quantize.cpp new file mode 100644 index 000000000..e9b7b3c37 --- /dev/null +++ b/cpp/src/utilities/models/bounds_strengthening_predictor/quantize.cpp @@ -0,0 +1,111 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ + +#include "header.h" + +static const double threshold[] = { + 331.50000000000006, 380.50000000000006, 444.00000000000006, 454.50000000000006, + 519.50000000000011, 555.00000000000011, 1454.0000000000002, 1496.5000000000002, + 2448.5000000000005, 4115.0000000000009, 4305.5000000000009, 4839.5000000000009, + 5346.5000000000009, 6228.5000000000009, 9689.5000000000018, 14879.500000000002, + 15693.000000000002, 17345.000000000004, 21083.000000000004, 24144.500000000004, + 34410.500000000007, 36239.500000000007, 37842.500000000007, 40918.500000000007, + 46263.500000000007, 50138.000000000007, 60708.500000000007, 72207.000000000015, + 77439.500000000015, 79608.500000000015, 91121.000000000015, 93826.000000000015, + 104579.50000000001, 163887.00000000003, 202377.00000000003, 221228.50000000003, + 246546.00000000003, 298845.00000000006, 323110.00000000006, 344863.50000000006, + 510463.50000000006, 1645.0000000000002, 1709.0000000000002, 3059.5000000000005, + 3953.5000000000005, 4013.0000000000005, 4025.5000000000005, 4837.5000000000009, + 5585.5000000000009, 5832.5000000000009, 6329.5000000000009, 6829.0000000000009, + 7266.0000000000009, 9604.5000000000018, 10543.000000000002, 11316.000000000002, + 13942.500000000002, 18312.500000000004, 20001.500000000004, 26121.500000000004, + 28899.500000000004, 31929.000000000004, 41385.000000000007, 49239.000000000007, + 52480.000000000007, 55778.000000000007, 59927.500000000007, 65717.000000000015, + 75092.000000000015, 106578.00000000001, 114790.50000000001, 120166.00000000001, + 125666.50000000001, 238985.00000000003, 257730.50000000003, 304621.00000000006, + 345125.50000000006, 467574.50000000006, 538765.50000000012, 600167.00000000012, + 651989.00000000012, 492.50000000000006, 6278.5000000000009, 7445.5000000000009, + 9808.5000000000018, 11088.500000000002, 12428.500000000002, 15493.000000000002, + 17632.500000000004, 20808.500000000004, 22190.000000000004, 22709.500000000004, + 23707.500000000004, 36926.500000000007, 38191.000000000007, 44292.000000000007, + 45843.000000000007, 47541.500000000007, 52712.500000000007, 54350.500000000007, + 59809.000000000007, 63098.000000000007, 68639.000000000015, 80444.000000000015, + 92079.500000000015, 98182.500000000015, 123427.00000000001, 125177.00000000001, + 167577.00000000003, 174836.00000000003, 186265.50000000003, 188362.00000000003, + 197007.50000000003, 211368.00000000003, 249795.00000000003, 260021.00000000003, + 266201.00000000006, 289613.50000000006, 292623.50000000006, 309845.50000000006, + 313121.50000000006, 319213.00000000006, 327694.00000000006, 336320.00000000006, + 388897.00000000006, 431435.50000000006, 443736.50000000006, 499102.50000000006, + 504094.00000000006, 547807.50000000012, 587180.00000000012, 603657.50000000012, + 624274.00000000012, 647468.00000000012, 699833.00000000012, 734235.50000000012, + 757689.00000000012, 780010.50000000012, 962875.00000000012, 1164224.5000000002, + 1416361.0000000002, 1630053.5000000002, 1863835.0000000002, 1921521.0000000002, + 1971655.0000000002, 1989499.0000000002, 2005190.0000000002, 2018692.0000000002, + 2032598.0000000002, 2092776.0000000002, 2836397.5000000005, 3398604.0000000005, + 3442503.5000000005, 3897359.5000000005, 5191813.5000000009, 5694629.5000000009, + 6838622.5000000009, 8540143.0000000019, 6310.0000000000009, 43748.000000000007, + 50829.500000000007, 69946.000000000015, 79995.000000000015, 110786.50000000001, + 140480.00000000003, 147046.50000000003, 180637.50000000003, 211441.50000000003, + 299525.50000000006, 386595.00000000006, 412447.00000000006, 430914.00000000006, + 474762.00000000006, 497602.00000000006, 587288.00000000012, 706723.50000000012, + 781740.50000000012, 853522.00000000012, 997296.00000000012, 1065333.0000000002, + 1204174.5000000002, 1222545.5000000002, 1806119.5000000002, 1879308.0000000002, + 1940828.0000000002, 2238905.0000000005, 2573759.0000000005, 7308634.0000000009, +}; + +static const int th_begin[] = { + 0, + 41, + 81, + 158, + 188, +}; + +static const int th_len[] = { + 41, + 40, + 77, + 30, + 0, +}; + +/* + * \brief Function to convert a feature value into bin index. + * \param val Feature value, in floating-point + * \param fid Feature identifier + * \return bin Index corresponding to given feature value + */ +int bounds_strengthening_predictor::quantize(double val, unsigned fid) +{ + const size_t offset = th_begin[fid]; + const double* array = &threshold[offset]; + int len = th_len[fid]; + int low = 0; + int high = len; + int mid; + double mval; + // It is possible th_begin[i] == [total_num_threshold]. This means that + // all features i, (i+1), ... are not used for any of the splits in the model. + // So in this case, just return something + if (offset == 188 || val < array[0]) { return -10; } + while (low + 1 < high) { + mid = (low + high) / 2; + mval = array[mid]; + if (val == mval) { + return mid * 2; + } else if (val < mval) { + high = mid; + } else { + low = mid; + } + } + if (array[low] == val) { + return low * 2; + } else if (high == len) { + return len * 2; + } else { + return low * 2 + 1; + } +} diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp index 949d84b2c..33c315c59 100644 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -25,6 +25,7 @@ #include #include +#include "models/bounds_strengthening_predictor/header.h" #include "models/cpufj_predictor/header.h" #include "models/dualsimplex_predictor/header.h" #include "models/fj_predictor/header.h" @@ -79,5 +80,6 @@ template class work_unit_predictor_t; template class work_unit_predictor_t; template class work_unit_predictor_t; template class work_unit_predictor_t; +template class work_unit_predictor_t; } // namespace cuopt From c0422edc9828414fa3f0644d3dc8f6da92e57b82 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 21 Jan 2026 14:24:59 +0100 Subject: [PATCH 232/366] fixed crash --- cpp/src/dual_simplex/branch_and_bound.cpp | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 6760c4fe1..06dfaee02 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1253,6 +1253,10 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut lower_bound_ceiling_ = inf; min_node_queue_size_ = 2 * settings_.num_threads; + if (settings_.diving_settings.coefficient_diving != 0) { + calculate_variable_locks(original_lp_, var_up_locks_, var_down_locks_); + } + settings_.log.printf( " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " "| Time |\n"); @@ -1291,7 +1295,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut node_queue_.best_first_queue_size()); for (auto type : worker_types) { - settings_.log.debug("%s: max num of workers = %d", + settings_.log.debug("%c: max num of workers = %d", feasible_solution_symbol(type), max_num_workers_per_type[type]); } @@ -1310,7 +1314,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut #ifdef CUOPT_LOG_DEBUG for (auto type : worker_types) { - settings_.log.debug("%s: max num of workers = %d", + settings_.log.debug("%c: max num of workers = %d", feasible_solution_symbol(type), max_num_workers_per_type[type]); } From a0a1d93678b2b77d2e88a28c4121d84141fc5b48 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 21 Jan 2026 14:34:02 +0100 Subject: [PATCH 233/366] better num thread initialization --- cpp/src/mip/presolve/bounds_presolve.cuh | 2 +- cpp/src/mip/presolve/probing_cache.cu | 5 ++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/cpp/src/mip/presolve/bounds_presolve.cuh b/cpp/src/mip/presolve/bounds_presolve.cuh index b82f442aa..88a6c740f 100644 --- a/cpp/src/mip/presolve/bounds_presolve.cuh +++ b/cpp/src/mip/presolve/bounds_presolve.cuh @@ -34,7 +34,7 @@ class bound_presolve_t { struct settings_t { f_t time_limit{60.0}; i_t iteration_limit{std::numeric_limits::max()}; - i_t num_threads = std::max(omp_get_max_threads(), 1); + i_t num_threads = -1; bool parallel_bounds_update{true}; }; diff --git a/cpp/src/mip/presolve/probing_cache.cu b/cpp/src/mip/presolve/probing_cache.cu index 0c3b34dbb..a6d41883d 100644 --- a/cpp/src/mip/presolve/probing_cache.cu +++ b/cpp/src/mip/presolve/probing_cache.cu @@ -856,7 +856,10 @@ bool compute_probing_cache(bound_presolve_t& bound_presolve, bound_presolve.settings.iteration_limit = 50; bound_presolve.settings.time_limit = timer.remaining_time(); - const size_t num_threads = bound_presolve.settings.num_threads; + size_t num_threads = bound_presolve.settings.num_threads < 0 + ? omp_get_max_threads() + : bound_presolve.settings.num_threads; + num_threads = std::max(num_threads, 1); // Create a vector of multi_probe_t objects std::vector> multi_probe_presolve_pool; From 9f0fe29db07aa5d7797585cbe567d32b466afd90 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 21 Jan 2026 14:38:08 +0100 Subject: [PATCH 234/366] fix compilation --- cpp/src/mip/presolve/probing_cache.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/presolve/probing_cache.cu b/cpp/src/mip/presolve/probing_cache.cu index a6d41883d..03b86940c 100644 --- a/cpp/src/mip/presolve/probing_cache.cu +++ b/cpp/src/mip/presolve/probing_cache.cu @@ -859,7 +859,7 @@ bool compute_probing_cache(bound_presolve_t& bound_presolve, size_t num_threads = bound_presolve.settings.num_threads < 0 ? omp_get_max_threads() : bound_presolve.settings.num_threads; - num_threads = std::max(num_threads, 1); + num_threads = std::max(num_threads, 1); // Create a vector of multi_probe_t objects std::vector> multi_probe_presolve_pool; From 298c68c8840b6745ff10571338f9c56043379e8b Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 21 Jan 2026 16:58:48 +0100 Subject: [PATCH 235/366] moved parameters to simplex_settings. added command line option --- .../linear_programming/cuopt/run_mip.cpp | 11 ++++++++++ .../mip/solver_settings.hpp | 4 +++- cpp/src/dual_simplex/pseudo_costs.cpp | 11 ++++++---- .../dual_simplex/simplex_solver_settings.hpp | 21 +++++++++++++++++++ 4 files changed, 42 insertions(+), 5 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 213e38e5e..3f9346201 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -147,6 +147,7 @@ int run_single_file(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, + bool enable_rb, double time_limit) { const raft::handle_t handle_{}; @@ -204,6 +205,7 @@ int run_single_file(std::string file_path, settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; + settings.enable_reliability_branching = enable_rb; cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; auto start_run_solver = std::chrono::high_resolution_clock::now(); @@ -256,6 +258,7 @@ void run_single_file_mp(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, + bool enable_rb, double time_limit) { std::cout << "running file " << file_path << " on gpu : " << device << std::endl; @@ -271,6 +274,7 @@ void run_single_file_mp(std::string file_path, num_cpu_threads, write_log_file, log_to_console, + enable_rb, time_limit); // this is a bad design to communicate the result but better than adding complexity of IPC or // pipes @@ -354,6 +358,10 @@ int main(int argc, char* argv[]) .help("track allocations (t/f)") .default_value(std::string("f")); + program.add_argument("--enable-reliability-branching") + .help("track allocations (t/f)") + .default_value(std::string("f")); + // Parse arguments try { program.parse_args(argc, argv); @@ -382,6 +390,7 @@ int main(int argc, char* argv[]) bool log_to_console = program.get("--log-to-console")[0] == 't'; double memory_limit = program.get("--memory-limit"); bool track_allocations = program.get("--track-allocations")[0] == 't'; + bool enable_rb = program.get("--enable-reliability-branching")[0] == 't'; if (num_cpu_threads < 0) { num_cpu_threads = omp_get_max_threads() / n_gpus; } @@ -469,6 +478,7 @@ int main(int argc, char* argv[]) num_cpu_threads, write_log_file, log_to_console, + enable_rb, time_limit); } else if (sys_pid < 0) { std::cerr << "Fork failed!" << std::endl; @@ -509,6 +519,7 @@ int main(int argc, char* argv[]) num_cpu_threads, write_log_file, log_to_console, + enable_rb, time_limit); } diff --git a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp index 4f6320752..653a71972 100644 --- a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp +++ b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2023-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -83,6 +83,8 @@ class mip_solver_settings_t { i_t num_cpu_threads = -1; // -1 means use default number of threads in branch and bound i_t num_gpus = 1; bool log_to_console = true; + bool enable_reliability_branching = false; + std::string log_file; std::string sol_file; std::string user_problem_file; diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 76dd8e125..836361e5f 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -156,7 +156,9 @@ f_t trial_branching(const lp_problem_t& original_lp, simplex_solver_settings_t child_settings = settings; child_settings.set_log(false); f_t lp_start_time = tic(); - child_settings.iteration_limit = std::clamp(bnb_lp_iter_per_node, 10, 100); + i_t lp_iter_upper = settings.reliability_branching_settings.upper_max_lp_iter; + i_t lp_iter_lower = settings.reliability_branching_settings.lower_max_lp_iter; + child_settings.iteration_limit = std::clamp(bnb_lp_iter_per_node, lp_iter_lower, lp_iter_upper); child_settings.cut_off = upper_bound + settings.dual_tol; child_settings.inside_mip = 2; child_settings.scale_columns = false; @@ -428,7 +430,8 @@ i_t pseudo_costs_t::reliable_variable_selection( // const i_t min_v = 1; // i_t reliable_threshold = std::clamp((1 - gamma) * min_v + gamma * max_v, min_v, max_v); // reliable_threshold = total_lp_iter < max_iter ? reliable_threshold : 0; - i_t reliable_threshold = 1; + i_t reliable_threshold = settings.reliability_branching_settings.reliable_threshold; + int task_priority = settings.reliability_branching_settings.task_priority; std::vector pending(fractional.size(), -1); std::vector next(fractional.size(), -1); @@ -462,7 +465,7 @@ i_t pseudo_costs_t::reliable_variable_selection( } if (num_pending != 0) { - settings.log.debug( + log.printf( "RB LP iterations = %d, B&B LP iterations = %d reliable_threshold = %d, num strong branches " "= %d\n", total_lp_iter.load(), @@ -472,7 +475,7 @@ i_t pseudo_costs_t::reliable_variable_selection( } while (num_pending != 0) { -#pragma omp taskloop priority(20) grainsize(2) if (num_pending > 2) +#pragma omp taskloop if (num_pending > 1) priority(task_priority) for (i_t i = 0; i < num_pending; ++i) { const i_t j = pending[i]; bool is_locked = pseudo_cost_mutex[j].try_lock(); diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index d86f84c39..125ba3ac7 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -36,6 +36,25 @@ struct diving_heuristics_settings_t { i_t backtrack_limit = 5; }; +template +struct reliability_branching_settings_t { + // For now, setting to 1, which correspond to pseudocost with strong branching + // initialization. Later, it can be set dynamically depending on the number + // of LP iterations in the strong branching and B&B. + // Set to 0 for disabling reliability branching + i_t reliable_threshold = 1; + + // Lower bound for the maximum number of LP iterations for a single trial branching + i_t lower_max_lp_iter = 10; + + // Upper bound for the maximum number of LP iterations for a single trial branching + i_t upper_max_lp_iter = 500; + + // Priority of the tasks created when running the trial branching in parallel. + // Set to 1 to have the same priority as the other tasks. + i_t task_priority = 5; +}; + template struct simplex_solver_settings_t { public: @@ -157,6 +176,8 @@ struct simplex_solver_settings_t { i_t num_bfs_workers; // number of threads dedicated to the best-first search diving_heuristics_settings_t diving_settings; // Settings for the diving heuristics + reliability_branching_settings_t + reliability_branching_settings; // Settings for reliability branching i_t inside_mip; // 0 if outside MIP, 1 if inside MIP at root node, 2 if inside MIP at leaf node std::function&, f_t)> solution_callback; From 8a890fdd20599ad8f0d47913f1c9ee6d0558c211 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 21 Jan 2026 17:14:14 +0100 Subject: [PATCH 236/366] handle cli arguments --- cpp/src/dual_simplex/branch_and_bound.cpp | 7 +++---- cpp/src/dual_simplex/pseudo_costs.cpp | 2 +- cpp/src/dual_simplex/simplex_solver_settings.hpp | 2 ++ cpp/src/mip/diversity/lns/rins.cu | 1 + cpp/src/mip/diversity/recombiners/sub_mip.cuh | 1 + cpp/src/mip/solver.cu | 2 ++ 6 files changed, 10 insertions(+), 5 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 06dfaee02..c84d4ac19 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -577,10 +577,7 @@ branch_variable_t branch_and_bound_t::variable_selection( switch (worker_data->worker_type) { case bnb_worker_type_t::BEST_FIRST: - // RINS/SubMIP path - if (!enable_concurrent_lp_root_solve()) { - branch_var = pc_.variable_selection(fractional, solution, log); - } else { + if (settings_.reliability_branching_settings.enable) { branch_var = pc_.reliable_variable_selection(worker_data->leaf_problem, settings_, var_types_, @@ -596,6 +593,8 @@ branch_variable_t branch_and_bound_t::variable_selection( exploration_stats_.total_lp_iters, exploration_stats_.nodes_explored, log); + } else { + branch_var = pc_.variable_selection(fractional, solution, log); } round_dir = martin_criteria(solution[branch_var], root_relax_soln_.x[branch_var]); diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 836361e5f..e6ef734e8 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -465,7 +465,7 @@ i_t pseudo_costs_t::reliable_variable_selection( } if (num_pending != 0) { - log.printf( + printf( "RB LP iterations = %d, B&B LP iterations = %d reliable_threshold = %d, num strong branches " "= %d\n", total_lp_iter.load(), diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 125ba3ac7..910f48e1f 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -38,6 +38,8 @@ struct diving_heuristics_settings_t { template struct reliability_branching_settings_t { + bool enable = false; + // For now, setting to 1, which correspond to pseudocost with strong branching // initialization. Later, it can be set dynamically depending on the number // of LP iterations in the strong branching and B&B. diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 7456b59ed..c3c280a03 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -259,6 +259,7 @@ void rins_t::run_rins() branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; branch_and_bound_settings.num_threads = 2; branch_and_bound_settings.num_bfs_workers = 1; + branch_and_bound_settings.reliability_branching_settings.enable = false; // In the future, let RINS use all the diving heuristics. For now, // restricting to guided diving. diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index 82670437a..ce232b514 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -104,6 +104,7 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; branch_and_bound_settings.num_threads = 2; branch_and_bound_settings.num_bfs_workers = 1; + branch_and_bound_settings.reliability_branching_settings.enable = false; // In the future, let SubMIP use all the diving heuristics. For now, // restricting to guided diving. diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 08e1806b9..9e9093285 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -167,6 +167,8 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.reliability_branching_settings.enable = + solver_settings_.enable_reliability_branching; if (context.settings.num_cpu_threads < 0) { branch_and_bound_settings.num_threads = omp_get_max_threads() - 1; From 2c2c5155edfceca80f60d63c89b210c268812dca Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 21 Jan 2026 17:44:18 +0100 Subject: [PATCH 237/366] set additional openmp flags --- cpp/src/dual_simplex/pseudo_costs.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index e6ef734e8..439fb7567 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -465,8 +465,8 @@ i_t pseudo_costs_t::reliable_variable_selection( } if (num_pending != 0) { - printf( - "RB LP iterations = %d, B&B LP iterations = %d reliable_threshold = %d, num strong branches " + settings.log.printf( + "RB LP iterations = %d, B&B LP iterations = %d, reliable_threshold = %d, num strong branches " "= %d\n", total_lp_iter.load(), bnb_lp_iter, @@ -475,7 +475,7 @@ i_t pseudo_costs_t::reliable_variable_selection( } while (num_pending != 0) { -#pragma omp taskloop if (num_pending > 1) priority(task_priority) +#pragma omp taskloop if (num_pending > 1) priority(task_priority) grainsize(1) untied for (i_t i = 0; i < num_pending; ++i) { const i_t j = pending[i]; bool is_locked = pseudo_cost_mutex[j].try_lock(); From 1aca951d254f0e5eb651f1173bdd4b778fea65ff Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 21 Jan 2026 16:51:10 +0000 Subject: [PATCH 238/366] Fix issue with work limits that aren't multiples of the horizon steps --- cpp/src/dual_simplex/branch_and_bound.cpp | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 81b5307fd..6fb3f948e 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2205,7 +2205,11 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) should_terminate = true; } - if (work_unit_context_.global_work_units_elapsed >= settings_.work_limit) { + // Check if the next horizon would exceed work limit. If so, terminate now rather than + // letting workers continue past the limit. This is conservative (stops slightly early) + // but prevents workers from processing nodes beyond the work budget. + // bsp_current_horizon_ now holds the NEXT horizon's end value after the increment above. + if (bsp_current_horizon_ > settings_.work_limit) { solver_status_ = mip_status_t::WORK_LIMIT; should_terminate = true; } From e593d36a5e7b924ef35a05f724ce1c7959787b47 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 21 Jan 2026 17:55:18 +0100 Subject: [PATCH 239/366] set the number of tasks for strong branching. --- cpp/src/dual_simplex/pseudo_costs.cpp | 10 +++++++--- cpp/src/dual_simplex/simplex_solver_settings.hpp | 4 ++++ 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 439fb7567..76e13a5e2 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -430,8 +430,11 @@ i_t pseudo_costs_t::reliable_variable_selection( // const i_t min_v = 1; // i_t reliable_threshold = std::clamp((1 - gamma) * min_v + gamma * max_v, min_v, max_v); // reliable_threshold = total_lp_iter < max_iter ? reliable_threshold : 0; - i_t reliable_threshold = settings.reliability_branching_settings.reliable_threshold; - int task_priority = settings.reliability_branching_settings.task_priority; + const i_t reliable_threshold = settings.reliability_branching_settings.reliable_threshold; + const int task_priority = settings.reliability_branching_settings.task_priority; + int num_tasks = settings.reliability_branching_settings.num_tasks; + if (num_tasks < 0) { num_tasks = omp_get_num_threads(); } + num_tasks = std::max(num_tasks, 1); std::vector pending(fractional.size(), -1); std::vector next(fractional.size(), -1); @@ -475,7 +478,8 @@ i_t pseudo_costs_t::reliable_variable_selection( } while (num_pending != 0) { -#pragma omp taskloop if (num_pending > 1) priority(task_priority) grainsize(1) untied +#pragma omp taskloop if (num_pending > 1 && num_tasks > 1) priority(task_priority) \ + num_tasks(num_tasks) untied for (i_t i = 0; i < num_pending; ++i) { const i_t j = pending[i]; bool is_locked = pseudo_cost_mutex[j].try_lock(); diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 910f48e1f..7dd8ffa03 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -38,6 +38,7 @@ struct diving_heuristics_settings_t { template struct reliability_branching_settings_t { + // Enable or disable reliability branching bool enable = false; // For now, setting to 1, which correspond to pseudocost with strong branching @@ -55,6 +56,9 @@ struct reliability_branching_settings_t { // Priority of the tasks created when running the trial branching in parallel. // Set to 1 to have the same priority as the other tasks. i_t task_priority = 5; + + // Set the max number of tasks spawned for performing strong branching + i_t num_tasks = -1; }; template From 19210f0f72a03e9e39d00d8183947996ffc55f6c Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 21 Jan 2026 17:11:05 +0000 Subject: [PATCH 240/366] propagate solutions to the solver in determinsitic mode --- cpp/src/dual_simplex/branch_and_bound.cpp | 24 ++++++++++++++++++++++ cpp/src/mip/diversity/diversity_manager.cu | 1 + 2 files changed, 25 insertions(+) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 6fb3f948e..218cb32a4 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2792,6 +2792,12 @@ void branch_and_bound_t::process_history_and_sync( user_lower, gap.c_str(), toc(exploration_stats_.start_time)); + + if (settings_.solution_callback != nullptr) { + std::vector original_x; + uncrush_primal_solution(original_problem_, original_lp_, hsol.solution, original_x); + settings_.solution_callback(original_x, hsol.objective); + } } } } @@ -2835,13 +2841,22 @@ void branch_and_bound_t::process_history_and_sync( toc(exploration_stats_.start_time)); // Update incumbent + bool improved = false; mutex_upper_.lock(); if (sol.objective < upper_bound_) { upper_bound_ = sol.objective; incumbent_.set_incumbent_solution(sol.objective, *sol.solution); current_upper = sol.objective; + improved = true; } mutex_upper_.unlock(); + + // Notify diversity manager of new incumbent + if (improved && settings_.solution_callback != nullptr) { + std::vector original_x; + uncrush_primal_solution(original_problem_, original_lp_, *sol.solution, original_x); + settings_.solution_callback(original_x, sol.objective); + } } } @@ -3133,13 +3148,22 @@ void branch_and_bound_t::merge_diving_solutions() user_mip_gap(user_obj, user_lower).c_str(), toc(exploration_stats_.start_time)); + bool improved = false; mutex_upper_.lock(); if (sol->objective < upper_bound_) { upper_bound_ = sol->objective; incumbent_.set_incumbent_solution(sol->objective, sol->solution); current_upper = sol->objective; + improved = true; } mutex_upper_.unlock(); + + // Notify diversity manager of new incumbent + if (improved && settings_.solution_callback != nullptr) { + std::vector original_x; + uncrush_primal_solution(original_problem_, original_lp_, sol->solution, original_x); + settings_.solution_callback(original_x, sol->objective); + } } } diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 208bfc2e5..dd31acaa3 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -326,6 +326,7 @@ solution_t diversity_manager_t::run_solver() // Stop CPUFJ when B&B is done ls.stop_cpufj_deterministic(); + population.add_external_solutions_to_population(); return population.best_feasible(); } if (disable_heuristics_env != nullptr && std::string(disable_heuristics_env) == "1") { From 92a870564234e72b89115b14ed26141d265cc5c5 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 21 Jan 2026 17:15:19 +0000 Subject: [PATCH 241/366] fix envvar --- cpp/src/mip/solve.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index 14f894b21..ca537dd75 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -141,7 +141,7 @@ mip_solution_t run_mip(detail::problem_t& problem, is_feasible_before_scaling || is_feasible_after_unscaling, solver.get_solver_stats(), false); int hidesol = - std::getenv("CUOPT_MIP_PRINT_SOLUTION") ? atoi(std::getenv("CUOPT_MIP_PRINT_SOLUTION")) : 0; + std::getenv("CUOPT_MIP_HIDE_SOLUTION") ? atoi(std::getenv("CUOPT_MIP_HIDE_SOLUTION")) : 0; if (!hidesol) { detail::print_solution(scaled_problem.handle_ptr, sol.get_solution()); } return sol; } From bbc966b9328ccb8a0eee4e102ed4c0005dd41266 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 21 Jan 2026 19:02:44 +0000 Subject: [PATCH 242/366] fix some issues with hashes --- cpp/src/dual_simplex/branch_and_bound.cpp | 28 +++++++++++-- cpp/src/linear_programming/translate.hpp | 12 +++--- cpp/src/mip/feasibility_jump/fj_cpu.cu | 2 +- cpp/src/mip/problem/problem.cu | 50 +++++++++++++++-------- 4 files changed, 65 insertions(+), 27 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 218cb32a4..6b8f862d9 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1658,10 +1658,29 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut original_lp_, log); - settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", - settings_.num_threads, - settings_.num_bfs_workers, - settings_.num_threads - settings_.num_bfs_workers); + { + uint32_t lp_hash = detail::compute_hash(original_lp_.objective); + lp_hash ^= detail::compute_hash(original_lp_.A.x.underlying()); + settings_.log.printf("lp A.x hash: %08x\n", + detail::compute_hash(original_lp_.A.x.underlying())); + lp_hash ^= detail::compute_hash(original_lp_.A.i.underlying()); + settings_.log.printf("lp A.j hash: %08x\n", + detail::compute_hash(original_lp_.A.i.underlying())); + lp_hash ^= detail::compute_hash(original_lp_.A.col_start.underlying()); + settings_.log.printf("lp A.col_start hash: %08x\n", + detail::compute_hash(original_lp_.A.col_start.underlying())); + lp_hash ^= detail::compute_hash(original_lp_.rhs); + settings_.log.printf("lp rhs hash: %08x\n", detail::compute_hash(original_lp_.rhs)); + lp_hash ^= detail::compute_hash(original_lp_.lower); + settings_.log.printf("lp lower hash: %08x\n", detail::compute_hash(original_lp_.lower)); + lp_hash ^= detail::compute_hash(original_lp_.upper); + settings_.log.printf( + "Exploring the B&B tree using %d threads (best-first = %d, diving = %d) [LP hash: %08x]\n", + settings_.num_threads, + settings_.num_bfs_workers, + settings_.num_threads - settings_.num_bfs_workers, + lp_hash); + } exploration_stats_.nodes_explored = 0; exploration_stats_.nodes_unexplored = 2; @@ -2127,6 +2146,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) } state_hash = detail::compute_hash(state_data); + state_hash ^= pc_.compute_state_hash(); BSP_DEBUG_LOG_HORIZON_HASH( bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end, state_hash); } diff --git a/cpp/src/linear_programming/translate.hpp b/cpp/src/linear_programming/translate.hpp index 19f6c024c..6ecb37c6e 100644 --- a/cpp/src/linear_programming/translate.hpp +++ b/cpp/src/linear_programming/translate.hpp @@ -30,9 +30,9 @@ static dual_simplex::user_problem_t cuopt_problem_to_simplex_problem( user_problem.objective = cuopt::host_copy(model.objective_coefficients, handle_ptr->get_stream()); dual_simplex::csr_matrix_t csr_A(m, n, nz); - csr_A.x = cuopt::host_copy(model.coefficients, handle_ptr->get_stream()); - csr_A.j = cuopt::host_copy(model.variables, handle_ptr->get_stream()); - csr_A.row_start = cuopt::host_copy(model.offsets, handle_ptr->get_stream()); + csr_A.x = ins_vector(cuopt::host_copy(model.coefficients, handle_ptr->get_stream())); + csr_A.j = ins_vector(cuopt::host_copy(model.variables, handle_ptr->get_stream())); + csr_A.row_start = ins_vector(cuopt::host_copy(model.offsets, handle_ptr->get_stream())); csr_A.to_compressed_col(user_problem.A); @@ -121,9 +121,9 @@ void translate_to_crossover_problem(const detail::problem_t& problem, dual_simplex::csr_matrix_t csr_A( problem.n_constraints, problem.n_variables, problem.nnz); - csr_A.x = cuopt::host_copy(problem.coefficients, stream); - csr_A.j = cuopt::host_copy(problem.variables, stream); - csr_A.row_start = cuopt::host_copy(problem.offsets, stream); + csr_A.x = ins_vector(cuopt::host_copy(problem.coefficients, stream)); + csr_A.j = ins_vector(cuopt::host_copy(problem.variables, stream)); + csr_A.row_start = ins_vector(cuopt::host_copy(problem.offsets, stream)); stream.synchronize(); CUOPT_LOG_DEBUG("Converting to compressed column"); diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index b1d8e2664..548f6aebc 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -1552,7 +1552,7 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l // Notify producer_sync if registered if (fj_cpu.producer_sync != nullptr) { fj_cpu.producer_sync->notify_progress(); } - CUOPT_LOG_DEBUG("CPUFJ work units: %f incumbent %g", + CUOPT_LOG_TRACE("CPUFJ work units: %f incumbent %g", fj_cpu.work_units_elapsed.load(std::memory_order_relaxed), fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective)); } diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 529354417..d4e6ea9da 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -19,6 +19,7 @@ #include #include +#include #include #include @@ -1970,10 +1971,12 @@ void problem_t::get_host_user_problem( auto stream = handle_ptr->get_stream(); user_problem.objective = cuopt::host_copy(objective_coefficients, stream); + // Explicitly construct ins_vector wrappers to ensure data_ptr is properly initialized + // (direct assignment via implicit conversion doesn't update data_ptr correctly) dual_simplex::csr_matrix_t csr_A(m, n, nz); - csr_A.x = cuopt::host_copy(coefficients, stream); - csr_A.j = cuopt::host_copy(variables, stream); - csr_A.row_start = cuopt::host_copy(offsets, stream); + csr_A.x = ins_vector(cuopt::host_copy(coefficients, stream)); + csr_A.j = ins_vector(cuopt::host_copy(variables, stream)); + csr_A.row_start = ins_vector(cuopt::host_copy(offsets, stream)); csr_A.to_compressed_col(user_problem.A); @@ -2062,21 +2065,36 @@ template uint32_t problem_t::get_fingerprint() const { // CSR representation should be unique and sorted at this point + auto stream = handle_ptr->get_stream(); + + uint32_t h_coeff = detail::compute_hash(coefficients, stream); + uint32_t h_vars = detail::compute_hash(variables, stream); + uint32_t h_offsets = detail::compute_hash(offsets, stream); + uint32_t h_rev_coeff = detail::compute_hash(reverse_coefficients, stream); + uint32_t h_rev_off = detail::compute_hash(reverse_offsets, stream); + uint32_t h_rev_constr = detail::compute_hash(reverse_constraints, stream); + uint32_t h_obj = detail::compute_hash(objective_coefficients, stream); + uint32_t h_varbounds = detail::compute_hash(variable_bounds, stream); + uint32_t h_clb = detail::compute_hash(constraint_lower_bounds, stream); + uint32_t h_cub = detail::compute_hash(constraint_upper_bounds, stream); + uint32_t h_vartypes = detail::compute_hash(variable_types, stream); + uint32_t h_obj_off = detail::compute_hash(presolve_data.objective_offset); + uint32_t h_obj_scale = detail::compute_hash(presolve_data.objective_scaling_factor); std::vector hashes = { - detail::compute_hash(coefficients, handle_ptr->get_stream()), - detail::compute_hash(variables, handle_ptr->get_stream()), - detail::compute_hash(offsets, handle_ptr->get_stream()), - detail::compute_hash(reverse_coefficients, handle_ptr->get_stream()), - detail::compute_hash(reverse_offsets, handle_ptr->get_stream()), - detail::compute_hash(reverse_constraints, handle_ptr->get_stream()), - detail::compute_hash(objective_coefficients, handle_ptr->get_stream()), - detail::compute_hash(variable_bounds, handle_ptr->get_stream()), - detail::compute_hash(constraint_lower_bounds, handle_ptr->get_stream()), - detail::compute_hash(constraint_upper_bounds, handle_ptr->get_stream()), - detail::compute_hash(variable_types, handle_ptr->get_stream()), - detail::compute_hash(presolve_data.objective_offset), - detail::compute_hash(presolve_data.objective_scaling_factor), + h_coeff, + h_vars, + h_offsets, + h_rev_coeff, + h_rev_off, + h_rev_constr, + h_obj, + h_varbounds, + h_clb, + h_cub, + h_vartypes, + h_obj_off, + h_obj_scale, }; return detail::compute_hash(hashes); } From cff46f338992a71c7dae1d8c003c9290d918c51a Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 21 Jan 2026 19:03:07 +0000 Subject: [PATCH 243/366] bump1 --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 6b8f862d9..8094bbb8f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1667,7 +1667,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut settings_.log.printf("lp A.j hash: %08x\n", detail::compute_hash(original_lp_.A.i.underlying())); lp_hash ^= detail::compute_hash(original_lp_.A.col_start.underlying()); - settings_.log.printf("lp A.col_start hash: %08x\n", + settings_.log.printf("lp A.col_start hash1: %08x\n", detail::compute_hash(original_lp_.A.col_start.underlying())); lp_hash ^= detail::compute_hash(original_lp_.rhs); settings_.log.printf("lp rhs hash: %08x\n", detail::compute_hash(original_lp_.rhs)); From 254be070e2e26976fe688909cb477435ea8dee84 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 21 Jan 2026 19:03:24 +0000 Subject: [PATCH 244/366] bump2 --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 8094bbb8f..6b8f862d9 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1667,7 +1667,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut settings_.log.printf("lp A.j hash: %08x\n", detail::compute_hash(original_lp_.A.i.underlying())); lp_hash ^= detail::compute_hash(original_lp_.A.col_start.underlying()); - settings_.log.printf("lp A.col_start hash1: %08x\n", + settings_.log.printf("lp A.col_start hash: %08x\n", detail::compute_hash(original_lp_.A.col_start.underlying())); lp_hash ^= detail::compute_hash(original_lp_.rhs); settings_.log.printf("lp rhs hash: %08x\n", detail::compute_hash(original_lp_.rhs)); From aa15d8ee956a04bd9a0a268cdf03c3bb8afdc875 Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 22 Jan 2026 13:29:07 +0100 Subject: [PATCH 245/366] added additional information in the logs when solving the root relaxation in concurrent mode. --- cpp/src/dual_simplex/branch_and_bound.cpp | 28 ++++++++++++---------- cpp/src/dual_simplex/branch_and_bound.hpp | 14 +++++++++-- cpp/src/mip/diversity/diversity_manager.cu | 5 ++-- cpp/src/mip/problem/problem.cuh | 2 +- cpp/src/mip/solver.cu | 3 ++- 5 files changed, 34 insertions(+), 18 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index b2c9f85d2..85c193599 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1308,6 +1308,8 @@ lp_status_t branch_and_bound_t::solve_root_relaxation( } if (root_crossover_solution_set_.load(std::memory_order_acquire)) { + settings_.log.printf("\nRunning crossover\n\n"); + // Crush the root relaxation solution on converted user problem std::vector crushed_root_x; crush_primal_solution( @@ -1338,12 +1340,11 @@ lp_status_t branch_and_bound_t::solve_root_relaxation( root_crossover_soln_, crossover_vstatus_); - if (crossover_status == crossover_status_t::OPTIMAL) { - settings_.log.printf("Crossover status: %d\n", crossover_status); - } - // Check if crossover was stopped by dual simplex if (crossover_status == crossover_status_t::OPTIMAL) { + settings_.log.printf("\nCrossover found an optimal solution for the root relaxation\n\n"); + root_solver_type_ = root_solver_type_t::CROSSOVER; + set_root_concurrent_halt(1); // Stop dual simplex root_status = root_status_future.get(); // Override the root relaxation solution with the crossover solution @@ -1351,10 +1352,12 @@ lp_status_t branch_and_bound_t::solve_root_relaxation( root_vstatus_ = crossover_vstatus_; root_status = lp_status_t::OPTIMAL; } else { - root_status = root_status_future.get(); + root_status = root_status_future.get(); + root_solver_type_ = root_solver_type_t::DUAL_SIMPLEX; } } else { - root_status = root_status_future.get(); + root_status = root_status_future.get(); + root_solver_type_ = root_solver_type_t::DUAL_SIMPLEX; } return root_status; } @@ -1414,14 +1417,13 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut root_relax_soln_.resize(original_lp_.num_rows, original_lp_.num_cols); - settings_.log.printf("Solving LP root relaxation\n"); - lp_status_t root_status; simplex_solver_settings_t lp_settings = settings_; lp_settings.inside_mip = 1; lp_settings.concurrent_halt = get_root_concurrent_halt(); // RINS/SUBMIP path if (!enable_concurrent_lp_root_solve()) { + settings_.log.printf("\nSolving LP root relaxation with dual simplex\n"); root_status = solve_linear_program_advanced(original_lp_, exploration_stats_.start_time, lp_settings, @@ -1430,6 +1432,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut edge_norms_); } else { + settings_.log.printf("\nSolving LP root relaxation in concurrent mode\n"); root_status = solve_root_relaxation(lp_settings); } @@ -1540,10 +1543,11 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut original_lp_, log); - settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n", - settings_.num_threads, - settings_.num_bfs_workers, - settings_.num_threads - settings_.num_bfs_workers); + settings_.log.printf( + "\nExploring the B&B tree using %d threads (best-first = %d, diving = %d)\n\n", + settings_.num_threads, + settings_.num_bfs_workers, + settings_.num_threads - settings_.num_bfs_workers); exploration_stats_.nodes_explored = 0; exploration_stats_.nodes_unexplored = 2; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index c22d3ef28..857c95a21 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -70,6 +70,9 @@ struct bnb_stats_t { template class branch_and_bound_t { public: + // Specify which solver was used for solving the root LP relaxation + enum class root_solver_type_t { NONE = 0, CROSSOVER = 1, DUAL_SIMPLEX = 2 }; + branch_and_bound_t(const user_problem_t& user_problem, const simplex_solver_settings_t& solver_settings); @@ -82,9 +85,15 @@ class branch_and_bound_t { const std::vector& reduced_costs, f_t objective, f_t user_objective, - i_t iterations) + i_t iterations, + f_t solve_time) { - if (!is_running) { + if (root_solver_type_ == root_solver_type_t::NONE) { + settings_.log.printf( + "\nRoot relaxation solution found in %d iterations and %.2fs by PDLP/Barrier\n", + iterations, + solve_time); + settings_.log.printf("Root relaxation objective = %+.8e\n", user_objective); root_crossover_soln_.x = primal; root_crossover_soln_.y = dual; root_crossover_soln_.z = reduced_costs; @@ -164,6 +173,7 @@ class branch_and_bound_t { std::atomic root_crossover_solution_set_{false}; bool enable_concurrent_lp_root_solve_{false}; std::atomic root_concurrent_halt_{0}; + std::atomic root_solver_type_{root_solver_type_t::NONE}; // Pseudocosts pseudo_costs_t pc_; diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index f3158316f..c992d7395 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -411,7 +411,7 @@ solution_t diversity_manager_t::run_solver() // to bring variables within the bounds } - // Send PDLP relaxed solution to branch and bound before it solves the root node + // Send PDLP relaxed solution to branch and bound if (problem_ptr->set_root_relaxation_solution_callback != nullptr) { auto& d_primal_solution = lp_result.get_primal_solution(); auto& d_dual_solution = lp_result.get_dual_solution(); @@ -438,9 +438,10 @@ solution_t diversity_manager_t::run_solver() auto user_obj = lp_result.get_objective_value(); auto solver_obj = problem_ptr->get_solver_obj_from_user_obj(user_obj); auto iterations = lp_result.get_additional_termination_information().number_of_steps_taken; + auto solve_time = lp_result.get_additional_termination_information().solve_time; // Set for the B&B (param4 expects solver space, param5 expects user space) problem_ptr->set_root_relaxation_solution_callback( - host_primal, host_dual, host_reduced_costs, solver_obj, user_obj, iterations); + host_primal, host_dual, host_reduced_costs, solver_obj, user_obj, iterations, solve_time); } // in case the pdlp returned var boudns that are out of bounds diff --git a/cpp/src/mip/problem/problem.cuh b/cpp/src/mip/problem/problem.cuh index 910079916..9b552fc69 100644 --- a/cpp/src/mip/problem/problem.cuh +++ b/cpp/src/mip/problem/problem.cuh @@ -216,7 +216,7 @@ class problem_t { std::function&)> branch_and_bound_callback; std::function&, const std::vector&, const std::vector&, f_t, f_t, i_t)> + const std::vector&, const std::vector&, const std::vector&, f_t, f_t, i_t, f_t)> set_root_relaxation_solution_callback; typename mip_solver_settings_t::tolerances_t tolerances{}; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 08e1806b9..be84379b9 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -221,7 +221,8 @@ solution_t mip_solver_t::run_solver() std::placeholders::_3, std::placeholders::_4, std::placeholders::_5, - std::placeholders::_6); + std::placeholders::_6, + std::placeholders::_7); // Fork a thread for branch and bound // std::async and std::future allow us to get the return value of bb::solve() From ae98cbd8f8d149843f24a46b41192698b311eb23 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 13:31:45 +0000 Subject: [PATCH 246/366] fix tie-breaking and ins_vector counters not being reset appropriately --- cpp/src/dual_simplex/bb_worker_state.hpp | 17 ++- cpp/src/dual_simplex/branch_and_bound.cpp | 117 +++++++++++++++---- cpp/src/dual_simplex/phase2.cpp | 26 ++++- cpp/src/utilities/memory_instrumentation.hpp | 8 +- 4 files changed, 138 insertions(+), 30 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index df026ad72..015b023bf 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -57,6 +57,15 @@ struct queued_integer_solution_t { f_t objective; std::vector solution; i_t depth; + int worker_id; + int sequence_id; + + bool operator<(const queued_integer_solution_t& other) const + { + if (objective != other.objective) return objective < other.objective; + if (worker_id != other.worker_id) return worker_id < other.worker_id; + return sequence_id < other.sequence_id; + } }; // Per-worker state for BSP (Bulk Synchronous Parallel) branch-and-bound @@ -145,6 +154,9 @@ struct bb_worker_state_t { // Queued integer solutions found during this horizon (merged at sync) std::vector> integer_solutions; + // Solution sequence counter for deterministic tie-breaking (cumulative across horizons) + int next_solution_seq{0}; + // Queued pseudo-cost updates (applied to global pseudo-costs at sync) std::vector> pseudo_cost_updates; @@ -530,6 +542,9 @@ struct bsp_diving_worker_state_t { // Queued integer solutions found during this horizon (merged at sync) std::vector> integer_solutions; + // Solution sequence counter for deterministic tie-breaking (cumulative across horizons) + int next_solution_seq{0}; + // ========================================================================== // Statistics // ========================================================================== @@ -626,7 +641,7 @@ struct bsp_diving_worker_state_t { void queue_integer_solution(f_t objective, const std::vector& solution, i_t depth) { - integer_solutions.push_back({objective, solution, depth}); + integer_solutions.push_back({objective, solution, depth, worker_id, next_solution_seq++}); ++total_integer_solutions; } diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 6b8f862d9..706aba751 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2102,6 +2102,45 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) bb_event_batch_t all_events = bsp_workers_->collect_and_sort_events(); + // // Diagnostic logging: event boundaries + // { + // settings_.log.printf("SYNC S%d: horizon=[%.6f, %.6f], %zu events\n", + // bsp_horizon_number_, horizon_start, horizon_end, + // all_events.events.size()); + + // if (!all_events.events.empty()) { + // double min_wut = all_events.events.front().wut; + // double max_wut = all_events.events.back().wut; + // settings_.log.printf(" Event WUT range: [%.6f, %.6f]\n", min_wut, max_wut); + + // // Log events near horizon boundary (within 1% of horizon_step) + // double boundary_epsilon = bsp_horizon_step_ * 0.01; + // for (const auto& event : all_events.events) { + // if (std::abs(event.wut - horizon_end) < boundary_epsilon || + // std::abs(event.wut - horizon_start) < boundary_epsilon) { + // const char* type_str = "?"; + // switch (event.type) { + // case bb_event_type_t::NODE_BRANCHED: type_str = "BRANCHED"; break; + // case bb_event_type_t::NODE_FATHOMED: type_str = "FATHOMED"; break; + // case bb_event_type_t::NODE_INTEGER: type_str = "INTEGER"; break; + // case bb_event_type_t::NODE_INFEASIBLE: type_str = "INFEASIBLE"; break; + // case bb_event_type_t::NODE_PAUSED: type_str = "PAUSED"; break; + // case bb_event_type_t::NODE_NUMERICAL: type_str = "NUMERICAL"; break; + // case bb_event_type_t::HEURISTIC_SOLUTION: type_str = "HEURISTIC"; break; + // } + // settings_.log.printf(" BOUNDARY EVENT: wut=%.9f, worker=%d, node=%d, type=%s\n", + // event.wut, event.worker_id, event.node_id, type_str); + // } + // } + // } + + // // Log per-worker clock values + // for (const auto& worker : *bsp_workers_) { + // settings_.log.printf(" Worker %d: clock=%.6f, work_this_horizon=%.6f\n", + // worker.worker_id, worker.clock, worker.work_units_this_horizon); + // } + // } + BSP_DEBUG_LOG_SYNC_PHASE_START( bsp_debug_settings_, bsp_debug_logger_, horizon_end, all_events.size()); @@ -2499,6 +2538,11 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.clock += work_performed; worker.work_units_this_horizon += work_performed; + // // Diagnostic: log work performed for each node solve + // settings_.log.printf("NODE_SOLVE: W%d N%d: iters=%d work=%.9f clock=%.9f horizon_end=%.6f\n", + // worker.worker_id, node_ptr->node_id, node_iter, + // work_performed, worker.clock, worker.horizon_end); + exploration_stats_.total_lp_solve_time += toc(lp_start_time); exploration_stats_.total_lp_iters += node_iter; ++exploration_stats_.nodes_explored; @@ -2577,7 +2621,11 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t // Integer feasible - queue for deterministic processing at sync if (leaf_objective < worker.local_upper_bound) { worker.local_upper_bound = leaf_objective; - worker.integer_solutions.push_back({leaf_objective, leaf_solution.x, node_ptr->depth}); + worker.integer_solutions.push_back({leaf_objective, + leaf_solution.x, + node_ptr->depth, + worker.worker_id, + worker.next_solution_seq++}); } worker.record_integer_solution(node_ptr, leaf_objective); @@ -2823,28 +2871,26 @@ void branch_and_bound_t::process_history_and_sync( } // Merge integer solutions from all workers and update global incumbent - // Sort by (objective, worker_id) for deterministic winner selection - // lexicographical sort as a fallback - struct worker_solution_t { - f_t objective; - const std::vector* solution; - i_t depth; - int worker_id; - }; - std::vector all_integer_solutions; + std::vector*> all_integer_solutions; for (auto& worker : *bsp_workers_) { for (auto& sol : worker.integer_solutions) { - all_integer_solutions.push_back({sol.objective, &sol.solution, sol.depth, worker.worker_id}); + all_integer_solutions.push_back(&sol); } } + // Sort solutions for deterministic processing order (uses built-in operator<) + std::sort(all_integer_solutions.begin(), + all_integer_solutions.end(), + [](const queued_integer_solution_t* a, + const queued_integer_solution_t* b) { return *a < *b; }); + f_t bsp_lower = compute_bsp_lower_bound(); f_t current_upper = upper_bound_.load(); - for (const auto& sol : all_integer_solutions) { + for (const auto* sol : all_integer_solutions) { // improving solution found, log it - if (sol.objective < current_upper) { - f_t user_obj = compute_user_objective(original_lp_, sol.objective); + if (sol->objective < current_upper) { + f_t user_obj = compute_user_objective(original_lp_, sol->objective); f_t user_lower = compute_user_objective(original_lp_, bsp_lower); i_t nodes_explored = exploration_stats_.nodes_explored.load(); i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); @@ -2855,7 +2901,7 @@ void branch_and_bound_t::process_history_and_sync( nodes_unexplored, user_obj, user_lower, - sol.depth, + sol->depth, nodes_explored > 0 ? exploration_stats_.total_lp_iters.load() / nodes_explored : 0.0, user_mip_gap(user_obj, user_lower).c_str(), toc(exploration_stats_.start_time)); @@ -2863,10 +2909,10 @@ void branch_and_bound_t::process_history_and_sync( // Update incumbent bool improved = false; mutex_upper_.lock(); - if (sol.objective < upper_bound_) { - upper_bound_ = sol.objective; - incumbent_.set_incumbent_solution(sol.objective, *sol.solution); - current_upper = sol.objective; + if (sol->objective < upper_bound_) { + upper_bound_ = sol->objective; + incumbent_.set_incumbent_solution(sol->objective, sol->solution); + current_upper = sol->objective; improved = true; } mutex_upper_.unlock(); @@ -2874,8 +2920,8 @@ void branch_and_bound_t::process_history_and_sync( // Notify diversity manager of new incumbent if (improved && settings_.solution_callback != nullptr) { std::vector original_x; - uncrush_primal_solution(original_problem_, original_lp_, *sol.solution, original_x); - settings_.solution_callback(original_x, sol.objective); + uncrush_primal_solution(original_problem_, original_lp_, sol->solution, original_x); + settings_.solution_callback(original_x, sol->objective); } } } @@ -3137,15 +3183,42 @@ void branch_and_bound_t::merge_diving_solutions() { if (!bsp_diving_workers_) return; + // // Diagnostic logging for diving workers + // { + // int total_solutions = 0; + // int total_nodes_explored = 0; + // for (const auto& worker : *bsp_diving_workers_) { + // total_solutions += worker.integer_solutions.size(); + // total_nodes_explored += worker.nodes_explored_this_horizon; + // } + // settings_.log.printf(" Diving workers: %d solutions, %d nodes explored this horizon\n", + // total_solutions, total_nodes_explored); + + // // Log per-diving-worker state + // for (const auto& worker : *bsp_diving_workers_) { + // if (worker.nodes_explored_this_horizon > 0 || !worker.integer_solutions.empty()) { + // settings_.log.printf(" DW%d: clock=%.6f, nodes=%d, sols=%zu\n", + // worker.worker_id, worker.clock, + // worker.nodes_explored_this_horizon, + // worker.integer_solutions.size()); + // } + // } + // } + // Collect all integer solutions from diving workers std::vector*> all_solutions; - for (auto& worker : *bsp_diving_workers_) { for (auto& sol : worker.integer_solutions) { all_solutions.push_back(&sol); } } + // Sort solutions for deterministic processing order (uses built-in operator<) + std::sort(all_solutions.begin(), + all_solutions.end(), + [](const queued_integer_solution_t* a, + const queued_integer_solution_t* b) { return *a < *b; }); + // Apply improving solutions to incumbent f_t current_upper = upper_bound_.load(); for (const auto* sol : all_solutions) { diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 0a4dda5a6..5e7bf1327 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2564,7 +2564,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, i_t last_feature_log_iter = iter; // Helper to compute scaled work units for a given number of iterations - cuopt::work_unit_predictor_t work_predictor{}; + thread_local cuopt::work_unit_predictor_t + work_predictor{}; auto predict_work_units = [&](i_t num_iters) -> f_t { // raft::common::nvtx::range scope("DualSimplex::predict_work_units"); std::map features_map; @@ -2587,8 +2588,27 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, features_map["byte_loads"] = static_cast(features.byte_loads); features_map["byte_stores"] = static_cast(features.byte_stores); - f_t base_prediction = std::max((f_t)0.0, (f_t)work_predictor.predict_scalar(features_map)); - return base_prediction * static_cast(num_iters) / FEATURE_LOG_INTERVAL; + f_t base_prediction = std::max((f_t)0.0, (f_t)work_predictor.predict_scalar(features_map)); + f_t scaled_prediction = base_prediction * static_cast(num_iters) / FEATURE_LOG_INTERVAL; + + // // Diagnostic: log features that could vary between runs + // const char* worker_name = work_unit_context ? work_unit_context->name.c_str() : "unknown"; + // CUOPT_LOG_DEBUG("PREDICT_WORK [%s]: iters=%d refacts=%d updates=%d sparse_dz=%d dense_dz=%d " + // "bound_flips=%d infeas=%d dy_nz=%.6f loads=%zu stores=%zu -> pred=%.9f", + // worker_name, + // num_iters, + // features.num_refactors, + // features.num_basis_updates, + // features.sparse_delta_z_count, + // features.dense_delta_z_count, + // features.total_bound_flips, + // features.num_infeasibilities, + // features.delta_y_nz_percentage, + // features.byte_loads, + // features.byte_stores, + // scaled_prediction); + + return scaled_prediction; }; cuopt::scope_guard work_unit_guard([&]() { diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 3839b1aa8..87d25f5c8 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -542,7 +542,7 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } memop_instrumentation_wrapper_t(const memop_instrumentation_wrapper_t& other) - : memory_instrumentation_base_t(other), array_(other.array_) + : memory_instrumentation_base_t(), array_(other.array_) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -552,7 +552,7 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { } memop_instrumentation_wrapper_t(memop_instrumentation_wrapper_t&& other) noexcept - : memory_instrumentation_base_t(std::move(other)), array_(std::move(other.array_)) + : memory_instrumentation_base_t(), array_(std::move(other.array_)) { if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -564,7 +564,7 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { memop_instrumentation_wrapper_t& operator=(const memop_instrumentation_wrapper_t& other) { if (this != &other) { - memory_instrumentation_base_t::operator=(other); + reset_counters(); array_ = other.array_; if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); @@ -578,7 +578,7 @@ struct memop_instrumentation_wrapper_t : public memory_instrumentation_base_t { memop_instrumentation_wrapper_t& operator=(memop_instrumentation_wrapper_t&& other) noexcept { if (this != &other) { - memory_instrumentation_base_t::operator=(std::move(other)); + reset_counters(); array_ = std::move(other.array_); if constexpr (type_traits_utils::has_data::value) { data_ptr = array_.data(); From 83e0b371c59f8b28ecd28743b824c475fcabe247 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 13:32:13 +0000 Subject: [PATCH 247/366] bump1 --- cpp/src/dual_simplex/phase2.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 5e7bf1327..cc6761941 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2591,7 +2591,6 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, f_t base_prediction = std::max((f_t)0.0, (f_t)work_predictor.predict_scalar(features_map)); f_t scaled_prediction = base_prediction * static_cast(num_iters) / FEATURE_LOG_INTERVAL; - // // Diagnostic: log features that could vary between runs // const char* worker_name = work_unit_context ? work_unit_context->name.c_str() : "unknown"; // CUOPT_LOG_DEBUG("PREDICT_WORK [%s]: iters=%d refacts=%d updates=%d sparse_dz=%d dense_dz=%d " // "bound_flips=%d infeas=%d dy_nz=%.6f loads=%zu stores=%zu -> pred=%.9f", From f6a908defbbee5c3b7a558f32233a534aaa621d5 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 13:36:37 +0000 Subject: [PATCH 248/366] no CPUFJ --- cpp/src/mip/local_search/local_search.cu | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 3f66a3c37..cf281dce5 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -178,10 +178,10 @@ void local_search_t::start_cpufj_deterministic( producer_sync.register_producer(&deterministic_cpu_fj.fj_cpu->work_units_elapsed); // Set up callback to send solutions to B&B with work unit timestamps - deterministic_cpu_fj.fj_cpu->improvement_callback = - [&bb](f_t obj, const std::vector& h_vec, double work_units) { - bb.set_new_solution_deterministic(h_vec, work_units); - }; + // deterministic_cpu_fj.fj_cpu->improvement_callback = + // [&bb](f_t obj, const std::vector& h_vec, double work_units) { + // bb.set_new_solution_deterministic(h_vec, work_units); + // }; // Start the CPUFJ thread deterministic_cpu_fj.start_cpu_solver(); From 5ed3732a2e8cbdf41e9e2ba8cf8c0bf5cbd296fb Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 13:36:57 +0000 Subject: [PATCH 249/366] bump1 --- cpp/src/mip/local_search/local_search.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index cf281dce5..07d8e31fe 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -178,7 +178,7 @@ void local_search_t::start_cpufj_deterministic( producer_sync.register_producer(&deterministic_cpu_fj.fj_cpu->work_units_elapsed); // Set up callback to send solutions to B&B with work unit timestamps - // deterministic_cpu_fj.fj_cpu->improvement_callback = + // deterministic_cpu_fj.fj_cpu->improvement_callback =1 // [&bb](f_t obj, const std::vector& h_vec, double work_units) { // bb.set_new_solution_deterministic(h_vec, work_units); // }; From d604cb6b1fa9c124113d5b98cff1f30cc770cc0a Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 22 Jan 2026 16:28:24 +0100 Subject: [PATCH 250/366] renamed macro --- cpp/src/linear_programming/solve.cu | 54 +++++++++++++++++------------ 1 file changed, 32 insertions(+), 22 deletions(-) diff --git a/cpp/src/linear_programming/solve.cu b/cpp/src/linear_programming/solve.cu index 9982924ea..829008651 100644 --- a/cpp/src/linear_programming/solve.cu +++ b/cpp/src/linear_programming/solve.cu @@ -42,8 +42,8 @@ #include // For std::thread -#define CUOPT_LP_SOLVER_LOG_INFO(...) \ - if (!settings.inside_mip) { CUOPT_LOG_INFO(__VA_ARGS__); } +#define CUOPT_LOG_CONDITIONAL_INFO(condition, ...) \ + if ((condition)) { CUOPT_LOG_INFO(__VA_ARGS__); } namespace cuopt::linear_programming { @@ -420,7 +420,8 @@ run_barrier(dual_simplex::user_problem_t& user_problem, auto status = dual_simplex::solve_linear_program_with_barrier( user_problem, barrier_settings, solution); - CUOPT_LP_SOLVER_LOG_INFO("Barrier finished in %.2f seconds", timer.elapsed_time()); + CUOPT_LOG_CONDITIONAL_INFO( + !settings.inside_mip, "Barrier finished in %.2f seconds", timer.elapsed_time()); if (settings.concurrent_halt != nullptr && (status == dual_simplex::lp_status_t::OPTIMAL || status == dual_simplex::lp_status_t::UNBOUNDED || @@ -492,9 +493,10 @@ run_dual_simplex(dual_simplex::user_problem_t& user_problem, auto status = dual_simplex::solve_linear_program(user_problem, dual_simplex_settings, solution); - CUOPT_LP_SOLVER_LOG_INFO("Dual simplex finished in %.2f seconds, total time %.2f", - timer_dual_simplex.elapsed_time(), - timer.elapsed_time()); + CUOPT_LOG_CONDITIONAL_INFO(!settings.inside_mip, + "Dual simplex finished in %.2f seconds, total time %.2f", + timer_dual_simplex.elapsed_time(), + timer.elapsed_time()); if (settings.concurrent_halt != nullptr && (status == dual_simplex::lp_status_t::OPTIMAL || status == dual_simplex::lp_status_t::UNBOUNDED || @@ -533,7 +535,8 @@ static optimization_problem_solution_t run_pdlp_solver( bool is_batch_mode) { if (problem.n_constraints == 0) { - CUOPT_LP_SOLVER_LOG_INFO( + CUOPT_LOG_CONDITIONAL_INFO( + !settings.inside_mip, "No constraints in the problem: PDLP can't be run, use Dual Simplex instead."); return optimization_problem_solution_t{pdlp_termination_status_t::NumericalError, problem.handle_ptr->get_stream()}; @@ -555,9 +558,10 @@ optimization_problem_solution_t run_pdlp(detail::problem_t& auto sol = run_pdlp_solver(problem, settings, timer, is_batch_mode); auto pdlp_solve_time = timer_pdlp.elapsed_time(); sol.set_solve_time(timer.elapsed_time()); - CUOPT_LP_SOLVER_LOG_INFO("PDLP finished"); + CUOPT_LOG_CONDITIONAL_INFO(!settings.inside_mip, "PDLP finished"); if (sol.get_termination_status() != pdlp_termination_status_t::ConcurrentLimit) { - CUOPT_LP_SOLVER_LOG_INFO( + CUOPT_LOG_CONDITIONAL_INFO( + !settings.inside_mip, "Status: %s Objective: %.8e Iterations: %d Time: %.3fs, Total time %.3fs", sol.get_termination_status_string().c_str(), sol.get_objective_value(), @@ -625,12 +629,13 @@ optimization_problem_solution_t run_pdlp(detail::problem_t& info, termination_status); sol.copy_from(problem.handle_ptr, sol_crossover); - CUOPT_LP_SOLVER_LOG_INFO("Crossover status %s", sol.get_termination_status_string().c_str()); + CUOPT_LOG_CONDITIONAL_INFO( + !settings.inside_mip, "Crossover status %s", sol.get_termination_status_string().c_str()); } if (settings.method == method_t::Concurrent && settings.concurrent_halt != nullptr && crossover_info == 0 && sol.get_termination_status() == pdlp_termination_status_t::Optimal) { // We finished. Tell dual simplex to stop if it is still running. - CUOPT_LP_SOLVER_LOG_INFO("PDLP finished. Telling others to stop"); + CUOPT_LOG_CONDITIONAL_INFO(!settings.inside_mip, "PDLP finished. Telling others to stop"); *settings.concurrent_halt = 1; } return sol; @@ -658,7 +663,7 @@ optimization_problem_solution_t run_concurrent( const timer_t& timer, bool is_batch_mode) { - CUOPT_LP_SOLVER_LOG_INFO("Running concurrent\n"); + CUOPT_LOG_CONDITIONAL_INFO(!settings.inside_mip, "Running concurrent\n"); timer_t timer_concurrent(timer.remaining_time()); // Copy the settings so that we can set the concurrent halt pointer @@ -673,7 +678,8 @@ optimization_problem_solution_t run_concurrent( if (settings.num_gpus > 1) { int device_count = raft::device_setter::get_device_count(); - CUOPT_LP_SOLVER_LOG_INFO("Running PDLP and Barrier on %d GPUs", device_count); + CUOPT_LOG_CONDITIONAL_INFO( + !settings.inside_mip, "Running PDLP and Barrier on %d GPUs", device_count); cuopt_expects( device_count > 1, error_type_t::RuntimeError, "Multi-GPU mode requires at least 2 GPUs"); } @@ -757,17 +763,20 @@ optimization_problem_solution_t run_concurrent( 1); f_t end_time = timer.elapsed_time(); - CUOPT_LP_SOLVER_LOG_INFO( - "Concurrent time: %.3fs, total time %.3fs", timer_concurrent.elapsed_time(), end_time); + CUOPT_LOG_CONDITIONAL_INFO(!settings.inside_mip, + "Concurrent time: %.3fs, total time %.3fs", + timer_concurrent.elapsed_time(), + end_time); // Check status to see if we should return the pdlp solution or the dual simplex solution if (!settings.inside_mip && (sol_dual_simplex.get_termination_status() == pdlp_termination_status_t::Optimal || sol_dual_simplex.get_termination_status() == pdlp_termination_status_t::PrimalInfeasible || sol_dual_simplex.get_termination_status() == pdlp_termination_status_t::DualInfeasible)) { - CUOPT_LP_SOLVER_LOG_INFO("Solved with dual simplex"); + CUOPT_LOG_CONDITIONAL_INFO(!settings.inside_mip, "Solved with dual simplex"); sol_pdlp.copy_from(problem.handle_ptr, sol_dual_simplex); sol_pdlp.set_solve_time(end_time); - CUOPT_LP_SOLVER_LOG_INFO( + CUOPT_LOG_CONDITIONAL_INFO( + !settings.inside_mip, "Status: %s Objective: %.8e Iterations: %d Time: %.3fs", sol_pdlp.get_termination_status_string().c_str(), sol_pdlp.get_objective_value(), @@ -775,10 +784,11 @@ optimization_problem_solution_t run_concurrent( end_time); return sol_pdlp; } else if (sol_barrier.get_termination_status() == pdlp_termination_status_t::Optimal) { - CUOPT_LP_SOLVER_LOG_INFO("Solved with barrier"); + CUOPT_LOG_CONDITIONAL_INFO(!settings.inside_mip, "Solved with barrier"); sol_pdlp.copy_from(problem.handle_ptr, sol_barrier); sol_pdlp.set_solve_time(end_time); - CUOPT_LP_SOLVER_LOG_INFO( + CUOPT_LOG_CONDITIONAL_INFO( + !settings.inside_mip, "Status: %s Objective: %.8e Iterations: %d Time: %.3fs", sol_pdlp.get_termination_status_string().c_str(), sol_pdlp.get_objective_value(), @@ -786,14 +796,14 @@ optimization_problem_solution_t run_concurrent( end_time); return sol_pdlp; } else if (sol_pdlp.get_termination_status() == pdlp_termination_status_t::Optimal) { - CUOPT_LP_SOLVER_LOG_INFO("Solved with PDLP"); + CUOPT_LOG_CONDITIONAL_INFO(!settings.inside_mip, "Solved with PDLP"); return sol_pdlp; } else if (!settings.inside_mip && sol_pdlp.get_termination_status() == pdlp_termination_status_t::ConcurrentLimit) { - CUOPT_LP_SOLVER_LOG_INFO("Using dual simplex solve info"); + CUOPT_LOG_CONDITIONAL_INFO(!settings.inside_mip, "Using dual simplex solve info"); return sol_dual_simplex; } else { - CUOPT_LP_SOLVER_LOG_INFO("Using PDLP solve info"); + CUOPT_LOG_CONDITIONAL_INFO(!settings.inside_mip, "Using PDLP solve info"); return sol_pdlp; } } From 4a5270fe256999162fb327672625e58648741486 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 15:41:12 +0000 Subject: [PATCH 251/366] Refactoring, fix incorrect optimality, add tests --- cpp/src/dual_simplex/bb_event.hpp | 51 +--- cpp/src/dual_simplex/bb_worker_state.hpp | 25 +- cpp/src/dual_simplex/branch_and_bound.cpp | 76 +----- cpp/src/mip/diversity/diversity_manager.cu | 4 + cpp/src/mip/local_search/local_search.cu | 8 +- cpp/src/utilities/work_unit_scheduler.cpp | 24 -- cpp/src/utilities/work_unit_scheduler.hpp | 21 -- .../c_api_tests/c_api_test.c | 110 ++++++++ .../c_api_tests/c_api_tests.cpp | 246 ++++++++++-------- .../c_api_tests/c_api_tests.h | 7 +- cpp/tests/mip/CMakeLists.txt | 5 +- cpp/tests/mip/determinism_test.cu | 198 ++++++++++++++ 12 files changed, 472 insertions(+), 303 deletions(-) create mode 100644 cpp/tests/mip/determinism_test.cu diff --git a/cpp/src/dual_simplex/bb_event.hpp b/cpp/src/dual_simplex/bb_event.hpp index 6c3f189fe..284371be3 100644 --- a/cpp/src/dual_simplex/bb_event.hpp +++ b/cpp/src/dual_simplex/bb_event.hpp @@ -10,20 +10,17 @@ #include #include #include -#include #include namespace cuopt::linear_programming::dual_simplex { // Event types generated by B&B workers during deterministic execution enum class bb_event_type_t : int8_t { - NODE_BRANCHED = 0, - NODE_FATHOMED = 1, - NODE_INTEGER = 2, - NODE_PAUSED = 3, - NODE_INFEASIBLE = 4, - NODE_NUMERICAL = 5, - HEURISTIC_SOLUTION = 6 + NODE_BRANCHED = 0, + NODE_FATHOMED = 1, + NODE_INTEGER = 2, + NODE_INFEASIBLE = 3, + NODE_NUMERICAL = 4, }; template @@ -45,17 +42,6 @@ struct fathomed_payload_t { f_t lower_bound; }; -template -struct paused_payload_t { - f_t accumulated_wut; -}; - -template -struct heuristic_solution_payload_t { - f_t objective_value; - size_t solution_index; -}; - template struct bb_event_t { bb_event_type_t type; @@ -68,8 +54,6 @@ struct bb_event_t { branched_payload_t branched; integer_solution_payload_t integer_solution; fathomed_payload_t fathomed; - paused_payload_t paused; - heuristic_solution_payload_t heuristic; } payload; bb_event_t() @@ -142,18 +126,6 @@ struct bb_event_t { return e; } - static bb_event_t make_paused(double work_unit_ts, int worker, i_t node, int seq, f_t accumulated) - { - bb_event_t e; - e.type = bb_event_type_t::NODE_PAUSED; - e.wut = work_unit_ts; - e.worker_id = worker; - e.node_id = node; - e.event_sequence = seq; - e.payload.paused = {accumulated}; - return e; - } - static bb_event_t make_numerical(double work_unit_ts, int worker, i_t node, int seq) { bb_event_t e; @@ -165,19 +137,6 @@ struct bb_event_t { e.payload.fathomed = {std::numeric_limits::infinity()}; return e; } - - static bb_event_t make_heuristic_solution( - double work_unit_ts, int worker, int seq, f_t objective, size_t sol_idx) - { - bb_event_t e; - e.type = bb_event_type_t::HEURISTIC_SOLUTION; - e.wut = work_unit_ts; - e.worker_id = worker; - e.node_id = -1; - e.event_sequence = seq; - e.payload.heuristic = {objective, sol_idx}; - return e; - } }; template diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 015b023bf..702dadc5d 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -133,7 +133,6 @@ struct bb_worker_state_t { // Per-horizon statistics (reset each horizon) i_t nodes_processed_this_horizon{0}; - double work_units_this_horizon{0.0}; // Cumulative statistics (across all horizons) i_t total_nodes_processed{0}; @@ -142,7 +141,6 @@ struct bb_worker_state_t { i_t total_nodes_infeasible{0}; i_t total_integer_solutions{0}; i_t total_nodes_assigned{0}; // via load balancing - double total_work_units{0.0}; // Timing statistics (in seconds) double total_runtime{0.0}; // Total time spent doing actual work @@ -213,7 +211,6 @@ struct bb_worker_state_t { events.horizon_end = horizon_end; event_sequence = 0; nodes_processed_this_horizon = 0; - work_units_this_horizon = 0.0; // Also sync work_context to match clock for consistent tracking work_context.global_work_units_elapsed = horizon_start; // Note: next_creation_seq is NOT reset - it's cumulative for unique identity @@ -383,15 +380,6 @@ struct bb_worker_state_t { return plunge_stack.size() + backlog.size() + (current_node != nullptr ? 1 : 0); } - // Extract only backlog nodes (for redistribution at horizon sync) - // Plunge stack nodes stay with worker for locality - std::vector*> extract_backlog_nodes() - { - std::vector*> nodes = std::move(backlog); - backlog.clear(); - return nodes; - } - // Record an event void record_event(bb_event_t event) { @@ -440,15 +428,6 @@ struct bb_worker_state_t { record_event(bb_event_t::make_numerical(clock, worker_id, node->node_id, 0)); } - // Update clock with work units - void advance_clock(double work_units) - { - clock += work_units; - work_units_this_horizon += work_units; - total_work_units += work_units; - work_context.record_work(work_units); - } - // Track node processed (called when a node LP solve completes) void track_node_processed() { @@ -549,7 +528,6 @@ struct bsp_diving_worker_state_t { // Statistics // ========================================================================== - i_t nodes_explored_this_horizon{0}; i_t total_nodes_explored{0}; i_t total_integer_solutions{0}; i_t total_dives{0}; @@ -596,8 +574,7 @@ struct bsp_diving_worker_state_t { horizon_end = end; work_context.global_work_units_elapsed = start; - local_upper_bound = upper_bound; - nodes_explored_this_horizon = 0; + local_upper_bound = upper_bound; // Note: Don't clear dive_queue here - workers may still have nodes to process integer_solutions.clear(); recompute_bounds_and_basis = true; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 706aba751..d885e0962 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2062,13 +2062,12 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& // Track last solved node for warm-start detection worker.last_solved_node = node; - // Handle result - worker.current_node = nullptr; if (status == node_solve_info_t::TIME_LIMIT || status == node_solve_info_t::WORK_LIMIT) { // Time/work limit hit - the loop head will detect this and terminate properly continue; } // Node completed successfully - loop back to process children + worker.current_node = nullptr; continue; } @@ -2102,45 +2101,6 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) bb_event_batch_t all_events = bsp_workers_->collect_and_sort_events(); - // // Diagnostic logging: event boundaries - // { - // settings_.log.printf("SYNC S%d: horizon=[%.6f, %.6f], %zu events\n", - // bsp_horizon_number_, horizon_start, horizon_end, - // all_events.events.size()); - - // if (!all_events.events.empty()) { - // double min_wut = all_events.events.front().wut; - // double max_wut = all_events.events.back().wut; - // settings_.log.printf(" Event WUT range: [%.6f, %.6f]\n", min_wut, max_wut); - - // // Log events near horizon boundary (within 1% of horizon_step) - // double boundary_epsilon = bsp_horizon_step_ * 0.01; - // for (const auto& event : all_events.events) { - // if (std::abs(event.wut - horizon_end) < boundary_epsilon || - // std::abs(event.wut - horizon_start) < boundary_epsilon) { - // const char* type_str = "?"; - // switch (event.type) { - // case bb_event_type_t::NODE_BRANCHED: type_str = "BRANCHED"; break; - // case bb_event_type_t::NODE_FATHOMED: type_str = "FATHOMED"; break; - // case bb_event_type_t::NODE_INTEGER: type_str = "INTEGER"; break; - // case bb_event_type_t::NODE_INFEASIBLE: type_str = "INFEASIBLE"; break; - // case bb_event_type_t::NODE_PAUSED: type_str = "PAUSED"; break; - // case bb_event_type_t::NODE_NUMERICAL: type_str = "NUMERICAL"; break; - // case bb_event_type_t::HEURISTIC_SOLUTION: type_str = "HEURISTIC"; break; - // } - // settings_.log.printf(" BOUNDARY EVENT: wut=%.9f, worker=%d, node=%d, type=%s\n", - // event.wut, event.worker_id, event.node_id, type_str); - // } - // } - // } - - // // Log per-worker clock values - // for (const auto& worker : *bsp_workers_) { - // settings_.log.printf(" Worker %d: clock=%.6f, work_this_horizon=%.6f\n", - // worker.worker_id, worker.clock, worker.work_units_this_horizon); - // } - // } - BSP_DEBUG_LOG_SYNC_PHASE_START( bsp_debug_settings_, bsp_debug_logger_, horizon_end, all_events.size()); @@ -2536,12 +2496,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t double work_performed = worker.work_context.global_work_units_elapsed - work_units_at_start; worker.clock += work_performed; - worker.work_units_this_horizon += work_performed; - - // // Diagnostic: log work performed for each node solve - // settings_.log.printf("NODE_SOLVE: W%d N%d: iters=%d work=%.9f clock=%.9f horizon_end=%.6f\n", - // worker.worker_id, node_ptr->node_id, node_iter, - // work_performed, worker.clock, worker.horizon_end); exploration_stats_.total_lp_solve_time += toc(lp_start_time); exploration_stats_.total_lp_iters += node_iter; @@ -2726,6 +2680,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } else if (lp_status == dual::status_t::TIME_LIMIT) { return node_solve_info_t::TIME_LIMIT; + } else if (lp_status == dual::status_t::WORK_LIMIT) { + return node_solve_info_t::WORK_LIMIT; } else { worker.record_numerical(node_ptr); @@ -2822,8 +2778,7 @@ void branch_and_bound_t::process_history_and_sync( case bb_event_type_t::NODE_BRANCHED: case bb_event_type_t::NODE_FATHOMED: case bb_event_type_t::NODE_INFEASIBLE: - case bb_event_type_t::NODE_NUMERICAL: - case bb_event_type_t::HEURISTIC_SOLUTION: break; + case bb_event_type_t::NODE_NUMERICAL: break; } } @@ -3183,28 +3138,6 @@ void branch_and_bound_t::merge_diving_solutions() { if (!bsp_diving_workers_) return; - // // Diagnostic logging for diving workers - // { - // int total_solutions = 0; - // int total_nodes_explored = 0; - // for (const auto& worker : *bsp_diving_workers_) { - // total_solutions += worker.integer_solutions.size(); - // total_nodes_explored += worker.nodes_explored_this_horizon; - // } - // settings_.log.printf(" Diving workers: %d solutions, %d nodes explored this horizon\n", - // total_solutions, total_nodes_explored); - - // // Log per-diving-worker state - // for (const auto& worker : *bsp_diving_workers_) { - // if (worker.nodes_explored_this_horizon > 0 || !worker.integer_solutions.empty()) { - // settings_.log.printf(" DW%d: clock=%.6f, nodes=%d, sols=%zu\n", - // worker.worker_id, worker.clock, - // worker.nodes_explored_this_horizon, - // worker.integer_solutions.size()); - // } - // } - // } - // Collect all integer solutions from diving workers std::vector*> all_solutions; for (auto& worker : *bsp_diving_workers_) { @@ -3399,7 +3332,6 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t::run_presolve(f_t time_limit) const f_t max_time_on_probing = diversity_config.max_time_on_probing; f_t time_for_probing_cache = std::min(max_time_on_probing, time_limit * time_ratio_of_probing_cache); + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + time_for_probing_cache = std::numeric_limits::infinity(); + } + time_for_probing_cache = 0; timer_t probing_timer{time_for_probing_cache}; // this function computes probing cache, finds singletons, substitutions and changes the problem bool problem_is_infeasible = diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 07d8e31fe..3f66a3c37 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -178,10 +178,10 @@ void local_search_t::start_cpufj_deterministic( producer_sync.register_producer(&deterministic_cpu_fj.fj_cpu->work_units_elapsed); // Set up callback to send solutions to B&B with work unit timestamps - // deterministic_cpu_fj.fj_cpu->improvement_callback =1 - // [&bb](f_t obj, const std::vector& h_vec, double work_units) { - // bb.set_new_solution_deterministic(h_vec, work_units); - // }; + deterministic_cpu_fj.fj_cpu->improvement_callback = + [&bb](f_t obj, const std::vector& h_vec, double work_units) { + bb.set_new_solution_deterministic(h_vec, work_units); + }; // Start the CPUFJ thread deterministic_cpu_fj.start_cpu_solver(); diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp index 746d5e6dd..66f3bfae0 100644 --- a/cpp/src/utilities/work_unit_scheduler.cpp +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -36,7 +36,6 @@ void work_unit_scheduler_t::register_context(work_limit_context_t& ctx) { std::lock_guard lock(mutex_); contexts_.push_back(ctx); - callback_queues_[&ctx] = callback_queue_t{}; last_sync_target_[&ctx] = 0.0; ctx.scheduler = this; } @@ -51,7 +50,6 @@ void work_unit_scheduler_t::deregister_context(work_limit_context_t& ctx) return &ref.get() == &ctx; }), contexts_.end()); - callback_queues_.erase(&ctx); last_sync_target_.erase(&ctx); cv_.notify_all(); } @@ -102,16 +100,6 @@ sync_result_t work_unit_scheduler_t::wait_for_next_sync(work_limit_context_t& ct return stopped_.load() ? sync_result_t::STOPPED : sync_result_t::CONTINUE; } -// void work_unit_scheduler_t::queue_callback(work_limit_context_t& source, -// work_limit_context_t& destination, -// callback_t callback) -// { -// std::lock_guard lock(mutex_); -// double tag = source.global_work_units_elapsed; -// auto it = callback_queues_.find(&destination); -// if (it != callback_queues_.end()) { it->second.push({tag, std::move(callback)}); } -// } - double work_unit_scheduler_t::current_sync_target() const { if (sync_interval_ <= 0) return std::numeric_limits::infinity(); @@ -166,12 +154,6 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double size_t my_exit_generation = exit_generation_; - if (verbose) { CUOPT_LOG_DEBUG("[%s] Processing callbacks", ctx.name.c_str()); } - // lock.unlock(); - // process_callbacks_for_context(ctx, sync_target); - // lock.lock(); - if (verbose) { CUOPT_LOG_DEBUG("[%s] Done processing callbacks", ctx.name.c_str()); } - contexts_at_barrier_--; if (contexts_at_barrier_ == 0) { exit_generation_++; @@ -205,10 +187,4 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double } } -// void work_unit_scheduler_t::process_callbacks_for_context(work_limit_context_t& ctx, -// double up_to_work_units) -// { - -// } - } // namespace cuopt diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index 59b7fbffe..1120c29ff 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -20,7 +20,6 @@ #include #include #include -#include #include #include @@ -35,8 +34,6 @@ enum class sync_result_t { class work_unit_scheduler_t { public: - using callback_t = std::function; - explicit work_unit_scheduler_t(double sync_interval = 5.0); void set_sync_interval(double interval); @@ -45,9 +42,6 @@ class work_unit_scheduler_t { void register_context(work_limit_context_t& ctx); void deregister_context(work_limit_context_t& ctx); void on_work_recorded(work_limit_context_t& ctx, double total_work); - // void queue_callback(work_limit_context_t& source, - // work_limit_context_t& destination, - // callback_t callback); // Sync callback support - callback is executed when all contexts reach sync point // If callback returns true, scheduler stops and all workers exit cleanly @@ -66,25 +60,10 @@ class work_unit_scheduler_t { double sync_interval_; private: - struct tagged_callback_t { - double work_unit_tag; - callback_t callback; - - bool operator>(const tagged_callback_t& other) const - { - return work_unit_tag > other.work_unit_tag; - } - }; - - using callback_queue_t = - std::priority_queue, std::greater<>>; - double current_sync_target() const; void wait_at_sync_point(work_limit_context_t& ctx, double sync_target); - void process_callbacks_for_context(work_limit_context_t& ctx, double up_to_work_units); std::vector> contexts_; - std::unordered_map callback_queues_; std::unordered_map last_sync_target_; std::mutex mutex_; diff --git a/cpp/tests/linear_programming/c_api_tests/c_api_test.c b/cpp/tests/linear_programming/c_api_tests/c_api_test.c index 52be9e16f..16f93275f 100644 --- a/cpp/tests/linear_programming/c_api_tests/c_api_test.c +++ b/cpp/tests/linear_programming/c_api_tests/c_api_test.c @@ -1201,3 +1201,113 @@ cuOptDestroySolution(&solution); return status; } + +cuopt_int_t test_deterministic_bb(const char* filename, + cuopt_int_t num_runs, + cuopt_int_t num_threads, + cuopt_float_t time_limit) +{ + cuOptOptimizationProblem problem = NULL; + cuOptSolverSettings settings = NULL; + cuopt_float_t first_objective = 0.0; + cuopt_int_t first_status = -1; + cuopt_int_t status; + cuopt_int_t run; + + printf("Testing deterministic B&B: %s with %d threads, %d runs\n", filename, num_threads, num_runs); + + status = cuOptReadProblem(filename, &problem); + if (status != CUOPT_SUCCESS) { + printf("Error reading problem: %d\n", status); + goto DONE; + } + + status = cuOptCreateSolverSettings(&settings); + if (status != CUOPT_SUCCESS) { + printf("Error creating solver settings: %d\n", status); + goto DONE; + } + + status = cuOptSetIntegerParameter(settings, CUOPT_MIP_DETERMINISM_MODE, CUOPT_MODE_DETERMINISTIC); + if (status != CUOPT_SUCCESS) { + printf("Error setting determinism mode: %d\n", status); + goto DONE; + } + + status = cuOptSetIntegerParameter(settings, CUOPT_NUM_CPU_THREADS, num_threads); + if (status != CUOPT_SUCCESS) { + printf("Error setting num threads: %d\n", status); + goto DONE; + } + + status = cuOptSetFloatParameter(settings, CUOPT_TIME_LIMIT, time_limit); + if (status != CUOPT_SUCCESS) { + printf("Error setting time limit: %d\n", status); + goto DONE; + } + + for (run = 0; run < num_runs; run++) { + cuOptSolution solution = NULL; + cuopt_float_t objective; + cuopt_int_t termination_status; + + status = cuOptSolve(problem, settings, &solution); + if (status != CUOPT_SUCCESS) { + printf("Error solving problem on run %d: %d\n", run, status); + cuOptDestroySolution(&solution); + goto DONE; + } + + status = cuOptGetObjectiveValue(solution, &objective); + if (status != CUOPT_SUCCESS) { + printf("Error getting objective value on run %d: %d\n", run, status); + cuOptDestroySolution(&solution); + goto DONE; + } + + status = cuOptGetTerminationStatus(solution, &termination_status); + if (status != CUOPT_SUCCESS) { + printf("Error getting termination status on run %d: %d\n", run, status); + cuOptDestroySolution(&solution); + goto DONE; + } + + printf("Run %d: status=%s (%d), objective=%f\n", + run, + termination_status_to_string(termination_status), + termination_status, + objective); + + if (run == 0) { + first_objective = objective; + first_status = termination_status; + } else { + if (first_status != termination_status) { + printf("Determinism failure: run %d termination status %d differs from run 0 status %d\n", + run, + termination_status, + first_status); + status = CUOPT_VALIDATION_ERROR; + cuOptDestroySolution(&solution); + goto DONE; + } + if (first_objective != objective) { + printf("Determinism failure: run %d objective %f differs from run 0 objective %f\n", + run, + objective, + first_objective); + status = CUOPT_VALIDATION_ERROR; + cuOptDestroySolution(&solution); + goto DONE; + } + } + cuOptDestroySolution(&solution); + } + + printf("Deterministic B&B test PASSED: all %d runs produced identical results\n", num_runs); + +DONE: + cuOptDestroyProblem(&problem); + cuOptDestroySolverSettings(&settings); + return status; +} diff --git a/cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp b/cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp index af1295298..d79affcd7 100644 --- a/cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp +++ b/cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -14,117 +14,143 @@ #include -TEST(c_api, int_size) { EXPECT_EQ(test_int_size(), sizeof(int32_t)); } - -TEST(c_api, float_size) { EXPECT_EQ(test_float_size(), sizeof(double)); } - -TEST(c_api, afiro) -{ - const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); - std::string filename = rapidsDatasetRootDir + "/linear_programming/" + "afiro_original.mps"; - int termination_status; - EXPECT_EQ(solve_mps_file(filename.c_str(), 60, CUOPT_INFINITY, &termination_status), - CUOPT_SUCCESS); - EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); -} - -// Test both LP and MIP codepaths -class TimeLimitTestFixture : public ::testing::TestWithParam> { -}; -TEST_P(TimeLimitTestFixture, time_limit) +// TEST(c_api, int_size) { EXPECT_EQ(test_int_size(), sizeof(int32_t)); } + +// TEST(c_api, float_size) { EXPECT_EQ(test_float_size(), sizeof(double)); } + +// TEST(c_api, afiro) +// { +// const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); +// std::string filename = rapidsDatasetRootDir + "/linear_programming/" + "afiro_original.mps"; +// int termination_status; +// EXPECT_EQ(solve_mps_file(filename.c_str(), 60, CUOPT_INFINITY, &termination_status), +// CUOPT_SUCCESS); +// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); +// } + +// // Test both LP and MIP codepaths +// class TimeLimitTestFixture : public ::testing::TestWithParam> { +// }; +// TEST_P(TimeLimitTestFixture, time_limit) +// { +// const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); +// std::string filename = rapidsDatasetRootDir + std::get<0>(GetParam()); +// double target_solve_time = std::get<1>(GetParam()); +// int method = std::get<2>(GetParam()); +// int termination_status; +// double solve_time = std::numeric_limits::quiet_NaN(); +// EXPECT_EQ(solve_mps_file(filename.c_str(), +// target_solve_time, +// CUOPT_INFINITY, +// &termination_status, +// &solve_time, +// method), +// CUOPT_SUCCESS); +// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_TIME_LIMIT); + +// Dual simplex is spending some time for factorizing the basis, and this computation does not +// check for time limit +double excess_allowed_time = 3.0; +// EXPECT_NEAR(solve_time, target_solve_time, excess_allowed_time); +// } +// INSTANTIATE_TEST_SUITE_P( +// c_api, +// TimeLimitTestFixture, +// ::testing::Values( +// std::make_tuple("/linear_programming/square41/square41.mps", +// 5, +// CUOPT_METHOD_DUAL_SIMPLEX), // LP, Dual Simplex +// std::make_tuple("/linear_programming/square41/square41.mps", 5, CUOPT_METHOD_PDLP), // LP, +// PDLP std::make_tuple("/mip/supportcase22.mps", 15, CUOPT_METHOD_DUAL_SIMPLEX) // +// MIP +// )); + +// TEST(c_api, iteration_limit) +// { +// const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); +// std::string filename = rapidsDatasetRootDir + "/linear_programming/" + "afiro_original.mps"; +// int termination_status; +// EXPECT_EQ(solve_mps_file(filename.c_str(), 60, 1, &termination_status), CUOPT_SUCCESS); +// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_ITERATION_LIMIT); +// } + +// TEST(c_api, solve_time_bb_preemption) +// { +// const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); +// std::string filename = rapidsDatasetRootDir + "/mip/" + "bb_optimality.mps"; +// int termination_status; +// double solve_time = std::numeric_limits::quiet_NaN(); +// EXPECT_EQ(solve_mps_file(filename.c_str(), 5, CUOPT_INFINITY, &termination_status, +// &solve_time), +// CUOPT_SUCCESS); +// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); +// EXPECT_GT(solve_time, 0); // solve time should not be equal to 0, even on very simple +// instances +// // solved by B&B before the diversity solver has time to run +// } + +// TEST(c_api, bad_parameter_name) { EXPECT_EQ(test_bad_parameter_name(), CUOPT_INVALID_ARGUMENT); } + +// TEST(c_api, burglar) { EXPECT_EQ(burglar_problem(), CUOPT_SUCCESS); } + +// TEST(c_api, test_missing_file) { EXPECT_EQ(test_missing_file(), CUOPT_MPS_FILE_ERROR); } + +// TEST(c_api, test_infeasible_problem) { EXPECT_EQ(test_infeasible_problem(), CUOPT_SUCCESS); } + +// TEST(c_api, test_ranged_problem) +// { +// cuopt_int_t termination_status; +// cuopt_float_t objective; +// EXPECT_EQ(test_ranged_problem(&termination_status, &objective), CUOPT_SUCCESS); +// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); +// EXPECT_NEAR(objective, 32.0, 1e-3); +// } + +// TEST(c_api, test_invalid_bounds) +// { +// // Test LP codepath +// EXPECT_EQ(test_invalid_bounds(false), CUOPT_SUCCESS); +// // Test MIP codepath +// EXPECT_EQ(test_invalid_bounds(true), CUOPT_SUCCESS); +// } + +// TEST(c_api, test_quadratic_problem) +// { +// cuopt_int_t termination_status; +// cuopt_float_t objective; +// EXPECT_EQ(test_quadratic_problem(&termination_status, &objective), CUOPT_SUCCESS); +// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); +// EXPECT_NEAR(objective, -32.0, 1e-3); +// } + +// TEST(c_api, test_quadratic_ranged_problem) +// { +// cuopt_int_t termination_status; +// cuopt_float_t objective; +// EXPECT_EQ(test_quadratic_ranged_problem(&termination_status, &objective), CUOPT_SUCCESS); +// EXPECT_EQ(termination_status, (int)CUOPT_TERIMINATION_STATUS_OPTIMAL); +// EXPECT_NEAR(objective, -32.0, 1e-3); +// } + +class DeterministicBBTestFixture + : public ::testing::TestWithParam> {}; +TEST_P(DeterministicBBTestFixture, deterministic_reproducibility) { const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); std::string filename = rapidsDatasetRootDir + std::get<0>(GetParam()); - double target_solve_time = std::get<1>(GetParam()); - int method = std::get<2>(GetParam()); - int termination_status; - double solve_time = std::numeric_limits::quiet_NaN(); - EXPECT_EQ(solve_mps_file(filename.c_str(), - target_solve_time, - CUOPT_INFINITY, - &termination_status, - &solve_time, - method), - CUOPT_SUCCESS); - EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_TIME_LIMIT); - - // Dual simplex is spending some time for factorizing the basis, and this computation does not - // check for time limit - double excess_allowed_time = 3.0; - EXPECT_NEAR(solve_time, target_solve_time, excess_allowed_time); -} -INSTANTIATE_TEST_SUITE_P( - c_api, - TimeLimitTestFixture, - ::testing::Values( - std::make_tuple("/linear_programming/square41/square41.mps", - 5, - CUOPT_METHOD_DUAL_SIMPLEX), // LP, Dual Simplex - std::make_tuple("/linear_programming/square41/square41.mps", 5, CUOPT_METHOD_PDLP), // LP, PDLP - std::make_tuple("/mip/supportcase22.mps", 15, CUOPT_METHOD_DUAL_SIMPLEX) // MIP - )); - -TEST(c_api, iteration_limit) -{ - const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); - std::string filename = rapidsDatasetRootDir + "/linear_programming/" + "afiro_original.mps"; - int termination_status; - EXPECT_EQ(solve_mps_file(filename.c_str(), 60, 1, &termination_status), CUOPT_SUCCESS); - EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_ITERATION_LIMIT); -} - -TEST(c_api, solve_time_bb_preemption) -{ - const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); - std::string filename = rapidsDatasetRootDir + "/mip/" + "bb_optimality.mps"; - int termination_status; - double solve_time = std::numeric_limits::quiet_NaN(); - EXPECT_EQ(solve_mps_file(filename.c_str(), 5, CUOPT_INFINITY, &termination_status, &solve_time), - CUOPT_SUCCESS); - EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); - EXPECT_GT(solve_time, 0); // solve time should not be equal to 0, even on very simple instances - // solved by B&B before the diversity solver has time to run -} + int num_threads = std::get<1>(GetParam()); + double time_limit = std::get<2>(GetParam()); -TEST(c_api, bad_parameter_name) { EXPECT_EQ(test_bad_parameter_name(), CUOPT_INVALID_ARGUMENT); } - -TEST(c_api, burglar) { EXPECT_EQ(burglar_problem(), CUOPT_SUCCESS); } - -TEST(c_api, test_missing_file) { EXPECT_EQ(test_missing_file(), CUOPT_MPS_FILE_ERROR); } - -TEST(c_api, test_infeasible_problem) { EXPECT_EQ(test_infeasible_problem(), CUOPT_SUCCESS); } - -TEST(c_api, test_ranged_problem) -{ - cuopt_int_t termination_status; - cuopt_float_t objective; - EXPECT_EQ(test_ranged_problem(&termination_status, &objective), CUOPT_SUCCESS); - EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); - EXPECT_NEAR(objective, 32.0, 1e-3); -} - -TEST(c_api, test_invalid_bounds) -{ - // Test LP codepath - EXPECT_EQ(test_invalid_bounds(false), CUOPT_SUCCESS); - // Test MIP codepath - EXPECT_EQ(test_invalid_bounds(true), CUOPT_SUCCESS); -} - -TEST(c_api, test_quadratic_problem) -{ - cuopt_int_t termination_status; - cuopt_float_t objective; - EXPECT_EQ(test_quadratic_problem(&termination_status, &objective), CUOPT_SUCCESS); - EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); - EXPECT_NEAR(objective, -32.0, 1e-3); -} - -TEST(c_api, test_quadratic_ranged_problem) -{ - cuopt_int_t termination_status; - cuopt_float_t objective; - EXPECT_EQ(test_quadratic_ranged_problem(&termination_status, &objective), CUOPT_SUCCESS); - EXPECT_EQ(termination_status, (int)CUOPT_TERIMINATION_STATUS_OPTIMAL); - EXPECT_NEAR(objective, -32.0, 1e-3); + // Run 3 times and verify identical results + EXPECT_EQ(test_deterministic_bb(filename.c_str(), 3, num_threads, time_limit), CUOPT_SUCCESS); } +INSTANTIATE_TEST_SUITE_P(c_api, + DeterministicBBTestFixture, + ::testing::Values( + // Low thread count + std::make_tuple("/mip/gen-ip054.mps", 4, 60.0), + // High thread count (high contention) + std::make_tuple("/mip/gen-ip054.mps", 128, 60.0), + // Different instance + std::make_tuple("/mip/bb_optimality.mps", 8, 60.0))); diff --git a/cpp/tests/linear_programming/c_api_tests/c_api_tests.h b/cpp/tests/linear_programming/c_api_tests/c_api_tests.h index 5726c3a99..04a49998c 100644 --- a/cpp/tests/linear_programming/c_api_tests/c_api_tests.h +++ b/cpp/tests/linear_programming/c_api_tests/c_api_tests.h @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -35,6 +35,11 @@ cuopt_int_t test_quadratic_problem(cuopt_int_t* termination_status_ptr, cuopt_int_t test_quadratic_ranged_problem(cuopt_int_t* termination_status_ptr, cuopt_float_t* objective_ptr); +cuopt_int_t test_deterministic_bb(const char* filename, + cuopt_int_t num_runs, + cuopt_int_t num_threads, + cuopt_float_t time_limit); + #ifdef __cplusplus } #endif diff --git a/cpp/tests/mip/CMakeLists.txt b/cpp/tests/mip/CMakeLists.txt index ce47f3144..3efcd5df8 100644 --- a/cpp/tests/mip/CMakeLists.txt +++ b/cpp/tests/mip/CMakeLists.txt @@ -1,5 +1,5 @@ # cmake-format: off -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # cmake-format: on @@ -43,3 +43,6 @@ ConfigureTest(PRESOLVE_TEST ConfigureTest(MIP_TERMINATION_STATUS_TEST ${CMAKE_CURRENT_SOURCE_DIR}/termination_test.cu ) +ConfigureTest(DETERMINISM_TEST + ${CMAKE_CURRENT_SOURCE_DIR}/determinism_test.cu +) diff --git a/cpp/tests/mip/determinism_test.cu b/cpp/tests/mip/determinism_test.cu new file mode 100644 index 000000000..15670e1a2 --- /dev/null +++ b/cpp/tests/mip/determinism_test.cu @@ -0,0 +1,198 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#include "../linear_programming/utilities/pdlp_test_utilities.cuh" +#include "mip_utils.cuh" + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include + +#include +#include +#include +#include + +namespace cuopt::linear_programming::test { + +namespace { + +void expect_solutions_bitwise_equal(const mip_solution_t& sol1, + const mip_solution_t& sol2, + raft::handle_t& handle, + const std::string& label = "") +{ + auto x1 = cuopt::host_copy(sol1.get_solution(), handle.get_stream()); + auto x2 = cuopt::host_copy(sol2.get_solution(), handle.get_stream()); + + ASSERT_EQ(x1.size(), x2.size()) << label << "Solution sizes differ"; + for (size_t i = 0; i < x1.size(); ++i) { + EXPECT_EQ(x1[i], x2[i]) << label << "Variable " << i << " differs"; + } +} + +} // namespace + +class DeterministicBBTest : public ::testing::Test { + protected: + raft::handle_t handle_; +}; + +// Test that multiple runs with deterministic mode produce identical objective values +TEST_F(DeterministicBBTest, reproducible_objective) +{ + auto path = make_path_absolute("/mip/gen-ip054.mps"); + auto problem = mps_parser::parse_mps(path, false); + handle_.sync_stream(); + + mip_solver_settings_t settings; + settings.time_limit = 60.0; + settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; + settings.num_cpu_threads = 8; + settings.work_limit = 4; + + auto solution1 = solve_mip(&handle_, problem, settings); + double obj1 = solution1.get_objective_value(); + auto status1 = solution1.get_termination_status(); + + auto solution2 = solve_mip(&handle_, problem, settings); + double obj2 = solution2.get_objective_value(); + auto status2 = solution2.get_termination_status(); + + EXPECT_EQ(status1, status2) << "Termination status differs between runs"; + EXPECT_DOUBLE_EQ(obj1, obj2) << "Objective value differs between runs"; + expect_solutions_bitwise_equal(solution1, solution2, handle_); +} + +// Test determinism under high thread contention +TEST_F(DeterministicBBTest, reproducible_high_contention) +{ + auto path = make_path_absolute("/mip/gen-ip054.mps"); + auto problem = mps_parser::parse_mps(path, false); + handle_.sync_stream(); + + mip_solver_settings_t settings; + settings.time_limit = 60.0; + settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; + settings.num_cpu_threads = 128; // High thread count to stress contention + settings.work_limit = 1; + + std::vector> solutions; + + constexpr int num_runs = 3; + for (int run = 0; run < num_runs; ++run) { + solutions.push_back(solve_mip(&handle_, problem, settings)); + } + + for (int i = 1; i < num_runs; ++i) { + EXPECT_EQ(solutions[0].get_termination_status(), solutions[i].get_termination_status()) + << "Run " << i << " termination status differs from run 0"; + EXPECT_DOUBLE_EQ(solutions[0].get_objective_value(), solutions[i].get_objective_value()) + << "Run " << i << " objective differs from run 0"; + expect_solutions_bitwise_equal( + solutions[0], solutions[i], handle_, "Run " + std::to_string(i) + " vs run 0: "); + } +} + +// Test that solution vectors are bitwise identical across runs +TEST_F(DeterministicBBTest, reproducible_solution_vector) +{ + auto path = make_path_absolute("/mip/swath1.mps"); + auto problem = mps_parser::parse_mps(path, false); + handle_.sync_stream(); + + mip_solver_settings_t settings; + settings.time_limit = 60.0; + settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; + settings.num_cpu_threads = 8; + settings.work_limit = 2; + + auto solution1 = solve_mip(&handle_, problem, settings); + auto solution2 = solve_mip(&handle_, problem, settings); + + EXPECT_EQ(solution1.get_termination_status(), solution2.get_termination_status()); + EXPECT_DOUBLE_EQ(solution1.get_objective_value(), solution2.get_objective_value()); + expect_solutions_bitwise_equal(solution1, solution2, handle_); +} + +// Parameterized test for different problem instances +class DeterministicBBInstanceTest + : public ::testing::TestWithParam> { + protected: + raft::handle_t handle_; +}; + +TEST_P(DeterministicBBInstanceTest, deterministic_across_runs) +{ + auto [instance_path, num_threads, time_limit, work_limit] = GetParam(); + auto path = make_path_absolute(instance_path); + auto problem = mps_parser::parse_mps(path, false); + handle_.sync_stream(); + + // Get a random seed for each run + auto seed = std::random_device{}(); + std::cout << "Tested with seed " << seed << "\n"; + + mip_solver_settings_t settings; + settings.time_limit = time_limit; + settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; + settings.num_cpu_threads = num_threads; + settings.work_limit = work_limit; + + cuopt::seed_generator::set_seed(seed); + auto solution1 = solve_mip(&handle_, problem, settings); + cuopt::seed_generator::set_seed(seed); + auto solution2 = solve_mip(&handle_, problem, settings); + cuopt::seed_generator::set_seed(seed); + auto solution3 = solve_mip(&handle_, problem, settings); + + EXPECT_EQ(solution1.get_termination_status(), solution2.get_termination_status()); + EXPECT_EQ(solution1.get_termination_status(), solution3.get_termination_status()); + + EXPECT_DOUBLE_EQ(solution1.get_objective_value(), solution2.get_objective_value()); + EXPECT_DOUBLE_EQ(solution1.get_objective_value(), solution3.get_objective_value()); + + EXPECT_DOUBLE_EQ(solution1.get_solution_bound(), solution2.get_solution_bound()); + EXPECT_DOUBLE_EQ(solution1.get_solution_bound(), solution3.get_solution_bound()); + + expect_solutions_bitwise_equal(solution1, solution2, handle_, "Run 1 vs 2: "); + expect_solutions_bitwise_equal(solution1, solution3, handle_, "Run 1 vs 3: "); +} + +INSTANTIATE_TEST_SUITE_P( + DeterministicBB, + DeterministicBBInstanceTest, + ::testing::Values( + // Instance, threads, time_limit + std::make_tuple("/mip/gen-ip054.mps", 4, 60.0, 4), + std::make_tuple("/mip/swath1.mps", 8, 60.0, 4), + std::make_tuple("/mip/gen-ip054.mps", 128, 120.0, 1), + std::make_tuple("/mip/bb_optimality.mps", 4, 60.0, 4), + std::make_tuple("/mip/neos5.mps", 16, 60.0, 1), + std::make_tuple("/mip/seymour1.mps", 16, 60.0, 1), + std::make_tuple("/mip/n2seq36q.mps", 16, 60.0, 4), + std::make_tuple("/mip/gmu-35-50.mps", 32, 60.0, 3)), + [](const ::testing::TestParamInfo& info) { + const auto& path = std::get<0>(info.param); + int threads = std::get<1>(info.param); + std::string name = path.substr(path.rfind('/') + 1); + name = name.substr(0, name.rfind('.')); + std::replace(name.begin(), name.end(), '-', '_'); + return name + "_threads" + std::to_string(threads); + }); + +} // namespace cuopt::linear_programming::test From 2ed103c362b270e12dc91b52a03e0f2a9d0dc690 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 15:57:21 +0000 Subject: [PATCH 252/366] add work limit as a parameter --- .../cuopt/linear_programming/constants.h | 2 + .../mip/solver_solution.hpp | 3 +- cpp/src/math_optimization/solver_settings.cu | 1 + cpp/src/mip/solve.cu | 1 + cpp/src/mip/solver_solution.cu | 3 +- .../c_api_tests/c_api_test.c | 9 +- .../c_api_tests/c_api_tests.cpp | 244 +++++++++--------- .../c_api_tests/c_api_tests.h | 3 +- 8 files changed, 139 insertions(+), 127 deletions(-) diff --git a/cpp/include/cuopt/linear_programming/constants.h b/cpp/include/cuopt/linear_programming/constants.h index eb32fa1fb..f524f32d5 100644 --- a/cpp/include/cuopt/linear_programming/constants.h +++ b/cpp/include/cuopt/linear_programming/constants.h @@ -32,6 +32,7 @@ #define CUOPT_DUAL_INFEASIBLE_TOLERANCE "dual_infeasible_tolerance" #define CUOPT_ITERATION_LIMIT "iteration_limit" #define CUOPT_TIME_LIMIT "time_limit" +#define CUOPT_WORK_LIMIT "work_limit" #define CUOPT_PDLP_SOLVER_MODE "pdlp_solver_mode" #define CUOPT_METHOD "method" #define CUOPT_PER_CONSTRAINT_RESIDUAL "per_constraint_residual" @@ -74,6 +75,7 @@ #define CUOPT_TERIMINATION_STATUS_UNBOUNDED 3 #define CUOPT_TERIMINATION_STATUS_ITERATION_LIMIT 4 #define CUOPT_TERIMINATION_STATUS_TIME_LIMIT 5 +#define CUOPT_TERIMINATION_STATUS_WORK_LIMIT 10 #define CUOPT_TERIMINATION_STATUS_NUMERICAL_ERROR 6 #define CUOPT_TERIMINATION_STATUS_PRIMAL_FEASIBLE 7 #define CUOPT_TERIMINATION_STATUS_FEASIBLE_FOUND 8 diff --git a/cpp/include/cuopt/linear_programming/mip/solver_solution.hpp b/cpp/include/cuopt/linear_programming/mip/solver_solution.hpp index 6ff8d324b..a6c28ac20 100644 --- a/cpp/include/cuopt/linear_programming/mip/solver_solution.hpp +++ b/cpp/include/cuopt/linear_programming/mip/solver_solution.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -30,6 +30,7 @@ enum class mip_termination_status_t : int8_t { Infeasible = CUOPT_TERIMINATION_STATUS_INFEASIBLE, Unbounded = CUOPT_TERIMINATION_STATUS_UNBOUNDED, TimeLimit = CUOPT_TERIMINATION_STATUS_TIME_LIMIT, + WorkLimit = CUOPT_TERIMINATION_STATUS_WORK_LIMIT, }; template diff --git a/cpp/src/math_optimization/solver_settings.cu b/cpp/src/math_optimization/solver_settings.cu index 1b5efeb0d..b5c7eed3f 100644 --- a/cpp/src/math_optimization/solver_settings.cu +++ b/cpp/src/math_optimization/solver_settings.cu @@ -60,6 +60,7 @@ solver_settings_t::solver_settings_t() : pdlp_settings(), mip_settings float_parameters = { {CUOPT_TIME_LIMIT, &mip_settings.time_limit, 0.0, std::numeric_limits::infinity(), std::numeric_limits::infinity()}, {CUOPT_TIME_LIMIT, &pdlp_settings.time_limit, 0.0, std::numeric_limits::infinity(), std::numeric_limits::infinity()}, + {CUOPT_WORK_LIMIT, &mip_settings.work_limit, 0.0, std::numeric_limits::infinity(), std::numeric_limits::infinity()}, {CUOPT_ABSOLUTE_DUAL_TOLERANCE, &pdlp_settings.tolerances.absolute_dual_tolerance, 0.0, 1e-1, 1e-4}, {CUOPT_RELATIVE_DUAL_TOLERANCE, &pdlp_settings.tolerances.relative_dual_tolerance, 0.0, 1e-1, 1e-4}, {CUOPT_ABSOLUTE_PRIMAL_TOLERANCE, &pdlp_settings.tolerances.absolute_primal_tolerance, 0.0, 1e-1, 1e-4}, diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index ca537dd75..ec128708e 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -244,6 +244,7 @@ mip_solution_t solve_mip(optimization_problem_t& op_problem, if (run_presolve) { auto status_to_skip = sol.get_termination_status() == mip_termination_status_t::TimeLimit || + sol.get_termination_status() == mip_termination_status_t::WorkLimit || sol.get_termination_status() == mip_termination_status_t::Infeasible; auto primal_solution = cuopt::device_copy(sol.get_solution(), op_problem.get_handle_ptr()->get_stream()); diff --git a/cpp/src/mip/solver_solution.cu b/cpp/src/mip/solver_solution.cu index 2ce6d5700..5689d444d 100644 --- a/cpp/src/mip/solver_solution.cu +++ b/cpp/src/mip/solver_solution.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -136,6 +136,7 @@ std::string mip_solution_t::get_termination_status_string( case mip_termination_status_t::FeasibleFound: return "FeasibleFound"; case mip_termination_status_t::Infeasible: return "Infeasible"; case mip_termination_status_t::TimeLimit: return "TimeLimit"; + case mip_termination_status_t::WorkLimit: return "WorkLimit"; case mip_termination_status_t::Unbounded: return "Unbounded"; // Do not implement default case to trigger compile time error if new enum is added diff --git a/cpp/tests/linear_programming/c_api_tests/c_api_test.c b/cpp/tests/linear_programming/c_api_tests/c_api_test.c index 16f93275f..c4da6f59e 100644 --- a/cpp/tests/linear_programming/c_api_tests/c_api_test.c +++ b/cpp/tests/linear_programming/c_api_tests/c_api_test.c @@ -1205,7 +1205,8 @@ return status; cuopt_int_t test_deterministic_bb(const char* filename, cuopt_int_t num_runs, cuopt_int_t num_threads, - cuopt_float_t time_limit) + cuopt_float_t time_limit, + cuopt_float_t work_limit) { cuOptOptimizationProblem problem = NULL; cuOptSolverSettings settings = NULL; @@ -1246,6 +1247,12 @@ cuopt_int_t test_deterministic_bb(const char* filename, goto DONE; } + status = cuOptSetFloatParameter(settings, CUOPT_WORK_LIMIT, work_limit); + if (status != CUOPT_SUCCESS) { + printf("Error setting work limit: %d\n", status); + goto DONE; + } + for (run = 0; run < num_runs; run++) { cuOptSolution solution = NULL; cuopt_float_t objective; diff --git a/cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp b/cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp index d79affcd7..d0bfcf264 100644 --- a/cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp +++ b/cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp @@ -14,143 +14,141 @@ #include -// TEST(c_api, int_size) { EXPECT_EQ(test_int_size(), sizeof(int32_t)); } - -// TEST(c_api, float_size) { EXPECT_EQ(test_float_size(), sizeof(double)); } - -// TEST(c_api, afiro) -// { -// const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); -// std::string filename = rapidsDatasetRootDir + "/linear_programming/" + "afiro_original.mps"; -// int termination_status; -// EXPECT_EQ(solve_mps_file(filename.c_str(), 60, CUOPT_INFINITY, &termination_status), -// CUOPT_SUCCESS); -// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); -// } - -// // Test both LP and MIP codepaths -// class TimeLimitTestFixture : public ::testing::TestWithParam> { -// }; -// TEST_P(TimeLimitTestFixture, time_limit) -// { -// const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); -// std::string filename = rapidsDatasetRootDir + std::get<0>(GetParam()); -// double target_solve_time = std::get<1>(GetParam()); -// int method = std::get<2>(GetParam()); -// int termination_status; -// double solve_time = std::numeric_limits::quiet_NaN(); -// EXPECT_EQ(solve_mps_file(filename.c_str(), -// target_solve_time, -// CUOPT_INFINITY, -// &termination_status, -// &solve_time, -// method), -// CUOPT_SUCCESS); -// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_TIME_LIMIT); - -// Dual simplex is spending some time for factorizing the basis, and this computation does not -// check for time limit -double excess_allowed_time = 3.0; -// EXPECT_NEAR(solve_time, target_solve_time, excess_allowed_time); -// } -// INSTANTIATE_TEST_SUITE_P( -// c_api, -// TimeLimitTestFixture, -// ::testing::Values( -// std::make_tuple("/linear_programming/square41/square41.mps", -// 5, -// CUOPT_METHOD_DUAL_SIMPLEX), // LP, Dual Simplex -// std::make_tuple("/linear_programming/square41/square41.mps", 5, CUOPT_METHOD_PDLP), // LP, -// PDLP std::make_tuple("/mip/supportcase22.mps", 15, CUOPT_METHOD_DUAL_SIMPLEX) // -// MIP -// )); - -// TEST(c_api, iteration_limit) -// { -// const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); -// std::string filename = rapidsDatasetRootDir + "/linear_programming/" + "afiro_original.mps"; -// int termination_status; -// EXPECT_EQ(solve_mps_file(filename.c_str(), 60, 1, &termination_status), CUOPT_SUCCESS); -// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_ITERATION_LIMIT); -// } - -// TEST(c_api, solve_time_bb_preemption) -// { -// const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); -// std::string filename = rapidsDatasetRootDir + "/mip/" + "bb_optimality.mps"; -// int termination_status; -// double solve_time = std::numeric_limits::quiet_NaN(); -// EXPECT_EQ(solve_mps_file(filename.c_str(), 5, CUOPT_INFINITY, &termination_status, -// &solve_time), -// CUOPT_SUCCESS); -// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); -// EXPECT_GT(solve_time, 0); // solve time should not be equal to 0, even on very simple -// instances -// // solved by B&B before the diversity solver has time to run -// } - -// TEST(c_api, bad_parameter_name) { EXPECT_EQ(test_bad_parameter_name(), CUOPT_INVALID_ARGUMENT); } - -// TEST(c_api, burglar) { EXPECT_EQ(burglar_problem(), CUOPT_SUCCESS); } - -// TEST(c_api, test_missing_file) { EXPECT_EQ(test_missing_file(), CUOPT_MPS_FILE_ERROR); } - -// TEST(c_api, test_infeasible_problem) { EXPECT_EQ(test_infeasible_problem(), CUOPT_SUCCESS); } - -// TEST(c_api, test_ranged_problem) -// { -// cuopt_int_t termination_status; -// cuopt_float_t objective; -// EXPECT_EQ(test_ranged_problem(&termination_status, &objective), CUOPT_SUCCESS); -// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); -// EXPECT_NEAR(objective, 32.0, 1e-3); -// } - -// TEST(c_api, test_invalid_bounds) -// { -// // Test LP codepath -// EXPECT_EQ(test_invalid_bounds(false), CUOPT_SUCCESS); -// // Test MIP codepath -// EXPECT_EQ(test_invalid_bounds(true), CUOPT_SUCCESS); -// } - -// TEST(c_api, test_quadratic_problem) -// { -// cuopt_int_t termination_status; -// cuopt_float_t objective; -// EXPECT_EQ(test_quadratic_problem(&termination_status, &objective), CUOPT_SUCCESS); -// EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); -// EXPECT_NEAR(objective, -32.0, 1e-3); -// } - -// TEST(c_api, test_quadratic_ranged_problem) -// { -// cuopt_int_t termination_status; -// cuopt_float_t objective; -// EXPECT_EQ(test_quadratic_ranged_problem(&termination_status, &objective), CUOPT_SUCCESS); -// EXPECT_EQ(termination_status, (int)CUOPT_TERIMINATION_STATUS_OPTIMAL); -// EXPECT_NEAR(objective, -32.0, 1e-3); -// } +TEST(c_api, int_size) { EXPECT_EQ(test_int_size(), sizeof(int32_t)); } + +TEST(c_api, float_size) { EXPECT_EQ(test_float_size(), sizeof(double)); } + +TEST(c_api, afiro) +{ + const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); + std::string filename = rapidsDatasetRootDir + "/linear_programming/" + "afiro_original.mps"; + int termination_status; + EXPECT_EQ(solve_mps_file(filename.c_str(), 60, CUOPT_INFINITY, &termination_status), + CUOPT_SUCCESS); + EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); +} + +// Test both LP and MIP codepaths +class TimeLimitTestFixture : public ::testing::TestWithParam> { +}; +TEST_P(TimeLimitTestFixture, time_limit) +{ + const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); + std::string filename = rapidsDatasetRootDir + std::get<0>(GetParam()); + double target_solve_time = std::get<1>(GetParam()); + int method = std::get<2>(GetParam()); + int termination_status; + double solve_time = std::numeric_limits::quiet_NaN(); + EXPECT_EQ(solve_mps_file(filename.c_str(), + target_solve_time, + CUOPT_INFINITY, + &termination_status, + &solve_time, + method), + CUOPT_SUCCESS); + EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_TIME_LIMIT); + + // Dual simplex is spending some time for factorizing the basis, and this computation does not + // check for time limit + double excess_allowed_time = 3.0; + EXPECT_NEAR(solve_time, target_solve_time, excess_allowed_time); +} +INSTANTIATE_TEST_SUITE_P( + c_api, + TimeLimitTestFixture, + ::testing::Values( + std::make_tuple("/linear_programming/square41/square41.mps", + 5, + CUOPT_METHOD_DUAL_SIMPLEX), // LP, Dual Simplex + std::make_tuple("/linear_programming/square41/square41.mps", 5, CUOPT_METHOD_PDLP), // LP, PDLP + std::make_tuple("/mip/supportcase22.mps", 15, CUOPT_METHOD_DUAL_SIMPLEX) // MIP + )); + +TEST(c_api, iteration_limit) +{ + const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); + std::string filename = rapidsDatasetRootDir + "/linear_programming/" + "afiro_original.mps"; + int termination_status; + EXPECT_EQ(solve_mps_file(filename.c_str(), 60, 1, &termination_status), CUOPT_SUCCESS); + EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_ITERATION_LIMIT); +} + +TEST(c_api, solve_time_bb_preemption) +{ + const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); + std::string filename = rapidsDatasetRootDir + "/mip/" + "bb_optimality.mps"; + int termination_status; + double solve_time = std::numeric_limits::quiet_NaN(); + EXPECT_EQ(solve_mps_file(filename.c_str(), 5, CUOPT_INFINITY, &termination_status, &solve_time), + CUOPT_SUCCESS); + EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); + EXPECT_GT(solve_time, 0); // solve time should not be equal to 0, even on very simple instances + // solved by B&B before the diversity solver has time to run +} + +TEST(c_api, bad_parameter_name) { EXPECT_EQ(test_bad_parameter_name(), CUOPT_INVALID_ARGUMENT); } + +TEST(c_api, burglar) { EXPECT_EQ(burglar_problem(), CUOPT_SUCCESS); } + +TEST(c_api, test_missing_file) { EXPECT_EQ(test_missing_file(), CUOPT_MPS_FILE_ERROR); } + +TEST(c_api, test_infeasible_problem) { EXPECT_EQ(test_infeasible_problem(), CUOPT_SUCCESS); } + +TEST(c_api, test_ranged_problem) +{ + cuopt_int_t termination_status; + cuopt_float_t objective; + EXPECT_EQ(test_ranged_problem(&termination_status, &objective), CUOPT_SUCCESS); + EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); + EXPECT_NEAR(objective, 32.0, 1e-3); +} + +TEST(c_api, test_invalid_bounds) +{ + // Test LP codepath + EXPECT_EQ(test_invalid_bounds(false), CUOPT_SUCCESS); + // Test MIP codepath + EXPECT_EQ(test_invalid_bounds(true), CUOPT_SUCCESS); +} + +TEST(c_api, test_quadratic_problem) +{ + cuopt_int_t termination_status; + cuopt_float_t objective; + EXPECT_EQ(test_quadratic_problem(&termination_status, &objective), CUOPT_SUCCESS); + EXPECT_EQ(termination_status, CUOPT_TERIMINATION_STATUS_OPTIMAL); + EXPECT_NEAR(objective, -32.0, 1e-3); +} + +TEST(c_api, test_quadratic_ranged_problem) +{ + cuopt_int_t termination_status; + cuopt_float_t objective; + EXPECT_EQ(test_quadratic_ranged_problem(&termination_status, &objective), CUOPT_SUCCESS); + EXPECT_EQ(termination_status, (int)CUOPT_TERIMINATION_STATUS_OPTIMAL); + EXPECT_NEAR(objective, -32.0, 1e-3); +} class DeterministicBBTestFixture - : public ::testing::TestWithParam> {}; + : public ::testing::TestWithParam> {}; TEST_P(DeterministicBBTestFixture, deterministic_reproducibility) { const std::string& rapidsDatasetRootDir = cuopt::test::get_rapids_dataset_root_dir(); std::string filename = rapidsDatasetRootDir + std::get<0>(GetParam()); int num_threads = std::get<1>(GetParam()); double time_limit = std::get<2>(GetParam()); + double work_limit = std::get<3>(GetParam()); // Run 3 times and verify identical results - EXPECT_EQ(test_deterministic_bb(filename.c_str(), 3, num_threads, time_limit), CUOPT_SUCCESS); + EXPECT_EQ(test_deterministic_bb(filename.c_str(), 3, num_threads, time_limit, work_limit), + CUOPT_SUCCESS); } INSTANTIATE_TEST_SUITE_P(c_api, DeterministicBBTestFixture, ::testing::Values( // Low thread count - std::make_tuple("/mip/gen-ip054.mps", 4, 60.0), + std::make_tuple("/mip/gen-ip054.mps", 4, 60.0, 2), // High thread count (high contention) - std::make_tuple("/mip/gen-ip054.mps", 128, 60.0), + std::make_tuple("/mip/gen-ip054.mps", 128, 60.0, 2), // Different instance - std::make_tuple("/mip/bb_optimality.mps", 8, 60.0))); + std::make_tuple("/mip/bb_optimality.mps", 8, 60.0, 2))); diff --git a/cpp/tests/linear_programming/c_api_tests/c_api_tests.h b/cpp/tests/linear_programming/c_api_tests/c_api_tests.h index 04a49998c..a52463606 100644 --- a/cpp/tests/linear_programming/c_api_tests/c_api_tests.h +++ b/cpp/tests/linear_programming/c_api_tests/c_api_tests.h @@ -38,7 +38,8 @@ cuopt_int_t test_quadratic_ranged_problem(cuopt_int_t* termination_status_ptr, cuopt_int_t test_deterministic_bb(const char* filename, cuopt_int_t num_runs, cuopt_int_t num_threads, - cuopt_float_t time_limit); + cuopt_float_t time_limit, + cuopt_float_t work_limit); #ifdef __cplusplus } From 628c22ba9a3f7d463e069b377a84d01c3380accc Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 22 Jan 2026 17:44:09 +0100 Subject: [PATCH 253/366] changed the number of threads in probing cache --- cpp/src/mip/presolve/probing_cache.cu | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/mip/presolve/probing_cache.cu b/cpp/src/mip/presolve/probing_cache.cu index 03b86940c..ddc82e84e 100644 --- a/cpp/src/mip/presolve/probing_cache.cu +++ b/cpp/src/mip/presolve/probing_cache.cu @@ -857,9 +857,9 @@ bool compute_probing_cache(bound_presolve_t& bound_presolve, bound_presolve.settings.time_limit = timer.remaining_time(); size_t num_threads = bound_presolve.settings.num_threads < 0 - ? omp_get_max_threads() + ? 0.2 * omp_get_max_threads() : bound_presolve.settings.num_threads; - num_threads = std::max(num_threads, 1); + num_threads = std::clamp(num_threads, 1, 8); // Create a vector of multi_probe_t objects std::vector> multi_probe_presolve_pool; From 0d2226cabfcb122664efe99676e6b4dd8071b379 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 16:45:34 +0000 Subject: [PATCH 254/366] add parameter for MIP seed --- cpp/include/cuopt/linear_programming/constants.h | 1 + .../cuopt/linear_programming/mip/solver_settings.hpp | 9 ++++++++- cpp/src/math_optimization/solver_settings.cu | 3 ++- cpp/src/mip/solve.cu | 4 ++++ cpp/tests/linear_programming/c_api_tests/c_api_test.c | 9 +++++++++ 5 files changed, 24 insertions(+), 2 deletions(-) diff --git a/cpp/include/cuopt/linear_programming/constants.h b/cpp/include/cuopt/linear_programming/constants.h index f524f32d5..a08a9ca2b 100644 --- a/cpp/include/cuopt/linear_programming/constants.h +++ b/cpp/include/cuopt/linear_programming/constants.h @@ -59,6 +59,7 @@ #define CUOPT_MIP_HEURISTICS_ONLY "mip_heuristics_only" #define CUOPT_MIP_SCALING "mip_scaling" #define CUOPT_MIP_PRESOLVE "mip_presolve" +#define CUOPT_MIP_SEED "mip_seed" #define CUOPT_SOLUTION_FILE "solution_file" #define CUOPT_NUM_CPU_THREADS "num_cpu_threads" #define CUOPT_NUM_GPUS "num_gpus" diff --git a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp index c99d0364e..8e7285df7 100644 --- a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp +++ b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2023-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -102,6 +102,13 @@ class mip_solver_settings_t { * at potential cost of performance */ int determinism_mode = CUOPT_MODE_OPPORTUNISTIC; + /** + * @brief Random seed for the MIP solver. + * + * Controls the initial seed for random number generation in the solver. + * Use -1 to generate a random seed. + */ + i_t seed = -1; // this is for extracting info from different places of the solver during // benchmarks benchmark_info_t* benchmark_info_ptr = nullptr; diff --git a/cpp/src/math_optimization/solver_settings.cu b/cpp/src/math_optimization/solver_settings.cu index b5c7eed3f..0a75edc5e 100644 --- a/cpp/src/math_optimization/solver_settings.cu +++ b/cpp/src/math_optimization/solver_settings.cu @@ -90,7 +90,8 @@ solver_settings_t::solver_settings_t() : pdlp_settings(), mip_settings {CUOPT_BARRIER_DUAL_INITIAL_POINT, &pdlp_settings.barrier_dual_initial_point, -1, 1, -1}, {CUOPT_NUM_GPUS, &pdlp_settings.num_gpus, 1, 2, 1}, {CUOPT_NUM_GPUS, &mip_settings.num_gpus, 1, 2, 1}, - {CUOPT_MIP_DETERMINISM_MODE, &mip_settings.determinism_mode, CUOPT_MODE_OPPORTUNISTIC, CUOPT_MODE_DETERMINISTIC, CUOPT_MODE_OPPORTUNISTIC} + {CUOPT_MIP_DETERMINISM_MODE, &mip_settings.determinism_mode, CUOPT_MODE_OPPORTUNISTIC, CUOPT_MODE_DETERMINISTIC, CUOPT_MODE_OPPORTUNISTIC}, + {CUOPT_MIP_SEED, &mip_settings.seed, -1, std::numeric_limits::max(), -1} }; // Bool parameters diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index ec128708e..aa5403507 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -21,6 +21,7 @@ #include #include #include +#include #include #include @@ -166,6 +167,9 @@ mip_solution_t solve_mip(optimization_problem_t& op_problem, print_version_info(); + // Initialize seed generator if a specific seed is requested + if (settings.seed >= 0) { cuopt::seed_generator::set_seed(settings.seed); } + raft::common::nvtx::range fun_scope("Running solver"); // This is required as user might forget to set some fields diff --git a/cpp/tests/linear_programming/c_api_tests/c_api_test.c b/cpp/tests/linear_programming/c_api_tests/c_api_test.c index c4da6f59e..76ab9c974 100644 --- a/cpp/tests/linear_programming/c_api_tests/c_api_test.c +++ b/cpp/tests/linear_programming/c_api_tests/c_api_test.c @@ -1253,11 +1253,20 @@ cuopt_int_t test_deterministic_bb(const char* filename, goto DONE; } + int seed = rand(); + printf("Seed: %d\n", seed); + for (run = 0; run < num_runs; run++) { cuOptSolution solution = NULL; cuopt_float_t objective; cuopt_int_t termination_status; + status = cuOptSetIntegerParameter(settings, CUOPT_MIP_SEED, seed); + if (status != CUOPT_SUCCESS) { + printf("Error setting seed: %d\n", status); + goto DONE; + } + status = cuOptSolve(problem, settings, &solution); if (status != CUOPT_SUCCESS) { printf("Error solving problem on run %d: %d\n", run, status); From 6956bbc968e44db7b40abe82d92b1158bb75a4aa Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 22 Jan 2026 17:57:30 +0100 Subject: [PATCH 255/366] fix type --- cpp/src/mip/presolve/probing_cache.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/presolve/probing_cache.cu b/cpp/src/mip/presolve/probing_cache.cu index ddc82e84e..c0c9f2f0a 100644 --- a/cpp/src/mip/presolve/probing_cache.cu +++ b/cpp/src/mip/presolve/probing_cache.cu @@ -859,7 +859,7 @@ bool compute_probing_cache(bound_presolve_t& bound_presolve, size_t num_threads = bound_presolve.settings.num_threads < 0 ? 0.2 * omp_get_max_threads() : bound_presolve.settings.num_threads; - num_threads = std::clamp(num_threads, 1, 8); + num_threads = std::clamp(num_threads, 1, 8); // Create a vector of multi_probe_t objects std::vector> multi_probe_presolve_pool; From 409b1ee9bb659ee270572746681692b49e0881e1 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 17:30:48 +0000 Subject: [PATCH 256/366] restore probing cache --- cpp/include/cuopt/linear_programming/constants.h | 2 +- cpp/src/mip/diversity/diversity_manager.cu | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/cpp/include/cuopt/linear_programming/constants.h b/cpp/include/cuopt/linear_programming/constants.h index a08a9ca2b..7b392313c 100644 --- a/cpp/include/cuopt/linear_programming/constants.h +++ b/cpp/include/cuopt/linear_programming/constants.h @@ -76,11 +76,11 @@ #define CUOPT_TERIMINATION_STATUS_UNBOUNDED 3 #define CUOPT_TERIMINATION_STATUS_ITERATION_LIMIT 4 #define CUOPT_TERIMINATION_STATUS_TIME_LIMIT 5 -#define CUOPT_TERIMINATION_STATUS_WORK_LIMIT 10 #define CUOPT_TERIMINATION_STATUS_NUMERICAL_ERROR 6 #define CUOPT_TERIMINATION_STATUS_PRIMAL_FEASIBLE 7 #define CUOPT_TERIMINATION_STATUS_FEASIBLE_FOUND 8 #define CUOPT_TERIMINATION_STATUS_CONCURRENT_LIMIT 9 +#define CUOPT_TERIMINATION_STATUS_WORK_LIMIT 10 /* @brief The objective sense constants */ #define CUOPT_MINIMIZE 1 diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index ebdd20ee8..ce6167544 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -194,7 +194,6 @@ bool diversity_manager_t::run_presolve(f_t time_limit) if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { time_for_probing_cache = std::numeric_limits::infinity(); } - time_for_probing_cache = 0; timer_t probing_timer{time_for_probing_cache}; // this function computes probing cache, finds singletons, substitutions and changes the problem bool problem_is_infeasible = From addae13a2789e3a122ac193ea313a408be459d5f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 17:31:13 +0000 Subject: [PATCH 257/366] bump1 --- cpp/src/mip/diversity/diversity_manager.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index ce6167544..87f62ae64 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -195,7 +195,7 @@ bool diversity_manager_t::run_presolve(f_t time_limit) time_for_probing_cache = std::numeric_limits::infinity(); } timer_t probing_timer{time_for_probing_cache}; - // this function computes probing cache, finds singletons, substitutions and changes the problem + // This function computes probing cache, finds singletons, substitutions and changes the problem bool problem_is_infeasible = compute_probing_cache(ls.constraint_prop.bounds_update, *problem_ptr, probing_timer); if (problem_is_infeasible) { return false; } From e9a5facae4a2c155684c9bc43c6a017f130f7855 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 17:31:32 +0000 Subject: [PATCH 258/366] bump2 --- cpp/src/mip/diversity/diversity_manager.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 87f62ae64..ce6167544 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -195,7 +195,7 @@ bool diversity_manager_t::run_presolve(f_t time_limit) time_for_probing_cache = std::numeric_limits::infinity(); } timer_t probing_timer{time_for_probing_cache}; - // This function computes probing cache, finds singletons, substitutions and changes the problem + // this function computes probing cache, finds singletons, substitutions and changes the problem bool problem_is_infeasible = compute_probing_cache(ls.constraint_prop.bounds_update, *problem_ptr, probing_timer); if (problem_is_infeasible) { return false; } From 4f751327baee81277fa56c7833cfa7436e27e6d0 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 19:11:39 +0000 Subject: [PATCH 259/366] fix compute_hash using the defautl stream and breaking graph capture --- cpp/src/mip/problem/problem.cu | 4 ++-- cpp/src/mip/utils.cuh | 6 ++---- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index d4e6ea9da..6c0a52c88 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1610,8 +1610,8 @@ problem_t problem_t::get_problem_after_fixing_vars( cuopt_assert(result_end - variable_map.data() == variable_map.size(), "Size issue in set_difference"); CUOPT_LOG_DEBUG("Fixing assignment hash 0x%x, vars to fix 0x%x", - detail::compute_hash(assignment), - detail::compute_hash(variables_to_fix)); + detail::compute_hash(assignment, handle_ptr->get_stream()), + detail::compute_hash(variables_to_fix, handle_ptr->get_stream())); problem.fix_given_variables(*this, assignment, variables_to_fix, handle_ptr); RAFT_CHECK_CUDA(handle_ptr->get_stream()); problem.remove_given_variables(*this, assignment, variable_map, handle_ptr); diff --git a/cpp/src/mip/utils.cuh b/cpp/src/mip/utils.cuh index dc9e3f09d..41ba8fa9e 100644 --- a/cpp/src/mip/utils.cuh +++ b/cpp/src/mip/utils.cuh @@ -31,8 +31,7 @@ constexpr int default_int_lower = std::numeric_limits::min(); constexpr double zero_bound = 0.; template -inline uint32_t compute_hash(raft::device_span values, - rmm::cuda_stream_view stream = rmm::cuda_stream_default) +inline uint32_t compute_hash(raft::device_span values, rmm::cuda_stream_view stream) { auto h_contents = cuopt::host_copy(values, stream); RAFT_CHECK_CUDA(stream); @@ -40,8 +39,7 @@ inline uint32_t compute_hash(raft::device_span values, } template -inline uint32_t compute_hash(const rmm::device_uvector& values, - rmm::cuda_stream_view stream = rmm::cuda_stream_default) +inline uint32_t compute_hash(const rmm::device_uvector& values, rmm::cuda_stream_view stream) { auto h_contents = cuopt::host_copy(values, stream); RAFT_CHECK_CUDA(stream); From 2fd68592f93195c18f1cf6c02f0fe96178c9b1f3 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 22 Jan 2026 19:12:32 +0000 Subject: [PATCH 260/366] bump1 --- cpp/src/mip/problem/problem.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 6c0a52c88..07e3c8e85 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1609,7 +1609,7 @@ problem_t problem_t::get_problem_after_fixing_vars( RAFT_CHECK_CUDA(handle_ptr->get_stream()); cuopt_assert(result_end - variable_map.data() == variable_map.size(), "Size issue in set_difference"); - CUOPT_LOG_DEBUG("Fixing assignment hash 0x%x, vars to fix 0x%x", + CUOPT_LOG_DEBUG("Fixing assignment hash 0x%x, vars to fix: 0x%x", detail::compute_hash(assignment, handle_ptr->get_stream()), detail::compute_hash(variables_to_fix, handle_ptr->get_stream())); problem.fix_given_variables(*this, assignment, variables_to_fix, handle_ptr); From 6bdd587155232bd058a582ae78b90672fc5a89e9 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 23 Jan 2026 10:39:48 +0100 Subject: [PATCH 261/366] changed the logs for the root relaxation --- cpp/src/dual_simplex/branch_and_bound.cpp | 46 +++++++++++++++------- cpp/src/dual_simplex/branch_and_bound.hpp | 15 ++----- cpp/src/dual_simplex/phase2.cpp | 6 --- cpp/src/mip/diversity/diversity_manager.cu | 3 +- cpp/src/mip/problem/problem.cuh | 2 +- cpp/src/mip/solver.cu | 3 +- 6 files changed, 38 insertions(+), 37 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 85c193599..fd6c0644a 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1288,6 +1288,11 @@ template lp_status_t branch_and_bound_t::solve_root_relaxation( simplex_solver_settings_t const& lp_settings) { + f_t start_time = tic(); + f_t user_objective = 0; + i_t iter = 0; + std::string solver_name = ""; + // Root node path lp_status_t root_status; std::future root_status_future; @@ -1308,8 +1313,6 @@ lp_status_t branch_and_bound_t::solve_root_relaxation( } if (root_crossover_solution_set_.load(std::memory_order_acquire)) { - settings_.log.printf("\nRunning crossover\n\n"); - // Crush the root relaxation solution on converted user problem std::vector crushed_root_x; crush_primal_solution( @@ -1342,23 +1345,39 @@ lp_status_t branch_and_bound_t::solve_root_relaxation( // Check if crossover was stopped by dual simplex if (crossover_status == crossover_status_t::OPTIMAL) { - settings_.log.printf("\nCrossover found an optimal solution for the root relaxation\n\n"); - root_solver_type_ = root_solver_type_t::CROSSOVER; - set_root_concurrent_halt(1); // Stop dual simplex root_status = root_status_future.get(); + // Override the root relaxation solution with the crossover solution root_relax_soln_ = root_crossover_soln_; root_vstatus_ = crossover_vstatus_; root_status = lp_status_t::OPTIMAL; + user_objective = root_crossover_soln_.user_objective; + iter = root_crossover_soln_.iterations; + solver_name = "Barrier/PDLP and Crossover"; + } else { - root_status = root_status_future.get(); - root_solver_type_ = root_solver_type_t::DUAL_SIMPLEX; + root_status = root_status_future.get(); + user_objective = root_relax_soln_.user_objective; + iter = root_relax_soln_.iterations; + solver_name = "Dual Simplex"; } } else { - root_status = root_status_future.get(); - root_solver_type_ = root_solver_type_t::DUAL_SIMPLEX; + root_status = root_status_future.get(); + user_objective = root_relax_soln_.user_objective; + iter = root_relax_soln_.iterations; + solver_name = "Dual Simplex"; } + + settings_.log.printf("\n"); + settings_.log.printf("Root relaxation solution found in %d iterations and %.2fs by %s\n", + iter, + toc(start_time), + solver_name.c_str()); + settings_.log.printf("Root relaxation objective %+.8e\n", user_objective); + settings_.log.printf("\n"); + + is_root_solution_set = true; return root_status; } @@ -1543,11 +1562,10 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut original_lp_, log); - settings_.log.printf( - "\nExploring the B&B tree using %d threads (best-first = %d, diving = %d)\n\n", - settings_.num_threads, - settings_.num_bfs_workers, - settings_.num_threads - settings_.num_bfs_workers); + settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n\n", + settings_.num_threads, + settings_.num_bfs_workers, + settings_.num_threads - settings_.num_bfs_workers); exploration_stats_.nodes_explored = 0; exploration_stats_.nodes_unexplored = 2; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 857c95a21..327f99bc4 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -70,9 +70,6 @@ struct bnb_stats_t { template class branch_and_bound_t { public: - // Specify which solver was used for solving the root LP relaxation - enum class root_solver_type_t { NONE = 0, CROSSOVER = 1, DUAL_SIMPLEX = 2 }; - branch_and_bound_t(const user_problem_t& user_problem, const simplex_solver_settings_t& solver_settings); @@ -85,15 +82,9 @@ class branch_and_bound_t { const std::vector& reduced_costs, f_t objective, f_t user_objective, - i_t iterations, - f_t solve_time) + i_t iterations) { - if (root_solver_type_ == root_solver_type_t::NONE) { - settings_.log.printf( - "\nRoot relaxation solution found in %d iterations and %.2fs by PDLP/Barrier\n", - iterations, - solve_time); - settings_.log.printf("Root relaxation objective = %+.8e\n", user_objective); + if (!is_root_solution_set) { root_crossover_soln_.x = primal; root_crossover_soln_.y = dual; root_crossover_soln_.z = reduced_costs; @@ -173,7 +164,7 @@ class branch_and_bound_t { std::atomic root_crossover_solution_set_{false}; bool enable_concurrent_lp_root_solve_{false}; std::atomic root_concurrent_halt_{0}; - std::atomic root_solver_type_{root_solver_type_t::NONE}; + bool is_root_solution_set{false}; // Pseudocosts pseudo_costs_t pc_; diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 2bc00f636..a54101ec8 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2077,12 +2077,6 @@ void prepare_optimality(const lp_problem_t& lp, settings.log.printf("Primal infeasibility (abs): %.2e\n", primal_infeas); settings.log.printf("Dual infeasibility (abs): %.2e\n", dual_infeas); settings.log.printf("Perturbation: %.2e\n", perturbation); - } else { - settings.log.printf("\n"); - settings.log.printf( - "Root relaxation solution found in %d iterations and %.2fs\n", iter, toc(start_time)); - settings.log.printf("Root relaxation objective %+.8e\n", sol.user_objective); - settings.log.printf("\n"); } } } diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index c992d7395..dcca0b1cc 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -438,10 +438,9 @@ solution_t diversity_manager_t::run_solver() auto user_obj = lp_result.get_objective_value(); auto solver_obj = problem_ptr->get_solver_obj_from_user_obj(user_obj); auto iterations = lp_result.get_additional_termination_information().number_of_steps_taken; - auto solve_time = lp_result.get_additional_termination_information().solve_time; // Set for the B&B (param4 expects solver space, param5 expects user space) problem_ptr->set_root_relaxation_solution_callback( - host_primal, host_dual, host_reduced_costs, solver_obj, user_obj, iterations, solve_time); + host_primal, host_dual, host_reduced_costs, solver_obj, user_obj, iterations); } // in case the pdlp returned var boudns that are out of bounds diff --git a/cpp/src/mip/problem/problem.cuh b/cpp/src/mip/problem/problem.cuh index 9b552fc69..910079916 100644 --- a/cpp/src/mip/problem/problem.cuh +++ b/cpp/src/mip/problem/problem.cuh @@ -216,7 +216,7 @@ class problem_t { std::function&)> branch_and_bound_callback; std::function&, const std::vector&, const std::vector&, f_t, f_t, i_t, f_t)> + const std::vector&, const std::vector&, const std::vector&, f_t, f_t, i_t)> set_root_relaxation_solution_callback; typename mip_solver_settings_t::tolerances_t tolerances{}; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index be84379b9..08e1806b9 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -221,8 +221,7 @@ solution_t mip_solver_t::run_solver() std::placeholders::_3, std::placeholders::_4, std::placeholders::_5, - std::placeholders::_6, - std::placeholders::_7); + std::placeholders::_6); // Fork a thread for branch and bound // std::async and std::future allow us to get the return value of bb::solve() From a10ac30190aaa50a4ae49fa494f1de57c22c702b Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 23 Jan 2026 10:33:35 +0000 Subject: [PATCH 262/366] fix concurrent LP solve and probing cache in deterministic mode --- cpp/src/dual_simplex/branch_and_bound.cpp | 8 ++------ cpp/src/mip/diversity/diversity_manager.cu | 9 +++++---- cpp/src/mip/solver.cu | 4 +++- 3 files changed, 10 insertions(+), 11 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d885e0962..ddebbbee7 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -519,12 +519,8 @@ mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t::run_presolve(f_t time_limit) if (termination_criterion_t::NO_UPDATE != term_crit) { ls.constraint_prop.bounds_update.set_updated_bounds(*problem_ptr); } - if (!fj_only_run) { + bool run_probing_cache = !fj_only_run; + // Don't run probing cache in deterministic mode yet as neither B&B nor CPUFJ needs it + // and it doesn't make use of work units yet + if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { run_probing_cache = false; } + if (run_probing_cache) { // Run probing cache before trivial presolve to discover variable implications const f_t time_ratio_of_probing_cache = diversity_config.time_ratio_of_probing_cache; const f_t max_time_on_probing = diversity_config.max_time_on_probing; f_t time_for_probing_cache = std::min(max_time_on_probing, time_limit * time_ratio_of_probing_cache); - if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { - time_for_probing_cache = std::numeric_limits::infinity(); - } timer_t probing_timer{time_for_probing_cache}; // this function computes probing cache, finds singletons, substitutions and changes the problem bool problem_is_infeasible = diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 90b4cac5c..8b2affe86 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -224,15 +224,17 @@ solution_t mip_solver_t::run_solver() branch_and_bound = std::make_unique>( branch_and_bound_problem, branch_and_bound_settings); context.branch_and_bound_ptr = branch_and_bound.get(); - branch_and_bound->set_concurrent_lp_root_solve(true); // Set the primal heuristics -> branch and bound callback if (context.settings.determinism_mode == CUOPT_MODE_OPPORTUNISTIC) { + branch_and_bound->set_concurrent_lp_root_solve(true); + context.problem_ptr->branch_and_bound_callback = std::bind(&dual_simplex::branch_and_bound_t::set_new_solution, branch_and_bound.get(), std::placeholders::_1); } else if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { + branch_and_bound->set_concurrent_lp_root_solve(false); // TODO once deterministic GPU heuristics are integrated context.problem_ptr->branch_and_bound_callback = [bb = branch_and_bound.get()](const std::vector& solution) { From 5000cdac9c9486bdfdbd8f6475254004db81cd6a Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 23 Jan 2026 13:47:09 +0000 Subject: [PATCH 263/366] more stats logging --- cpp/src/dual_simplex/branch_and_bound.cpp | 21 +++++++++- cpp/src/dual_simplex/branch_and_bound.hpp | 5 +++ cpp/src/dual_simplex/phase2.cpp | 49 ++++++++++++++--------- cpp/src/mip/feasibility_jump/fj_cpu.cu | 25 +++++++++++- cpp/src/mip/solver.cu | 1 + 5 files changed, 79 insertions(+), 22 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index ddebbbee7..0f85bdb85 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1763,6 +1763,8 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t(bsp_horizon_step_); + // bsp_scheduler_->verbose = true; scoped_context_registrations_t context_registrations(*bsp_scheduler_); for (auto& worker : *bsp_workers_) { @@ -1944,7 +1947,18 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t 0 || producer_wait_count_ > 0) { + double avg_wait = + (producer_wait_count_ > 0) ? total_producer_wait_time_ / producer_wait_count_ : 0.0; + settings_.log.printf("Producer Sync Statistics:\n"); + settings_.log.printf( + " Producers: %zu, Syncs: %d\n", producer_sync_.num_producers(), producer_wait_count_); + settings_.log.printf(" Total wait: %.3fs, Avg: %.4fs, Max: %.4fs\n", + total_producer_wait_time_, + avg_wait, + max_producer_wait_time_); + } // Finalize debug logger BSP_DEBUG_FINALIZE(bsp_debug_settings_, bsp_debug_logger_); @@ -2088,7 +2102,12 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) // Wait for external producers (CPUFJ) to reach horizon_start before processing // This ensures we don't process B&B events before producers have caught up + double wait_start = tic(); producer_sync_.wait_for_producers(horizon_start); + double wait_time = toc(wait_start); + total_producer_wait_time_ += wait_time; + max_producer_wait_time_ = std::max(max_producer_wait_time_, wait_time); + ++producer_wait_count_; work_unit_context_.global_work_units_elapsed = horizon_end; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index b8dbe561f..7be43e50b 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -373,6 +373,11 @@ class branch_and_bound_t { // B&B waits for registered producers at each horizon sync producer_sync_t producer_sync_; + // Producer wait time statistics + double total_producer_wait_time_{0.0}; + double max_producer_wait_time_{0.0}; + i_t producer_wait_count_{0}; + // BSP node heap - priority queue for unexplored nodes (ordered by lower bound) struct node_ptr_lower_bound_comp { bool operator()(const mip_node_t* a, const mip_node_t* b) const diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index cc6761941..64adc881a 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -27,6 +27,17 @@ #include +#define PHASE2_NVTX_RANGES + +#ifdef PHASE2_NVTX_RANGES +#define PHASE2_NVTX_RANGE(name) raft::common::nvtx::range NVTX_UNIQUE_NAME(nvtx_scope_)(name) +#define NVTX_UNIQUE_NAME(base) NVTX_CONCAT(base, __LINE__) +#define NVTX_CONCAT(a, b) NVTX_CONCAT_INNER(a, b) +#define NVTX_CONCAT_INNER(a, b) a##b +#else +#define PHASE2_NVTX_RANGE(name) ((void)0) +#endif + #include #include #include @@ -441,7 +452,7 @@ void compute_reduced_costs(const std::vector& objective, const std::vector& nonbasic_list, std::vector& z) { - // raft::common::nvtx::range scope("DualSimplex::compute_reduced_costs"); + PHASE2_NVTX_RANGE("DualSimplex::compute_reduced_costs"); const i_t m = A.m; const i_t n = A.n; @@ -476,7 +487,7 @@ void compute_primal_variables(const basis_update_mpf_t& ft, std::vector& x, ins_vector& xB_workspace) { - // raft::common::nvtx::range scope("DualSimplex::compute_primal_variables"); + PHASE2_NVTX_RANGE("DualSimplex::compute_primal_variables"); const i_t m = A.m; const i_t n = A.n; std::vector rhs = lp_rhs; @@ -543,7 +554,7 @@ void compute_dual_residual(const csc_matrix_t& A, const std::vector& z, std::vector& dual_residual) { - // raft::common::nvtx::range scope("DualSimplex::compute_dual_residual"); + PHASE2_NVTX_RANGE("DualSimplex::compute_dual_residual"); dual_residual = z; const i_t n = A.n; @@ -700,7 +711,7 @@ f_t compute_initial_primal_infeasibilities(const lp_problem_t& lp, ins_vector& infeasibility_indices, f_t& primal_inf) { - // raft::common::nvtx::range scope("DualSimplex::compute_initial_primal_infeasibilities"); + PHASE2_NVTX_RANGE("DualSimplex::compute_initial_primal_infeasibilities"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; squared_infeasibilities.resize(n); @@ -1992,7 +2003,7 @@ void set_primal_variables_on_bounds(const lp_problem_t& lp, std::vector& vstatus, std::vector& x) { - // raft::common::nvtx::range scope("DualSimplex::set_primal_variables_on_bounds"); + PHASE2_NVTX_RANGE("DualSimplex::set_primal_variables_on_bounds"); const i_t n = lp.num_cols; for (i_t j = 0; j < n; ++j) { // We set z_j = 0 for basic variables @@ -2255,7 +2266,7 @@ dual::status_t dual_phase2(i_t phase, i_t& iter, std::vector& delta_y_steepest_edge) { - // raft::common::nvtx::range scope("DualSimplex::phase2"); + PHASE2_NVTX_RANGE("DualSimplex::phase2"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; std::vector basic_list(m); @@ -2294,7 +2305,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector& delta_y_steepest_edge, work_limit_context_t* work_unit_context) { - // raft::common::nvtx::range scope("DualSimplex::phase2_advanced"); + PHASE2_NVTX_RANGE("DualSimplex::phase2_advanced"); const i_t m = lp.num_rows; const i_t n = lp.num_cols; assert(m <= n); @@ -2335,7 +2346,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::bound_info(lp, settings); if (initialize_basis) { - // raft::common::nvtx::range init_basis_scope("DualSimplex::init_basis"); + PHASE2_NVTX_RANGE("DualSimplex::init_basis"); std::vector superbasic_list; nonbasic_list.clear(); nonbasic_list.reserve(n - m); @@ -2417,7 +2428,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif if (delta_y_steepest_edge.size() == 0) { - // raft::common::nvtx::range scope("DualSimplex::initialize_steepest_edge_norms"); + PHASE2_NVTX_RANGE("DualSimplex::initialize_steepest_edge_norms"); delta_y_steepest_edge.resize(n); if (slack_basis) { phase2::initialize_steepest_edge_norms_from_slack_basis( @@ -2482,7 +2493,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, manifold.add("A_transpose.i", A_transpose.i); manifold.add("A_transpose.x", A_transpose.x); { - // raft::common::nvtx::range scope("DualSimplex::transpose_A"); + PHASE2_NVTX_RANGE("DualSimplex::transpose_A"); lp.A.transpose(A_transpose); } f_t obj = compute_objective(lp, x); @@ -2567,7 +2578,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, thread_local cuopt::work_unit_predictor_t work_predictor{}; auto predict_work_units = [&](i_t num_iters) -> f_t { - // raft::common::nvtx::range scope("DualSimplex::predict_work_units"); + PHASE2_NVTX_RANGE("DualSimplex::predict_work_units"); std::map features_map; features_map["m"] = static_cast(features.num_rows); features_map["n"] = static_cast(features.num_cols); @@ -2641,7 +2652,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, }); while (iter < iter_limit) { - // raft::common::nvtx::range scope_main("DualSimplex::phase2_main_loop"); + PHASE2_NVTX_RANGE("DualSimplex::phase2_main_loop"); // Pricing i_t direction = 0; i_t basic_leaving_index = -1; @@ -2649,7 +2660,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, f_t max_val; timers.start_timer(); { - // raft::common::nvtx::range scope_pricing("DualSimplex::pricing"); + PHASE2_NVTX_RANGE("DualSimplex::pricing"); if (settings.use_steepest_edge_pricing) { leaving_index = phase2::steepest_edge_pricing_with_infeasibilities(lp, settings, @@ -2694,7 +2705,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_y_sparse.clear(); UTsol_sparse.clear(); { - // raft::common::nvtx::range scope_btran("DualSimplex::btran"); + PHASE2_NVTX_RANGE("DualSimplex::btran"); phase2::compute_delta_y(ft, basic_leaving_index, direction, delta_y_sparse, UTsol_sparse); } timers.btran_time += timers.stop_timer(); @@ -2724,7 +2735,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, delta_y_nz_percentage = delta_y_nz0 / static_cast(m) * 100.0; const bool use_transpose = delta_y_nz_percentage <= 30.0; { - // raft::common::nvtx::range scope_compute_delta_z("DualSimplex::delta_z"); + PHASE2_NVTX_RANGE("DualSimplex::delta_z"); if (use_transpose) { sparse_delta_z++; phase2::compute_delta_z(A_transpose, @@ -2769,7 +2780,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, const bool harris_ratio = settings.use_harris_ratio; const bool bound_flip_ratio = settings.use_bound_flip_ratio; { - // raft::common::nvtx::range scope_ratio("DualSimplex::ratio_test"); + PHASE2_NVTX_RANGE("DualSimplex::ratio_test"); if (harris_ratio) { f_t max_step_length = phase2::first_stage_harris(lp, vstatus, nonbasic_list, z, delta_z); entering_index = phase2::second_stage_harris(lp, @@ -3021,7 +3032,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, scaled_delta_xB_sparse.clear(); rhs_sparse.from_csc_column(lp.A, entering_index); { - // raft::common::nvtx::range scope_ftran("DualSimplex::ftran"); + PHASE2_NVTX_RANGE("DualSimplex::ftran"); if (phase2::compute_delta_x(lp, ft, entering_index, @@ -3167,7 +3178,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, timers.start_timer(); // Refactor or update the basis factorization { - // raft::common::nvtx::range scope_update("DualSimplex::basis_update"); + PHASE2_NVTX_RANGE("DualSimplex::basis_update"); bool should_refactor = ft.num_updates() > settings.refactor_frequency; if (!should_refactor) { i_t recommend_refactor = ft.update(utilde_sparse, UTsol_sparse, basic_leaving_index); @@ -3181,7 +3192,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, phase2::check_basic_infeasibilities(basic_list, basic_mark, infeasibility_indices, 6); #endif if (should_refactor) { - // raft::common::nvtx::range scope_refactor("DualSimplex::refactorization"); + PHASE2_NVTX_RANGE("DualSimplex::refactorization"); num_refactors++; bool should_recompute_x = false; if (ft.refactor_basis( diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 548f6aebc..52972a126 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -13,6 +13,8 @@ #include +#include + #include #include #include @@ -24,6 +26,16 @@ #define CPUFJ_TIMING_TRACE 0 +// Define CPUFJ_NVTX_RANGES to enable detailed NVTX profiling ranges +#ifdef CPUFJ_NVTX_RANGES +#define CPUFJ_NVTX_RANGE(name) raft::common::nvtx::range NVTX_UNIQUE_NAME(nvtx_scope_)(name) +#define NVTX_UNIQUE_NAME(base) NVTX_CONCAT(base, __LINE__) +#define NVTX_CONCAT(a, b) NVTX_CONCAT_INNER(a, b) +#define NVTX_CONCAT_INNER(a, b) a##b +#else +#define CPUFJ_NVTX_RANGE(name) ((void)0) +#endif + namespace cuopt::linear_programming::detail { template @@ -607,6 +619,7 @@ static inline std::pair compute_score(fj_cpu_climber_t static void smooth_weights(fj_cpu_climber_t& fj_cpu) { + CPUFJ_NVTX_RANGE("CPUFJ::smooth_weights"); for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; cstr_idx++) { // consider only satisfied constraints if (fj_cpu.violated_constraints.count(cstr_idx)) continue; @@ -627,6 +640,7 @@ template static void update_weights(fj_cpu_climber_t& fj_cpu) { timing_raii_t timer(fj_cpu.update_weights_times); + CPUFJ_NVTX_RANGE("CPUFJ::update_weights"); raft::random::PCGenerator rng(fj_cpu.settings.seed + fj_cpu.iterations, 0, 0); bool smoothing = rng.next_float() <= fj_cpu.settings.parameters.weight_smoothing_probability; @@ -685,6 +699,7 @@ static void apply_move(fj_cpu_climber_t& fj_cpu, bool localmin = false) { timing_raii_t timer(fj_cpu.apply_move_times); + CPUFJ_NVTX_RANGE("CPUFJ::apply_move"); raft::random::PCGenerator rng(fj_cpu.settings.seed + fj_cpu.iterations, 0, 0); @@ -807,6 +822,7 @@ template static thrust::tuple find_mtm_move( fj_cpu_climber_t& fj_cpu, const std::vector& target_cstrs, bool localmin = false) { + CPUFJ_NVTX_RANGE("CPUFJ::find_mtm_move"); auto& problem = *fj_cpu.pb_ptr; raft::random::PCGenerator rng(fj_cpu.settings.seed + fj_cpu.iterations, 0, 0); @@ -969,6 +985,7 @@ static thrust::tuple find_mtm_move_viol( fj_cpu_climber_t& fj_cpu, i_t sample_size = 100, bool localmin = false) { timing_raii_t timer(fj_cpu.find_mtm_move_viol_times); + CPUFJ_NVTX_RANGE("CPUFJ::find_mtm_move_viol"); std::vector sampled_cstrs; sampled_cstrs.reserve(sample_size); @@ -986,6 +1003,7 @@ static thrust::tuple find_mtm_move_sat( fj_cpu_climber_t& fj_cpu, i_t sample_size = 100) { timing_raii_t timer(fj_cpu.find_mtm_move_sat_times); + CPUFJ_NVTX_RANGE("CPUFJ::find_mtm_move_sat"); std::vector sampled_cstrs; sampled_cstrs.reserve(sample_size); @@ -1001,6 +1019,7 @@ static thrust::tuple find_mtm_move_sat( template static void recompute_lhs(fj_cpu_climber_t& fj_cpu) { + CPUFJ_NVTX_RANGE("CPUFJ::recompute_lhs"); cuopt_assert(fj_cpu.h_lhs.size() == fj_cpu.view.pb.n_constraints, "h_lhs size mismatch"); fj_cpu.violated_constraints.clear(); @@ -1037,6 +1056,7 @@ static thrust::tuple find_lift_move( fj_cpu_climber_t& fj_cpu) { timing_raii_t timer(fj_cpu.find_lift_move_times); + CPUFJ_NVTX_RANGE("CPUFJ::find_lift_move"); fj_move_t best_move = fj_move_t{-1, 0}; fj_staged_score_t best_score = fj_staged_score_t::zero(); @@ -1144,6 +1164,7 @@ static thrust::tuple find_lift_move( template static void perturb(fj_cpu_climber_t& fj_cpu) { + CPUFJ_NVTX_RANGE("CPUFJ::perturb"); // select N variables, assign them a random value between their bounds std::vector sampled_vars; std::sample(fj_cpu.h_objective_vars.begin(), @@ -1514,7 +1535,7 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l #endif // Collect and print PAPI metrics and regression features every 1000 iterations - if (fj_cpu.iterations % 1000 == 0 && fj_cpu.iterations > 0) { + if (fj_cpu.iterations % 100 == 0 && fj_cpu.iterations > 0) { auto now = std::chrono::high_resolution_clock::now(); double time_window_ms = std::chrono::duration_cast>( now - fj_cpu.last_feature_log_time) @@ -1552,7 +1573,7 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l // Notify producer_sync if registered if (fj_cpu.producer_sync != nullptr) { fj_cpu.producer_sync->notify_progress(); } - CUOPT_LOG_TRACE("CPUFJ work units: %f incumbent %g", + CUOPT_LOG_DEBUG("CPUFJ work units: %f incumbent %g", fj_cpu.work_units_elapsed.load(std::memory_order_relaxed), fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective)); } diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 8b2affe86..954b35eef 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -243,6 +243,7 @@ solution_t mip_solver_t::run_solver() } context.work_unit_scheduler_.register_context(branch_and_bound->get_work_unit_context()); + // context.work_unit_scheduler_.verbose = true; context.problem_ptr->set_root_relaxation_solution_callback = std::bind(&dual_simplex::branch_and_bound_t::set_root_relaxation_solution, From fab2ffe0cacee72df258d054b0761112fa4e031f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 23 Jan 2026 13:47:56 +0000 Subject: [PATCH 264/366] horizon 0.15 --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 0f85bdb85..5b6e622b6 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1763,7 +1763,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t Date: Fri, 23 Jan 2026 13:48:23 +0000 Subject: [PATCH 265/366] horizon 0.25 --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 5b6e622b6..9648501d7 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1763,7 +1763,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t Date: Fri, 23 Jan 2026 13:49:03 +0000 Subject: [PATCH 266/366] horizon 0.5 --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 9648501d7..3f93b9648 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1763,7 +1763,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t Date: Fri, 23 Jan 2026 13:49:46 +0000 Subject: [PATCH 267/366] horizon step 1.00 --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 3f93b9648..f5492c354 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1763,7 +1763,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t Date: Fri, 23 Jan 2026 13:50:17 +0000 Subject: [PATCH 268/366] restore --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index f5492c354..e7a481fc9 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1763,8 +1763,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t Date: Fri, 23 Jan 2026 14:50:35 +0100 Subject: [PATCH 269/366] limited the number of candidates for strong branching. refactoring to reduce the number of arguments. --- cpp/src/dual_simplex/bnb_worker.hpp | 17 +- cpp/src/dual_simplex/branch_and_bound.cpp | 25 +- cpp/src/dual_simplex/pseudo_costs.cpp | 232 ++++++++++-------- cpp/src/dual_simplex/pseudo_costs.hpp | 20 +- .../dual_simplex/simplex_solver_settings.hpp | 25 +- cpp/src/dual_simplex/solution.hpp | 6 +- cpp/src/utilities/omp_helpers.hpp | 2 +- cpp/src/utilities/pcg.hpp | 157 ++++++++++++ 8 files changed, 343 insertions(+), 141 deletions(-) create mode 100644 cpp/src/utilities/pcg.hpp diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index bb1b0b1b2..8ddbf4e31 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -12,6 +12,8 @@ #include #include +#include + #include #include #include @@ -38,9 +40,9 @@ template struct bnb_stats_t { f_t start_time = 0.0; omp_atomic_t total_lp_solve_time = 0.0; - omp_atomic_t nodes_explored = 0; - omp_atomic_t nodes_unexplored = 0; - omp_atomic_t total_lp_iters = 0; + omp_atomic_t nodes_explored = 0; + omp_atomic_t nodes_unexplored = 0; + omp_atomic_t total_lp_iters = 0; omp_atomic_t nodes_since_last_log = 0; omp_atomic_t last_log = 0.0; }; @@ -68,6 +70,8 @@ class bnb_worker_data_t { std::vector start_upper; mip_node_t* start_node; + PCG rng; + bool recompute_basis = true; bool recompute_bounds = true; @@ -86,7 +90,8 @@ class bnb_worker_data_t { basic_list(original_lp.num_rows), nonbasic_list(), node_presolver(leaf_problem, Arow, {}, var_type), - bounds_changed(original_lp.num_cols, false) + bounds_changed(original_lp.num_cols, false), + rng(PCG::default_seed ^ worker_id, PCG::default_stream + worker_id) { } @@ -262,9 +267,9 @@ std::array bnb_get_num_workers_round_robin( auto worker_types = bnb_get_worker_types(settings); max_num_workers.fill(0); - max_num_workers[BEST_FIRST] = std::max(1, num_threads / 2); + max_num_workers[BEST_FIRST] = std::max(1, num_threads / 4); - i_t diving_workers = 2 * settings.num_diving_workers; + i_t diving_workers = settings.num_diving_workers; i_t m = worker_types.size() - 1; for (size_t i = 1, k = 0; i < worker_types.size(); ++i) { diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 3f045c767..d63943aab 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -578,20 +578,17 @@ branch_variable_t branch_and_bound_t::variable_selection( case bnb_worker_type_t::BEST_FIRST: if (settings_.reliability_branching_settings.enable) { - branch_var = pc_.reliable_variable_selection(worker_data->leaf_problem, - settings_, - var_types_, - node_ptr->vstatus, - worker_data->leaf_edge_norms, + simplex_solver_settings_t rb_settings = settings_; + rb_settings.reliability_branching_settings.num_tasks = worker_pool_.num_idle_workers(); + + branch_var = pc_.reliable_variable_selection(node_ptr, fractional, solution, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, - node_ptr->lower_bound, + rb_settings, + var_types_, + worker_data, + exploration_stats_, upper_bound_, - exploration_stats_.total_lp_iters, - exploration_stats_.nodes_explored, log); } else { branch_var = pc_.variable_selection(fractional, solution, log); @@ -640,9 +637,9 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* lp_settings.scale_columns = false; if (worker_data->worker_type != bnb_worker_type_t::BEST_FIRST) { - i_t bnb_lp_iters = exploration_stats_.total_lp_iters; + int64_t bnb_lp_iters = exploration_stats_.total_lp_iters; f_t factor = settings_.diving_settings.iteration_limit_factor; - f_t max_iter = factor * bnb_lp_iters; + int64_t max_iter = factor * bnb_lp_iters; lp_settings.iteration_limit = max_iter - stats.total_lp_iters; if (lp_settings.iteration_limit <= 0) { return dual::status_t::ITERATION_LIMIT; } } @@ -1258,7 +1255,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut std::array max_num_workers_per_type; max_num_workers_per_type.fill(0); max_num_workers_per_type[BEST_FIRST] = settings_.num_threads; - worker_pool_.init(2 * settings_.num_threads, original_lp_, Arow_, var_types_, settings_); + worker_pool_.init(settings_.num_threads, original_lp_, Arow_, var_types_, settings_); active_workers_per_type.fill(0); settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n\n", diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 76e13a5e2..36d2f296b 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -147,7 +147,7 @@ f_t trial_branching(const lp_problem_t& original_lp, f_t branch_var_upper, f_t upper_bound, i_t bnb_lp_iter_per_node, - omp_atomic_t& total_lp_iter) + omp_atomic_t& total_lp_iter) { lp_problem_t child_problem = original_lp; child_problem.lower[branch_var] = branch_var_lower; @@ -393,20 +393,14 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio template i_t pseudo_costs_t::reliable_variable_selection( - const lp_problem_t& lp, - const simplex_solver_settings_t& settings, - const std::vector& var_types, - const std::vector& vstatus, - const std::vector& edge_norms, + mip_node_t* node_ptr, const std::vector& fractional, const std::vector& solution, - const basis_update_mpf_t& basis_factors, - const std::vector& basic_list, - const std::vector& nonbasic_list, - f_t current_obj, + const simplex_solver_settings_t& settings, + const std::vector& var_types, + bnb_worker_data_t* worker_data, + const bnb_stats_t& bnb_stats, f_t upper_bound, - i_t bnb_lp_iter, - i_t bnb_explored_nodes, logger_t& log) { i_t branch_var = fractional[0]; @@ -430,16 +424,12 @@ i_t pseudo_costs_t::reliable_variable_selection( // const i_t min_v = 1; // i_t reliable_threshold = std::clamp((1 - gamma) * min_v + gamma * max_v, min_v, max_v); // reliable_threshold = total_lp_iter < max_iter ? reliable_threshold : 0; + const int64_t bnb_total_lp_iter = bnb_stats.total_lp_iters; + const int64_t bnb_nodes_explored = bnb_stats.nodes_explored; + const i_t bnb_lp_iter_per_node = + bnb_stats.total_lp_iters.load() / bnb_stats.nodes_explored.load(); const i_t reliable_threshold = settings.reliability_branching_settings.reliable_threshold; - const int task_priority = settings.reliability_branching_settings.task_priority; - int num_tasks = settings.reliability_branching_settings.num_tasks; - if (num_tasks < 0) { num_tasks = omp_get_num_threads(); } - num_tasks = std::max(num_tasks, 1); - - std::vector pending(fractional.size(), -1); - std::vector next(fractional.size(), -1); - omp_atomic_t num_pending = 0; - omp_atomic_t num_next = 0; + std::vector unreliable_list; omp_mutex_t score_mutex; for (auto j : fractional) { @@ -447,7 +437,7 @@ i_t pseudo_costs_t::reliable_variable_selection( if (pseudo_cost_num_down[j] < reliable_threshold || pseudo_cost_num_up[j] < reliable_threshold) { - pending[num_pending++] = j; + unreliable_list.push_back(j); continue; } @@ -467,97 +457,137 @@ i_t pseudo_costs_t::reliable_variable_selection( } } - if (num_pending != 0) { - settings.log.printf( - "RB LP iterations = %d, B&B LP iterations = %d, reliable_threshold = %d, num strong branches " - "= %d\n", - total_lp_iter.load(), - bnb_lp_iter, - reliable_threshold, - num_pending.load()); + if (unreliable_list.empty()) { + log.printf( + "pc branching on %d. Value %e. Score %e\n", branch_var, solution[branch_var], max_score); + + return branch_var; } - while (num_pending != 0) { -#pragma omp taskloop if (num_pending > 1 && num_tasks > 1) priority(task_priority) \ - num_tasks(num_tasks) untied - for (i_t i = 0; i < num_pending; ++i) { - const i_t j = pending[i]; - bool is_locked = pseudo_cost_mutex[j].try_lock(); + const int task_priority = settings.reliability_branching_settings.task_priority; + const i_t max_num_candidates = settings.reliability_branching_settings.max_num_candidates; + const i_t max_lookahead = settings.reliability_branching_settings.max_lookahead; + const size_t num_sb_vars = std::min(unreliable_list.size(), max_num_candidates); - if (!is_locked) { - next[num_next++] = j; - continue; - } + assert(task_priority > 0); + assert(max_num_candidates > 0); + assert(max_lookahead > 0); + assert(num_sb_vars > 0); - if (pseudo_cost_num_down[j] < reliable_threshold) { - // Do trial branching on the down branch - f_t obj = trial_branching(lp, - settings, - var_types, - vstatus, - edge_norms, - basis_factors, - basic_list, - nonbasic_list, - j, - lp.lower[j], - std::floor(solution[j]), - upper_bound, - bnb_lp_iter / bnb_explored_nodes, - total_lp_iter); - if (!std::isnan(obj)) { - f_t change_in_obj = obj - current_obj; - f_t change_in_x = solution[j] - std::floor(solution[j]); - pseudo_cost_sum_down[j] += change_in_obj / change_in_x; - pseudo_cost_num_down[j]++; - } - } + // Shuffle the unreliable list so every variable has the same chance to be selected. + if (unreliable_list.size() > max_num_candidates) { worker_data->rng.shuffle(unreliable_list); } + + int num_tasks = settings.reliability_branching_settings.num_tasks; + num_tasks = std::clamp(num_tasks, 1, unreliable_list.size()); + assert(num_tasks > 0); + + settings.log.printf("RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d\n", + sb_total_lp_iter.load(), + bnb_total_lp_iter, + unreliable_list.size(), + num_tasks); - if (pseudo_cost_num_up[j] < reliable_threshold) { - f_t obj = trial_branching(lp, - settings, - var_types, - vstatus, - edge_norms, - basis_factors, - basic_list, - nonbasic_list, - j, - std::ceil(solution[j]), - lp.upper[j], - upper_bound, - bnb_lp_iter / bnb_explored_nodes, - total_lp_iter); - - if (!std::isnan(obj)) { - f_t change_in_obj = obj - current_obj; - f_t change_in_x = std::ceil(solution[j]) - solution[j]; - pseudo_cost_sum_up[j] += change_in_obj / change_in_x; - pseudo_cost_num_up[j]++; + omp_atomic_t unchanged = 0; + +#pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) untied + for (int task_id = 0; task_id < num_tasks; ++task_id) { + size_t start = (double)task_id * num_sb_vars / num_tasks; + size_t end = (double)(task_id + 1) * num_sb_vars / num_tasks; + + std::vector conflict; + + while (end > start) { + for (i_t i = start; i < end; ++i) { + if (unchanged > max_lookahead) { break; } + + const i_t j = unreliable_list[i]; + bool is_locked = pseudo_cost_mutex[j].try_lock(); + + if (!is_locked) { + conflict.push_back(j); + continue; } - } - f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] - : pseudo_cost_down_avg; + if (pseudo_cost_num_down[j] < reliable_threshold) { + // Do trial branching on the down branch + f_t obj = trial_branching(worker_data->leaf_problem, + settings, + var_types, + node_ptr->vstatus, + worker_data->leaf_edge_norms, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, + j, + worker_data->leaf_problem.lower[j], + std::floor(solution[j]), + upper_bound, + bnb_lp_iter_per_node, + sb_total_lp_iter); + if (!std::isnan(obj)) { + f_t change_in_obj = obj - node_ptr->lower_bound; + f_t change_in_x = solution[j] - std::floor(solution[j]); + pseudo_cost_sum_down[j] += change_in_obj / change_in_x; + pseudo_cost_num_down[j]++; + } + } - pseudo_cost_mutex[j].unlock(); + if (pseudo_cost_num_up[j] < reliable_threshold) { + f_t obj = trial_branching(worker_data->leaf_problem, + settings, + var_types, + node_ptr->vstatus, + worker_data->leaf_edge_norms, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, + j, + std::ceil(solution[j]), + worker_data->leaf_problem.upper[j], + upper_bound, + bnb_lp_iter_per_node, + sb_total_lp_iter); + + if (!std::isnan(obj)) { + f_t change_in_obj = obj - node_ptr->lower_bound; + f_t change_in_x = std::ceil(solution[j]) - solution[j]; + pseudo_cost_sum_up[j] += change_in_obj / change_in_x; + pseudo_cost_num_up[j]++; + } + } - constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); + f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + f_t pc_down = pseudo_cost_sum_down[j] > 0 + ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] + : pseudo_cost_down_avg; + + pseudo_cost_mutex[j].unlock(); + + constexpr f_t eps = 1e-6; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); + + score_mutex.lock(); + if (score > max_score) { + max_score = score; + branch_var = j; + unchanged = 0; + } else { + unchanged++; + } + score_mutex.unlock(); + } - if (std::lock_guard lock(score_mutex); score > max_score) { - max_score = score; - branch_var = j; + if (unchanged > max_lookahead) { break; } + if (!conflict.empty()) { + std::copy(conflict.begin(), conflict.end(), unreliable_list.begin() + start); } - } - std::swap(pending, next); - num_pending = num_next; - num_next = 0; + end = start + conflict.size(); + conflict.clear(); + } } log.printf( diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 7e596628e..24f0ed0c4 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -8,11 +8,13 @@ #pragma once #include +#include #include #include #include #include #include +#include #include @@ -55,20 +57,14 @@ class pseudo_costs_t { const std::vector& solution, logger_t& log); - i_t reliable_variable_selection(const lp_problem_t& lp, - const simplex_solver_settings_t& settings, - const std::vector& var_types, - const std::vector& vstatus, - const std::vector& edge_norms, + i_t reliable_variable_selection(mip_node_t* node_ptr, const std::vector& fractional, const std::vector& solution, - const basis_update_mpf_t& basis_factors, - const std::vector& basic_list, - const std::vector& nonbasic_list, - f_t current_obj, + const simplex_solver_settings_t& settings, + const std::vector& var_types, + bnb_worker_data_t* worker_data, + const bnb_stats_t& bnb_stats, f_t upper_bound, - i_t bnb_lp_iter, - i_t bnb_explored_nodes, logger_t& log); void update_pseudo_costs_from_strong_branching(const std::vector& fractional, @@ -81,7 +77,7 @@ class pseudo_costs_t { std::vector strong_branch_up; std::vector pseudo_cost_mutex; omp_atomic_t num_strong_branches_completed = 0; - omp_atomic_t total_lp_iter = 0; + omp_atomic_t sb_total_lp_iter = 0; }; template diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 7dd8ffa03..6d7ffe9dd 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -22,6 +22,7 @@ namespace cuopt::linear_programming::dual_simplex { template struct diving_heuristics_settings_t { + // The number of workers dedicated to the diving heuristics. i_t num_diving_workers = -1; // -1 automatic, 0 disabled, 1 enabled @@ -30,10 +31,19 @@ struct diving_heuristics_settings_t { i_t guided_diving = -1; i_t coefficient_diving = -1; - i_t min_node_depth = 10; - i_t node_limit = 500; + // The minimum depth to start diving from. + i_t min_node_depth = 10; + + // The maximum number of nodes when performing a dive. + i_t node_limit = 500; + + // The maximum number of dual simplex iteration allowed + // in a single dive. This set in terms of the total number of + // iterations in the best-first threads. f_t iteration_limit_factor = 0.05; - i_t backtrack_limit = 5; + + // The maximum backtracking allowed. + i_t backtrack_limit = 5; }; template @@ -57,8 +67,15 @@ struct reliability_branching_settings_t { // Set to 1 to have the same priority as the other tasks. i_t task_priority = 5; - // Set the max number of tasks spawned for performing strong branching + // The number of tasks spawned for performing strong branching. i_t num_tasks = -1; + + // The maximum number of candidates initialized by strong branching in a single + // node + i_t max_num_candidates = 100; + + // The maximum number of candidates evaluated that does not improve the best score. + i_t max_lookahead = 10; }; template diff --git a/cpp/src/dual_simplex/solution.hpp b/cpp/src/dual_simplex/solution.hpp index d1d745cbd..4e8bcaf75 100644 --- a/cpp/src/dual_simplex/solution.hpp +++ b/cpp/src/dual_simplex/solution.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -72,8 +72,8 @@ class mip_solution_t { std::vector x; f_t objective; f_t lower_bound; - i_t nodes_explored; - i_t simplex_iterations; + int64_t nodes_explored; + int64_t simplex_iterations; bool has_incumbent; }; diff --git a/cpp/src/utilities/omp_helpers.hpp b/cpp/src/utilities/omp_helpers.hpp index e1b68bf88..b79026762 100644 --- a/cpp/src/utilities/omp_helpers.hpp +++ b/cpp/src/utilities/omp_helpers.hpp @@ -42,7 +42,7 @@ class omp_atomic_t { return new_val; } - operator T() { return load(); } + operator T() const { return load(); } T operator+=(T inc) { return fetch_add(inc) + inc; } T operator-=(T inc) { return fetch_sub(inc) - inc; } diff --git a/cpp/src/utilities/pcg.hpp b/cpp/src/utilities/pcg.hpp new file mode 100644 index 000000000..dcae5a9ec --- /dev/null +++ b/cpp/src/utilities/pcg.hpp @@ -0,0 +1,157 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#include +#include + +// Copied from raft/PCGenerator (rng_device.cuh). We cannot use raft implementation +// on the CPU (.cpp file) since the raft header includes CUDA code. +// The original code is from https://www.pcg-random.org/. +namespace cuopt { +class PCG { + public: + static constexpr uint64_t default_seed = 0x853c49e6748fea9bULL; + static constexpr uint64_t default_stream = 0xda3e39cb94b95bdbULL; + + /** + * @brief ctor. Initializes the PCG + * @param rng_state is the generator state used for initializing the generator + * @param subsequence specifies the subsequence to be generated out of 2^64 possible subsequences + * In a parallel setting, like threads of a CUDA kernel, each thread is required to generate a + * unique set of random numbers. This can be achieved by initializing the generator with same + * rng_state for all the threads and diststreamt values for subsequence. + */ + PCG(const uint64_t seed = default_seed, + const uint64_t subsequence = default_stream, + uint64_t offset = 0) + { + set_seed(seed, subsequence, offset); + } + + // Set the seed, subsequence and offset of the PCG + void set_seed(uint64_t seed, const uint64_t subsequence = default_stream, uint64_t offset = 0) + { + state = uint64_t(0); + stream = (subsequence << 1u) | 1u; + uint32_t discard; + next(discard); + state += seed; + next(discard); + skipahead(offset); + } + + // Based on "Random Number Generation with Arbitrary Strides" F. B. Brown + // Link https://mcnp.lanl.gov/pdf_files/anl-rn-arb-stride.pdf + void skipahead(uint64_t offset) + { + uint64_t G = 1; + uint64_t h = 6364136223846793005ULL; + uint64_t C = 0; + uint64_t f = stream; + while (offset) { + if (offset & 1) { + G = G * h; + C = C * h + f; + } + f = f * (h + 1); + h = h * h; + offset >>= 1; + } + state = state * G + C; + } + + /** + * @defgroup NextRand Generate the next random number + * @brief This code is derived from PCG basic code + * @{ + */ + uint32_t next_u32() + { + uint32_t ret; + uint64_t oldstate = state; + state = oldstate * 6364136223846793005ULL + stream; + uint32_t xorshifted = ((oldstate >> 18u) ^ oldstate) >> 27u; + uint32_t rot = oldstate >> 59u; + ret = (xorshifted >> rot) | (xorshifted << ((-rot) & 31)); + return ret; + } + + uint64_t next_u64() + { + uint64_t ret; + uint32_t a, b; + a = next_u32(); + b = next_u32(); + ret = uint64_t(a) | (uint64_t(b) << 32); + return ret; + } + + int32_t next_i32() + { + int32_t ret; + uint32_t val; + val = next_u32(); + ret = int32_t(val & 0x7fffffff); + return ret; + } + + int64_t next_i64() + { + int64_t ret; + uint64_t val; + val = next_u64(); + ret = int64_t(val & 0x7fffffffffffffff); + return ret; + } + + float next_float() { return static_cast((next_u32() >> 8) * 0x1.0p-24); } + + double next_double() { return static_cast((next_u64() >> 11) * 0x1.0p-53); } + + template + T next() + { + T val; + next(val); + return val; + } + + void next(uint32_t& ret) { ret = next_u32(); } + void next(uint64_t& ret) { ret = next_u64(); } + void next(int32_t& ret) { ret = next_i32(); } + void next(int64_t& ret) { ret = next_i64(); } + void next(float& ret) { ret = next_float(); } + void next(double& ret) { ret = next_double(); } + + /// Draws a sample from a uniform distribution. The samples are uniformly distributed over + /// the semi-closed interval `[low, high)`. This routine may have a **slight bias** toward + /// some numbers in the range (scaling by floating-point). + template + T uniform(T low, T high) + { + double val = next_double(); + T range = high - low; + return low + (val * range); + } + + // Shuffles the contents of a sequence using the Fisher–Yates algorithm. + template + void shuffle(std::vector& seq) + { + for (size_t i = 0; i < seq.size() - 1; ++i) { + size_t j = uniform(i, seq.size()); + if (j != i) std::swap(seq[i], seq[j]); + } + } + + private: + uint64_t state; + uint64_t stream; +}; +} // namespace cuopt From 444b95b1e5b6f23caaa2230dc2f29529bf717c74 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 23 Jan 2026 15:06:50 +0100 Subject: [PATCH 270/366] removed try_lock --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- cpp/src/dual_simplex/pseudo_costs.cpp | 152 ++++++++++------------ 2 files changed, 68 insertions(+), 86 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d63943aab..1490c43d8 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -579,7 +579,7 @@ branch_variable_t branch_and_bound_t::variable_selection( if (settings_.reliability_branching_settings.enable) { simplex_solver_settings_t rb_settings = settings_; - rb_settings.reliability_branching_settings.num_tasks = worker_pool_.num_idle_workers(); + rb_settings.reliability_branching_settings.num_tasks = 2 * worker_pool_.num_idle_workers(); branch_var = pc_.reliable_variable_selection(node_ptr, fractional, diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 36d2f296b..d443e0a89 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -494,99 +494,81 @@ i_t pseudo_costs_t::reliable_variable_selection( size_t start = (double)task_id * num_sb_vars / num_tasks; size_t end = (double)(task_id + 1) * num_sb_vars / num_tasks; - std::vector conflict; - - while (end > start) { - for (i_t i = start; i < end; ++i) { - if (unchanged > max_lookahead) { break; } - - const i_t j = unreliable_list[i]; - bool is_locked = pseudo_cost_mutex[j].try_lock(); + for (i_t i = start; i < end; ++i) { + if (unchanged > max_lookahead) { break; } - if (!is_locked) { - conflict.push_back(j); - continue; + const i_t j = unreliable_list[i]; + pseudo_cost_mutex[j].lock(); + + if (pseudo_cost_num_down[j] < reliable_threshold) { + // Do trial branching on the down branch + f_t obj = trial_branching(worker_data->leaf_problem, + settings, + var_types, + node_ptr->vstatus, + worker_data->leaf_edge_norms, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, + j, + worker_data->leaf_problem.lower[j], + std::floor(solution[j]), + upper_bound, + bnb_lp_iter_per_node, + sb_total_lp_iter); + if (!std::isnan(obj)) { + f_t change_in_obj = obj - node_ptr->lower_bound; + f_t change_in_x = solution[j] - std::floor(solution[j]); + pseudo_cost_sum_down[j] += change_in_obj / change_in_x; + pseudo_cost_num_down[j]++; } + } - if (pseudo_cost_num_down[j] < reliable_threshold) { - // Do trial branching on the down branch - f_t obj = trial_branching(worker_data->leaf_problem, - settings, - var_types, - node_ptr->vstatus, - worker_data->leaf_edge_norms, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, - j, - worker_data->leaf_problem.lower[j], - std::floor(solution[j]), - upper_bound, - bnb_lp_iter_per_node, - sb_total_lp_iter); - if (!std::isnan(obj)) { - f_t change_in_obj = obj - node_ptr->lower_bound; - f_t change_in_x = solution[j] - std::floor(solution[j]); - pseudo_cost_sum_down[j] += change_in_obj / change_in_x; - pseudo_cost_num_down[j]++; - } + if (pseudo_cost_num_up[j] < reliable_threshold) { + f_t obj = trial_branching(worker_data->leaf_problem, + settings, + var_types, + node_ptr->vstatus, + worker_data->leaf_edge_norms, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, + j, + std::ceil(solution[j]), + worker_data->leaf_problem.upper[j], + upper_bound, + bnb_lp_iter_per_node, + sb_total_lp_iter); + + if (!std::isnan(obj)) { + f_t change_in_obj = obj - node_ptr->lower_bound; + f_t change_in_x = std::ceil(solution[j]) - solution[j]; + pseudo_cost_sum_up[j] += change_in_obj / change_in_x; + pseudo_cost_num_up[j]++; } + } - if (pseudo_cost_num_up[j] < reliable_threshold) { - f_t obj = trial_branching(worker_data->leaf_problem, - settings, - var_types, - node_ptr->vstatus, - worker_data->leaf_edge_norms, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, - j, - std::ceil(solution[j]), - worker_data->leaf_problem.upper[j], - upper_bound, - bnb_lp_iter_per_node, - sb_total_lp_iter); - - if (!std::isnan(obj)) { - f_t change_in_obj = obj - node_ptr->lower_bound; - f_t change_in_x = std::ceil(solution[j]) - solution[j]; - pseudo_cost_sum_up[j] += change_in_obj / change_in_x; - pseudo_cost_num_up[j]++; - } - } + f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] + : pseudo_cost_down_avg; - f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - f_t pc_down = pseudo_cost_sum_down[j] > 0 - ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - - pseudo_cost_mutex[j].unlock(); - - constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); - - score_mutex.lock(); - if (score > max_score) { - max_score = score; - branch_var = j; - unchanged = 0; - } else { - unchanged++; - } - score_mutex.unlock(); - } + pseudo_cost_mutex[j].unlock(); - if (unchanged > max_lookahead) { break; } - if (!conflict.empty()) { - std::copy(conflict.begin(), conflict.end(), unreliable_list.begin() + start); - } + constexpr f_t eps = 1e-6; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); - end = start + conflict.size(); - conflict.clear(); + score_mutex.lock(); + if (score > max_score) { + max_score = score; + branch_var = j; + unchanged = 0; + } else { + unchanged++; + } + score_mutex.unlock(); } } From 61c5f77d9a3eb09bb3121fcdce2d73a8b4dce938 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 23 Jan 2026 14:07:46 +0000 Subject: [PATCH 271/366] fix incorrect optimal report --- cpp/src/dual_simplex/branch_and_bound.cpp | 3 ++- cpp/src/mip/feasibility_jump/fj_cpu.cu | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index e7a481fc9..3ef438f73 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1745,7 +1745,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut lower_bound = node_queue_.best_first_queue_size() > 0 ? node_queue_.get_lower_bound() : search_tree_.root.lower_bound; // If queue is empty and we have an incumbent, the tree is fully explored - if (node_queue_.best_first_queue_size() == 0 && incumbent_.has_incumbent) { + if (node_queue_.best_first_queue_size() == 0 && exploration_stats_.nodes_unexplored == 0 && + incumbent_.has_incumbent) { lower_bound = upper_bound_.load(); } } diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 52972a126..d7f627b19 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -1573,7 +1573,7 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l // Notify producer_sync if registered if (fj_cpu.producer_sync != nullptr) { fj_cpu.producer_sync->notify_progress(); } - CUOPT_LOG_DEBUG("CPUFJ work units: %f incumbent %g", + CUOPT_LOG_TRACE("CPUFJ work units: %f incumbent %g", fj_cpu.work_units_elapsed.load(std::memory_order_relaxed), fj_cpu.pb_ptr->get_user_obj_from_solver_obj(fj_cpu.h_best_objective)); } From 90dcdc94b1986c0085ccc4be4cdb77af775ddef2 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 23 Jan 2026 15:47:08 +0100 Subject: [PATCH 272/366] removed unused settings --- cpp/src/dual_simplex/bnb_worker.hpp | 14 ++++++------ cpp/src/dual_simplex/branch_and_bound.cpp | 22 ++++++++----------- cpp/src/dual_simplex/pseudo_costs.cpp | 7 +++--- .../dual_simplex/simplex_solver_settings.hpp | 6 ----- cpp/src/mip/diversity/lns/rins.cu | 6 ++--- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 6 ++--- cpp/src/mip/solver.cu | 6 ----- 7 files changed, 23 insertions(+), 44 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index 8ddbf4e31..83e3a0640 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -259,17 +259,17 @@ std::vector bnb_get_worker_types(diving_heuristics_settings_t return types; } -template -std::array bnb_get_num_workers_round_robin( - i_t num_threads, diving_heuristics_settings_t settings) +template +std::array bnb_get_max_workers( + i_t num_workers, std::vector worker_types) { std::array max_num_workers; - auto worker_types = bnb_get_worker_types(settings); - max_num_workers.fill(0); - max_num_workers[BEST_FIRST] = std::max(1, num_threads / 4); - i_t diving_workers = settings.num_diving_workers; + i_t bfs_workers = std::max(worker_types.size() == 1 ? num_workers : num_workers / 4, 1); + max_num_workers[BEST_FIRST] = bfs_workers; + + i_t diving_workers = (num_workers - bfs_workers); i_t m = worker_types.size() - 1; for (size_t i = 1, k = 0; i < worker_types.size(); ++i) { diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 1490c43d8..2f7375d8d 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1249,19 +1249,17 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut node_queue_.push(search_tree_.root.get_up_child()); diving_heuristics_settings_t diving_settings = settings_.diving_settings; + const i_t num_workers = 2 * settings_.num_threads; bool is_ramp_up_finished = false; std::vector worker_types = {BEST_FIRST}; std::array max_num_workers_per_type; max_num_workers_per_type.fill(0); - max_num_workers_per_type[BEST_FIRST] = settings_.num_threads; - worker_pool_.init(settings_.num_threads, original_lp_, Arow_, var_types_, settings_); + max_num_workers_per_type[BEST_FIRST] = num_workers; + worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); active_workers_per_type.fill(0); - settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n\n", - settings_.num_threads, - settings_.num_bfs_workers, - settings_.num_threads - settings_.num_bfs_workers); + settings_.log.printf("Exploring the B&B tree using %d threads\n\n", settings_.num_threads); exploration_stats_.nodes_explored = 0; exploration_stats_.nodes_unexplored = 2; @@ -1301,10 +1299,9 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut if (!is_ramp_up_finished) { if (node_queue_.best_first_queue_size() >= min_node_queue_size_) { if (!std::isfinite(upper_bound_)) { diving_settings.guided_diving = false; } - max_num_workers_per_type = - bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); - worker_types = bnb_get_worker_types(diving_settings); - is_ramp_up_finished = true; + worker_types = bnb_get_worker_types(diving_settings); + max_num_workers_per_type = bnb_get_max_workers(num_workers, worker_types); + is_ramp_up_finished = true; #ifdef CUOPT_LOG_DEBUG settings_.log.debug( @@ -1326,9 +1323,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut if (settings_.diving_settings.guided_diving != diving_settings.guided_diving) { if (std::isfinite(upper_bound_)) { diving_settings.guided_diving = settings_.diving_settings.guided_diving; - max_num_workers_per_type = - bnb_get_num_workers_round_robin(settings_.num_threads, diving_settings); - worker_types = bnb_get_worker_types(diving_settings); + worker_types = bnb_get_worker_types(diving_settings); + max_num_workers_per_type = bnb_get_max_workers(num_workers, worker_types); #ifdef CUOPT_LOG_DEBUG for (auto type : worker_types) { diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index d443e0a89..837adf3d6 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -477,8 +477,9 @@ i_t pseudo_costs_t::reliable_variable_selection( // Shuffle the unreliable list so every variable has the same chance to be selected. if (unreliable_list.size() > max_num_candidates) { worker_data->rng.shuffle(unreliable_list); } - int num_tasks = settings.reliability_branching_settings.num_tasks; - num_tasks = std::clamp(num_tasks, 1, unreliable_list.size()); + omp_atomic_t unchanged = 0; + int num_tasks = settings.reliability_branching_settings.num_tasks; + num_tasks = std::clamp(num_tasks, 1, unreliable_list.size()); assert(num_tasks > 0); settings.log.printf("RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d\n", @@ -487,8 +488,6 @@ i_t pseudo_costs_t::reliable_variable_selection( unreliable_list.size(), num_tasks); - omp_atomic_t unchanged = 0; - #pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) untied for (int task_id = 0; task_id < num_tasks; ++task_id) { size_t start = (double)task_id * num_sb_vars / num_tasks; diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 6d7ffe9dd..c04e8379a 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -22,9 +22,6 @@ namespace cuopt::linear_programming::dual_simplex { template struct diving_heuristics_settings_t { - // The number of workers dedicated to the diving heuristics. - i_t num_diving_workers = -1; - // -1 automatic, 0 disabled, 1 enabled i_t line_search_diving = -1; i_t pseudocost_diving = -1; @@ -129,14 +126,12 @@ struct simplex_solver_settings_t { iteration_log_frequency(1000), first_iteration_log(2), num_threads(omp_get_max_threads() - 1), - num_bfs_workers(std::max(num_threads / 4, 1)), random_seed(0), inside_mip(0), solution_callback(nullptr), heuristic_preemption_callback(nullptr), concurrent_halt(nullptr) { - diving_settings.num_diving_workers = std::max(num_threads - num_bfs_workers, 1); } void set_log(bool logging) const { log.log = logging; } @@ -196,7 +191,6 @@ struct simplex_solver_settings_t { i_t first_iteration_log; // number of iterations to log at beginning of solve i_t num_threads; // number of threads to use i_t random_seed; // random seed - i_t num_bfs_workers; // number of threads dedicated to the best-first search diving_heuristics_settings_t diving_settings; // Settings for the diving heuristics reliability_branching_settings_t diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index c3c280a03..49864d195 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -256,14 +256,12 @@ void rins_t::run_rins() branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = std::min(current_mip_gap, (f_t)settings.target_mip_gap); - branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; - branch_and_bound_settings.num_threads = 2; - branch_and_bound_settings.num_bfs_workers = 1; + branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.num_threads = 2; branch_and_bound_settings.reliability_branching_settings.enable = false; // In the future, let RINS use all the diving heuristics. For now, // restricting to guided diving. - branch_and_bound_settings.diving_settings.num_diving_workers = 1; branch_and_bound_settings.diving_settings.line_search_diving = 0; branch_and_bound_settings.diving_settings.coefficient_diving = 0; branch_and_bound_settings.diving_settings.pseudocost_diving = 0; diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index ce232b514..c3c0b0f1d 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -101,14 +101,12 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.print_presolve_stats = false; branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; - branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; - branch_and_bound_settings.num_threads = 2; - branch_and_bound_settings.num_bfs_workers = 1; + branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; + branch_and_bound_settings.num_threads = 2; branch_and_bound_settings.reliability_branching_settings.enable = false; // In the future, let SubMIP use all the diving heuristics. For now, // restricting to guided diving. - branch_and_bound_settings.diving_settings.num_diving_workers = 1; branch_and_bound_settings.diving_settings.line_search_diving = 0; branch_and_bound_settings.diving_settings.coefficient_diving = 0; branch_and_bound_settings.diving_settings.pseudocost_diving = 0; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 9e9093285..383dd013b 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -176,12 +176,6 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.num_threads = std::max(1, context.settings.num_cpu_threads); } - i_t num_threads = branch_and_bound_settings.num_threads; - i_t num_bfs_workers = std::max(1, num_threads / 4); - i_t num_diving_workers = std::max(1, num_threads - num_bfs_workers); - branch_and_bound_settings.num_bfs_workers = num_bfs_workers; - branch_and_bound_settings.diving_settings.num_diving_workers = num_diving_workers; - // Set the branch and bound -> primal heuristics callback branch_and_bound_settings.solution_callback = std::bind(&branch_and_bound_solution_helper_t::solution_callback, From 5260b519c14793c88181de4ca09c5e6446608a6f Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 23 Jan 2026 17:29:16 +0100 Subject: [PATCH 273/366] adjusted number of workers for rb --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- cpp/src/dual_simplex/pseudo_costs.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 2f7375d8d..d82056b65 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -579,7 +579,7 @@ branch_variable_t branch_and_bound_t::variable_selection( if (settings_.reliability_branching_settings.enable) { simplex_solver_settings_t rb_settings = settings_; - rb_settings.reliability_branching_settings.num_tasks = 2 * worker_pool_.num_idle_workers(); + rb_settings.reliability_branching_settings.num_tasks = worker_pool_.num_idle_workers(); branch_var = pc_.reliable_variable_selection(node_ptr, fractional, diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 837adf3d6..9f6ff85f2 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -479,7 +479,7 @@ i_t pseudo_costs_t::reliable_variable_selection( omp_atomic_t unchanged = 0; int num_tasks = settings.reliability_branching_settings.num_tasks; - num_tasks = std::clamp(num_tasks, 1, unreliable_list.size()); + num_tasks = std::clamp(num_tasks, 1, num_sb_vars); assert(num_tasks > 0); settings.log.printf("RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d\n", From 87098e38030c4ecf38bbcdeb7b5c069c4b2700fb Mon Sep 17 00:00:00 2001 From: nicolas Date: Sat, 24 Jan 2026 09:29:31 +0100 Subject: [PATCH 274/366] adjusting number of workers for rb --- cpp/src/dual_simplex/branch_and_bound.cpp | 21 ++++++++++++--------- cpp/src/dual_simplex/pseudo_costs.cpp | 7 ++++--- 2 files changed, 16 insertions(+), 12 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d82056b65..9cb2090d0 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1078,15 +1078,18 @@ lp_status_t branch_and_bound_t::solve_root_relaxation( solver_name = "Dual Simplex"; } - settings_.log.printf("\n"); - settings_.log.printf("Root relaxation solution found in %d iterations and %.2fs by %s\n", - iter, - toc(start_time), - solver_name.c_str()); - settings_.log.printf("Root relaxation objective %+.8e\n", user_objective); - settings_.log.printf("\n"); - - is_root_solution_set = true; + if (root_status == lp_status_t::OPTIMAL) { + settings_.log.printf("\n"); + settings_.log.printf("Root relaxation solution found in %d iterations and %.2fs by %s\n", + iter, + toc(start_time), + solver_name.c_str()); + settings_.log.printf("Root relaxation objective %+.8e\n", user_objective); + settings_.log.printf("\n"); + + is_root_solution_set = true; + } + return root_status; } diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 9f6ff85f2..9f1506e43 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -467,7 +467,7 @@ i_t pseudo_costs_t::reliable_variable_selection( const int task_priority = settings.reliability_branching_settings.task_priority; const i_t max_num_candidates = settings.reliability_branching_settings.max_num_candidates; const i_t max_lookahead = settings.reliability_branching_settings.max_lookahead; - const size_t num_sb_vars = std::min(unreliable_list.size(), max_num_candidates); + const i_t num_sb_vars = std::min(unreliable_list.size(), max_num_candidates); assert(task_priority > 0); assert(max_num_candidates > 0); @@ -478,8 +478,9 @@ i_t pseudo_costs_t::reliable_variable_selection( if (unreliable_list.size() > max_num_candidates) { worker_data->rng.shuffle(unreliable_list); } omp_atomic_t unchanged = 0; - int num_tasks = settings.reliability_branching_settings.num_tasks; - num_tasks = std::clamp(num_tasks, 1, num_sb_vars); + i_t num_tasks = settings.reliability_branching_settings.num_tasks; + num_tasks = std::clamp(num_tasks, 1, num_sb_vars / 4); + num_tasks = std::min(num_tasks, max_lookahead); assert(num_tasks > 0); settings.log.printf("RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d\n", From b66ebaea610b0834c81eec0febe93584cf557174 Mon Sep 17 00:00:00 2001 From: nicolas Date: Sat, 24 Jan 2026 17:35:35 +0100 Subject: [PATCH 275/366] fix incorrect max tasks --- cpp/src/dual_simplex/pseudo_costs.cpp | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 9f1506e43..6b6c2d0bd 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -478,16 +478,16 @@ i_t pseudo_costs_t::reliable_variable_selection( if (unreliable_list.size() > max_num_candidates) { worker_data->rng.shuffle(unreliable_list); } omp_atomic_t unchanged = 0; + const i_t max_tasks = std::clamp(num_sb_vars / 2, 1, max_lookahead); i_t num_tasks = settings.reliability_branching_settings.num_tasks; - num_tasks = std::clamp(num_tasks, 1, num_sb_vars / 4); - num_tasks = std::min(num_tasks, max_lookahead); + num_tasks = std::clamp(num_tasks, 1, max_tasks); assert(num_tasks > 0); - settings.log.printf("RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d\n", - sb_total_lp_iter.load(), - bnb_total_lp_iter, - unreliable_list.size(), - num_tasks); + log.printf("RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d\n", + sb_total_lp_iter.load(), + bnb_total_lp_iter, + unreliable_list.size(), + num_tasks); #pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) untied for (int task_id = 0; task_id < num_tasks; ++task_id) { From 9e8488cec60c7d75f35c327bb376ea1a11cd6617 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sat, 24 Jan 2026 16:36:33 +0000 Subject: [PATCH 276/366] fix pseudocost updates --- cpp/src/dual_simplex/branch_and_bound.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 3ef438f73..122c79208 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2387,6 +2387,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t lp_settings.scale_columns = false; bool feasible = true; + // TODO: fix once the bounds strengthening predictor is more accurate if (false) { raft::common::nvtx::range scope_bs("BB::bound_strengthening"); feasible = worker.node_presolver->bounds_strengthening( @@ -2570,8 +2571,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t i_t leaf_num_fractional = fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); - node_ptr->lower_bound = leaf_objective; + f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); // Queue pseudo-cost update for deterministic application at sync if (node_ptr->branch_var >= 0) { @@ -2585,6 +2585,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } } + node_ptr->lower_bound = leaf_objective; + if (leaf_num_fractional == 0) { // Integer feasible - queue for deterministic processing at sync if (leaf_objective < worker.local_upper_bound) { From 30eb52eb0e4a901dcb79857eb4dd43cf58ff2f0c Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 25 Jan 2026 15:30:15 +0000 Subject: [PATCH 277/366] w/ bounds strenght --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 122c79208..ac5751ceb 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2388,7 +2388,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t bool feasible = true; // TODO: fix once the bounds strengthening predictor is more accurate - if (false) { + if (true) { raft::common::nvtx::range scope_bs("BB::bound_strengthening"); feasible = worker.node_presolver->bounds_strengthening( worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); From 98171318798f86ffe1fbd239cbde595fcf7e0e6d Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 25 Jan 2026 16:41:46 +0000 Subject: [PATCH 278/366] fix holes in implementation --- cpp/src/dual_simplex/bb_worker_state.hpp | 20 +++- cpp/src/dual_simplex/branch_and_bound.cpp | 132 +++++++++++++++------- cpp/src/dual_simplex/branch_and_bound.hpp | 5 - 3 files changed, 108 insertions(+), 49 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 702dadc5d..56cd128f8 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -524,6 +524,9 @@ struct bsp_diving_worker_state_t { // Solution sequence counter for deterministic tie-breaking (cumulative across horizons) int next_solution_seq{0}; + // Queued pseudo-cost updates (applied to global pseudo-costs at sync) + std::vector> pseudo_cost_updates; + // ========================================================================== // Statistics // ========================================================================== @@ -531,6 +534,7 @@ struct bsp_diving_worker_state_t { i_t total_nodes_explored{0}; i_t total_integer_solutions{0}; i_t total_dives{0}; + i_t lp_iters_this_dive{0}; // LP iterations in current dive (for iteration limit) double total_runtime{0.0}; double total_nowork_time{0.0}; @@ -575,8 +579,8 @@ struct bsp_diving_worker_state_t { work_context.global_work_units_elapsed = start; local_upper_bound = upper_bound; - // Note: Don't clear dive_queue here - workers may still have nodes to process integer_solutions.clear(); + pseudo_cost_updates.clear(); recompute_bounds_and_basis = true; } @@ -622,6 +626,20 @@ struct bsp_diving_worker_state_t { ++total_integer_solutions; } + // Queue a pseudo-cost update for global sync AND apply it to local snapshot immediately. + void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) + { + pseudo_cost_updates.push_back({variable, direction, delta, clock, worker_id}); + + if (direction == rounding_direction_t::DOWN) { + pc_sum_down_snapshot[variable] += delta; + pc_num_down_snapshot[variable]++; + } else { + pc_sum_up_snapshot[variable] += delta; + pc_num_up_snapshot[variable]++; + } + } + // Variable selection using snapshot pseudo-costs (for pseudocost diving) branch_variable_t variable_selection_from_snapshot(const std::vector& fractional, const std::vector& solution) const diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index ac5751ceb..394ba0e6a 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -39,6 +39,9 @@ #include #include +// Compile-time flag to disable bounds strengthening in BSP code path (for debugging/profiling) +#define BSP_DISABLE_BOUNDS_STRENGTHENING + namespace cuopt::linear_programming::dual_simplex { namespace { @@ -2276,45 +2279,6 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) idle_workers.c_str()); } -template -void branch_and_bound_t::run_worker_until_horizon(bb_worker_state_t& worker, - search_tree_t& search_tree, - double current_horizon) -{ - raft::common::nvtx::range scope("BB::worker_run"); - - while (worker.clock < current_horizon && worker.has_work() && - solver_status_ == mip_status_t::UNSET) { - mip_node_t* node = worker.dequeue_node(); - if (node == nullptr) break; - - // Check if node should be pruned - f_t upper_bound = upper_bound_.load(); - if (node->lower_bound >= upper_bound) { - worker.record_fathomed(node, node->lower_bound); - worker.track_node_pruned(); - search_tree.update(node, node_status_t::FATHOMED); - --exploration_stats_.nodes_unexplored; - continue; - } - - // basis warm-start detection - bool is_child = (node->parent == worker.last_solved_node); - worker.recompute_bounds_and_basis = !is_child; - - node_solve_info_t status = solve_node_bsp(worker, node, search_tree, current_horizon); - worker.last_solved_node = node; - - if (status == node_solve_info_t::TIME_LIMIT) { - solver_status_ = mip_status_t::TIME_LIMIT; - break; - } else if (status == node_solve_info_t::WORK_LIMIT) { - solver_status_ = mip_status_t::WORK_LIMIT; - break; - } - } -} - template node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t& worker, mip_node_t* node_ptr, @@ -2387,6 +2351,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t lp_settings.scale_columns = false; bool feasible = true; +#ifndef BSP_DISABLE_BOUNDS_STRENGTHENING // TODO: fix once the bounds strengthening predictor is more accurate if (true) { raft::common::nvtx::range scope_bs("BB::bound_strengthening"); @@ -2419,6 +2384,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.work_context.record_work(prediction); } } +#endif if (!feasible) { node_ptr->lower_bound = std::numeric_limits::infinity(); @@ -2471,6 +2437,20 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t leaf_edge_norms, &worker.work_context); + if (lp_status == dual::status_t::NUMERICAL) { + settings_.log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); + lp_status_t second_status = solve_linear_program_with_advanced_basis(*worker.leaf_problem, + lp_start_time, + lp_settings, + leaf_solution, + *worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + leaf_vstatus, + leaf_edge_norms); + lp_status = convert_lp_status_to_dual_status(second_status); + } + if (bsp_debug_settings_.any_enabled()) { uint64_t path_hash = node_ptr->compute_path_hash(); uint64_t sol_hash = detail::compute_hash(leaf_solution.x); @@ -2573,6 +2553,13 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); + // TODO + if (settings_.node_processed_callback != nullptr) { + // std::vector original_x; + // uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); + // settings_.node_processed_callback(original_x, leaf_objective); + } + // Queue pseudo-cost update for deterministic application at sync if (node_ptr->branch_var >= 0) { const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; @@ -2630,6 +2617,9 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t logger_t log; log.log = false; + node_ptr->objective_estimate = + pc_.obj_estimate(leaf_fractional, leaf_solution.x, node_ptr->lower_bound, log); + search_tree.branch( node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, *worker.leaf_problem, log); search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); @@ -3178,7 +3168,11 @@ void branch_and_bound_t::merge_diving_solutions() i_t nodes_explored = exploration_stats_.nodes_explored.load(); i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); - settings_.log.printf("D %10d %10d %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + // Get diving type from worker for proper symbol + bnb_worker_type_t diving_type = (*bsp_diving_workers_)[sol->worker_id].diving_type; + + settings_.log.printf("%c %10d %10d %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + feasible_solution_symbol(diving_type), nodes_explored, nodes_unexplored, user_obj, @@ -3209,9 +3203,28 @@ void branch_and_bound_t::merge_diving_solutions() } } - // Clear solution queues + // Merge pseudo-cost updates from diving workers + std::vector> all_diving_pc_updates; + for (auto& worker : *bsp_diving_workers_) { + for (auto& upd : worker.pseudo_cost_updates) { + all_diving_pc_updates.push_back(upd); + } + } + + for (const auto& upd : all_diving_pc_updates) { + if (upd.direction == rounding_direction_t::DOWN) { + pc_.pseudo_cost_sum_down[upd.variable] += upd.delta; + pc_.pseudo_cost_num_down[upd.variable]++; + } else { + pc_.pseudo_cost_sum_up[upd.variable] += upd.delta; + pc_.pseudo_cost_num_up[upd.variable]++; + } + } + + // Clear solution and pseudo-cost update queues for (auto& worker : *bsp_diving_workers_) { worker.integer_solutions.clear(); + worker.pseudo_cost_updates.clear(); } } @@ -3269,6 +3282,7 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t::dive_from_bsp(bsp_diving_worker_state_tbounds_strengthening( + worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + + if (!feasible) { + worker.recompute_bounds_and_basis = true; + continue; + } +#endif + + { + i_t bnb_lp_iters = exploration_stats_.total_lp_iters.load(); + f_t factor = settings_.diving_settings.iteration_limit_factor; + i_t max_iter = (i_t)(factor * bnb_lp_iters); + lp_settings.iteration_limit = max_iter - worker.lp_iters_this_dive; + if (lp_settings.iteration_limit <= 0) { break; } + } + // Solve LP relaxation lp_solution_t leaf_solution(worker.leaf_problem->num_rows, worker.leaf_problem->num_cols); @@ -3349,10 +3381,12 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t::dive_from_bsp(bsp_diving_worker_state_t leaf_fractional; fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); + f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); + + if (node_ptr->branch_var >= 0) { + const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; + const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN + ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) + : std::ceil(node_ptr->fractional_val) - node_ptr->fractional_val; + if (frac > 1e-10) { + worker.queue_pseudo_cost_update( + node_ptr->branch_var, node_ptr->branch_dir, change_in_obj / frac); + } + } + node_ptr->lower_bound = leaf_objective; if (leaf_fractional.empty()) { diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 7be43e50b..b6e4ae058 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -310,11 +310,6 @@ class branch_and_bound_t { // Refill worker queues from the global pool void refill_worker_queues(i_t target_queue_size); - // Run a single worker until it reaches the horizon - void run_worker_until_horizon(bb_worker_state_t& worker, - search_tree_t& search_tree, - double current_horizon); - // Process history and synchronize - the "brain" of BSP void process_history_and_sync(const bb_event_batch_t& events); From 383e69a6f9db803ac611d7155a8ae7835dfeefed Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 25 Jan 2026 16:43:08 +0000 Subject: [PATCH 279/366] no BS --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 394ba0e6a..5388249ef 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -40,7 +40,7 @@ #include // Compile-time flag to disable bounds strengthening in BSP code path (for debugging/profiling) -#define BSP_DISABLE_BOUNDS_STRENGTHENING +// #define BSP_DISABLE_BOUNDS_STRENGTHENING namespace cuopt::linear_programming::dual_simplex { From 585bdf0cf90bc7d65cf668d7e638fe45ebe2f644 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 25 Jan 2026 16:43:27 +0000 Subject: [PATCH 280/366] no BS typo --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 5388249ef..9a197f447 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -39,7 +39,7 @@ #include #include -// Compile-time flag to disable bounds strengthening in BSP code path (for debugging/profiling) +// bounds strenghtening work unit predictor is not yet accurate enough // #define BSP_DISABLE_BOUNDS_STRENGTHENING namespace cuopt::linear_programming::dual_simplex { From dcf054298d3e5db7847fef17bb5e67fbce2009e6 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 25 Jan 2026 17:59:34 +0000 Subject: [PATCH 281/366] greater horizon --- cpp/src/dual_simplex/branch_and_bound.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 9a197f447..b7f8d65be 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -40,7 +40,7 @@ #include // bounds strenghtening work unit predictor is not yet accurate enough -// #define BSP_DISABLE_BOUNDS_STRENGTHENING +#define BSP_DISABLE_BOUNDS_STRENGTHENING namespace cuopt::linear_programming::dual_simplex { @@ -1765,7 +1765,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t Date: Sun, 25 Jan 2026 20:00:03 +0100 Subject: [PATCH 282/366] removed ramp-up phase --- cpp/src/dual_simplex/branch_and_bound.cpp | 44 +++++++++-------------- 1 file changed, 16 insertions(+), 28 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 9cb2090d0..2f062f712 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1253,12 +1253,21 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut diving_heuristics_settings_t diving_settings = settings_.diving_settings; const i_t num_workers = 2 * settings_.num_threads; - bool is_ramp_up_finished = false; - std::vector worker_types = {BEST_FIRST}; - std::array max_num_workers_per_type; - max_num_workers_per_type.fill(0); - max_num_workers_per_type[BEST_FIRST] = num_workers; + if (!std::isfinite(upper_bound_)) { diving_settings.guided_diving = false; } + std::vector worker_types = bnb_get_worker_types(diving_settings); + std::array max_num_workers_per_type = + bnb_get_max_workers(num_workers, worker_types); + +#ifdef CUOPT_LOG_DEBUG + for (auto type : worker_types) { + settings_.log.debug("%c%d: max num of workers = %d", + feasible_solution_symbol(type), + type, + max_num_workers_per_type[type]); + } +#endif + worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); active_workers_per_type.fill(0); @@ -1299,28 +1308,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut repair_heuristic_solutions(); - if (!is_ramp_up_finished) { - if (node_queue_.best_first_queue_size() >= min_node_queue_size_) { - if (!std::isfinite(upper_bound_)) { diving_settings.guided_diving = false; } - worker_types = bnb_get_worker_types(diving_settings); - max_num_workers_per_type = bnb_get_max_workers(num_workers, worker_types); - is_ramp_up_finished = true; - -#ifdef CUOPT_LOG_DEBUG - settings_.log.debug( - "Ramp-up phase is finished. num active workers = %d, heap size = %d\n", - active_workers_per_type[BEST_FIRST], - node_queue_.best_first_queue_size()); - - for (auto type : worker_types) { - settings_.log.debug("%c: max num of workers = %d", - feasible_solution_symbol(type), - max_num_workers_per_type[type]); - } -#endif - } - } - // If the guided diving was disabled previously due to the lack of an incumbent solution, // re-enable as soon as a new incumbent is found. if (settings_.diving_settings.guided_diving != diving_settings.guided_diving) { @@ -1331,8 +1318,9 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut #ifdef CUOPT_LOG_DEBUG for (auto type : worker_types) { - settings_.log.debug("%c: max num of workers = %d", + settings_.log.debug("%c%d: max num of workers = %d", feasible_solution_symbol(type), + type, max_num_workers_per_type[type]); } #endif From 175ffc8cd1275d368787f96a63eecf4eb22dc93a Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 25 Jan 2026 19:18:14 +0000 Subject: [PATCH 283/366] BS back --- cpp/src/dual_simplex/branch_and_bound.cpp | 24 +++++++++++++++++-- .../dual_simplex/dual_simplex_features.hpp | 2 ++ cpp/src/dual_simplex/phase2.cpp | 24 +++++++++++++++++-- 3 files changed, 46 insertions(+), 4 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index b7f8d65be..a360022bf 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -40,7 +40,7 @@ #include // bounds strenghtening work unit predictor is not yet accurate enough -#define BSP_DISABLE_BOUNDS_STRENGTHENING +// #define BSP_DISABLE_BOUNDS_STRENGTHENING namespace cuopt::linear_programming::dual_simplex { @@ -2355,8 +2355,10 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t // TODO: fix once the bounds strengthening predictor is more accurate if (true) { raft::common::nvtx::range scope_bs("BB::bound_strengthening"); - feasible = worker.node_presolver->bounds_strengthening( + f_t bs_start_time = tic(); + feasible = worker.node_presolver->bounds_strengthening( worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + f_t bs_actual_time = toc(bs_start_time); if (settings_.deterministic) { static cuopt::work_unit_predictor_t @@ -2381,6 +2383,24 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t // predicts milliseconds f_t prediction = std::max(f_t(0), static_cast(bs_predictor.predict_scalar(features))) / 1000; + +#ifdef CUOPT_DEBUG_WORK_PREDICTION + f_t ratio = (prediction > 0.0) ? (bs_actual_time / prediction) : 0.0; + settings_.log.printf( + "[WORK_PRED_BS] W%d N%d: actual=%.6fs predicted=%.6fwu ratio=%.3f (m=%d n=%d nnz=%d " + "processed=%d changed=%d)\n", + worker.worker_id, + node_ptr->node_id, + bs_actual_time, + prediction, + ratio, + m, + n, + nnz, + worker.node_presolver->last_nnz_processed, + num_bounds_changed); +#endif + worker.work_context.record_work(prediction); } } diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index e715f23b6..08096437c 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -15,6 +15,8 @@ #include +// #define CUOPT_DEBUG_WORK_PREDICTION + namespace cuopt::linear_programming::dual_simplex { /** diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 64adc881a..cb51e631b 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2645,8 +2645,28 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if (work_unit_context) { f_t prediction = predict_work_units(remaining_iters); - // printf("DualSimplex determ (final): %d iters, predicted %.4f\n", remaining_iters, - // prediction); + +#ifdef CUOPT_DEBUG_WORK_PREDICTION + f_t actual_time = features.interval_runtime; + f_t ratio = (prediction > 0.0) ? (actual_time / prediction) : 0.0; + const char* worker_name = work_unit_context->name.c_str(); + printf( + "[WORK_PRED_DS] %s: actual=%.6fs predicted=%.6fwu ratio=%.3f (iters=%d refacts=%d " + "updates=%d sparse_dz=%d dense_dz=%d flips=%d infeas=%d dy_nz=%.2f%%)\n", + worker_name, + actual_time, + prediction, + ratio, + remaining_iters, + features.num_refactors, + features.num_basis_updates, + features.sparse_delta_z_count, + features.dense_delta_z_count, + features.total_bound_flips, + features.num_infeasibilities, + features.delta_y_nz_percentage * 100.0); +#endif + work_unit_context->record_work(prediction); } }); From 0e89356a561944cb652c755d3240f311501693b8 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Sun, 25 Jan 2026 19:18:50 +0000 Subject: [PATCH 284/366] with logging --- cpp/src/dual_simplex/dual_simplex_features.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 08096437c..0783037f2 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -15,7 +15,7 @@ #include -// #define CUOPT_DEBUG_WORK_PREDICTION +#define CUOPT_DEBUG_WORK_PREDICTION namespace cuopt::linear_programming::dual_simplex { From 15dd371467196b080ee5a74a09542f15d4f92313 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 09:45:25 +0000 Subject: [PATCH 285/366] add numericla restart to diving and lower bound ceiling updates in BSP --- cpp/src/dual_simplex/bb_worker_state.hpp | 5 ++++ cpp/src/dual_simplex/branch_and_bound.cpp | 34 +++++++++++++++++++++-- cpp/src/dual_simplex/bsp_debug.hpp | 4 +-- 3 files changed, 37 insertions(+), 6 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 56cd128f8..22163615b 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -149,6 +149,9 @@ struct bb_worker_state_t { // Worker-local upper bound for BSP determinism (prevents cross-worker pruning races) f_t local_upper_bound{std::numeric_limits::infinity()}; + // Worker-local lower bound ceiling for numerical issues (merged to global at sync) + f_t local_lower_bound_ceiling{std::numeric_limits::infinity()}; + // Queued integer solutions found during this horizon (merged at sync) std::vector> integer_solutions; @@ -218,6 +221,8 @@ struct bb_worker_state_t { // Initialize worker-local upper bound from global (for BSP determinism) local_upper_bound = global_upper_bound; + local_lower_bound_ceiling = std::numeric_limits::infinity(); + // Clear queued updates from previous horizon integer_solutions.clear(); pseudo_cost_updates.clear(); diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index a360022bf..787da3fc4 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2710,6 +2710,15 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t return node_solve_info_t::WORK_LIMIT; } else { + // Update local lower bound ceiling for numerical issues (merged to global at sync) + if (node_ptr->lower_bound < worker.local_lower_bound_ceiling) { + worker.local_lower_bound_ceiling = node_ptr->lower_bound; + } + settings_.log.printf( + "LP returned numerical issue on node %d. Local best bound set to %+10.6e.\n", + node_ptr->node_id, + compute_user_objective(original_lp_, worker.local_lower_bound_ceiling)); + worker.record_numerical(node_ptr); worker.recompute_bounds_and_basis = true; search_tree.update(node_ptr, node_status_t::NUMERICAL); @@ -2924,6 +2933,10 @@ void branch_and_bound_t::process_history_and_sync( pc_.pseudo_cost_num_up[upd.variable]++; } } + + for (const auto& worker : *bsp_workers_) { + fetch_min(lower_bound_ceiling_, worker.local_lower_bound_ceiling); + } } template @@ -3299,8 +3312,8 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tbounds_changed); - const i_t max_nodes_per_dive = 100; - const i_t max_backtrack_depth = 5; + const i_t max_nodes_per_dive = settings_.diving_settings.max_nodes_per_dive; + const i_t max_backtrack_depth = settings_.diving_settings.backtrack_limit; i_t nodes_this_dive = 0; worker.lp_iters_this_dive = 0; worker.recompute_bounds_and_basis = true; @@ -3325,7 +3338,9 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tlower_bound >= worker.local_upper_bound) { + f_t rel_gap = user_relative_gap(original_lp_, worker.local_upper_bound, node_ptr->lower_bound); + if (node_ptr->lower_bound >= worker.local_upper_bound || + rel_gap < settings_.relative_mip_gap_tol) { worker.recompute_bounds_and_basis = true; continue; } @@ -3399,6 +3414,19 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t Date: Mon, 26 Jan 2026 10:06:12 +0000 Subject: [PATCH 286/366] cleanup --- cpp/src/dual_simplex/branch_and_bound.cpp | 92 ++++++++++------------- 1 file changed, 41 insertions(+), 51 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 787da3fc4..9ba427834 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2054,7 +2054,8 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& // Check if node should be pruned (use worker's snapshot for determinism) f_t upper_bound = worker.local_upper_bound; - if (node->lower_bound >= upper_bound) { + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node->lower_bound); + if (node->lower_bound >= upper_bound || rel_gap < settings_.relative_mip_gap_tol) { worker.current_node = nullptr; worker.record_fathomed(node, node->lower_bound); worker.track_node_pruned(); @@ -2352,57 +2353,54 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t bool feasible = true; #ifndef BSP_DISABLE_BOUNDS_STRENGTHENING - // TODO: fix once the bounds strengthening predictor is more accurate - if (true) { - raft::common::nvtx::range scope_bs("BB::bound_strengthening"); - f_t bs_start_time = tic(); - feasible = worker.node_presolver->bounds_strengthening( - worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); - f_t bs_actual_time = toc(bs_start_time); + raft::common::nvtx::range scope_bs("BB::bound_strengthening"); + f_t bs_start_time = tic(); + feasible = worker.node_presolver->bounds_strengthening( + worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + f_t bs_actual_time = toc(bs_start_time); - if (settings_.deterministic) { - static cuopt::work_unit_predictor_t - bs_predictor; + if (settings_.deterministic) { + static cuopt::work_unit_predictor_t + bs_predictor; - const i_t m = worker.leaf_problem->num_rows; - const i_t n = worker.leaf_problem->num_cols; - const i_t nnz = worker.leaf_problem->A.col_start[n]; + const i_t m = worker.leaf_problem->num_rows; + const i_t n = worker.leaf_problem->num_cols; + const i_t nnz = worker.leaf_problem->A.col_start[n]; - i_t num_bounds_changed = 0; - for (bool changed : worker.node_presolver->bounds_changed) { - if (changed) ++num_bounds_changed; - } + i_t num_bounds_changed = 0; + for (bool changed : worker.node_presolver->bounds_changed) { + if (changed) ++num_bounds_changed; + } - std::map features; - features["m"] = static_cast(m); - features["n"] = static_cast(n); - features["nnz"] = static_cast(nnz); - features["nnz_processed"] = static_cast(worker.node_presolver->last_nnz_processed); - features["bounds_changed"] = static_cast(num_bounds_changed); + std::map features; + features["m"] = static_cast(m); + features["n"] = static_cast(n); + features["nnz"] = static_cast(nnz); + features["nnz_processed"] = static_cast(worker.node_presolver->last_nnz_processed); + features["bounds_changed"] = static_cast(num_bounds_changed); - // predicts milliseconds - f_t prediction = - std::max(f_t(0), static_cast(bs_predictor.predict_scalar(features))) / 1000; + // predicts milliseconds + f_t prediction = + std::max(f_t(0), static_cast(bs_predictor.predict_scalar(features))) / 1000; #ifdef CUOPT_DEBUG_WORK_PREDICTION - f_t ratio = (prediction > 0.0) ? (bs_actual_time / prediction) : 0.0; - settings_.log.printf( - "[WORK_PRED_BS] W%d N%d: actual=%.6fs predicted=%.6fwu ratio=%.3f (m=%d n=%d nnz=%d " - "processed=%d changed=%d)\n", - worker.worker_id, - node_ptr->node_id, - bs_actual_time, - prediction, - ratio, - m, - n, - nnz, - worker.node_presolver->last_nnz_processed, - num_bounds_changed); + f_t ratio = (prediction > 0.0) ? (bs_actual_time / prediction) : 0.0; + settings_.log.printf( + "[WORK_PRED_BS] W%d N%d: actual=%.6fs predicted=%.6fwu ratio=%.3f (m=%d n=%d nnz=%d " + "processed=%d changed=%d)\n", + worker.worker_id, + node_ptr->node_id, + bs_actual_time, + prediction, + ratio, + m, + n, + nnz, + worker.node_presolver->last_nnz_processed, + num_bounds_changed); #endif - worker.work_context.record_work(prediction); - } + worker.work_context.record_work(prediction); } #endif @@ -3008,20 +3006,12 @@ void branch_and_bound_t::balance_worker_loads() if (!needs_balance) return; - // Collect all redistributable nodes from worker queues std::vector*> all_nodes; for (auto& worker : *bsp_workers_) { - // Extract backlog nodes for (auto* node : worker.backlog) { all_nodes.push_back(node); } worker.backlog.clear(); - - // Extract plunge stack nodes - for (auto* node : worker.plunge_stack) { - all_nodes.push_back(node); - } - worker.plunge_stack.clear(); } if (all_nodes.empty()) return; From 91c95e27d8b2e5b25235c38a7cff85b2050ba82c Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 10:19:32 +0000 Subject: [PATCH 287/366] heap instead of rebuild --- cpp/src/dual_simplex/bb_worker_state.hpp | 47 ++++++------- cpp/src/dual_simplex/branch_and_bound.cpp | 84 ++++------------------- cpp/src/dual_simplex/branch_and_bound.hpp | 3 - 3 files changed, 37 insertions(+), 97 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 22163615b..1dfe9e3f6 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -13,6 +13,7 @@ #include #include #include +#include #include #include @@ -40,6 +41,22 @@ enum class bnb_worker_type_t { COEFFICIENT_DIVING = 4 // Coefficient diving (9.2.1) }; +// Comparator for backlog heap: best-first by lower_bound with deterministic BSP identity tie-break +// Returns true if 'a' has lower priority than 'b' (for max-heap behavior in std::push_heap) +template +struct backlog_node_compare_t { + bool operator()(const mip_node_t* a, const mip_node_t* b) const + { + // Primary: prefer smaller lower_bound (best-first search) + if (a->lower_bound != b->lower_bound) { return a->lower_bound > b->lower_bound; } + // Deterministic tie-breaking by BSP identity tuple + if (a->origin_worker_id != b->origin_worker_id) { + return a->origin_worker_id > b->origin_worker_id; + } + return a->creation_seq > b->creation_seq; + } +}; + // Queued pseudo-cost update for BSP determinism // Updates are collected during horizon, then applied in deterministic order at sync template @@ -86,7 +103,8 @@ struct bb_worker_state_t { // Backlog: nodes "plugged" when branching - candidates for load balancing // When branching with a sibling on the plunge stack, that sibling moves here. // At horizon sync, backlog nodes participate in redistribution. - std::vector*> backlog; + // Implemented as a priority heap ordered by lower_bound (best-first) with BSP identity tie-break. + heap_t*, backlog_node_compare_t> backlog; // Current node being processed (may be paused at horizon boundary) mip_node_t* current_node{nullptr}; @@ -297,7 +315,7 @@ struct bb_worker_state_t { if (!plunge_stack.empty()) { mip_node_t* sibling = plunge_stack.back(); plunge_stack.pop_back(); - backlog.push_back(sibling); + backlog.push(sibling); } // Assign BSP identity to children @@ -328,7 +346,7 @@ struct bb_worker_state_t { // Get next node to process using plunging strategy: // 1. Resume paused node if any // 2. Pop from plunge stack (depth-first continuation) - // 3. Fall back to backlog (best-first from plugged nodes) + // 3. Fall back to backlog heap (best-first from plugged nodes) mip_node_t* dequeue_node() { // 1. Resume paused node if any @@ -345,26 +363,9 @@ struct bb_worker_state_t { return node; } - // 3. Fall back to backlog - select best node (lowest lower_bound) - if (!backlog.empty()) { - auto best_it = - std::min_element(backlog.begin(), - backlog.end(), - [](const mip_node_t* a, const mip_node_t* b) { - // Best-first: prefer lower bound - if (a->lower_bound != b->lower_bound) { - return a->lower_bound < b->lower_bound; - } - // Deterministic tie-breaking by BSP identity - if (a->origin_worker_id != b->origin_worker_id) { - return a->origin_worker_id < b->origin_worker_id; - } - return a->creation_seq < b->creation_seq; - }); - mip_node_t* node = *best_it; - backlog.erase(best_it); - return node; - } + // 3. Fall back to backlog heap - pop best node (lowest lower_bound with BSP identity tie-break) + auto node_opt = backlog.pop(); + if (node_opt.has_value()) { return node_opt.value(); } return nullptr; } diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 9ba427834..1f7ef9707 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1966,67 +1966,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t -void branch_and_bound_t::refill_worker_queues(i_t target_queue_size) -{ - // Distribute nodes from global pool to workers in round-robin fashion - // This ensures deterministic assignment based on node ordering in the heap - - std::vector*> nodes_to_assign; - - // Pop nodes from heap while respecting incumbent bound - mutex_heap_.lock(); - while (!heap_.empty()) { - mip_node_t* node = heap_.top(); - - // Skip pruned nodes - if (node->lower_bound >= upper_bound_.load()) { - heap_.pop(); - search_tree_.update(node, node_status_t::FATHOMED); - --exploration_stats_.nodes_unexplored; - continue; - } - - // Check if we have enough nodes - if (nodes_to_assign.size() >= static_cast(target_queue_size * bsp_workers_->size())) { - break; - } - - heap_.pop(); - nodes_to_assign.push_back(node); - } - mutex_heap_.unlock(); - - // Sort by BSP identity for deterministic distribution - // Uses lexicographic order of (origin_worker_id, creation_seq) - auto deterministic_less = [](const mip_node_t* a, const mip_node_t* b) { - // Lexicographic comparison of BSP identity tuple - if (a->origin_worker_id != b->origin_worker_id) { - return a->origin_worker_id < b->origin_worker_id; - } - return a->creation_seq < b->creation_seq; - }; - std::sort(nodes_to_assign.begin(), nodes_to_assign.end(), deterministic_less); - - for (size_t i = 0; i < nodes_to_assign.size(); ++i) { - int worker_id = i % bsp_workers_->size(); - auto* node = nodes_to_assign[i]; - // Use enqueue_node_with_identity since these nodes already have BSP identity from root setup - (*bsp_workers_)[worker_id].enqueue_node_with_identity(node); - (*bsp_workers_)[worker_id].track_node_assigned(); - - // Debug: Log node assignment - double wut = bsp_current_horizon_ - bsp_horizon_step_; // Start of current horizon - BSP_DEBUG_LOG_NODE_ASSIGNED(bsp_debug_settings_, - bsp_debug_logger_, - wut, - worker_id, - node->node_id, - node->origin_worker_id, - node->lower_bound); - } -} - template void branch_and_bound_t::run_worker_loop(bb_worker_state_t& worker, search_tree_t& search_tree) @@ -2157,7 +2096,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) for (auto* node : worker.plunge_stack) { state_data.push_back(node->get_id_packed()); } - for (auto* node : worker.backlog) { + for (auto* node : worker.backlog.data()) { state_data.push_back(node->get_id_packed()); } } @@ -2957,10 +2896,10 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() worker.plunge_stack = std::move(surviving); } - // Check nodes in backlog - filter in place + // Check nodes in backlog heap - filter and rebuild { std::vector*> surviving; - for (auto* node : worker.backlog) { + for (auto* node : worker.backlog.data()) { if (node->lower_bound >= upper_bound) { search_tree_.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; @@ -2968,7 +2907,10 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() surviving.push_back(node); } } - worker.backlog = std::move(surviving); + worker.backlog.clear(); + for (auto* node : surviving) { + worker.backlog.push(node); + } } } } @@ -3008,7 +2950,7 @@ void branch_and_bound_t::balance_worker_loads() std::vector*> all_nodes; for (auto& worker : *bsp_workers_) { - for (auto* node : worker.backlog) { + for (auto* node : worker.backlog.data()) { all_nodes.push_back(node); } worker.backlog.clear(); @@ -3068,8 +3010,8 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() lower_bound = std::min(node->lower_bound, lower_bound); } - // Check backlog nodes - for (auto* node : worker.backlog) { + // Check backlog heap nodes + for (auto* node : worker.backlog.data()) { lower_bound = std::min(node->lower_bound, lower_bound); } } @@ -3094,11 +3036,11 @@ void branch_and_bound_t::populate_diving_heap_at_sync() const int target_total = num_diving * target_nodes_per_worker; f_t upper_bound = upper_bound_.load(); - // Collect candidate nodes from BFS worker backlogs + // Collect candidate nodes from BFS worker backlog heaps std::vector*, f_t>> candidates; for (auto& worker : *bsp_workers_) { - for (auto* node : worker.backlog) { + for (auto* node : worker.backlog.data()) { if (node->lower_bound < upper_bound) { f_t score = node->objective_estimate; if (!std::isfinite(score)) { score = node->lower_bound; } @@ -3302,7 +3244,7 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tbounds_changed); - const i_t max_nodes_per_dive = settings_.diving_settings.max_nodes_per_dive; + const i_t max_nodes_per_dive = settings_.diving_settings.node_limit; const i_t max_backtrack_depth = settings_.diving_settings.backtrack_limit; i_t nodes_this_dive = 0; worker.lp_iters_this_dive = 0; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index b6e4ae058..69f0fac42 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -307,9 +307,6 @@ class branch_and_bound_t { // Main BSP coordinator loop - runs in deterministic mode void run_bsp_coordinator(const csr_matrix_t& Arow); - // Refill worker queues from the global pool - void refill_worker_queues(i_t target_queue_size); - // Process history and synchronize - the "brain" of BSP void process_history_and_sync(const bb_event_batch_t& events); From 1fdd13e03c9f0329c06596907140cd20b857e7c5 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 26 Jan 2026 11:27:21 +0100 Subject: [PATCH 288/366] fix root relaxation message when the solution is not optimal --- cpp/src/dual_simplex/branch_and_bound.cpp | 18 ++++++++++++------ cpp/src/dual_simplex/solve.hpp | 18 +++++++++++++++++- 2 files changed, 29 insertions(+), 7 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index fd6c0644a..7fbe90284 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1370,14 +1370,20 @@ lp_status_t branch_and_bound_t::solve_root_relaxation( } settings_.log.printf("\n"); - settings_.log.printf("Root relaxation solution found in %d iterations and %.2fs by %s\n", - iter, - toc(start_time), - solver_name.c_str()); - settings_.log.printf("Root relaxation objective %+.8e\n", user_objective); - settings_.log.printf("\n"); + if (root_status == lp_status_t::OPTIMAL) { + settings_.log.printf("Root relaxation solution found in %d iterations and %.2fs by %s\n", + iter, + toc(start_time), + solver_name.c_str()); + settings_.log.printf("Root relaxation objective %+.8e\n", user_objective); + } else { + settings_.log.printf("Root relaxation returned status: %s\n", + lp_status_to_string(root_status).c_str()); + } + settings_.log.printf("\n"); is_root_solution_set = true; + return root_status; } diff --git a/cpp/src/dual_simplex/solve.hpp b/cpp/src/dual_simplex/solve.hpp index e96229784..6292df637 100644 --- a/cpp/src/dual_simplex/solve.hpp +++ b/cpp/src/dual_simplex/solve.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -30,6 +30,22 @@ enum class lp_status_t { UNSET = 8 }; +static std::string lp_status_to_string(lp_status_t status) +{ + switch (status) { + case lp_status_t::OPTIMAL: return "OPTIMAL"; + case lp_status_t::INFEASIBLE: return "INFEASIBLE"; + case lp_status_t::UNBOUNDED: return "UNBOUNDED"; + case lp_status_t::ITERATION_LIMIT: return "ITERATION_LIMIT"; + case lp_status_t::TIME_LIMIT: return "TIME_LIMIT"; + case lp_status_t::NUMERICAL_ISSUES: return "NUMERICAL_ISSUES"; + case lp_status_t::CUTOFF: return "CUTOFF"; + case lp_status_t::CONCURRENT_LIMIT: return "CONCURRENT_LIMIT"; + case lp_status_t::UNSET: return "UNSET"; + } + return "UNKNOWN"; +} + template f_t compute_objective(const lp_problem_t& problem, const std::vector& x); From 5ea16217d781b7d0707b22dba376392fde2fff6a Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 11:03:28 +0000 Subject: [PATCH 289/366] cleanup, fix loss of determinism --- cpp/src/dual_simplex/basis_solves.cpp | 24 +- cpp/src/dual_simplex/basis_updates.cpp | 26 +- cpp/src/dual_simplex/bb_worker_state.hpp | 288 +++--------------- cpp/src/dual_simplex/branch_and_bound.cpp | 96 ++---- cpp/src/dual_simplex/branch_and_bound.hpp | 8 +- .../dual_simplex/dual_simplex_features.hpp | 2 +- 6 files changed, 112 insertions(+), 332 deletions(-) diff --git a/cpp/src/dual_simplex/basis_solves.cpp b/cpp/src/dual_simplex/basis_solves.cpp index 8d488139a..409656064 100644 --- a/cpp/src/dual_simplex/basis_solves.cpp +++ b/cpp/src/dual_simplex/basis_solves.cpp @@ -182,12 +182,12 @@ i_t factorize_basis(const csc_matrix_t& A, f_t fact_start = tic(); csc_matrix_t B(A.m, A.m, 1); form_b(A, basic_list, B); - ins_vector row_perm(m); - ins_vector col_perm(m); + std::vector row_perm(m); + std::vector col_perm(m); i_t row_singletons; i_t col_singletons; find_singletons(B, row_singletons, row_perm, col_singletons, col_perm); - ins_vector row_perm_inv(m); + std::vector row_perm_inv(m); inverse_permutation(row_perm, row_perm_inv); #ifdef PRINT_SINGLETONS @@ -351,12 +351,12 @@ i_t factorize_basis(const csc_matrix_t& A, csc_matrix_t SL(Sdim, Sdim, Snz); csc_matrix_t SU(Sdim, Sdim, Snz); // Factorize S - ins_vector S_perm_inv(Sdim); + std::vector S_perm_inv(Sdim); std::optional> empty = std::nullopt; f_t actual_factor_start = tic(); - ins_vector S_col_perm(Sdim); - ins_vector identity(Sdim); + std::vector S_col_perm(Sdim); + std::vector identity(Sdim); for (i_t h = 0; h < Sdim; ++h) { identity[h] = h; } @@ -380,7 +380,7 @@ i_t factorize_basis(const csc_matrix_t& A, deficient[h - Srank] = col_perm[num_singletons + S_col_perm[h]]; } // Get S_perm - ins_vector S_perm(Sdim); + std::vector S_perm(Sdim); inverse_permutation(S_perm_inv, S_perm); // Get the slacks needed slacks_needed.resize(Sdim - Srank); @@ -392,7 +392,7 @@ i_t factorize_basis(const csc_matrix_t& A, } // Need to permute col_perm[k] according to q - ins_vector col_perm_sav(m - num_singletons); + std::vector col_perm_sav(m - num_singletons); i_t q_j = 0; for (i_t h = num_singletons; h < m; ++h) { col_perm_sav[q_j] = col_perm[h]; @@ -404,7 +404,7 @@ i_t factorize_basis(const csc_matrix_t& A, q_j++; } - ins_vector S_perm(m); + std::vector S_perm(m); inverse_permutation(S_perm_inv, S_perm); actual_factor = toc(actual_factor_start); @@ -479,7 +479,7 @@ i_t factorize_basis(const csc_matrix_t& A, assert(Unz <= Unz_max); U.col_start[m] = Unz; // Finalize U - ins_vector last_perm(Sdim); + std::vector last_perm(Sdim); for (i_t k = 0; k < Sdim; ++k) { last_perm[k] = row_perm[num_singletons + k]; } @@ -634,7 +634,7 @@ i_t basis_repair(const csc_matrix_t& A, assert(nonbasic_list.size() == n - m); // Create slack_map - ins_vector slack_map(m); // slack_map[i] = j if column j is e_i + std::vector slack_map(m); // slack_map[i] = j if column j is e_i i_t slacks_found = 0; for (i_t j = n - 1; j >= n - m; j--) { const i_t col_start = A.col_start[j]; @@ -650,7 +650,7 @@ i_t basis_repair(const csc_matrix_t& A, assert(slacks_found == m); // Create nonbasic_map - ins_vector nonbasic_map( + std::vector nonbasic_map( n, -1); // nonbasic_map[j] = p if nonbasic[p] = j, -1 if j is basic/superbasic const i_t num_nonbasic = nonbasic_list.size(); for (i_t k = 0; k < num_nonbasic; ++k) { diff --git a/cpp/src/dual_simplex/basis_updates.cpp b/cpp/src/dual_simplex/basis_updates.cpp index 5a2b5007e..4a7b216e0 100644 --- a/cpp/src/dual_simplex/basis_updates.cpp +++ b/cpp/src/dual_simplex/basis_updates.cpp @@ -556,7 +556,7 @@ i_t basis_update_t::u_solve(std::vector& x) const // 2. Solve for y such that U*y = bprime // 3. Compute Q*y = x const i_t m = U_.m; - ins_vector bprime(m); + std::vector bprime(m); inverse_permute_vector(col_permutation_, x, bprime); #ifdef CHECK_UPPER_SOLVE @@ -607,7 +607,7 @@ i_t basis_update_t::u_transpose_solve(std::vector& x) const // 2. Solve for y such that U'*y = bprime // 3. Compute Q*y = x const i_t m = U_.m; - ins_vector bprime(m); + std::vector bprime(m); inverse_permute_vector(col_permutation_, x, bprime); dual_simplex::upper_triangular_transpose_solve(U_, bprime); permute_vector(col_permutation_, bprime, x); @@ -864,7 +864,7 @@ i_t basis_update_t::update(std::vector& utilde, i_t leaving_index #endif // ubar = Q'*utilde - ins_vector ubar(m); + std::vector ubar(m); inverse_permute_vector(col_permutation_, utilde, ubar); // Find t @@ -875,7 +875,7 @@ i_t basis_update_t::update(std::vector& utilde, i_t leaving_index const f_t delta = u_diagonal(t); // Solve U'*w = delta*et - ins_vector w(m); + std::vector w(m); w[t] = delta; dual_simplex::upper_triangular_transpose_solve(U_, w); #ifdef PARANOID @@ -901,7 +901,7 @@ i_t basis_update_t::update(std::vector& utilde, i_t leaving_index // Set deltabar = w'*ubar const f_t deltabar = update_L ? dot(w, ubar) : ubar[t]; assert(std::abs(deltabar) > 0); - ins_vector baru(m); + std::vector baru(m); for (i_t k = 0; k < t; ++k) { baru[k] = ubar[k]; } @@ -918,11 +918,11 @@ i_t basis_update_t::update(std::vector& utilde, i_t leaving_index } } - ins_vector d(m); + std::vector d(m); d = w; d[t] = 0.0; // dtilde^T = d^T Q^T -> dtilde = Q*d - ins_vector dtilde(m); + std::vector dtilde(m); permute_vector(col_permutation_, d, dtilde); update_upper(baru_ind, baru_val, t); @@ -1032,7 +1032,7 @@ i_t basis_update_t::lower_triangular_multiply(const csc_matrix_t sval; const i_t in_col_start = in.col_start[in_col]; const i_t in_col_end = in.col_start[in_col + 1]; - ins_vector sbuffer(m); + std::vector sbuffer(m); for (i_t p = in_col_start; p < in_col_end; ++p) { sbuffer[inverse_col_permutation_[in.i[p]]] = in.x[p]; } @@ -1066,7 +1066,7 @@ i_t basis_update_t::lower_triangular_multiply(const csc_matrix_t work2(m); + std::vector work2(m); sind.push_back(r); sval.push_back(-dot); @@ -1085,14 +1085,14 @@ i_t basis_update_t::lower_triangular_multiply(const csc_matrix_t workspace(m); + std::vector workspace(m); const i_t nx = sind.size(); for (i_t k = 0; k < nx; ++k) { const i_t j = sind[k]; const f_t x = sval[k]; workspace[j] = x; } - ins_vector workspace2(m); + std::vector workspace2(m); matrix_vector_multiply(L0_, 1.0, workspace, 0.0, workspace2); workspace = workspace2; @@ -1985,7 +1985,7 @@ void basis_update_mpf_t::l_multiply(std::vector& inout) const add_sparse_column(S_, u_col, theta, inout); } - ins_vector out(m, 0.0); + std::vector out(m, 0.0); matrix_vector_multiply(L0_, 1.0, inout, 0.0, out); inout = out; } @@ -1994,7 +1994,7 @@ template void basis_update_mpf_t::l_transpose_multiply(std::vector& inout) const { const i_t m = L0_.m; - ins_vector out(m, 0.0); + std::vector out(m, 0.0); matrix_vector_multiply(L0_transpose_, 1.0, inout, 0.0, out); inout = out; diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 1dfe9e3f6..5f82fa6e6 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -66,6 +66,14 @@ struct pseudo_cost_update_t { f_t delta; // change_in_obj / frac double wut; // work unit timestamp when update occurred (for deterministic ordering) int worker_id; // for tie-breaking in sort + + bool operator<(const pseudo_cost_update_t& other) const + { + if (wut != other.wut) return wut < other.wut; + if (variable != other.variable) return variable < other.variable; + if (delta != other.delta) return delta < other.delta; + return worker_id < other.worker_id; + } }; // Queued integer solution found during a horizon (merged at sync) @@ -85,168 +93,98 @@ struct queued_integer_solution_t { } }; -// Per-worker state for BSP (Bulk Synchronous Parallel) branch-and-bound +// Per-worker state for BSP branch-and-bound template struct bb_worker_state_t { int worker_id{0}; - // ========================================================================== - // Plunging data structures (matching explore_subtree strategy) - // ========================================================================== - - // Plunge stack: depth-first path through the tree - // - Front = next node to process (LIFO) - // - Max size = 2 (current node's sibling only) - // - When branching with sibling on stack, sibling is moved to backlog + // Plunge stack: LIFO queue for depth-first exploration std::deque*> plunge_stack; - // Backlog: nodes "plugged" when branching - candidates for load balancing - // When branching with a sibling on the plunge stack, that sibling moves here. - // At horizon sync, backlog nodes participate in redistribution. - // Implemented as a priority heap ordered by lower_bound (best-first) with BSP identity tie-break. + // Backlog heap: nodes plugged when branching, ordered by lower_bound for best-first selection heap_t*, backlog_node_compare_t> backlog; - // Current node being processed (may be paused at horizon boundary) mip_node_t* current_node{nullptr}; + mip_node_t* last_solved_node{nullptr}; // For basis warm-start detection - // Last node that was solved (for basis warm-start detection) - // If next node's parent == last_solved_node, we can reuse basis - mip_node_t* last_solved_node{nullptr}; - - // Worker's work unit clock (cumulative) double clock{0.0}; - - // Current horizon boundaries (for BSP sync) double horizon_start{0.0}; double horizon_end{0.0}; - // Creation sequence counter - cumulative across horizons for unique identity - // Each node created by this worker gets (worker_id, next_creation_seq++) + // Cumulative counter for unique node identity: (worker_id, next_creation_seq++) int32_t next_creation_seq{0}; - // Events generated during this horizon bb_event_batch_t events; - - // Event sequence counter for deterministic tie-breaking int event_sequence{0}; - // LP problem copy for this worker (bounds modified per node) std::unique_ptr> leaf_problem; - - // Basis factorization state std::unique_ptr> basis_factors; - - // Bounds strengthening (node presolver) std::unique_ptr> node_presolver; - - // Working vectors for basis std::vector basic_list; std::vector nonbasic_list; - - // Work unit context for this worker work_limit_context_t work_context; - - // Whether basis needs recomputation for next node bool recompute_bounds_and_basis{true}; - // Per-horizon statistics (reset each horizon) i_t nodes_processed_this_horizon{0}; - - // Cumulative statistics (across all horizons) i_t total_nodes_processed{0}; i_t total_nodes_pruned{0}; i_t total_nodes_branched{0}; i_t total_nodes_infeasible{0}; i_t total_integer_solutions{0}; - i_t total_nodes_assigned{0}; // via load balancing - - // Timing statistics (in seconds) - double total_runtime{0.0}; // Total time spent doing actual work - double total_nowork_time{0.0}; // Total time spent with no nodes to work on + i_t total_nodes_assigned{0}; + double total_runtime{0.0}; + double total_nowork_time{0.0}; - // Worker-local upper bound for BSP determinism (prevents cross-worker pruning races) f_t local_upper_bound{std::numeric_limits::infinity()}; - - // Worker-local lower bound ceiling for numerical issues (merged to global at sync) f_t local_lower_bound_ceiling{std::numeric_limits::infinity()}; - // Queued integer solutions found during this horizon (merged at sync) std::vector> integer_solutions; - - // Solution sequence counter for deterministic tie-breaking (cumulative across horizons) int next_solution_seq{0}; - - // Queued pseudo-cost updates (applied to global pseudo-costs at sync) std::vector> pseudo_cost_updates; - // Pseudo-cost snapshot for deterministic variable selection - // Initialized from global pseudo-costs at horizon start, then updated locally - // during the horizon as the worker processes nodes. This allows within-horizon - // learning while maintaining determinism (each worker's updates are sequential). - // At sync, all workers' updates are merged into global pseudo-costs, and new - // snapshots are taken at the next horizon start. + // Pseudo-cost snapshots: local copies updated within horizon, merged at sync std::vector pc_sum_up_snapshot; std::vector pc_sum_down_snapshot; std::vector pc_num_up_snapshot; std::vector pc_num_down_snapshot; - // Constructor explicit bb_worker_state_t(int id) : worker_id(id), work_context("BB_Worker_" + std::to_string(id)) { } - // Initialize worker with problem data void initialize(const lp_problem_t& original_lp, const csr_matrix_t& Arow, const std::vector& var_types, i_t refactor_frequency, bool deterministic) { - // Create copy of LP problem for this worker - leaf_problem = std::make_unique>(original_lp); - - // Initialize basis factors + leaf_problem = std::make_unique>(original_lp); const i_t m = leaf_problem->num_rows; basis_factors = std::make_unique>(m, refactor_frequency); - - // Initialize bounds strengthening std::vector row_sense; node_presolver = std::make_unique>(*leaf_problem, Arow, row_sense, var_types); - - // Initialize working vectors basic_list.resize(m); nonbasic_list.clear(); - - // Configure work context work_context.deterministic = deterministic; } - // Reset for new horizon void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) { - clock = horizon_start; + clock = horizon_start; + work_context.global_work_units_elapsed = horizon_start; events.clear(); events.horizon_start = horizon_start; events.horizon_end = horizon_end; event_sequence = 0; nodes_processed_this_horizon = 0; - // Also sync work_context to match clock for consistent tracking - work_context.global_work_units_elapsed = horizon_start; - // Note: next_creation_seq is NOT reset - it's cumulative for unique identity - - // Initialize worker-local upper bound from global (for BSP determinism) - local_upper_bound = global_upper_bound; - - local_lower_bound_ceiling = std::numeric_limits::infinity(); - - // Clear queued updates from previous horizon + local_upper_bound = global_upper_bound; + local_lower_bound_ceiling = std::numeric_limits::infinity(); integer_solutions.clear(); pseudo_cost_updates.clear(); } - // Update snapshots from global state at horizon boundary void set_snapshots(f_t global_upper_bound, const std::vector& pc_sum_up, const std::vector& pc_sum_down, @@ -264,15 +202,10 @@ struct bb_worker_state_t { horizon_end = new_horizon_end; } - // Queue a pseudo-cost update for global sync AND apply it to local snapshot immediately. - // Local snapshot updates are sequential within each worker (deterministic). - // Global updates are merged at sync in sorted (wut, worker_id) order (deterministic). + // Queue pseudo-cost update for global sync and apply to local snapshot void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) { - // Queue for global sync at horizon end pseudo_cost_updates.push_back({variable, direction, delta, clock, worker_id}); - - // Also apply to local snapshot immediately for better variable selection if (direction == rounding_direction_t::DOWN) { pc_sum_down_snapshot[variable] += delta; pc_num_down_snapshot[variable]++; @@ -282,8 +215,6 @@ struct bb_worker_state_t { } } - // Variable selection using snapshot (for BSP determinism) - // Returns the best variable to branch on based on pseudo-cost scores i_t variable_selection_from_snapshot(const std::vector& fractional, const std::vector& solution) const { @@ -296,104 +227,69 @@ struct bb_worker_state_t { solution); } - // ========================================================================== - // Node enqueueing methods - // ========================================================================== - - // Add a node that already has BSP identity (from load balancing or initial distribution) - void enqueue_node_with_identity(mip_node_t* node) { plunge_stack.push_front(node); } + void enqueue_node(mip_node_t* node) { plunge_stack.push_front(node); } - // Add children after branching with proper plunging behavior: - // 1. If plunge stack has a sibling, move it to backlog (plugging) - // 2. Push both children to plunge stack with preferred child on top - // Returns the child that was placed on top (to be explored first) + // Enqueue children with plunging: move any existing sibling to backlog, push both children mip_node_t* enqueue_children_for_plunge(mip_node_t* down_child, mip_node_t* up_child, rounding_direction_t preferred_direction) { - // PLUGGING: If plunge stack has a sibling from previous branch, move it to backlog if (!plunge_stack.empty()) { - mip_node_t* sibling = plunge_stack.back(); + backlog.push(plunge_stack.back()); plunge_stack.pop_back(); - backlog.push(sibling); } - // Assign BSP identity to children down_child->origin_worker_id = worker_id; down_child->creation_seq = next_creation_seq++; up_child->origin_worker_id = worker_id; up_child->creation_seq = next_creation_seq++; - // Push children - preferred child on top (front) for immediate exploration mip_node_t* first_child; if (preferred_direction == rounding_direction_t::UP) { - plunge_stack.push_front(down_child); // Second to explore - plunge_stack.push_front(up_child); // First to explore (on top) + plunge_stack.push_front(down_child); + plunge_stack.push_front(up_child); first_child = up_child; } else { - plunge_stack.push_front(up_child); // Second to explore - plunge_stack.push_front(down_child); // First to explore (on top) + plunge_stack.push_front(up_child); + plunge_stack.push_front(down_child); first_child = down_child; } - return first_child; } - // ========================================================================== - // Node dequeueing methods - // ========================================================================== - - // Get next node to process using plunging strategy: - // 1. Resume paused node if any - // 2. Pop from plunge stack (depth-first continuation) - // 3. Fall back to backlog heap (best-first from plugged nodes) + // Dequeue: current_node first, then plunge_stack, then backlog heap mip_node_t* dequeue_node() { - // 1. Resume paused node if any if (current_node != nullptr) { mip_node_t* node = current_node; current_node = nullptr; return node; } - - // 2. Prefer plunge stack (depth-first continuation) if (!plunge_stack.empty()) { mip_node_t* node = plunge_stack.front(); plunge_stack.pop_front(); return node; } - - // 3. Fall back to backlog heap - pop best node (lowest lower_bound with BSP identity tie-break) auto node_opt = backlog.pop(); - if (node_opt.has_value()) { return node_opt.value(); } - - return nullptr; + return node_opt.has_value() ? node_opt.value() : nullptr; } - // ========================================================================== - // Queue state queries - // ========================================================================== - - // Check if worker has work available bool has_work() const { return current_node != nullptr || !plunge_stack.empty() || !backlog.empty(); } - // Get number of nodes in worker's queues (including paused node) size_t queue_size() const { return plunge_stack.size() + backlog.size() + (current_node != nullptr ? 1 : 0); } - // Record an event void record_event(bb_event_t event) { event.event_sequence = event_sequence++; events.add(std::move(event)); } - // Record node branching event void record_branched( mip_node_t* node, i_t down_child_id, i_t up_child_id, i_t branch_var, f_t branch_val) { @@ -408,146 +304,84 @@ struct bb_worker_state_t { branch_val)); } - // Record integer solution found void record_integer_solution(mip_node_t* node, f_t objective) { record_event( bb_event_t::make_integer_solution(clock, worker_id, node->node_id, 0, objective)); } - // Record node fathomed void record_fathomed(mip_node_t* node, f_t lower_bound) { record_event( bb_event_t::make_fathomed(clock, worker_id, node->node_id, 0, lower_bound)); } - // Record node infeasible void record_infeasible(mip_node_t* node) { record_event(bb_event_t::make_infeasible(clock, worker_id, node->node_id, 0)); } - // Record numerical error void record_numerical(mip_node_t* node) { record_event(bb_event_t::make_numerical(clock, worker_id, node->node_id, 0)); } - // Track node processed (called when a node LP solve completes) void track_node_processed() { ++nodes_processed_this_horizon; ++total_nodes_processed; } - // Track node branched void track_node_branched() { ++total_nodes_branched; } - - // Track node pruned (fathomed due to bound) void track_node_pruned() { ++total_nodes_pruned; } - - // Track node infeasible void track_node_infeasible() { ++total_nodes_infeasible; } - - // Track integer solution found void track_integer_solution() { ++total_integer_solutions; } - - // Track node assigned via load balancing void track_node_assigned() { ++total_nodes_assigned; } }; -// ============================================================================= -// BSP Diving Worker State -// ============================================================================= - -// Per-worker state for BSP deterministic diving -// Diving workers operate on detached copies of nodes and don't modify the main tree +// Per-worker state for BSP diving (operates on detached node copies) template struct bsp_diving_worker_state_t { int worker_id{0}; bnb_worker_type_t diving_type{bnb_worker_type_t::PSEUDOCOST_DIVING}; - // Worker's work unit clock (cumulative) double clock{0.0}; - - // Current horizon boundaries double horizon_start{0.0}; double horizon_end{0.0}; - - // Work context for horizon sync work_limit_context_t work_context; - // LP problem copy for this worker std::unique_ptr> leaf_problem; - - // Basis factorization state std::unique_ptr> basis_factors; - - // Bounds strengthening std::unique_ptr> node_presolver; - - // Working vectors for basis std::vector basic_list; std::vector nonbasic_list; - - // Whether basis needs recomputation for next node bool recompute_bounds_and_basis{true}; - // ========================================================================== - // Snapshots for determinism (taken at horizon start) - // ========================================================================== - + // Snapshots taken at horizon start f_t local_upper_bound{std::numeric_limits::infinity()}; - - // Incumbent snapshot for guided diving + i_t total_lp_iters_snapshot{0}; std::vector incumbent_snapshot; - - // Pseudo-cost snapshots std::vector pc_sum_up_snapshot; std::vector pc_sum_down_snapshot; std::vector pc_num_up_snapshot; std::vector pc_num_down_snapshot; - - // Root relaxation solution (for line search diving) const std::vector* root_solution{nullptr}; - // ========================================================================== - // Diving-specific state - // ========================================================================== - - // Queue of starting nodes for dives (detached copies assigned at sync) - // Worker processes these until queue empty or horizon exhausted std::deque> dive_queue; - - // Current lower/upper bounds for the dive (initialized from starting node) std::vector dive_lower; std::vector dive_upper; - // Queued integer solutions found during this horizon (merged at sync) std::vector> integer_solutions; - - // Solution sequence counter for deterministic tie-breaking (cumulative across horizons) int next_solution_seq{0}; - - // Queued pseudo-cost updates (applied to global pseudo-costs at sync) std::vector> pseudo_cost_updates; - // ========================================================================== - // Statistics - // ========================================================================== - i_t total_nodes_explored{0}; i_t total_integer_solutions{0}; i_t total_dives{0}; - i_t lp_iters_this_dive{0}; // LP iterations in current dive (for iteration limit) + i_t lp_iters_this_dive{0}; double total_runtime{0.0}; double total_nowork_time{0.0}; - // ========================================================================== - // Constructor and initialization - // ========================================================================== - explicit bsp_diving_worker_state_t(int id, bnb_worker_type_t type) : worker_id(id), diving_type(type), work_context("Diving_Worker_" + std::to_string(id)) { @@ -559,21 +393,16 @@ struct bsp_diving_worker_state_t { i_t refactor_frequency, bool deterministic) { - leaf_problem = std::make_unique>(original_lp); - + leaf_problem = std::make_unique>(original_lp); const i_t m = leaf_problem->num_rows; basis_factors = std::make_unique>(m, refactor_frequency); - std::vector row_sense; node_presolver = std::make_unique>(*leaf_problem, Arow, row_sense, var_types); - basic_list.resize(m); nonbasic_list.clear(); - - dive_lower = original_lp.lower; - dive_upper = original_lp.upper; - + dive_lower = original_lp.lower; + dive_upper = original_lp.upper; work_context.deterministic = deterministic; } @@ -591,6 +420,7 @@ struct bsp_diving_worker_state_t { } void set_snapshots(f_t global_upper_bound, + i_t total_lp_iters, const std::vector& pc_sum_up, const std::vector& pc_sum_down, const std::vector& pc_num_up, @@ -600,15 +430,16 @@ struct bsp_diving_worker_state_t { double new_horizon_start, double new_horizon_end) { - local_upper_bound = global_upper_bound; - pc_sum_up_snapshot = pc_sum_up; - pc_sum_down_snapshot = pc_sum_down; - pc_num_up_snapshot = pc_num_up; - pc_num_down_snapshot = pc_num_down; - incumbent_snapshot = incumbent; - root_solution = root_sol; - horizon_start = new_horizon_start; - horizon_end = new_horizon_end; + local_upper_bound = global_upper_bound; + total_lp_iters_snapshot = total_lp_iters; + pc_sum_up_snapshot = pc_sum_up; + pc_sum_down_snapshot = pc_sum_down; + pc_num_up_snapshot = pc_num_up; + pc_num_down_snapshot = pc_num_down; + incumbent_snapshot = incumbent; + root_solution = root_sol; + horizon_start = new_horizon_start; + horizon_end = new_horizon_end; } void enqueue_dive_node(mip_node_t* node) { dive_queue.push_back(node->detach_copy()); } @@ -632,11 +463,9 @@ struct bsp_diving_worker_state_t { ++total_integer_solutions; } - // Queue a pseudo-cost update for global sync AND apply it to local snapshot immediately. void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) { pseudo_cost_updates.push_back({variable, direction, delta, clock, worker_id}); - if (direction == rounding_direction_t::DOWN) { pc_sum_down_snapshot[variable] += delta; pc_num_down_snapshot[variable]++; @@ -646,11 +475,9 @@ struct bsp_diving_worker_state_t { } } - // Variable selection using snapshot pseudo-costs (for pseudocost diving) branch_variable_t variable_selection_from_snapshot(const std::vector& fractional, const std::vector& solution) const { - // Use root_solution if available, otherwise use solution as fallback const std::vector& root_sol = (root_solution != nullptr) ? *root_solution : solution; return pseudocost_diving_from_arrays(pc_sum_down_snapshot.data(), pc_sum_up_snapshot.data(), @@ -662,14 +489,12 @@ struct bsp_diving_worker_state_t { root_sol); } - // Guided diving variable selection using incumbent snapshot branch_variable_t guided_variable_selection(const std::vector& fractional, const std::vector& solution) const { if (incumbent_snapshot.empty()) { return variable_selection_from_snapshot(fractional, solution); } - return guided_diving_from_arrays(pc_sum_down_snapshot.data(), pc_sum_up_snapshot.data(), pc_num_down_snapshot.data(), @@ -680,8 +505,6 @@ struct bsp_diving_worker_state_t { incumbent_snapshot); } }; - -// Container for all diving worker states template class bsp_diving_worker_pool_t { public: @@ -736,13 +559,11 @@ class bsp_diving_worker_pool_t { std::vector> workers_; }; -// Container for all worker states in BSP B&B template class bb_worker_pool_t { public: bb_worker_pool_t() = default; - // Initialize pool with specified number of workers void initialize(int num_workers, const lp_problem_t& original_lp, const csr_matrix_t& Arow, @@ -758,15 +579,10 @@ class bb_worker_pool_t { } } - // Get worker by ID bb_worker_state_t& operator[](int worker_id) { return workers_[worker_id]; } - const bb_worker_state_t& operator[](int worker_id) const { return workers_[worker_id]; } - - // Get number of workers int size() const { return static_cast(workers_.size()); } - // Reset all workers for new horizon void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) { for (auto& worker : workers_) { @@ -774,7 +590,6 @@ class bb_worker_pool_t { } } - // Collect all events from all workers into a single sorted batch bb_event_batch_t collect_and_sort_events() { bb_event_batch_t all_events; @@ -788,7 +603,6 @@ class bb_worker_pool_t { return all_events; } - // Check if any worker has work bool any_has_work() const { for (const auto& worker : workers_) { @@ -797,7 +611,6 @@ class bb_worker_pool_t { return false; } - // Get total queue size across all workers size_t total_queue_size() const { size_t total = 0; @@ -807,7 +620,6 @@ class bb_worker_pool_t { return total; } - // Iterator support auto begin() { return workers_.begin(); } auto end() { return workers_.end(); } auto begin() const { return workers_.begin(); } diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 1f7ef9707..845a28476 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -39,14 +39,13 @@ #include #include -// bounds strenghtening work unit predictor is not yet accurate enough // #define BSP_DISABLE_BOUNDS_STRENGTHENING namespace cuopt::linear_programming::dual_simplex { namespace { -static constexpr double FEATURE_LOG_INTERVAL = 0.25; // Log at most every 500ms +static constexpr double FEATURE_LOG_INTERVAL = 0.25; // Log at most every 250ms template bool is_fractional(f_t x, variable_type_t var_type, f_t integer_tol) @@ -1660,18 +1659,16 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut { uint32_t lp_hash = detail::compute_hash(original_lp_.objective); lp_hash ^= detail::compute_hash(original_lp_.A.x.underlying()); - settings_.log.printf("lp A.x hash: %08x\n", - detail::compute_hash(original_lp_.A.x.underlying())); + settings_.log.debug("lp A.x hash: %08x\n", detail::compute_hash(original_lp_.A.x.underlying())); lp_hash ^= detail::compute_hash(original_lp_.A.i.underlying()); - settings_.log.printf("lp A.j hash: %08x\n", - detail::compute_hash(original_lp_.A.i.underlying())); + settings_.log.debug("lp A.j hash: %08x\n", detail::compute_hash(original_lp_.A.i.underlying())); lp_hash ^= detail::compute_hash(original_lp_.A.col_start.underlying()); - settings_.log.printf("lp A.col_start hash: %08x\n", - detail::compute_hash(original_lp_.A.col_start.underlying())); + settings_.log.debug("lp A.col_start hash: %08x\n", + detail::compute_hash(original_lp_.A.col_start.underlying())); lp_hash ^= detail::compute_hash(original_lp_.rhs); - settings_.log.printf("lp rhs hash: %08x\n", detail::compute_hash(original_lp_.rhs)); + settings_.log.debug("lp rhs hash: %08x\n", detail::compute_hash(original_lp_.rhs)); lp_hash ^= detail::compute_hash(original_lp_.lower); - settings_.log.printf("lp lower hash: %08x\n", detail::compute_hash(original_lp_.lower)); + settings_.log.debug("lp lower hash: %08x\n", detail::compute_hash(original_lp_.lower)); lp_hash ^= detail::compute_hash(original_lp_.upper); settings_.log.printf( "Exploring the B&B tree using %d threads (best-first = %d, diving = %d) [LP hash: %08x]\n", @@ -1775,7 +1772,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t>(); bsp_workers_->initialize(num_bfs_workers, original_lp_, @@ -1784,7 +1780,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t 0) { std::vector diving_types = {bnb_worker_type_t::PSEUDOCOST_DIVING, bnb_worker_type_t::LINE_SEARCH_DIVING, @@ -1798,12 +1793,9 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t(bsp_horizon_step_); // bsp_scheduler_->verbose = true; @@ -1828,26 +1820,22 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_torigin_worker_id = -1; // Pre-BSP marker + search_tree_.root.get_down_child()->origin_worker_id = -1; search_tree_.root.get_down_child()->creation_seq = 0; search_tree_.root.get_up_child()->origin_worker_id = -1; search_tree_.root.get_up_child()->creation_seq = 1; - (*bsp_workers_)[0].enqueue_node_with_identity(search_tree_.root.get_down_child()); + (*bsp_workers_)[0].enqueue_node(search_tree_.root.get_down_child()); (*bsp_workers_)[0].track_node_assigned(); - (*bsp_workers_)[1 % num_bfs_workers].enqueue_node_with_identity(search_tree_.root.get_up_child()); + (*bsp_workers_)[1 % num_bfs_workers].enqueue_node(search_tree_.root.get_up_child()); (*bsp_workers_)[1 % num_bfs_workers].track_node_assigned(); BSP_DEBUG_FLUSH_ASSIGN_TRACE(bsp_debug_settings_, bsp_debug_logger_); - // Set sync callback - executed when all workers arrive at barrier - // Returns true to stop the scheduler (and all workers exit cleanly together) bsp_scheduler_->set_sync_callback([this](double sync_target) -> bool { bsp_sync_callback(0); return bsp_terminated_.load(); }); - // initialize global state snapshots for (auto& worker : *bsp_workers_) { worker.set_snapshots(upper_bound_.load(), pc_.pseudo_cost_sum_up, @@ -1864,6 +1852,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::run_worker_loop(bb_worker_state_t& { raft::common::nvtx::range scope("BB::worker_loop"); - // Workers run continuously until scheduler signals stop (via sync callback) - // The scheduler handles synchronization at horizon boundaries via record_work() while (!bsp_terminated_.load() && !bsp_scheduler_->is_stopped() && solver_status_ == mip_status_t::UNSET) { - // Check time limit directly - don't wait for sync if time is up if (toc(exploration_stats_.start_time) > settings_.time_limit) { solver_status_ = mip_status_t::TIME_LIMIT; bsp_terminated_.store(true); - bsp_scheduler_->stop(); // Wake up workers waiting at barrier + bsp_scheduler_->stop(); break; } @@ -1988,10 +1968,8 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& mip_node_t* node = worker.dequeue_node(); if (node == nullptr) { continue; } - // Track that this node is being actively processed worker.current_node = node; - // Check if node should be pruned (use worker's snapshot for determinism) f_t upper_bound = worker.local_upper_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node->lower_bound); if (node->lower_bound >= upper_bound || rel_gap < settings_.relative_mip_gap_tol) { @@ -2003,33 +1981,24 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& continue; } - // Check if we can warm-start from the previous solve's basis bool is_child = (node->parent == worker.last_solved_node); worker.recompute_bounds_and_basis = !is_child; - // Solve the node - record_work() inside may block at sync points - // The scheduler's sync callback will execute during barrier waits node_solve_info_t status = solve_node_bsp(worker, node, search_tree, worker.horizon_end); - - // Track last solved node for warm-start detection - worker.last_solved_node = node; + worker.last_solved_node = node; if (status == node_solve_info_t::TIME_LIMIT || status == node_solve_info_t::WORK_LIMIT) { - // Time/work limit hit - the loop head will detect this and terminate properly continue; } - // Node completed successfully - loop back to process children worker.current_node = nullptr; continue; } - // No work available - advance to next sync point to participate in barrier - // This ensures all workers reach the sync point even if some have no work + // No work - advance to sync point to participate in barrier f_t nowork_start = tic(); cuopt::sync_result_t result = bsp_scheduler_->wait_for_next_sync(worker.work_context); worker.total_nowork_time += toc(nowork_start); if (result == cuopt::sync_result_t::STOPPED) { break; } - // After sync, bsp_sync_callback may have redistributed nodes to us } } @@ -2042,8 +2011,6 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) double horizon_start = bsp_current_horizon_ - bsp_horizon_step_; double horizon_end = bsp_current_horizon_; - // Wait for external producers (CPUFJ) to reach horizon_start before processing - // This ensures we don't process B&B events before producers have caught up double wait_start = tic(); producer_sync_.wait_for_producers(horizon_start); double wait_time = toc(wait_start); @@ -2128,10 +2095,8 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) heap_snapshot, all_events); - // Advance the horizon for next sync bsp_current_horizon_ += bsp_horizon_step_; - // Update worker snapshots for next horizon for (auto& worker : *bsp_workers_) { worker.set_snapshots(upper_bound_.load(), pc_.pseudo_cost_sum_up, @@ -2142,13 +2107,12 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) bsp_current_horizon_); } - // Update diving worker snapshots for next horizon if (bsp_diving_workers_) { std::vector incumbent_snapshot; if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } - for (auto& worker : *bsp_diving_workers_) { worker.set_snapshots(upper_bound_.load(), + exploration_stats_.total_lp_iters.load(), pc_.pseudo_cost_sum_up, pc_.pseudo_cost_sum_down, pc_.pseudo_cost_num_up, @@ -2160,7 +2124,6 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) } } - // Check termination conditions f_t lower_bound = compute_bsp_lower_bound(); f_t upper_bound = upper_bound_.load(); f_t abs_gap = upper_bound - lower_bound; @@ -2168,7 +2131,6 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) bool should_terminate = false; - // Gap tolerance reached if (abs_gap <= settings_.absolute_mip_gap_tol || rel_gap <= settings_.relative_mip_gap_tol) { should_terminate = true; } @@ -2181,10 +2143,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) should_terminate = true; } - // Check if the next horizon would exceed work limit. If so, terminate now rather than - // letting workers continue past the limit. This is conservative (stops slightly early) - // but prevents workers from processing nodes beyond the work budget. - // bsp_current_horizon_ now holds the NEXT horizon's end value after the increment above. + // Stop early if next horizon exceeds work limit if (bsp_current_horizon_ > settings_.work_limit) { solver_status_ = mip_status_t::WORK_LIMIT; should_terminate = true; @@ -2192,12 +2151,10 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) if (should_terminate) { bsp_terminated_.store(true); } - // Progress logging with horizon number and state hash f_t obj = compute_user_objective(original_lp_, upper_bound); f_t user_lower = compute_user_objective(original_lp_, lower_bound); std::string gap_user = user_mip_gap(obj, user_lower); - // Build list of workers that reached sync with no work std::string idle_workers; for (const auto& w : *bsp_workers_) { if (!w.has_work() && w.current_node == nullptr) { @@ -2861,6 +2818,8 @@ void branch_and_bound_t::process_history_and_sync( } } + std::sort(all_pc_updates.begin(), all_pc_updates.end()); + for (const auto& upd : all_pc_updates) { if (upd.direction == rounding_direction_t::DOWN) { pc_.pseudo_cost_sum_down[upd.variable] += upd.delta; @@ -2974,10 +2933,10 @@ void branch_and_bound_t::balance_worker_loads() worker_order.push_back(w); } - // Distribute nodes - use enqueue_node_with_identity to preserve existing identity + // Distribute nodes - use enqueue_node to preserve existing identity for (size_t i = 0; i < all_nodes.size(); ++i) { size_t worker_idx = worker_order[i % num_workers]; - (*bsp_workers_)[worker_idx].enqueue_node_with_identity(all_nodes[i]); + (*bsp_workers_)[worker_idx].enqueue_node(all_nodes[i]); (*bsp_workers_)[worker_idx].track_node_assigned(); double wut = bsp_current_horizon_; @@ -3051,9 +3010,13 @@ void branch_and_bound_t::populate_diving_heap_at_sync() if (candidates.empty()) return; - // Sort candidates by score (lower is better for diving - closer to optimum) + // Sort candidates by score with deterministic tie-breaking by BSP identity std::sort(candidates.begin(), candidates.end(), [](const auto& a, const auto& b) { - return a.second < b.second; + if (a.second != b.second) return a.second < b.second; + if (a.first->origin_worker_id != b.first->origin_worker_id) { + return a.first->origin_worker_id < b.first->origin_worker_id; + } + return a.first->creation_seq < b.first->creation_seq; }); int nodes_to_take = std::min(target_total, (int)candidates.size()); @@ -3168,7 +3131,7 @@ void branch_and_bound_t::merge_diving_solutions() } } - // Merge pseudo-cost updates from diving workers + // Merge pseudo-cost updates from diving workers in deterministic order std::vector> all_diving_pc_updates; for (auto& worker : *bsp_diving_workers_) { for (auto& upd : worker.pseudo_cost_updates) { @@ -3176,6 +3139,8 @@ void branch_and_bound_t::merge_diving_solutions() } } + std::sort(all_diving_pc_updates.begin(), all_diving_pc_updates.end()); + for (const auto& upd : all_diving_pc_updates) { if (upd.direction == rounding_direction_t::DOWN) { pc_.pseudo_cost_sum_down[upd.variable] += upd.delta; @@ -3316,9 +3281,8 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t* node; - f_t score; // objective_estimate for diving priority + f_t score; }; struct diving_score_comp { bool operator()(const diving_entry_t& a, const diving_entry_t& b) const { - return a.score > b.score; // Min-heap by score (lower is better) + if (a.score != b.score) return a.score > b.score; // Min-heap by score + if (a.node->origin_worker_id != b.node->origin_worker_id) { + return a.node->origin_worker_id > b.node->origin_worker_id; + } + return a.node->creation_seq > b.node->creation_seq; } }; heap_t diving_heap_; diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 0783037f2..08096437c 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -15,7 +15,7 @@ #include -#define CUOPT_DEBUG_WORK_PREDICTION +// #define CUOPT_DEBUG_WORK_PREDICTION namespace cuopt::linear_programming::dual_simplex { From b9960c021722c44467c40a3fb00bccf11c64c800 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 11:32:31 +0000 Subject: [PATCH 290/366] more cleanup --- cpp/src/dual_simplex/branch_and_bound.cpp | 31 ++++++++++++---------- cpp/src/mip/diversity/diversity_manager.cu | 2 +- cpp/src/mip/problem/problem.cu | 2 -- 3 files changed, 18 insertions(+), 17 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 845a28476..1ccce9f6b 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2053,8 +2053,8 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) state_data.push_back(static_cast(exploration_stats_.nodes_unexplored)); f_t ub = upper_bound_.load(); f_t lb = compute_bsp_lower_bound(); - state_data.push_back(static_cast(ub * 1000000)); - state_data.push_back(static_cast(lb * 1000000)); + state_data.push_back(std::bit_cast(ub)); + state_data.push_back(std::bit_cast(lb)); for (auto& worker : *bsp_workers_) { if (worker.current_node != nullptr) { @@ -2627,12 +2627,17 @@ void branch_and_bound_t::process_history_and_sync( // Infeasible solutions from GPU heuristics are queued for repair; process them now { std::vector> to_repair; - mutex_repair_.lock(); - if (repair_queue_.size() > 0) { - to_repair = repair_queue_; - repair_queue_.clear(); - } - mutex_repair_.unlock(); + // TODO: support repair queue in BSP mode + // mutex_repair_.lock(); + // if (repair_queue_.size() > 0) { + // to_repair = repair_queue_; + // repair_queue_.clear(); + // } + // mutex_repair_.unlock(); + + std::sort(to_repair.begin(), + to_repair.end(), + [](const std::vector& a, const std::vector& b) { return a < b; }); if (to_repair.size() > 0) { settings_.log.debug("BSP sync: Attempting to repair %ld injected solutions\n", @@ -2721,7 +2726,6 @@ void branch_and_bound_t::process_history_and_sync( // Process heuristic solution at its correct work unit timestamp position f_t new_upper = std::numeric_limits::infinity(); - mutex_upper_.lock(); if (hsol.objective < upper_bound_) { upper_bound_ = hsol.objective; incumbent_.set_incumbent_solution(hsol.objective, hsol.solution); @@ -2731,7 +2735,6 @@ void branch_and_bound_t::process_history_and_sync( BSP_DEBUG_LOG_INCUMBENT_UPDATE( bsp_debug_settings_, bsp_debug_logger_, hsol.wut, hsol.objective, "heuristic"); } - mutex_upper_.unlock(); if (new_upper < std::numeric_limits::infinity()) { f_t user_obj = compute_user_objective(original_lp_, new_upper); @@ -2792,14 +2795,12 @@ void branch_and_bound_t::process_history_and_sync( // Update incumbent bool improved = false; - mutex_upper_.lock(); if (sol->objective < upper_bound_) { upper_bound_ = sol->objective; incumbent_.set_incumbent_solution(sol->objective, sol->solution); current_upper = sol->objective; improved = true; } - mutex_upper_.unlock(); // Notify diversity manager of new incumbent if (improved && settings_.solution_callback != nullptr) { @@ -2880,7 +2881,7 @@ void branch_and_bound_t::balance_worker_loads() const size_t num_workers = bsp_workers_->size(); if (num_workers <= 1) return; - constexpr bool force_rebalance_every_sync = true; + constexpr bool force_rebalance_every_sync = false; // Count work for each worker: current_node (if any) + plunge_stack + backlog std::vector work_counts(num_workers); @@ -2933,7 +2934,7 @@ void branch_and_bound_t::balance_worker_loads() worker_order.push_back(w); } - // Distribute nodes - use enqueue_node to preserve existing identity + // Distribute nodes for (size_t i = 0; i < all_nodes.size(); ++i) { size_t worker_idx = worker_order[i % num_workers]; (*bsp_workers_)[worker_idx].enqueue_node(all_nodes[i]); @@ -3011,6 +3012,8 @@ void branch_and_bound_t::populate_diving_heap_at_sync() if (candidates.empty()) return; // Sort candidates by score with deterministic tie-breaking by BSP identity + // Technically not necessary as it stands since the worker assignments and ordering are + // deterministic std::sort(candidates.begin(), candidates.end(), [](const auto& a, const auto& b) { if (a.second != b.second) return a.second < b.second; if (a.first->origin_worker_id != b.first->origin_worker_id) { diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index fda37d067..8f8e27bc2 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -186,7 +186,7 @@ bool diversity_manager_t::run_presolve(f_t time_limit) ls.constraint_prop.bounds_update.set_updated_bounds(*problem_ptr); } bool run_probing_cache = !fj_only_run; - // Don't run probing cache in deterministic mode yet as neither B&B nor CPUFJ needs it + // Don't run probing cache in deterministic mode yet as neither B&B nor CPUFJ need it // and it doesn't make use of work units yet if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { run_probing_cache = false; } if (run_probing_cache) { diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 07e3c8e85..850c2b167 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -1971,8 +1971,6 @@ void problem_t::get_host_user_problem( auto stream = handle_ptr->get_stream(); user_problem.objective = cuopt::host_copy(objective_coefficients, stream); - // Explicitly construct ins_vector wrappers to ensure data_ptr is properly initialized - // (direct assignment via implicit conversion doesn't update data_ptr correctly) dual_simplex::csr_matrix_t csr_A(m, n, nz); csr_A.x = ins_vector(cuopt::host_copy(coefficients, stream)); csr_A.j = ins_vector(cuopt::host_copy(variables, stream)); From 66f6cfd636f4e697e9a8107b5051506ab68acbf8 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 26 Jan 2026 12:49:23 +0100 Subject: [PATCH 291/366] increase max_lookahead --- cpp/src/dual_simplex/simplex_solver_settings.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index c04e8379a..223f11bbc 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -72,7 +72,7 @@ struct reliability_branching_settings_t { i_t max_num_candidates = 100; // The maximum number of candidates evaluated that does not improve the best score. - i_t max_lookahead = 10; + i_t max_lookahead = 20; }; template From 2c6e12ebd3c84d72a89db17d17ac920cbdaa2449 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 12:06:41 +0000 Subject: [PATCH 292/366] fix determinism test seed --- .../linear_programming/cuopt/run_mip.cpp | 1 + cpp/src/dual_simplex/branch_and_bound.cpp | 8 +++--- cpp/tests/mip/determinism_test.cu | 27 ++++++++++++++----- 3 files changed, 25 insertions(+), 11 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 28feab130..7dbfe8c26 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -208,6 +208,7 @@ int run_single_file(std::string file_path, settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; + settings.seed = 42; cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; auto start_run_solver = std::chrono::high_resolution_clock::now(); diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 1ccce9f6b..0e2b8d886 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -3284,10 +3284,10 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t> solutions; constexpr int num_runs = 3; @@ -144,7 +156,8 @@ TEST_P(DeterministicBBInstanceTest, deterministic_across_runs) handle_.sync_stream(); // Get a random seed for each run - auto seed = std::random_device{}(); + auto seed = std::random_device{}() & 0x7fffffff; + ; std::cout << "Tested with seed " << seed << "\n"; mip_solver_settings_t settings; From be15f6326e97eac5ac601e455c662a9c19a0416e Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 13:23:46 +0000 Subject: [PATCH 293/366] fix fjcpu bug --- cpp/src/mip/feasibility_jump/fj_cpu.cu | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index d7f627b19..4eb4f83ab 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -851,8 +851,9 @@ static thrust::tuple find_mtm_move( if (nnz_sum > fj_cpu.nnz_samples) nnz_pick_probability = (f_t)fj_cpu.nnz_samples / nnz_sum; for (size_t cstr_idx : target_cstrs) { - auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); - f_t cstr_tol = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); + auto c_lb = fj_cpu.h_cstr_lb[cstr_idx]; + auto c_ub = fj_cpu.h_cstr_ub[cstr_idx]; + f_t cstr_tol = fj_cpu.view.get_corrected_tolerance(cstr_idx, c_lb, c_ub); cuopt_assert(cstr_idx < fj_cpu.h_cstr_lb.size(), "cstr_idx is out of bounds"); auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); @@ -1027,7 +1028,8 @@ static void recompute_lhs(fj_cpu_climber_t& fj_cpu) fj_cpu.total_violations = 0; for (i_t cstr_idx = 0; cstr_idx < fj_cpu.view.pb.n_constraints; ++cstr_idx) { auto [offset_begin, offset_end] = range_for_constraint(fj_cpu, cstr_idx); - auto [c_lb, c_ub] = fj_cpu.cached_cstr_bounds[cstr_idx].get(); + auto c_lb = fj_cpu.h_cstr_lb[cstr_idx]; + auto c_ub = fj_cpu.h_cstr_ub[cstr_idx]; auto delta_it = thrust::make_transform_iterator(thrust::make_counting_iterator(0), [&fj_cpu](i_t j) { return fj_cpu.h_coefficients[j] * fj_cpu.h_assignment[fj_cpu.h_variables[j]]; From 3b93226d80dd2754a61ccb2cf0657022b9ab3adc Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 13:24:21 +0000 Subject: [PATCH 294/366] bump --- cpp/src/mip/feasibility_jump/fj_cpu.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 4eb4f83ab..bb6129dbe 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -868,7 +868,7 @@ static thrust::tuple find_mtm_move( best_move = fj_move_t{var_idx, cached_move.first}; } // cuopt_assert(fj_cpu.view.pb.check_variable_within_bounds(var_idx, - // fj_cpu.h_assignment[var_idx] + cached_move.first), "best move not within bounds"); + // fj_cpu.h_assignment[var_idx] + cached_move.first), "best move is not within bounds"); } fj_cpu.hit_count++; continue; From 9e4b2eeb32c231abf2d5b5c6d5a3ff33b1d2cfb0 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 13:39:56 +0000 Subject: [PATCH 295/366] add comment description --- cpp/src/dual_simplex/branch_and_bound.cpp | 58 +++++++++++++++++++++++ 1 file changed, 58 insertions(+) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 0e2b8d886..d21711d81 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1757,6 +1757,64 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut // BSP (Bulk Synchronous Parallel) Deterministic implementation // ============================================================================ +// The BSP model is based on letting independent workers execute during virtual time intervals, +// and exchange data during serialized interval sync points. +/* + +Work Units: 0 0.5 1.0 + │ │ │ + │◄──────── Horizon 0 ──────────►│◄───────── Horizon 1 ──────────►│ + │ │ │ +══════════════╪═══════════════════════════════╪════════════════════════════════╪════ + │ │ │ + │ ┌──────────────┐ ┌──────────────┐ + BFS Worker 0 │ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ │ │ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ │ │ + ├ plunge │ explore nodes │ │ explore nodes │ │ + │ stack │ emit events (wut) │ │ emit events │ │ + ├ backlog │ │ SYNC S1 │ │ SYNC S2 │ + │ heap │ │ │ │ │ + ├ PC snap │ │ • Sort by │ │ • Sort by │ + ├ events[] │ │ (wut, w, │ │ (wut, w, │ + └ solutions[]│ │ seq) │ │ seq) │ +──────────────┼────────────────────────│ • Replay │──────────────────│ • Replay │ + │ │ • Merge PC │ │ • Merge PC │ + BFS Worker 1 │ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ │ • Merge sols │ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ │ • Merge sols │ + ├ plunge │ explore nodes │ • Prune │ explore nodes │ • Prune │ + │ stack │ emit events (wut) │ • Balance │ emit events │ • Balance │ + ├ backlog │ │ • Assign │ │ • Assign │ + │ heap │ │ • Snapshot │ │ • Snapshot │ + ├ PC snap │ │ │ │ │ + ├ events[] │ │ [38779ebd] │ │ [2ad65699] │ + └ solutions[]│ │ │ │ │ +──────────────┼────────────────────────│ │──────────────────│ │ + │ │ │ │ │ + Diving D0 │ ░░░░░░░░░░░░░░░░░░░░░░ │ │ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ │ │ + ├ dive_queue │ (waiting) │ │ dive, find sols │ │ + ├ PC snap │ │ │ │ │ + ├ incumbent │ │ │ │ │ + │ snap │ │ │ │ │ + ├ pc_updates │ │ │ │ │ + └ solutions[]│ │ │ │ │ +──────────────┼────────────────────────│ │──────────────────│ │ + │ │ │ │ │ + Diving D1 │ ░░░░░░░░░░░░░░░░░░░░░░ │ │ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ │ │ + ├ dive_queue │ (waiting) │ │ dive, find sols │ │ + ├ PC snap │ │ │ │ │ + ├ incumbent │ └──────────────┘ └──────────────┘ + │ snap │ + ├ pc_updates │ + └ solutions[]│ +══════════════╪═══════════════════════════════════════════════════════════════════════════ + │ + ▼ +──────────────────────────────────────────────────────────────────────────────────────────► + Work Unit Time + +Legend: ▓▓▓ = actively working ░░░ = waiting at barrier [hash] = state hash for verification + wut = work unit timestamp PC = pseudo-costs snap = snapshot (local copy) + +*/ + template void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t& Arow) { From deff89912af97f9e25891c078e111554cc64dee0 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 14:03:26 +0000 Subject: [PATCH 296/366] copyright fixes --- cpp/src/dual_simplex/folding.cpp | 2 +- cpp/src/dual_simplex/initial_basis.cpp | 2 +- cpp/src/dual_simplex/phase2.hpp | 2 +- cpp/src/dual_simplex/solve.cpp | 2 +- cpp/src/utilities/models/cpufj_predictor/header.h | 2 +- cpp/src/utilities/models/cpufj_predictor/main.cpp | 2 +- cpp/src/utilities/models/cpufj_predictor/quantize.cpp | 2 +- cpp/src/utilities/models/dualsimplex_predictor/header.h | 2 +- cpp/src/utilities/models/dualsimplex_predictor/main.cpp | 2 +- cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp | 2 +- cpp/src/utilities/models/fj_predictor/header.h | 2 +- cpp/src/utilities/models/fj_predictor/main.cpp | 2 +- cpp/src/utilities/models/fj_predictor/quantize.cpp | 2 +- cpp/src/utilities/models/pdlp_predictor/header.h | 2 +- cpp/src/utilities/models/pdlp_predictor/main.cpp | 2 +- cpp/src/utilities/models/pdlp_predictor/quantize.cpp | 2 +- cpp/src/utilities/version_info.hpp | 2 +- cpp/src/utilities/work_unit_predictor.hpp | 2 +- 18 files changed, 18 insertions(+), 18 deletions(-) diff --git a/cpp/src/dual_simplex/folding.cpp b/cpp/src/dual_simplex/folding.cpp index 3f5dc41ab..b72cdd1b5 100644 --- a/cpp/src/dual_simplex/folding.cpp +++ b/cpp/src/dual_simplex/folding.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/initial_basis.cpp b/cpp/src/dual_simplex/initial_basis.cpp index 7d2ec9288..5da844904 100644 --- a/cpp/src/dual_simplex/initial_basis.cpp +++ b/cpp/src/dual_simplex/initial_basis.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/phase2.hpp b/cpp/src/dual_simplex/phase2.hpp index 54e2b4087..4fd83b8f1 100644 --- a/cpp/src/dual_simplex/phase2.hpp +++ b/cpp/src/dual_simplex/phase2.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/dual_simplex/solve.cpp b/cpp/src/dual_simplex/solve.cpp index 945429b5c..fe1136ab3 100644 --- a/cpp/src/dual_simplex/solve.cpp +++ b/cpp/src/dual_simplex/solve.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/utilities/models/cpufj_predictor/header.h b/cpp/src/utilities/models/cpufj_predictor/header.h index 800072da2..409f669a6 100644 --- a/cpp/src/utilities/models/cpufj_predictor/header.h +++ b/cpp/src/utilities/models/cpufj_predictor/header.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ diff --git a/cpp/src/utilities/models/cpufj_predictor/main.cpp b/cpp/src/utilities/models/cpufj_predictor/main.cpp index 3f4bf0837..ce8fd7c29 100644 --- a/cpp/src/utilities/models/cpufj_predictor/main.cpp +++ b/cpp/src/utilities/models/cpufj_predictor/main.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ diff --git a/cpp/src/utilities/models/cpufj_predictor/quantize.cpp b/cpp/src/utilities/models/cpufj_predictor/quantize.cpp index 0b292d03f..f036eb5cf 100644 --- a/cpp/src/utilities/models/cpufj_predictor/quantize.cpp +++ b/cpp/src/utilities/models/cpufj_predictor/quantize.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ diff --git a/cpp/src/utilities/models/dualsimplex_predictor/header.h b/cpp/src/utilities/models/dualsimplex_predictor/header.h index 990607eba..75dcbb661 100644 --- a/cpp/src/utilities/models/dualsimplex_predictor/header.h +++ b/cpp/src/utilities/models/dualsimplex_predictor/header.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ diff --git a/cpp/src/utilities/models/dualsimplex_predictor/main.cpp b/cpp/src/utilities/models/dualsimplex_predictor/main.cpp index 211e2d097..b89a429c6 100644 --- a/cpp/src/utilities/models/dualsimplex_predictor/main.cpp +++ b/cpp/src/utilities/models/dualsimplex_predictor/main.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ diff --git a/cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp b/cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp index ab62e5687..bad128a30 100644 --- a/cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp +++ b/cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ diff --git a/cpp/src/utilities/models/fj_predictor/header.h b/cpp/src/utilities/models/fj_predictor/header.h index ccae87627..ae58b2591 100644 --- a/cpp/src/utilities/models/fj_predictor/header.h +++ b/cpp/src/utilities/models/fj_predictor/header.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/cpp/src/utilities/models/fj_predictor/main.cpp b/cpp/src/utilities/models/fj_predictor/main.cpp index ac5cd1ed1..4c3d30022 100644 --- a/cpp/src/utilities/models/fj_predictor/main.cpp +++ b/cpp/src/utilities/models/fj_predictor/main.cpp @@ -1,6 +1,6 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/cpp/src/utilities/models/fj_predictor/quantize.cpp b/cpp/src/utilities/models/fj_predictor/quantize.cpp index 4bd50efaf..ef43dd4c9 100644 --- a/cpp/src/utilities/models/fj_predictor/quantize.cpp +++ b/cpp/src/utilities/models/fj_predictor/quantize.cpp @@ -1,6 +1,6 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/cpp/src/utilities/models/pdlp_predictor/header.h b/cpp/src/utilities/models/pdlp_predictor/header.h index 09d2fddc5..49c9cfd0e 100644 --- a/cpp/src/utilities/models/pdlp_predictor/header.h +++ b/cpp/src/utilities/models/pdlp_predictor/header.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ diff --git a/cpp/src/utilities/models/pdlp_predictor/main.cpp b/cpp/src/utilities/models/pdlp_predictor/main.cpp index 6ae348640..d8b0b91d1 100644 --- a/cpp/src/utilities/models/pdlp_predictor/main.cpp +++ b/cpp/src/utilities/models/pdlp_predictor/main.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ diff --git a/cpp/src/utilities/models/pdlp_predictor/quantize.cpp b/cpp/src/utilities/models/pdlp_predictor/quantize.cpp index b0c135615..f5d7ec690 100644 --- a/cpp/src/utilities/models/pdlp_predictor/quantize.cpp +++ b/cpp/src/utilities/models/pdlp_predictor/quantize.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ diff --git a/cpp/src/utilities/version_info.hpp b/cpp/src/utilities/version_info.hpp index ea909e7c1..edb183d64 100644 --- a/cpp/src/utilities/version_info.hpp +++ b/cpp/src/utilities/version_info.hpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ diff --git a/cpp/src/utilities/work_unit_predictor.hpp b/cpp/src/utilities/work_unit_predictor.hpp index 45bc6b97b..4ebee6923 100644 --- a/cpp/src/utilities/work_unit_predictor.hpp +++ b/cpp/src/utilities/work_unit_predictor.hpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights * reserved. SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); From 0b0e65bcc40c4c9b65345965874702272ffa1640 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 14:41:40 +0000 Subject: [PATCH 297/366] review comments --- cpp/src/dual_simplex/branch_and_bound.cpp | 46 +++++++++++++------ cpp/src/dual_simplex/branch_and_bound.hpp | 10 ---- cpp/src/dual_simplex/phase2.cpp | 5 -- cpp/src/dual_simplex/pseudo_costs.cpp | 4 +- .../dual_simplex/simplex_solver_settings.hpp | 1 + cpp/src/dual_simplex/solve.hpp | 1 + cpp/src/mip/diversity/diversity_manager.cu | 1 + cpp/src/mip/utilities/cpu_worker_thread.cuh | 2 + cpp/src/utilities/hashing.hpp | 7 +-- .../utilities/models/fj_predictor/header.h | 2 +- cpp/src/utilities/producer_sync.hpp | 1 + cpp/src/utilities/work_unit_predictor.cpp | 22 ++++----- cpp/src/utilities/work_unit_predictor.hpp | 4 +- cpp/src/utilities/work_unit_scheduler.cpp | 1 + cpp/src/utilities/work_unit_scheduler.hpp | 2 +- cpp/tests/mip/determinism_test.cu | 1 + 16 files changed, 61 insertions(+), 49 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index c472a9bdf..e074fdc7a 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1867,19 +1867,33 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t 0) { - std::vector diving_types = {bnb_worker_type_t::PSEUDOCOST_DIVING, - bnb_worker_type_t::LINE_SEARCH_DIVING, - bnb_worker_type_t::GUIDED_DIVING, - bnb_worker_type_t::COEFFICIENT_DIVING}; - bsp_diving_workers_ = std::make_unique>(); - bsp_diving_workers_->initialize(num_diving_workers, - diving_types, - original_lp_, - Arow, - var_types_, - settings_.refactor_frequency, - settings_.deterministic); - calculate_variable_locks(original_lp_, var_up_locks_, var_down_locks_); + std::vector diving_types; + diving_types.reserve(4); + + if (settings_.diving_settings.pseudocost_diving != 0) { + diving_types.push_back(bnb_worker_type_t::PSEUDOCOST_DIVING); + } + if (settings_.diving_settings.line_search_diving != 0) { + diving_types.push_back(bnb_worker_type_t::LINE_SEARCH_DIVING); + } + if (settings_.diving_settings.guided_diving != 0) { + diving_types.push_back(bnb_worker_type_t::GUIDED_DIVING); + } + if (settings_.diving_settings.coefficient_diving != 0) { + diving_types.push_back(bnb_worker_type_t::COEFFICIENT_DIVING); + calculate_variable_locks(original_lp_, var_up_locks_, var_down_locks_); + } + + if (!diving_types.empty()) { + bsp_diving_workers_ = std::make_unique>(); + bsp_diving_workers_->initialize(num_diving_workers, + diving_types, + original_lp_, + Arow, + var_types_, + settings_.refactor_frequency, + settings_.deterministic); + } } bsp_scheduler_ = std::make_unique(bsp_horizon_step_); @@ -1899,11 +1913,12 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tsize() : 0; settings_.log.printf( "BSP Mode: %d BFS workers + %d diving workers, horizon step = %.2f work " "units\n", num_bfs_workers, - num_diving_workers, + actual_diving_workers, bsp_horizon_step_); search_tree_.root.get_down_child()->origin_worker_id = -1; @@ -3042,7 +3057,8 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() { // Compute lower bound from BFS worker local structures only const f_t inf = std::numeric_limits::infinity(); - f_t lower_bound = inf; + f_t lower_bound = lower_bound_ceiling_.load(); + if (!std::isfinite(lower_bound)) lower_bound = inf; // Check all BFS worker queues for (const auto& worker : *bsp_workers_) { diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 08337d328..1f6b8a561 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -373,16 +373,6 @@ class branch_and_bound_t { double max_producer_wait_time_{0.0}; i_t producer_wait_count_{0}; - // BSP node heap - priority queue for unexplored nodes (ordered by lower bound) - struct node_ptr_lower_bound_comp { - bool operator()(const mip_node_t* a, const mip_node_t* b) const - { - return a->lower_bound > b->lower_bound; - } - }; - heap_t*, node_ptr_lower_bound_comp> heap_; - omp_mutex_t mutex_heap_; - // BSP heuristic solution queue - solutions received from GPU heuristics // Stored with work unit timestamp for deterministic ordering struct queued_heuristic_solution_t { diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 3379e247b..81c25c224 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2554,11 +2554,6 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, manifold.add("atilde_sparse.i", atilde_sparse.i); manifold.add("atilde_sparse.x", atilde_sparse.x); - // Add A_transpose matrix arrays to manifold for memory tracking - manifold.add("A_transpose.col_start", A_transpose.col_start); - manifold.add("A_transpose.i", A_transpose.i); - manifold.add("A_transpose.x", A_transpose.x); - // Add A matrix for entering column access during basis update manifold.add("A.col_start", lp.A.col_start); manifold.add("A.i", lp.A.i); diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 206e45807..dc45728d4 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -274,7 +274,9 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio fractional, solution); - log.printf("pc branching on %d. Value %e.\n", branch_var, solution[branch_var]); + if (branch_var >= 0) { + log.printf("pc branching on %d. Value %e.\n", branch_var, solution[branch_var]); + } return branch_var; } diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 97ca19f47..616254026 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -74,6 +74,7 @@ struct simplex_solver_settings_t { print_presolve_stats(true), barrier_presolve(false), cudss_deterministic(false), + deterministic(false), barrier(false), eliminate_dense_columns(true), num_gpus(1), diff --git a/cpp/src/dual_simplex/solve.hpp b/cpp/src/dual_simplex/solve.hpp index 1ba4bd7b3..6e23f6e4c 100644 --- a/cpp/src/dual_simplex/solve.hpp +++ b/cpp/src/dual_simplex/solve.hpp @@ -43,6 +43,7 @@ static std::string lp_status_to_string(lp_status_t status) case lp_status_t::CUTOFF: return "CUTOFF"; case lp_status_t::CONCURRENT_LIMIT: return "CONCURRENT_LIMIT"; case lp_status_t::UNSET: return "UNSET"; + case lp_status_t::WORK_LIMIT: return "WORK_LIMIT"; } return "UNKNOWN"; } diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 2369bdac8..d29154c57 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -324,6 +324,7 @@ solution_t diversity_manager_t::run_solver() } while (!check_b_b_preemption()) { + if (timer.check_time_limit()) break; std::this_thread::sleep_for(std::chrono::milliseconds(100)); } diff --git a/cpp/src/mip/utilities/cpu_worker_thread.cuh b/cpp/src/mip/utilities/cpu_worker_thread.cuh index a7abddb63..2b982e1f4 100644 --- a/cpp/src/mip/utilities/cpu_worker_thread.cuh +++ b/cpp/src/mip/utilities/cpu_worker_thread.cuh @@ -18,9 +18,11 @@ #pragma once #include +#include #include #include #include +#include namespace cuopt::linear_programming::detail { diff --git a/cpp/src/utilities/hashing.hpp b/cpp/src/utilities/hashing.hpp index a1fa9220f..9e8dc09e1 100644 --- a/cpp/src/utilities/hashing.hpp +++ b/cpp/src/utilities/hashing.hpp @@ -6,12 +6,13 @@ #pragma once #include +#include #include namespace cuopt::linear_programming::detail { template -inline uint32_t compute_hash(std::vector h_contents) +inline uint32_t compute_hash(const std::vector& h_contents) { // FNV-1a hash @@ -26,8 +27,8 @@ inline uint32_t compute_hash(std::vector h_contents) } template -#ifdef __CUDA_ARCH__ -__device__ +#if defined(__CUDACC__) +__host__ __device__ #endif inline uint32_t compute_hash(const i_t val) diff --git a/cpp/src/utilities/models/fj_predictor/header.h b/cpp/src/utilities/models/fj_predictor/header.h index ae58b2591..61215ede6 100644 --- a/cpp/src/utilities/models/fj_predictor/header.h +++ b/cpp/src/utilities/models/fj_predictor/header.h @@ -37,7 +37,7 @@ class fj_predictor { static int32_t get_num_feature(void); static const char* get_threshold_type(void); static const char* get_leaf_output_type(void); - static void predict(union Entry* data, int pred_margin, double* result); + static void predict(Entry* data, int pred_margin, double* result); static void postprocess(double* result); static int quantize(double val, unsigned fid); diff --git a/cpp/src/utilities/producer_sync.hpp b/cpp/src/utilities/producer_sync.hpp index df410fca8..fed4201b8 100644 --- a/cpp/src/utilities/producer_sync.hpp +++ b/cpp/src/utilities/producer_sync.hpp @@ -16,6 +16,7 @@ */ #pragma once +#include #include #include #include diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp index 33c315c59..d218cacb5 100644 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ b/cpp/src/utilities/work_unit_predictor.cpp @@ -51,16 +51,16 @@ float work_unit_predictor_t::predict_scalar( } } - std::vector cache_vec; - cache_vec.reserve(model_t::NUM_FEATURES); - for (int i = 0; i < model_t::NUM_FEATURES; ++i) { - cache_vec.push_back(data[i].missing != -1 ? data[i].fvalue - : std::numeric_limits::quiet_NaN()); - } - uint32_t key = cuopt::linear_programming::detail::compute_hash(cache_vec); + // std::vector cache_vec; + // cache_vec.reserve(model_t::NUM_FEATURES); + // for (int i = 0; i < model_t::NUM_FEATURES; ++i) { + // cache_vec.push_back(data[i].missing != -1 ? data[i].fvalue + // : std::numeric_limits::quiet_NaN()); + // } + // uint32_t key = cuopt::linear_programming::detail::compute_hash(cache_vec); - auto cached_it = prediction_cache.find(key); - if (cached_it != prediction_cache.end()) { return cached_it->second; } + // auto cached_it = prediction_cache.find(key); + // if (cached_it != prediction_cache.end()) { return cached_it->second; } double result = 0.0; auto start = std::chrono::high_resolution_clock::now(); @@ -69,8 +69,8 @@ float work_unit_predictor_t::predict_scalar( std::chrono::duration elapsed = end - start; if (debug) CUOPT_LOG_DEBUG("Prediction time: %f ms", elapsed.count()); - float scaled_result = scaler_.scale_work_units(result); - prediction_cache[key] = scaled_result; + float scaled_result = scaler_.scale_work_units(result); + // prediction_cache[key] = scaled_result; if (debug) CUOPT_LOG_DEBUG("Result: %f (scaled: %f)", result, scaled_result); return scaled_result; diff --git a/cpp/src/utilities/work_unit_predictor.hpp b/cpp/src/utilities/work_unit_predictor.hpp index 4ebee6923..9d65510f0 100644 --- a/cpp/src/utilities/work_unit_predictor.hpp +++ b/cpp/src/utilities/work_unit_predictor.hpp @@ -34,7 +34,7 @@ struct cpu_work_unit_scaler_t { { constexpr double baseline_max_clock = 3800.0; double max_clock = get_cpu_max_clock_mhz(); - scaling_factor_ = baseline_max_clock / max_clock; + scaling_factor_ = max_clock == 0 ? 1 : baseline_max_clock / max_clock; } double scale_work_units(double work_units) const { return work_units * scaling_factor_; } @@ -56,7 +56,7 @@ class work_unit_predictor_t { bool debug{false}; private: - mutable std::unordered_map prediction_cache; + // mutable std::unordered_map prediction_cache; scaler_t scaler_; }; diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp index 66f3bfae0..11a3c1f62 100644 --- a/cpp/src/utilities/work_unit_scheduler.cpp +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -103,6 +103,7 @@ sync_result_t work_unit_scheduler_t::wait_for_next_sync(work_limit_context_t& ct double work_unit_scheduler_t::current_sync_target() const { if (sync_interval_ <= 0) return std::numeric_limits::infinity(); + std::unique_lock lock(mutex_); return (barrier_generation_ + 1) * sync_interval_; } diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index 1120c29ff..773117b51 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -66,7 +66,7 @@ class work_unit_scheduler_t { std::vector> contexts_; std::unordered_map last_sync_target_; - std::mutex mutex_; + mutable std::mutex mutex_; std::condition_variable cv_; size_t contexts_at_barrier_{0}; double current_sync_target_{0}; diff --git a/cpp/tests/mip/determinism_test.cu b/cpp/tests/mip/determinism_test.cu index b2968c0bc..7806b8ffb 100644 --- a/cpp/tests/mip/determinism_test.cu +++ b/cpp/tests/mip/determinism_test.cu @@ -165,6 +165,7 @@ TEST_P(DeterministicBBInstanceTest, deterministic_across_runs) settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; settings.num_cpu_threads = num_threads; settings.work_limit = work_limit; + settings.seed = seed; cuopt::seed_generator::set_seed(seed); auto solution1 = solve_mip(&handle_, problem, settings); From c5438a48e175e883c1b260d47923aefa049b46e4 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 16:52:51 +0000 Subject: [PATCH 298/366] fix wait_for_producers target --- cpp/src/dual_simplex/branch_and_bound.cpp | 7 ++++++- cpp/src/mip/solver.cu | 8 ++++---- cpp/tests/mip/determinism_test.cu | 3 ++- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index e074fdc7a..5bbf8b5ec 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2113,7 +2113,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) double horizon_end = bsp_current_horizon_; double wait_start = tic(); - producer_sync_.wait_for_producers(horizon_start); + producer_sync_.wait_for_producers(horizon_end); double wait_time = toc(wait_start); total_producer_wait_time_ += wait_time; max_producer_wait_time_ = std::max(max_producer_wait_time_, wait_time); @@ -2820,6 +2820,11 @@ void branch_and_bound_t::process_history_and_sync( if (process_heuristic) { const auto& hsol = heuristic_solutions[heuristic_idx++]; + CUOPT_LOG_TRACE( + "BSP sync: Heuristic solution received at WUT %f with objective %g, current horizon %f", + hsol.wut, + hsol.objective, + bsp_current_horizon_); // Debug: Log heuristic received BSP_DEBUG_LOG_HEURISTIC_RECEIVED( bsp_debug_settings_, bsp_debug_logger_, hsol.wut, hsol.objective); diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 954b35eef..586b005e6 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -236,10 +236,10 @@ solution_t mip_solver_t::run_solver() } else if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { branch_and_bound->set_concurrent_lp_root_solve(false); // TODO once deterministic GPU heuristics are integrated - context.problem_ptr->branch_and_bound_callback = - [bb = branch_and_bound.get()](const std::vector& solution) { - bb->set_new_solution_deterministic(solution, 0.0); - }; + // context.problem_ptr->branch_and_bound_callback = + // [bb = branch_and_bound.get()](const std::vector& solution) { + // bb->set_new_solution_deterministic(solution, 0.0); + // }; } context.work_unit_scheduler_.register_context(branch_and_bound->get_work_unit_context()); diff --git a/cpp/tests/mip/determinism_test.cu b/cpp/tests/mip/determinism_test.cu index 7806b8ffb..ff2922f8f 100644 --- a/cpp/tests/mip/determinism_test.cu +++ b/cpp/tests/mip/determinism_test.cu @@ -198,7 +198,8 @@ INSTANTIATE_TEST_SUITE_P( std::make_tuple("/mip/bb_optimality.mps", 4, 60.0, 4), std::make_tuple("/mip/neos5.mps", 16, 60.0, 1), std::make_tuple("/mip/seymour1.mps", 16, 60.0, 1), - std::make_tuple("/mip/n2seq36q.mps", 16, 60.0, 4), + // too heavy for CI + // std::make_tuple("/mip/n2seq36q.mps", 16, 60.0, 4), std::make_tuple("/mip/gmu-35-50.mps", 32, 60.0, 3)), [](const ::testing::TestParamInfo& info) { const auto& path = std::get<0>(info.param); From 31b5285666ea66143a793cf5ebfb6ce412b50b86 Mon Sep 17 00:00:00 2001 From: nicolas Date: Mon, 26 Jan 2026 18:17:41 +0100 Subject: [PATCH 299/366] set the reliable threshold dynamically --- cpp/src/dual_simplex/pseudo_costs.cpp | 41 ++++++++++++------- .../dual_simplex/simplex_solver_settings.hpp | 29 +++++++++---- 2 files changed, 49 insertions(+), 21 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 6b6c2d0bd..8fa5adb2b 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -375,6 +375,7 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio i_t branch_var = fractional[0]; f_t max_score = -1; i_t select = -1; + for (i_t k = 0; k < num_fractional; k++) { if (score[k] > max_score) { max_score = score[k]; @@ -418,17 +419,27 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_down_avg, pseudo_cost_up_avg); - // const i_t max_iter = 0.5 * bnb_lp_iter; - // const i_t gamma = (max_iter - total_lp_iter) / (total_lp_iter + 1); - // const i_t max_v = 5; - // const i_t min_v = 1; - // i_t reliable_threshold = std::clamp((1 - gamma) * min_v + gamma * max_v, min_v, max_v); - // reliable_threshold = total_lp_iter < max_iter ? reliable_threshold : 0; const int64_t bnb_total_lp_iter = bnb_stats.total_lp_iters; const int64_t bnb_nodes_explored = bnb_stats.nodes_explored; - const i_t bnb_lp_iter_per_node = - bnb_stats.total_lp_iters.load() / bnb_stats.nodes_explored.load(); - const i_t reliable_threshold = settings.reliability_branching_settings.reliable_threshold; + const i_t bnb_lp_iter_per_node = bnb_total_lp_iter / bnb_stats.nodes_explored; + + const i_t max_threshold = settings.reliability_branching_settings.max_reliable_threshold; + const i_t min_threshold = settings.reliability_branching_settings.min_reliable_threshold; + const i_t iter_factor = settings.reliability_branching_settings.bnb_lp_factor; + const i_t iter_offset = settings.reliability_branching_settings.bnb_lp_offset; + const int64_t alpha = iter_factor * bnb_total_lp_iter; + const int64_t max_iter = alpha + settings.reliability_branching_settings.bnb_lp_offset; + + i_t reliable_threshold = settings.reliability_branching_settings.reliable_threshold; + if (reliable_threshold < 0) { + i_t gamma = (max_iter - sb_total_lp_iter) / (sb_total_lp_iter + 1); + gamma = std::min(1, gamma); + gamma = std::max((alpha - sb_total_lp_iter) / (sb_total_lp_iter + 1), gamma); + + reliable_threshold = (1 - gamma) * min_threshold + gamma * max_threshold; + reliable_threshold = sb_total_lp_iter < max_iter ? reliable_threshold : 0; + } + std::vector unreliable_list; omp_mutex_t score_mutex; @@ -483,11 +494,13 @@ i_t pseudo_costs_t::reliable_variable_selection( num_tasks = std::clamp(num_tasks, 1, max_tasks); assert(num_tasks > 0); - log.printf("RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d\n", - sb_total_lp_iter.load(), - bnb_total_lp_iter, - unreliable_list.size(), - num_tasks); + settings.log.printf( + "RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d, reliable_threshold = %d\n", + sb_total_lp_iter.load(), + bnb_total_lp_iter, + unreliable_list.size(), + num_tasks, + reliable_threshold); #pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) untied for (int task_id = 0; task_id < num_tasks; ++task_id) { diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 223f11bbc..fc32b44c7 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -48,12 +48,6 @@ struct reliability_branching_settings_t { // Enable or disable reliability branching bool enable = false; - // For now, setting to 1, which correspond to pseudocost with strong branching - // initialization. Later, it can be set dynamically depending on the number - // of LP iterations in the strong branching and B&B. - // Set to 0 for disabling reliability branching - i_t reliable_threshold = 1; - // Lower bound for the maximum number of LP iterations for a single trial branching i_t lower_max_lp_iter = 10; @@ -72,7 +66,28 @@ struct reliability_branching_settings_t { i_t max_num_candidates = 100; // The maximum number of candidates evaluated that does not improve the best score. - i_t max_lookahead = 20; + i_t max_lookahead = 10; + + // Define the maximum number of iteration spent in strong branching. + // Let `bnb_lp_iter` = total number of iterations in B&B, then + // `max iter in strong branching = bnb_lp_factor * bnb_lp_iter + bnb_lp_offset`. + // This is used for determining the `reliable_threshold`. + i_t bnb_lp_factor = 0.5; + i_t bnb_lp_offset = 100000; + + // Threshold for determining for the number of pseudocost updates. Used for + // determining if the pseudocost is reliable or not. + // - <0: automatic + // - 0: disable (use pseudocost branching instead) + // - >0: will use the value for the threshold. + i_t reliable_threshold = -1; + + // Maximum and minimum values for `reliable_threshold`. If strong branching is + // cheap, then the value of the `reliable_threshold` can be greater + // than the `max_reliable_threshold`. + // Only used when `reliable_threshold` is negative + i_t max_reliable_threshold = 5; + i_t min_reliable_threshold = 1; }; template From 433ae0ee3c3ac68f6a28068c6da34e8b26e6a0a3 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Mon, 26 Jan 2026 18:55:25 +0000 Subject: [PATCH 300/366] no cpufj for bench --- cpp/src/mip/diversity/diversity_manager.cu | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index d29154c57..965faf9f5 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -320,8 +320,9 @@ solution_t diversity_manager_t::run_solver() // Start CPUFJ in deterministic mode with B&B integration if (context.branch_and_bound_ptr != nullptr) { - ls.start_cpufj_deterministic(*context.branch_and_bound_ptr); + // ls.start_cpufj_deterministic(*context.branch_and_bound_ptr); } + context.branch_and_bound_ptr->get_producer_sync().registration_complete(); while (!check_b_b_preemption()) { if (timer.check_time_limit()) break; @@ -329,7 +330,7 @@ solution_t diversity_manager_t::run_solver() } // Stop CPUFJ when B&B is done - ls.stop_cpufj_deterministic(); + // ls.stop_cpufj_deterministic(); population.add_external_solutions_to_population(); return population.best_feasible(); From 9448dd789ef25cfd8ca12becaa97a4034969cd82 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 27 Jan 2026 11:09:28 +0000 Subject: [PATCH 301/366] Revert "no cpufj for bench" This reverts commit 433ae0ee3c3ac68f6a28068c6da34e8b26e6a0a3. --- cpp/src/mip/diversity/diversity_manager.cu | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index 965faf9f5..d29154c57 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -320,9 +320,8 @@ solution_t diversity_manager_t::run_solver() // Start CPUFJ in deterministic mode with B&B integration if (context.branch_and_bound_ptr != nullptr) { - // ls.start_cpufj_deterministic(*context.branch_and_bound_ptr); + ls.start_cpufj_deterministic(*context.branch_and_bound_ptr); } - context.branch_and_bound_ptr->get_producer_sync().registration_complete(); while (!check_b_b_preemption()) { if (timer.check_time_limit()) break; @@ -330,7 +329,7 @@ solution_t diversity_manager_t::run_solver() } // Stop CPUFJ when B&B is done - // ls.stop_cpufj_deterministic(); + ls.stop_cpufj_deterministic(); population.add_external_solutions_to_population(); return population.best_feasible(); From 7bac62d71fa257b9827eb152e309dbeaa0d9f00d Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 27 Jan 2026 14:31:36 +0100 Subject: [PATCH 302/366] fixed incorrect threshold formula. fixed time limit violation. --- .../linear_programming/cuopt/run_mip.cpp | 2 + cpp/src/dual_simplex/pseudo_costs.cpp | 60 +++++++++---------- .../dual_simplex/simplex_solver_settings.hpp | 11 ++-- 3 files changed, 35 insertions(+), 38 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 3f9346201..f1259c7a1 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -198,6 +198,8 @@ int run_single_file(std::string file_path, } } + CUOPT_LOG_INFO("Reliability branching: %d\n", enable_rb); + settings.time_limit = time_limit; settings.heuristics_only = heuristics_only; settings.num_cpu_threads = num_cpu_threads; diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 8fa5adb2b..330f2369c 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -147,6 +147,7 @@ f_t trial_branching(const lp_problem_t& original_lp, f_t branch_var_upper, f_t upper_bound, i_t bnb_lp_iter_per_node, + f_t start_time, omp_atomic_t& total_lp_iter) { lp_problem_t child_problem = original_lp; @@ -155,7 +156,6 @@ f_t trial_branching(const lp_problem_t& original_lp, simplex_solver_settings_t child_settings = settings; child_settings.set_log(false); - f_t lp_start_time = tic(); i_t lp_iter_upper = settings.reliability_branching_settings.upper_max_lp_iter; i_t lp_iter_lower = settings.reliability_branching_settings.lower_max_lp_iter; child_settings.iteration_limit = std::clamp(bnb_lp_iter_per_node, lp_iter_lower, lp_iter_upper); @@ -174,7 +174,7 @@ f_t trial_branching(const lp_problem_t& original_lp, dual::status_t status = dual_phase2_with_advanced_basis(2, 0, false, - lp_start_time, + start_time, child_problem, child_settings, child_vstatus, @@ -404,6 +404,7 @@ i_t pseudo_costs_t::reliable_variable_selection( f_t upper_bound, logger_t& log) { + f_t start_time = bnb_stats.start_time; i_t branch_var = fractional[0]; f_t max_score = -1; i_t num_initialized_down; @@ -423,19 +424,18 @@ i_t pseudo_costs_t::reliable_variable_selection( const int64_t bnb_nodes_explored = bnb_stats.nodes_explored; const i_t bnb_lp_iter_per_node = bnb_total_lp_iter / bnb_stats.nodes_explored; - const i_t max_threshold = settings.reliability_branching_settings.max_reliable_threshold; - const i_t min_threshold = settings.reliability_branching_settings.min_reliable_threshold; - const i_t iter_factor = settings.reliability_branching_settings.bnb_lp_factor; - const i_t iter_offset = settings.reliability_branching_settings.bnb_lp_offset; - const int64_t alpha = iter_factor * bnb_total_lp_iter; - const int64_t max_iter = alpha + settings.reliability_branching_settings.bnb_lp_offset; - i_t reliable_threshold = settings.reliability_branching_settings.reliable_threshold; if (reliable_threshold < 0) { - i_t gamma = (max_iter - sb_total_lp_iter) / (sb_total_lp_iter + 1); - gamma = std::min(1, gamma); - gamma = std::max((alpha - sb_total_lp_iter) / (sb_total_lp_iter + 1), gamma); - + const i_t max_threshold = settings.reliability_branching_settings.max_reliable_threshold; + const i_t min_threshold = settings.reliability_branching_settings.min_reliable_threshold; + const f_t iter_factor = settings.reliability_branching_settings.bnb_lp_factor; + const i_t iter_offset = settings.reliability_branching_settings.bnb_lp_offset; + const int64_t alpha = iter_factor * bnb_total_lp_iter; + const int64_t max_iter = alpha + settings.reliability_branching_settings.bnb_lp_offset; + + f_t gamma = (max_iter - sb_total_lp_iter) / (sb_total_lp_iter + 1.0); + gamma = std::min(1.0, gamma); + gamma = std::max((alpha - sb_total_lp_iter) / (sb_total_lp_iter + 1.0), gamma); reliable_threshold = (1 - gamma) * min_threshold + gamma * max_threshold; reliable_threshold = sb_total_lp_iter < max_iter ? reliable_threshold : 0; } @@ -477,24 +477,18 @@ i_t pseudo_costs_t::reliable_variable_selection( const int task_priority = settings.reliability_branching_settings.task_priority; const i_t max_num_candidates = settings.reliability_branching_settings.max_num_candidates; - const i_t max_lookahead = settings.reliability_branching_settings.max_lookahead; const i_t num_sb_vars = std::min(unreliable_list.size(), max_num_candidates); assert(task_priority > 0); assert(max_num_candidates > 0); - assert(max_lookahead > 0); assert(num_sb_vars > 0); - // Shuffle the unreliable list so every variable has the same chance to be selected. - if (unreliable_list.size() > max_num_candidates) { worker_data->rng.shuffle(unreliable_list); } - - omp_atomic_t unchanged = 0; - const i_t max_tasks = std::clamp(num_sb_vars / 2, 1, max_lookahead); - i_t num_tasks = settings.reliability_branching_settings.num_tasks; - num_tasks = std::clamp(num_tasks, 1, max_tasks); + const i_t max_tasks = std::max(num_sb_vars / 2, 1); + i_t num_tasks = settings.reliability_branching_settings.num_tasks; + num_tasks = std::clamp(num_tasks, 1, max_tasks); assert(num_tasks > 0); - settings.log.printf( + log.printf( "RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d, reliable_threshold = %d\n", sb_total_lp_iter.load(), bnb_total_lp_iter, @@ -502,17 +496,19 @@ i_t pseudo_costs_t::reliable_variable_selection( num_tasks, reliable_threshold); + // Shuffle the unreliable list so every variable has the same chance to be selected. + if (unreliable_list.size() > max_num_candidates) { worker_data->rng.shuffle(unreliable_list); } + #pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) untied for (int task_id = 0; task_id < num_tasks; ++task_id) { size_t start = (double)task_id * num_sb_vars / num_tasks; size_t end = (double)(task_id + 1) * num_sb_vars / num_tasks; for (i_t i = start; i < end; ++i) { - if (unchanged > max_lookahead) { break; } + if (toc(start_time) > settings.time_limit) { break; } const i_t j = unreliable_list[i]; pseudo_cost_mutex[j].lock(); - if (pseudo_cost_num_down[j] < reliable_threshold) { // Do trial branching on the down branch f_t obj = trial_branching(worker_data->leaf_problem, @@ -528,7 +524,9 @@ i_t pseudo_costs_t::reliable_variable_selection( std::floor(solution[j]), upper_bound, bnb_lp_iter_per_node, + start_time, sb_total_lp_iter); + if (!std::isnan(obj)) { f_t change_in_obj = obj - node_ptr->lower_bound; f_t change_in_x = solution[j] - std::floor(solution[j]); @@ -536,7 +534,10 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_num_down[j]++; } } + pseudo_cost_mutex[j].unlock(); + if (toc(start_time) > settings.time_limit) { break; } + pseudo_cost_mutex[j].lock(); if (pseudo_cost_num_up[j] < reliable_threshold) { f_t obj = trial_branching(worker_data->leaf_problem, settings, @@ -551,6 +552,7 @@ i_t pseudo_costs_t::reliable_variable_selection( worker_data->leaf_problem.upper[j], upper_bound, bnb_lp_iter_per_node, + start_time, sb_total_lp_iter); if (!std::isnan(obj)) { @@ -560,14 +562,13 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_num_up[j]++; } } + pseudo_cost_mutex[j].unlock(); + if (toc(start_time) > settings.time_limit) { break; } f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] : pseudo_cost_up_avg; f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] : pseudo_cost_down_avg; - - pseudo_cost_mutex[j].unlock(); - constexpr f_t eps = 1e-6; const f_t f_down = solution[j] - std::floor(solution[j]); const f_t f_up = std::ceil(solution[j]) - solution[j]; @@ -577,9 +578,6 @@ i_t pseudo_costs_t::reliable_variable_selection( if (score > max_score) { max_score = score; branch_var = j; - unchanged = 0; - } else { - unchanged++; } score_mutex.unlock(); } diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index fc32b44c7..2fdaf0356 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -65,14 +65,11 @@ struct reliability_branching_settings_t { // node i_t max_num_candidates = 100; - // The maximum number of candidates evaluated that does not improve the best score. - i_t max_lookahead = 10; - // Define the maximum number of iteration spent in strong branching. // Let `bnb_lp_iter` = total number of iterations in B&B, then // `max iter in strong branching = bnb_lp_factor * bnb_lp_iter + bnb_lp_offset`. // This is used for determining the `reliable_threshold`. - i_t bnb_lp_factor = 0.5; + f_t bnb_lp_factor = 0.5; i_t bnb_lp_offset = 100000; // Threshold for determining for the number of pseudocost updates. Used for @@ -82,9 +79,9 @@ struct reliability_branching_settings_t { // - >0: will use the value for the threshold. i_t reliable_threshold = -1; - // Maximum and minimum values for `reliable_threshold`. If strong branching is - // cheap, then the value of the `reliable_threshold` can be greater - // than the `max_reliable_threshold`. + // Maximum and minimum points of the curve to determine the value + // of the `reliable_threshold` based on the current number of LP + // iterations in strong branching and B&B. // Only used when `reliable_threshold` is negative i_t max_reliable_threshold = 5; i_t min_reliable_threshold = 1; From a6e055c9739bc1cd266bf133389f33b97667410d Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 27 Jan 2026 14:47:35 +0100 Subject: [PATCH 303/366] simplified parallel loop --- cpp/src/dual_simplex/pseudo_costs.cpp | 155 ++++++++++++-------------- 1 file changed, 73 insertions(+), 82 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 330f2369c..e74684a69 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -475,18 +475,14 @@ i_t pseudo_costs_t::reliable_variable_selection( return branch_var; } + const int num_tasks = std::max(settings.reliability_branching_settings.num_tasks, 1); const int task_priority = settings.reliability_branching_settings.task_priority; const i_t max_num_candidates = settings.reliability_branching_settings.max_num_candidates; - const i_t num_sb_vars = std::min(unreliable_list.size(), max_num_candidates); + const i_t num_candidates = std::min(unreliable_list.size(), max_num_candidates); assert(task_priority > 0); assert(max_num_candidates > 0); - assert(num_sb_vars > 0); - - const i_t max_tasks = std::max(num_sb_vars / 2, 1); - i_t num_tasks = settings.reliability_branching_settings.num_tasks; - num_tasks = std::clamp(num_tasks, 1, max_tasks); - assert(num_tasks > 0); + assert(num_candidates > 0); log.printf( "RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d, reliable_threshold = %d\n", @@ -500,87 +496,82 @@ i_t pseudo_costs_t::reliable_variable_selection( if (unreliable_list.size() > max_num_candidates) { worker_data->rng.shuffle(unreliable_list); } #pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) untied - for (int task_id = 0; task_id < num_tasks; ++task_id) { - size_t start = (double)task_id * num_sb_vars / num_tasks; - size_t end = (double)(task_id + 1) * num_sb_vars / num_tasks; - - for (i_t i = start; i < end; ++i) { - if (toc(start_time) > settings.time_limit) { break; } + for (i_t i = 0; i < num_candidates; ++i) { + if (toc(start_time) > settings.time_limit) { continue; } - const i_t j = unreliable_list[i]; - pseudo_cost_mutex[j].lock(); - if (pseudo_cost_num_down[j] < reliable_threshold) { - // Do trial branching on the down branch - f_t obj = trial_branching(worker_data->leaf_problem, - settings, - var_types, - node_ptr->vstatus, - worker_data->leaf_edge_norms, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, - j, - worker_data->leaf_problem.lower[j], - std::floor(solution[j]), - upper_bound, - bnb_lp_iter_per_node, - start_time, - sb_total_lp_iter); - - if (!std::isnan(obj)) { - f_t change_in_obj = obj - node_ptr->lower_bound; - f_t change_in_x = solution[j] - std::floor(solution[j]); - pseudo_cost_sum_down[j] += change_in_obj / change_in_x; - pseudo_cost_num_down[j]++; - } + const i_t j = unreliable_list[i]; + pseudo_cost_mutex[j].lock(); + if (pseudo_cost_num_down[j] < reliable_threshold) { + // Do trial branching on the down branch + f_t obj = trial_branching(worker_data->leaf_problem, + settings, + var_types, + node_ptr->vstatus, + worker_data->leaf_edge_norms, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, + j, + worker_data->leaf_problem.lower[j], + std::floor(solution[j]), + upper_bound, + bnb_lp_iter_per_node, + start_time, + sb_total_lp_iter); + + if (!std::isnan(obj)) { + f_t change_in_obj = obj - node_ptr->lower_bound; + f_t change_in_x = solution[j] - std::floor(solution[j]); + pseudo_cost_sum_down[j] += change_in_obj / change_in_x; + pseudo_cost_num_down[j]++; } - pseudo_cost_mutex[j].unlock(); - if (toc(start_time) > settings.time_limit) { break; } + } + pseudo_cost_mutex[j].unlock(); + if (toc(start_time) > settings.time_limit) { continue; } - pseudo_cost_mutex[j].lock(); - if (pseudo_cost_num_up[j] < reliable_threshold) { - f_t obj = trial_branching(worker_data->leaf_problem, - settings, - var_types, - node_ptr->vstatus, - worker_data->leaf_edge_norms, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, - j, - std::ceil(solution[j]), - worker_data->leaf_problem.upper[j], - upper_bound, - bnb_lp_iter_per_node, - start_time, - sb_total_lp_iter); - - if (!std::isnan(obj)) { - f_t change_in_obj = obj - node_ptr->lower_bound; - f_t change_in_x = std::ceil(solution[j]) - solution[j]; - pseudo_cost_sum_up[j] += change_in_obj / change_in_x; - pseudo_cost_num_up[j]++; - } + pseudo_cost_mutex[j].lock(); + if (pseudo_cost_num_up[j] < reliable_threshold) { + f_t obj = trial_branching(worker_data->leaf_problem, + settings, + var_types, + node_ptr->vstatus, + worker_data->leaf_edge_norms, + worker_data->basis_factors, + worker_data->basic_list, + worker_data->nonbasic_list, + j, + std::ceil(solution[j]), + worker_data->leaf_problem.upper[j], + upper_bound, + bnb_lp_iter_per_node, + start_time, + sb_total_lp_iter); + + if (!std::isnan(obj)) { + f_t change_in_obj = obj - node_ptr->lower_bound; + f_t change_in_x = std::ceil(solution[j]) - solution[j]; + pseudo_cost_sum_up[j] += change_in_obj / change_in_x; + pseudo_cost_num_up[j]++; } - pseudo_cost_mutex[j].unlock(); - if (toc(start_time) > settings.time_limit) { break; } + } + pseudo_cost_mutex[j].unlock(); + if (toc(start_time) > settings.time_limit) { continue; } - f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); - - score_mutex.lock(); - if (score > max_score) { - max_score = score; - branch_var = j; - } - score_mutex.unlock(); + f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] + : pseudo_cost_up_avg; + f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] + : pseudo_cost_down_avg; + constexpr f_t eps = 1e-6; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); + + score_mutex.lock(); + if (score > max_score) { + max_score = score; + branch_var = j; } + score_mutex.unlock(); } log.printf( From de4389b67fe26a2d9f65c6bdada230514f2a9703 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 27 Jan 2026 15:56:57 +0100 Subject: [PATCH 304/366] added single-threaded mode for rins and submip --- cpp/src/dual_simplex/branch_and_bound.cpp | 350 +++++++++++------- cpp/src/dual_simplex/branch_and_bound.hpp | 19 +- cpp/src/dual_simplex/solve.cpp | 6 +- cpp/src/mip/diversity/lns/rins.cu | 15 +- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 11 +- cpp/src/mip/solver.cu | 3 +- 6 files changed, 239 insertions(+), 165 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 8bb9505c0..51e99e0d9 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -816,7 +816,8 @@ std::pair branch_and_bound_t::upd } template -void branch_and_bound_t::plunge_with(bnb_worker_data_t* worker_data) +void branch_and_bound_t::plunge_with(bnb_worker_data_t* worker_data, + mip_solve_mode_t mode) { std::deque*> stack; stack.push_front(worker_data->start_node); @@ -911,8 +912,10 @@ void branch_and_bound_t::plunge_with(bnb_worker_data_t* work } } - worker_pool_.return_worker_to_pool(worker_data); - active_workers_per_type[BEST_FIRST]--; + if (mode == mip_solve_mode_t::BNB_PARALLEL) { + worker_pool_.return_worker_to_pool(worker_data); + active_workers_per_type_[BEST_FIRST]--; + } } template @@ -990,7 +993,201 @@ void branch_and_bound_t::dive_with(bnb_worker_data_t* worker } worker_pool_.return_worker_to_pool(worker_data); - active_workers_per_type[diving_type]--; + active_workers_per_type_[diving_type]--; +} + +template +void branch_and_bound_t::run_scheduler() +{ + diving_heuristics_settings_t diving_settings = settings_.diving_settings; + const i_t num_workers = 2 * settings_.num_threads; + + if (!std::isfinite(upper_bound_)) { diving_settings.guided_diving = false; } + std::vector worker_types = bnb_get_worker_types(diving_settings); + std::array max_num_workers_per_type = + bnb_get_max_workers(num_workers, worker_types); + +#ifdef CUOPT_LOG_DEBUG + for (auto type : worker_types) { + settings_.log.debug("%c%d: max num of workers = %d", + feasible_solution_symbol(type), + type, + max_num_workers_per_type[type]); + } +#endif + + worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); + active_workers_per_type_.fill(0); + + f_t lower_bound = get_lower_bound(); + f_t abs_gap = upper_bound_ - lower_bound; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); + i_t last_node_depth = 0; + + while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && + rel_gap > settings_.relative_mip_gap_tol && + (active_workers_per_type_[0] > 0 || node_queue_.best_first_queue_size() > 0)) { + bool launched_any_task = false; + lower_bound = get_lower_bound(); + abs_gap = upper_bound_ - lower_bound; + rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); + + repair_heuristic_solutions(); + + // If the guided diving was disabled previously due to the lack of an incumbent solution, + // re-enable as soon as a new incumbent is found. + if (settings_.diving_settings.guided_diving != diving_settings.guided_diving) { + if (std::isfinite(upper_bound_)) { + diving_settings.guided_diving = settings_.diving_settings.guided_diving; + worker_types = bnb_get_worker_types(diving_settings); + max_num_workers_per_type = bnb_get_max_workers(num_workers, worker_types); + +#ifdef CUOPT_LOG_DEBUG + for (auto type : worker_types) { + settings_.log.debug("%c%d: max num of workers = %d", + feasible_solution_symbol(type), + type, + max_num_workers_per_type[type]); + } +#endif + } + } + + f_t now = toc(exploration_stats_.start_time); + f_t time_since_last_log = + exploration_stats_.last_log == 0 ? 1.0 : toc(exploration_stats_.last_log); + i_t nodes_since_last_log = exploration_stats_.nodes_since_last_log; + + if (((nodes_since_last_log >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && + time_since_last_log >= 1) || + (time_since_last_log > 30) || now > settings_.time_limit) { + i_t depth = + node_queue_.best_first_queue_size() > 0 ? node_queue_.bfs_top()->depth : last_node_depth; + report(' ', upper_bound_, lower_bound, depth); + exploration_stats_.last_log = tic(); + exploration_stats_.nodes_since_last_log = 0; + } + + if (now > settings_.time_limit) { + solver_status_ = mip_status_t::TIME_LIMIT; + break; + } + + for (auto type : worker_types) { + if (active_workers_per_type_[type] >= max_num_workers_per_type[type]) { continue; } + + // Get an idle worker. + bnb_worker_data_t* worker = worker_pool_.get_idle_worker(); + if (worker == nullptr) { break; } + + if (type == BEST_FIRST) { + // If there any node left in the heap, we pop the top node and explore it. + std::optional*> start_node = node_queue_.pop_best_first(); + + if (!start_node.has_value()) { continue; } + if (upper_bound_ < start_node.value()->lower_bound) { + // This node was put on the heap earlier but its lower bound is now greater than the + // current upper bound + search_tree_.graphviz_node( + settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); + search_tree_.update(start_node.value(), node_status_t::FATHOMED); + continue; + } + + // Remove the worker from the idle list. + worker_pool_.pop_idle_worker(); + worker->init_best_first(start_node.value(), original_lp_); + last_node_depth = start_node.value()->depth; + active_workers_per_type_[type]++; + launched_any_task = true; + +#pragma omp task affinity(worker) + plunge_with(worker, mip_solve_mode_t::BNB_PARALLEL); + + } else { + std::optional*> start_node = node_queue_.pop_diving(); + + if (!start_node.has_value()) { continue; } + if (upper_bound_ < start_node.value()->lower_bound || + start_node.value()->depth < diving_settings.min_node_depth) { + continue; + } + + bool is_feasible = worker->init_diving(start_node.value(), type, original_lp_, settings_); + if (!is_feasible) { continue; } + + // Remove the worker from the idle list. + worker_pool_.pop_idle_worker(); + active_workers_per_type_[type]++; + launched_any_task = true; + +#pragma omp task affinity(worker) + dive_with(worker); + } + } + + // If no new task was launched in this iteration, suspend temporarily the + // execution of the master. As of 8/Jan/2026, GCC does not + // implement taskyield, but LLVM does. + if (!launched_any_task) { std::this_thread::sleep_for(std::chrono::milliseconds(1)); } + } +} + +template +void branch_and_bound_t::single_threaded_solve() +{ + bnb_worker_data_t worker(0, original_lp_, Arow_, var_types_, settings_); + + f_t lower_bound = get_lower_bound(); + f_t abs_gap = upper_bound_ - lower_bound; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); + i_t last_node_depth = 0; + + while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && + rel_gap > settings_.relative_mip_gap_tol && node_queue_.best_first_queue_size() > 0) { + bool launched_any_task = false; + lower_bound = get_lower_bound(); + abs_gap = upper_bound_ - lower_bound; + rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); + + repair_heuristic_solutions(); + + f_t now = toc(exploration_stats_.start_time); + f_t time_since_last_log = + exploration_stats_.last_log == 0 ? 1.0 : toc(exploration_stats_.last_log); + i_t nodes_since_last_log = exploration_stats_.nodes_since_last_log; + + if (((nodes_since_last_log >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && + time_since_last_log >= 1) || + (time_since_last_log > 30) || now > settings_.time_limit) { + i_t depth = + node_queue_.best_first_queue_size() > 0 ? node_queue_.bfs_top()->depth : last_node_depth; + report(' ', upper_bound_, lower_bound, depth); + exploration_stats_.last_log = tic(); + exploration_stats_.nodes_since_last_log = 0; + } + + if (now > settings_.time_limit) { + solver_status_ = mip_status_t::TIME_LIMIT; + break; + } + + // If there any node left in the heap, we pop the top node and explore it. + std::optional*> start_node = node_queue_.pop_best_first(); + + if (!start_node.has_value()) { continue; } + if (upper_bound_ < start_node.value()->lower_bound) { + // This node was put on the heap earlier but its lower bound is now greater than the + // current upper bound + search_tree_.graphviz_node( + settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); + search_tree_.update(start_node.value(), node_status_t::FATHOMED); + continue; + } + + worker.init_best_first(start_node.value(), original_lp_); + plunge_with(&worker, mip_solve_mode_t::BNB_SINGLE_THREADED); + } } template @@ -1097,7 +1294,8 @@ lp_status_t branch_and_bound_t::solve_root_relaxation( } template -mip_status_t branch_and_bound_t::solve(mip_solution_t& solution) +mip_status_t branch_and_bound_t::solve(mip_solution_t& solution, + mip_solve_mode_t solve_mode) { logger_t log; log.log = false; @@ -1131,6 +1329,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut simplex_solver_settings_t lp_settings = settings_; lp_settings.inside_mip = 1; lp_settings.concurrent_halt = get_root_concurrent_halt(); + // RINS/SUBMIP path if (!enable_concurrent_lp_root_solve()) { settings_.log.printf("\nSolving LP root relaxation with dual simplex\n"); @@ -1254,26 +1453,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut node_queue_.push(search_tree_.root.get_down_child()); node_queue_.push(search_tree_.root.get_up_child()); - diving_heuristics_settings_t diving_settings = settings_.diving_settings; - const i_t num_workers = 2 * settings_.num_threads; - - if (!std::isfinite(upper_bound_)) { diving_settings.guided_diving = false; } - std::vector worker_types = bnb_get_worker_types(diving_settings); - std::array max_num_workers_per_type = - bnb_get_max_workers(num_workers, worker_types); - -#ifdef CUOPT_LOG_DEBUG - for (auto type : worker_types) { - settings_.log.debug("%c%d: max num of workers = %d", - feasible_solution_symbol(type), - type, - max_num_workers_per_type[type]); - } -#endif - - worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); - active_workers_per_type.fill(0); - settings_.log.printf("Exploring the B&B tree using %d threads\n\n", settings_.num_threads); exploration_stats_.nodes_explored = 0; @@ -1292,124 +1471,15 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " "| Time |\n"); + if (solve_mode == mip_solve_mode_t::BNB_PARALLEL) { #pragma omp parallel num_threads(settings_.num_threads) - { -#pragma omp master { - f_t lower_bound = get_lower_bound(); - f_t abs_gap = upper_bound_ - lower_bound; - f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); - i_t last_node_depth = 0; - - while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && - rel_gap > settings_.relative_mip_gap_tol && - (active_workers_per_type[0] > 0 || node_queue_.best_first_queue_size() > 0)) { - bool launched_any_task = false; - lower_bound = get_lower_bound(); - abs_gap = upper_bound_ - lower_bound; - rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); - - repair_heuristic_solutions(); - - // If the guided diving was disabled previously due to the lack of an incumbent solution, - // re-enable as soon as a new incumbent is found. - if (settings_.diving_settings.guided_diving != diving_settings.guided_diving) { - if (std::isfinite(upper_bound_)) { - diving_settings.guided_diving = settings_.diving_settings.guided_diving; - worker_types = bnb_get_worker_types(diving_settings); - max_num_workers_per_type = bnb_get_max_workers(num_workers, worker_types); - -#ifdef CUOPT_LOG_DEBUG - for (auto type : worker_types) { - settings_.log.debug("%c%d: max num of workers = %d", - feasible_solution_symbol(type), - type, - max_num_workers_per_type[type]); - } -#endif - } - } - - f_t now = toc(exploration_stats_.start_time); - f_t time_since_last_log = - exploration_stats_.last_log == 0 ? 1.0 : toc(exploration_stats_.last_log); - i_t nodes_since_last_log = exploration_stats_.nodes_since_last_log; - - if (((nodes_since_last_log >= 1000 || abs_gap < 10 * settings_.absolute_mip_gap_tol) && - time_since_last_log >= 1) || - (time_since_last_log > 30) || now > settings_.time_limit) { - i_t depth = node_queue_.best_first_queue_size() > 0 ? node_queue_.bfs_top()->depth - : last_node_depth; - report(' ', upper_bound_, lower_bound, depth); - exploration_stats_.last_log = tic(); - exploration_stats_.nodes_since_last_log = 0; - } - - if (now > settings_.time_limit) { - solver_status_ = mip_status_t::TIME_LIMIT; - break; - } - - for (auto type : worker_types) { - if (active_workers_per_type[type] >= max_num_workers_per_type[type]) { continue; } - - // Get an idle worker. - bnb_worker_data_t* worker = worker_pool_.get_idle_worker(); - if (worker == nullptr) { break; } - - if (type == BEST_FIRST) { - // If there any node left in the heap, we pop the top node and explore it. - std::optional*> start_node = node_queue_.pop_best_first(); - - if (!start_node.has_value()) { continue; } - if (upper_bound_ < start_node.value()->lower_bound) { - // This node was put on the heap earlier but its lower bound is now greater than the - // current upper bound - search_tree_.graphviz_node( - settings_.log, start_node.value(), "cutoff", start_node.value()->lower_bound); - search_tree_.update(start_node.value(), node_status_t::FATHOMED); - continue; - } - - // Remove the worker from the idle list. - worker_pool_.pop_idle_worker(); - worker->init_best_first(start_node.value(), original_lp_); - last_node_depth = start_node.value()->depth; - active_workers_per_type[type]++; - launched_any_task = true; - -#pragma omp task affinity(worker) - plunge_with(worker); - - } else { - std::optional*> start_node = node_queue_.pop_diving(); - - if (!start_node.has_value()) { continue; } - if (upper_bound_ < start_node.value()->lower_bound || - start_node.value()->depth < diving_settings.min_node_depth) { - continue; - } - - bool is_feasible = - worker->init_diving(start_node.value(), type, original_lp_, settings_); - if (!is_feasible) { continue; } - - // Remove the worker from the idle list. - worker_pool_.pop_idle_worker(); - active_workers_per_type[type]++; - launched_any_task = true; - -#pragma omp task affinity(worker) - dive_with(worker); - } - } - - // If no new task was launched in this iteration, suspend temporarily the - // execution of the master. As of 8/Jan/2026, GCC does not - // implement taskyield, but LLVM does. - if (!launched_any_task) { std::this_thread::sleep_for(std::chrono::milliseconds(1)); } - } +#pragma omp master + run_scheduler(); } + + } else { + single_threaded_solve(); } is_running = false; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 8644e2a01..e7d2b0aff 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -36,6 +36,11 @@ enum class mip_status_t { UNSET = 6, // The status is not set }; +enum class mip_solve_mode_t { + BNB_PARALLEL = 0, // Parallel B&B (default) + BNB_SINGLE_THREADED = 1, // Single threaded B&B for SubMIP and RINS +}; + template void upper_bound_callback(f_t upper_bound); @@ -86,7 +91,7 @@ class branch_and_bound_t { lp_status_t solve_root_relaxation(simplex_solver_settings_t const& lp_settings); // The main entry routine. Returns the solver status and populates solution with the incumbent. - mip_status_t solve(mip_solution_t& solution); + mip_status_t solve(mip_solution_t& solution, mip_solve_mode_t solve_mode); private: const user_problem_t& original_problem_; @@ -146,7 +151,7 @@ class branch_and_bound_t { // Count the number of workers per type that either are being executed or // are waiting to be executed. - std::array, bnb_num_worker_types> active_workers_per_type; + std::array, bnb_num_worker_types> active_workers_per_type_; // Worker pool bnb_worker_pool_t worker_pool_; @@ -183,12 +188,20 @@ class branch_and_bound_t { // We use best-first to pick the `start_node` and then perform a depth-first search // from this node (i.e., a plunge). It can only backtrack to a sibling node. // Unexplored nodes in the subtree are inserted back into the global heap. - void plunge_with(bnb_worker_data_t* worker_data); + void plunge_with(bnb_worker_data_t* worker_data, mip_solve_mode_t mode); // Perform a deep dive in the subtree determined by the `start_node` in order // to find integer feasible solutions. void dive_with(bnb_worker_data_t* worker_data); + // Run the scheduler (aka the master) whose will schedule and manage + // all the other workers. + void run_scheduler(); + + // Run the branch-and-bound algorithm in single threaded mode. + // This disable all diving heuristics. + void single_threaded_solve(); + // Solve the LP relaxation of a leaf node dual::status_t solve_node_lp(mip_node_t* node_ptr, bnb_worker_data_t* worker_data, diff --git a/cpp/src/dual_simplex/solve.cpp b/cpp/src/dual_simplex/solve.cpp index 1f31a757d..cec1eb92d 100644 --- a/cpp/src/dual_simplex/solve.cpp +++ b/cpp/src/dual_simplex/solve.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -599,7 +599,7 @@ i_t solve(const user_problem_t& problem, if (is_mip(problem) && !settings.relaxation) { branch_and_bound_t branch_and_bound(problem, settings); mip_solution_t mip_solution(problem.num_cols); - mip_status_t mip_status = branch_and_bound.solve(mip_solution); + mip_status_t mip_status = branch_and_bound.solve(mip_solution, mip_solve_mode_t::BNB_PARALLEL); if (mip_status == mip_status_t::OPTIMAL) { status = 0; } else { @@ -638,7 +638,7 @@ i_t solve_mip_with_guess(const user_problem_t& problem, if (is_mip(problem)) { branch_and_bound_t branch_and_bound(problem, settings); branch_and_bound.set_initial_guess(guess); - mip_status_t mip_status = branch_and_bound.solve(solution); + mip_status_t mip_status = branch_and_bound.solve(solution, mip_solve_mode_t::BNB_PARALLEL); if (mip_status == mip_status_t::OPTIMAL) { status = 0; } else { diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index 49864d195..c4927c9c2 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -257,16 +257,10 @@ void rins_t::run_rins() branch_and_bound_settings.relative_mip_gap_tol = std::min(current_mip_gap, (f_t)settings.target_mip_gap); branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; - branch_and_bound_settings.num_threads = 2; + branch_and_bound_settings.num_threads = 1; branch_and_bound_settings.reliability_branching_settings.enable = false; - - // In the future, let RINS use all the diving heuristics. For now, - // restricting to guided diving. - branch_and_bound_settings.diving_settings.line_search_diving = 0; - branch_and_bound_settings.diving_settings.coefficient_diving = 0; - branch_and_bound_settings.diving_settings.pseudocost_diving = 0; - branch_and_bound_settings.log.log = false; - branch_and_bound_settings.log.log_prefix = "[RINS] "; + branch_and_bound_settings.log.log = false; + branch_and_bound_settings.log.log_prefix = "[RINS] "; branch_and_bound_settings.solution_callback = [&rins_solution_queue](std::vector& solution, f_t objective) { rins_solution_queue.push_back(solution); @@ -274,7 +268,8 @@ void rins_t::run_rins() dual_simplex::branch_and_bound_t branch_and_bound(branch_and_bound_problem, branch_and_bound_settings); branch_and_bound.set_initial_guess(cuopt::host_copy(fixed_assignment, rins_handle.get_stream())); - branch_and_bound_status = branch_and_bound.solve(branch_and_bound_solution); + branch_and_bound_status = branch_and_bound.solve( + branch_and_bound_solution, dual_simplex::mip_solve_mode_t::BNB_SINGLE_THREADED); if (!std::isnan(branch_and_bound_solution.objective)) { CUOPT_LOG_DEBUG("RINS submip solution found. Objective %.16e. Status %d", diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index c3c0b0f1d..340e0b9ff 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -102,14 +102,8 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; - branch_and_bound_settings.num_threads = 2; + branch_and_bound_settings.num_threads = 1; branch_and_bound_settings.reliability_branching_settings.enable = false; - - // In the future, let SubMIP use all the diving heuristics. For now, - // restricting to guided diving. - branch_and_bound_settings.diving_settings.line_search_diving = 0; - branch_and_bound_settings.diving_settings.coefficient_diving = 0; - branch_and_bound_settings.diving_settings.pseudocost_diving = 0; branch_and_bound_settings.solution_callback = [this](std::vector& solution, f_t objective) { this->solution_callback(solution, objective); @@ -119,7 +113,8 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.log.log = false; dual_simplex::branch_and_bound_t branch_and_bound(branch_and_bound_problem, branch_and_bound_settings); - branch_and_bound_status = branch_and_bound.solve(branch_and_bound_solution); + branch_and_bound_status = branch_and_bound.solve( + branch_and_bound_solution, dual_simplex::mip_solve_mode_t::BNB_SINGLE_THREADED); if (solution_vector.size() > 0) { cuopt_assert(fixed_assignment.size() == branch_and_bound_solution.x.size(), "Assignment size mismatch"); diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 383dd013b..a1cb200d9 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -226,7 +226,8 @@ solution_t mip_solver_t::run_solver() branch_and_bound_status_future = std::async(std::launch::async, &dual_simplex::branch_and_bound_t::solve, branch_and_bound.get(), - std::ref(branch_and_bound_solution)); + std::ref(branch_and_bound_solution), + dual_simplex::mip_solve_mode_t::BNB_PARALLEL); } // Start the primal heuristics From 799f9f5a47e6dbe11b31ddae111ebaa2657faa7e Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 27 Jan 2026 15:53:22 +0000 Subject: [PATCH 305/366] more logging for bounds strength --- cpp/src/dual_simplex/bounds_strengthening.cpp | 72 ++++++++++++++----- cpp/src/dual_simplex/bounds_strengthening.hpp | 7 ++ cpp/src/dual_simplex/branch_and_bound.cpp | 23 ++++-- .../dual_simplex/dual_simplex_features.hpp | 41 ++++++++--- 4 files changed, 111 insertions(+), 32 deletions(-) diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index 4fbe1525b..b2b74fb5a 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -68,6 +68,9 @@ bounds_strengthening_t::bounds_strengthening_t( constraint_lb(problem.num_rows), constraint_ub(problem.num_rows) { + constexpr i_t ELEMS_PER_CACHE_LINE = 64 / sizeof(f_t); + const i_t num_cache_lines = (problem.num_cols + ELEMS_PER_CACHE_LINE - 1) / ELEMS_PER_CACHE_LINE; + cache_lines_touched_.resize(num_cache_lines, false); const bool is_row_sense_empty = row_sense.empty(); if (is_row_sense_empty) { std::copy(problem.rhs.begin(), problem.rhs.end(), constraint_lb.begin()); @@ -95,20 +98,22 @@ bool bounds_strengthening_t::bounds_strengthening( std::vector& upper_bounds, const simplex_solver_settings_t& settings) { + constexpr i_t ELEMS_PER_CACHE_LINE = 64 / sizeof(f_t); + const i_t m = A.m; const i_t n = A.n; + size_t nnz_processed = 0; + i_t max_row_len = 0; + int64_t total_col_span = 0; + i_t rows_processed = 0; + i_t unique_cache_lines = 0; + std::fill(cache_lines_touched_.begin(), cache_lines_touched_.end(), false); + std::vector constraint_changed(m, true); std::vector variable_changed(n, false); std::vector constraint_changed_next(m, false); - auto& A_i = A.i.underlying(); - auto& A_x = A.x.underlying(); - auto& Arow_j = Arow.j.underlying(); - auto& Arow_x = Arow.x.underlying(); - - size_t nnz_processed = 0; - if (!bounds_changed.empty()) { std::fill(constraint_changed.begin(), constraint_changed.end(), false); for (i_t i = 0; i < n; ++i) { @@ -116,7 +121,7 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t row_start = A.col_start[i]; const i_t row_end = A.col_start[i + 1]; for (i_t p = row_start; p < row_end; ++p) { - const i_t j = A_i[p]; + const i_t j = A.i[p]; constraint_changed[j] = true; } } @@ -134,13 +139,28 @@ bool bounds_strengthening_t::bounds_strengthening( if (!constraint_changed[i]) { continue; } const i_t row_start = Arow.row_start[i]; const i_t row_end = Arow.row_start[i + 1]; - nnz_processed += (row_end - row_start); + const i_t row_len = row_end - row_start; + + nnz_processed += row_len; + rows_processed++; + max_row_len = std::max(max_row_len, row_len); f_t min_a = 0.0; f_t max_a = 0.0; + i_t min_j = n; + i_t max_j = 0; for (i_t p = row_start; p < row_end; ++p) { - const i_t j = Arow_j[p]; - const f_t a_ij = Arow_x[p]; + const i_t j = Arow.j[p]; + const f_t a_ij = Arow.x[p]; + + min_j = std::min(min_j, j); + max_j = std::max(max_j, j); + + const i_t cache_line_id = j / ELEMS_PER_CACHE_LINE; + if (!cache_lines_touched_[cache_line_id]) { + cache_lines_touched_[cache_line_id] = true; + unique_cache_lines++; + } variable_changed[j] = true; if (a_ij > 0) { @@ -157,6 +177,8 @@ bool bounds_strengthening_t::bounds_strengthening( if (upper[j] == inf && a_ij < 0) { min_a = -inf; } } + if (row_len > 0) { total_col_span += (max_j - min_j); } + f_t cnst_lb = constraint_lb[i]; f_t cnst_ub = constraint_ub[i]; bool is_infeasible = @@ -170,7 +192,12 @@ bool bounds_strengthening_t::bounds_strengthening( cnst_ub, min_a, max_a); - last_nnz_processed = nnz_processed; + last_nnz_processed = nnz_processed; + last_num_iterations = iter + 1; + last_max_row_len = max_row_len; + last_total_col_span = total_col_span; + last_rows_processed = rows_processed; + last_unique_cache_lines = unique_cache_lines; return false; } @@ -192,10 +219,10 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t row_end = A.col_start[k + 1]; nnz_processed += (row_end - row_start); for (i_t p = row_start; p < row_end; ++p) { - const i_t i = A_i[p]; + const i_t i = A.i[p]; if (!constraint_changed[i]) { continue; } - const f_t a_ik = A_x[p]; + const f_t a_ik = A.x[p]; f_t delta_min_act = delta_min_activity[i]; f_t delta_max_act = delta_max_activity[i]; @@ -223,12 +250,17 @@ bool bounds_strengthening_t::bounds_strengthening( if (new_lb > new_ub + 1e-6) { settings.log.debug( "Iter:: %d, Infeasible variable after update %d, %e > %e\n", iter, k, new_lb, new_ub); - last_nnz_processed = nnz_processed; + last_nnz_processed = nnz_processed; + last_num_iterations = iter + 1; + last_max_row_len = max_row_len; + last_total_col_span = total_col_span; + last_rows_processed = rows_processed; + last_unique_cache_lines = unique_cache_lines; return false; } if (new_lb != old_lb || new_ub != old_ub) { for (i_t p = row_start; p < row_end; ++p) { - const i_t i = A_i[p]; + const i_t i = A.i[p]; constraint_changed_next[i] = true; } } @@ -249,6 +281,13 @@ bool bounds_strengthening_t::bounds_strengthening( iter++; } + last_nnz_processed = nnz_processed; + last_num_iterations = iter + 1; + last_max_row_len = max_row_len; + last_total_col_span = total_col_span; + last_rows_processed = rows_processed; + last_unique_cache_lines = unique_cache_lines; + // settings.log.printf("Total strengthened variables %d\n", total_strengthened_variables); #if DEBUG_BOUND_STRENGTHENING @@ -291,7 +330,6 @@ bool bounds_strengthening_t::bounds_strengthening( lower_bounds = lower; upper_bounds = upper; - last_nnz_processed = nnz_processed; return true; } diff --git a/cpp/src/dual_simplex/bounds_strengthening.hpp b/cpp/src/dual_simplex/bounds_strengthening.hpp index bb069a067..5bb1d163a 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.hpp +++ b/cpp/src/dual_simplex/bounds_strengthening.hpp @@ -26,6 +26,11 @@ class bounds_strengthening_t { std::vector bounds_changed; size_t last_nnz_processed{0}; + i_t last_num_iterations{0}; + i_t last_max_row_len{0}; + int64_t last_total_col_span{0}; + i_t last_rows_processed{0}; + i_t last_unique_cache_lines{0}; private: const csc_matrix_t& A; @@ -39,5 +44,7 @@ class bounds_strengthening_t { std::vector delta_max_activity; std::vector constraint_lb; std::vector constraint_ub; + + std::vector cache_lines_touched_; }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 5bbf8b5ec..6975bed19 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -758,11 +758,16 @@ dual::status_t branch_and_bound_t::solve_node_lp( node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); f_t bs_runtime = toc(bs_start); - bs_features_.m = leaf_problem.num_rows; - bs_features_.n = leaf_problem.num_cols; - bs_features_.nnz = leaf_problem.A.col_start[leaf_problem.num_cols]; - bs_features_.nnz_processed = node_presolver.last_nnz_processed; - bs_features_.runtime = bs_runtime; + bs_features_.m = leaf_problem.num_rows; + bs_features_.n = leaf_problem.num_cols; + bs_features_.nnz = leaf_problem.A.col_start[leaf_problem.num_cols]; + bs_features_.nnz_processed = node_presolver.last_nnz_processed; + bs_features_.num_iterations = node_presolver.last_num_iterations; + bs_features_.max_row_len = node_presolver.last_max_row_len; + bs_features_.total_col_span = node_presolver.last_total_col_span; + bs_features_.rows_processed = node_presolver.last_rows_processed; + bs_features_.unique_cache_lines = node_presolver.last_unique_cache_lines; + bs_features_.runtime = bs_runtime; bs_features_.log_single(bs_features_.m, bs_features_.n, bs_features_.nnz); } @@ -2375,6 +2380,14 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t features["nnz"] = static_cast(nnz); features["nnz_processed"] = static_cast(worker.node_presolver->last_nnz_processed); features["bounds_changed"] = static_cast(num_bounds_changed); + features["num_iterations"] = static_cast(worker.node_presolver->last_num_iterations); + features["max_row_len"] = static_cast(worker.node_presolver->last_max_row_len); + features["avg_col_span"] = worker.node_presolver->last_rows_processed > 0 + ? static_cast(worker.node_presolver->last_total_col_span) / + worker.node_presolver->last_rows_processed + : 0.0f; + features["unique_cache_lines"] = + static_cast(worker.node_presolver->last_unique_cache_lines); // predicts milliseconds f_t prediction = diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 08096437c..12e42c5e8 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -172,6 +172,22 @@ struct bounds_strengthening_features_t { size_t nnz_processed{0}; // non-zeros traversed (work metric) f_t runtime{0.0}; + // Cache locality metrics + i_t max_row_len{0}; // longest row encountered + int64_t total_col_span{0}; // sum of (max_col - min_col) per processed row + i_t rows_processed{0}; // number of constraint rows processed + i_t unique_cache_lines{0}; // unique 64-byte cache lines touched + + f_t avg_col_span() const + { + return rows_processed > 0 ? static_cast(total_col_span) / rows_processed : 0.0; + } + + f_t cache_line_reuse_ratio() const + { + return unique_cache_lines > 0 ? static_cast(nnz_processed) / unique_cache_lines : 0.0; + } + // Interval aggregates (for when bounds strengthening is called multiple times) i_t call_count{0}; i_t total_iterations{0}; @@ -190,16 +206,21 @@ struct bounds_strengthening_features_t { void log_single(i_t m_val, i_t n_val, i_t nnz_val) const { - // printf( - // "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " - // "iterations=%d bounds_changed=%d nnz_processed=%zu runtime=%.6f\n", - // m_val, - // n_val, - // nnz_val, - // num_iterations, - // num_bounds_changed, - // nnz_processed, - // runtime); + printf( + "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " + "iterations=%d bounds_changed=%d nnz_processed=%zu " + "max_row_len=%d avg_col_span=%.2f unique_cache_lines=%d cache_reuse=%.2f runtime=%.6f\n", + m_val, + n_val, + nnz_val, + num_iterations, + num_bounds_changed, + nnz_processed, + max_row_len, + avg_col_span(), + unique_cache_lines, + cache_line_reuse_ratio(), + runtime); } void reset() From eb510807378cda64770a9bc6ea70a5421ec76661 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 27 Jan 2026 17:04:55 +0100 Subject: [PATCH 306/366] added missing mutexes --- cpp/src/dual_simplex/pseudo_costs.cpp | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index e74684a69..b8ca23606 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -497,9 +497,10 @@ i_t pseudo_costs_t::reliable_variable_selection( #pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) untied for (i_t i = 0; i < num_candidates; ++i) { + const i_t j = unreliable_list[i]; + if (toc(start_time) > settings.time_limit) { continue; } - const i_t j = unreliable_list[i]; pseudo_cost_mutex[j].lock(); if (pseudo_cost_num_down[j] < reliable_threshold) { // Do trial branching on the down branch @@ -527,6 +528,7 @@ i_t pseudo_costs_t::reliable_variable_selection( } } pseudo_cost_mutex[j].unlock(); + if (toc(start_time) > settings.time_limit) { continue; } pseudo_cost_mutex[j].lock(); @@ -555,12 +557,16 @@ i_t pseudo_costs_t::reliable_variable_selection( } } pseudo_cost_mutex[j].unlock(); + if (toc(start_time) > settings.time_limit) { continue; } + pseudo_cost_mutex[j].lock(); f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] : pseudo_cost_up_avg; f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] : pseudo_cost_down_avg; + pseudo_cost_mutex[j].unlock(); + constexpr f_t eps = 1e-6; const f_t f_down = solution[j] - std::floor(solution[j]); const f_t f_up = std::ceil(solution[j]) - solution[j]; From f3567cbf7c853acee82f76e2fa7def0aa1e973df Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 27 Jan 2026 17:13:30 +0100 Subject: [PATCH 307/366] fixed empty vector in shuffle --- cpp/src/utilities/pcg.hpp | 1 + 1 file changed, 1 insertion(+) diff --git a/cpp/src/utilities/pcg.hpp b/cpp/src/utilities/pcg.hpp index dcae5a9ec..5422fd8df 100644 --- a/cpp/src/utilities/pcg.hpp +++ b/cpp/src/utilities/pcg.hpp @@ -144,6 +144,7 @@ class PCG { template void shuffle(std::vector& seq) { + if (seq.empty()) { return; } for (size_t i = 0; i < seq.size() - 1; ++i) { size_t j = uniform(i, seq.size()); if (j != i) std::swap(seq[i], seq[j]); From 233933ff7a851b79336681a6a3edd7364f3ffaa9 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 27 Jan 2026 18:23:45 +0100 Subject: [PATCH 308/366] reverted some code changes --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- cpp/src/dual_simplex/pseudo_costs.cpp | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 51e99e0d9..2da5ea4dd 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -939,7 +939,7 @@ void branch_and_bound_t::dive_with(bnb_worker_data_t* worker dive_stats.total_lp_iters = 0; dive_stats.total_lp_solve_time = 0; dive_stats.nodes_explored = 0; - dive_stats.nodes_unexplored = 0; + dive_stats.nodes_unexplored = 1; while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET && is_running) { mip_node_t* node_ptr = stack.front(); diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index b8ca23606..4825fadef 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -342,11 +342,11 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - log.debug("PC: num initialized down %d up %d avg down %e up %e\n", - num_initialized_down, - num_initialized_up, - pseudo_cost_down_avg, - pseudo_cost_up_avg); + log.printf("PC: num initialized down %d up %d avg down %e up %e\n", + num_initialized_down, + num_initialized_up, + pseudo_cost_down_avg, + pseudo_cost_up_avg); for (i_t k = 0; k < num_fractional; k++) { const i_t j = fractional[k]; @@ -629,7 +629,7 @@ f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, std::min(std::max(pseudo_cost_down * f_down, eps), std::max(pseudo_cost_up * f_up, eps)); } - log.debug("pseudocost estimate = %e\n", estimate); + log.printf("pseudocost estimate = %e\n", estimate); return estimate; } From 527754c744077952f5e0634c883bccb07d9bb8fe Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 27 Jan 2026 20:50:13 +0100 Subject: [PATCH 309/366] replaced with lock_guards --- cpp/src/dual_simplex/pseudo_costs.cpp | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 4825fadef..add5c9154 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -498,10 +498,9 @@ i_t pseudo_costs_t::reliable_variable_selection( #pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) untied for (i_t i = 0; i < num_candidates; ++i) { const i_t j = unreliable_list[i]; + std::lock_guard lock(pseudo_cost_mutex[j]); if (toc(start_time) > settings.time_limit) { continue; } - - pseudo_cost_mutex[j].lock(); if (pseudo_cost_num_down[j] < reliable_threshold) { // Do trial branching on the down branch f_t obj = trial_branching(worker_data->leaf_problem, @@ -527,11 +526,8 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_num_down[j]++; } } - pseudo_cost_mutex[j].unlock(); if (toc(start_time) > settings.time_limit) { continue; } - - pseudo_cost_mutex[j].lock(); if (pseudo_cost_num_up[j] < reliable_threshold) { f_t obj = trial_branching(worker_data->leaf_problem, settings, @@ -556,28 +552,23 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_num_up[j]++; } } - pseudo_cost_mutex[j].unlock(); if (toc(start_time) > settings.time_limit) { continue; } - - pseudo_cost_mutex[j].lock(); f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] : pseudo_cost_up_avg; f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] : pseudo_cost_down_avg; - pseudo_cost_mutex[j].unlock(); constexpr f_t eps = 1e-6; const f_t f_down = solution[j] - std::floor(solution[j]); const f_t f_up = std::ceil(solution[j]) - solution[j]; f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); - score_mutex.lock(); + std::lock_guard score_lock(score_mutex); if (score > max_score) { max_score = score; branch_var = j; } - score_mutex.unlock(); } log.printf( From 2251b873772ca9e2b31e5bc6c5c63e3179036d3f Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 27 Jan 2026 21:23:31 +0100 Subject: [PATCH 310/366] fixed crash --- cpp/src/dual_simplex/branch_and_bound.cpp | 30 +++++++++++++++-------- cpp/src/dual_simplex/branch_and_bound.hpp | 2 +- cpp/src/dual_simplex/pseudo_costs.cpp | 2 +- 3 files changed, 22 insertions(+), 12 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 2da5ea4dd..16bd75022 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -257,7 +257,7 @@ f_t branch_and_bound_t::get_lower_bound() template void branch_and_bound_t::report_heuristic(f_t obj) { - if (is_running) { + if (is_running_) { f_t user_obj = compute_user_objective(original_lp_, obj); f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); std::string user_gap = user_mip_gap(user_obj, user_lower); @@ -282,7 +282,7 @@ void branch_and_bound_t::report(char symbol, f_t obj, f_t lower_bound, i_t nodes_unexplored = exploration_stats_.nodes_unexplored; f_t user_obj = compute_user_objective(original_lp_, obj); f_t user_lower = compute_user_objective(original_lp_, lower_bound); - f_t iter_node = exploration_stats_.total_lp_iters / nodes_explored; + f_t iter_node = (f_t)exploration_stats_.total_lp_iters / nodes_explored; std::string user_gap = user_mip_gap(user_obj, user_lower); settings_.log.printf("%c %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", symbol, @@ -941,7 +941,7 @@ void branch_and_bound_t::dive_with(bnb_worker_data_t* worker dive_stats.nodes_explored = 0; dive_stats.nodes_unexplored = 1; - while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET && is_running) { + while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET && is_running_) { mip_node_t* node_ptr = stack.front(); stack.pop_front(); @@ -1024,6 +1024,12 @@ void branch_and_bound_t::run_scheduler() f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); i_t last_node_depth = 0; + is_running_ = true; + + settings_.log.printf( + " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " + "| Time |\n"); + while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && (active_workers_per_type_[0] > 0 || node_queue_.best_first_queue_size() > 0)) { @@ -1131,6 +1137,8 @@ void branch_and_bound_t::run_scheduler() // implement taskyield, but LLVM does. if (!launched_any_task) { std::this_thread::sleep_for(std::chrono::milliseconds(1)); } } + + is_running_ = false; } template @@ -1143,6 +1151,12 @@ void branch_and_bound_t::single_threaded_solve() f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); i_t last_node_depth = 0; + is_running_ = true; + + settings_.log.printf( + " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " + "| Time |\n"); + while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && node_queue_.best_first_queue_size() > 0) { bool launched_any_task = false; @@ -1188,6 +1202,8 @@ void branch_and_bound_t::single_threaded_solve() worker.init_best_first(start_node.value(), original_lp_); plunge_with(&worker, mip_solve_mode_t::BNB_SINGLE_THREADED); } + + is_running_ = false; } template @@ -1301,7 +1317,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut log.log = false; log.log_prefix = settings_.log.log_prefix; solver_status_ = mip_status_t::UNSET; - is_running = false; + is_running_ = false; exploration_stats_.nodes_unexplored = 0; exploration_stats_.nodes_explored = 0; original_lp_.A.to_compressed_row(Arow_); @@ -1459,7 +1475,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut exploration_stats_.nodes_unexplored = 2; exploration_stats_.nodes_since_last_log = 0; exploration_stats_.last_log = tic(); - is_running = true; lower_bound_ceiling_ = inf; min_node_queue_size_ = 2 * settings_.num_threads; @@ -1467,10 +1482,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut calculate_variable_locks(original_lp_, var_up_locks_, var_down_locks_); } - settings_.log.printf( - " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " - "| Time |\n"); - if (solve_mode == mip_solve_mode_t::BNB_PARALLEL) { #pragma omp parallel num_threads(settings_.num_threads) { @@ -1482,7 +1493,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut single_threaded_solve(); } - is_running = false; f_t lower_bound = node_queue_.best_first_queue_size() > 0 ? node_queue_.get_lower_bound() : search_tree_.root.lower_bound; set_final_solution(solution, lower_bound); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index e7d2b0aff..3efaaa6d6 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -158,7 +158,7 @@ class branch_and_bound_t { // Global status of the solver. omp_atomic_t solver_status_; - omp_atomic_t is_running{false}; + omp_atomic_t is_running_{false}; // Minimum number of node in the queue. When the queue size is less than // this variable, the nodes are added directly to the queue instead of diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index add5c9154..84feb33be 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -495,7 +495,7 @@ i_t pseudo_costs_t::reliable_variable_selection( // Shuffle the unreliable list so every variable has the same chance to be selected. if (unreliable_list.size() > max_num_candidates) { worker_data->rng.shuffle(unreliable_list); } -#pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) untied +#pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) for (i_t i = 0; i < num_candidates; ++i) { const i_t j = unreliable_list[i]; std::lock_guard lock(pseudo_cost_mutex[j]); From 79192886a2a102299cf7e653a85846c9d3d7e78a Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 28 Jan 2026 10:19:35 +0100 Subject: [PATCH 311/366] fixed number of threads set to 0 --- cpp/src/mip/solver.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index a1cb200d9..a7e514f17 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -171,7 +171,7 @@ solution_t mip_solver_t::run_solver() solver_settings_.enable_reliability_branching; if (context.settings.num_cpu_threads < 0) { - branch_and_bound_settings.num_threads = omp_get_max_threads() - 1; + branch_and_bound_settings.num_threads = std::max(1, omp_get_max_threads() - 1); } else { branch_and_bound_settings.num_threads = std::max(1, context.settings.num_cpu_threads); } From cec6f3886c1584262e292ef16794fbc4f8bc3c67 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 28 Jan 2026 10:24:29 +0100 Subject: [PATCH 312/366] enable reliablity branching by default --- .../linear_programming/cuopt/run_mip.cpp | 34 +++++++++---------- .../mip/solver_settings.hpp | 3 +- cpp/src/mip/solver.cu | 2 +- 3 files changed, 20 insertions(+), 19 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index f1259c7a1..71034ff12 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -147,7 +147,7 @@ int run_single_file(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, - bool enable_rb, + bool reliability_branching, double time_limit) { const raft::handle_t handle_{}; @@ -198,7 +198,7 @@ int run_single_file(std::string file_path, } } - CUOPT_LOG_INFO("Reliability branching: %d\n", enable_rb); + CUOPT_LOG_INFO("Reliability branching: %d\n", reliability_branching); settings.time_limit = time_limit; settings.heuristics_only = heuristics_only; @@ -207,7 +207,7 @@ int run_single_file(std::string file_path, settings.tolerances.relative_tolerance = 1e-12; settings.tolerances.absolute_tolerance = 1e-6; settings.presolve = true; - settings.enable_reliability_branching = enable_rb; + settings.reliability_branching = reliability_branching; cuopt::linear_programming::benchmark_info_t benchmark_info; settings.benchmark_info_ptr = &benchmark_info; auto start_run_solver = std::chrono::high_resolution_clock::now(); @@ -260,7 +260,7 @@ void run_single_file_mp(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, - bool enable_rb, + bool reliability_branching, double time_limit) { std::cout << "running file " << file_path << " on gpu : " << device << std::endl; @@ -276,7 +276,7 @@ void run_single_file_mp(std::string file_path, num_cpu_threads, write_log_file, log_to_console, - enable_rb, + reliability_branching, time_limit); // this is a bad design to communicate the result but better than adding complexity of IPC or // pipes @@ -360,9 +360,9 @@ int main(int argc, char* argv[]) .help("track allocations (t/f)") .default_value(std::string("f")); - program.add_argument("--enable-reliability-branching") - .help("track allocations (t/f)") - .default_value(std::string("f")); + program.add_argument("--reliability-branching") + .help("enable reliability branching (t/f)") + .default_value(std::string("t")); // Parse arguments try { @@ -386,13 +386,13 @@ int main(int argc, char* argv[]) std::string result_file; int batch_num = -1; - bool heuristics_only = program.get("--heuristics-only")[0] == 't'; - int num_cpu_threads = program.get("--num-cpu-threads"); - bool write_log_file = program.get("--write-log-file")[0] == 't'; - bool log_to_console = program.get("--log-to-console")[0] == 't'; - double memory_limit = program.get("--memory-limit"); - bool track_allocations = program.get("--track-allocations")[0] == 't'; - bool enable_rb = program.get("--enable-reliability-branching")[0] == 't'; + bool heuristics_only = program.get("--heuristics-only")[0] == 't'; + int num_cpu_threads = program.get("--num-cpu-threads"); + bool write_log_file = program.get("--write-log-file")[0] == 't'; + bool log_to_console = program.get("--log-to-console")[0] == 't'; + double memory_limit = program.get("--memory-limit"); + bool track_allocations = program.get("--track-allocations")[0] == 't'; + bool reliability_branching = program.get("--reliability-branching")[0] == 't'; if (num_cpu_threads < 0) { num_cpu_threads = omp_get_max_threads() / n_gpus; } @@ -480,7 +480,7 @@ int main(int argc, char* argv[]) num_cpu_threads, write_log_file, log_to_console, - enable_rb, + reliability_branching, time_limit); } else if (sys_pid < 0) { std::cerr << "Fork failed!" << std::endl; @@ -521,7 +521,7 @@ int main(int argc, char* argv[]) num_cpu_threads, write_log_file, log_to_console, - enable_rb, + reliability_branching, time_limit); } diff --git a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp index 653a71972..f5cb1c80e 100644 --- a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp +++ b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp @@ -83,7 +83,8 @@ class mip_solver_settings_t { i_t num_cpu_threads = -1; // -1 means use default number of threads in branch and bound i_t num_gpus = 1; bool log_to_console = true; - bool enable_reliability_branching = false; + + bool reliability_branching = true; std::string log_file; std::string sol_file; diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index a7e514f17..5f9f21da6 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -168,7 +168,7 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; branch_and_bound_settings.reliability_branching_settings.enable = - solver_settings_.enable_reliability_branching; + solver_settings_.reliability_branching; if (context.settings.num_cpu_threads < 0) { branch_and_bound_settings.num_threads = std::max(1, omp_get_max_threads() - 1); From 6c408f229311419f8492a3a6a652f2474be1ab08 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 28 Jan 2026 14:54:23 +0000 Subject: [PATCH 313/366] fix logging --- cpp/src/dual_simplex/dual_simplex_features.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 12e42c5e8..d70b85b10 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -206,7 +206,7 @@ struct bounds_strengthening_features_t { void log_single(i_t m_val, i_t n_val, i_t nnz_val) const { - printf( + CUOPT_LOG_DEBUG( "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " "iterations=%d bounds_changed=%d nnz_processed=%zu " "max_row_len=%d avg_col_span=%.2f unique_cache_lines=%d cache_reuse=%.2f runtime=%.6f\n", From 27ee9271e40bfe384f087424311e16dbff67c354 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 28 Jan 2026 17:13:01 +0100 Subject: [PATCH 314/366] set the solve mode based on the number of threads --- cpp/src/mip/solver.cu | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 5f9f21da6..f18e25732 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -176,6 +176,11 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.num_threads = std::max(1, context.settings.num_cpu_threads); } + dual_simplex::mip_solve_mode_t solve_mode = + branch_and_bound_settings.num_threads > 1 + ? dual_simplex::mip_solve_mode_t::BNB_PARALLEL + : dual_simplex::mip_solve_mode_t::BNB_SINGLE_THREADED; + // Set the branch and bound -> primal heuristics callback branch_and_bound_settings.solution_callback = std::bind(&branch_and_bound_solution_helper_t::solution_callback, @@ -227,7 +232,7 @@ solution_t mip_solver_t::run_solver() &dual_simplex::branch_and_bound_t::solve, branch_and_bound.get(), std::ref(branch_and_bound_solution), - dual_simplex::mip_solve_mode_t::BNB_PARALLEL); + solve_mode); } // Start the primal heuristics From 4d1f6840a4266c9a1893ac663e1a863445cae2cb Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 28 Jan 2026 17:34:59 +0000 Subject: [PATCH 315/366] disable RUNPATH --- cpp/CMakeLists.txt | 18 +++++++++--------- cpp/tests/CMakeLists.txt | 14 +++++++------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index b6f9a12e1..7ba1872b4 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -491,9 +491,9 @@ target_link_libraries(cuopt_cli PRIVATE ) # Use RUNPATH when building locally in order to allow LD_LIBRARY_PATH to override the conda env path -if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") - target_link_options(cuopt_cli PRIVATE -Wl,--enable-new-dtags) -endif() +# if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") +# target_link_options(cuopt_cli PRIVATE -Wl,--enable-new-dtags) +# endif() set_property(TARGET cuopt_cli PROPERTY INSTALL_RPATH "$ORIGIN/../${lib_dir}") # adds the cuopt_cli executable to the runtime deb package @@ -525,9 +525,9 @@ if(BUILD_MIP_BENCHMARKS AND NOT BUILD_LP_ONLY) OpenMP::OpenMP_CXX PRIVATE ) - if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") - target_link_options(solve_MIP PRIVATE -Wl,--enable-new-dtags) - endif() + # if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") + # target_link_options(solve_MIP PRIVATE -Wl,--enable-new-dtags) + # endif() target_include_directories(solve_MIP PRIVATE @@ -559,9 +559,9 @@ if(BUILD_LP_BENCHMARKS) OpenMP::OpenMP_CXX PRIVATE ) - if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") - target_link_options(solve_LP PRIVATE -Wl,--enable-new-dtags) - endif() + # if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") + # target_link_options(solve_LP PRIVATE -Wl,--enable-new-dtags) + # endif() endif() diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index fb8f0e26b..79c0eb5b6 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -1,5 +1,5 @@ # cmake-format: off -# SPDX-FileCopyrightText: Copyright (c) 2021-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2021-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # cmake-format: on @@ -25,9 +25,9 @@ if(BUILD_TESTS) GTest::gmock GTest::gtest ) - if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") - target_link_options(cuopttestutils PRIVATE -Wl,--enable-new-dtags) - endif() + # if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") + # target_link_options(cuopttestutils PRIVATE -Wl,--enable-new-dtags) + # endif() endif() @@ -55,9 +55,9 @@ function(ConfigureTest CMAKE_TEST_NAME) GTest::gtest_main ${CUOPT_PRIVATE_CUDA_LIBS} ) - if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") - target_link_options(${CMAKE_TEST_NAME} PRIVATE -Wl,--enable-new-dtags) - endif() + # if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") + # target_link_options(${CMAKE_TEST_NAME} PRIVATE -Wl,--enable-new-dtags) + # endif() add_test(NAME ${CMAKE_TEST_NAME} COMMAND ${CMAKE_TEST_NAME}) From 8810401559b680e7b7fc72e5e8add65aec29d12b Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 28 Jan 2026 17:35:49 +0000 Subject: [PATCH 316/366] Revert "fix logging" This reverts commit 6c408f229311419f8492a3a6a652f2474be1ab08. --- cpp/src/dual_simplex/dual_simplex_features.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index d70b85b10..12e42c5e8 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -206,7 +206,7 @@ struct bounds_strengthening_features_t { void log_single(i_t m_val, i_t n_val, i_t nnz_val) const { - CUOPT_LOG_DEBUG( + printf( "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " "iterations=%d bounds_changed=%d nnz_processed=%zu " "max_row_len=%d avg_col_span=%.2f unique_cache_lines=%d cache_reuse=%.2f runtime=%.6f\n", From a811e86dd588a77ed4f76e6b4b7536d19d0017a3 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 28 Jan 2026 17:36:07 +0000 Subject: [PATCH 317/366] Revert "more logging for bounds strength" This reverts commit 799f9f5a47e6dbe11b31ddae111ebaa2657faa7e. --- cpp/src/dual_simplex/bounds_strengthening.cpp | 72 +++++-------------- cpp/src/dual_simplex/bounds_strengthening.hpp | 7 -- cpp/src/dual_simplex/branch_and_bound.cpp | 23 ++---- .../dual_simplex/dual_simplex_features.hpp | 41 +++-------- 4 files changed, 32 insertions(+), 111 deletions(-) diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index b2b74fb5a..4fbe1525b 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -68,9 +68,6 @@ bounds_strengthening_t::bounds_strengthening_t( constraint_lb(problem.num_rows), constraint_ub(problem.num_rows) { - constexpr i_t ELEMS_PER_CACHE_LINE = 64 / sizeof(f_t); - const i_t num_cache_lines = (problem.num_cols + ELEMS_PER_CACHE_LINE - 1) / ELEMS_PER_CACHE_LINE; - cache_lines_touched_.resize(num_cache_lines, false); const bool is_row_sense_empty = row_sense.empty(); if (is_row_sense_empty) { std::copy(problem.rhs.begin(), problem.rhs.end(), constraint_lb.begin()); @@ -98,22 +95,20 @@ bool bounds_strengthening_t::bounds_strengthening( std::vector& upper_bounds, const simplex_solver_settings_t& settings) { - constexpr i_t ELEMS_PER_CACHE_LINE = 64 / sizeof(f_t); - const i_t m = A.m; const i_t n = A.n; - size_t nnz_processed = 0; - i_t max_row_len = 0; - int64_t total_col_span = 0; - i_t rows_processed = 0; - i_t unique_cache_lines = 0; - std::fill(cache_lines_touched_.begin(), cache_lines_touched_.end(), false); - std::vector constraint_changed(m, true); std::vector variable_changed(n, false); std::vector constraint_changed_next(m, false); + auto& A_i = A.i.underlying(); + auto& A_x = A.x.underlying(); + auto& Arow_j = Arow.j.underlying(); + auto& Arow_x = Arow.x.underlying(); + + size_t nnz_processed = 0; + if (!bounds_changed.empty()) { std::fill(constraint_changed.begin(), constraint_changed.end(), false); for (i_t i = 0; i < n; ++i) { @@ -121,7 +116,7 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t row_start = A.col_start[i]; const i_t row_end = A.col_start[i + 1]; for (i_t p = row_start; p < row_end; ++p) { - const i_t j = A.i[p]; + const i_t j = A_i[p]; constraint_changed[j] = true; } } @@ -139,28 +134,13 @@ bool bounds_strengthening_t::bounds_strengthening( if (!constraint_changed[i]) { continue; } const i_t row_start = Arow.row_start[i]; const i_t row_end = Arow.row_start[i + 1]; - const i_t row_len = row_end - row_start; - - nnz_processed += row_len; - rows_processed++; - max_row_len = std::max(max_row_len, row_len); + nnz_processed += (row_end - row_start); f_t min_a = 0.0; f_t max_a = 0.0; - i_t min_j = n; - i_t max_j = 0; for (i_t p = row_start; p < row_end; ++p) { - const i_t j = Arow.j[p]; - const f_t a_ij = Arow.x[p]; - - min_j = std::min(min_j, j); - max_j = std::max(max_j, j); - - const i_t cache_line_id = j / ELEMS_PER_CACHE_LINE; - if (!cache_lines_touched_[cache_line_id]) { - cache_lines_touched_[cache_line_id] = true; - unique_cache_lines++; - } + const i_t j = Arow_j[p]; + const f_t a_ij = Arow_x[p]; variable_changed[j] = true; if (a_ij > 0) { @@ -177,8 +157,6 @@ bool bounds_strengthening_t::bounds_strengthening( if (upper[j] == inf && a_ij < 0) { min_a = -inf; } } - if (row_len > 0) { total_col_span += (max_j - min_j); } - f_t cnst_lb = constraint_lb[i]; f_t cnst_ub = constraint_ub[i]; bool is_infeasible = @@ -192,12 +170,7 @@ bool bounds_strengthening_t::bounds_strengthening( cnst_ub, min_a, max_a); - last_nnz_processed = nnz_processed; - last_num_iterations = iter + 1; - last_max_row_len = max_row_len; - last_total_col_span = total_col_span; - last_rows_processed = rows_processed; - last_unique_cache_lines = unique_cache_lines; + last_nnz_processed = nnz_processed; return false; } @@ -219,10 +192,10 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t row_end = A.col_start[k + 1]; nnz_processed += (row_end - row_start); for (i_t p = row_start; p < row_end; ++p) { - const i_t i = A.i[p]; + const i_t i = A_i[p]; if (!constraint_changed[i]) { continue; } - const f_t a_ik = A.x[p]; + const f_t a_ik = A_x[p]; f_t delta_min_act = delta_min_activity[i]; f_t delta_max_act = delta_max_activity[i]; @@ -250,17 +223,12 @@ bool bounds_strengthening_t::bounds_strengthening( if (new_lb > new_ub + 1e-6) { settings.log.debug( "Iter:: %d, Infeasible variable after update %d, %e > %e\n", iter, k, new_lb, new_ub); - last_nnz_processed = nnz_processed; - last_num_iterations = iter + 1; - last_max_row_len = max_row_len; - last_total_col_span = total_col_span; - last_rows_processed = rows_processed; - last_unique_cache_lines = unique_cache_lines; + last_nnz_processed = nnz_processed; return false; } if (new_lb != old_lb || new_ub != old_ub) { for (i_t p = row_start; p < row_end; ++p) { - const i_t i = A.i[p]; + const i_t i = A_i[p]; constraint_changed_next[i] = true; } } @@ -281,13 +249,6 @@ bool bounds_strengthening_t::bounds_strengthening( iter++; } - last_nnz_processed = nnz_processed; - last_num_iterations = iter + 1; - last_max_row_len = max_row_len; - last_total_col_span = total_col_span; - last_rows_processed = rows_processed; - last_unique_cache_lines = unique_cache_lines; - // settings.log.printf("Total strengthened variables %d\n", total_strengthened_variables); #if DEBUG_BOUND_STRENGTHENING @@ -330,6 +291,7 @@ bool bounds_strengthening_t::bounds_strengthening( lower_bounds = lower; upper_bounds = upper; + last_nnz_processed = nnz_processed; return true; } diff --git a/cpp/src/dual_simplex/bounds_strengthening.hpp b/cpp/src/dual_simplex/bounds_strengthening.hpp index 5bb1d163a..bb069a067 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.hpp +++ b/cpp/src/dual_simplex/bounds_strengthening.hpp @@ -26,11 +26,6 @@ class bounds_strengthening_t { std::vector bounds_changed; size_t last_nnz_processed{0}; - i_t last_num_iterations{0}; - i_t last_max_row_len{0}; - int64_t last_total_col_span{0}; - i_t last_rows_processed{0}; - i_t last_unique_cache_lines{0}; private: const csc_matrix_t& A; @@ -44,7 +39,5 @@ class bounds_strengthening_t { std::vector delta_max_activity; std::vector constraint_lb; std::vector constraint_ub; - - std::vector cache_lines_touched_; }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 6975bed19..5bbf8b5ec 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -758,16 +758,11 @@ dual::status_t branch_and_bound_t::solve_node_lp( node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); f_t bs_runtime = toc(bs_start); - bs_features_.m = leaf_problem.num_rows; - bs_features_.n = leaf_problem.num_cols; - bs_features_.nnz = leaf_problem.A.col_start[leaf_problem.num_cols]; - bs_features_.nnz_processed = node_presolver.last_nnz_processed; - bs_features_.num_iterations = node_presolver.last_num_iterations; - bs_features_.max_row_len = node_presolver.last_max_row_len; - bs_features_.total_col_span = node_presolver.last_total_col_span; - bs_features_.rows_processed = node_presolver.last_rows_processed; - bs_features_.unique_cache_lines = node_presolver.last_unique_cache_lines; - bs_features_.runtime = bs_runtime; + bs_features_.m = leaf_problem.num_rows; + bs_features_.n = leaf_problem.num_cols; + bs_features_.nnz = leaf_problem.A.col_start[leaf_problem.num_cols]; + bs_features_.nnz_processed = node_presolver.last_nnz_processed; + bs_features_.runtime = bs_runtime; bs_features_.log_single(bs_features_.m, bs_features_.n, bs_features_.nnz); } @@ -2380,14 +2375,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t features["nnz"] = static_cast(nnz); features["nnz_processed"] = static_cast(worker.node_presolver->last_nnz_processed); features["bounds_changed"] = static_cast(num_bounds_changed); - features["num_iterations"] = static_cast(worker.node_presolver->last_num_iterations); - features["max_row_len"] = static_cast(worker.node_presolver->last_max_row_len); - features["avg_col_span"] = worker.node_presolver->last_rows_processed > 0 - ? static_cast(worker.node_presolver->last_total_col_span) / - worker.node_presolver->last_rows_processed - : 0.0f; - features["unique_cache_lines"] = - static_cast(worker.node_presolver->last_unique_cache_lines); // predicts milliseconds f_t prediction = diff --git a/cpp/src/dual_simplex/dual_simplex_features.hpp b/cpp/src/dual_simplex/dual_simplex_features.hpp index 12e42c5e8..08096437c 100644 --- a/cpp/src/dual_simplex/dual_simplex_features.hpp +++ b/cpp/src/dual_simplex/dual_simplex_features.hpp @@ -172,22 +172,6 @@ struct bounds_strengthening_features_t { size_t nnz_processed{0}; // non-zeros traversed (work metric) f_t runtime{0.0}; - // Cache locality metrics - i_t max_row_len{0}; // longest row encountered - int64_t total_col_span{0}; // sum of (max_col - min_col) per processed row - i_t rows_processed{0}; // number of constraint rows processed - i_t unique_cache_lines{0}; // unique 64-byte cache lines touched - - f_t avg_col_span() const - { - return rows_processed > 0 ? static_cast(total_col_span) / rows_processed : 0.0; - } - - f_t cache_line_reuse_ratio() const - { - return unique_cache_lines > 0 ? static_cast(nnz_processed) / unique_cache_lines : 0.0; - } - // Interval aggregates (for when bounds strengthening is called multiple times) i_t call_count{0}; i_t total_iterations{0}; @@ -206,21 +190,16 @@ struct bounds_strengthening_features_t { void log_single(i_t m_val, i_t n_val, i_t nnz_val) const { - printf( - "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " - "iterations=%d bounds_changed=%d nnz_processed=%zu " - "max_row_len=%d avg_col_span=%.2f unique_cache_lines=%d cache_reuse=%.2f runtime=%.6f\n", - m_val, - n_val, - nnz_val, - num_iterations, - num_bounds_changed, - nnz_processed, - max_row_len, - avg_col_span(), - unique_cache_lines, - cache_line_reuse_ratio(), - runtime); + // printf( + // "BOUNDS_STRENGTH_FEATURES: m=%d n=%d nnz=%d " + // "iterations=%d bounds_changed=%d nnz_processed=%zu runtime=%.6f\n", + // m_val, + // n_val, + // nnz_val, + // num_iterations, + // num_bounds_changed, + // nnz_processed, + // runtime); } void reset() From b761b22c4f5b0d205a3256694ce9b2751a64b679 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 29 Jan 2026 17:35:13 +0000 Subject: [PATCH 318/366] naive prediction --- cpp/src/dual_simplex/branch_and_bound.cpp | 3 ++- cpp/src/dual_simplex/phase2.cpp | 7 +++++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 5bbf8b5ec..eff9c5ab6 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2397,7 +2397,8 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t num_bounds_changed); #endif - worker.work_context.record_work(prediction); + // worker.work_context.record_work(prediction); + worker.work_context.record_work(worker.node_presolver->last_nnz_processed); } #endif diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 81c25c224..204879fe1 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2656,7 +2656,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, features.delta_y_nz_percentage * 100.0); #endif - work_unit_context->record_work(prediction); + // work_unit_context->record_work(prediction); + work_unit_context->record_work(total_loads + total_stores); } }); @@ -3301,7 +3302,9 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // features.interval_runtime, // prediction - features.interval_runtime); // printf("Current iter %d\n", iter); - work_unit_context->record_work(prediction); + + // work_unit_context->record_work(prediction); + work_unit_context->record_work(total_loads + total_stores); } last_feature_log_iter = iter; From 8dfb7c3b2063e222c553e9c1507074a08867d593 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 29 Jan 2026 19:39:21 +0000 Subject: [PATCH 319/366] scaled mem prediction --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- cpp/src/dual_simplex/phase2.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index eff9c5ab6..2856ec9c4 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2398,7 +2398,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t #endif // worker.work_context.record_work(prediction); - worker.work_context.record_work(worker.node_presolver->last_nnz_processed); + worker.work_context.record_work(worker.node_presolver->last_nnz_processed / 1e8); } #endif diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 204879fe1..7bc6486a2 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2657,7 +2657,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif // work_unit_context->record_work(prediction); - work_unit_context->record_work(total_loads + total_stores); + work_unit_context->record_work((total_loads + total_stores) / 1e8); } }); @@ -3304,7 +3304,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // printf("Current iter %d\n", iter); // work_unit_context->record_work(prediction); - work_unit_context->record_work(total_loads + total_stores); + work_unit_context->record_work((total_loads + total_stores) / 1e8); } last_feature_log_iter = iter; From d55054746fbfebe8699dd75103782b3a9a28b398 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 29 Jan 2026 19:44:05 +0000 Subject: [PATCH 320/366] no cpufj --- cpp/src/mip/local_search/local_search.cu | 68 ++++++++++++------------ 1 file changed, 34 insertions(+), 34 deletions(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 3f66a3c37..26bc813cd 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -152,39 +152,39 @@ template void local_search_t::start_cpufj_deterministic( dual_simplex::branch_and_bound_t& bb) { - std::vector default_weights(context.problem_ptr->n_constraints, 1.); - - solution_t solution(*context.problem_ptr); - thrust::fill(solution.handle_ptr->get_thrust_policy(), - solution.assignment.begin(), - solution.assignment.end(), - 0.0); - solution.clamp_within_bounds(); - - deterministic_cpu_fj.fj_ptr = &fj; - deterministic_cpu_fj.fj_cpu = fj.create_cpu_climber(solution, - default_weights, - default_weights, - 0., - context.preempt_heuristic_solver_, - fj_settings_t{}, - /*randomize=*/true); - - deterministic_cpu_fj.fj_cpu->log_prefix = "******* deterministic CPUFJ: "; - - // Register with producer_sync for B&B synchronization - producer_sync_t& producer_sync = bb.get_producer_sync(); - deterministic_cpu_fj.fj_cpu->producer_sync = &producer_sync; - producer_sync.register_producer(&deterministic_cpu_fj.fj_cpu->work_units_elapsed); - - // Set up callback to send solutions to B&B with work unit timestamps - deterministic_cpu_fj.fj_cpu->improvement_callback = - [&bb](f_t obj, const std::vector& h_vec, double work_units) { - bb.set_new_solution_deterministic(h_vec, work_units); - }; - - // Start the CPUFJ thread - deterministic_cpu_fj.start_cpu_solver(); + // std::vector default_weights(context.problem_ptr->n_constraints, 1.); + + // solution_t solution(*context.problem_ptr); + // thrust::fill(solution.handle_ptr->get_thrust_policy(), + // solution.assignment.begin(), + // solution.assignment.end(), + // 0.0); + // solution.clamp_within_bounds(); + + // deterministic_cpu_fj.fj_ptr = &fj; + // deterministic_cpu_fj.fj_cpu = fj.create_cpu_climber(solution, + // default_weights, + // default_weights, + // 0., + // context.preempt_heuristic_solver_, + // fj_settings_t{}, + // /*randomize=*/true); + + // deterministic_cpu_fj.fj_cpu->log_prefix = "******* deterministic CPUFJ: "; + + // // Register with producer_sync for B&B synchronization + // producer_sync_t& producer_sync = bb.get_producer_sync(); + // deterministic_cpu_fj.fj_cpu->producer_sync = &producer_sync; + // producer_sync.register_producer(&deterministic_cpu_fj.fj_cpu->work_units_elapsed); + + // // Set up callback to send solutions to B&B with work unit timestamps + // deterministic_cpu_fj.fj_cpu->improvement_callback = + // [&bb](f_t obj, const std::vector& h_vec, double work_units) { + // bb.set_new_solution_deterministic(h_vec, work_units); + // }; + + // // Start the CPUFJ thread + // deterministic_cpu_fj.start_cpu_solver(); // Signal that registration is complete - B&B can now wait on producers producer_sync.registration_complete(); @@ -198,7 +198,7 @@ void local_search_t::stop_cpufj_deterministic() deterministic_cpu_fj.fj_cpu->producer_sync->deregister_producer( &deterministic_cpu_fj.fj_cpu->work_units_elapsed); } - deterministic_cpu_fj.request_termination(); + // deterministic_cpu_fj.request_termination(); } } From 4d75fb0f5f67ba9c5a0322747e13b7bf0fa3571f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 30 Jan 2026 09:16:10 +0000 Subject: [PATCH 321/366] Revert "no cpufj" This reverts commit d55054746fbfebe8699dd75103782b3a9a28b398. --- cpp/src/mip/local_search/local_search.cu | 68 ++++++++++++------------ 1 file changed, 34 insertions(+), 34 deletions(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 26bc813cd..3f66a3c37 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -152,39 +152,39 @@ template void local_search_t::start_cpufj_deterministic( dual_simplex::branch_and_bound_t& bb) { - // std::vector default_weights(context.problem_ptr->n_constraints, 1.); - - // solution_t solution(*context.problem_ptr); - // thrust::fill(solution.handle_ptr->get_thrust_policy(), - // solution.assignment.begin(), - // solution.assignment.end(), - // 0.0); - // solution.clamp_within_bounds(); - - // deterministic_cpu_fj.fj_ptr = &fj; - // deterministic_cpu_fj.fj_cpu = fj.create_cpu_climber(solution, - // default_weights, - // default_weights, - // 0., - // context.preempt_heuristic_solver_, - // fj_settings_t{}, - // /*randomize=*/true); - - // deterministic_cpu_fj.fj_cpu->log_prefix = "******* deterministic CPUFJ: "; - - // // Register with producer_sync for B&B synchronization - // producer_sync_t& producer_sync = bb.get_producer_sync(); - // deterministic_cpu_fj.fj_cpu->producer_sync = &producer_sync; - // producer_sync.register_producer(&deterministic_cpu_fj.fj_cpu->work_units_elapsed); - - // // Set up callback to send solutions to B&B with work unit timestamps - // deterministic_cpu_fj.fj_cpu->improvement_callback = - // [&bb](f_t obj, const std::vector& h_vec, double work_units) { - // bb.set_new_solution_deterministic(h_vec, work_units); - // }; - - // // Start the CPUFJ thread - // deterministic_cpu_fj.start_cpu_solver(); + std::vector default_weights(context.problem_ptr->n_constraints, 1.); + + solution_t solution(*context.problem_ptr); + thrust::fill(solution.handle_ptr->get_thrust_policy(), + solution.assignment.begin(), + solution.assignment.end(), + 0.0); + solution.clamp_within_bounds(); + + deterministic_cpu_fj.fj_ptr = &fj; + deterministic_cpu_fj.fj_cpu = fj.create_cpu_climber(solution, + default_weights, + default_weights, + 0., + context.preempt_heuristic_solver_, + fj_settings_t{}, + /*randomize=*/true); + + deterministic_cpu_fj.fj_cpu->log_prefix = "******* deterministic CPUFJ: "; + + // Register with producer_sync for B&B synchronization + producer_sync_t& producer_sync = bb.get_producer_sync(); + deterministic_cpu_fj.fj_cpu->producer_sync = &producer_sync; + producer_sync.register_producer(&deterministic_cpu_fj.fj_cpu->work_units_elapsed); + + // Set up callback to send solutions to B&B with work unit timestamps + deterministic_cpu_fj.fj_cpu->improvement_callback = + [&bb](f_t obj, const std::vector& h_vec, double work_units) { + bb.set_new_solution_deterministic(h_vec, work_units); + }; + + // Start the CPUFJ thread + deterministic_cpu_fj.start_cpu_solver(); // Signal that registration is complete - B&B can now wait on producers producer_sync.registration_complete(); @@ -198,7 +198,7 @@ void local_search_t::stop_cpufj_deterministic() deterministic_cpu_fj.fj_cpu->producer_sync->deregister_producer( &deterministic_cpu_fj.fj_cpu->work_units_elapsed); } - // deterministic_cpu_fj.request_termination(); + deterministic_cpu_fj.request_termination(); } } From da6dfe099cd6474d599f53c2f2fd072e50f00161 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 30 Jan 2026 09:16:46 +0000 Subject: [PATCH 322/366] Revert "disable RUNPATH" This reverts commit 4d1f6840a4266c9a1893ac663e1a863445cae2cb. --- cpp/CMakeLists.txt | 18 +++++++++--------- cpp/tests/CMakeLists.txt | 14 +++++++------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 7ba1872b4..b6f9a12e1 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -491,9 +491,9 @@ target_link_libraries(cuopt_cli PRIVATE ) # Use RUNPATH when building locally in order to allow LD_LIBRARY_PATH to override the conda env path -# if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") -# target_link_options(cuopt_cli PRIVATE -Wl,--enable-new-dtags) -# endif() +if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") + target_link_options(cuopt_cli PRIVATE -Wl,--enable-new-dtags) +endif() set_property(TARGET cuopt_cli PROPERTY INSTALL_RPATH "$ORIGIN/../${lib_dir}") # adds the cuopt_cli executable to the runtime deb package @@ -525,9 +525,9 @@ if(BUILD_MIP_BENCHMARKS AND NOT BUILD_LP_ONLY) OpenMP::OpenMP_CXX PRIVATE ) - # if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") - # target_link_options(solve_MIP PRIVATE -Wl,--enable-new-dtags) - # endif() + if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") + target_link_options(solve_MIP PRIVATE -Wl,--enable-new-dtags) + endif() target_include_directories(solve_MIP PRIVATE @@ -559,9 +559,9 @@ if(BUILD_LP_BENCHMARKS) OpenMP::OpenMP_CXX PRIVATE ) - # if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") - # target_link_options(solve_LP PRIVATE -Wl,--enable-new-dtags) - # endif() + if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") + target_link_options(solve_LP PRIVATE -Wl,--enable-new-dtags) + endif() endif() diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index 79c0eb5b6..fb8f0e26b 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -1,5 +1,5 @@ # cmake-format: off -# SPDX-FileCopyrightText: Copyright (c) 2021-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2021-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # cmake-format: on @@ -25,9 +25,9 @@ if(BUILD_TESTS) GTest::gmock GTest::gtest ) - # if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") - # target_link_options(cuopttestutils PRIVATE -Wl,--enable-new-dtags) - # endif() + if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") + target_link_options(cuopttestutils PRIVATE -Wl,--enable-new-dtags) + endif() endif() @@ -55,9 +55,9 @@ function(ConfigureTest CMAKE_TEST_NAME) GTest::gtest_main ${CUOPT_PRIVATE_CUDA_LIBS} ) - # if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") - # target_link_options(${CMAKE_TEST_NAME} PRIVATE -Wl,--enable-new-dtags) - # endif() + if(NOT DEFINED INSTALL_TARGET OR "${INSTALL_TARGET}" STREQUAL "") + target_link_options(${CMAKE_TEST_NAME} PRIVATE -Wl,--enable-new-dtags) + endif() add_test(NAME ${CMAKE_TEST_NAME} COMMAND ${CMAKE_TEST_NAME}) From 25388d8afdf5cbb80a1c9d99a064ca36c1745e92 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 30 Jan 2026 09:56:46 +0000 Subject: [PATCH 323/366] replace work predictor machinery with mem ops only --- cpp/src/CMakeLists.txt | 13 +- cpp/src/dual_simplex/branch_and_bound.cpp | 44 +- cpp/src/dual_simplex/branch_and_bound.hpp | 1 - cpp/src/dual_simplex/phase2.cpp | 84 +- cpp/src/mip/feasibility_jump/fj_cpu.cu | 44 +- cpp/src/mip/feasibility_jump/fj_cpu.cuh | 2 +- cpp/src/mip/solver_context.cuh | 11 - .../bounds_strengthening_predictor/header.h | 35 - .../bounds_strengthening_predictor/main.cpp | 3423 --- .../quantize.cpp | 111 - .../utilities/models/cpufj_predictor/header.h | 35 - .../utilities/models/cpufj_predictor/main.cpp | 5412 ----- .../models/cpufj_predictor/quantize.cpp | 293 - .../models/dualsimplex_predictor/header.h | 35 - .../models/dualsimplex_predictor/main.cpp | 19265 ---------------- .../models/dualsimplex_predictor/quantize.cpp | 1891 -- .../utilities/models/fj_predictor/header.h | 47 - .../utilities/models/fj_predictor/main.cpp | 11828 ---------- .../models/fj_predictor/quantize.cpp | 1180 - .../utilities/models/pdlp_predictor/header.h | 35 - .../utilities/models/pdlp_predictor/main.cpp | 16893 -------------- .../models/pdlp_predictor/quantize.cpp | 1006 - cpp/src/utilities/producer_sync.hpp | 3 +- cpp/src/utilities/version_info.cpp | 39 +- cpp/src/utilities/version_info.hpp | 1 - cpp/src/utilities/work_unit_predictor.cpp | 85 - cpp/src/utilities/work_unit_predictor.hpp | 63 - 27 files changed, 15 insertions(+), 61864 deletions(-) delete mode 100644 cpp/src/utilities/models/bounds_strengthening_predictor/header.h delete mode 100644 cpp/src/utilities/models/bounds_strengthening_predictor/main.cpp delete mode 100644 cpp/src/utilities/models/bounds_strengthening_predictor/quantize.cpp delete mode 100644 cpp/src/utilities/models/cpufj_predictor/header.h delete mode 100644 cpp/src/utilities/models/cpufj_predictor/main.cpp delete mode 100644 cpp/src/utilities/models/cpufj_predictor/quantize.cpp delete mode 100644 cpp/src/utilities/models/dualsimplex_predictor/header.h delete mode 100644 cpp/src/utilities/models/dualsimplex_predictor/main.cpp delete mode 100644 cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp delete mode 100644 cpp/src/utilities/models/fj_predictor/header.h delete mode 100644 cpp/src/utilities/models/fj_predictor/main.cpp delete mode 100644 cpp/src/utilities/models/fj_predictor/quantize.cpp delete mode 100644 cpp/src/utilities/models/pdlp_predictor/header.h delete mode 100644 cpp/src/utilities/models/pdlp_predictor/main.cpp delete mode 100644 cpp/src/utilities/models/pdlp_predictor/quantize.cpp delete mode 100644 cpp/src/utilities/work_unit_predictor.cpp delete mode 100644 cpp/src/utilities/work_unit_predictor.hpp diff --git a/cpp/src/CMakeLists.txt b/cpp/src/CMakeLists.txt index 06f820a34..59c06c844 100644 --- a/cpp/src/CMakeLists.txt +++ b/cpp/src/CMakeLists.txt @@ -7,18 +7,7 @@ set(UTIL_SRC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/utilities/seed_generator.cu ${CMAKE_CURRENT_SOURCE_DIR}/utilities/logger.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/version_info.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utilities/timestamp_utils.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/work_unit_predictor.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/work_unit_scheduler.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/main.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/fj_predictor/quantize.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/cpufj_predictor/main.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/cpufj_predictor/quantize.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/dualsimplex_predictor/main.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/dualsimplex_predictor/quantize.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/pdlp_predictor/main.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/pdlp_predictor/quantize.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/bounds_strengthening_predictor/main.cpp - ${CMAKE_CURRENT_SOURCE_DIR}/utilities/models/bounds_strengthening_predictor/quantize.cpp) + ${CMAKE_CURRENT_SOURCE_DIR}/utilities/work_unit_scheduler.cpp) add_subdirectory(linear_programming) add_subdirectory(math_optimization) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 2856ec9c4..3ef512f89 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -7,7 +7,6 @@ #include -#include #include #include #include @@ -21,7 +20,6 @@ #include #include #include -#include #include @@ -2357,47 +2355,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t f_t bs_actual_time = toc(bs_start_time); if (settings_.deterministic) { - static cuopt::work_unit_predictor_t - bs_predictor; - - const i_t m = worker.leaf_problem->num_rows; - const i_t n = worker.leaf_problem->num_cols; - const i_t nnz = worker.leaf_problem->A.col_start[n]; - - i_t num_bounds_changed = 0; - for (bool changed : worker.node_presolver->bounds_changed) { - if (changed) ++num_bounds_changed; - } - - std::map features; - features["m"] = static_cast(m); - features["n"] = static_cast(n); - features["nnz"] = static_cast(nnz); - features["nnz_processed"] = static_cast(worker.node_presolver->last_nnz_processed); - features["bounds_changed"] = static_cast(num_bounds_changed); - - // predicts milliseconds - f_t prediction = - std::max(f_t(0), static_cast(bs_predictor.predict_scalar(features))) / 1000; - -#ifdef CUOPT_DEBUG_WORK_PREDICTION - f_t ratio = (prediction > 0.0) ? (bs_actual_time / prediction) : 0.0; - settings_.log.printf( - "[WORK_PRED_BS] W%d N%d: actual=%.6fs predicted=%.6fwu ratio=%.3f (m=%d n=%d nnz=%d " - "processed=%d changed=%d)\n", - worker.worker_id, - node_ptr->node_id, - bs_actual_time, - prediction, - ratio, - m, - n, - nnz, - worker.node_presolver->last_nnz_processed, - num_bounds_changed); -#endif - - // worker.work_context.record_work(prediction); + // TEMP; worker.work_context.record_work(worker.node_presolver->last_nnz_processed / 1e8); } #endif diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 1f6b8a561..567f6e286 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -26,7 +26,6 @@ #include #include #include -#include #include #include diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 7bc6486a2..5e9faff0d 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -17,11 +17,9 @@ #include #include -#include #include #include #include -#include #include @@ -2563,53 +2561,6 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, f_t interval_start_time = toc(start_time); i_t last_feature_log_iter = iter; - // Helper to compute scaled work units for a given number of iterations - thread_local cuopt::work_unit_predictor_t - work_predictor{}; - auto predict_work_units = [&](i_t num_iters) -> f_t { - PHASE2_NVTX_RANGE("DualSimplex::predict_work_units"); - std::map features_map; - features_map["m"] = static_cast(features.num_rows); - features_map["n"] = static_cast(features.num_cols); - features_map["nnz"] = static_cast(features.num_nonzeros); - features_map["density"] = static_cast(features.matrix_density); - features_map["avg_nnz_col"] = static_cast(features.avg_nnz_per_col); - features_map["avg_nnz_row"] = static_cast(features.avg_nnz_per_row); - features_map["bounded"] = static_cast(features.num_bounded_vars); - features_map["free"] = static_cast(features.num_free_vars); - features_map["refact_freq"] = static_cast(features.refactor_frequency); - features_map["num_refacts"] = static_cast(features.num_refactors); - features_map["num_updates"] = static_cast(features.num_basis_updates); - features_map["sparse_dz"] = static_cast(features.sparse_delta_z_count); - features_map["dense_dz"] = static_cast(features.dense_delta_z_count); - features_map["bound_flips"] = static_cast(features.total_bound_flips); - features_map["num_infeas"] = static_cast(features.num_infeasibilities); - features_map["dy_nz_pct"] = static_cast(features.delta_y_nz_percentage); - features_map["byte_loads"] = static_cast(features.byte_loads); - features_map["byte_stores"] = static_cast(features.byte_stores); - - f_t base_prediction = std::max((f_t)0.0, (f_t)work_predictor.predict_scalar(features_map)); - f_t scaled_prediction = base_prediction * static_cast(num_iters) / FEATURE_LOG_INTERVAL; - - // const char* worker_name = work_unit_context ? work_unit_context->name.c_str() : "unknown"; - // CUOPT_LOG_DEBUG("PREDICT_WORK [%s]: iters=%d refacts=%d updates=%d sparse_dz=%d dense_dz=%d " - // "bound_flips=%d infeas=%d dy_nz=%.6f loads=%zu stores=%zu -> pred=%.9f", - // worker_name, - // num_iters, - // features.num_refactors, - // features.num_basis_updates, - // features.sparse_delta_z_count, - // features.dense_delta_z_count, - // features.total_bound_flips, - // features.num_infeasibilities, - // features.delta_y_nz_percentage, - // features.byte_loads, - // features.byte_stores, - // scaled_prediction); - - return scaled_prediction; - }; - cuopt::scope_guard work_unit_guard([&]() { i_t remaining_iters = iter - last_feature_log_iter; if (remaining_iters <= 0) return; @@ -2633,30 +2584,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, features.log_features(settings); if (work_unit_context) { - f_t prediction = predict_work_units(remaining_iters); - -#ifdef CUOPT_DEBUG_WORK_PREDICTION - f_t actual_time = features.interval_runtime; - f_t ratio = (prediction > 0.0) ? (actual_time / prediction) : 0.0; - const char* worker_name = work_unit_context->name.c_str(); - printf( - "[WORK_PRED_DS] %s: actual=%.6fs predicted=%.6fwu ratio=%.3f (iters=%d refacts=%d " - "updates=%d sparse_dz=%d dense_dz=%d flips=%d infeas=%d dy_nz=%.2f%%)\n", - worker_name, - actual_time, - prediction, - ratio, - remaining_iters, - features.num_refactors, - features.num_basis_updates, - features.sparse_delta_z_count, - features.dense_delta_z_count, - features.total_bound_flips, - features.num_infeasibilities, - features.delta_y_nz_percentage * 100.0); -#endif - - // work_unit_context->record_work(prediction); + // TEMP; work_unit_context->record_work((total_loads + total_stores) / 1e8); } }); @@ -3295,15 +3223,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, features.log_features(settings); if (work_unit_context) { - f_t prediction = predict_work_units(iters_elapsed); - // printf("DualSimplex determ: %d iters, predicted %.4f, actual %.4f, error %.4f\n", - // iters_elapsed, - // prediction, - // features.interval_runtime, - // prediction - features.interval_runtime); - // printf("Current iter %d\n", iter); - - // work_unit_context->record_work(prediction); + // TEMP; work_unit_context->record_work((total_loads + total_stores) / 1e8); } diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index bb6129dbe..2fa510959 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -28,10 +28,10 @@ // Define CPUFJ_NVTX_RANGES to enable detailed NVTX profiling ranges #ifdef CPUFJ_NVTX_RANGES -#define CPUFJ_NVTX_RANGE(name) raft::common::nvtx::range NVTX_UNIQUE_NAME(nvtx_scope_)(name) -#define NVTX_UNIQUE_NAME(base) NVTX_CONCAT(base, __LINE__) -#define NVTX_CONCAT(a, b) NVTX_CONCAT_INNER(a, b) -#define NVTX_CONCAT_INNER(a, b) a##b +#define CPUFJ_NVTX_RANGE(name) raft::common::nvtx::range CPUFJ_NVTX_UNIQUE_NAME(nvtx_scope_)(name) +#define CPUFJ_NVTX_UNIQUE_NAME(base) CPUFJ_NVTX_CONCAT(base, __LINE__) +#define CPUFJ_NVTX_CONCAT(a, b) CPUFJ_NVTX_CONCAT_INNER(a, b) +#define CPUFJ_NVTX_CONCAT_INNER(a, b) a##b #else #define CPUFJ_NVTX_RANGE(name) ((void)0) #endif @@ -1536,43 +1536,13 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l } #endif - // Collect and print PAPI metrics and regression features every 1000 iterations if (fj_cpu.iterations % 100 == 0 && fj_cpu.iterations > 0) { - auto now = std::chrono::high_resolution_clock::now(); - double time_window_ms = std::chrono::duration_cast>( - now - fj_cpu.last_feature_log_time) - .count() * - 1000.0; - double total_time_ms = - std::chrono::duration_cast>(now - loop_start).count() * - 1000.0; - // Collect memory statistics auto [loads, stores] = fj_cpu.memory_manifold.collect(); - // Log all features including memory statistics - // log_regression_features(fj_cpu, time_window_ms, total_time_ms, loads, stores); - - fj_cpu.last_feature_log_time = now; - - std::map features_map; - features_map["n_vars"] = (float)fj_cpu.h_reverse_offsets.size() - 1; - features_map["n_cstrs"] = (float)fj_cpu.h_offsets.size() - 1; - features_map["total_nnz"] = (float)fj_cpu.h_reverse_offsets.back(); - features_map["mem_total_mb"] = (float)(loads + stores) / 1e6; - float time_prediction = - std::max( - (f_t)0.0, - (f_t)ceil(context.work_unit_predictors.cpufj_predictor.predict_scalar(features_map))) / - 1000.0; - - // Record work units with bias applied (bias < 1.0 makes CPUFJ appear faster) - double biased_work = time_prediction * fj_cpu.work_unit_bias; - double new_work_units = - fj_cpu.work_units_elapsed.load(std::memory_order_relaxed) + biased_work; - fj_cpu.work_units_elapsed.store(new_work_units, std::memory_order_release); - - // Notify producer_sync if registered + double biased_work = (loads + stores) * fj_cpu.work_unit_bias / 1e10; + fj_cpu.work_units_elapsed += biased_work; + if (fj_cpu.producer_sync != nullptr) { fj_cpu.producer_sync->notify_progress(); } CUOPT_LOG_TRACE("CPUFJ work units: %f incumbent %g", diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cuh b/cpp/src/mip/feasibility_jump/fj_cpu.cuh index a1ec24054..a69ce5930 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cuh +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cuh @@ -154,7 +154,7 @@ struct fj_cpu_climber_t { // Work unit tracking for deterministic synchronization std::atomic work_units_elapsed{0.0}; - double work_unit_bias{0.8}; // Bias factor to keep CPUFJ ahead of B&B (< 1.0 means faster) + double work_unit_bias{1.5}; // Bias factor to keep CPUFJ ahead of B&B producer_sync_t* producer_sync{nullptr}; // Optional sync utility for notifying progress std::atomic halted{false}; diff --git a/cpp/src/mip/solver_context.cuh b/cpp/src/mip/solver_context.cuh index 055ad165f..705823970 100644 --- a/cpp/src/mip/solver_context.cuh +++ b/cpp/src/mip/solver_context.cuh @@ -11,11 +11,7 @@ #include #include -#include -#include -#include #include -#include #pragma once @@ -27,12 +23,6 @@ class branch_and_bound_t; namespace cuopt::linear_programming::detail { -struct mip_solver_work_unit_predictors_t { - work_unit_predictor_t fj_predictor{}; - work_unit_predictor_t cpufj_predictor{}; - work_unit_predictor_t pdlp_predictor{}; -}; - // Aggregate structure containing the global context of the solving process for convenience: // The current problem, user settings, raft handle and statistics objects template @@ -59,7 +49,6 @@ struct mip_solver_context_t { const mip_solver_settings_t settings; pdlp_initial_scaling_strategy_t& scaling; solver_stats_t stats; - mip_solver_work_unit_predictors_t work_unit_predictors; // Work limit context for tracking work units in deterministic mode (shared across all timers in // GPU heuristic loop) work_limit_context_t gpu_heur_loop{"GPUHeur"}; diff --git a/cpp/src/utilities/models/bounds_strengthening_predictor/header.h b/cpp/src/utilities/models/bounds_strengthening_predictor/header.h deleted file mode 100644 index d9cb0e12c..000000000 --- a/cpp/src/utilities/models/bounds_strengthening_predictor/header.h +++ /dev/null @@ -1,35 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#pragma once - -#include -#include -#include -#include -#include -#include - -class bounds_strengthening_predictor { - public: - union Entry { - int missing; - double fvalue; - int qvalue; - }; - - static int32_t get_num_target(void); - static void get_num_class(int32_t* out); - static int32_t get_num_feature(void); - static const char* get_threshold_type(void); - static const char* get_leaf_output_type(void); - static void predict(union Entry* data, int pred_margin, double* result); - static void postprocess(double* result); - static int quantize(double val, unsigned fid); - - // Feature names - static constexpr int NUM_FEATURES = 5; - static const char* feature_names[NUM_FEATURES]; -}; // class bounds_strengthening_predictor diff --git a/cpp/src/utilities/models/bounds_strengthening_predictor/main.cpp b/cpp/src/utilities/models/bounds_strengthening_predictor/main.cpp deleted file mode 100644 index 1b1488c99..000000000 --- a/cpp/src/utilities/models/bounds_strengthening_predictor/main.cpp +++ /dev/null @@ -1,3423 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#include -#include "header.h" - -#if defined(__clang__) || defined(__GNUC__) -#define LIKELY(x) __builtin_expect(!!(x), 1) -#define UNLIKELY(x) __builtin_expect(!!(x), 0) -#else -#define LIKELY(x) (x) -#define UNLIKELY(x) (x) -#endif -#define N_TARGET 1 -#define MAX_N_CLASS 1 - -const unsigned char is_categorical[] = { - 0, - 0, - 0, - 0, - 0, -}; -static const int32_t num_class[] = { - 1, -}; - -int32_t bounds_strengthening_predictor::get_num_target(void) { return std::exp(N_TARGET) - (100); } -void bounds_strengthening_predictor::get_num_class(int32_t* out) -{ - for (int i = 0; i < N_TARGET; ++i) { - out[i] = num_class[i]; - } -} -int32_t bounds_strengthening_predictor::get_num_feature(void) { return std::exp(5) - (100); } -const char* bounds_strengthening_predictor::get_threshold_type(void) { return "float64"; } -const char* bounds_strengthening_predictor::get_leaf_output_type(void) { return "float64"; } - -void bounds_strengthening_predictor::predict(union Entry* data, int pred_margin, double* result) -{ - // Quantize data - for (int i = 0; i < 5; ++i) { - if (data[i].missing != -1 && !is_categorical[i]) { - data[i].qvalue = quantize(data[i].fvalue, i); - } - } - - unsigned int tmp; - if (LIKELY(false || (data[3].qvalue <= 36))) { - if (LIKELY(false || (data[3].qvalue <= 4))) { - if (LIKELY(false || (data[0].qvalue <= 0))) { - result[0] += 4.6207593335207156; - } else { - result[0] += 4.621034455402758; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 10))) { - if (LIKELY(false || (data[0].qvalue <= 12))) { - if (UNLIKELY(false || (data[3].qvalue <= 6))) { - result[0] += 4.6219609086662095; - } else { - result[0] += 4.622183037068742; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 20))) { - result[0] += 4.621033941004809; - } else { - if (LIKELY(false || (data[0].qvalue <= 26))) { - result[0] += 4.621791267961554; - } else { - result[0] += 4.621426104022116; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 6))) { - if (LIKELY(false || (data[3].qvalue <= 18))) { - result[0] += 4.625919220945453; - } else { - result[0] += 4.628513174443221; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 26))) { - if (UNLIKELY(false || (data[1].qvalue <= 10))) { - result[0] += 4.621784796275526; - } else { - result[0] += 4.622332428952099; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 4.622921220395778; - } else { - result[0] += 4.625300570663533; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - result[0] += 4.640761672090703; - } else { - if (LIKELY(false || (data[3].qvalue <= 56))) { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - if (UNLIKELY(false || (data[0].qvalue <= 24))) { - result[0] += 4.6249068107234645; - } else { - result[0] += 4.622329447390833; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 44))) { - result[0] += 4.629801464601299; - } else { - result[0] += 4.631153243632111; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 50))) { - result[0] += 4.661969246306176; - } else { - result[0] += 4.634038239440175; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 32))) { - result[0] += 4.661584263398344; - } else { - result[0] += 4.670140606548272; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 106))) { - if (LIKELY(false || (data[1].qvalue <= 68))) { - if (LIKELY(false || (data[2].qvalue <= 44))) { - if (LIKELY(false || (data[0].qvalue <= 44))) { - if (LIKELY(false || (data[0].qvalue <= 4))) { - result[0] += -0.0015367070074457825; - } else { - result[0] += -0.0013136363962750828; - } - } else { - result[0] += -4.5316595658010896e-05; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 26))) { - if (LIKELY(false || (data[2].qvalue <= 94))) { - result[0] += -0.0004993478758971943; - } else { - result[0] += 0.001360991802963148; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 48))) { - result[0] += 0.0003523832199634608; - } else { - result[0] += 0.00182867874633131; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (UNLIKELY(false || (data[2].qvalue <= 30))) { - result[0] += 0.002593102353418926; - } else { - result[0] += 0.005728859642902398; - } - } else { - result[0] += 0.008440219358081076; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 116))) { - if (LIKELY(false || (data[1].qvalue <= 54))) { - result[0] += 0.002535670244021562; - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.006909437253271745; - } else { - result[0] += 0.02304069650359452; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 56))) { - if (UNLIKELY(false || (data[2].qvalue <= 120))) { - result[0] += 0.004730323543486456; - } else { - result[0] += 0.0061965453575640326; - } - } else { - result[0] += 0.013205196280244542; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (UNLIKELY(false || (data[2].qvalue <= 140))) { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - result[0] += 0.00807777003523674; - } else { - result[0] += 0.010705611526849181; - } - } else { - result[0] += 0.012723627762072781; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (UNLIKELY(false || (data[2].qvalue <= 146))) { - result[0] += 0.015703792435409165; - } else { - result[0] += 0.020898327013036955; - } - } else { - result[0] += 0.02921710257052448; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.03916204050259713; - } else { - result[0] += 0.060036659967154266; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 32))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.02552313877403762; - } else { - result[0] += 0.03311200053743086; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.1013872077379908; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[1].qvalue <= 72))) { - result[0] += 0.026368786688846874; - } else { - result[0] += 0.0433115207428156; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.029699057000023982; - } else { - result[0] += 0.06580137491588535; - } - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 104))) { - if (LIKELY(false || (data[1].qvalue <= 68))) { - if (LIKELY(false || (data[2].qvalue <= 46))) { - if (LIKELY(false || (data[1].qvalue <= 44))) { - if (LIKELY(false || (data[2].qvalue <= 18))) { - result[0] += -0.0013768933256704324; - } else { - result[0] += -0.0011262398007476182; - } - } else { - result[0] += -0.00032975454434196595; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 26))) { - if (LIKELY(false || (data[2].qvalue <= 60))) { - result[0] += -0.0006276937122835658; - } else { - result[0] += -0.00018352024583244162; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 48))) { - result[0] += 0.00028540160605117557; - } else { - result[0] += 0.0016549226422226974; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (UNLIKELY(false || (data[2].qvalue <= 32))) { - result[0] += 0.0023646158978421383; - } else { - result[0] += 0.0051637399936750455; - } - } else { - result[0] += 0.0075761109087648; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 114))) { - if (LIKELY(false || (data[1].qvalue <= 54))) { - result[0] += 0.0019452567655093584; - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - result[0] += 0.005666648643122513; - } else { - result[0] += 0.01829621969461441; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 56))) { - if (UNLIKELY(false || (data[2].qvalue <= 120))) { - result[0] += 0.003990524959078667; - } else { - result[0] += 0.0055769135986496665; - } - } else { - result[0] += 0.011831853925204368; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (UNLIKELY(false || (data[2].qvalue <= 140))) { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - result[0] += 0.007270356084799834; - } else { - result[0] += 0.009635304835365038; - } - } else { - result[0] += 0.011451321149275655; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 52))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - result[0] += 0.017390748142516205; - } else { - result[0] += 0.02630676100748059; - } - } else { - result[0] += 0.009110929474456986; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - result[0] += 0.02801222951444861; - } else { - result[0] += 0.042642993176138254; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.022974970733987354; - } else { - result[0] += 0.029654697301807826; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[1].qvalue <= 72))) { - result[0] += 0.019513382427394393; - } else { - result[0] += 0.039000512019146324; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 64))) { - result[0] += 0.03719209477305412; - } else { - result[0] += 0.059241237849631215; - } - } - } else { - result[0] += 0.09132090954478399; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 36))) { - if (LIKELY(false || (data[3].qvalue <= 4))) { - if (LIKELY(false || (data[3].qvalue <= 0))) { - result[0] += -0.00125673851948232; - } else { - result[0] += -0.0010632420050787612; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 10))) { - if (LIKELY(false || (data[0].qvalue <= 12))) { - result[0] += -0.00024632899498939104; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 20))) { - result[0] += -0.0010346225974928775; - } else { - result[0] += -0.0005449025485433642; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 10))) { - if (UNLIKELY(false || (data[1].qvalue <= 10))) { - if (LIKELY(false || (data[1].qvalue <= 2))) { - result[0] += 0.0025363360589947676; - } else { - result[0] += 0.004674142936566838; - } - } else { - result[0] += 0.0015267965552685568; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 60))) { - if (UNLIKELY(false || (data[1].qvalue <= 26))) { - result[0] += -0.00027483790298776263; - } else { - result[0] += 0.0002972757256301511; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 62))) { - result[0] += 0.0041428877040166215; - } else { - result[0] += 0.0011655918164156917; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - result[0] += 0.01333787139964387; - } else { - if (LIKELY(false || (data[3].qvalue <= 56))) { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - if (UNLIKELY(false || (data[0].qvalue <= 24))) { - result[0] += 0.0015462619991967958; - } else { - result[0] += -0.0001502519938051531; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 48))) { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 0.005627108368440385; - } else { - result[0] += 0.004311373641741069; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 50))) { - result[0] += 0.029228057063747634; - } else { - result[0] += 0.008457099724201248; - } - } - } - } else { - result[0] += 0.03349293840846523; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 94))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (LIKELY(false || (data[2].qvalue <= 42))) { - if (LIKELY(false || (data[0].qvalue <= 44))) { - result[0] += -0.0010804696960751716; - } else { - result[0] += -7.891687944899684e-05; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 26))) { - if (LIKELY(false || (data[2].qvalue <= 60))) { - result[0] += -0.0005406252210800222; - } else { - result[0] += -0.00018499077307288617; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 52))) { - result[0] += 9.144939740089358e-05; - } else { - result[0] += 0.0011602440613788997; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (UNLIKELY(false || (data[2].qvalue <= 30))) { - result[0] += 0.0015989411665534034; - } else { - result[0] += 0.004244323292266292; - } - } else { - result[0] += 0.005838572629904283; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 114))) { - if (LIKELY(false || (data[1].qvalue <= 52))) { - result[0] += 0.0014231677129056524; - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.004423386593859449; - } else { - result[0] += 0.014837017284128172; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 56))) { - if (UNLIKELY(false || (data[2].qvalue <= 122))) { - if (LIKELY(false || (data[1].qvalue <= 46))) { - result[0] += 0.0030916099442218725; - } else { - result[0] += 0.0051002350582270845; - } - } else { - result[0] += 0.00452717826425259; - } - } else { - result[0] += 0.011297589064915738; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[1].qvalue <= 38))) { - result[0] += 0.005096665266400936; - } else { - result[0] += 0.00914843088656596; - } - } else { - result[0] += 0.008873224907066796; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (UNLIKELY(false || (data[2].qvalue <= 146))) { - result[0] += 0.011259071094333009; - } else { - result[0] += 0.0157599764415071; - } - } else { - result[0] += 0.022078909557385205; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.030722327275153918; - } else { - result[0] += 0.04927028425037861; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 32))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.019342689499895135; - } else { - result[0] += 0.02546834143335123; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[1].qvalue <= 72))) { - result[0] += 0.018988974551944173; - } else { - result[0] += 0.03220939183373785; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.08284748123076036; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.023946994628225055; - } else { - result[0] += 0.05204241935061829; - } - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 102))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[0].qvalue <= 30))) { - if (LIKELY(false || (data[2].qvalue <= 18))) { - result[0] += -0.001013660458835288; - } else { - result[0] += -0.0008091843403196929; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 32))) { - result[0] += -0.0003093376277910651; - } else { - result[0] += 0.0008342959644312034; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 62))) { - if (LIKELY(false || (data[3].qvalue <= 8))) { - result[0] += -0.00025794934360278315; - } else { - result[0] += -0.0006182054733506302; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 94))) { - result[0] += 3.786358371332886e-05; - } else { - result[0] += 0.0011058684623919252; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 38))) { - result[0] += 0.003947623029554656; - } else { - if (LIKELY(false || (data[2].qvalue <= 88))) { - result[0] += 0.005537522805788919; - } else { - result[0] += 0.012393589503644558; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 118))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 114))) { - result[0] += 0.0015414300409981696; - } else { - result[0] += 0.0025675242802216634; - } - } else { - result[0] += 0.00484969263475652; - } - } else { - result[0] += 0.016452803226945166; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 56))) { - result[0] += 0.0039797931775412965; - } else { - result[0] += 0.011972692174657348; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 0.008268968051726045; - } else { - result[0] += 0.004574969868580042; - } - } else { - result[0] += 0.007985935393142055; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 56))) { - if (LIKELY(false || (data[0].qvalue <= 30))) { - result[0] += 0.012855216147807361; - } else { - result[0] += 0.018209631592896635; - } - } else { - result[0] += 0.0011929211910353282; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - result[0] += 0.0213851068444448; - } else { - result[0] += 0.03414051433662315; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.017427519954483866; - } else { - result[0] += 0.022839697665974004; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.027770178962905873; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.027731845133727596; - } else { - result[0] += 0.04685399499752964; - } - } - } else { - result[0] += 0.07097678249608726; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 102))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[2].qvalue <= 40))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - if (LIKELY(false || (data[2].qvalue <= 18))) { - result[0] += -0.0009069312057087865; - } else { - result[0] += -0.0007209240090653536; - } - } else { - result[0] += 9.266279868544054e-06; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 60))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - result[0] += -0.00045145656190525603; - } else { - result[0] += 0.000736445256361047; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += 3.824640547813124e-05; - } else { - result[0] += 0.0019395439371273169; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.0032973085317471182; - } else { - if (LIKELY(false || (data[2].qvalue <= 72))) { - result[0] += 0.0046992006748856376; - } else { - result[0] += 0.010662218181265368; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 114))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += 0.0013872983040295432; - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.003922717377135387; - } else { - result[0] += 0.012402207983552287; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 56))) { - if (UNLIKELY(false || (data[2].qvalue <= 122))) { - result[0] += 0.0027351189679491643; - } else { - result[0] += 0.003676515596417783; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.008212890325666373; - } else { - result[0] += 0.018472826568917796; - } - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.004094328568438451; - } else { - result[0] += 0.007676761387855601; - } - } else { - result[0] += 0.00718737123039425; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 56))) { - if (LIKELY(false || (data[0].qvalue <= 30))) { - result[0] += 0.011570012574530879; - } else { - result[0] += 0.016393931404272945; - } - } else { - result[0] += 0.0010741865297096182; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.024740276188422474; - } else { - result[0] += 0.041189863514155156; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.01568551045560334; - } else { - result[0] += 0.020556299621416432; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.025004828468340786; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.02500338864662955; - } else { - result[0] += 0.0421828362800883; - } - } - } else { - result[0] += 0.06392980175664915; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 94))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[2].qvalue <= 40))) { - if (LIKELY(false || (data[0].qvalue <= 30))) { - result[0] += -0.0007939024435866282; - } else { - if (LIKELY(false || (data[3].qvalue <= 32))) { - result[0] += -0.00028064729815836673; - } else { - result[0] += 0.0007573829970680826; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 46))) { - if (LIKELY(false || (data[3].qvalue <= 46))) { - result[0] += -0.0002029265825135938; - } else { - result[0] += 0.0049281532625337285; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 66))) { - result[0] += 0.0006539904593123779; - } else { - result[0] += 0.002101938298715612; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.002966716939082758; - } else { - result[0] += 0.004282679307075282; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 114))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += 0.0010976994464251622; - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.003309816493639741; - } else { - result[0] += 0.010820436719805003; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 56))) { - if (UNLIKELY(false || (data[2].qvalue <= 118))) { - result[0] += 0.0020919978318753476; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 28))) { - result[0] += 0.005176501864572617; - } else { - result[0] += 0.0031809497994157152; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.007393664821488642; - } else { - result[0] += 0.016667527793483305; - } - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 80))) { - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (UNLIKELY(false || (data[2].qvalue <= 140))) { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.005229670373125717; - } else { - result[0] += 0.015644584188655934; - } - } else { - result[0] += 0.006607172961087171; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 56))) { - if (LIKELY(false || (data[0].qvalue <= 30))) { - result[0] += 0.010413297684558929; - } else { - result[0] += 0.014759276311661552; - } - } else { - result[0] += 0.0009672698351743883; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.024270801343479934; - } else { - result[0] += 0.03719959639012814; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.014117627727620947; - } else { - result[0] += 0.018501183876499263; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.022514852274741447; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.022543378573271537; - } else { - result[0] += 0.03797737476673532; - } - } - } else { - result[0] += 0.057582486043418105; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 94))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[2].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += -0.0007216230613638112; - } else { - result[0] += 1.2243669808756593e-05; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 60))) { - if (LIKELY(false || (data[0].qvalue <= 30))) { - result[0] += -0.00040489502028823784; - } else { - result[0] += 0.00042280612429353794; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 46))) { - result[0] += 6.1268474691525876e-06; - } else { - result[0] += 0.004594043928912372; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 38))) { - result[0] += 0.002846796010984943; - } else { - result[0] += 0.004154787212310116; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 112))) { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += 0.0009445975360671696; - } else { - result[0] += 0.0025639599906144878; - } - } else { - result[0] += 0.008383097287811591; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 56))) { - if (UNLIKELY(false || (data[2].qvalue <= 118))) { - result[0] += 0.0017724753898680124; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 28))) { - result[0] += 0.004659576685877428; - } else { - result[0] += 0.002862865819246229; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 120))) { - result[0] += 0.0046025811935695195; - } else { - result[0] += 0.010899977739561688; - } - } else { - result[0] += 0.014507038097267284; - } - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 80))) { - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.003162446156626471; - } else { - result[0] += 0.007225625842537261; - } - } else { - result[0] += 0.005829436446592702; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 56))) { - if (UNLIKELY(false || (data[2].qvalue <= 146))) { - result[0] += 0.008519365467784451; - } else { - result[0] += 0.010728541856931856; - } - } else { - result[0] += 0.0008709948831324892; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.021860231652754504; - } else { - result[0] += 0.0335958852712065; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.012706466408332943; - } else { - result[0] += 0.01665152863778353; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.02027282733611074; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.02032540112733841; - } else { - result[0] += 0.03419118077768789; - } - } - } else { - result[0] += 0.051865370802847414; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 94))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[0].qvalue <= 30))) { - if (LIKELY(false || (data[2].qvalue <= 16))) { - result[0] += -0.0006709370987019684; - } else { - result[0] += -0.0005270795525272784; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 32))) { - result[0] += -0.00019736253512332336; - } else { - result[0] += 0.0006554788837920564; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 46))) { - if (LIKELY(false || (data[3].qvalue <= 46))) { - result[0] += -0.00014697141542902774; - } else { - result[0] += 0.004114945413306209; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 66))) { - result[0] += 0.000608774604580438; - } else { - result[0] += 0.0018912343738969528; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.0023794517398263364; - } else { - result[0] += 0.0034944486152153164; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 112))) { - if (LIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 0.0009107471036068726; - } else { - result[0] += 0.007554206584444208; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 52))) { - if (UNLIKELY(false || (data[2].qvalue <= 122))) { - result[0] += 0.0018167266037088525; - } else { - result[0] += 0.002703491325573408; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 120))) { - result[0] += 0.004131123450839368; - } else { - result[0] += 0.009561926307650204; - } - } else { - result[0] += 0.013076206701871469; - } - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 0.005894735890468045; - } else { - result[0] += 0.0027635595215004857; - } - } else { - result[0] += 0.005246514394410695; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 56))) { - if (LIKELY(false || (data[0].qvalue <= 30))) { - result[0] += 0.008396656550227073; - } else { - result[0] += 0.012412600592852905; - } - } else { - result[0] += 0.0007843023806235918; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - result[0] += 0.013815322846990745; - } else { - result[0] += 0.022879901539002146; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.011436360873006497; - } else { - result[0] += 0.014986792630805573; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.018254062129975056; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.018325642903990143; - } else { - result[0] += 0.03078245439443534; - } - } - } else { - result[0] += 0.04671587835039412; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 36))) { - if (LIKELY(false || (data[3].qvalue <= 2))) { - if (LIKELY(false || (data[3].qvalue <= 0))) { - result[0] += -0.0006125589231343043; - } else { - result[0] += -0.0004916423019606601; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 8))) { - if (UNLIKELY(false || (data[1].qvalue <= 10))) { - if (LIKELY(false || (data[1].qvalue <= 2))) { - result[0] += 0.001216506849361631; - } else { - result[0] += 0.00271299155013397; - } - } else { - result[0] += 0.0008440598879826304; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 34))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (LIKELY(false || (data[3].qvalue <= 10))) { - result[0] += -0.00012183459142403321; - } else { - result[0] += 0.00029635306618271324; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 22))) { - result[0] += -0.00047278908965180035; - } else { - result[0] += -0.00026271782481790195; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 26))) { - if (LIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 0.00016567086050993653; - } else { - result[0] += 0.0012740789559656865; - } - } else { - result[0] += 0.00119403700340191; - } - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - result[0] += 0.006378599209839731; - } else { - if (LIKELY(false || (data[3].qvalue <= 56))) { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += -0.00013731745398177464; - } else { - if (LIKELY(false || (data[3].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 44))) { - result[0] += 0.0024863318876363182; - } else { - result[0] += 0.003201281592347354; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 50))) { - result[0] += 0.014536959755708252; - } else { - result[0] += 0.004109250766509175; - } - } - } - } else { - result[0] += 0.0156613343672559; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 94))) { - if (LIKELY(false || (data[1].qvalue <= 66))) { - if (LIKELY(false || (data[2].qvalue <= 58))) { - if (LIKELY(false || (data[1].qvalue <= 22))) { - result[0] += -0.0005258393188517168; - } else { - if (LIKELY(false || (data[2].qvalue <= 54))) { - result[0] += -0.00033817430900207735; - } else { - result[0] += 1.2707417395591901e-05; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 52))) { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - result[0] += -0.0004450469071967257; - } else { - result[0] += 5.459686661712705e-05; - } - } else { - result[0] += 0.0010611375199245895; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 36))) { - result[0] += 0.0014671398186970237; - } else { - result[0] += 0.002626165800361098; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 112))) { - if (LIKELY(false || (data[1].qvalue <= 52))) { - result[0] += 0.0007307625021520472; - } else { - result[0] += 0.0025431188996113325; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 54))) { - if (UNLIKELY(false || (data[2].qvalue <= 122))) { - if (LIKELY(false || (data[1].qvalue <= 40))) { - result[0] += 0.0011336443912753236; - } else { - result[0] += 0.0026154250468263807; - } - } else { - result[0] += 0.0021727864966215654; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 120))) { - result[0] += 0.0036860385022813268; - } else { - result[0] += 0.0074261350156955945; - } - } else { - result[0] += 0.011381202510825986; - } - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - result[0] += 0.0025260287423364753; - } else { - result[0] += 0.004205124307871224; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.01428713906478215; - } else { - result[0] += 0.006449499560970644; - } - } - } else { - result[0] += 0.008158350036622746; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.015911680867656684; - } else { - result[0] += 0.027726709544658664; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 32))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.009635705460455004; - } else { - if (LIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.012077164092969977; - } else { - result[0] += 0.013736665018938832; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.0021803621484432372; - } else { - result[0] += 0.015298270954553467; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.04418030004892776; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.012503185447837626; - } else { - result[0] += 0.02704296985360522; - } - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 94))) { - if (LIKELY(false || (data[1].qvalue <= 66))) { - if (LIKELY(false || (data[2].qvalue <= 58))) { - if (LIKELY(false || (data[1].qvalue <= 22))) { - if (LIKELY(false || (data[2].qvalue <= 12))) { - result[0] += -0.0005057327530247406; - } else { - result[0] += -0.0004075698534556644; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - result[0] += -0.0003907346454170061; - } else { - result[0] += -0.00013304885629976585; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 52))) { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - result[0] += -0.000400544289864025; - } else { - result[0] += 4.913722296179108e-05; - } - } else { - result[0] += 0.0009550458929996977; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (UNLIKELY(false || (data[2].qvalue <= 34))) { - result[0] += 0.0007601159979557645; - } else { - result[0] += 0.0019476041107708356; - } - } else { - result[0] += 0.0025254091774788738; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 116))) { - if (LIKELY(false || (data[1].qvalue <= 52))) { - result[0] += 0.000687914586801239; - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.0024117269694104632; - } else { - result[0] += 0.007822948252922929; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 50))) { - result[0] += 0.0018173927952783312; - } else { - result[0] += 0.005066485649762486; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (UNLIKELY(false || (data[2].qvalue <= 138))) { - result[0] += 0.002273550315729467; - } else { - result[0] += 0.0037846264364073015; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.012868415887070285; - } else { - result[0] += 0.005806636735887348; - } - } - } else { - result[0] += 0.007342867370677843; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.014328672563322842; - } else { - result[0] += 0.025040684528648854; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 32))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.008672550231782946; - } else { - if (LIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.010870030198642126; - } else { - result[0] += 0.012363709845251715; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += -0.0016214965620348532; - } else { - result[0] += 0.009768780560755148; - } - } else { - result[0] += 0.014496867406714756; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.03979524121133249; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.011297521186726434; - } else { - result[0] += 0.024346893042280684; - } - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 96))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[2].qvalue <= 58))) { - if (LIKELY(false || (data[2].qvalue <= 24))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += -0.0004359996480215986; - } else { - result[0] += 0; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 40))) { - result[0] += -0.0002523395445968973; - } else { - result[0] += 0.0003694161280901895; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 12))) { - if (LIKELY(false || (data[2].qvalue <= 78))) { - result[0] += 7.525930391762016e-06; - } else { - result[0] += 0.0008929398582163592; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += -0.0001610122359790055; - } else { - result[0] += 0.0010397722343388125; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 52))) { - if (UNLIKELY(false || (data[2].qvalue <= 4))) { - result[0] += -0.0005520984639497772; - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.0014389645609665816; - } else { - result[0] += 0.0022859821912433556; - } - } - } else { - result[0] += 0.004341192593591669; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 118))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 42))) { - result[0] += 0.0007445782254113342; - } else { - result[0] += -0.0004863660791352827; - } - } else { - result[0] += 0.0022185971404081856; - } - } else { - result[0] += 0.00764254255032432; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 28))) { - result[0] += 0.003993263532652023; - } else { - result[0] += 0.0016802334304175982; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 80))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (LIKELY(false || (data[2].qvalue <= 142))) { - result[0] += 0.0032242882960233418; - } else { - result[0] += 0.004681685073819564; - } - } else { - result[0] += -0.0019765970439377645; - } - } else { - result[0] += 0.006608897421772834; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.01415305377345304; - } else { - result[0] += 0.02261486836243421; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.007850465295499818; - } else { - result[0] += 0.0104151568660406; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += -0.00899228106430244; - } else { - result[0] += 0.012754785278791262; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.01241382271659771; - } else { - result[0] += 0.02191960403899695; - } - } else { - result[0] += 0.035845413210231866; - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 104))) { - if (LIKELY(false || (data[1].qvalue <= 66))) { - if (LIKELY(false || (data[2].qvalue <= 60))) { - if (LIKELY(false || (data[1].qvalue <= 22))) { - if (LIKELY(false || (data[3].qvalue <= 38))) { - result[0] += -0.00038117538124280044; - } else { - result[0] += -0.0044417730590026435; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 54))) { - result[0] += -0.00024944182998305845; - } else { - result[0] += 4.7726306723714356e-05; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 48))) { - if (UNLIKELY(false || (data[3].qvalue <= 12))) { - result[0] += 0.00017780524533208728; - } else { - result[0] += -0.00014198949040707064; - } - } else { - result[0] += 0.0008690091555703179; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 6))) { - result[0] += -4.2794447077009146e-05; - } else { - result[0] += 0.0013736436855736134; - } - } else { - result[0] += 0.0023003491149758966; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 48))) { - if (UNLIKELY(false || (data[2].qvalue <= 122))) { - if (LIKELY(false || (data[3].qvalue <= 38))) { - result[0] += 0.0008322321038850688; - } else { - result[0] += -0.0007022737671788451; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 0.0040552217853155985; - } else { - result[0] += 0.001568199690420882; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 74))) { - if (LIKELY(false || (data[2].qvalue <= 118))) { - result[0] += 0.002158096393788496; - } else { - result[0] += 0.00418981399519502; - } - } else { - result[0] += 0.007976203340311152; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (LIKELY(false || (data[2].qvalue <= 142))) { - result[0] += 0.0029787778286427856; - } else { - result[0] += 0.00387467999983618; - } - } else { - result[0] += -0.0016447041228178653; - } - } else { - result[0] += 0.005939025993708955; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - result[0] += 0.009542470806448608; - } else { - result[0] += 0.015164196475275924; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.0070657533568465065; - } else { - if (LIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.008770727747783912; - } else { - result[0] += 0.010031409549326995; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[1].qvalue <= 72))) { - result[0] += -0.005887114820381005; - } else { - result[0] += 0.011992559542402971; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[1].qvalue <= 64))) { - result[0] += 0.011192463174135607; - } else { - result[0] += 0.019734306209876366; - } - } else { - result[0] += 0.032287623295143474; - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 98))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[2].qvalue <= 62))) { - if (LIKELY(false || (data[2].qvalue <= 22))) { - if (LIKELY(false || (data[3].qvalue <= 34))) { - result[0] += -0.00034750254877484433; - } else { - result[0] += -0.0009661001233083217; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 46))) { - result[0] += -0.0002100971251621207; - } else { - result[0] += 0.0003227294420283331; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 12))) { - if (LIKELY(false || (data[2].qvalue <= 80))) { - result[0] += 6.630874663410788e-05; - } else { - result[0] += 0.0007797657979206323; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += -0.00012124022680195402; - } else { - result[0] += 0.0009324062666283081; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 52))) { - result[0] += 0.0014435021333467982; - } else { - result[0] += 0.003681684849152054; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 118))) { - if (LIKELY(false || (data[3].qvalue <= 46))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 42))) { - result[0] += 0.0006546173228667891; - } else { - result[0] += -0.0008103936292200276; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 50))) { - result[0] += 0.006212216600054695; - } else { - result[0] += 0.001728401157423842; - } - } - } else { - result[0] += 0.005068273878703855; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 0.003464285315254766; - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - result[0] += 0.001344884231078165; - } else { - result[0] += 0.007599763760233628; - } - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 80))) { - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (UNLIKELY(false || (data[3].qvalue <= 24))) { - result[0] += 0.008852681654884861; - } else { - result[0] += 0.002711342951735739; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 30))) { - result[0] += 0.004675440064623506; - } else { - result[0] += 0.007392307943903377; - } - } else { - result[0] += -0.001327311393919067; - } - } - } else { - result[0] += 0.013657623668096947; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.0063594791059816335; - } else { - result[0] += 0.008437232533364402; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += -0.007575196287639085; - } else { - result[0] += 0.010362936929949294; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.010091268861320833; - } else { - result[0] += 0.017766873342447524; - } - } else { - result[0] += 0.029082956597010896; - } - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 36))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -0.000321332335062971; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 6))) { - if (UNLIKELY(false || (data[0].qvalue <= 2))) { - result[0] += -0.0002294363570388214; - } else { - if (LIKELY(false || (data[1].qvalue <= 2))) { - result[0] += 0.0005885804579918629; - } else { - result[0] += 0.0017855147306627796; - } - } - } else { - result[0] += -0.00022345013791362544; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 16))) { - if (LIKELY(false || (data[0].qvalue <= 12))) { - result[0] += 2.4700241334642348e-05; - } else { - if (LIKELY(false || (data[0].qvalue <= 42))) { - result[0] += -0.00020036238717468835; - } else { - result[0] += 0.0012242986912053152; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 18))) { - result[0] += 0.000495591293193325; - } else { - if (LIKELY(false || (data[1].qvalue <= 58))) { - result[0] += 8.398870862786576e-05; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 62))) { - result[0] += 0.0009488573573794414; - } else { - result[0] += 0.00024307072370918857; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - result[0] += 0.0033778298438371006; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 32))) { - result[0] += -0.00018653029821255553; - } else { - if (LIKELY(false || (data[3].qvalue <= 48))) { - result[0] += 0.0013578038474610836; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 0.007771589888056477; - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - result[0] += 0.002061396852944069; - } else { - result[0] += 0.003952529031744175; - } - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 94))) { - if (LIKELY(false || (data[1].qvalue <= 66))) { - if (LIKELY(false || (data[2].qvalue <= 64))) { - if (LIKELY(false || (data[1].qvalue <= 22))) { - if (LIKELY(false || (data[3].qvalue <= 38))) { - result[0] += -0.0002819994363762382; - } else { - result[0] += -0.004239400330393072; - } - } else { - result[0] += -0.00013827393706384455; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 14))) { - if (LIKELY(false || (data[2].qvalue <= 80))) { - result[0] += 7.513439279655798e-05; - } else { - result[0] += 0.0007472308363421268; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 30))) { - result[0] += -0.00026325700404833315; - } else { - result[0] += 0.00020351968979620772; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 56))) { - result[0] += 0.000994229445321635; - } else { - result[0] += 0.002762669975718151; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 60))) { - if (LIKELY(false || (data[2].qvalue <= 122))) { - if (LIKELY(false || (data[1].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 38))) { - result[0] += 0.00048738447627366677; - } else { - result[0] += -0.0008016673054020819; - } - } else { - result[0] += 0.0013491982872427825; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 0.003373441637139947; - } else { - result[0] += 0.0011426012802899458; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 74))) { - if (UNLIKELY(false || (data[1].qvalue <= 62))) { - result[0] += 0.004820049065765553; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 70))) { - result[0] += -0.0005516126023022891; - } else { - result[0] += 0.002897319749185366; - } - } - } else { - result[0] += 0.0058772611298245165; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (UNLIKELY(false || (data[3].qvalue <= 24))) { - result[0] += 0.008367746274452657; - } else { - result[0] += 0.0022013121979746215; - } - } else { - result[0] += -0.0021588341517871502; - } - } else { - result[0] += 0.004543503641829177; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - result[0] += 0.007435827781432688; - } else { - result[0] += 0.01190804336570028; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.005390048627758562; - } else { - result[0] += 0.007261544693340969; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[1].qvalue <= 72))) { - result[0] += -0.005337102150777355; - } else { - result[0] += 0.008993863393954641; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[1].qvalue <= 64))) { - result[0] += 0.008333794155773979; - } else { - result[0] += 0.01566137192688039; - } - } else { - result[0] += 0.025425005405029257; - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 108))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[2].qvalue <= 76))) { - if (LIKELY(false || (data[2].qvalue <= 24))) { - if (LIKELY(false || (data[3].qvalue <= 34))) { - result[0] += -0.0002526916639581458; - } else { - result[0] += -0.0008324735013533947; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 16))) { - result[0] += -3.7354343032152795e-05; - } else { - result[0] += -0.00023134344820509463; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 12))) { - result[0] += 0.0006301467008408141; - } else { - if (LIKELY(false || (data[3].qvalue <= 30))) { - result[0] += 0.00017263467395112425; - } else { - result[0] += -0.000222732211427673; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 56))) { - if (UNLIKELY(false || (data[2].qvalue <= 4))) { - result[0] += -0.0009052675927216569; - } else { - result[0] += 0.0010961202845499573; - } - } else { - result[0] += 0.0034438631349503835; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - if (UNLIKELY(false || (data[2].qvalue <= 124))) { - result[0] += 0.0006888856342971111; - } else { - result[0] += 0.0011003002879949233; - } - } else { - result[0] += 0.0021751931006627804; - } - } else { - result[0] += 0.006117324596259431; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 80))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (LIKELY(false || (data[2].qvalue <= 142))) { - result[0] += 0.001886015702980746; - } else { - result[0] += 0.0028879528128823518; - } - } else { - result[0] += -0.0019439231080232771; - } - } else { - result[0] += 0.00409802749099343; - } - } else { - result[0] += 0.010724971462071377; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.004851273450709325; - } else { - if (LIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.005993594323114087; - } else { - result[0] += 0.007126830483296861; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += -0.006591833172632115; - } else { - result[0] += 0.007745557909157859; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.007513856667529552; - } else { - result[0] += 0.014099994844270129; - } - } else { - result[0] += 0.022901478010549474; - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 100))) { - if (LIKELY(false || (data[1].qvalue <= 66))) { - if (LIKELY(false || (data[2].qvalue <= 64))) { - if (LIKELY(false || (data[1].qvalue <= 22))) { - result[0] += -0.00023332139181383586; - } else { - if (LIKELY(false || (data[2].qvalue <= 50))) { - result[0] += -0.00017980571342344313; - } else { - result[0] += -2.0718875487255795e-05; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 48))) { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - result[0] += -0.0002518917005409201; - } else { - result[0] += 7.227477975380027e-05; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 60))) { - result[0] += 0.0005108640832635015; - } else { - result[0] += 0.0016008570594736012; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 70))) { - if (UNLIKELY(false || (data[2].qvalue <= 2))) { - result[0] += -0.0004895282097672478; - } else { - result[0] += 0.0009271045263167995; - } - } else { - result[0] += 0.003029910672478344; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 48))) { - if (UNLIKELY(false || (data[2].qvalue <= 122))) { - result[0] += 0.00040218816684723627; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 12))) { - result[0] += 0.0032437816301035304; - } else { - result[0] += 0.0009341826233583182; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 116))) { - result[0] += 0.0014590413273968005; - } else { - result[0] += 0.0029815343815250677; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[0].qvalue <= 32))) { - if (UNLIKELY(false || (data[1].qvalue <= 14))) { - result[0] += 0.006437739111759044; - } else { - result[0] += 0.0017784105014231725; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 36))) { - result[0] += -0.001771490979701454; - } else { - result[0] += 0.0020923301462197823; - } - } - } else { - result[0] += 0.0036797237719870812; - } - } else { - result[0] += 0.007668662148627889; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.004320617911165859; - } else { - result[0] += 0.005898161657802002; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0; - } else { - result[0] += 0.0069151463115944434; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.020628421008308875; - } else { - result[0] += 0.012233663540640245; - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 96))) { - if (LIKELY(false || (data[1].qvalue <= 66))) { - if (LIKELY(false || (data[2].qvalue <= 50))) { - if (LIKELY(false || (data[3].qvalue <= 34))) { - result[0] += -0.0001966547586316513; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 52))) { - result[0] += -0.001949253703254182; - } else { - result[0] += -0.0003961865052170259; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 60))) { - if (LIKELY(false || (data[2].qvalue <= 80))) { - result[0] += -8.702945155336081e-05; - } else { - result[0] += 0.00010101002842960764; - } - } else { - result[0] += 0.0011539892201556142; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 0.0006699104798973902; - } else { - result[0] += 0.0016097103650539448; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 122))) { - if (LIKELY(false || (data[3].qvalue <= 38))) { - result[0] += 0.00037384006745287045; - } else { - result[0] += -0.00078417496305398; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 0.002583360550126892; - } else { - result[0] += 0.0008286684098961815; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 74))) { - if (LIKELY(false || (data[2].qvalue <= 118))) { - result[0] += 0.001147831022132617; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 20))) { - result[0] += 0.006146182855694659; - } else { - result[0] += 0.002161465650341462; - } - } - } else { - result[0] += 0.004769212968618104; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (UNLIKELY(false || (data[3].qvalue <= 28))) { - result[0] += 0.006289090271765258; - } else { - result[0] += 0.0015995773242235114; - } - } else { - result[0] += -0.0015852486767579577; - } - } else { - result[0] += 0.0033119101068960115; - } - } else { - result[0] += 0.006903666361705358; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - if (LIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.004350684674853294; - } else { - result[0] += 0.0058160004156852805; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[1].qvalue <= 72))) { - result[0] += -0.005029844201946011; - } else { - result[0] += 0.006639408804302992; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[1].qvalue <= 64))) { - result[0] += 0.00619428725665315; - } else { - result[0] += 0.011474633151106254; - } - } else { - result[0] += 0.01858097359565879; - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 108))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - if (LIKELY(false || (data[2].qvalue <= 76))) { - if (LIKELY(false || (data[2].qvalue <= 20))) { - if (LIKELY(false || (data[3].qvalue <= 38))) { - result[0] += -0.00019940358387546183; - } else { - result[0] += -0.000703113116348102; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 16))) { - result[0] += -4.638458464186633e-05; - } else { - result[0] += -0.0002294784137776043; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 12))) { - result[0] += 0.0005471125373971178; - } else { - if (LIKELY(false || (data[3].qvalue <= 30))) { - result[0] += 9.670414976687889e-05; - } else { - result[0] += -0.00021767801684133135; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - result[0] += -3.166344582237319e-05; - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (UNLIKELY(false || (data[0].qvalue <= 58))) { - result[0] += 0.0008152326933001966; - } else { - result[0] += 0.00025174464498736135; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 80))) { - result[0] += 0.0013971405413898312; - } else { - result[0] += -0.0006916749685131233; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[0].qvalue <= 42))) { - result[0] += 0.0006645427754437797; - } else { - result[0] += 0.0013310551905353592; - } - } else { - result[0] += 0.004798737931736182; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 150))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[0].qvalue <= 80))) { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (LIKELY(false || (data[2].qvalue <= 142))) { - result[0] += 0.0013586733582895156; - } else { - result[0] += 0.002186283295362004; - } - } else { - result[0] += -0.0014274378502657555; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.006650116111876395; - } else { - result[0] += 0.013644770354148933; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - result[0] += 0.0032603013506928773; - } else { - result[0] += -0.010240866610706277; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 48))) { - result[0] += 0.004779958775547548; - } else { - result[0] += 0.00686445672987769; - } - } else { - result[0] += 0.010596439063715636; - } - } else { - result[0] += 0.016736742650200403; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 104))) { - if (LIKELY(false || (data[1].qvalue <= 66))) { - if (LIKELY(false || (data[2].qvalue <= 74))) { - if (LIKELY(false || (data[1].qvalue <= 22))) { - result[0] += -0.00017265302736832624; - } else { - result[0] += -6.590425058702988e-05; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - result[0] += -0.00021500489047340408; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 28))) { - result[0] += 0.0003260276251636524; - } else { - result[0] += 1.1618765633385006e-05; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 56))) { - result[0] += 0.0006091551148605127; - } else { - result[0] += 0.002041106193822838; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 48))) { - if (UNLIKELY(false || (data[2].qvalue <= 124))) { - result[0] += 0.0003264125370269558; - } else { - result[0] += 0.0007305621801641551; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 74))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += 0.0015308893471321235; - } else { - result[0] += -0.0011053771592844164; - } - } else { - result[0] += 0.004019341238603161; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 150))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - result[0] += 0.0012825109063898839; - } else { - result[0] += 0.005737490966667883; - } - } else { - result[0] += 0.002875416523570694; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - result[0] += 0.0043220277108669625; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.01467062094307446; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.00487359498506815; - } else { - result[0] += 0.009006784051361535; - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 108))) { - if (LIKELY(false || (data[1].qvalue <= 60))) { - if (LIKELY(false || (data[2].qvalue <= 82))) { - if (LIKELY(false || (data[2].qvalue <= 12))) { - result[0] += -0.0001702686007892128; - } else { - if (LIKELY(false || (data[0].qvalue <= 16))) { - result[0] += -4.754655862563027e-05; - } else { - result[0] += -0.00017474323658447127; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 18))) { - result[0] += -0.00018950144326743167; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 24))) { - result[0] += 0.0005661111931274635; - } else { - result[0] += 0.00014359699750018804; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 4))) { - result[0] += -9.242813453442713e-05; - } else { - result[0] += 0.0004318537730927437; - } - } else { - result[0] += 0.000996025787596676; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (UNLIKELY(false || (data[1].qvalue <= 10))) { - result[0] += 0.0013570211830916067; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 6.900395934849614e-05; - } else { - result[0] += 0.000526718399212838; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 74))) { - result[0] += 0.0012271596277449244; - } else { - result[0] += 0.0038837618064354986; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (LIKELY(false || (data[2].qvalue <= 146))) { - result[0] += 0.0011542642646145598; - } else { - result[0] += 0.0023728018844858226; - } - } else { - result[0] += 0.00520880423871823; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - if (LIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.003135293929630502; - } else { - result[0] += 0.004343381199101189; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.0005685181748121977; - } else { - result[0] += 0.004925780552866418; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.013619444400285816; - } else { - result[0] += 0.008108635586280942; - } - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 36))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - result[0] += -0.00012650171567985005; - } else { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += -4.938551105130385e-07; - } else { - result[0] += 0.00023777414317281933; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - result[0] += 0.0014209088284525466; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 32))) { - result[0] += -0.00013475701157859363; - } else { - if (LIKELY(false || (data[3].qvalue <= 48))) { - result[0] += 0.0005692054876058483; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 0.002924335912848483; - } else { - result[0] += 0.0011432533753282545; - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 142))) { - if (LIKELY(false || (data[2].qvalue <= 108))) { - if (LIKELY(false || (data[2].qvalue <= 26))) { - result[0] += -0.00012932196425586976; - } else { - if (LIKELY(false || (data[0].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 80))) { - result[0] += -5.748038407974522e-05; - } else { - result[0] += 9.26994872808481e-05; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 58))) { - result[0] += 0.0008936605731170518; - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - result[0] += -1.2440258612188177e-05; - } else { - result[0] += 0.0005585540355690144; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (UNLIKELY(false || (data[2].qvalue <= 132))) { - result[0] += 0.00040803348778467856; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 0.0025997388488009357; - } else { - if (LIKELY(false || (data[3].qvalue <= 54))) { - result[0] += 0.0007607395473800554; - } else { - result[0] += -0.006367553819648243; - } - } - } - } else { - result[0] += 0.004347370506359142; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 152))) { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 80))) { - if (UNLIKELY(false || (data[3].qvalue <= 26))) { - result[0] += 0.008218266539471714; - } else { - result[0] += 0.001689778047040225; - } - } else { - result[0] += 0.010197310490906239; - } - } else { - result[0] += 0.002771954438342974; - } - } else { - result[0] += -0.00263078958160482; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 32))) { - result[0] += 0.003762501649809371; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.011977412732067837; - } else { - result[0] += 0.007180131322773255; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 142))) { - if (LIKELY(false || (data[2].qvalue <= 110))) { - if (LIKELY(false || (data[1].qvalue <= 60))) { - if (LIKELY(false || (data[2].qvalue <= 84))) { - if (LIKELY(false || (data[3].qvalue <= 38))) { - result[0] += -9.548885583609136e-05; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 40))) { - result[0] += -0.003540081420843614; - } else { - result[0] += -0.00024649854685375276; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 20))) { - result[0] += 0.0003053226725768677; - } else { - result[0] += -6.602264761802435e-05; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 68))) { - if (UNLIKELY(false || (data[2].qvalue <= 4))) { - result[0] += -9.762664695776116e-05; - } else { - result[0] += 0.00038670152054575205; - } - } else { - result[0] += 0.0014758023283282069; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[2].qvalue <= 138))) { - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (LIKELY(false || (data[3].qvalue <= 38))) { - result[0] += 0.0003950345531674714; - } else { - result[0] += -0.0006780454492008814; - } - } else { - result[0] += 0.0011275851879113837; - } - } else { - result[0] += 0.000772236033582265; - } - } else { - result[0] += 0.004143313796252665; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 150))) { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[3].qvalue <= 26))) { - result[0] += 0.009037786461412907; - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - result[0] += 0.0016797847044042091; - } else { - result[0] += 0.009218368388153613; - } - } - } else { - result[0] += -0.002368890210880294; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (LIKELY(false || (data[1].qvalue <= 64))) { - result[0] += 0.0030468957829948803; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.003570656648731198; - } else { - result[0] += 0.006838003122049602; - } - } - } else { - result[0] += 0.010788609192028134; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (LIKELY(false || (data[2].qvalue <= 116))) { - if (LIKELY(false || (data[1].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 50))) { - result[0] += -0.00010835034231308543; - } else { - result[0] += -3.592209291896241e-06; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 10))) { - result[0] += -6.758697818328856e-05; - } else { - if (LIKELY(false || (data[2].qvalue <= 68))) { - result[0] += 0.0002567234765659431; - } else { - result[0] += 0.0006684446514199459; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 38))) { - if (UNLIKELY(false || (data[1].qvalue <= 4))) { - result[0] += 0.0019020060332526339; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 126))) { - result[0] += 0.00014095668063188527; - } else { - result[0] += 0.0006037159852907501; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - result[0] += 0.001560769119473446; - } else { - result[0] += 0.004308113069032753; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 152))) { - if (LIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.001609181337370336; - } else { - result[0] += 0.0025188474001257414; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 32))) { - result[0] += 0.0030819144903457287; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.009717799607497545; - } else { - result[0] += 0.005811008536776794; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 142))) { - if (LIKELY(false || (data[2].qvalue <= 108))) { - if (LIKELY(false || (data[2].qvalue <= 26))) { - result[0] += -9.633149362070105e-05; - } else { - if (LIKELY(false || (data[3].qvalue <= 46))) { - if (LIKELY(false || (data[3].qvalue <= 42))) { - result[0] += -2.3786842856587217e-06; - } else { - result[0] += -0.0008784412376800542; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 52))) { - result[0] += 0.0005592856904334903; - } else { - result[0] += -0.0010997740778614094; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 134))) { - result[0] += 0.0003003870717462951; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 0.0021186501027940312; - } else { - result[0] += 0.0005632598779634045; - } - } - } else { - result[0] += 0.003155110609239992; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 152))) { - if (LIKELY(false || (data[3].qvalue <= 58))) { - if (UNLIKELY(false || (data[3].qvalue <= 26))) { - if (LIKELY(false || (data[3].qvalue <= 22))) { - result[0] += 0.002531461699501328; - } else { - result[0] += 0.013085974014940716; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - result[0] += 0.0012521484883360095; - } else { - result[0] += 0.002029939821163165; - } - } - } else { - result[0] += -0.002291005669175251; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - result[0] += 0.0027415007075521983; - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - result[0] += 0.00517479080318933; - } else { - result[0] += 0.008753272564108693; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 36))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - result[0] += -7.805033787009575e-05; - } else { - result[0] += 2.3801570604239546e-05; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - result[0] += 0.0008308640887867767; - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (UNLIKELY(false || (data[1].qvalue <= 32))) { - result[0] += -0.0001255589210486739; - } else { - result[0] += 0.0002974589212792894; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 66))) { - result[0] += 0.001434875231823905; - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.00015393892861622098; - } else { - result[0] += 0.0008355777768832701; - } - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (LIKELY(false || (data[2].qvalue <= 100))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - if (LIKELY(false || (data[2].qvalue <= 80))) { - result[0] += -7.09997632022635e-05; - } else { - if (LIKELY(false || (data[2].qvalue <= 92))) { - if (LIKELY(false || (data[2].qvalue <= 90))) { - result[0] += 6.700198529498124e-05; - } else { - result[0] += 0.0006739111265931478; - } - } else { - result[0] += -7.873394714685852e-05; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += -0.00043781627567509543; - } else { - result[0] += 0.00020885997574915155; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 80))) { - if (UNLIKELY(false || (data[2].qvalue <= 130))) { - result[0] += 0.00021999474610715451; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 0.001993992760088811; - } else { - result[0] += 0.00044991030401214167; - } - } - } else { - result[0] += 0.004000181025335582; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 152))) { - if (LIKELY(false || (data[3].qvalue <= 58))) { - result[0] += 0.0015042104367088394; - } else { - result[0] += -0.0022578272852008534; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 32))) { - result[0] += 0.002410829276957461; - } else { - result[0] += 0.0055512269901312646; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[2].qvalue <= 104))) { - if (UNLIKELY(false || (data[2].qvalue <= 12))) { - result[0] += -8.496817920340536e-05; - } else { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += -1.7540259968379833e-05; - } else { - result[0] += 0.00025745579920166244; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 138))) { - result[0] += 0.00023710498818372454; - } else { - result[0] += 0.0005008600118271386; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.0021586178965176087; - } else { - result[0] += 0.008461107069451827; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - if (LIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.0012443975867339444; - } else { - result[0] += 0.0019477678730867058; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - result[0] += 0.0019342118146449738; - } else { - result[0] += 0.004997239694835386; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 32))) { - if (LIKELY(false || (data[1].qvalue <= 16))) { - result[0] += -6.499770451690952e-05; - } else { - result[0] += 9.93532909532843e-06; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - result[0] += 0.0005901691687912098; - } else { - if (LIKELY(false || (data[1].qvalue <= 70))) { - result[0] += 0.00015807135019769365; - } else { - result[0] += 0.0005244958255941764; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[2].qvalue <= 86))) { - result[0] += -4.676198214734394e-05; - } else { - if (LIKELY(false || (data[1].qvalue <= 60))) { - if (UNLIKELY(false || (data[1].qvalue <= 14))) { - if (LIKELY(false || (data[2].qvalue <= 118))) { - result[0] += -0.00010600660656932565; - } else { - result[0] += 0.0015161007267231429; - } - } else { - result[0] += 0.0002612619805398477; - } - } else { - result[0] += 0.0015693112213150122; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 44))) { - result[0] += 0.0013664188809999066; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 150))) { - result[0] += -0.010022143385034394; - } else { - result[0] += 0.0037975534515937988; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 142))) { - if (LIKELY(false || (data[2].qvalue <= 86))) { - result[0] += -4.208578851825266e-05; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 14))) { - if (LIKELY(false || (data[2].qvalue <= 116))) { - result[0] += -0.00011403525794098995; - } else { - result[0] += 0.0009114640165412386; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 60))) { - result[0] += 0.00022356508848759622; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 62))) { - result[0] += 0.0027995720407202294; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 70))) { - result[0] += -0.0008798782950704978; - } else { - result[0] += 0.0014448416755765952; - } - } - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 152))) { - if (LIKELY(false || (data[1].qvalue <= 70))) { - result[0] += 0.0008384216911964453; - } else { - result[0] += 0.002447881422746419; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 32))) { - result[0] += 0.0017733230820123455; - } else { - result[0] += 0.004110382169902663; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[2].qvalue <= 86))) { - result[0] += -3.787721484857798e-05; - } else { - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (UNLIKELY(false || (data[1].qvalue <= 14))) { - if (LIKELY(false || (data[2].qvalue <= 118))) { - result[0] += -8.689697470917008e-05; - } else { - result[0] += 0.0012740214207030438; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 54))) { - result[0] += 0.00021910612133028753; - } else { - result[0] += -0.0019394310314736379; - } - } - } else { - result[0] += 0.001605288614427337; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 152))) { - if (LIKELY(false || (data[3].qvalue <= 52))) { - result[0] += 0.0010209367212090006; - } else { - result[0] += -0.0070652788154780865; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - result[0] += 0.001794208969053978; - } else { - result[0] += 0.00589450452114028; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 142))) { - if (LIKELY(false || (data[2].qvalue <= 86))) { - result[0] += -3.4089497281904435e-05; - } else { - if (LIKELY(false || (data[1].qvalue <= 48))) { - result[0] += 0.00011859360145590661; - } else { - result[0] += 0.0005882149306744061; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 150))) { - result[0] += 0.0006289461581674275; - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - result[0] += 0.0012857444628638962; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.0053522964003919205; - } else { - result[0] += 0.002180858513372534; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 142))) { - if (LIKELY(false || (data[2].qvalue <= 122))) { - if (LIKELY(false || (data[1].qvalue <= 48))) { - result[0] += -3.231127347736361e-05; - } else { - result[0] += 0.0001305453669476947; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 0.0015725449376088275; - } else { - result[0] += 0.0002082949912808661; - } - } else { - result[0] += -0.006547446409612894; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 150))) { - if (UNLIKELY(false || (data[3].qvalue <= 26))) { - result[0] += 0.0065892055427092455; - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - result[0] += 0.000599515916925125; - } else { - result[0] += -0.001937681221394829; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 58))) { - result[0] += 0.00127534830686723; - } else { - result[0] += 0.004778217595251305; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 38))) { - result[0] += -1.2646257755117516e-05; - } else { - result[0] += 0.0002454812344733204; - } - if (LIKELY(false || (data[3].qvalue <= 32))) { - result[0] += -1.5786346014169378e-05; - } else { - result[0] += 0.00016700826916388908; - } - if (LIKELY(false || (data[2].qvalue <= 146))) { - if (LIKELY(false || (data[2].qvalue <= 80))) { - result[0] += -2.887770604298617e-05; - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += 0.00010275584396454631; - } else { - result[0] += -0.001987768939441655; - } - } else { - result[0] += 0.0019405763097977533; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 0.0008150039520986579; - } else { - result[0] += 0.003428131746816993; - } - } - if (LIKELY(false || (data[3].qvalue <= 38))) { - result[0] += -9.428440320689368e-06; - } else { - result[0] += 0.00018303284915371275; - } - if (LIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[2].qvalue <= 126))) { - result[0] += -1.6525789323637374e-05; - } else { - result[0] += 0.00019819706642161615; - } - } else { - result[0] += 0.0009033266907285568; - } - if (LIKELY(false || (data[2].qvalue <= 142))) { - if (LIKELY(false || (data[2].qvalue <= 80))) { - result[0] += -2.3731074344715385e-05; - } else { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - if (UNLIKELY(false || (data[3].qvalue <= 12))) { - result[0] += 0.0003259490041081464; - } else { - result[0] += 3.657792922172123e-05; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 50))) { - result[0] += 0.00406008107199644; - } else { - result[0] += 0.0005606708031635196; - } - } - } else { - result[0] += -0.005101178498007357; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 0.0005373762500503928; - } else { - result[0] += 0.0027015011546189507; - } - } - if (LIKELY(false || (data[2].qvalue <= 150))) { - if (LIKELY(false || (data[2].qvalue <= 128))) { - result[0] += -1.3661286266953386e-05; - } else { - result[0] += 0.00018261991840147; - } - } else { - result[0] += 0.000931821730518591; - } - if (LIKELY(false || (data[2].qvalue <= 150))) { - if (LIKELY(false || (data[2].qvalue <= 126))) { - result[0] += -1.2529314401332627e-05; - } else { - result[0] += 0.00016018169881967258; - } - } else { - result[0] += 0.0008386591836191829; - } - if (LIKELY(false || (data[2].qvalue <= 144))) { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[2].qvalue <= 64))) { - result[0] += -2.4302315497459032e-05; - } else { - result[0] += 5.194281681207711e-05; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += 0.00045022060778704474; - } else { - result[0] += 0.006445361257938202; - } - } - } else { - result[0] += 0.0004861295980061241; - } - if (LIKELY(false || (data[2].qvalue <= 150))) { - if (LIKELY(false || (data[2].qvalue <= 86))) { - result[0] += -1.559693370405162e-05; - } else { - result[0] += 8.018493624717838e-05; - } - } else { - result[0] += 0.0007062082614175126; - } - if (LIKELY(false || (data[2].qvalue <= 148))) { - result[0] += -3.500609763214595e-06; - } else { - result[0] += 0.0005238382736763112; - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - result[0] += -7.872314087503873e-06; - } else { - if (LIKELY(false || (data[1].qvalue <= 40))) { - result[0] += 0.00016982337133831138; - } else { - result[0] += 0.0015582625369628602; - } - } - if (LIKELY(false || (data[2].qvalue <= 152))) { - result[0] += -1.7980922930840279e-06; - } else { - result[0] += 0.0008003180445416104; - } - if (LIKELY(false || (data[2].qvalue <= 138))) { - result[0] += -6.905273724778028e-06; - } else { - result[0] += 0.00018194518281833516; - } - if (LIKELY(false || (data[1].qvalue <= 48))) { - result[0] += -7.5322283086623e-06; - } else { - result[0] += 0.00013574551159436845; - } - if (LIKELY(false || (data[2].qvalue <= 152))) { - result[0] += -1.5802465245244315e-06; - } else { - result[0] += 0.0007009422698652187; - } - if (LIKELY(false || (data[2].qvalue <= 142))) { - result[0] += -3.6413942554064128e-06; - } else { - result[0] += 0.0002746418585991201; - } - if (UNLIKELY(false || (data[2].qvalue <= 12))) { - result[0] += -3.379293117509959e-05; - } else { - result[0] += 2.828160884444577e-05; - } - - // Apply base_scores - result[0] += 0; - - // Apply postprocessor - if (!pred_margin) { postprocess(result); } -} - -void bounds_strengthening_predictor::postprocess(double* result) -{ - // Do nothing -} - -// Feature names array -const char* - bounds_strengthening_predictor::feature_names[bounds_strengthening_predictor::NUM_FEATURES] = { - "m", "n", "nnz_processed", "nnz", "bounds_changed"}; diff --git a/cpp/src/utilities/models/bounds_strengthening_predictor/quantize.cpp b/cpp/src/utilities/models/bounds_strengthening_predictor/quantize.cpp deleted file mode 100644 index e9b7b3c37..000000000 --- a/cpp/src/utilities/models/bounds_strengthening_predictor/quantize.cpp +++ /dev/null @@ -1,111 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#include "header.h" - -static const double threshold[] = { - 331.50000000000006, 380.50000000000006, 444.00000000000006, 454.50000000000006, - 519.50000000000011, 555.00000000000011, 1454.0000000000002, 1496.5000000000002, - 2448.5000000000005, 4115.0000000000009, 4305.5000000000009, 4839.5000000000009, - 5346.5000000000009, 6228.5000000000009, 9689.5000000000018, 14879.500000000002, - 15693.000000000002, 17345.000000000004, 21083.000000000004, 24144.500000000004, - 34410.500000000007, 36239.500000000007, 37842.500000000007, 40918.500000000007, - 46263.500000000007, 50138.000000000007, 60708.500000000007, 72207.000000000015, - 77439.500000000015, 79608.500000000015, 91121.000000000015, 93826.000000000015, - 104579.50000000001, 163887.00000000003, 202377.00000000003, 221228.50000000003, - 246546.00000000003, 298845.00000000006, 323110.00000000006, 344863.50000000006, - 510463.50000000006, 1645.0000000000002, 1709.0000000000002, 3059.5000000000005, - 3953.5000000000005, 4013.0000000000005, 4025.5000000000005, 4837.5000000000009, - 5585.5000000000009, 5832.5000000000009, 6329.5000000000009, 6829.0000000000009, - 7266.0000000000009, 9604.5000000000018, 10543.000000000002, 11316.000000000002, - 13942.500000000002, 18312.500000000004, 20001.500000000004, 26121.500000000004, - 28899.500000000004, 31929.000000000004, 41385.000000000007, 49239.000000000007, - 52480.000000000007, 55778.000000000007, 59927.500000000007, 65717.000000000015, - 75092.000000000015, 106578.00000000001, 114790.50000000001, 120166.00000000001, - 125666.50000000001, 238985.00000000003, 257730.50000000003, 304621.00000000006, - 345125.50000000006, 467574.50000000006, 538765.50000000012, 600167.00000000012, - 651989.00000000012, 492.50000000000006, 6278.5000000000009, 7445.5000000000009, - 9808.5000000000018, 11088.500000000002, 12428.500000000002, 15493.000000000002, - 17632.500000000004, 20808.500000000004, 22190.000000000004, 22709.500000000004, - 23707.500000000004, 36926.500000000007, 38191.000000000007, 44292.000000000007, - 45843.000000000007, 47541.500000000007, 52712.500000000007, 54350.500000000007, - 59809.000000000007, 63098.000000000007, 68639.000000000015, 80444.000000000015, - 92079.500000000015, 98182.500000000015, 123427.00000000001, 125177.00000000001, - 167577.00000000003, 174836.00000000003, 186265.50000000003, 188362.00000000003, - 197007.50000000003, 211368.00000000003, 249795.00000000003, 260021.00000000003, - 266201.00000000006, 289613.50000000006, 292623.50000000006, 309845.50000000006, - 313121.50000000006, 319213.00000000006, 327694.00000000006, 336320.00000000006, - 388897.00000000006, 431435.50000000006, 443736.50000000006, 499102.50000000006, - 504094.00000000006, 547807.50000000012, 587180.00000000012, 603657.50000000012, - 624274.00000000012, 647468.00000000012, 699833.00000000012, 734235.50000000012, - 757689.00000000012, 780010.50000000012, 962875.00000000012, 1164224.5000000002, - 1416361.0000000002, 1630053.5000000002, 1863835.0000000002, 1921521.0000000002, - 1971655.0000000002, 1989499.0000000002, 2005190.0000000002, 2018692.0000000002, - 2032598.0000000002, 2092776.0000000002, 2836397.5000000005, 3398604.0000000005, - 3442503.5000000005, 3897359.5000000005, 5191813.5000000009, 5694629.5000000009, - 6838622.5000000009, 8540143.0000000019, 6310.0000000000009, 43748.000000000007, - 50829.500000000007, 69946.000000000015, 79995.000000000015, 110786.50000000001, - 140480.00000000003, 147046.50000000003, 180637.50000000003, 211441.50000000003, - 299525.50000000006, 386595.00000000006, 412447.00000000006, 430914.00000000006, - 474762.00000000006, 497602.00000000006, 587288.00000000012, 706723.50000000012, - 781740.50000000012, 853522.00000000012, 997296.00000000012, 1065333.0000000002, - 1204174.5000000002, 1222545.5000000002, 1806119.5000000002, 1879308.0000000002, - 1940828.0000000002, 2238905.0000000005, 2573759.0000000005, 7308634.0000000009, -}; - -static const int th_begin[] = { - 0, - 41, - 81, - 158, - 188, -}; - -static const int th_len[] = { - 41, - 40, - 77, - 30, - 0, -}; - -/* - * \brief Function to convert a feature value into bin index. - * \param val Feature value, in floating-point - * \param fid Feature identifier - * \return bin Index corresponding to given feature value - */ -int bounds_strengthening_predictor::quantize(double val, unsigned fid) -{ - const size_t offset = th_begin[fid]; - const double* array = &threshold[offset]; - int len = th_len[fid]; - int low = 0; - int high = len; - int mid; - double mval; - // It is possible th_begin[i] == [total_num_threshold]. This means that - // all features i, (i+1), ... are not used for any of the splits in the model. - // So in this case, just return something - if (offset == 188 || val < array[0]) { return -10; } - while (low + 1 < high) { - mid = (low + high) / 2; - mval = array[mid]; - if (val == mval) { - return mid * 2; - } else if (val < mval) { - high = mid; - } else { - low = mid; - } - } - if (array[low] == val) { - return low * 2; - } else if (high == len) { - return len * 2; - } else { - return low * 2 + 1; - } -} diff --git a/cpp/src/utilities/models/cpufj_predictor/header.h b/cpp/src/utilities/models/cpufj_predictor/header.h deleted file mode 100644 index 409f669a6..000000000 --- a/cpp/src/utilities/models/cpufj_predictor/header.h +++ /dev/null @@ -1,35 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#pragma once - -#include -#include -#include -#include -#include -#include - -class cpufj_predictor { - public: - union Entry { - int missing; - double fvalue; - int qvalue; - }; - - static int32_t get_num_target(void); - static void get_num_class(int32_t* out); - static int32_t get_num_feature(void); - static const char* get_threshold_type(void); - static const char* get_leaf_output_type(void); - static void predict(union Entry* data, int pred_margin, double* result); - static void postprocess(double* result); - static int quantize(double val, unsigned fid); - - // Feature names - static constexpr int NUM_FEATURES = 4; - static const char* feature_names[NUM_FEATURES]; -}; // class cpufj_data diff --git a/cpp/src/utilities/models/cpufj_predictor/main.cpp b/cpp/src/utilities/models/cpufj_predictor/main.cpp deleted file mode 100644 index ce8fd7c29..000000000 --- a/cpp/src/utilities/models/cpufj_predictor/main.cpp +++ /dev/null @@ -1,5412 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#include "header.h" - -#if defined(__clang__) || defined(__GNUC__) -#define LIKELY(x) __builtin_expect(!!(x), 1) -#define UNLIKELY(x) __builtin_expect(!!(x), 0) -#else -#define LIKELY(x) (x) -#define UNLIKELY(x) (x) -#endif -#define N_TARGET 1 -#define MAX_N_CLASS 1 - -const unsigned char is_categorical[] = { - 0, - 0, - 0, - 0, -}; -static const int32_t num_class[] = { - 1, -}; - -int32_t cpufj_predictor::get_num_target(void) { return N_TARGET; } -void cpufj_predictor::get_num_class(int32_t* out) -{ - for (int i = 0; i < N_TARGET; ++i) { - out[i] = num_class[i]; - } -} -int32_t cpufj_predictor::get_num_feature(void) { return 4; } -const char* cpufj_predictor::get_threshold_type(void) { return "float64"; } -const char* cpufj_predictor::get_leaf_output_type(void) { return "float64"; } - -void cpufj_predictor::predict(union Entry* data, int pred_margin, double* result) -{ - // Quantize data - for (int i = 0; i < 4; ++i) { - if (data[i].missing != -1 && !is_categorical[i]) { - data[i].qvalue = quantize(data[i].fvalue, i); - } - } - - unsigned int tmp; - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += 156.82697390964927; - } else { - result[0] += 160.51072839782955; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 24))) { - result[0] += 167.07152558194775; - } else { - result[0] += 173.16044577743776; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 197.67049116907555; - } else { - result[0] += 193.73228835720744; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 204.19401403625514; - } else { - result[0] += 233.21262399057034; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - result[0] += 271.7565681764123; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 384.56067848347016; - } else { - result[0] += 490.5143183873164; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 50))) { - if (LIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[3].qvalue <= 114))) { - if (LIKELY(false || (data[3].qvalue <= 74))) { - result[0] += -13.943317103763608; - } else { - result[0] += -10.411451244915156; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -1.6607479643348968; - } else { - result[0] += 1.873995960655583; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += 13.317445150475752; - } else { - result[0] += -3.2833804644580917; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 42))) { - result[0] += 28.787436474976477; - } else { - result[0] += 22.808869505615306; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (UNLIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 116))) { - result[0] += 3.5889216009186664; - } else { - result[0] += 5.910052863589392; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 210))) { - result[0] += 89.99770637090484; - } else { - result[0] += 127.79448643063864; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 191.2644805975275; - } else { - result[0] += 286.4874098858173; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 50))) { - if (LIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[3].qvalue <= 114))) { - if (LIKELY(false || (data[3].qvalue <= 76))) { - result[0] += -12.539408056743746; - } else { - result[0] += -9.311441710639189; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -1.4947050956018029; - } else { - result[0] += 1.6866257811476963; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += 11.98745272164608; - } else { - result[0] += -2.9554856143381034; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 22.718528792836054; - } else { - result[0] += 52.594838808257; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (UNLIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 116))) { - result[0] += 3.2306536812875586; - } else { - result[0] += 5.321595177064682; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 79.5197036158342; - } else { - result[0] += 112.02363049281628; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 172.34821010044644; - } else { - result[0] += 257.92682935697115; - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -11.281112483305671; - } else { - result[0] += -8.601904513350851; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 24))) { - result[0] += -4.457196969778106; - } else { - result[0] += 0.6775720991155234; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 18.28399618068051; - } else { - result[0] += 15.34024990698243; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 23.56902428310345; - } else { - result[0] += 47.371626254377695; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - result[0] += 72.50774203412541; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 155.30278921274038; - } else { - result[0] += 232.21351482872598; - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[3].qvalue <= 114))) { - if (LIKELY(false || (data[3].qvalue <= 78))) { - result[0] += -10.157360048782069; - } else { - result[0] += -7.469901314898597; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -1.2836068490987675; - } else { - result[0] += 1.4928852675361874; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 68))) { - result[0] += 11.862787216911165; - } else { - result[0] += -3.825179380397125; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - result[0] += 16.467439176084763; - } else { - result[0] += 22.870842463159736; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - if (UNLIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 116))) { - result[0] += -4.329979207208883; - } else { - result[0] += -2.4277052596041226; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 206))) { - result[0] += 62.899179793202485; - } else { - result[0] += 89.86758030525095; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 144.9081294228374; - } else { - result[0] += 209.06360369591349; - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[3].qvalue <= 118))) { - if (LIKELY(false || (data[3].qvalue <= 80))) { - result[0] += -9.111244309609868; - } else { - result[0] += -6.450300041406144; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 146))) { - result[0] += -0.908464880592677; - } else { - result[0] += 1.3456266411166913; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 144))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 9.598829002806253; - } else { - result[0] += -3.458980194428218; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - result[0] += 14.685884008167873; - } else { - result[0] += 19.70403341262041; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (UNLIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 116))) { - result[0] += -3.8977343521833427; - } else { - result[0] += -2.185981180004069; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 202))) { - result[0] += 53.636361895007624; - } else { - result[0] += 73.84635747136831; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 54))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 109.30218868014822; - } else { - result[0] += 130.5223189467278; - } - } else { - result[0] += 188.22157308443514; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 50))) { - if (LIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[3].qvalue <= 114))) { - if (LIKELY(false || (data[3].qvalue <= 74))) { - result[0] += -8.242985704258855; - } else { - result[0] += -6.103964758757681; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 122))) { - result[0] += -3.0003459256849077; - } else { - result[0] += 0.5938833671659278; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += 9.054639032941743; - } else { - result[0] += -3.3592089510831986; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 18.040143668653474; - } else { - result[0] += 13.175784904763427; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 236))) { - if (UNLIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 118))) { - result[0] += -3.214705374718561; - } else { - result[0] += 0.31848526393665993; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 202))) { - result[0] += 48.27447091272866; - } else { - result[0] += 66.18473238316001; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 97.70898775652987; - } else { - result[0] += 117.56467429340749; - } - } else { - result[0] += 169.45732965745194; - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -7.402034186828849; - } else { - result[0] += -5.63837490580214; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 24))) { - result[0] += -3.2673346602099738; - } else { - result[0] += 0.4720904756386771; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 11.693457400016031; - } else { - result[0] += 10.03852332044065; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 15.437715046858271; - } else { - result[0] += 36.647436411890496; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - result[0] += 47.53118689085973; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 106.35664032988495; - } else { - result[0] += 152.56374281850964; - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -6.661840917407535; - } else { - result[0] += -5.074574846660258; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 24))) { - result[0] += -2.94088446825355; - } else { - result[0] += 0.4248859491848843; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 10.52521173349777; - } else { - result[0] += 9.034789845952849; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 13.894155617598422; - } else { - result[0] += 33.00796459750472; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 42.823961164678224; - } else { - result[0] += 36.98781896591187; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 95.8378507576408; - } else { - result[0] += 137.35430146859977; - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 12))) { - result[0] += -5.922535414763472; - } else { - result[0] += -3.9535302977307456; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 24))) { - result[0] += -2.647051125523947; - } else { - result[0] += 0.38240139128726525; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 9.473680911813272; - } else { - result[0] += 8.131417758482929; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 42))) { - result[0] += 12.474218356244899; - } else { - result[0] += 28.32144924784816; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 38.542156344073405; - } else { - result[0] += 33.34683068752289; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 86.359383424193; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 130.56753145370016; - } else { - result[0] += 115.99602850603911; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -5.403420533141926; - } else { - result[0] += -4.075259880702465; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 24))) { - result[0] += -2.3825756020703164; - } else { - result[0] += 0.34416491637505503; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 62))) { - result[0] += 6.690473585247339; - } else { - result[0] += 7.675205616748645; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 11.251804458709806; - } else { - result[0] += 26.917319955760036; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 34.68847201632427; - } else { - result[0] += 30.064251933097843; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 77.81834311684409; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 117.59088283047355; - } else { - result[0] += 104.46758514965971; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - if (LIKELY(false || (data[3].qvalue <= 96))) { - if (UNLIKELY(false || (data[3].qvalue <= 22))) { - result[0] += -5.645610033293023; - } else { - result[0] += -4.712904234092419; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 68))) { - result[0] += -1.7467118855770272; - } else { - result[0] += -6.7806729780163355; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -3.3298901489085715; - } else { - result[0] += 0.9879303535654625; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 156))) { - result[0] += 4.765656733422152; - } else { - result[0] += 10.436091866890687; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 118))) { - result[0] += -19.231066348531115; - } else { - result[0] += -15.589280898150276; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 218))) { - if (UNLIKELY(false || (data[3].qvalue <= 200))) { - result[0] += 25.747728264474212; - } else { - result[0] += 40.04473103921778; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 64.38265010597043; - } else { - result[0] += 95.58272802311622; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - if (LIKELY(false || (data[3].qvalue <= 98))) { - if (LIKELY(false || (data[3].qvalue <= 74))) { - result[0] += -4.409030071223973; - } else { - result[0] += -3.5647114194984613; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 68))) { - result[0] += -1.3542190966799321; - } else { - result[0] += -6.103673214709666; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -2.999404705305745; - } else { - result[0] += 0.8891527883879043; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 156))) { - result[0] += 4.289215943564471; - } else { - result[0] += 9.392568858155158; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 116))) { - result[0] += -17.598092511782436; - } else { - result[0] += -16.123547517184555; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 216))) { - if (UNLIKELY(false || (data[3].qvalue <= 200))) { - result[0] += 23.17394211472633; - } else { - result[0] += 35.171000449187375; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 55.995760587885144; - } else { - result[0] += 86.04514530858954; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 22))) { - if (LIKELY(false || (data[1].qvalue <= 12))) { - result[0] += -3.8888486100132043; - } else { - result[0] += -2.5693198969248536; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 0.301134908182464; - } else { - result[0] += 4.7321266664777495; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 5.997381523357489; - } else { - result[0] += 5.0375213284435185; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 8.559482811493893; - } else { - result[0] += 22.27496138368804; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 25.27353965981968; - } else { - result[0] += 21.13871028184891; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 55.52418384593922; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 87.85257342192293; - } else { - result[0] += 76.03356082097153; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 50))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - if (LIKELY(false || (data[3].qvalue <= 90))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - result[0] += -3.5064268267703387; - } else { - result[0] += -6.065722823494794; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 68))) { - result[0] += -1.4039415524212295; - } else { - result[0] += -5.997225846159178; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -2.9652186181689757; - } else { - result[0] += 0.7283126984643262; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 154))) { - result[0] += 2.789907904600108; - } else { - result[0] += 7.718533347299168; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 188))) { - result[0] += -18.00435797338746; - } else { - if (LIKELY(false || (data[3].qvalue <= 214))) { - if (UNLIKELY(false || (data[3].qvalue <= 198))) { - result[0] += 17.330223423965826; - } else { - result[0] += 27.46055877083792; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 46.12967429173014; - } else { - result[0] += 70.70123466855004; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - if (LIKELY(false || (data[3].qvalue <= 102))) { - if (UNLIKELY(false || (data[3].qvalue <= 22))) { - result[0] += -3.888827166320002; - } else { - result[0] += -3.04723191570044; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 34))) { - result[0] += -0.7424769082893876; - } else { - result[0] += -5.398447803084307; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -2.670943097291571; - } else { - result[0] += 0.6554928174570391; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 158))) { - result[0] += 2.9919951855699423; - } else { - result[0] += 7.128361704676317; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 118))) { - result[0] += -16.31666198848907; - } else { - result[0] += -13.216809526331284; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 220))) { - if (LIKELY(false || (data[3].qvalue <= 206))) { - result[0] += 18.698365978047963; - } else { - result[0] += 30.323819222880072; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 54))) { - result[0] += 48.19287170494928; - } else { - result[0] += 66.97776871619591; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[0].qvalue <= 22))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -2.8871386766483713; - } else { - result[0] += -2.194022438199927; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 24))) { - result[0] += -1.2643512510325772; - } else { - result[0] += 0.2946143237550731; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (UNLIKELY(false || (data[0].qvalue <= 28))) { - result[0] += 6.18334904875928; - } else { - result[0] += 3.4917401374748973; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 42))) { - result[0] += 6.637851347005048; - } else { - result[0] += 17.628961355963423; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 18.427701194716725; - } else { - result[0] += 15.068101975917816; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 39.624109077872816; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 65.43777984806364; - } else { - result[0] += 54.793414778446135; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - if (LIKELY(false || (data[2].qvalue <= 52))) { - if (LIKELY(false || (data[3].qvalue <= 86))) { - result[0] += -2.5929923519240776; - } else { - result[0] += -1.2308422313969718; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 116))) { - result[0] += -16.777735206670346; - } else { - result[0] += -15.496213790630472; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - if (LIKELY(false || (data[3].qvalue <= 166))) { - result[0] += 3.153837529703604; - } else { - result[0] += 10.653343513120348; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 1.2707685237496547; - } else { - result[0] += -7.577582551412722; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 50))) { - if (LIKELY(false || (data[0].qvalue <= 70))) { - if (UNLIKELY(false || (data[3].qvalue <= 216))) { - result[0] += 5.086264511434565; - } else { - result[0] += 4.004129196876291; - } - } else { - result[0] += 18.011869379087937; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 214))) { - if (UNLIKELY(false || (data[3].qvalue <= 200))) { - result[0] += 12.716813581606937; - } else { - result[0] += 21.19923662562386; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - result[0] += 33.0928592549443; - } else { - result[0] += 48.6196284504888; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (LIKELY(false || (data[3].qvalue <= 104))) { - result[0] += -2.2842871895780763; - } else { - result[0] += -0.6940580648135516; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 118))) { - result[0] += -14.884714915484075; - } else { - result[0] += -12.21852856355555; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (LIKELY(false || (data[0].qvalue <= 68))) { - result[0] += 1.1320691013866366; - } else { - result[0] += -6.824281661647207; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 168))) { - result[0] += 3.133632898542928; - } else { - result[0] += 10.26787174244473; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 56))) { - if (UNLIKELY(false || (data[3].qvalue <= 216))) { - if (UNLIKELY(false || (data[0].qvalue <= 66))) { - result[0] += 0.3519373175810123; - } else { - result[0] += 4.883879225540344; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 234))) { - result[0] += 3.4475335994592777; - } else { - result[0] += 4.289605817529637; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 214))) { - if (UNLIKELY(false || (data[3].qvalue <= 198))) { - result[0] += 10.944053551737898; - } else { - result[0] += 18.520104025075135; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 31.9020659327635; - } else { - result[0] += 47.722870097304835; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (LIKELY(false || (data[3].qvalue <= 84))) { - result[0] += -2.1126641262849875; - } else { - result[0] += -1.0034167734194253; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 114))) { - result[0] += -14.666533134028597; - } else { - result[0] += -13.091642320227521; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -2.67357385169376; - } else { - result[0] += 0.317539563282027; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 172))) { - result[0] += 2.9924052201762423; - } else { - result[0] += 10.018166909712392; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 56))) { - if (UNLIKELY(false || (data[3].qvalue <= 216))) { - if (UNLIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 0.3173503774211839; - } else { - result[0] += 4.396111790242462; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 234))) { - result[0] += 3.1028551632609607; - } else { - result[0] += 3.8610566164274576; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 212))) { - if (UNLIKELY(false || (data[3].qvalue <= 196))) { - result[0] += 9.079411213991389; - } else { - result[0] += 15.631981353661537; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 230))) { - result[0] += 24.953231107660457; - } else { - result[0] += 38.732870695055425; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (LIKELY(false || (data[3].qvalue <= 106))) { - result[0] += -1.8507802734637893; - } else { - result[0] += -0.4780342275095875; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 116))) { - result[0] += -12.281350727645211; - } else { - result[0] += -11.20561954498291; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -2.4082419443490855; - } else { - result[0] += 0.2857906276722831; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 164))) { - result[0] += 1.8193510979340806; - } else { - result[0] += 7.12452591373636; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 56))) { - if (UNLIKELY(false || (data[3].qvalue <= 216))) { - if (UNLIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 0.28616249149215633; - } else { - result[0] += 3.957059242184614; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 236))) { - result[0] += 2.8268370028874497; - } else { - result[0] += 3.686134112733879; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 212))) { - if (LIKELY(false || (data[3].qvalue <= 200))) { - result[0] += 9.321451071849161; - } else { - result[0] += 15.02085895338778; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 24.772053316683984; - } else { - result[0] += 39.09600893416962; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 150))) { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (LIKELY(false || (data[3].qvalue <= 124))) { - result[0] += -1.6348218331553022; - } else { - result[0] += 0.16446761997202702; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 114))) { - result[0] += -11.997166693345555; - } else { - result[0] += -10.593410837279578; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 176))) { - result[0] += -0.26077036500572887; - } else { - result[0] += 0.26721227295908545; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 174))) { - result[0] += 3.50927093938965; - } else { - result[0] += 9.399961201456877; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 56))) { - if (UNLIKELY(false || (data[3].qvalue <= 218))) { - if (UNLIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 0.25803962657163887; - } else { - result[0] += 3.413014759107607; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 230))) { - result[0] += 2.36220596981744; - } else { - result[0] += 2.9185929060991107; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 218))) { - if (LIKELY(false || (data[3].qvalue <= 204))) { - result[0] += 9.258923590762315; - } else { - result[0] += 15.922847318580807; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 25.53935408466994; - } else { - result[0] += 35.194870544491394; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - if (LIKELY(false || (data[2].qvalue <= 52))) { - if (LIKELY(false || (data[3].qvalue <= 82))) { - result[0] += -1.5559766022197978; - } else { - result[0] += -0.6821089851736815; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 118))) { - result[0] += -9.793275858100605; - } else { - result[0] += -7.646164855957032; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -2.185564344146035; - } else { - result[0] += 0.23136153315509006; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 164))) { - result[0] += 1.4281790213369323; - } else { - result[0] += 5.862740580154604; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 52))) { - if (LIKELY(false || (data[0].qvalue <= 70))) { - if (UNLIKELY(false || (data[3].qvalue <= 214))) { - result[0] += 3.1620136193354766; - } else { - result[0] += 2.374787311783622; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 12.394186796439712; - } else { - result[0] += 7.221490024859086; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 222))) { - if (LIKELY(false || (data[3].qvalue <= 202))) { - result[0] += 7.724518171783787; - } else { - result[0] += 14.107049853741623; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 25.09318574808453; - } else { - result[0] += 33.43533238246624; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 150))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 110))) { - result[0] += -1.348448669905975; - } else { - result[0] += 0.3221871974704163; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 126))) { - result[0] += -8.519520394077722; - } else { - result[0] += -4.128400709863002; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 16))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - result[0] += 0.20971876754977306; - } else { - result[0] += 2.000764232879495; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 176))) { - result[0] += 3.2250018408327294; - } else { - result[0] += 9.091428443343196; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 56))) { - if (UNLIKELY(false || (data[2].qvalue <= 46))) { - result[0] += -0.07772423557166397; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 206))) { - result[0] += 7.465886690298717; - } else { - result[0] += 2.199134462061703; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 212))) { - if (UNLIKELY(false || (data[3].qvalue <= 196))) { - result[0] += 5.510852046313868; - } else { - result[0] += 10.349949332911033; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - result[0] += 17.106146295672758; - } else { - result[0] += 26.416688466239478; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 152))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 120))) { - result[0] += -1.1991422347150693; - } else { - result[0] += 0.46237283922421873; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 126))) { - result[0] += -7.6684254163131875; - } else { - result[0] += -3.7949608540857165; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 16))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += 0.18039368614868972; - } else { - result[0] += 1.85527472375075; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 172))) { - result[0] += 2.72143013117147; - } else { - result[0] += 6.582942954251957; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 52))) { - if (LIKELY(false || (data[0].qvalue <= 70))) { - if (UNLIKELY(false || (data[3].qvalue <= 214))) { - result[0] += 2.622990881969633; - } else { - result[0] += 1.9174728788131057; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 10.617562740828639; - } else { - result[0] += 5.75643968641758; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 214))) { - if (UNLIKELY(false || (data[3].qvalue <= 196))) { - result[0] += 4.573403401881785; - } else { - result[0] += 9.61596988143479; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 17.169701079810455; - } else { - result[0] += 25.958129802402993; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 152))) { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (LIKELY(false || (data[3].qvalue <= 92))) { - result[0] += -1.130718809808625; - } else { - result[0] += -0.2762627382286883; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 114))) { - result[0] += -8.235156694808095; - } else { - result[0] += -6.951272825766222; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 32))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += 0.16235716809342288; - } else { - result[0] += 1.707373695884848; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 176))) { - result[0] += 2.8296562924600828; - } else { - result[0] += 7.488739806649071; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 56))) { - if (UNLIKELY(false || (data[0].qvalue <= 66))) { - result[0] += -0.32786249211636087; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 206))) { - result[0] += 6.490628203531107; - } else { - result[0] += 1.7818277591800613; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 212))) { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - result[0] += 3.807200463755219; - } else { - result[0] += 8.111758332078471; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 226))) { - result[0] += 13.144779116022248; - } else { - result[0] += 20.863007074513657; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 70))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 22))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -1.0274672289090832; - } else { - result[0] += -0.6823801552731604; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - result[0] += 1.0283586431978333; - } else { - result[0] += -0.23713978185064843; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[0].qvalue <= 62))) { - result[0] += 2.1905285297360657; - } else { - result[0] += 6.504341335474002; - } - } else { - result[0] += 0.9435717207184171; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (UNLIKELY(false || (data[1].qvalue <= 74))) { - if (UNLIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 3.5741883312100953; - } else { - result[0] += 4.626694408655167; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - result[0] += 6.38776066924514; - } else { - result[0] += 7.9433524366525505; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 27.524095430198624; - } else { - result[0] += 17.93763900587164; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 154))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 108))) { - result[0] += -0.8928303502649112; - } else { - result[0] += 0.1926267660463724; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 122))) { - result[0] += -7.0074780337366285; - } else { - result[0] += -3.8229517707148526; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 16))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - result[0] += 0.17887769899557107; - } else { - result[0] += 1.2261906364605684; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 178))) { - result[0] += 2.7207480610255765; - } else { - result[0] += 8.3997445237607; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 56))) { - if (LIKELY(false || (data[3].qvalue <= 236))) { - if (UNLIKELY(false || (data[3].qvalue <= 218))) { - result[0] += 1.928126817594764; - } else { - result[0] += 1.3411342581129906; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - result[0] += 1.9525385201789491; - } else { - result[0] += 2.4821401437493256; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 212))) { - if (UNLIKELY(false || (data[3].qvalue <= 198))) { - result[0] += 3.713535595855519; - } else { - result[0] += 7.180656615177431; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 226))) { - result[0] += 11.193661980597666; - } else { - result[0] += 17.555807665127727; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[3].qvalue <= 154))) { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -0.7003375835348792; - } else { - result[0] += -3.7302757340894694; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 116))) { - result[0] += -6.599387818444294; - } else { - result[0] += -5.72489429399885; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 32))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += 0.15262275808585868; - } else { - result[0] += 1.1954652973697173; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 178))) { - result[0] += 2.4487311442319593; - } else { - result[0] += 7.490679951544426; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 56))) { - if (UNLIKELY(false || (data[3].qvalue <= 214))) { - if (UNLIKELY(false || (data[0].qvalue <= 66))) { - result[0] += -0.4618247323919986; - } else { - result[0] += 2.1528753353821717; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 234))) { - result[0] += 1.2097848819662675; - } else { - result[0] += 1.752838457626999; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 212))) { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - result[0] += 2.420871486643817; - } else { - result[0] += 6.029272785166614; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 230))) { - result[0] += 10.227185956634905; - } else { - result[0] += 15.974606997518203; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 192))) { - if (LIKELY(false || (data[3].qvalue <= 124))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += -2.865659975119095; - } else { - result[0] += -0.6896181889893488; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 116))) { - result[0] += -5.944063382797502; - } else { - result[0] += -5.094536373408761; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 52))) { - if (LIKELY(false || (data[3].qvalue <= 160))) { - result[0] += 0.6397310298301389; - } else { - result[0] += 2.9220460179454832; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 138))) { - result[0] += -2.666624663971065; - } else { - result[0] += 0.4270094967652458; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[3].qvalue <= 236))) { - if (UNLIKELY(false || (data[3].qvalue <= 218))) { - result[0] += 1.521282712441143; - } else { - result[0] += 1.0810978861849587; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - result[0] += 1.582856585015188; - } else { - result[0] += 2.0611044482921446; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 216))) { - if (LIKELY(false || (data[3].qvalue <= 206))) { - result[0] += 4.439645479292941; - } else { - result[0] += 7.705468000634004; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 11.427783097865925; - } else { - result[0] += 17.19202195652448; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 192))) { - if (LIKELY(false || (data[3].qvalue <= 124))) { - if (LIKELY(false || (data[2].qvalue <= 28))) { - if (UNLIKELY(false || (data[3].qvalue <= 24))) { - result[0] += -1.2367334931972405; - } else { - result[0] += -0.568679959736126; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 112))) { - result[0] += -8.32192050869182; - } else { - result[0] += -5.006960492312537; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 0.4550818668115695; - } else { - result[0] += -3.0743532402463507; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 160))) { - result[0] += 0.13097623312980142; - } else { - result[0] += 2.786328266103951; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - if (LIKELY(false || (data[1].qvalue <= 72))) { - result[0] += 1.0925176197221544; - } else { - result[0] += 7.582882419296458; - } - } else { - result[0] += -3.9018135684874005; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 224))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 4.15326719153954; - } else { - result[0] += 7.678964746257113; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 78))) { - result[0] += 19.747446627865543; - } else { - result[0] += 12.500771902929218; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 192))) { - if (LIKELY(false || (data[3].qvalue <= 124))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (UNLIKELY(false || (data[3].qvalue <= 22))) { - result[0] += -1.167979990569673; - } else { - result[0] += -0.5133742356132379; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 114))) { - result[0] += -5.603649494204901; - } else { - result[0] += -4.409295198837661; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 52))) { - if (LIKELY(false || (data[3].qvalue <= 160))) { - result[0] += 0.5480854924955038; - } else { - result[0] += 2.363109929409574; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 138))) { - result[0] += -2.302846027254356; - } else { - result[0] += 0.34306161983695516; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 0.9832828581009277; - } else { - result[0] += 6.834192669120016; - } - } else { - result[0] += -3.521149036128347; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 224))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 3.738054988080166; - } else { - result[0] += 6.911557381148448; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 78))) { - result[0] += 17.78481683344929; - } else { - result[0] += 11.252482971264655; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 56))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -0.5726678321736987; - } else { - if (LIKELY(false || (data[2].qvalue <= 46))) { - result[0] += -0.14924717663033055; - } else { - result[0] += 6.235019242422922; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 1.162497192248416; - } else { - result[0] += 2.0120424899788536; - } - } else { - result[0] += 0.47169244387428955; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (UNLIKELY(false || (data[2].qvalue <= 42))) { - if (UNLIKELY(false || (data[2].qvalue <= 34))) { - result[0] += -0.357844108120637; - } else { - result[0] += 1.7605654400603017; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 70))) { - result[0] += 6.52940587259161; - } else { - result[0] += 3.3080480937743113; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 16.01724653501452; - } else { - result[0] += 8.753001223254058; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 192))) { - if (LIKELY(false || (data[3].qvalue <= 156))) { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -0.4137669463081151; - } else { - result[0] += -2.6409307308959122; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 114))) { - result[0] += -5.376507786444898; - } else { - result[0] += -4.389560249429967; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 32))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += 0.025008362302337356; - } else { - result[0] += 1.019241657971192; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 72))) { - result[0] += 2.1411528979907484; - } else { - result[0] += -0.47484663873831007; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 0.8378079743141229; - } else { - result[0] += 5.832806958458091; - } - } else { - result[0] += -3.500358439422236; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 222))) { - if (LIKELY(false || (data[3].qvalue <= 206))) { - result[0] += 2.851221265160206; - } else { - result[0] += 5.566558597933049; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 78))) { - result[0] += 14.425347984875641; - } else { - result[0] += 9.578878534245574; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 192))) { - if (LIKELY(false || (data[3].qvalue <= 124))) { - if (LIKELY(false || (data[2].qvalue <= 28))) { - if (UNLIKELY(false || (data[3].qvalue <= 22))) { - result[0] += -0.9195523527649098; - } else { - result[0] += -0.3719639557405976; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 74))) { - result[0] += -7.786786672274272; - } else { - result[0] += -3.9362138288079898; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += 0.6022720589428014; - } else { - result[0] += 7.092300986713834; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 190))) { - result[0] += -2.1043147125075716; - } else { - result[0] += 0.12674302444068922; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 50))) { - if (LIKELY(false || (data[3].qvalue <= 232))) { - if (UNLIKELY(false || (data[3].qvalue <= 218))) { - result[0] += 1.031090335758177; - } else { - result[0] += 0.5392873356886984; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - result[0] += 0.9291700147586669; - } else { - result[0] += 1.5196304502959777; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 216))) { - if (LIKELY(false || (data[3].qvalue <= 206))) { - result[0] += 2.5829880193644055; - } else { - result[0] += 4.763908216949989; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 6.935099835666378; - } else { - result[0] += 10.036216690922197; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 164))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 6))) { - result[0] += -0.43590572608869044; - } else { - result[0] += -2.4316455018950878; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 42))) { - result[0] += -0.04380141068835597; - } else { - result[0] += 1.7110383905553694; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 144))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += -3.530168794490836; - } else { - result[0] += -1.0214699319685518; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 148))) { - result[0] += -6.775844377790179; - } else { - result[0] += -10.790508870091934; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[3].qvalue <= 188))) { - result[0] += -0.02275360025237618; - } else { - result[0] += -0.5838206132835355; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 1.7417272007672004; - } else { - result[0] += 0.660660463610099; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 208))) { - if (LIKELY(false || (data[3].qvalue <= 200))) { - result[0] += 1.9156509211867474; - } else { - result[0] += 3.135028625615758; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - result[0] += 4.960511180617512; - } else { - result[0] += 8.359091821368834; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 194))) { - if (LIKELY(false || (data[3].qvalue <= 160))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 70))) { - result[0] += -0.42133291745401114; - } else { - result[0] += -0.010851476443648929; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -2.4223382968632965; - } else { - result[0] += -6.899222971006882; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 16))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += -0.03413012978096565; - } else { - result[0] += 0.5317297186286644; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 2.211563570567631; - } else { - result[0] += -0.16486923946003673; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 50))) { - if (LIKELY(false || (data[3].qvalue <= 234))) { - if (UNLIKELY(false || (data[3].qvalue <= 218))) { - result[0] += 0.8620758848535985; - } else { - result[0] += 0.47621186848670294; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - result[0] += 0.8507697571985345; - } else { - result[0] += 1.3028689973708651; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 216))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 2.41099243454025; - } else { - result[0] += 4.090173047276771; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 5.632316419302413; - } else { - result[0] += 8.200667309946828; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 166))) { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (LIKELY(false || (data[3].qvalue <= 68))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - result[0] += -0.38327232438457565; - } else { - result[0] += -2.1415671585396967; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 68))) { - result[0] += -0.048803059306061464; - } else { - result[0] += -1.7470427286358465; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 118))) { - if (UNLIKELY(false || (data[3].qvalue <= 114))) { - result[0] += -3.8643859740923037; - } else { - result[0] += -3.030297649090965; - } - } else { - result[0] += -1.569212761065539; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (UNLIKELY(false || (data[0].qvalue <= 54))) { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - result[0] += 1.1504605549094933; - } else { - result[0] += -0.02918860846727582; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - result[0] += 2.077977738321157; - } else { - result[0] += 0.5359649431721014; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 212))) { - if (LIKELY(false || (data[3].qvalue <= 202))) { - result[0] += 1.652849064266966; - } else { - result[0] += 2.954255485765737; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 76))) { - result[0] += -0.5669657233750427; - } else { - result[0] += 5.676198660468206; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 168))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 38))) { - if (UNLIKELY(false || (data[3].qvalue <= 40))) { - result[0] += -0.5244874333393534; - } else { - result[0] += -0.18256259456848412; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 74))) { - result[0] += -5.51355459890058; - } else { - result[0] += 1.411684886661277; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 144))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += -2.6606175581636338; - } else { - result[0] += -0.47356034191920265; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 152))) { - result[0] += -5.942664098495093; - } else { - result[0] += -10.448295749482655; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 38))) { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[3].qvalue <= 188))) { - result[0] += -0.011800565116617453; - } else { - result[0] += -0.5202224220078566; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 1.7775453851705354; - } else { - result[0] += 0.49081054656235107; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 222))) { - if (LIKELY(false || (data[3].qvalue <= 204))) { - result[0] += 1.614115694236453; - } else { - result[0] += 3.0704803932483227; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 76))) { - result[0] += -0.5108923716568848; - } else { - result[0] += 6.589366885775977; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (LIKELY(false || (data[0].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -0.2892059588012491; - } else { - if (LIKELY(false || (data[0].qvalue <= 14))) { - result[0] += -0.08035600794549216; - } else { - result[0] += -0.4720559073325542; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 0.8967780238095124; - } else { - result[0] += 6.595205778394427; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - result[0] += -0.3520288227121683; - } else { - result[0] += 0.38974464605775183; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (UNLIKELY(false || (data[2].qvalue <= 38))) { - if (LIKELY(false || (data[1].qvalue <= 64))) { - result[0] += 0.7589099157010143; - } else { - result[0] += -1.366375525846731; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 66))) { - result[0] += 4.22967240474964; - } else { - result[0] += 1.4943104257462279; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 9.129350705249179; - } else { - result[0] += 3.068747078527702; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 168))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 42))) { - if (LIKELY(false || (data[0].qvalue <= 50))) { - result[0] += -0.18545114512070737; - } else { - result[0] += -1.7692822476814012; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 74))) { - result[0] += -4.801651736039382; - } else { - result[0] += 1.3646239105274842; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 142))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += -2.5169209574656417; - } else { - result[0] += -0.18660289084267964; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 148))) { - result[0] += -4.541809815605482; - } else { - result[0] += -8.652151740789414; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 1.3663672464257006; - } else { - result[0] += 3.04170718867983; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 0.2559705229975156; - } else { - result[0] += 2.9168273909132068; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 4.97017162342233; - } else { - result[0] += 11.503874847499691; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 0.4665647305038452; - } else { - result[0] += 5.509817603230935; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 162))) { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - result[0] += -1.3283728127725134; - } else { - result[0] += -6.130559274355571; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 62))) { - result[0] += -0.2896825481817099; - } else { - result[0] += -0.07162487032383137; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 118))) { - if (UNLIKELY(false || (data[3].qvalue <= 114))) { - result[0] += -3.1167449558905838; - } else { - result[0] += -2.3599501347059975; - } - } else { - result[0] += -1.059417066398789; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (UNLIKELY(false || (data[0].qvalue <= 54))) { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - result[0] += 0.834224641552816; - } else { - result[0] += -0.015370587395285587; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - result[0] += 0.9254950158058017; - } else { - result[0] += 0.36421699256211726; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 212))) { - if (LIKELY(false || (data[3].qvalue <= 200))) { - result[0] += 1.0215455914435474; - } else { - result[0] += 1.9619948363613522; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 76))) { - result[0] += -0.6542972311475775; - } else { - result[0] += 3.9645617071617623; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (LIKELY(false || (data[0].qvalue <= 22))) { - if (LIKELY(false || (data[1].qvalue <= 14))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -0.22003805724566253; - } else { - result[0] += -0.05306048893366094; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 16))) { - result[0] += -1.3451441339924273; - } else { - result[0] += -1.5488482760393083; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - if (UNLIKELY(false || (data[2].qvalue <= 10))) { - result[0] += 0.21580833438485533; - } else { - result[0] += 1.177993229883637; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 56))) { - result[0] += -0.2994307611075257; - } else { - result[0] += 0.305894106833898; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (UNLIKELY(false || (data[2].qvalue <= 34))) { - if (LIKELY(false || (data[2].qvalue <= 32))) { - result[0] += 0.6300423600302785; - } else { - result[0] += -0.4677024667015657; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 60))) { - result[0] += 2.92097058914907; - } else { - result[0] += 0.9749046067206072; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 7.280391073314689; - } else { - result[0] += 1.8221294908582069; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 170))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 42))) { - if (UNLIKELY(false || (data[3].qvalue <= 20))) { - result[0] += -0.5336229335357556; - } else { - result[0] += -0.12326033481423136; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 120))) { - result[0] += -3.7782573749400954; - } else { - result[0] += 1.2031945074524026; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 142))) { - if (LIKELY(false || (data[3].qvalue <= 136))) { - result[0] += -2.0138202324018293; - } else { - result[0] += 0.4287253040269907; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 150))) { - result[0] += -4.26031586774496; - } else { - result[0] += -8.189678469057437; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[3].qvalue <= 188))) { - result[0] += 0.03067279449262314; - } else { - result[0] += -0.4287195475348111; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 1.8796244512104765; - } else { - result[0] += 0.3056819085450995; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 226))) { - if (LIKELY(false || (data[3].qvalue <= 202))) { - result[0] += 0.9636725612848687; - } else { - result[0] += 1.932067083622997; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 76))) { - result[0] += -0.6860055204538198; - } else { - result[0] += 4.570361189431599; - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (LIKELY(false || (data[0].qvalue <= 22))) { - if (LIKELY(false || (data[1].qvalue <= 14))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -0.1814554734769651; - } else { - result[0] += -0.03493688255810839; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 16))) { - result[0] += -1.1580987454621139; - } else { - result[0] += -1.3413217625073497; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 0.6065784994119716; - } else { - result[0] += 6.64156315122332; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 56))) { - result[0] += -0.2710747772797691; - } else { - result[0] += 0.24590059195978556; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (UNLIKELY(false || (data[2].qvalue <= 38))) { - if (LIKELY(false || (data[1].qvalue <= 64))) { - result[0] += 0.52839377206309; - } else { - result[0] += -1.3097734584676666; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 66))) { - result[0] += 3.0327157128432702; - } else { - result[0] += 0.8301613199220086; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 6.102585921419179; - } else { - result[0] += 1.1868023468270625; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 170))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 42))) { - if (LIKELY(false || (data[0].qvalue <= 50))) { - result[0] += -0.1099508760937471; - } else { - result[0] += -1.4799074079672025; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 74))) { - result[0] += -3.9533722562056326; - } else { - result[0] += 1.0527497525643543; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 142))) { - if (LIKELY(false || (data[3].qvalue <= 136))) { - result[0] += -1.874856650108779; - } else { - result[0] += 0.36151445149430145; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 152))) { - result[0] += -4.013347158834968; - } else { - result[0] += -7.940386810302735; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - if (LIKELY(false || (data[3].qvalue <= 206))) { - result[0] += 0.8194996574746792; - } else { - result[0] += 1.9073466437063578; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 0.1914437036803978; - } else { - result[0] += 1.9462096802861646; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 3.445255372400778; - } else { - result[0] += 9.341778161720354; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 0.3243846697838024; - } else { - result[0] += 3.292591407943689; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 160))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - result[0] += -0.9774807565883121; - } else { - result[0] += -5.423777811527253; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += -0.12073383773519127; - } else { - result[0] += 0.9911425235059078; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 142))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += -1.8435700376685866; - } else { - result[0] += -0.10737506746612407; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 148))) { - result[0] += -3.3288382873336477; - } else { - result[0] += -5.702344606187609; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - if (LIKELY(false || (data[3].qvalue <= 202))) { - result[0] += 0.6121670947641649; - } else { - result[0] += 1.5478825951517399; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 0.15159187149918565; - } else { - result[0] += 1.7525244521134302; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 3.1021024062818072; - } else { - result[0] += 8.417132546913868; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 0.29207001641087005; - } else { - result[0] += 2.9643452592336215; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -0.14003554272994065; - } else { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - result[0] += -0.015514364763912126; - } else { - result[0] += 1.0510222973719732; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 30))) { - result[0] += -0.27117390740511166; - } else { - result[0] += 0.14354185812943476; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (LIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[0].qvalue <= 62))) { - result[0] += 0.5018898936445311; - } else { - result[0] += 2.5336810498326523; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 2.3642130662022995; - } else { - result[0] += 0.07374194009502717; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 4.874216369284443; - } else { - result[0] += 0.44699526391786304; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 170))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (UNLIKELY(false || (data[3].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - result[0] += -0.35060793036231724; - } else { - result[0] += -5.047665471633276; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 38))) { - result[0] += -0.07028608501406199; - } else { - result[0] += 0.7109949202510606; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 144))) { - if (LIKELY(false || (data[3].qvalue <= 122))) { - result[0] += -1.8132749471478289; - } else { - result[0] += -0.4798894299710876; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 152))) { - result[0] += -3.6392164039153325; - } else { - result[0] += -6.78748151870001; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - if (LIKELY(false || (data[0].qvalue <= 32))) { - result[0] += 0.6818792886595983; - } else { - result[0] += 2.0346700524088903; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += 0.1650370627648028; - } else { - result[0] += 1.4122047675458287; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 2.743139011175984; - } else { - result[0] += 7.534331532576863; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 0.25562844944051427; - } else { - result[0] += 2.4035809405106767; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -0.1149636651708558; - } else { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - result[0] += -0.005786597801570078; - } else { - result[0] += 0.9536320919638706; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 30))) { - result[0] += -0.25774071878779176; - } else { - result[0] += 0.12494917135148927; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (LIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += 0.416251413237658; - } else { - result[0] += 4.65468264502448; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 1.9891757155656817; - } else { - result[0] += 0.06062405904969436; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 4.150901784204815; - } else { - result[0] += 0.1636860781066988; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -0.10346745600044038; - } else { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - result[0] += -0.005207970482558494; - } else { - result[0] += 0.8583114141430566; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 30))) { - result[0] += -0.2319702347159344; - } else { - result[0] += 0.11246252282811312; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (LIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[0].qvalue <= 62))) { - result[0] += 0.36237019282195543; - } else { - result[0] += 2.185361301747475; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 1.7916299525787094; - } else { - result[0] += 0.05456246786985419; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 3.7383578847668653; - } else { - result[0] += 0.1474179036193099; - } - } - } - if (LIKELY(false || (data[2].qvalue <= 20))) { - if (LIKELY(false || (data[0].qvalue <= 22))) { - if (LIKELY(false || (data[1].qvalue <= 14))) { - if (LIKELY(false || (data[0].qvalue <= 16))) { - result[0] += -0.07824623744678852; - } else { - result[0] += 2.4147477472795025; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 20))) { - result[0] += -0.9602189832977626; - } else { - result[0] += -1.2336897440823642; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[2].qvalue <= 10))) { - result[0] += 0.17808383312156362; - } else { - result[0] += 0.7764366538076664; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 30))) { - result[0] += -0.20877644063379633; - } else { - result[0] += 0.10122370049456518; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (LIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += 0.3366817124844217; - } else { - result[0] += 3.9891647478052095; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 1.613702605033743; - } else { - result[0] += 0.04910696472777197; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 3.3668153467865807; - } else { - result[0] += 0.13276691204199761; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 170))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - if (LIKELY(false || (data[2].qvalue <= 28))) { - result[0] += -0.8003296552631252; - } else { - result[0] += -5.168554219404857; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 28))) { - result[0] += -0.04191612245860841; - } else { - result[0] += -0.31322033502373364; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 142))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += -1.6260716001993314; - } else { - result[0] += -0.07253028186242319; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 152))) { - result[0] += -3.059761641596405; - } else { - result[0] += -6.156701689220611; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (LIKELY(false || (data[1].qvalue <= 36))) { - if (UNLIKELY(false || (data[3].qvalue <= 176))) { - result[0] += 0.8382331706298907; - } else { - result[0] += 0.1470265483651418; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 68))) { - result[0] += 1.4136662728343927; - } else { - result[0] += 0.42639604831684264; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (UNLIKELY(false || (data[1].qvalue <= 76))) { - result[0] += 0.2137965369719358; 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- } else { - result[0] += -0.03741247869342383; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 198))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 170))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += -0.9280781442850904; - } else { - result[0] += -0.05519963885812408; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 18))) { - result[0] += 0.13575882302728218; - } else { - result[0] += 1.3530812716367793; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 192))) { - if (LIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -1.0690103616905782; - } else { - result[0] += -2.214087039734459; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - result[0] += -0.6739287079004651; - } else { - result[0] += 0.08192197004299273; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - if (LIKELY(false || (data[1].qvalue <= 72))) { - result[0] += 0.16779935065337956; - } else { - result[0] += 2.7936280922346484; - } - } else { - result[0] += -6.01828142584824; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 216))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 0.6464965410008258; - } else { - result[0] += 1.2127821355938402; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 56))) { - result[0] += 2.304393523269968; - } else { - result[0] += -0.03369406329707866; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 44))) { - if (LIKELY(false || (data[2].qvalue <= 6))) { - result[0] += -0.053129047160861614; - } else { - result[0] += -0.6797773812157768; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 50))) { - result[0] += 0.4863998644119598; - } else { - result[0] += 3.517865025900506; - } - } - } else { - result[0] += -0.18996639918014335; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - if (LIKELY(false || (data[0].qvalue <= 34))) { - result[0] += 0.21126964469127507; - } else { - result[0] += 0.9292041399773837; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 0.38898047466443736; - } else { - result[0] += 0.0343316630215836; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 2.3603220532788822; - } else { - result[0] += -0.030345338012543195; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 198))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 170))) { - if (LIKELY(false || (data[2].qvalue <= 12))) { - result[0] += -0.03196203652937306; - } else { - result[0] += -0.32291491116843224; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 14))) { - result[0] += 0.1059633693894297; - } else { - result[0] += 1.1109217701835943; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 192))) { - if (LIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -0.9750124564919093; - } else { - result[0] += -2.011326445104133; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - result[0] += -0.6277616333681295; - } else { - result[0] += 0.052653140828700706; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - if (LIKELY(false || (data[1].qvalue <= 72))) { - result[0] += 0.14758938512729353; - } else { - result[0] += 2.5144118215047015; - } - } else { - result[0] += -5.434481533329662; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 218))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 0.5607584407428231; - } else { - result[0] += 1.097345812808785; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 56))) { - result[0] += 2.188365555677249; - } else { - result[0] += -0.027329945962853226; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 196))) { - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[3].qvalue <= 174))) { - if (UNLIKELY(false || (data[3].qvalue <= 16))) { - result[0] += -0.339980565686603; - } else { - result[0] += -0.03185759232203992; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 42))) { - result[0] += 0.1301433477915698; - } else { - result[0] += 1.580000992364498; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 192))) { - if (LIKELY(false || (data[3].qvalue <= 190))) { - result[0] += -1.685654765519544; - } else { - result[0] += -1.0927866830676793; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - result[0] += -0.5651196538004228; - } else { - result[0] += -0.10798605171852133; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - result[0] += 0.13680401406260853; - } else { - result[0] += 2.8238424551720716; - } - } else { - result[0] += -4.904288046301866; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 226))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 0.45463644431627187; - } else { - result[0] += 1.0130458030200111; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 2.236542576950049; - } else { - result[0] += -0.024613159223933895; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 198))) { - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[3].qvalue <= 172))) { - if (LIKELY(false || (data[0].qvalue <= 50))) { - result[0] += -0.032568909202472296; - } else { - result[0] += -0.4469851314428541; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 30))) { - result[0] += 0.11294361883379918; - } else { - result[0] += 1.0056842141087612; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 192))) { - if (LIKELY(false || (data[3].qvalue <= 190))) { - result[0] += -1.517244761721867; - } else { - result[0] += -0.9837414394446418; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - result[0] += -0.508728386740654; - } else { - result[0] += 0.03608265112965852; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 0.11915447057376925; 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- } else { - result[0] += -1.5843785915044357; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - result[0] += -0.45796421690851963; - } else { - result[0] += 0.026405314510040745; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 50))) { - if (LIKELY(false || (data[3].qvalue <= 230))) { - if (UNLIKELY(false || (data[3].qvalue <= 218))) { - result[0] += 0.3385478130008445; - } else { - result[0] += -0.10764744508292204; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 236))) { - result[0] += 0.20488484351232758; - } else { - result[0] += 0.47811499378647015; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 212))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 0.4209993070324332; - } else { - result[0] += 0.7287778763739126; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 1.187064839378982; - } else { - result[0] += -0.01996407508027334; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 176))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[0].qvalue <= 60))) { - if (LIKELY(false || (data[0].qvalue <= 50))) { - result[0] += -0.027774129306039258; - } else { - result[0] += -0.8385662967485399; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 120))) { - result[0] += -3.519087969462077; - } else { - result[0] += 0.7293787751512967; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 148))) { - if (LIKELY(false || (data[3].qvalue <= 118))) { - result[0] += -1.1172824090308644; - } else { - result[0] += -0.20902441584933884; - } - } else { - result[0] += -5.111647744634573; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - result[0] += 1.8609278771357982; - } else { - result[0] += 0.12480108283901199; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 196))) { - result[0] += -0.08227578925901015; - } else { - result[0] += 0.25625433402161485; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.9637861349021059; - } else { - result[0] += 5.291859932724311; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.9927995753498028; - } else { - result[0] += 0.14113094589752082; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 160))) { - if (LIKELY(false || (data[2].qvalue <= 12))) { - if (LIKELY(false || (data[3].qvalue <= 130))) { - if (UNLIKELY(false || (data[3].qvalue <= 46))) { - result[0] += -0.13887715472257542; - } else { - result[0] += 0.01749027468530986; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += 0.8747182175833713; - } else { - result[0] += 3.3873552828056868; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - result[0] += -2.0719160557169487; - } else { - result[0] += -0.9527323590531762; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 112))) { - result[0] += -4.278540461588714; - } else { - result[0] += -0.22532110219787005; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - if (LIKELY(false || (data[3].qvalue <= 204))) { - result[0] += 0.13994283376545083; - } else { - result[0] += 0.628766931454757; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 168))) { - result[0] += -0.2077112285433431; - } else { - result[0] += 0.0971080345193495; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.8677912951323141; - } else { - result[0] += 4.768073993215755; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.8939668307876266; - } else { - result[0] += 0.1270565048333717; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 160))) { - if (LIKELY(false || (data[2].qvalue <= 12))) { - if (LIKELY(false || (data[3].qvalue <= 130))) { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += -0.166060933557046; - } else { - result[0] += 0.004294648853588787; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += 0.7873344634071392; - } else { - result[0] += 3.0512455477455793; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - if (UNLIKELY(false || (data[1].qvalue <= 16))) { - result[0] += 3.321445830033885; - } else { - result[0] += -1.8410162796558962; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 0.5648229020291158; - } else { - result[0] += -0.40508307941792243; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (LIKELY(false || (data[1].qvalue <= 38))) { - if (LIKELY(false || (data[3].qvalue <= 218))) { - result[0] += 0.116824997205243; - } else { - result[0] += 0.0017106091707403482; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 200))) { - result[0] += 0.09749333921964695; - } else { - result[0] += 0.512061380766423; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (UNLIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.8049728208270159; - } else { - result[0] += 0.11438560255422983; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.7813580416089511; - } else { - result[0] += 4.296131911715682; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 160))) { - if (LIKELY(false || (data[2].qvalue <= 12))) { - if (LIKELY(false || (data[3].qvalue <= 132))) { - if (UNLIKELY(false || (data[3].qvalue <= 50))) { - result[0] += -0.10165509214748221; - } else { - result[0] += 0.027573987237460126; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += 0.8965004316498251; - } else { - result[0] += 2.7484863860847417; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - result[0] += -1.6997008955624044; - } else { - result[0] += -0.678268726322754; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 118))) { - result[0] += -1.114816829956962; - } else { - result[0] += -0.07990083268080249; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (LIKELY(false || (data[3].qvalue <= 178))) { - result[0] += 0.08501821194709441; - } else { - result[0] += 0.7073547907959752; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 196))) { - result[0] += -0.09598099681424313; - } else { - result[0] += 0.1882562171309855; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.7035334691233133; - } else { - result[0] += 3.8709025872240264; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += -0.03477044912415934; - } else { - result[0] += 0.6388646830943916; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 4))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -0.034231165787913284; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += -0.008538500447320395; - } else { - result[0] += 0.030661489227495748; - } - } - } else { - result[0] += -0.6560679714832831; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[2].qvalue <= 46))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - result[0] += 0.05367898474501495; - } else { - result[0] += 0.44735028827783085; - } - } else { - result[0] += 6.229511664254325; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - result[0] += -0.14752365398422523; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 0.38398941999281766; - } else { - result[0] += 0.04404838897933778; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 176))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (UNLIKELY(false || (data[3].qvalue <= 0))) { - if (LIKELY(false || (data[2].qvalue <= 28))) { - result[0] += -0.7549177495924217; - } else { - result[0] += -4.158142198324204; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 42))) { - result[0] += -0.02530038694314765; - } else { - result[0] += 0.5546607631795957; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 148))) { - if (LIKELY(false || (data[3].qvalue <= 136))) { - result[0] += -0.6813185032064286; - } else { - result[0] += 0.29119811621696806; - } - } else { - result[0] += -4.5671472818360614; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (LIKELY(false || (data[1].qvalue <= 40))) { - if (LIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 0.06089688550626267; - } else { - result[0] += -0.9885366824653841; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 180))) { - result[0] += 0.8258845435182254; - } else { - result[0] += 5.761587441940307; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (UNLIKELY(false || (data[3].qvalue <= 196))) { - result[0] += -0.07740265934103074; - } else { - result[0] += 0.16502792587020448; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 80))) { - result[0] += 0.3005847261842887; - } else { - result[0] += 1.4347149255319134; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 152))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - result[0] += -0.010806768205029583; - } else { - result[0] += -3.3904279910193553; - } - } else { - result[0] += 3.6360500988061872; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 56))) { - if (UNLIKELY(false || (data[1].qvalue <= 48))) { - result[0] += -1.3970841696833907; - } else { - result[0] += -0.5310959589164045; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 136))) { - result[0] += -0.43780724551513983; - } else { - result[0] += 0.38160829548283537; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 238))) { - if (LIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - result[0] += 0.1260601616574519; - } else { - result[0] += 2.3280856465403716; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 188))) { - result[0] += -6.748794538567707; - } else { - result[0] += 0.03388208818968886; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.4861732172767124; - } else { - result[0] += 3.34139486286105; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.6939914549665678; - } else { - result[0] += 0.012280274946395664; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 176))) { - if (LIKELY(false || (data[1].qvalue <= 54))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - result[0] += -0.464813733628163; - } else { - result[0] += -3.2180759784840705; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 48))) { - result[0] += -0.07920781023396078; - } else { - result[0] += 0.019886892967067187; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 146))) { - if (UNLIKELY(false || (data[1].qvalue <= 60))) { - result[0] += -1.3944523714625319; - } else { - result[0] += -0.6633490458105974; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 58))) { - result[0] += -0.4361867431137107; - } else { - result[0] += 0.09866640502908253; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (LIKELY(false || (data[1].qvalue <= 40))) { - if (LIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 0.04221310244085489; - } else { - result[0] += -0.8607172810069976; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 180))) { - result[0] += 0.7308448858222341; - } else { - result[0] += 5.17753285443306; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 198))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 0.12604388146867565; - } else { - result[0] += -0.5198739781442024; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - result[0] += 0.0001483644827354292; - } else { - result[0] += 0.4382489570445441; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 12))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += 0.2715686357969588; - } else { - result[0] += -0.17382652834003876; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - result[0] += -0.5295756487690885; - } else { - result[0] += -1.247911300102006; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -4.132038760807204; - } else { - result[0] += -2.048248645525712; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 236))) { - if (LIKELY(false || (data[3].qvalue <= 152))) { - if (LIKELY(false || (data[2].qvalue <= 12))) { - result[0] += 0.009045345990511855; - } else { - result[0] += -0.36819514234182105; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 0.15071853324608264; - } else { - result[0] += 0.0023504781977204342; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.4615429381822286; - } else { - result[0] += 2.967287079375618; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.39428092203572945; - } else { - result[0] += -0.027793250841637182; - } - } - } - } - if (LIKELY(false || (data[2].qvalue <= 4))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (LIKELY(false || (data[1].qvalue <= 2))) { - if (UNLIKELY(false || (data[0].qvalue <= 0))) { - result[0] += 0.02095625550210984; - } else { - result[0] += -0.02470681499709372; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += -0.006936135990179306; - } else { - result[0] += 0.028313332943848205; - } - } - } else { - result[0] += -0.45814704971029124; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[2].qvalue <= 46))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - result[0] += 0.059813599712526144; - } else { - result[0] += 0.4052626695215156; - } - } else { - result[0] += 6.220781436647688; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - result[0] += -0.14287609636708531; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 0.28909888374082054; - } else { - result[0] += 0.0223884762447394; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 178))) { - if (LIKELY(false || (data[1].qvalue <= 52))) { - if (UNLIKELY(false || (data[3].qvalue <= 10))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - result[0] += -0.20717493847839827; - } else { - result[0] += -2.852040849850889; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 30))) { - result[0] += -0.008160190303522096; - } else { - result[0] += 0.27599937314740675; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 146))) { - if (UNLIKELY(false || (data[1].qvalue <= 60))) { - result[0] += -1.1881260380224656; - } else { - result[0] += -0.5625438965153544; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 58))) { - result[0] += -0.403338839639409; - } else { - result[0] += 0.10268439573411683; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 184))) { - if (LIKELY(false || (data[1].qvalue <= 40))) { - if (LIKELY(false || (data[3].qvalue <= 182))) { - result[0] += -0.013368157221764328; - } else { - result[0] += 0.11043304821096991; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 180))) { - result[0] += 1.5817916361431956; - } else { - result[0] += 4.191132561870824; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 236))) { - if (UNLIKELY(false || (data[3].qvalue <= 196))) { - result[0] += -0.05418531103810077; - } else { - result[0] += 0.11582636097487908; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 80))) { - result[0] += 0.21679116682674598; - } else { - result[0] += 0.9937182303717; - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (LIKELY(false || (data[0].qvalue <= 74))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -0.020289773669981; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - result[0] += 0.07888539366069491; - } else { - result[0] += -0.0043942118674004035; - } - } - } else { - result[0] += -0.8819353631826549; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 1.166342015953883; - } else { - result[0] += -0.21953414862872633; - } - } - if (LIKELY(false || (data[3].qvalue <= 178))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - if (LIKELY(false || (data[3].qvalue <= 128))) { - result[0] += -0.014985389955920162; - } else { - result[0] += 0.5030502722795092; - } - } else { - result[0] += 3.209527899147808; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[3].qvalue <= 146))) { - result[0] += -1.359497708335802; - } else { - result[0] += -0.048409546375607654; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 0.2462483312344813; - } else { - result[0] += -0.15689708895777443; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (LIKELY(false || (data[1].qvalue <= 40))) { - if (UNLIKELY(false || (data[3].qvalue <= 182))) { - result[0] += -0.011608086477160455; - } else { - result[0] += 0.054071549124002416; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 180))) { - result[0] += 1.4247202960054501; - } else { - result[0] += 4.292557497723102; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 196))) { - if (LIKELY(false || (data[1].qvalue <= 72))) { - result[0] += 0.11744769877683264; - } else { - result[0] += -0.6228392828110428; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - result[0] += -0.01810292778067102; - } else { - result[0] += 0.33713691230011644; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 32))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += 0.271129972670566; - } else { - result[0] += -0.0968952898723599; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - result[0] += -0.4419965803483365; - } else { - result[0] += -1.1427374036977256; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -2.964990935176611; - } else { - result[0] += -2.0024242342435397; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 236))) { - if (LIKELY(false || (data[3].qvalue <= 152))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - result[0] += 0.019913351953155762; - } else { - result[0] += -0.3451557406567103; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 68))) { - result[0] += 0.09431825657210167; - } else { - result[0] += -0.040080507458776934; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.2811388814670214; - } else { - result[0] += 2.54007776661795; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.33998703221387494; - } else { - result[0] += -0.11096001409545336; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 54))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 18))) { - result[0] += -0.10381124910582794; - } else { - result[0] += -0.036053796621203345; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 18))) { - result[0] += -0.4795079805215589; - } else { - result[0] += -0.3343803406439497; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 58))) { - if (UNLIKELY(false || (data[0].qvalue <= 50))) { - result[0] += -1.3594113743305207; - } else { - result[0] += -0.8394250588417054; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -3.2252649987262227; - } else { - result[0] += -1.8098834428420432; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 234))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += 0.03700457930168985; - } else { - result[0] += 17.47663833459218; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 138))) { - result[0] += -0.5601782767579584; - } else { - result[0] += 0.017180070918654807; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.2387817133710211; - } else { - result[0] += -4.100601076265661; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.843944808030713; - } else { - result[0] += -0.2201947167753442; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 200))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 188))) { - if (LIKELY(false || (data[0].qvalue <= 66))) { - result[0] += -0.0005495605667856106; - } else { - result[0] += -3.58726428370322; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 66))) { - result[0] += -0.3697917053822814; - } else { - result[0] += 2.6442676259341997; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 194))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - result[0] += -0.1322526748337809; - } else { - result[0] += -0.9165228440058644; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 198))) { - result[0] += 0.015399111888784775; - } else { - result[0] += -0.03293383786820958; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 50))) { - if (UNLIKELY(false || (data[3].qvalue <= 206))) { - result[0] += -1.74668638865153; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 214))) { - result[0] += 0.41404376746989746; - } else { - result[0] += -0.04320938041815753; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 208))) { - if (LIKELY(false || (data[0].qvalue <= 72))) { - result[0] += 0.15385322801835455; - } else { - result[0] += 0.8175728843952048; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.4756074244340375; - } else { - result[0] += -0.1983103516972138; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 56))) { - if (LIKELY(false || (data[0].qvalue <= 24))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 26))) { - result[0] += -0.08484652763250765; - } else { - result[0] += -0.028152113688230847; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 6))) { - result[0] += -0.4321762504373764; - } else { - result[0] += -0.2651707672412985; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 50))) { - result[0] += -1.0555429015159608; - } else { - result[0] += -0.7582275236447653; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -2.9167088172746745; - } else { - result[0] += -1.4978527782972042; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 234))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 38))) { - result[0] += 0.02016281760562264; - } else { - result[0] += 0.5381136571935687; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - result[0] += -0.4649008843759111; - } else { - result[0] += 0.06919020340221031; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.15702109026459932; - } else { - result[0] += 2.1580531330984467; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.21686901261145552; - } else { - result[0] += -0.1001601769605728; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 42))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 8))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += 0.19641092388804365; - } else { - result[0] += -0.055276022085065206; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 8))) { - result[0] += -0.3392746616594763; - } else { - result[0] += -0.7843794295296335; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 44))) { - result[0] += -2.238581113219261; - } else { - result[0] += -1.3538284465441337; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 200))) { - if (LIKELY(false || (data[1].qvalue <= 62))) { - if (LIKELY(false || (data[3].qvalue <= 188))) { - result[0] += 0.017158471900924056; - } else { - result[0] += 1.4687521383872566; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 192))) { - result[0] += -0.48506636615045484; - } else { - result[0] += -0.047229123064139726; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += -0.003343629263232336; - } else { - result[0] += -3.737171672379098; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.24658213105746754; - } else { - result[0] += 0.7366530590168956; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 60))) { - if (LIKELY(false || (data[0].qvalue <= 24))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 18))) { - result[0] += -0.07999229789885372; - } else { - result[0] += -0.024486695046509094; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 6))) { - result[0] += -0.3591096819916043; - } else { - result[0] += -0.19716339864139187; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 50))) { - result[0] += -0.8995184892360121; - } else { - result[0] += -0.6091088518301646; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -2.423594094981318; - } else { - result[0] += -1.2236526361795572; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 236))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 38))) { - result[0] += 0.017590391024004715; - } else { - result[0] += 0.48376995639076664; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 192))) { - result[0] += -0.4336376838327545; - } else { - result[0] += 0.04774652419020946; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.08141260086235247; - } else { - result[0] += 1.8715363113490904; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.2612265991556262; - } else { - result[0] += -0.11460675569674739; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 178))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - if (LIKELY(false || (data[3].qvalue <= 128))) { - result[0] += -0.010014087252989079; - } else { - result[0] += 0.44165882919228355; - } - } else { - result[0] += 2.767749158126721; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - result[0] += -1.265884517270139; - } else { - result[0] += -0.326555396705971; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 0.17874279127753523; - } else { - result[0] += -0.10461815372106785; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (LIKELY(false || (data[1].qvalue <= 40))) { - if (UNLIKELY(false || (data[3].qvalue <= 182))) { - result[0] += -0.0291664181175232; - } else { - result[0] += 0.0321031669500698; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 180))) { - result[0] += 1.2655710841109187; - } else { - result[0] += 3.8452706585121152; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 200))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 0.07346629730705066; - } else { - result[0] += -0.2790375844518317; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - result[0] += -0.030938073075268954; - } else { - result[0] += 0.2584663489831829; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 8))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 8))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += 0.1897708646960375; - } else { - result[0] += -0.10591389661914655; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 8))) { - result[0] += -0.27429189760805434; - } else { - result[0] += -0.6438250486029867; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 22))) { - result[0] += -1.9822659349441527; - } else { - result[0] += -1.1856181401364942; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 176))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += 0.004593246195533139; - } else { - result[0] += 2.4929798120346622; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - result[0] += -0.9223952289248065; - } else { - result[0] += -0.044695212630605684; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (LIKELY(false || (data[1].qvalue <= 42))) { - result[0] += 0.0022744264617153534; - } else { - result[0] += 0.9887394270605384; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - result[0] += -0.05980165741450547; - } else { - result[0] += 0.06763695160928662; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += 0.23353499906984243; - } else { - result[0] += -0.030567457307068176; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 6))) { - result[0] += -0.3026360332604611; - } else { - result[0] += -0.15352749305440105; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 58))) { - if (UNLIKELY(false || (data[0].qvalue <= 50))) { - result[0] += -0.9208640919129055; - } else { - result[0] += -0.4870238716204962; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -2.028915634984555; - } else { - result[0] += -0.9910290916149432; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 230))) { - if (LIKELY(false || (data[3].qvalue <= 218))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 0.012556621103070601; - } else { - result[0] += 0.27622010195921237; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - result[0] += 0.4583737829879677; - } else { - result[0] += -0.1957218213752224; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.13572258094742976; - } else { - result[0] += 1.654014792332844; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.10699664077497309; - } else { - result[0] += -0.26287929402538607; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (LIKELY(false || (data[1].qvalue <= 0))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += 0.21031418917361988; - } else { - result[0] += -0.05299115840815619; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 8))) { - result[0] += -0.21890068136134136; - } else { - result[0] += -0.5242896185723835; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 22))) { - result[0] += -1.6681880847613018; - } else { - result[0] += -0.9499344626594992; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 230))) { - if (LIKELY(false || (data[3].qvalue <= 218))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += 0.003964550672321244; - } else { - result[0] += 0.248609910955958; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 80))) { - result[0] += -0.17615619642542008; - } else { - result[0] += 0.41270129405980494; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.1074364755511174; - } else { - result[0] += -3.3754073861750165; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 56))) { - result[0] += 0.5143961430823207; - } else { - result[0] += -0.23675266527233682; - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 10))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - if (LIKELY(false || (data[1].qvalue <= 4))) { - if (LIKELY(false || (data[0].qvalue <= 6))) { - result[0] += -0.02091550687275058; - } else { - result[0] += 0.05471821127697738; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 8))) { - result[0] += -0.06265624329964264; - } else { - result[0] += -0.003474992384641048; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 4))) { - if (LIKELY(false || (data[0].qvalue <= 2))) { - result[0] += -0.05813859439852901; - } else { - result[0] += 1.685043735887323; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 2))) { - result[0] += 0.005356865798300517; - } else { - result[0] += -0.009951643674742359; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - if (LIKELY(false || (data[1].qvalue <= 26))) { - result[0] += 0.04421518555621426; 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- } else { - result[0] += -1.2668292170304518; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 178))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += 0.003420268764872335; - } else { - result[0] += 2.213071489783301; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - result[0] += -0.7776470923789272; - } else { - result[0] += -0.04327273641201862; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (LIKELY(false || (data[1].qvalue <= 40))) { - result[0] += 0.00861831306648558; - } else { - result[0] += 1.848056525397011; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 196))) { - result[0] += -0.04828241624484346; - } else { - result[0] += 0.05563476812699414; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 64))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 26))) { - result[0] += -0.05078687692889694; - } else { - result[0] += -0.014356568909767928; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - result[0] += -0.17534628583710493; - } else { - result[0] += -0.5621430105402849; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -1.7740939281297767; - } else { - result[0] += -1.145018720260033; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - if (LIKELY(false || (data[3].qvalue <= 130))) { - result[0] += 0.018993981486318878; - } else { - result[0] += 0.5365927638406978; - } - } else { - result[0] += 15.05643708864848; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 152))) { - if (LIKELY(false || (data[0].qvalue <= 56))) { - result[0] += -0.4728617369458554; - } else { - result[0] += 0.20752761050703802; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += 0.09305968936956194; - } else { - result[0] += -0.009524064343686378; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 52))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += 0.18727818329014223; - } else { - result[0] += -0.025890677188174106; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - result[0] += -0.15753058727404748; - } else { - result[0] += -0.5067434101778527; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -1.604397966136103; - } else { - result[0] += -1.0349207672706018; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 234))) { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += 0.020542942769893005; - } else { - result[0] += 13.61352872212728; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 152))) { - result[0] += -0.20312529336650453; - } else { - result[0] += 0.019356250676449485; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.1529675914478733; - } else { - result[0] += -3.046054223456034; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 0.4578596813081651; - } else { - result[0] += -0.21317670897106453; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 100))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 12))) { - if (LIKELY(false || (data[3].qvalue <= 88))) { - result[0] += -0.009515774573242132; - } else { - result[0] += 0.10722986880689861; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - result[0] += -0.08947096205225108; - } else { - result[0] += -0.4843778472947222; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -1.9798399291992188; - } else { - result[0] += -1.155246785481771; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 12))) { - if (LIKELY(false || (data[3].qvalue <= 132))) { - if (LIKELY(false || (data[3].qvalue <= 126))) { - result[0] += 0.058783065329708964; - } else { - result[0] += 0.20552873062508797; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += 0.5012681102076195; - } else { - result[0] += 1.1655076967659646; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 152))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += -0.4052823818071666; - } else { - result[0] += 0.2663182250508323; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 0.09131312159878124; - } else { - result[0] += -0.023826012683033533; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 14))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 22))) { - if (LIKELY(false || (data[1].qvalue <= 16))) { - result[0] += -0.05587497206644211; - } else { - result[0] += -0.18871587509128254; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 50))) { - result[0] += -0.471164876208749; - } else { - result[0] += -0.2899516754150391; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 64))) { - result[0] += -1.2615578046052351; - } else { - result[0] += -0.824327716093797; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 230))) { - if (LIKELY(false || (data[3].qvalue <= 218))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - result[0] += -0.0018346942873173101; - } else { - result[0] += 0.16034261671985583; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - result[0] += 0.3582006235217019; - } else { - result[0] += -0.15805343752114553; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.09120323986998596; - } else { - result[0] += -2.746553548719825; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 240))) { - result[0] += 0.020237036833509074; - } else { - result[0] += 0.5211988226836327; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 178))) { - if (LIKELY(false || (data[2].qvalue <= 12))) { - if (LIKELY(false || (data[3].qvalue <= 130))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += -0.005991282302809805; - } else { - result[0] += 0.21808256969336925; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += 0.3648159058387132; - } else { - result[0] += 1.0501265993764848; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[3].qvalue <= 140))) { - result[0] += -0.7560950076917835; - } else { - result[0] += -0.011609365583746693; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 114))) { - result[0] += -0.9929890502662195; - } else { - result[0] += -0.018430796040420066; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - if (LIKELY(false || (data[3].qvalue <= 182))) { - result[0] += 1.4941535253935205; - } else { - result[0] += 5.01069572504829; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 40))) { - result[0] += -1.6721194921221052; - } else { - result[0] += 0.06600910509196743; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - if (LIKELY(false || (data[2].qvalue <= 50))) { - result[0] += 0.06599082621417188; - } else { - result[0] += -0.5629778590534413; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 50))) { - result[0] += -0.031091763815894752; - } else { - result[0] += 0.12480377527902276; - } - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 38))) { - if (LIKELY(false || (data[2].qvalue <= 36))) { - if (LIKELY(false || (data[0].qvalue <= 14))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += 0.1757544212263416; - } else { - result[0] += -0.03321362620945462; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 24))) { - result[0] += -0.1209793900016442; - } else { - result[0] += -0.3390527655087508; - } - } - } else { - result[0] += -0.9544804904460907; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 228))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - if (LIKELY(false || (data[3].qvalue <= 188))) { - result[0] += 0.00850193251229063; - } else { - result[0] += 1.253315189034531; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 192))) { - result[0] += -0.3207565361013706; - } else { - result[0] += 0.01040093706091699; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[0].qvalue <= 76))) { - result[0] += 0.07622775564010142; - } else { - result[0] += 0.6434668296100172; - } - } else { - result[0] += -0.25382570830590884; - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 66))) { - if (LIKELY(false || (data[2].qvalue <= 36))) { - if (UNLIKELY(false || (data[3].qvalue <= 6))) { - if (UNLIKELY(false || (data[3].qvalue <= 0))) { - result[0] += -0.15891414485012728; - } else { - result[0] += -0.06083332526536694; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 30))) { - result[0] += -0.025560713454775653; - } else { - result[0] += -0.009423279209306063; - } - } - } else { - result[0] += -0.8614186177253723; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[2].qvalue <= 12))) { - if (LIKELY(false || (data[3].qvalue <= 130))) { - result[0] += 0.014946627847899083; - } else { - result[0] += 0.3928370868714558; - } - } else { - result[0] += 12.309830587704978; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 146))) { - if (UNLIKELY(false || (data[2].qvalue <= 40))) { - result[0] += -0.7278951537054682; - } else { - result[0] += 0.10042924111104164; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 0.048389262179927645; - } else { - result[0] += -0.024802333325858735; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 228))) { - if (LIKELY(false || (data[3].qvalue <= 218))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 0.0026677754941615505; - } else { - result[0] += -0.11795346042813584; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 214))) { - result[0] += 0.25660651477274304; - } else { - result[0] += 0.12738714081176464; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (UNLIKELY(false || (data[3].qvalue <= 222))) { - result[0] += -0.36182422209697296; - } else { - result[0] += -0.09068035614479159; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 226))) { - result[0] += 0.29626333166012725; - } else { - result[0] += 0.501580802754658; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - if (LIKELY(false || (data[1].qvalue <= 74))) { - result[0] += 0.04606551220989991; - } else { - result[0] += 1.6768793160307642; - } - } else { - result[0] += -2.4957903161863; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 56))) { - if (LIKELY(false || (data[3].qvalue <= 240))) { - result[0] += -0.001995007368938002; - } else { - result[0] += 0.8600447828586285; - } - } else { - result[0] += -0.22613400126344585; - } - } - } - if (LIKELY(false || (data[3].qvalue <= 100))) { - if (LIKELY(false || (data[2].qvalue <= 28))) { - if (LIKELY(false || (data[1].qvalue <= 10))) { - if (LIKELY(false || (data[3].qvalue <= 88))) { - result[0] += -0.007032593132406662; - } else { - result[0] += 0.10789191115072033; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 94))) { - result[0] += -0.14776386905895944; - } else { - result[0] += -0.04740764971575276; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 44))) { - result[0] += -1.5670684486389161; - } else { - result[0] += -0.7503883746818261; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 10))) { - result[0] += 2.769067636305286; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - result[0] += 0.07602934068056531; - } else { - result[0] += 2.8183202544858474; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 146))) { - result[0] += -0.16233532230561315; - } else { - result[0] += 0.01202817781399166; - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 54))) { - if (LIKELY(false || (data[0].qvalue <= 50))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - if (LIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.0029886117550836865; - } else { - result[0] += -0.06408425115195845; - } - } else { - result[0] += 0.26602660123013633; - } - } else { - result[0] += -0.07929562686095666; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - if (LIKELY(false || (data[2].qvalue <= 46))) { - if (UNLIKELY(false || (data[2].qvalue <= 36))) { - result[0] += 3.7119815171616426; - } else { - result[0] += 0.547443312065942; - } - } else { - result[0] += 4.166473594393049; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - if (UNLIKELY(false || (data[0].qvalue <= 60))) { - result[0] += 0.0017977573714891022; - } else { - result[0] += 0.23845299048938143; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 76))) { - result[0] += -0.037150860814943704; - } else { - result[0] += 0.14564257745376; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 178))) { - if (LIKELY(false || (data[0].qvalue <= 60))) { - if (LIKELY(false || (data[0].qvalue <= 50))) { - if (LIKELY(false || (data[0].qvalue <= 48))) { - result[0] += -0.007183108829239199; - } else { - result[0] += 0.2394491795303417; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 140))) { - result[0] += -0.6344022675495424; - } else { - result[0] += 0.007260844712462359; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 142))) { - if (UNLIKELY(false || (data[3].qvalue <= 138))) { - result[0] += -0.08417562885148172; - } else { - result[0] += 0.5906773558105556; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 68))) { - result[0] += 0.18764238988337378; - } else { - result[0] += -2.326712134777498; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - if (LIKELY(false || (data[3].qvalue <= 182))) { - result[0] += 1.339431215953403; - } else { - result[0] += 4.521662556143368; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 52))) { - result[0] += 0.7292394052527168; - } else { - result[0] += -0.012391112078889212; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 194))) { - if (LIKELY(false || (data[2].qvalue <= 50))) { - result[0] += 0.05246317581619821; - } else { - result[0] += -0.4745475472223324; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 50))) { - result[0] += -0.024886056556697605; - } else { - result[0] += 0.10675635333641575; - } - } - } - } - if (LIKELY(false || (data[3].qvalue <= 178))) { - if (LIKELY(false || (data[1].qvalue <= 46))) { - if (LIKELY(false || (data[1].qvalue <= 44))) { - if (LIKELY(false || (data[3].qvalue <= 170))) { - result[0] += -0.0035541639556744805; - } else { - result[0] += 0.23776616044647245; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 22))) { - result[0] += 1.6540061310768128; - } else { - result[0] += -0.007316129265448475; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 24))) { - if (UNLIKELY(false || (data[3].qvalue <= 156))) { - result[0] += -0.9207533250588412; - } else { - result[0] += -0.19604021761441262; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 26))) { - result[0] += 0.39069615612658914; - } else { - result[0] += -0.07078300532699774; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 186))) { - if (LIKELY(false || (data[1].qvalue <= 42))) { - if (UNLIKELY(false || (data[3].qvalue <= 182))) { - result[0] += -0.0537103470292483; - } else { - result[0] += 0.01762700340556235; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 180))) { - result[0] += 0.6253090474974919; - } else { - result[0] += 2.807307553162299; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 202))) { - if (LIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 0.04721793457525833; - } else { - result[0] += -0.17271753814495214; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 76))) { - result[0] += -0.01901583571747938; - } else { - result[0] += 0.1447237594141037; - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[0].qvalue <= 76))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - if (LIKELY(false || (data[0].qvalue <= 50))) { - result[0] += 0.0021335393865604085; - } else { - result[0] += -0.07177648191161112; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 30))) { - result[0] += 2.634354959784485; - } else { - result[0] += 0.004247555141275988; - } - } - } else { - result[0] += 0.45583923039077984; - } - } else { - result[0] += -0.24432320779772257; - } - if (UNLIKELY(false || (data[3].qvalue <= 6))) { - if (LIKELY(false || (data[0].qvalue <= 58))) { - if (LIKELY(false || (data[0].qvalue <= 46))) { - if (LIKELY(false || (data[1].qvalue <= 18))) { - result[0] += -0.053137056565348964; - } else { - result[0] += -0.1615091542830301; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 50))) { - result[0] += -0.48585747241973887; - } else { - result[0] += -0.2905982590516409; - } - } - } else { - result[0] += -1.0460150627295177; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 228))) { - if (LIKELY(false || (data[3].qvalue <= 218))) { - if (LIKELY(false || (data[3].qvalue <= 208))) { - result[0] += -0.0007595050498410175; - } else { - result[0] += 0.17149519474492791; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - result[0] += 0.2696969214216211; - } else { - result[0] += -0.148602349719807; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 78))) { - if (LIKELY(false || (data[1].qvalue <= 76))) { - result[0] += 0.041070141212163; - } else { - result[0] += 0.27300094236384387; - } - } else { - result[0] += -0.220040733112148; - } - } - } - if (UNLIKELY(false || (data[3].qvalue <= 66))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - if (UNLIKELY(false || (data[3].qvalue <= 8))) { - if (LIKELY(false || (data[0].qvalue <= 24))) { - result[0] += -0.04786216375242139; - } else { - result[0] += -0.2854615034882365; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 44))) { - result[0] += -0.0157676019115889; - } else { - result[0] += -0.00516823734649734; - } - } - } else { - result[0] += -0.8536656070967852; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 54))) { - if (LIKELY(false || (data[3].qvalue <= 130))) { - result[0] += 0.01178608871677298; - } else { - result[0] += 0.31573188820888726; - } - } else { - result[0] += 10.428866252899171; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 146))) { - if (LIKELY(false || (data[0].qvalue <= 60))) { - result[0] += -0.39214777400557815; - } else { - result[0] += 0.15464362462318024; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += 0.05795968864116782; - } else { - result[0] += -0.018277034463468993; - } - } - } - } - - // Apply base_scores - result[0] += 0; - - // Apply postprocessor - if (!pred_margin) { postprocess(result); } -} - -void cpufj_predictor::postprocess(double* result) -{ - // Do nothing -} - -// Feature names array -const char* cpufj_predictor::feature_names[cpufj_predictor::NUM_FEATURES] = { - "n_vars", "n_cstrs", "total_nnz", "mem_total_mb"}; diff --git a/cpp/src/utilities/models/cpufj_predictor/quantize.cpp b/cpp/src/utilities/models/cpufj_predictor/quantize.cpp deleted file mode 100644 index f036eb5cf..000000000 --- a/cpp/src/utilities/models/cpufj_predictor/quantize.cpp +++ /dev/null @@ -1,293 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#include "header.h" - -static const double threshold[] = { - 940.50000000000011, - 962.50000000000011, - 964.50000000000011, - 1001.5000000000001, - 1010.5000000000001, - 1088.5000000000002, - 1250.5000000000002, - 1435.5000000000002, - 3395.5000000000005, - 3603.0000000000005, - 4535.5000000000009, - 5197.5000000000009, - 5419.0000000000009, - 5949.5000000000009, - 5959.5000000000009, - 5962.5000000000009, - 5963.5000000000009, - 5964.5000000000009, - 5966.5000000000009, - 5969.5000000000009, - 5972.5000000000009, - 5984.0000000000009, - 6803.5000000000009, - 7344.0000000000009, - 8291.0000000000018, - 9184.0000000000018, - 9992.0000000000018, - 10730.500000000002, - 12831.000000000002, - 14626.500000000002, - 15810.500000000002, - 16371.000000000002, - 17411.000000000004, - 18723.500000000004, - 19548.000000000004, - 20752.500000000004, - 23386.000000000004, - 35418.500000000007, - 49402.000000000007, - 53915.500000000007, - 598.50000000000011, - 1717.5000000000002, - 1767.5000000000002, - 1847.5000000000002, - 2005.5000000000002, - 2290.0000000000005, - 2305.5000000000005, - 2649.0000000000005, - 5179.5000000000009, - 5250.5000000000009, - 5362.5000000000009, - 6412.5000000000009, - 7682.0000000000009, - 9243.0000000000018, - 10037.000000000002, - 12675.000000000002, - 18404.000000000004, - 19600.500000000004, - 21814.500000000004, - 23409.000000000004, - 23497.000000000004, - 23508.000000000004, - 23558.000000000004, - 23603.500000000004, - 23652.500000000004, - 23744.500000000004, - 23818.500000000004, - 23827.000000000004, - 23874.000000000004, - 23897.500000000004, - 23920.500000000004, - 23949.500000000004, - 23978.000000000004, - 24015.000000000004, - 24102.000000000004, - 24164.500000000004, - 26354.000000000004, - 40391.500000000007, - 58010.500000000007, - 64224.000000000007, - 64894.000000000007, - 5656.5000000000009, - 7438.5000000000009, - 7622.5000000000009, - 18609.000000000004, - 23424.500000000004, - 24989.500000000004, - 29342.000000000004, - 43154.000000000007, - 46402.000000000007, - 46815.000000000007, - 47052.000000000007, - 47178.000000000007, - 47358.000000000007, - 47388.000000000007, - 47485.000000000007, - 47659.000000000007, - 47752.500000000007, - 47839.500000000007, - 48007.000000000007, - 48085.500000000007, - 48300.500000000007, - 48410.000000000007, - 54272.500000000007, - 57404.000000000007, - 76382.000000000015, - 83368.000000000015, - 108268.00000000001, - 170279.00000000003, - 186892.00000000003, - 3.8570000000000007, - 9.8880000000000017, - 11.190500000000002, - 11.561500000000001, - 11.861500000000001, - 12.013500000000002, - 12.3085, - 12.4625, - 12.647500000000003, - 13.102500000000001, - 13.439500000000001, - 14.092500000000003, - 15.505500000000003, - 16.265500000000003, - 16.586500000000004, - 16.893500000000003, - 17.051500000000001, - 17.255500000000001, - 17.432500000000001, - 17.537500000000005, - 17.814500000000006, - 18.095500000000005, - 18.167500000000004, - 18.334500000000002, - 18.546500000000005, - 18.743500000000001, - 18.851500000000005, - 18.949500000000004, - 19.151500000000002, - 19.201500000000006, - 19.275500000000005, - 19.453500000000002, - 19.604500000000005, - 19.704500000000003, - 20.009500000000006, - 20.944500000000001, - 21.655500000000004, - 24.058500000000006, - 56.284500000000001, - 58.642500000000005, - 63.514000000000003, - 66.785000000000011, - 67.949500000000015, - 70.709000000000017, - 72.252000000000024, - 74.410500000000013, - 76.675500000000014, - 78.813500000000019, - 81.035000000000011, - 86.201000000000008, - 87.71850000000002, - 90.722000000000023, - 93.919500000000014, - 95.885000000000005, - 98.094500000000025, - 101.17400000000002, - 105.48300000000002, - 119.34800000000001, - 135.33800000000005, - 148.20100000000002, - 155.32500000000002, - 202.12250000000003, - 215.85450000000003, - 222.94300000000001, - 227.80600000000001, - 231.09150000000002, - 235.98200000000006, - 238.51450000000003, - 289.45400000000001, - 328.42950000000002, - 358.75900000000007, - 402.21050000000008, - 420.89000000000004, - 436.57650000000007, - 443.69200000000006, - 453.70450000000005, - 462.34700000000004, - 471.61900000000009, - 478.55100000000004, - 486.96150000000006, - 495.03150000000005, - 501.28050000000007, - 505.49750000000006, - 510.61650000000003, - 518.64250000000004, - 524.98750000000007, - 530.02200000000005, - 538.52950000000021, - 548.17750000000012, - 563.13350000000003, - 585.95900000000017, - 606.86650000000009, - 622.19850000000008, - 632.28650000000016, - 662.49250000000006, - 870.10250000000008, - 885.30550000000005, - 899.1785000000001, - 914.79400000000021, - 932.5870000000001, - 945.74600000000009, - 1009.9455000000002, - 1021.0850000000002, - 1048.7770000000003, - 1084.9000000000003, - 1115.9705000000001, - 1145.3940000000002, - 1175.9330000000002, - 1208.2720000000002, - 1235.9550000000002, - 1253.6805000000002, - 1266.5855000000004, - 1276.1255000000003, - 1282.5315000000003, - 1291.7940000000001, - 1295.7590000000002, - 1299.4545000000001, - 1307.3780000000004, - 1312.9645000000003, - 1322.5440000000001, - 1465.1910000000003, -}; - -static const int th_begin[] = { - 0, - 40, - 81, - 110, -}; - -static const int th_len[] = { - 40, - 41, - 29, - 121, -}; - -/* - * \brief Function to convert a feature value into bin index. - * \param val Feature value, in floating-point - * \param fid Feature identifier - * \return bin Index corresponding to given feature value - */ -int cpufj_predictor::quantize(double val, unsigned fid) -{ - const size_t offset = th_begin[fid]; - const double* array = &threshold[offset]; - int len = th_len[fid]; - int low = 0; - int high = len; - int mid; - double mval; - // It is possible th_begin[i] == [total_num_threshold]. This means that - // all features i, (i+1), ... are not used for any of the splits in the model. - // So in this case, just return something - if (offset == 231 || val < array[0]) { return -10; } - while (low + 1 < high) { - mid = (low + high) / 2; - mval = array[mid]; - if (val == mval) { - return mid * 2; - } else if (val < mval) { - high = mid; - } else { - low = mid; - } - } - if (array[low] == val) { - return low * 2; - } else if (high == len) { - return len * 2; - } else { - return low * 2 + 1; - } -} diff --git a/cpp/src/utilities/models/dualsimplex_predictor/header.h b/cpp/src/utilities/models/dualsimplex_predictor/header.h deleted file mode 100644 index 75dcbb661..000000000 --- a/cpp/src/utilities/models/dualsimplex_predictor/header.h +++ /dev/null @@ -1,35 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#pragma once - -#include -#include -#include -#include -#include -#include - -class dualsimplex_predictor { - public: - union Entry { - int missing; - float fvalue; - int qvalue; - }; - - static int32_t get_num_target(void); - static void get_num_class(int32_t* out); - static int32_t get_num_feature(void); - static const char* get_threshold_type(void); - static const char* get_leaf_output_type(void); - static void predict(union Entry* data, int pred_margin, double* result); - static void postprocess(double* result); - static int quantize(float val, unsigned fid); - - // Feature names - static constexpr int NUM_FEATURES = 18; - static const char* feature_names[NUM_FEATURES]; -}; // class dualsimplex_predictor diff --git a/cpp/src/utilities/models/dualsimplex_predictor/main.cpp b/cpp/src/utilities/models/dualsimplex_predictor/main.cpp deleted file mode 100644 index b89a429c6..000000000 --- a/cpp/src/utilities/models/dualsimplex_predictor/main.cpp +++ /dev/null @@ -1,19265 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#include -#include "header.h" - -#if defined(__clang__) || defined(__GNUC__) -#define LIKELY(x) __builtin_expect(!!(x), 1) -#define UNLIKELY(x) __builtin_expect(!!(x), 0) -#else -#define LIKELY(x) (x) -#define UNLIKELY(x) (x) -#endif -#define N_TARGET 1 -#define MAX_N_CLASS 1 - -const unsigned char is_categorical[] = { - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, -}; -static const int32_t num_class[] = { - 1, -}; - -int32_t dualsimplex_predictor::get_num_target(void) { return N_TARGET; } -void dualsimplex_predictor::get_num_class(int32_t* out) -{ - for (int i = 0; i < N_TARGET; ++i) { - out[i] = num_class[i]; - } -} -int32_t dualsimplex_predictor::get_num_feature(void) { return 18; } -const char* dualsimplex_predictor::get_threshold_type(void) { return "float32"; } -const char* dualsimplex_predictor::get_leaf_output_type(void) { return "float32"; } - -void dualsimplex_predictor::predict(union Entry* data, int pred_margin, double* result) -{ - // Quantize data - for (int i = 0; i < 18; ++i) { - if (data[i].missing != -1 && !is_categorical[i]) { - data[i].qvalue = quantize(data[i].fvalue, i); - } - } - - unsigned int tmp; - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 30))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += -0.23892987; - } else { - result[0] += -0.41049096; - } - } else { - result[0] += -0.32070833; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { - result[0] += -0.3301402; - } else { - result[0] += -0.27258733; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 158))) { - result[0] += -0.2263252; - } else { - result[0] += -0.03232357; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 244))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += -0.21953094; - } else { - result[0] += -0.2782724; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 252))) { - result[0] += -0.10610497; - } else { - result[0] += -0.18065642; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 238))) { - result[0] += -0.14425437; - } else { - result[0] += -0.027287258; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 4))) { - result[0] += -0.06345942; - } else { - result[0] += -0.026511494; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { - result[0] += -0.20900369; - } else { - result[0] += -0.03617132; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - result[0] += -0.17355898; - } else { - result[0] += -0.094911605; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 174))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { - result[0] += -0.064165816; - } else { - result[0] += -0.12049206; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 116))) { - result[0] += -0.11783655; - } else { - result[0] += 0.031279184; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 14))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - result[0] += -0.16023064; - } else { - result[0] += -0.01693966; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 92))) { - result[0] += -0.03719565; - } else { - result[0] += 0.002473806; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 180))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 194))) { - result[0] += 0.015176967; - } else { - result[0] += -0.033581402; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 52))) { - result[0] += 0.060070585; - } else { - result[0] += 0.102768324; - } - } - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 210))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 152))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - result[0] += -0.018653875; - } else { - result[0] += 0.21873346; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 158))) { - result[0] += 0.020794097; - } else { - result[0] += 0.0017005502; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 54))) { - result[0] += -0.15759692; - } else { - result[0] += 0.037903294; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - result[0] += 0.073502585; - } else { - result[0] += 0.1481389; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 72))) { - result[0] += -0.20302705; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 298))) { - result[0] += -0.0740808; - } else { - result[0] += -0.0013614334; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 36))) { - result[0] += 0.007832853; - } else { - result[0] += 0.05149386; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 334))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 286))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { - result[0] += 0.038612913; - } else { - result[0] += -0.011268199; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 246))) { - result[0] += 0.12736425; - } else { - result[0] += 0.057369012; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 270))) { - result[0] += 0.09891705; - } else { - result[0] += 0.1404978; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 200))) { - result[0] += -0.09467173; - } else { - result[0] += 0.058953542; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 98))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 12))) { - result[0] += 0.24495018; - } else { - result[0] += 0.4303154; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - result[0] += 0.23759733; - } else { - result[0] += 0.31459442; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { - result[0] += 0.18160371; - } else { - result[0] += 0.119219065; - } - } else { - result[0] += 0.2541432; - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += -0.13811207; - } else { - result[0] += -0.39534476; - } - } else { - result[0] += -0.3041533; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 20))) { - result[0] += -0.29433212; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 78))) { - result[0] += -0.19736992; - } else { - result[0] += -0.25767314; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - result[0] += -0.19146764; - } else { - result[0] += -0.15695396; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { - result[0] += -0.23099203; - } else { - result[0] += -0.117888466; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 16))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { - result[0] += -0.1496231; - } else { - result[0] += -0.10690468; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 160))) { - result[0] += -0.027002404; - } else { - result[0] += -0.08140517; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { - result[0] += -0.18216138; - } else { - result[0] += -0.023672506; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { - result[0] += -0.12947556; - } else { - result[0] += -0.04930939; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { - result[0] += -0.10542603; - } else { - result[0] += -0.05690325; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 70))) { - result[0] += -0.11127614; - } else { - result[0] += 0.029446835; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 24))) { - result[0] += -0.07772863; - } else { - result[0] += -0.02781359; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 86))) { - result[0] += -0.13588855; - } else { - result[0] += -0.0629831; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 180))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 298))) { - result[0] += 0.008945032; - } else { - result[0] += -0.030025298; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 52))) { - result[0] += 0.053789187; - } else { - result[0] += 0.09317514; - } - } - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 212))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 172))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 150))) { - result[0] += -0.01571287; - } else { - result[0] += 0.01397138; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 22))) { - result[0] += -0.004348435; - } else { - result[0] += 0.28950748; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 62))) { - result[0] += 0.073582016; - } else { - result[0] += 0.15677379; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { - result[0] += -0.031289082; - } else { - result[0] += 0.06539955; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 298))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { - result[0] += -0.18994586; - } else { - result[0] += -0.066469155; - } - } else { - result[0] += 0.026545838; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 82))) { - result[0] += 0.0066967257; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 122))) { - result[0] += 0.04904222; - } else { - result[0] += 0.012652091; - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 308))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 54))) { - result[0] += 0.036342237; - } else { - result[0] += -0.0249609; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { - result[0] += 0.10429803; - } else { - result[0] += 0.3026218; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 252))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.19158468; - } else { - result[0] += -0.07063005; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 264))) { - result[0] += -0.019653698; - } else { - result[0] += 0.029620511; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 328))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 222))) { - result[0] += 0.16111112; - } else { - result[0] += 0.25347957; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 128))) { - result[0] += 0.084318936; - } else { - result[0] += 0.14822438; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 92))) { - result[0] += 0.22045271; - } else { - result[0] += 0.26758388; - } - } else { - result[0] += 0.46477053; - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 134))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 4))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += -0.1654226; - } else { - result[0] += -0.36575767; - } - } else { - result[0] += -0.28903162; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 30))) { - result[0] += -0.24255578; - } else { - result[0] += -0.27643654; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { - result[0] += -0.29263017; - } else { - result[0] += -0.20599626; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { - result[0] += -0.18999758; - } else { - result[0] += -0.15109059; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { - result[0] += -0.11279365; - } else { - result[0] += -0.027640833; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { - result[0] += -0.12278713; - } else { - result[0] += -0.029810125; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 100))) { - result[0] += -0.21111389; - } else { - result[0] += -0.13208562; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 146))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 78))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { - result[0] += -0.11854438; - } else { - result[0] += -0.17666526; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 64))) { - result[0] += -0.012537091; - } else { - result[0] += -0.081729345; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 80))) { - result[0] += -0.10848161; - } else { - result[0] += -0.06910487; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - result[0] += -0.048394956; - } else { - result[0] += -0.10724862; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 18))) { - result[0] += -0.05678125; - } else { - result[0] += -0.15847282; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { - result[0] += -0.02826718; - } else { - result[0] += 0.016257456; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 170))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 194))) { - result[0] += 0.009919471; - } else { - result[0] += -0.02828365; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 52))) { - result[0] += 0.048264306; - } else { - result[0] += 0.084261395; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 220))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 204))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 154))) { - result[0] += -0.015983945; - } else { - result[0] += 0.0104940515; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - result[0] += 0.059158504; - } else { - result[0] += -0.013891051; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 166))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - result[0] += -0.05883024; - } else { - result[0] += -0.15169065; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 50))) { - result[0] += 0.017066706; - } else { - result[0] += 0.046345506; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 302))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - result[0] += 0.114555776; - } else { - result[0] += 0.40284094; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - result[0] += 0.027867997; - } else { - result[0] += -0.05320992; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 160))) { - result[0] += 0.02757929; - } else { - result[0] += 0.0005367231; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { - result[0] += 0.0925671; - } else { - result[0] += -0.04869043; - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 334))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 54))) { - result[0] += 0.033666294; - } else { - result[0] += -0.019405624; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 138))) { - result[0] += 0.085760355; - } else { - result[0] += 0.11623438; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 136))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 78))) { - result[0] += -0.078748904; - } else { - result[0] += -0.03139619; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { - result[0] += 0.020764524; - } else { - result[0] += 0.15907182; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 156))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - result[0] += 0.19595024; - } else { - result[0] += 0.25730672; - } - } else { - result[0] += 0.41746393; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 90))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 20))) { - result[0] += 0.18315254; - } else { - result[0] += 0.3391243; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { - result[0] += 0.1157012; - } else { - result[0] += 0.20929395; - } - } - } - } - } - } - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 108))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 74))) { - result[0] += -0.14199895; - } else { - result[0] += -0.0956778; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 140))) { - result[0] += -0.010430599; - } else { - result[0] += 0.019095603; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { - result[0] += -0.3010808; - } else { - result[0] += -0.23643827; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { - result[0] += -0.20573525; - } else { - result[0] += -0.02070453; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 204))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 68))) { - result[0] += -0.120953314; - } else { - result[0] += -0.075053215; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { - result[0] += 0.023893813; - } else { - result[0] += -0.05592366; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - result[0] += -0.16090266; - } else { - result[0] += -0.22481023; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 86))) { - result[0] += -0.14591117; - } else { - result[0] += -0.081503354; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 124))) { - result[0] += 0.014306935; - } else { - result[0] += -0.15783915; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 156))) { - result[0] += -0.04105756; - } else { - result[0] += -0.014158033; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 142))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 82))) { - result[0] += -0.120835006; - } else { - result[0] += -0.073639594; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 60))) { - result[0] += -0.19654752; - } else { - result[0] += -0.14132342; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 192))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 26))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 76))) { - result[0] += 0.01790429; - } else { - result[0] += -0.00943239; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 238))) { - result[0] += 0.04468555; - } else { - result[0] += 0.011115446; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 62))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 270))) { - result[0] += 0.07178088; - } else { - result[0] += 0.011843091; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { - result[0] += 0.0443505; - } else { - result[0] += 0.14387575; - } - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 288))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 254))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 148))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { - result[0] += -0.04890412; - } else { - result[0] += -0.0113124; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 190))) { - result[0] += 0.015577379; - } else { - result[0] += -0.0047443947; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 300))) { - result[0] += 0.03677338; - } else { - result[0] += 0.08133157; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 58))) { - result[0] += 0.11096779; - } else { - result[0] += 0.15190493; - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 186))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 70))) { - result[0] += 0.0974144; - } else { - result[0] += 0.012945512; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 228))) { - result[0] += -0.016480766; - } else { - result[0] += 0.020737674; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 176))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { - result[0] += -0.105966404; - } else { - result[0] += -0.01857656; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { - result[0] += -0.033622157; - } else { - result[0] += 0.042780038; - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 334))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 14))) { - result[0] += -0.0662734; - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 140))) { - result[0] += 0.07991419; - } else { - result[0] += 0.039648328; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 172))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 120))) { - result[0] += 0.10550159; - } else { - result[0] += 0.03400041; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { - result[0] += 0.11472396; - } else { - result[0] += 0.1589642; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 8))) { - result[0] += 0.14915675; - } else { - result[0] += 0.3244665; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - result[0] += 0.17384307; - } else { - result[0] += 0.23245731; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { - result[0] += 0.13902494; - } else { - result[0] += 0.07743486; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - result[0] += 0.19507462; - } else { - result[0] += 0.12790385; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 14))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += -0.12810235; - } else { - result[0] += -0.30201578; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { - result[0] += -0.25788355; - } else { - result[0] += -0.21340947; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { - result[0] += -0.21370807; - } else { - result[0] += -0.167573; - } - } else { - result[0] += -0.024952482; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 100))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { - result[0] += -0.171652; - } else { - result[0] += -0.12910992; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 110))) { - result[0] += -0.07442858; - } else { - result[0] += -0.12269157; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 12))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { - result[0] += -0.11061738; - } else { - result[0] += -0.06790245; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 160))) { - result[0] += -0.017483674; - } else { - result[0] += -0.059728812; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 146))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 82))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { - result[0] += -0.13760419; - } else { - result[0] += -0.01986711; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { - result[0] += -0.0773085; - } else { - result[0] += 0.004780628; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 62))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 144))) { - result[0] += -0.08369659; - } else { - result[0] += -0.0037746632; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - result[0] += -0.033646163; - } else { - result[0] += -0.09390003; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 64))) { - result[0] += -0.10188579; - } else { - result[0] += -0.022887342; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { - result[0] += -0.025158664; - } else { - result[0] += 0.011464008; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 170))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 204))) { - result[0] += 0.009676366; - } else { - result[0] += -0.025687149; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 52))) { - result[0] += 0.03910928; - } else { - result[0] += 0.06689557; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 220))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 204))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 80))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { - result[0] += 0.039443213; - } else { - result[0] += 0.20666161; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 128))) { - result[0] += -0.01548522; - } else { - result[0] += 0.015606319; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 392))) { - result[0] += -0.0018438485; - } else { - result[0] += -0.03514717; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 90))) { - result[0] += -0.029947493; - } else { - result[0] += -0.056570943; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 302))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - result[0] += 0.15045454; - } else { - result[0] += 0.01461363; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - result[0] += 0.031331427; - } else { - result[0] += 0.2708533; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += -0.044682816; - } else { - result[0] += 0.019505082; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 278))) { - result[0] += 0.10829522; - } else { - result[0] += 0.07073908; - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 312))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { - result[0] += 0.027972206; - } else { - result[0] += 0.005189055; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { - result[0] += -0.066226505; - } else { - result[0] += 0.0877003; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 148))) { - result[0] += 0.055681467; - } else { - result[0] += -0.06665366; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 280))) { - result[0] += 0.0809778; - } else { - result[0] += 0.1468879; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { - result[0] += 0.15474413; - } else { - result[0] += 0.20727856; - } - } else { - result[0] += 0.35305986; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 158))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { - result[0] += 0.07976506; - } else { - result[0] += 0.1597683; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - result[0] += 0.13799594; - } else { - result[0] += 0.23070168; - } - } - } - } - } - } - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 104))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.24169908; - } else { - result[0] += -0.107800476; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { - result[0] += 0.0015814465; - } else { - result[0] += -0.14862476; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 82))) { - result[0] += -0.2477706; - } else { - result[0] += -0.1950383; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { - result[0] += -0.17119522; - } else { - result[0] += -0.045236852; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 178))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { - result[0] += 0.025135875; - } else { - result[0] += -0.08694006; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { - result[0] += 0.029477531; - } else { - result[0] += -0.04847209; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 116))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - result[0] += 0.011227789; - } else { - result[0] += -0.09084964; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 82))) { - result[0] += -0.13153963; - } else { - result[0] += -0.18369597; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 152))) { - result[0] += -0.061374586; - } else { - result[0] += -0.111760534; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 276))) { - result[0] += -0.03116385; - } else { - result[0] += -0.085914634; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 142))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - result[0] += 0.0005968995; - } else { - result[0] += -0.08622521; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 70))) { - result[0] += -0.17008883; - } else { - result[0] += -0.11829443; - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 34))) { - result[0] += 0.0151279615; - } else { - result[0] += 0.037952267; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 58))) { - result[0] += 0.059345204; - } else { - result[0] += 0.11877208; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 206))) { - result[0] += -0.12507677; - } else { - result[0] += -0.025324045; - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 4))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 102))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 246))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - result[0] += 0.044818092; - } else { - result[0] += -0.01875449; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 148))) { - result[0] += -0.11071397; - } else { - result[0] += -0.059991468; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { - result[0] += 0.049274206; - } else { - result[0] += 0.012975462; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { - result[0] += -0.0189122; - } else { - result[0] += 0.060573917; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 148))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - result[0] += 0.02068209; - } else { - result[0] += -0.027593452; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 60))) { - result[0] += -0.008190083; - } else { - result[0] += 0.01192194; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 268))) { - result[0] += 0.02700766; - } else { - result[0] += 0.07031421; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 58))) { - result[0] += 0.09329746; - } else { - result[0] += 0.12738243; - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 336))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 128))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { - result[0] += 0.04464999; - } else { - result[0] += 0.0775819; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 62))) { - result[0] += -0.05400176; - } else { - result[0] += 0.0332122; - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 60))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { - result[0] += 0.06202929; - } else { - result[0] += 0.0011356722; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - result[0] += 0.093274996; - } else { - result[0] += 0.12987015; - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 124))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { - result[0] += 0.15837678; - } else { - result[0] += 0.3200806; - } - } else { - result[0] += 0.34475276; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { - result[0] += 0.09141225; - } else { - result[0] += 0.15360029; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - result[0] += 0.14422126; - } else { - result[0] += 0.18972561; - } - } - } - } - } - } - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 106))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 102))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 38))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { - result[0] += -0.22463696; - } else { - result[0] += -0.17236648; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { - result[0] += -0.15470378; - } else { - result[0] += -0.014106827; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 80))) { - result[0] += -0.104941204; - } else { - result[0] += -0.06797316; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { - result[0] += 0.0034419482; - } else { - result[0] += -0.04432465; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 220))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 66))) { - result[0] += -0.09611558; - } else { - result[0] += -0.05750701; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { - result[0] += 0.02356928; - } else { - result[0] += -0.045556568; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { - result[0] += -0.12279941; - } else { - result[0] += -0.16899776; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 124))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 130))) { - result[0] += -0.017295085; - } else { - result[0] += 0.031519514; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 156))) { - result[0] += -0.16593443; - } else { - result[0] += -0.110011; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 112))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { - result[0] += -0.010664171; - } else { - result[0] += -0.032727893; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 142))) { - result[0] += -0.07378478; - } else { - result[0] += -0.13850205; - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 134))) { - result[0] += 0.007132952; - } else { - result[0] += 0.033364255; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 62))) { - result[0] += 0.053948194; - } else { - result[0] += 0.105476275; - } - } - } else { - result[0] += -0.07528159; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 288))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 254))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 154))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - result[0] += 0.017853899; - } else { - result[0] += -0.021608775; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 190))) { - result[0] += 0.012284887; - } else { - result[0] += -0.0040449556; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { - result[0] += 0.027741803; - } else { - result[0] += 0.063282035; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 56))) { - result[0] += 0.08446597; - } else { - result[0] += 0.116814755; - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 200))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 70))) { - result[0] += 0.0801467; - } else { - result[0] += 0.00028896204; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 206))) { - result[0] += -0.0006912606; - } else { - result[0] += 0.038264047; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 152))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { - result[0] += -0.09579014; - } else { - result[0] += -0.011496059; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 124))) { - result[0] += 0.028059239; - } else { - result[0] += -0.029746488; - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 332))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 60))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { - result[0] += 0.04334253; - } else { - result[0] += 0.094181366; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { - result[0] += 0.010113914; - } else { - result[0] += 0.12312211; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 12))) { - result[0] += 0.008063223; - } else { - result[0] += 0.05981284; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 44))) { - result[0] += 0.055709254; - } else { - result[0] += 0.102501765; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 126))) { - result[0] += 0.19393599; - } else { - result[0] += 0.31415454; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - result[0] += 0.12953043; - } else { - result[0] += 0.17184684; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 152))) { - result[0] += 0.105829634; - } else { - result[0] += 0.05405576; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 342))) { - result[0] += 0.11501894; - } else { - result[0] += 0.14885037; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 142))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { - result[0] += -0.22360039; - } else { - result[0] += -0.16961168; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 62))) { - result[0] += -0.17449044; - } else { - result[0] += -0.13112317; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { - result[0] += -0.19438948; - } else { - result[0] += -0.07163301; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 58))) { - result[0] += -0.12211982; - } else { - result[0] += -0.1598212; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 104))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { - result[0] += -0.1360238; - } else { - result[0] += -0.08778509; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 172))) { - result[0] += -0.058016848; - } else { - result[0] += -0.01712372; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += -0.10796758; - } else { - result[0] += -0.1462996; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { - result[0] += -0.026550526; - } else { - result[0] += -0.09057524; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { - result[0] += -0.1045504; - } else { - result[0] += -0.06835998; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - result[0] += -0.16272134; - } else { - result[0] += -0.10771642; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 236))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { - result[0] += -0.1004568; - } else { - result[0] += -0.02189359; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 12))) { - result[0] += -0.11555834; - } else { - result[0] += -0.064784154; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 110))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 78))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 138))) { - result[0] += -0.09062075; - } else { - result[0] += -0.01616345; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 144))) { - result[0] += -0.046594307; - } else { - result[0] += -0.00848129; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 220))) { - result[0] += 0.048658125; - } else { - result[0] += 0.015779605; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += -0.003939248; - } else { - result[0] += -0.031186854; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 222))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 254))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 154))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { - result[0] += 0.0046192957; - } else { - result[0] += 0.02478583; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { - result[0] += 0.24694644; - } else { - result[0] += -0.104915835; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - result[0] += -0.0028355815; - } else { - result[0] += -0.03066038; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 288))) { - result[0] += -0.038436286; - } else { - result[0] += 0.03750279; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 78))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 186))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 318))) { - result[0] += -0.012060843; - } else { - result[0] += -0.0011414163; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 314))) { - result[0] += -0.008850611; - } else { - result[0] += 0.015718905; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 262))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 196))) { - result[0] += 0.05882001; - } else { - result[0] += 0.036198247; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 184))) { - result[0] += -0.065134026; - } else { - result[0] += -0.01131267; - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 312))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 122))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { - result[0] += 0.2215176; - } else { - result[0] += -0.10633468; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - result[0] += 0.1440095; - } else { - result[0] += 0.051038798; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 254))) { - result[0] += -0.014680609; - } else { - result[0] += 0.014171252; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 348))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 224))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 94))) { - result[0] += 0.056311846; - } else { - result[0] += 0.1058489; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 362))) { - result[0] += 0.037274715; - } else { - result[0] += 0.11873694; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 152))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 34))) { - result[0] += 0.11126106; - } else { - result[0] += 0.14717433; - } - } else { - result[0] += 0.26775053; - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 134))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - result[0] += -0.20885597; - } else { - result[0] += -0.16570954; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += -0.13637789; - } else { - result[0] += -0.1756009; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { - result[0] += -0.21495152; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { - result[0] += -0.15078692; - } else { - result[0] += -0.11473024; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - result[0] += -0.08377748; - } else { - result[0] += -0.020055672; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - result[0] += -0.12106979; - } else { - result[0] += -0.092178635; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 16))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { - result[0] += -0.07951072; - } else { - result[0] += -0.04535841; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 52))) { - result[0] += 0.015479415; - } else { - result[0] += -0.034244347; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 202))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 76))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { - result[0] += -0.10090798; - } else { - result[0] += -0.072621964; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - result[0] += -0.08769948; - } else { - result[0] += -0.020480268; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 52))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 158))) { - result[0] += -0.06127919; - } else { - result[0] += 0.0065026283; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { - result[0] += -0.06342202; - } else { - result[0] += -0.021188881; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 72))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 100))) { - result[0] += -0.04954914; - } else { - result[0] += 0.00018559814; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 44))) { - result[0] += 0.011565405; - } else { - result[0] += -0.011750548; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 146))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { - result[0] += 0.075431995; - } else { - result[0] += -0.005443739; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 62))) { - result[0] += 0.025297; - } else { - result[0] += 0.044371624; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 234))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 206))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 112))) { - result[0] += -0.012151074; - } else { - result[0] += 0.0095630875; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.19431071; - } else { - result[0] += 0.039510164; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - result[0] += -0.002731852; - } else { - result[0] += -0.02748406; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { - result[0] += -0.039663028; - } else { - result[0] += -0.002819218; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 294))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - result[0] += 0.012211016; - } else { - result[0] += 0.04506604; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 108))) { - result[0] += -8.271459e-05; - } else { - result[0] += -0.031217247; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 10))) { - result[0] += -0.027022822; - } else { - result[0] += 0.015010217; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - result[0] += 0.08849927; - } else { - result[0] += 0.04585655; - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 330))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 56))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { - result[0] += 0.015790751; - } else { - result[0] += 0.0709383; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 302))) { - result[0] += 0.014195338; - } else { - result[0] += 0.043147992; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 306))) { - result[0] += 0.027121753; - } else { - result[0] += 0.06687878; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { - result[0] += 0.06856555; - } else { - result[0] += 0.11084541; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 12))) { - result[0] += 0.023235757; - } else { - result[0] += 0.12572078; - } - } else { - result[0] += 0.24674617; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 362))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { - result[0] += 0.08457372; - } else { - result[0] += 0.04069808; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { - result[0] += 0.11791612; - } else { - result[0] += 0.07502523; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 142))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 28))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += -0.054601975; - } else { - result[0] += -0.17477903; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 48))) { - result[0] += -0.00046666022; - } else { - result[0] += -0.13438587; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 8))) { - result[0] += -0.07183619; - } else { - result[0] += -0.17046754; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { - result[0] += -0.10375444; - } else { - result[0] += -0.14129147; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - result[0] += -0.07520628; - } else { - result[0] += -0.01688747; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 14))) { - result[0] += -0.11755419; - } else { - result[0] += -0.08725922; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 28))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 2))) { - result[0] += -0.06106487; - } else { - result[0] += -0.09542205; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 202))) { - result[0] += 0.008344304; - } else { - result[0] += -0.03182082; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 76))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 64))) { - result[0] += -0.061504174; - } else { - result[0] += -0.13946581; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - result[0] += -0.13620088; - } else { - result[0] += -0.08506644; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 236))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 78))) { - result[0] += -0.10902418; - } else { - result[0] += -0.016639745; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 244))) { - result[0] += -0.10518378; - } else { - result[0] += -0.05821876; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 116))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 90))) { - result[0] += -0.06888045; - } else { - result[0] += -0.0376487; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 88))) { - result[0] += -0.012150378; - } else { - result[0] += 0.015397436; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 216))) { - result[0] += 0.039432753; - } else { - result[0] += 0.013889983; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += -0.0010314513; - } else { - result[0] += -0.02555494; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 232))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 206))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 200))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 80))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { - result[0] += 0.030333001; - } else { - result[0] += 0.17310323; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { - result[0] += 0.02483304; - } else { - result[0] += 0.0021163383; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { - result[0] += -0.017784828; - } else { - result[0] += -0.039406788; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 234))) { - result[0] += -0.0026142828; - } else { - result[0] += 0.03959627; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 284))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { - result[0] += 0.019107591; - } else { - result[0] += -0.011726064; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { - result[0] += 0.096682; - } else { - result[0] += 0.22430603; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { - result[0] += 0.012120399; - } else { - result[0] += -0.03992904; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - result[0] += 0.06968047; - } else { - result[0] += 0.04038763; - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 304))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - result[0] += 0.11028786; - } else { - result[0] += 0.040963266; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { - result[0] += 0.19369291; - } else { - result[0] += 0.0747213; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 78))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - result[0] += -0.057252772; - } else { - result[0] += -0.14311497; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 232))) { - result[0] += -0.0102287065; - } else { - result[0] += 0.016080577; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 330))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { - result[0] += 0.07653522; - } else { - result[0] += 0.13459362; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 130))) { - result[0] += 0.03455645; - } else { - result[0] += 0.06964803; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - result[0] += 0.09500605; - } else { - result[0] += 0.12430712; - } - } else { - result[0] += 0.22509204; - } - } - } - } - } - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 74))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 20))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 48))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - result[0] += -0.16930752; - } else { - result[0] += -0.1269749; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += -0.06503186; - } else { - result[0] += -0.12498716; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { - result[0] += -0.1927099; - } else { - result[0] += -0.08201694; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 38))) { - result[0] += -0.068333164; - } else { - result[0] += -0.13142657; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 12))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { - result[0] += 0.042084176; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.14493789; - } else { - result[0] += -0.09196159; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 98))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { - result[0] += 0.03108189; - } else { - result[0] += -0.067129865; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - result[0] += 0.0054919203; - } else { - result[0] += -0.05392629; - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 144))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 98))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { - result[0] += -0.21116984; - } else { - result[0] += -0.0651527; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { - result[0] += 0.116855875; - } else { - result[0] += 0.050092816; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 148))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 20))) { - result[0] += -0.08852083; - } else { - result[0] += -0.04572432; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 270))) { - result[0] += -0.0053127306; - } else { - result[0] += 0.024463532; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 32))) { - result[0] += -0.22384882; - } else { - result[0] += -0.093135744; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 16))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 74))) { - result[0] += -0.031588506; - } else { - result[0] += -0.08342363; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 116))) { - result[0] += 0.012553929; - } else { - result[0] += 0.058796555; - } - } - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 146))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 300))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 4))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 46))) { - result[0] += 0.08138124; - } else { - result[0] += 0.13923828; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 202))) { - result[0] += 0.021075254; - } else { - result[0] += 0.064726695; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 98))) { - result[0] += 0.016024219; - } else { - result[0] += -0.014299775; - } - } else { - result[0] += -0.04494404; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 86))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 82))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - result[0] += -0.03340462; - } else { - result[0] += -0.002975365; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 130))) { - result[0] += -0.13237435; - } else { - result[0] += -0.098908305; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - result[0] += -0.019236477; - } else { - result[0] += -0.033017647; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { - result[0] += 0.0251749; - } else { - result[0] += 0.0012758941; - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - result[0] += 0.016069753; - } else { - result[0] += -0.010248724; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - result[0] += -0.028139247; - } else { - result[0] += -0.06801506; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 164))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 238))) { - result[0] += 0.031309973; - } else { - result[0] += 0.07697828; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 258))) { - result[0] += 0.03671503; - } else { - result[0] += 0.0007923256; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 282))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { - result[0] += 0.02349461; - } else { - result[0] += -0.00087367493; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { - result[0] += 0.03977532; - } else { - result[0] += 0.096183844; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 290))) { - result[0] += -0.08533253; - } else { - result[0] += -0.06052809; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 238))) { - result[0] += -0.055827565; - } else { - result[0] += -0.0045159357; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 152))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { - result[0] += -0.17435847; - } else { - result[0] += -0.09712316; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 88))) { - result[0] += -0.121227026; - } else { - result[0] += -0.09049445; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 90))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 148))) { - result[0] += -0.07094587; - } else { - result[0] += -0.13034694; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - result[0] += -0.0074528246; - } else { - result[0] += -0.1171114; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - result[0] += -0.03242543; - } else { - result[0] += -0.09674471; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 136))) { - result[0] += -0.060467966; - } else { - result[0] += -0.08785518; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 28))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 54))) { - result[0] += -0.047304705; - } else { - result[0] += -0.070193194; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 238))) { - result[0] += -0.027161488; - } else { - result[0] += 0.0049651144; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 124))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - result[0] += -0.05672009; - } else { - result[0] += -0.133577; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { - result[0] += -0.034619935; - } else { - result[0] += -0.074495845; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { - result[0] += -0.016871883; - } else { - result[0] += 0.0060241153; - } - } else { - result[0] += -0.07992915; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 256))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 90))) { - result[0] += -0.059571445; - } else { - result[0] += -0.026049837; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 164))) { - result[0] += 0.0035159283; - } else { - result[0] += -0.047432255; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { - result[0] += -0.009207371; - } else { - result[0] += 0.026254697; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 28))) { - result[0] += -0.083354376; - } else { - result[0] += -0.025358835; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 234))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 158))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 246))) { - result[0] += 0.004427155; - } else { - result[0] += 0.030195776; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - result[0] += 0.043274168; - } else { - result[0] += 0.0072428077; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 92))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 164))) { - result[0] += -0.052554864; - } else { - result[0] += 0.0033617232; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { - result[0] += -0.037242856; - } else { - result[0] += -0.008167746; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 112))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.094510235; - } else { - result[0] += 0.01284566; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 26))) { - result[0] += 0.07989215; - } else { - result[0] += 0.17329922; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 76))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - result[0] += 0.058576096; - } else { - result[0] += -0.055972952; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { - result[0] += 0.21819083; - } else { - result[0] += 0.09395635; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 130))) { - result[0] += 0.01516776; - } else { - result[0] += 0.033623938; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 106))) { - result[0] += 0.043214235; - } else { - result[0] += 0.119155124; - } - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 38))) { - result[0] += 0.09584941; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - result[0] += 0.080481224; - } else { - result[0] += 0.03998689; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 318))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 126))) { - result[0] += 0.026821358; - } else { - result[0] += -0.031004164; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 202))) { - result[0] += 0.06501947; - } else { - result[0] += 0.19631971; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 166))) { - result[0] += 0.07131386; - } else { - result[0] += 0.028573189; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 314))) { - result[0] += 0.083791435; - } else { - result[0] += 0.17529456; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 124))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 22))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { - result[0] += -0.01719068; - } else { - result[0] += -0.064043045; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { - result[0] += -0.1791604; - } else { - result[0] += -0.13283774; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { - result[0] += -0.056413215; - } else { - result[0] += -0.14871566; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { - result[0] += -0.09970745; - } else { - result[0] += -0.010530283; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 34))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { - result[0] += -0.019443454; - } else { - result[0] += -0.09627802; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { - result[0] += -0.072033755; - } else { - result[0] += -0.029162338; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 56))) { - result[0] += -0.04072467; - } else { - result[0] += -0.07095223; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { - result[0] += 9.782781e-05; - } else { - result[0] += -0.028554544; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - result[0] += -0.10857904; - } else { - result[0] += -0.17578779; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 148))) { - result[0] += -0.05417463; - } else { - result[0] += -0.08070096; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 300))) { - result[0] += -0.06944825; - } else { - result[0] += -0.0195438; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 96))) { - result[0] += -0.032838166; - } else { - result[0] += -0.013822776; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 298))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 232))) { - result[0] += -0.034293767; - } else { - result[0] += -0.010428538; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 38))) { - result[0] += -0.008245598; - } else { - result[0] += 0.0061710696; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { - result[0] += -0.00058508094; - } else { - result[0] += -0.048794635; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { - result[0] += 0.014517671; - } else { - result[0] += 0.029143566; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 210))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 162))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 10))) { - result[0] += -0.051883638; - } else { - result[0] += -0.121891774; - } - } else { - result[0] += 0.006774173; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 108))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { - result[0] += -0.05183841; - } else { - result[0] += -0.023333719; - } - } else { - result[0] += 0.041755553; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 128))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += -0.021229211; - } else { - result[0] += 0.023228422; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 76))) { - result[0] += 0.0065774354; - } else { - result[0] += -0.0068485746; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 28))) { - result[0] += 0.049541216; - } else { - result[0] += 0.21175821; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 10))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 292))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.1843134; - } else { - result[0] += 0.011620779; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 280))) { - result[0] += -0.049533408; - } else { - result[0] += 0.019996347; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 272))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - result[0] += 0.07229465; - } else { - result[0] += 0.019160887; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - result[0] += 0.0056915763; - } else { - result[0] += 0.04898624; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 328))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 56))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 124))) { - result[0] += 0.010618052; - } else { - result[0] += 0.039828878; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - result[0] += 0.08413791; - } else { - result[0] += 0.05419789; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 128))) { - result[0] += 0.04146382; - } else { - result[0] += 0.08484615; - } - } else { - result[0] += 0.17491151; - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 146))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 30))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { - result[0] += -0.14357229; - } else { - result[0] += -0.06613964; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { - result[0] += -0.07836775; - } else { - result[0] += 0.008682779; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 20))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { - result[0] += -0.085223936; - } else { - result[0] += -0.10550176; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 84))) { - result[0] += -0.04918786; - } else { - result[0] += -0.0712194; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { - result[0] += -0.0485091; - } else { - result[0] += -0.07085284; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 154))) { - result[0] += -0.04310399; - } else { - result[0] += -0; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 168))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { - result[0] += -0.03786544; - } else { - result[0] += -0.06086583; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 24))) { - result[0] += -0.026798641; - } else { - result[0] += -0.0029160806; - } - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 66))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { - result[0] += -0.041853126; - } else { - result[0] += -0.067471944; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.10217434; - } else { - result[0] += -0.031203955; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { - result[0] += -0.020556139; - } else { - result[0] += 0.0036511559; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { - result[0] += -0.0052104336; - } else { - result[0] += 0.022399493; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 266))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { - result[0] += -0.050368812; - } else { - result[0] += -0.015018652; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 98))) { - result[0] += 0.050732918; - } else { - result[0] += -0.017873917; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { - result[0] += 0.16741006; - } else { - result[0] += 0.08613744; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { - result[0] += 0.02335409; - } else { - result[0] += -0.0018855215; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 234))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 200))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 172))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - result[0] += 0.0073416415; - } else { - result[0] += 0.029692546; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 146))) { - result[0] += -0.0061891414; - } else { - result[0] += -0.021139516; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 378))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 224))) { - result[0] += -0.018264985; - } else { - result[0] += -0.0061430032; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - result[0] += 0.024317574; - } else { - result[0] += -0.023863515; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 294))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 90))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.06744468; - } else { - result[0] += 0.2128239; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - result[0] += 0.013560965; - } else { - result[0] += -0.010385789; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 156))) { - result[0] += 0.036912605; - } else { - result[0] += 0.008162293; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { - result[0] += 0.07527434; - } else { - result[0] += 0.013765368; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 362))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 74))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 132))) { - result[0] += -0.040987097; - } else { - result[0] += 0.009021326; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { - result[0] += 0.026009029; - } else { - result[0] += 0.08366104; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 34))) { - result[0] += 0.064916424; - } else { - result[0] += 0.09441544; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 4))) { - result[0] += 0.08905909; - } else { - result[0] += 0.020877834; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 318))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 126))) { - result[0] += 0.02257039; - } else { - result[0] += -0.025986714; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 202))) { - result[0] += 0.05338145; - } else { - result[0] += 0.15790914; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 220))) { - result[0] += 0.018759893; - } else { - result[0] += 0.056597423; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 318))) { - result[0] += 0.06810229; - } else { - result[0] += 0.16054772; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 156))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 48))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 180))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { - result[0] += -0.06675883; - } else { - result[0] += -0.04909338; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 80))) { - result[0] += -0.045537602; - } else { - result[0] += -0.021407653; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 102))) { - result[0] += -0.036747407; - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 38))) { - result[0] += -0.0055241673; - } else { - result[0] += 0.0064017037; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { - result[0] += -0.1232142; - } else { - result[0] += -0.08944999; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { - result[0] += -0.103527024; - } else { - result[0] += -0.06685146; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 8))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 110))) { - result[0] += -0.087132424; - } else { - result[0] += -0.0155411465; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { - result[0] += 0.007360445; - } else { - result[0] += -0.042419024; - } - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 104))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 102))) { - result[0] += -0.02354617; - } else { - result[0] += 0.0067315414; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { - result[0] += -0.02450421; - } else { - result[0] += -0.058597732; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 32))) { - result[0] += 0.023825407; - } else { - result[0] += 0.18043439; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 8))) { - result[0] += -0.043022137; - } else { - result[0] += 0.029165251; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 222))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 208))) { - result[0] += -0.07280388; - } else { - result[0] += -0.046989717; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - result[0] += 0.016371334; - } else { - result[0] += -0.0062531256; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { - result[0] += -0.00938953; - } else { - result[0] += 0.011327556; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 98))) { - result[0] += -0.057005074; - } else { - result[0] += -0.011778428; - } - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 268))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 264))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 46))) { - result[0] += 0.030093227; - } else { - result[0] += 0.11276569; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - result[0] += 0.0048071328; - } else { - result[0] += -0.030283172; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { - result[0] += 0.07801795; - } else { - result[0] += 0.010139103; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 100))) { - result[0] += 0.015687512; - } else { - result[0] += 0.057320017; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 128))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 162))) { - result[0] += 0.16135572; - } else { - result[0] += 0.10743537; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 316))) { - result[0] += 0.0299298; - } else { - result[0] += -0.025237197; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 62))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { - result[0] += 0.01356712; - } else { - result[0] += 0.027944017; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 284))) { - result[0] += 0.09368516; - } else { - result[0] += 0.00735568; - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 212))) { - result[0] += 0.06613142; - } else { - result[0] += 0.030379802; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 296))) { - result[0] += 0.037492864; - } else { - result[0] += 0.08968493; - } - } - } else { - result[0] += 0.14285138; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 240))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 122))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 294))) { - result[0] += 0.10012319; - } else { - result[0] += 0.04607804; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { - result[0] += 0.00882749; - } else { - result[0] += 0.034421023; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 188))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { - result[0] += 0.020126143; - } else { - result[0] += 0.056154665; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 344))) { - result[0] += 0.06664233; - } else { - result[0] += 0.028045407; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 124))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 56))) { - result[0] += -0.08264944; - } else { - result[0] += -0.017300455; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 132))) { - result[0] += -0.09584019; - } else { - result[0] += -0.03871246; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 68))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 8))) { - result[0] += -0.027366564; - } else { - result[0] += 0.005001399; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { - result[0] += -0.015287772; - } else { - result[0] += -0.06076094; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - result[0] += -0.01928839; - } else { - result[0] += -0.05802682; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 14))) { - result[0] += -0.069114976; - } else { - result[0] += -0.050886452; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 168))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 58))) { - result[0] += -0.02828979; - } else { - result[0] += -0.049417857; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 54))) { - result[0] += 0.018446451; - } else { - result[0] += -0.017286932; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { - result[0] += -0; - } else { - result[0] += -0.1095063; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { - result[0] += -0.112913184; - } else { - result[0] += -0.041024093; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 236))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { - result[0] += -0.036767628; - } else { - result[0] += -0.0056491303; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 50))) { - result[0] += -0.05497813; - } else { - result[0] += -0.02247507; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 124))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 94))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { - result[0] += -0.04725503; - } else { - result[0] += -0.022799945; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - result[0] += -0.03265686; - } else { - result[0] += 0.0007802085; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 54))) { - result[0] += 0.008465467; - } else { - result[0] += 0.022264598; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { - result[0] += -0.008557126; - } else { - result[0] += -0.050682377; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 230))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 56))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 84))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { - result[0] += -0.23723964; - } else { - result[0] += -0.0674392; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 96))) { - result[0] += 0.017011134; - } else { - result[0] += -0.023735067; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 156))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { - result[0] += -0.021249495; - } else { - result[0] += 0.0016016475; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { - result[0] += 0.004315733; - } else { - result[0] += 0.029064924; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 30))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 18))) { - result[0] += 0.016252542; - } else { - result[0] += 0.07114914; - } - } else { - result[0] += 0.18867145; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 4))) { - result[0] += -0.043993976; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 52))) { - result[0] += 0.04012975; - } else { - result[0] += 0.0784916; - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 312))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 68))) { - result[0] += -0.1251441; - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 124))) { - result[0] += 0.1326; - } else { - result[0] += 0.07419976; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - result[0] += 0.070240654; - } else { - result[0] += 0.0142424395; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - result[0] += 0.008635036; - } else { - result[0] += 0.05565952; - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 104))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 326))) { - result[0] += 0.0021902567; - } else { - result[0] += 0.12992251; - } - } else { - result[0] += 0.14213897; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 342))) { - result[0] += 0.018112415; - } else { - result[0] += 0.048771314; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { - result[0] += 0.040432267; - } else { - result[0] += 0.06690516; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 158))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 48))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 4))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { - result[0] += -0.084643945; - } else { - result[0] += -0.05658353; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { - result[0] += -0.14270312; - } else { - result[0] += -0.096725866; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 10))) { - result[0] += -0.072900295; - } else { - result[0] += -0.050103154; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { - result[0] += 0.0026472413; - } else { - result[0] += -0.034194175; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 210))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 172))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - result[0] += -0.08699354; - } else { - result[0] += -0.029962752; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 258))) { - result[0] += -0.012024823; - } else { - result[0] += -0.040718153; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 38))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 278))) { - result[0] += 1.4413594e-05; - } else { - result[0] += -0.01927815; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 52))) { - result[0] += -0.004854994; - } else { - result[0] += 0.0106780855; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 88))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 28))) { - result[0] += -0.07230939; - } else { - result[0] += -0.029629577; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { - result[0] += -0.018495014; - } else { - result[0] += 0.019453213; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 90))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - result[0] += -0.01461862; - } else { - result[0] += 0.10096019; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - result[0] += -0.029421015; - } else { - result[0] += 9.543482e-05; - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 90))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 60))) { - result[0] += -0.021362139; - } else { - result[0] += -0.063853346; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 62))) { - result[0] += 0.00073504547; - } else { - result[0] += 0.019957332; - } - } - } else { - result[0] += 0.09791734; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 294))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 60))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 18))) { - result[0] += -0.005831562; - } else { - result[0] += -0.06499735; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 132))) { - result[0] += -0.0071320483; - } else { - result[0] += -0.055945735; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 58))) { - result[0] += 0.0037681882; - } else { - result[0] += 0.05542149; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 82))) { - result[0] += 0.011588092; - } else { - result[0] += -0.0017960105; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { - result[0] += -0.16209017; - } else { - result[0] += 0.019222626; - } - } else { - result[0] += 0.13090767; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 270))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 14))) { - result[0] += -0.030162899; - } else { - result[0] += -0.008859159; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 184))) { - result[0] += 0.028086677; - } else { - result[0] += 0.0010727844; - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 110))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - result[0] += 0.07769736; - } else { - result[0] += 0.114956; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 62))) { - result[0] += 0.026280258; - } else { - result[0] += 0.063517205; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 198))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 184))) { - result[0] += 0.028841827; - } else { - result[0] += 0.07754768; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 220))) { - result[0] += 0.023495244; - } else { - result[0] += 0.058550116; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 230))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { - result[0] += 0.006005039; - } else { - result[0] += 0.0330562; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 182))) { - result[0] += 0.02288873; - } else { - result[0] += 0.052012473; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 92))) { - result[0] += -0.023153892; - } else { - result[0] += 0.06317775; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 122))) { - result[0] += 0.09178891; - } else { - result[0] += 0.17105517; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 162))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 48))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 180))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { - result[0] += -0.051588424; - } else { - result[0] += -0.03475411; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { - result[0] += -0.0234789; - } else { - result[0] += 0.0038708851; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 214))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - result[0] += -0.008086841; - } else { - result[0] += -0.027431283; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 68))) { - result[0] += -0.001538304; - } else { - result[0] += 0.0073204334; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 56))) { - result[0] += -0.05180776; - } else { - result[0] += 0.005863587; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { - result[0] += -0.09820559; - } else { - result[0] += -0.042626407; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { - result[0] += -0.048510984; - } else { - result[0] += -0.071686305; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 58))) { - result[0] += -0.04765085; - } else { - result[0] += -0.010419096; - } - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 28))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { - result[0] += 0.06322976; - } else { - result[0] += -0.03347231; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 12))) { - result[0] += -0.17174184; - } else { - result[0] += -0.08866473; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { - result[0] += -0.019928426; - } else { - result[0] += -0.07354166; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { - result[0] += -0.020242494; - } else { - result[0] += -0.040445287; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - result[0] += -0.008752908; - } else { - result[0] += 0.09380058; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 176))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 198))) { - result[0] += 0.017689208; - } else { - result[0] += -0.0005958941; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 2))) { - result[0] += -0.0046095774; - } else { - result[0] += -0.038875457; - } - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 272))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { - result[0] += -0.046054542; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 132))) { - result[0] += -0.21343382; - } else { - result[0] += -0.087919936; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 50))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - result[0] += 0.012674006; - } else { - result[0] += 0.11185076; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 308))) { - result[0] += 0.041192535; - } else { - result[0] += 0.070588894; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 124))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 246))) { - result[0] += 0.0028114773; - } else { - result[0] += 0.019441808; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 90))) { - result[0] += 0.011045033; - } else { - result[0] += -0.022086669; - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 20))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 306))) { - result[0] += 0.071599804; - } else { - result[0] += 0.03955371; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 130))) { - result[0] += 0.010970717; - } else { - result[0] += 0.03211321; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 334))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 68))) { - result[0] += 0.059979778; - } else { - result[0] += 0.036047976; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 66))) { - result[0] += 0.10536631; - } else { - result[0] += 0.024781441; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 322))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 254))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 210))) { - result[0] += -0.052476525; - } else { - result[0] += -0.019687535; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - result[0] += 0.012497628; - } else { - result[0] += -0.011794323; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 362))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 120))) { - result[0] += 0.013189244; - } else { - result[0] += 0.03505987; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { - result[0] += 0.056928415; - } else { - result[0] += 0.022988325; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 126))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 22))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 56))) { - result[0] += -0.058271427; - } else { - result[0] += 0.0006807263; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { - result[0] += -0.10462822; - } else { - result[0] += -0.068169676; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 12))) { - result[0] += -0.0072206096; - } else { - result[0] += 0.0307555; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 50))) { - result[0] += -0.050685406; - } else { - result[0] += -0.023581887; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 296))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - result[0] += -0.005921537; - } else { - result[0] += -0.042507444; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += -0.052036572; - } else { - result[0] += -0.034811404; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - result[0] += 0.0008109753; - } else { - result[0] += -0.023712752; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { - result[0] += -0.081094466; - } else { - result[0] += -0.017712194; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { - result[0] += -0.09813706; - } else { - result[0] += -0.03229853; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 236))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 82))) { - result[0] += -0.028393775; - } else { - result[0] += -0.0027526347; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 290))) { - result[0] += -0.047838878; - } else { - result[0] += -0.026909484; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 18))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 12))) { - result[0] += -0.13414939; - } else { - result[0] += -0.05853174; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 8))) { - result[0] += -0.08385962; - } else { - result[0] += -0.029232157; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 112))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 94))) { - result[0] += -0.012187723; - } else { - result[0] += -0.00030637285; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - result[0] += -0.05476123; - } else { - result[0] += -0.002212812; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 234))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 62))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 88))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 56))) { - result[0] += 0.034629773; - } else { - result[0] += 0.090757936; - } - } else { - result[0] += -0.015683705; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 4))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { - result[0] += 0.0014823362; - } else { - result[0] += -0.11727782; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 328))) { - result[0] += -0.011885715; - } else { - result[0] += 0.06458332; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - result[0] += 0.009296111; - } else { - result[0] += -0.0022434364; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 390))) { - result[0] += 0.09074904; - } else { - result[0] += 0.022570081; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - result[0] += -0.010527322; - } else { - result[0] += 0.0112762675; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 170))) { - result[0] += -0.026221529; - } else { - result[0] += -0.0167725; - } - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 128))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 156))) { - result[0] += 0.134217; - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 242))) { - result[0] += 0.055591363; - } else { - result[0] += 0.09201013; - } - } - } else { - result[0] += -0.07688349; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 10))) { - result[0] += 0.008139768; - } else { - result[0] += 0.027951911; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - result[0] += 0.03482144; - } else { - result[0] += 0.12041791; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 330))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 56))) { - result[0] += 0.019060235; - } else { - result[0] += 0.03520998; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { - result[0] += 0.05164655; - } else { - result[0] += 0.10352349; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 166))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 62))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 4))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 6))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { - result[0] += -0.05586126; - } else { - result[0] += -0.0062299026; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { - result[0] += -0.08666905; - } else { - result[0] += -0.057932485; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 106))) { - result[0] += -0.037010778; - } else { - result[0] += -0.060017806; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { - result[0] += 0.008048224; - } else { - result[0] += -0.025649264; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 180))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - result[0] += -0.018599523; - } else { - result[0] += -0.063888915; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { - result[0] += -0.055958636; - } else { - result[0] += 0.0054995283; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 242))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { - result[0] += -0.015452884; - } else { - result[0] += -0.0031880846; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { - result[0] += 6.0049097e-06; - } else { - result[0] += 0.011630835; - } - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 80))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 104))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - result[0] += -0.012825543; - } else { - result[0] += 0.0061619915; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - result[0] += -0.035948735; - } else { - result[0] += -0.005659299; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 22))) { - result[0] += -0.0034629384; - } else { - result[0] += 0.11748111; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 14))) { - result[0] += -0.045964625; - } else { - result[0] += 0.027189994; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 220))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 288))) { - result[0] += -0.043134686; - } else { - result[0] += -0.008114795; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 136))) { - result[0] += 0.022731418; - } else { - result[0] += -0.0039848676; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { - result[0] += -0.0025353373; - } else { - result[0] += 0.017748738; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 68))) { - result[0] += -0.00209215; - } else { - result[0] += -0.026470816; - } - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 268))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += -0.0426252; - } else { - result[0] += -0.17894174; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 46))) { - result[0] += 0.034646492; - } else { - result[0] += 0.08830755; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - result[0] += 0.0036898192; - } else { - result[0] += 0.10474273; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 278))) { - result[0] += 0.0591173; - } else { - result[0] += 0.10110114; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 316))) { - result[0] += 0.017330313; - } else { - result[0] += -0.03201217; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 92))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { - result[0] += 0.020424707; - } else { - result[0] += 0.06761751; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 374))) { - result[0] += 0.0006410443; - } else { - result[0] += 0.025116405; - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 342))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 228))) { - result[0] += 0.002580609; - } else { - result[0] += 0.015501295; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 26))) { - result[0] += -0.021991013; - } else { - result[0] += 0.1534664; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 272))) { - result[0] += -0.004891763; - } else { - result[0] += 0.024440682; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 344))) { - result[0] += 0.04597979; - } else { - result[0] += 0.013181034; - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - result[0] += 0.08511517; - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 12))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { - result[0] += 0.017548429; - } else { - result[0] += -0.051454138; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 312))) { - result[0] += 0.0665479; - } else { - result[0] += 0.03926411; - } - } - } - } - } - } - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 52))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 4))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 120))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += -0.034856033; - } else { - result[0] += 0.035472337; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.082092844; - } else { - result[0] += -0.057657477; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 122))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 96))) { - result[0] += 0.042515777; - } else { - result[0] += -0.018864583; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 142))) { - result[0] += -0.057759553; - } else { - result[0] += -0.020723581; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 26))) { - result[0] += 0.054745015; - } else { - result[0] += 0.017524384; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 164))) { - result[0] += -0.03406791; - } else { - result[0] += -0.08434524; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 28))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 142))) { - result[0] += 0.017600825; - } else { - result[0] += -0.03500613; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { - result[0] += -0.046339255; - } else { - result[0] += -0.018127348; - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { - result[0] += -0.037658464; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { - result[0] += 0.0075216624; - } else { - result[0] += 0.0364659; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 134))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 102))) { - result[0] += 0.033616688; - } else { - result[0] += 0.083399825; - } - } else { - result[0] += 0.12963086; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.074191265; - } else { - result[0] += 0.007478449; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 42))) { - result[0] += 0.098748855; - } else { - result[0] += 0.030946104; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - result[0] += -0.026153265; - } else { - result[0] += -0.15095265; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 74))) { - result[0] += -0.018939627; - } else { - result[0] += -0.06403255; - } - } - } - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - result[0] += -0.021922093; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 134))) { - result[0] += -0.08688069; - } else { - result[0] += -0.033859696; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 86))) { - result[0] += -0.016842697; - } else { - result[0] += -0.0006648888; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 242))) { - result[0] += -0.023793818; - } else { - result[0] += -0.035974815; - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 302))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { - result[0] += 0.012690376; - } else { - result[0] += -0.0033918878; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { - result[0] += -0.005548475; - } else { - result[0] += -0.036335457; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 206))) { - result[0] += -0.0005064619; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 376))) { - result[0] += 0.03696196; - } else { - result[0] += 0.015889136; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 86))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 84))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 44))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 108))) { - result[0] += -0.019162795; - } else { - result[0] += -0.0054707523; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - result[0] += 0.0037607402; - } else { - result[0] += -0.009698882; - } - } - } else { - result[0] += -0.069641024; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 256))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { - result[0] += 0.045991335; - } else { - result[0] += 0.0303468; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { - result[0] += 0.018556785; - } else { - result[0] += -0.028926486; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 222))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 126))) { - result[0] += 0.014895338; - } else { - result[0] += -0.0033647448; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - result[0] += 0.031980734; - } else { - result[0] += 0.0069378153; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 28))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 30))) { - result[0] += -0.091816425; - } else { - result[0] += -0.053907175; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += -0.035644125; - } else { - result[0] += -0.08623389; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - result[0] += -0.038305838; - } else { - result[0] += -0.022053199; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 34))) { - result[0] += -0.041282233; - } else { - result[0] += -0.011869354; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 136))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 118))) { - result[0] += -0.008556094; - } else { - result[0] += -0.023536053; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { - result[0] += -0.023365794; - } else { - result[0] += -0.0052509154; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { - result[0] += -0.008722498; - } else { - result[0] += -0.08987408; - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { - result[0] += 0.032462854; - } else { - result[0] += -0.013032225; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 44))) { - result[0] += 0.0020141487; - } else { - result[0] += 0.016696744; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 172))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { - result[0] += 0.0063471817; - } else { - result[0] += 0.01982216; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { - result[0] += 0.00396263; - } else { - result[0] += -0.017814338; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 310))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 22))) { - result[0] += -0.042612687; - } else { - result[0] += -0.0046601035; - } - } else { - result[0] += -0.007730845; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 66))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 48))) { - result[0] += -0.039969202; - } else { - result[0] += -0.116455294; - } - } else { - result[0] += -0.02463033; - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 208))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 60))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 144))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 42))) { - result[0] += 0.022728456; - } else { - result[0] += -0.023419065; - } - } else { - result[0] += 0.06361882; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 74))) { - result[0] += -0.025523862; - } else { - result[0] += -0.051117957; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - result[0] += -0.006611984; - } else { - result[0] += -0.04705615; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += 0.010768503; - } else { - result[0] += -0.0008824992; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 298))) { - result[0] += -0.040758982; - } else { - result[0] += -0.008637625; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 130))) { - result[0] += 0.029029582; - } else { - result[0] += 0.08626362; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 302))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { - result[0] += 0.011424046; - } else { - result[0] += 0.04683126; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 72))) { - result[0] += -0.033311624; - } else { - result[0] += 0.076620966; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 264))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 106))) { - result[0] += -0.01699209; - } else { - result[0] += -0.032285534; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 104))) { - result[0] += 0.008964994; - } else { - result[0] += -0.01650845; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 60))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 316))) { - result[0] += 0.025268046; - } else { - result[0] += 0.055473793; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 66))) { - result[0] += 0.08008851; - } else { - result[0] += 0.01032548; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 326))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 138))) { - result[0] += 0.0054397047; - } else { - result[0] += 0.02308716; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { - result[0] += 0.08063211; - } else { - result[0] += 0.03532891; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 166))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 48))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 10))) { - result[0] += -0.04106638; - } else { - result[0] += -0.02793704; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { - result[0] += 0.022730371; - } else { - result[0] += -0.015745891; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 38))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += -0.056902584; - } else { - result[0] += -0.11115749; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 52))) { - result[0] += -0.0027035407; - } else { - result[0] += -0.0484084; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 64))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - result[0] += -0.06669981; - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 8))) { - result[0] += -0.058218993; - } else { - result[0] += -0.01607107; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { - result[0] += -0.004913365; - } else { - result[0] += 0.010270695; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 288))) { - result[0] += -0.013211744; - } else { - result[0] += -0.029026702; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 96))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { - result[0] += -0.017454801; - } else { - result[0] += 0.0241193; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.07878145; - } else { - result[0] += -0.021389706; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 150))) { - result[0] += 0.005198505; - } else { - result[0] += -0.025203273; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { - result[0] += -0.0036238257; - } else { - result[0] += 0.030112168; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 206))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { - result[0] += -0.014281181; - } else { - result[0] += 0.044825673; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { - result[0] += 0.00095986744; - } else { - result[0] += 0.014824574; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 78))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 248))) { - result[0] += -0.004780053; - } else { - result[0] += 0.023393003; - } - } else { - result[0] += -0.05524193; - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 240))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 264))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 200))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - result[0] += 0.008043467; - } else { - result[0] += -0.035642993; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - result[0] += 0.025907112; - } else { - result[0] += 0.0037340687; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 164))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 90))) { - result[0] += 0.0053138766; - } else { - result[0] += -0.019315336; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - result[0] += 0.012754604; - } else { - result[0] += -0.017428217; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 316))) { - result[0] += -0.018819278; - } else { - result[0] += 0.005015341; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 322))) { - result[0] += -0.046993464; - } else { - result[0] += -0.00060153054; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 334))) { - result[0] += 0.032121107; - } else { - result[0] += 0.07720378; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 206))) { - result[0] += -0.01248775; - } else { - result[0] += 0.021081064; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 220))) { - result[0] += -0.002822095; - } else { - result[0] += 0.010395023; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 24))) { - result[0] += -0.00028166902; - } else { - result[0] += 0.05817884; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 368))) { - result[0] += 0.033362225; - } else { - result[0] += 0.059052836; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 76))) { - result[0] += -0.022754088; - } else { - result[0] += 0.032660067; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 164))) { - result[0] += 0.05608514; - } else { - result[0] += 0.028797118; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 310))) { - result[0] += 0.0028316458; - } else { - result[0] += 0.023484427; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 308))) { - result[0] += 0.029160103; - } else { - result[0] += 0.06451139; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 212))) { - result[0] += -0.008154633; - } else { - result[0] += 0.02224008; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 102))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 66))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 34))) { - result[0] += -0.025441715; - } else { - result[0] += -0; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.09877158; - } else { - result[0] += -0.05378988; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { - result[0] += -0.02562209; - } else { - result[0] += -0.042403318; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { - result[0] += 0.02021136; - } else { - result[0] += -0.01660019; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 28))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - result[0] += 0.01719434; - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { - result[0] += -0.018652868; - } else { - result[0] += -0.072789736; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += -0.0050430708; - } else { - result[0] += -0.020398756; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 88))) { - result[0] += 0.0035772503; - } else { - result[0] += 0.04239833; - } - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 222))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { - result[0] += -0.008277955; - } else { - result[0] += -0.02545519; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { - result[0] += 0.035175905; - } else { - result[0] += -0.0068374695; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 274))) { - result[0] += 0.0034994117; - } else { - result[0] += -0.012190169; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { - result[0] += -0.0075547704; - } else { - result[0] += 0.01670718; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 100))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 46))) { - result[0] += -0.043555766; - } else { - result[0] += -0.09016201; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 176))) { - result[0] += -0.043037914; - } else { - result[0] += -2.9695482e-05; - } - } - } else { - result[0] += -0.024823932; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 230))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 6))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 120))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += 0.05400118; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { - result[0] += -0.07639449; - } else { - result[0] += -0.016434733; - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 148))) { - result[0] += -0.05022436; - } else { - result[0] += 0.00233022; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 126))) { - result[0] += -0.055654902; - } else { - result[0] += -0.13723731; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 104))) { - result[0] += -0.003547279; - } else { - result[0] += 0.0030764283; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { - result[0] += 0.016396696; - } else { - result[0] += 0.07968046; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - result[0] += 0.017225603; - } else { - result[0] += 0.037653413; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.077591464; - } else { - result[0] += 0.15476403; - } - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 238))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 298))) { - result[0] += -0.016144833; - } else { - result[0] += 0.032200087; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 282))) { - result[0] += 0.06538327; - } else { - result[0] += 0.10325643; - } - } - } else { - result[0] += -0.10228916; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 272))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - result[0] += -0.008800676; - } else { - result[0] += 0.009970379; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += -0.015078469; - } else { - result[0] += -0.03748675; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - result[0] += 0.034970764; - } else { - result[0] += -0.026505647; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { - result[0] += 0.037449267; - } else { - result[0] += 0.012034763; - } - } - } - } - } - } - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 160))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 64))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 32))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { - result[0] += -0.042211335; - } else { - result[0] += -0.0153662665; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.08464201; - } else { - result[0] += -0.039633475; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 18))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 32))) { - result[0] += -0.0023176766; - } else { - result[0] += 0.011839011; - } - } else { - result[0] += -0.024568608; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 114))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 50))) { - result[0] += -0.011506539; - } else { - result[0] += -0.041527543; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 30))) { - result[0] += 0.006311471; - } else { - result[0] += 0.096107006; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { - result[0] += -0.07760205; - } else { - result[0] += -0.024386143; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { - result[0] += 0.0042932476; - } else { - result[0] += -0.024321787; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 278))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 108))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 78))) { - result[0] += -0.00662189; - } else { - result[0] += 0.01475962; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 130))) { - result[0] += 0.03366244; - } else { - result[0] += 0.01162212; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 114))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 28))) { - result[0] += 0.030401085; - } else { - result[0] += 0.1034078; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 76))) { - result[0] += 0.06511612; - } else { - result[0] += 0.004562137; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 258))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 132))) { - result[0] += -0.017277284; - } else { - result[0] += -0.00040395462; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 274))) { - result[0] += -0.028246973; - } else { - result[0] += -0.043952625; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 212))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - result[0] += -0.045117795; - } else { - result[0] += -0.00930117; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 90))) { - result[0] += 0.013196451; - } else { - result[0] += -0.019043565; - } - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 310))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 42))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 84))) { - result[0] += -0.026572356; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 164))) { - result[0] += 0.051293522; - } else { - result[0] += -0; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 88))) { - result[0] += 0.08133901; - } else { - result[0] += 0.05325532; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 114))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 6))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { - result[0] += -0.015609048; - } else { - result[0] += -0.038890243; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 102))) { - result[0] += -0.008053626; - } else { - result[0] += 0.0021804231; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { - result[0] += 0.013441979; - } else { - result[0] += 0.0015576767; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 48))) { - result[0] += -0.012684277; - } else { - result[0] += 0.0018506636; - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 66))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 146))) { - result[0] += 0.1326864; - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { - result[0] += 0.0802758; - } else { - result[0] += 0.04734662; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 162))) { - result[0] += -0.0053350735; - } else { - result[0] += -0.040489998; - } - } else { - result[0] += 0.057940014; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 228))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 318))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 38))) { - result[0] += -0.00303559; - } else { - result[0] += 0.027338777; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 98))) { - result[0] += 0.021263791; - } else { - result[0] += 0.0065736147; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 220))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 294))) { - result[0] += 0.036004547; - } else { - result[0] += 0.00041290582; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { - result[0] += 0.003415088; - } else { - result[0] += 0.03065363; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 112))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 182))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 30))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { - result[0] += -0.061424643; - } else { - result[0] += -0.004366158; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 12))) { - result[0] += -0.029432973; - } else { - result[0] += 0.024416348; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 44))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 38))) { - result[0] += -0.028231425; - } else { - result[0] += 0.013277724; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { - result[0] += -0.05933566; - } else { - result[0] += -0.033664998; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 64))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 276))) { - result[0] += 0.0039472985; - } else { - result[0] += -0.010128739; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 96))) { - result[0] += -0.032275613; - } else { - result[0] += -0.007163971; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 148))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { - result[0] += -0.009553356; - } else { - result[0] += -0.024487672; - } - } else { - result[0] += -0.04257847; - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 106))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - result[0] += -0.017238883; - } else { - result[0] += -0.005016202; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - result[0] += -0.031886365; - } else { - result[0] += -0.013730754; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 64))) { - result[0] += 0.008240308; - } else { - result[0] += 0.03919262; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 38))) { - result[0] += 0.005747178; - } else { - result[0] += -0.058204617; - } - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { - result[0] += -0.055713423; - } else { - result[0] += -0.126311; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 160))) { - result[0] += -0.010013968; - } else { - result[0] += -0.03667828; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 62))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 90))) { - result[0] += -0.021773633; - } else { - result[0] += -0.060646553; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 76))) { - result[0] += -0.028524075; - } else { - result[0] += -0.0014835782; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 240))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 250))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 58))) { - result[0] += -0.009143605; - } else { - result[0] += 0.0004789403; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { - result[0] += 0.017569097; - } else { - result[0] += 0.06969844; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.053881604; - } else { - result[0] += 0.030100072; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 28))) { - result[0] += 0.07642787; - } else { - result[0] += 0.14859438; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 276))) { - result[0] += -0.0064661647; - } else { - result[0] += 0.0021556418; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { - result[0] += -0.042718846; - } else { - result[0] += 0.004742034; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 96))) { - result[0] += 0.0150559945; - } else { - result[0] += 0.02904155; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 196))) { - result[0] += 0.030410746; - } else { - result[0] += -0.00021071863; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 130))) { - result[0] += -0.0019750185; - } else { - result[0] += 0.010826028; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 92))) { - result[0] += -0.027783621; - } else { - result[0] += 0.040064003; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 34))) { - result[0] += 0.025106177; - } else { - result[0] += 0.04847647; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { - result[0] += -0.020649737; - } else { - result[0] += 0.047101017; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 110))) { - result[0] += 0.0425664; - } else { - result[0] += 0.010982556; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 320))) { - result[0] += 0.0027861602; - } else { - result[0] += 0.024253966; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - result[0] += 0.040306833; - } else { - result[0] += 0.019420182; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 250))) { - result[0] += -0.07389223; - } else { - result[0] += -0.018163418; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 166))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 76))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 32))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 134))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - result[0] += -0.033120375; - } else { - result[0] += -0.019771839; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - result[0] += 0.033069532; - } else { - result[0] += -0.024330398; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - result[0] += -0.00891195; - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 260))) { - result[0] += -0.06895655; - } else { - result[0] += -0.029440561; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 112))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { - result[0] += -0.008321228; - } else { - result[0] += -0.032401457; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 28))) { - result[0] += 0.01681263; - } else { - result[0] += -0.011843439; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { - result[0] += 0.009037083; - } else { - result[0] += 0.084029466; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { - result[0] += -0.039284047; - } else { - result[0] += 0.0024618404; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 30))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 138))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 284))) { - result[0] += -0.004148552; - } else { - result[0] += -0.028972754; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 182))) { - result[0] += -0.060149025; - } else { - result[0] += -0.023092858; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 246))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 124))) { - result[0] += -0.0048736627; - } else { - result[0] += 0.011900749; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 152))) { - result[0] += -0.027425656; - } else { - result[0] += -0.0049680914; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - result[0] += 0.018673597; - } else { - result[0] += -0.002092194; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 158))) { - result[0] += 0.025586123; - } else { - result[0] += 0.043332465; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 140))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 134))) { - result[0] += -0.009704775; - } else { - result[0] += 0.033585932; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 130))) { - result[0] += -0.04070072; - } else { - result[0] += -0.02585305; - } - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 132))) { - result[0] += 0.026704356; - } else { - result[0] += -0.0026301623; - } - } else { - result[0] += -0.083959244; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 116))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - result[0] += 0.00727385; - } else { - result[0] += -0.00793132; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 34))) { - result[0] += -0.0025618703; - } else { - result[0] += 0.03066427; - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 80))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 192))) { - result[0] += -0.0036948516; - } else { - result[0] += 0.014927791; - } - } else { - result[0] += 0.06770062; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 112))) { - result[0] += 0.021252338; - } else { - result[0] += 0.08691488; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 110))) { - result[0] += 0.030034242; - } else { - result[0] += 0.0126524735; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 84))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 146))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 294))) { - result[0] += 0.03448388; - } else { - result[0] += 0.012207389; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 82))) { - result[0] += -0.011361829; - } else { - result[0] += 0.010800744; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 374))) { - result[0] += -0.019659309; - } else { - result[0] += -0.010299957; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 222))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 128))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { - result[0] += -0.022616882; - } else { - result[0] += 0.0058049723; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 210))) { - result[0] += -0.05356568; - } else { - result[0] += -0.011999037; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - result[0] += 0.007046343; - } else { - result[0] += 0.04422299; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 200))) { - result[0] += -0.023396257; - } else { - result[0] += -0.015149494; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 110))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 4))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 38))) { - result[0] += -0.012656443; - } else { - result[0] += 0.04053043; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 30))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { - result[0] += -0.04405209; - } else { - result[0] += -0.07002534; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += 0.0050658057; - } else { - result[0] += -0.035065096; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 36))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 44))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { - result[0] += -0.016180787; - } else { - result[0] += 0.035941508; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 62))) { - result[0] += 0.0028223798; - } else { - result[0] += -0.028867302; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { - result[0] += -0.0008212989; - } else { - result[0] += -0.019350568; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 100))) { - result[0] += -0.012334666; - } else { - result[0] += -0.02596775; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { - result[0] += -0.03201983; - } else { - result[0] += -0.009261032; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.043002192; - } else { - result[0] += -0.004841908; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { - result[0] += -0.034105346; - } else { - result[0] += -0.001769832; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 18))) { - result[0] += -0.026078701; - } else { - result[0] += -0.0034125247; - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 68))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 204))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 72))) { - result[0] += -0.035768237; - } else { - result[0] += 0.007326287; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { - result[0] += -0.013266384; - } else { - result[0] += 0.0073778555; - } - } - } else { - result[0] += 0.04042886; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 262))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 314))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - result[0] += -0.031073779; - } else { - result[0] += 0.009902022; - } - } else { - result[0] += -0.10413245; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 304))) { - result[0] += -0.0015145864; - } else { - result[0] += 0.0044673397; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { - result[0] += 0.016366184; - } else { - result[0] += 0.06682373; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { - result[0] += 0.1125614; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 158))) { - result[0] += 0.015199658; - } else { - result[0] += -0.047179397; - } - } else { - result[0] += 0.07871269; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 304))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 140))) { - result[0] += 0.037271794; - } else { - result[0] += 0.055074245; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 350))) { - result[0] += 0.01456887; - } else { - result[0] += 0.043336138; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 62))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - result[0] += -0.011651535; - } else { - result[0] += 0.02064044; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 326))) { - result[0] += 0.040328953; - } else { - result[0] += 0.016403003; - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 12))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 122))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 262))) { - result[0] += -0.0107279355; - } else { - result[0] += 0.005842201; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { - result[0] += -0.038425323; - } else { - result[0] += 0.021354888; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { - result[0] += 0.045149498; - } else { - result[0] += -0; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 272))) { - result[0] += 0.0030525408; - } else { - result[0] += 0.016887192; - } - } - } - } - } - } - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 80))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 80))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 138))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 76))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { - result[0] += -0.022748465; - } else { - result[0] += -0.0015031536; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 58))) { - result[0] += 0.038551223; - } else { - result[0] += -0.02340021; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { - result[0] += -0.022547202; - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 64))) { - result[0] += 0.006482385; - } else { - result[0] += 0.043839242; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 84))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { - result[0] += -0.032270882; - } else { - result[0] += 0.041581474; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 74))) { - result[0] += -0.02748827; - } else { - result[0] += -0.06621357; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { - result[0] += -0.15229563; - } else { - result[0] += -0.056743264; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 290))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - result[0] += 0.0090522; - } else { - result[0] += -0.021619566; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { - result[0] += 0.011768992; - } else { - result[0] += 0.034092333; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 174))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 296))) { - result[0] += -0.0050243097; - } else { - result[0] += 0.0058637843; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 218))) { - result[0] += -0.022230322; - } else { - result[0] += -0; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 234))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 178))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - result[0] += -0.0040832614; - } else { - result[0] += -0.026436746; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 188))) { - result[0] += 0.0104423; - } else { - result[0] += -0.009707513; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 146))) { - result[0] += 0.021574568; - } else { - result[0] += -0.01677997; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - result[0] += -0.01483564; - } else { - result[0] += -0.044649884; - } - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 266))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 270))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 58))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { - result[0] += 0.027206311; - } else { - result[0] += -0.0112104425; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - result[0] += 0.002884076; - } else { - result[0] += -0.008946375; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 242))) { - result[0] += -0.021231398; - } else { - result[0] += -0.007831926; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - result[0] += -0.010156151; - } else { - result[0] += 0.006795832; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - result[0] += -0; - } else { - result[0] += 0.01808864; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 156))) { - result[0] += 0.0849195; - } else { - result[0] += 0.040363234; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 208))) { - result[0] += 0.018572433; - } else { - result[0] += -0.016821038; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 238))) { - result[0] += -0.013755165; - } else { - result[0] += 0.010804783; - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 124))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { - result[0] += 0.03828322; - } else { - result[0] += 0.0062655816; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 4))) { - result[0] += -0.039595883; - } else { - result[0] += -0.00876256; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 162))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { - result[0] += 0.028006807; - } else { - result[0] += 0.011207984; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 104))) { - result[0] += -0; - } else { - result[0] += 0.04828812; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 236))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 134))) { - result[0] += 0.019871203; - } else { - result[0] += -0.0020931112; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 164))) { - result[0] += -0.054860026; - } else { - result[0] += -0.016543666; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 126))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - result[0] += 0.017333688; - } else { - result[0] += -0.014761999; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { - result[0] += 0.0047273724; - } else { - result[0] += 0.018399872; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 90))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 8))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { - result[0] += -0.07626998; - } else { - result[0] += 0.020595873; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - result[0] += 0.0014158572; - } else { - result[0] += 0.039809644; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 26))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 28))) { - result[0] += -0.047608785; - } else { - result[0] += -0.08177885; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { - result[0] += -0.018627753; - } else { - result[0] += -0.03965402; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 152))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - result[0] += -0.016198985; - } else { - result[0] += -0.034763936; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { - result[0] += 0.0029702466; - } else { - result[0] += -0.010453141; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 80))) { - result[0] += 0.002711076; - } else { - result[0] += -0.040787876; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { - result[0] += -0.0030517352; - } else { - result[0] += -0.018486565; - } - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 204))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 296))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { - result[0] += -0.01466268; - } else { - result[0] += 0.0016207602; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - result[0] += 0.014294909; - } else { - result[0] += -0.00088145724; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 270))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 230))) { - result[0] += 0.017696586; - } else { - result[0] += 0.0033850037; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 96))) { - result[0] += 0.003758549; - } else { - result[0] += -0.008070237; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 108))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 38))) { - result[0] += -0.012763503; - } else { - result[0] += 0.015098961; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 260))) { - result[0] += -0.06455403; - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { - result[0] += -0.017203202; - } else { - result[0] += 0.0007284813; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 260))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 264))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - result[0] += -0.0004502135; - } else { - result[0] += -0.013600699; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 104))) { - result[0] += 0.0078685535; - } else { - result[0] += 0.052134164; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { - result[0] += -0.047534015; - } else { - result[0] += 0.021326948; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.056880232; - } else { - result[0] += 0.09747363; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 92))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 242))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 276))) { - result[0] += 0.023308432; - } else { - result[0] += 0.011319171; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += 0.051132478; - } else { - result[0] += 0.02103916; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 238))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 288))) { - result[0] += 0.006215774; - } else { - result[0] += -0.0061574043; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 330))) { - result[0] += 0.0041284435; - } else { - result[0] += 0.023122046; - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 288))) { - result[0] += 0.034480914; - } else { - result[0] += 0.01336052; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.056260873; - } else { - result[0] += 0.042514727; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 172))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { - result[0] += 0.02588587; - } else { - result[0] += -0.032997902; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 316))) { - result[0] += -0.0100605395; - } else { - result[0] += 0.020955171; - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 12))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 122))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 320))) { - result[0] += 0.0012241629; - } else { - result[0] += 0.010477929; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { - result[0] += -0.038784113; - } else { - result[0] += 0.019064387; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 308))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 128))) { - result[0] += 0.014094616; - } else { - result[0] += -0.0027222696; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { - result[0] += 0.043263398; - } else { - result[0] += 0.016619174; - } - } - } - } - } - } - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 164))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 106))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 52))) { - result[0] += -0.07700386; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { - result[0] += -0.027042255; - } else { - result[0] += 0.0029873038; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { - result[0] += -0.063133374; - } else { - result[0] += -0.0046702675; - } - } else { - result[0] += -0.097486384; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 110))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 258))) { - result[0] += -0.011726489; - } else { - result[0] += -0.058912832; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 90))) { - result[0] += -0.0659786; - } else { - result[0] += -0.02953866; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 58))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 90))) { - result[0] += 0.0242778; - } else { - result[0] += -0.0023044557; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 118))) { - result[0] += 0.029503396; - } else { - result[0] += -0.015513777; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 278))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 100))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { - result[0] += -0.003784867; - } else { - result[0] += 0.012548617; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 26))) { - result[0] += 0.0061970223; - } else { - result[0] += 0.023837453; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 330))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { - result[0] += 0.050173163; - } else { - result[0] += -0; - } - } else { - result[0] += -0.007900632; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 86))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 288))) { - result[0] += -0.027921408; - } else { - result[0] += -0.0038356972; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 126))) { - result[0] += -0.0057223137; - } else { - result[0] += 0.010252192; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 188))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 232))) { - result[0] += -0.0093339635; - } else { - result[0] += -0.002617002; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - result[0] += -0.01492499; - } else { - result[0] += -0.025125777; - } - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 308))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 42))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { - result[0] += 0.03968758; - } else { - result[0] += -0.001241333; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 6))) { - result[0] += -0.01191957; - } else { - result[0] += 0.002430247; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 116))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 54))) { - result[0] += -0; - } else { - result[0] += 0.08623359; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 168))) { - result[0] += -0.0005277793; - } else { - result[0] += 0.01962237; - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { - result[0] += -0.0047836783; - } else { - result[0] += -0.015227089; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { - result[0] += 0.008447981; - } else { - result[0] += 0.04540634; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 232))) { - result[0] += -0.022486782; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - result[0] += -0; - } else { - result[0] += -0.015179853; - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 16))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 4))) { - result[0] += -0.041306626; - } else { - result[0] += -0.008218481; - } - } else { - result[0] += 0.042096373; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 316))) { - result[0] += 0.022573106; - } else { - result[0] += 0.07205055; - } - } else { - result[0] += 0.028815215; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 318))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 38))) { - result[0] += -0.0054102; - } else { - result[0] += 0.019898819; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 98))) { - result[0] += 0.013574933; - } else { - result[0] += 0.0023076402; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 294))) { - result[0] += 0.023586577; - } else { - result[0] += -0.004840189; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { - result[0] += -0.0051436094; - } else { - result[0] += 0.020131659; - } - } - } - } - } - } - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 162))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 88))) { - result[0] += -0.01574367; - } else { - result[0] += 0.0011124586; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 46))) { - result[0] += -0.07613191; - } else { - result[0] += -0.01636207; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 74))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 24))) { - result[0] += 0.014280135; - } else { - result[0] += 0.037306037; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 24))) { - result[0] += -0.024091965; - } else { - result[0] += 0.0048261676; - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 124))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 202))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { - result[0] += -0.04113116; - } else { - result[0] += -0.061350565; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 50))) { - result[0] += -0.03143156; - } else { - result[0] += -0.0036942845; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 4))) { - result[0] += 0.024413232; - } else { - result[0] += -0.01646574; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { - result[0] += -0.016089493; - } else { - result[0] += 0.006952781; - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 144))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 274))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 20))) { - result[0] += -0.023807703; - } else { - result[0] += 0.0015813807; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 332))) { - result[0] += 0.029507706; - } else { - result[0] += -0.009649085; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 36))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 24))) { - result[0] += 0.0048679006; - } else { - result[0] += -0.043877617; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 116))) { - result[0] += 0.014281779; - } else { - result[0] += 0.047004733; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 164))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 92))) { - result[0] += -0.0021749143; - } else { - result[0] += -0.012218022; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { - result[0] += 0.0060172398; - } else { - result[0] += 0.033813428; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 228))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { - result[0] += -0.029434139; - } else { - result[0] += -0.014660637; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - result[0] += -0.008835907; - } else { - result[0] += 0.0022141964; - } - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 310))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 212))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 42))) { - result[0] += 0.023493351; - } else { - result[0] += -0.0012283614; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - result[0] += 0.002782393; - } else { - result[0] += 0.019489653; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 116))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 160))) { - result[0] += 0.07486535; - } else { - result[0] += -0.009162361; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 168))) { - result[0] += -0.0022064948; - } else { - result[0] += 0.018015308; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 128))) { - result[0] += -0.01574024; - } else { - result[0] += -0.0039653988; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 140))) { - result[0] += 0.020983376; - } else { - result[0] += 0.0030928531; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 238))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 244))) { - result[0] += -0.018868484; - } else { - result[0] += -0.012392565; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 234))) { - result[0] += 0.00805167; - } else { - result[0] += -0.011693968; - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 86))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 96))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 84))) { - result[0] += -0.038049206; - } else { - result[0] += -0.002771721; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 320))) { - result[0] += 0.012239266; - } else { - result[0] += 0.03609361; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { - result[0] += 0.027658317; - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 136))) { - result[0] += 0.051759947; - } else { - result[0] += 0.094818436; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 230))) { - result[0] += 0.005940085; - } else { - result[0] += 0.017185425; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 300))) { - result[0] += -0.02267663; - } else { - result[0] += -0.00748341; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 116))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 22))) { - result[0] += 0.005678715; - } else { - result[0] += 0.02415133; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 76))) { - result[0] += -0.026114738; - } else { - result[0] += 0.06968238; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 102))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 144))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { - result[0] += -0.042726804; - } else { - result[0] += -0.0135103455; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 162))) { - result[0] += -0.007055785; - } else { - result[0] += 0.014698033; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { - result[0] += -0.007809716; - } else { - result[0] += -0.025070379; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - result[0] += -0; - } else { - result[0] += 0.02416715; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 242))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - result[0] += -0.004747329; - } else { - result[0] += -0.030286456; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 38))) { - result[0] += 0.004049407; - } else { - result[0] += -0.003883451; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 280))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 44))) { - result[0] += 0.0048826905; - } else { - result[0] += 0.01918045; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { - result[0] += -0.010770427; - } else { - result[0] += 0.010454954; - } - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 126))) { - result[0] += -0.04999471; - } else { - result[0] += -0.1126702; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 6))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 8))) { - result[0] += -0.0024832468; - } else { - result[0] += 0.034831993; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 82))) { - result[0] += 0.018345565; - } else { - result[0] += -0.03647999; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 72))) { - result[0] += -0.025135571; - } else { - result[0] += -0; - } - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 158))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 42))) { - result[0] += -0.031185156; - } else { - result[0] += -0.011169832; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { - result[0] += -0.014554634; - } else { - result[0] += 0.05174926; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 134))) { - result[0] += 0.033395614; - } else { - result[0] += -0.015813565; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 274))) { - result[0] += 0.00033646842; - } else { - result[0] += 0.0078946445; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 114))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 154))) { - result[0] += 0.008154803; - } else { - result[0] += -0.014786914; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.06375181; - } else { - result[0] += 0.014291716; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 46))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += -0.0068405718; - } else { - result[0] += -0.034137234; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 288))) { - result[0] += 0.027196486; - } else { - result[0] += -0.029254882; - } - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 304))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 246))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 104))) { - result[0] += -0.00011478314; - } else { - result[0] += -0.008755667; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 106))) { - result[0] += 0.00041385577; - } else { - result[0] += 0.01698925; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 324))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - result[0] += -0.008199277; - } else { - result[0] += -0.01730309; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { - result[0] += 0.0039300146; - } else { - result[0] += 0.015076749; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 218))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 232))) { - result[0] += -0.0025358626; - } else { - result[0] += -0.014611224; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 16))) { - result[0] += 0.021280123; - } else { - result[0] += -0.008725259; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { - result[0] += 0.04309545; - } else { - result[0] += 0.0142627945; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 34))) { - result[0] += 0.024396539; - } else { - result[0] += 0.0048766937; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 170))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 16))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 28))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 70))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 68))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { - result[0] += -0.024164472; - } else { - result[0] += 0.008937138; - } - } else { - result[0] += 0.038625453; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 76))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 98))) { - result[0] += -0.04647706; - } else { - result[0] += -0.019371232; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 154))) { - result[0] += -0.007284378; - } else { - result[0] += -0.041731447; - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 14))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - result[0] += -0.049801502; - } else { - result[0] += 0.007428868; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { - result[0] += 0.061795365; - } else { - result[0] += 0.022740616; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - result[0] += 0.022688469; - } else { - result[0] += -0.016254207; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { - result[0] += 0.012587378; - } else { - result[0] += -0.019465229; - } - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 60))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 294))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 70))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += -0.016392779; - } else { - result[0] += 0.0076769763; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 132))) { - result[0] += -0.0070873285; - } else { - result[0] += -0.017376283; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 180))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 58))) { - result[0] += -0.008054349; - } else { - result[0] += 0.00049257686; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { - result[0] += 0.010894717; - } else { - result[0] += 0.0022375863; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 260))) { - result[0] += -0.018788002; - } else { - result[0] += 0.009024569; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 266))) { - result[0] += -0.0017262193; - } else { - result[0] += 0.015350415; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 172))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 194))) { - result[0] += 0.01940038; - } else { - result[0] += 0.0023542785; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { - result[0] += 0.003066004; - } else { - result[0] += -0.010361836; - } - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 290))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 104))) { - result[0] += -0.00023940679; - } else { - result[0] += -0.024299331; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 140))) { - result[0] += 0.018921724; - } else { - result[0] += -0.012017216; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 304))) { - result[0] += -0.009317097; - } else { - result[0] += 0.0070780194; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { - result[0] += 0.029872233; - } else { - result[0] += 0.0070561725; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - result[0] += -0.012874908; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 140))) { - result[0] += -0.03384575; - } else { - result[0] += -0.09957864; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 92))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - result[0] += 0.007720273; - } else { - result[0] += 0.050167393; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 288))) { - result[0] += 0.030057851; - } else { - result[0] += 0.012027963; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { - result[0] += -0.03683686; - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { - result[0] += -0.018220117; - } else { - result[0] += 0.0014780884; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 328))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 146))) { - result[0] += 0.02194166; - } else { - result[0] += -0.00030332725; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 84))) { - result[0] += -0.007878302; - } else { - result[0] += -0.016368117; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 104))) { - result[0] += -0.015569873; - } else { - result[0] += -0.00039696257; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 2))) { - result[0] += 0.022349734; - } else { - result[0] += 0.06912997; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - result[0] += -0.0011338064; - } else { - result[0] += -0.015420935; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 218))) { - result[0] += -0.0004335566; - } else { - result[0] += 0.012481102; - } - } - } - } - } - } - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 226))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 36))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 150))) { - result[0] += -0.008768398; - } else { - result[0] += -0.031017033; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - result[0] += -0.0019974862; - } else { - result[0] += -0.010033133; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 282))) { - result[0] += 0.0018652402; - } else { - result[0] += 0.028417757; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - result[0] += -0.02573219; - } else { - result[0] += 0.00014164243; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - result[0] += -0.012296872; - } else { - result[0] += 0.018667158; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 52))) { - result[0] += 0.0111978315; - } else { - result[0] += 0.00019159181; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 104))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - result[0] += -0.018547757; - } else { - result[0] += 0.00039879596; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { - result[0] += -0.025369791; - } else { - result[0] += -0.006152769; - } - } - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - result[0] += 0.0284749; - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 4))) { - result[0] += -0.12034156; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 90))) { - result[0] += 0.01555708; - } else { - result[0] += -0.04060086; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 28))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - result[0] += -0.00899754; - } else { - result[0] += -0.041497704; - } - } else { - result[0] += 0.012691744; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 38))) { - result[0] += -0.011100016; - } else { - result[0] += -0.065193616; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { - result[0] += -0.026525358; - } else { - result[0] += -0.007171394; - } - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 74))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { - result[0] += 0.017233951; - } else { - result[0] += 0.047289252; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 292))) { - result[0] += -0.020638349; - } else { - result[0] += 0.02203339; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 102))) { - result[0] += -0.028595468; - } else { - result[0] += 0.035886366; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - result[0] += 0.023079382; - } else { - result[0] += -0.0015904735; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { - result[0] += 0.01960182; - } else { - result[0] += 0.004229198; - } - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 110))) { - result[0] += 0.042509545; - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 78))) { - result[0] += 0.01140577; - } else { - result[0] += 0.03407; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 134))) { - result[0] += -0.002405419; - } else { - result[0] += -0.010636522; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { - result[0] += 0.012451133; - } else { - result[0] += -0.0039769937; - } - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { - result[0] += -0.01115589; - } else { - result[0] += 0.01320644; - } - } else { - result[0] += -0.013309436; - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 90))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 24))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 18))) { - result[0] += -0.054960348; - } else { - result[0] += -0.008308151; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 116))) { - result[0] += 0.040893547; - } else { - result[0] += -0.029395109; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 108))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 14))) { - result[0] += -0.011575009; - } else { - result[0] += 0.0045230165; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { - result[0] += -0.01649066; - } else { - result[0] += 0.0117938975; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 28))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 92))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 44))) { - result[0] += -0.017786995; - } else { - result[0] += 0.0030950578; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 38))) { - result[0] += -0.044471394; - } else { - result[0] += 0.022061352; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 52))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { - result[0] += -0.014604069; - } else { - result[0] += 0.016241714; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 42))) { - result[0] += 0.018912865; - } else { - result[0] += 0.0019412668; - } - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 306))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 64))) { - result[0] += -0.006083592; - } else { - result[0] += 0.046224184; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += 0.015739193; - } else { - result[0] += -0.022769345; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { - result[0] += 0.03625956; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 212))) { - result[0] += 0.00036286406; - } else { - result[0] += 0.009678096; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 240))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { - result[0] += -0.010175624; - } else { - result[0] += 0.0050961208; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 46))) { - result[0] += -0.0105675245; - } else { - result[0] += -0.026643395; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 226))) { - result[0] += -0; - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 86))) { - result[0] += 0.023571983; - } else { - result[0] += -0; - } - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 222))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 106))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 66))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 54))) { - result[0] += -0.07242959; - } else { - result[0] += -0.00901923; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 190))) { - result[0] += 0.0003989732; - } else { - result[0] += -0.014672895; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 56))) { - result[0] += -0.0052175657; - } else { - result[0] += 0.038002204; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 86))) { - result[0] += -0.030148897; - } else { - result[0] += -0.006241556; - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { - result[0] += -5.1738414e-05; - } else { - result[0] += 0.0063861213; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { - result[0] += 0.022810614; - } else { - result[0] += -0.016576035; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 234))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - result[0] += -0.0050715203; - } else { - result[0] += 0.005531682; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 238))) { - result[0] += -0.01301794; - } else { - result[0] += 0.00030336596; - } - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 180))) { - result[0] += -0.0094006555; - } else { - result[0] += 0.02063076; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - result[0] += 0.010461995; - } else { - result[0] += 0.0025512462; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 62))) { - result[0] += 0.010800346; - } else { - result[0] += 0.052793957; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 16))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { - result[0] += -0.02969619; - } else { - result[0] += -0.01354564; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 126))) { - result[0] += -0.0019324312; - } else { - result[0] += 0.018997952; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 228))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { - result[0] += 0.01697753; - } else { - result[0] += -0.0008568109; - } - } else { - result[0] += -0.0515616; - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 106))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 86))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { - result[0] += -0.036433112; - } else { - result[0] += -0.011925978; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 56))) { - result[0] += 0.03285799; - } else { - result[0] += 0.0010874722; - } - } - } else { - result[0] += -0.060846817; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 92))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 34))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += -0.012450369; - } else { - result[0] += -0.04985917; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 24))) { - result[0] += 0.0078088613; - } else { - result[0] += -0.00958295; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 168))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 24))) { - result[0] += -0.043195866; - } else { - result[0] += -0.014012662; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 26))) { - result[0] += -0.019311419; - } else { - result[0] += 0.007864608; - } - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 152))) { - result[0] += -0.012414033; - } else { - result[0] += 0.029668832; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - result[0] += -0.0030567853; - } else { - result[0] += 0.01868884; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 254))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { - result[0] += 0.026466904; - } else { - result[0] += -0.00078509795; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 256))) { - result[0] += -0.017463585; - } else { - result[0] += -0.004959351; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 90))) { - result[0] += 0.054318376; - } else { - result[0] += 0.016744314; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - result[0] += -0.008082875; - } else { - result[0] += 0.004647791; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 136))) { - result[0] += -0.019794006; - } else { - result[0] += 0.037679102; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - result[0] += 0.044813335; - } else { - result[0] += -0.0018291153; - } - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 68))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 76))) { - result[0] += -0.011341448; - } else { - result[0] += -0.060769796; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { - result[0] += -0.00012510354; - } else { - result[0] += 0.019792138; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 110))) { - result[0] += -0.005990359; - } else { - result[0] += 0.017361904; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - result[0] += 0.008660458; - } else { - result[0] += 0.0014748373; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 220))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 272))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 118))) { - result[0] += -0.0027236396; - } else { - result[0] += -0.029521126; - } - } else { - result[0] += -0; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += -0.058680933; - } else { - result[0] += 0.0022811424; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 206))) { - result[0] += -0.017125372; - } else { - result[0] += 0.008216227; - } - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { - result[0] += -0.01995611; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 30))) { - result[0] += 0.021725012; - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 220))) { - result[0] += 0.050011344; - } else { - result[0] += 0.014028007; - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 56))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 4))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 104))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 44))) { - result[0] += 0.021376295; - } else { - result[0] += 0.0036938381; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 274))) { - result[0] += -0.0331207; - } else { - result[0] += -0.005488401; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 8))) { - result[0] += 0.015083553; - } else { - result[0] += -0.016786192; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 30))) { - result[0] += -0.037689086; - } else { - result[0] += -0.019093947; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 130))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 8))) { - result[0] += 0.059979796; - } else { - result[0] += 0.0069023515; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 290))) { - result[0] += -0.024388736; - } else { - result[0] += -0; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 52))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 64))) { - result[0] += 0.030230714; - } else { - result[0] += -0.002469063; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - result[0] += -0.00685675; - } else { - result[0] += -0.019919405; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 40))) { - result[0] += 0.007914934; - } else { - result[0] += -0.0072328295; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 168))) { - result[0] += -0.023456104; - } else { - result[0] += -0.007590213; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 278))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 268))) { - result[0] += -0.00084484427; - } else { - result[0] += 0.008248358; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 98))) { - result[0] += 0.008069568; - } else { - result[0] += -0.01922637; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 58))) { - result[0] += -0.0034377978; - } else { - result[0] += 0.02219229; - } - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 114))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { - result[0] += -0.0008113146; - } else { - result[0] += -0.011595509; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 30))) { - result[0] += -0.00054977357; - } else { - result[0] += 0.0060809394; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { - result[0] += -0.013630775; - } else { - result[0] += 0.029257402; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { - result[0] += 0.01473201; - } else { - result[0] += 0.004033634; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 326))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - result[0] += 0.0005843948; - } else { - result[0] += -0.015655208; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 368))) { - result[0] += -0.007192479; - } else { - result[0] += 0.006543958; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 346))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { - result[0] += 0.01452767; - } else { - result[0] += 0.0055364254; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { - result[0] += 0.026855484; - } else { - result[0] += -0.005540358; - } - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 88))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 92))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 60))) { - result[0] += 0.04485644; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { - result[0] += -0.042145204; - } else { - result[0] += 7.136203e-05; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 32))) { - result[0] += -0.005530101; - } else { - result[0] += -0.041934248; - } - } else { - result[0] += -0.07572525; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 246))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 282))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - result[0] += 0.025418038; - } else { - result[0] += -0.0084718885; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 214))) { - result[0] += -0.019256549; - } else { - result[0] += 0.00998631; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 98))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { - result[0] += -0.054605898; - } else { - result[0] += -0.011759637; - } - } else { - result[0] += -0.046184976; - } - } - } - } - } - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 176))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 36))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { - result[0] += -0.022035845; - } else { - result[0] += 0.046122007; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { - result[0] += 0.0077522853; - } else { - result[0] += -0.02736899; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 128))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 120))) { - result[0] += -0.0018629817; - } else { - result[0] += 0.02747905; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += 0.02887457; - } else { - result[0] += -0.013981772; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 108))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { - result[0] += -0.01683332; - } else { - result[0] += -0.0050804014; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 52))) { - result[0] += 0.03178656; - } else { - result[0] += -0.0057087517; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 90))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 152))) { - result[0] += -0.014723192; - } else { - result[0] += -0.04805673; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 144))) { - result[0] += -0.017328734; - } else { - result[0] += -0.03817048; - } - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 28))) { - result[0] += -0.0028061648; - } else { - result[0] += 0.002789692; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { - result[0] += -0.025860507; - } else { - result[0] += -0.0057830475; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 330))) { - result[0] += 0.006883178; - } else { - result[0] += -0.010597587; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 66))) { - result[0] += -0.03156317; - } else { - result[0] += -0.00012491019; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 36))) { - result[0] += -0.049023475; - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { - result[0] += -0.015445833; - } else { - result[0] += -0.038803425; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 164))) { - result[0] += -0.0042772954; - } else { - result[0] += -0.02979202; - } - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 4))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 18))) { - result[0] += -0.105805; - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 156))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 218))) { - result[0] += -0.013860464; - } else { - result[0] += -0.047610518; - } - } else { - result[0] += 0.015889838; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 296))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 150))) { - result[0] += 0.0039272998; - } else { - result[0] += 0.04100207; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { - result[0] += 0.066427186; - } else { - result[0] += 0.019468648; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { - result[0] += -0.013523097; - } else { - result[0] += -0.035645615; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 302))) { - result[0] += -0.017095027; - } else { - result[0] += 0.008768201; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 84))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 196))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 88))) { - result[0] += -0.0047009015; - } else { - result[0] += 0.0095943585; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { - result[0] += -0.024147784; - } else { - result[0] += -0.007935442; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 100))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 360))) { - result[0] += -0.025299666; - } else { - result[0] += -0.015776524; - } - } else { - result[0] += -0.00817149; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { - result[0] += -0.013373877; - } else { - result[0] += 0.06051935; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 112))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 54))) { - result[0] += 0.013844582; - } else { - result[0] += -0.018000482; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 116))) { - result[0] += 0.025009016; - } else { - result[0] += 0.0011969743; - } - } - } - } - } - } - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 6))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 46))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 96))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { - result[0] += -0.005377132; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { - result[0] += 0.033936527; - } else { - result[0] += 0.06770065; - } - } else { - result[0] += -0; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 34))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 28))) { - result[0] += -0.016085489; - } else { - result[0] += 0.012783072; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 26))) { - result[0] += -0.038858794; - } else { - result[0] += -0.004968185; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 36))) { - result[0] += 0.04801505; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { - result[0] += 0.01772366; - } else { - result[0] += -0.026647871; - } - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 84))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { - result[0] += -0.02566973; - } else { - result[0] += 0.028348282; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 120))) { - result[0] += -0.043836653; - } else { - result[0] += -0.012338279; - } - } - } else { - result[0] += -0.10324136; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 36))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 2))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 12))) { - result[0] += -0.033641063; - } else { - result[0] += 0.00022004887; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 116))) { - result[0] += -0.044643078; - } else { - result[0] += -0.008762143; - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { - result[0] += 0.0021203114; - } else { - result[0] += -0.017186115; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 88))) { - result[0] += -0.019821104; - } else { - result[0] += 0.0021318241; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 184))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 222))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { - result[0] += 0.0009353072; - } else { - result[0] += -0.0056581837; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { - result[0] += -0.0009079633; - } else { - result[0] += 0.008418952; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 108))) { - result[0] += -0; - } else { - result[0] += -0.03857514; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { - result[0] += 0.0028833076; - } else { - result[0] += 0.030077396; - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 86))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 66))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 44))) { - result[0] += 0.0097111445; - } else { - result[0] += -0.004844829; - } - } else { - result[0] += 0.022152675; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 58))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 36))) { - result[0] += 0.012857464; - } else { - result[0] += 0.04939564; - } - } else { - result[0] += 0.0038712837; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 194))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 96))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 142))) { - result[0] += 0.022889886; - } else { - result[0] += -0.025423696; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 194))) { - result[0] += 0.0012433341; - } else { - result[0] += -0.027690545; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 42))) { - result[0] += 0.019493537; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { - result[0] += 0.04888856; - } else { - result[0] += 0.0047187754; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { - result[0] += -0.038507808; - } else { - result[0] += -0.0059399325; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 190))) { - result[0] += -0.00081904867; - } else { - result[0] += 0.023750668; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 62))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 180))) { - result[0] += 0.007010121; - } else { - result[0] += 0.00020330278; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { - result[0] += 0.022976872; - } else { - result[0] += -0.008908423; - } - } - } - } - } - } - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 16))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 20))) { - result[0] += 0.04029838; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { - result[0] += -0.0076732375; - } else { - result[0] += -0.044110678; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 140))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 138))) { - result[0] += 0.013151512; - } else { - result[0] += 0.04138301; - } - } else { - result[0] += -0.031063614; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 38))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 244))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 212))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 128))) { - result[0] += -0.01187767; - } else { - result[0] += -0.024798945; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { - result[0] += -0.014814547; - } else { - result[0] += -0.0693584; - } - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { - result[0] += -0.019700082; - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 12))) { - result[0] += 0.020823775; - } else { - result[0] += -0.0007175183; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 28))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { - result[0] += -0.04782397; - } else { - result[0] += -0.00023042485; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 18))) { - result[0] += 0.011482454; - } else { - result[0] += -0.025828404; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 68))) { - result[0] += 0.07567041; - } else { - result[0] += 0.03777515; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 36))) { - result[0] += 0.019245345; - } else { - result[0] += -0.007408004; - } - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { - result[0] += -0.0023852098; - } else { - result[0] += -0.01571614; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - result[0] += -0.0008245474; - } else { - result[0] += -0.01959662; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 52))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 160))) { - result[0] += 0.01735154; - } else { - result[0] += -0.01864585; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 104))) { - result[0] += -0.0019429872; - } else { - result[0] += 0.001837825; - } - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 86))) { - result[0] += 0.013189456; - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { - result[0] += 0.007143569; - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 44))) { - result[0] += 0.025064811; - } else { - result[0] += 0.0470048; - } - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 138))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 2))) { - result[0] += -0.028255967; - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 330))) { - result[0] += 0.0038780514; - } else { - result[0] += 0.027806832; - } - } else { - result[0] += -0.030956248; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 144))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 98))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { - result[0] += 0.0049754553; - } else { - result[0] += 0.07370368; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 54))) { - result[0] += 0.0024109604; - } else { - result[0] += -0.021205995; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 232))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - result[0] += 0.006072202; - } else { - result[0] += 0.021960644; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 60))) { - result[0] += -0.016412217; - } else { - result[0] += 0.02891737; - } - } - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 176))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { - result[0] += -0.013008319; - } else { - result[0] += 0.014044277; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 8))) { - result[0] += -0.04008707; - } else { - result[0] += 0.011214378; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 8))) { - result[0] += -0.021072047; - } else { - result[0] += -0.032819312; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 16))) { - result[0] += -0.014122496; - } else { - result[0] += -0.0027466726; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { - result[0] += 0.003206402; - } else { - result[0] += 0.023088232; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { - result[0] += -0.029293472; - } else { - result[0] += -0.00044284094; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 88))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { - result[0] += -0.037337154; - } else { - result[0] += -0.054280948; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - result[0] += 0.000117012045; - } else { - result[0] += -0.009735559; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 152))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 194))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { - result[0] += -0.024764068; - } else { - result[0] += 0.0052268724; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 64))) { - result[0] += 0.014035463; - } else { - result[0] += 0.042725824; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 78))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 118))) { - result[0] += -0.016555734; - } else { - result[0] += 0.0024362335; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 132))) { - result[0] += 0.013627543; - } else { - result[0] += 0.0015473928; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 168))) { - result[0] += 0.04074187; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { - result[0] += 0.029437272; - } else { - result[0] += 0.0063492656; - } - } - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 36))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { - result[0] += -0.007086392; - } else { - result[0] += -0.028627744; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { - result[0] += -0.01253838; - } else { - result[0] += 0.020658642; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 100))) { - result[0] += -0.015626702; - } else { - result[0] += -0.07547299; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - result[0] += -0.098590024; - } else { - result[0] += -0.016383274; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 156))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 262))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 238))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 148))) { - result[0] += -0.005975528; - } else { - result[0] += -0.017585494; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 134))) { - result[0] += -0.06808423; - } else { - result[0] += -0.022014553; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 292))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 128))) { - result[0] += -0.010686524; - } else { - result[0] += 0.011010774; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 284))) { - result[0] += 0.03166331; - } else { - result[0] += 0.0053174673; - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 82))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 258))) { - result[0] += -0.049204163; - } else { - result[0] += -0.0052781263; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 108))) { - result[0] += 0.007684131; - } else { - result[0] += -0.01196891; - } - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 250))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 168))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 16))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 120))) { - result[0] += -0; - } else { - result[0] += 0.025176907; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 158))) { - result[0] += -0.009614422; - } else { - result[0] += -0.025206959; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 216))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 82))) { - result[0] += -0.0041216165; - } else { - result[0] += 0.042651124; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 84))) { - result[0] += -0.038773373; - } else { - result[0] += 0.023100061; - } - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.006539114; - } else { - result[0] += -0.062450953; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - result[0] += -0.004460583; - } else { - result[0] += 0.0019909479; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - result[0] += -0.00021329732; - } else { - result[0] += 0.011201916; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { - result[0] += 0.01424145; - } else { - result[0] += 0.0014522666; - } - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 254))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 50))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - result[0] += 0.033733904; - } else { - result[0] += -0.0006381403; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 28))) { - result[0] += -0.015036389; - } else { - result[0] += 0.0023278003; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 2))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 280))) { - result[0] += -0.020510865; - } else { - result[0] += 0.02703706; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 52))) { - result[0] += 0.022050345; - } else { - result[0] += -0.0019060083; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 118))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 314))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 300))) { - result[0] += -0.019168613; - } else { - result[0] += 0.006976162; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 264))) { - result[0] += -0.015453433; - } else { - result[0] += 0.0013623396; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 60))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 206))) { - result[0] += 0.004762128; - } else { - result[0] += -0.006663674; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 66))) { - result[0] += 0.025200857; - } else { - result[0] += -0.005030225; - } - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 54))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 44))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 42))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 2))) { - result[0] += -0.012792389; - } else { - result[0] += 0.017205698; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - result[0] += -0.042184174; - } else { - result[0] += 0.004857573; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 66))) { - result[0] += -0.028872743; - } else { - result[0] += -0.006002301; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { - result[0] += -0.0013448104; - } else { - result[0] += -0.04370745; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 214))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 302))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { - result[0] += 0.0069149467; - } else { - result[0] += -0.0046409685; - } - } else { - result[0] += 0.02378996; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 62))) { - result[0] += 0.012926725; - } else { - result[0] += -0.008747402; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 50))) { - result[0] += -0.046444967; - } else { - result[0] += -0.018540045; - } - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 146))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 134))) { - result[0] += -0.09067112; - } else { - result[0] += -0.010558448; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { - result[0] += -0.023714645; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 194))) { - result[0] += -0.010494131; - } else { - result[0] += 0.01534995; - } - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 308))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 110))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - result[0] += -0.03064735; - } else { - result[0] += 0.0034591525; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { - result[0] += 0.026732335; - } else { - result[0] += 0.008400806; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { - result[0] += -0.005901861; - } else { - result[0] += 0.009023737; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - result[0] += -0.012533008; - } else { - result[0] += -0.0020645715; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { - result[0] += -0.013980149; - } else { - result[0] += 0.0019120906; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 54))) { - result[0] += -0.0057765585; - } else { - result[0] += 0.017128821; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { - result[0] += -0.018316587; - } else { - result[0] += -0.005805789; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { - result[0] += 0.03265365; - } else { - result[0] += -0.0010156564; - } - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 240))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 122))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 184))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 132))) { - result[0] += 0.014779656; - } else { - result[0] += -0.020928297; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 206))) { - result[0] += 0.07677117; - } else { - result[0] += -0.004149875; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 254))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 108))) { - result[0] += 0.0061775236; - } else { - result[0] += -0.002984956; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 192))) { - result[0] += -0.008194422; - } else { - result[0] += -0.05728069; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 192))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 280))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { - result[0] += 0.022544114; - } else { - result[0] += 0.0022025593; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { - result[0] += -0.004058608; - } else { - result[0] += 0.014417312; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 318))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 338))) { - result[0] += 0.0165982; - } else { - result[0] += 0.038578045; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { - result[0] += -0.015475737; - } else { - result[0] += 0.01142684; - } - } - } - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 20))) { - result[0] += -0.0928144; - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 180))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 46))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { - result[0] += -0.020081904; - } else { - result[0] += 0.002259192; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - result[0] += -0.07451444; - } else { - result[0] += -0.0156236235; - } - } - } else { - result[0] += -0.07355082; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 108))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 38))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { - result[0] += -0.037877556; - } else { - result[0] += 0.0013853526; - } - } else { - result[0] += -0.050581098; - } - } else { - result[0] += -0; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 178))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 176))) { - result[0] += -0.0043627243; - } else { - result[0] += -0.020921404; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { - result[0] += 0.01141603; - } else { - result[0] += -0.0036946; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 228))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 94))) { - result[0] += -0.027430529; - } else { - result[0] += 0.00075300987; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 74))) { - result[0] += -0.05395777; - } else { - result[0] += -0.013114258; - } - } - } - } - } - } - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 226))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 270))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 266))) { - result[0] += -0.0013336329; - } else { - result[0] += -0.012101962; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 96))) { - result[0] += -0.012478279; - } else { - result[0] += -0.042821463; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 108))) { - result[0] += -0.00018370214; - } else { - result[0] += 0.018782655; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 90))) { - result[0] += 0.0019190798; - } else { - result[0] += -0.0032492876; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 114))) { - result[0] += -0.041382577; - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { - result[0] += 0.019699065; - } else { - result[0] += -0.023365228; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 242))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 152))) { - result[0] += -0.014805789; - } else { - result[0] += -5.5329445e-05; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 276))) { - result[0] += 0.031025484; - } else { - result[0] += 0.00553753; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 282))) { - result[0] += 0.03033769; - } else { - result[0] += 0.053280126; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { - result[0] += 0.0059688077; - } else { - result[0] += -0.027727133; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 314))) { - result[0] += 0.009310781; - } else { - result[0] += -0.008495542; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 40))) { - result[0] += 0.038052257; - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 12))) { - result[0] += 0.012323047; - } else { - result[0] += -0.023586523; - } - } - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 144))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 130))) { - result[0] += 0.0018252156; - } else { - result[0] += -0.00692114; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 312))) { - result[0] += 0.032022245; - } else { - result[0] += -0.006053284; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 338))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 132))) { - result[0] += -0.019810393; - } else { - result[0] += -0.0073889843; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 36))) { - result[0] += 0.016521052; - } else { - result[0] += 0.0061617037; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 76))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 244))) { - result[0] += 0.012791178; - } else { - result[0] += -0.020304035; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 258))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { - result[0] += 0.009074631; - } else { - result[0] += -0.0016534543; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { - result[0] += 0.027765274; - } else { - result[0] += -0.0001559796; - } - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 234))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 258))) { - result[0] += -0.009072641; - } else { - result[0] += 0.008848451; - } - } else { - result[0] += -0.012117184; - } - } - } - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 6))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 148))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { - result[0] += 0.009714896; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 18))) { - result[0] += -0.035071608; - } else { - result[0] += -0.015023454; - } - } - } else { - result[0] += -0.073392645; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 90))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 132))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 38))) { - result[0] += -0.0044015315; - } else { - result[0] += 0.012927837; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 4))) { - result[0] += -0.0023782412; - } else { - result[0] += -0.021760104; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 42))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 112))) { - result[0] += -0.0147892255; - } else { - result[0] += 0.0073062084; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { - result[0] += -0.026250172; - } else { - result[0] += 0.0071073617; - } - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 0))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 162))) { - result[0] += 0.040685426; - } else { - result[0] += 0.0120480135; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 308))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 18))) { - result[0] += -0.06254916; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 98))) { - result[0] += -0.0067300186; - } else { - result[0] += -0.0331382; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { - result[0] += -0.019220524; - } else { - result[0] += 0.009100321; - } - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 206))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - result[0] += 0.009733505; - } else { - result[0] += -0.004346859; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - result[0] += 8.9312605e-05; - } else { - result[0] += -0.003453482; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 280))) { - result[0] += -0.0062282663; - } else { - result[0] += 0.0049122893; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - result[0] += -0.002204797; - } else { - result[0] += 0.0064123496; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 44))) { - result[0] += 0.06116004; - } else { - result[0] += 0.010365079; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 146))) { - result[0] += -0.018945072; - } else { - result[0] += -0.010433878; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 118))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 168))) { - result[0] += 0.01741377; - } else { - result[0] += 0.07594368; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 170))) { - result[0] += -0.0018027859; - } else { - result[0] += 0.016592477; - } - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { - result[0] += 0.043439865; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 82))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 58))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { - result[0] += -0.036944382; - } else { - result[0] += -0.012312688; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - result[0] += 0.043840874; - } else { - result[0] += -0.014361912; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - result[0] += -0.004802558; - } else { - result[0] += 0.007698638; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 170))) { - result[0] += -0.017537218; - } else { - result[0] += -0.009052196; - } - } - } - } - } - } - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 10))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 76))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 18))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 16))) { - result[0] += -0.011083994; - } else { - result[0] += -0.033235386; - } - } else { - result[0] += 0.03099644; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { - result[0] += -0.020493729; - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 102))) { - result[0] += 0.03927904; - } else { - result[0] += -0.0013705323; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 172))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 80))) { - result[0] += 0.06984255; - } else { - result[0] += 0.024543915; - } - } else { - result[0] += 0.008717417; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - result[0] += -0.034200173; - } else { - result[0] += 0.020928657; - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 40))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 34))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 30))) { - result[0] += -0.018539859; - } else { - result[0] += 0.006696008; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { - result[0] += 0.0006399334; - } else { - result[0] += 0.023955312; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 46))) { - result[0] += 0.0061688256; - } else { - result[0] += 0.044047784; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 102))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 156))) { - result[0] += -0.016056351; - } else { - result[0] += 0.0054231877; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { - result[0] += -0.0025266295; - } else { - result[0] += 0.018242473; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 270))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 104))) { - result[0] += -0.017715478; - } else { - result[0] += -0.04547394; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 152))) { - result[0] += 0.013087104; - } else { - result[0] += -0.029149592; - } - } - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 264))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 52))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 188))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 22))) { - result[0] += 0.0041657714; - } else { - result[0] += -0.022834495; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { - result[0] += 0.016674465; - } else { - result[0] += 0.032325614; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 176))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 154))) { - result[0] += -0.017698428; - } else { - result[0] += 0.03289035; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { - result[0] += 0.0020410677; - } else { - result[0] += -0.020820608; - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 74))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 70))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 50))) { - result[0] += -4.355009e-05; - } else { - result[0] += -0.032748487; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 78))) { - result[0] += 0.035664223; - } else { - result[0] += -0.0039311326; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - result[0] += -0.0035012837; - } else { - result[0] += 0.02525419; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - result[0] += -0.0004375557; - } else { - result[0] += -0.032447618; - } - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 68))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 284))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 76))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 202))) { - result[0] += 0.06691403; - } else { - result[0] += 0.026116902; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 228))) { - result[0] += -0.0024726556; - } else { - result[0] += 0.0035805923; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 112))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { - result[0] += 0.008209762; - } else { - result[0] += 0.046135128; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 142))) { - result[0] += -0.0095024295; - } else { - result[0] += 0.0053031766; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 156))) { - result[0] += 0.02656824; - } else { - result[0] += -0.00404437; - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 142))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 40))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 46))) { - result[0] += 0.06587588; - } else { - result[0] += 0.0105197765; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 70))) { - result[0] += 0.0041911616; - } else { - result[0] += -0.012468158; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { - result[0] += 0.009541268; - } else { - result[0] += 0.058547605; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 152))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 290))) { - result[0] += -0.021359572; - } else { - result[0] += -0.004128001; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 162))) { - result[0] += 0.011844903; - } else { - result[0] += 0.023288697; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 316))) { - result[0] += -7.127906e-05; - } else { - result[0] += 0.0055441954; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 44))) { - result[0] += -0.033032846; - } else { - result[0] += 0.008700618; - } - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 100))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 28))) { - result[0] += -0.020282162; - } else { - result[0] += -0; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 252))) { - result[0] += 0.0372028; - } else { - result[0] += 0.020493207; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 114))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 192))) { - result[0] += 0.0023391566; - } else { - result[0] += -0.012876572; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { - result[0] += 0.028000802; - } else { - result[0] += 0.008097276; - } - } - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 12))) { - result[0] += -0.030859623; - } else { - result[0] += 0.059427258; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 296))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 12))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 158))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { - result[0] += 0.023903606; - } else { - result[0] += -0.01342114; - } - } else { - result[0] += -0.03418419; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 52))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { - result[0] += -0.014690627; - } else { - result[0] += -0.046283614; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { - result[0] += -0.018795181; - } else { - result[0] += -0.005358623; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 162))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 300))) { - result[0] += -0.013268234; - } else { - result[0] += 0.011121328; - } - } else { - result[0] += -0.0279394; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 176))) { - result[0] += 0.03626206; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 326))) { - result[0] += -0.0057000555; - } else { - result[0] += 0.0147212865; - } - } - } - } - } else { - result[0] += -0.06643397; - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 6))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 48))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 126))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { - result[0] += -0.0010974932; - } else { - result[0] += 0.020823766; - } - } else { - result[0] += 0.0449019; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 34))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - result[0] += -0.008742712; - } else { - result[0] += 0.016725272; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 36))) { - result[0] += -0.026789738; - } else { - result[0] += 0.005821276; - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { - result[0] += -0.033222493; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 40))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { - result[0] += -0.0058179474; - } else { - result[0] += 0.04347635; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 112))) { - result[0] += -0.015091679; - } else { - result[0] += 0.0024027491; - } - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 232))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 32))) { - result[0] += 0.0399952; - } else { - result[0] += -0.00027889368; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 62))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { - result[0] += -0.01870556; - } else { - result[0] += 0.0057035587; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { - result[0] += 0.02278183; - } else { - result[0] += -0.008421278; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { - result[0] += -0.042912465; - } else { - result[0] += -0; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 242))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 160))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 194))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 110))) { - result[0] += 0.003376493; - } else { - result[0] += 0.011122108; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { - result[0] += 0.03045251; - } else { - result[0] += 0.012394785; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { - result[0] += -0.027083784; - } else { - result[0] += 0.0126619935; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { - result[0] += 0.008825673; - } else { - result[0] += 0.044847894; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 144))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 10))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { - result[0] += 0.023729367; - } else { - result[0] += -0.016748834; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 64))) { - result[0] += -0.020931631; - } else { - result[0] += -0.05402078; - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 30))) { - result[0] += 0.020663498; - } else { - result[0] += 0.00616918; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 20))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 122))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 12))) { - result[0] += -0.017938469; - } else { - result[0] += 0.03910426; - } - } else { - result[0] += 0.0053874785; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 14))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.022196533; - } else { - result[0] += -0.00451347; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - result[0] += 0.00037458728; - } else { - result[0] += 0.01688151; - } - } - } - } - } - } - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 254))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 90))) { - result[0] += 0.027586067; - } else { - result[0] += -0.029394219; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 212))) { - result[0] += -0.008463705; - } else { - result[0] += -0.028911496; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 194))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 154))) { - result[0] += -0.0010301826; - } else { - result[0] += 0.0035315962; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { - result[0] += -0.0011929054; - } else { - result[0] += -0.00760438; - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 62))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 110))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - result[0] += 0.011337014; - } else { - result[0] += 0.0015774952; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 86))) { - result[0] += -0.0043284963; - } else { - result[0] += 0.0071344683; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 256))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 206))) { - result[0] += 0.0046818857; - } else { - result[0] += 0.01623973; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 332))) { - result[0] += -0.025880137; - } else { - result[0] += 0.00024090149; - } - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 44))) { - result[0] += 0.062326457; - } else { - result[0] += -0.012353135; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 154))) { - result[0] += -0.01557938; - } else { - result[0] += -0.007663093; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 114))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 156))) { - result[0] += 0.07925903; - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 160))) { - result[0] += -0.022038413; - } else { - result[0] += 0.010596351; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 118))) { - result[0] += 0.009866733; - } else { - result[0] += 0.04229749; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 78))) { - result[0] += -0.027659763; - } else { - result[0] += -0.0035324506; - } - } - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 168))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 130))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 84))) { - result[0] += -0.01782277; - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 212))) { - result[0] += -0; - } else { - result[0] += -0.01930575; - } - } - } else { - result[0] += 0.0040967353; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 402))) { - result[0] += -0.021184763; - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 140))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - result[0] += -0.0055727926; - } else { - result[0] += 0.009987557; - } - } else { - result[0] += -0.016817084; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 128))) { - result[0] += -0.011004246; - } else { - result[0] += 0.0059432825; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { - result[0] += -0.0151095195; - } else { - result[0] += -0.0033481915; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 92))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { - result[0] += 0.0145778125; - } else { - result[0] += -0.0010819718; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 182))) { - result[0] += -0.01369258; - } else { - result[0] += 0.010227516; - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 238))) { - result[0] += -0.009665318; - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 180))) { - result[0] += 0.026355088; - } else { - result[0] += -0.0077078748; - } - } - } - } - } - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 230))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 268))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 52))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { - result[0] += -0.0125539; - } else { - result[0] += 0.014108579; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - result[0] += 0.021760508; - } else { - result[0] += -0.019894226; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 268))) { - result[0] += -0.029441832; - } else { - result[0] += -0.010534534; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 112))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 68))) { - result[0] += -0.031425703; - } else { - result[0] += -0.019633126; - } - } else { - result[0] += -0.01066619; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { - result[0] += -0.0006799324; - } else { - result[0] += -0.027934993; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - result[0] += -0.015189849; - } else { - result[0] += -0.0026831003; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 236))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 166))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - result[0] += -1.1786798e-05; - } else { - result[0] += 0.02334137; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 290))) { - result[0] += 0.0047730226; - } else { - result[0] += 0.01909187; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 284))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 246))) { - result[0] += 0.024219202; - } else { - result[0] += 0.04463927; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 28))) { - result[0] += -0; - } else { - result[0] += 0.057420652; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 240))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 202))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 296))) { - result[0] += 0.005736035; - } else { - result[0] += -0.018226644; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { - result[0] += -0.014074256; - } else { - result[0] += -0.00035597832; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 250))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 218))) { - result[0] += -0.00022620684; - } else { - result[0] += 0.021930484; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 212))) { - result[0] += -0.00031011814; - } else { - result[0] += 0.018323947; - } - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - result[0] += 0.029248917; - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 292))) { - result[0] += -0.014892972; - } else { - result[0] += 0.017006356; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 294))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 252))) { - result[0] += 0.0011380663; - } else { - result[0] += 0.0074514025; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { - result[0] += -0.027498512; - } else { - result[0] += -0.00029022736; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 256))) { - result[0] += 0.034170583; - } else { - result[0] += 0.022245234; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 328))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 324))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 298))) { - result[0] += -0.027805215; - } else { - result[0] += -0.017054409; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 294))) { - result[0] += -0.016550997; - } else { - result[0] += -0.0043134186; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 346))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - result[0] += -0.00029596835; - } else { - result[0] += -0.030671025; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - result[0] += 0.022249522; - } else { - result[0] += -0.014686073; - } - } - } - } - } - } - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 400))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 116))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 16))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { - result[0] += -0.014927131; - } else { - result[0] += -0.0050526834; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - result[0] += 0.0031292983; - } else { - result[0] += -0.0021366577; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 14))) { - result[0] += -0.019448534; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { - result[0] += 0.031444933; - } else { - result[0] += 0.010930794; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 36))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 152))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 20))) { - result[0] += -0.017589707; - } else { - result[0] += -0.00019221655; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - result[0] += -0.020101383; - } else { - result[0] += -0; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 170))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - result[0] += 0.0022330189; - } else { - result[0] += 0.012290857; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 14))) { - result[0] += -0.0006709326; - } else { - result[0] += 0.0086814575; - } - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 392))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 156))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 154))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0; - } else { - result[0] += 0.067005776; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 102))) { - result[0] += 0.07170707; - } else { - result[0] += -0.014048599; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 166))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 164))) { - result[0] += -0.030913204; - } else { - result[0] += -0.007337024; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 160))) { - result[0] += 0.065959476; - } else { - result[0] += 0.0030737682; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 176))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 112))) { - result[0] += -0.016045328; - } else { - result[0] += -0.008878782; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 118))) { - result[0] += 0.00972886; - } else { - result[0] += 0.03835055; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 130))) { - result[0] += -0.009170124; - } else { - result[0] += 0.0057587116; - } - } - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { - result[0] += -0.015629305; - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 142))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 140))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - result[0] += -0.004336093; - } else { - result[0] += 0.015490188; - } - } else { - result[0] += -0.01513085; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { - result[0] += 0.00940407; - } else { - result[0] += 0.032563876; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 178))) { - result[0] += -0.01413842; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 244))) { - result[0] += 0.0270089; - } else { - result[0] += -0.00225509; - } - } else { - result[0] += -0.008365121; - } - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 110))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 168))) { - result[0] += -0.0023444246; - } else { - result[0] += 0.007994309; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { - result[0] += 0.00061645266; - } else { - result[0] += -0.04576966; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 62))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 20))) { - result[0] += 0.01534583; - } else { - result[0] += 0.039503228; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 96))) { - result[0] += 0.01222286; - } else { - result[0] += -0.0050080977; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 92))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { - result[0] += -0.00016676041; - } else { - result[0] += 0.045995515; - } - } else { - result[0] += -0.019292504; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 270))) { - result[0] += -0.0106395595; - } else { - result[0] += 0.024322292; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 196))) { - result[0] += 0.0038009014; - } else { - result[0] += -0.006002572; - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 112))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 104))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 90))) { - result[0] += 0.00059230387; - } else { - result[0] += 0.02190473; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - result[0] += -0.0012604637; - } else { - result[0] += -0.025116524; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { - result[0] += 0.01818419; - } else { - result[0] += -0.0041689784; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 54))) { - result[0] += -0.0046932534; - } else { - result[0] += 0.012534521; - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 100))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 212))) { - result[0] += -0.0053321766; - } else { - result[0] += 3.2746604e-05; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 160))) { - result[0] += 0.04412394; - } else { - result[0] += -0.019410737; - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 148))) { - result[0] += -0.015239002; - } else { - result[0] += -0.007977055; - } - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 134))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 150))) { - result[0] += 0.026527822; - } else { - result[0] += -0.015368457; - } - } else { - result[0] += -0.05488323; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 240))) { - result[0] += -0.07350321; - } else { - result[0] += -0.01900329; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 12))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 96))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { - result[0] += 0.0044366815; - } else { - result[0] += 0.05480617; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { - result[0] += -0.011877809; - } else { - result[0] += 0.01703535; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 106))) { - result[0] += -0.02123229; - } else { - result[0] += -0.07076668; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 242))) { - result[0] += -0.0073331865; - } else { - result[0] += -0.039508924; - } - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 32))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 108))) { - result[0] += 0.028880654; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 8))) { - result[0] += -0.015295302; - } else { - result[0] += 0.0052049416; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 100))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - result[0] += -0.0003331708; - } else { - result[0] += 0.025730435; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 280))) { - result[0] += -0.0062754015; - } else { - result[0] += 0.013416797; - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 82))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 4))) { - result[0] += -0; - } else { - result[0] += -0.031346504; - } - } else { - result[0] += 0.0021177256; - } - } - } - } - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 314))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { - result[0] += -0.0009686186; - } else { - result[0] += -0.027624989; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 222))) { - result[0] += 0.012920478; - } else { - result[0] += 0.03591418; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 262))) { - result[0] += -0.027306026; - } else { - result[0] += -0.010613002; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { - result[0] += 0.028817087; - } else { - result[0] += -0.021536713; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 218))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 150))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 60))) { - result[0] += -0.014519043; - } else { - result[0] += -0.0013858235; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { - result[0] += 0.011594709; - } else { - result[0] += -0.0014000179; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 312))) { - result[0] += -0.005692505; - } else { - result[0] += 0.009161465; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { - result[0] += -3.463562e-05; - } else { - result[0] += 0.004579789; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 296))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 260))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 164))) { - result[0] += -0.00439987; - } else { - result[0] += -0.020940205; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += -0.062401365; - } else { - result[0] += -0.011520316; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 150))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 326))) { - result[0] += 0.016910944; - } else { - result[0] += 0.002540021; - } - } else { - result[0] += -0.010653024; - } - } - } else { - result[0] += -0.056521356; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 384))) { - result[0] += 0.03409591; - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 34))) { - result[0] += -0; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 386))) { - result[0] += -0.0031993948; - } else { - result[0] += -0.02678071; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 254))) { - result[0] += 0.031704232; - } else { - result[0] += 0.005365833; - } - } - } - } - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 52))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 6))) { - result[0] += 0.0014249064; - } else { - result[0] += -0.01947671; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 94))) { - result[0] += 0.026210293; - } else { - result[0] += 0.003069733; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - result[0] += -0.02217819; - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 40))) { - result[0] += 0.023008201; - } else { - result[0] += 0.0086527765; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 14))) { - result[0] += 0.028070768; - } else { - result[0] += -0; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 16))) { - result[0] += -0.025724366; - } else { - result[0] += -0.008844643; - } - } - } else { - result[0] += -0.047070276; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 134))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 44))) { - result[0] += 0.004072014; - } else { - result[0] += -0.0108394185; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 170))) { - result[0] += 0.03304889; - } else { - result[0] += 0.0103535205; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 114))) { - result[0] += -0.023122396; - } else { - result[0] += -0.010224661; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 178))) { - result[0] += 0.001644756; - } else { - result[0] += -0.002623936; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { - result[0] += -0.0045392714; - } else { - result[0] += -0.025003826; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 98))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 318))) { - result[0] += 0.015446856; - } else { - result[0] += -0.034815937; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 34))) { - result[0] += -0.00036249825; - } else { - result[0] += 0.004029076; - } - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 86))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 48))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { - result[0] += 0.0035753883; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 160))) { - result[0] += -0.046707038; - } else { - result[0] += -0.026563475; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 136))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 48))) { - result[0] += 0.011157422; - } else { - result[0] += 0.025159726; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 228))) { - result[0] += -0.0022385176; - } else { - result[0] += 0.009932112; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 214))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 186))) { - result[0] += -0.0044850186; - } else { - result[0] += 0.007804101; - } - } else { - result[0] += 0.023426604; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 312))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 90))) { - result[0] += -0.0033166704; - } else { - result[0] += -0.010364393; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 142))) { - result[0] += -0.013235124; - } else { - result[0] += 0.015832936; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 104))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 98))) { - result[0] += 0.022907645; - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 158))) { - result[0] += 0.00681618; - } else { - result[0] += -0.012386585; - } - } - } else { - result[0] += 0.04736806; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - result[0] += -0.0017811867; - } else { - result[0] += 0.024726752; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 300))) { - result[0] += -0.024868438; - } else { - result[0] += -0.008288312; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 300))) { - result[0] += 0.023393013; - } else { - result[0] += 0.000140571; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { - result[0] += -0.0001585513; - } else { - result[0] += 0.0125155775; - } - } - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 400))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 136))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 106))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 100))) { - result[0] += 0.00032556072; - } else { - result[0] += 0.02669864; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 144))) { - result[0] += -0.01502426; - } else { - result[0] += -0.007827666; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 108))) { - result[0] += -0.042772368; - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { - result[0] += 0.0073166452; - } else { - result[0] += -0.003959548; - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 168))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 104))) { - result[0] += 0.00850385; - } else { - result[0] += -0.0007850647; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 174))) { - result[0] += 0.022124942; - } else { - result[0] += 0.0037423302; - } - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 12))) { - result[0] += -0.03591499; - } else { - result[0] += 0.04963535; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { - result[0] += -0.014330697; - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 142))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 140))) { - result[0] += 0.00258422; - } else { - result[0] += -0.011271856; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { - result[0] += 0.008900406; - } else { - result[0] += 0.025473619; - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 226))) { - result[0] += -0.013987194; - } else { - result[0] += -0.0071587036; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 404))) { - result[0] += -0.007607888; - } else { - result[0] += 0.021506222; - } - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 164))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { - result[0] += 0.017460436; - } else { - result[0] += -0.0049960585; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 48))) { - result[0] += 0.03522423; - } else { - result[0] += 0.0048457957; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 92))) { - result[0] += -0.025125725; - } else { - result[0] += -0; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 4))) { - result[0] += -0.058159776; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 292))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 80))) { - result[0] += -0.0017305056; - } else { - result[0] += -0.01667433; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 306))) { - result[0] += -0.027741238; - } else { - result[0] += 0.009779043; - } - } - } - } - } else { - result[0] += 0.009787267; - } - } - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 342))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 136))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 218))) { - result[0] += -0.00073015277; - } else { - result[0] += 0.0008299645; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { - result[0] += -0.003797351; - } else { - result[0] += -0.026151488; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 164))) { - result[0] += -0.0062180604; - } else { - result[0] += -0.018368756; - } - } else { - result[0] += 0.03389421; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 202))) { - result[0] += -0.001612652; - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 242))) { - result[0] += 0.029014328; - } else { - result[0] += 0.018497443; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 366))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 298))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 326))) { - result[0] += -0.02712582; - } else { - result[0] += -0.014455877; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 296))) { - result[0] += -0.017788123; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 306))) { - result[0] += 0.003964351; - } else { - result[0] += -0.0068141944; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 334))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 304))) { - result[0] += 0.046189606; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - result[0] += 0.024067063; - } else { - result[0] += -0; - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 56))) { - result[0] += 0.002387124; - } else { - result[0] += -0.021191007; - } - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 316))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 48))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 140))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { - result[0] += 0.017918406; - } else { - result[0] += 0.0007088012; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 90))) { - result[0] += 0.008024058; - } else { - result[0] += 0.03525953; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 50))) { - result[0] += -0.017354771; - } else { - result[0] += 0.011550955; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += 0.016495919; - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 50))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 82))) { - result[0] += 0.0069151595; - } else { - result[0] += -0.009860008; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { - result[0] += -0.02526379; - } else { - result[0] += -0.010914913; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { - result[0] += 0.023363909; - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 188))) { - result[0] += -0.017829457; - } else { - result[0] += 0.0066434382; - } - } else { - result[0] += 0.031978313; - } - } else { - result[0] += -0.026702777; - } - } - } - } - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 18))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 72))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 0))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 28))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 8))) { - result[0] += 0.0016082926; - } else { - result[0] += -0.01875299; - } - } else { - result[0] += 0.0011138814; - } - } else { - result[0] += -0.05503887; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 116))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += -0.034357756; - } else { - result[0] += -0.00018943613; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 10))) { - result[0] += -0.015174734; - } else { - result[0] += 0.006123707; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 54))) { - result[0] += -0; - } else { - result[0] += -0.032295134; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { - result[0] += 0.022839814; - } else { - result[0] += -0.0064965202; - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 114))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 78))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 10))) { - result[0] += -0; - } else { - result[0] += -0.025930112; - } - } else { - result[0] += -0.040734638; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { - result[0] += 0.0056574023; - } else { - result[0] += 0.045485523; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 30))) { - result[0] += -0.014559778; - } else { - result[0] += -0.0022656727; - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 178))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 22))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { - result[0] += 0.009212374; - } else { - result[0] += -0.023791295; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 122))) { - result[0] += -0; - } else { - result[0] += -0.030348688; - } - } - } else { - result[0] += 0.011739956; - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 116))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 318))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 80))) { - result[0] += 0.00070217456; - } else { - result[0] += -0.01572331; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 296))) { - result[0] += 0.0059441226; - } else { - result[0] += -0.015418323; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 86))) { - result[0] += 0.0038127878; - } else { - result[0] += -0.0063350433; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { - result[0] += 0.023485564; - } else { - result[0] += -0.0005745096; - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 28))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 396))) { - result[0] += -0.008549287; - } else { - result[0] += 0.03102727; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 76))) { - result[0] += 0.03656302; - } else { - result[0] += 0.0037815608; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { - result[0] += 0.036549788; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 80))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 56))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 202))) { - result[0] += -0.0316082; - } else { - result[0] += -0.011285119; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - result[0] += 0.044548456; - } else { - result[0] += -0.011524561; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - result[0] += -0.0036371031; - } else { - result[0] += 0.0054557663; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 250))) { - result[0] += -0.010413558; - } else { - result[0] += -0.005399991; - } - } - } - } - } - } - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 244))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 92))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 26))) { - result[0] += -0.02938572; - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 36))) { - result[0] += -0.0011291165; - } else { - result[0] += -0.023937851; - } - } else { - result[0] += 0.014182518; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 48))) { - result[0] += 0.09786916; - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 46))) { - result[0] += -0.039628785; - } else { - result[0] += -0; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 174))) { - result[0] += 0.008892446; - } else { - result[0] += -0.002574872; - } - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { - result[0] += 0.0067228116; - } else { - result[0] += 0.039215095; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 106))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 50))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - result[0] += -0.007054515; - } else { - result[0] += 0.035221007; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 274))) { - result[0] += -0.030953402; - } else { - result[0] += -0.008404141; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 146))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 106))) { - result[0] += -0.011643419; - } else { - result[0] += -0.05229321; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 288))) { - result[0] += -0.016430408; - } else { - result[0] += -0.039948575; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { - result[0] += -0.015282759; - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 80))) { - result[0] += 0.007892172; - } else { - result[0] += 0.078224584; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 148))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 4))) { - result[0] += -0.0037962466; - } else { - result[0] += -0.015912866; - } - } else { - result[0] += 0.013302639; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 340))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { - result[0] += -0.00012223067; - } else { - result[0] += -0.012624851; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 256))) { - result[0] += 0.027083063; - } else { - result[0] += 0.015543193; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 20))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 298))) { - result[0] += -0.020818194; - } else { - result[0] += -0.006904413; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 320))) { - result[0] += 0.049202304; - } else { - result[0] += 0.009114965; - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 34))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 82))) { - result[0] += 0.003950732; - } else { - result[0] += -0.011314066; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - result[0] += 0.0068105734; - } else { - result[0] += 0.028154967; - } - } - } else { - result[0] += -0.021689637; - } - } - } - } - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 296))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 232))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 6))) { - result[0] += -0.004839664; - } else { - result[0] += 0.00013240986; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 194))) { - result[0] += 0.012408934; - } else { - result[0] += -0.00019027379; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 318))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 260))) { - result[0] += -0.0021018793; - } else { - result[0] += 0.013699087; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 262))) { - result[0] += -0.046322387; - } else { - result[0] += -0; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 260))) { - result[0] += 0.02145579; - } else { - result[0] += 0.010288753; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 358))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 286))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 112))) { - result[0] += -0.026122052; - } else { - result[0] += -0.014107632; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 12))) { - result[0] += -0.004089996; - } else { - result[0] += -0.017688856; - } - } - } else { - result[0] += 0.021606075; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 12))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 326))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 200))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 166))) { - result[0] += -0.00194148; - } else { - result[0] += -0.012741702; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 210))) { - result[0] += 0.036462914; - } else { - result[0] += -0.008751659; - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 102))) { - result[0] += -0.0035663412; - } else { - result[0] += -0.02093569; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 0))) { - result[0] += 0.014931956; - } else { - result[0] += 0.038147915; - } - } else { - result[0] += 0.0034323465; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 14))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 88))) { - result[0] += -0; - } else { - result[0] += 0.02190816; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 60))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 54))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { - result[0] += 0.0031788598; - } else { - result[0] += -0.017724153; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 46))) { - result[0] += 0.00170696; - } else { - result[0] += -0.021163156; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 62))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 186))) { - result[0] += -0.015714565; - } else { - result[0] += 0.055118978; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 346))) { - result[0] += 0.0018268081; - } else { - result[0] += 0.012591888; - } - } - } - } - } - } - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 258))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 144))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 98))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += -0.005291102; - } else { - result[0] += -0.029125659; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 232))) { - result[0] += 0.003732911; - } else { - result[0] += -0.018547367; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 240))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 152))) { - result[0] += 0.0017839748; - } else { - result[0] += -0.008801985; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 118))) { - result[0] += 0.0042691375; - } else { - result[0] += 0.03554597; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 208))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 224))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 284))) { - result[0] += -0.003548905; - } else { - result[0] += -0.012224059; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 226))) { - result[0] += 0.0020274771; - } else { - result[0] += 0.019364875; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 180))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { - result[0] += 0.016102543; - } else { - result[0] += -0.013677455; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 248))) { - result[0] += -0.05339232; - } else { - result[0] += -0.00876738; - } - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 106))) { - result[0] += -0.013133674; - } else { - result[0] += -0.0521831; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 38))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { - result[0] += 0.008359418; - } else { - result[0] += 0.038568888; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 102))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 60))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 122))) { - result[0] += 0.0013149813; - } else { - result[0] += -0.0191629; - } - } else { - result[0] += -0.030204315; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 54))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 78))) { - result[0] += 0.024884596; - } else { - result[0] += 0.0094683515; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 50))) { - result[0] += 0.0068674767; - } else { - result[0] += -0.007323086; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 94))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 104))) { - result[0] += -0.0076974086; - } else { - result[0] += -0.029438093; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 110))) { - result[0] += 0.03408525; - } else { - result[0] += -0.006439855; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 62))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 76))) { - result[0] += 0.0068958416; - } else { - result[0] += 0.04007944; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { - result[0] += 0.00047964975; - } else { - result[0] += 0.0132646635; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 88))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 86))) { - result[0] += 0.0022136592; - } else { - result[0] += 0.026103059; - } - } else { - result[0] += -0.0099972095; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 120))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { - result[0] += -0.011941642; - } else { - result[0] += 0.0021292144; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 20))) { - result[0] += -0.014601464; - } else { - result[0] += -0.0005000638; - } - } - } - } - } - } - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 250))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 224))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 246))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 196))) { - result[0] += 0.000114969465; - } else { - result[0] += -0.0023860415; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 14))) { - result[0] += -0.035199355; - } else { - result[0] += -0.017500581; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 314))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - result[0] += 0.00627641; - } else { - result[0] += -0.0026602177; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { - result[0] += -0.008631534; - } else { - result[0] += 0.008578693; - } - } - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 30))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 216))) { - result[0] += 0.0028546187; - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 14))) { - result[0] += 0.014946878; - } else { - result[0] += 0.041622277; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 250))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - result[0] += 0.030365026; - } else { - result[0] += 0.005026304; - } - } else { - result[0] += -0.030540321; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 130))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - result[0] += -0.034515306; - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 150))) { - result[0] += 0.04119061; - } else { - result[0] += 0.018316824; - } - } else { - result[0] += -0.017891763; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 54))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 200))) { - result[0] += -0.028790927; - } else { - result[0] += -0.0069620805; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += 0.04099756; - } else { - result[0] += -0.003357871; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 260))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 118))) { - result[0] += -0.0026708224; - } else { - result[0] += -0.011060624; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - result[0] += 0.0011356926; - } else { - result[0] += 0.041363157; - } - } - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 190))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 188))) { - result[0] += 0.024280345; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 320))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 0))) { - result[0] += 0.010236905; - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 182))) { - result[0] += -0.02040131; - } else { - result[0] += -0.008407633; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 326))) { - result[0] += 0.012552517; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - result[0] += -0.021955565; - } else { - result[0] += -0.0035318558; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 120))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 302))) { - result[0] += 0.009495072; - } else { - result[0] += 0.00013142404; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { - result[0] += -0.006438227; - } else { - result[0] += -0.030158589; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 306))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 276))) { - result[0] += -0; - } else { - result[0] += 0.018983908; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { - result[0] += -0.044639092; - } else { - result[0] += 0.00017055031; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 60))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 346))) { - result[0] += -0.008808951; - } else { - result[0] += -0.032180067; - } - } else { - result[0] += 0.008250392; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 62))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 56))) { - result[0] += 0.056063425; - } else { - result[0] += -0.0065511647; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { - result[0] += -0.0092850765; - } else { - result[0] += 0.0021918796; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 30))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - result[0] += 0.007402791; - } else { - result[0] += -0.011684748; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += -0.006889455; - } else { - result[0] += 0.01800547; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 36))) { - result[0] += -0.024754832; - } else { - result[0] += -0.0028222399; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 30))) { - result[0] += -0.020031106; - } else { - result[0] += 0.0067768777; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 52))) { - result[0] += 0.041279774; - } else { - result[0] += 0.012748748; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 144))) { - result[0] += -0; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { - result[0] += 0.015250462; - } else { - result[0] += 0.026752282; - } - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 216))) { - result[0] += -0.02535589; - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 156))) { - result[0] += 0.006471269; - } else { - result[0] += 0.029563783; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 170))) { - result[0] += -0.025211362; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 186))) { - result[0] += -0.023914715; - } else { - result[0] += -0.009117383; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 154))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { - result[0] += 0.0012569004; - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 118))) { - result[0] += -0.008115943; - } else { - result[0] += -0.026224598; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 340))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 196))) { - result[0] += 0.042526796; - } else { - result[0] += 0.022222739; - } - } else { - result[0] += 0.012392859; - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 12))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { - result[0] += -0.028314574; - } else { - result[0] += -0.002670089; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { - result[0] += 0.0057236534; - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 120))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 50))) { - result[0] += -0.017261336; - } else { - result[0] += -0.007837756; - } - } else { - result[0] += -0.0055499817; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 216))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 78))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 56))) { - result[0] += -0.0009610457; - } else { - result[0] += 0.0076379874; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 16))) { - result[0] += 0.05212106; - } else { - result[0] += 0.02506253; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { - result[0] += -0.0006034739; - } else { - result[0] += 0.021560185; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { - result[0] += -0.034164276; - } else { - result[0] += -0.004662303; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 78))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { - result[0] += -0.001952683; - } else { - result[0] += 0.037713937; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 152))) { - result[0] += -0.008823862; - } else { - result[0] += -0.0014422481; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 76))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 242))) { - result[0] += 0.014311755; - } else { - result[0] += -0.018534161; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { - result[0] += 0.0027180712; - } else { - result[0] += -0.0037262347; - } - } - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 152))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 296))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 46))) { - result[0] += -0.00358946; - } else { - result[0] += 0.00086048665; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 298))) { - result[0] += -0.020779364; - } else { - result[0] += -0; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 184))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 34))) { - result[0] += -0.039312568; - } else { - result[0] += -0.005182448; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 82))) { - result[0] += 0.0015322726; - } else { - result[0] += -0.0016843865; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 218))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 312))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { - result[0] += -0.006095136; - } else { - result[0] += 0.028541317; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 188))) { - result[0] += 0.015441231; - } else { - result[0] += -0.01365337; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { - result[0] += 0.028764328; - } else { - result[0] += -0; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - result[0] += 0.01359984; - } else { - result[0] += -0.0053497027; - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 118))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 216))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 254))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 38))) { - result[0] += 0.0073710456; - } else { - result[0] += 0.00038945695; - } - } else { - result[0] += -0.00408677; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { - result[0] += -0.016966945; - } else { - result[0] += -0; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 130))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 106))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 70))) { - result[0] += 0.0059221475; - } else { - result[0] += 0.018299853; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { - result[0] += 0.005721331; - } else { - result[0] += -0.010450677; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 214))) { - result[0] += 0.03640103; - } else { - result[0] += 0.01191198; - } - } - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 96))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 88))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 70))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 76))) { - result[0] += -0.036111545; - } else { - result[0] += -0; - } - } else { - result[0] += 0.013476851; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 74))) { - result[0] += -0.02820078; - } else { - result[0] += -0.08100428; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 76))) { - result[0] += 0.001866552; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 48))) { - result[0] += -0.021744216; - } else { - result[0] += -0; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 48))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 42))) { - result[0] += -0.004817111; - } else { - result[0] += -0.019636374; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 70))) { - result[0] += 0.017743232; - } else { - result[0] += -0.010167501; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 66))) { - result[0] += -0.05105884; - } else { - result[0] += -0.011944066; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 102))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 68))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 24))) { - result[0] += -0; - } else { - result[0] += 0.02911661; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 66))) { - result[0] += -0.010146369; - } else { - result[0] += 0.009323521; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 2))) { - result[0] += -0.03494624; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { - result[0] += -0.003336961; - } else { - result[0] += -0.02113694; - } - } - } - } - } - } - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 92))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 166))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 114))) { - result[0] += -0.0013070774; - } else { - result[0] += 0.0005617281; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { - result[0] += -0.00045852616; - } else { - result[0] += 0.021694412; - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 190))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 54))) { - result[0] += -0.009019866; - } else { - result[0] += 0.0010552766; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 382))) { - result[0] += 0.042176425; - } else { - result[0] += -0.019407976; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 54))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 94))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 22))) { - result[0] += -0.008323395; - } else { - result[0] += -0.0172386; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 96))) { - result[0] += 0.0036288549; - } else { - result[0] += -0.013044089; - } - } - } else { - result[0] += 0.00021139935; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 392))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 158))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 74))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 78))) { - result[0] += -0.00079369405; - } else { - result[0] += 0.04720639; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 390))) { - result[0] += 0.05325351; - } else { - result[0] += -0.043466292; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 66))) { - result[0] += -0.032283835; - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 198))) { - result[0] += -0.0008110626; - } else { - result[0] += 0.050249096; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 68))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 158))) { - result[0] += -0.010387595; - } else { - result[0] += -0.0034609255; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 110))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 394))) { - result[0] += 0.0030221925; - } else { - result[0] += 0.028211419; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 114))) { - result[0] += -0.0093119275; - } else { - result[0] += 0.0069152773; - } - } - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 164))) { - result[0] += -0.009060017; - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 128))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 126))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 46))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { - result[0] += -0.006120693; - } else { - result[0] += 0.009823619; - } - } else { - result[0] += -0.009353681; - } - } else { - result[0] += -0.0127736945; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 400))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { - result[0] += 0.013670566; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 86))) { - result[0] += -0.004946747; - } else { - result[0] += 0.0048705908; - } - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 136))) { - result[0] += -0.010391137; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 92))) { - result[0] += 0.004601255; - } else { - result[0] += -0.0049207225; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 22))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 112))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { - result[0] += -0.018509652; - } else { - result[0] += -0.0028016684; - } - } else { - result[0] += 0.012803587; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 14))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.013896561; - } else { - result[0] += -0.009757071; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 2))) { - result[0] += 0.035226237; - } else { - result[0] += 0.003883094; - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 108))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 72))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { - result[0] += 0.009042918; - } else { - result[0] += 0.03158817; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 138))) { - result[0] += 0.007958132; - } else { - result[0] += 0.03468901; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 146))) { - result[0] += -0.0020058847; - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 174))) { - result[0] += 0.026864871; - } else { - result[0] += 0.014916946; - } - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 216))) { - result[0] += -0.022077216; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { - result[0] += 0.007497831; - } else { - result[0] += 0.028885541; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 250))) { - result[0] += -0.02360869; - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 174))) { - result[0] += -0.018704783; - } else { - result[0] += -0.0076525756; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 294))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - result[0] += 9.27179e-05; - } else { - result[0] += 0.031941853; - } - } else { - result[0] += 0.009628982; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 350))) { - result[0] += -0.021527562; - } else { - result[0] += -0.0072527933; - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 74))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 104))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 70))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 60))) { - result[0] += 0.02288001; - } else { - result[0] += -0.016261838; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 146))) { - result[0] += -0.03769898; - } else { - result[0] += -0.018141152; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 46))) { - result[0] += -0.0023420886; - } else { - result[0] += 0.021080358; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 140))) { - result[0] += -0.012038226; - } else { - result[0] += 0.008399843; - } - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 72))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { - result[0] += 0.002315519; - } else { - result[0] += 0.062828414; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { - result[0] += 0.0130506335; - } else { - result[0] += -0.007596428; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 40))) { - result[0] += 0.007198368; - } else { - result[0] += -0.0058715134; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - result[0] += 0.02399823; - } else { - result[0] += -0.008211115; - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 84))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 26))) { - result[0] += 0.02461793; - } else { - result[0] += 0.008668258; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 28))) { - result[0] += -0.030365562; - } else { - result[0] += 0.010669919; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 94))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 16))) { - result[0] += -0.018534234; - } else { - result[0] += -0.0031345394; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - result[0] += -0.010914135; - } else { - result[0] += -0.00012393964; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 214))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 88))) { - result[0] += 0.022872802; - } else { - result[0] += 0.0050913426; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 132))) { - result[0] += 0.0009941938; - } else { - result[0] += -0.0025577117; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += 0.012239212; - } else { - result[0] += 0.032537233; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 74))) { - result[0] += -0.012231956; - } else { - result[0] += 0.00096511666; - } - } - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 244))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 50))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 100))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 190))) { - result[0] += -0.015980741; - } else { - result[0] += 0.013686332; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 106))) { - result[0] += 0.030151868; - } else { - result[0] += 0.008428541; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 20))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 192))) { - result[0] += 0.001345482; - } else { - result[0] += 0.02078061; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 126))) { - result[0] += -0.029191677; - } else { - result[0] += 0.00064531795; - } - } - } - } else { - result[0] += 0.035184488; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 30))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 212))) { - result[0] += 0.009157064; - } else { - result[0] += -0.03418844; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { - result[0] += -0.01716392; - } else { - result[0] += -0.045232523; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { - result[0] += -0.01179669; - } else { - result[0] += 0.06294518; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 140))) { - result[0] += -0.011332896; - } else { - result[0] += 0.01226867; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { - result[0] += 0.0011395406; - } else { - result[0] += -0.0025050612; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 138))) { - result[0] += -0.037857305; - } else { - result[0] += -0.00218284; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 226))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 102))) { - result[0] += -0.0008166955; - } else { - result[0] += 0.004205488; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.0056326287; - } else { - result[0] += 0.0007912867; - } - } - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 6))) { - result[0] += 0.023435187; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 40))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 42))) { - result[0] += 0.03141637; - } else { - result[0] += -0.0015305742; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 8))) { - result[0] += -0.017991172; - } else { - result[0] += 0.002195324; - } - } else { - result[0] += 0.013269196; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 44))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 120))) { - result[0] += -0.01257918; - } else { - result[0] += 0.0010349855; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 154))) { - result[0] += -0.051812388; - } else { - result[0] += -0.010208193; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 30))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 278))) { - result[0] += 0.0073652193; - } else { - result[0] += -0.013559197; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 56))) { - result[0] += -0.02485094; - } else { - result[0] += -0.0037162665; - } - } - } - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 12))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 42))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 2))) { - result[0] += -0.034541044; - } else { - result[0] += 0.0027651105; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { - result[0] += -0.019249316; - } else { - result[0] += -0.0034680355; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 8))) { - result[0] += -0.02443984; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += 0.013286717; - } else { - result[0] += -0.0025256465; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 46))) { - result[0] += 0.0061984803; - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 48))) { - result[0] += 0.079341814; - } else { - result[0] += 0.025329694; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 6))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 126))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { - result[0] += 0.021308701; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 118))) { - result[0] += -0.006669204; - } else { - result[0] += -0.019334236; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 140))) { - result[0] += 0.04223214; - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 74))) { - result[0] += -0.01822163; - } else { - result[0] += 0.011897653; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 12))) { - result[0] += -0.03937181; - } else { - result[0] += -0.021595992; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 58))) { - result[0] += 0.008440125; - } else { - result[0] += -0.018780667; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 32))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { - result[0] += -0.0025710927; - } else { - result[0] += -0.015579129; - } - } else { - result[0] += 0.013569686; - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 118))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 10))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 102))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 90))) { - result[0] += -0.008451057; - } else { - result[0] += 0.010793014; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 244))) { - result[0] += -0.007276828; - } else { - result[0] += -0.00065764156; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { - result[0] += 0.00065685576; - } else { - result[0] += 0.025356445; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 142))) { - result[0] += -0.012041635; - } else { - result[0] += -0.05446145; - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 84))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 120))) { - result[0] += 0.044229705; - } else { - result[0] += 0.0006534785; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 30))) { - result[0] += 0.005015543; - } else { - result[0] += -0.01020183; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += 0.06759548; - } else { - result[0] += 0.007752572; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { - result[0] += 0.011832262; - } else { - result[0] += 0.0015743548; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 208))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 196))) { - result[0] += -0.0024539565; - } else { - result[0] += 0.001751725; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { - result[0] += -0; - } else { - result[0] += 0.021780172; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 66))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 272))) { - result[0] += 0.008948053; - } else { - result[0] += -0.0063331993; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { - result[0] += -0.0073422543; - } else { - result[0] += -0.026588783; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 210))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 104))) { - result[0] += 0.0029568898; - } else { - result[0] += -0.042533282; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { - result[0] += -0.0074031386; - } else { - result[0] += 0.014356777; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - result[0] += 0.00092436554; - } else { - result[0] += 0.024809679; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 326))) { - result[0] += -0.00329214; - } else { - result[0] += 0.00526762; - } - } - } - } - } - } - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 142))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 10))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 200))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 72))) { - result[0] += 0.0010467954; - } else { - result[0] += 0.030300766; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 62))) { - result[0] += 0.02035891; - } else { - result[0] += -0.010212629; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { - result[0] += -0.001989516; - } else { - result[0] += -0.013169396; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 346))) { - result[0] += 0.020909293; - } else { - result[0] += -0.0079391515; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 306))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 262))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 116))) { - result[0] += -0.0052688015; - } else { - result[0] += 0.0019646974; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 286))) { - result[0] += -0.031940565; - } else { - result[0] += -0; - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 80))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 322))) { - result[0] += 0.020321852; - } else { - result[0] += 0.034146465; - } - } else { - result[0] += -0.03401268; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 118))) { - result[0] += 0.0016832197; - } else { - result[0] += 0.027038325; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 166))) { - result[0] += -0.0071495273; - } else { - result[0] += -0.0014786319; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 258))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { - result[0] += 0.01780109; - } else { - result[0] += -0.01263422; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 228))) { - result[0] += 0.039167188; - } else { - result[0] += 0.018858656; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 42))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 26))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { - result[0] += -0.0019396268; - } else { - result[0] += 0.005724409; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 6))) { - result[0] += 0.027276058; - } else { - result[0] += 0.008047543; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 130))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { - result[0] += -0.012420956; - } else { - result[0] += -0.0021616125; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - result[0] += -0.0018222294; - } else { - result[0] += 0.0021800934; - } - } - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 150))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 92))) { - result[0] += -0.006505067; - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 220))) { - result[0] += 0.040462743; - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 46))) { - result[0] += 0.015701324; - } else { - result[0] += -0.0201608; - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 116))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 52))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 94))) { - result[0] += 0.0005646117; - } else { - result[0] += 0.010826596; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - result[0] += -0.013457968; - } else { - result[0] += -1.2906838e-05; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += -0.0008355248; - } else { - result[0] += -0.0226526; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 92))) { - result[0] += 0.020389868; - } else { - result[0] += 0.0054127253; - } - } - } - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 14))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 76))) { - result[0] += -0.032427344; - } else { - result[0] += -0; - } - } else { - result[0] += 0.04492506; - } - } - } - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 40))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 8))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { - result[0] += -0.008939014; - } else { - result[0] += -0.031956512; - } - } else { - result[0] += 0.017092446; - } - } else { - result[0] += 0.022510538; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 4))) { - result[0] += 0.042899013; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - result[0] += -0.0038211767; - } else { - result[0] += 0.029483128; - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 72))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 160))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 82))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { - result[0] += -0.007246212; - } else { - result[0] += 0.0062661204; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 88))) { - result[0] += -0.02025948; - } else { - result[0] += -0.005702542; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 116))) { - result[0] += -0.030373631; - } else { - result[0] += -0.004761403; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 90))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { - result[0] += 0.010341472; - } else { - result[0] += 0.0385042; - } - } else { - result[0] += -0.0025437989; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 204))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - result[0] += -0.0027436535; - } else { - result[0] += -0.020981843; - } - } else { - result[0] += 0.011424384; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { - result[0] += -0.026424287; - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 22))) { - result[0] += 0.015828108; - } else { - result[0] += -0.004946768; - } - } - } else { - result[0] += 0.036206927; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 0))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 46))) { - result[0] += -0.0018861439; - } else { - result[0] += 0.019979624; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 16))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 64))) { - result[0] += -0.012420944; - } else { - result[0] += -0.04097703; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 46))) { - result[0] += -0.014519716; - } else { - result[0] += 0.0064661787; - } - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 116))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { - result[0] += -0.0007507774; - } else { - result[0] += 0.011509613; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 74))) { - result[0] += 0.0015937341; - } else { - result[0] += 0.026918387; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 268))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 76))) { - result[0] += -0.001206148; - } else { - result[0] += -0.01964714; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 98))) { - result[0] += 0.008147176; - } else { - result[0] += -0.0026112716; - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 92))) { - result[0] += -0.00044672118; - } else { - result[0] += -0.009276496; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 156))) { - result[0] += 0.039460566; - } else { - result[0] += -0.0033604186; - } - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 148))) { - result[0] += -0.0112617565; - } else { - result[0] += -0.0053185816; - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 32))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 16))) { - result[0] += -0.027565295; - } else { - result[0] += -0.004120046; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - result[0] += -0.0030114742; - } else { - result[0] += 0.010990894; - } - } - } else { - result[0] += -0.018377742; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - result[0] += 0.0032660365; - } else { - result[0] += 0.014772459; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 240))) { - result[0] += 0.01916596; - } else { - result[0] += 0.043604817; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 188))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { - result[0] += 0.020365; - } else { - result[0] += -0.004660358; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 182))) { - result[0] += -0.026131554; - } else { - result[0] += -0.012585315; - } - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 208))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 144))) { - result[0] += -0.009168918; - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 186))) { - result[0] += -0.017280573; - } else { - result[0] += 0.013135247; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 294))) { - result[0] += 0.027347783; - } else { - result[0] += 0.0058005466; - } - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 228))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 262))) { - result[0] += -0.01863461; - } else { - result[0] += -0.009977842; - } - } else { - result[0] += 0.0074623013; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 18))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 82))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 10))) { - result[0] += -0.027271813; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - result[0] += 0.03428016; - } else { - result[0] += 0.005898777; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 124))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 120))) { - result[0] += -0.0031157867; - } else { - result[0] += 0.012381009; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 40))) { - result[0] += -0.032176975; - } else { - result[0] += -0.0047460934; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 128))) { - result[0] += -0.011950021; - } else { - result[0] += -0.029780094; - } - } else { - result[0] += -0.038288817; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 14))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 58))) { - result[0] += -0.013251813; - } else { - result[0] += 0.009832563; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 40))) { - result[0] += -0.0028266981; - } else { - result[0] += 0.010589391; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 26))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 0))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 144))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 8))) { - result[0] += -0; - } else { - result[0] += 0.02299802; - } - } else { - result[0] += -0.0005336401; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 16))) { - result[0] += 0.024464216; - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 24))) { - result[0] += -0.02506329; - } else { - result[0] += -0.00617628; - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 146))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 228))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 168))) { - result[0] += 0.000546928; - } else { - result[0] += -0.0015107197; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 244))) { - result[0] += 0.0052600866; - } else { - result[0] += 0.0005289404; - } - } - } else { - result[0] += -0.0067100944; - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 92))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 56))) { - result[0] += 0.000776489; - } else { - result[0] += -0.00053459004; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.0025825037; - } else { - result[0] += 0.033680957; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 54))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 94))) { - result[0] += -0.010514084; - } else { - result[0] += 0.00012713655; - } - } else { - result[0] += -8.464566e-05; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 46))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 192))) { - result[0] += -0.008038435; - } else { - result[0] += 0.03207838; - } - } else { - result[0] += -0.0059311604; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 196))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 104))) { - result[0] += -0.0027816568; - } else { - result[0] += 0.008232006; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - result[0] += 0.03486943; - } else { - result[0] += -0.009670003; - } - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 78))) { - result[0] += -0.021940826; - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 160))) { - result[0] += -0.007266288; - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 128))) { - result[0] += -0.004397231; - } else { - result[0] += 0.0011502573; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { - result[0] += -0.005171144; - } else { - result[0] += 0.009149543; - } - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 132))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 68))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - result[0] += 0.033398908; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 34))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 146))) { - result[0] += -0.011877987; - } else { - result[0] += 0.009033947; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 32))) { - result[0] += 0.017914457; - } else { - result[0] += 0.00086382544; - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 114))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 106))) { - result[0] += -0; - } else { - result[0] += -0.025332725; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 136))) { - result[0] += 0.010888358; - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 132))) { - result[0] += -0.002672884; - } else { - result[0] += -0.0126517145; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { - result[0] += -0.01176235; - } else { - result[0] += -0.04480323; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 132))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 80))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 146))) { - result[0] += 0.022533778; - } else { - result[0] += 0.0015053398; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { - result[0] += -0.029711738; - } else { - result[0] += -0.00018641823; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 194))) { - result[0] += -0.0029512055; - } else { - result[0] += -0.014446958; - } - } else { - result[0] += -0.04525297; - } - } - } - } - } - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 160))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 116))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 126))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 108))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 104))) { - result[0] += 0.0049565816; - } else { - result[0] += 0.063526; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 114))) { - result[0] += -0.019255767; - } else { - result[0] += 0.0012636579; - } - } - } else { - result[0] += 0.008695122; - } - } else { - result[0] += 0.019830158; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 190))) { - result[0] += -0.007866302; - } else { - result[0] += 0.012916532; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 30))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { - result[0] += 0.06366762; - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 98))) { - result[0] += -0.004975738; - } else { - result[0] += -0.026275873; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 238))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 148))) { - result[0] += -0.009829768; - } else { - result[0] += -0.028741485; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { - result[0] += -0.0046568112; - } else { - result[0] += -0.028392253; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { - result[0] += -0.01022596; - } else { - result[0] += 0.057439942; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 142))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 10))) { - result[0] += -0.005370389; - } else { - result[0] += -0.019819807; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 70))) { - result[0] += 0.011075004; - } else { - result[0] += -0.0060161785; - } - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 158))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 20))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { - result[0] += 0.062947415; - } else { - result[0] += 0.0032754538; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 146))) { - result[0] += -0.0023154307; - } else { - result[0] += 0.013836692; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { - result[0] += -0.0051080496; - } else { - result[0] += -0.026022715; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 130))) { - result[0] += -0.021521274; - } else { - result[0] += 0.0008544709; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 44))) { - result[0] += 0.011564689; - } else { - result[0] += 0.039744; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 134))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 202))) { - result[0] += 0.00052881095; - } else { - result[0] += -0.0008797978; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 56))) { - result[0] += 0.002648249; - } else { - result[0] += 0.02056101; - } - } - } - } - } - } - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 160))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 142))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 114))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 92))) { - result[0] += -0.0069403; - } else { - result[0] += 0.0036496245; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { - result[0] += -0.041129064; - } else { - result[0] += -0.007193537; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 138))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { - result[0] += -0.016804745; - } else { - result[0] += -0.035601314; - } - } else { - result[0] += 0.0045456374; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 30))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 154))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { - result[0] += 0.051934537; - } else { - result[0] += -0.0017292331; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 18))) { - result[0] += -0.018062782; - } else { - result[0] += -0.0065657706; - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 36))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 12))) { - result[0] += -0.0018053079; - } else { - result[0] += 0.008867439; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - result[0] += 0.024381882; - } else { - result[0] += 0.0078328205; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 194))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 126))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 118))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { - result[0] += -0.0050182706; - } else { - result[0] += 0.0074505755; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 48))) { - result[0] += -0.027363313; - } else { - result[0] += -0.008215737; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 238))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 132))) { - result[0] += 0.0108621; - } else { - result[0] += 0.001027461; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { - result[0] += 0.0018521305; - } else { - result[0] += -0.019366777; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 196))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 66))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 42))) { - result[0] += -0.021509835; - } else { - result[0] += -0.0051732687; - } - } else { - result[0] += -0.036974095; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 10))) { - result[0] += 0.013641315; - } else { - result[0] += -0.0012665674; - } - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 168))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 110))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 120))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 382))) { - result[0] += -0.0007951208; - } else { - result[0] += -0.012272722; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { - result[0] += 0.013259816; - } else { - result[0] += 0.00076070236; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 124))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 86))) { - result[0] += -0.023466855; - } else { - result[0] += -0.0015092399; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - result[0] += 0.0067120204; - } else { - result[0] += -0.0027905267; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 180))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 132))) { - result[0] += 0.0012523715; - } else { - result[0] += 0.019288411; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 72))) { - result[0] += -0.0024032074; - } else { - result[0] += 0.020834908; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 100))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 302))) { - result[0] += -0.0045833127; - } else { - result[0] += 0.002748456; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - result[0] += -0.00035413975; - } else { - result[0] += 0.0021194927; - } - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 352))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 196))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 162))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 36))) { - result[0] += 0.012191023; - } else { - result[0] += -0; - } - } else { - result[0] += 0.026147142; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 82))) { - result[0] += -0.0044075255; - } else { - result[0] += -0.018723859; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 368))) { - result[0] += 0.02115663; - } else { - result[0] += -0.008576654; - } - } - } - } - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 116))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 312))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 78))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 146))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 108))) { - result[0] += -0.0011369804; - } else { - result[0] += 0.007111168; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { - result[0] += -0.0066426345; - } else { - result[0] += 0.004026822; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 166))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 126))) { - result[0] += -0.016191296; - } else { - result[0] += -0.008390819; - } - } else { - result[0] += 0.039027248; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 338))) { - result[0] += 0.011132075; - } else { - result[0] += 0.00045876933; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 108))) { - result[0] += 0.03038956; - } else { - result[0] += 0.005012113; - } - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 128))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 126))) { - result[0] += -0.002966301; - } else { - result[0] += -0.012031978; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 400))) { - result[0] += 0.0043450063; - } else { - result[0] += -0.0033799019; - } - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 74))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 72))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 172))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { - result[0] += 0.00018884889; - } else { - result[0] += 0.004433022; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 140))) { - result[0] += -0.0016364238; - } else { - result[0] += 0.011106547; - } - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 34))) { - result[0] += 0.010573897; - } else { - result[0] += -0.046158385; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { - result[0] += 0.025685733; - } else { - result[0] += -0.012269548; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 298))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 258))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 328))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 132))) { - result[0] += 0.012633822; - } else { - result[0] += -0.004591215; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 68))) { - result[0] += 0.027154177; - } else { - result[0] += -0.0073612966; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 90))) { - result[0] += -0.009671869; - } else { - result[0] += 0.034116954; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 82))) { - result[0] += 0.003360726; - } else { - result[0] += -0.0010045286; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 242))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 80))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 294))) { - result[0] += -0.0067084306; - } else { - result[0] += 0.017508617; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 52))) { - result[0] += -0.025245184; - } else { - result[0] += -0.009711; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 266))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 96))) { - result[0] += 0.0024178706; - } else { - result[0] += 0.02038781; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { - result[0] += -0.005333036; - } else { - result[0] += -0.021810295; - } - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 314))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 148))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 44))) { - result[0] += 0.002272234; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 302))) { - result[0] += 0.0017241904; - } else { - result[0] += 0.014623227; - } - } - } else { - result[0] += -0.0057367506; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 140))) { - result[0] += -0.029135648; - } else { - result[0] += -0.0059048724; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 80))) { - result[0] += 0.0016456817; - } else { - result[0] += 0.036199924; - } - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 314))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 312))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - result[0] += 5.137796e-05; - } else { - result[0] += 0.016802512; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 344))) { - result[0] += -0.007183667; - } else { - result[0] += 0.0086045405; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { - result[0] += -0.0025281047; - } else { - result[0] += -0.022966359; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 250))) { - result[0] += 0.023195243; - } else { - result[0] += 0.003728645; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 186))) { - result[0] += 0.008694565; - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - result[0] += -0.012267268; - } else { - result[0] += 0.012734731; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { - result[0] += 0.023913363; - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 228))) { - result[0] += -0.0029125137; - } else { - result[0] += -0.030048529; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 226))) { - result[0] += -0; - } else { - result[0] += 0.04022684; - } - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 296))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 8))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 2))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - result[0] += 0.010619841; - } else { - result[0] += -0.015812444; - } - } else { - result[0] += 0.036461692; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 10))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 92))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 10))) { - result[0] += 0.006464646; - } else { - result[0] += -0.03794737; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { - result[0] += -0.014366518; - } else { - result[0] += 0.00065602706; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += -0.036709867; - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 26))) { - result[0] += 0.009573085; - } else { - result[0] += -0.0042258967; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { - result[0] += 0.025289237; - } else { - result[0] += 0.0031290874; - } - } - } - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 32))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 314))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 176))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 38))) { - result[0] += 0.0070433277; - } else { - result[0] += -0.0021675983; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += 0.00069358293; - } else { - result[0] += 0.014411396; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 224))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 182))) { - result[0] += -0.021365121; - } else { - result[0] += -0.009108902; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 258))) { - result[0] += -0.002070323; - } else { - result[0] += -0.0070032175; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 58))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 116))) { - result[0] += 0.015421606; - } else { - result[0] += 0.0065408074; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 68))) { - result[0] += -0.00017713972; - } else { - result[0] += 0.0036349527; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 66))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 112))) { - result[0] += -0.015431674; - } else { - result[0] += 0.0069513856; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 86))) { - result[0] += -0.0017071629; - } else { - result[0] += 0.0010942215; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 60))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 328))) { - result[0] += -0.018242605; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 114))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 334))) { - result[0] += 0.013435415; - } else { - result[0] += 0.000363245; - } - } else { - result[0] += -0.011918341; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 62))) { - result[0] += 0.057550073; - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 64))) { - result[0] += -0.010455087; - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 292))) { - result[0] += 0.025410349; - } else { - result[0] += 0.0067503005; - } - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 48))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 250))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 4))) { - result[0] += 0.0038494274; - } else { - result[0] += -0.012583888; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { - result[0] += 0.0052445154; - } else { - result[0] += 0.022994613; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 110))) { - result[0] += 0.010131297; - } else { - result[0] += 0.030005908; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 36))) { - result[0] += -0.015509938; - } else { - result[0] += 0.033259317; - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 256))) { - result[0] += -0.018772317; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 324))) { - result[0] += -0.0010802757; - } else { - result[0] += -0.019551003; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { - result[0] += 0.015505517; - } else { - result[0] += -0.014615647; - } - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 228))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 266))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { - result[0] += -0.0011533442; - } else { - result[0] += 0.0037619993; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 42))) { - result[0] += -0.0050083552; - } else { - result[0] += -0.020327281; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += -0.01845751; - } else { - result[0] += -0.0027648432; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 270))) { - result[0] += 0.014990672; - } else { - result[0] += -0.0010248877; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { - result[0] += -0.00588067; - } else { - result[0] += -0.0011169165; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - result[0] += 0.0015550206; - } else { - result[0] += -0.0028203435; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 10))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { - result[0] += 0.00057445857; - } else { - result[0] += -0.020817637; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 176))) { - result[0] += 0.004375774; - } else { - result[0] += -0.00017658187; - } - } - } - } - } - } - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 116))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 92))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 40))) { - result[0] += -0.029479165; - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 36))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { - result[0] += -0.0015629175; - } else { - result[0] += 0.014696643; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { - result[0] += -0.012695557; - } else { - result[0] += 0.002633101; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 38))) { - result[0] += 0.088580444; - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 18))) { - result[0] += -0.0013535247; - } else { - result[0] += -0.041627154; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 4))) { - result[0] += 0.010081653; - } else { - result[0] += 0.0011864647; - } - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 158))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { - result[0] += 0.024709804; - } else { - result[0] += 0.006805406; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { - result[0] += -0.020788606; - } else { - result[0] += 0.010531965; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 22))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { - result[0] += 0.029835364; - } else { - result[0] += 0.072482795; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 98))) { - result[0] += -0.0033422098; - } else { - result[0] += -0.027484313; - } - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 198))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 130))) { - result[0] += -0.0117129935; - } else { - result[0] += -0.027794067; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { - result[0] += -0.0058866264; - } else { - result[0] += -0.022589795; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 96))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { - result[0] += -0.007361448; - } else { - result[0] += 0.05226938; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 152))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { - result[0] += -0.0011431018; - } else { - result[0] += -0.013976167; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 34))) { - result[0] += -0; - } else { - result[0] += 0.0123240035; - } - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 16))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 130))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 42))) { - result[0] += 0.00010176472; - } else { - result[0] += 0.0130502; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - result[0] += -0.00884224; - } else { - result[0] += -0.0399912; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 166))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 102))) { - result[0] += -0.0070857047; - } else { - result[0] += 0.0050304467; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - result[0] += -0.018932706; - } else { - result[0] += -0.0011952891; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { - result[0] += 0.03236731; - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 66))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { - result[0] += 0.00022758842; - } else { - result[0] += 0.0045965556; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 40))) { - result[0] += 0.0054974416; - } else { - result[0] += -0.00038611452; - } - } - } - } - } - } - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 128))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 118))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 66))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 140))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - result[0] += -0.030644778; - } else { - result[0] += -0.0012927776; - } - } else { - result[0] += -0.027898073; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 270))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 10))) { - result[0] += -0.0026217196; - } else { - result[0] += 0.00031906084; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { - result[0] += 0.053565513; - } else { - result[0] += 0.011517114; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 242))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 206))) { - result[0] += 0.0022491028; - } else { - result[0] += 0.017666435; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 48))) { - result[0] += -0.039555483; - } else { - result[0] += -0.007261213; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 24))) { - result[0] += -0.0013352408; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 194))) { - result[0] += 0.019903926; - } else { - result[0] += -0.0018211514; - } - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 98))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 260))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { - result[0] += -0.003761566; - } else { - result[0] += 5.4693035e-05; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 44))) { - result[0] += 0.066539966; - } else { - result[0] += 0.0024916714; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 282))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { - result[0] += 0.0024568306; - } else { - result[0] += 0.013490746; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 144))) { - result[0] += 0.0017763426; - } else { - result[0] += -0.0017636567; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 66))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += -0.016284099; - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { - result[0] += 0.022716116; - } else { - result[0] += 0.0030027088; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 108))) { - result[0] += -0.008329183; - } else { - result[0] += -0.02752207; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { - result[0] += 0.024196664; - } else { - result[0] += -0.0069028423; - } - } - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 268))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 266))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 150))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 50))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 6))) { - result[0] += 0.03250914; - } else { - result[0] += 0.0055702133; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { - result[0] += 0.008322871; - } else { - result[0] += -0.015406762; - } - } - } else { - result[0] += 0.0395282; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 262))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 260))) { - result[0] += -0.018292183; - } else { - result[0] += -0; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 294))) { - result[0] += -0.0053772978; - } else { - result[0] += 0.0050149076; - } - } - } else { - result[0] += 0.006939339; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 88))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 282))) { - result[0] += 0.00481271; - } else { - result[0] += -0.0038877416; - } - } else { - result[0] += -0.0105433585; - } - } - } - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 320))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 284))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 212))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 252))) { - result[0] += 8.760832e-05; - } else { - result[0] += 0.0031985168; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 250))) { - result[0] += -0.0029068196; - } else { - result[0] += 0.0005925631; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 242))) { - result[0] += 0.019893859; - } else { - result[0] += 0.009958875; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 266))) { - result[0] += -0.017648466; - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 304))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 302))) { - result[0] += -0.011696982; - } else { - result[0] += 0.004698274; - } - } else { - result[0] += -0.0020677263; - } - } - } - } else { - result[0] += 0.04254042; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 162))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 168))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 122))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 76))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 98))) { - result[0] += -0.0029215065; - } else { - result[0] += 0.011221336; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 112))) { - result[0] += 0.020535378; - } else { - result[0] += 0.0062516048; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 62))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 302))) { - result[0] += -0.0063436194; - } else { - result[0] += -0.01713322; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 162))) { - result[0] += 0.010334019; - } else { - result[0] += -0.0069331317; - } - } - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 322))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 182))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 164))) { - result[0] += -0.0077398308; - } else { - result[0] += -0.03391137; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 190))) { - result[0] += 0.018701833; - } else { - result[0] += 0.005074762; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 192))) { - result[0] += 0.05592671; - } else { - result[0] += 0.012150154; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 138))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 44))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 84))) { - result[0] += -0.004530545; - } else { - result[0] += -0.014109983; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 34))) { - result[0] += 0.016917644; - } else { - result[0] += -0.0003956896; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 168))) { - result[0] += 0.018351296; - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 222))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 326))) { - result[0] += 0.009391616; - } else { - result[0] += -0.012847346; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - result[0] += 0.0028710503; - } else { - result[0] += -0.014146092; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 272))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 24))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { - result[0] += 0.004130573; - } else { - result[0] += -0.02545332; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 136))) { - result[0] += -0.011545375; - } else { - result[0] += 0.0030030604; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 124))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 76))) { - result[0] += 0.0053757923; - } else { - result[0] += 0.026795724; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += 0.0073435195; - } else { - result[0] += -0.011538415; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 84))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 14))) { - result[0] += 0.010773177; - } else { - result[0] += -0.0014024094; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 90))) { - result[0] += -0.00650954; - } else { - result[0] += 0.017750045; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 280))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 256))) { - result[0] += -0.01760956; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 184))) { - result[0] += -0.017441498; - } else { - result[0] += -0.0037122124; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 216))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 182))) { - result[0] += -0.024908429; - } else { - result[0] += -0.012610437; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { - result[0] += 0.020257859; - } else { - result[0] += -0.0005201062; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 20))) { - result[0] += 0.031078367; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 338))) { - result[0] += -0.03464001; - } else { - result[0] += 0.020424169; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 348))) { - result[0] += 0.0028230688; - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 114))) { - result[0] += -0.003942101; - } else { - result[0] += -0.017917613; - } - } - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 56))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 12))) { - result[0] += -0.018274682; - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 88))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { - result[0] += -0.014930627; - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 22))) { - result[0] += 0.009653761; - } else { - result[0] += -0.005647427; - } - } - } else { - result[0] += -0.017578453; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 216))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 116))) { - result[0] += -0.004387402; - } else { - result[0] += 0.004307063; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 244))) { - result[0] += 0.0050197043; - } else { - result[0] += -0.009600091; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 264))) { - result[0] += -0.0004044637; - } else { - result[0] += 0.013372946; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 138))) { - result[0] += -0.030934876; - } else { - result[0] += -0.005925291; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 6))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { - result[0] += -0.018131623; - } else { - result[0] += 0.007491207; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 158))) { - result[0] += 0.00448369; - } else { - result[0] += -0.004200383; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 124))) { - result[0] += 0.00013818998; - } else { - result[0] += -0.0060928585; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 226))) { - result[0] += 0.003693558; - } else { - result[0] += -0.0001647349; - } - } - } - } - } - } - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 400))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 388))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 346))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { - result[0] += 0.00025481472; - } else { - result[0] += 0.015072713; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 56))) { - result[0] += -0.0068883398; - } else { - result[0] += 0.005798895; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - result[0] += -0.012064995; - } else { - result[0] += -0.00055150193; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 236))) { - result[0] += 0.025693614; - } else { - result[0] += -0.0082374755; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 108))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 4))) { - result[0] += 0.020123675; - } else { - result[0] += 0.00013938252; - } - } else { - result[0] += -0.009953358; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 196))) { - result[0] += 0.016889976; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 236))) { - result[0] += -0.010803269; - } else { - result[0] += 0.010580909; - } - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 392))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 198))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 154))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 144))) { - result[0] += -0; - } else { - result[0] += 0.05305763; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 74))) { - result[0] += -0.0071868575; - } else { - result[0] += 0.06390004; - } - } - } else { - result[0] += 0.057614993; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 176))) { - result[0] += -0.0067237015; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 398))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 52))) { - result[0] += 0.0020582944; - } else { - result[0] += 0.009856465; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 86))) { - result[0] += -0.0054150624; - } else { - result[0] += 0.0054157143; - } - } - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 90))) { - result[0] += -0.010108463; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - result[0] += 0.0012366887; - } else { - result[0] += -0.005106163; - } - } - } - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 162))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 150))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 52))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - result[0] += -0.0030484337; - } else { - result[0] += 0.007103072; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { - result[0] += -0.020119462; - } else { - result[0] += 0.0049716155; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 42))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 18))) { - result[0] += -0.020776983; - } else { - result[0] += 0.0001524161; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - result[0] += 0.009727257; - } else { - result[0] += 0.0006957262; - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 54))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 158))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 152))) { - result[0] += -0.011365656; - } else { - result[0] += -0.0006019767; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 154))) { - result[0] += 0.02111136; - } else { - result[0] += 0.001328925; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 216))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 298))) { - result[0] += -0.009145065; - } else { - result[0] += 0.011142318; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 250))) { - result[0] += -0.05126782; - } else { - result[0] += -0.02713782; - } - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 138))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 268))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 66))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 42))) { - result[0] += -0.003784838; - } else { - result[0] += 0.0068541127; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 30))) { - result[0] += -0.029539952; - } else { - result[0] += -0.0067226845; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 166))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 44))) { - result[0] += 0.0031095946; - } else { - result[0] += -0.031267606; - } - } else { - result[0] += 0.03802244; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - result[0] += -0.008942714; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 332))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { - result[0] += 0.018265491; - } else { - result[0] += 0.01069651; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - result[0] += 0.0040097563; - } else { - result[0] += -0.03305195; - } - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 118))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 194))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 70))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 30))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 60))) { - result[0] += -0.019308351; - } else { - result[0] += 0.0002994246; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { - result[0] += 0.0010901977; - } else { - result[0] += 0.0058678756; - } - } - } else { - result[0] += 0.024462478; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 290))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 74))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - result[0] += -0.012439872; - } else { - result[0] += 0.017688526; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 242))) { - result[0] += -0.0040794015; - } else { - result[0] += -0.009616309; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 316))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 246))) { - result[0] += 0.0013879368; - } else { - result[0] += 0.017187012; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 336))) { - result[0] += -0.0060414565; - } else { - result[0] += 0.00086067413; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 210))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 288))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { - result[0] += -0.022543944; - } else { - result[0] += 0.0002652502; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { - result[0] += 0.010252799; - } else { - result[0] += -0.0059008473; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 68))) { - result[0] += -0.00482871; - } else { - result[0] += -0.036633775; - } - } else { - result[0] += -0.020131879; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 88))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += 0.019643709; - } else { - result[0] += -0.0055163004; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 110))) { - result[0] += -0.023596639; - } else { - result[0] += -0.005768484; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 214))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 282))) { - result[0] += 0.024731327; - } else { - result[0] += 0.0059899557; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 216))) { - result[0] += -0.012860804; - } else { - result[0] += 0.00026836878; - } - } - } - } - } - } - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 250))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 270))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 216))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 4))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 188))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 20))) { - result[0] += -0.015227991; - } else { - result[0] += 0.007824044; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { - result[0] += -0.0030827313; - } else { - result[0] += 0.04585096; - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 44))) { - result[0] += 0.0038916196; - } else { - result[0] += -0.00018074422; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += -0; - } else { - result[0] += 0.01431708; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 8))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 40))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 254))) { - result[0] += -0.03443868; - } else { - result[0] += -0.016926734; - } - } else { - result[0] += 0.016495222; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 318))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 150))) { - result[0] += -0.0018124201; - } else { - result[0] += -0.008941532; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - result[0] += 0.010480117; - } else { - result[0] += -0.0053374222; - } - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 144))) { - result[0] += -0.0025247212; - } else { - result[0] += -0.021172313; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 278))) { - result[0] += 0.0136889; - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 24))) { - result[0] += -0; - } else { - result[0] += -0.028621003; - } - } else { - result[0] += 0.008883018; - } - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 272))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 178))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 200))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 120))) { - result[0] += 0.004496847; - } else { - result[0] += -0.0019887805; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 60))) { - result[0] += 0.011425511; - } else { - result[0] += 0.001471782; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 164))) { - result[0] += 0.004373122; - } else { - result[0] += -0.0050846357; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 312))) { - result[0] += -0.01642145; - } else { - result[0] += -0.0041971803; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 240))) { - result[0] += -0.019498728; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 296))) { - result[0] += 0.016818725; - } else { - result[0] += 0.031616382; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 256))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 152))) { - result[0] += 0.0041652615; - } else { - result[0] += 0.024725467; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 4))) { - result[0] += -0.017824838; - } else { - result[0] += 0.00069643237; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 104))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 346))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 138))) { - result[0] += 0.057328846; - } else { - result[0] += -0.009282811; - } - } else { - result[0] += -0.027812624; - } - } else { - result[0] += 0.006232637; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 208))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 184))) { - result[0] += -0.0007241729; - } else { - result[0] += 0.01345086; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 372))) { - result[0] += -0.01243884; - } else { - result[0] += -0.0010584429; - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 66))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 48))) { - result[0] += 0.012493608; - } else { - result[0] += 0.0014906918; - } - } else { - result[0] += -0.011661606; - } - } - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 110))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 2))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 216))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 58))) { - result[0] += 0.0059683393; - } else { - result[0] += 0.047656022; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 76))) { - result[0] += -0.01823411; - } else { - result[0] += 0.011243396; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 88))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 36))) { - result[0] += -0.015964573; - } else { - result[0] += 0.018772287; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 84))) { - result[0] += -0.024348408; - } else { - result[0] += 0.0071454565; - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 60))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 10))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 118))) { - result[0] += 0.0038073042; - } else { - result[0] += -0.010677494; - } - } else { - result[0] += -0.010760325; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 166))) { - result[0] += -0.013037783; - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 196))) { - result[0] += 0.04078368; - } else { - result[0] += -0; - } - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 6))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 110))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 24))) { - result[0] += -0.019799097; - } else { - result[0] += 0.0018352288; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 14))) { - result[0] += 0.016346006; - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 266))) { - result[0] += -0.018813994; - } else { - result[0] += -0.007913149; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 174))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 296))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 280))) { - result[0] += 0.000356507; - } else { - result[0] += 0.004809927; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 298))) { - result[0] += -0.015981428; - } else { - result[0] += -0.0013808893; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 194))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 84))) { - result[0] += -0.0013645035; - } else { - result[0] += -0.009478527; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 196))) { - result[0] += 0.01796195; - } else { - result[0] += -0.00023798608; - } - } - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 142))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 210))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 26))) { - result[0] += -0.0045832377; - } else { - result[0] += -0.03190832; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { - result[0] += 0.0066647097; - } else { - result[0] += -0.002318192; - } - } - } else { - result[0] += -0.040041517; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 264))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 270))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 54))) { - result[0] += -0.0019740702; - } else { - result[0] += -0.024438191; - } - } else { - result[0] += -0; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 166))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 2))) { - result[0] += 0.029889846; - } else { - result[0] += 0.0023792638; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 268))) { - result[0] += 0.0048653954; - } else { - result[0] += -0.013145282; - } - } - } - } - } else { - result[0] += 0.026439697; - } - } - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 50))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 92))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 154))) { - result[0] += -0.023412313; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 164))) { - result[0] += 0.020196555; - } else { - result[0] += -0.012161604; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 38))) { - result[0] += 0.08170753; - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 94))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 18))) { - result[0] += -0.002143487; - } else { - result[0] += -0.038541686; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 50))) { - result[0] += -0.006611839; - } else { - result[0] += 0.008542123; - } - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 200))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 132))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 110))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 46))) { - result[0] += 0.0018188714; - } else { - result[0] += -0.015560075; - } - } else { - result[0] += 0.021364069; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 112))) { - result[0] += -0.029760977; - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 148))) { - result[0] += 0.0078332; - } else { - result[0] += -0.011854128; - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 64))) { - result[0] += 0.014748133; - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { - result[0] += -0.012884839; - } else { - result[0] += 0.0029220346; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 42))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 148))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { - result[0] += 0.015425849; - } else { - result[0] += -0.004185417; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 98))) { - result[0] += -0.0011755283; - } else { - result[0] += -0.03457963; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 266))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 48))) { - result[0] += -0.009746331; - } else { - result[0] += -0.030245284; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { - result[0] += -0.006777007; - } else { - result[0] += -0.02345091; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 92))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { - result[0] += -0.008947103; - } else { - result[0] += 0.03920984; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 148))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 6))) { - result[0] += 0.009707036; - } else { - result[0] += -0.0060035028; - } - } else { - result[0] += 0.01016276; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 28))) { - result[0] += -0; - } else { - result[0] += 0.032831904; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 118))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 98))) { - result[0] += 6.658281e-06; - } else { - result[0] += 0.0031512368; - } - } else { - result[0] += 0.02200143; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { - result[0] += 0.033468585; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 82))) { - result[0] += -0.008753168; - } else { - result[0] += -0.0016586205; - } - } - } - } - } - } - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 314))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 26))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - result[0] += -0.033794925; - } else { - result[0] += 0.0085956175; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 8))) { - result[0] += 0.02062062; - } else { - result[0] += -0.0024983874; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 30))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { - result[0] += -0; - } else { - result[0] += 0.02111803; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 66))) { - result[0] += 0.048572876; - } else { - result[0] += 0.007911704; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 112))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 68))) { - result[0] += -0.0077246563; - } else { - result[0] += 0.005386382; - } - } else { - result[0] += -0.020841127; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 48))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 4))) { - result[0] += -0.014042695; - } else { - result[0] += 0.008498053; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 12))) { - result[0] += -0.024558064; - } else { - result[0] += -0.0025908693; - } - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 32))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 24))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 170))) { - result[0] += 2.548086e-05; - } else { - result[0] += 0.026205242; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 92))) { - result[0] += 0.009812611; - } else { - result[0] += 0.00306112; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { - result[0] += 0.027721763; - } else { - result[0] += 0.006687283; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 16))) { - result[0] += 0.035541125; - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 18))) { - result[0] += -0.017473036; - } else { - result[0] += -0.0030920196; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { - result[0] += -0.00029661623; - } else { - result[0] += -0.006844879; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += 0.009076519; - } else { - result[0] += 0.00021811183; - } - } - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 36))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 34))) { - result[0] += 0.026330112; - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 248))) { - result[0] += 0.01319269; - } else { - result[0] += -0.004578778; - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 62))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { - result[0] += -0; - } else { - result[0] += -0.017771253; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 68))) { - result[0] += 0.041357838; - } else { - result[0] += 0.0004609677; - } - } - } - } - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 120))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 116))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 0))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 12))) { - result[0] += -0.03449745; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 10))) { - result[0] += -0.000830644; - } else { - result[0] += 0.027823929; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { - result[0] += -0.014675349; - } else { - result[0] += 0.002744519; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { - result[0] += 0.00021282148; - } else { - result[0] += 0.012012251; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { - result[0] += -0.023646448; - } else { - result[0] += -0.0012802199; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 64))) { - result[0] += 0.005379937; - } else { - result[0] += 0.020074537; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { - result[0] += -0.017483069; - } else { - result[0] += 0.00045991183; - } - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 28))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 118))) { - result[0] += -0.010277932; - } else { - result[0] += 0.020262284; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 78))) { - result[0] += 0.026524289; - } else { - result[0] += 0.0005923277; - } - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 78))) { - result[0] += -0.023216326; - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 160))) { - result[0] += -0.0063289674; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 80))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 56))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 124))) { - result[0] += -0.026424408; - } else { - result[0] += -0.00650081; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { - result[0] += 0.035873644; - } else { - result[0] += -0.0067583695; - } - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 134))) { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 122))) { - result[0] += 0.019181004; - } else { - result[0] += 0.0012452018; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - result[0] += -0.006571722; - } else { - result[0] += -0.00113232; - } - } - } - } - } - } - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 248))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 270))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 184))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 104))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 312))) { - result[0] += -0.001673722; - } else { - result[0] += 0.0015081331; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { - result[0] += 0.019442735; - } else { - result[0] += -0.0041994313; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 116))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 170))) { - result[0] += 0.00018035778; - } else { - result[0] += -0.012916463; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 356))) { - result[0] += 0.003256688; - } else { - result[0] += -0.0061490457; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 28))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 80))) { - result[0] += -0.002768964; - } else { - result[0] += 0.036568955; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { - result[0] += 0.0035748954; - } else { - result[0] += -0.0018361468; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { - result[0] += 0.0008316287; - } else { - result[0] += -0.012621154; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 212))) { - result[0] += 0.0009690163; - } else { - result[0] += -0.009806303; - } - } - } - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 226))) { - result[0] += -0.008160834; - } else { - result[0] += 0.010674962; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 66))) { - result[0] += -0.018799648; - } else { - result[0] += -0.0010747229; - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 274))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 252))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { - result[0] += 0.013668454; - } else { - result[0] += 0.0015821367; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 254))) { - result[0] += 0.0013118708; - } else { - result[0] += -0.0027904937; - } - } - } else { - result[0] += 0.013429761; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 150))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 312))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 278))) { - result[0] += 0.0026331868; - } else { - result[0] += 0.02152044; - } - } else { - result[0] += 0.010367058; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 308))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - result[0] += -0.009165699; - } else { - result[0] += 0.012586451; - } - } else { - result[0] += 0.017478531; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 130))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 354))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 56))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 138))) { - result[0] += 0.03761896; - } else { - result[0] += -0.009919451; - } - } else { - result[0] += -0.024488965; - } - } else { - result[0] += 0.0063245073; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 178))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 292))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 342))) { - result[0] += 0.004199187; - } else { - result[0] += -0.0270401; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 12))) { - result[0] += -0.034475163; - } else { - result[0] += 0.03449012; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 204))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 312))) { - result[0] += -0.008276438; - } else { - result[0] += -0.021829268; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 348))) { - result[0] += -0.00024369739; - } else { - result[0] += 0.018283892; - } - } - } - } - } - } - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 0))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 180))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - result[0] += 0.015541151; - } else { - result[0] += -0.004526817; - } - } else { - result[0] += 0.04517481; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 44))) { - result[0] += -0.008011468; - } else { - result[0] += -0.0023013349; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 16))) { - result[0] += 0.027930424; - } else { - result[0] += 0.001679267; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 92))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 20))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 32))) { - result[0] += 0.022154698; - } else { - result[0] += -0.01756174; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 76))) { - result[0] += -0.045003; - } else { - result[0] += -0.010276439; - } - } - } else { - result[0] += 0.001000655; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 116))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { - result[0] += 0.0012434544; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 160))) { - result[0] += 0.018014176; - } else { - result[0] += -0.00070313603; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 126))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 114))) { - result[0] += 0.0062645376; - } else { - result[0] += -0.015609448; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 16))) { - result[0] += 0.01479733; - } else { - result[0] += 0.0014734569; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 134))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 290))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 304))) { - result[0] += -0; - } else { - result[0] += -0.012774549; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 40))) { - result[0] += 0.014705256; - } else { - result[0] += -0.004232665; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 314))) { - result[0] += 0.0029532865; - } else { - result[0] += -0.0029254516; - } - } else { - result[0] += 0.026304556; - } - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 128))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 256))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 150))) { - result[0] += 0.00030722877; - } else { - result[0] += -0.0051551643; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { - result[0] += 0.016633255; - } else { - result[0] += 0.001668491; - } - } - } else { - result[0] += -0.014828484; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 236))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 254))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 286))) { - result[0] += 0.0071044452; - } else { - result[0] += -0.0076894383; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 54))) { - result[0] += 0.02084865; - } else { - result[0] += 0.007473866; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 278))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 290))) { - result[0] += 0.005741518; - } else { - result[0] += 0.03405652; - } - } else { - result[0] += 0.025841344; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 22))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 150))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 154))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 152))) { - result[0] += 0.00015940488; - } else { - result[0] += 0.05232349; - } - } else { - result[0] += -0.018910853; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { - result[0] += -0.051273484; - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 106))) { - result[0] += -0.02290248; - } else { - result[0] += -0.0016118204; - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 64))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 192))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 162))) { - result[0] += 0.0010482629; - } else { - result[0] += -0.016297817; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 196))) { - result[0] += 0.013967775; - } else { - result[0] += 0.027914885; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 74))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 2))) { - result[0] += 0.038794536; - } else { - result[0] += -0.0062946193; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - result[0] += -2.64479e-05; - } else { - result[0] += 0.021996183; - } - } - } - } - } - } - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 104))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 40))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 32))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 28))) { - result[0] += -0.005473042; - } else { - result[0] += 0.011005775; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 136))) { - result[0] += -0.011721667; - } else { - result[0] += 0.0078737205; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 46))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 38))) { - result[0] += 0.0025443584; - } else { - result[0] += -0.016291086; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += 0.00024633956; - } else { - result[0] += 0.027108392; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 26))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 10))) { - result[0] += 0.03744457; - } else { - result[0] += 0.0014647435; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 136))) { - result[0] += -0.0058734766; - } else { - result[0] += -0.02578007; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 50))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - result[0] += -0.00944722; - } else { - result[0] += -0.03411841; - } - } else { - result[0] += -0.001750114; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 14))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 100))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 24))) { - result[0] += 0.0006245327; - } else { - result[0] += 0.02487868; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 18))) { - result[0] += 0.003864918; - } else { - result[0] += 0.04562244; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 114))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 94))) { - result[0] += -0; - } else { - result[0] += -0.037629705; - } - } else { - result[0] += 0.017797282; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 20))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { - result[0] += 0.03347585; - } else { - result[0] += -0.00015754919; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - result[0] += 0.042547952; - } else { - result[0] += 0.008394706; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 304))) { - result[0] += -0.0053476547; - } else { - result[0] += 0.019664986; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 274))) { - result[0] += 0.00061302836; - } else { - result[0] += -0.008260283; - } - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 112))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 164))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { - result[0] += -0.0029065635; - } else { - result[0] += 0.012601085; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { - result[0] += 0.011005281; - } else { - result[0] += -0.010062504; - } - } - } else { - result[0] += 0.015878765; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 202))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 182))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 154))) { - result[0] += -0.012548672; - } else { - result[0] += -0.036353104; - } - } else { - result[0] += -0.0037594133; - } - } else { - result[0] += -0.026142243; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 110))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 108))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 194))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 162))) { - result[0] += 0.0049291016; - } else { - result[0] += 0.02395081; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 234))) { - result[0] += -0.0030472444; - } else { - result[0] += 0.0040821205; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 100))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 114))) { - result[0] += -0.020026503; - } else { - result[0] += -0.0037345327; - } - } else { - result[0] += -0.025079226; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 128))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 128))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 160))) { - result[0] += 0.006035849; - } else { - result[0] += -0.005563633; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 134))) { - result[0] += -0.017343946; - } else { - result[0] += 0.014413935; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 130))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 308))) { - result[0] += 0.007634467; - } else { - result[0] += -0.024592249; - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 106))) { - result[0] += 0.0005188165; - } else { - result[0] += -0.006757953; - } - } - } - } - } - } - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 298))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 52))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { - result[0] += 0.00393571; - } else { - result[0] += 0.02043746; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 262))) { - result[0] += -0.01545644; - } else { - result[0] += -0.002095156; - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 210))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 128))) { - result[0] += -0.00013221658; - } else { - result[0] += -0.002142299; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - result[0] += -0.0012591215; - } else { - result[0] += 0.0015919211; - } - } - } - } else { - result[0] += 0.011080801; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 364))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 300))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 142))) { - result[0] += -0.030891055; - } else { - result[0] += -0.011523842; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 296))) { - result[0] += -0.0094786985; - } else { - result[0] += 0.00014606201; - } - } - } else { - result[0] += 0.045800555; - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 18))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 0))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 6))) { - result[0] += 0.021174032; - } else { - result[0] += -0; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 54))) { - result[0] += -0; - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 170))) { - result[0] += -0.02313884; - } else { - result[0] += -0; - } - } - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 310))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 120))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 170))) { - result[0] += 0.0034402236; - } else { - result[0] += 0.03586924; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 150))) { - result[0] += -0.007850896; - } else { - result[0] += 0.0023356122; - } - } - } else { - result[0] += 0.019080771; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 340))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 216))) { - result[0] += -0.006365792; - } else { - result[0] += 0.012724089; - } - } else { - result[0] += -0.03172406; - } - } - } - } - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 350))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 144))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 142))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 116))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 138))) { - result[0] += -0.00062813837; - } else { - result[0] += 0.002671611; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 86))) { - result[0] += 0.00652712; - } else { - result[0] += 0.00037897687; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 152))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 286))) { - result[0] += -0.00025623475; - } else { - result[0] += -0.004133305; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 82))) { - result[0] += -0.01589259; - } else { - result[0] += 0.019172618; - } - } - } - } else { - result[0] += 0.009757864; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 250))) { - result[0] += -0.0065281326; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 94))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 254))) { - result[0] += 0.0007117824; - } else { - result[0] += 0.027270088; - } - } else { - result[0] += -0.0023996648; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 248))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 48))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { - result[0] += -0.0019857192; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 222))) { - result[0] += 0.025358735; - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 232))) { - result[0] += -0.0021736256; - } else { - result[0] += 0.016332442; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 52))) { - result[0] += -0.0129296705; - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 224))) { - result[0] += -0; - } else { - result[0] += 0.018950501; - } - } - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 68))) { - result[0] += -0.0070409672; - } else { - result[0] += 0.010926886; - } - } - } - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 314))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 312))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 248))) { - result[0] += -0.00022246773; - } else { - result[0] += 0.00076635665; - } - } else { - result[0] += 0.012082838; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 286))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 298))) { - result[0] += -0.015522775; - } else { - result[0] += -0.0064152265; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 348))) { - result[0] += -0.0013468252; - } else { - result[0] += 0.020515444; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 208))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 276))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 150))) { - result[0] += 0.0077754804; - } else { - result[0] += -0.0030992497; - } - } else { - result[0] += -0.017411573; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 250))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { - result[0] += 0.003438398; - } else { - result[0] += 0.032958325; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 30))) { - result[0] += 0.013451094; - } else { - result[0] += -0.006546424; - } - } - } - } - } else { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 32))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 188))) { - result[0] += 0.009157052; - } else { - result[0] += -0.010829896; - } - } else { - result[0] += 0.015605274; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { - result[0] += 0.01869001; - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 228))) { - result[0] += -0.0042858357; - } else { - result[0] += -0.02192712; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 226))) { - result[0] += -0.0021264374; - } else { - result[0] += 0.028851261; - } - } - } - } - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 150))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 6))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 282))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 2))) { - result[0] += 0.008089608; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 252))) { - result[0] += -0.02760528; - } else { - result[0] += -0.014671764; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 248))) { - result[0] += 0.0007742315; - } else { - result[0] += -0.014570135; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 310))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 264))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 52))) { - result[0] += 0.004098795; - } else { - result[0] += -0.0013311962; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 250))) { - result[0] += -0.016194416; - } else { - result[0] += -0.004321107; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 246))) { - result[0] += 0.012578972; - } else { - result[0] += 0.031848185; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 200))) { - result[0] += 0.002893864; - } else { - result[0] += -0.0014323759; - } - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 48))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 54))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 24))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += -0.0058673653; - } else { - result[0] += 0.014972388; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 32))) { - result[0] += -0.016248314; - } else { - result[0] += -0; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 42))) { - result[0] += 0.001427743; - } else { - result[0] += 0.013427376; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 218))) { - result[0] += 0.004056875; - } else { - result[0] += -0.0111101195; - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 142))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 108))) { - result[0] += 0.009302656; - } else { - result[0] += 0.0009259971; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { - result[0] += -0.0071252473; - } else { - result[0] += 0.0005085042; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 118))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 100))) { - result[0] += 0.0055279406; - } else { - result[0] += -0.015505721; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 152))) { - result[0] += 0.022024345; - } else { - result[0] += 0.013201664; - } - } - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 30))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 140))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 24))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 154))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 34))) { - result[0] += -0.0031792417; - } else { - result[0] += -0.024321148; - } - } else { - result[0] += 0.0074777827; - } - } else { - if (UNLIKELY((data[15].missing != -1) && (data[15].qvalue < 2))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 52))) { - result[0] += -0.015280488; - } else { - result[0] += 0.004024551; - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 26))) { - result[0] += 0.013732604; - } else { - result[0] += -0; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 74))) { - result[0] += -0.028851384; - } else { - result[0] += -0.051958527; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 26))) { - result[0] += 0.010219454; - } else { - result[0] += -0.009982391; - } - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 162))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 256))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 158))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 24))) { - result[0] += 0.014080286; - } else { - result[0] += -0.004486435; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 136))) { - result[0] += 0.005933493; - } else { - result[0] += 0.02153199; - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 316))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - result[0] += -0.02032683; - } else { - result[0] += 0.002681443; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 214))) { - result[0] += -0.0042459033; - } else { - result[0] += 0.0068887584; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 196))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 392))) { - result[0] += 0.00047894806; - } else { - result[0] += -0.0061863232; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 230))) { - result[0] += 0.008450966; - } else { - result[0] += 0.0015639787; - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 200))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 224))) { - result[0] += -0.007651621; - } else { - result[0] += -0.0009728819; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { - result[0] += 0.0062414994; - } else { - result[0] += -0.0019789643; - } - } - } - } - } - } - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 314))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 312))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 306))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 84))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { - result[0] += -0.0028604511; - } else { - result[0] += 0.0004893718; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 224))) { - result[0] += 0.026684532; - } else { - result[0] += 0.0063786306; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 16))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 206))) { - result[0] += -0.009385272; - } else { - result[0] += 0.008824005; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 256))) { - result[0] += -0.00080077257; - } else { - result[0] += 0.0010740731; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 374))) { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 34))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 224))) { - result[0] += 0.014482776; - } else { - result[0] += -0.0036570956; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 308))) { - result[0] += 0.009366672; - } else { - result[0] += -0.00948626; - } - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 26))) { - result[0] += -0.005847118; - } else { - result[0] += 0.033575047; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 218))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 188))) { - result[0] += 0.0021236583; - } else { - result[0] += -0.01067119; - } - } else { - result[0] += 0.0088947555; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 222))) { - result[0] += 0.018932056; - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 64))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 230))) { - result[0] += -0.0033845974; - } else { - result[0] += -0.020902513; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 250))) { - result[0] += 0.0032020702; - } else { - result[0] += 0.042684905; - } - } - } - } - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 18))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 70))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 68))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 6))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 10))) { - result[0] += -0.008612228; - } else { - result[0] += 0.0040322477; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 22))) { - result[0] += -0.008511812; - } else { - result[0] += 0.011585411; - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 18))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 44))) { - result[0] += 1.8034565e-05; - } else { - result[0] += 0.027274786; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 64))) { - result[0] += 0.008447873; - } else { - result[0] += -0.025014887; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 130))) { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 0))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 58))) { - result[0] += -0.007062527; - } else { - result[0] += 0.014613422; - } - } else { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 24))) { - result[0] += 0.0011185434; - } else { - result[0] += 0.008517242; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { - result[0] += -0.025690308; - } else { - result[0] += -0.0029228963; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 120))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 30))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 46))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { - result[0] += -0.017073292; - } else { - result[0] += 0.0035216322; - } - } else { - result[0] += -0.023047855; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 22))) { - result[0] += -0.025554648; - } else { - result[0] += -0.0017376606; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 106))) { - result[0] += -0.0054964907; - } else { - result[0] += 0.037392512; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { - result[0] += -0.05761969; - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 34))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 98))) { - result[0] += -0.016015155; - } else { - result[0] += 0.0068704; - } - } else { - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 76))) { - result[0] += -0.015397488; - } else { - result[0] += -0.04052282; - } - } - } - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 138))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 370))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 86))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 4))) { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 52))) { - result[0] += 0.0003938696; - } else { - result[0] += 0.0045072534; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 64))) { - result[0] += -0.0024714952; - } else { - result[0] += 0.0049051414; - } - } - } else { - result[0] += 0.017899476; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 380))) { - result[0] += -0.008237792; - } else { - result[0] += -0.0031683035; - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 288))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 282))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 174))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 100))) { - result[0] += -0.0050061867; - } else { - result[0] += 0.00018127509; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 26))) { - result[0] += -0.0051624607; - } else { - result[0] += 0.00025673906; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 156))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 240))) { - result[0] += -0.0046179085; - } else { - result[0] += 0.026366374; - } - } else { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 8))) { - result[0] += -0; - } else { - result[0] += 0.018350547; - } - } - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 268))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 50))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 220))) { - result[0] += -0.0091535505; - } else { - result[0] += -0.042126443; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 248))) { - result[0] += -0.03904317; - } else { - result[0] += -0.0013817366; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 44))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 122))) { - result[0] += 0.0032674843; - } else { - result[0] += 0.038644753; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { - result[0] += -0.003548465; - } else { - result[0] += 0.044352487; - } - } - } - } - } - } - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 168))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 102))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { - result[0] += -0.0006659017; - } else { - result[0] += -0.017484738; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 124))) { - result[0] += 0.021091629; - } else { - result[0] += -0.0017686317; - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 0))) { - result[0] += -0.035111748; - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 46))) { - result[0] += -0.0162628; - } else { - result[0] += -0.004374349; - } - } - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 8))) { - result[0] += -0.031120077; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { - result[0] += -0.0055634947; - } else { - result[0] += -0.017814448; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 120))) { - result[0] += 0.042426206; - } else { - result[0] += -0.0013250493; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 58))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 132))) { - result[0] += 0.03963465; - } else { - result[0] += 0.018596584; - } - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 114))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 94))) { - result[0] += -0.0008957187; - } else { - result[0] += -0.03431357; - } - } else { - result[0] += 0.015205196; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 84))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 102))) { - result[0] += 0.012878765; - } else { - result[0] += -0.013091402; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 140))) { - result[0] += -0.022308733; - } else { - result[0] += 0.0014070682; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 38))) { - result[0] += -0.018751746; - } else { - result[0] += -0.041268926; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 52))) { - result[0] += -0.0035958923; - } else { - result[0] += 9.794186e-05; - } - } - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 352))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 198))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 120))) { - result[0] += 0.013142203; - } else { - result[0] += -0.0014631682; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += -0; - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 16))) { - result[0] += -0.01765822; - } else { - result[0] += 0.0049266764; - } - } - } - } else { - result[0] += -0.0039253565; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 368))) { - result[0] += 0.015282332; - } else { - result[0] += -0.0067160674; - } - } - } - if (LIKELY((data[10].missing != -1) && (data[10].qvalue < 84))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 214))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 176))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 160))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 32))) { - result[0] += -0.0012096122; - } else { - result[0] += 0.004681909; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 118))) { - result[0] += -0.013531153; - } else { - result[0] += 0.004223753; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 72))) { - result[0] += -0.0026232149; - } else { - result[0] += -0.01515016; - } - } else { - if (LIKELY((data[11].missing != -1) && (data[11].qvalue < 170))) { - result[0] += -0.00014745975; - } else { - result[0] += 0.0059427386; - } - } - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 86))) { - if (LIKELY((data[17].missing != -1) && (data[17].qvalue < 322))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 146))) { - result[0] += 0.011954216; - } else { - result[0] += -0.00069962896; - } - } else { - result[0] += 0.03788197; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - result[0] += -0.0036799812; - } else { - result[0] += -0.014614584; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 194))) { - result[0] += -0; - } else { - result[0] += 0.020192096; - } - } - } - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 246))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 96))) { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 218))) { - if (UNLIKELY((data[10].missing != -1) && (data[10].qvalue < 36))) { - result[0] += 0.010108634; - } else { - result[0] += 0.0014457417; - } - } else { - if (UNLIKELY((data[12].missing != -1) && (data[12].qvalue < 138))) { - result[0] += -0.0062636524; - } else { - result[0] += 0.00020081793; - } - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 6))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 74))) { - result[0] += -0.011251283; - } else { - result[0] += 0.0056470656; - } - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 232))) { - result[0] += 0.0014956547; - } else { - result[0] += 0.0059374445; - } - } - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 266))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 58))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 114))) { - result[0] += -0.010740235; - } else { - result[0] += 0.00111787; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - result[0] += -0.001297196; - } else { - result[0] += 0.0012389937; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 240))) { - result[0] += 0.01125375; - } else { - result[0] += -0.03845505; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 292))) { - result[0] += -0.008091416; - } else { - result[0] += 0.0020921114; - } - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 30))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 8))) { - result[0] += -0.040072184; - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 136))) { - result[0] += 0.013737966; - } else { - result[0] += -0.00681287; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 132))) { - if (LIKELY((data[12].missing != -1) && (data[12].qvalue < 0))) { - result[0] += 0.028193597; - } else { - result[0] += -0.0037449375; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - result[0] += 0.015007503; - } else { - result[0] += -0.009241991; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 94))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 18))) { - result[0] += -0.003027987; - } else { - result[0] += -0.019359348; - } - } else { - result[0] += 0.0025615941; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - result[0] += 0.023024825; - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 218))) { - result[0] += -0.0007633574; - } else { - result[0] += -0.006595341; - } - } - } - } - } else { - if (LIKELY((data[15].missing != -1) && (data[15].qvalue < 6))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 146))) { - result[0] += -0.039751314; - } else { - if (UNLIKELY((data[11].missing != -1) && (data[11].qvalue < 108))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 308))) { - result[0] += 0.011828893; - } else { - result[0] += -0.009816745; - } - } else { - result[0] += -0.015453085; - } - } - } else { - result[0] += 0.0023617218; - } - } - } - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 322))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 282))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 276))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 274))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 272))) { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 268))) { - result[0] += -2.5651694e-05; - } else { - result[0] += 0.004297465; - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 234))) { - result[0] += -0.012060625; - } else { - result[0] += 0.027334878; - } - } - } else { - result[0] += 0.0077310125; - } - } else { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 112))) { - result[0] += -0.012176646; - } else { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 306))) { - result[0] += 0.0043146396; - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 278))) { - result[0] += -0.010458501; - } else { - result[0] += -0.0025602286; - } - } - } - } - } else { - result[0] += 0.026633803; - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 60))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 130))) { - if (UNLIKELY((data[14].missing != -1) && (data[14].qvalue < 14))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 122))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 106))) { - result[0] += 0.009240702; - } else { - result[0] += 0.02637141; - } - } else { - result[0] += -0.0056706998; - } - } else { - if (LIKELY((data[13].missing != -1) && (data[13].qvalue < 10))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 286))) { - result[0] += -0.003625507; - } else { - result[0] += 0.0070409435; - } - } else { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 280))) { - result[0] += 0.014894609; - } else { - result[0] += -0.003168697; - } - } - } - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 178))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { - result[0] += -0.015371713; - } else { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 148))) { - result[0] += -0.009225684; - } else { - result[0] += 0.00080344983; - } - } - } else { - result[0] += -0.034244932; - } - } - } else { - if (UNLIKELY((data[13].missing != -1) && (data[13].qvalue < 72))) { - if (LIKELY((data[9].missing != -1) && (data[9].qvalue < 208))) { - if (UNLIKELY((data[9].missing != -1) && (data[9].qvalue < 184))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += -0.013472222; - } else { - result[0] += 0.00748849; - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 304))) { - result[0] += 0.0054984465; - } else { - result[0] += 0.048147988; - } - } - } else { - result[0] += -0.010059855; - } - } else { - if (LIKELY((data[16].missing != -1) && (data[16].qvalue < 346))) { - if (LIKELY((data[14].missing != -1) && (data[14].qvalue < 250))) { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 278))) { - result[0] += 0.0179956; - } else { - result[0] += 0.00028234924; - } - } else { - if (UNLIKELY((data[16].missing != -1) && (data[16].qvalue < 330))) { - result[0] += -0.005519641; - } else { - result[0] += -0.03736005; - } - } - } else { - if (UNLIKELY((data[17].missing != -1) && (data[17].qvalue < 226))) { - result[0] += -0.0048794984; - } else { - result[0] += 0.007994923; - } - } - } - } - } - - // Apply base_scores - result[0] += -1.882233619689941406; - result[0] = std::exp(result[0]); - - // Apply postprocessor - if (!pred_margin) { postprocess(result); } -} - -void dualsimplex_predictor::postprocess(double* result) -{ - // Do nothing -} - -// Feature names array -const char* dualsimplex_predictor::feature_names[dualsimplex_predictor::NUM_FEATURES] = { - "m", - "n", - "nnz", - "density", - "avg_nnz_col", - "avg_nnz_row", - "bounded", - "free", - "refact_freq", - "num_refacts", - "num_updates", - "sparse_dz", - "dense_dz", - "bound_flips", - "num_infeas", - "dy_nz_pct", - "byte_loads", - "byte_stores"}; diff --git a/cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp b/cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp deleted file mode 100644 index bad128a30..000000000 --- a/cpp/src/utilities/models/dualsimplex_predictor/quantize.cpp +++ /dev/null @@ -1,1891 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#include "header.h" - -static const float threshold[] = { - 125, - 162, - 329, - 335, - 359, - 402, - 443, - 447, - 465, - 482, - 550, - 581, - 677, - 810, - 836, - 865, - 1004, - 1082, - 1388, - 1652, - 1690, - 1754, - 1820, - 1916, - 2387, - 2432, - 2471, - 2487, - 2676, - 3022, - 3128, - 3174, - 3472, - 3480, - 3897, - 4179, - 4193, - 4233, - 4240, - 4462, - 4480, - 4561, - 4661, - 4813, - 4934, - 5355, - 5593, - 6119, - 6332, - 6474, - 6504, - 6574, - 7260, - 8469, - 8488, - 8579, - 9001, - 9809, - 10400, - 10418, - 10711, - 10837, - 12702, - 13228, - 13552, - 14039, - 14653, - 15663, - 16026, - 16418, - 16488, - 16924, - 18083, - 18157, - 18525, - 18558, - 18584, - 18608, - 19134, - 20335, - 22063, - 24884, - 32736, - 33373, - 37617, - 39261, - 45220, - 48604, - 49951, - 53360, - 56116, - 57099, - 59576, - 67583, - 71393, - 88441, - 90924, - 99789, - 105209, - 105933, - 131865, - 163021, - 169576, - 224453, - 259962, - 265341, - 320520, - 349602, - 957446, - 1266, - 1314, - 2224, - 2246, - 2802, - 2825, - 2979, - 2984, - 4950, - 5769, - 5783, - 5970, - 6042, - 6519, - 6734, - 6742, - 7295, - 7784, - 8179, - 8393, - 8634, - 9240, - 9756, - 13075, - 13650, - 13802, - 14563, - 14619, - 15528, - 15655, - 15789, - 16634, - 16952, - 18099, - 18166, - 18178, - 18904, - 19457, - 19794, - 21196, - 22038, - 22645, - 24627, - 24951, - 25483, - 25720, - 30733, - 33499, - 33508, - 34436, - 37008, - 45637, - 47542, - 52526, - 56443, - 60016, - 60697, - 65771, - 65861, - 74900, - 75951, - 78534, - 88088, - 90196, - 114164, - 132419, - 141311, - 143793, - 152283, - 154622, - 159620, - 164010, - 164045, - 228960, - 233606, - 263018, - 266451, - 327865, - 408066, - 449518, - 471906, - 563256, - 1439711, - 1667610, - 2884312, - 2094, - 2530, - 2978, - 4192, - 5810, - 7766, - 8669, - 14451, - 15023, - 16817, - 19230, - 20237, - 20696, - 20930, - 21776, - 29160, - 36272, - 37026, - 40332, - 41996, - 43680, - 45193, - 50261, - 56378, - 77968, - 81543, - 81717, - 81884, - 84033, - 103839, - 109475, - 115299, - 120841, - 138700, - 141395, - 143805, - 144427, - 154634, - 171928, - 187719, - 188804, - 217438, - 217800, - 244409, - 260869, - 265420, - 280821, - 290749, - 292903, - 311318, - 352291, - 367831, - 373297, - 391677, - 394271, - 414508, - 435069, - 436515, - 466492, - 490740, - 580585, - 584200, - 819580, - 859035, - 1003742, - 1096149, - 1102562, - 1117965, - 1348523, - 1706240, - 1754504, - 1764516, - 1987895, - 2108293, - 2188365, - 4297708, - 5183486, - 7959863, - 11797396, - 1.01236e-05, - 1.146592e-05, - 1.7583379e-05, - 1.819257e-05, - 1.8350371e-05, - 2.181823e-05, - 2.1838039e-05, - 2.269307e-05, - 2.609327e-05, - 2.8569561e-05, - 3.116237e-05, - 3.2021151e-05, - 3.4602828e-05, - 3.5382109e-05, - 3.5911838e-05, - 3.710145e-05, - 3.8838469e-05, - 4.0873962e-05, - 4.824765e-05, - 5.1134e-05, - 5.5583841e-05, - 6.4015032e-05, - 7.5606709e-05, - 7.8921999e-05, - 8.1764643e-05, - 0.0001105412, - 0.0001109023, - 0.0001204482, - 0.0001509623, - 0.00016829019, - 0.0001782324, - 0.0001799585, - 0.0001880354, - 0.00019853171, - 0.0002269696, - 0.0002535714, - 0.0002745305, - 0.00028268999, - 0.00028290771, - 0.0002968506, - 0.0003150893, - 0.0003669617, - 0.00043851949, - 0.00044883709, - 0.00045417139, - 0.00049108238, - 0.00051668438, - 0.00067281991, - 0.00075661199, - 0.00083260372, - 0.0008424538, - 0.00090097258, - 0.001032864, - 0.001152342, - 0.001232837, - 0.001377049, - 0.001454633, - 0.001720742, - 0.001807331, - 0.0019736169, - 0.002305151, - 0.002327027, - 0.002512071, - 0.003719569, - 0.0038111249, - 0.0071778512, - 0.0079891831, - 0.0096212169, - 0.01194467, - 0.01507965, - 0.095635854, - 0.14575709, - 0.33324131, - 1.6, - 1.63, - 1.64, - 1.65, - 1.66, - 1.7, - 1.87, - 1.92, - 1.99, - 2, - 2.04, - 2.0899999, - 2.26, - 2.3199999, - 2.3299999, - 2.3399999, - 2.4000001, - 2.4100001, - 2.45, - 2.46, - 2.47, - 2.49, - 2.53, - 2.6900001, - 2.75, - 2.8099999, - 2.8199999, - 2.8299999, - 2.8900001, - 2.9000001, - 2.9400001, - 2.98, - 2.99, - 3, - 3.01, - 3.0599999, - 3.0999999, - 3.1500001, - 3.1700001, - 3.2, - 3.22, - 3.27, - 3.3099999, - 3.4200001, - 3.47, - 3.6199999, - 3.6400001, - 3.6900001, - 3.72, - 3.8499999, - 4.2800002, - 4.8200002, - 5.2600002, - 5.3200002, - 5.5, - 5.6599998, - 5.9099998, - 6.0300002, - 6.4000001, - 6.4200001, - 6.5300002, - 6.6799998, - 7.23, - 7.4899998, - 8.1599998, - 8.2799997, - 9.3400002, - 9.3900003, - 10.26, - 10.41, - 11.14, - 12.08, - 12.45, - 12.77, - 14.58, - 14.81, - 15.15, - 16.870001, - 17.219999, - 21.440001, - 25.24, - 34.73, - 37.759998, - 56.080002, - 69.279999, - 109.43, - 3.3299999, - 3.4200001, - 3.5, - 3.52, - 3.71, - 3.72, - 3.8, - 3.8599999, - 3.9400001, - 4.0100002, - 4.0300002, - 4.0900002, - 4.1100001, - 4.1300001, - 4.1999998, - 4.3099999, - 4.4200001, - 4.4299998, - 4.5700002, - 4.5999999, - 4.6300001, - 4.7800002, - 4.79, - 4.8200002, - 4.9099998, - 4.96, - 4.9699998, - 5, - 5.02, - 5.1500001, - 5.1799998, - 5.1900001, - 5.4099998, - 5.5900002, - 5.7399998, - 6.0700002, - 6.21, - 6.2800002, - 6.73, - 6.8200002, - 7.9400001, - 8.9200001, - 10.38, - 10.97, - 11, - 11.32, - 11.45, - 12.78, - 13.48, - 14.09, - 14.52, - 15.03, - 16.059999, - 18.5, - 20.07, - 20.99, - 25.85, - 42.599998, - 46.860001, - 50.740002, - 59.700001, - 67.800003, - 84.120003, - 89.410004, - 103.92, - 108.27, - 154.06, - 313.94, - 401.23999, - 742.96997, - 765.65997, - 214, - 363, - 630, - 792, - 955, - 1028, - 1110, - 1134, - 1143, - 1216, - 1302, - 1400, - 1414, - 1660, - 1683, - 1944, - 1946, - 2312, - 2376, - 2458, - 2595, - 2600, - 2795, - 2985, - 3034, - 3250, - 3252, - 3301, - 4136, - 4445, - 4914, - 5068, - 5376, - 5865, - 5936, - 5958, - 6730, - 7336, - 8214, - 8232, - 8450, - 8464, - 8955, - 8957, - 9520, - 10210, - 10716, - 11218, - 12414, - 12631, - 13265, - 13410, - 14099, - 15911, - 15977, - 16318, - 17187, - 18405, - 18865, - 20800, - 22480, - 24923, - 25096, - 26629, - 27542, - 29136, - 29435, - 30199, - 30731, - 31598, - 32428, - 40161, - 46727, - 53593, - 56994, - 65671, - 72468, - 73728, - 74860, - 78265, - 106260, - 122304, - 129171, - 129931, - 138134, - 167056, - 172094, - 185002, - 187879, - 242736, - 550539, - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 19, - 20, - 21, - 22, - 23, - 25, - 27, - 29, - 32, - 33, - 35, - 36, - 38, - 39, - 41, - 43, - 45, - 48, - 50, - 51, - 53, - 54, - 56, - 58, - 60, - 64, - 68, - 70, - 72, - 74, - 76, - 78, - 83, - 85, - 87, - 89, - 91, - 94, - 96, - 98, - 100, - 102, - 104, - 107, - 114, - 117, - 119, - 122, - 124, - 127, - 129, - 132, - 137, - 142, - 145, - 148, - 150, - 155, - 157, - 160, - 163, - 165, - 168, - 171, - 182, - 185, - 188, - 191, - 194, - 197, - 200, - 202, - 205, - 208, - 211, - 214, - 217, - 220, - 223, - 226, - 229, - 233, - 236, - 243, - 246, - 249, - 252, - 255, - 263, - 266, - 270, - 274, - 278, - 281, - 285, - 294, - 298, - 306, - 315, - 320, - 324, - 329, - 338, - 343, - 347, - 351, - 360, - 365, - 370, - 374, - 379, - 388, - 393, - 398, - 402, - 407, - 412, - 422, - 427, - 438, - 443, - 449, - 453, - 459, - 470, - 476, - 482, - 489, - 495, - 501, - 520, - 526, - 533, - 540, - 547, - 553, - 560, - 567, - 574, - 582, - 589, - 597, - 604, - 612, - 637, - 645, - 653, - 661, - 669, - 678, - 687, - 696, - 704, - 742, - 751, - 762, - 772, - 783, - 796, - 808, - 821, - 835, - 850, - 864, - 881, - 897, - 913, - 1006, - 1025, - 1046, - 1098, - 1126, - 1154, - 1183, - 1240, - 1301, - 1340, - 1494, - 1550, - 1607, - 1722, - 2180, - 2294, - 2636, - 2750, - 3093, - 3, - 7, - 9, - 11, - 12, - 21, - 23, - 25, - 31, - 33, - 34, - 35, - 37, - 39, - 41, - 43, - 46, - 47, - 49, - 51, - 53, - 55, - 57, - 59, - 61, - 63, - 65, - 67, - 69, - 71, - 73, - 75, - 77, - 83, - 85, - 87, - 89, - 91, - 93, - 95, - 97, - 99, - 100, - 100, - 200, - 400, - 500, - 600, - 700, - 781, - 800, - 1100, - 1200, - 1300, - 1400, - 1500, - 1600, - 1800, - 1900, - 2000, - 2200, - 2631, - 2900, - 2991, - 3215, - 3229, - 3500, - 3600, - 3752, - 3900, - 4200, - 4334, - 4430, - 4993, - 5226, - 5400, - 5580, - 6800, - 7200, - 7600, - 8500, - 8700, - 9100, - 9389, - 9500, - 9700, - 9800, - 10088, - 10367, - 10621, - 10783, - 10900, - 11000, - 11189, - 11300, - 11467, - 11700, - 11853, - 11958, - 12068, - 12133, - 12200, - 12300, - 12700, - 12900, - 13100, - 13400, - 13900, - 14100, - 14400, - 14700, - 15700, - 15900, - 16200, - 16400, - 16700, - 16900, - 17400, - 17700, - 18500, - 19000, - 19500, - 20000, - 20300, - 20800, - 21600, - 21900, - 22100, - 22400, - 22700, - 22900, - 23401, - 23700, - 24000, - 24300, - 24600, - 24900, - 25100, - 25400, - 25700, - 26000, - 26900, - 27200, - 28100, - 28400, - 29000, - 29300, - 29900, - 30166, - 30800, - 31200, - 31859, - 32531, - 32900, - 33300, - 33638, - 34000, - 34348, - 34700, - 35031, - 35400, - 36100, - 37131, - 37700, - 39400, - 40500, - 42300, - 44300, - 45000, - 45700, - 48700, - 49531, - 50400, - 51200, - 52900, - 53700, - 54500, - 55300, - 56100, - 56900, - 57800, - 60550, - 61500, - 62600, - 65585, - 66455, - 67400, - 69531, - 70573, - 71700, - 75800, - 79153, - 83000, - 85200, - 87500, - 91100, - 98700, - 102620, - 106608, - 116538, - 128326, - 2, - 7, - 8, - 19, - 51, - 59, - 62, - 65, - 83, - 122, - 140, - 210, - 235, - 280, - 320, - 342, - 374, - 402, - 511, - 584, - 640, - 804, - 966, - 1055, - 1176, - 1365, - 1861, - 2032, - 2923, - 3175, - 3297, - 3691, - 5827, - 9936, - 10785, - 15388, - 15974, - 19297, - 20249, - 29707, - 32244, - 65988, - 75104, - 81627, - 102279, - 107471, - 115309, - 130401, - 136081, - 137992, - 139946, - 141867, - 147702, - 151565, - 157335, - 161066, - 163019, - 166929, - 189052, - 192643, - 196469, - 200185, - 203906, - 211277, - 218538, - 222118, - 225779, - 236984, - 244345, - 251930, - 255672, - 259464, - 263326, - 267093, - 1, - 2, - 4, - 6, - 7, - 8, - 11, - 13, - 16, - 20, - 24, - 29, - 33, - 35, - 40, - 45, - 50, - 55, - 58, - 61, - 65, - 68, - 74, - 79, - 86, - 92, - 99, - 103, - 110, - 114, - 120, - 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85, - 79, - 73, - 86, - 71, - 91, - 0, - 0, - 203, - 43, - 158, - 74, - 143, - 158, - 86, - 176, - 166, -}; - -/* - * \brief Function to convert a feature value into bin index. - * \param val Feature value, in floating-point - * \param fid Feature identifier - * \return bin Index corresponding to given feature value - */ -int dualsimplex_predictor::quantize(float val, unsigned fid) -{ - const size_t offset = th_begin[fid]; - const float* array = &threshold[offset]; - int len = th_len[fid]; - int low = 0; - int high = len; - int mid; - float mval; - // It is possible th_begin[i] == [total_num_threshold]. This means that - // all features i, (i+1), ... are not used for any of the splits in the model. - // So in this case, just return something - if (offset == 1801 || val < array[0]) { return -10; } - while (low + 1 < high) { - mid = (low + high) / 2; - mval = array[mid]; - if (val == mval) { - return mid * 2; - } else if (val < mval) { - high = mid; - } else { - low = mid; - } - } - if (array[low] == val) { - return low * 2; - } else if (high == len) { - return len * 2; - } else { - return low * 2 + 1; - } -} diff --git a/cpp/src/utilities/models/fj_predictor/header.h b/cpp/src/utilities/models/fj_predictor/header.h deleted file mode 100644 index 61215ede6..000000000 --- a/cpp/src/utilities/models/fj_predictor/header.h +++ /dev/null @@ -1,47 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include -#include -#include -#include -#include -#include - -class fj_predictor { - public: - union Entry { - int missing; - double fvalue; - int qvalue; - }; - - static int32_t get_num_target(void); - static void get_num_class(int32_t* out); - static int32_t get_num_feature(void); - static const char* get_threshold_type(void); - static const char* get_leaf_output_type(void); - static void predict(Entry* data, int pred_margin, double* result); - static void postprocess(double* result); - static int quantize(double val, unsigned fid); - - // Feature names - static constexpr int NUM_FEATURES = 12; - static const char* feature_names[NUM_FEATURES]; -}; // class fj_predictor diff --git a/cpp/src/utilities/models/fj_predictor/main.cpp b/cpp/src/utilities/models/fj_predictor/main.cpp deleted file mode 100644 index 4c3d30022..000000000 --- a/cpp/src/utilities/models/fj_predictor/main.cpp +++ /dev/null @@ -1,11828 +0,0 @@ - -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "header.h" - -#if defined(__clang__) || defined(__GNUC__) -#define LIKELY(x) __builtin_expect(!!(x), 1) -#define UNLIKELY(x) __builtin_expect(!!(x), 0) -#else -#define LIKELY(x) (x) -#define UNLIKELY(x) (x) -#endif -#define N_TARGET 1 -#define MAX_N_CLASS 1 - -const unsigned char is_categorical[] = { - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, -}; -static const int32_t num_class[] = { - 1, -}; - -int32_t fj_predictor::get_num_target(void) { return N_TARGET; } -void fj_predictor::get_num_class(int32_t* out) -{ - for (int i = 0; i < N_TARGET; ++i) { - out[i] = num_class[i]; - } -} -int32_t fj_predictor::get_num_feature(void) { return 12; } -const char* fj_predictor::get_threshold_type(void) { return "float64"; } -const char* fj_predictor::get_leaf_output_type(void) { return "float64"; } - -void fj_predictor::predict(union Entry* data, int pred_margin, double* result) -{ - // Quantize data - for (int i = 0; i < 12; ++i) { - if (data[i].missing != -1 && !is_categorical[i]) { - data[i].qvalue = quantize(data[i].fvalue, i); - } - } - - unsigned int tmp; - if (UNLIKELY(false || (data[0].qvalue <= 186))) { - if (LIKELY(false || (data[0].qvalue <= 98))) { - if (UNLIKELY(false || (data[0].qvalue <= 44))) { - result[0] += 22901.255344838846; - } else { - if (LIKELY(false || (data[0].qvalue <= 74))) { - result[0] += 23329.74375127941; - } else { - result[0] += 23615.614222033248; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 144))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - result[0] += 24209.472824119093; - } else { - if (LIKELY(false || (data[6].qvalue <= 100))) { - result[0] += 23869.313116934638; - } else { - result[0] += 23405.54395751655; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 88))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 24902.415701614485; - } else { - result[0] += 24520.27915996134; - } - } else { - result[0] += 23891.56119652762; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 262))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (UNLIKELY(false || (data[0].qvalue <= 218))) { - result[0] += 25411.420502457517; - } else { - result[0] += 25917.434788937655; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[1].qvalue <= 116))) { - if (LIKELY(false || (data[0].qvalue <= 224))) { - result[0] += 24824.90282590602; - } else { - result[0] += 25260.302972714355; - } - } else { - result[0] += 24241.872897906287; - } - } else { - result[0] += 23668.087700157346; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 112))) { - if (UNLIKELY(false || (data[0].qvalue <= 312))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 26558.857562746103; - } else { - result[0] += 26202.38213380121; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 102))) { - result[0] += 25849.940581446823; - } else { - result[0] += 25230.335505164217; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 92))) { - if (LIKELY(false || (data[8].qvalue <= 126))) { - result[0] += 26911.44811674291; - } else { - result[0] += 26503.327098291113; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - result[0] += 25835.236675109372; - } else { - result[0] += 26394.354620052833; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 430))) { - if (LIKELY(false || (data[6].qvalue <= 152))) { - if (UNLIKELY(false || (data[0].qvalue <= 352))) { - result[0] += 25138.58875113828; - } else { - result[0] += 25959.666864308958; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 406))) { - result[0] += 23920.482800539634; - } else { - result[0] += 24831.920082473396; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 174))) { - if (LIKELY(false || (data[1].qvalue <= 152))) { - result[0] += 26247.05905907499; - } else { - result[0] += 25812.009772798196; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 464))) { - result[0] += 24485.25504118496; - } else { - result[0] += 25971.80731105497; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 188))) { - if (LIKELY(false || (data[0].qvalue <= 104))) { - if (LIKELY(false || (data[0].qvalue <= 50))) { - if (LIKELY(false || (data[0].qvalue <= 24))) { - result[0] += -2102.2335038546057; - } else { - result[0] += -1814.143470277424; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 62))) { - result[0] += -1350.4930205440783; - } else { - result[0] += -1691.59714939238; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 62))) { - if (LIKELY(false || (data[0].qvalue <= 156))) { - if (UNLIKELY(false || (data[0].qvalue <= 126))) { - result[0] += -925.0448121271659; - } else { - result[0] += -609.5991225040165; - } - } else { - result[0] += -202.96527072586937; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 116))) { - if (LIKELY(false || (data[0].qvalue <= 152))) { - result[0] += -1181.817220363881; - } else { - result[0] += -759.0207821022941; - } - } else { - result[0] += -1560.8110503642508; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 266))) { - if (LIKELY(false || (data[7].qvalue <= 66))) { - if (LIKELY(false || (data[0].qvalue <= 228))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 578.7799431040581; - } else { - result[0] += 127.52408840507688; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 933.7806696735934; - } else { - result[0] += 471.418662728938; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 142))) { - if (LIKELY(false || (data[7].qvalue <= 128))) { - if (LIKELY(false || (data[6].qvalue <= 100))) { - result[0] += 24.61431879503551; - } else { - result[0] += -514.7441916892188; - } - } else { - result[0] += -840.288058328428; - } - } else { - result[0] += -1369.132803656919; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 88))) { - if (UNLIKELY(false || (data[0].qvalue <= 320))) { - if (LIKELY(false || (data[6].qvalue <= 60))) { - result[0] += 1297.8937605229921; - } else { - if (UNLIKELY(false || (data[9].qvalue <= 48))) { - result[0] += 159.84923661334855; - } else { - result[0] += 806.892953963784; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 92))) { - result[0] += 1631.4555218267105; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - result[0] += 602.1591975811427; - } else { - result[0] += 1225.4498129353617; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 346))) { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[7].qvalue <= 134))) { - result[0] += 477.26815448941943; - } else { - result[0] += -266.49089797874365; - } - } else { - result[0] += -1085.9174116108277; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 136))) { - if (UNLIKELY(false || (data[9].qvalue <= 30))) { - result[0] += 710.5340339475686; - } else { - result[0] += 1160.9702839561826; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 426))) { - result[0] += -336.2082424932214; - } else { - result[0] += 697.2377446648411; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 184))) { - if (UNLIKELY(false || (data[0].qvalue <= 92))) { - if (UNLIKELY(false || (data[0].qvalue <= 38))) { - result[0] += -1827.1103815856493; - } else { - if (LIKELY(false || (data[6].qvalue <= 92))) { - if (LIKELY(false || (data[0].qvalue <= 68))) { - result[0] += -1460.9279929556785; - } else { - result[0] += -1204.5954946215916; - } - } else { - result[0] += -1703.764420874855; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 136))) { - if (LIKELY(false || (data[7].qvalue <= 62))) { - result[0] += -834.0606253314141; - } else { - result[0] += -1268.097736051363; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 62))) { - if (LIKELY(false || (data[0].qvalue <= 168))) { - result[0] += -450.7445362623278; - } else { - result[0] += -151.5065914814049; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - result[0] += -813.8840731408728; - } else { - result[0] += -1552.9886128870096; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 258))) { - if (LIKELY(false || (data[7].qvalue <= 62))) { - if (UNLIKELY(false || (data[0].qvalue <= 212))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 300.18949268400604; - } else { - result[0] += -82.16209082891513; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 821.3155839598878; - } else { - result[0] += 395.4300146794792; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 142))) { - if (LIKELY(false || (data[7].qvalue <= 134))) { - if (LIKELY(false || (data[6].qvalue <= 94))) { - result[0] += -36.622858072571994; - } else { - result[0] += -472.4429272195769; - } - } else { - result[0] += -886.8842878988258; - } - } else { - result[0] += -1274.9255101678516; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 82))) { - if (UNLIKELY(false || (data[0].qvalue <= 302))) { - if (LIKELY(false || (data[6].qvalue <= 50))) { - result[0] += 1102.9306261545728; - } else { - if (UNLIKELY(false || (data[9].qvalue <= 48))) { - result[0] += 4.269252395168786; - } else { - result[0] += 673.9694942587568; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 48))) { - result[0] += 844.0310059878134; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 336))) { - result[0] += 1254.6320984291067; - } else { - result[0] += 1496.534953542473; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 342))) { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[7].qvalue <= 134))) { - result[0] += 369.8856508010498; - } else { - result[0] += -301.745500216277; - } - } else { - result[0] += -971.5869097857602; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 152))) { - if (LIKELY(false || (data[1].qvalue <= 124))) { - result[0] += 1049.0667116337365; - } else { - result[0] += 655.8288897611133; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 430))) { - result[0] += -483.20221095743983; - } else { - result[0] += 574.5979992744856; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 192))) { - if (LIKELY(false || (data[0].qvalue <= 110))) { - if (LIKELY(false || (data[0].qvalue <= 56))) { - if (UNLIKELY(false || (data[0].qvalue <= 20))) { - result[0] += -1731.6694255800344; - } else { - result[0] += -1463.8734271807368; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 62))) { - result[0] += -1013.7125063069707; - } else { - result[0] += -1295.792308734329; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (LIKELY(false || (data[0].qvalue <= 162))) { - result[0] += -512.040169492541; - } else { - result[0] += -62.54166123570826; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 90))) { - if (LIKELY(false || (data[0].qvalue <= 160))) { - result[0] += -807.711585982142; - } else { - result[0] += -447.3244114975565; - } - } else { - result[0] += -1112.4075523200308; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 274))) { - if (LIKELY(false || (data[7].qvalue <= 68))) { - if (LIKELY(false || (data[0].qvalue <= 236))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 434.1102182190951; - } else { - result[0] += 64.6833316545062; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 24))) { - result[0] += 911.5851039227344; - } else { - result[0] += 509.70074610455003; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 36))) { - result[0] += -922.6420870752214; - } else { - if (LIKELY(false || (data[7].qvalue <= 134))) { - result[0] += -43.11646969985167; - } else { - result[0] += -711.5443228439684; - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 112))) { - if (LIKELY(false || (data[7].qvalue <= 50))) { - if (UNLIKELY(false || (data[0].qvalue <= 322))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 1217.5857081225881; - } else { - result[0] += 862.7945760545485; - } - } else { - result[0] += 1381.341515141895; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 340))) { - if (LIKELY(false || (data[7].qvalue <= 128))) { - result[0] += 618.2916036455348; - } else { - result[0] += -156.99063121911638; - } - } else { - if (LIKELY(false || (data[10].qvalue <= 78))) { - result[0] += 863.993956946003; - } else { - result[0] += 1217.2914460169281; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 432))) { - if (LIKELY(false || (data[1].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 202))) { - result[0] += 554.0458524017479; - } else { - result[0] += -451.2824254081932; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 132))) { - result[0] += -205.44955983215206; - } else { - result[0] += -1114.6634922865608; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 174))) { - if (UNLIKELY(false || (data[8].qvalue <= 44))) { - result[0] += 434.76778433173723; - } else { - result[0] += 874.836013742844; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - result[0] += -415.8493090687832; - } else { - result[0] += 895.0728029685396; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 180))) { - if (UNLIKELY(false || (data[0].qvalue <= 86))) { - if (UNLIKELY(false || (data[0].qvalue <= 32))) { - result[0] += -1510.0101454113396; - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - result[0] += -1126.5760081015337; - } else { - result[0] += -1392.4200114552195; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 128))) { - if (LIKELY(false || (data[1].qvalue <= 78))) { - result[0] += -742.4787091372192; - } else { - result[0] += -1128.3169223737384; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 82))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += -217.1747383855503; - } else { - result[0] += -505.7066609860621; - } - } else { - result[0] += -926.1157369421153; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 252))) { - if (LIKELY(false || (data[7].qvalue <= 50))) { - if (UNLIKELY(false || (data[0].qvalue <= 208))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 312.45787313138067; - } else { - result[0] += -10.665507478881368; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 562.9336214602067; - } else { - result[0] += 238.03704733164884; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 116))) { - if (LIKELY(false || (data[7].qvalue <= 126))) { - if (LIKELY(false || (data[0].qvalue <= 212))) { - result[0] += -279.2242547615078; - } else { - result[0] += 42.62552906704628; - } - } else { - result[0] += -677.2319392589053; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 152))) { - result[0] += -619.1669830813266; - } else { - result[0] += -1313.98491170941; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 76))) { - if (UNLIKELY(false || (data[0].qvalue <= 296))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 874.524930910277; - } else { - result[0] += 493.74627459103635; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 48))) { - if (UNLIKELY(false || (data[10].qvalue <= 32))) { - result[0] += 305.25905254947634; - } else { - result[0] += 893.8383559035191; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 330))) { - result[0] += 1017.5358679778622; - } else { - result[0] += 1243.0211544735387; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 330))) { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (UNLIKELY(false || (data[9].qvalue <= 40))) { - result[0] += -310.9808580488324; - } else { - result[0] += 277.1404180803976; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 68))) { - result[0] += -1191.3641199442363; - } else { - result[0] += -376.5469216582162; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[7].qvalue <= 134))) { - result[0] += 906.2480551197423; - } else { - result[0] += 554.4928258443013; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 424))) { - result[0] += -334.1159618884123; - } else { - result[0] += 515.865642037843; - } - } - } - } - } - } - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (UNLIKELY(false || (data[3].qvalue <= 44))) { - if (LIKELY(false || (data[7].qvalue <= 24))) { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - if (LIKELY(false || (data[2].qvalue <= 128))) { - result[0] += 200.2716866984727; - } else { - result[0] += -33.31807134567322; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 34))) { - if (LIKELY(false || (data[2].qvalue <= 124))) { - result[0] += 2.1267132045491657; - } else { - result[0] += -376.52573890556465; - } - } else { - result[0] += 146.5594659396158; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 84))) { - if (UNLIKELY(false || (data[6].qvalue <= 40))) { - if (LIKELY(false || (data[2].qvalue <= 88))) { - result[0] += -78.39211587508055; - } else { - result[0] += -211.1780949469055; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 94))) { - result[0] += -50.54335036012877; - } else { - result[0] += 80.00020842727285; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 40))) { - result[0] += -483.63985186717866; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 56))) { - result[0] += 31.9766310228433; - } else { - result[0] += -151.25515710703033; - } - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 152))) { - if (LIKELY(false || (data[10].qvalue <= 108))) { - if (UNLIKELY(false || (data[8].qvalue <= 20))) { - if (UNLIKELY(false || (data[8].qvalue <= 0))) { - result[0] += 111.60897433537673; - } else { - result[0] += -38.459174422337334; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 152))) { - result[0] += 39.893177773342615; - } else { - result[0] += 117.1746253290821; - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 40))) { - if (UNLIKELY(false || (data[3].qvalue <= 58))) { - result[0] += -269.8313885970149; - } else { - result[0] += -44.81099090001982; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 50))) { - result[0] += 44.36810505858426; - } else { - result[0] += -17.031285003457658; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 66))) { - result[0] += 29.134840065248483; - } else { - result[0] += -142.57181028298447; - } - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 100))) { - if (LIKELY(false || (data[7].qvalue <= 196))) { - if (UNLIKELY(false || (data[4].qvalue <= 52))) { - result[0] += -122.23715116402353; - } else { - if (UNLIKELY(false || (data[8].qvalue <= 124))) { - result[0] += 97.82653352215696; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 88))) { - result[0] += 19.540553416535854; - } else { - result[0] += -98.26047713895753; - } - } - } - } else { - result[0] += -208.9897177114526; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += -9.013216655506389; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 154))) { - result[0] += -171.41131706373193; - } else { - if (LIKELY(false || (data[7].qvalue <= 188))) { - result[0] += -228.5813161018793; - } else { - result[0] += -325.7003509988099; - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 196))) { - if (LIKELY(false || (data[0].qvalue <= 114))) { - if (LIKELY(false || (data[0].qvalue <= 60))) { - if (UNLIKELY(false || (data[0].qvalue <= 16))) { - result[0] += -1431.9188559535999; - } else { - result[0] += -1190.2844870798897; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 82))) { - result[0] += -829.9317534554965; - } else { - result[0] += -1180.4578431880107; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 94))) { - if (LIKELY(false || (data[0].qvalue <= 166))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += -258.2731590553095; - } else { - result[0] += -523.7930385002326; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 24))) { - result[0] += 148.2458712176789; - } else { - result[0] += -208.97850691995546; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 152))) { - result[0] += -766.4846303120403; - } else { - result[0] += -1387.2597830966474; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 282))) { - if (LIKELY(false || (data[7].qvalue <= 82))) { - if (LIKELY(false || (data[6].qvalue <= 54))) { - if (UNLIKELY(false || (data[0].qvalue <= 234))) { - result[0] += 347.56940876633854; - } else { - result[0] += 659.3973774082955; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 48))) { - result[0] += -299.8000443073724; - } else { - result[0] += 213.67905068511027; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - result[0] += -241.4835022721958; - } else { - result[0] += -964.8334869254049; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 96))) { - if (LIKELY(false || (data[7].qvalue <= 50))) { - if (UNLIKELY(false || (data[0].qvalue <= 326))) { - if (LIKELY(false || (data[6].qvalue <= 54))) { - result[0] += 1024.8241421866958; - } else { - result[0] += 691.4425294017764; - } - } else { - result[0] += 1134.476489243314; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 348))) { - if (LIKELY(false || (data[7].qvalue <= 178))) { - result[0] += 607.890368257065; - } else { - result[0] += -428.4659382893519; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 414))) { - result[0] += 1014.748894801393; - } else { - result[0] += 597.6335980398155; - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 156))) { - if (UNLIKELY(false || (data[0].qvalue <= 366))) { - if (LIKELY(false || (data[7].qvalue <= 102))) { - result[0] += 342.26282113720123; - } else { - result[0] += -230.30740062057927; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 104))) { - result[0] += 813.8543527953774; - } else { - result[0] += 415.6624160363879; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 446))) { - if (LIKELY(false || (data[6].qvalue <= 174))) { - result[0] += -259.7429923675005; - } else { - result[0] += -1426.7951683026927; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 174))) { - result[0] += 762.73962100054; - } else { - result[0] += 231.27954235732147; - } - } - } - } - } - } - if (LIKELY(false || (data[1].qvalue <= 152))) { - if (UNLIKELY(false || (data[3].qvalue <= 44))) { - if (LIKELY(false || (data[7].qvalue <= 24))) { - if (UNLIKELY(false || (data[7].qvalue <= 2))) { - if (UNLIKELY(false || (data[2].qvalue <= 10))) { - result[0] += 301.84658146708784; - } else { - result[0] += 144.51444115717683; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 34))) { - if (UNLIKELY(false || (data[3].qvalue <= 0))) { - result[0] += -133.3520185035297; - } else { - result[0] += 7.825198511802189; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 186.55141223016244; - } else { - result[0] += 66.07816401815487; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 84))) { - if (UNLIKELY(false || (data[9].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 30))) { - result[0] += 190.81711934969553; - } else { - result[0] += -49.06287353594854; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 6))) { - result[0] += -190.1399731591398; - } else { - result[0] += -58.312564377257914; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 40))) { - result[0] += -486.8565320491534; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 122))) { - result[0] += -23.79376937838657; - } else { - result[0] += -193.64446213252586; - } - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (LIKELY(false || (data[10].qvalue <= 108))) { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - if (UNLIKELY(false || (data[10].qvalue <= 0))) { - result[0] += 154.980784796019; - } else { - result[0] += -70.70779749773081; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 166))) { - result[0] += 42.70546399312704; - } else { - result[0] += 231.67165411885554; - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 40))) { - if (UNLIKELY(false || (data[3].qvalue <= 58))) { - result[0] += -276.0507296705326; - } else { - result[0] += -48.24184527320793; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 50))) { - result[0] += 46.29969181016952; - } else { - result[0] += -22.46817209136037; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 196))) { - if (LIKELY(false || (data[2].qvalue <= 184))) { - if (UNLIKELY(false || (data[9].qvalue <= 70))) { - result[0] += 259.7266642867935; - } else { - result[0] += -33.9062832423356; - } - } else { - result[0] += -130.08036962157004; - } - } else { - result[0] += -206.5135454754138; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 188))) { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 21.01496012390576; - } else { - if (LIKELY(false || (data[9].qvalue <= 16))) { - if (LIKELY(false || (data[9].qvalue <= 2))) { - result[0] += -166.20109103415467; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 22))) { - result[0] += 43.76493225425393; - } else { - result[0] += -110.8984323288921; - } - } - } else { - result[0] += -265.77242681150005; - } - } - } else { - result[0] += -316.46151789497696; - } - } - if (LIKELY(false || (data[1].qvalue <= 152))) { - if (UNLIKELY(false || (data[3].qvalue <= 44))) { - if (LIKELY(false || (data[7].qvalue <= 42))) { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - if (LIKELY(false || (data[2].qvalue <= 128))) { - result[0] += 196.29561739518834; - } else { - result[0] += -31.524131824672523; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 154))) { - if (UNLIKELY(false || (data[4].qvalue <= 6))) { - result[0] += 101.91406015487678; - } else { - result[0] += 1.3921745492571436; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 20))) { - result[0] += -150.8055895533361; - } else { - result[0] += 146.0669399096082; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 38))) { - if (LIKELY(false || (data[7].qvalue <= 122))) { - if (LIKELY(false || (data[4].qvalue <= 76))) { - result[0] += -101.79580164134511; - } else { - result[0] += 51.3302149880617; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 108))) { - result[0] += -334.80985020089327; - } else { - result[0] += -145.88767910265673; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 82))) { - result[0] += -560.2505700484534; - } else { - result[0] += -213.96025392317247; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (LIKELY(false || (data[10].qvalue <= 96))) { - if (UNLIKELY(false || (data[8].qvalue <= 20))) { - if (UNLIKELY(false || (data[8].qvalue <= 0))) { - result[0] += 105.68674793264172; - } else { - result[0] += -43.57721621182288; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -147.9863609603396; - } else { - result[0] += 50.11914192647265; - } - } - } else { - if (LIKELY(false || (data[8].qvalue <= 138))) { - if (LIKELY(false || (data[3].qvalue <= 120))) { - result[0] += -21.50623989909495; - } else { - result[0] += 36.087102588326; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 172))) { - result[0] += -401.56511373374224; - } else { - result[0] += -47.54506742735205; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 196))) { - if (LIKELY(false || (data[10].qvalue <= 136))) { - if (LIKELY(false || (data[4].qvalue <= 70))) { - result[0] += -58.788243146060125; - } else { - result[0] += 35.01063889842818; - } - } else { - result[0] += -167.15525202440935; - } - } else { - result[0] += -185.86643283746199; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 188))) { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 18.914144121003567; - } else { - if (LIKELY(false || (data[9].qvalue <= 16))) { - if (LIKELY(false || (data[9].qvalue <= 2))) { - result[0] += -149.58318971768668; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 22))) { - result[0] += 39.39279508530201; - } else { - result[0] += -99.81036974381612; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 28))) { - result[0] += -574.5809531834742; - } else { - result[0] += -223.51903615680277; - } - } - } - } else { - result[0] += -284.8251127560464; - } - } - if (UNLIKELY(false || (data[0].qvalue <= 178))) { - if (UNLIKELY(false || (data[0].qvalue <= 84))) { - if (UNLIKELY(false || (data[0].qvalue <= 34))) { - result[0] += -1224.3672336316197; - } else { - if (LIKELY(false || (data[6].qvalue <= 62))) { - result[0] += -890.0738935144229; - } else { - result[0] += -1083.6868213970283; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 88))) { - if (LIKELY(false || (data[0].qvalue <= 132))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += -449.253175915189; - } else { - result[0] += -666.9058347442864; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 36))) { - result[0] += -171.0454878027209; - } else { - result[0] += -404.9936499215746; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 108))) { - result[0] += -738.8736900979811; - } else { - result[0] += -1156.8338657457468; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 246))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 376.54293691682807; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 204))) { - result[0] += -19.164395129970544; - } else { - result[0] += 229.71203236622483; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 116))) { - if (LIKELY(false || (data[7].qvalue <= 92))) { - result[0] += -93.71602815702187; - } else { - result[0] += -442.3743373003131; - } - } else { - result[0] += -794.9240557679232; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 96))) { - if (UNLIKELY(false || (data[0].qvalue <= 294))) { - if (LIKELY(false || (data[7].qvalue <= 44))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 776.7722682804047; - } else { - result[0] += 551.7601378282178; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 126))) { - result[0] += 359.00317634305014; - } else { - result[0] += -261.0799150747045; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 54))) { - if (UNLIKELY(false || (data[0].qvalue <= 340))) { - result[0] += 868.4127360515845; - } else { - result[0] += 1030.2866556923334; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 174))) { - result[0] += 759.8335007432447; - } else { - result[0] += 375.57847778528725; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 362))) { - if (LIKELY(false || (data[1].qvalue <= 124))) { - if (LIKELY(false || (data[7].qvalue <= 152))) { - result[0] += 192.76419647400402; - } else { - result[0] += -627.210504284817; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 122))) { - result[0] += -248.81615213762672; - } else { - result[0] += -969.1369200758126; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 152))) { - if (LIKELY(false || (data[1].qvalue <= 124))) { - result[0] += 773.3204928636587; - } else { - result[0] += 406.4377394893374; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 448))) { - result[0] += -133.64446813672; - } else { - result[0] += 531.8523379173158; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 198))) { - if (LIKELY(false || (data[0].qvalue <= 116))) { - if (LIKELY(false || (data[0].qvalue <= 64))) { - if (UNLIKELY(false || (data[0].qvalue <= 14))) { - result[0] += -1181.6504962663746; - } else { - result[0] += -963.5370227469084; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 70))) { - result[0] += -638.280415142769; - } else { - result[0] += -906.2068637569266; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - if (UNLIKELY(false || (data[0].qvalue <= 160))) { - result[0] += -355.3777345047579; - } else { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 38.63569197354808; - } else { - result[0] += -203.4298594886395; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 106))) { - result[0] += -501.25518228442854; - } else { - result[0] += -904.0956495312481; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 284))) { - if (LIKELY(false || (data[6].qvalue <= 74))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - if (LIKELY(false || (data[0].qvalue <= 242))) { - result[0] += 325.0351233983085; - } else { - result[0] += 574.7980032330352; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 240))) { - result[0] += 50.603122386774714; - } else { - result[0] += 307.4265167122256; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 124))) { - if (LIKELY(false || (data[2].qvalue <= 200))) { - result[0] += -75.63749275285782; - } else { - result[0] += -878.8670678802445; - } - } else { - result[0] += -784.825049044781; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 108))) { - if (LIKELY(false || (data[1].qvalue <= 82))) { - if (LIKELY(false || (data[9].qvalue <= 128))) { - if (LIKELY(false || (data[0].qvalue <= 354))) { - result[0] += 768.9030375927771; - } else { - result[0] += 982.7632961026947; - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 26))) { - result[0] += 881.7519427243232; - } else { - result[0] += 460.8225104674566; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 352))) { - result[0] += 336.6542893696312; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 12))) { - result[0] += 232.48634402594894; - } else { - result[0] += 754.2862591795262; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 434))) { - if (LIKELY(false || (data[1].qvalue <= 138))) { - if (LIKELY(false || (data[10].qvalue <= 116))) { - result[0] += 486.1115775722369; - } else { - result[0] += -240.0215539033738; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 152))) { - result[0] += -340.1982716887718; - } else { - result[0] += -1142.2235138961084; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 174))) { - if (UNLIKELY(false || (data[2].qvalue <= 106))) { - result[0] += 356.11751909622217; - } else { - result[0] += 708.5483451506925; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - result[0] += -425.3011321722016; - } else { - result[0] += 737.8486431169086; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 176))) { - if (UNLIKELY(false || (data[0].qvalue <= 82))) { - if (UNLIKELY(false || (data[0].qvalue <= 30))) { - result[0] += -1008.2347845448372; - } else { - if (LIKELY(false || (data[6].qvalue <= 92))) { - result[0] += -759.2605409681978; - } else { - result[0] += -1002.0185264155406; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 90))) { - if (UNLIKELY(false || (data[0].qvalue <= 122))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += -430.8085418741898; - } else { - result[0] += -615.9206678101054; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += -160.1037858692929; - } else { - result[0] += -361.25591627953935; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - result[0] += -677.2268849569755; - } else { - result[0] += -1084.1504346623935; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 256))) { - if (LIKELY(false || (data[7].qvalue <= 48))) { - if (LIKELY(false || (data[0].qvalue <= 216))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 219.18790221063765; - } else { - result[0] += 8.224416225928314; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 473.5370228126004; - } else { - result[0] += 241.42467866161678; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 126))) { - if (LIKELY(false || (data[6].qvalue <= 100))) { - result[0] += -37.498571958273004; - } else { - result[0] += -365.5446484967875; - } - } else { - result[0] += -627.0927668810837; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 88))) { - if (UNLIKELY(false || (data[0].qvalue <= 306))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 703.8669257065242; - } else { - result[0] += 489.86267404805426; - } - } else { - result[0] += 234.87338838535948; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 64))) { - if (LIKELY(false || (data[0].qvalue <= 356))) { - result[0] += 717.6793071720917; - } else { - result[0] += 869.7251021367146; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 6))) { - result[0] += 129.6795962020335; - } else { - result[0] += 601.4460117834096; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 374))) { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[7].qvalue <= 176))) { - result[0] += 214.20152575675183; - } else { - result[0] += -537.860116518967; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 24))) { - result[0] += 39.286767186140054; - } else { - result[0] += -860.9173103201238; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 156))) { - if (UNLIKELY(false || (data[6].qvalue <= 36))) { - result[0] += -207.54940006849526; - } else { - result[0] += 555.8581322313454; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 448))) { - result[0] += -233.03910512550647; - } else { - result[0] += 411.0057503246483; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 200))) { - if (UNLIKELY(false || (data[0].qvalue <= 102))) { - if (UNLIKELY(false || (data[0].qvalue <= 42))) { - result[0] += -879.2505320646146; - } else { - if (LIKELY(false || (data[6].qvalue <= 100))) { - result[0] += -613.8765129117388; - } else { - result[0] += -898.2402256092773; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (UNLIKELY(false || (data[0].qvalue <= 152))) { - result[0] += -310.0254085675876; - } else { - result[0] += -23.926126961634566; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (UNLIKELY(false || (data[0].qvalue <= 150))) { - result[0] += -532.7012177870323; - } else { - result[0] += -303.0535459955343; - } - } else { - result[0] += -885.9999951852365; - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 96))) { - if (UNLIKELY(false || (data[0].qvalue <= 288))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (UNLIKELY(false || (data[0].qvalue <= 236))) { - result[0] += 215.01429644416353; - } else { - result[0] += 421.53246886972823; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 48))) { - result[0] += -286.0785455584352; - } else { - result[0] += 116.1256792417381; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 120))) { - if (LIKELY(false || (data[8].qvalue <= 118))) { - if (LIKELY(false || (data[0].qvalue <= 414))) { - result[0] += 706.4179843328923; - } else { - result[0] += 374.35058115512874; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 28))) { - result[0] += -23.434409262940505; - } else { - result[0] += 567.1115038047287; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 386))) { - if (LIKELY(false || (data[8].qvalue <= 18))) { - result[0] += 273.6642642670474; - } else { - result[0] += -1046.2285662989975; - } - } else { - result[0] += 487.25359122330957; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 354))) { - if (LIKELY(false || (data[6].qvalue <= 152))) { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (LIKELY(false || (data[4].qvalue <= 98))) { - result[0] += 86.77607178167547; - } else { - result[0] += -327.9227215464309; - } - } else { - result[0] += -702.536120284601; - } - } else { - result[0] += -816.76285821889; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 168))) { - if (LIKELY(false || (data[4].qvalue <= 104))) { - if (LIKELY(false || (data[2].qvalue <= 202))) { - result[0] += 585.7714665368296; - } else { - result[0] += 211.97898615341128; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 446))) { - result[0] += 45.872711470194794; - } else { - result[0] += 596.7727695600357; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 460))) { - if (LIKELY(false || (data[6].qvalue <= 174))) { - result[0] += -110.3734753492252; - } else { - result[0] += -993.643156799906; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 134))) { - result[0] += 513.9132377712247; - } else { - result[0] += -156.94258247387484; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 174))) { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - if (UNLIKELY(false || (data[0].qvalue <= 28))) { - result[0] += -825.7182786124127; - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - result[0] += -620.5383438315085; - } else { - result[0] += -812.4844205870289; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - if (UNLIKELY(false || (data[0].qvalue <= 124))) { - result[0] += -409.88504033635036; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += -72.60757573397173; - } else { - result[0] += -250.66315233138621; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 32))) { - result[0] += -830.1444150421403; - } else { - result[0] += -512.6141494941938; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 244))) { - if (LIKELY(false || (data[7].qvalue <= 48))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - if (UNLIKELY(false || (data[10].qvalue <= 8))) { - result[0] += 654.5140923867807; - } else { - result[0] += 216.223797321274; - } - } else { - result[0] += 59.127335346599445; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 42))) { - if (LIKELY(false || (data[7].qvalue <= 120))) { - result[0] += -408.9401043985653; - } else { - result[0] += -900.5928970741843; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 134))) { - result[0] += -88.4273489833162; - } else { - result[0] += -494.5124549516256; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 126))) { - if (UNLIKELY(false || (data[0].qvalue <= 316))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 577.7159751120596; - } else { - result[0] += 390.25605025593114; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 100))) { - result[0] += 225.37808240171967; - } else { - result[0] += -134.1896384710644; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 40))) { - if (UNLIKELY(false || (data[0].qvalue <= 380))) { - result[0] += 26.38536976794332; - } else { - result[0] += 404.9857810684197; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 206))) { - result[0] += 640.5372498970368; - } else { - result[0] += 146.7850504947837; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 398))) { - if (UNLIKELY(false || (data[9].qvalue <= 46))) { - if (UNLIKELY(false || (data[2].qvalue <= 32))) { - result[0] += -69.72606543209433; - } else { - result[0] += -890.4547484128167; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 194))) { - result[0] += 43.29913016336363; - } else { - result[0] += -1135.7449808644187; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 156))) { - if (UNLIKELY(false || (data[4].qvalue <= 72))) { - result[0] += 619.8183215898755; - } else { - result[0] += 285.5489715529262; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 448))) { - result[0] += -358.75757336334124; - } else { - result[0] += 304.16119148341835; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 202))) { - if (LIKELY(false || (data[0].qvalue <= 118))) { - if (LIKELY(false || (data[0].qvalue <= 66))) { - if (UNLIKELY(false || (data[0].qvalue <= 10))) { - result[0] += -817.0480700539837; - } else { - result[0] += -635.8069750267712; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - result[0] += -345.96669659122944; - } else { - result[0] += -534.0482572373161; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 90))) { - if (LIKELY(false || (data[0].qvalue <= 170))) { - result[0] += -211.68858516839794; - } else { - if (LIKELY(false || (data[6].qvalue <= 50))) { - result[0] += 62.76650514311201; - } else { - result[0] += -136.44892442998687; - } - } - } else { - result[0] += -491.2641598575405; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 134))) { - if (UNLIKELY(false || (data[0].qvalue <= 292))) { - if (LIKELY(false || (data[7].qvalue <= 48))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - if (LIKELY(false || (data[6].qvalue <= 32))) { - result[0] += 331.57071848199166; - } else { - result[0] += 679.3219985689888; - } - } else { - result[0] += 218.00776335345145; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 42))) { - result[0] += -277.030356546739; - } else { - result[0] += 67.28341346905603; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 40))) { - if (LIKELY(false || (data[0].qvalue <= 428))) { - if (LIKELY(false || (data[6].qvalue <= 134))) { - result[0] += 324.7275088735298; - } else { - result[0] += -419.6036244916827; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 90))) { - result[0] += 170.7438608997793; - } else { - result[0] += 713.0065984964028; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (LIKELY(false || (data[0].qvalue <= 356))) { - result[0] += 471.87466419027; - } else { - result[0] += 637.1788630246149; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 408))) { - result[0] += -529.7467726888689; - } else { - result[0] += 412.8057422873779; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 404))) { - if (UNLIKELY(false || (data[9].qvalue <= 46))) { - result[0] += -712.0507049531527; - } else { - if (LIKELY(false || (data[7].qvalue <= 194))) { - if (LIKELY(false || (data[2].qvalue <= 202))) { - result[0] += 36.347398334304295; - } else { - result[0] += -675.8496819191378; - } - } else { - result[0] += -1026.391767021813; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - if (LIKELY(false || (data[6].qvalue <= 174))) { - if (LIKELY(false || (data[2].qvalue <= 218))) { - result[0] += 300.59738999141763; - } else { - result[0] += -768.8823600737215; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 462))) { - result[0] += -815.3971702532364; - } else { - result[0] += -98.98096938280344; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 188))) { - result[0] += 772.6721143780254; - } else { - result[0] += 184.03196143296145; - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 204))) { - if (UNLIKELY(false || (data[0].qvalue <= 106))) { - if (LIKELY(false || (data[0].qvalue <= 52))) { - result[0] += -628.1023483072281; - } else { - if (LIKELY(false || (data[7].qvalue <= 90))) { - result[0] += -417.2006348985446; - } else { - result[0] += -652.72384554584; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - if (UNLIKELY(false || (data[0].qvalue <= 150))) { - result[0] += -244.5303558275764; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += 81.86220471198173; - } else { - result[0] += -93.51539141150997; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 42))) { - result[0] += -604.2798075362174; - } else { - result[0] += -312.7206975646505; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 76))) { - if (UNLIKELY(false || (data[0].qvalue <= 280))) { - if (UNLIKELY(false || (data[7].qvalue <= 30))) { - if (UNLIKELY(false || (data[7].qvalue <= 2))) { - result[0] += 807.0167727723248; - } else { - result[0] += 289.0442076002897; - } - } else { - result[0] += 124.5150431079561; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - if (UNLIKELY(false || (data[0].qvalue <= 330))) { - if (UNLIKELY(false || (data[7].qvalue <= 30))) { - result[0] += 565.5660231034293; - } else { - result[0] += 342.6451209197193; - } - } else { - result[0] += 592.3830913005597; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 6))) { - if (LIKELY(false || (data[0].qvalue <= 444))) { - result[0] += -430.7701175762352; - } else { - result[0] += 555.2205548100666; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 356))) { - result[0] += 220.55563498286244; - } else { - result[0] += 609.8515804605789; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 364))) { - if (UNLIKELY(false || (data[9].qvalue <= 40))) { - if (LIKELY(false || (data[7].qvalue <= 112))) { - result[0] += -186.288076755465; - } else { - result[0] += -654.6528922882809; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (LIKELY(false || (data[7].qvalue <= 180))) { - result[0] += 134.73234216388812; - } else { - result[0] += -411.6205521150166; - } - } else { - result[0] += -601.6441239663638; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 152))) { - if (LIKELY(false || (data[2].qvalue <= 214))) { - if (UNLIKELY(false || (data[8].qvalue <= 48))) { - result[0] += 159.54229602414563; - } else { - result[0] += 399.7957684439738; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -623.3887244049713; - } else { - result[0] += 412.91921366956416; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 448))) { - if (LIKELY(false || (data[9].qvalue <= 18))) { - result[0] += -725.573342147161; - } else { - result[0] += 232.5245626809719; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 62))) { - result[0] += 781.8973840507092; - } else { - result[0] += 102.27963999730173; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 206))) { - if (LIKELY(false || (data[0].qvalue <= 118))) { - if (LIKELY(false || (data[0].qvalue <= 70))) { - result[0] += -541.6072754781043; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 40))) { - result[0] += -266.3645156358689; - } else { - result[0] += -438.26985938213795; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 90))) { - if (LIKELY(false || (data[0].qvalue <= 170))) { - result[0] += -171.34773263938322; - } else { - result[0] += -11.303977068049283; - } - } else { - result[0] += -407.71415355827446; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 126))) { - if (UNLIKELY(false || (data[0].qvalue <= 298))) { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (UNLIKELY(false || (data[7].qvalue <= 16))) { - if (LIKELY(false || (data[5].qvalue <= 46))) { - result[0] += 305.49013378917147; - } else { - result[0] += 766.8660544561868; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 12))) { - result[0] += -212.69432422518977; - } else { - result[0] += 203.45215339752818; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 100))) { - if (UNLIKELY(false || (data[2].qvalue <= 6))) { - result[0] += -1306.979742409561; - } else { - result[0] += 84.56250938982618; - } - } else { - result[0] += -296.8758971391223; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 92))) { - if (LIKELY(false || (data[2].qvalue <= 212))) { - if (UNLIKELY(false || (data[7].qvalue <= 16))) { - result[0] += 634.2960749274997; - } else { - result[0] += 446.27587321034673; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 442))) { - result[0] += -656.3271831376737; - } else { - result[0] += 649.7533136866848; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 346))) { - if (LIKELY(false || (data[6].qvalue <= 100))) { - result[0] += 272.18984613419735; - } else { - result[0] += -183.6208158354411; - } - } else { - result[0] += 335.6468220356205; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 418))) { - if (LIKELY(false || (data[1].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 190))) { - if (LIKELY(false || (data[7].qvalue <= 194))) { - result[0] += 155.81121294253342; - } else { - result[0] += -775.8409014979492; - } - } else { - result[0] += -546.6139529208316; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 279.2996966849545; - } else { - result[0] += -746.3491117424761; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 174))) { - if (UNLIKELY(false || (data[0].qvalue <= 444))) { - if (UNLIKELY(false || (data[2].qvalue <= 76))) { - result[0] += -794.9146466173518; - } else { - result[0] += 238.59142556484136; - } - } else { - result[0] += 427.50310495259396; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - if (LIKELY(false || (data[4].qvalue <= 112))) { - result[0] += -91.71744859578774; - } else { - result[0] += -899.7782100028055; - } - } else { - result[0] += 481.37569784189236; - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 182))) { - if (UNLIKELY(false || (data[0].qvalue <= 90))) { - if (UNLIKELY(false || (data[0].qvalue <= 26))) { - result[0] += -559.2465183228969; - } else { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - result[0] += -339.48268673463207; - } else { - result[0] += -477.7038556835741; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 100))) { - if (LIKELY(false || (data[0].qvalue <= 142))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += -173.83777524062293; - } else { - result[0] += -308.64588699940225; - } - } else { - result[0] += -87.70355551216173; - } - } else { - result[0] += -460.0791599316315; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 264))) { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[6].qvalue <= 46))) { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - if (UNLIKELY(false || (data[4].qvalue <= 2))) { - result[0] += 519.5076559696931; - } else { - result[0] += 36.579262885789255; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 8))) { - result[0] += 850.2335034126149; - } else { - result[0] += 225.76397079413547; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 200))) { - result[0] += 31.634082363361337; - } else { - result[0] += -491.7231693795383; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 134))) { - result[0] += -143.97621323045203; - } else { - result[0] += -587.7222010882514; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (LIKELY(false || (data[9].qvalue <= 128))) { - if (LIKELY(false || (data[0].qvalue <= 328))) { - result[0] += 341.70375333102265; - } else { - result[0] += 492.9064526262677; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 32))) { - result[0] += 291.11846627413223; - } else { - result[0] += -67.70442593342548; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 336))) { - if (UNLIKELY(false || (data[9].qvalue <= 42))) { - result[0] += -178.5440203148846; - } else { - result[0] += 126.46282552428471; - } - } else { - if (LIKELY(false || (data[10].qvalue <= 76))) { - result[0] += 222.41058195803035; - } else { - result[0] += 434.73453399120257; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 426))) { - if (LIKELY(false || (data[9].qvalue <= 68))) { - if (LIKELY(false || (data[0].qvalue <= 416))) { - result[0] += -640.7399976249752; - } else { - result[0] += -167.72938945515875; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 46))) { - result[0] += -1344.5726371900926; - } else { - result[0] += 230.06133289043484; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 168))) { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -84.2627136369062; - } else { - result[0] += 415.7428703783992; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 460))) { - result[0] += -384.29322175155045; - } else { - result[0] += 296.1675101952608; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 214))) { - if (LIKELY(false || (data[0].qvalue <= 122))) { - if (UNLIKELY(false || (data[0].qvalue <= 46))) { - result[0] += -470.73918641723026; - } else { - if (LIKELY(false || (data[7].qvalue <= 90))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += -198.32164705298905; - } else { - result[0] += -321.39079912240294; - } - } else { - result[0] += -498.8080870646863; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 317.2121545851497; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 172))) { - result[0] += -88.36582704181914; - } else { - result[0] += 46.798653356184104; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 126))) { - if (LIKELY(false || (data[1].qvalue <= 92))) { - result[0] += -119.53396623757338; - } else { - result[0] += -262.3878864323929; - } - } else { - result[0] += -497.48244794654096; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 134))) { - if (UNLIKELY(false || (data[0].qvalue <= 304))) { - if (LIKELY(false || (data[7].qvalue <= 44))) { - if (UNLIKELY(false || (data[7].qvalue <= 2))) { - result[0] += 746.0045878549049; - } else { - if (LIKELY(false || (data[10].qvalue <= 124))) { - result[0] += 255.0791650107591; - } else { - result[0] += -320.7492571200465; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 90))) { - result[0] += 96.84399754507324; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 18))) { - result[0] += -880.8032147114733; - } else { - result[0] += -73.20637853492896; - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 38))) { - result[0] += 461.5993365359995; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 358))) { - if (LIKELY(false || (data[2].qvalue <= 180))) { - result[0] += 212.73235839758036; - } else { - result[0] += -154.54318766724592; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 124))) { - result[0] += 395.0997024493214; - } else { - result[0] += 210.88997718544783; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 404))) { - if (LIKELY(false || (data[7].qvalue <= 192))) { - if (LIKELY(false || (data[10].qvalue <= 112))) { - if (UNLIKELY(false || (data[5].qvalue <= 50))) { - result[0] += -969.6328168630031; - } else { - result[0] += -86.652192967571; - } - } else { - result[0] += -593.8688924415013; - } - } else { - result[0] += -859.0755121136438; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (LIKELY(false || (data[5].qvalue <= 120))) { - if (LIKELY(false || (data[2].qvalue <= 222))) { - result[0] += 179.3501661826762; - } else { - result[0] += -755.4195928241176; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 464))) { - result[0] += -1101.7895944684535; - } else { - result[0] += -169.1089745696061; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 132))) { - result[0] += 735.5941243655279; - } else { - result[0] += -9.689280563857821; - } - } - } - } - } - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - if (LIKELY(false || (data[7].qvalue <= 16))) { - if (UNLIKELY(false || (data[7].qvalue <= 2))) { - result[0] += 241.07465429893853; - } else { - if (UNLIKELY(false || (data[5].qvalue <= 2))) { - result[0] += -167.05234881826266; - } else { - if (LIKELY(false || (data[9].qvalue <= 160))) { - result[0] += 23.635093021555477; - } else { - result[0] += 247.95569232139462; - } - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 24))) { - if (UNLIKELY(false || (data[5].qvalue <= 26))) { - result[0] += -11.35953345193686; - } else { - if (LIKELY(false || (data[8].qvalue <= 46))) { - result[0] += -241.87320597288937; - } else { - result[0] += -558.2222728620737; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 22))) { - result[0] += -717.6843774931726; - } else { - if (UNLIKELY(false || (data[5].qvalue <= 18))) { - result[0] += -119.9905475497901; - } else { - result[0] += 12.781811278483842; - } - } - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 54))) { - if (UNLIKELY(false || (data[5].qvalue <= 78))) { - if (UNLIKELY(false || (data[8].qvalue <= 68))) { - if (UNLIKELY(false || (data[8].qvalue <= 22))) { - result[0] += -383.60019278447663; - } else { - result[0] += 70.39617109267411; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 62))) { - result[0] += 14.894390887322576; - } else { - result[0] += -246.88224072530934; - } - } - } else { - if (LIKELY(false || (data[10].qvalue <= 134))) { - if (LIKELY(false || (data[9].qvalue <= 32))) { - result[0] += -26.468777991984183; - } else { - result[0] += 124.62962202584231; - } - } else { - result[0] += -125.32103441709326; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 98))) { - if (UNLIKELY(false || (data[6].qvalue <= 50))) { - if (LIKELY(false || (data[8].qvalue <= 120))) { - result[0] += 79.94063628810217; - } else { - result[0] += -99.91998726218483; - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 54))) { - result[0] += -102.42726831173952; - } else { - result[0] += 19.419260663393466; - } - } - } else { - if (LIKELY(false || (data[8].qvalue <= 120))) { - if (LIKELY(false || (data[10].qvalue <= 90))) { - result[0] += 179.7748013112496; - } else { - result[0] += -7.882776932047753; - } - } else { - result[0] += 448.60994004642566; - } - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 134))) { - if (LIKELY(false || (data[7].qvalue <= 198))) { - if (LIKELY(false || (data[3].qvalue <= 168))) { - if (LIKELY(false || (data[6].qvalue <= 178))) { - result[0] += -30.950902106565053; - } else { - result[0] += 476.7456995983057; - } - } else { - result[0] += -198.82706671026182; - } - } else { - result[0] += -249.8175053383445; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 188))) { - if (UNLIKELY(false || (data[3].qvalue <= 98))) { - result[0] += -378.6732367385603; - } else { - result[0] += -109.64341915005078; - } - } else { - result[0] += -425.2420138470435; - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 220))) { - if (LIKELY(false || (data[0].qvalue <= 134))) { - if (LIKELY(false || (data[0].qvalue <= 72))) { - if (UNLIKELY(false || (data[0].qvalue <= 8))) { - result[0] += -532.059203726686; - } else { - result[0] += -372.9090262225109; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 82))) { - result[0] += -209.07044382236805; - } else { - result[0] += -400.00349533828; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (UNLIKELY(false || (data[7].qvalue <= 2))) { - result[0] += 412.17374136976844; - } else { - result[0] += -14.036003053192212; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 116))) { - result[0] += -154.44905243467502; - } else { - result[0] += -436.1506043761763; - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 116))) { - if (LIKELY(false || (data[0].qvalue <= 324))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (LIKELY(false || (data[0].qvalue <= 276))) { - result[0] += 180.81716708771435; - } else { - result[0] += 323.80885408996255; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 4))) { - result[0] += -804.687929563034; - } else { - if (LIKELY(false || (data[7].qvalue <= 144))) { - result[0] += 75.45041773548873; - } else { - result[0] += -308.6438664376922; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 198))) { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - if (UNLIKELY(false || (data[9].qvalue <= 60))) { - result[0] += -136.42148889369125; - } else { - result[0] += 347.35402497810156; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 40))) { - result[0] += 519.5328967780671; - } else { - result[0] += 317.09389087410756; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 442))) { - result[0] += -830.9681326018041; - } else { - result[0] += 776.5378702508172; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 392))) { - if (LIKELY(false || (data[1].qvalue <= 120))) { - if (UNLIKELY(false || (data[3].qvalue <= 96))) { - result[0] += -916.9602444683026; - } else { - if (LIKELY(false || (data[2].qvalue <= 190))) { - result[0] += 151.14410198089988; - } else { - result[0] += -534.6138410777346; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 64))) { - result[0] += -20.321297171216408; - } else { - result[0] += -553.6897184690705; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 152))) { - if (LIKELY(false || (data[2].qvalue <= 214))) { - if (UNLIKELY(false || (data[8].qvalue <= 44))) { - result[0] += -94.32615099423269; - } else { - result[0] += 326.8245357585306; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -617.350459951314; - } else { - result[0] += 276.127751759582; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 442))) { - result[0] += -733.0199493164838; - } else { - if (LIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -116.42395706265687; - } else { - result[0] += 335.32040559141655; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 172))) { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - if (UNLIKELY(false || (data[0].qvalue <= 6))) { - result[0] += -493.1717265491526; - } else { - if (LIKELY(false || (data[1].qvalue <= 72))) { - result[0] += -307.1327071737299; - } else { - result[0] += -442.19784021780106; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 116))) { - if (LIKELY(false || (data[1].qvalue <= 72))) { - if (LIKELY(false || (data[0].qvalue <= 138))) { - result[0] += -167.9842807798525; - } else { - result[0] += -70.31231390063265; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 120))) { - result[0] += -254.86498043371085; - } else { - result[0] += -703.766647232002; - } - } - } else { - result[0] += -458.17666670208604; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 240))) { - if (LIKELY(false || (data[6].qvalue <= 72))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 400.7108318320836; - } else { - if (LIKELY(false || (data[4].qvalue <= 128))) { - if (UNLIKELY(false || (data[4].qvalue <= 6))) { - result[0] += 227.72272060240076; - } else { - result[0] += 16.088229395762443; - } - } else { - result[0] += -679.3987618413844; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 146))) { - if (UNLIKELY(false || (data[3].qvalue <= 112))) { - result[0] += -287.0516773864561; - } else { - result[0] += -82.35735858878537; - } - } else { - result[0] += -473.44254046258266; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (UNLIKELY(false || (data[0].qvalue <= 326))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - result[0] += 167.85776493357912; - } else { - result[0] += 297.3702646687886; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 116))) { - result[0] += 67.52512843170207; - } else { - result[0] += -189.67754872059376; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 40))) { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 818.1555179158704; - } else { - result[0] += 130.45031708535106; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 454))) { - result[0] += 327.4166938133286; - } else { - result[0] += -120.66748291707741; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 428))) { - if (LIKELY(false || (data[9].qvalue <= 68))) { - if (LIKELY(false || (data[0].qvalue <= 418))) { - result[0] += -535.7497746001039; - } else { - result[0] += -120.65339683228727; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 46))) { - result[0] += -1201.76536866533; - } else { - result[0] += 160.46676494029293; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 152))) { - if (LIKELY(false || (data[2].qvalue <= 214))) { - result[0] += 379.9797915421976; - } else { - result[0] += -12.579853903701677; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 452))) { - result[0] += -539.1034312262643; - } else { - result[0] += 118.1495781414129; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 210))) { - if (UNLIKELY(false || (data[0].qvalue <= 96))) { - if (UNLIKELY(false || (data[0].qvalue <= 12))) { - result[0] += -414.82218364010805; - } else { - if (LIKELY(false || (data[6].qvalue <= 100))) { - result[0] += -261.7354787568685; - } else { - result[0] += -436.9782510571122; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 126))) { - if (LIKELY(false || (data[6].qvalue <= 50))) { - if (UNLIKELY(false || (data[0].qvalue <= 158))) { - result[0] += -86.99960309582809; - } else { - result[0] += 36.01621466834996; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 54))) { - result[0] += -276.15518055633515; - } else { - result[0] += -117.47322502980383; - } - } - } else { - result[0] += -418.57103843448124; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 134))) { - if (UNLIKELY(false || (data[0].qvalue <= 300))) { - if (LIKELY(false || (data[6].qvalue <= 100))) { - if (UNLIKELY(false || (data[7].qvalue <= 24))) { - result[0] += 221.38591506553996; - } else { - if (UNLIKELY(false || (data[5].qvalue <= 34))) { - result[0] += -114.10943519348092; - } else { - result[0] += 124.41383507415142; - } - } - } else { - result[0] += -124.776312637069; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 40))) { - if (LIKELY(false || (data[0].qvalue <= 434))) { - if (LIKELY(false || (data[6].qvalue <= 134))) { - result[0] += 173.27180862569062; - } else { - result[0] += -230.0263780267813; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 90))) { - result[0] += 93.55822981381293; - } else { - result[0] += 531.9618157197915; - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 138))) { - if (UNLIKELY(false || (data[7].qvalue <= 12))) { - result[0] += 529.342886411045; - } else { - result[0] += 275.38241535187143; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 32))) { - result[0] += 168.94637195832345; - } else { - result[0] += -664.1470891916572; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 442))) { - if (LIKELY(false || (data[6].qvalue <= 174))) { - if (LIKELY(false || (data[7].qvalue <= 198))) { - if (UNLIKELY(false || (data[9].qvalue <= 68))) { - result[0] += -259.076207652103; - } else { - result[0] += 97.97671807149003; - } - } else { - result[0] += -695.1606225313006; - } - } else { - result[0] += -796.0742621530271; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 180))) { - if (UNLIKELY(false || (data[2].qvalue <= 104))) { - if (LIKELY(false || (data[0].qvalue <= 464))) { - result[0] += -153.93531708038253; - } else { - result[0] += 448.69780310463796; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 216))) { - result[0] += 477.95650201824304; - } else { - result[0] += -28.61899971217077; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -909.6241555301945; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 470))) { - result[0] += -188.47445082878266; - } else { - result[0] += 377.35719169796516; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 172))) { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - if (UNLIKELY(false || (data[0].qvalue <= 8))) { - result[0] += -390.84856797995377; - } else { - if (LIKELY(false || (data[1].qvalue <= 64))) { - result[0] += -240.40122514681448; - } else { - result[0] += -347.4550413344793; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 134))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - result[0] += -39.25816550113254; - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[0].qvalue <= 142))) { - result[0] += -186.1240171676131; - } else { - result[0] += -98.0111804997198; - } - } else { - result[0] += -436.0524831294065; - } - } - } else { - result[0] += -471.83682540539394; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 238))) { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 348.9273763963965; - } else { - if (LIKELY(false || (data[7].qvalue <= 104))) { - result[0] += 30.53430231649784; - } else { - result[0] += -202.87449384945464; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 120))) { - if (UNLIKELY(false || (data[2].qvalue <= 4))) { - result[0] += -836.9509277269852; - } else { - if (UNLIKELY(false || (data[6].qvalue <= 22))) { - result[0] += -1508.3799402707123; - } else { - result[0] += -94.06253649645048; - } - } - } else { - result[0] += -348.5371075782411; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 126))) { - if (LIKELY(false || (data[0].qvalue <= 346))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (UNLIKELY(false || (data[0].qvalue <= 286))) { - result[0] += 162.75459965948465; - } else { - result[0] += 282.56625214886793; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 10))) { - result[0] += -440.06926455976475; - } else { - result[0] += 35.45780394100668; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 212))) { - if (UNLIKELY(false || (data[8].qvalue <= 72))) { - result[0] += 180.74606636101373; - } else { - result[0] += 317.0047465523858; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 438))) { - result[0] += -588.0578066297068; - } else { - result[0] += 433.5588979682989; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 422))) { - if (LIKELY(false || (data[1].qvalue <= 136))) { - if (LIKELY(false || (data[7].qvalue <= 192))) { - result[0] += 45.9325195881808; - } else { - result[0] += -679.3813065404768; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 270.29103457509785; - } else { - result[0] += -517.5531688785617; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 118))) { - if (UNLIKELY(false || (data[10].qvalue <= 56))) { - result[0] += 117.54636858837371; - } else { - result[0] += 647.912058999977; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 464))) { - result[0] += -41.193603415175254; - } else { - result[0] += 296.9100448809772; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 222))) { - if (LIKELY(false || (data[0].qvalue <= 138))) { - if (UNLIKELY(false || (data[0].qvalue <= 58))) { - result[0] += -275.2115013121635; - } else { - if (LIKELY(false || (data[6].qvalue <= 116))) { - result[0] += -155.46609912259618; - } else { - result[0] += -389.7954080308127; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 100))) { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - result[0] += 384.7520448135499; - } else { - if (LIKELY(false || (data[9].qvalue <= 154))) { - if (UNLIKELY(false || (data[4].qvalue <= 6))) { - result[0] += 292.1491245847539; - } else { - result[0] += -30.543242192258454; - } - } else { - result[0] += -385.76320075157514; - } - } - } else { - result[0] += -224.14550590009833; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 68))) { - if (UNLIKELY(false || (data[0].qvalue <= 272))) { - result[0] += 106.3555671944755; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - if (UNLIKELY(false || (data[9].qvalue <= 66))) { - if (UNLIKELY(false || (data[0].qvalue <= 410))) { - result[0] += -941.4297952572747; - } else { - result[0] += 56.28649828645251; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 8))) { - result[0] += 507.91991997031147; - } else { - result[0] += 35.20700760987173; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 348))) { - if (UNLIKELY(false || (data[7].qvalue <= 24))) { - result[0] += 324.4895405944488; - } else { - result[0] += 153.59057096574398; - } - } else { - if (LIKELY(false || (data[5].qvalue <= 60))) { - result[0] += 236.3224976398137; - } else { - result[0] += 444.2538183675668; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 372))) { - if (UNLIKELY(false || (data[9].qvalue <= 42))) { - if (UNLIKELY(false || (data[10].qvalue <= 64))) { - result[0] += 22.100327800787746; - } else { - result[0] += -285.1797635732232; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 182))) { - if (UNLIKELY(false || (data[3].qvalue <= 12))) { - result[0] += -916.6873662194294; - } else { - result[0] += 129.80380277726957; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 208))) { - result[0] += -121.90878827484735; - } else { - result[0] += -647.3935446930476; - } - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 10))) { - if (UNLIKELY(false || (data[0].qvalue <= 444))) { - if (LIKELY(false || (data[3].qvalue <= 168))) { - result[0] += -658.7332388174912; - } else { - result[0] += -100.44944033027124; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 174))) { - result[0] += 286.17922512478475; - } else { - result[0] += -166.4328133167238; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 94))) { - if (LIKELY(false || (data[3].qvalue <= 92))) { - result[0] += 56.97705669543027; - } else { - result[0] += -910.3064928974122; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 108))) { - result[0] += 459.24011774105895; - } else { - result[0] += 187.54878983259766; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 170))) { - if (UNLIKELY(false || (data[0].qvalue <= 80))) { - if (UNLIKELY(false || (data[11].qvalue <= 0))) { - if (UNLIKELY(false || (data[0].qvalue <= 18))) { - result[0] += -272.8015586187466; - } else { - result[0] += -158.01002915185765; - } - } else { - result[0] += -270.0109117909505; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 134))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - if (LIKELY(false || (data[5].qvalue <= 44))) { - result[0] += -47.655816285394785; - } else { - result[0] += 148.45176574086; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 48))) { - result[0] += -274.2183049595403; - } else { - result[0] += -120.42739359905393; - } - } - } else { - result[0] += -404.63297777523053; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 250))) { - if (LIKELY(false || (data[7].qvalue <= 86))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 310.3087236621802; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 6))) { - result[0] += -261.86875131184433; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 30))) { - result[0] += 77.33362724018633; - } else { - result[0] += -9.835991514809445; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 178))) { - if (LIKELY(false || (data[10].qvalue <= 94))) { - if (UNLIKELY(false || (data[3].qvalue <= 18))) { - result[0] += -491.7147359309727; - } else { - result[0] += -26.424686375898183; - } - } else { - result[0] += -256.5814333154503; - } - } else { - result[0] += -401.38775248670413; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 134))) { - if (LIKELY(false || (data[0].qvalue <= 364))) { - if (UNLIKELY(false || (data[9].qvalue <= 54))) { - if (LIKELY(false || (data[8].qvalue <= 78))) { - result[0] += 24.807904435156793; - } else { - result[0] += -255.08075851432937; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 180))) { - result[0] += 171.3936836582958; - } else { - result[0] += -146.48986963094893; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 10))) { - if (UNLIKELY(false || (data[0].qvalue <= 446))) { - result[0] += -527.9370629462896; - } else { - result[0] += 311.74412615306; - } - } else { - if (UNLIKELY(false || (data[11].qvalue <= 0))) { - result[0] += 61.275444153721516; - } else { - result[0] += 266.8647138392115; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 464))) { - if (LIKELY(false || (data[2].qvalue <= 218))) { - if (UNLIKELY(false || (data[9].qvalue <= 2))) { - result[0] += -553.7238254263838; - } else { - result[0] += 30.964493797225533; - } - } else { - result[0] += -1039.405250832381; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 132))) { - if (LIKELY(false || (data[0].qvalue <= 470))) { - result[0] += 221.3473424703826; - } else { - result[0] += 578.2036295192621; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 470))) { - result[0] += -1067.1325237122935; - } else { - result[0] += -72.41418684700736; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 222))) { - if (LIKELY(false || (data[0].qvalue <= 120))) { - if (UNLIKELY(false || (data[0].qvalue <= 36))) { - result[0] += -245.63449384858325; - } else { - if (LIKELY(false || (data[1].qvalue <= 102))) { - result[0] += -141.07821937677804; - } else { - result[0] += -311.46095137887164; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 78))) { - result[0] += -18.828476188261668; - } else { - result[0] += -158.60479952768688; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[0].qvalue <= 334))) { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - if (UNLIKELY(false || (data[5].qvalue <= 28))) { - if (LIKELY(false || (data[2].qvalue <= 122))) { - result[0] += 85.17185179060556; - } else { - result[0] += -1488.3371391950336; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 120))) { - result[0] += 251.47188880049714; - } else { - result[0] += -337.59370375551794; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 176))) { - if (LIKELY(false || (data[7].qvalue <= 122))) { - result[0] += 64.67449717812703; - } else { - result[0] += -198.4096965608494; - } - } else { - result[0] += -109.54175072971603; - } - } - } else { - if (LIKELY(false || (data[5].qvalue <= 96))) { - if (UNLIKELY(false || (data[7].qvalue <= 64))) { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - result[0] += 25.027805981227203; - } else { - result[0] += 254.11335581266104; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 22))) { - result[0] += -327.20805261276865; - } else { - result[0] += 158.88433974650883; - } - } - } else { - result[0] += 635.3039299555891; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 422))) { - if (UNLIKELY(false || (data[5].qvalue <= 98))) { - if (LIKELY(false || (data[4].qvalue <= 116))) { - result[0] += -674.4791597614653; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 154))) { - result[0] += -621.8229750354634; - } else { - result[0] += 421.9420604815607; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 102))) { - if (LIKELY(false || (data[0].qvalue <= 390))) { - result[0] += 85.124602127931; - } else { - result[0] += 949.6197614387934; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 144))) { - result[0] += 251.2728036404487; - } else { - result[0] += -309.15702771287084; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 184))) { - if (LIKELY(false || (data[0].qvalue <= 460))) { - if (UNLIKELY(false || (data[10].qvalue <= 38))) { - result[0] += -633.4429247336111; - } else { - result[0] += 160.50792355093574; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 176))) { - result[0] += 479.12605526304554; - } else { - result[0] += 116.33385550515398; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -930.2431709848779; - } else { - if (LIKELY(false || (data[1].qvalue <= 156))) { - result[0] += 214.15884274598116; - } else { - result[0] += -277.81632186374355; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 168))) { - if (UNLIKELY(false || (data[0].qvalue <= 72))) { - if (LIKELY(false || (data[6].qvalue <= 88))) { - if (UNLIKELY(false || (data[0].qvalue <= 4))) { - result[0] += -294.9481294960577; - } else { - result[0] += -165.43274636129723; - } - } else { - result[0] += -283.98106032598395; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 137.94773272215207; - } else { - result[0] += -74.90747793962899; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - result[0] += -146.0253896376931; - } else { - result[0] += -355.419967719613; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 268))) { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 312.51485279750943; - } else { - if (LIKELY(false || (data[2].qvalue <= 208))) { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - result[0] += -26.67715098965897; - } else { - result[0] += 59.16521507626189; - } - } else { - result[0] += -514.0817805406466; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 22))) { - result[0] += -1382.1700903888081; - } else { - if (LIKELY(false || (data[1].qvalue <= 148))) { - if (UNLIKELY(false || (data[6].qvalue <= 84))) { - result[0] += 2.2238582017569652; - } else { - result[0] += -149.07329069335975; - } - } else { - result[0] += -389.67704456086886; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 134))) { - if (LIKELY(false || (data[2].qvalue <= 212))) { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - if (LIKELY(false || (data[0].qvalue <= 462))) { - result[0] += -139.5342233564615; - } else { - result[0] += 425.4054517675065; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 370))) { - result[0] += 119.1045005005382; - } else { - result[0] += 225.06622562801806; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 428))) { - if (UNLIKELY(false || (data[10].qvalue <= 116))) { - result[0] += -314.0145429164164; - } else { - result[0] += -791.7715009965185; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 156))) { - result[0] += 299.89845236333366; - } else { - result[0] += -121.70249001324471; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 438))) { - if (LIKELY(false || (data[6].qvalue <= 152))) { - if (UNLIKELY(false || (data[2].qvalue <= 42))) { - result[0] += 308.86651700943054; - } else { - result[0] += -123.15739696081778; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 162))) { - result[0] += -543.1460199892057; - } else { - result[0] += -268.8247484125225; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 170))) { - if (LIKELY(false || (data[5].qvalue <= 114))) { - result[0] += 219.69052341180478; - } else { - result[0] += 768.652154361283; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -330.30502416245446; - } else { - result[0] += 226.83056005764217; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 194))) { - if (UNLIKELY(false || (data[0].qvalue <= 94))) { - result[0] += -164.85997854401103; - } else { - if (LIKELY(false || (data[7].qvalue <= 134))) { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - if (LIKELY(false || (data[6].qvalue <= 32))) { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - result[0] += 272.37997423411696; - } else { - result[0] += -38.01170141177444; - } - } else { - result[0] += 179.2649038053183; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 40))) { - if (UNLIKELY(false || (data[10].qvalue <= 14))) { - result[0] += -447.1000423177568; - } else { - result[0] += -129.8926570286136; - } - } else { - result[0] += -58.31804328980124; - } - } - } else { - result[0] += -307.58593821488535; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (UNLIKELY(false || (data[0].qvalue <= 308))) { - if (LIKELY(false || (data[6].qvalue <= 116))) { - if (UNLIKELY(false || (data[7].qvalue <= 2))) { - result[0] += 484.75118404823917; - } else { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 101.86133520052235; - } else { - result[0] += 18.262784206337113; - } - } - } else { - result[0] += -171.38940241656513; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 76))) { - if (LIKELY(false || (data[0].qvalue <= 420))) { - if (LIKELY(false || (data[4].qvalue <= 80))) { - result[0] += 164.27136795332518; - } else { - result[0] += 420.19522838999177; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 70))) { - result[0] += 288.60771862284395; - } else { - result[0] += -317.37860129812316; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 380))) { - if (UNLIKELY(false || (data[3].qvalue <= 112))) { - result[0] += -302.83673649889585; - } else { - result[0] += 54.622892990725916; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 98))) { - result[0] += -14.652198735183687; - } else { - result[0] += 188.62633009457414; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 410))) { - if (LIKELY(false || (data[1].qvalue <= 88))) { - if (UNLIKELY(false || (data[10].qvalue <= 68))) { - result[0] += -453.6048808127357; - } else { - result[0] += 39.27686618322581; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 20))) { - result[0] += 0.2890822714437353; - } else { - result[0] += -681.1445664481982; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 114))) { - if (LIKELY(false || (data[7].qvalue <= 198))) { - result[0] += 448.49867267672437; - } else { - if (LIKELY(false || (data[0].qvalue <= 446))) { - result[0] += -510.11854071816606; - } else { - result[0] += 725.828009865931; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - if (UNLIKELY(false || (data[10].qvalue <= 70))) { - result[0] += 82.63133907289594; - } else { - result[0] += -564.6009321848745; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 188))) { - result[0] += 485.04570202598234; - } else { - result[0] += -44.949579597135376; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 226))) { - if (LIKELY(false || (data[0].qvalue <= 140))) { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - result[0] += -89.22639695175398; - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - result[0] += -154.49086546432346; - } else { - result[0] += -329.5903225571293; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 126))) { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - result[0] += 318.48761171753574; - } else { - result[0] += -18.571568031593554; - } - } else { - result[0] += -220.49206840771905; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 202))) { - if (LIKELY(false || (data[1].qvalue <= 152))) { - if (LIKELY(false || (data[0].qvalue <= 366))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (LIKELY(false || (data[8].qvalue <= 120))) { - result[0] += 150.42799764081784; - } else { - result[0] += -76.38500429830752; - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 56))) { - result[0] += -211.0027314849032; - } else { - result[0] += 24.230230872826798; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 164))) { - if (LIKELY(false || (data[8].qvalue <= 142))) { - result[0] += 132.68242055886515; - } else { - result[0] += -882.8096755983888; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 192))) { - result[0] += 334.11768457333994; - } else { - result[0] += -53.48084930478878; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 450))) { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 233.21157693531032; - } else { - if (LIKELY(false || (data[10].qvalue <= 138))) { - result[0] += -582.6982780907397; - } else { - result[0] += 0.7508374869349193; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 174))) { - if (UNLIKELY(false || (data[2].qvalue <= 66))) { - result[0] += 844.2241348987232; - } else { - result[0] += 166.47618379149745; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -770.8958350747589; - } else { - result[0] += 106.39982734044628; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 424))) { - if (UNLIKELY(false || (data[10].qvalue <= 116))) { - if (LIKELY(false || (data[10].qvalue <= 114))) { - result[0] += -318.826948941992; - } else { - result[0] += 300.5051761631031; - } - } else { - result[0] += -663.2498735346456; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 68))) { - if (LIKELY(false || (data[7].qvalue <= 176))) { - result[0] += 330.2362671942275; - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - result[0] += -261.9859689644222; - } else { - result[0] += 470.337710070637; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - if (UNLIKELY(false || (data[8].qvalue <= 134))) { - result[0] += 52.55390876938948; - } else { - result[0] += -820.466862953795; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 172))) { - result[0] += 678.0504747681679; - } else { - result[0] += -30.65402416986749; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 230))) { - if (LIKELY(false || (data[0].qvalue <= 146))) { - if (LIKELY(false || (data[7].qvalue <= 124))) { - if (UNLIKELY(false || (data[0].qvalue <= 40))) { - result[0] += -163.25748720727836; - } else { - result[0] += -83.1950205740847; - } - } else { - result[0] += -269.08136712131557; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 54))) { - result[0] += -164.01997292662278; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 228.21827507549563; - } else { - result[0] += -9.402288377382439; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 142))) { - if (UNLIKELY(false || (data[7].qvalue <= 16))) { - if (LIKELY(false || (data[6].qvalue <= 30))) { - if (UNLIKELY(false || (data[7].qvalue <= 4))) { - result[0] += 381.64939051951137; - } else { - if (LIKELY(false || (data[0].qvalue <= 302))) { - result[0] += 58.00057357423904; - } else { - result[0] += 223.74792550499748; - } - } - } else { - result[0] += 454.5662709049777; - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 36))) { - if (LIKELY(false || (data[8].qvalue <= 128))) { - if (LIKELY(false || (data[0].qvalue <= 378))) { - result[0] += -93.01200660095587; - } else { - result[0] += 134.32252549902134; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 444))) { - result[0] += -2078.6414691042096; - } else { - result[0] += -159.18640228976722; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 32))) { - if (UNLIKELY(false || (data[0].qvalue <= 396))) { - result[0] += -163.73514121181952; - } else { - result[0] += 100.75922992115221; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 360))) { - result[0] += 84.30972820399808; - } else { - result[0] += 197.85022347944687; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 442))) { - if (LIKELY(false || (data[7].qvalue <= 198))) { - if (UNLIKELY(false || (data[6].qvalue <= 114))) { - if (LIKELY(false || (data[0].qvalue <= 406))) { - result[0] += -124.87742350731226; - } else { - result[0] += 371.4284844619934; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 130))) { - result[0] += -1022.9075285275169; - } else { - result[0] += -154.11889775108097; - } - } - } else { - result[0] += -609.5631862224477; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 180))) { - if (UNLIKELY(false || (data[2].qvalue <= 104))) { - if (LIKELY(false || (data[0].qvalue <= 464))) { - result[0] += -172.46788335138166; - } else { - result[0] += 267.92756125694655; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 216))) { - result[0] += 344.21102212490365; - } else { - result[0] += -63.20922723234142; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (UNLIKELY(false || (data[4].qvalue <= 88))) { - result[0] += -98.6686560628389; - } else { - result[0] += -824.7416534209821; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 162))) { - result[0] += 423.0262534565941; - } else { - result[0] += -172.900534785997; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 164))) { - if (UNLIKELY(false || (data[7].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 22))) { - result[0] += -151.00440148400935; - } else { - result[0] += -40.118590528856686; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 134))) { - result[0] += -118.83158145049806; - } else { - result[0] += -265.5488061301905; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (UNLIKELY(false || (data[0].qvalue <= 276))) { - if (LIKELY(false || (data[1].qvalue <= 76))) { - if (UNLIKELY(false || (data[7].qvalue <= 2))) { - result[0] += 337.5770961546419; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - result[0] += -45.53149621844132; - } else { - result[0] += 57.69099405123824; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 22))) { - result[0] += -1262.4368580820865; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 4))) { - result[0] += -499.135631046245; - } else { - result[0] += -51.130463115279184; - } - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 10))) { - if (UNLIKELY(false || (data[0].qvalue <= 446))) { - if (LIKELY(false || (data[10].qvalue <= 132))) { - result[0] += -452.52727046733617; - } else { - result[0] += 207.2426999628595; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 186))) { - result[0] += 147.30212851601848; - } else { - result[0] += -378.60903812177116; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - if (LIKELY(false || (data[7].qvalue <= 116))) { - result[0] += 60.647838286510584; - } else { - result[0] += -403.6642612098349; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 144))) { - result[0] += 97.61644906336824; - } else { - result[0] += 243.6057368289787; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 440))) { - if (LIKELY(false || (data[10].qvalue <= 116))) { - if (LIKELY(false || (data[0].qvalue <= 412))) { - if (LIKELY(false || (data[10].qvalue <= 114))) { - result[0] += -372.46208551501405; - } else { - result[0] += 150.12385303560893; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 154))) { - result[0] += 378.2084024000228; - } else { - result[0] += -115.22596011254524; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 104))) { - result[0] += -1013.8954317267925; - } else { - result[0] += -458.7466793813837; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 162))) { - if (LIKELY(false || (data[0].qvalue <= 460))) { - if (UNLIKELY(false || (data[10].qvalue <= 118))) { - result[0] += 573.7495286165021; - } else { - result[0] += -39.64820127548395; - } - } else { - result[0] += 747.9256037479381; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 464))) { - if (UNLIKELY(false || (data[2].qvalue <= 218))) { - result[0] += -322.32729930754635; - } else { - result[0] += -889.5131323414915; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - result[0] += -49.77314604177496; - } else { - result[0] += 452.922151113344; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 230))) { - if (UNLIKELY(false || (data[0].qvalue <= 100))) { - result[0] += -110.27471806521078; - } else { - if (UNLIKELY(false || (data[9].qvalue <= 54))) { - if (UNLIKELY(false || (data[1].qvalue <= 36))) { - result[0] += 405.5242126958143; - } else { - result[0] += -170.43432845409902; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - result[0] += 256.63050409714157; - } else { - if (LIKELY(false || (data[9].qvalue <= 154))) { - if (UNLIKELY(false || (data[4].qvalue <= 6))) { - result[0] += 199.22002184764415; - } else { - result[0] += -21.456223281242632; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 20))) { - result[0] += -394.6713467594481; - } else { - result[0] += 400.4595635278145; - } - } - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (UNLIKELY(false || (data[9].qvalue <= 2))) { - if (LIKELY(false || (data[0].qvalue <= 466))) { - if (LIKELY(false || (data[6].qvalue <= 170))) { - if (UNLIKELY(false || (data[1].qvalue <= 146))) { - result[0] += -1171.2407195011317; - } else { - result[0] += 62.25614012625213; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 462))) { - result[0] += -747.9426165897981; - } else { - result[0] += -245.0583143986272; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 184))) { - result[0] += 389.8618473333866; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -714.1912396897699; - } else { - result[0] += 105.56232441101614; - } - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 30))) { - if (LIKELY(false || (data[6].qvalue <= 152))) { - if (LIKELY(false || (data[0].qvalue <= 452))) { - result[0] += 8.267421890159907; - } else { - result[0] += 532.9815498007549; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 450))) { - result[0] += -998.5163824104064; - } else { - result[0] += 67.53749679257139; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 38))) { - if (LIKELY(false || (data[0].qvalue <= 314))) { - result[0] += 128.68056584630503; - } else { - result[0] += 304.65594504617775; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 378))) { - result[0] += 38.864839831291896; - } else { - result[0] += 132.95011491298308; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 422))) { - if (LIKELY(false || (data[10].qvalue <= 116))) { - result[0] += -200.50051859519212; - } else { - result[0] += -570.0695420065967; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 76))) { - if (LIKELY(false || (data[2].qvalue <= 214))) { - result[0] += 284.7667993502012; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 452))) { - result[0] += -439.2896395697744; - } else { - result[0] += 195.2703219505385; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 448))) { - result[0] += -719.4958031369861; - } else { - if (LIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -48.870417355715304; - } else { - result[0] += 558.0516523176542; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 158))) { - if (LIKELY(false || (data[1].qvalue <= 102))) { - if (UNLIKELY(false || (data[0].qvalue <= 28))) { - result[0] += -132.8644217361974; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 20))) { - if (LIKELY(false || (data[5].qvalue <= 44))) { - if (UNLIKELY(false || (data[6].qvalue <= 0))) { - result[0] += 184.7567664677024; - } else { - result[0] += -31.01544671430151; - } - } else { - result[0] += 93.76937878382813; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 158))) { - result[0] += -70.3541078253051; - } else { - result[0] += -256.18565932246213; - } - } - } - } else { - result[0] += -186.9601821997848; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 192))) { - if (UNLIKELY(false || (data[0].qvalue <= 272))) { - if (UNLIKELY(false || (data[7].qvalue <= 22))) { - if (LIKELY(false || (data[6].qvalue <= 32))) { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - result[0] += 301.99251039980805; - } else { - result[0] += 8.317443045497088; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 285.9447580619998; - } else { - result[0] += -199.02985809976943; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 34))) { - if (UNLIKELY(false || (data[7].qvalue <= 28))) { - result[0] += -757.7155546033136; - } else { - result[0] += -100.8798688536487; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 54))) { - result[0] += -108.18906289397617; - } else { - result[0] += 16.427955171508895; - } - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 32))) { - if (LIKELY(false || (data[0].qvalue <= 450))) { - if (LIKELY(false || (data[6].qvalue <= 168))) { - result[0] += -31.079261573945725; - } else { - result[0] += -439.24160085690585; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 176))) { - result[0] += 196.17828368025368; - } else { - result[0] += -96.92802902335563; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - if (LIKELY(false || (data[7].qvalue <= 150))) { - result[0] += 5.472886535824474; - } else { - result[0] += -322.6822453952255; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 104))) { - result[0] += 71.15954913651206; - } else { - result[0] += 170.40770380917564; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 434))) { - if (LIKELY(false || (data[1].qvalue <= 98))) { - result[0] += -431.7182112009705; - } else { - result[0] += -761.9070576282425; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 174))) { - if (LIKELY(false || (data[0].qvalue <= 450))) { - if (LIKELY(false || (data[3].qvalue <= 170))) { - result[0] += 223.6763270971365; - } else { - result[0] += -863.1311602081029; - } - } else { - result[0] += 633.5684765097419; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 464))) { - result[0] += -822.025766073113; - } else { - if (LIKELY(false || (data[8].qvalue <= 144))) { - result[0] += -357.65137650350306; - } else { - result[0] += 161.10489597769953; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 230))) { - if (UNLIKELY(false || (data[0].qvalue <= 94))) { - result[0] += -92.57565870144303; - } else { - if (LIKELY(false || (data[7].qvalue <= 142))) { - result[0] += -18.109772204939024; - } else { - result[0] += -209.49572955406617; - } - } - } else { - if (LIKELY(false || (data[10].qvalue <= 116))) { - if (LIKELY(false || (data[1].qvalue <= 152))) { - if (UNLIKELY(false || (data[10].qvalue <= 30))) { - if (LIKELY(false || (data[0].qvalue <= 462))) { - if (LIKELY(false || (data[5].qvalue <= 102))) { - result[0] += 3.2575921560660412; - } else { - result[0] += -901.3597327991677; - } - } else { - result[0] += 507.2677983999125; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 128))) { - if (UNLIKELY(false || (data[5].qvalue <= 12))) { - result[0] += -59.086766158297294; - } else { - result[0] += 112.28787145525055; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 58))) { - result[0] += 323.6859359964492; - } else { - result[0] += -58.36682033574347; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 460))) { - if (LIKELY(false || (data[2].qvalue <= 84))) { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 300.9633139092185; - } else { - result[0] += -192.01700771623888; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 166))) { - result[0] += -1598.762896930197; - } else { - result[0] += -603.9295642909062; - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 16))) { - if (UNLIKELY(false || (data[6].qvalue <= 174))) { - result[0] += 461.56736902180194; - } else { - result[0] += 8.737578542205178; - } - } else { - result[0] += -643.983821829543; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 440))) { - if (LIKELY(false || (data[6].qvalue <= 104))) { - if (UNLIKELY(false || (data[2].qvalue <= 148))) { - if (LIKELY(false || (data[2].qvalue <= 134))) { - result[0] += 179.91548506297573; - } else { - result[0] += -912.5235714991372; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 174))) { - result[0] += 301.8534329221878; - } else { - result[0] += 38.18076419361558; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 94))) { - if (LIKELY(false || (data[0].qvalue <= 406))) { - result[0] += -361.8709774599796; - } else { - result[0] += 270.3263916029786; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 6))) { - result[0] += 104.81627908969739; - } else { - result[0] += -535.2156269218489; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 170))) { - if (LIKELY(false || (data[4].qvalue <= 122))) { - if (UNLIKELY(false || (data[7].qvalue <= 114))) { - result[0] += 594.5833564826081; - } else { - result[0] += 64.11583431949299; - } - } else { - result[0] += 652.8845003559351; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -763.9957389916799; - } else { - if (UNLIKELY(false || (data[4].qvalue <= 102))) { - result[0] += 315.48470650562535; - } else { - result[0] += -203.67379945157032; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 154))) { - if (UNLIKELY(false || (data[4].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 12))) { - result[0] += -126.5718042782856; - } else { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 111.5057760402395; - } else { - result[0] += -28.4538638965687; - } - } else { - result[0] += -313.9852425571987; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 142))) { - if (UNLIKELY(false || (data[9].qvalue <= 48))) { - result[0] += -159.27654309724835; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 54))) { - result[0] += -114.2328146382158; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 18))) { - result[0] += -169.53442010531597; - } else { - result[0] += -51.819614520602755; - } - } - } - } else { - result[0] += -237.49538829330947; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 192))) { - if (UNLIKELY(false || (data[7].qvalue <= 16))) { - if (LIKELY(false || (data[5].qvalue <= 46))) { - if (LIKELY(false || (data[0].qvalue <= 290))) { - if (UNLIKELY(false || (data[7].qvalue <= 2))) { - result[0] += 287.9491607426837; - } else { - result[0] += 22.668345828570306; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 0))) { - result[0] += -77.53473409498233; - } else { - result[0] += 204.6386641118168; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 16))) { - result[0] += 593.7874627921306; - } else { - result[0] += 312.8524644986319; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 42))) { - if (UNLIKELY(false || (data[6].qvalue <= 24))) { - if (LIKELY(false || (data[2].qvalue <= 122))) { - result[0] += -142.83280406635228; - } else { - result[0] += -693.2501230727516; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 122))) { - result[0] += 37.74683956549833; - } else { - result[0] += -198.748856011046; - } - } - } else { - if (LIKELY(false || (data[10].qvalue <= 108))) { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - result[0] += -89.22275972296083; - } else { - result[0] += 86.32327653910806; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 442))) { - result[0] += -55.02130370736227; - } else { - result[0] += 177.35435049468094; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 432))) { - if (UNLIKELY(false || (data[2].qvalue <= 74))) { - result[0] += -882.5317237443489; - } else { - result[0] += -481.30010328266326; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 132))) { - if (LIKELY(false || (data[0].qvalue <= 450))) { - if (UNLIKELY(false || (data[7].qvalue <= 198))) { - result[0] += 647.320083633933; - } else { - result[0] += 38.11111140878454; - } - } else { - result[0] += 745.2928537341078; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -789.9197396160231; - } else { - if (UNLIKELY(false || (data[8].qvalue <= 144))) { - result[0] += -357.5527732739339; - } else { - result[0] += 102.50578713894222; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 232))) { - if (LIKELY(false || (data[6].qvalue <= 116))) { - if (UNLIKELY(false || (data[0].qvalue <= 62))) { - result[0] += -81.42467173085487; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 16))) { - if (LIKELY(false || (data[5].qvalue <= 48))) { - result[0] += 9.502633625283542; - } else { - result[0] += 279.42600545633803; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 28))) { - if (UNLIKELY(false || (data[6].qvalue <= 18))) { - result[0] += -361.821022373354; - } else { - result[0] += -88.28610792754384; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 120))) { - result[0] += -20.102121037240444; - } else { - result[0] += -420.12674867604005; - } - } - } - } - } else { - result[0] += -159.62279487679336; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 38))) { - if (LIKELY(false || (data[10].qvalue <= 124))) { - if (UNLIKELY(false || (data[6].qvalue <= 10))) { - if (LIKELY(false || (data[7].qvalue <= 18))) { - result[0] += 88.01385578444962; - } else { - if (UNLIKELY(false || (data[6].qvalue <= 4))) { - result[0] += 493.08930447766016; - } else { - result[0] += -285.19813158141295; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 82))) { - result[0] += 106.27335201243972; - } else { - result[0] += 226.93623921451922; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 50))) { - if (LIKELY(false || (data[0].qvalue <= 412))) { - result[0] += -1343.712162589175; - } else { - result[0] += 132.58954443144634; - } - } else { - result[0] += 91.6622701261032; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 44))) { - if (LIKELY(false || (data[3].qvalue <= 38))) { - if (LIKELY(false || (data[4].qvalue <= 84))) { - if (LIKELY(false || (data[0].qvalue <= 368))) { - result[0] += -236.33002335788538; - } else { - result[0] += 20.986096347583267; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 128))) { - result[0] += 344.9152673102923; - } else { - result[0] += -105.45712817788554; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 328.6203717386541; - } else { - if (LIKELY(false || (data[0].qvalue <= 448))) { - result[0] += -650.199452548778; - } else { - result[0] += -47.9905026661036; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 132))) { - if (LIKELY(false || (data[0].qvalue <= 366))) { - if (LIKELY(false || (data[2].qvalue <= 180))) { - result[0] += 42.03195219241939; - } else { - result[0] += -158.93413279533422; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 74))) { - result[0] += -34.071317241183344; - } else { - result[0] += 156.0271506153512; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 440))) { - if (UNLIKELY(false || (data[6].qvalue <= 102))) { - result[0] += 133.87095453891186; - } else { - result[0] += -228.7502100788784; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 108))) { - result[0] += 347.50113932020776; - } else { - result[0] += 14.191860694706207; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 230))) { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - if (LIKELY(false || (data[2].qvalue <= 122))) { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - result[0] += 177.32409766602134; - } else { - if (UNLIKELY(false || (data[10].qvalue <= 10))) { - result[0] += -222.24918504973667; - } else { - result[0] += -35.30855443997724; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 4))) { - result[0] += -176.9665709227076; - } else { - result[0] += -753.9332796594691; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 116))) { - result[0] += -28.342368225619726; - } else { - if (UNLIKELY(false || (data[4].qvalue <= 8))) { - result[0] += 421.668098564295; - } else { - if (LIKELY(false || (data[3].qvalue <= 70))) { - result[0] += 43.5107313173905; - } else { - result[0] += 227.90844897767985; - } - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - result[0] += -57.17411824360336; - } else { - result[0] += -189.687353931762; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 202))) { - if (UNLIKELY(false || (data[3].qvalue <= 0))) { - if (LIKELY(false || (data[4].qvalue <= 54))) { - if (LIKELY(false || (data[9].qvalue <= 156))) { - result[0] += 143.81130747694493; - } else { - if (LIKELY(false || (data[0].qvalue <= 378))) { - result[0] += -412.9308129291959; - } else { - result[0] += 174.61191720224775; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 422))) { - if (LIKELY(false || (data[9].qvalue <= 88))) { - result[0] += -729.9085027051653; - } else { - result[0] += -1582.3957851064256; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 82))) { - result[0] += 255.17536551772466; - } else { - result[0] += -411.7747098985702; - } - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 0))) { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -687.4964190637061; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 156))) { - result[0] += 579.2665867229997; - } else { - result[0] += -202.15282940470115; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 378))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - result[0] += 87.73491261546084; - } else { - result[0] += -32.02969863953809; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 166))) { - result[0] += 64.28717265542389; - } else { - result[0] += 229.17843828172778; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (LIKELY(false || (data[6].qvalue <= 176))) { - if (LIKELY(false || (data[0].qvalue <= 442))) { - if (UNLIKELY(false || (data[10].qvalue <= 116))) { - result[0] += -44.67740033036628; - } else { - result[0] += -394.97654476097733; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 222))) { - result[0] += 206.95175345062236; - } else { - result[0] += -422.41503848096585; - } - } - } else { - result[0] += -637.136586228852; - } - } else { - result[0] += 376.9378106237276; - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 162))) { - if (LIKELY(false || (data[1].qvalue <= 64))) { - if (UNLIKELY(false || (data[0].qvalue <= 4))) { - result[0] += -145.63790783710485; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 92.69381886875624; - } else { - if (LIKELY(false || (data[2].qvalue <= 200))) { - result[0] += -29.52565796333189; - } else { - result[0] += -264.4833581843737; - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 80))) { - result[0] += -65.00862983366554; - } else { - result[0] += -128.3574858335414; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 192))) { - if (LIKELY(false || (data[10].qvalue <= 116))) { - if (LIKELY(false || (data[0].qvalue <= 334))) { - if (LIKELY(false || (data[7].qvalue <= 44))) { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - result[0] += 2.638363742314313; - } else { - result[0] += 90.02482928313995; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 46))) { - result[0] += -159.46662656973402; - } else { - result[0] += -6.479090823017612; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 10))) { - if (LIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -181.28880117455623; - } else { - result[0] += 348.385125441513; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - result[0] += -95.60162541279402; - } else { - result[0] += 108.90915732052065; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 448))) { - if (LIKELY(false || (data[6].qvalue <= 104))) { - if (UNLIKELY(false || (data[8].qvalue <= 92))) { - result[0] += -264.2515788707746; - } else { - result[0] += 101.55470547812884; - } - } else { - if (LIKELY(false || (data[8].qvalue <= 148))) { - result[0] += -178.22322886037145; - } else { - result[0] += -572.0017383471462; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 176))) { - if (LIKELY(false || (data[2].qvalue <= 216))) { - result[0] += 360.0600154694514; - } else { - result[0] += -2.277701777775852; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - result[0] += -592.4446434254904; - } else { - result[0] += 311.89032842128967; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 434))) { - if (LIKELY(false || (data[1].qvalue <= 98))) { - if (LIKELY(false || (data[3].qvalue <= 170))) { - result[0] += -269.5216917245177; - } else { - result[0] += -763.2129798350616; - } - } else { - result[0] += -630.6861669974678; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 132))) { - if (LIKELY(false || (data[0].qvalue <= 452))) { - if (UNLIKELY(false || (data[1].qvalue <= 98))) { - result[0] += 491.16969306572764; - } else { - result[0] += -1.8378346933226348; - } - } else { - result[0] += 735.4661909012816; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -714.8393720262166; - } else { - if (LIKELY(false || (data[9].qvalue <= 124))) { - result[0] += -185.85739524553466; - } else { - result[0] += 534.3912650447504; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 234))) { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - if (LIKELY(false || (data[2].qvalue <= 122))) { - if (LIKELY(false || (data[7].qvalue <= 34))) { - result[0] += -17.516199145518204; - } else { - if (LIKELY(false || (data[5].qvalue <= 40))) { - result[0] += -106.45476753502933; - } else { - result[0] += -903.8252212915366; - } - } - } else { - result[0] += -429.58429904156355; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 108))) { - result[0] += -24.64488868029451; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 6))) { - result[0] += 442.263546116001; - } else { - result[0] += 55.12907189754925; - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - result[0] += -46.866944249531215; - } else { - result[0] += -159.04948180790302; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (UNLIKELY(false || (data[3].qvalue <= 0))) { - if (LIKELY(false || (data[7].qvalue <= 110))) { - result[0] += -7.415599980597751; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 420))) { - if (LIKELY(false || (data[2].qvalue <= 196))) { - result[0] += -1399.7966503370856; - } else { - result[0] += -610.3226946773707; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 96))) { - result[0] += 354.8632074839545; - } else { - result[0] += -377.08280725284146; - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 12))) { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (LIKELY(false || (data[0].qvalue <= 312))) { - result[0] += 78.41314807466219; - } else { - result[0] += 229.81473981996055; - } - } else { - result[0] += 656.2836431782829; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 370))) { - if (UNLIKELY(false || (data[6].qvalue <= 62))) { - result[0] += 59.2010780966252; - } else { - result[0] += -33.600013356306874; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 10))) { - result[0] += -40.555513916002155; - } else { - result[0] += 98.08389359507521; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 412))) { - if (UNLIKELY(false || (data[8].qvalue <= 136))) { - if (LIKELY(false || (data[8].qvalue <= 122))) { - result[0] += -319.74524116929115; - } else { - result[0] += -767.7796729203433; - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 82))) { - result[0] += 122.08054350335395; - } else { - result[0] += -301.9722820682188; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 114))) { - if (LIKELY(false || (data[7].qvalue <= 198))) { - result[0] += 313.9124097005065; - } else { - result[0] += -8.79354690498097; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (LIKELY(false || (data[6].qvalue <= 180))) { - result[0] += -72.7211246346664; - } else { - result[0] += -510.6446546492408; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 188))) { - result[0] += 560.0836128367328; - } else { - result[0] += 17.985013800397187; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 148))) { - if (UNLIKELY(false || (data[4].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 2))) { - result[0] += -141.96709416955443; - } else { - result[0] += -15.705324092558222; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 152))) { - result[0] += -61.382323220403975; - } else { - result[0] += -187.5709723722473; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (LIKELY(false || (data[0].qvalue <= 352))) { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - if (LIKELY(false || (data[7].qvalue <= 16))) { - result[0] += 44.92602753182352; - } else { - result[0] += -211.1546710682142; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 70))) { - result[0] += 84.82973193387151; - } else { - result[0] += 297.5136117027242; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 54))) { - if (LIKELY(false || (data[8].qvalue <= 80))) { - result[0] += 17.890869053801463; - } else { - result[0] += -233.23923140903472; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 42))) { - result[0] += -115.18248063786203; - } else { - result[0] += 19.654733381799502; - } - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 38))) { - if (UNLIKELY(false || (data[8].qvalue <= 48))) { - if (UNLIKELY(false || (data[6].qvalue <= 108))) { - result[0] += 73.48571042663086; - } else { - result[0] += -228.89571182954265; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 412))) { - result[0] += -172.0892135464257; - } else { - result[0] += 115.89684884981358; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 74))) { - if (UNLIKELY(false || (data[2].qvalue <= 2))) { - result[0] += -192.70183326405345; - } else { - result[0] += 228.22523400305982; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 164))) { - result[0] += -14.495336023506457; - } else { - result[0] += 164.58338032467216; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 418))) { - if (UNLIKELY(false || (data[6].qvalue <= 110))) { - result[0] += 92.44304518392212; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 142))) { - result[0] += -549.5535909490123; - } else { - result[0] += -283.7280102420611; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 170))) { - if (UNLIKELY(false || (data[3].qvalue <= 110))) { - if (UNLIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -751.8919251402435; - } else { - result[0] += 185.12085880810656; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 442))) { - result[0] += 106.48103521611722; - } else { - result[0] += 402.22669875787847; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 460))) { - if (UNLIKELY(false || (data[4].qvalue <= 14))) { - result[0] += -36.04581623793381; - } else { - result[0] += -576.7489848172199; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 204))) { - result[0] += 811.3997608577275; - } else { - result[0] += 54.07893988355018; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 190))) { - if (LIKELY(false || (data[1].qvalue <= 118))) { - if (UNLIKELY(false || (data[0].qvalue <= 40))) { - result[0] += -66.6266648878957; - } else { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (UNLIKELY(false || (data[7].qvalue <= 0))) { - result[0] += 218.52501238124137; - } else { - if (LIKELY(false || (data[7].qvalue <= 184))) { - result[0] += -13.543587342327356; - } else { - result[0] += -215.10060994815572; - } - } - } else { - result[0] += -235.9788695560659; - } - } - } else { - result[0] += -124.00456972107527; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 192))) { - if (LIKELY(false || (data[10].qvalue <= 122))) { - if (UNLIKELY(false || (data[10].qvalue <= 30))) { - if (LIKELY(false || (data[0].qvalue <= 462))) { - if (LIKELY(false || (data[5].qvalue <= 102))) { - result[0] += -18.111064688282102; - } else { - result[0] += -830.4402494030709; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 114))) { - result[0] += 549.3375303639418; - } else { - result[0] += -471.1944868822796; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 38))) { - if (LIKELY(false || (data[0].qvalue <= 306))) { - result[0] += 58.15499047997176; - } else { - result[0] += 202.68651234520863; - } - } else { - if (LIKELY(false || (data[5].qvalue <= 84))) { - result[0] += 5.292449943571185; - } else { - result[0] += 88.29265314882701; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[0].qvalue <= 414))) { - if (LIKELY(false || (data[1].qvalue <= 128))) { - result[0] += -1380.2032030523546; - } else { - result[0] += -290.5204067553985; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 428))) { - result[0] += -114.53892152149231; - } else { - result[0] += 514.5293420314608; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 58))) { - if (UNLIKELY(false || (data[4].qvalue <= 48))) { - result[0] += -61.89760896238173; - } else { - result[0] += 238.5724533454146; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 440))) { - result[0] += -129.87454115256398; - } else { - result[0] += 80.61545671612187; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 436))) { - if (UNLIKELY(false || (data[0].qvalue <= 346))) { - result[0] += -205.98214313987833; - } else { - result[0] += -501.7217730494733; - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 94))) { - if (UNLIKELY(false || (data[9].qvalue <= 74))) { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -1647.808151855469; - } else { - result[0] += -111.63345227808891; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 448))) { - result[0] += 135.65529729266765; - } else { - result[0] += 542.5839931646244; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -605.3494305872034; - } else { - if (LIKELY(false || (data[7].qvalue <= 200))) { - result[0] += 23.37492749029042; - } else { - result[0] += -504.33767145881876; - } - } - } - } - } - } - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - if (LIKELY(false || (data[3].qvalue <= 134))) { - if (UNLIKELY(false || (data[8].qvalue <= 6))) { - if (UNLIKELY(false || (data[2].qvalue <= 28))) { - result[0] += 147.6732444791957; - } else { - if (LIKELY(false || (data[2].qvalue <= 46))) { - result[0] += -10.796001760756946; - } else { - result[0] += 119.76151703653773; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 16))) { - result[0] += -80.64564777701082; - } else { - result[0] += -137.81918275187488; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 188))) { - result[0] += -301.13288115967265; - } else { - result[0] += -677.8347078414617; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 102))) { - if (LIKELY(false || (data[7].qvalue <= 48))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - if (LIKELY(false || (data[1].qvalue <= 66))) { - result[0] += 177.5420680296681; - } else { - result[0] += -218.59350937699685; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 20))) { - result[0] += -72.74967625197816; - } else { - result[0] += 20.742745610897746; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 84))) { - if (LIKELY(false || (data[4].qvalue <= 78))) { - result[0] += -90.48006952525736; - } else { - result[0] += -828.7521117629706; - } - } else { - if (LIKELY(false || (data[5].qvalue <= 66))) { - result[0] += 120.02128594767765; - } else { - result[0] += -101.64457682331833; - } - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 32))) { - if (UNLIKELY(false || (data[8].qvalue <= 48))) { - if (LIKELY(false || (data[8].qvalue <= 24))) { - result[0] += -29.977235441363906; - } else { - result[0] += -517.2676348797457; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 138))) { - result[0] += 79.82233930374132; - } else { - result[0] += -65.66862801060336; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 176))) { - if (UNLIKELY(false || (data[2].qvalue <= 4))) { - result[0] += -246.17761450724754; - } else { - result[0] += 128.63335245716783; - } - } else { - if (LIKELY(false || (data[10].qvalue <= 134))) { - result[0] += 25.606045866379198; - } else { - result[0] += -125.59366437898994; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 32))) { - result[0] += -1022.6641412510904; - } else { - if (LIKELY(false || (data[7].qvalue <= 200))) { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 256.4779043789177; - } else { - if (LIKELY(false || (data[1].qvalue <= 126))) { - if (LIKELY(false || (data[4].qvalue <= 68))) { - result[0] += -87.1798391962891; - } else { - result[0] += 43.00407443678074; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 62))) { - result[0] += -130.4952039313895; - } else { - result[0] += -240.59828244124444; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 164))) { - result[0] += -424.70860218767706; - } else { - result[0] += -596.5247694936194; - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 260))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - if (UNLIKELY(false || (data[8].qvalue <= 2))) { - result[0] += 174.72777599410847; - } else { - result[0] += 11.536037972850965; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 6))) { - if (LIKELY(false || (data[9].qvalue <= 96))) { - if (LIKELY(false || (data[7].qvalue <= 72))) { - if (LIKELY(false || (data[0].qvalue <= 132))) { - result[0] += -134.39734952576543; - } else { - result[0] += -367.3809402303026; - } - } else { - result[0] += 85.4518440435695; - } - } else { - result[0] += 30.478639892590373; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 124))) { - if (UNLIKELY(false || (data[0].qvalue <= 68))) { - result[0] += -50.893812931197466; - } else { - if (LIKELY(false || (data[2].qvalue <= 200))) { - result[0] += -4.69601116410534; - } else { - result[0] += -265.1217064966412; - } - } - } else { - result[0] += -104.769463503831; - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 64))) { - if (UNLIKELY(false || (data[9].qvalue <= 36))) { - result[0] += 428.87545627753326; - } else { - if (UNLIKELY(false || (data[9].qvalue <= 38))) { - if (UNLIKELY(false || (data[0].qvalue <= 442))) { - result[0] += -1773.848510347332; - } else { - result[0] += 55.659715716371515; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 359.22617617244686; - } else { - if (UNLIKELY(false || (data[8].qvalue <= 20))) { - result[0] += -44.87315510911888; - } else { - result[0] += 74.38569188031644; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 382))) { - if (UNLIKELY(false || (data[2].qvalue <= 140))) { - if (UNLIKELY(false || (data[3].qvalue <= 12))) { - if (UNLIKELY(false || (data[10].qvalue <= 40))) { - result[0] += -1410.981056764837; - } else { - result[0] += -637.2921393197482; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 56))) { - result[0] += -70.36055469147344; - } else { - result[0] += 175.7175261222059; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 146))) { - result[0] += -613.9698554996957; - } else { - if (LIKELY(false || (data[8].qvalue <= 154))) { - result[0] += -92.27993947562106; - } else { - result[0] += -383.51208633267225; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 88))) { - if (UNLIKELY(false || (data[7].qvalue <= 80))) { - if (UNLIKELY(false || (data[2].qvalue <= 64))) { - result[0] += -11.98521625723796; - } else { - result[0] += -714.7673310356126; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 96))) { - result[0] += 284.6663603250349; - } else { - result[0] += -61.60569809902938; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 40))) { - if (UNLIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -1260.7217681991474; - } else { - result[0] += 135.29439352456583; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 114))) { - result[0] += 204.2658499096425; - } else { - result[0] += 26.57998407789056; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 278))) { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - if (LIKELY(false || (data[1].qvalue <= 10))) { - result[0] += 88.29835564376643; - } else { - result[0] += 433.48530065382903; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 158))) { - if (UNLIKELY(false || (data[4].qvalue <= 6))) { - if (UNLIKELY(false || (data[9].qvalue <= 134))) { - result[0] += 355.11754045683864; - } else { - result[0] += 50.689614734965716; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 10))) { - if (UNLIKELY(false || (data[9].qvalue <= 142))) { - result[0] += -67.45276443587568; - } else { - result[0] += -258.89458735449244; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 150))) { - result[0] += -21.308012427711205; - } else { - result[0] += 244.9050850901425; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 166))) { - result[0] += -159.91554699358346; - } else { - result[0] += -467.3807915108788; - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 180))) { - if (LIKELY(false || (data[0].qvalue <= 460))) { - if (LIKELY(false || (data[2].qvalue <= 214))) { - if (UNLIKELY(false || (data[9].qvalue <= 2))) { - if (UNLIKELY(false || (data[3].qvalue <= 98))) { - result[0] += -888.854216500035; - } else { - result[0] += -60.23569984287638; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 10))) { - result[0] += -107.7332013111863; - } else { - result[0] += 44.24648581216298; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 32))) { - if (LIKELY(false || (data[0].qvalue <= 446))) { - result[0] += -226.15311381622098; - } else { - result[0] += 710.1847247221976; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -521.4681372500785; - } else { - result[0] += -121.81009418188125; - } - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 156))) { - if (LIKELY(false || (data[4].qvalue <= 108))) { - if (UNLIKELY(false || (data[9].qvalue <= 36))) { - result[0] += 578.8864769214038; - } else { - result[0] += -65.43432145431503; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 470))) { - result[0] += -1140.9993698987162; - } else { - result[0] += -277.1047243415079; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 124))) { - if (LIKELY(false || (data[2].qvalue <= 218))) { - result[0] += 625.8667118148287; - } else { - result[0] += 133.56293016701576; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 464))) { - result[0] += -257.6768725464037; - } else { - result[0] += 235.27089761951143; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (UNLIKELY(false || (data[4].qvalue <= 88))) { - if (LIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -510.7544463506701; - } else { - result[0] += 309.52861873156445; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 186))) { - result[0] += -475.554284048819; - } else { - result[0] += -931.2955241523669; - } - } - } else { - result[0] += 204.12262051329452; - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 148))) { - if (LIKELY(false || (data[1].qvalue <= 60))) { - if (UNLIKELY(false || (data[0].qvalue <= 4))) { - result[0] += -105.41033083691937; - } else { - if (LIKELY(false || (data[7].qvalue <= 118))) { - result[0] += -9.957538169716495; - } else { - result[0] += -116.44279176689716; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 80))) { - if (LIKELY(false || (data[7].qvalue <= 138))) { - result[0] += -36.47666931987333; - } else { - result[0] += -181.90346126434108; - } - } else { - result[0] += -92.3026154859271; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 192))) { - if (LIKELY(false || (data[0].qvalue <= 380))) { - if (UNLIKELY(false || (data[9].qvalue <= 54))) { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 635.4266731901135; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 56))) { - result[0] += -956.053733984008; - } else { - result[0] += -104.63367772570996; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 132))) { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - result[0] += -20.173896617001866; - } else { - result[0] += 65.25040747317426; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 136))) { - result[0] += -570.0499875540565; - } else { - result[0] += -4.395243842020363; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 96))) { - if (LIKELY(false || (data[2].qvalue <= 150))) { - if (UNLIKELY(false || (data[7].qvalue <= 56))) { - result[0] += 82.3347657422682; - } else { - result[0] += -151.85939690566192; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 202))) { - result[0] += 306.7932008675081; - } else { - result[0] += -193.98901427646175; - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 30))) { - if (UNLIKELY(false || (data[10].qvalue <= 80))) { - result[0] += -101.31894339788478; - } else { - result[0] += 57.66783954209549; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 160))) { - result[0] += 304.3481193163534; - } else { - result[0] += 101.37427473767308; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 438))) { - if (LIKELY(false || (data[1].qvalue <= 98))) { - if (LIKELY(false || (data[3].qvalue <= 170))) { - result[0] += -173.79619958480487; - } else { - result[0] += -611.7431741530887; - } - } else { - result[0] += -479.5994428796921; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 174))) { - if (LIKELY(false || (data[0].qvalue <= 452))) { - if (LIKELY(false || (data[2].qvalue <= 194))) { - result[0] += 205.09978107028988; - } else { - result[0] += -552.4708829680587; - } - } else { - result[0] += 465.99445615301477; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 466))) { - if (UNLIKELY(false || (data[6].qvalue <= 180))) { - result[0] += -38.14040161366896; - } else { - result[0] += -654.1612824926979; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 164))) { - result[0] += 89.03239010969071; - } else { - result[0] += -334.95950567599243; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 324))) { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - if (LIKELY(false || (data[3].qvalue <= 70))) { - if (LIKELY(false || (data[3].qvalue <= 66))) { - if (LIKELY(false || (data[3].qvalue <= 34))) { - if (LIKELY(false || (data[2].qvalue <= 122))) { - result[0] += -18.433723366725584; - } else { - result[0] += -472.18904133491907; - } - } else { - result[0] += 41.144657963793065; - } - } else { - if (LIKELY(false || (data[10].qvalue <= 126))) { - if (UNLIKELY(false || (data[2].qvalue <= 70))) { - result[0] += -791.0825554146527; - } else { - result[0] += -77.87558566467489; - } - } else { - result[0] += -1273.8300701538087; - } - } - } else { - result[0] += 162.91309292890534; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -374.78034701138165; - } else { - result[0] += -30.23314802748434; - } - } - } else { - if (LIKELY(false || (data[10].qvalue <= 116))) { - if (LIKELY(false || (data[1].qvalue <= 152))) { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - if (LIKELY(false || (data[0].qvalue <= 458))) { - if (LIKELY(false || (data[6].qvalue <= 82))) { - result[0] += 19.475576346353392; - } else { - result[0] += -352.9733362293148; - } - } else { - result[0] += 349.1018486741264; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 416))) { - result[0] += 212.94808346197698; - } else { - result[0] += -374.8210809697834; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 46))) { - result[0] += -34.43423741643343; - } else { - result[0] += 75.34901971114162; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 312.18232536892253; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 454))) { - result[0] += -380.2729414924723; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 74))) { - result[0] += 384.2278192657615; - } else { - result[0] += -48.08512286278479; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 448))) { - if (LIKELY(false || (data[6].qvalue <= 124))) { - if (UNLIKELY(false || (data[2].qvalue <= 148))) { - result[0] += -339.175501983996; - } else { - result[0] += 73.82088972486432; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 122))) { - if (UNLIKELY(false || (data[3].qvalue <= 114))) { - result[0] += -690.6636434137862; - } else { - result[0] += -233.4604631893612; - } - } else { - result[0] += 61.13592985094737; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 176))) { - if (LIKELY(false || (data[2].qvalue <= 216))) { - if (LIKELY(false || (data[0].qvalue <= 466))) { - result[0] += 336.27393994775974; - } else { - result[0] += -1244.2348096296523; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -288.7929275242086; - } else { - result[0] += 303.1087488248426; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 470))) { - result[0] += -522.5254243038571; - } else { - result[0] += 147.10931954259047; - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 150))) { - if (UNLIKELY(false || (data[0].qvalue <= 2))) { - result[0] += -114.0935731263952; - } else { - if (LIKELY(false || (data[1].qvalue <= 102))) { - if (LIKELY(false || (data[4].qvalue <= 36))) { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (LIKELY(false || (data[5].qvalue <= 42))) { - result[0] += -18.695361121395486; - } else { - result[0] += 10.588982002368144; - } - } else { - result[0] += -172.85563078964458; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 30))) { - if (LIKELY(false || (data[10].qvalue <= 22))) { - result[0] += -51.938265058110325; - } else { - result[0] += -246.80507747694102; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 94))) { - result[0] += -27.959993628946236; - } else { - result[0] += 73.25664987336096; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 154))) { - if (UNLIKELY(false || (data[8].qvalue <= 66))) { - if (UNLIKELY(false || (data[9].qvalue <= 34))) { - result[0] += -101.97494811248283; - } else { - result[0] += 17.881559562839353; - } - } else { - result[0] += -100.47614812809579; - } - } else { - result[0] += -213.33125633625428; - } - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - if (LIKELY(false || (data[1].qvalue <= 10))) { - if (UNLIKELY(false || (data[2].qvalue <= 24))) { - if (LIKELY(false || (data[0].qvalue <= 222))) { - result[0] += 434.3221282795032; - } else { - result[0] += 823.0770567103796; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 258))) { - if (LIKELY(false || (data[2].qvalue <= 128))) { - result[0] += 87.83445346165345; - } else { - result[0] += -212.4695657744424; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 128))) { - result[0] += 464.3977186968871; - } else { - result[0] += 163.9938488166673; - } - } - } - } else { - result[0] += 637.6504024717447; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 154))) { - if (UNLIKELY(false || (data[8].qvalue <= 0))) { - if (LIKELY(false || (data[0].qvalue <= 406))) { - if (LIKELY(false || (data[9].qvalue <= 90))) { - result[0] += 282.83342280184934; - } else { - result[0] += 120.66910156313747; - } - } else { - result[0] += 1028.390014143319; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - if (LIKELY(false || (data[0].qvalue <= 464))) { - result[0] += -292.64750935522983; - } else { - result[0] += 696.0738841280834; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - result[0] += -98.38508109993742; - } else { - result[0] += 20.07618047102507; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 20))) { - if (LIKELY(false || (data[0].qvalue <= 384))) { - if (UNLIKELY(false || (data[1].qvalue <= 14))) { - result[0] += -153.79848410394547; - } else { - result[0] += -491.2192559340425; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 430))) { - result[0] += 170.48551507912254; - } else { - result[0] += 726.1771275054432; - } - } - } else { - result[0] += 582.2067601142297; - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 384))) { - if (LIKELY(false || (data[6].qvalue <= 144))) { - if (LIKELY(false || (data[4].qvalue <= 120))) { - if (LIKELY(false || (data[8].qvalue <= 138))) { - if (LIKELY(false || (data[3].qvalue <= 122))) { - if (LIKELY(false || (data[6].qvalue <= 78))) { - result[0] += 6.611296900045464; - } else { - result[0] += -146.7747758716442; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 106))) { - result[0] += 117.39498296773701; - } else { - result[0] += -52.139056254522906; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 64))) { - result[0] += -786.3744050601753; - } else { - result[0] += -75.75876514356197; - } - } - } else { - result[0] += -251.69019507498334; - } - } else { - result[0] += -159.81350735348502; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 10))) { - if (UNLIKELY(false || (data[0].qvalue <= 450))) { - if (LIKELY(false || (data[3].qvalue <= 168))) { - if (UNLIKELY(false || (data[8].qvalue <= 78))) { - result[0] += -655.1579692026768; - } else { - result[0] += -234.92840903417084; - } - } else { - result[0] += 129.69681018242764; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 174))) { - if (LIKELY(false || (data[7].qvalue <= 174))) { - if (UNLIKELY(false || (data[8].qvalue <= 54))) { - result[0] += -139.81070363809008; - } else { - result[0] += 236.49651798822757; - } - } else { - result[0] += 997.2638005655676; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 468))) { - result[0] += -440.5340347804863; - } else { - result[0] += 133.34746925104315; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 96))) { - if (LIKELY(false || (data[3].qvalue <= 76))) { - if (UNLIKELY(false || (data[8].qvalue <= 80))) { - if (UNLIKELY(false || (data[9].qvalue <= 16))) { - result[0] += 953.3985019502752; - } else { - result[0] += 115.36154895354196; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 68))) { - result[0] += -75.90121598789128; - } else { - result[0] += 321.1318897275337; - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 78))) { - if (LIKELY(false || (data[0].qvalue <= 442))) { - result[0] += -896.7922993531249; - } else { - result[0] += -192.64263220387716; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 82))) { - result[0] += -157.71189744728704; - } else { - result[0] += 388.1757773943014; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 128))) { - if (LIKELY(false || (data[0].qvalue <= 426))) { - if (LIKELY(false || (data[6].qvalue <= 140))) { - result[0] += 239.69457507298395; - } else { - result[0] += -339.18339092719594; - } - } else { - result[0] += 440.89438255994565; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 48))) { - if (LIKELY(false || (data[8].qvalue <= 28))) { - result[0] += 94.78432124431386; - } else { - result[0] += -585.5090672534587; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 118))) { - result[0] += 261.6165504519048; - } else { - result[0] += -0.9669883257810821; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 130))) { - if (UNLIKELY(false || (data[0].qvalue <= 6))) { - result[0] += -85.38452227119578; - } else { - if (LIKELY(false || (data[1].qvalue <= 60))) { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - result[0] += 71.39641555324053; - } else { - result[0] += -12.679337084314707; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 70))) { - if (UNLIKELY(false || (data[9].qvalue <= 46))) { - result[0] += -328.3515745861993; - } else { - result[0] += -24.65068942389365; - } - } else { - result[0] += -65.54687009952785; - } - } - } - } else { - if (LIKELY(false || (data[10].qvalue <= 122))) { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - if (LIKELY(false || (data[1].qvalue <= 10))) { - if (UNLIKELY(false || (data[2].qvalue <= 24))) { - result[0] += 448.8186025793332; - } else { - if (LIKELY(false || (data[0].qvalue <= 262))) { - result[0] += 28.507570304853807; - } else { - result[0] += 334.06403364310586; - } - } - } else { - result[0] += 565.4514138078015; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 154))) { - if (LIKELY(false || (data[9].qvalue <= 148))) { - if (UNLIKELY(false || (data[1].qvalue <= 16))) { - result[0] += -96.69821678256282; - } else { - result[0] += 21.631660184261378; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 204))) { - result[0] += 354.2452862416222; - } else { - result[0] += -51.61557505903728; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 12))) { - if (LIKELY(false || (data[0].qvalue <= 372))) { - result[0] += -328.0629299855443; - } else { - result[0] += 135.76618575654695; - } - } else { - result[0] += 497.32398174001605; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[0].qvalue <= 416))) { - if (UNLIKELY(false || (data[2].qvalue <= 134))) { - if (UNLIKELY(false || (data[0].qvalue <= 354))) { - result[0] += -15.704885988027627; - } else { - result[0] += -432.2700366601563; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 398))) { - result[0] += -1249.1625247677366; - } else { - result[0] += -669.3611102779328; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 428))) { - result[0] += -58.35712345907448; - } else { - if (LIKELY(false || (data[0].qvalue <= 438))) { - result[0] += 338.03248915141415; - } else { - result[0] += 777.2208330972686; - } - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 84))) { - if (LIKELY(false || (data[0].qvalue <= 426))) { - if (LIKELY(false || (data[1].qvalue <= 154))) { - result[0] += 120.71513738942753; - } else { - result[0] += -308.17490477956534; - } - } else { - result[0] += 833.731541937934; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 438))) { - if (UNLIKELY(false || (data[9].qvalue <= 6))) { - result[0] += 149.7928136109008; - } else { - result[0] += -223.98734671939062; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 152))) { - result[0] += 365.23772234337326; - } else { - result[0] += -24.192037541431123; - } - } - } - } - } - } - if (LIKELY(false || (data[7].qvalue <= 192))) { - if (LIKELY(false || (data[0].qvalue <= 388))) { - if (LIKELY(false || (data[1].qvalue <= 124))) { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (LIKELY(false || (data[3].qvalue <= 122))) { - if (LIKELY(false || (data[8].qvalue <= 116))) { - result[0] += 4.536955058087513; - } else { - result[0] += -72.38105398500589; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -344.4937340104233; - } else { - result[0] += 54.04370832477616; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 352))) { - result[0] += -155.8470275480651; - } else { - result[0] += -517.2572856835848; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 64))) { - if (LIKELY(false || (data[8].qvalue <= 64))) { - if (UNLIKELY(false || (data[3].qvalue <= 62))) { - result[0] += 140.37617439343134; - } else { - result[0] += -55.112467004567236; - } - } else { - result[0] += 774.980876755253; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 346))) { - if (UNLIKELY(false || (data[8].qvalue <= 38))) { - result[0] += -397.1965385921262; - } else { - result[0] += -92.74338066885616; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - result[0] += 475.3460832912639; - } else { - result[0] += -489.2890878250337; - } - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 96))) { - if (UNLIKELY(false || (data[8].qvalue <= 10))) { - result[0] += 386.3622072624303; - } else { - if (LIKELY(false || (data[2].qvalue <= 150))) { - if (LIKELY(false || (data[0].qvalue <= 452))) { - result[0] += -138.2046386901946; - } else { - result[0] += 109.19811106405169; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 202))) { - result[0] += 279.484508613883; - } else { - result[0] += -160.50141487078906; - } - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 30))) { - if (UNLIKELY(false || (data[8].qvalue <= 48))) { - if (LIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 96.50006530829478; - } else { - result[0] += -311.4323024052799; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 114))) { - result[0] += 221.35954689453885; - } else { - result[0] += -11.21222614010631; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 12))) { - if (LIKELY(false || (data[0].qvalue <= 462))) { - result[0] += -250.44082389008545; - } else { - result[0] += 259.77584765088653; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 86))) { - result[0] += 361.4943477960914; - } else { - result[0] += 91.34722785277711; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 436))) { - if (UNLIKELY(false || (data[0].qvalue <= 350))) { - result[0] += -160.4323829658149; - } else { - result[0] += -425.59656420877354; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 146))) { - result[0] += 180.15448233040198; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -433.82026787118116; - } else { - result[0] += -59.26399277285995; - } - } - } - } - if (LIKELY(false || (data[7].qvalue <= 178))) { - if (LIKELY(false || (data[0].qvalue <= 460))) { - if (UNLIKELY(false || (data[9].qvalue <= 2))) { - if (LIKELY(false || (data[7].qvalue <= 152))) { - if (LIKELY(false || (data[0].qvalue <= 442))) { - result[0] += -308.1244882642975; - } else { - if (LIKELY(false || (data[1].qvalue <= 154))) { - result[0] += 509.7187192299381; - } else { - result[0] += -455.2150339355469; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 428))) { - result[0] += -189.49508286587985; - } else { - result[0] += -760.2093864229564; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 212))) { - if (LIKELY(false || (data[7].qvalue <= 166))) { - if (LIKELY(false || (data[7].qvalue <= 164))) { - result[0] += 3.4436391818207746; - } else { - result[0] += -682.0310140822548; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 388))) { - result[0] += 92.34103234198413; - } else { - result[0] += 380.69157714277566; - } - } - } else { - result[0] += -165.7227313266843; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 156))) { - if (LIKELY(false || (data[4].qvalue <= 108))) { - if (UNLIKELY(false || (data[9].qvalue <= 36))) { - result[0] += 549.4816677105838; - } else { - if (LIKELY(false || (data[1].qvalue <= 58))) { - result[0] += 194.29034273702734; - } else { - result[0] += -515.5656251305937; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 472))) { - if (UNLIKELY(false || (data[9].qvalue <= 6))) { - result[0] += 310.8152990014213; - } else { - result[0] += -1141.971330367939; - } - } else { - result[0] += -60.374245828492555; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 176))) { - result[0] += 351.2745651968375; - } else { - result[0] += 86.38384679612086; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 416))) { - if (UNLIKELY(false || (data[2].qvalue <= 162))) { - if (LIKELY(false || (data[2].qvalue <= 142))) { - result[0] += -197.5086799826019; - } else { - if (LIKELY(false || (data[0].qvalue <= 332))) { - result[0] += -304.0321579015652; - } else { - result[0] += -814.8576146638849; - } - } - } else { - result[0] += -77.31156609799358; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 114))) { - if (LIKELY(false || (data[7].qvalue <= 198))) { - result[0] += 364.03422368739893; - } else { - if (LIKELY(false || (data[0].qvalue <= 448))) { - result[0] += -262.12017291355977; - } else { - result[0] += 355.1467205041031; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (LIKELY(false || (data[6].qvalue <= 180))) { - if (LIKELY(false || (data[2].qvalue <= 222))) { - result[0] += 34.07266539417179; - } else { - result[0] += -461.43650166079533; - } - } else { - result[0] += -392.65138527512556; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 188))) { - result[0] += 424.2608283476747; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -401.74970612209404; - } else { - result[0] += 149.51617217256646; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 318))) { - if (UNLIKELY(false || (data[7].qvalue <= 16))) { - if (LIKELY(false || (data[5].qvalue <= 46))) { - result[0] += 6.274580412893512; - } else { - result[0] += 170.65850290785482; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 28))) { - if (UNLIKELY(false || (data[7].qvalue <= 28))) { - result[0] += -321.26713386171923; - } else { - if (LIKELY(false || (data[10].qvalue <= 24))) { - if (UNLIKELY(false || (data[3].qvalue <= 0))) { - result[0] += -883.3009822319002; - } else { - result[0] += -30.131313419296436; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 44))) { - result[0] += 145.86018225187783; - } else { - result[0] += -533.3653617127321; - } - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - if (LIKELY(false || (data[8].qvalue <= 114))) { - if (UNLIKELY(false || (data[3].qvalue <= 32))) { - result[0] += -64.30753875016195; - } else { - result[0] += 70.480411627325; - } - } else { - if (LIKELY(false || (data[8].qvalue <= 132))) { - result[0] += -282.2392212961099; - } else { - result[0] += 67.65616622116742; - } - } - } else { - result[0] += -24.453451185191113; - } - } - } - } else { - if (LIKELY(false || (data[10].qvalue <= 116))) { - if (UNLIKELY(false || (data[9].qvalue <= 10))) { - if (LIKELY(false || (data[0].qvalue <= 466))) { - if (UNLIKELY(false || (data[8].qvalue <= 26))) { - result[0] += -607.6977777871718; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 442))) { - result[0] += -273.64198324787975; - } else { - result[0] += 45.47924148212965; - } - } - } else { - result[0] += 215.11954437098967; - } - } else { - result[0] += 45.61441585551685; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 440))) { - if (LIKELY(false || (data[6].qvalue <= 124))) { - if (LIKELY(false || (data[0].qvalue <= 414))) { - if (UNLIKELY(false || (data[6].qvalue <= 42))) { - result[0] += -454.72131809174454; - } else { - result[0] += 0.13988354882495432; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 56))) { - result[0] += 38.582001327207514; - } else { - result[0] += 525.027278044562; - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 20))) { - if (UNLIKELY(false || (data[6].qvalue <= 132))) { - result[0] += -698.2304850150304; - } else { - result[0] += -55.05691788452429; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 130))) { - result[0] += -678.96875763753; - } else { - result[0] += -223.46103248730483; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 170))) { - if (LIKELY(false || (data[4].qvalue <= 122))) { - if (LIKELY(false || (data[9].qvalue <= 16))) { - result[0] += -90.61321602369662; - } else { - result[0] += 261.0377505016003; - } - } else { - result[0] += 460.8651737454858; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -518.3029692442528; - } else { - if (UNLIKELY(false || (data[4].qvalue <= 102))) { - result[0] += 251.99443410934916; - } else { - result[0] += -168.5258406883114; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 88))) { - if (UNLIKELY(false || (data[0].qvalue <= 2))) { - result[0] += -93.52752297088361; - } else { - if (LIKELY(false || (data[1].qvalue <= 92))) { - if (UNLIKELY(false || (data[1].qvalue <= 38))) { - if (UNLIKELY(false || (data[0].qvalue <= 22))) { - result[0] += -28.36906776557731; - } else { - if (LIKELY(false || (data[4].qvalue <= 94))) { - result[0] += -3.2423893406338657; - } else { - result[0] += 138.541654434318; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 22))) { - result[0] += -46.28911386105568; - } else { - if (LIKELY(false || (data[3].qvalue <= 150))) { - result[0] += -22.298009717414818; - } else { - result[0] += -81.59335058549901; - } - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 88))) { - if (LIKELY(false || (data[4].qvalue <= 132))) { - result[0] += -66.17939589753632; - } else { - result[0] += -205.33954408242673; - } - } else { - result[0] += 8.493295470465377; - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 2))) { - if (LIKELY(false || (data[5].qvalue <= 6))) { - if (LIKELY(false || (data[0].qvalue <= 264))) { - if (UNLIKELY(false || (data[5].qvalue <= 0))) { - if (LIKELY(false || (data[0].qvalue <= 224))) { - result[0] += 326.1908307836102; - } else { - result[0] += 712.5672110896917; - } - } else { - result[0] += 165.97117149989344; - } - } else { - result[0] += 512.7394095968342; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 290))) { - result[0] += -88.61952314673287; - } else { - result[0] += 257.63671136127255; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - if (LIKELY(false || (data[0].qvalue <= 464))) { - if (LIKELY(false || (data[3].qvalue <= 166))) { - if (LIKELY(false || (data[4].qvalue <= 82))) { - result[0] += -64.2972163819839; - } else { - result[0] += -678.5598075508324; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -743.1003478462518; - } else { - result[0] += 22.380609435146425; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (LIKELY(false || (data[1].qvalue <= 2))) { - result[0] += 668.8097667814556; - } else { - result[0] += 172.2438155603644; - } - } else { - result[0] += -103.48953914040013; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 14))) { - if (LIKELY(false || (data[9].qvalue <= 150))) { - if (UNLIKELY(false || (data[9].qvalue <= 112))) { - result[0] += 210.79347178390668; - } else { - result[0] += -66.34475012824036; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 8))) { - result[0] += 79.44162948474975; - } else { - result[0] += 423.58682172057206; - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 154))) { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 58.63080216913425; - } else { - result[0] += 0.56660428680394; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 388))) { - result[0] += -354.99641077814067; - } else { - result[0] += 163.82541020213822; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 428))) { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[0].qvalue <= 364))) { - result[0] += -4.43201919585627; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 2))) { - if (LIKELY(false || (data[3].qvalue <= 148))) { - if (LIKELY(false || (data[4].qvalue <= 96))) { - result[0] += -461.34171468334483; - } else { - result[0] += -1974.1613360699155; - } - } else { - result[0] += 464.12272874367665; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 16))) { - if (UNLIKELY(false || (data[2].qvalue <= 116))) { - result[0] += -2425.075299189815; - } else { - result[0] += -330.4655251495208; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 72))) { - result[0] += 166.19696442999253; - } else { - result[0] += 9.303779051447991; - } - } - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 42))) { - result[0] += -481.590488188672; - } else { - if (UNLIKELY(false || (data[10].qvalue <= 84))) { - if (UNLIKELY(false || (data[2].qvalue <= 154))) { - result[0] += -201.8241770516161; - } else { - if (LIKELY(false || (data[0].qvalue <= 400))) { - result[0] += 51.47643177844849; - } else { - result[0] += 557.8587391367337; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 356))) { - result[0] += -68.84035678322192; - } else { - result[0] += -259.4256502962815; - } - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 104))) { - if (LIKELY(false || (data[5].qvalue <= 100))) { - if (UNLIKELY(false || (data[0].qvalue <= 454))) { - if (UNLIKELY(false || (data[4].qvalue <= 78))) { - result[0] += 156.73836309324633; - } else { - if (LIKELY(false || (data[4].qvalue <= 138))) { - result[0] += -607.0488890568329; - } else { - result[0] += 9.03668551193256; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 100))) { - if (UNLIKELY(false || (data[6].qvalue <= 40))) { - result[0] += -1017.2723550166346; - } else { - result[0] += 91.3800484259382; - } - } else { - result[0] += -942.7966219121105; - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 26))) { - if (UNLIKELY(false || (data[0].qvalue <= 464))) { - if (UNLIKELY(false || (data[4].qvalue <= 114))) { - result[0] += 346.1853239128133; - } else { - result[0] += -870.2639669615536; - } - } else { - result[0] += 136.37634000604268; - } - } else { - result[0] += 483.8076223319302; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 104))) { - if (UNLIKELY(false || (data[8].qvalue <= 76))) { - result[0] += 632.1932202439316; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 114))) { - result[0] += 859.7961658998064; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 110))) { - result[0] += 6.445434627545658; - } else { - result[0] += 232.20573728889286; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 450))) { - if (UNLIKELY(false || (data[10].qvalue <= 62))) { - result[0] += -766.6301885459153; - } else { - result[0] += -66.31539816252801; - } - } else { - result[0] += 73.09136207866754; - } - } - } - } - if (LIKELY(false || (data[7].qvalue <= 192))) { - if (LIKELY(false || (data[10].qvalue <= 122))) { - if (LIKELY(false || (data[3].qvalue <= 102))) { - if (LIKELY(false || (data[7].qvalue <= 44))) { - if (UNLIKELY(false || (data[6].qvalue <= 24))) { - if (LIKELY(false || (data[7].qvalue <= 8))) { - result[0] += 28.34884333239003; - } else { - result[0] += -46.41782030735656; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 73.71689530827445; - } else { - result[0] += 1.4571574901978375; - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 68))) { - if (UNLIKELY(false || (data[3].qvalue <= 2))) { - result[0] += -319.74328138081097; - } else { - result[0] += 28.72734330489961; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 74))) { - result[0] += -392.30286128897285; - } else { - result[0] += -56.63552344488173; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - if (LIKELY(false || (data[8].qvalue <= 148))) { - if (UNLIKELY(false || (data[3].qvalue <= 134))) { - result[0] += -61.214933692783916; - } else { - result[0] += -207.6113943042064; - } - } else { - result[0] += -632.4056164963666; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 36))) { - if (LIKELY(false || (data[8].qvalue <= 28))) { - result[0] += 0.776759507119416; - } else { - result[0] += -563.4328564341248; - } - } else { - if (LIKELY(false || (data[8].qvalue <= 140))) { - result[0] += 80.66532309064849; - } else { - result[0] += -37.64303198703362; - } - } - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 128))) { - if (LIKELY(false || (data[9].qvalue <= 80))) { - if (UNLIKELY(false || (data[1].qvalue <= 142))) { - result[0] += 3.0907605764638824; - } else { - result[0] += -191.9470886917915; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 42))) { - result[0] += -535.3539427021846; - } else { - result[0] += -747.1267209093315; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 84))) { - if (UNLIKELY(false || (data[4].qvalue <= 48))) { - if (UNLIKELY(false || (data[1].qvalue <= 48))) { - result[0] += 39.65250739364507; - } else { - result[0] += -83.33176284276131; - } - } else { - if (LIKELY(false || (data[10].qvalue <= 144))) { - result[0] += 137.86981971555903; - } else { - result[0] += -47.78103683170895; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 6))) { - if (LIKELY(false || (data[8].qvalue <= 108))) { - result[0] += 265.07465212054825; - } else { - result[0] += -112.5511941043707; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 130))) { - result[0] += -185.10561993544567; - } else { - result[0] += -55.42540507337779; - } - } - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 200))) { - if (LIKELY(false || (data[1].qvalue <= 164))) { - result[0] += -71.05204569728328; - } else { - if (LIKELY(false || (data[3].qvalue <= 174))) { - result[0] += -229.96092256575585; - } else { - result[0] += -663.863689584278; - } - } - } else { - result[0] += -404.1617066176453; - } - } - if (LIKELY(false || (data[0].qvalue <= 434))) { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[0].qvalue <= 374))) { - if (LIKELY(false || (data[4].qvalue <= 120))) { - if (LIKELY(false || (data[2].qvalue <= 176))) { - if (LIKELY(false || (data[5].qvalue <= 80))) { - result[0] += -4.033785114047066; - } else { - result[0] += 51.688746674364644; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 42))) { - result[0] += -308.48199870168713; - } else { - result[0] += -36.130910480232906; - } - } - } else { - result[0] += -195.1197382760659; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 2))) { - if (LIKELY(false || (data[4].qvalue <= 110))) { - if (LIKELY(false || (data[4].qvalue <= 96))) { - result[0] += -408.3567730759215; - } else { - result[0] += -1707.6448987068966; - } - } else { - result[0] += 485.8821080747238; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 16))) { - if (UNLIKELY(false || (data[2].qvalue <= 116))) { - result[0] += -2096.4662113083464; - } else { - result[0] += -276.14738618844734; - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 68))) { - result[0] += -8.171064906722554; - } else { - result[0] += 155.11413889664198; - } - } - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 46))) { - result[0] += -426.0821034776104; - } else { - if (UNLIKELY(false || (data[10].qvalue <= 84))) { - if (UNLIKELY(false || (data[2].qvalue <= 154))) { - if (LIKELY(false || (data[0].qvalue <= 420))) { - result[0] += -251.87299899417505; - } else { - result[0] += 171.7813777604329; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 188))) { - result[0] += 266.03680241951855; - } else { - result[0] += -230.94110204440864; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 98))) { - if (UNLIKELY(false || (data[9].qvalue <= 14))) { - result[0] += -51.4712788511078; - } else { - result[0] += -349.1585791389674; - } - } else { - result[0] += -87.62955894052737; - } - } - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 14))) { - result[0] += -962.914071451823; - } else { - if (LIKELY(false || (data[6].qvalue <= 180))) { - if (UNLIKELY(false || (data[2].qvalue <= 104))) { - if (UNLIKELY(false || (data[10].qvalue <= 4))) { - result[0] += 410.01446934535943; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 144))) { - result[0] += -158.79533984060308; - } else { - result[0] += 54.19442054392661; - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 64))) { - result[0] += 800.9582024560236; - } else { - if (LIKELY(false || (data[2].qvalue <= 214))) { - result[0] += 152.70184903575273; - } else { - result[0] += -39.258349058638146; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 472))) { - if (LIKELY(false || (data[10].qvalue <= 106))) { - if (UNLIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -385.9941662617043; - } else { - result[0] += 109.98038694597618; - } - } else { - result[0] += -519.7043617216136; - } - } else { - result[0] += 312.6413190598439; - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 450))) { - if (LIKELY(false || (data[6].qvalue <= 146))) { - if (LIKELY(false || (data[7].qvalue <= 198))) { - if (LIKELY(false || (data[0].qvalue <= 366))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - if (LIKELY(false || (data[1].qvalue <= 74))) { - result[0] += 137.297555227154; - } else { - result[0] += -254.9833262356102; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - result[0] += -123.87983644564534; - } else { - result[0] += -2.4145863679975013; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 6))) { - if (LIKELY(false || (data[6].qvalue <= 82))) { - result[0] += 51.07636765965205; - } else { - result[0] += -390.1200629057833; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 28))) { - result[0] += 215.48035596295546; - } else { - result[0] += 39.12939193887718; - } - } - } - } else { - result[0] += -235.5281315434429; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 28))) { - result[0] += -534.086589766028; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 156))) { - if (LIKELY(false || (data[7].qvalue <= 166))) { - result[0] += -253.52810060662947; - } else { - if (UNLIKELY(false || (data[6].qvalue <= 164))) { - result[0] += 451.54660129512826; - } else { - result[0] += -6.922248765373611; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 168))) { - if (LIKELY(false || (data[0].qvalue <= 422))) { - result[0] += -87.41127255128306; - } else { - result[0] += 161.7062666527756; - } - } else { - if (LIKELY(false || (data[8].qvalue <= 96))) { - result[0] += -362.4373441748169; - } else { - result[0] += -47.688779690032106; - } - } - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 180))) { - if (UNLIKELY(false || (data[6].qvalue <= 70))) { - if (LIKELY(false || (data[3].qvalue <= 118))) { - result[0] += -109.71948701870082; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -3168.276688179348; - } else { - result[0] += -655.8877042161603; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 108))) { - if (UNLIKELY(false || (data[7].qvalue <= 148))) { - if (LIKELY(false || (data[6].qvalue <= 138))) { - result[0] += 211.55392830640332; - } else { - result[0] += 544.2392075566188; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 460))) { - result[0] += -137.766397826111; - } else { - result[0] += 227.29084096946204; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 114))) { - if (UNLIKELY(false || (data[0].qvalue <= 470))) { - result[0] += -1219.702639935603; - } else { - result[0] += 19.073660451518418; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 168))) { - result[0] += 180.8219311362737; - } else { - result[0] += -23.62961539090255; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (UNLIKELY(false || (data[4].qvalue <= 88))) { - result[0] += -24.096509384288314; - } else { - result[0] += -474.0824089535872; - } - } else { - result[0] += 151.94918465665242; - } - } - } - if (LIKELY(false || (data[8].qvalue <= 156))) { - if (LIKELY(false || (data[0].qvalue <= 450))) { - if (LIKELY(false || (data[6].qvalue <= 146))) { - if (LIKELY(false || (data[0].qvalue <= 344))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - if (LIKELY(false || (data[0].qvalue <= 222))) { - result[0] += 39.972789080579226; - } else { - result[0] += 214.53042647495874; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - result[0] += -103.36098764508137; - } else { - result[0] += -3.5862855800631728; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 144))) { - if (LIKELY(false || (data[5].qvalue <= 92))) { - result[0] += 33.48501259384832; - } else { - result[0] += -526.1972342777782; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 188))) { - result[0] += 291.7875686572814; - } else { - result[0] += -154.8400445536322; - } - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 50))) { - if (UNLIKELY(false || (data[6].qvalue <= 152))) { - if (LIKELY(false || (data[0].qvalue <= 436))) { - result[0] += -209.00331042067378; - } else { - result[0] += 476.30810024115374; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 284))) { - result[0] += -174.50986784466022; - } else { - result[0] += -612.8591006144763; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 154))) { - if (LIKELY(false || (data[6].qvalue <= 160))) { - result[0] += -17.361861237499827; - } else { - result[0] += -227.84455664039766; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 170))) { - result[0] += 141.6717472259187; - } else { - result[0] += -152.85677116643248; - } - } - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 16))) { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -2290.8527835669884; - } else { - result[0] += -90.00394883304172; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 0))) { - if (UNLIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -670.6849931746262; - } else { - result[0] += -81.08626512407443; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 124))) { - if (UNLIKELY(false || (data[10].qvalue <= 44))) { - result[0] += 252.92553318096964; - } else { - result[0] += -237.02792802064724; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 26))) { - result[0] += -131.25931683115354; - } else { - result[0] += 155.92595301503033; - } - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 416))) { - if (LIKELY(false || (data[0].qvalue <= 350))) { - result[0] += -135.57120230355562; - } else { - result[0] += -444.05965584753494; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 130))) { - if (UNLIKELY(false || (data[0].qvalue <= 454))) { - result[0] += -617.9283577463137; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 168))) { - result[0] += 379.3453933956734; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 468))) { - result[0] += -555.1703256848654; - } else { - result[0] += 183.81435330137998; - } - } - } - } else { - result[0] += 287.9785027492729; - } - } - } - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (LIKELY(false || (data[0].qvalue <= 462))) { - if (LIKELY(false || (data[1].qvalue <= 160))) { - if (LIKELY(false || (data[6].qvalue <= 174))) { - if (LIKELY(false || (data[7].qvalue <= 166))) { - if (LIKELY(false || (data[7].qvalue <= 164))) { - result[0] += 1.9227744347010873; - } else { - result[0] += -372.9806790342511; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 162))) { - result[0] += 38.027655688643975; - } else { - result[0] += 330.84712805066437; - } - } - } else { - result[0] += -254.51641015239204; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 430))) { - result[0] += -29.47274599196618; - } else { - result[0] += -712.7969392811483; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 156))) { - if (LIKELY(false || (data[4].qvalue <= 108))) { - if (UNLIKELY(false || (data[9].qvalue <= 36))) { - result[0] += 557.7390203473873; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 58))) { - result[0] += 232.35239699183904; - } else { - result[0] += -374.7341949643208; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 470))) { - if (UNLIKELY(false || (data[9].qvalue <= 6))) { - result[0] += 438.92836082611086; - } else { - result[0] += -1439.2171157594041; - } - } else { - result[0] += -146.03654903994473; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 176))) { - if (LIKELY(false || (data[4].qvalue <= 130))) { - if (LIKELY(false || (data[5].qvalue <= 108))) { - result[0] += 321.2116732677101; - } else { - result[0] += 754.9708259292751; - } - } else { - result[0] += -22.672436608018547; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 70))) { - result[0] += 795.3453671875; - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - result[0] += -213.941419408782; - } else { - result[0] += 282.16809176472924; - } - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (UNLIKELY(false || (data[5].qvalue <= 32))) { - result[0] += -1600.845136858259; - } else { - if (LIKELY(false || (data[6].qvalue <= 180))) { - if (LIKELY(false || (data[0].qvalue <= 440))) { - if (UNLIKELY(false || (data[5].qvalue <= 58))) { - result[0] += -521.5318192486991; - } else { - result[0] += -96.30084920460428; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 218))) { - result[0] += 166.08593687552778; - } else { - result[0] += -341.6197044372843; - } - } - } else { - result[0] += -270.16752261143085; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 188))) { - result[0] += 379.4922879662453; - } else { - if (LIKELY(false || (data[1].qvalue <= 164))) { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - if (LIKELY(false || (data[3].qvalue <= 178))) { - result[0] += 59.33023477478028; - } else { - result[0] += -944.8980152625645; - } - } else { - result[0] += 362.7230938296502; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -1894.5816613281252; - } else { - result[0] += -151.54009237179278; - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 466))) { - if (LIKELY(false || (data[6].qvalue <= 180))) { - if (LIKELY(false || (data[2].qvalue <= 218))) { - if (LIKELY(false || (data[0].qvalue <= 428))) { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[10].qvalue <= 146))) { - result[0] += 2.61150493746397; - } else { - result[0] += -294.8989575123201; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 154))) { - result[0] += -144.860709390818; - } else { - result[0] += 49.05217218783569; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 104))) { - if (LIKELY(false || (data[2].qvalue <= 102))) { - result[0] += -69.84846406106546; - } else { - result[0] += -1099.694205050492; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 64))) { - result[0] += 547.2763598289783; - } else { - result[0] += 121.02960542039312; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 430))) { - if (UNLIKELY(false || (data[1].qvalue <= 112))) { - result[0] += -299.9131036348163; - } else { - result[0] += 87.20580911412755; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 162))) { - if (LIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -434.7498970777533; - } else { - result[0] += 375.0325198630567; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 174))) { - result[0] += -761.131324712887; - } else { - result[0] += -387.49766429457327; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 392))) { - result[0] += -94.7425416912909; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 154))) { - result[0] += -190.41458346534543; - } else { - result[0] += -405.4827892391865; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 200))) { - if (UNLIKELY(false || (data[7].qvalue <= 78))) { - if (UNLIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 373.04881527855287; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -1545.4975544084823; - } else { - result[0] += -255.72717774402136; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 98))) { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (LIKELY(false || (data[1].qvalue <= 116))) { - result[0] += 450.93153398469025; - } else { - result[0] += 1014.2956421685988; - } - } else { - result[0] += 1245.0624796875002; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 148))) { - if (UNLIKELY(false || (data[8].qvalue <= 34))) { - result[0] += -547.0456131590985; - } else { - result[0] += -9.966885090949628; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 104))) { - result[0] += 305.58289721685486; - } else { - result[0] += 51.01148885239249; - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - if (LIKELY(false || (data[1].qvalue <= 162))) { - result[0] += -921.1548865874474; - } else { - result[0] += -1870.7389783653846; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 162))) { - result[0] += 236.7134294836506; - } else { - result[0] += -285.6914099083137; - } - } - } - } - if (LIKELY(false || (data[7].qvalue <= 174))) { - if (UNLIKELY(false || (data[5].qvalue <= 14))) { - if (UNLIKELY(false || (data[4].qvalue <= 2))) { - if (LIKELY(false || (data[2].qvalue <= 58))) { - if (LIKELY(false || (data[2].qvalue <= 52))) { - result[0] += 138.46445954751613; - } else { - result[0] += 30.25739363045126; - } - } else { - result[0] += 308.1558521464535; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 40))) { - if (LIKELY(false || (data[10].qvalue <= 52))) { - if (LIKELY(false || (data[8].qvalue <= 12))) { - result[0] += -8.275799037633549; - } else { - result[0] += -133.54923320626136; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 6))) { - result[0] += 175.8079980773678; - } else { - result[0] += 60.614919146381446; - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 42))) { - result[0] += -434.16667085044185; - } else { - if (LIKELY(false || (data[4].qvalue <= 38))) { - result[0] += -41.16427694169654; - } else { - result[0] += -185.9750821120982; - } - } - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 22))) { - if (LIKELY(false || (data[3].qvalue <= 18))) { - if (LIKELY(false || (data[5].qvalue <= 20))) { - if (UNLIKELY(false || (data[4].qvalue <= 12))) { - result[0] += -122.25698184102133; - } else { - result[0] += 80.57705277421712; - } - } else { - result[0] += -153.60843419206896; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 22))) { - result[0] += 408.69803721206245; - } else { - if (UNLIKELY(false || (data[8].qvalue <= 90))) { - result[0] += 136.97672358425646; - } else { - result[0] += 270.9923573487174; - } - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 28))) { - if (LIKELY(false || (data[2].qvalue <= 122))) { - if (UNLIKELY(false || (data[3].qvalue <= 24))) { - result[0] += -159.08908713728795; - } else { - result[0] += -9.923411608506871; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 148))) { - result[0] += -521.1873971145586; - } else { - result[0] += 17.25752566881542; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 2))) { - if (UNLIKELY(false || (data[3].qvalue <= 64))) { - result[0] += 301.7622074237134; - } else { - result[0] += 122.86657003996795; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 4))) { - result[0] += -254.85463978758668; - } else { - result[0] += 5.494987706460857; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 32))) { - result[0] += -834.7163352102; - } else { - if (LIKELY(false || (data[7].qvalue <= 200))) { - if (UNLIKELY(false || (data[8].qvalue <= 6))) { - result[0] += 172.7220039048895; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += 161.15720525096583; - } else { - if (UNLIKELY(false || (data[8].qvalue <= 136))) { - result[0] += -107.08335478914324; - } else { - result[0] += -12.489971356584766; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 180))) { - result[0] += -405.35269670345906; - } else { - result[0] += -207.23569477608535; - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (LIKELY(false || (data[6].qvalue <= 180))) { - if (LIKELY(false || (data[0].qvalue <= 392))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (LIKELY(false || (data[0].qvalue <= 292))) { - if (UNLIKELY(false || (data[3].qvalue <= 34))) { - result[0] += -26.43222222269896; - } else { - result[0] += 7.909229573729924; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 30))) { - result[0] += 8.617729518857145; - } else { - result[0] += 108.90607449231234; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 74))) { - if (UNLIKELY(false || (data[10].qvalue <= 70))) { - result[0] += -1.9284671174533046; - } else { - result[0] += -105.2363711810878; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 106))) { - result[0] += 35.07140832310961; - } else { - result[0] += -52.187791710622804; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 222))) { - if (UNLIKELY(false || (data[2].qvalue <= 86))) { - if (LIKELY(false || (data[2].qvalue <= 64))) { - result[0] += 53.903161702785724; - } else { - result[0] += -171.4159662862328; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 114))) { - result[0] += 123.56275048732999; - } else { - result[0] += 26.26029225107184; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -422.00141694181985; - } else { - result[0] += -107.9116343442738; - } - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 88))) { - if (LIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -288.46097298039064; - } else { - if (LIKELY(false || (data[7].qvalue <= 196))) { - result[0] += 375.28961351398226; - } else { - result[0] += -281.2952544835409; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 446))) { - result[0] += -17.901971338973112; - } else { - if (LIKELY(false || (data[6].qvalue <= 186))) { - result[0] += -312.93686064175296; - } else { - result[0] += -716.0857770302855; - } - } - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 88))) { - result[0] += 451.3160600734765; - } else { - if (UNLIKELY(false || (data[10].qvalue <= 54))) { - if (UNLIKELY(false || (data[8].qvalue <= 16))) { - result[0] += 120.12627461414955; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -1770.8570323768029; - } else { - result[0] += -297.6523306745511; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 184))) { - if (LIKELY(false || (data[7].qvalue <= 196))) { - if (LIKELY(false || (data[0].qvalue <= 472))) { - result[0] += 199.35980767050484; - } else { - result[0] += 503.56640093198286; - } - } else { - result[0] += -518.0686588935853; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - if (LIKELY(false || (data[7].qvalue <= 188))) { - result[0] += -150.74680039702164; - } else { - result[0] += -1055.9052195002068; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 176))) { - result[0] += -46.963809276019184; - } else { - result[0] += 394.9533442330038; - } - } - } - } - } - } - if (LIKELY(false || (data[10].qvalue <= 120))) { - if (LIKELY(false || (data[5].qvalue <= 84))) { - if (LIKELY(false || (data[6].qvalue <= 72))) { - if (LIKELY(false || (data[1].qvalue <= 96))) { - if (LIKELY(false || (data[1].qvalue <= 92))) { - if (LIKELY(false || (data[8].qvalue <= 116))) { - result[0] += 14.869440540791798; - } else { - result[0] += -36.73557311757522; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 64))) { - result[0] += -538.0900500719082; - } else { - result[0] += -27.241161506715144; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 174))) { - result[0] += 132.0541996118715; - } else { - result[0] += -113.04252572422735; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 24))) { - if (LIKELY(false || (data[8].qvalue <= 68))) { - if (LIKELY(false || (data[1].qvalue <= 160))) { - result[0] += 244.86366814112222; - } else { - result[0] += -95.24641504542937; - } - } else { - result[0] += -76.8715657600925; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 114))) { - if (LIKELY(false || (data[5].qvalue <= 76))) { - result[0] += -120.29792651308429; - } else { - result[0] += 5.15502948111146; - } - } else { - result[0] += -227.26424899945584; - } - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 48))) { - if (LIKELY(false || (data[10].qvalue <= 72))) { - if (LIKELY(false || (data[6].qvalue <= 126))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += -511.47464005858114; - } else { - result[0] += 99.31151638687581; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 2))) { - result[0] += 139.52958863442427; - } else { - result[0] += -222.6339569962306; - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 112))) { - result[0] += -706.5550247771737; - } else { - result[0] += 32.62538379320284; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 152))) { - if (LIKELY(false || (data[8].qvalue <= 150))) { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -225.27583884895458; - } else { - result[0] += 97.88878371179209; - } - } else { - result[0] += -91.48452493663109; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 156))) { - if (UNLIKELY(false || (data[1].qvalue <= 148))) { - result[0] += -972.0166516770522; - } else { - result[0] += -3.8444739361171365; - } - } else { - result[0] += -30.275824314188043; - } - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[1].qvalue <= 128))) { - result[0] += -591.4492984187848; - } else { - result[0] += 52.69617940013822; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 86))) { - if (LIKELY(false || (data[2].qvalue <= 180))) { - result[0] += 84.41514196973255; - } else { - result[0] += -58.25664931323567; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 6))) { - result[0] += 79.8372134768803; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 190))) { - result[0] += -166.72699212355798; - } else { - result[0] += -39.28083649832746; - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 436))) { - if (LIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[0].qvalue <= 384))) { - if (LIKELY(false || (data[1].qvalue <= 126))) { - if (LIKELY(false || (data[4].qvalue <= 102))) { - if (LIKELY(false || (data[1].qvalue <= 102))) { - result[0] += 0.7528430355879543; - } else { - result[0] += -81.79712338566776; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 108))) { - result[0] += 169.93142796462274; - } else { - result[0] += -185.79991240603783; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 126))) { - if (LIKELY(false || (data[2].qvalue <= 120))) { - result[0] += -28.331180770557967; - } else { - result[0] += 672.7509955188011; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 274))) { - result[0] += -114.78306332631581; - } else { - result[0] += -408.53260808925637; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 198))) { - if (LIKELY(false || (data[6].qvalue <= 128))) { - if (UNLIKELY(false || (data[9].qvalue <= 16))) { - result[0] += -1018.0208814686271; - } else { - result[0] += 59.74134293981621; - } - } else { - result[0] += 293.35493295005193; - } - } else { - result[0] += -400.56582852441784; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 98))) { - if (UNLIKELY(false || (data[9].qvalue <= 14))) { - if (LIKELY(false || (data[0].qvalue <= 420))) { - result[0] += -74.3543481633016; - } else { - if (LIKELY(false || (data[1].qvalue <= 150))) { - result[0] += 571.9786783643856; - } else { - result[0] += -219.12600540624658; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 342))) { - result[0] += -101.20421903443618; - } else { - result[0] += -425.1879279279357; - } - } - } else { - result[0] += -38.8853994688072; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 0))) { - result[0] += -129.9772116102948; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 100))) { - if (UNLIKELY(false || (data[7].qvalue <= 56))) { - result[0] += 352.2167561334443; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 452))) { - if (LIKELY(false || (data[4].qvalue <= 104))) { - result[0] += -179.85826237208437; - } else { - result[0] += -1026.3738449938005; - } - } else { - result[0] += 86.91047265123943; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 140))) { - if (UNLIKELY(false || (data[9].qvalue <= 10))) { - if (UNLIKELY(false || (data[0].qvalue <= 448))) { - result[0] += -194.66018635911382; - } else { - result[0] += 165.22462004866262; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 112))) { - result[0] += 280.5581946286645; - } else { - result[0] += 803.432975071445; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 464))) { - if (LIKELY(false || (data[2].qvalue <= 218))) { - result[0] += 2.697588396559468; - } else { - result[0] += -478.252898298942; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 154))) { - result[0] += -208.4543974390897; - } else { - result[0] += 185.3089271840308; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 426))) { - if (LIKELY(false || (data[1].qvalue <= 124))) { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (LIKELY(false || (data[5].qvalue <= 86))) { - if (LIKELY(false || (data[6].qvalue <= 76))) { - if (LIKELY(false || (data[4].qvalue <= 80))) { - result[0] += -1.486803701497208; - } else { - result[0] += 82.51456493961574; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 26))) { - result[0] += -1724.8354696321032; - } else { - result[0] += -82.71048025173705; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 22))) { - if (UNLIKELY(false || (data[4].qvalue <= 110))) { - result[0] += -790.0620438667581; - } else { - result[0] += 106.26605184431772; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 378))) { - result[0] += 33.9153156537546; - } else { - result[0] += 173.09121370877705; - } - } - } - } else { - result[0] += -154.93054492116286; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 54))) { - if (LIKELY(false || (data[2].qvalue <= 50))) { - if (LIKELY(false || (data[2].qvalue <= 42))) { - if (LIKELY(false || (data[0].qvalue <= 392))) { - result[0] += -1.9879083035835015; - } else { - result[0] += 398.5109346540218; - } - } else { - result[0] += -786.5885010553891; - } - } else { - result[0] += 430.4466811037737; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 342))) { - result[0] += -47.917740653423564; - } else { - if (LIKELY(false || (data[0].qvalue <= 412))) { - if (LIKELY(false || (data[2].qvalue <= 164))) { - result[0] += -361.3887876613474; - } else { - result[0] += -59.68743934462101; - } - } else { - result[0] += -24.34286763077365; - } - } - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 10))) { - result[0] += 711.5911056007745; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 88))) { - if (LIKELY(false || (data[0].qvalue <= 454))) { - if (LIKELY(false || (data[2].qvalue <= 64))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += -939.5055510805191; - } else { - result[0] += 49.38415531008706; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 128))) { - result[0] += -607.7336297438973; - } else { - result[0] += 231.05599252528052; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 40))) { - result[0] += -676.7676630675595; - } else { - result[0] += 107.56624878796568; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 104))) { - if (UNLIKELY(false || (data[3].qvalue <= 110))) { - if (UNLIKELY(false || (data[7].qvalue <= 52))) { - result[0] += 470.39671526527695; - } else { - result[0] += 19.64177427856826; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 456))) { - result[0] += 257.8561111510292; - } else { - result[0] += -156.72749393540863; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 112))) { - result[0] += 541.0799146065921; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 444))) { - result[0] += -223.96127054701944; - } else { - result[0] += 35.39412132031614; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 458))) { - if (LIKELY(false || (data[2].qvalue <= 200))) { - if (LIKELY(false || (data[3].qvalue <= 172))) { - if (LIKELY(false || (data[2].qvalue <= 198))) { - if (LIKELY(false || (data[0].qvalue <= 392))) { - if (LIKELY(false || (data[2].qvalue <= 180))) { - result[0] += -1.1948610469474203; - } else { - result[0] += -80.88158309915174; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 10))) { - result[0] += -96.14071101510345; - } else { - result[0] += 63.358216911141255; - } - } - } else { - result[0] += 176.15313222545365; - } - } else { - result[0] += -326.1906626097021; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 116))) { - if (LIKELY(false || (data[0].qvalue <= 430))) { - if (UNLIKELY(false || (data[8].qvalue <= 146))) { - if (LIKELY(false || (data[0].qvalue <= 400))) { - result[0] += 40.65129948483353; - } else { - result[0] += 623.8755186699341; - } - } else { - result[0] += -139.52193586780237; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (LIKELY(false || (data[0].qvalue <= 446))) { - result[0] += 215.51009220017923; - } else { - result[0] += 626.7644565408536; - } - } else { - result[0] += -57.77880667756001; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 78))) { - result[0] += -345.1537827658937; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 206))) { - if (UNLIKELY(false || (data[0].qvalue <= 368))) { - result[0] += 120.97750370682058; - } else { - result[0] += -530.0146207114068; - } - } else { - result[0] += -90.90617312134356; - } - } - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 128))) { - if (LIKELY(false || (data[2].qvalue <= 222))) { - if (UNLIKELY(false || (data[8].qvalue <= 44))) { - if (UNLIKELY(false || (data[2].qvalue <= 30))) { - if (LIKELY(false || (data[4].qvalue <= 114))) { - result[0] += 405.6146661410597; - } else { - result[0] += 22.37476339760467; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 78))) { - result[0] += -499.28634142340854; - } else { - result[0] += 68.9305749234034; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 90))) { - if (LIKELY(false || (data[9].qvalue <= 86))) { - result[0] += 529.6389184175102; - } else { - result[0] += 95.91732717933235; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 158))) { - result[0] += 194.2271286977213; - } else { - result[0] += -22.243321533686604; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - result[0] += -322.8706246634995; - } else { - result[0] += 171.93787845774688; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -280.2205757227194; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 16))) { - result[0] += 805.6007065468135; - } else { - if (LIKELY(false || (data[9].qvalue <= 16))) { - if (LIKELY(false || (data[7].qvalue <= 192))) { - result[0] += 103.74300879356734; - } else { - result[0] += -219.28913112606025; - } - } else { - result[0] += -516.2170829126527; - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 54))) { - if (UNLIKELY(false || (data[0].qvalue <= 0))) { - result[0] += -98.93297408058446; - } else { - if (LIKELY(false || (data[4].qvalue <= 60))) { - if (LIKELY(false || (data[3].qvalue <= 126))) { - if (UNLIKELY(false || (data[0].qvalue <= 18))) { - result[0] += -35.246449455925166; - } else { - result[0] += -11.913885864248398; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 12))) { - result[0] += 244.38629528569243; - } else { - result[0] += -59.63764056448275; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 136))) { - result[0] += -67.53814213480176; - } else { - result[0] += 13.863392069333836; - } - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - if (LIKELY(false || (data[1].qvalue <= 10))) { - if (LIKELY(false || (data[0].qvalue <= 232))) { - if (UNLIKELY(false || (data[2].qvalue <= 24))) { - result[0] += 243.2273344714578; - } else { - if (LIKELY(false || (data[2].qvalue <= 52))) { - result[0] += 56.08540095837141; - } else { - result[0] += -73.3251905895068; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 128))) { - if (UNLIKELY(false || (data[2].qvalue <= 24))) { - result[0] += 643.1260108343162; - } else { - result[0] += 308.232898319556; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 318))) { - result[0] += -85.94227185477129; - } else { - result[0] += 339.32448533355216; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 196))) { - result[0] += 295.57345918020815; - } else { - result[0] += 541.3180556951017; - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 156))) { - if (UNLIKELY(false || (data[4].qvalue <= 6))) { - if (LIKELY(false || (data[4].qvalue <= 4))) { - if (UNLIKELY(false || (data[6].qvalue <= 2))) { - result[0] += 176.28854142574565; - } else { - result[0] += -16.26659984192861; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 224))) { - result[0] += 272.5245026026528; - } else { - result[0] += 630.8310847742418; - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 10))) { - if (LIKELY(false || (data[10].qvalue <= 76))) { - result[0] += -99.36810379968507; - } else { - result[0] += -462.5017447936385; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 146))) { - result[0] += 3.9186464796296914; - } else { - result[0] += 225.22750105396563; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 364))) { - if (UNLIKELY(false || (data[0].qvalue <= 178))) { - if (LIKELY(false || (data[1].qvalue <= 22))) { - result[0] += -168.88397604209945; - } else { - result[0] += 38.12118179055833; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 14))) { - result[0] += -208.349456121144; - } else { - result[0] += -434.7176030497106; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 428))) { - if (LIKELY(false || (data[1].qvalue <= 22))) { - result[0] += 147.36975160660015; - } else { - result[0] += -427.1472748332084; - } - } else { - result[0] += 468.6802177857324; - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (LIKELY(false || (data[5].qvalue <= 118))) { - if (LIKELY(false || (data[0].qvalue <= 438))) { - if (LIKELY(false || (data[7].qvalue <= 194))) { - if (LIKELY(false || (data[1].qvalue <= 130))) { - if (LIKELY(false || (data[4].qvalue <= 106))) { - result[0] += 0.3368638092539733; - } else { - result[0] += 173.775034577441; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 60))) { - result[0] += 48.85885497450373; - } else { - result[0] += -96.87483192360062; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 346))) { - result[0] += -78.03276293917031; - } else { - if (LIKELY(false || (data[0].qvalue <= 428))) { - result[0] += -418.6164374426745; - } else { - result[0] += -98.44042608027418; - } - } - } - } else { - if (LIKELY(false || (data[5].qvalue <= 112))) { - if (UNLIKELY(false || (data[9].qvalue <= 8))) { - if (UNLIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -275.91209245174423; - } else { - result[0] += 99.25590915465185; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 82))) { - result[0] += -41.70434165862219; - } else { - result[0] += 130.83735776026924; - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 4))) { - if (UNLIKELY(false || (data[0].qvalue <= 444))) { - result[0] += 208.90021667361708; - } else { - result[0] += 614.78246500265; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 452))) { - result[0] += -186.66325247313335; - } else { - result[0] += 252.69463305407146; - } - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 158))) { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -243.87323720092883; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 160))) { - result[0] += 607.3841937390166; - } else { - if (LIKELY(false || (data[2].qvalue <= 224))) { - result[0] += -4.642945887934479; - } else { - result[0] += -425.1676612575311; - } - } - } - } else { - result[0] += -226.78722863564013; - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 88))) { - if (LIKELY(false || (data[2].qvalue <= 226))) { - result[0] += 457.093631103365; - } else { - result[0] += -195.01605350860177; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 54))) { - if (UNLIKELY(false || (data[8].qvalue <= 16))) { - result[0] += 121.71032578695974; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -1535.090860877404; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 48))) { - result[0] += -642.94729679988; - } else { - result[0] += -89.58887867028852; - } - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 88))) { - if (UNLIKELY(false || (data[5].qvalue <= 72))) { - result[0] += -178.884099836437; - } else { - result[0] += 349.2407402511536; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 144))) { - result[0] += -1194.8087333496094; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -154.55357593204099; - } else { - result[0] += 257.1888927003237; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 112))) { - if (UNLIKELY(false || (data[0].qvalue <= 0))) { - result[0] += -89.14531598408416; - } else { - result[0] += -18.084829562955424; - } - } else { - if (LIKELY(false || (data[8].qvalue <= 138))) { - if (LIKELY(false || (data[3].qvalue <= 116))) { - if (LIKELY(false || (data[8].qvalue <= 118))) { - if (UNLIKELY(false || (data[4].qvalue <= 38))) { - if (LIKELY(false || (data[1].qvalue <= 44))) { - result[0] += 16.943599637772344; - } else { - result[0] += 106.06836417622998; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 42))) { - result[0] += -528.7742969051144; - } else { - result[0] += -9.291722918418776; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 132))) { - result[0] += -118.09966496394628; - } else { - result[0] += -560.1406224711944; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 140))) { - result[0] += -51.02960535934701; - } else { - result[0] += 338.4129630811006; - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 104))) { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - if (LIKELY(false || (data[0].qvalue <= 464))) { - result[0] += -248.21126531471361; - } else { - result[0] += 386.6061213337075; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 376))) { - result[0] += 94.34550988961043; - } else { - result[0] += 304.2723529855085; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 436))) { - if (LIKELY(false || (data[8].qvalue <= 106))) { - result[0] += -8.896943765616806; - } else { - result[0] += -245.20061258455573; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 60))) { - result[0] += -113.87022533457596; - } else { - result[0] += 116.77525773183898; - } - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 152))) { - if (LIKELY(false || (data[0].qvalue <= 428))) { - if (UNLIKELY(false || (data[0].qvalue <= 224))) { - result[0] += -624.1711191522509; - } else { - if (LIKELY(false || (data[0].qvalue <= 414))) { - result[0] += -1347.2302644189722; - } else { - result[0] += -733.7942435464515; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 60))) { - result[0] += 471.0712008104827; - } else { - result[0] += -142.56709666372134; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - if (UNLIKELY(false || (data[9].qvalue <= 104))) { - if (UNLIKELY(false || (data[2].qvalue <= 192))) { - result[0] += -1567.6200324035817; - } else { - result[0] += -98.50162535806925; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 398))) { - result[0] += -50.25901125894962; - } else { - result[0] += 60.40281064144929; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 102))) { - if (LIKELY(false || (data[3].qvalue <= 178))) { - result[0] += 379.87726438502557; - } else { - result[0] += -199.4001040982219; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -287.5563657821928; - } else { - result[0] += 332.3956673905034; - } - } - } - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 232))) { - if (UNLIKELY(false || (data[4].qvalue <= 36))) { - if (LIKELY(false || (data[1].qvalue <= 40))) { - if (LIKELY(false || (data[4].qvalue <= 30))) { - result[0] += -1.565030040146726; - } else { - result[0] += -104.9566604701055; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 138))) { - if (UNLIKELY(false || (data[0].qvalue <= 76))) { - result[0] += -17.037935890389736; - } else { - result[0] += 63.289328847278654; - } - } else { - result[0] += -1324.6271531918176; - } - } - } else { - result[0] += -20.81789198275515; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 34))) { - if (LIKELY(false || (data[8].qvalue <= 100))) { - if (LIKELY(false || (data[8].qvalue <= 98))) { - if (LIKELY(false || (data[2].qvalue <= 134))) { - if (UNLIKELY(false || (data[0].qvalue <= 290))) { - result[0] += 2.015327035742837; - } else { - result[0] += 76.49554789592639; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 402))) { - result[0] += -823.5010456874027; - } else { - result[0] += 182.88330508869717; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 376))) { - result[0] += -684.3245220762328; - } else { - result[0] += 114.75508963752516; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 92))) { - result[0] += 584.9961795342024; - } else { - if (UNLIKELY(false || (data[9].qvalue <= 118))) { - result[0] += 284.6160485461698; - } else { - if (LIKELY(false || (data[0].qvalue <= 342))) { - result[0] += -0.3173057744465385; - } else { - result[0] += 255.28193335274855; - } - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 44))) { - if (UNLIKELY(false || (data[5].qvalue <= 24))) { - if (LIKELY(false || (data[3].qvalue <= 18))) { - if (LIKELY(false || (data[0].qvalue <= 368))) { - result[0] += -109.2669803718638; - } else { - result[0] += 49.7918489437198; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 26))) { - result[0] += -35.33314939837475; - } else { - result[0] += 383.1659807220039; - } - } - } else { - if (UNLIKELY(false || (data[11].qvalue <= 0))) { - if (LIKELY(false || (data[8].qvalue <= 80))) { - result[0] += -119.6036066769413; - } else { - result[0] += -530.947990035426; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - result[0] += -80.63511119863223; - } else { - result[0] += 415.2734672989832; - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 8))) { - if (UNLIKELY(false || (data[3].qvalue <= 68))) { - result[0] += 704.1141519325657; - } else { - if (UNLIKELY(false || (data[8].qvalue <= 2))) { - result[0] += 84.60147882812862; - } else { - result[0] += -187.8254210699395; - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 70))) { - if (LIKELY(false || (data[5].qvalue <= 90))) { - result[0] += 121.89790445304102; - } else { - result[0] += -115.0763981218671; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 72))) { - result[0] += -654.9303551006227; - } else { - result[0] += 7.981521248472231; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 420))) { - if (LIKELY(false || (data[6].qvalue <= 144))) { - if (LIKELY(false || (data[10].qvalue <= 146))) { - if (LIKELY(false || (data[2].qvalue <= 210))) { - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (LIKELY(false || (data[0].qvalue <= 358))) { - result[0] += -2.9256693373180407; - } else { - result[0] += 52.86899999050905; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 162))) { - result[0] += -236.3933458780788; - } else { - result[0] += -14.339195301019586; - } - } - } else { - result[0] += -219.14539537485498; - } - } else { - result[0] += -318.42234277715806; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 154))) { - if (UNLIKELY(false || (data[0].qvalue <= 274))) { - result[0] += -22.289702159300546; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 42))) { - result[0] += 180.36521926437015; - } else { - result[0] += -260.09045231531485; - } - } - } else { - result[0] += 44.37903776463972; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 86))) { - if (UNLIKELY(false || (data[0].qvalue <= 440))) { - if (UNLIKELY(false || (data[7].qvalue <= 46))) { - if (LIKELY(false || (data[1].qvalue <= 90))) { - result[0] += -68.81869559889574; - } else { - result[0] += 716.3498421781522; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 106))) { - if (LIKELY(false || (data[4].qvalue <= 78))) { - result[0] += -171.83362174216776; - } else { - result[0] += -984.3537857945884; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 100))) { - result[0] += -338.7075403591485; - } else { - result[0] += 123.96422662235261; - } - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 164))) { - if (LIKELY(false || (data[9].qvalue <= 126))) { - if (UNLIKELY(false || (data[0].qvalue <= 454))) { - result[0] += -204.18509525988378; - } else { - result[0] += 64.71659005621359; - } - } else { - result[0] += 817.3359237308946; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 26))) { - result[0] += -262.7801965080877; - } else { - result[0] += 612.6525648856328; - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 140))) { - if (LIKELY(false || (data[8].qvalue <= 128))) { - if (LIKELY(false || (data[6].qvalue <= 150))) { - if (LIKELY(false || (data[1].qvalue <= 148))) { - result[0] += 369.7289746082688; - } else { - result[0] += -103.32913617071652; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 22))) { - result[0] += -25.94812018229829; - } else { - result[0] += 299.29536905910857; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - result[0] += 18.803068756821325; - } else { - result[0] += 861.4705320103184; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 118))) { - result[0] += 148.92751319365524; - } else { - if (UNLIKELY(false || (data[6].qvalue <= 126))) { - if (UNLIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -1775.7163414874296; - } else { - result[0] += 74.55151140491274; - } - } else { - result[0] += -10.06076847351882; - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 450))) { - if (LIKELY(false || (data[1].qvalue <= 148))) { - if (UNLIKELY(false || (data[10].qvalue <= 30))) { - if (LIKELY(false || (data[6].qvalue <= 152))) { - if (UNLIKELY(false || (data[9].qvalue <= 92))) { - if (LIKELY(false || (data[8].qvalue <= 12))) { - result[0] += 21.302765815704674; - } else { - result[0] += -269.2691797738821; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 14))) { - result[0] += -63.12874563850693; - } else { - result[0] += 62.93227886630939; - } - } - } else { - result[0] += -400.8612725252076; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 28))) { - if (LIKELY(false || (data[0].qvalue <= 424))) { - if (LIKELY(false || (data[0].qvalue <= 386))) { - result[0] += 58.49853608139253; - } else { - result[0] += 379.42843601568325; - } - } else { - result[0] += -1179.5767049009407; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 30))) { - if (LIKELY(false || (data[1].qvalue <= 100))) { - result[0] += 19.778425640043217; - } else { - result[0] += -856.1976997050459; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 424))) { - result[0] += -5.97846202630444; - } else { - result[0] += 86.90154167537219; - } - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 102))) { - result[0] += 410.7033838491514; - } else { - if (UNLIKELY(false || (data[4].qvalue <= 112))) { - result[0] += -278.12154040332797; - } else { - result[0] += -70.26805980080006; - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 180))) { - if (LIKELY(false || (data[4].qvalue <= 136))) { - if (UNLIKELY(false || (data[8].qvalue <= 46))) { - if (LIKELY(false || (data[9].qvalue <= 52))) { - if (UNLIKELY(false || (data[4].qvalue <= 108))) { - result[0] += 202.68141819077738; - } else { - result[0] += -167.2132011812022; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -1384.5104673530457; - } else { - result[0] += -189.4566731860187; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 140))) { - if (LIKELY(false || (data[7].qvalue <= 132))) { - result[0] += 187.09927614728926; - } else { - result[0] += 567.0117546161409; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -93.51709182438407; - } else { - result[0] += 121.54129938504501; - } - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 140))) { - result[0] += 465.57152458676364; - } else { - result[0] += -479.2019554780659; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 472))) { - if (LIKELY(false || (data[10].qvalue <= 106))) { - if (LIKELY(false || (data[7].qvalue <= 200))) { - if (UNLIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -220.95806736832415; - } else { - result[0] += 116.17247093054512; - } - } else { - result[0] += -729.6717337371826; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 164))) { - result[0] += -312.7238552339285; - } else { - result[0] += -1140.467401624576; - } - } - } else { - result[0] += 195.02478855827175; - } - } - } - if (LIKELY(false || (data[8].qvalue <= 154))) { - if (LIKELY(false || (data[0].qvalue <= 392))) { - if (LIKELY(false || (data[6].qvalue <= 62))) { - if (LIKELY(false || (data[0].qvalue <= 310))) { - if (LIKELY(false || (data[8].qvalue <= 120))) { - if (LIKELY(false || (data[8].qvalue <= 100))) { - result[0] += -9.71572720333726; - } else { - result[0] += 67.59923309810226; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 52))) { - result[0] += -351.55140420047667; - } else { - result[0] += -16.616159911071314; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 72))) { - if (LIKELY(false || (data[4].qvalue <= 66))) { - result[0] += 69.88081147655924; - } else { - result[0] += -969.7781215245079; - } - } else { - result[0] += 228.67764875201647; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 4))) { - if (UNLIKELY(false || (data[10].qvalue <= 2))) { - result[0] += 74.35381121983716; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 138))) { - result[0] += -62.154687912270276; - } else { - result[0] += -796.2456169363136; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 56))) { - if (LIKELY(false || (data[4].qvalue <= 86))) { - result[0] += -156.1506230350774; - } else { - result[0] += 119.82853149552272; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 106))) { - result[0] += 13.900388157788383; - } else { - result[0] += -42.87238835775265; - } - } - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 78))) { - if (UNLIKELY(false || (data[6].qvalue <= 58))) { - if (LIKELY(false || (data[6].qvalue <= 56))) { - if (LIKELY(false || (data[2].qvalue <= 156))) { - result[0] += -25.79300413884613; - } else { - result[0] += 449.6201546856599; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 424))) { - result[0] += -1246.7840846746014; - } else { - result[0] += -60.57121668348967; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 18))) { - if (UNLIKELY(false || (data[0].qvalue <= 416))) { - result[0] += -507.2811346565345; - } else { - result[0] += 1.6959367259683802; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 130))) { - result[0] += 256.5119388340715; - } else { - result[0] += 61.901222326179735; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 4))) { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -712.841039622884; - } else { - result[0] += -50.285859444198785; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 168))) { - if (UNLIKELY(false || (data[7].qvalue <= 80))) { - result[0] += -89.82284215276091; - } else { - result[0] += 57.2312430185315; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 462))) { - result[0] += -196.41216488031796; - } else { - result[0] += 64.87819986422424; - } - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 422))) { - if (LIKELY(false || (data[0].qvalue <= 352))) { - result[0] += -54.40201249787878; - } else { - result[0] += -323.09540305923275; - } - } else { - result[0] += 22.58190522531515; - } - } - if (LIKELY(false || (data[7].qvalue <= 192))) { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (LIKELY(false || (data[2].qvalue <= 212))) { - if (LIKELY(false || (data[0].qvalue <= 396))) { - if (UNLIKELY(false || (data[9].qvalue <= 62))) { - if (UNLIKELY(false || (data[1].qvalue <= 34))) { - result[0] += 320.8771929320019; - } else { - result[0] += -48.74134204044459; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 64))) { - result[0] += 144.15507685887556; - } else { - result[0] += 2.061937768452418; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 168))) { - if (LIKELY(false || (data[8].qvalue <= 142))) { - result[0] += 14.310048813060831; - } else { - result[0] += -891.4821913653136; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 186))) { - result[0] += 230.68364280672665; - } else { - result[0] += 31.828982825162395; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 460))) { - if (UNLIKELY(false || (data[8].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 446))) { - result[0] += 5.3923912609412765; - } else { - result[0] += -296.2815883663324; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 18))) { - result[0] += 0.33503809236265447; - } else { - result[0] += -222.23518809470139; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 162))) { - result[0] += 435.30908950076656; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 220))) { - result[0] += 151.71039930723558; - } else { - result[0] += -220.68651987712957; - } - } - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 158))) { - if (LIKELY(false || (data[5].qvalue <= 106))) { - if (LIKELY(false || (data[8].qvalue <= 34))) { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -761.8192641261176; - } else { - result[0] += -24.696202075383436; - } - } else { - result[0] += 297.8870552382013; - } - } else { - result[0] += -1111.3451507308962; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 180))) { - result[0] += 399.4329665421262; - } else { - if (LIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -33.631317828261764; - } else { - if (LIKELY(false || (data[6].qvalue <= 186))) { - result[0] += 521.4533408866705; - } else { - result[0] += -47.855811708641056; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 438))) { - if (UNLIKELY(false || (data[0].qvalue <= 352))) { - result[0] += -42.87267583810598; - } else { - result[0] += -282.3784003570174; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 132))) { - result[0] += 156.2716050426377; - } else { - if (UNLIKELY(false || (data[8].qvalue <= 144))) { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - if (UNLIKELY(false || (data[2].qvalue <= 154))) { - result[0] += -106.3379635111628; - } else { - result[0] += -799.4368728340669; - } - } else { - result[0] += -30.901543195994442; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -278.9996333283842; - } else { - result[0] += 97.21542436889949; - } - } - } - } - } - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - if (LIKELY(false || (data[0].qvalue <= 198))) { - if (LIKELY(false || (data[1].qvalue <= 10))) { - if (UNLIKELY(false || (data[2].qvalue <= 24))) { - if (UNLIKELY(false || (data[0].qvalue <= 62))) { - result[0] += -71.54944060418909; - } else { - result[0] += 200.91246808639335; - } - } else { - result[0] += -16.524995964984008; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 56))) { - result[0] += -6.615663904946698; - } else { - result[0] += 277.0038505094405; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 128))) { - if (LIKELY(false || (data[1].qvalue <= 10))) { - result[0] += 242.12226853180888; - } else { - result[0] += 495.40205234661437; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 308))) { - result[0] += -144.40748272556144; - } else { - result[0] += 244.60940232429508; - } - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 156))) { - if (UNLIKELY(false || (data[4].qvalue <= 6))) { - if (LIKELY(false || (data[4].qvalue <= 4))) { - if (LIKELY(false || (data[9].qvalue <= 144))) { - if (UNLIKELY(false || (data[10].qvalue <= 12))) { - result[0] += 151.95679595717183; - } else { - result[0] += -75.40462882328015; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 248))) { - result[0] += 59.16238539562008; - } else { - result[0] += 258.58095117618336; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 194))) { - if (UNLIKELY(false || (data[0].qvalue <= 78))) { - result[0] += 34.722618898156114; - } else { - result[0] += 229.7578373765491; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 254))) { - result[0] += 416.11542151307907; - } else { - result[0] += 657.1399509168027; - } - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 10))) { - if (LIKELY(false || (data[0].qvalue <= 396))) { - if (LIKELY(false || (data[10].qvalue <= 76))) { - result[0] += -99.76580517101934; - } else { - result[0] += -406.79586814929917; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 410))) { - result[0] += 67.21864653476916; - } else { - result[0] += 318.3788762612092; - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 152))) { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - result[0] += -89.91859493216367; - } else { - result[0] += 2.265520650788892; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 182))) { - result[0] += 86.60159851878495; - } else { - result[0] += 446.4563385244049; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 426))) { - if (UNLIKELY(false || (data[0].qvalue <= 166))) { - result[0] += -85.71042457905949; - } else { - if (LIKELY(false || (data[0].qvalue <= 356))) { - if (LIKELY(false || (data[1].qvalue <= 14))) { - result[0] += -197.6560417052055; - } else { - result[0] += -382.76909364031883; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 22))) { - result[0] += 86.90468637807797; - } else { - result[0] += -409.69153384359265; - } - } - } - } else { - result[0] += 370.0893122985466; - } - } - } - if (LIKELY(false || (data[0].qvalue <= 450))) { - if (LIKELY(false || (data[1].qvalue <= 130))) { - if (LIKELY(false || (data[4].qvalue <= 106))) { - if (UNLIKELY(false || (data[9].qvalue <= 16))) { - result[0] += -778.7936840058063; - } else { - if (UNLIKELY(false || (data[9].qvalue <= 20))) { - result[0] += 194.97101648633375; - } else { - if (UNLIKELY(false || (data[10].qvalue <= 30))) { - result[0] += -35.3509348883947; - } else { - result[0] += 5.30025526653546; - } - } - } - } else { - result[0] += 128.95468067576573; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 106))) { - if (UNLIKELY(false || (data[8].qvalue <= 68))) { - if (LIKELY(false || (data[2].qvalue <= 120))) { - result[0] += -34.18950997644831; - } else { - result[0] += 621.8798392741791; - } - } else { - result[0] += -155.4137360966938; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 108))) { - if (LIKELY(false || (data[0].qvalue <= 430))) { - if (LIKELY(false || (data[7].qvalue <= 108))) { - result[0] += 256.5188225022511; - } else { - result[0] += -99.67316593885579; - } - } else { - result[0] += 620.485300659375; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 100))) { - result[0] += -2286.0077275800704; - } else { - if (LIKELY(false || (data[5].qvalue <= 116))) { - result[0] += -53.309956527903466; - } else { - result[0] += 190.13761434101764; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 136))) { - if (UNLIKELY(false || (data[2].qvalue <= 78))) { - if (LIKELY(false || (data[1].qvalue <= 142))) { - if (UNLIKELY(false || (data[7].qvalue <= 76))) { - if (LIKELY(false || (data[2].qvalue <= 68))) { - result[0] += 253.43645195632422; - } else { - result[0] += -1413.0786233422066; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 462))) { - result[0] += -690.7561180054086; - } else { - result[0] += -63.707054709028625; - } - } - } else { - result[0] += 291.7091149857328; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 120))) { - if (UNLIKELY(false || (data[8].qvalue <= 128))) { - result[0] += 478.56900909093986; - } else { - result[0] += 167.99342824787573; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 464))) { - result[0] += 60.9554692627185; - } else { - result[0] += -1073.8503825143669; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 464))) { - if (LIKELY(false || (data[4].qvalue <= 136))) { - if (UNLIKELY(false || (data[8].qvalue <= 52))) { - result[0] += -539.7433428985337; - } else { - if (LIKELY(false || (data[1].qvalue <= 160))) { - result[0] += 20.89762446651438; - } else { - result[0] += -416.20782851777005; - } - } - } else { - result[0] += 361.5994213025975; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 172))) { - if (LIKELY(false || (data[1].qvalue <= 160))) { - if (UNLIKELY(false || (data[10].qvalue <= 34))) { - result[0] += 537.4931809409429; - } else { - result[0] += 93.3123402983442; - } - } else { - result[0] += 509.56219742394296; - } - } else { - result[0] += -30.502598904934388; - } - } - } - } - if (UNLIKELY(false || (data[4].qvalue <= 18))) { - if (UNLIKELY(false || (data[9].qvalue <= 108))) { - if (UNLIKELY(false || (data[0].qvalue <= 170))) { - result[0] += 32.02621609860586; - } else { - result[0] += 195.61137056108709; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 116))) { - result[0] += -56.120014772691675; - } else { - if (UNLIKELY(false || (data[9].qvalue <= 120))) { - if (UNLIKELY(false || (data[8].qvalue <= 38))) { - result[0] += 258.16901404153344; - } else { - result[0] += 53.61413424410968; - } - } else { - result[0] += 3.8083516584014774; - } - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 30))) { - if (LIKELY(false || (data[8].qvalue <= 12))) { - if (UNLIKELY(false || (data[10].qvalue <= 2))) { - result[0] += 95.68803265337215; - } else { - if (UNLIKELY(false || (data[10].qvalue <= 8))) { - if (LIKELY(false || (data[3].qvalue <= 132))) { - result[0] += -103.63143270824092; - } else { - result[0] += -416.841719094304; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 452))) { - result[0] += 29.00180398543003; - } else { - result[0] += 739.6504496232433; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 442))) { - if (UNLIKELY(false || (data[9].qvalue <= 44))) { - result[0] += -1170.886850360489; - } else { - if (UNLIKELY(false || (data[9].qvalue <= 78))) { - result[0] += -23.599978133418663; - } else { - result[0] += -233.86794128469188; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 100))) { - result[0] += 296.8371355378329; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -578.3205363336284; - } else { - result[0] += 28.31820900489086; - } - } - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 110))) { - if (UNLIKELY(false || (data[2].qvalue <= 34))) { - if (LIKELY(false || (data[0].qvalue <= 270))) { - result[0] += 15.084899225242879; - } else { - if (UNLIKELY(false || (data[6].qvalue <= 44))) { - result[0] += 227.37067720434766; - } else { - result[0] += 79.75576158468904; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 66))) { - if (LIKELY(false || (data[2].qvalue <= 64))) { - result[0] += -21.445175019440377; - } else { - result[0] += -736.583490831738; - } - } else { - if (LIKELY(false || (data[10].qvalue <= 122))) { - result[0] += 19.304014117388515; - } else { - result[0] += -31.807962860341632; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 52))) { - if (LIKELY(false || (data[6].qvalue <= 20))) { - if (LIKELY(false || (data[2].qvalue <= 76))) { - result[0] += -2.770553189772293; - } else { - result[0] += -459.67841100976295; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 402))) { - result[0] += -654.9440592987792; - } else { - result[0] += -113.53853189933453; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 52))) { - result[0] += 141.69815316018148; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 74))) { - result[0] += -502.039703472783; - } else { - result[0] += -32.3855029558517; - } - } - } - } - } - } - if (LIKELY(false || (data[7].qvalue <= 176))) { - if (LIKELY(false || (data[3].qvalue <= 122))) { - if (LIKELY(false || (data[6].qvalue <= 68))) { - if (LIKELY(false || (data[7].qvalue <= 96))) { - if (LIKELY(false || (data[7].qvalue <= 92))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += 92.56955997074317; - } else { - result[0] += -0.9922155287745735; - } - } else { - result[0] += -500.10035950605436; - } - } else { - if (LIKELY(false || (data[10].qvalue <= 82))) { - if (UNLIKELY(false || (data[3].qvalue <= 18))) { - result[0] += -349.0415762351967; - } else { - result[0] += 171.85769845927226; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 156))) { - result[0] += -451.9129687933409; - } else { - result[0] += -27.180822178728683; - } - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 24))) { - if (UNLIKELY(false || (data[5].qvalue <= 52))) { - if (LIKELY(false || (data[6].qvalue <= 170))) { - result[0] += 257.80955723579154; - } else { - result[0] += 14.067277977079232; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 78))) { - result[0] += -187.1084046068555; - } else { - result[0] += -16.47644404213514; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 58))) { - if (UNLIKELY(false || (data[1].qvalue <= 68))) { - result[0] += -459.1713788092935; - } else { - result[0] += -121.28232839389386; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 82))) { - result[0] += 45.911193575042404; - } else { - result[0] += -65.09204602729294; - } - } - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - if (LIKELY(false || (data[8].qvalue <= 148))) { - result[0] += -112.3564328986315; - } else { - result[0] += -581.5931838175455; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 104))) { - if (LIKELY(false || (data[8].qvalue <= 130))) { - if (LIKELY(false || (data[7].qvalue <= 140))) { - result[0] += 99.49014850118782; - } else { - result[0] += 283.8674441857837; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 166))) { - result[0] += -196.33975801441395; - } else { - result[0] += 5.911638064587168; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 128))) { - if (UNLIKELY(false || (data[1].qvalue <= 114))) { - result[0] += 220.39824119047162; - } else { - result[0] += 86.35899353404471; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 136))) { - result[0] += -135.11781706233776; - } else { - result[0] += 1.7254921740277542; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 32))) { - result[0] += -729.2271141921474; - } else { - if (LIKELY(false || (data[1].qvalue <= 164))) { - if (UNLIKELY(false || (data[8].qvalue <= 6))) { - result[0] += 163.01861591388177; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 182))) { - result[0] += -90.15761066666771; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 184))) { - result[0] += 105.67463769590789; - } else { - result[0] += -40.65848461097406; - } - } - } - } else { - result[0] += -149.36907697589524; - } - } - } - if (LIKELY(false || (data[0].qvalue <= 436))) { - if (UNLIKELY(false || (data[9].qvalue <= 54))) { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (UNLIKELY(false || (data[10].qvalue <= 2))) { - result[0] += 73.61434867220245; - } else { - if (LIKELY(false || (data[4].qvalue <= 92))) { - result[0] += -219.52771218319805; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 182))) { - result[0] += -261.44554618186174; - } else { - result[0] += -1230.408535736753; - } - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 24))) { - if (LIKELY(false || (data[0].qvalue <= 386))) { - if (LIKELY(false || (data[2].qvalue <= 112))) { - result[0] += -5.198946846457009; - } else { - result[0] += 249.1763981732631; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 34))) { - result[0] += 641.3825622819111; - } else { - result[0] += 205.60287406527715; - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 32))) { - if (UNLIKELY(false || (data[4].qvalue <= 126))) { - result[0] += -887.6161494419807; - } else { - result[0] += -171.10035394400404; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 408))) { - result[0] += -58.06284362537737; - } else { - result[0] += 25.218332263091998; - } - } - } - } - } else { - if (LIKELY(false || (data[8].qvalue <= 154))) { - if (LIKELY(false || (data[0].qvalue <= 346))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - if (LIKELY(false || (data[0].qvalue <= 232))) { - result[0] += 25.584773238822454; - } else { - result[0] += 219.8658888633042; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 20))) { - result[0] += -51.10525196126827; - } else { - result[0] += 0.46333939293857984; - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 72))) { - if (LIKELY(false || (data[8].qvalue <= 130))) { - result[0] += 164.15191019337982; - } else { - result[0] += -335.2814106754929; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 84))) { - result[0] += -89.39119339130295; - } else { - result[0] += 34.512913855389506; - } - } - } - } else { - result[0] += -108.08090718754343; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 158))) { - if (UNLIKELY(false || (data[8].qvalue <= 46))) { - if (LIKELY(false || (data[2].qvalue <= 118))) { - if (UNLIKELY(false || (data[7].qvalue <= 76))) { - result[0] += 131.26162098672742; - } else { - if (LIKELY(false || (data[0].qvalue <= 464))) { - result[0] += -442.8722988609171; - } else { - result[0] += 73.24052956825756; - } - } - } else { - result[0] += 686.9491978638054; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 54))) { - result[0] += 687.8496107636925; - } else { - if (LIKELY(false || (data[0].qvalue <= 456))) { - if (UNLIKELY(false || (data[9].qvalue <= 12))) { - result[0] += -77.24307302441612; - } else { - result[0] += 147.57566065994; - } - } else { - if (LIKELY(false || (data[5].qvalue <= 110))) { - result[0] += 138.145849125284; - } else { - result[0] += 445.5193884395197; - } - } - } - } - } else { - result[0] += -13.479785177056158; - } - } - if (LIKELY(false || (data[0].qvalue <= 418))) { - if (LIKELY(false || (data[1].qvalue <= 124))) { - if (LIKELY(false || (data[4].qvalue <= 68))) { - if (LIKELY(false || (data[1].qvalue <= 80))) { - if (UNLIKELY(false || (data[9].qvalue <= 50))) { - result[0] += -577.1611446622182; - } else { - if (LIKELY(false || (data[10].qvalue <= 142))) { - result[0] += 3.8314250912323637; - } else { - result[0] += -329.28055580520333; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 26))) { - if (UNLIKELY(false || (data[0].qvalue <= 198))) { - result[0] += -194.40768818753736; - } else { - result[0] += -854.4809931534361; - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 70))) { - result[0] += -160.52174054303293; - } else { - result[0] += 2.741586163284305; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 372))) { - if (UNLIKELY(false || (data[1].qvalue <= 46))) { - if (UNLIKELY(false || (data[0].qvalue <= 168))) { - result[0] += 18.984540004822804; - } else { - result[0] += 327.11800611524313; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 20))) { - result[0] += -129.20406003130722; - } else { - result[0] += 17.92278831555766; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 150))) { - if (UNLIKELY(false || (data[2].qvalue <= 6))) { - result[0] += -221.08946446962568; - } else { - result[0] += 229.63359317462343; - } - } else { - result[0] += -223.7223770166941; - } - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 64))) { - if (LIKELY(false || (data[0].qvalue <= 394))) { - result[0] += 5.729375564939926; - } else { - if (LIKELY(false || (data[4].qvalue <= 136))) { - result[0] += 349.6954434120244; - } else { - result[0] += -447.2340327004826; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 346))) { - result[0] += -18.934394820420263; - } else { - if (LIKELY(false || (data[2].qvalue <= 164))) { - if (LIKELY(false || (data[1].qvalue <= 140))) { - result[0] += -512.2810219610269; - } else { - result[0] += -123.2775600600666; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 176))) { - result[0] += 335.8072972282146; - } else { - result[0] += -135.40134282126365; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 10))) { - result[0] += 408.30240571312294; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 56))) { - result[0] += 203.85368488394235; - } else { - if (UNLIKELY(false || (data[7].qvalue <= 100))) { - if (LIKELY(false || (data[0].qvalue <= 448))) { - if (LIKELY(false || (data[10].qvalue <= 96))) { - result[0] += -457.3360616844447; - } else { - result[0] += 124.47860255821743; - } - } else { - if (LIKELY(false || (data[4].qvalue <= 108))) { - result[0] += 110.48892123347983; - } else { - result[0] += -391.09864189814425; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 114))) { - result[0] += 211.6228610309497; - } else { - if (UNLIKELY(false || (data[10].qvalue <= 100))) { - result[0] += 56.71696470804632; - } else { - result[0] += -35.66565365297374; - } - } - } - } - } - } - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - if (LIKELY(false || (data[1].qvalue <= 10))) { - if (LIKELY(false || (data[2].qvalue <= 52))) { - if (UNLIKELY(false || (data[2].qvalue <= 24))) { - result[0] += 147.91960717089773; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - if (LIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 43.27359384398152; - } else { - result[0] += 111.57729094083193; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 8))) { - result[0] += 113.34997104546899; - } else { - result[0] += 72.55342688666062; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 128))) { - if (LIKELY(false || (data[1].qvalue <= 8))) { - if (LIKELY(false || (data[1].qvalue <= 6))) { - result[0] += 31.246573605653715; - } else { - result[0] += 46.606138483456206; - } - } else { - result[0] += 25.9373059644741; - } - } else { - result[0] += -31.626364376909578; - } - } - } else { - result[0] += 240.78696773473405; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 156))) { - if (UNLIKELY(false || (data[4].qvalue <= 6))) { - if (LIKELY(false || (data[4].qvalue <= 4))) { - if (LIKELY(false || (data[1].qvalue <= 20))) { - if (UNLIKELY(false || (data[9].qvalue <= 136))) { - result[0] += -48.976438574076354; - } else { - result[0] += 84.65299344607978; - } - } else { - result[0] += -130.88113764489268; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 72))) { - if (UNLIKELY(false || (data[1].qvalue <= 22))) { - result[0] += 338.1827440503927; - } else { - result[0] += 298.08608821916727; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 22))) { - result[0] += 204.95094375285473; - } else { - result[0] += 165.99111228283587; - } - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 10))) { - if (LIKELY(false || (data[10].qvalue <= 76))) { - if (LIKELY(false || (data[2].qvalue <= 92))) { - result[0] += -108.03954054155902; - } else { - result[0] += 14.418807341740987; - } - } else { - result[0] += -321.311952308832; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 146))) { - if (UNLIKELY(false || (data[1].qvalue <= 8))) { - result[0] += -76.25024484457143; - } else { - result[0] += 1.263274912881202; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 6))) { - result[0] += -66.04814325674907; - } else { - result[0] += 205.46869851639258; - } - } - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 36))) { - if (UNLIKELY(false || (data[1].qvalue <= 14))) { - result[0] += -104.7552531611455; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 18))) { - if (LIKELY(false || (data[1].qvalue <= 16))) { - result[0] += -179.2056109143998; - } else { - result[0] += -196.72295793090106; - } - } else { - result[0] += -228.7848599462564; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -62.70206554713903; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 22))) { - result[0] += -109.57286387525; - } else { - result[0] += -86.90439645392793; - } - } - } - } - } - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - if (LIKELY(false || (data[1].qvalue <= 10))) { - if (LIKELY(false || (data[2].qvalue <= 52))) { - if (UNLIKELY(false || (data[2].qvalue <= 24))) { - result[0] += 133.14242400479026; - } else { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - if (LIKELY(false || (data[2].qvalue <= 46))) { - result[0] += 38.96136485201495; - } else { - result[0] += 100.46528939864675; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 8))) { - result[0] += 102.02497851147024; - } else { - result[0] += 65.31936047920384; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 128))) { - if (LIKELY(false || (data[1].qvalue <= 8))) { - if (LIKELY(false || (data[1].qvalue <= 6))) { - result[0] += 28.128267190495155; - } else { - result[0] += 42.006052734820884; - } - } else { - result[0] += 23.349456995785104; - } - } else { - result[0] += -28.466887237596946; - } - } - } else { - result[0] += 216.73249472971176; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 154))) { - if (LIKELY(false || (data[9].qvalue <= 148))) { - if (UNLIKELY(false || (data[10].qvalue <= 2))) { - if (LIKELY(false || (data[2].qvalue <= 156))) { - if (LIKELY(false || (data[9].qvalue <= 114))) { - result[0] += 101.975488812464; - } else { - result[0] += -25.287212545092885; - } - } else { - result[0] += -99.57496398377107; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 30))) { - if (UNLIKELY(false || (data[4].qvalue <= 16))) { - result[0] += 58.55564779565954; - } else { - result[0] += -70.77906117415272; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 18))) { - result[0] += 56.64272064066464; - } else { - result[0] += -1.102421664746258; - } - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 204))) { - if (UNLIKELY(false || (data[4].qvalue <= 4))) { - if (UNLIKELY(false || (data[1].qvalue <= 12))) { - result[0] += 76.95158297864288; - } else { - result[0] += 30.779853089335912; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 12))) { - result[0] += 184.47632299122395; - } else { - result[0] += 240.0561058810638; - } - } - } else { - result[0] += -59.44992973601068; - } - } - } else { - if (LIKELY(false || (data[3].qvalue <= 20))) { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - if (UNLIKELY(false || (data[2].qvalue <= 36))) { - result[0] += 90.32707990001026; - } else { - result[0] += -56.438123774542795; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 92))) { - if (LIKELY(false || (data[2].qvalue <= 88))) { - result[0] += -97.11633504657749; - } else { - result[0] += -327.052109099788; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 24))) { - result[0] += -88.46877715199902; - } else { - result[0] += 36.38893057075731; - } - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 14))) { - result[0] += 164.4902900819997; - } else { - result[0] += 201.51887436344938; - } - } - } - } - if (LIKELY(false || (data[7].qvalue <= 190))) { - if (LIKELY(false || (data[3].qvalue <= 102))) { - if (LIKELY(false || (data[6].qvalue <= 72))) { - if (LIKELY(false || (data[8].qvalue <= 112))) { - if (LIKELY(false || (data[8].qvalue <= 100))) { - if (LIKELY(false || (data[10].qvalue <= 124))) { - result[0] += 2.677490165277401; - } else { - result[0] += -230.8237998800555; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 106))) { - result[0] += 25.99249929961273; - } else { - result[0] += 143.56253715641475; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 158))) { - if (LIKELY(false || (data[4].qvalue <= 32))) { - result[0] += -56.96864498437293; - } else { - result[0] += -361.08262388656516; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 130))) { - result[0] += -1.2571125993192631; - } else { - result[0] += 139.96081546293442; - } - } - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 24))) { - if (LIKELY(false || (data[4].qvalue <= 126))) { - if (LIKELY(false || (data[8].qvalue <= 68))) { - result[0] += 157.68101550278936; - } else { - result[0] += -24.160628623142866; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 100))) { - result[0] += -87.49349951075376; - } else { - result[0] += -651.2774869908153; - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 104))) { - if (UNLIKELY(false || (data[8].qvalue <= 82))) { - result[0] += -72.49802639529806; - } else { - result[0] += -213.66048931924266; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 54))) { - result[0] += 78.72485385773093; - } else { - result[0] += -55.249394110207554; - } - } - } - } - } else { - if (LIKELY(false || (data[10].qvalue <= 134))) { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - if (LIKELY(false || (data[8].qvalue <= 148))) { - result[0] += -94.97855070131733; - } else { - result[0] += -527.4611841577268; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 32))) { - if (LIKELY(false || (data[9].qvalue <= 28))) { - result[0] += 0.9950631307094829; - } else { - result[0] += -188.09164012193523; - } - } else { - if (LIKELY(false || (data[8].qvalue <= 140))) { - result[0] += 64.8246177611995; - } else { - result[0] += -21.226393353713657; - } - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 140))) { - if (UNLIKELY(false || (data[10].qvalue <= 138))) { - result[0] += -69.54835276237708; - } else { - if (LIKELY(false || (data[1].qvalue <= 158))) { - result[0] += -236.0745244061219; - } else { - result[0] += -12.63559195083826; - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 52))) { - result[0] += -236.792523303151; - } else { - if (LIKELY(false || (data[10].qvalue <= 144))) { - result[0] += 52.206924149774636; - } else { - result[0] += -39.51021768049611; - } - } - } - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 140))) { - if (LIKELY(false || (data[2].qvalue <= 226))) { - result[0] += -52.42843817107941; - } else { - result[0] += -306.65785355623103; - } - } else { - result[0] += -385.0662454001109; - } - } - if (LIKELY(false || (data[0].qvalue <= 398))) { - if (LIKELY(false || (data[1].qvalue <= 110))) { - if (LIKELY(false || (data[4].qvalue <= 70))) { - if (LIKELY(false || (data[1].qvalue <= 86))) { - if (UNLIKELY(false || (data[5].qvalue <= 42))) { - if (LIKELY(false || (data[5].qvalue <= 40))) { - result[0] += -11.28284578878582; - } else { - result[0] += -558.1753204017739; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 26))) { - result[0] += 133.21891876863342; - } else { - result[0] += 2.7832862656512685; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 26))) { - if (LIKELY(false || (data[0].qvalue <= 246))) { - result[0] += -324.1019695078817; - } else { - result[0] += -1005.9270602557989; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 88))) { - result[0] += -134.86731075700266; - } else { - result[0] += 10.66748293923189; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 336))) { - if (LIKELY(false || (data[6].qvalue <= 98))) { - result[0] += 2.8200100312590166; - } else { - result[0] += 101.03470891273177; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 14))) { - if (LIKELY(false || (data[6].qvalue <= 82))) { - result[0] += 102.04557161945678; - } else { - result[0] += -1199.8811078319284; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 178))) { - result[0] += 209.67226520445524; - } else { - result[0] += -245.61747308533063; - } - } - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 60))) { - result[0] += 42.785881512673626; - } else { - if (LIKELY(false || (data[6].qvalue <= 154))) { - if (LIKELY(false || (data[0].qvalue <= 270))) { - result[0] += -45.95588289959001; - } else { - if (UNLIKELY(false || (data[6].qvalue <= 90))) { - result[0] += -6.815612347789953; - } else { - result[0] += -266.0110871919803; - } - } - } else { - result[0] += 39.02030164716074; - } - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 8))) { - result[0] += 324.9267206663035; - } else { - if (LIKELY(false || (data[2].qvalue <= 214))) { - if (UNLIKELY(false || (data[1].qvalue <= 16))) { - result[0] += -112.18559323452085; - } else { - if (LIKELY(false || (data[1].qvalue <= 132))) { - if (UNLIKELY(false || (data[2].qvalue <= 80))) { - result[0] += -56.2055045870944; - } else { - result[0] += 116.07989618400472; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 118))) { - result[0] += 141.36324262903074; - } else { - result[0] += -22.395839800711574; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 458))) { - if (UNLIKELY(false || (data[10].qvalue <= 118))) { - result[0] += 100.73381070844493; - } else { - if (LIKELY(false || (data[6].qvalue <= 174))) { - result[0] += -365.7173433437338; - } else { - result[0] += 21.744193287907777; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 162))) { - result[0] += 280.70190659426964; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 466))) { - result[0] += -456.7905672944539; - } else { - result[0] += 86.74089207808544; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 438))) { - if (LIKELY(false || (data[6].qvalue <= 146))) { - if (LIKELY(false || (data[7].qvalue <= 198))) { - if (LIKELY(false || (data[0].qvalue <= 388))) { - if (LIKELY(false || (data[1].qvalue <= 126))) { - if (LIKELY(false || (data[4].qvalue <= 102))) { - result[0] += -3.0799897635647393; - } else { - result[0] += 83.18986261549072; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 36))) { - result[0] += 225.15234009730352; - } else { - result[0] += -85.63258649231685; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 82))) { - if (LIKELY(false || (data[3].qvalue <= 80))) { - result[0] += 35.515553203945544; - } else { - result[0] += -529.1932694931496; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 124))) { - result[0] += 183.78815671074506; - } else { - result[0] += 39.81733903217535; - } - } - } - } else { - result[0] += -186.6083734780767; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 50))) { - if (UNLIKELY(false || (data[0].qvalue <= 272))) { - result[0] += 16.882292766847286; - } else { - result[0] += -340.80931681790474; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 154))) { - if (LIKELY(false || (data[3].qvalue <= 170))) { - if (LIKELY(false || (data[10].qvalue <= 102))) { - result[0] += -8.494744851894465; - } else { - result[0] += -138.4317062673716; - } - } else { - result[0] += -365.5767086328917; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 172))) { - if (UNLIKELY(false || (data[0].qvalue <= 304))) { - result[0] += -4.6291599966349635; - } else { - result[0] += 233.27933991789052; - } - } else { - result[0] += -47.89610265065409; - } - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 182))) { - if (LIKELY(false || (data[10].qvalue <= 148))) { - if (UNLIKELY(false || (data[8].qvalue <= 46))) { - if (UNLIKELY(false || (data[7].qvalue <= 56))) { - result[0] += 408.84774771757384; - } else { - if (LIKELY(false || (data[4].qvalue <= 136))) { - result[0] += -81.22211370546637; - } else { - result[0] += 149.79791446348088; - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 60))) { - result[0] += 507.31584249730014; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 108))) { - result[0] += 3.6960386503909706; - } else { - result[0] += 132.51686944699037; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 472))) { - if (LIKELY(false || (data[7].qvalue <= 192))) { - if (UNLIKELY(false || (data[0].qvalue <= 460))) { - result[0] += -289.98766205200593; - } else { - result[0] += -37.620573467202114; - } - } else { - result[0] += -1064.0260380735235; - } - } else { - result[0] += 301.66872530386996; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 472))) { - if (LIKELY(false || (data[1].qvalue <= 160))) { - if (LIKELY(false || (data[3].qvalue <= 178))) { - result[0] += -66.47823654447284; - } else { - result[0] += -479.9069173542369; - } - } else { - result[0] += -776.3112829887955; - } - } else { - result[0] += 122.73633086275382; - } - } - } - if (LIKELY(false || (data[0].qvalue <= 458))) { - if (LIKELY(false || (data[6].qvalue <= 168))) { - if (LIKELY(false || (data[6].qvalue <= 166))) { - if (LIKELY(false || (data[6].qvalue <= 164))) { - if (LIKELY(false || (data[0].qvalue <= 424))) { - if (LIKELY(false || (data[6].qvalue <= 144))) { - result[0] += -1.16853189584804; - } else { - result[0] += -76.18237147015647; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += -565.1080728858356; - } else { - result[0] += 55.856898322571; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 428))) { - result[0] += -22.36169324873774; - } else { - result[0] += -588.8674501865916; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 444))) { - result[0] += 45.149287996873234; - } else { - result[0] += 472.9437979483076; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 110))) { - if (UNLIKELY(false || (data[0].qvalue <= 426))) { - result[0] += -14.394012939959836; - } else { - result[0] += -486.3581999710685; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 58))) { - result[0] += -223.70340084114315; - } else { - if (UNLIKELY(false || (data[10].qvalue <= 88))) { - if (LIKELY(false || (data[0].qvalue <= 400))) { - result[0] += -62.55465904994526; - } else { - result[0] += 711.6569177288845; - } - } else { - result[0] += -22.12709356395005; - } - } - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 176))) { - if (UNLIKELY(false || (data[6].qvalue <= 136))) { - if (LIKELY(false || (data[4].qvalue <= 108))) { - if (UNLIKELY(false || (data[9].qvalue <= 36))) { - result[0] += 353.08425684031425; - } else { - if (UNLIKELY(false || (data[8].qvalue <= 70))) { - result[0] += -488.1757777871809; - } else { - result[0] += 63.64288315962378; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 470))) { - if (LIKELY(false || (data[1].qvalue <= 164))) { - result[0] += -1340.3345402421837; - } else { - result[0] += 611.7688119589316; - } - } else { - result[0] += -5.555477568010339; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 124))) { - if (LIKELY(false || (data[2].qvalue <= 216))) { - if (UNLIKELY(false || (data[10].qvalue <= 48))) { - result[0] += 101.80136762073124; - } else { - result[0] += 394.0454932921462; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - result[0] += -54.51295272481636; - } else { - result[0] += 369.30662351586926; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 464))) { - if (LIKELY(false || (data[2].qvalue <= 168))) { - result[0] += -280.5682849399643; - } else { - result[0] += 363.49964354819656; - } - } else { - if (LIKELY(false || (data[2].qvalue <= 94))) { - result[0] += 287.00161641580763; - } else { - result[0] += -27.192437609622456; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (UNLIKELY(false || (data[4].qvalue <= 102))) { - result[0] += 4.886466438190986; - } else { - result[0] += -273.6325616457949; - } - } else { - result[0] += 83.09343404390128; - } - } - } - if (LIKELY(false || (data[0].qvalue <= 466))) { - if (LIKELY(false || (data[6].qvalue <= 168))) { - if (LIKELY(false || (data[0].qvalue <= 426))) { - if (UNLIKELY(false || (data[9].qvalue <= 42))) { - if (LIKELY(false || (data[9].qvalue <= 28))) { - if (LIKELY(false || (data[9].qvalue <= 26))) { - result[0] += -12.741637583507675; - } else { - result[0] += 237.9588004111412; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 22))) { - result[0] += -668.1312059622767; - } else { - result[0] += -74.96557902347062; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 96))) { - if (LIKELY(false || (data[1].qvalue <= 92))) { - result[0] += 1.2337343498563946; - } else { - result[0] += -70.38800250372651; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 98))) { - result[0] += 57.91224145436725; - } else { - result[0] += -291.61695102438495; - } - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 46))) { - if (LIKELY(false || (data[3].qvalue <= 138))) { - if (LIKELY(false || (data[2].qvalue <= 78))) { - result[0] += -16.243170204026153; - } else { - result[0] += 316.91551138966975; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 122))) { - result[0] += -653.2681828545267; - } else { - result[0] += -140.38689952692195; - } - } - } else { - if (LIKELY(false || (data[10].qvalue <= 116))) { - if (UNLIKELY(false || (data[9].qvalue <= 76))) { - result[0] += 227.09410027903155; - } else { - result[0] += 41.580049580383246; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 112))) { - result[0] += 421.02462529251864; - } else { - result[0] += -29.85412788966988; - } - } - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 84))) { - if (LIKELY(false || (data[0].qvalue <= 462))) { - if (UNLIKELY(false || (data[0].qvalue <= 430))) { - result[0] += -40.88170364268475; - } else { - if (LIKELY(false || (data[0].qvalue <= 458))) { - result[0] += -438.26722679955304; - } else { - result[0] += -153.8759027518579; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 170))) { - result[0] += 596.3699073028564; - } else { - result[0] += -65.1138629905028; - } - } - } else { - if (LIKELY(false || (data[2].qvalue <= 170))) { - if (LIKELY(false || (data[0].qvalue <= 450))) { - if (UNLIKELY(false || (data[8].qvalue <= 96))) { - result[0] += -174.18902213573074; - } else { - result[0] += 92.92329912293484; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 140))) { - result[0] += 1029.8844129302536; - } else { - result[0] += 173.42569967619715; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 398))) { - result[0] += 117.61714884981923; - } else { - result[0] += -165.11066227116845; - } - } - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 132))) { - if (UNLIKELY(false || (data[3].qvalue <= 140))) { - if (LIKELY(false || (data[8].qvalue <= 152))) { - result[0] += 275.79848862506384; - } else { - result[0] += -208.23065829261407; - } - } else { - result[0] += 44.68615918739647; - } - } else { - result[0] += -104.44556106745557; - } - } - if (LIKELY(false || (data[0].qvalue <= 452))) { - if (LIKELY(false || (data[7].qvalue <= 178))) { - if (LIKELY(false || (data[2].qvalue <= 212))) { - if (LIKELY(false || (data[7].qvalue <= 166))) { - if (LIKELY(false || (data[6].qvalue <= 168))) { - if (LIKELY(false || (data[6].qvalue <= 166))) { - result[0] += 0.17111650034346793; - } else { - result[0] += 170.19199658576656; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 114))) { - result[0] += -7.762253214369399; - } else { - result[0] += -229.65839448385577; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 154))) { - if (UNLIKELY(false || (data[7].qvalue <= 168))) { - result[0] += -1527.4651782070064; - } else { - result[0] += -32.341146195408825; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 400))) { - result[0] += 53.78926807880971; - } else { - result[0] += 365.35095312342645; - } - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 40))) { - result[0] += -195.8247595287494; - } else { - if (LIKELY(false || (data[0].qvalue <= 446))) { - result[0] += -6.730419002246698; - } else { - result[0] += -485.7584640261381; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 68))) { - result[0] += -302.41592403705386; - } else { - result[0] += -41.37635294838609; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 146))) { - if (UNLIKELY(false || (data[8].qvalue <= 68))) { - if (LIKELY(false || (data[9].qvalue <= 52))) { - if (LIKELY(false || (data[4].qvalue <= 108))) { - if (LIKELY(false || (data[0].qvalue <= 462))) { - result[0] += 105.6696751827489; - } else { - result[0] += 422.3152713156167; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 154))) { - result[0] += -338.9649649852936; - } else { - result[0] += 94.81444748000506; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 472))) { - if (UNLIKELY(false || (data[2].qvalue <= 32))) { - result[0] += 307.59982556152346; - } else { - result[0] += -2059.814399773849; - } - } else { - result[0] += -114.64047998985878; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 120))) { - if (LIKELY(false || (data[0].qvalue <= 468))) { - if (UNLIKELY(false || (data[6].qvalue <= 56))) { - result[0] += -105.53208465517486; - } else { - result[0] += 242.79092160164484; - } - } else { - result[0] += 794.8344802547236; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 462))) { - result[0] += 211.67574313790456; - } else { - result[0] += -1052.0226856718868; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 470))) { - if (LIKELY(false || (data[2].qvalue <= 220))) { - if (LIKELY(false || (data[5].qvalue <= 122))) { - if (UNLIKELY(false || (data[8].qvalue <= 14))) { - result[0] += 607.7257898100032; - } else { - result[0] += 31.625663758552975; - } - } else { - result[0] += -165.26478050781773; - } - } else { - result[0] += -212.34245839321358; - } - } else { - if (LIKELY(false || (data[7].qvalue <= 188))) { - result[0] += 195.00720541235728; - } else { - result[0] += -16.708118864398536; - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 442))) { - if (LIKELY(false || (data[1].qvalue <= 148))) { - if (LIKELY(false || (data[8].qvalue <= 154))) { - if (UNLIKELY(false || (data[0].qvalue <= 54))) { - result[0] += -25.89138246104899; - } else { - if (LIKELY(false || (data[5].qvalue <= 84))) { - if (LIKELY(false || (data[8].qvalue <= 116))) { - result[0] += 6.395755334851975; - } else { - result[0] += -41.8874025902268; - } - } else { - if (LIKELY(false || (data[10].qvalue <= 136))) { - result[0] += 43.17739670120483; - } else { - result[0] += -113.04299411317503; - } - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 342))) { - result[0] += -9.406748281219956; - } else { - result[0] += -151.8391684094208; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 102))) { - if (LIKELY(false || (data[0].qvalue <= 308))) { - result[0] += 68.15272951691435; - } else { - result[0] += 653.7243028200384; - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 118))) { - if (UNLIKELY(false || (data[0].qvalue <= 360))) { - result[0] += 4.132956299238849; - } else { - if (LIKELY(false || (data[2].qvalue <= 140))) { - result[0] += -391.93226074781654; - } else { - result[0] += -64.14020916590836; - } - } - } else { - result[0] += -42.79033951970415; - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 140))) { - if (UNLIKELY(false || (data[3].qvalue <= 16))) { - result[0] += 673.2372143809691; - } else { - if (LIKELY(false || (data[8].qvalue <= 72))) { - if (UNLIKELY(false || (data[0].qvalue <= 456))) { - if (LIKELY(false || (data[2].qvalue <= 68))) { - result[0] += 72.78571585943284; - } else { - result[0] += -372.52419624016306; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 84))) { - result[0] += -470.8106385006224; - } else { - result[0] += 129.07388124253447; - } - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 448))) { - if (UNLIKELY(false || (data[3].qvalue <= 106))) { - result[0] += -167.83421517433203; - } else { - result[0] += 120.89570583762936; - } - } else { - if (LIKELY(false || (data[1].qvalue <= 144))) { - result[0] += 166.50278950598; - } else { - result[0] += 479.51545503859353; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - if (LIKELY(false || (data[2].qvalue <= 216))) { - if (LIKELY(false || (data[8].qvalue <= 102))) { - if (LIKELY(false || (data[4].qvalue <= 136))) { - result[0] += -85.88265694904688; - } else { - result[0] += 158.08464637061354; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 160))) { - result[0] += 566.823827422359; - } else { - result[0] += 36.90095759323719; - } - } - } else { - result[0] += -221.08459607201044; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 162))) { - result[0] += 279.3051543277019; - } else { - if (UNLIKELY(false || (data[4].qvalue <= 88))) { - result[0] += 225.3999401051908; - } else { - if (LIKELY(false || (data[0].qvalue <= 472))) { - result[0] += -146.03870534454174; - } else { - result[0] += 135.266458595812; - } - } - } - } - } - } - if (UNLIKELY(false || (data[7].qvalue <= 10))) { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (LIKELY(false || (data[8].qvalue <= 56))) { - if (LIKELY(false || (data[5].qvalue <= 30))) { - if (UNLIKELY(false || (data[4].qvalue <= 2))) { - result[0] += 95.33481345166494; - } else { - if (UNLIKELY(false || (data[10].qvalue <= 36))) { - result[0] += -51.475241127929905; - } else { - result[0] += 45.54996053721217; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 18))) { - result[0] += 193.47951816282492; - } else { - result[0] += 41.14647330634432; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 8))) { - result[0] += -256.5011877316934; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 44))) { - result[0] += -78.031723920287; - } else { - if (UNLIKELY(false || (data[10].qvalue <= 58))) { - result[0] += -70.18547029244044; - } else { - result[0] += 58.299178728803355; - } - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 56))) { - result[0] += 377.13982577866017; - } else { - result[0] += 125.3708679348752; - } - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 22))) { - if (LIKELY(false || (data[6].qvalue <= 20))) { - if (LIKELY(false || (data[3].qvalue <= 48))) { - if (LIKELY(false || (data[5].qvalue <= 38))) { - if (LIKELY(false || (data[2].qvalue <= 122))) { - result[0] += -27.262907617303707; - } else { - result[0] += -296.1225155241601; - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 94))) { - result[0] += -744.6855172554128; - } else { - result[0] += -295.5306383117119; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 74))) { - result[0] += 131.65876610363532; - } else { - result[0] += 275.9221477857152; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 54))) { - result[0] += -266.18327484029714; - } else { - result[0] += -500.5391151498484; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 14))) { - if (LIKELY(false || (data[4].qvalue <= 34))) { - if (LIKELY(false || (data[8].qvalue <= 86))) { - result[0] += -10.58236111787111; - } else { - if (UNLIKELY(false || (data[5].qvalue <= 8))) { - result[0] += -39.932805444944165; - } else { - result[0] += 159.2150451080038; - } - } - } else { - result[0] += 204.44418412478637; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 30))) { - if (LIKELY(false || (data[8].qvalue <= 12))) { - if (UNLIKELY(false || (data[1].qvalue <= 62))) { - result[0] += 125.99859295088723; - } else { - result[0] += -10.15551076665495; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 20))) { - result[0] += -190.22917265383091; - } else { - result[0] += -33.04391029665733; - } - } - } else { - if (LIKELY(false || (data[10].qvalue <= 98))) { - if (UNLIKELY(false || (data[7].qvalue <= 32))) { - result[0] += 63.784678651859224; - } else { - result[0] += 4.687841489245975; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 70))) { - result[0] += -193.67925649583466; - } else { - result[0] += -11.97318656822869; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 410))) { - if (UNLIKELY(false || (data[9].qvalue <= 54))) { - if (UNLIKELY(false || (data[4].qvalue <= 74))) { - if (LIKELY(false || (data[0].qvalue <= 298))) { - result[0] += -37.83989902430271; - } else { - result[0] += -246.41304263463073; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 36))) { - if (UNLIKELY(false || (data[0].qvalue <= 196))) { - result[0] += -24.698954353394658; - } else { - result[0] += 269.8421976574775; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 82))) { - if (LIKELY(false || (data[4].qvalue <= 128))) { - result[0] += 121.42816960444512; - } else { - result[0] += -204.12026096629276; - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 86))) { - result[0] += -150.30284718707338; - } else { - result[0] += -9.526517783514938; - } - } - } - } - } else { - result[0] += 0.6374046148668437; - } - } else { - if (UNLIKELY(false || (data[9].qvalue <= 10))) { - if (LIKELY(false || (data[0].qvalue <= 456))) { - if (UNLIKELY(false || (data[3].qvalue <= 160))) { - if (UNLIKELY(false || (data[2].qvalue <= 56))) { - result[0] += -844.9505630005908; - } else { - result[0] += -266.4489226017028; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 448))) { - result[0] += -59.61765022395716; - } else { - if (UNLIKELY(false || (data[9].qvalue <= 0))) { - result[0] += -422.1597338477869; - } else { - result[0] += 307.5185329018873; - } - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 84))) { - result[0] += 259.874337152467; - } else { - result[0] += -0.14677676347402133; - } - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 76))) { - if (LIKELY(false || (data[2].qvalue <= 60))) { - if (UNLIKELY(false || (data[4].qvalue <= 82))) { - if (LIKELY(false || (data[6].qvalue <= 66))) { - result[0] += 6.901818537027612; - } else { - result[0] += 346.73948874314306; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 0))) { - result[0] += 767.3329148187853; - } else { - result[0] += -55.71428668797978; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 462))) { - if (UNLIKELY(false || (data[8].qvalue <= 62))) { - result[0] += -1186.4827177702393; - } else { - result[0] += -248.10230638115846; - } - } else { - result[0] += 317.2999956644618; - } - } - } else { - if (LIKELY(false || (data[8].qvalue <= 128))) { - if (UNLIKELY(false || (data[10].qvalue <= 46))) { - if (UNLIKELY(false || (data[8].qvalue <= 6))) { - result[0] += 424.73738367278276; - } else { - result[0] += -69.29711136275134; - } - } else { - if (LIKELY(false || (data[9].qvalue <= 38))) { - result[0] += 93.41031827005968; - } else { - result[0] += 256.43713558484785; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 86))) { - if (LIKELY(false || (data[0].qvalue <= 456))) { - result[0] += -194.80499023272242; - } else { - result[0] += 84.16720477646794; - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 130))) { - result[0] += 1117.9205291606104; - } else { - result[0] += 32.52858858984742; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 442))) { - if (LIKELY(false || (data[6].qvalue <= 146))) { - if (LIKELY(false || (data[0].qvalue <= 360))) { - if (UNLIKELY(false || (data[4].qvalue <= 38))) { - if (UNLIKELY(false || (data[6].qvalue <= 24))) { - if (UNLIKELY(false || (data[9].qvalue <= 94))) { - result[0] += -509.2763123794946; - } else { - result[0] += -10.28309254566162; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 46))) { - result[0] += 65.60802974294101; - } else { - result[0] += -11.172650598142237; - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 40))) { - if (UNLIKELY(false || (data[0].qvalue <= 170))) { - result[0] += -379.1246497217576; - } else { - result[0] += -880.7154812437998; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 38))) { - result[0] += 33.59073760216112; - } else { - result[0] += -19.2301464816471; - } - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 198))) { - if (LIKELY(false || (data[2].qvalue <= 186))) { - if (LIKELY(false || (data[3].qvalue <= 144))) { - result[0] += 33.69989668265042; - } else { - result[0] += 208.11777026466976; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 126))) { - result[0] += -4.6241213678677076; - } else { - result[0] += -258.29323587381907; - } - } - } else { - result[0] += -240.03769405637382; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 94))) { - if (UNLIKELY(false || (data[4].qvalue <= 20))) { - if (LIKELY(false || (data[0].qvalue <= 400))) { - result[0] += -1.3019828319392528; - } else { - result[0] += 595.7005340010636; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 258))) { - result[0] += -9.190887637624767; - } else { - result[0] += -285.64672594943085; - } - } - } else { - if (LIKELY(false || (data[7].qvalue <= 154))) { - if (UNLIKELY(false || (data[0].qvalue <= 344))) { - result[0] += 40.545254733724846; - } else { - if (UNLIKELY(false || (data[0].qvalue <= 416))) { - result[0] += -233.93172162375063; - } else { - result[0] += -22.286785557997515; - } - } - } else { - result[0] += 70.10413972244889; - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 140))) { - if (LIKELY(false || (data[4].qvalue <= 114))) { - if (LIKELY(false || (data[4].qvalue <= 112))) { - if (UNLIKELY(false || (data[3].qvalue <= 16))) { - result[0] += 634.2191665019159; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 78))) { - result[0] += 6.665393953447264; - } else { - result[0] += 149.39801863389712; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 154))) { - result[0] += 1226.4539406622023; - } else { - result[0] += 294.9277796445255; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 158))) { - result[0] += 235.1520118587282; - } else { - result[0] += -166.61985769647302; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - if (LIKELY(false || (data[2].qvalue <= 216))) { - result[0] += 5.169623713259927; - } else { - result[0] += -199.41738547469845; - } - } else { - result[0] += 71.9476904143754; - } - } - } - if (UNLIKELY(false || (data[0].qvalue <= 48))) { - if (UNLIKELY(false || (data[0].qvalue <= 0))) { - result[0] += -78.00407393572347; - } else { - if (LIKELY(false || (data[2].qvalue <= 202))) { - if (LIKELY(false || (data[3].qvalue <= 104))) { - if (LIKELY(false || (data[4].qvalue <= 90))) { - if (UNLIKELY(false || (data[2].qvalue <= 0))) { - result[0] += -70.6888999910638; - } else { - result[0] += -10.627670242516414; - } - } else { - if (LIKELY(false || (data[10].qvalue <= 66))) { - result[0] += -107.69047239009149; - } else { - result[0] += -13.414907874287316; - } - } - } else { - if (LIKELY(false || (data[1].qvalue <= 138))) { - if (UNLIKELY(false || (data[10].qvalue <= 12))) { - result[0] += 69.98431682634853; - } else { - result[0] += -59.43734140381587; - } - } else { - result[0] += 78.30655993653257; - } - } - } else { - result[0] += 82.01495795861331; - } - } - } else { - if (UNLIKELY(false || (data[4].qvalue <= 0))) { - if (LIKELY(false || (data[2].qvalue <= 128))) { - if (LIKELY(false || (data[0].qvalue <= 226))) { - if (LIKELY(false || (data[2].qvalue <= 58))) { - if (UNLIKELY(false || (data[2].qvalue <= 24))) { - result[0] += 150.52913267303913; - } else { - result[0] += 6.758849638068968; - } - } else { - result[0] += 219.10434569977826; - } - } else { - result[0] += 274.1316917335302; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 332))) { - result[0] += -84.8367008241603; - } else { - result[0] += 317.87177416169243; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 14))) { - if (LIKELY(false || (data[0].qvalue <= 406))) { - if (LIKELY(false || (data[4].qvalue <= 28))) { - if (UNLIKELY(false || (data[9].qvalue <= 108))) { - result[0] += 152.21239710120096; - } else { - result[0] += -49.218505477191925; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 30))) { - result[0] += -369.09166571523195; - } else { - result[0] += -59.62264580017301; - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 122))) { - if (UNLIKELY(false || (data[2].qvalue <= 72))) { - result[0] += 225.48342290509692; - } else { - result[0] += 454.8914585206501; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 420))) { - result[0] += -316.42278833229403; - } else { - result[0] += 89.88278332332641; - } - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 22))) { - if (LIKELY(false || (data[3].qvalue <= 18))) { - if (LIKELY(false || (data[4].qvalue <= 26))) { - result[0] += 83.70108736546864; - } else { - result[0] += -47.03238163468578; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 172))) { - result[0] += 64.00534235112683; - } else { - result[0] += 241.06317309770884; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 28))) { - if (UNLIKELY(false || (data[9].qvalue <= 108))) { - result[0] += 41.85255742051009; - } else { - result[0] += -122.59387579963109; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 12))) { - result[0] += 72.23509636314206; - } else { - result[0] += 2.3792085384878088; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 460))) { - if (LIKELY(false || (data[6].qvalue <= 168))) { - if (LIKELY(false || (data[0].qvalue <= 432))) { - if (LIKELY(false || (data[1].qvalue <= 124))) { - if (LIKELY(false || (data[5].qvalue <= 62))) { - if (LIKELY(false || (data[8].qvalue <= 110))) { - result[0] += -1.818322290568549; - } else { - result[0] += -86.51820960810343; - } - } else { - if (UNLIKELY(false || (data[6].qvalue <= 48))) { - result[0] += 122.64593189161003; - } else { - result[0] += 8.90299476140962; - } - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 64))) { - if (LIKELY(false || (data[8].qvalue <= 64))) { - result[0] += 27.811889151480592; - } else { - result[0] += 564.1322969672179; - } - } else { - if (UNLIKELY(false || (data[10].qvalue <= 66))) { - result[0] += -961.542648801726; - } else { - result[0] += -67.5677271302586; - } - } - } - } else { - if (LIKELY(false || (data[10].qvalue <= 148))) { - if (UNLIKELY(false || (data[1].qvalue <= 0))) { - result[0] += -271.4318221979432; - } else { - if (UNLIKELY(false || (data[5].qvalue <= 16))) { - result[0] += 688.4114417873475; - } else { - result[0] += 58.42035899984279; - } - } - } else { - result[0] += -307.4296972195184; - } - } - } else { - if (UNLIKELY(false || (data[8].qvalue <= 78))) { - if (UNLIKELY(false || (data[0].qvalue <= 434))) { - result[0] += -47.84229037947061; - } else { - result[0] += -341.83457990151675; - } - } else { - result[0] += -24.69473744066359; - } - } - } else { - if (LIKELY(false || (data[6].qvalue <= 176))) { - if (UNLIKELY(false || (data[6].qvalue <= 156))) { - if (LIKELY(false || (data[4].qvalue <= 108))) { - if (LIKELY(false || (data[1].qvalue <= 90))) { - if (LIKELY(false || (data[0].qvalue <= 468))) { - result[0] += -237.2028222385908; - } else { - result[0] += 235.23343926289132; - } - } else { - result[0] += 275.68252452796696; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 470))) { - if (UNLIKELY(false || (data[9].qvalue <= 6))) { - result[0] += 276.655894681045; - } else { - result[0] += -1138.9810647848005; - } - } else { - result[0] += -24.815816631740525; - } - } - } else { - if (LIKELY(false || (data[4].qvalue <= 124))) { - if (UNLIKELY(false || (data[8].qvalue <= 94))) { - result[0] += 468.636846801675; - } else { - result[0] += 173.66365185212408; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 464))) { - result[0] += -156.75158265385107; - } else { - if (UNLIKELY(false || (data[5].qvalue <= 72))) { - result[0] += 418.66415661473707; - } else { - result[0] += 16.60197418193807; - } - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 472))) { - if (UNLIKELY(false || (data[4].qvalue <= 102))) { - if (LIKELY(false || (data[3].qvalue <= 178))) { - result[0] += 112.29216412676556; - } else { - result[0] += -589.6749782826543; - } - } else { - if (LIKELY(false || (data[6].qvalue <= 186))) { - result[0] += -126.14743791646418; - } else { - result[0] += -697.0503549298137; - } - } - } else { - result[0] += 130.01903028897132; - } - } - } - if (LIKELY(false || (data[0].qvalue <= 250))) { - if (LIKELY(false || (data[2].qvalue <= 96))) { - if (LIKELY(false || (data[10].qvalue <= 110))) { - result[0] += -15.209203725866741; - } else { - result[0] += -151.01702601859964; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 116))) { - result[0] += 33.169088504059346; - } else { - if (UNLIKELY(false || (data[2].qvalue <= 136))) { - if (LIKELY(false || (data[2].qvalue <= 134))) { - if (LIKELY(false || (data[9].qvalue <= 132))) { - result[0] += -0.5460734226255192; - } else { - result[0] += -166.18365097405575; - } - } else { - result[0] += -630.4456930959705; - } - } else { - result[0] += 9.610897218995794; - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 12))) { - if (UNLIKELY(false || (data[5].qvalue <= 4))) { - if (UNLIKELY(false || (data[7].qvalue <= 4))) { - result[0] += 292.5131264640451; - } else { - if (LIKELY(false || (data[0].qvalue <= 346))) { - if (LIKELY(false || (data[2].qvalue <= 46))) { - result[0] += -90.96452643248735; - } else { - result[0] += -554.2646443755457; - } - } else { - result[0] += 78.62976969599482; - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 316))) { - if (LIKELY(false || (data[3].qvalue <= 54))) { - if (UNLIKELY(false || (data[9].qvalue <= 102))) { - result[0] += 178.17593860823325; - } else { - result[0] += 25.683590998151445; - } - } else { - result[0] += 478.77786108066925; - } - } else { - result[0] += 181.52015147560186; - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 122))) { - if (UNLIKELY(false || (data[6].qvalue <= 54))) { - if (UNLIKELY(false || (data[3].qvalue <= 10))) { - if (LIKELY(false || (data[4].qvalue <= 46))) { - result[0] += 392.13693925483733; - } else { - result[0] += 93.50921667351855; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 26))) { - result[0] += -171.2455308856315; - } else { - result[0] += 65.00191321740634; - } - } - } else { - if (UNLIKELY(false || (data[5].qvalue <= 54))) { - if (LIKELY(false || (data[3].qvalue <= 60))) { - result[0] += -29.670526192638437; - } else { - result[0] += -250.61118646403057; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 72))) { - result[0] += 139.50112391138077; - } else { - result[0] += 5.183830796614544; - } - } - } - } else { - if (LIKELY(false || (data[0].qvalue <= 410))) { - if (UNLIKELY(false || (data[6].qvalue <= 6))) { - if (LIKELY(false || (data[0].qvalue <= 376))) { - result[0] += 72.4511482444548; - } else { - result[0] += 480.9499826432033; - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 50))) { - result[0] += -339.82229606896135; - } else { - result[0] += -34.16011839278788; - } - } - } else { - if (UNLIKELY(false || (data[1].qvalue <= 52))) { - if (LIKELY(false || (data[4].qvalue <= 22))) { - result[0] += 110.97363027456811; - } else { - result[0] += 409.03280051924963; - } - } else { - if (LIKELY(false || (data[0].qvalue <= 468))) { - result[0] += -25.656918184151955; - } else { - result[0] += 673.645954177365; - } - } - } - } - } - } - if (LIKELY(false || (data[0].qvalue <= 338))) { - if (LIKELY(false || (data[2].qvalue <= 96))) { - if (LIKELY(false || (data[10].qvalue <= 110))) { - if (LIKELY(false || (data[2].qvalue <= 86))) { - if (LIKELY(false || (data[2].qvalue <= 82))) { - if (LIKELY(false || (data[10].qvalue <= 74))) { - result[0] += -0.803059421195404; - } else { - result[0] += -41.251005891223926; - } - } else { - result[0] += 143.4559149834235; - } - } else { - if (LIKELY(false || (data[3].qvalue <= 116))) { - if (UNLIKELY(false || (data[7].qvalue <= 26))) { - result[0] += 2.624252580548391; - } else { - result[0] += -227.38952669158417; - } - } else { - result[0] += 45.837014796557646; - } - } - } else { - result[0] += -195.38067959274719; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 116))) { - if (LIKELY(false || (data[0].qvalue <= 176))) { - result[0] += 12.646781663778135; - } else { - if (UNLIKELY(false || (data[8].qvalue <= 50))) { - result[0] += 217.37905842024804; - } else { - if (LIKELY(false || (data[9].qvalue <= 100))) { - result[0] += 18.490879448389602; - } else { - result[0] += 157.2895805126459; - } - } - } - } else { - if (LIKELY(false || (data[5].qvalue <= 88))) { - if (LIKELY(false || (data[10].qvalue <= 130))) { - if (LIKELY(false || (data[10].qvalue <= 124))) { - result[0] += -25.376404193377994; - } else { - result[0] += -234.0117708294; - } - } else { - result[0] += 44.70662468521388; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 130))) { - if (UNLIKELY(false || (data[0].qvalue <= 146))) { - result[0] += 12.86322888112687; - } else { - result[0] += 200.2312601015275; - } - } else { - if (UNLIKELY(false || (data[2].qvalue <= 162))) { - result[0] += -49.82927755893318; - } else { - result[0] += 34.59858546002059; - } - } - } - } - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 64))) { - if (LIKELY(false || (data[7].qvalue <= 60))) { - result[0] += 44.53611652626999; - } else { - if (UNLIKELY(false || (data[3].qvalue <= 100))) { - result[0] += 378.92912612636616; - } else { - result[0] += 114.52994706164273; - } - } - } else { - if (UNLIKELY(false || (data[3].qvalue <= 12))) { - if (UNLIKELY(false || (data[0].qvalue <= 416))) { - if (LIKELY(false || (data[10].qvalue <= 92))) { - result[0] += -628.9086683084666; - } else { - result[0] += 107.65691728510829; - } - } else { - if (UNLIKELY(false || (data[7].qvalue <= 148))) { - if (LIKELY(false || (data[0].qvalue <= 432))) { - result[0] += 30.295081044275346; - } else { - result[0] += 409.9041492892673; - } - } else { - result[0] += -143.73134957553725; - } - } - } else { - if (LIKELY(false || (data[9].qvalue <= 146))) { - if (LIKELY(false || (data[9].qvalue <= 138))) { - if (UNLIKELY(false || (data[6].qvalue <= 54))) { - result[0] += 149.66087572300066; - } else { - result[0] += -2.415057229240939; - } - } else { - if (UNLIKELY(false || (data[0].qvalue <= 418))) { - result[0] += -377.3930807574788; - } else { - result[0] += 56.193799271376434; - } - } - } else { - result[0] += 545.2850103362496; - } - } - } - } - - // Apply base_scores - result[0] += 0; - - // Apply postprocessor - if (!pred_margin) { postprocess(result); } -} - -void fj_predictor::postprocess(double* result) -{ - // Do nothing -} - -// Feature names array -const char* fj_predictor::feature_names[fj_predictor::NUM_FEATURES] = {"time", - "initial_violation_count", - "max_nnz_per_row", - "n_binary_vars", - "n_constraints", - "n_integer_vars", - "n_variables", - "nnz", - "nnz_stddev", - "sparsity", - "unbalancedness", - "uses_load_balancing"}; diff --git a/cpp/src/utilities/models/fj_predictor/quantize.cpp b/cpp/src/utilities/models/fj_predictor/quantize.cpp deleted file mode 100644 index ef43dd4c9..000000000 --- a/cpp/src/utilities/models/fj_predictor/quantize.cpp +++ /dev/null @@ -1,1180 +0,0 @@ - -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "header.h" - -static const double threshold[] = { - 0.054247050000000012, - 0.077229550000000022, - 0.099138400000000002, - 0.12088200000000002, - 0.14051650000000002, - 0.15852850000000004, - 0.17679700000000004, - 0.19352650000000002, - 0.21021800000000002, - 0.22558550000000002, - 0.24112700000000004, - 0.25703600000000004, - 0.27504900000000004, - 0.29268150000000009, - 0.31128750000000005, - 0.33090700000000001, - 0.34973950000000004, - 0.36837650000000005, - 0.38755100000000003, - 0.40692750000000005, - 0.42464700000000005, - 0.44212900000000005, - 0.46115050000000007, - 0.47875750000000006, - 0.49512500000000004, - 0.51183000000000012, - 0.52816850000000015, - 0.54488850000000011, - 0.56229750000000012, - 0.57881900000000008, - 0.59550200000000009, - 0.61214950000000012, - 0.62885000000000013, - 0.64696700000000018, - 0.66350950000000009, - 0.67997150000000006, - 0.69847550000000014, - 0.7159675000000002, - 0.73463250000000013, - 0.7525940000000001, - 0.77200500000000016, - 0.79034150000000014, - 0.80839950000000005, - 0.82770850000000007, - 0.8455950000000001, - 0.86400500000000013, - 0.88264050000000005, - 0.9004295000000001, - 0.91857200000000006, - 0.93668050000000014, - 0.95404050000000018, - 0.97391950000000016, - 0.99200750000000004, - 1.0099000000000002, - 1.0289150000000002, - 1.0465600000000002, - 1.0646550000000004, - 1.08342, - 1.1014450000000002, - 1.1397000000000002, - 1.1587800000000004, - 1.1770050000000001, - 1.1970650000000003, - 1.2149600000000003, - 1.2334100000000003, - 1.2509550000000003, - 1.2698400000000001, - 1.2892250000000003, - 1.3068250000000001, - 1.3257700000000001, - 1.3439900000000002, - 1.3627650000000002, - 1.3818950000000003, - 1.3997500000000003, - 1.4181450000000002, - 1.4367350000000003, - 1.4547850000000002, - 1.472415, - 1.4914700000000003, - 1.5101100000000003, - 1.5280200000000004, - 1.5462100000000001, - 1.5645300000000002, - 1.5825350000000002, - 1.6018750000000004, - 1.6398350000000002, - 1.6586050000000003, - 1.6957200000000003, - 1.7142950000000001, - 1.7329100000000002, - 1.7519050000000003, - 1.7889300000000004, - 1.8084400000000003, - 1.84535, - 1.8819950000000003, - 1.9005400000000001, - 1.9191650000000002, - 1.9559750000000002, - 1.9729050000000001, - 1.9908450000000002, - 2.0093400000000003, - 2.0269500000000007, - 2.0634850000000005, - 2.0812900000000005, - 2.0995600000000008, - 2.1174250000000003, - 2.1351400000000003, - 2.1524100000000006, - 2.1703750000000004, - 2.1880300000000004, - 2.2057200000000008, - 2.2244050000000004, - 2.2413600000000007, - 2.2589850000000005, - 2.2762350000000002, - 2.2941550000000004, - 2.3126850000000005, - 2.3300800000000002, - 2.3486300000000004, - 2.3668800000000005, - 2.3849800000000001, - 2.4039000000000006, - 2.4207550000000002, - 2.4380750000000004, - 2.4563950000000006, - 2.4738150000000005, - 2.4917700000000003, - 2.5087150000000005, - 2.5256750000000001, - 2.541865, - 2.5750850000000001, - 2.5926700000000005, - 2.6103800000000006, - 2.6462700000000008, - 2.6646600000000005, - 2.6821750000000004, - 2.6995250000000008, - 2.7161250000000003, - 2.7339100000000003, - 2.7519550000000002, - 2.7684900000000003, - 2.7861950000000006, - 2.8210050000000004, - 2.8391800000000003, - 2.8555100000000002, - 2.8724000000000003, - 2.8905950000000007, - 2.9086600000000007, - 2.9273950000000002, - 2.9445150000000004, - 2.9631150000000006, - 2.9813800000000006, - 3.0161400000000005, - 3.0351250000000003, - 3.0535200000000002, - 3.0715450000000004, - 3.0891000000000006, - 3.1077850000000002, - 3.1259600000000005, - 3.1445250000000002, - 3.1638600000000001, - 3.2029950000000005, - 3.2231000000000005, - 3.2427850000000005, - 3.2622000000000004, - 3.2821500000000001, - 3.3022150000000008, - 3.3210900000000003, - 3.3419400000000006, - 3.3625650000000005, - 3.3821600000000003, - 3.4255600000000004, - 3.4476550000000006, - 3.4694350000000003, - 3.4931250000000005, - 3.5172500000000002, - 3.5405750000000005, - 3.5633350000000004, - 3.5881150000000006, - 3.6123700000000007, - 3.6388050000000005, - 3.6645250000000007, - 3.6929500000000002, - 3.7206100000000002, - 3.7487850000000003, - 3.8103150000000006, - 3.8410400000000005, - 3.8753900000000003, - 3.9096100000000003, - 3.9434900000000002, - 3.9779100000000001, - 4.0157400000000001, - 4.0538750000000006, - 4.0947550000000001, - 4.134875000000001, - 4.1789850000000008, - 4.2277500000000012, - 4.2804200000000003, - 4.3315450000000011, - 4.3857550000000005, - 4.4510850000000008, - 4.5179800000000006, - 4.5945550000000006, - 4.6744550000000009, - 4.7680650000000009, - 4.8691100000000009, - 4.9748750000000008, - 5.1040400000000004, - 5.2357750000000012, - 5.3817050000000011, - 5.5487400000000013, - 5.7226800000000013, - 5.9013650000000011, - 6.0933350000000006, - 6.2959750000000012, - 6.5019050000000016, - 6.7376150000000008, - 6.9850100000000008, - 7.2452850000000009, - 7.5387250000000003, - 7.8497500000000011, - 8.206685000000002, - 8.5881250000000016, - 9.0114350000000005, - 9.5016150000000028, - 10.006050000000002, - 10.534950000000002, - 11.213450000000003, - 12.138300000000003, - 13.343550000000002, - 14.829750000000002, - 16.520350000000004, - 19.149450000000005, - 22.676800000000004, - 27.456400000000002, - 34.351750000000003, - 47.268800000000006, - 1.0000000180025095e-35, - 1.5000000000000002, - 2.5000000000000004, - 3.5000000000000004, - 4.5000000000000009, - 7.5000000000000009, - 9.5000000000000018, - 13.500000000000002, - 20.500000000000004, - 27.500000000000004, - 32.500000000000007, - 35.500000000000007, - 73.500000000000014, - 145.50000000000003, - 210.50000000000003, - 256.50000000000006, - 267.50000000000006, - 271.50000000000006, - 338.50000000000006, - 416.50000000000006, - 627.50000000000011, - 646.50000000000011, - 654.50000000000011, - 728.50000000000011, - 785.50000000000011, - 917.50000000000011, - 943.50000000000011, - 1105.5000000000002, - 1168.5000000000002, - 1414.5000000000002, - 1511.5000000000002, - 1766.5000000000002, - 2142.5000000000005, - 2424.5000000000005, - 2476.5000000000005, - 2824.5000000000005, - 3182.5000000000005, - 3233.5000000000005, - 3334.5000000000005, - 3428.5000000000005, - 3734.5000000000005, - 3862.0000000000005, - 4068.5000000000005, - 4221.5000000000009, - 4296.5000000000009, - 4701.5000000000009, - 4781.5000000000009, - 6990.5000000000009, - 7213.5000000000009, - 7449.5000000000009, - 8540.5000000000018, - 9118.5000000000018, - 9521.5000000000018, - 10539.500000000002, - 11717.500000000002, - 12095.500000000002, - 13029.500000000002, - 14400.000000000002, - 14615.500000000002, - 15096.500000000002, - 15817.500000000002, - 16830.500000000004, - 17346.500000000004, - 20007.000000000004, - 20988.500000000004, - 21532.500000000004, - 23313.500000000004, - 24562.500000000004, - 30402.000000000004, - 35983.500000000007, - 40040.000000000007, - 41987.500000000007, - 43540.000000000007, - 44215.500000000007, - 45601.500000000007, - 47232.500000000007, - 54221.000000000007, - 68471.500000000015, - 74712.500000000015, - 89913.000000000015, - 125519.00000000001, - 149049.00000000003, - 230573.00000000003, - 3.5000000000000004, - 4.5000000000000009, - 5.5000000000000009, - 6.5000000000000009, - 7.5000000000000009, - 8.5000000000000018, - 11.500000000000002, - 13.500000000000002, - 14.500000000000002, - 15.500000000000002, - 17.500000000000004, - 24.500000000000004, - 29.500000000000004, - 30.500000000000004, - 31.500000000000004, - 33.500000000000007, - 35.500000000000007, - 37.500000000000007, - 40.500000000000007, - 41.500000000000007, - 42.500000000000007, - 43.500000000000007, - 45.500000000000007, - 49.500000000000007, - 50.500000000000007, - 55.500000000000007, - 59.500000000000007, - 60.500000000000007, - 65.500000000000014, - 68.000000000000014, - 71.000000000000014, - 72.500000000000014, - 73.500000000000014, - 74.500000000000014, - 80.500000000000014, - 90.000000000000014, - 96.500000000000014, - 98.500000000000014, - 99.500000000000014, - 105.50000000000001, - 108.50000000000001, - 112.50000000000001, - 113.50000000000001, - 116.50000000000001, - 117.50000000000001, - 118.50000000000001, - 121.50000000000001, - 124.50000000000001, - 127.00000000000001, - 156.50000000000003, - 158.50000000000003, - 174.50000000000003, - 182.00000000000003, - 186.50000000000003, - 190.50000000000003, - 193.00000000000003, - 197.50000000000003, - 215.50000000000003, - 230.50000000000003, - 238.50000000000003, - 242.50000000000003, - 274.50000000000006, - 287.50000000000006, - 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1.9725750000000002, - 2.0513250000000007, - 2.0544200000000008, - 2.1066100000000003, - 2.2829500000000005, - 2.3961550000000003, - 2.6080850000000004, - 2.7410500000000004, - 3.0611950000000001, - 3.5134100000000008, - 3.5836800000000006, - 4.7010550000000011, - 4.7679900000000011, - 5.106815000000001, - 5.1937250000000015, - 5.2231500000000013, - 5.349705000000001, - 5.4792200000000006, - 5.6649900000000004, - 5.8800200000000009, - 6.6231050000000007, - 6.9449300000000003, - 7.3326800000000008, - 8.199600000000002, - 11.794000000000002, - 16.289250000000003, - 19.559350000000006, - 24.766300000000005, - 1.0000000180025095e-35, -}; - -static const int th_begin[] = { - 0, - 237, - 320, - 434, - 525, - 596, - 658, - 752, - 853, - 932, - 1013, - 1088, -}; - -static const int th_len[] = { - 237, - 83, - 114, - 91, - 71, - 62, - 94, - 101, - 79, - 81, - 75, - 1, -}; - -/* - * \brief Function to convert a feature value into bin index. - * \param val Feature value, in floating-point - * \param fid Feature identifier - * \return bin Index corresponding to given feature value - */ -int fj_predictor::quantize(double val, unsigned fid) -{ - const size_t offset = th_begin[fid]; - const double* array = &threshold[offset]; - int len = th_len[fid]; - int low = 0; - int high = len; - int mid; - double mval; - // It is possible th_begin[i] == [total_num_threshold]. This means that - // all features i, (i+1), ... are not used for any of the splits in the model. - // So in this case, just return something - if (offset == 1089 || val < array[0]) { return -10; } - while (low + 1 < high) { - mid = (low + high) / 2; - mval = array[mid]; - if (val == mval) { - return mid * 2; - } else if (val < mval) { - high = mid; - } else { - low = mid; - } - } - if (array[low] == val) { - return low * 2; - } else if (high == len) { - return len * 2; - } else { - return low * 2 + 1; - } -} diff --git a/cpp/src/utilities/models/pdlp_predictor/header.h b/cpp/src/utilities/models/pdlp_predictor/header.h deleted file mode 100644 index 49c9cfd0e..000000000 --- a/cpp/src/utilities/models/pdlp_predictor/header.h +++ /dev/null @@ -1,35 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#pragma once - -#include -#include -#include -#include -#include -#include - -class pdlp_predictor { - public: - union Entry { - int missing; - float fvalue; - int qvalue; - }; - - static int32_t get_num_target(void); - static void get_num_class(int32_t* out); - static int32_t get_num_feature(void); - static const char* get_threshold_type(void); - static const char* get_leaf_output_type(void); - static void predict(union Entry* data, int pred_margin, double* result); - static void postprocess(double* result); - static int quantize(float val, unsigned fid); - - // Feature names - static constexpr int NUM_FEATURES = 8; - static const char* feature_names[NUM_FEATURES]; -}; // class pdlp_predictor diff --git a/cpp/src/utilities/models/pdlp_predictor/main.cpp b/cpp/src/utilities/models/pdlp_predictor/main.cpp deleted file mode 100644 index d8b0b91d1..000000000 --- a/cpp/src/utilities/models/pdlp_predictor/main.cpp +++ /dev/null @@ -1,16893 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#include -#include "header.h" - -#if defined(__clang__) || defined(__GNUC__) -#define LIKELY(x) __builtin_expect(!!(x), 1) -#define UNLIKELY(x) __builtin_expect(!!(x), 0) -#else -#define LIKELY(x) (x) -#define UNLIKELY(x) (x) -#endif -#define N_TARGET 1 -#define MAX_N_CLASS 1 - -const unsigned char is_categorical[] = { - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, -}; -static const int32_t num_class[] = { - 1, -}; - -int32_t pdlp_predictor::get_num_target(void) { return N_TARGET; } -void pdlp_predictor::get_num_class(int32_t* out) -{ - for (int i = 0; i < N_TARGET; ++i) { - out[i] = num_class[i]; - } -} -int32_t pdlp_predictor::get_num_feature(void) { return 8; } -const char* pdlp_predictor::get_threshold_type(void) { return "float32"; } -const char* pdlp_predictor::get_leaf_output_type(void) { return "float32"; } - -void pdlp_predictor::predict(union Entry* data, int pred_margin, double* result) -{ - // Quantize data - for (int i = 0; i < 8; ++i) { - if (data[i].missing != -1 && !is_categorical[i]) { - data[i].qvalue = quantize(data[i].fvalue, i); - } - } - - unsigned int tmp; - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 30))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - result[0] += -0.014820111; - } else { - result[0] += 0.0105687855; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - result[0] += -0.053651925; - } else { - result[0] += -0.10176092; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 220))) { - result[0] += -0.012739944; - } else { - result[0] += -0.036262058; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { - result[0] += -0.0063560107; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 258))) { - result[0] += 0.08327574; - } else { - result[0] += 0.026058618; - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 230))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 164))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { - result[0] += 0.031961214; - } else { - result[0] += 0.04515064; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { - result[0] += 0.018566301; - } else { - result[0] += -0.009213644; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { - result[0] += 0.14206316; - } else { - result[0] += 0.07914438; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { - result[0] += 0.025240844; - } else { - result[0] += 0.058222026; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += 0.26381668; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - result[0] += 0.085295476; - } else { - result[0] += 0.16802387; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 258))) { - result[0] += 0.19548021; - } else { - result[0] += 0.14702512; - } - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += 0.0039215386; - } else { - result[0] += -0.020411594; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { - result[0] += -0.030691355; - } else { - result[0] += -0.066723615; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 182))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { - result[0] += -0.039679535; - } else { - result[0] += -0.07033275; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 226))) { - result[0] += -0.017522344; - } else { - result[0] += 0.024236064; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 76))) { - result[0] += -0.01896934; - } else { - result[0] += 0.005805307; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += -0.05819216; - } else { - result[0] += -0.030700704; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 210))) { - result[0] += -0.016364044; - } else { - result[0] += -0.0079360735; - } - } else { - result[0] += 0.09915119; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 260))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { - result[0] += 0.0082174195; - } else { - result[0] += -0.0037611835; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { - result[0] += 0.05758765; - } else { - result[0] += 0.00845852; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 188))) { - result[0] += 0.08059614; - } else { - result[0] += 0.060384076; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 62))) { - result[0] += -0; - } else { - result[0] += 0.07759698; - } - } else { - result[0] += 0.12749861; - } - } else { - result[0] += 0.16187607; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 22))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - result[0] += -0.0052639237; - } else { - result[0] += 0.022940176; - } - } else { - result[0] += -0.03480838; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { - result[0] += -0.04990984; - } else { - result[0] += -0.080560416; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - result[0] += -0.014295327; - } else { - result[0] += -0.03235691; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { - result[0] += -0; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 258))) { - result[0] += 0.07372898; - } else { - result[0] += 0.024104217; - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 230))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 176))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 232))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { - result[0] += 0.02912726; - } else { - result[0] += 0.04095058; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += 0.016825125; - } else { - result[0] += -0.007768286; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { - result[0] += 0.1265556; - } else { - result[0] += 0.07124209; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - result[0] += 0.040830098; - } else { - result[0] += 0.0559672; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += 0.23753951; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - result[0] += 0.07685151; - } else { - result[0] += 0.1544579; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 260))) { - result[0] += 0.046362683; - } else { - result[0] += 0.17204122; - } - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { - result[0] += -0.022617795; - } else { - result[0] += -0.0122455275; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { - result[0] += -0.027298182; - } else { - result[0] += -0.059523176; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 182))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 248))) { - result[0] += -0.035259727; - } else { - result[0] += -0.061327398; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 236))) { - result[0] += -0.01425799; - } else { - result[0] += 0.017331526; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { - result[0] += 0.00193814; - } else { - result[0] += -0.017574918; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 196))) { - result[0] += -0.02869076; - } else { - result[0] += -0.065549426; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - result[0] += 0.0057360423; - } else { - result[0] += -0.013489055; - } - } else { - result[0] += 0.089799576; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 260))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { - result[0] += 0.007112731; - } else { - result[0] += -0.0027204938; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { - result[0] += 0.054216303; - } else { - result[0] += 0.007258077; - } - } - } else { - result[0] += 0.059579976; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 152))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 62))) { - result[0] += 0.0030776516; - } else { - result[0] += 0.05093806; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { - result[0] += 0.07593134; - } else { - result[0] += 0.11205458; - } - } - } else { - result[0] += 0.14210285; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 32))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - result[0] += -0.014897979; - } else { - result[0] += 0.003548234; - } - } else { - result[0] += 0.018050188; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 88))) { - result[0] += -0.008003016; - } else { - result[0] += -0.029365538; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - result[0] += -0.008477488; - } else { - result[0] += -0.05125452; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { - result[0] += 0.02146909; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 8))) { - result[0] += -0.038308386; - } else { - result[0] += 0.0050441376; - } - } - } else { - result[0] += 0.045694016; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - result[0] += 0.010466478; - } else { - result[0] += 0.032060686; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - result[0] += 0.055632647; - } else { - result[0] += 0.10817957; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 244))) { - result[0] += 0.05290089; - } else { - result[0] += 0.15234004; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += 0.11994175; - } else { - result[0] += 0.16568963; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += 0.21408843; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { - result[0] += 0.109505884; - } else { - result[0] += 0.16057345; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += -0.021672187; - } else { - result[0] += -0.05343589; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += 0.0054487307; - } else { - result[0] += -0.017187914; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 92))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 188))) { - result[0] += -0.019816956; - } else { - result[0] += -0.0050099557; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 248))) { - result[0] += -0.03200326; - } else { - result[0] += -0.056725156; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - result[0] += -0.011511999; - } else { - result[0] += -0.028344637; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += 0.024851266; - } else { - result[0] += -0.05057622; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { - result[0] += -0.0015090223; - } else { - result[0] += 0.04174643; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 206))) { - result[0] += -0.014773312; - } else { - result[0] += -0.001539268; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 210))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - result[0] += 0.007884377; - } else { - result[0] += 0.061007004; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - result[0] += 0.021275358; - } else { - result[0] += 0.004744153; - } - } - } else { - result[0] += 0.053870168; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - result[0] += 0.00918735; - } else { - result[0] += 0.06173278; - } - } else { - result[0] += 0.098605655; - } - } else { - result[0] += 0.13258949; - } - } - } - } - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 266))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { - result[0] += 0.0034634469; - } else { - result[0] += -0.01012047; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 202))) { - result[0] += 0.026168926; - } else { - result[0] += -0.003920808; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 34))) { - result[0] += 0.01599789; - } else { - result[0] += -0.02167696; - } - } else { - result[0] += -0.057240784; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 180))) { - result[0] += 0.016893527; - } else { - result[0] += -0.0182766; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { - result[0] += -0.027289895; - } else { - result[0] += -0.0045931367; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 236))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - result[0] += 0.06542163; - } else { - result[0] += 0.048862282; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 248))) { - result[0] += 0.012741444; - } else { - result[0] += -0.0075932243; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 250))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 64))) { - result[0] += 0.090353966; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 260))) { - result[0] += 0.042119607; - } else { - result[0] += 0.053020816; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 46))) { - result[0] += 0.03130157; - } else { - result[0] += 0.007205482; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { - result[0] += 0.10881876; - } else { - result[0] += 0.054842055; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - result[0] += 0.031206427; - } else { - result[0] += 0.093665555; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { - result[0] += 0.058885314; - } else { - result[0] += 0.009160067; - } - } - } else { - result[0] += 0.12904838; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 196))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 132))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 126))) { - result[0] += 0.009280808; - } else { - result[0] += 0.039755784; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 184))) { - result[0] += -0.0042881714; - } else { - result[0] += 0.02849459; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 176))) { - result[0] += 0.013534146; - } else { - result[0] += 0.042829912; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 196))) { - result[0] += 0.060934413; - } else { - result[0] += 0.04222716; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 166))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 252))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 242))) { - result[0] += -0.014791851; - } else { - result[0] += 0.03050575; - } - } else { - result[0] += -0.051454216; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 244))) { - result[0] += -0.00703993; - } else { - result[0] += 0.006275458; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 230))) { - result[0] += 0.05098787; - } else { - result[0] += 0.0045872494; - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 236))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 156))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 204))) { - result[0] += 0.010856108; - } else { - result[0] += -0.028078709; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 196))) { - result[0] += 0.059345823; - } else { - result[0] += 0.025444312; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 246))) { - result[0] += 0.1018459; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 226))) { - result[0] += 0.06307183; - } else { - result[0] += 0.049246445; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 192))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 248))) { - result[0] += -0.0057842466; - } else { - result[0] += 0.03709874; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 272))) { - result[0] += 0.10474528; - } else { - result[0] += 0.0065343017; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { - result[0] += 0.14898759; - } else { - result[0] += 0.09902468; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 204))) { - result[0] += -0.020444617; - } else { - result[0] += 0.08464522; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - result[0] += -0.010948095; - } else { - result[0] += -0.023803001; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - result[0] += -0.011822949; - } else { - result[0] += 0.045119066; - } - } - } else { - result[0] += -0.06180344; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { - result[0] += -0.005668474; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 258))) { - result[0] += 0.06234616; - } else { - result[0] += 0.017185496; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 178))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 100))) { - result[0] += 0.024365185; - } else { - result[0] += 0.034411617; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - result[0] += 0.0037991663; - } else { - result[0] += 0.06540612; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - result[0] += 0.04412658; - } else { - result[0] += 0.09097751; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { - result[0] += 0.109255195; - } else { - result[0] += 0.15402773; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += 0.18228906; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { - result[0] += 0.091413066; - } else { - result[0] += 0.12995003; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 66))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - result[0] += -0.013535394; - } else { - result[0] += -0.030926574; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - result[0] += -0.02790393; - } else { - result[0] += -0.0042497306; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - result[0] += -0.016862048; - } else { - result[0] += -0.042121567; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - result[0] += 0.043057855; - } else { - result[0] += -0.010520599; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 268))) { - result[0] += 0.0006321628; - } else { - result[0] += 0.043750294; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { - result[0] += 0.017152889; - } else { - result[0] += 0.0047487523; - } - } - } else { - result[0] += 0.047594197; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - result[0] += -0.0008511189; - } else { - result[0] += -0.015743343; - } - } else { - result[0] += 0.020423314; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { - result[0] += 0.071650326; - } else { - result[0] += 0.038795434; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.106261484; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { - result[0] += 0.05060423; - } else { - result[0] += 0.09152374; - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 32))) { - result[0] += -0.0061984994; - } else { - result[0] += 0.037181865; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { - result[0] += 0.05393646; - } else { - result[0] += 0.015753375; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 134))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - result[0] += -0.021209892; - } else { - result[0] += 0.0069300993; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { - result[0] += -0.0077880546; - } else { - result[0] += -0.04197134; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 12))) { - result[0] += 0.040751807; - } else { - result[0] += -0.002328172; - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 254))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 198))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { - result[0] += 0.011177325; - } else { - result[0] += -0.0057386267; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 100))) { - result[0] += 0.022035722; - } else { - result[0] += 0.031592578; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 154))) { - result[0] += 0.046259467; - } else { - result[0] += 0.07964405; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.037900005; - } else { - result[0] += 0.006691271; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - result[0] += 0.045320172; - } else { - result[0] += 0.08683389; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - result[0] += 0.112302594; - } else { - result[0] += 0.15148906; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 218))) { - result[0] += 0.09804384; - } else { - result[0] += 0.15994717; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += -0.014513046; - } else { - result[0] += -0.04194841; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 60))) { - result[0] += -0.018427247; - } else { - result[0] += -0.009509578; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 182))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { - result[0] += -0.021712733; - } else { - result[0] += -0.033654116; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { - result[0] += 0.017070105; - } else { - result[0] += -0.010606452; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { - result[0] += 0.003849056; - } else { - result[0] += -0.012776096; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += -0.01715595; - } else { - result[0] += -0.034953322; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - result[0] += 0.004928622; - } else { - result[0] += -0.009454594; - } - } else { - result[0] += 0.06586691; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - result[0] += 0.00017961742; - } else { - result[0] += -0.0068662055; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += 0.034061044; - } else { - result[0] += 0.004285582; - } - } - } else { - result[0] += 0.041407876; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - result[0] += 0.0008129136; - } else { - result[0] += 0.046276525; - } - } else { - result[0] += 0.08599146; - } - } else { - result[0] += 0.092344016; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 6))) { - result[0] += 0.0022713204; - } else { - result[0] += -0.01581463; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - result[0] += -0.019155947; - } else { - result[0] += -0.031192351; - } - } - } else { - result[0] += -0.054840814; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += 0.034809362; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - result[0] += -0.03447778; - } else { - result[0] += -0.0010464619; - } - } - } else { - result[0] += 0.036184475; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 178))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { - result[0] += 0.011472473; - } else { - result[0] += -0.00536993; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { - result[0] += 0.017885301; - } else { - result[0] += 0.026684735; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 36))) { - result[0] += 0.050483853; - } else { - result[0] += 0.032498028; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { - result[0] += 0.107258014; - } else { - result[0] += 0.05922347; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += 0.14826694; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { - result[0] += 0.07493075; - } else { - result[0] += 0.105560794; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { - result[0] += -0.024180638; - } else { - result[0] += -0.010499556; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 218))) { - result[0] += 0.016562223; - } else { - result[0] += -0.013134924; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { - result[0] += -0.018523889; - } else { - result[0] += -0.030611722; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += -0.017535986; - } else { - result[0] += -0.004351136; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { - result[0] += -0.021926327; - } else { - result[0] += -0.008350995; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 42))) { - result[0] += -0.011140397; - } else { - result[0] += -0.04280148; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - result[0] += 0.03963171; - } else { - result[0] += 0.019383498; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 214))) { - result[0] += -0.00729556; - } else { - result[0] += -0.040553037; - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.08876854; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.067758165; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { - result[0] += 0.03981258; - } else { - result[0] += 0.05752442; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { - result[0] += -0.00089354016; - } else { - result[0] += 0.0065446827; - } - } else { - result[0] += -0.014053066; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 222))) { - result[0] += 0.06474081; - } else { - result[0] += 0.017832294; - } - } - } - } - } - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 206))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 202))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 100))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 30))) { - result[0] += 0.0055920025; - } else { - result[0] += -0.013393702; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { - result[0] += 0.03213504; - } else { - result[0] += 0.004373153; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { - result[0] += -0.026502002; - } else { - result[0] += -0.0089536635; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 108))) { - result[0] += 0.023841746; - } else { - result[0] += -0.009630135; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 132))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 84))) { - result[0] += 0.03338425; - } else { - result[0] += -0.007993841; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 98))) { - result[0] += 0.039039984; - } else { - result[0] += 0.017829265; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 122))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - result[0] += -0.008180471; - } else { - result[0] += -0.017685762; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 172))) { - result[0] += 0.024588756; - } else { - result[0] += 0.00014103504; - } - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 120))) { - result[0] += -0.017513642; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 228))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 204))) { - result[0] += 0.053347778; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { - result[0] += 0.036463372; - } else { - result[0] += 0.019543491; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - result[0] += 0.06796604; - } else { - result[0] += 0.011818393; - } - } else { - result[0] += -0.003918262; - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 260))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 252))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 224))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 108))) { - result[0] += 0.055681136; - } else { - result[0] += 0.03390036; - } - } else { - result[0] += 0.014793222; - } - } else { - result[0] += 0.06464329; - } - } else { - result[0] += 0.1336614; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 214))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 200))) { - result[0] += -0.010694483; - } else { - result[0] += 0.0089774085; - } - } else { - result[0] += 0.09665075; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 214))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { - result[0] += 0.005944577; - } else { - result[0] += 0.039748937; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { - result[0] += -0.036054015; - } else { - result[0] += -0.03007635; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { - result[0] += 0.07873906; - } else { - result[0] += 0.040747557; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 280))) { - result[0] += 0.10715278; - } else { - result[0] += 0.07093804; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 272))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 228))) { - result[0] += 0.0400591; - } else { - result[0] += 0.070340686; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { - result[0] += 0.006239193; - } else { - result[0] += 0.04050245; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 20))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - result[0] += 0.0143945515; - } else { - result[0] += -0.01724617; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 124))) { - result[0] += 0.06920057; - } else { - result[0] += 0.012906248; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { - result[0] += -0.028011957; - } else { - result[0] += -0.054441918; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 222))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += -0; - } else { - result[0] += -0.015584071; - } - } else { - result[0] += 0.04294205; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 176))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - result[0] += 0.0010215238; - } else { - result[0] += 0.018780788; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 36))) { - result[0] += 0.038763847; - } else { - result[0] += 0.023370415; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 224))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - result[0] += 0.032697503; - } else { - result[0] += 0.13195097; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += 0.074951015; - } else { - result[0] += 0.10605689; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += 0.12042389; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 6))) { - result[0] += 0.07597848; - } else { - result[0] += 0.059126675; - } - } else { - result[0] += 0.087247975; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - result[0] += -0.011189851; - } else { - result[0] += -0.023633793; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - result[0] += -0.009847272; - } else { - result[0] += 0.0011721178; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { - result[0] += -0.033823837; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - result[0] += -0.028593678; - } else { - result[0] += -0.023168823; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { - result[0] += 0.048958402; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 226))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { - result[0] += -0.0030530605; - } else { - result[0] += 0.009326304; - } - } else { - result[0] += 0.015771423; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - result[0] += -0.019048717; - } else { - result[0] += -0.00045981476; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - result[0] += 0.036297392; - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { - result[0] += 0.004649827; - } else { - result[0] += 0.024824895; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { - result[0] += 0.0042260056; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - result[0] += 0.035964742; - } else { - result[0] += 0.05283689; - } - } - } else { - result[0] += 0.072465; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - result[0] += -0.012068967; - } else { - result[0] += -0.027094675; - } - } else { - result[0] += -0.066233106; - } - } else { - result[0] += 0.025606213; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { - result[0] += 0.0134207625; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - result[0] += -0.051151592; - } else { - result[0] += -0.013505876; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - result[0] += 0.033789955; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 226))) { - result[0] += -0.0063351872; - } else { - result[0] += 0.01952872; - } - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 232))) { - result[0] += 0.017679365; - } else { - result[0] += 0.0012839021; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 188))) { - result[0] += 0.01832609; - } else { - result[0] += 0.031185871; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { - result[0] += 0.07488849; - } else { - result[0] += 0.046187967; - } - } else { - result[0] += 0.09955382; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { - result[0] += 0.069774024; - } else { - result[0] += 0.10838503; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { - result[0] += 0.05327609; - } else { - result[0] += 0.077984296; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - result[0] += 0.0047698705; - } else { - result[0] += 0.017100412; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - result[0] += -0.0088116955; - } else { - result[0] += 0.0046756053; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 46))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 138))) { - result[0] += -0.021546138; - } else { - result[0] += -0.06706627; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { - result[0] += 0.0009891371; - } else { - result[0] += -0.015693253; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 76))) { - result[0] += -0.033967104; - } else { - result[0] += -0.010871164; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - result[0] += -0.01983557; - } else { - result[0] += -0.033842903; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += 3.485963e-05; - } else { - result[0] += 0.029934336; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { - result[0] += -0.009020819; - } else { - result[0] += -0.0016374525; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { - result[0] += -0.00035558367; - } else { - result[0] += 0.031120656; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { - result[0] += 0.0042784135; - } else { - result[0] += -0.003890027; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - result[0] += 0.03780892; - } else { - result[0] += 0.020871228; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { - result[0] += 0.032693; - } else { - result[0] += 0.016958345; - } - } else { - result[0] += 0.056264985; - } - } else { - result[0] += 0.061108418; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - result[0] += 0.016505312; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 58))) { - result[0] += -0.012738267; - } else { - result[0] += -0.020164208; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - result[0] += 0.0137371225; - } else { - result[0] += -0.010237254; - } - } - } else { - result[0] += -0.037308313; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 112))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - result[0] += 0.0016659134; - } else { - result[0] += 0.022526134; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { - result[0] += 0.02430934; - } else { - result[0] += 0.010316737; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - result[0] += 0.03054412; - } else { - result[0] += 0.013778204; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 218))) { - result[0] += 0.062106986; - } else { - result[0] += 0.08695674; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += 0.097699985; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { - result[0] += 0.048410773; - } else { - result[0] += 0.061695654; - } - } else { - result[0] += 0.0699871; - } - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { - result[0] += 0.017755916; - } else { - result[0] += -0.0076434105; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 80))) { - result[0] += -0.013907449; - } else { - result[0] += -0.028985953; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += -0.03517434; - } else { - result[0] += 0.02803059; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - result[0] += -0.005040298; - } else { - result[0] += 0.029063394; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 18))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 252))) { - result[0] += -0.0004860425; - } else { - result[0] += -0.00823578; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - result[0] += 0.020955933; - } else { - result[0] += -0.005362306; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - result[0] += -0.022735301; - } else { - result[0] += -0.006933318; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { - result[0] += 0.008230773; - } else { - result[0] += -0.011880973; - } - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 232))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 34))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 192))) { - result[0] += 0.00536417; - } else { - result[0] += -0.020807503; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { - result[0] += 0.029748295; - } else { - result[0] += 0.041834693; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 196))) { - result[0] += -0.00087343634; - } else { - result[0] += -0.026689067; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 182))) { - result[0] += -0.0014600045; - } else { - result[0] += 0.013319497; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 256))) { - result[0] += 0.0012430011; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { - result[0] += -0.028792778; - } else { - result[0] += -0.024853468; - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 4))) { - result[0] += -0.009469046; - } else { - result[0] += 0.0108768735; - } - } else { - result[0] += -0.025166774; - } - } else { - result[0] += -0.061156314; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { - result[0] += -0.011232008; - } else { - result[0] += -0.025154117; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - result[0] += 0.008252529; - } else { - result[0] += 0.046288077; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 6))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 220))) { - result[0] += 0.040085025; - } else { - result[0] += -0.008784636; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - result[0] += -0.019954626; - } else { - result[0] += 0.0048681814; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 178))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += 0.007502618; - } else { - result[0] += -0.0053296923; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { - result[0] += 0.013098051; - } else { - result[0] += 0.022515437; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 222))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 234))) { - result[0] += 0.02392622; - } else { - result[0] += 0.006717711; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 92))) { - result[0] += 0.036931966; - } else { - result[0] += 0.024680862; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 192))) { - result[0] += 0.049618512; - } else { - result[0] += 0.030690536; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 248))) { - result[0] += 0.087034844; - } else { - result[0] += 0.06434413; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.076431125; - } else { - result[0] += 0.050094455; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.08487356; - } else { - result[0] += 0.016335858; - } - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { - result[0] += 0.0068731843; - } else { - result[0] += -0.027962524; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 30))) { - result[0] += -0.005616961; - } else { - result[0] += -0.020151896; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 160))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - result[0] += -0.011980721; - } else { - result[0] += -0.022607153; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { - result[0] += 0.045720205; - } else { - result[0] += 0.011621847; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 58))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - result[0] += 0.02517539; - } else { - result[0] += -0.0003533575; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { - result[0] += 0.00938051; - } else { - result[0] += -0.011884357; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += -0.0057867505; - } else { - result[0] += -0.025664538; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { - result[0] += -0.008385401; - } else { - result[0] += -0.0017240072; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 266))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - result[0] += -0.000107964406; - } else { - result[0] += 0.0060515343; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 244))) { - result[0] += -0.005519016; - } else { - result[0] += 0.0014766084; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.02811285; - } else { - result[0] += 0.013522627; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 152))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { - result[0] += -0; - } else { - result[0] += 0.015318493; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { - result[0] += 0.02693863; - } else { - result[0] += 0.042594276; - } - } - } else { - result[0] += 0.05915534; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - result[0] += -0.015769325; - } else { - result[0] += 0.009167842; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - result[0] += -0.066482425; - } else { - result[0] += -0.029134443; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 58))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { - result[0] += -0.010345658; - } else { - result[0] += -0.0004427325; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - result[0] += 0.00778659; - } else { - result[0] += -0.052467596; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += 0.025486384; - } else { - result[0] += 0.011895447; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - result[0] += 0.022794057; - } else { - result[0] += 0.048023786; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - result[0] += 0.024896143; - } else { - result[0] += 0.010721759; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += 0.04730309; - } else { - result[0] += 0.07061689; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += 0.079474784; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 6))) { - result[0] += 0.050025214; - } else { - result[0] += 0.03944496; - } - } else { - result[0] += 0.05712953; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 54))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += 0.0070698797; - } else { - result[0] += -0.009966139; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - result[0] += 0.015061869; - } else { - result[0] += -0.0023575744; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { - result[0] += -0.021192541; - } else { - result[0] += -0.00409846; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 156))) { - result[0] += -0.01823308; - } else { - result[0] += 0.00014080759; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 206))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 100))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - result[0] += 0.02206879; - } else { - result[0] += -0.00015570642; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - result[0] += -0.015682366; - } else { - result[0] += -0.006868744; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { - result[0] += 0.0135331275; - } else { - result[0] += -0.023557609; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { - result[0] += 0.04654594; - } else { - result[0] += 0.0058658714; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - result[0] += -0.016888324; - } else { - result[0] += -0.00063805765; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { - result[0] += 0.029826907; - } else { - result[0] += 0.007284993; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { - result[0] += 0.001150948; - } else { - result[0] += 0.0069423555; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 152))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - result[0] += -0.0042433054; - } else { - result[0] += 0.011705535; - } - } else { - result[0] += 0.025982574; - } - } else { - result[0] += 0.048386928; - } - } - } - } - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 206))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += -0.042859327; - } else { - result[0] += -0.0044231373; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { - result[0] += 0.049170107; - } else { - result[0] += 0.035520416; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { - result[0] += -0.0009743745; - } else { - result[0] += 0.009931285; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { - result[0] += -0.0017889539; - } else { - result[0] += -0.007930504; - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 226))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 204))) { - result[0] += 0.0063184327; - } else { - result[0] += -0.015213072; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - result[0] += 0.014672543; - } else { - result[0] += 0.026392935; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 154))) { - result[0] += -0.037449803; - } else { - result[0] += -0.053623743; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { - result[0] += 0.002925502; - } else { - result[0] += -0.010573565; - } - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 264))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 152))) { - result[0] += 0.00568271; - } else { - result[0] += -0.004481957; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 212))) { - result[0] += 0.053150166; - } else { - result[0] += 0.014763187; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 194))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 80))) { - result[0] += -0.008710187; - } else { - result[0] += 0.075820915; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - result[0] += -0.023372917; - } else { - result[0] += -0.012099934; - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 204))) { - result[0] += 0.037964396; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 206))) { - result[0] += 0.0036865615; - } else { - result[0] += 0.021090202; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { - result[0] += -0.025623156; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 200))) { - result[0] += -0.004627872; - } else { - result[0] += 0.004686881; - } - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 262))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 248))) { - result[0] += 0.0714253; - } else { - result[0] += 0.048554998; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 232))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 230))) { - result[0] += 0.02269533; - } else { - result[0] += 0.014301536; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 220))) { - result[0] += 0.0025220858; - } else { - result[0] += 0.011460328; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 56))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - result[0] += 0.019217266; - } else { - result[0] += 0.03249597; - } - } else { - result[0] += 0.035739582; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 248))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { - result[0] += -0.023269765; - } else { - result[0] += -0.01868113; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 180))) { - result[0] += -0.0065037594; - } else { - result[0] += -0.022253536; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { - result[0] += 0.0036995602; - } else { - result[0] += 0.0520568; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 146))) { - result[0] += 0.02432552; - } else { - result[0] += -0.01594592; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { - result[0] += 0.0497043; - } else { - result[0] += 0.022733098; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.055147793; - } else { - result[0] += 0.041854013; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 166))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - result[0] += 0.0019326921; - } else { - result[0] += -0.013451728; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 280))) { - result[0] += 0.029732933; - } else { - result[0] += 0.009648888; - } - } - } - } - } - } - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 206))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += -0.002109048; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 92))) { - result[0] += 0.04531033; - } else { - result[0] += 0.028058738; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { - result[0] += -0.0014130161; - } else { - result[0] += 0.009621711; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { - result[0] += -0.0014670437; - } else { - result[0] += -0.007695043; - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 226))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 156))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 192))) { - result[0] += 0.007604044; - } else { - result[0] += -0.0070991814; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 220))) { - result[0] += 0.020077487; - } else { - result[0] += 0.006828181; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 154))) { - result[0] += -0.03365531; - } else { - result[0] += -0.049779564; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { - result[0] += 0.0017921542; - } else { - result[0] += -0.009079668; - } - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 260))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 152))) { - result[0] += 0.005504231; - } else { - result[0] += -0.0039019831; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 212))) { - result[0] += 0.044337142; - } else { - result[0] += 0.0070573166; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 194))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { - result[0] += 0.028603448; - } else { - result[0] += -0.0078216465; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { - result[0] += 0.015729161; - } else { - result[0] += -0.016172249; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 204))) { - result[0] += 0.03005304; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { - result[0] += 0.016138185; - } else { - result[0] += 0.033333454; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { - result[0] += -0.02334422; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - result[0] += 0.0137236165; - } else { - result[0] += 0.003765919; - } - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 260))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += 0.06424896; - } else { - result[0] += 0.045115866; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 232))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { - result[0] += 0.020254442; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { - result[0] += 0.003927156; - } else { - result[0] += -0.00030379518; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 56))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - result[0] += 0.01626153; - } else { - result[0] += 0.030240763; - } - } else { - result[0] += 0.031179471; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { - result[0] += -0.019889602; - } else { - result[0] += -0.006115454; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { - result[0] += 0.02490984; - } else { - result[0] += 0.0096712075; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { - result[0] += -0.016214207; - } else { - result[0] += 0.0054753; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 162))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.04828751; - } else { - result[0] += 0.02183148; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { - result[0] += 0.095508024; - } else { - result[0] += 0.037357938; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { - result[0] += 0.024429439; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += -0.032644305; - } else { - result[0] += -0.007228152; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 92))) { - result[0] += -0; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - result[0] += -0.0005319937; - } else { - result[0] += 0.04273183; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += -0; - } else { - result[0] += -0.015805844; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 220))) { - result[0] += 0.028671984; - } else { - result[0] += -0; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { - result[0] += -0.015004277; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { - result[0] += -0.043184254; - } else { - result[0] += -0.015434514; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { - result[0] += 0.027698422; - } else { - result[0] += 0.019098751; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { - result[0] += 0.0024283626; - } else { - result[0] += 0.019441115; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 58))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += 0.07004071; - } else { - result[0] += 0.022275856; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { - result[0] += -0.0013316347; - } else { - result[0] += 0.012960051; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 224))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.06203411; - } else { - result[0] += 0.035423633; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.057469275; - } else { - result[0] += 0.009981114; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 212))) { - result[0] += 0.023559755; - } else { - result[0] += 0.035965245; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { - result[0] += 0.058553632; - } else { - result[0] += 0.04478031; - } - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { - result[0] += 0.01235016; - } else { - result[0] += -0.0039201183; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 80))) { - result[0] += -0.007764698; - } else { - result[0] += -0.019835753; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += -0.006487711; - } else { - result[0] += 0.018991722; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - result[0] += -0.0039943634; - } else { - result[0] += 0.024546368; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 154))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { - result[0] += -0.0147830695; - } else { - result[0] += -0.008563632; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { - result[0] += -0.030682612; - } else { - result[0] += 0.017968642; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 166))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { - result[0] += 0.068739615; - } else { - result[0] += 0.00021970407; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - result[0] += -0.0028976346; - } else { - result[0] += -0.009871369; - } - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 236))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { - result[0] += -0.021564588; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { - result[0] += 0.0040140515; - } else { - result[0] += 0.016122099; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 210))) { - result[0] += -0.0033681232; - } else { - result[0] += 0.0073165386; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { - result[0] += -0.025544493; - } else { - result[0] += -0.01873306; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 230))) { - result[0] += -0.019029168; - } else { - result[0] += 0.004258202; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 254))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { - result[0] += -0.00011749373; - } else { - result[0] += -0.010187042; - } - } else { - result[0] += 0.037634984; - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { - result[0] += -0.0064992644; - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 146))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 144))) { - result[0] += 0.022600017; - } else { - result[0] += -0.0016712642; - } - } else { - result[0] += 0.025729222; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 20))) { - result[0] += -0.007915151; - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - result[0] += 0.0005776281; - } else { - result[0] += 0.031780552; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { - result[0] += -0.017479556; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 178))) { - result[0] += -0.04499294; - } else { - result[0] += -0.01988611; - } - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { - result[0] += 0.008722408; - } else { - result[0] += 0.032495603; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += -0.0015038172; - } else { - result[0] += 0.009706586; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.08136292; - } else { - result[0] += 0.015997507; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { - result[0] += 0.03901923; - } else { - result[0] += 0.0564677; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - result[0] += 0.0416153; - } else { - result[0] += 0.026928617; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { - result[0] += 0.041233912; - } else { - result[0] += 0.05209862; - } - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { - result[0] += 0.00972145; - } else { - result[0] += 0.04575323; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { - result[0] += 0.02476694; - } else { - result[0] += -0.03629762; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.021036565; - } else { - result[0] += -0.045760524; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 224))) { - result[0] += 0.007162188; - } else { - result[0] += -0.018060943; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 22))) { - result[0] += -0.007815191; - } else { - result[0] += -0.017675815; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { - result[0] += -0.0029981257; - } else { - result[0] += 0.020084158; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { - result[0] += 0.02050746; - } else { - result[0] += -0.0026271623; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - result[0] += -0.011671978; - } else { - result[0] += -0.0055001257; - } - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 228))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { - result[0] += -0.019566214; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { - result[0] += 0.0054924428; - } else { - result[0] += -0.00093082106; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - result[0] += 0.0126124; - } else { - result[0] += 0.032813326; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 272))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - result[0] += -0.023916144; - } else { - result[0] += -0.017258545; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += 0.05414641; - } else { - result[0] += 0.000113278526; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 254))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { - result[0] += -0.00055813155; - } else { - result[0] += -0.008351603; - } - } else { - result[0] += 0.032734703; - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - result[0] += -0.0139853405; - } else { - result[0] += 0.003684853; - } - } else { - result[0] += 0.008061817; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { - result[0] += -0.06050358; - } else { - result[0] += -0.0023646136; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += 0.028764948; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += -0.028082406; - } else { - result[0] += -0.0058664638; - } - } else { - result[0] += 0.023923343; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 170))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - result[0] += 0.009201097; - } else { - result[0] += -0.010257622; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - result[0] += 0.012351765; - } else { - result[0] += 0.08077949; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { - result[0] += 0.037493166; - } else { - result[0] += 0.052167382; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { - result[0] += 0.036415916; - } else { - result[0] += 0.046912543; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { - result[0] += 0.023727143; - } else { - result[0] += 0.036457557; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { - result[0] += -0.002685583; - } else { - result[0] += -0.006960124; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { - result[0] += 0.04494014; - } else { - result[0] += 0.0043056607; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 220))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - result[0] += -0.0039729676; - } else { - result[0] += -0.014122757; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 178))) { - result[0] += -0.025312701; - } else { - result[0] += -0.014867464; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - result[0] += 0.015924158; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 190))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 184))) { - result[0] += -0.0075506503; - } else { - result[0] += 0.010475333; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - result[0] += -0.027276382; - } else { - result[0] += -0.004030099; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 98))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { - result[0] += 0.014477782; - } else { - result[0] += -0.015422763; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { - result[0] += 0.03197563; - } else { - result[0] += 0.020037709; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { - result[0] += -0.019650823; - } else { - result[0] += 0.002236197; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 150))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { - result[0] += -0.02055483; - } else { - result[0] += -0.0014581191; - } - } else { - result[0] += -0.016465666; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 242))) { - result[0] += -0.05338929; - } else { - result[0] += -0.014811342; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { - result[0] += 0.01168551; - } else { - result[0] += 0.0010410798; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - result[0] += -0.005663588; - } else { - result[0] += 0.012237726; - } - } else { - result[0] += -0.015190408; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { - result[0] += -0.048400734; - } else { - result[0] += -0.01567449; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { - result[0] += 0.034811612; - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 166))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - result[0] += -0.005241947; - } else { - result[0] += 0.0036905694; - } - } else { - result[0] += 0.027789101; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 172))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 168))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - result[0] += -0.0023323249; - } else { - result[0] += 0.008576729; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - result[0] += -0.02920859; - } else { - result[0] += -0.005879428; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 236))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { - result[0] += 0.01825313; - } else { - result[0] += 0.01146; - } - } else { - result[0] += -0.0015520846; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - result[0] += 0.050887883; - } else { - result[0] += 0.0008823246; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { - result[0] += 0.04027082; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { - result[0] += 0.02058533; - } else { - result[0] += 0.039607793; - } - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 28))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += 0.0065161586; - } else { - result[0] += -0.007997109; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { - result[0] += 0.005459733; - } else { - result[0] += -0.0032214918; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { - result[0] += -0.015889943; - } else { - result[0] += -0.054882165; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 160))) { - result[0] += 0.013258442; - } else { - result[0] += -0.008985414; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { - result[0] += 0.03941143; - } else { - result[0] += 0.01124495; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 100))) { - result[0] += -0.01534603; - } else { - result[0] += 0.0053413953; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 236))) { - result[0] += -0.017410088; - } else { - result[0] += 0.013132173; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { - result[0] += -0.0060942764; - } else { - result[0] += -0.0017843047; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 222))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { - result[0] += 0.035340164; - } else { - result[0] += 0.0032424498; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.02138096; - } else { - result[0] += 0.011583105; - } - } - } else { - result[0] += -0.0026775673; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 152))) { - result[0] += 0.0032441877; - } else { - result[0] += 0.013359073; - } - } else { - result[0] += 0.030153606; - } - } else { - result[0] += 0.036456186; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - result[0] += -0.0117252795; - } else { - result[0] += 0.0043645753; - } - } else { - result[0] += -0.041562933; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - result[0] += 0.0008401942; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - result[0] += -0.01901227; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { - result[0] += 0.014339037; - } else { - result[0] += -0.00501071; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 16))) { - result[0] += 0.012573126; - } else { - result[0] += 0.0028887743; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 38))) { - result[0] += 0.046177443; - } else { - result[0] += 0.015051349; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { - result[0] += 0.010199251; - } else { - result[0] += -0.039779864; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { - result[0] += -0.004799234; - } else { - result[0] += 0.008129753; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += 0.04275175; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { - result[0] += -0.008011124; - } else { - result[0] += 0.002239405; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 268))) { - result[0] += 0.017378718; - } else { - result[0] += 0.03415826; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 218))) { - result[0] += 0.02640838; - } else { - result[0] += 0.039306883; - } - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += 0.0055097975; - } else { - result[0] += -0.007307568; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - result[0] += 0.0022932815; - } else { - result[0] += -0.0064788023; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { - result[0] += -0.014033759; - } else { - result[0] += -0.049122956; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { - result[0] += 0.005159875; - } else { - result[0] += -0.009883177; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { - result[0] += -0.013826482; - } else { - result[0] += 0.022371221; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { - result[0] += -0.0141291395; - } else { - result[0] += 0.004760613; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 132))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { - result[0] += -0.007064409; - } else { - result[0] += 0.023824794; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - result[0] += -0.00016228954; - } else { - result[0] += -0.007653934; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 222))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 208))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { - result[0] += 0.004959669; - } else { - result[0] += -0.001015551; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { - result[0] += 0.027703479; - } else { - result[0] += 0.0036117423; - } - } - } else { - result[0] += -0.0026553113; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { - result[0] += 0.013676906; - } else { - result[0] += -0.0007326856; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - result[0] += -0; - } else { - result[0] += 0.030973986; - } - } - } else { - result[0] += 0.029461041; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 8))) { - result[0] += -0.009251536; - } else { - result[0] += 0.009845628; - } - } else { - result[0] += -0.04083568; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += 0.029976163; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - result[0] += -0.027244702; - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 166))) { - result[0] += -0.004565258; - } else { - result[0] += 0.025788173; - } - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 254))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 34))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - result[0] += 0.011977387; - } else { - result[0] += 0.00021163402; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { - result[0] += 0.038581323; - } else { - result[0] += 0.013672948; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { - result[0] += 0.0043165404; - } else { - result[0] += -0.035845544; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { - result[0] += -0.0024726556; - } else { - result[0] += 0.008141091; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += 0.03638802; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 166))) { - result[0] += 0.0001506592; - } else { - result[0] += 0.009602265; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 218))) { - result[0] += 0.022467962; - } else { - result[0] += 0.03540038; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.06481004; - } else { - result[0] += 0.017584896; - } - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 140))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 104))) { - result[0] += 0.045290753; - } else { - result[0] += -0.01791346; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - result[0] += -0.058552768; - } else { - result[0] += 0.0349788; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 256))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 198))) { - result[0] += 0.0336964; - } else { - result[0] += 0.0018466607; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 176))) { - result[0] += -0.0049308436; - } else { - result[0] += -0.012199649; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 86))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 100))) { - result[0] += -0.007085; - } else { - result[0] += -0.016483508; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - result[0] += 0.006847045; - } else { - result[0] += -0.0070480793; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { - result[0] += -0.0041096415; - } else { - result[0] += 0.0021135767; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { - result[0] += -0.0060591055; - } else { - result[0] += -0.0011625281; - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 138))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 28))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - result[0] += 0.02211509; - } else { - result[0] += 0.060023166; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { - result[0] += -0.013461961; - } else { - result[0] += 0.04670935; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - result[0] += -0; - } else { - result[0] += 0.025248567; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 154))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 148))) { - result[0] += -0.008415373; - } else { - result[0] += 0.013447831; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 138))) { - result[0] += -0.016349984; - } else { - result[0] += -0.030504474; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += 0.00021573504; - } else { - result[0] += -0.013515745; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { - result[0] += 8.818205e-05; - } else { - result[0] += 0.010008925; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 18))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - result[0] += 0.0037289502; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 124))) { - result[0] += 0.054951753; - } else { - result[0] += -0.0039979555; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - result[0] += -0.00068969437; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - result[0] += -0.012617968; - } else { - result[0] += -0.027266933; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 160))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 48))) { - result[0] += 0.0068334453; - } else { - result[0] += -0.0039404687; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { - result[0] += -0; - } else { - result[0] += 0.039604023; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 178))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 36))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 154))) { - result[0] += -0.0074695237; - } else { - result[0] += 0.0059394534; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 218))) { - result[0] += 0.02302828; - } else { - result[0] += -0.047223628; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 156))) { - result[0] += -0.016071025; - } else { - result[0] += 0.0044617057; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - result[0] += -0.0016089712; - } else { - result[0] += 0.0069275023; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += 0.030206159; - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { - result[0] += 0.010797313; - } else { - result[0] += -0.002882598; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { - result[0] += 0.020259693; - } else { - result[0] += 0.0355315; - } - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 192))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 78))) { - result[0] += -0.0044226176; - } else { - result[0] += -0.02530554; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { - result[0] += 0.008079353; - } else { - result[0] += -0.003107704; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { - result[0] += -0.026262758; - } else { - result[0] += -0.013569482; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { - result[0] += -0.007361694; - } else { - result[0] += 0.01589146; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 186))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - result[0] += 0.0066897767; - } else { - result[0] += -0.03870764; - } - } else { - result[0] += 0.022245135; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 156))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 46))) { - result[0] += 0.0011395493; - } else { - result[0] += -0.0057673557; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { - result[0] += 0.022744471; - } else { - result[0] += 0.008478357; - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 222))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.028410172; - } else { - result[0] += 0.014004901; - } - } else { - result[0] += 0.0010523595; - } - } else { - result[0] += 0.02651517; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 176))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { - result[0] += -0.0015872531; - } else { - result[0] += -0.017748317; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += -0.0032637853; - } else { - result[0] += 0.010829237; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 242))) { - result[0] += 0.034405578; - } else { - result[0] += 0.00891859; - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 20))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 254))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { - result[0] += 0.007226427; - } else { - result[0] += -0.012499331; - } - } else { - result[0] += 0.04246553; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 88))) { - result[0] += 0.002265221; - } else { - result[0] += -0.0032362868; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - result[0] += -0.011394487; - } else { - result[0] += -0.0008440514; - } - } - } else { - result[0] += -0.028067807; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { - result[0] += 0.08105516; - } else { - result[0] += 0.018269602; - } - } else { - result[0] += 0.015147194; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { - result[0] += 0.014643987; - } else { - result[0] += 0.00034420882; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { - result[0] += 0.019848578; - } else { - result[0] += 0.0055640773; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += 0.0281445; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { - result[0] += 0.016479755; - } else { - result[0] += 0.023855714; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 252))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { - result[0] += 0.0043632514; - } else { - result[0] += -0.0032153563; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += -0.0008953023; - } else { - result[0] += -0.012973676; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 224))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { - result[0] += 0.06142653; - } else { - result[0] += 0.014740917; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { - result[0] += 0.036935415; - } else { - result[0] += -0.0027422463; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 206))) { - result[0] += -0.043081205; - } else { - result[0] += -0.020138515; - } - } else { - result[0] += -0.012991915; - } - } else { - result[0] += 0.005466835; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { - result[0] += -0.00065770803; - } else { - result[0] += 0.020950794; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 98))) { - result[0] += -0.0020690185; - } else { - result[0] += 0.0022426536; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.01779854; - } else { - result[0] += 0.007740788; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - result[0] += 0.010289395; - } else { - result[0] += 0.022736436; - } - } else { - result[0] += 0.02622193; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { - result[0] += -0.0030147363; - } else { - result[0] += -0.01984936; - } - } else { - result[0] += -0.014292531; - } - } else { - result[0] += 0.019834789; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 12))) { - result[0] += 0.012920295; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { - result[0] += 0.07497602; - } else { - result[0] += 0.01954972; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { - result[0] += 0.010222583; - } else { - result[0] += 0.00049927505; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - result[0] += -0.0032804606; - } else { - result[0] += 0.0070060194; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += 0.024341205; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { - result[0] += 0.015133323; - } else { - result[0] += 0.021515321; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - result[0] += 0.0021268248; - } else { - result[0] += -0.004917339; - } - } else { - result[0] += 0.020086113; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.023110032; - } else { - result[0] += -0.008982231; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 206))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { - result[0] += -0.01757216; - } else { - result[0] += -5.0931576e-05; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - result[0] += 0.017951632; - } else { - result[0] += 0.0057268315; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 62))) { - result[0] += 0.0030992; - } else { - result[0] += -0.010930783; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { - result[0] += -0.00038469338; - } else { - result[0] += -0.004318268; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { - result[0] += -0; - } else { - result[0] += 0.017621709; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - result[0] += 0.0036806122; - } else { - result[0] += -4.4085446e-05; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.021112496; - } else { - result[0] += 0.007883241; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - result[0] += 0.007880076; - } else { - result[0] += 0.02039106; - } - } else { - result[0] += 0.023736782; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 14))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - result[0] += -0; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { - result[0] += 0.022109; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { - result[0] += -0.011896599; - } else { - result[0] += -0.02695978; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 28))) { - result[0] += 0.017046606; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 86))) { - result[0] += -0.022864131; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { - result[0] += 0.01655387; - } else { - result[0] += -0.0029186176; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - result[0] += 0.006224805; - } else { - result[0] += -0.0006160491; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { - result[0] += 0.03912334; - } else { - result[0] += 0.0064465962; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - result[0] += 0.03801068; - } else { - result[0] += -0; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { - result[0] += -0.0056696637; - } else { - result[0] += 0.002394879; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { - result[0] += 0.0026905416; - } else { - result[0] += 0.014434603; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 18))) { - result[0] += -0.0066401684; - } else { - result[0] += -0.02399191; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 216))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - result[0] += 0.01420292; - } else { - result[0] += 0.007666865; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 258))) { - result[0] += 0.015625248; - } else { - result[0] += 0.035386283; - } - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 168))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 164))) { - result[0] += -0.0026949244; - } else { - result[0] += 0.010536998; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 172))) { - result[0] += -0.02333055; - } else { - result[0] += -0.004258865; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 190))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 256))) { - result[0] += 0.010322237; - } else { - result[0] += -0.004513755; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 194))) { - result[0] += -0.013793135; - } else { - result[0] += 0.00041879248; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { - result[0] += -0.0012282294; - } else { - result[0] += 0.012586914; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { - result[0] += -0.013370514; - } else { - result[0] += -0.009561422; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.020673957; - } else { - result[0] += 0.01021281; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += 0.029428396; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - result[0] += -0.0008754981; - } else { - result[0] += -0.0100570135; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { - result[0] += 0.016708953; - } else { - result[0] += 0.009689736; - } - } else { - result[0] += 0.00040766338; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { - result[0] += 0.024215741; - } else { - result[0] += 0.012051038; - } - } - } - } - } - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - result[0] += 0.0013062642; - } else { - result[0] += 0.019394014; - } - } else { - result[0] += -0.0104289735; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 48))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += -0.05682643; - } else { - result[0] += 0.026358023; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 126))) { - result[0] += 0.0067068855; - } else { - result[0] += -0.0033787123; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += -0.017422771; - } else { - result[0] += -0.013566106; - } - } else { - result[0] += 0.05098955; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 264))) { - result[0] += 0.0021389506; - } else { - result[0] += 0.022104675; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 196))) { - result[0] += -0.0034482577; - } else { - result[0] += -0.04897159; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - result[0] += 0.04547104; - } else { - result[0] += -0.0036854; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += 0.031185264; - } else { - result[0] += -0.00029240636; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - result[0] += 0.048903078; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { - result[0] += -0.012713486; - } else { - result[0] += -0.0045942473; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { - result[0] += -0.024384957; - } else { - result[0] += 0.010758282; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { - result[0] += 0.030021293; - } else { - result[0] += 0.017209774; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 220))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 88))) { - result[0] += -0.0025992983; - } else { - result[0] += 0.0014243874; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += -0.014005645; - } else { - result[0] += 0.00015922675; - } - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 184))) { - result[0] += -0.0023512202; - } else { - result[0] += 0.00030087968; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { - result[0] += 0.015333219; - } else { - result[0] += 0.050305735; - } - } else { - result[0] += 0.0038954068; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 220))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { - result[0] += 0.005740883; - } else { - result[0] += -0.011134521; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { - result[0] += -0.0102155125; - } else { - result[0] += -0.01861443; - } - } else { - result[0] += -0.008878123; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 36))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 58))) { - result[0] += 0.07084565; - } else { - result[0] += 0.019467566; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 106))) { - result[0] += -0.005714934; - } else { - result[0] += 0.0072955615; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { - result[0] += 0.0217909; - } else { - result[0] += 0.008551905; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - result[0] += 0.016447848; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { - result[0] += 0.028782597; - } else { - result[0] += -0.027478626; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { - result[0] += 0.007580962; - } else { - result[0] += 0.0014997128; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 214))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { - result[0] += -0.0022094585; - } else { - result[0] += -0.02451445; - } - } else { - result[0] += 0.024397848; - } - } else { - result[0] += -0.01616343; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - result[0] += 0.011657309; - } else { - result[0] += 0.0012069183; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += 0.040601656; - } else { - result[0] += 0.009451126; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { - result[0] += -0.022806326; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - result[0] += 0.03292076; - } else { - result[0] += 0.001937497; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += 0.0035285496; - } else { - result[0] += -0.0058691064; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - result[0] += 0.012688639; - } else { - result[0] += 0.005491004; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { - result[0] += 0.0044394704; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 192))) { - result[0] += 0.012408857; - } else { - result[0] += 0.019069476; - } - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { - result[0] += 0.006046584; - } else { - result[0] += -0.015792418; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += 0.027976854; - } else { - result[0] += 0.012421743; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += 0.054596562; - } else { - result[0] += 0.01953054; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { - result[0] += -0.003245589; - } else { - result[0] += 0.013587843; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.021004315; - } else { - result[0] += -0.008656485; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - result[0] += 0.01555752; - } else { - result[0] += -0.0006588964; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { - result[0] += -0.0020559; - } else { - result[0] += -0.010479237; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += 0.021051032; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 220))) { - result[0] += 0.006872528; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { - result[0] += -0.00075457775; - } else { - result[0] += -0.008546843; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { - result[0] += 0.009004579; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 2))) { - result[0] += 0.013524537; - } else { - result[0] += 0.02246733; - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 202))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { - result[0] += -0.004806658; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - result[0] += -0.02193972; - } else { - result[0] += -0.0058797724; - } - } - } else { - result[0] += -0.023569103; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 264))) { - result[0] += 0.016511796; - } else { - result[0] += -0.0023302487; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += 0.004636476; - } else { - result[0] += 0.03744141; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 154))) { - result[0] += -0.0021920146; - } else { - result[0] += 0.0044151954; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += 0.013442422; - } else { - result[0] += 0.025949672; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 252))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { - result[0] += 0.012082203; - } else { - result[0] += 0.017159306; - } - } else { - result[0] += 0.018528607; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - result[0] += 0.012809316; - } else { - result[0] += 0.0020744025; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - result[0] += 0.02068119; - } else { - result[0] += 0.012666582; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += -0.025207102; - } else { - result[0] += -0.0042135157; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - result[0] += 0.019998292; - } else { - result[0] += -0.011731009; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - result[0] += 0.04935061; - } else { - result[0] += 0.00985283; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 2))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 10))) { - result[0] += -0.0114418; - } else { - result[0] += 0.001085467; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { - result[0] += 0.005336402; - } else { - result[0] += 0.040240493; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - result[0] += -0.0003519562; - } else { - result[0] += -0.016158544; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 22))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 186))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += -0.052682735; - } else { - result[0] += -0.024834817; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - result[0] += 0.0076021785; - } else { - result[0] += -0.009852809; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 24))) { - result[0] += 0.07371118; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - result[0] += -0.0062063127; - } else { - result[0] += -0.0016409116; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 166))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 138))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 96))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += -0.0056977295; - } else { - result[0] += -0.0020788263; - } - } else { - result[0] += 0.013674322; - } - } else { - result[0] += -0.012557744; - } - } else { - result[0] += 0.020858606; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 100))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { - result[0] += 0.004243218; - } else { - result[0] += -0.0012915577; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { - result[0] += 0.012041297; - } else { - result[0] += 0.0036917683; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 226))) { - result[0] += 0.021245888; - } else { - result[0] += 0.010770134; - } - } else { - result[0] += 0.026461909; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 252))) { - result[0] += 0.011941756; - } else { - result[0] += 0.01697006; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 190))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - result[0] += 0.0059637506; - } else { - result[0] += 0.0009332538; - } - } else { - result[0] += -0.01690738; - } - } else { - result[0] += 0.018049363; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { - result[0] += -0.003757357; - } else { - result[0] += -0.010965187; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 138))) { - result[0] += 0.054250848; - } else { - result[0] += -0.00039252196; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 42))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { - result[0] += 0.01451879; - } else { - result[0] += 0.037112333; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 36))) { - result[0] += -0.017370671; - } else { - result[0] += -0.0013852807; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 222))) { - result[0] += 0.009491138; - } else { - result[0] += 0.03599734; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { - result[0] += 0.0021581096; - } else { - result[0] += -0.0014275697; - } - } else { - result[0] += 0.009684578; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { - result[0] += 0.009782214; - } else { - result[0] += -0.0025362705; - } - } else { - result[0] += 0.015987474; - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { - result[0] += 0.031121805; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - result[0] += -0.0017203381; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 10))) { - result[0] += 0.030785752; - } else { - result[0] += -0.005102741; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - result[0] += -0.005013264; - } else { - result[0] += 0.027989835; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { - result[0] += -0.024730321; - } else { - result[0] += -0.0032983057; - } - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 244))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += 0.009897011; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { - result[0] += -0.0021574632; - } else { - result[0] += 0.057048213; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { - result[0] += 0.010313834; - } else { - result[0] += 0.002621749; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += 0.015189426; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { - result[0] += 0.009792722; - } else { - result[0] += 0.014417933; - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += -0.0014002819; - } else { - result[0] += 0.006653227; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - result[0] += -0.023881285; - } else { - result[0] += -0; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - result[0] += 0.018696984; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += -0.0133892; - } else { - result[0] += 0.008133677; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - result[0] += 0.043852713; - } else { - result[0] += 0.0058281594; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 212))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 144))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 174))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { - result[0] += -0.0026800435; - } else { - result[0] += 0.0020921542; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - result[0] += 0.0018816863; - } else { - result[0] += -0.027228499; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - result[0] += -0.014789981; - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 150))) { - result[0] += -0.009289259; - } else { - result[0] += -0.0037157678; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 158))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { - result[0] += -0.009539143; - } else { - result[0] += 0.006927042; - } - } else { - result[0] += 0.039872702; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 160))) { - result[0] += -0.039126202; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 166))) { - result[0] += -0.002670629; - } else { - result[0] += 0.001439321; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - result[0] += -5.299445e-05; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - result[0] += -0.019934397; - } else { - result[0] += -0.0012183964; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 154))) { - result[0] += -0.0013647849; - } else { - result[0] += 0.038733196; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - result[0] += 0.008902398; - } else { - result[0] += -0.00011572992; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 14))) { - result[0] += 3.4862278e-06; - } else { - result[0] += 0.012706413; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { - result[0] += 0.007615597; - } else { - result[0] += -0.0051007196; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 48))) { - result[0] += 0.015438475; - } else { - result[0] += 0.0018905745; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 246))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 230))) { - result[0] += 0.008593912; - } else { - result[0] += 0.014445068; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += -0.00014397338; - } else { - result[0] += 0.0083941175; - } - } - } else { - result[0] += -0.004308312; - } - } else { - result[0] += 0.046102088; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { - result[0] += -0.0013631996; - } else { - result[0] += -0.011290415; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 200))) { - result[0] += 0.00060525746; - } else { - result[0] += 0.013914026; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 248))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - result[0] += -0.023959834; - } else { - result[0] += -0.010296635; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { - result[0] += 0.008832153; - } else { - result[0] += 0.0023550882; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.015583684; - } else { - result[0] += 0.010421765; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { - result[0] += -0.00011256654; - } else { - result[0] += -0.02525649; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 232))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 230))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { - result[0] += 0.005643142; - } else { - result[0] += -0.00010354039; - } - } else { - result[0] += -0.015879504; - } - } else { - result[0] += 0.014167144; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 246))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += 0.049543332; - } else { - result[0] += 0.0046836296; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { - result[0] += -0.017134476; - } else { - result[0] += -0.007789578; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += -0.008826947; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 250))) { - result[0] += -0.0007550689; - } else { - result[0] += 0.0071488456; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 188))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 238))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 106))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { - result[0] += -0.019138163; - } else { - result[0] += 0.008177842; - } - } else { - result[0] += 0.03202384; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { - result[0] += 0.03914841; - } else { - result[0] += 0.027094522; - } - } else { - result[0] += 0.0053101527; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += 0.0403822; - } else { - result[0] += 0.013740751; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { - result[0] += -0.030392349; - } else { - result[0] += 0.0028747516; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 80))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { - result[0] += -0.008877286; - } else { - result[0] += 0.012316696; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 118))) { - result[0] += 0.004029528; - } else { - result[0] += -0.00029411144; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { - result[0] += 0.011664437; - } else { - result[0] += 0.0006082279; - } - } else { - result[0] += -0.006005393; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 166))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { - result[0] += 0.09773924; - } else { - result[0] += 0.02065113; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 200))) { - result[0] += 0.0035237612; - } else { - result[0] += 0.009829774; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 208))) { - result[0] += -0.007697291; - } else { - result[0] += 0.0015825549; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - result[0] += 0.0036124797; - } else { - result[0] += 0.014532133; - } - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - result[0] += 0.0012666411; - } else { - result[0] += -0.03202522; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { - result[0] += 0.020200811; - } else { - result[0] += 0.004590917; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 28))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { - result[0] += 0.03727207; - } else { - result[0] += 0.016316103; - } - } else { - result[0] += -0.003320934; - } - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += 0.053239834; - } else { - result[0] += 0.015470129; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - result[0] += -0.0021254455; - } else { - result[0] += -0.009888625; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { - result[0] += 0.062025126; - } else { - result[0] += -0.00554525; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { - result[0] += 0.057511933; - } else { - result[0] += -0.008578778; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { - result[0] += -0.046820056; - } else { - result[0] += -0.02672096; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 70))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - result[0] += -0.03445236; - } else { - result[0] += 0.01575507; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { - result[0] += -0.029678077; - } else { - result[0] += -0.0074849683; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - result[0] += 0.013723537; - } else { - result[0] += -0.0020312674; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { - result[0] += -0.004165123; - } else { - result[0] += -0.00078670814; - } - } - } - } - } - } - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 12))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += -0; - } else { - result[0] += -0.03194522; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { - result[0] += 0.025147652; - } else { - result[0] += 0.0062306984; - } - } - } else { - result[0] += -0.0008928859; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += 0.13331777; - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 206))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 144))) { - result[0] += 0.030818671; - } else { - result[0] += 0.011000312; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - result[0] += 0.0052034413; - } else { - result[0] += -0.002728651; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += 0.0031897593; - } else { - result[0] += 0.017273722; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { - result[0] += -0.02854721; - } else { - result[0] += 0.00037428603; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - result[0] += -0.0019407201; - } else { - result[0] += -0.007221583; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 62))) { - result[0] += 0.047634736; - } else { - result[0] += -0.017209576; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { - result[0] += 0.025280062; - } else { - result[0] += 0.012840076; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { - result[0] += 0.038194098; - } else { - result[0] += 0.008874618; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { - result[0] += -0.03347216; - } else { - result[0] += -0.0004954495; - } - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 50))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 172))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 202))) { - result[0] += -0.009764884; - } else { - result[0] += -0.027206961; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 202))) { - result[0] += 0.00784516; - } else { - result[0] += 0.0012178732; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 170))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 168))) { - result[0] += 0.024105137; - } else { - result[0] += -0.0019981433; - } - } else { - result[0] += 0.031363543; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - result[0] += -0.0072048977; - } else { - result[0] += -0.022911293; - } - } else { - result[0] += 0.010616104; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 258))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 248))) { - result[0] += 0.008845729; - } else { - result[0] += -0.0013868009; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 246))) { - result[0] += -0.0024586073; - } else { - result[0] += -0.0086732535; - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 196))) { - result[0] += 0.01107409; - } else { - result[0] += -0.0011529119; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - result[0] += 0.013766219; - } else { - result[0] += 0.026060035; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 210))) { - result[0] += -0.004132199; - } else { - result[0] += -0.01266666; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 264))) { - result[0] += 0.011719443; - } else { - result[0] += -0.0067507224; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 90))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 188))) { - result[0] += 0.0155691; - } else { - result[0] += 0.010912938; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 198))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 188))) { - result[0] += 0.0051367334; - } else { - result[0] += -0.00038397807; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += 0.044876117; - } else { - result[0] += 0.005380819; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 124))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 250))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - result[0] += -0.01634503; - } else { - result[0] += -0; - } - } else { - result[0] += -0.002735744; - } - } else { - result[0] += 0.012586943; - } - } else { - result[0] += -0.0009229357; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 106))) { - result[0] += -0.013541499; - } else { - result[0] += 0.016169539; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += -0; - } else { - result[0] += 0.026296902; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { - result[0] += 0.0010251206; - } else { - result[0] += 0.007284665; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 100))) { - result[0] += -0.0034584904; - } else { - result[0] += 0.0022243506; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { - result[0] += 0.008507351; - } else { - result[0] += 0.00037087366; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 220))) { - result[0] += -0.0015936692; - } else { - result[0] += -0.00584162; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += 0.03250199; - } else { - result[0] += 0.014099632; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 174))) { - result[0] += 0.0007955474; - } else { - result[0] += 0.0064386777; - } - } - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 144))) { - result[0] += -0.032964613; - } else { - result[0] += 0.017446639; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { - result[0] += -0.015205093; - } else { - result[0] += -0; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - result[0] += 0.003139617; - } else { - result[0] += -0.014359586; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += -0.0014178219; - } else { - result[0] += 0.002840144; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 62))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - result[0] += 0.041497182; - } else { - result[0] += 0.001743556; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { - result[0] += -0.014737419; - } else { - result[0] += -0.0060345526; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 188))) { - result[0] += -0.0014039263; - } else { - result[0] += 0.009091877; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { - result[0] += -0.013735535; - } else { - result[0] += -0.0066661197; - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 100))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { - result[0] += 0.05542008; - } else { - result[0] += 0.0056177597; - } - } else { - result[0] += 0.021063056; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += -0.008560927; - } else { - result[0] += 0.007372921; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - result[0] += 0.0033190027; - } else { - result[0] += 0.02229972; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 162))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { - result[0] += 0.0031475432; - } else { - result[0] += 0.01872689; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 42))) { - result[0] += -0.0027235162; - } else { - result[0] += -0.011199118; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { - result[0] += -0.013897911; - } else { - result[0] += -0.0017742496; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 144))) { - result[0] += 0.045032453; - } else { - result[0] += 0.0035365508; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { - result[0] += 0.015004215; - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - result[0] += -0.02693589; - } else { - result[0] += 0.0030036382; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 236))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 220))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { - result[0] += -0.005196058; - } else { - result[0] += -0.0010462991; - } - } else { - result[0] += -0.036202352; - } - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - result[0] += 0.009205636; - } else { - result[0] += -0.008914505; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { - result[0] += 0.00077702984; - } else { - result[0] += -0.006057858; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 78))) { - result[0] += 0.011342842; - } else { - result[0] += 0.003530657; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - result[0] += 0.03748219; - } else { - result[0] += 0.014057839; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 154))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 102))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += -0.0016399727; - } else { - result[0] += -0.011802055; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - result[0] += -0.0016713021; - } else { - result[0] += 0.0056822044; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 34))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { - result[0] += 0.00386166; - } else { - result[0] += -0.0027170626; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 46))) { - result[0] += 0.019643234; - } else { - result[0] += 0.0028665168; - } - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { - result[0] += 0.0005789458; - } else { - result[0] += 0.0143351955; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { - result[0] += 0.011239639; - } else { - result[0] += -0.016711237; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { - result[0] += 0.019091167; - } else { - result[0] += 0.0046307947; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += 0.050568253; - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += 0.03797291; - } else { - result[0] += 0.007982335; - } - } - } else { - result[0] += 0.0035218038; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - result[0] += -0.016012253; - } else { - result[0] += 0.0023404376; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - result[0] += -0.001683798; - } else { - result[0] += 0.013443462; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 232))) { - result[0] += -0.0012087579; - } else { - result[0] += 0.009097977; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 192))) { - result[0] += -0.010690723; - } else { - result[0] += -0.001824428; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += 0.017058346; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 220))) { - result[0] += 0.004044805; - } else { - result[0] += -0.0013507138; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 242))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { - result[0] += 0.008160069; - } else { - result[0] += 0.015147629; - } - } else { - result[0] += 0.024462283; - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { - result[0] += -0.000107747815; - } else { - result[0] += 0.0067256675; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { - result[0] += -0.0076294737; - } else { - result[0] += -0.043650094; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 178))) { - result[0] += 0.034688678; - } else { - result[0] += -0.02312972; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 100))) { - result[0] += -0.002360337; - } else { - result[0] += 0.012530643; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { - result[0] += -0.01826018; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 64))) { - result[0] += 0.0056892973; - } else { - result[0] += 0.03400869; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 80))) { - result[0] += -0.009709366; - } else { - result[0] += -0.00013415277; - } - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 156))) { - result[0] += 0.010154608; - } else { - result[0] += 0.001891119; - } - } else { - result[0] += 0.03333105; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 154))) { - result[0] += -0.022681896; - } else { - result[0] += -0.03837011; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { - result[0] += 0.007119199; - } else { - result[0] += -0.015321928; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 134))) { - result[0] += 0.08953688; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { - result[0] += -0.022155154; - } else { - result[0] += 0.004006593; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - result[0] += 0.013605259; - } else { - result[0] += 0.027043974; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { - result[0] += -0.0115665; - } else { - result[0] += 0.007095266; - } - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += 0.035634317; - } else { - result[0] += 0.017916704; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - result[0] += -0.0043744547; - } else { - result[0] += -0.00026779916; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 236))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - result[0] += 0.0133640645; - } else { - result[0] += 0.004636778; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += 0.012312482; - } else { - result[0] += 0.06577807; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 132))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += 0.0068262727; - } else { - result[0] += -0.04068326; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { - result[0] += -0.0010328897; - } else { - result[0] += -0.005453972; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { - result[0] += -0.0008340759; - } else { - result[0] += -0.008702307; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 16))) { - result[0] += 0.040576342; - } else { - result[0] += 0.0024016355; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 226))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 192))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - result[0] += 0.029419197; - } else { - result[0] += 0.0010929945; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 156))) { - result[0] += -0.0012289739; - } else { - result[0] += -0.0070381216; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.0115409745; - } else { - result[0] += 0.0048038764; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 242))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 222))) { - result[0] += 0.023705222; - } else { - result[0] += 0.010082607; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.012382432; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 228))) { - result[0] += 0.004983134; - } else { - result[0] += -0; - } - } - } - } - } - } - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 208))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += 0.061853677; - } else { - result[0] += -0.0063631344; - } - } else { - result[0] += -0.036711987; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 28))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { - result[0] += 0.0031359557; - } else { - result[0] += -0.0016080218; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { - result[0] += 0.036603495; - } else { - result[0] += 0.015373263; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += -0.055269193; - } else { - result[0] += 0.019025035; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - result[0] += -0.022278577; - } else { - result[0] += 0.016397122; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.01897105; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { - result[0] += 0.00051737565; - } else { - result[0] += 0.0041289385; - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += -0.036939483; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { - result[0] += 0.009198739; - } else { - result[0] += -0.013264345; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { - result[0] += 0.022775693; - } else { - result[0] += 0.007945242; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 156))) { - result[0] += -0.0018210205; - } else { - result[0] += 0.0037362184; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 128))) { - result[0] += -0.012762427; - } else { - result[0] += -0.0007420523; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { - result[0] += 0.017958501; - } else { - result[0] += 0.0061608446; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { - result[0] += -0.0049462705; - } else { - result[0] += 0.0019659882; - } - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { - result[0] += 0.00022622973; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 250))) { - result[0] += 0.005413284; - } else { - result[0] += 0.030752031; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 220))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { - result[0] += 0.003555048; - } else { - result[0] += -0.0069270222; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { - result[0] += -0.008863601; - } else { - result[0] += -0.015783666; - } - } else { - result[0] += -0.0067091286; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 36))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 58))) { - result[0] += 0.062294442; - } else { - result[0] += 0.01808921; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { - result[0] += 0.08561305; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 190))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - result[0] += 0.0024828026; - } else { - result[0] += 0.018881498; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 204))) { - result[0] += -0.007572981; - } else { - result[0] += 0.001793948; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 126))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { - result[0] += -0.000932767; - } else { - result[0] += 0.006823505; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - result[0] += -0.036651295; - } else { - result[0] += -0.0058056363; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { - result[0] += -0.0055914307; - } else { - result[0] += 0.03341748; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 100))) { - result[0] += -0.00073259696; - } else { - result[0] += 0.010309592; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { - result[0] += -0.019160004; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 64))) { - result[0] += 0.0046965308; - } else { - result[0] += 0.026486978; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 80))) { - result[0] += -0.008773739; - } else { - result[0] += -0.0007050743; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { - result[0] += 0.025857493; - } else { - result[0] += -0.0017104832; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { - result[0] += -0.04572242; - } else { - result[0] += -0.013417631; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 128))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - result[0] += 0.02735805; - } else { - result[0] += 0.008384274; - } - } else { - result[0] += 0.025471484; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 146))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 144))) { - result[0] += -0.0019103719; - } else { - result[0] += -0.015735812; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - result[0] += 0.003526498; - } else { - result[0] += 0.0004870697; - } - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 252))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 230))) { - result[0] += -0.0010278291; - } else { - result[0] += 0.0035127073; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - result[0] += 0.0007956819; - } else { - result[0] += -0.007078775; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { - result[0] += 0.06692124; - } else { - result[0] += 0.012191664; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { - result[0] += -0.0037893832; - } else { - result[0] += 0.0049554044; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 262))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.0026184532; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - result[0] += -0.03463073; - } else { - result[0] += -0.01464231; - } - } - } else { - result[0] += 0.01621621; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { - result[0] += -0.0019204362; - } else { - result[0] += 0.012864575; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - result[0] += -0.008148334; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 46))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { - result[0] += 0.04631585; - } else { - result[0] += 0.008748725; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 58))) { - result[0] += -0.0012717935; - } else { - result[0] += 0.0031659238; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { - result[0] += 0.023349551; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - result[0] += 0.0070865126; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 220))) { - result[0] += -0.0010378936; - } else { - result[0] += -0.03451775; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 58))) { - result[0] += -0.0030549995; - } else { - result[0] += 0.042464044; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 196))) { - result[0] += -0.0068810317; - } else { - result[0] += -0.01983464; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 28))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - result[0] += 0.021494946; - } else { - result[0] += 0.046589624; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 246))) { - result[0] += 0.0016733175; - } else { - result[0] += -0.002286189; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { - result[0] += -0.023560746; - } else { - result[0] += 0.025176898; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - result[0] += -0.0032716715; - } else { - result[0] += -0.0107773235; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 158))) { - result[0] += 9.897955e-07; - } else { - result[0] += 0.0075915637; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 238))) { - result[0] += -0.018133821; - } else { - result[0] += -0.003638253; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { - result[0] += 0.013451889; - } else { - result[0] += 0.006885908; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 2))) { - result[0] += 0.016480822; - } else { - result[0] += 0.0028402929; - } - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 182))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - result[0] += 0.009219227; - } else { - result[0] += -0.025390211; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += 0.025732726; - } else { - result[0] += -0.0023830726; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { - result[0] += 0.0035509118; - } else { - result[0] += 0.013229172; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += -0.007854169; - } else { - result[0] += 0.0072692037; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += 0.047711357; - } else { - result[0] += 0.025087982; - } - } else { - result[0] += 0.0016552185; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 36))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += 0.0398016; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - result[0] += 0.0008522939; - } else { - result[0] += 0.022818817; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 8))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { - result[0] += -0.047148183; - } else { - result[0] += -0.014362188; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - result[0] += 0.033707537; - } else { - result[0] += -0.005045968; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 38))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 52))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - result[0] += -0.015981285; - } else { - result[0] += 0.061333533; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.011986034; - } else { - result[0] += 0.014650764; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 156))) { - result[0] += 0.03378779; - } else { - result[0] += 0.001349721; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - result[0] += -0.00046623495; - } else { - result[0] += -0.0032709301; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 42))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += 0.0026227154; - } else { - result[0] += -0.004871029; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += 0.024236446; - } else { - result[0] += 0.00875364; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 86))) { - result[0] += -0.010391722; - } else { - result[0] += -0.0003864034; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { - result[0] += 0.0091726575; - } else { - result[0] += 0.00034549323; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += -0.016140882; - } else { - result[0] += -0.0051438985; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 86))) { - result[0] += 0.0077548027; - } else { - result[0] += -0.0023856275; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 96))) { - result[0] += 0.0072452063; - } else { - result[0] += 0.021926673; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { - result[0] += 0.007803672; - } else { - result[0] += 0.07842522; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { - result[0] += 0.0030498195; - } else { - result[0] += -0.030465296; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 138))) { - result[0] += 0.027338846; - } else { - result[0] += 0.018519042; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 104))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 150))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { - result[0] += -0.0038131357; - } else { - result[0] += -0.014322984; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - result[0] += 0.011482879; - } else { - result[0] += -0.0018702493; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 38))) { - result[0] += 0.04208499; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 48))) { - result[0] += -0.013598467; - } else { - result[0] += 0.0022218376; - } - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += 0.014965734; - } else { - result[0] += 0.006420965; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - result[0] += 0.0035459124; - } else { - result[0] += -0.03692886; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += -0.00581108; - } else { - result[0] += 0.0022818777; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - result[0] += 0.02711085; - } else { - result[0] += 0.0037428073; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 232))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 230))) { - result[0] += -0.0006429512; - } else { - result[0] += -0.013558619; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - result[0] += 0.006967464; - } else { - result[0] += 0.020120773; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - result[0] += -0.0188818; - } else { - result[0] += -0.011276356; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 248))) { - result[0] += -0.0036909913; - } else { - result[0] += 0.0023398104; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 228))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 220))) { - result[0] += 0.0067386203; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += 0.009782809; - } else { - result[0] += -0.0003835666; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 242))) { - result[0] += 0.008386671; - } else { - result[0] += 0.01509656; - } - } - } - } - } - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 230))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += 0.0035304844; - } else { - result[0] += 0.00860195; - } - } else { - result[0] += 0.044866074; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += -0.0027610434; - } else { - result[0] += 0.032996777; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 30))) { - result[0] += 0.009394963; - } else { - result[0] += -0.0004232897; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { - result[0] += 0.019008147; - } else { - result[0] += 0.009637523; - } - } else { - result[0] += 0.041156612; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { - result[0] += 0.0076490478; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 182))) { - result[0] += 0.01141602; - } else { - result[0] += 0.0032564194; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 4))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - result[0] += 0.0054725604; - } else { - result[0] += -0.006845514; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { - result[0] += -0; - } else { - result[0] += 0.04772381; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += -0.016645087; - } else { - result[0] += -0.032690536; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { - result[0] += 0.003037467; - } else { - result[0] += -0.009953394; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 12))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { - result[0] += 0.01011019; - } else { - result[0] += 0.017591367; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 258))) { - result[0] += 0.0020601144; - } else { - result[0] += -0.0052700583; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 172))) { - result[0] += -0.0001318003; - } else { - result[0] += -0.0058084927; - } - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 160))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 158))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 8))) { - result[0] += -0.021778304; - } else { - result[0] += -0.05481277; - } - } else { - result[0] += 0.01922178; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += -0.02884279; - } else { - result[0] += -0.05432384; - } - } else { - result[0] += -0.008790418; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += -0.0039755395; - } else { - result[0] += 0.0508219; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 128))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 28))) { - result[0] += -0.047565266; - } else { - result[0] += -0.034321543; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { - result[0] += 0.011836532; - } else { - result[0] += -0.0025003483; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 58))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 68))) { - result[0] += 0.0057843057; - } else { - result[0] += 0.036940213; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { - result[0] += -0.011010564; - } else { - result[0] += 0.014086082; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 226))) { - result[0] += -0.00865682; - } else { - result[0] += 0.018309247; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { - result[0] += 0.00883958; - } else { - result[0] += -0.0026359025; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 98))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { - result[0] += -0.0024397147; - } else { - result[0] += -0.023152852; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - result[0] += 0.0015076617; - } else { - result[0] += -0.009175034; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { - result[0] += 0.02444943; - } else { - result[0] += 0.0023199369; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - result[0] += -0.0028377601; - } else { - result[0] += 0.00046475232; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { - result[0] += 0.00531006; - } else { - result[0] += -0.00012114655; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { - result[0] += -0.004693984; - } else { - result[0] += -0.032999605; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - result[0] += 0.029490167; - } else { - result[0] += 0.007921627; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 58))) { - result[0] += -0.0028598853; - } else { - result[0] += 0.0077966005; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 66))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 70))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - result[0] += 0.014692311; - } else { - result[0] += -0.021459384; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { - result[0] += -0.016421737; - } else { - result[0] += -0.0066403346; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - result[0] += 0.0074493513; - } else { - result[0] += -0.0040894877; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - result[0] += 0.01759093; - } else { - result[0] += -0.00061179395; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += 0.0076502063; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 236))) { - result[0] += 0.00010772177; - } else { - result[0] += -0.003537047; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { - result[0] += -0.012459938; - } else { - result[0] += -0.0037218079; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 260))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - result[0] += 0.002977729; - } else { - result[0] += -0.019293662; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - result[0] += 0.015566354; - } else { - result[0] += 0.0037241564; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.03374357; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += 0.0041862135; - } else { - result[0] += 0.009111217; - } - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 204))) { - result[0] += 0.001820308; - } else { - result[0] += 0.0113331815; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { - result[0] += -0.01062336; - } else { - result[0] += 0.0023687228; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - result[0] += 0.0050820797; - } else { - result[0] += -0.010689573; - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += 0.039545674; - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += 0.023330238; - } else { - result[0] += 0.0062547647; - } - } - } else { - result[0] += 0.00083993434; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 58))) { - result[0] += -0.0019025843; - } else { - result[0] += 0.0075600627; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - result[0] += 0.042078517; - } else { - result[0] += 0.0026668767; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.015797917; - } else { - result[0] += -0.006350769; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 96))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { - result[0] += 0.048628982; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { - result[0] += 0.021388667; - } else { - result[0] += 0.0020165527; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 110))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 106))) { - result[0] += -0.0051917243; - } else { - result[0] += 0.010212748; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - result[0] += 0.0015873375; - } else { - result[0] += -0.0008199594; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 64))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 212))) { - result[0] += 0.01901365; - } else { - result[0] += -0.0028177812; - } - } else { - result[0] += 0.02409505; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 206))) { - result[0] += -0.0063365176; - } else { - result[0] += 0.00086170074; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - result[0] += -0.016780075; - } else { - result[0] += 0.012628958; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { - result[0] += -0.009542297; - } else { - result[0] += 0.053097267; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 250))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 238))) { - result[0] += 0.003721561; - } else { - result[0] += 0.019598227; - } - } else { - result[0] += 0.03498596; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 68))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { - result[0] += 0.00025514522; - } else { - result[0] += 0.0040203123; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { - result[0] += -0.0074286456; - } else { - result[0] += 0.00093450863; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 166))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - result[0] += -0.00087522157; - } else { - result[0] += 0.007016174; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { - result[0] += -0.034118887; - } else { - result[0] += -0.011294309; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 260))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 256))) { - result[0] += 0.022198912; - } else { - result[0] += 0.012400496; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { - result[0] += -0.010423508; - } else { - result[0] += 0.004261658; - } - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 96))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 52))) { - result[0] += -0.001626932; - } else { - result[0] += 0.0061591933; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - result[0] += -0.0022012205; - } else { - result[0] += -0.0123000285; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 144))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 122))) { - result[0] += 0.007472606; - } else { - result[0] += 0.05678388; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 196))) { - result[0] += 3.3709428e-05; - } else { - result[0] += -0.0143476175; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 50))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { - result[0] += 0.03503921; - } else { - result[0] += -0.036016237; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 182))) { - result[0] += -0.0048608086; - } else { - result[0] += 0.022741511; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += -0.035393443; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 88))) { - result[0] += -0.002815163; - } else { - result[0] += -2.7660133e-05; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 174))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 158))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 148))) { - result[0] += 0.0017332671; - } else { - result[0] += 0.026441107; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 222))) { - result[0] += -0.0073182597; - } else { - result[0] += 0.0006405428; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { - result[0] += 0.02126172; - } else { - result[0] += 0.011408723; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 68))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 46))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - result[0] += -0.014773398; - } else { - result[0] += 0.009008159; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += 0.0047272076; - } else { - result[0] += -0.01409996; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 160))) { - result[0] += 0.009959228; - } else { - result[0] += 0.04201186; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { - result[0] += -0.008849121; - } else { - result[0] += -0.00033711825; - } - } - } - } - } - } - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 16))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - result[0] += 0.002547614; - } else { - result[0] += -0.016982568; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - result[0] += 0.0087383045; - } else { - result[0] += -0.0019732136; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - result[0] += -0.027964113; - } else { - result[0] += -0.0070363963; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 12))) { - result[0] += 0.015301444; - } else { - result[0] += -0.0009437213; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 218))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 22))) { - result[0] += 0.011154254; - } else { - result[0] += 0.03460535; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { - result[0] += 0.006955503; - } else { - result[0] += 0.002656106; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 30))) { - result[0] += 0.008213889; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 234))) { - result[0] += -0.0014978464; - } else { - result[0] += 0.0018082943; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 166))) { - result[0] += -0.010998022; - } else { - result[0] += -0.028136352; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 28))) { - result[0] += 0.056712236; - } else { - result[0] += 0.00025265655; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += -0.032531813; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.012461627; - } else { - result[0] += -0.0026103533; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 62))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - result[0] += 0.003723057; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 44))) { - result[0] += 0.050172295; - } else { - result[0] += 0.03318845; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - result[0] += 0.038616996; - } else { - result[0] += -0.010049296; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { - result[0] += -0.0062160105; - } else { - result[0] += -4.625852e-05; - } - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 228))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 220))) { - result[0] += 0.027368566; - } else { - result[0] += -0.0007781659; - } - } else { - result[0] += -0.010964031; - } - } else { - result[0] += 0.0031334688; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 286))) { - result[0] += 0.023848334; - } else { - result[0] += 0.008595723; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { - result[0] += 0.0113761; - } else { - result[0] += 0.0038027503; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { - result[0] += 0.028975934; - } else { - result[0] += 0.01324735; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { - result[0] += -0; - } else { - result[0] += 0.011437596; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { - result[0] += 0.01134281; - } else { - result[0] += 0.0018215694; - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 242))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 230))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 114))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 106))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 180))) { - result[0] += 0.0014854757; - } else { - result[0] += -0.0047508436; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { - result[0] += -0.012526001; - } else { - result[0] += -0.0042038257; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - result[0] += 0.027467722; - } else { - result[0] += 0.005847614; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - result[0] += -0.016319519; - } else { - result[0] += 0.0018143759; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 138))) { - result[0] += 0.0017114735; - } else { - result[0] += -0.04394586; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { - result[0] += 0.014061573; - } else { - result[0] += 0.0076026404; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { - result[0] += -0.0018655244; - } else { - result[0] += 0.011241974; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 54))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - result[0] += -0.013273408; - } else { - result[0] += 0.015772188; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 90))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 58))) { - result[0] += -0.00058818224; - } else { - result[0] += 0.008676103; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - result[0] += -0.010151858; - } else { - result[0] += 0.001280522; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - result[0] += -0.010351776; - } else { - result[0] += 0.012125977; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { - result[0] += -0.005976769; - } else { - result[0] += 0.0058461004; - } - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 22))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - result[0] += 0.0014196447; - } else { - result[0] += -0.016857633; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 18))) { - result[0] += 0.0388976; - } else { - result[0] += 0.0022651523; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - result[0] += -0.0031505611; - } else { - result[0] += -0.02737777; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - result[0] += 0.027305475; - } else { - result[0] += -0.00032103845; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 28))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - result[0] += -0.033979844; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 44))) { - result[0] += -0.00036602697; - } else { - result[0] += -0.009034148; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { - result[0] += 0.02605836; - } else { - result[0] += 0.0035190487; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - result[0] += 0.0030745373; - } else { - result[0] += 0.021341039; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 112))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - result[0] += -0.00765642; - } else { - result[0] += 0.012637125; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 92))) { - result[0] += -0.031098261; - } else { - result[0] += -0.018325815; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 48))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 74))) { - result[0] += 0.007115057; - } else { - result[0] += 0.025569752; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 50))) { - result[0] += -0.030833412; - } else { - result[0] += -0.0027387291; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 118))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += -0.010184347; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { - result[0] += 0.009563519; - } else { - result[0] += 0.0019127353; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { - result[0] += -0.0016026145; - } else { - result[0] += -0.012827705; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { - result[0] += 0.006836459; - } else { - result[0] += -0.00047072658; - } - } - } - } - } - } - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { - result[0] += 0.016250825; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { - result[0] += -0.009732073; - } else { - result[0] += 0.017143272; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - result[0] += -0.009671597; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 270))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 134))) { - result[0] += -0.00035089705; - } else { - result[0] += 0.004113893; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { - result[0] += 0.01820572; - } else { - result[0] += 0.009231458; - } - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += 0.00027064016; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.0017144865; - } else { - result[0] += -0.0095933005; - } - } else { - result[0] += -0.012053235; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 194))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 250))) { - result[0] += -0.0034071447; - } else { - result[0] += 0.046097945; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 132))) { - result[0] += -0.0055225524; - } else { - result[0] += -0.0019266952; - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 250))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 86))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - result[0] += 0.0048346436; - } else { - result[0] += 0.01435117; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { - result[0] += -0.019338546; - } else { - result[0] += -0.0010408615; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 178))) { - result[0] += 0.011773284; - } else { - result[0] += 0.002931762; - } - } else { - result[0] += 0.040974855; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { - result[0] += 0.08329225; - } else { - result[0] += -0.0002780066; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { - result[0] += -0.0042001484; - } else { - result[0] += -0.02084507; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += -0.00010066136; - } else { - result[0] += 0.0027292925; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { - result[0] += -0.0122973155; - } else { - result[0] += 0.035760295; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += -0.001996606; - } else { - result[0] += 0.021067668; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 118))) { - result[0] += -0.013935153; - } else { - result[0] += 0.031183664; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += 0.061950255; - } else { - result[0] += 0.0058407323; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 142))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { - result[0] += 0.0829713; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.03793059; - } else { - result[0] += -0.00907617; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 68))) { - result[0] += 0.0025514578; - } else { - result[0] += -0.0012025639; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 16))) { - result[0] += 0.046701174; - } else { - result[0] += 0.0046325247; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { - result[0] += 0.027825123; - } else { - result[0] += -0; - } - } else { - result[0] += -0.021053893; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 82))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 152))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 106))) { - result[0] += -0.0013631139; - } else { - result[0] += -0.012569079; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - result[0] += -0.0008349316; - } else { - result[0] += 0.022382598; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 156))) { - result[0] += 0.013735485; - } else { - result[0] += 0.0033138485; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - result[0] += -0.0037839809; - } else { - result[0] += 0.002062925; - } - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += 0.0013772444; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { - result[0] += -0.011463809; - } else { - result[0] += -0.0047910134; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { - result[0] += 0.060537297; - } else { - result[0] += 0.010533939; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += 0.016672423; - } else { - result[0] += -0.00052434875; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 250))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - result[0] += 0.0070519983; - } else { - result[0] += 0.016810419; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { - result[0] += -0.026641011; - } else { - result[0] += 0.00010854311; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 232))) { - result[0] += 0.03818845; - } else { - result[0] += 0.012029546; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { - result[0] += -0.007278714; - } else { - result[0] += -0.03333238; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 144))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 46))) { - result[0] += 0.007880951; - } else { - result[0] += -0.00031094736; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - result[0] += 0.013625564; - } else { - result[0] += 0.03885763; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 214))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - result[0] += -0.0029387611; - } else { - result[0] += 7.662349e-05; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += 0.029973779; - } else { - result[0] += 0.0043506036; - } - } - } - } - } - } - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 78))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - result[0] += -0.017686712; - } else { - result[0] += 0.015614047; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 242))) { - result[0] += 0.0010816348; - } else { - result[0] += 0.010603439; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 122))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += -0.013882413; - } else { - result[0] += -3.773386e-06; - } - } else { - result[0] += -0.029591149; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 52))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - result[0] += 0.0029416822; - } else { - result[0] += 0.060454708; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { - result[0] += -0.03178696; - } else { - result[0] += -0.011132187; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 164))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { - result[0] += 0.06373953; - } else { - result[0] += -0.0049581574; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { - result[0] += -0.011388632; - } else { - result[0] += 0.0049218778; - } - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 14))) { - result[0] += 0.0012467568; - } else { - result[0] += -0.019324591; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { - result[0] += 0.001289832; - } else { - result[0] += -0.00096984406; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { - result[0] += -0.011180186; - } else { - result[0] += -0.0001193571; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { - result[0] += 0.017248346; - } else { - result[0] += 0.003171424; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 18))) { - result[0] += 0.04394267; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 176))) { - result[0] += -0.026358157; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += 0.0084524695; - } else { - result[0] += -0.008720714; - } - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 286))) { - result[0] += 0.02462693; - } else { - result[0] += 0.0102696465; - } - } - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 236))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { - result[0] += -9.075111e-05; - } else { - result[0] += -0.0032513805; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { - result[0] += 0.0011170026; - } else { - result[0] += 0.023266837; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 204))) { - result[0] += -0.012011125; - } else { - result[0] += -0.0051451973; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 202))) { - result[0] += 0.023120182; - } else { - result[0] += -0.008411562; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 212))) { - result[0] += 0.017263098; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 230))) { - result[0] += 0.003063401; - } else { - result[0] += 0.021211077; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 234))) { - result[0] += -0.0117486585; - } else { - result[0] += -0.0008188842; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 248))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 40))) { - result[0] += -0.025011426; - } else { - result[0] += -0.01188937; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 90))) { - result[0] += 0.000581751; - } else { - result[0] += 0.035827655; - } - } - } else { - result[0] += -0.056811877; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { - result[0] += 0.03448122; - } else { - result[0] += 0.0075843707; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 120))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 150))) { - result[0] += 0.02317158; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { - result[0] += -0.011207158; - } else { - result[0] += 0.00603795; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - result[0] += 1.6565642e-05; - } else { - result[0] += 0.0018936057; - } - } else { - result[0] += -0.0073121726; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += 0.013665982; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { - result[0] += 0.02345163; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 90))) { - result[0] += 0.031935133; - } else { - result[0] += 0.05211563; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 108))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 150))) { - result[0] += 0.006929166; - } else { - result[0] += 0.0018365175; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { - result[0] += -0.028940136; - } else { - result[0] += -0.0025783076; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.010843903; - } else { - result[0] += 0.006668359; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 84))) { - result[0] += -0.022552337; - } else { - result[0] += -0.0126125915; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 188))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { - result[0] += -0.0033015904; - } else { - result[0] += 0.0023345288; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 172))) { - result[0] += 0.027362315; - } else { - result[0] += 0.008473503; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 198))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - result[0] += -0.0086487485; - } else { - result[0] += 0.0011204499; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - result[0] += -0.0040217172; - } else { - result[0] += 0.0013360606; - } - } - } - } - } - } - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { - result[0] += -0.0038944704; - } else { - result[0] += -0.024639545; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - result[0] += 0.010567961; - } else { - result[0] += -0.0008910143; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 222))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 208))) { - result[0] += 0.0022678128; - } else { - result[0] += 0.010532487; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 20))) { - result[0] += 0.0032915582; - } else { - result[0] += -0.0005411969; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 152))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - result[0] += -0.01407851; - } else { - result[0] += 0.026896363; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 42))) { - result[0] += -0.007195405; - } else { - result[0] += 0.004968326; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 174))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { - result[0] += 0.008754817; - } else { - result[0] += -0.0017400451; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { - result[0] += -0.00865253; - } else { - result[0] += 0.0009031243; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 244))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { - result[0] += 0.00038980006; - } else { - result[0] += -0.016266255; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 18))) { - result[0] += -0.011384182; - } else { - result[0] += -0.005449654; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - result[0] += 0.029928738; - } else { - result[0] += -0.005479242; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 50))) { - result[0] += -0.02587893; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 182))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - result[0] += -0.005359716; - } else { - result[0] += 0.06967212; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 236))) { - result[0] += 0.0018839792; - } else { - result[0] += 0.00916981; - } - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 122))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 104))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += 0.0006954739; - } else { - result[0] += 0.048395343; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { - result[0] += 1.917278e-05; - } else { - result[0] += 0.03755577; - } - } - } else { - result[0] += -0.011912975; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 118))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 220))) { - result[0] += 0.00019196502; - } else { - result[0] += 0.014448823; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - result[0] += 0.019970976; - } else { - result[0] += -0.0011340084; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { - result[0] += -0.009018285; - } else { - result[0] += 0.020422988; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { - result[0] += 0.02001476; - } else { - result[0] += 0.034766663; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 152))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - result[0] += -0.0060089673; - } else { - result[0] += -0.026388485; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 102))) { - result[0] += -0.0338698; - } else { - result[0] += -0.0058778613; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 192))) { - result[0] += 0.025475418; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - result[0] += 0.0019010755; - } else { - result[0] += 0.011219644; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 128))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - result[0] += -0.021041693; - } else { - result[0] += 0.008361343; - } - } else { - result[0] += 0.03766183; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - result[0] += -0.010491718; - } else { - result[0] += 0.003890305; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.011144113; - } else { - result[0] += -8.4675696e-05; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 212))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 166))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { - result[0] += 0.001330308; - } else { - result[0] += 0.02066918; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 130))) { - result[0] += 6.2536885e-05; - } else { - result[0] += -0.013168806; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 90))) { - result[0] += 0.019082962; - } else { - result[0] += 0.0053747944; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - result[0] += -0.0076252148; - } else { - result[0] += 0.0009393127; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 168))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.011447157; - } else { - result[0] += 0.0061047096; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { - result[0] += -0; - } else { - result[0] += -0.020285398; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += 0.01896581; - } else { - result[0] += 0.00095191033; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 172))) { - result[0] += 0.0154045075; - } else { - result[0] += -0.001370645; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += 0.006603177; - } else { - result[0] += 0.00034456272; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - result[0] += -0.0029918782; - } else { - result[0] += -0.012741557; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 166))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 164))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - result[0] += 0.0071618394; - } else { - result[0] += -0.012161553; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { - result[0] += 0.03479741; - } else { - result[0] += 0.00893333; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 178))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 228))) { - result[0] += 0.0005804447; - } else { - result[0] += -0.006457042; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 186))) { - result[0] += 0.016656972; - } else { - result[0] += 0.002414147; - } - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 20))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 124))) { - result[0] += 0.022543216; - } else { - result[0] += 0.0024742563; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 50))) { - result[0] += -0.025850767; - } else { - result[0] += -0.0073662284; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 46))) { - result[0] += 0.0006718464; - } else { - result[0] += -0.0011177754; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { - result[0] += 0.003656535; - } else { - result[0] += -0.0034576224; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 122))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { - result[0] += 0.01316909; - } else { - result[0] += -0.00022220907; - } - } else { - result[0] += 0.0322718; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 132))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - result[0] += 0.0029699726; - } else { - result[0] += -0.009400754; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - result[0] += 0.0157076; - } else { - result[0] += -0.0039490475; - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 136))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 192))) { - result[0] += -0.030938972; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 174))) { - result[0] += 0.027307421; - } else { - result[0] += -0.00040336605; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - result[0] += 0.029921725; - } else { - result[0] += 0.013266409; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 168))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 62))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 144))) { - result[0] += 0.044555373; - } else { - result[0] += 0.0020996393; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 90))) { - result[0] += -0.0010202875; - } else { - result[0] += -0.0097485995; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 170))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - result[0] += 0.024518076; - } else { - result[0] += -0.0032617298; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += -0.002727569; - } else { - result[0] += -0.00034776315; - } - } - } - } - } - } - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 188))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - result[0] += -0.00013192062; - } else { - result[0] += -0.007137733; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - result[0] += 0.0058402964; - } else { - result[0] += 0.00019643598; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 170))) { - result[0] += -0.015256891; - } else { - result[0] += 0.027069787; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { - result[0] += 0.016238226; - } else { - result[0] += 0.045753922; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { - result[0] += -0.019134747; - } else { - result[0] += 0.010413033; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 178))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 122))) { - result[0] += -0.0016630454; - } else { - result[0] += -0.024183424; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { - result[0] += 0.012589683; - } else { - result[0] += 0.0009457501; - } - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 210))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 208))) { - result[0] += -0.0066352724; - } else { - result[0] += -0.017913656; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.0011060528; - } else { - result[0] += 0.0039828527; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 200))) { - result[0] += -0.0031051391; - } else { - result[0] += -0.016724786; - } - } else { - result[0] += -0.024875712; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 162))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 116))) { - result[0] += -0.016162679; - } else { - result[0] += 0.0026748257; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 160))) { - result[0] += 0.01718258; - } else { - result[0] += 0.0035327424; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 114))) { - result[0] += 0.0027622434; - } else { - result[0] += -0.019732043; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { - result[0] += -0.009568975; - } else { - result[0] += -0.0024784; - } - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 212))) { - result[0] += 0.01407246; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - result[0] += -0.04716774; - } else { - result[0] += -0.008298879; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { - result[0] += -0.012182956; - } else { - result[0] += -0.0051582577; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 124))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 128))) { - result[0] += -0.0067259143; - } else { - result[0] += 0.013526896; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { - result[0] += 0.002809637; - } else { - result[0] += -0.0016421535; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 82))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - result[0] += -0.0011062829; - } else { - result[0] += 0.014348181; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { - result[0] += 0.0076159174; - } else { - result[0] += -0.0151206255; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 124))) { - result[0] += 0.0045484654; - } else { - result[0] += 0.015772928; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - result[0] += -0.0059127775; - } else { - result[0] += 0.0025872302; - } - } - } - } - } - } - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 206))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += 0.115350716; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += 0.030523414; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { - result[0] += 0.00818327; - } else { - result[0] += 0.042636707; - } - } else { - result[0] += 0.002394322; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += -0; - } else { - result[0] += 0.022605298; - } - } else { - result[0] += -0.018712113; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - result[0] += -0.018390236; - } else { - result[0] += 0.038224217; - } - } else { - result[0] += 0.0023876673; - } - } - } else { - result[0] += -0.0001913241; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 182))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { - result[0] += 0.002910438; - } else { - result[0] += -0.036367122; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - result[0] += 0.0029667527; - } else { - result[0] += 0.04206534; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.012119245; - } else { - result[0] += -0.023951335; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 178))) { - result[0] += -0.034575075; - } else { - result[0] += 0.007937021; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 138))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { - result[0] += -0.0042928117; - } else { - result[0] += -0.0012923562; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 184))) { - result[0] += -0.028960615; - } else { - result[0] += -0.016311336; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += -0.053593513; - } else { - result[0] += 0.009981692; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 78))) { - result[0] += -0.017819297; - } else { - result[0] += -0.00070253096; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 200))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 44))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 32))) { - result[0] += 0.004783728; - } else { - result[0] += 0.016091399; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { - result[0] += 0.029074514; - } else { - result[0] += 0.002160437; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 250))) { - result[0] += 0.055964243; - } else { - result[0] += 0.01622332; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 250))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 20))) { - result[0] += -0.008321747; - } else { - result[0] += -0.0040490143; - } - } else { - result[0] += -0.018693618; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 250))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - result[0] += 6.4203756e-05; - } else { - result[0] += -0.0021725388; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 268))) { - result[0] += 0.0055606547; - } else { - result[0] += -0.0023256661; - } - } - } - } - } - } - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 182))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - result[0] += -6.4768305e-06; - } else { - result[0] += -0.013116406; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += 0.019466847; - } else { - result[0] += 0.006013643; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 178))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - result[0] += -0.026702961; - } else { - result[0] += 0.0020189236; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { - result[0] += -0.0006026015; - } else { - result[0] += 0.01105925; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - result[0] += 0.029843587; - } else { - result[0] += 0.0661096; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 98))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 100))) { - result[0] += 0.00522026; - } else { - result[0] += -0.0006855772; - } - } else { - result[0] += 0.013178887; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 124))) { - result[0] += -0.020214727; - } else { - result[0] += 0.0038588562; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 200))) { - result[0] += -0.00505647; - } else { - result[0] += -0.026435608; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 270))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { - result[0] += -0.0028923058; - } else { - result[0] += -0.008900782; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { - result[0] += 0.050368883; - } else { - result[0] += 0.0024831446; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 46))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 42))) { - result[0] += 0.011402026; - } else { - result[0] += -0.031253975; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { - result[0] += -0.012896495; - } else { - result[0] += -0.0015336399; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 68))) { - result[0] += 0.011413305; - } else { - result[0] += 0.02147497; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 212))) { - result[0] += 0.013494618; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 46))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 260))) { - result[0] += 0.0066242903; - } else { - result[0] += 0.0001638933; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - result[0] += -0.009525633; - } else { - result[0] += 0.0022819052; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 176))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 70))) { - result[0] += 0.0022807613; - } else { - result[0] += -0.0062286295; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - result[0] += -0.01628782; - } else { - result[0] += -0.004168268; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 104))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 102))) { - result[0] += 0.011599141; - } else { - result[0] += 0.0024420898; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 162))) { - result[0] += 0.013803795; - } else { - result[0] += -0.0013663528; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 146))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { - result[0] += -0.005095913; - } else { - result[0] += 0.00041055848; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 78))) { - result[0] += -0.004392452; - } else { - result[0] += 0.0040476876; - } - } - } - } - } - } - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { - result[0] += -0.00044016525; - } else { - result[0] += 0.0027606068; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 180))) { - result[0] += -0.0027404772; - } else { - result[0] += -0.012484104; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += 0.1066999; - } else { - result[0] += 0.005611764; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { - result[0] += -0.0044804937; - } else { - result[0] += -0.00094216457; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 86))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 20))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += -0.016515603; - } else { - result[0] += -0.0019510689; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += 0.04879087; - } else { - result[0] += 0.019489786; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 174))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - result[0] += 0.028882284; - } else { - result[0] += 0.0029704724; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { - result[0] += -0.008776897; - } else { - result[0] += 0.00076872477; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 22))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 246))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += -0.00024617615; - } else { - result[0] += -0.013363178; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.012836638; - } else { - result[0] += -0.00578899; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 74))) { - result[0] += 0.02552943; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 74))) { - result[0] += 0.014522381; - } else { - result[0] += -0.0055740518; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 50))) { - result[0] += -0.024675723; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { - result[0] += 0.036256976; - } else { - result[0] += -0.010159162; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 186))) { - result[0] += -0.0034239579; - } else { - result[0] += 0.0019339178; - } - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 130))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 126))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 124))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { - result[0] += 0.0021977962; - } else { - result[0] += -0.0026835771; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { - result[0] += 0.014414914; - } else { - result[0] += 0.010211523; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - result[0] += -0.008403643; - } else { - result[0] += -0.024761902; - } - } else { - result[0] += 0.0032713003; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - result[0] += -0.02039499; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.009458961; - } else { - result[0] += -0.0018666508; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - result[0] += 0.0327911; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.036110453; - } else { - result[0] += 0.0033109114; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 48))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 150))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 48))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 104))) { - result[0] += 0.010583217; - } else { - result[0] += -0.01392099; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - result[0] += 0.032042317; - } else { - result[0] += 0.011768063; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 154))) { - result[0] += -0.04997726; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - result[0] += -0.008824881; - } else { - result[0] += 0.0048329984; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - result[0] += 0.00096224155; - } else { - result[0] += -0.009902506; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 146))) { - result[0] += 0.02890099; - } else { - result[0] += 5.074874e-05; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += -0.0082296785; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 96))) { - result[0] += 0.0020531386; - } else { - result[0] += -0.001512293; - } - } - } - } - } - } - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { - result[0] += -0.009371125; - } else { - result[0] += 0.016960299; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - result[0] += -0.008832243; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 254))) { - result[0] += 0.008071503; - } else { - result[0] += 0.013252865; - } - } else { - result[0] += -0.0039770114; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { - result[0] += -0.0074452083; - } else { - result[0] += 0.0010280218; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { - result[0] += 0.020828962; - } else { - result[0] += 0.006464574; - } - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 244))) { - result[0] += 0.044012897; - } else { - result[0] += -0.01173665; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 76))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { - result[0] += 0.013455394; - } else { - result[0] += -0.014296198; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - result[0] += 0.00890102; - } else { - result[0] += -0.00038118512; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { - result[0] += 0.0076601445; - } else { - result[0] += 0.037503462; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { - result[0] += 0.00028127801; - } else { - result[0] += -0.0077372934; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { - result[0] += -0.0058348128; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - result[0] += -0.0035285298; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += 0.0027004466; - } else { - result[0] += 0.009092315; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 4))) { - result[0] += 0.009767297; - } else { - result[0] += -0.008808373; - } - } else { - result[0] += 0.009121577; - } - } else { - result[0] += -0.0003377227; - } - } else { - result[0] += -0.0081705395; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 112))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 110))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - result[0] += 0.012397877; - } else { - result[0] += 0.003961118; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { - result[0] += -0.003340742; - } else { - result[0] += 0.001318198; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { - result[0] += 0.04594936; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - result[0] += -0.012992064; - } else { - result[0] += 0.013072202; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 146))) { - result[0] += -0.016053863; - } else { - result[0] += -0.0013210791; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 120))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { - result[0] += -0.0011712526; - } else { - result[0] += 0.010021153; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 124))) { - result[0] += -0.007040714; - } else { - result[0] += 0.0005415445; - } - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 8))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - result[0] += 0.0068953778; - } else { - result[0] += -0.0090838885; - } - } else { - result[0] += 0.024001935; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - result[0] += -0.009051267; - } else { - result[0] += -0.02782002; - } - } else { - result[0] += 0.007848546; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 8))) { - result[0] += 0.0028921093; - } else { - result[0] += 0.023708088; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - result[0] += 0.0013097594; - } else { - result[0] += -0.001009873; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 190))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - result[0] += 0.017365938; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += -0.026752142; - } else { - result[0] += -0.00315145; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - result[0] += -0.0013150068; - } else { - result[0] += 0.05037552; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - result[0] += -0.02626193; - } else { - result[0] += 0.030513177; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 8))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 44))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.0053047254; - } else { - result[0] += -0.027583335; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 54))) { - result[0] += 0.043980025; - } else { - result[0] += -0.006328375; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { - result[0] += 0.0048042852; - } else { - result[0] += 0.042036824; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.012095607; - } else { - result[0] += -0.00045656387; - } - } - } - } - } - } - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 236))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 112))) { - result[0] += -0.0005225084; - } else { - result[0] += 0.0013189358; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - result[0] += -0.01325873; - } else { - result[0] += 0.008976769; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 98))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { - result[0] += -0.004084352; - } else { - result[0] += 0.0038890864; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 204))) { - result[0] += -0.010790448; - } else { - result[0] += -0.0041690473; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 214))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += 0.00092866377; - } else { - result[0] += -0.021240106; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { - result[0] += 0.016957764; - } else { - result[0] += 0.009066544; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 56))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 120))) { - result[0] += -0.008545898; - } else { - result[0] += -0.0013856884; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 216))) { - result[0] += -0.0058118394; - } else { - result[0] += 0.0030270133; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.004897514; - } else { - result[0] += 0.012878601; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 136))) { - result[0] += -0.009626532; - } else { - result[0] += -0.005689178; - } - } - } else { - result[0] += -0.04376392; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 136))) { - result[0] += 0.03378177; - } else { - result[0] += 0.01186064; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 120))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { - result[0] += -0.0032555745; - } else { - result[0] += 0.0158323; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 188))) { - result[0] += 0.00046772003; - } else { - result[0] += 0.0077147195; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - result[0] += 0.0011735325; - } else { - result[0] += -0.006349434; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 146))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.023203839; - } else { - result[0] += 0.00827275; - } - } else { - result[0] += 0.039015923; - } - } else { - result[0] += 0.016087564; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 156))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 206))) { - result[0] += -0.0005247008; - } else { - result[0] += -0.0068671047; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 188))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 182))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - result[0] += -0.00041580052; - } else { - result[0] += 0.005611544; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { - result[0] += -0.015564476; - } else { - result[0] += -0.005183963; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 172))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 218))) { - result[0] += 0.023443257; - } else { - result[0] += -0.002142666; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { - result[0] += 0.05721947; - } else { - result[0] += 0.000846235; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 8))) { - result[0] += -0.010377513; - } else { - result[0] += 0.002864465; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { - result[0] += -0.041931864; - } else { - result[0] += -0.01965585; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { - result[0] += -0.0147984475; - } else { - result[0] += 0.031754512; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { - result[0] += 0.0027872298; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { - result[0] += -0.0018927314; - } else { - result[0] += 0.00035012572; - } - } - } else { - result[0] += 0.011900772; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += 0.02755776; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { - result[0] += -0.03298561; - } else { - result[0] += -0.008792006; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 260))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 166))) { - result[0] += -0.00033489856; - } else { - result[0] += 0.006294869; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 216))) { - result[0] += -0.00025057443; - } else { - result[0] += 0.005475367; - } - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 22))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 194))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 172))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += 0.034542117; - } else { - result[0] += 0.0051070247; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 38))) { - result[0] += -0.011878603; - } else { - result[0] += 0.015853336; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 12))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 204))) { - result[0] += -0.017843468; - } else { - result[0] += 0.0025024624; - } - } else { - result[0] += -0.038026553; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 0))) { - result[0] += 0.0004767517; - } else { - result[0] += -0.005413917; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { - result[0] += -0.00014004558; - } else { - result[0] += 0.0038309437; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { - result[0] += -0.025853286; - } else { - result[0] += 0.016463885; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { - result[0] += 0.008300542; - } else { - result[0] += -0.0022710604; - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 26))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { - result[0] += 0.06824603; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - result[0] += 0.0344267; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 24))) { - result[0] += -0.02413011; - } else { - result[0] += 0.009757657; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 240))) { - result[0] += 0.04187591; - } else { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += 0.021772258; - } else { - result[0] += 0.00023637361; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - result[0] += -0.00018605802; - } else { - result[0] += 0.00892628; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - result[0] += -0.0029304621; - } else { - result[0] += 0.005322406; - } - } - } - } - } - } - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { - result[0] += 0.058786936; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 204))) { - result[0] += -0.001987611; - } else { - result[0] += 0.0012095956; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += -0.013887591; - } else { - result[0] += -0.005378675; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - result[0] += 0.004469014; - } else { - result[0] += -0.0019160692; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - result[0] += -0.052244443; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 20))) { - result[0] += 0.034024667; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 184))) { - result[0] += -0.028885717; - } else { - result[0] += -0.016080601; - } - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 28))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 196))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 206))) { - result[0] += 0.004683802; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - result[0] += 0.0038840864; - } else { - result[0] += -0.0010689719; - } - } - } else { - result[0] += 0.040237945; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 174))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { - result[0] += 0.009488398; - } else { - result[0] += 0.0063879974; - } - } else { - result[0] += 0.040055256; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 240))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 124))) { - result[0] += 0.0063549983; - } else { - result[0] += 0.0503232; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { - result[0] += -0.0018827593; - } else { - result[0] += 0.0051954878; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 232))) { - result[0] += 0.032296978; - } else { - result[0] += 0.0139222685; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { - result[0] += 0.04671686; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - result[0] += 0.0009886044; - } else { - result[0] += 0.006239516; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 30))) { - result[0] += 0.031537697; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { - result[0] += -0.019081848; - } else { - result[0] += -0.0026591418; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - result[0] += 0.026619418; - } else { - result[0] += 0.0041615735; - } - } else { - result[0] += 0.047726057; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - result[0] += 0.001571185; - } else { - result[0] += -0.00050108694; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 140))) { - result[0] += 0.009636164; - } else { - result[0] += 0.0010100096; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - result[0] += -0.0026403468; - } else { - result[0] += 0.0114326365; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 50))) { - result[0] += -0.021934291; - } else { - result[0] += -0.0050015096; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 238))) { - result[0] += -0.007851736; - } else { - result[0] += 0.03168834; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - result[0] += 0.033044577; - } else { - result[0] += 9.3673785e-05; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 188))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 156))) { - result[0] += 0.00030188911; - } else { - result[0] += 0.002446159; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { - result[0] += -0.0055051325; - } else { - result[0] += 0.0012621379; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { - result[0] += 0.014215629; - } else { - result[0] += 0.005867033; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { - result[0] += -0.006319243; - } else { - result[0] += 0.0017076422; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 166))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 218))) { - result[0] += -0.0017003525; - } else { - result[0] += 0.010260985; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 236))) { - result[0] += -0.015775895; - } else { - result[0] += -0.007966612; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - result[0] += 0.0038435606; - } else { - result[0] += 0.0005556598; - } - } else { - result[0] += 0.0073271217; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 230))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 196))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { - result[0] += -0.00010884991; - } else { - result[0] += -0.0013555246; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - result[0] += 0.00045378748; - } else { - result[0] += 0.00696068; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 180))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - result[0] += 0.0034289937; - } else { - result[0] += -0.005728068; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { - result[0] += -0.009315341; - } else { - result[0] += -0.004357322; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { - result[0] += -0.024190784; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { - result[0] += -0; - } else { - result[0] += -0.019713525; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 220))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 64))) { - result[0] += -0.0078073195; - } else { - result[0] += 5.9448754e-05; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 186))) { - result[0] += -0.006281379; - } else { - result[0] += -0.010314364; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - result[0] += 0.001542045; - } else { - result[0] += -0.0030066923; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { - result[0] += 0.045451414; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 250))) { - result[0] += -0.0048594624; - } else { - result[0] += 0.002339422; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 236))) { - result[0] += 0.0038202107; - } else { - result[0] += -0.0043049804; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 222))) { - result[0] += 0.035619907; - } else { - result[0] += 0.011654694; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { - result[0] += 0.011329205; - } else { - result[0] += -0.0026530891; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - result[0] += 0.00091164187; - } else { - result[0] += 0.0065599764; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 180))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - result[0] += 0.0003863835; - } else { - result[0] += 0.012511316; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 178))) { - result[0] += -0.012533942; - } else { - result[0] += 0.000940961; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - result[0] += 0.02408296; - } else { - result[0] += 0.006255468; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 188))) { - result[0] += 0.01437206; - } else { - result[0] += 0.005517607; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 156))) { - result[0] += -0.004864832; - } else { - result[0] += 0.004785055; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 206))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - result[0] += -0.008446374; - } else { - result[0] += 3.6487032e-05; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { - result[0] += -0.013399954; - } else { - result[0] += 0.0013180381; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 56))) { - result[0] += -0.01676119; - } else { - result[0] += 0.019005135; - } - } else { - result[0] += 0.026536051; - } - } else { - result[0] += -0.017303595; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 128))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 234))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { - result[0] += -0.005325236; - } else { - result[0] += 0.0026667507; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { - result[0] += 0.006688422; - } else { - result[0] += 0.0130848605; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 200))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 180))) { - result[0] += -6.232897e-05; - } else { - result[0] += -0.011703821; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { - result[0] += 0.01631069; - } else { - result[0] += 0.0024924437; - } - } - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 118))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += -0.020456076; - } else { - result[0] += 1.7096074e-05; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += -0.0043996517; - } else { - result[0] += 0.037505116; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { - result[0] += 0.03947706; - } else { - result[0] += 0.00013893798; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { - result[0] += -0.00020466889; - } else { - result[0] += -0.0056823995; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 90))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { - result[0] += 0.0015122422; - } else { - result[0] += 0.014326178; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 98))) { - result[0] += -0.03659178; - } else { - result[0] += -0.00897372; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 234))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { - result[0] += -0.0012049631; - } else { - result[0] += -0.010048345; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - result[0] += 0.023757316; - } else { - result[0] += -0.00096084067; - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { - result[0] += 0.007543098; - } else { - result[0] += 0.05037853; - } - } else { - result[0] += -0.020506142; - } - } else { - result[0] += 0.041784402; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 106))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 168))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - result[0] += 0.022379946; - } else { - result[0] += 0.001123256; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 176))) { - result[0] += -0.0040573548; - } else { - result[0] += -0.00018302095; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { - result[0] += 0.026662258; - } else { - result[0] += 0.001635097; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 172))) { - result[0] += 0.0157876; - } else { - result[0] += 0.00078219484; - } - } - } - } - } - } - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 168))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { - result[0] += -0.003070421; - } else { - result[0] += 0.003986802; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - result[0] += -0.016073924; - } else { - result[0] += -0.00033199994; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 82))) { - result[0] += -0.0026495587; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 180))) { - result[0] += 0.01088603; - } else { - result[0] += 0.0028157744; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 128))) { - result[0] += 0.035645917; - } else { - result[0] += 0.011242066; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 158))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 120))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - result[0] += -0.004172708; - } else { - result[0] += -0.013315861; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - result[0] += 0.0016847762; - } else { - result[0] += -0.019520441; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 116))) { - result[0] += -0.0012042717; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - result[0] += 0.010144642; - } else { - result[0] += 0.021312661; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 122))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { - result[0] += -0.0060045626; - } else { - result[0] += -0.019477958; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 154))) { - result[0] += -0.010557742; - } else { - result[0] += -0.019895343; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 164))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { - result[0] += 0.0023980096; - } else { - result[0] += -0.0058308984; - } - } else { - result[0] += 0.0054435167; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 180))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 170))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 144))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 52))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - result[0] += 0.01054426; - } else { - result[0] += -0.013723351; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 190))) { - result[0] += 0.0035272748; - } else { - result[0] += -0.0014124735; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 128))) { - result[0] += -0.020531978; - } else { - result[0] += -0.008468418; - } - } - } else { - result[0] += 0.027421832; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 108))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 136))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { - result[0] += -0.0014708912; - } else { - result[0] += 0.010910965; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { - result[0] += 0.02383196; - } else { - result[0] += -0.005580553; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 192))) { - result[0] += -0.015012925; - } else { - result[0] += -0.038420197; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 102))) { - result[0] += 0.027166018; - } else { - result[0] += -0.0051050577; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 158))) { - result[0] += 0.005120454; - } else { - result[0] += -0.0066694873; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - result[0] += 0.040612247; - } else { - result[0] += 0.010608687; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 206))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 186))) { - result[0] += 9.48829e-05; - } else { - result[0] += -0.0028726817; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 222))) { - result[0] += 0.0036865217; - } else { - result[0] += -0.0017621756; - } - } - } - } - } - } - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 12))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { - result[0] += 0.010326921; - } else { - result[0] += -6.580556e-05; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 14))) { - result[0] += 0.035937645; - } else { - result[0] += 0.0061092353; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += 0.09306122; - } else { - result[0] += 0.004483877; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { - result[0] += -0.0021131127; - } else { - result[0] += -0.0002122157; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 16))) { - result[0] += -0.0010388399; - } else { - result[0] += 0.04628381; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 170))) { - result[0] += -0.012791668; - } else { - result[0] += 0.012744421; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 14))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { - result[0] += 0.001258762; - } else { - result[0] += -0.010475395; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 18))) { - result[0] += 0.025982514; - } else { - result[0] += 0.0025854723; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 26))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 96))) { - result[0] += -0.020725088; - } else { - result[0] += 0.014970714; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 176))) { - result[0] += -0.0017299574; - } else { - result[0] += 0.026476989; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 158))) { - result[0] += -0.004242038; - } else { - result[0] += -0.029603764; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 100))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 126))) { - result[0] += 0.0020087094; - } else { - result[0] += -0.001871386; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 44))) { - result[0] += 0.014256491; - } else { - result[0] += 0.007524089; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 94))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 206))) { - result[0] += -0.014824388; - } else { - result[0] += -0.0017853121; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - result[0] += -0.004817825; - } else { - result[0] += 0.00016148947; - } - } - } - } - } - } else { - result[0] += 0.0088046715; - } - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 168))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 154))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - result[0] += 9.3958115e-05; - } else { - result[0] += 0.0067572976; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 222))) { - result[0] += -0.0049092458; - } else { - result[0] += -0.0005594916; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 178))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 108))) { - result[0] += 0.00977688; - } else { - result[0] += 0.0015517873; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 204))) { - result[0] += -0.008679122; - } else { - result[0] += -0.002352368; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 158))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - result[0] += 0.009798234; - } else { - result[0] += 0.0032348556; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 182))) { - result[0] += 0.014364332; - } else { - result[0] += 0.025256773; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 118))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { - result[0] += -0.019178228; - } else { - result[0] += -0.0023992255; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { - result[0] += 0.026579231; - } else { - result[0] += 0.0040463707; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 172))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 170))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { - result[0] += -0.012679273; - } else { - result[0] += -0.03488743; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 162))) { - result[0] += 0.012128734; - } else { - result[0] += -0.0036993285; - } - } - } else { - result[0] += -0.026699487; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 164))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 46))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { - result[0] += 0.008520793; - } else { - result[0] += -0.0012307116; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 174))) { - result[0] += -0.0020589442; - } else { - result[0] += -0.0074132094; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 22))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 258))) { - result[0] += -0.00428136; - } else { - result[0] += 0.042908087; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { - result[0] += 0.034873586; - } else { - result[0] += 0.0018836738; - } - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 190))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { - result[0] += -0.0036578246; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 208))) { - result[0] += 0.020423703; - } else { - result[0] += 0.030946225; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - result[0] += -0.006327176; - } else { - result[0] += -0.015637185; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 258))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 232))) { - result[0] += 0.0020563873; - } else { - result[0] += 0.009788949; - } - } else { - result[0] += -0.0091839805; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 194))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { - result[0] += 0.051798742; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 252))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - result[0] += -0.006234445; - } else { - result[0] += 0.008611916; - } - } else { - result[0] += 0.0055716326; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 196))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 194))) { - result[0] += 0.003886501; - } else { - result[0] += 0.008659224; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 198))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { - result[0] += -0.03154314; - } else { - result[0] += -0.0031137222; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - result[0] += 0.00038270376; - } else { - result[0] += 0.0020157003; - } - } - } - } - } - } - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { - result[0] += 0.026274556; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - result[0] += -0.014014624; - } else { - result[0] += 0.0017416707; - } - } else { - result[0] += -0.020999283; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { - result[0] += 0.019021433; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 2))) { - result[0] += 0.00134224; - } else { - result[0] += 0.0040796655; - } - } else { - result[0] += -0.0030546128; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 90))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 46))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - result[0] += -0.0019856154; - } else { - result[0] += -0.0002087965; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - result[0] += 0.013158979; - } else { - result[0] += -0.0030576333; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { - result[0] += 0.003060523; - } else { - result[0] += 0.0005930412; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 48))) { - result[0] += 0.03627108; - } else { - result[0] += -0.0035451804; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - result[0] += -0.0046488056; - } else { - result[0] += 0.00013075671; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 120))) { - result[0] += 0.012213302; - } else { - result[0] += 0.04960065; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { - result[0] += -0.0069404887; - } else { - result[0] += -0.01752409; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - result[0] += 0.0142224645; - } else { - result[0] += -0.0028541964; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - result[0] += -0.00048694783; - } else { - result[0] += -0.01978219; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - result[0] += 0.023798453; - } else { - result[0] += 0.0028695017; - } - } - } else { - result[0] += 0.049870964; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { - result[0] += -0.011660507; - } else { - result[0] += 0.020355195; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { - result[0] += -0.010959279; - } else { - result[0] += -0.022352552; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 104))) { - result[0] += 0.0019975018; - } else { - result[0] += 0.02552997; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.005593583; - } else { - result[0] += 0.0001567727; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 36))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - result[0] += 0.0025115781; - } else { - result[0] += 0.01276709; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += 0.0009829837; - } else { - result[0] += -0.0020405638; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 176))) { - result[0] += -0.0027318567; - } else { - result[0] += 0.00036724083; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { - result[0] += 0.00952085; - } else { - result[0] += 0.00021925311; - } - } - } - } else { - result[0] += 0.0028065902; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 264))) { - result[0] += -0.010198384; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 68))) { - result[0] += 0.008980814; - } else { - result[0] += -0.00083386357; - } - } - } else { - result[0] += 0.034579825; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - result[0] += -0.0005227964; - } else { - result[0] += 0.00078901247; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 264))) { - result[0] += -0.0031115639; - } else { - result[0] += -0.008557741; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 80))) { - result[0] += 0.044274148; - } else { - result[0] += 0.004722073; - } - } else { - result[0] += -0.020457005; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 208))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 214))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { - result[0] += -0.011751657; - } else { - result[0] += -0.004513866; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 174))) { - result[0] += 0.0004462137; - } else { - result[0] += 0.0044212313; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 202))) { - result[0] += -0.015617072; - } else { - result[0] += -0.0095054805; - } - } else { - result[0] += -3.9380015e-05; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 160))) { - result[0] += 0.002189336; - } else { - result[0] += 0.011331045; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 230))) { - result[0] += 0.0016538227; - } else { - result[0] += -0.0024343396; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 186))) { - result[0] += 0.030128485; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { - result[0] += 0.0012932423; - } else { - result[0] += 0.004705872; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += 0.001994251; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 252))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 138))) { - result[0] += -0.00304999; - } else { - result[0] += 0.011378044; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { - result[0] += 0.004555874; - } else { - result[0] += -0.00066039886; - } - } - } - } - } - } - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { - result[0] += 0.020459097; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 234))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 56))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - result[0] += -0.006080057; - } else { - result[0] += 0.0015269167; - } - } else { - result[0] += 0.004850093; - } - } else { - result[0] += 0.0372449; - } - } else { - result[0] += -0.0033630708; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - result[0] += 0.0020560354; - } else { - result[0] += -0.01611949; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { - result[0] += 0.02037514; - } else { - result[0] += 0.0024216073; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { - result[0] += -1.3009435e-06; - } else { - result[0] += -0.010717098; - } - } else { - result[0] += -0.026966467; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { - result[0] += -0.035333917; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 60))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 34))) { - result[0] += -0.0039970325; - } else { - result[0] += -0.018680593; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { - result[0] += 0.060415726; - } else { - result[0] += -0.0035995846; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 12))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { - result[0] += 0.079136275; - } else { - result[0] += 0.006364348; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - result[0] += 0.009026817; - } else { - result[0] += -0.00017536264; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 258))) { - result[0] += 0.0025470634; - } else { - result[0] += -0.0051941304; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 78))) { - result[0] += -0.0008757123; - } else { - result[0] += -0.008570026; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 52))) { - result[0] += 0.005017677; - } else { - result[0] += -7.974629e-05; - } - } - } - } - } - } - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 212))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 190))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 174))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - result[0] += -8.178638e-05; - } else { - result[0] += 0.0078121508; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { - result[0] += -0.005830926; - } else { - result[0] += -0.0010021594; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 262))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 244))) { - result[0] += 0.0016010083; - } else { - result[0] += 0.00825313; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 206))) { - result[0] += -0.003538803; - } else { - result[0] += 0.0063129514; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 198))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 190))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 170))) { - result[0] += -0.005545571; - } else { - result[0] += -0.0232496; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 52))) { - result[0] += 0.012393202; - } else { - result[0] += -4.4827804e-05; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 208))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 200))) { - result[0] += 0.003246121; - } else { - result[0] += -0.00059273635; - } - } else { - result[0] += -0.013444054; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 70))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 180))) { - result[0] += 0.00011026966; - } else { - result[0] += -0.012288004; - } - } else { - result[0] += 0.009097963; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 210))) { - result[0] += -0.004248265; - } else { - result[0] += -0.008213413; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += 0.041847315; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 214))) { - result[0] += 0.008025631; - } else { - result[0] += 0.0023118325; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 238))) { - result[0] += -0.004090266; - } else { - result[0] += 0.001676759; - } - } - } - } - } - } else { - result[0] += 0.0124376435; - } - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 148))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 142))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 130))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 126))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { - result[0] += 8.788087e-05; - } else { - result[0] += -0.0007693504; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 62))) { - result[0] += 0.028994996; - } else { - result[0] += 0.0019165861; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 82))) { - result[0] += 0.0018343481; - } else { - result[0] += -0.001085312; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - result[0] += -0.00844089; - } else { - result[0] += -0.0010173704; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 118))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { - result[0] += 0.03216304; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.019660346; - } else { - result[0] += 0.010661392; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { - result[0] += -0.011746697; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 120))) { - result[0] += 0.040727697; - } else { - result[0] += 0.007775008; - } - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 146))) { - result[0] += -0.017095553; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += -0.010490341; - } else { - result[0] += -0.0029303418; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { - result[0] += 0.0037032042; - } else { - result[0] += 0.019236496; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 88))) { - result[0] += -0.00065161986; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 154))) { - result[0] += 0.013047007; - } else { - result[0] += 0.018410644; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { - result[0] += -0.0025795808; - } else { - result[0] += 0.0025427365; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 44))) { - result[0] += 0.0063687847; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 158))) { - result[0] += 0.021263849; - } else { - result[0] += 0.037114166; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 12))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { - result[0] += -0.007888737; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.0043152072; - } else { - result[0] += -0.00065306626; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 184))) { - result[0] += 0.0002832889; - } else { - result[0] += 0.023496693; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 164))) { - result[0] += -0.004027024; - } else { - result[0] += 0.0036689022; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 52))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 168))) { - result[0] += -0.006764188; - } else { - result[0] += -0.015382841; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 188))) { - result[0] += -0.0006603152; - } else { - result[0] += -0.0053936313; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { - result[0] += 0.0016898311; - } else { - result[0] += 0.010389189; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { - result[0] += -0.0190918; - } else { - result[0] += 6.013682e-05; - } - } - } - } - } - } - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 284))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 30))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 258))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += 0.0022224819; - } else { - result[0] += -9.1448324e-05; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { - result[0] += 0.008647813; - } else { - result[0] += 0.0013861794; - } - } - } else { - result[0] += -0.0051472313; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { - result[0] += 0.022298444; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { - result[0] += 0.014823428; - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 78))) { - result[0] += 0.0028555428; - } else { - result[0] += -0.0060178605; - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 8))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 204))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 144))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { - result[0] += -0.011365654; - } else { - result[0] += 0.008808569; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 166))) { - result[0] += -0.024276486; - } else { - result[0] += -0.0131767215; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 86))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - result[0] += 0.00949425; - } else { - result[0] += -0.013411551; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - result[0] += -0; - } else { - result[0] += 0.012317988; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - result[0] += 0.0853606; - } else { - result[0] += 0.0014590746; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 42))) { - result[0] += -0.04024057; - } else { - result[0] += 0.025248704; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { - result[0] += -0.00020014495; - } else { - result[0] += -0.008825987; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { - result[0] += -0.0026628354; - } else { - result[0] += 0.00010321446; - } - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { - result[0] += -0.002153416; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - result[0] += -0.0011085857; - } else { - result[0] += 0.00558898; - } - } - } - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 64))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 106))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { - result[0] += -0.0072398344; - } else { - result[0] += -0.00012400204; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { - result[0] += 0.025649467; - } else { - result[0] += -0.0001327928; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 94))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { - result[0] += -0.0015077029; - } else { - result[0] += -0.014433789; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += 0.026124759; - } else { - result[0] += 0.0094175115; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 98))) { - result[0] += -0.009028183; - } else { - result[0] += -0.05055972; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - result[0] += 0.008288766; - } else { - result[0] += -0.03171199; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 74))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += 0.029759515; - } else { - result[0] += -0.019024173; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 86))) { - result[0] += 0.0045653116; - } else { - result[0] += 6.555731e-05; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 106))) { - result[0] += 0.029950727; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.0046233633; - } else { - result[0] += 0.020635402; - } - } else { - result[0] += 0.0056214184; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += -0.03250946; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - result[0] += 0.013577218; - } else { - result[0] += -0.031636387; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 62))) { - result[0] += -0.005065638; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.004201682; - } else { - result[0] += 0.0023832265; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 84))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 84))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 152))) { - result[0] += -0.023720464; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { - result[0] += -0.0023072253; - } else { - result[0] += 0.0019047937; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 158))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { - result[0] += -0.0026599832; - } else { - result[0] += 0.03371409; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 98))) { - result[0] += -0.010501557; - } else { - result[0] += 0.03556975; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 92))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 96))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - result[0] += 0.005952064; - } else { - result[0] += 0.024354918; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - result[0] += 0.0024803034; - } else { - result[0] += -0.007694894; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 110))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 56))) { - result[0] += 0.016107908; - } else { - result[0] += -0.0016387564; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { - result[0] += 0.003588716; - } else { - result[0] += -1.9828594e-05; - } - } - } - } - } - } - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 206))) { - result[0] += 0.0046148184; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { - result[0] += 0.0024027901; - } else { - result[0] += 0.00039783234; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - result[0] += 0.044661757; - } else { - result[0] += 0.0023485457; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - result[0] += -0.04294611; - } else { - result[0] += -0.02216121; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += 0.0008537049; - } else { - result[0] += -0.020757614; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 192))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 134))) { - result[0] += -0.0010473432; - } else { - result[0] += -0.012557432; - } - } else { - result[0] += 0.0345631; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 10))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { - result[0] += 0.009390167; - } else { - result[0] += 0.0029766764; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 12))) { - result[0] += -0.00874328; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - result[0] += -0.011705816; - } else { - result[0] += 0.0072610932; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { - result[0] += -0.00043213202; - } else { - result[0] += 0.0003012484; - } - } - } - } - } - } - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 14))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - result[0] += -0.0078075933; - } else { - result[0] += -0.03296116; - } - } else { - result[0] += 0.0045325574; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 12))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - result[0] += 0.0027738758; - } else { - result[0] += -0.0002652021; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.017156854; - } else { - result[0] += 0.0061222934; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - result[0] += 0.009275707; - } else { - result[0] += -0.008091964; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - result[0] += 0.02927331; - } else { - result[0] += 0.0036204413; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - result[0] += -0.009472274; - } else { - result[0] += -0.036276244; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 76))) { - result[0] += -0.0055947257; - } else { - result[0] += -0.016915802; - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - result[0] += 0.0031531143; - } else { - result[0] += -0.020101098; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 10))) { - result[0] += -0.019649826; - } else { - result[0] += -0.0027359962; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { - result[0] += 0.02526692; - } else { - result[0] += -0; - } - } else { - result[0] += 0.0012190904; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 12))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { - result[0] += 0.079478934; - } else { - result[0] += 0.004041801; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.05100611; - } else { - result[0] += 0.013841884; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - result[0] += -0.0001244545; - } else { - result[0] += 0.0013780014; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - result[0] += 0.004001136; - } else { - result[0] += -0.011721842; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - result[0] += 0.015507097; - } else { - result[0] += -0.002207271; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 68))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 174))) { - result[0] += -0.0029817559; - } else { - result[0] += 0.0030468712; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 154))) { - result[0] += 0.008323856; - } else { - result[0] += 0.0378311; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - result[0] += 0.019646537; - } else { - result[0] += -0.009434113; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 80))) { - result[0] += 0.015092474; - } else { - result[0] += 0.00023066562; - } - } - } - } - } - } - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 26))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 12))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 14))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { - result[0] += 0.00044216277; - } else { - result[0] += 0.0030401708; - } - } else { - result[0] += -0.053345073; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - result[0] += 0.024727738; - } else { - result[0] += -0.008567747; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 38))) { - result[0] += 0.036883164; - } else { - result[0] += 0.014384936; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.004501282; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 14))) { - result[0] += -0.023340857; - } else { - result[0] += 0.0061771907; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 76))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.024513956; - } else { - result[0] += -0.0015961519; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { - result[0] += 0.0052294037; - } else { - result[0] += 0.00016616772; - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 218))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 62))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { - result[0] += 0.0042944904; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 122))) { - result[0] += -0.035668056; - } else { - result[0] += 0.033606566; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { - result[0] += 0.013674865; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 98))) { - result[0] += 0.0019538465; - } else { - result[0] += -0.001950983; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 6))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - result[0] += 0.0020924758; - } else { - result[0] += 0.00730929; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 78))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 10))) { - result[0] += 0.00075499614; - } else { - result[0] += 0.0019689128; - } - } else { - result[0] += -0.0016815666; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 58))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 226))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 8))) { - result[0] += -0.0115894945; - } else { - result[0] += -0.051073868; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - result[0] += 0.011092906; - } else { - result[0] += 0.00051702786; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 210))) { - result[0] += -0.013087346; - } else { - result[0] += -0.0062652132; - } - } else { - result[0] += 0.009260368; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 92))) { - result[0] += -0.013211399; - } else { - result[0] += -0.005936895; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 64))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += 0.018809779; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { - result[0] += -0.00044671824; - } else { - result[0] += 0.0025783246; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { - result[0] += -0.008156588; - } else { - result[0] += 0.011215605; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { - result[0] += 0.009001787; - } else { - result[0] += 0.027359499; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += 0.03523052; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { - result[0] += -0.016149698; - } else { - result[0] += -0.0014326718; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 82))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 172))) { - result[0] += 0.009203473; - } else { - result[0] += -0.01101023; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 30))) { - result[0] += 0.007282652; - } else { - result[0] += -3.0036234e-05; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 240))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 160))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { - result[0] += 0.00011574587; - } else { - result[0] += 0.021244911; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { - result[0] += 0.0015066937; - } else { - result[0] += 0.01799863; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 116))) { - result[0] += 0.002711843; - } else { - result[0] += 0.01596604; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 178))) { - result[0] += -0.0002499475; - } else { - result[0] += 0.0038768928; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { - result[0] += 0.022616291; - } else { - result[0] += -0; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { - result[0] += -0.018833179; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { - result[0] += -0.007184063; - } else { - result[0] += -0.00079069706; - } - } - } - } - } else { - result[0] += 0.0025515545; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 254))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 208))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 230))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 218))) { - result[0] += -9.1387315e-05; - } else { - result[0] += -0.0014537438; - } - } else { - result[0] += 0.0155031625; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 142))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 140))) { - result[0] += -0.003084584; - } else { - result[0] += -0.01318902; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 160))) { - result[0] += 0.0037594668; - } else { - result[0] += -0.0014401844; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 88))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { - result[0] += 0.0035882175; - } else { - result[0] += -0.0048747966; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 162))) { - result[0] += 0.0041894084; - } else { - result[0] += 0.0011686251; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 210))) { - result[0] += -0.0026981125; - } else { - result[0] += -0.009141969; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { - result[0] += 0.0005261341; - } else { - result[0] += -0.0018949058; - } - } - } - } - } else { - result[0] += 0.018716332; - } - } - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 236))) { - result[0] += 2.3203478e-05; - } else { - result[0] += -0.0077802497; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - result[0] += 0.031958427; - } else { - result[0] += 0.0035587456; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 128))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { - result[0] += 0.002486235; - } else { - result[0] += 0.011185441; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 204))) { - result[0] += -0.0068403906; - } else { - result[0] += -0.001759151; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 140))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.0073994272; - } else { - result[0] += -0.016759716; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 108))) { - result[0] += 0.028997958; - } else { - result[0] += -0.00054327224; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 232))) { - result[0] += 0.02114836; - } else { - result[0] += -0.011946307; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 264))) { - result[0] += 0.002376872; - } else { - result[0] += -0.0016341202; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - result[0] += -0.03481881; - } else { - result[0] += -0.007797651; - } - } else { - result[0] += -0.00034481628; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { - result[0] += 0.0057917973; - } else { - result[0] += 0.0218565; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 152))) { - result[0] += -0.001166375; - } else { - result[0] += 0.0030204598; - } - } - } - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 100))) { - result[0] += -8.485738e-05; - } else { - result[0] += 0.0070774257; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 148))) { - result[0] += -0.0021743942; - } else { - result[0] += -0.029421; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 172))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - result[0] += 0.0006834134; - } else { - result[0] += 0.010325692; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { - result[0] += -0.001693792; - } else { - result[0] += 0.0011259892; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { - result[0] += -0.0077881524; - } else { - result[0] += 0.0026733195; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 166))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 32))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 72))) { - result[0] += 0.028013662; - } else { - result[0] += 0.0070042475; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { - result[0] += 0.004207076; - } else { - result[0] += -0.016646465; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 182))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { - result[0] += 0.014890812; - } else { - result[0] += -0.0032379036; - } - } else { - result[0] += 0.0058797766; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 166))) { - result[0] += -0.004874174; - } else { - result[0] += -0.014910466; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 194))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 178))) { - result[0] += -0.0024306828; - } else { - result[0] += -0.021217367; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 232))) { - result[0] += 0.0033338335; - } else { - result[0] += -0.00023477618; - } - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 194))) { - result[0] += -0.037188523; - } else { - result[0] += -0.012556768; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 4))) { - result[0] += 0.0015031213; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - result[0] += 0.038557947; - } else { - result[0] += 0.018226711; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 84))) { - result[0] += -0.02505053; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 204))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { - result[0] += -0.0005712052; - } else { - result[0] += -0.0054953173; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 216))) { - result[0] += 0.0013088783; - } else { - result[0] += -0.0013262478; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 214))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 92))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 246))) { - result[0] += -0.0016769763; - } else { - result[0] += 0.004949599; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - result[0] += -0.022474889; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - result[0] += 0.011304743; - } else { - result[0] += 0.0049235076; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 86))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 82))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - result[0] += -0.0013205075; - } else { - result[0] += 0.005631945; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { - result[0] += -0.013254325; - } else { - result[0] += -0.0048218477; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 234))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - result[0] += 0.0018386835; - } else { - result[0] += -0.010448969; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { - result[0] += -0.0026523883; - } else { - result[0] += 0.00015089083; - } - } - } - } - } - } - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 198))) { - result[0] += 0.0024612967; - } else { - result[0] += -0.00267146; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 134))) { - result[0] += 0.0072369473; - } else { - result[0] += 0.03716041; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 34))) { - result[0] += 0.016798072; - } else { - result[0] += -0.020668425; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { - result[0] += -0.0026954925; - } else { - result[0] += 0.00023960511; - } - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 140))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.009252391; - } else { - result[0] += 0.0032986738; - } - } else { - result[0] += -0.01665429; - } - } else { - result[0] += 0.0028180957; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 128))) { - result[0] += 0.02164252; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - result[0] += -0.012067605; - } else { - result[0] += 0.0026951; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 48))) { - result[0] += 0.015092296; - } else { - result[0] += -0.038058575; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { - result[0] += 0.00892017; - } else { - result[0] += 0.031467702; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 216))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 154))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - result[0] += 0.0022178197; - } else { - result[0] += -0.005610294; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { - result[0] += 0.011955344; - } else { - result[0] += -0.0074412758; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - result[0] += 0.0014451804; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.0032450047; - } else { - result[0] += 0.00080591295; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 164))) { - result[0] += 0.010952067; - } else { - result[0] += -0.003597115; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 236))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 144))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 230))) { - result[0] += 0.0010651236; - } else { - result[0] += 0.019118952; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { - result[0] += -0.0010132345; - } else { - result[0] += 0.0005361346; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - result[0] += 0.0063631246; - } else { - result[0] += -0.005013407; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 216))) { - result[0] += -0.0018120991; - } else { - result[0] += 0.0013921553; - } - } - } - } - } - } - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 90))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 6))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { - result[0] += -0.0027856524; - } else { - result[0] += 0.011518478; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 52))) { - result[0] += -0.024774041; - } else { - result[0] += -0.0019057058; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 10))) { - result[0] += -0.01394673; - } else { - result[0] += 0.006964297; - } - } else { - result[0] += 0.05194057; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 152))) { - result[0] += -0.028471593; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 186))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 20))) { - result[0] += -0.0014699342; - } else { - result[0] += 0.024541676; - } - } else { - result[0] += -0.010517646; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 210))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - result[0] += -0.006757573; - } else { - result[0] += 0.0052805874; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 52))) { - result[0] += -0.01105545; - } else { - result[0] += 0.001222587; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 72))) { - result[0] += -0.011149263; - } else { - result[0] += -0.031825926; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 80))) { - result[0] += -6.1052204e-05; - } else { - result[0] += -0.0018558489; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += -0; - } else { - result[0] += 0.037658174; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 184))) { - result[0] += -0.022644173; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 46))) { - result[0] += -0.011395801; - } else { - result[0] += -0.004704497; - } - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 40))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 26))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 116))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { - result[0] += 0.018175911; - } else { - result[0] += 0.0022316524; - } - } else { - result[0] += -0.01448056; - } - } else { - result[0] += 0.03850323; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 10))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { - result[0] += -0.0056385025; - } else { - result[0] += -0.001911032; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 142))) { - result[0] += 0.006841138; - } else { - result[0] += 0.0015013752; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { - result[0] += -0.003987257; - } else { - result[0] += 0.0014403629; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 112))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 140))) { - result[0] += 0.0001458845; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 168))) { - result[0] += -0.018207422; - } else { - result[0] += 0.0007071493; - } - } - } else { - result[0] += -0.014048204; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { - result[0] += 0.024706252; - } else { - result[0] += 0.012115999; - } - } else { - result[0] += -0.010074422; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 160))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - result[0] += 0.00012816093; - } else { - result[0] += -0.0048219212; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - result[0] += 0.00042065704; - } else { - result[0] += -0.00044052277; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 2))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - result[0] += 0.012054322; - } else { - result[0] += -0.012439433; - } - } else { - result[0] += 0.004142578; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 246))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { - result[0] += 0.0005554498; - } else { - result[0] += 0.0034215685; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { - result[0] += 0.00583103; - } else { - result[0] += 0.00022540719; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 0))) { - result[0] += 0.003226608; - } else { - result[0] += 0.029922882; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 20))) { - result[0] += -0.006730991; - } else { - result[0] += -0.001442519; - } - } - } - } - } else { - result[0] += 0.020426724; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 254))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 16))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - result[0] += 0.0003546644; - } else { - result[0] += -0.0044249105; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 132))) { - result[0] += 0.0020044944; - } else { - result[0] += -0.0026672701; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 10))) { - result[0] += -0.007007061; - } else { - result[0] += -0.0024277617; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 156))) { - result[0] += 0.0035193504; - } else { - result[0] += 0.016300239; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - result[0] += -0.002883124; - } else { - result[0] += 0.0034831774; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 32))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 222))) { - result[0] += -0.004244165; - } else { - result[0] += -0.02417766; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 36))) { - result[0] += -0.0009843176; - } else { - result[0] += -0.00013388977; - } - } - } - } - } else { - result[0] += 0.013616696; - } - } - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 44))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 42))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 20))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 28))) { - result[0] += 0.00050606584; - } else { - result[0] += -0.0017093392; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - result[0] += -0.0051450143; - } else { - result[0] += 0.0035812582; - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += -0.010360922; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 6))) { - result[0] += 0.0013417737; - } else { - result[0] += -0.0003085478; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 102))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += 0.06123736; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { - result[0] += -0.010781719; - } else { - result[0] += -0.002857905; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 52))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - result[0] += 0.013251043; - } else { - result[0] += -0.020714538; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 76))) { - result[0] += -0.0037599222; - } else { - result[0] += -0.0004986952; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 24))) { - result[0] += 0.08917741; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 38))) { - result[0] += -0.030496597; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 252))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 274))) { - result[0] += -0.0025217582; - } else { - result[0] += 0.00018528964; - } - } else { - result[0] += -0.014542065; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 222))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.009864878; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 50))) { - result[0] += 0.017176276; - } else { - result[0] += 0.030590722; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 92))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { - result[0] += 0.0058373446; - } else { - result[0] += -0.012375738; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += 0.047506116; - } else { - result[0] += 0.011469667; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { - result[0] += 0.004630673; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 38))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 252))) { - result[0] += -0.004851036; - } else { - result[0] += -0.023372188; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 50))) { - result[0] += 0.036052324; - } else { - result[0] += -0.002185328; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 60))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 56))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 224))) { - result[0] += -0.00040355555; - } else { - result[0] += -0.0124766445; - } - } else { - result[0] += 0.008253347; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 92))) { - result[0] += -0.011534996; - } else { - result[0] += -0.0021382947; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { - result[0] += -0.0014395117; - } else { - result[0] += 0.019073278; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - result[0] += 0.019316873; - } else { - result[0] += 0.0020871195; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { - result[0] += 0.004829779; - } else { - result[0] += 0.021349184; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - result[0] += -0.0045497594; - } else { - result[0] += 0.015120694; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 66))) { - result[0] += 0.0015102568; - } else { - result[0] += -3.608707e-06; - } - } - } - } - } - } - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 54))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - result[0] += 0.00032929177; - } else { - result[0] += 0.029328126; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 216))) { - result[0] += -0.00073194504; - } else { - result[0] += -0.009144339; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += -0.032370485; - } else { - result[0] += 0.0061941766; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 50))) { - result[0] += 0.053673845; - } else { - result[0] += 0.018453414; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 178))) { - result[0] += -0.010862867; - } else { - result[0] += 0.0031142721; - } - } else { - result[0] += 0.04961296; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 28))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 150))) { - result[0] += -0.035367988; - } else { - result[0] += -0.014287199; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 18))) { - result[0] += 0.0022301266; - } else { - result[0] += -0.0076341094; - } - } - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 122))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 74))) { - result[0] += 0.009890891; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 32))) { - result[0] += -0.034998115; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { - result[0] += 0.003447539; - } else { - result[0] += 0.0015428506; - } - } - } - } else { - result[0] += 0.021317383; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { - result[0] += 0.06219096; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 106))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 64))) { - result[0] += 0.0083704125; - } else { - result[0] += 0.0011106033; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - result[0] += -0.006979663; - } else { - result[0] += -0.004151942; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { - result[0] += 0.017166223; - } else { - result[0] += 0.0019738798; - } - } else { - result[0] += -0.025185201; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 70))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 114))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 66))) { - result[0] += 0.0008634852; - } else { - result[0] += -0.008104631; - } - } else { - result[0] += 0.0040236074; - } - } else { - result[0] += 0.010268874; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 82))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 74))) { - result[0] += -0.029302115; - } else { - result[0] += 0.027285008; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { - result[0] += -0.004545824; - } else { - result[0] += -0.00053453137; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - result[0] += 0.0019230042; - } else { - result[0] += -4.2119464e-06; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { - result[0] += -0.010225962; - } else { - result[0] += -0.00024979832; - } - } - } - } - } - } - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 34))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 36))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 26))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 200))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 4))) { - result[0] += -0.0014482096; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 196))) { - result[0] += 0.0071594575; - } else { - result[0] += 0.027249644; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - result[0] += 0.011023153; - } else { - result[0] += -0.010220974; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - result[0] += 0.0010131482; - } else { - result[0] += -0.00073193485; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 208))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - result[0] += -0.049486402; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - result[0] += -0.00315987; - } else { - result[0] += -0.014758741; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 26))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 22))) { - result[0] += 0.004376946; - } else { - result[0] += 0.040119316; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 16))) { - result[0] += -0.010627906; - } else { - result[0] += 0.0038438165; - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 38))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 16))) { - result[0] += 0.0069011683; - } else { - result[0] += 0.016726343; - } - } else { - result[0] += 0.0049917004; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 258))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 186))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - result[0] += 0.01811142; - } else { - result[0] += 0.0030194859; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.002317766; - } else { - result[0] += 0.00087052287; - } - } - } else { - result[0] += -0.0039971955; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 40))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 162))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 38))) { - result[0] += -0.0014173501; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 154))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 152))) { - result[0] += -0.0050792135; - } else { - result[0] += 0.006009587; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { - result[0] += -0.00039048417; - } else { - result[0] += -0.009521704; - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 182))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 182))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 38))) { - result[0] += -0.0080691455; - } else { - result[0] += 0.016010879; - } - } else { - result[0] += -0.01479948; - } - } else { - result[0] += -0.02853345; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 46))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 82))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 38))) { - result[0] += -0.015648594; - } else { - result[0] += 0.005108214; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - result[0] += 0.011618692; - } else { - result[0] += 0.0042830543; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 200))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 42))) { - result[0] += 0.005279971; - } else { - result[0] += -0.009966015; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { - result[0] += -0.0035748226; - } else { - result[0] += 0.0021337403; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 172))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { - result[0] += -0.025904989; - } else { - result[0] += 0.0019193542; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - result[0] += -0.00022157452; - } else { - result[0] += -0.0015951125; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 28))) { - result[0] += -0.00094731955; - } else { - result[0] += 0.029716676; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { - result[0] += 0.00062934245; - } else { - result[0] += -0.00041988047; - } - } - } - } - } - } - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 112))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 110))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 112))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.0063841576; - } else { - result[0] += 0.0017673693; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - result[0] += -0.0013868456; - } else { - result[0] += 0.00015485496; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 72))) { - result[0] += -0.009897975; - } else { - result[0] += -0.030677944; - } - } else { - result[0] += 0.021359773; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - result[0] += 0.021139013; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.0034971682; - } else { - result[0] += -0.0043157833; - } - } else { - result[0] += -0.010502246; - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 144))) { - result[0] += 0.02943166; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.005999833; - } else { - result[0] += -0.0019856528; - } - } else { - result[0] += 0.013738169; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 70))) { - result[0] += 0.008470384; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - result[0] += -0.003441482; - } else { - result[0] += -0.011388111; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 110))) { - result[0] += 0.0024586066; - } else { - result[0] += -0.021055488; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 50))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 76))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 26))) { - result[0] += -0.026231218; - } else { - result[0] += -0.0044914987; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { - result[0] += 0.027324816; - } else { - result[0] += 0.0072242687; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 186))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { - result[0] += -0.0015850182; - } else { - result[0] += -0.010035633; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 134))) { - result[0] += 0.054534066; - } else { - result[0] += 0.0008897401; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 166))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += 0.075174846; - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += -0.0014329397; - } else { - result[0] += 0.002409567; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 180))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - result[0] += -0.007922634; - } else { - result[0] += -0.00042070524; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 158))) { - result[0] += -0.00044648108; - } else { - result[0] += 0.0013399239; - } - } - } - } - } - } - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 180))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 168))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += 0.0013971846; - } else { - result[0] += -6.5496147e-06; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { - result[0] += -0.02558857; - } else { - result[0] += -0.0035198168; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 68))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { - result[0] += -0.0007052002; - } else { - result[0] += 0.011330684; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 110))) { - result[0] += 0.008754961; - } else { - result[0] += 0.017457347; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 6))) { - result[0] += 0.021995483; - } else { - result[0] += 0.0037829764; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.013063647; - } else { - result[0] += -0.008626144; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 218))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 210))) { - result[0] += -0.00048324605; - } else { - result[0] += 0.014737451; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 168))) { - result[0] += 0.03996015; - } else { - result[0] += -0.004091793; - } - } - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 170))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 144))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - result[0] += 0.0020200752; - } else { - result[0] += 0.016987968; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 192))) { - result[0] += -0.013594501; - } else { - result[0] += 0.0012423365; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - result[0] += -0.0187645; - } else { - result[0] += -0.007347082; - } - } - } else { - result[0] += 0.021339634; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 198))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 112))) { - result[0] += 0.06586228; - } else { - result[0] += 0.021907127; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 94))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 142))) { - result[0] += 0.0030923286; - } else { - result[0] += 0.00036826293; - } - } else { - result[0] += 0.04356821; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += 0.01220308; - } else { - result[0] += -0.007081172; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 54))) { - result[0] += 3.0494935e-05; - } else { - result[0] += -0.006945651; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 16))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 10))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - result[0] += 0.00072253105; - } else { - result[0] += -0.020929243; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 194))) { - result[0] += 0.057745833; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 32))) { - result[0] += -0.0037926536; - } else { - result[0] += 0.042418174; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.034033656; - } else { - result[0] += -0.021374384; - } - } else { - result[0] += -0; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 40))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - result[0] += -0.0036783994; - } else { - result[0] += -0.012444709; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 156))) { - result[0] += -0.00028157423; - } else { - result[0] += -0.0030722509; - } - } - } - } - } - } - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 6))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 248))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - result[0] += -0.018552605; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 212))) { - result[0] += 0.013609043; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - result[0] += -0.0035600197; - } else { - result[0] += 0.005957694; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 254))) { - result[0] += -0.0039241104; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 42))) { - result[0] += -0.0053416244; - } else { - result[0] += 0.0076100454; - } - } else { - result[0] += 0.0010679305; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 266))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 8))) { - result[0] += -0.005295289; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 234))) { - result[0] += -1.6435115e-05; - } else { - result[0] += -0.0010428902; - } - } - } else { - result[0] += -0.0050287596; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 228))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 274))) { - result[0] += 0.025240282; - } else { - result[0] += 0.006482261; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 264))) { - result[0] += 0.0052149496; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 202))) { - result[0] += 0.006406661; - } else { - result[0] += -0.0011082895; - } - } - } - } - } - } else { - result[0] += 0.010802027; - } - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 168))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 140))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 136))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 132))) { - result[0] += 2.1751774e-05; - } else { - result[0] += -0.0076580383; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 68))) { - result[0] += -0.00075408455; - } else { - result[0] += 0.015074461; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - result[0] += -0.0018395454; - } else { - result[0] += 0.015883565; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - result[0] += -0.0091815125; - } else { - result[0] += 0.0014608675; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 172))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - result[0] += 0.0026474108; - } else { - result[0] += 0.01692606; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 140))) { - result[0] += 0.02202346; - } else { - result[0] += -0.012683655; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 228))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { - result[0] += -0.0071168602; - } else { - result[0] += -0.00039252793; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - result[0] += 0.001928371; - } else { - result[0] += -0.0002336656; - } - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 148))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 30))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 26))) { - result[0] += -0.025342045; - } else { - result[0] += 0.020604817; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { - result[0] += -0.013844515; - } else { - result[0] += -0.0027904052; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 142))) { - result[0] += 0.019259404; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 170))) { - result[0] += 0.0053436733; - } else { - result[0] += 0.0006968994; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 72))) { - result[0] += -0.013023679; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 96))) { - result[0] += -0.0089388685; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 140))) { - result[0] += -0.013734943; - } else { - result[0] += 0.0016584283; - } - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 158))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 4))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - result[0] += -0.00055690337; - } else { - result[0] += -0.016434593; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - result[0] += 0.076187894; - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 144))) { - result[0] += 0.0040504658; - } else { - result[0] += 0.010756901; - } - } - } - } else { - result[0] += 0.023556888; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 184))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 0))) { - result[0] += 0.06953845; - } else { - result[0] += -0.0023770647; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 186))) { - result[0] += 0.021977352; - } else { - result[0] += 0.0001357905; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 44))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 72))) { - result[0] += 0.0026280086; - } else { - result[0] += 0.015929218; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 44))) { - result[0] += 0.0037350792; - } else { - result[0] += 0.00039957697; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 18))) { - result[0] += 0.03222883; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 176))) { - result[0] += -0.011439302; - } else { - result[0] += -0.0039858185; - } - } - } - } - } - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 0))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 16))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - result[0] += -0.0012402734; - } else { - result[0] += -0.019989545; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 8))) { - result[0] += 0.0026303197; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.040178392; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += -0.0065984237; - } else { - result[0] += 0.02927506; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.020329256; - } else { - result[0] += -0; - } - } else { - result[0] += 0.012915528; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 2))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - result[0] += 0.009343422; - } else { - result[0] += -0.012758999; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - result[0] += -0.027498607; - } else { - result[0] += -0.012787706; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 134))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 24))) { - result[0] += 0.013563978; - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { - result[0] += -0; - } else { - result[0] += 0.017173748; - } - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 88))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 42))) { - result[0] += -0; - } else { - result[0] += -0.018669667; - } - } else { - result[0] += -0; - } - } - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 6))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - result[0] += 0.0013438625; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 94))) { - result[0] += 0.0075284163; - } else { - result[0] += 0.020848578; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 10))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 224))) { - result[0] += 0.0053101867; - } else { - result[0] += -0.005638622; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 226))) { - result[0] += 0.00015324964; - } else { - result[0] += 0.0012270033; - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 240))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 238))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 208))) { - result[0] += -0.0004296944; - } else { - result[0] += 0.00052584225; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 174))) { - result[0] += 0.0010627261; - } else { - result[0] += -0.00823095; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 242))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 256))) { - result[0] += 0.0063353493; - } else { - result[0] += 0.00052647333; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 190))) { - result[0] += 0.0017266994; - } else { - result[0] += -0.0006555208; - } - } - } - } - } - } - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 94))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 80))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 34))) { - result[0] += 0.0004357934; - } else { - result[0] += -0.0016803583; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - result[0] += 0.013963197; - } else { - result[0] += 0.0010920282; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 70))) { - result[0] += -0.0020354139; - } else { - result[0] += -0.0084240325; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { - result[0] += 0.00018836466; - } else { - result[0] += -0.001789179; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 98))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { - result[0] += -0.0021141667; - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 142))) { - result[0] += 0.0008162449; - } else { - result[0] += -0.0007128279; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 118))) { - result[0] += 0.005020843; - } else { - result[0] += 0.015004709; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - result[0] += 0.004781536; - } else { - result[0] += 0.0005883411; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 130))) { - result[0] += -0.035336528; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - result[0] += 0.023583809; - } else { - result[0] += 0.0007455337; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 90))) { - result[0] += 0.0023794405; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - result[0] += 0.023832815; - } else { - result[0] += 0.010761608; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 116))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 146))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 196))) { - result[0] += -0.010108376; - } else { - result[0] += -0.0007176217; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 106))) { - result[0] += 0.0009595863; - } else { - result[0] += -0.004075228; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 148))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 144))) { - result[0] += -0; - } else { - result[0] += 0.01168954; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 142))) { - result[0] += -0.0033294098; - } else { - result[0] += -6.6527915e-05; - } - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 108))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 118))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 48))) { - result[0] += 0.033734445; - } else { - result[0] += 0.0013792046; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { - result[0] += 0.011527828; - } else { - result[0] += 0.0049809148; - } - } - } else { - result[0] += 0.013943759; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 178))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - result[0] += -0.008578103; - } else { - result[0] += 0.0029740678; - } - } else { - result[0] += -0.018524783; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 140))) { - result[0] += -0.00042957798; - } else { - result[0] += -0.009110951; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 132))) { - result[0] += 0.021989485; - } else { - result[0] += 0.0028953014; - } - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 200))) { - result[0] += -0.0064930986; - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 188))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 184))) { - result[0] += 0.00023702074; - } else { - result[0] += 0.0032349098; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 232))) { - result[0] += -0.0035499786; - } else { - result[0] += -0; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 216))) { - result[0] += -0.008582824; - } else { - result[0] += 0.0003269389; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 56))) { - result[0] += 0.006198867; - } else { - result[0] += 0.0029896083; - } - } - } - } - } - } - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 198))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 194))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 148))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 76))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 72))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 24))) { - result[0] += -0.008292492; - } else { - result[0] += 0.048605617; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 22))) { - result[0] += -0.011937882; - } else { - result[0] += -0.0048466916; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 138))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 36))) { - result[0] += -0.0032074524; - } else { - result[0] += 6.964723e-05; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 52))) { - result[0] += 0.024765873; - } else { - result[0] += -0.0021545556; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 110))) { - result[0] += -1.2665853e-05; - } else { - result[0] += -0.023850983; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 154))) { - result[0] += 0.0025796972; - } else { - result[0] += 0.00040402517; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 160))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.0170327; - } else { - result[0] += -0.00829741; - } - } else { - result[0] += -0.0026483634; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 86))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 188))) { - result[0] += 0.0010909947; - } else { - result[0] += 0.0053282953; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 110))) { - result[0] += 0.026514; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 100))) { - result[0] += 0.037232116; - } else { - result[0] += 0.004267938; - } - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 78))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 248))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 126))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 202))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 18))) { - result[0] += 0.026404385; - } else { - result[0] += -0.001118703; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - result[0] += 0.0017804791; - } else { - result[0] += -0.007409166; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 130))) { - result[0] += -0.004484429; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 222))) { - result[0] += 0.031472586; - } else { - result[0] += 0.0001007054; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 4))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 252))) { - result[0] += -0.003407167; - } else { - result[0] += 0.001687183; - } - } else { - result[0] += 0.014353302; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 28))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 28))) { - result[0] += -0.0015060778; - } else { - result[0] += -0.0003457068; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 10))) { - result[0] += -0.013887319; - } else { - result[0] += -0.0035536517; - } - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 238))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 160))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 74))) { - result[0] += -0.0055618854; - } else { - result[0] += -0.0014347414; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 224))) { - result[0] += -0.00070218346; - } else { - result[0] += 0.0014398324; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 8))) { - result[0] += 0.023142483; - } else { - result[0] += -0.000307219; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 24))) { - result[0] += 0.0010922215; - } else { - result[0] += 0.017112693; - } - } - } - } - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 214))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 12))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 2))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - result[0] += -0.010769006; - } else { - result[0] += 0.0069697066; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 24))) { - result[0] += 0.02430601; - } else { - result[0] += 0.00639099; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 62))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 20))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 34))) { - result[0] += 0.015791424; - } else { - result[0] += -0.0046518813; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 36))) { - result[0] += -0.03668975; - } else { - result[0] += -0.012895368; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 142))) { - result[0] += 0.03604609; - } else { - result[0] += 0.012836625; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 50))) { - result[0] += -0.010758377; - } else { - result[0] += -0.001967916; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 220))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 80))) { - result[0] += 0.034449853; - } else { - result[0] += 0.0030996487; - } - } else { - result[0] += -0.015721142; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 254))) { - result[0] += 0.039080422; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 222))) { - result[0] += 0.020187657; - } else { - result[0] += 0.0016309306; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 224))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { - result[0] += -0.01591835; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 64))) { - result[0] += -0.006518031; - } else { - result[0] += -0.0026554007; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 248))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - result[0] += 0.00103551; - } else { - result[0] += 0.00901093; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - result[0] += -0.0012550136; - } else { - result[0] += -1.612309e-05; - } - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 116))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 114))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 130))) { - result[0] += 0.00788109; - } else { - result[0] += -0.0018598955; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 60))) { - result[0] += 0.005137967; - } else { - result[0] += -0.0032950975; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 98))) { - result[0] += -0.042013966; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 72))) { - result[0] += -0.0046039354; - } else { - result[0] += 0.011869277; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 172))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { - result[0] += -0.026546886; - } else { - result[0] += 0.009092043; - } - } else { - result[0] += 0.017844815; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 230))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 102))) { - result[0] += 0.0068409294; - } else { - result[0] += 0.01327271; - } - } else { - result[0] += 0.000659167; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 40))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 84))) { - result[0] += 0.004663289; - } else { - result[0] += -0.006825484; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 56))) { - result[0] += 0.008568982; - } else { - result[0] += 0.0030761992; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 150))) { - result[0] += -0.014622196; - } else { - result[0] += 0.00041277093; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 42))) { - result[0] += 0.0016062713; - } else { - result[0] += 0.046605274; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 172))) { - result[0] += -0.004711694; - } else { - result[0] += -0.0015061334; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 54))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 222))) { - result[0] += 0.012894141; - } else { - result[0] += -0.0020437974; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 124))) { - result[0] += -0.0009532698; - } else { - result[0] += 0.00017820772; - } - } - } - } - } - } - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 274))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 120))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += 0.0047425376; - } else { - result[0] += 0.009442864; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 58))) { - result[0] += -0.016419962; - } else { - result[0] += 0.00078140077; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { - result[0] += -0.016464185; - } else { - result[0] += -0.0012191174; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 106))) { - result[0] += 0.00046863809; - } else { - result[0] += 0.0035006986; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 124))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 160))) { - result[0] += -0.0128822; - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 170))) { - result[0] += -0.0021174157; - } else { - result[0] += -0.01799887; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 80))) { - result[0] += 0.043405965; - } else { - result[0] += 0.0019986776; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - result[0] += -0.00859444; - } else { - result[0] += 0.00027344722; - } - } - } - } - } else { - result[0] += 0.013666634; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 88))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 70))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 228))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 0))) { - result[0] += 0.0035516606; - } else { - result[0] += -1.4989321e-05; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 230))) { - result[0] += -0.003034349; - } else { - result[0] += -0.0006231525; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 34))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 244))) { - result[0] += 0.0052153706; - } else { - result[0] += -0.0051266435; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 84))) { - result[0] += -0.0033818122; - } else { - result[0] += -0.0005771067; - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 30))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 244))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { - result[0] += 0.042181622; - } else { - result[0] += 0.004492736; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { - result[0] += -0.01460122; - } else { - result[0] += 0.00014612076; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 146))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 92))) { - result[0] += -0.001969084; - } else { - result[0] += -6.331263e-05; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 160))) { - result[0] += 0.0028343403; - } else { - result[0] += 0.00031744837; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { - result[0] += -0.006944129; - } else { - result[0] += -0.0027726921; - } - } - } - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 278))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 246))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 232))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 66))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 60))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { - result[0] += 0.0005400666; - } else { - result[0] += -0.0019886708; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { - result[0] += -0.009072294; - } else { - result[0] += -0.0026447035; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 76))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - result[0] += -0.0027691973; - } else { - result[0] += 0.00404422; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 276))) { - result[0] += -2.3040471e-05; - } else { - result[0] += 0.012937434; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 22))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += -0.015312892; - } else { - result[0] += -0.004679353; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 102))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { - result[0] += 0.006205307; - } else { - result[0] += 0.0010604822; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { - result[0] += -0.0003646382; - } else { - result[0] += 0.0056393673; - } - } - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 246))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 98))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - result[0] += 0.0009229826; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 262))) { - result[0] += -0.002368063; - } else { - result[0] += -0.00024388141; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 250))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - result[0] += -0.012024042; - } else { - result[0] += -0.0031967615; - } - } else { - result[0] += -0.00085695874; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 260))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 204))) { - result[0] += -0.00046735056; - } else { - result[0] += 0.0040739644; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 258))) { - result[0] += -0.0032260884; - } else { - result[0] += 0.0010031442; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { - result[0] += -0.009778283; - } else { - result[0] += 0.019902207; - } - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 120))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 282))) { - result[0] += -0.0005751638; - } else { - result[0] += -0.01039412; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 230))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 122))) { - result[0] += -0.01584022; - } else { - result[0] += 0.0018113088; - } - } else { - result[0] += 0.0046630637; - } - } - } - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 24))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 250))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 4))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 36))) { - result[0] += -0.00047560144; - } else { - result[0] += 0.0013354685; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 90))) { - result[0] += -0.0026048406; - } else { - result[0] += 0.00038235518; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 256))) { - result[0] += 0.0044472557; - } else { - result[0] += 0.03397901; - } - } else { - result[0] += -0.0003922035; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 114))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 14))) { - result[0] += 0.031751767; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 100))) { - result[0] += -0.0036668777; - } else { - result[0] += 0.00452039; - } - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 22))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 18))) { - result[0] += -0.002395912; - } else { - result[0] += 0.030751167; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 42))) { - result[0] += -0.015407211; - } else { - result[0] += 0.011755302; - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 32))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 6))) { - result[0] += -0.03115373; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 8))) { - result[0] += 0.0073042098; - } else { - result[0] += 0.033008788; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 94))) { - result[0] += -0.0064700614; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 4))) { - result[0] += -0.007395641; - } else { - result[0] += -0.02954722; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 14))) { - result[0] += -0.01630216; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 184))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 32))) { - result[0] += 0.008391211; - } else { - result[0] += -0.0021036936; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 62))) { - result[0] += -0.00060320273; - } else { - result[0] += 0.0022427232; - } - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 60))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 24))) { - if (LIKELY((data[6].missing != -1) && (data[6].qvalue < 6))) { - result[0] += -0.000107325846; - } else { - result[0] += 0.029023886; - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 48))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 50))) { - result[0] += -0.003524834; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 20))) { - result[0] += 0.002008538; - } else { - result[0] += 0.00040226732; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 132))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 164))) { - result[0] += 0.0028264725; - } else { - result[0] += 0.0078762965; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 190))) { - result[0] += -0.0036010318; - } else { - result[0] += -0.020989217; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 10))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 76))) { - result[0] += -0.025969082; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 76))) { - result[0] += -0.0037539073; - } else { - result[0] += -0.015046534; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 52))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 46))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - result[0] += 0.008154599; - } else { - result[0] += -0.00034012404; - } - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.00019781369; - } else { - result[0] += -0.0046178605; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 60))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 122))) { - result[0] += 0.0022514695; - } else { - result[0] += 0.02182677; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 66))) { - result[0] += -0.002138548; - } else { - result[0] += 0.00019392448; - } - } - } - } - } - } - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 2))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 4))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 4))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 2))) { - result[0] += -0.018533966; - } else { - result[0] += 0.009372647; - } - } else { - result[0] += -0.013326846; - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 0))) { - result[0] += 0.017056702; - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 250))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 260))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 36))) { - result[0] += 0.0010617184; - } else { - result[0] += 0.003547955; - } - } else { - result[0] += -0.002409021; - } - } else { - result[0] += 0.022888973; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 178))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 218))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 240))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 238))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 176))) { - result[0] += -6.381352e-05; - } else { - result[0] += 0.01608517; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - result[0] += 0.031114805; - } else { - result[0] += 0.0031871856; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { - result[0] += 0.0010918784; - } else { - result[0] += 0.010223559; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - result[0] += -0.0053569055; - } else { - result[0] += -0.00082102185; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 140))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 122))) { - result[0] += 0.017271513; - } else { - result[0] += -0.0010735758; - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 210))) { - result[0] += -0.0022431507; - } else { - result[0] += -0.0044196337; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 114))) { - result[0] += 0.03313246; - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 198))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 166))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 116))) { - result[0] += 0.0011855942; - } else { - result[0] += 0.0060151634; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 142))) { - result[0] += -0.007734927; - } else { - result[0] += 0.0012392174; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 200))) { - result[0] += -0.0054051043; - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 252))) { - result[0] += -0.00018920467; - } else { - result[0] += 0.0010806855; - } - } - } - } - } - } - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 270))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 210))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 186))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 184))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 174))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 170))) { - result[0] += -5.627934e-05; - } else { - result[0] += 0.004937595; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 176))) { - result[0] += -0.0081005385; - } else { - result[0] += -0.00034580208; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 214))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 112))) { - result[0] += 0.0017502941; - } else { - result[0] += 0.0067032403; - } - } else { - result[0] += 0.05561105; - } - } - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 114))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 148))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 104))) { - result[0] += -0.017684242; - } else { - result[0] += -0.008623506; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 210))) { - result[0] += -0.0030512868; - } else { - result[0] += 0.00268173; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 138))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 116))) { - result[0] += -0.008280813; - } else { - result[0] += -0.0006436382; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 68))) { - result[0] += 0.005869223; - } else { - result[0] += 0.0157161; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 218))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 38))) { - result[0] += -0.040014755; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 68))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 64))) { - result[0] += 0.0034462882; - } else { - result[0] += 0.012366778; - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 126))) { - result[0] += 0.0029618521; - } else { - result[0] += -0.0031078772; - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 78))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 68))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 156))) { - result[0] += -0.0013473729; - } else { - result[0] += 0.0062149167; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { - result[0] += 0.017057097; - } else { - result[0] += -0.010900224; - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 144))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 212))) { - result[0] += 0.0012661046; - } else { - result[0] += 0.0069153532; - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { - result[0] += -0.016097846; - } else { - result[0] += -0.00017735653; - } - } - } - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 0))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 272))) { - result[0] += 0.010808589; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 218))) { - result[0] += -0.009866092; - } else { - result[0] += 0.0047606938; - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 216))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 108))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 266))) { - result[0] += -0; - } else { - result[0] += 0.014268956; - } - } else { - result[0] += 0.0023136109; - } - } else { - result[0] += -0.0024249533; - } - } - } - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 96))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 74))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 64))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 60))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 40))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 136))) { - result[0] += -0.0002939115; - } else { - result[0] += 0.03095895; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 36))) { - result[0] += -0.016124493; - } else { - result[0] += 0.0012605732; - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 128))) { - result[0] += 0.00055091607; - } else { - result[0] += -0.0038312187; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 62))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - result[0] += 0.020991528; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 30))) { - result[0] += 0.00425994; - } else { - result[0] += -0.012923174; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 72))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 112))) { - result[0] += -0.008987924; - } else { - result[0] += 0.0027231926; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 136))) { - result[0] += 0.00033489117; - } else { - result[0] += 0.0031028683; - } - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 94))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 76))) { - result[0] += -0.023087258; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 40))) { - result[0] += 0.040452894; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 20))) { - result[0] += -0.004355786; - } else { - result[0] += 0.00052141724; - } - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 98))) { - result[0] += -0.026337389; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 78))) { - result[0] += -0.011440942; - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 80))) { - result[0] += 0.0051072245; - } else { - result[0] += -0.0016136405; - } - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 94))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 104))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 66))) { - result[0] += -0.016175192; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 90))) { - result[0] += -0.003506745; - } else { - result[0] += -0.0008637124; - } - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 164))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 80))) { - result[0] += 0.039352115; - } else { - result[0] += 0.009527031; - } - } else { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 168))) { - result[0] += -0.014133029; - } else { - result[0] += 0.0009379142; - } - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 124))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { - result[0] += 0.0014655776; - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 94))) { - result[0] += 0.00799122; - } else { - result[0] += 0.02205122; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 150))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 116))) { - result[0] += 0.00084181456; - } else { - result[0] += 0.03656875; - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - result[0] += -0.0324972; - } else { - result[0] += -0.0043041403; - } - } - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 108))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 130))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 106))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 120))) { - result[0] += -0.014786902; - } else { - result[0] += -0.027247995; - } - } else { - result[0] += -0.013442961; - } - } else { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 106))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 96))) { - result[0] += 0.014649215; - } else { - result[0] += 0.00018173472; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 82))) { - result[0] += -0.003195444; - } else { - result[0] += -0.017632857; - } - } - } - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 218))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 172))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 180))) { - result[0] += 0.0011816436; - } else { - result[0] += 0.006784923; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 174))) { - result[0] += -0.0025611892; - } else { - result[0] += 0.00039800836; - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 222))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 108))) { - result[0] += -0.015747108; - } else { - result[0] += -0.0025709863; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 222))) { - result[0] += 0.00016036122; - } else { - result[0] += -0.004456304; - } - } - } - } - } - } - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 116))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 44))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 0))) { - result[0] += -0.043082695; - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 2))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 14))) { - result[0] += -0.015321686; - } else { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 18))) { - result[0] += 0.009543039; - } else { - result[0] += -0.00492233; - } - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 60))) { - result[0] += -0.018702324; - } else { - result[0] += 0.0030421054; - } - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 110))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 96))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 104))) { - result[0] += 0.023666665; - } else { - result[0] += -0.003085631; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 102))) { - result[0] += 0.0025167426; - } else { - result[0] += -0.0006228736; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 126))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 52))) { - result[0] += 0.025037153; - } else { - result[0] += 0.00086976105; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 130))) { - result[0] += -0.00833582; - } else { - result[0] += 6.457727e-05; - } - } - } - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 48))) { - result[0] += -0.034489177; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 50))) { - result[0] += 0.059084423; - } else { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 54))) { - result[0] += -0.008554864; - } else { - result[0] += -0.000933964; - } - } - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 98))) { - if (UNLIKELY((data[6].missing != -1) && (data[6].qvalue < 2))) { - result[0] += -0.005567677; - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 74))) { - result[0] += 0.026860246; - } else { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 240))) { - if (LIKELY((data[1].missing != -1) && (data[1].qvalue < 238))) { - result[0] += 0.003906974; - } else { - result[0] += 0.016403912; - } - } else { - result[0] += -0.0005779523; - } - } - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 102))) { - if (UNLIKELY((data[1].missing != -1) && (data[1].qvalue < 68))) { - result[0] += -0.021588845; - } else { - result[0] += -0.0072186044; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 152))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 88))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 56))) { - result[0] += 0.00058102736; - } else { - result[0] += 0.0042956616; - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { - result[0] += -0.0021127523; - } else { - result[0] += 0.0012841078; - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 162))) { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 186))) { - result[0] += -0.004427323; - } else { - result[0] += -0.0004450692; - } - } else { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 188))) { - result[0] += 0.0005094443; - } else { - result[0] += -0.00016178952; - } - } - } - } - } - } - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 162))) { - if (LIKELY((data[4].missing != -1) && (data[4].qvalue < 152))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 232))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 230))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 272))) { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 50))) { - result[0] += -0.00010068305; - } else { - result[0] += 0.0004361026; - } - } else { - result[0] += 0.027371911; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 130))) { - result[0] += 0.023174508; - } else { - result[0] += 0.011960031; - } - } - } else { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 242))) { - if (LIKELY((data[2].missing != -1) && (data[2].qvalue < 100))) { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 62))) { - result[0] += -0.015924754; - } else { - result[0] += -0.0012669711; - } - } else { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 188))) { - result[0] += 0.018523319; - } else { - result[0] += -0.00022792198; - } - } - } else { - result[0] += 0.039226815; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 112))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 146))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 102))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 90))) { - result[0] += 0.022155268; - } else { - result[0] += -0.0030770514; - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 66))) { - result[0] += 0.03438976; - } else { - result[0] += 0.016168883; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 80))) { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 128))) { - result[0] += 0.023656188; - } else { - result[0] += 0.009587833; - } - } else { - if (LIKELY((data[7].missing != -1) && (data[7].qvalue < 118))) { - result[0] += 0.003995668; - } else { - result[0] += -0.00010666515; - } - } - } - } else { - if (UNLIKELY((data[0].missing != -1) && (data[0].qvalue < 108))) { - if (UNLIKELY((data[7].missing != -1) && (data[7].qvalue < 150))) { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 136))) { - result[0] += 0.004109849; - } else { - result[0] += 0.027041653; - } - } else { - result[0] += -9.42766e-05; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 154))) { - result[0] += 0.0130727235; - } else { - if (LIKELY((data[3].missing != -1) && (data[3].qvalue < 78))) { - result[0] += -0.0015822932; - } else { - result[0] += -0.008224895; - } - } - } - } - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 164))) { - result[0] += -0.012683782; - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 38))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 268))) { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 146))) { - result[0] += -0.0033396825; - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 162))) { - result[0] += 0.01649657; - } else { - result[0] += 0.0024240483; - } - } - } else { - if (UNLIKELY((data[2].missing != -1) && (data[2].qvalue < 186))) { - result[0] += -0.004931916; - } else { - result[0] += 4.3331627e-05; - } - } - } else { - if (UNLIKELY((data[3].missing != -1) && (data[3].qvalue < 52))) { - if (LIKELY((data[0].missing != -1) && (data[0].qvalue < 202))) { - if (UNLIKELY((data[5].missing != -1) && (data[5].qvalue < 254))) { - result[0] += -0.0077444026; - } else { - result[0] += -0.0008859424; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 178))) { - result[0] += 0.0073036547; - } else { - result[0] += -0.007702441; - } - } - } else { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 228))) { - if (LIKELY((data[5].missing != -1) && (data[5].qvalue < 220))) { - result[0] += -0.00019439656; - } else { - result[0] += -0.0056553436; - } - } else { - if (UNLIKELY((data[4].missing != -1) && (data[4].qvalue < 180))) { - result[0] += 0.0066337893; - } else { - result[0] += 0.00068436674; - } - } - } - } - } - } - - // Apply base_scores - result[0] += 3.356329679489135742; - - // Apply postprocessor - if (!pred_margin) { postprocess(result); } -} - -void pdlp_predictor::postprocess(double* result) -{ - // Do nothing -} - -// Feature names array -const char* pdlp_predictor::feature_names[pdlp_predictor::NUM_FEATURES] = { - "n_vars", "n_cstrs", "nnz", "sparsity", "nnz_stddev", "unbalancedness", "spmv_ops", "total_nnz"}; diff --git a/cpp/src/utilities/models/pdlp_predictor/quantize.cpp b/cpp/src/utilities/models/pdlp_predictor/quantize.cpp deleted file mode 100644 index f5d7ec690..000000000 --- a/cpp/src/utilities/models/pdlp_predictor/quantize.cpp +++ /dev/null @@ -1,1006 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ - -#include "header.h" - -static const float threshold[] = { - 17, - 26, - 31, - 52, - 56, - 58, - 60, - 69, - 86, - 145, - 156, - 160, - 210, - 214, - 219, - 253, - 269, - 272, - 365, - 420, - 479, - 504, - 511, - 649, - 655, - 770, - 780, - 784, - 875, - 888, - 955, - 1024, - 1106, - 1184, - 1187, - 1216, - 1344, - 1400, - 1455, - 1635, - 2025, - 2036, - 2164, - 2272, - 2303, - 2376, - 2442, - 2595, - 2600, - 3034, - 3060, - 3073, - 3079, - 3261, - 3338, - 3472, - 3510, - 3561, - 3654, - 4009, - 4171, - 4272, - 4462, - 4484, - 4704, - 4745, - 4768, - 4798, - 5068, - 5376, - 5662, - 5942, - 5951, - 5956, - 5958, - 6260, - 6481, - 6483, - 6730, - 6868, - 7389, - 7418, - 7745, - 8538, - 10218, - 10720, - 11185, - 11928, - 12414, - 12631, - 12927, - 13410, - 13688, - 13839, - 13869, - 14060, - 14099, - 14370, - 14636, - 15977, - 17187, - 17188, - 18405, - 18865, - 20602, - 20849, - 22480, - 23530, - 23836, - 24923, - 27542, - 29397, - 29435, - 30199, - 32450, - 33154, - 35734, - 37616, - 37648, - 37816, - 40272, - 53593, - 62171, - 63708, - 72468, - 74873, - 74884, - 74918, - 78287, - 86262, - 94595, - 129171, - 129931, - 138056, - 167056, - 184865, - 268358, - 1428996, - 8, - 12, - 13, - 40, - 41, - 45, - 46, - 49, - 61, - 108, - 112, - 126, - 130, - 158, - 162, - 225, - 234, - 241, - 242, - 260, - 270, - 296, - 322, - 332, - 359, - 360, - 397, - 403, - 443, - 444, - 447, - 448, - 482, - 483, - 484, - 487, - 566, - 569, - 577, - 582, - 643, - 659, - 711, - 721, - 723, - 764, - 811, - 867, - 879, - 900, - 1005, - 1025, - 1299, - 1389, - 1500, - 1513, - 1573, - 1634, - 1653, - 1691, - 1759, - 1863, - 1898, - 1917, - 1947, - 2225, - 2301, - 2344, - 2362, - 2366, - 2372, - 2376, - 2433, - 2472, - 2522, - 2526, - 2898, - 3174, - 3249, - 3250, - 3480, - 3491, - 3492, - 3760, - 3897, - 3925, - 4234, - 4241, - 4424, - 4454, - 4463, - 4634, - 4660, - 4811, - 4813, - 5190, - 5594, - 6120, - 6323, - 6333, - 6654, - 6754, - 6894, - 6956, - 8469, - 8620, - 10427, - 10479, - 11334, - 13361, - 14040, - 14766, - 16027, - 16488, - 16925, - 16927, - 18304, - 18609, - 22191, - 24885, - 24971, - 30499, - 32737, - 32933, - 37617, - 43882, - 45221, - 48604, - 53361, - 59577, - 67584, - 99790, - 131866, - 146326, - 169577, - 259962, - 314413, - 2881228, - 11, - 90, - 130, - 300, - 403, - 463, - 480, - 840, - 916, - 1011, - 1023, - 1035, - 1602, - 2221, - 2298, - 2710, - 3120, - 3382, - 3456, - 3718, - 4448, - 4496, - 4875, - 5096, - 5849, - 5940, - 6208, - 6498, - 7023, - 7490, - 7592, - 7755, - 7875, - 8379, - 8991, - 9168, - 9180, - 9714, - 10300, - 10530, - 12612, - 12613, - 14030, - 14576, - 15688, - 17774, - 19143, - 20810, - 22736, - 23965, - 26394, - 29040, - 30477, - 30489, - 31720, - 39920, - 40392, - 43256, - 43305, - 43345, - 43357, - 47777, - 48695, - 50150, - 58368, - 59790, - 65145, - 78260, - 83617, - 94254, - 101208, - 102473, - 110022, - 113048, - 118141, - 119459, - 127416, - 132112, - 147024, - 152533, - 154570, - 178965, - 182574, - 185099, - 187236, - 187968, - 188780, - 203770, - 210725, - 228952, - 228988, - 229044, - 247107, - 271063, - 283189, - 283914, - 293410, - 295358, - 302709, - 319705, - 346211, - 409850, - 433829, - 518476, - 648112, - 648715, - 834722, - 858766, - 1024230, - 1632480, - 1697945, - 2087795, - 2192958, - 4313301, - 2.4999999e-05, - 2.9000001e-05, - 3.7999998e-05, - 4.7000001e-05, - 5.3e-05, - 5.6000001e-05, - 5.9000002e-05, - 7.8999998e-05, - 7.9999998e-05, - 0.000109, - 0.00012899999, - 0.000157, - 0.00017499999, - 0.000181, - 0.000199, - 0.000256, - 0.00028000001, - 0.000283, - 0.00028400001, - 0.000287, - 0.000397, - 0.000401, - 0.00045200001, - 0.00047299999, - 0.00050600001, - 0.00053899997, - 0.00056000001, - 0.00062599999, - 0.00067400001, - 0.00072399998, - 0.00079299998, - 0.00090599997, - 0.001052, - 0.001054, - 0.001064, - 0.00107, - 0.001084, - 0.00122, - 0.001233, - 0.001236, - 0.001297, - 0.001321, - 0.001404, - 0.001618, - 0.001706, - 0.001785, - 0.001862, - 0.0020570001, - 0.0021569999, - 0.002267, - 0.0025269999, - 0.002664, - 0.00278, - 0.0029440001, - 0.003064, - 0.0030789999, - 0.0031069999, - 0.003267, - 0.0032800001, - 0.003491, - 0.0038409999, - 0.0039599999, - 0.0040600002, - 0.0042429999, - 0.004373, - 0.0047650002, - 0.0049749999, - 0.0053010001, - 0.005316, - 0.0054930001, - 0.0055149999, - 0.005903, - 0.0061130002, - 0.0071859998, - 0.007557, - 0.0079190005, - 0.0082029998, - 0.0083109997, - 0.0085450001, - 0.0088409996, - 0.008986, - 0.0096119996, - 0.010158, - 0.010454, - 0.010491, - 0.010588, - 0.011062, - 0.011189, - 0.01136, - 0.011947, - 0.012388, - 0.01285, - 0.013316, - 0.014028, - 0.014862, - 0.017344, - 0.017529, - 0.018440999, - 0.019753, - 0.020732, - 0.021379, - 0.024308, - 0.028986, - 0.029084001, - 0.029973, - 0.031771, - 0.032212, - 0.032758001, - 0.036036, - 0.036471002, - 0.048811998, - 0.048967998, - 0.057353001, - 0.064516, - 0.087884001, - 0.101821, - 0.13953499, - 0.14706101, - 0.229167, - 0.231547, - 0.258656, - 0.26689899, - 0.329083, - 0.34957099, - 0.35977599, - 0.42875499, - 0.91632003, - 0.073445998, - 0.233594, - 0.27638501, - 0.38584599, - 0.39129001, - 0.39430001, - 0.47627199, - 0.49999601, - 0.64261901, - 0.75658399, - 0.767389, - 0.84350997, - 1.015707, - 1.1055419, - 1.15354, - 1.4087991, - 1.722888, - 1.833473, - 1.891134, - 1.944816, - 2.2556751, - 2.4810159, - 2.66401, - 3.046876, - 3.5996871, - 3.6403749, - 4.273489, - 4.50634, - 5.4227982, - 5.6764622, - 6.6741581, - 6.9359412, - 6.9459491, - 9.0884533, - 9.3204279, - 11.047834, - 11.05003, - 11.34829, - 11.977816, - 12.56616, - 12.896968, - 13.082605, - 13.121695, - 15.218937, - 15.219805, - 15.422595, - 15.908748, - 16.333834, - 16.442045, - 17.222109, - 18.255583, - 18.337471, - 18.482592, - 19.853386, - 20.08359, - 20.493109, - 20.575439, - 21.781368, - 21.850649, - 23.025219, - 23.394861, - 23.543165, - 24.943169, - 26.903715, - 29.262121, - 30.220488, - 30.346951, - 30.993811, - 31.029835, - 32.219078, - 34.479328, - 39.028454, - 39.205608, - 39.3573, - 43.941006, - 44.133533, - 49.547768, - 52.339855, - 53.529537, - 56.239407, - 57.230946, - 60.911472, - 61.411575, - 66.006073, - 75.513832, - 76.959404, - 79.399498, - 94.297729, - 101.50806, - 104.90943, - 108.45773, - 125.96421, - 131.78522, - 149.06885, - 149.10501, - 159.80826, - 166.47375, - 190.44113, - 193.85234, - 214.411, - 228.99097, - 243.77342, - 251.07321, - 265.90009, - 277.75555, - 290.5871, - 321.94229, - 349.17996, - 435.58398, - 461.15714, - 480.75259, - 555.98761, - 556.20483, - 632.65863, - 828.06183, - 836.67456, - 857.39771, - 1038.6504, - 1445.1395, - 2359.3203, - 2750.1714, - 3077.2048, - 3270.0989, - 3929.8506, - 3954.1118, - 5246.2725, - 7567.3979, - 14218.984, - 0.0041490002, - 0.036821999, - 0.049214002, - 0.130913, - 0.177389, - 0.18044201, - 0.183018, - 0.19227199, - 0.244839, - 0.30151099, - 0.33716401, - 0.33797899, - 0.33922401, - 0.376284, - 0.436288, - 0.488325, - 0.50635898, - 0.51155603, - 0.53070199, - 0.55729002, - 0.576186, - 0.60621798, - 0.62735999, - 0.63285798, - 0.64827198, - 0.69477397, - 0.727198, - 0.74622601, - 0.76021701, - 0.77570999, - 0.817267, - 0.84039903, - 0.874892, - 0.88309401, - 0.91301, - 0.914644, - 0.92933702, - 0.94280899, - 1.017756, - 1.11876, - 1.1953419, - 1.205152, - 1.207976, - 1.208634, - 1.295553, - 1.2959141, - 1.326723, - 1.347288, - 1.3574491, - 1.390246, - 1.390783, - 1.428421, - 1.4407949, - 1.45542, - 1.502932, - 1.5636179, - 1.581139, - 1.78348, - 1.78504, - 1.801465, - 1.873001, - 2.0050731, - 2.062494, - 2.1306751, - 2.1410699, - 2.142792, - 2.1604609, - 2.301403, - 2.3202839, - 2.320334, - 2.342221, - 2.3494289, - 2.392698, - 2.4694431, - 2.4709809, - 2.548645, - 2.7524309, - 2.8433869, - 2.8817151, - 2.9853411, - 3.0516059, - 3.0590241, - 3.1028869, - 3.2358611, - 3.3461559, - 3.357758, - 3.364897, - 3.3847311, - 3.4308331, - 3.538095, - 3.712795, - 3.7464709, - 3.7710979, - 4.0210929, - 4.3894181, - 4.4247661, - 4.4356871, - 4.64078, - 4.7781639, - 5.100903, - 5.298172, - 5.2985978, - 5.4433179, - 5.4726572, - 5.5151229, - 5.6058269, - 5.6186318, - 5.738615, - 5.7462459, - 5.792984, - 5.9997821, - 6.0248361, - 6.1309428, - 6.1651001, - 6.6284609, - 7.162158, - 7.2263432, - 7.5340571, - 7.7937908, - 8.3784914, - 9.5885639, - 9.5996771, - 9.8454266, - 9.853548, - 10.502564, - 12.32793, - 13.122874, - 14.426889, - 15.470113, - 16.773743, - 19.316851, - 19.475534, - 22.584642, - 28.079082, - 46.668407, - 9, - 12, - 15, - 18, - 21, - 24, - 27, - 30, - 60, - 90, - 120, - 150, - 1620, - 7800, - 13500, - 24200, - 25700, - 28800, - 45400, - 55000, - 60400, - 61900, - 72000, - 96100, - 104000, - 106000, - 133000, - 137000, - 138000, - 152000, - 155000, - 177000, - 191000, - 222000, - 240000, - 264000, - 288000, - 304000, - 333000, - 345000, - 391000, - 408000, - 425000, - 449000, - 468000, - 518000, - 554000, - 583000, - 665000, - 719000, - 731000, - 756000, - 757000, - 772000, - 837000, - 931000, - 975000, - 1050000, - 1060000, - 1120000, - 1150000, - 1160000, - 1210000, - 1350000, - 1440000, - 1740000, - 1820000, - 1830000, - 1890000, - 2020000, - 2400000, - 2430000, - 2550000, - 2600000, - 2680000, - 2760000, - 2870000, - 2920000, - 3050000, - 3310000, - 3410000, - 3500000, - 3590000, - 3630000, - 3740000, - 3850000, - 3910000, - 4360000, - 4570000, - 4760000, - 5080000, - 5660000, - 6060000, - 6070000, - 6150000, - 6490000, - 6500000, - 6600000, - 6780000, - 7090000, - 7170000, - 8100000, - 8760000, - 9040000, - 9270000, - 10700000, - 11300000, - 11600000, - 12100000, - 12600000, - 12700000, - 13700000, - 14100000, - 15200000, - 15900000, - 16500000, - 17100000, - 17600000, - 17900000, - 19200000, - 20800000, - 22500000, - 23200000, - 26000000, - 26800000, - 27800000, - 31100000, - 31400000, - 31600000, - 31700000, - 34300000, - 34400000, - 37100000, - 39800000, - 42800000, - 44300000, - 45400000, - 51500000, - 51900000, - 56600000, - 61500000, - 65100000, - 77500000, - 78400000, - 97200000, - 1.07e+08, - 1.24e+08, - 1.29e+08, - 1.34e+08, - 1.54e+08, - 2.14e+08, - 2.49e+08, - 3.13e+08, - 3.34e+08, - 5.06e+08, - 1.34e+09, -}; - -static const int th_begin[] = { - 0, - 138, - 276, - 390, - 517, - 645, - 780, - 792, -}; - -static const int th_len[] = { - 138, - 138, - 114, - 127, - 128, - 135, - 12, - 144, -}; - -/* - * \brief Function to convert a feature value into bin index. - * \param val Feature value, in floating-point - * \param fid Feature identifier - * \return bin Index corresponding to given feature value - */ -int pdlp_predictor::quantize(float val, unsigned fid) -{ - const size_t offset = th_begin[fid]; - const float* array = &threshold[offset]; - int len = th_len[fid]; - int low = 0; - int high = len; - int mid; - float mval; - // It is possible th_begin[i] == [total_num_threshold]. This means that - // all features i, (i+1), ... are not used for any of the splits in the model. - // So in this case, just return something - if (offset == 936 || val < array[0]) { return -10; } - while (low + 1 < high) { - mid = (low + high) / 2; - mval = array[mid]; - if (val == mval) { - return mid * 2; - } else if (val < mval) { - high = mid; - } else { - low = mid; - } - } - if (array[low] == val) { - return low * 2; - } else if (high == len) { - return len * 2; - } else { - return low * 2 + 1; - } -} diff --git a/cpp/src/utilities/producer_sync.hpp b/cpp/src/utilities/producer_sync.hpp index fed4201b8..dfc316c24 100644 --- a/cpp/src/utilities/producer_sync.hpp +++ b/cpp/src/utilities/producer_sync.hpp @@ -32,7 +32,8 @@ namespace cuopt { * target work unit threshold before proceeding. * * Key invariant: Producers must not fall behind the consumer's horizon. The consumer - * waits at sync points until all producers have caught up. + * waits at sync points until all producers have caught up. The producers are biased + * to ensure they remain likely ahead of the consumer. */ class producer_sync_t { public: diff --git a/cpp/src/utilities/version_info.cpp b/cpp/src/utilities/version_info.cpp index 6b2192afd..ec9db5130 100644 --- a/cpp/src/utilities/version_info.cpp +++ b/cpp/src/utilities/version_info.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -163,43 +163,6 @@ static double get_available_memory_gb() return kb / (1024.0 * 1024.0); // Convert KB to GB } -double get_cpu_max_clock_mhz() -{ - // thread_local to avoid an unecessary sync inserted by the compiler - // due to the standard mandating thread-safe static local variable initialization - // the extra work here is minimal. - thread_local static double cached_mhz = []() { - // Try sysfs cpufreq interface first (returns frequency in KHz) - // FIXME: assumes all available CPUs have the same max clock as CPU0 - std::ifstream freq_file("/sys/devices/system/cpu/cpu0/cpufreq/cpuinfo_max_freq"); - if (freq_file.is_open()) { - long khz = 0; - freq_file >> khz; - if (khz > 0) { return khz / 1e3; } - } - - // Fallback: parse /proc/cpuinfo for "cpu MHz" - std::ifstream cpuinfo("/proc/cpuinfo"); - if (!cpuinfo.is_open()) return 0.0; - - std::string line; - double max_mhz = 0.0; - while (std::getline(cpuinfo, line)) { - if (line.find("cpu MHz") != std::string::npos) { - std::size_t colon = line.find(':'); - if (colon != std::string::npos) { - double mhz = std::stod(line.substr(colon + 1)); - if (mhz > max_mhz) { max_mhz = mhz; } - } - } - } - - return max_mhz; - }(); - - return cached_mhz; -} - void print_version_info() { int device_id = 0; diff --git a/cpp/src/utilities/version_info.hpp b/cpp/src/utilities/version_info.hpp index edb183d64..4f2a725da 100644 --- a/cpp/src/utilities/version_info.hpp +++ b/cpp/src/utilities/version_info.hpp @@ -8,5 +8,4 @@ namespace cuopt { void print_version_info(); -double get_cpu_max_clock_mhz(); } // namespace cuopt diff --git a/cpp/src/utilities/work_unit_predictor.cpp b/cpp/src/utilities/work_unit_predictor.cpp deleted file mode 100644 index d218cacb5..000000000 --- a/cpp/src/utilities/work_unit_predictor.cpp +++ /dev/null @@ -1,85 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "work_unit_predictor.hpp" - -#include -#include -#include -#include -#include -#include -#include - -#include "models/bounds_strengthening_predictor/header.h" -#include "models/cpufj_predictor/header.h" -#include "models/dualsimplex_predictor/header.h" -#include "models/fj_predictor/header.h" -#include "models/pdlp_predictor/header.h" - -#include "hashing.hpp" - -namespace cuopt { - -template -float work_unit_predictor_t::predict_scalar( - const std::map& features) const -{ - raft::common::nvtx::range range("work_unit_predictor_t::predict_scalar"); - - typename model_t::Entry data[model_t::NUM_FEATURES]; - for (int i = 0; i < model_t::NUM_FEATURES; ++i) { - if (features.find(std::string(model_t::feature_names[i])) == features.end()) { - data[i].missing = -1; - CUOPT_LOG_WARN("Feature %s: missing\n", model_t::feature_names[i]); - } else { - data[i].fvalue = features.at(std::string(model_t::feature_names[i])); - } - } - - // std::vector cache_vec; - // cache_vec.reserve(model_t::NUM_FEATURES); - // for (int i = 0; i < model_t::NUM_FEATURES; ++i) { - // cache_vec.push_back(data[i].missing != -1 ? data[i].fvalue - // : std::numeric_limits::quiet_NaN()); - // } - // uint32_t key = cuopt::linear_programming::detail::compute_hash(cache_vec); - - // auto cached_it = prediction_cache.find(key); - // if (cached_it != prediction_cache.end()) { return cached_it->second; } - - double result = 0.0; - auto start = std::chrono::high_resolution_clock::now(); - model_t::predict(data, 0, &result); - auto end = std::chrono::high_resolution_clock::now(); - std::chrono::duration elapsed = end - start; - if (debug) CUOPT_LOG_DEBUG("Prediction time: %f ms", elapsed.count()); - - float scaled_result = scaler_.scale_work_units(result); - // prediction_cache[key] = scaled_result; - if (debug) CUOPT_LOG_DEBUG("Result: %f (scaled: %f)", result, scaled_result); - - return scaled_result; -} - -template class work_unit_predictor_t; -template class work_unit_predictor_t; -template class work_unit_predictor_t; -template class work_unit_predictor_t; -template class work_unit_predictor_t; - -} // namespace cuopt diff --git a/cpp/src/utilities/work_unit_predictor.hpp b/cpp/src/utilities/work_unit_predictor.hpp deleted file mode 100644 index 9d65510f0..000000000 --- a/cpp/src/utilities/work_unit_predictor.hpp +++ /dev/null @@ -1,63 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#pragma once - -#include -#include -#include -#include -#include - -#include - -namespace cuopt { - -// Temporary scaling classes until I figure out better ways to do this -// to account for performance differences between the regression learning machine and the user -// machine. (e.g. integrate memory latency/bandwidth, cache topology, user-provided tuning...) -struct cpu_work_unit_scaler_t { - cpu_work_unit_scaler_t() - { - constexpr double baseline_max_clock = 3800.0; - double max_clock = get_cpu_max_clock_mhz(); - scaling_factor_ = max_clock == 0 ? 1 : baseline_max_clock / max_clock; - } - - double scale_work_units(double work_units) const { return work_units * scaling_factor_; } - - private: - double scaling_factor_; -}; - -struct gpu_work_unit_scaler_t { - double scale_work_units(double work_units) const { return work_units; } -}; - -template -class work_unit_predictor_t { - public: - float predict_scalar(const std::map& features) const; - - public: - bool debug{false}; - - private: - // mutable std::unordered_map prediction_cache; - scaler_t scaler_; -}; - -} // namespace cuopt From e88d5effe9e80029543e5be72f0a4e2028c53403 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 30 Jan 2026 10:12:18 +0000 Subject: [PATCH 324/366] remove debug log machinery --- cpp/src/dual_simplex/branch_and_bound.cpp | 181 +--- cpp/src/dual_simplex/branch_and_bound.hpp | 5 - cpp/src/dual_simplex/bsp_debug.hpp | 1148 --------------------- 3 files changed, 2 insertions(+), 1332 deletions(-) delete mode 100644 cpp/src/dual_simplex/bsp_debug.hpp diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 3ef512f89..c3c46a3ca 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -245,8 +245,7 @@ branch_and_bound_t::branch_and_bound_t( root_relax_soln_(1, 1), root_crossover_soln_(1, 1), pc_(1), - solver_status_(mip_status_t::UNSET), - bsp_debug_settings_(bsp_debug_settings_t::from_environment()) + solver_status_(mip_status_t::UNSET) { exploration_stats_.start_time = tic(); dualize_info_t dualize_info; @@ -1907,10 +1906,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tsize() : 0; settings_.log.printf( "BSP Mode: %d BFS workers + %d diving workers, horizon step = %.2f work " @@ -1928,7 +1923,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tset_sync_callback([this](double sync_target) -> bool { bsp_sync_callback(0); @@ -2044,8 +2038,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t @@ -2119,18 +2111,10 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) work_unit_context_.global_work_units_elapsed = horizon_end; - BSP_DEBUG_LOG_HORIZON_START( - bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_start, horizon_end); - bb_event_batch_t all_events = bsp_workers_->collect_and_sort_events(); - BSP_DEBUG_LOG_SYNC_PHASE_START( - bsp_debug_settings_, bsp_debug_logger_, horizon_end, all_events.size()); - process_history_and_sync(all_events); - BSP_DEBUG_LOG_SYNC_PHASE_END(bsp_debug_settings_, bsp_debug_logger_, horizon_end); - prune_worker_nodes_vs_incumbent(); merge_diving_solutions(); @@ -2140,10 +2124,6 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) assign_diving_nodes(); balance_worker_loads(); - BSP_DEBUG_FLUSH_ASSIGN_TRACE(bsp_debug_settings_, bsp_debug_logger_); - - BSP_DEBUG_LOG_HORIZON_END( - bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end); uint32_t state_hash = 0; { @@ -2169,30 +2149,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) state_hash = detail::compute_hash(state_data); state_hash ^= pc_.compute_state_hash(); - BSP_DEBUG_LOG_HORIZON_HASH( - bsp_debug_settings_, bsp_debug_logger_, bsp_horizon_number_, horizon_end, state_hash); - } - - BSP_DEBUG_EMIT_TREE_STATE(bsp_debug_settings_, - bsp_debug_logger_, - bsp_horizon_number_, - search_tree_.root, - upper_bound_.load()); - - std::vector*> heap_snapshot; - BSP_DEBUG_EMIT_STATE_JSON(bsp_debug_settings_, - bsp_debug_logger_, - bsp_horizon_number_, - horizon_start, - horizon_end, - 0, - upper_bound_.load(), - compute_bsp_lower_bound(), - exploration_stats_.nodes_explored, - exploration_stats_.nodes_unexplored, - *bsp_workers_, - heap_snapshot, - all_events); + } bsp_current_horizon_ += bsp_horizon_step_; @@ -2308,16 +2265,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t double work_units_at_start = worker.work_context.global_work_units_elapsed; double clock_at_start = worker.clock; - double work_limit = worker.horizon_end - worker.clock; - BSP_DEBUG_LOG_SOLVE_START(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - work_limit, - false); - std::fill(worker.node_presolver->bounds_changed.begin(), worker.node_presolver->bounds_changed.end(), false); @@ -2380,22 +2327,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; - // Debug: Log LP input for determinism analysis - if (bsp_debug_settings_.any_enabled()) { - uint64_t path_hash = node_ptr->compute_path_hash(); - uint64_t vstatus_hash = detail::compute_hash(leaf_vstatus); - uint64_t bounds_hash = detail::compute_hash(worker.leaf_problem->lower) ^ - detail::compute_hash(worker.leaf_problem->upper); - BSP_DEBUG_LOG_LP_INPUT(bsp_debug_settings_, - bsp_debug_logger_, - worker.worker_id, - node_ptr->node_id, - path_hash, - node_ptr->depth, - vstatus_hash, - bounds_hash); - } - dual::status_t lp_status = dual_phase2_with_advanced_basis(2, 0, worker.recompute_bounds_and_basis, @@ -2425,24 +2356,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t lp_status = convert_lp_status_to_dual_status(second_status); } - if (bsp_debug_settings_.any_enabled()) { - uint64_t path_hash = node_ptr->compute_path_hash(); - uint64_t sol_hash = detail::compute_hash(leaf_solution.x); - f_t obj = (lp_status == dual::status_t::OPTIMAL) - ? compute_objective(*worker.leaf_problem, leaf_solution.x) - : std::numeric_limits::infinity(); - uint64_t obj_hash = detail::compute_hash(obj); - BSP_DEBUG_LOG_LP_OUTPUT(bsp_debug_settings_, - bsp_debug_logger_, - worker.worker_id, - node_ptr->node_id, - path_hash, - static_cast(lp_status), - node_iter, - obj_hash, - sol_hash); - } - // Validate vstatus after LP solve - check for corruption during simplex { const i_t expected_basic_count = original_lp_.num_rows; @@ -2480,17 +2393,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.track_node_processed(); worker.recompute_bounds_and_basis = true; - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "INFEASIBLE", - node_ptr->lower_bound); - BSP_DEBUG_LOG_INFEASIBLE( - bsp_debug_settings_, bsp_debug_logger_, worker.clock, worker.worker_id, node_ptr->node_id); - search_tree.update(node_ptr, node_status_t::INFEASIBLE); return node_solve_info_t::NO_CHILDREN; @@ -2502,21 +2404,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.track_node_processed(); worker.recompute_bounds_and_basis = true; - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "FATHOMED", - node_ptr->lower_bound); - BSP_DEBUG_LOG_FATHOMED(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->lower_bound); - search_tree.update(node_ptr, node_status_t::FATHOMED); return node_solve_info_t::NO_CHILDREN; @@ -2564,21 +2451,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.track_node_processed(); worker.recompute_bounds_and_basis = true; - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "INTEGER", - leaf_objective); - BSP_DEBUG_LOG_INTEGER(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - leaf_objective); - search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); return node_solve_info_t::NO_CHILDREN; @@ -2605,23 +2477,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t worker.track_node_branched(); worker.track_node_processed(); - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "BRANCH", - leaf_objective); - BSP_DEBUG_LOG_BRANCHED(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - down_child_id, - up_child_id); - exploration_stats_.nodes_unexplored += 2; rounding_direction_t preferred = @@ -2633,27 +2488,11 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t : node_solve_info_t::UP_CHILD_FIRST; } else { - // Record event and debug logs BEFORE search_tree.update() which may delete the node worker.record_fathomed(node_ptr, leaf_objective); worker.track_node_pruned(); worker.track_node_processed(); worker.recompute_bounds_and_basis = true; - BSP_DEBUG_LOG_SOLVE_END(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - node_ptr->origin_worker_id, - "FATHOMED", - leaf_objective); - BSP_DEBUG_LOG_FATHOMED(bsp_debug_settings_, - bsp_debug_logger_, - worker.clock, - worker.worker_id, - node_ptr->node_id, - leaf_objective); - search_tree.update(node_ptr, node_status_t::FATHOMED); return node_solve_info_t::NO_CHILDREN; } @@ -2784,9 +2623,6 @@ void branch_and_bound_t::process_history_and_sync( hsol.wut, hsol.objective, bsp_current_horizon_); - // Debug: Log heuristic received - BSP_DEBUG_LOG_HEURISTIC_RECEIVED( - bsp_debug_settings_, bsp_debug_logger_, hsol.wut, hsol.objective); // Process heuristic solution at its correct work unit timestamp position f_t new_upper = std::numeric_limits::infinity(); @@ -2795,10 +2631,6 @@ void branch_and_bound_t::process_history_and_sync( upper_bound_ = hsol.objective; incumbent_.set_incumbent_solution(hsol.objective, hsol.solution); new_upper = hsol.objective; - - // Debug: Log incumbent update - BSP_DEBUG_LOG_INCUMBENT_UPDATE( - bsp_debug_settings_, bsp_debug_logger_, hsol.wut, hsol.objective, "heuristic"); } if (new_upper < std::numeric_limits::infinity()) { @@ -3004,15 +2836,6 @@ void branch_and_bound_t::balance_worker_loads() size_t worker_idx = worker_order[i % num_workers]; (*bsp_workers_)[worker_idx].enqueue_node(all_nodes[i]); (*bsp_workers_)[worker_idx].track_node_assigned(); - - double wut = bsp_current_horizon_; - BSP_DEBUG_LOG_NODE_ASSIGNED(bsp_debug_settings_, - bsp_debug_logger_, - wut, - static_cast(worker_idx), - all_nodes[i]->node_id, - all_nodes[i]->origin_worker_id, - all_nodes[i]->lower_bound); } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 567f6e286..8e7f22d20 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -9,7 +9,6 @@ #include #include -#include #include #include #include @@ -382,10 +381,6 @@ class branch_and_bound_t { omp_mutex_t mutex_heuristic_queue_; std::vector heuristic_solution_queue_; - // BSP debug logger (settings read from environment variables) - bsp_debug_settings_t bsp_debug_settings_; - bsp_debug_logger_t bsp_debug_logger_; - // ============================================================================ // BSP Diving state // ============================================================================ diff --git a/cpp/src/dual_simplex/bsp_debug.hpp b/cpp/src/dual_simplex/bsp_debug.hpp deleted file mode 100644 index 3ba2aeda6..000000000 --- a/cpp/src/dual_simplex/bsp_debug.hpp +++ /dev/null @@ -1,1148 +0,0 @@ -/* clang-format off */ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ -/* clang-format on */ - -#pragma once - -// Enable BSP debug macros. The actual logging is controlled at runtime via -// environment variables (CUOPT_BSP_DEBUG_*). This define enables the macro -// infrastructure; without it, all BSP_DEBUG_* macros become complete no-ops. -// #define BSP_DEBUG_ENABLED - -#include -#include - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -namespace cuopt::linear_programming::dual_simplex { - -// ============================================================================ -// BSP Debug Event Types -// ============================================================================ - -enum class bsp_log_event_t { - HORIZON_START, // New horizon begins - HORIZON_END, // Horizon completed - HORIZON_HASH, // Determinism fingerprint for horizon - NODE_ASSIGNED, // Node assigned to worker - NODE_SOLVE_START, // Worker starts solving node - NODE_SOLVE_END, // Worker finishes node (with result type) - NODE_PAUSED, // Node paused at horizon - NODE_RESUMED, // Paused node resumed - NODE_BRANCHED, // Node branched into children - NODE_PRUNED, // Node pruned (cutoff) - NODE_INTEGER, // Integer solution found - NODE_INFEASIBLE, // Node infeasible - NODE_FATHOMED, // Node fathomed - FINAL_ID_ASSIGNED, // Final ID assigned during sync - HEURISTIC_RECEIVED, // Heuristic solution received - INCUMBENT_UPDATE, // New best solution found - SYNC_PHASE_START, // Sync phase begins - SYNC_PHASE_END, // Sync phase ends - WORKER_IDLE, // Worker has no work - LP_INPUT, // LP solver input hashes (for determinism debugging) - LP_OUTPUT, // LP solver output hashes (for determinism debugging) -}; - -inline const char* bsp_log_event_name(bsp_log_event_t event) -{ - switch (event) { - case bsp_log_event_t::HORIZON_START: return "HORIZON_START"; - case bsp_log_event_t::HORIZON_END: return "HORIZON_END"; - case bsp_log_event_t::HORIZON_HASH: return "HORIZON_HASH"; - case bsp_log_event_t::NODE_ASSIGNED: return "NODE_ASSIGNED"; - case bsp_log_event_t::NODE_SOLVE_START: return "NODE_SOLVE_START"; - case bsp_log_event_t::NODE_SOLVE_END: return "NODE_SOLVE_END"; - case bsp_log_event_t::NODE_PAUSED: return "NODE_PAUSED"; - case bsp_log_event_t::NODE_RESUMED: return "NODE_RESUMED"; - case bsp_log_event_t::NODE_BRANCHED: return "NODE_BRANCHED"; - case bsp_log_event_t::NODE_PRUNED: return "NODE_PRUNED"; - case bsp_log_event_t::NODE_INTEGER: return "NODE_INTEGER"; - case bsp_log_event_t::NODE_INFEASIBLE: return "NODE_INFEASIBLE"; - case bsp_log_event_t::NODE_FATHOMED: return "NODE_FATHOMED"; - case bsp_log_event_t::FINAL_ID_ASSIGNED: return "FINAL_ID_ASSIGNED"; - case bsp_log_event_t::HEURISTIC_RECEIVED: return "HEURISTIC_RECEIVED"; - case bsp_log_event_t::INCUMBENT_UPDATE: return "INCUMBENT_UPDATE"; - case bsp_log_event_t::SYNC_PHASE_START: return "SYNC_PHASE_START"; - case bsp_log_event_t::SYNC_PHASE_END: return "SYNC_PHASE_END"; - case bsp_log_event_t::WORKER_IDLE: return "WORKER_IDLE"; - case bsp_log_event_t::LP_INPUT: return "LP_INPUT"; - case bsp_log_event_t::LP_OUTPUT: return "LP_OUTPUT"; - default: return "UNKNOWN"; - } -} - -// ============================================================================ -// BSP Debug Settings -// ============================================================================ - -/** - * @brief BSP debug settings controlled via environment variables. - * - * Environment variables: - * CUOPT_BSP_DEBUG_LOG=1 Enable structured event log - * CUOPT_BSP_DEBUG_TIMELINE=1 Emit ASCII timeline visualization - * CUOPT_BSP_DEBUG_TREE=1 Emit DOT tree state files - * CUOPT_BSP_DEBUG_JSON=1 Emit JSON state dumps - * CUOPT_BSP_DEBUG_TRACE=1 Emit determinism trace file - * CUOPT_BSP_DEBUG_ALL=1 Enable all debug output - * CUOPT_BSP_DEBUG_DIR=path Output directory (default: ./bsp_debug/) - * CUOPT_BSP_DEBUG_LEVEL=N Log level: 0=off, 1=major events, 2=all events - * CUOPT_BSP_DEBUG_FLUSH_EVERY_HORIZON=0 Disable flushing debug output every horizon (default: 1) - */ -struct bsp_debug_settings_t { - bool enable_event_log{false}; - bool enable_timeline{false}; - bool enable_tree_dot{false}; - bool enable_state_json{false}; - bool enable_determinism_trace{false}; - bool flush_every_horizon{true}; // Flush debug output at end of each horizon - std::string output_dir{"./bsp_debug/"}; - int log_level{1}; // 0=off, 1=major events, 2=all events - - bool any_enabled() const - { - return enable_event_log || enable_timeline || enable_tree_dot || enable_state_json || - enable_determinism_trace; - } - - void enable_all() - { - enable_event_log = true; - enable_timeline = true; - enable_tree_dot = true; - enable_state_json = true; - enable_determinism_trace = true; - } - - void disable_all() - { - enable_event_log = false; - enable_timeline = false; - enable_tree_dot = false; - enable_state_json = false; - enable_determinism_trace = false; - } - - /** - * @brief Initialize settings from environment variables. - * @return A bsp_debug_settings_t populated from environment variables. - */ - static bsp_debug_settings_t from_environment() - { - bsp_debug_settings_t settings; - - auto get_env_bool = [](const char* name) -> bool { - const char* val = std::getenv(name); - if (val == nullptr) return false; - return std::string(val) == "1" || std::string(val) == "true" || std::string(val) == "TRUE"; - }; - - auto get_env_int = [](const char* name, int default_val) -> int { - const char* val = std::getenv(name); - if (val == nullptr) return default_val; - try { - return std::stoi(val); - } catch (...) { - return default_val; - } - }; - - auto get_env_string = [](const char* name, const std::string& default_val) -> std::string { - const char* val = std::getenv(name); - if (val == nullptr) return default_val; - return std::string(val); - }; - - // Check for CUOPT_BSP_DEBUG_ALL first - if (get_env_bool("CUOPT_BSP_DEBUG_ALL")) { - settings.enable_all(); - } else { - settings.enable_event_log = get_env_bool("CUOPT_BSP_DEBUG_LOG"); - settings.enable_timeline = get_env_bool("CUOPT_BSP_DEBUG_TIMELINE"); - settings.enable_tree_dot = get_env_bool("CUOPT_BSP_DEBUG_TREE"); - settings.enable_state_json = get_env_bool("CUOPT_BSP_DEBUG_JSON"); - settings.enable_determinism_trace = get_env_bool("CUOPT_BSP_DEBUG_TRACE"); - } - - settings.output_dir = get_env_string("CUOPT_BSP_DEBUG_DIR", "./bsp_debug/"); - settings.log_level = get_env_int("CUOPT_BSP_DEBUG_LEVEL", 1); - - // Flush every horizon is ON by default; set to 0 to disable - const char* flush_env = std::getenv("CUOPT_BSP_DEBUG_FLUSH_EVERY_HORIZON"); - if (flush_env != nullptr) { - settings.flush_every_horizon = - (std::string(flush_env) == "1" || std::string(flush_env) == "true"); - } - - return settings; - } -}; - -// ============================================================================ -// Timeline Event (for ASCII visualization) -// ============================================================================ - -struct timeline_event_t { - double wut_start; - double wut_end; - int worker_id; - int node_id; - int final_id; - std::string result; // "BRANCH", "INTEGER", "FATHOMED", "PAUSED", etc. -}; - -// ============================================================================ -// BSP Debug Logger Class -// ============================================================================ - -template -class bsp_debug_logger_t { - public: - bsp_debug_logger_t() : start_time_(std::chrono::steady_clock::now()) {} - - void set_settings(const bsp_debug_settings_t& settings) - { - settings_ = settings; - if (settings_.any_enabled()) { - try { - std::filesystem::create_directories(settings_.output_dir); - } catch (const std::filesystem::filesystem_error& e) { - settings_.enable_event_log = false; - settings_.enable_timeline = false; - settings_.enable_tree_dot = false; - settings_.enable_state_json = false; - settings_.enable_determinism_trace = false; - } - - // Clear timeline file at start (it uses append mode for each horizon) - if (settings_.enable_timeline) { - std::string filename = settings_.output_dir + "bsp_timeline.txt"; - std::ofstream file(filename, std::ios::trunc); - if (file.is_open()) { - file << "BSP Timeline Visualization\n"; - file << "==========================\n"; - file.close(); - } - } - } - } - - void set_num_workers(int num_workers) { num_workers_ = num_workers; } - - void set_horizon_step(double step) { horizon_step_ = step; } - - // ======================================================================== - // Event Logging - // ======================================================================== - - void log_horizon_start(int horizon_num, double wut_start, double wut_end) - { - current_horizon_ = horizon_num; - horizon_wut_start_ = wut_start; - horizon_wut_end_ = wut_end; - - if (settings_.enable_event_log) { - log_event(wut_start, - -1, - bsp_log_event_t::HORIZON_START, - -1, - -1, - "horizon=[" + std::to_string(wut_start) + "," + std::to_string(wut_end) + ")"); - } - - if (settings_.enable_determinism_trace) { - trace_ss_ << "H" << horizon_num << ":START:wut=[" << wut_start << "," << wut_end << ")\n"; - } - - // Clear timeline events for new horizon - timeline_events_.clear(); - } - - void log_horizon_end(int horizon_num, double work_unit_ts) - { - if (settings_.enable_event_log) { - log_event(work_unit_ts, -1, bsp_log_event_t::HORIZON_END, -1, -1, ""); - } - - if (settings_.enable_timeline) { emit_timeline_for_horizon(horizon_num); } - } - - // Log determinism fingerprint hash for the horizon - // This hash captures all state that should be identical across deterministic runs - void log_horizon_hash(int horizon_num, double work_unit_ts, uint32_t hash) - { - std::lock_guard lock(mutex_); - - if (settings_.enable_event_log) { - std::stringstream ss; - ss << "hash=0x" << std::hex << std::setfill('0') << std::setw(8) << hash; - log_event_unlocked(work_unit_ts, -1, bsp_log_event_t::HORIZON_HASH, -1, -1, ss.str()); - } - - if (settings_.enable_determinism_trace) { - trace_ss_ << "H" << horizon_num << ":HASH:0x" << std::hex << std::setfill('0') << std::setw(8) - << hash << std::dec << "\n"; - } - } - - void log_node_assigned( - double work_unit_ts, int worker_id, i_t node_id, i_t final_id, f_t lower_bound) - { - if (settings_.enable_event_log) { - log_event(work_unit_ts, - worker_id, - bsp_log_event_t::NODE_ASSIGNED, - node_id, - final_id, - "lb=" + std::to_string(lower_bound)); - } - - if (settings_.enable_determinism_trace) { - assign_trace_ss_ << "W" << worker_id << "=" << final_id << ","; - } - } - - void flush_assign_trace() - { - if (settings_.enable_determinism_trace && assign_trace_ss_.str().length() > 0) { - std::string s = assign_trace_ss_.str(); - if (!s.empty() && s.back() == ',') s.pop_back(); - trace_ss_ << "H" << current_horizon_ << ":ASSIGN:" << s << "\n"; - assign_trace_ss_.str(""); - assign_trace_ss_.clear(); - } - } - - void log_solve_start(double work_unit_ts, - int worker_id, - i_t node_id, - i_t final_id, - double work_limit, - bool is_resumed) - { - std::lock_guard lock(mutex_); - - if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked(work_unit_ts, - worker_id, - bsp_log_event_t::NODE_SOLVE_START, - node_id, - final_id, - "work_limit=" + std::to_string(work_limit)); - } - - // Start timeline event - current_solve_start_[worker_id] = work_unit_ts; - current_solve_node_[worker_id] = node_id; - current_solve_fid_[worker_id] = final_id; - } - - void log_solve_end(double work_unit_ts, - int worker_id, - i_t node_id, - i_t final_id, - const std::string& result, - f_t lower_bound) - { - std::lock_guard lock(mutex_); - - if (settings_.enable_event_log) { - log_event_unlocked(work_unit_ts, - worker_id, - bsp_log_event_t::NODE_SOLVE_END, - node_id, - final_id, - "result=" + result + ",lb=" + std::to_string(lower_bound)); - } - - // Complete timeline event - if (settings_.enable_timeline) { - timeline_event_t te; - te.wut_start = current_solve_start_[worker_id]; - te.wut_end = work_unit_ts; - te.worker_id = worker_id; - te.node_id = node_id; - te.final_id = final_id; - te.result = result; - timeline_events_.push_back(te); - } - } - - void log_branched( - double work_unit_ts, int worker_id, i_t parent_id, i_t parent_fid, i_t down_id, i_t up_id) - { - std::lock_guard lock(mutex_); - - if (settings_.enable_event_log) { - log_event_unlocked(work_unit_ts, - worker_id, - bsp_log_event_t::NODE_BRANCHED, - parent_id, - parent_fid, - "children=" + std::to_string(down_id) + "," + std::to_string(up_id)); - } - - if (settings_.enable_determinism_trace) { - // Log parent final_id (deterministic) and children node_ids (non-deterministic until sync) - events_trace_ss_ << "BRANCH(" << work_unit_ts << ",W" << worker_id << ",F" << parent_fid - << "->" << down_id << "," << up_id << "),"; - } - } - - void log_paused( - double work_unit_ts, int worker_id, i_t node_id, i_t final_id, f_t accumulated_wut) - { - std::lock_guard lock(mutex_); - - if (settings_.enable_event_log) { - log_event_unlocked(work_unit_ts, - worker_id, - bsp_log_event_t::NODE_PAUSED, - node_id, - final_id, - "acc_wut=" + std::to_string(accumulated_wut)); - } - - if (settings_.enable_determinism_trace) { - events_trace_ss_ << "PAUSE(" << work_unit_ts << ",W" << worker_id << "," << node_id << "),"; - } - } - - void log_integer(double work_unit_ts, int worker_id, i_t node_id, f_t objective) - { - std::lock_guard lock(mutex_); - - if (settings_.enable_event_log) { - log_event_unlocked(work_unit_ts, - worker_id, - bsp_log_event_t::NODE_INTEGER, - node_id, - -1, - "obj=" + std::to_string(objective)); - } - - if (settings_.enable_determinism_trace) { - events_trace_ss_ << "INT(" << work_unit_ts << ",W" << worker_id << "," << objective << "),"; - } - } - - void log_fathomed(double work_unit_ts, int worker_id, i_t node_id, f_t lower_bound) - { - std::lock_guard lock(mutex_); - - if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked(work_unit_ts, - worker_id, - bsp_log_event_t::NODE_FATHOMED, - node_id, - -1, - "lb=" + std::to_string(lower_bound)); - } - } - - void log_infeasible(double work_unit_ts, int worker_id, i_t node_id) - { - std::lock_guard lock(mutex_); - - if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked( - work_unit_ts, worker_id, bsp_log_event_t::NODE_INFEASIBLE, node_id, -1, ""); - } - } - - void log_pruned(double work_unit_ts, i_t node_id, i_t final_id, f_t lower_bound, f_t upper_bound) - { - std::lock_guard lock(mutex_); - - if (settings_.enable_event_log && settings_.log_level >= 2) { - log_event_unlocked( - work_unit_ts, - -1, - bsp_log_event_t::NODE_PRUNED, - node_id, - final_id, - "lb=" + std::to_string(lower_bound) + ",ub=" + std::to_string(upper_bound)); - } - } - - void log_sync_phase_start(double work_unit_ts, size_t num_events) - { - if (settings_.enable_event_log) { - log_event(work_unit_ts, - -1, - bsp_log_event_t::SYNC_PHASE_START, - -1, - -1, - "num_events=" + std::to_string(num_events)); - } - } - - void log_sync_phase_end(double work_unit_ts) - { - if (settings_.enable_event_log) { - log_event(work_unit_ts, -1, bsp_log_event_t::SYNC_PHASE_END, -1, -1, ""); - } - - // Flush event trace - if (settings_.enable_determinism_trace) { - std::string s = events_trace_ss_.str(); - if (!s.empty() && s.back() == ',') s.pop_back(); - trace_ss_ << "H" << current_horizon_ << ":EVENTS:" << s << "\n"; - events_trace_ss_.str(""); - events_trace_ss_.clear(); - } - } - - void log_final_id_assigned(i_t provisional_id, i_t final_id) - { - if (settings_.enable_determinism_trace) { - final_ids_trace_ss_ << provisional_id << "->" << final_id << ","; - } - } - - void flush_final_ids_trace() - { - if (settings_.enable_determinism_trace && final_ids_trace_ss_.str().length() > 0) { - std::string s = final_ids_trace_ss_.str(); - if (!s.empty() && s.back() == ',') s.pop_back(); - trace_ss_ << "H" << current_horizon_ << ":FINAL_IDS:" << s << "\n"; - final_ids_trace_ss_.str(""); - final_ids_trace_ss_.clear(); - } - } - - void log_heuristic_received(double work_unit_ts, f_t objective) - { - if (settings_.enable_event_log) { - log_event(work_unit_ts, - -1, - bsp_log_event_t::HEURISTIC_RECEIVED, - -1, - -1, - "obj=" + std::to_string(objective)); - } - - if (settings_.enable_determinism_trace) { - trace_ss_ << "H" << current_horizon_ << ":HEURISTIC:" << objective << "@" << work_unit_ts - << "\n"; - } - } - - void log_incumbent_update(double work_unit_ts, f_t objective, const std::string& source) - { - if (settings_.enable_event_log) { - log_event(work_unit_ts, - -1, - bsp_log_event_t::INCUMBENT_UPDATE, - -1, - -1, - "obj=" + std::to_string(objective) + ",source=" + source); - } - - if (settings_.enable_determinism_trace) { - trace_ss_ << "H" << current_horizon_ << ":INCUMBENT:" << objective << "@" << work_unit_ts - << "(" << source << ")\n"; - } - } - - void log_heap_order(const std::vector& final_ids) - { - if (settings_.enable_determinism_trace) { - trace_ss_ << "H" << current_horizon_ << ":HEAP_ORDER:"; - for (size_t i = 0; i < final_ids.size(); ++i) { - trace_ss_ << final_ids[i]; - if (i < final_ids.size() - 1) trace_ss_ << ","; - } - trace_ss_ << "\n"; - } - } - - // ======================================================================== - // LP Determinism Logging - // ======================================================================== - - void log_lp_input(int worker_id, - i_t node_id, - uint64_t path_hash, - i_t depth, - uint64_t vstatus_hash, - uint64_t bounds_hash) - { - std::lock_guard lock(mutex_); - - // Log to main events file (always when BSP debug is enabled) - if (settings_.enable_event_log) { - char details[320]; - std::snprintf(details, - sizeof(details), - "path=0x%016llx,d=%d,vstatus=0x%016llx,bounds=0x%016llx", - static_cast(path_hash), - depth, - static_cast(vstatus_hash), - static_cast(bounds_hash)); - log_event_unlocked(-1.0, worker_id, bsp_log_event_t::LP_INPUT, node_id, -1, details); - } - - // Also log to determinism trace if enabled - if (settings_.enable_determinism_trace) { - char buf[320]; - std::snprintf(buf, - sizeof(buf), - "LP_IN(W%d,N%d,path=0x%016llx,d=%d,vs=0x%016llx,bnd=0x%016llx)", - worker_id, - node_id, - static_cast(path_hash), - depth, - static_cast(vstatus_hash), - static_cast(bounds_hash)); - events_trace_ss_ << buf << ","; - } - } - - void log_lp_output(int worker_id, - i_t node_id, - uint64_t path_hash, - int status, - i_t iters, - uint64_t obj_bits, - uint64_t sol_hash) - { - std::lock_guard lock(mutex_); - - // Log to main events file (always when BSP debug is enabled) - if (settings_.enable_event_log) { - char details[320]; - std::snprintf(details, - sizeof(details), - "path=0x%016llx,status=%d,iters=%d,obj=0x%016llx,sol=0x%016llx", - static_cast(path_hash), - status, - iters, - static_cast(obj_bits), - static_cast(sol_hash)); - log_event_unlocked(-1.0, worker_id, bsp_log_event_t::LP_OUTPUT, node_id, -1, details); - } - - // Also log to determinism trace if enabled - if (settings_.enable_determinism_trace) { - char buf[320]; - std::snprintf(buf, - sizeof(buf), - "LP_OUT(W%d,N%d,path=0x%016llx,st=%d,it=%d,obj=0x%016llx,sol=0x%016llx)", - worker_id, - node_id, - static_cast(path_hash), - status, - iters, - static_cast(obj_bits), - static_cast(sol_hash)); - events_trace_ss_ << buf << ","; - } - } - - void log_branch_decision(i_t branch_var, uint64_t score_hash, size_t num_ties) - { - if (settings_.enable_determinism_trace && settings_.log_level >= 2) { - std::lock_guard lock(mutex_); - char buf[128]; - std::snprintf(buf, - sizeof(buf), - "BVAR(v=%d,sc=0x%016llx,ties=%zu)", - branch_var, - static_cast(score_hash), - num_ties); - events_trace_ss_ << buf << ","; - } - } - - // ======================================================================== - // Tree State (DOT format) - // ======================================================================== - - void emit_tree_state(int horizon_num, const mip_node_t& root, f_t upper_bound) - { - if (!settings_.enable_tree_dot) return; - - std::string filename = - settings_.output_dir + "bsp_tree_h" + std::to_string(horizon_num) + ".dot"; - std::ofstream file(filename); - if (!file.is_open()) return; - - file << "digraph BSPTree_Horizon" << horizon_num << " {\n"; - file << " rankdir=TB;\n"; - file << " node [shape=box];\n\n"; - - // Traverse tree and emit nodes - emit_tree_node_recursive(file, &root, upper_bound); - - file << "}\n"; - file.close(); - } - - // ======================================================================== - // JSON State Dump - // ======================================================================== - - template - void emit_state_json(int horizon_num, - double wut_start, - double wut_end, - i_t next_final_id, - f_t upper_bound, - f_t lower_bound, - i_t nodes_explored, - i_t nodes_unexplored, - const WorkerPool& workers, - const std::vector*>& heap_nodes, - const bb_event_batch_t& events) - { - if (!settings_.enable_state_json) return; - - std::string filename = - settings_.output_dir + "bsp_state_h" + std::to_string(horizon_num) + ".json"; - std::ofstream file(filename); - if (!file.is_open()) return; - - file << "{\n"; - file << " \"horizon\": " << horizon_num << ",\n"; - file << " \"wut_range\": [" << wut_start << ", " << wut_end << "],\n"; - file << " \"bsp_next_final_id\": " << next_final_id << ",\n"; - file << " \"upper_bound\": " << (upper_bound >= 1e30 ? "\"inf\"" : std::to_string(upper_bound)) - << ",\n"; - file << " \"lower_bound\": " << lower_bound << ",\n"; - file << " \"nodes_explored\": " << nodes_explored << ",\n"; - file << " \"nodes_unexplored\": " << nodes_unexplored << ",\n"; - - // Workers - file << " \"workers\": [\n"; - for (int i = 0; i < workers.size(); ++i) { - const auto& w = workers[i]; - file << " {\n"; - file << " \"id\": " << i << ",\n"; - file << " \"clock\": " << w.clock << ",\n"; - if (w.current_node != nullptr) { - file << " \"current_node\": {\"id\": " << w.current_node->node_id - << ", \"origin_worker\": " << w.current_node->origin_worker_id - << ", \"creation_seq\": " << w.current_node->creation_seq - << ", \"acc_wut\": " << w.current_node->accumulated_wut << "},\n"; - } else { - file << " \"current_node\": null,\n"; - } - file << " \"plunge_stack_size\": " << w.plunge_stack.size() << ",\n"; - file << " \"backlog_size\": " << w.backlog.size() << "\n"; - file << " }" << (i < workers.size() - 1 ? "," : "") << "\n"; - } - file << " ],\n"; - - // Heap - file << " \"heap\": [\n"; - for (size_t i = 0; i < heap_nodes.size(); ++i) { - const auto* n = heap_nodes[i]; - file << " {\"id\": " << n->node_id << ", \"origin_worker\": " << n->origin_worker_id - << ", \"creation_seq\": " << n->creation_seq << ", \"lb\": " << n->lower_bound << "}" - << (i < heap_nodes.size() - 1 ? "," : "") << "\n"; - } - file << " ],\n"; - - // Events this horizon - file << " \"events_count\": " << events.events.size() << "\n"; - - file << "}\n"; - file.close(); - } - - // ======================================================================== - // Finalization - // ======================================================================== - - void finalize() - { - if (settings_.enable_event_log) { flush_event_log(); } - - if (settings_.enable_determinism_trace) { flush_trace(); } - } - - private: - bsp_debug_settings_t settings_; - std::chrono::steady_clock::time_point start_time_; - int num_workers_{0}; - double horizon_step_{5.0}; - int current_horizon_{0}; - double horizon_wut_start_{0.0}; - double horizon_wut_end_{0.0}; - - std::mutex mutex_; - std::stringstream log_ss_; - std::stringstream trace_ss_; - std::stringstream assign_trace_ss_; - std::stringstream events_trace_ss_; - std::stringstream final_ids_trace_ss_; - - // Timeline tracking - std::vector timeline_events_; - std::map current_solve_start_; - std::map current_solve_node_; - std::map current_solve_fid_; - - double get_real_time() const - { - auto now = std::chrono::steady_clock::now(); - return std::chrono::duration(now - start_time_).count(); - } - - void log_event(double work_unit_ts, - int worker_id, - bsp_log_event_t event, - i_t node_id, - i_t final_id, - const std::string& details) - { - std::lock_guard lock(mutex_); - log_event_unlocked(work_unit_ts, worker_id, event, node_id, final_id, details); - } - - void log_event_unlocked(double work_unit_ts, - int worker_id, - bsp_log_event_t event, - i_t node_id, - i_t final_id, - const std::string& details) - { - log_ss_ << std::fixed << std::setprecision(3) << work_unit_ts << "\t" << get_real_time() << "\t" - << current_horizon_ << "\t" << (worker_id < 0 ? "COORD" : std::to_string(worker_id)) - << "\t" << bsp_log_event_name(event) << "\t" - << (node_id < 0 ? "-" : std::to_string(node_id)) << "\t" - << (final_id < 0 ? "-" : std::to_string(final_id)) << "\t" << details << "\n"; - } - - void flush_event_log() - { - std::string filename = settings_.output_dir + "bsp_events.log"; - std::ofstream file(filename); - if (file.is_open()) { - file << "VT\tREAL_TIME\tHORIZON\tWORKER\tEVENT\tNODE_ID\tFINAL_ID\tDETAILS\n"; - file << log_ss_.str(); - file.close(); - } - } - - void flush_trace() - { - std::string filename = settings_.output_dir + "bsp_trace.txt"; - std::ofstream file(filename); - if (file.is_open()) { - file << "# BSP Determinism Trace\n"; - file << "# Compare this file across runs - must be identical for determinism\n\n"; - file << trace_ss_.str(); - file.close(); - } - } - - void emit_timeline_for_horizon(int horizon_num) - { - std::string filename = settings_.output_dir + "bsp_timeline.txt"; - std::ofstream file(filename, std::ios::app); - if (!file.is_open()) return; - - const int width = 80; - const double wut_min = horizon_wut_start_; - const double wut_max = horizon_wut_end_; - const double wut_range = wut_max - wut_min; - - // Avoid division by zero - if (wut_range <= 0.0) { - file << "\nHorizon " << horizon_num << ": Empty or invalid WUT range\n"; - return; - } - - file << "\n"; - file << std::string(width, '=') << "\n"; - file << "Horizon " << horizon_num << ": WUT [" << wut_min << ", " << wut_max << ")\n"; - file << std::string(width, '-') << "\n"; - - // WUT scale - file << " WUT: "; - for (int i = 0; i <= 5; ++i) { - double wut = wut_min + (wut_range * i / 5.0); - file << std::setw(10) << std::fixed << std::setprecision(1) << wut; - } - file << "\n"; - - // Worker rows - for (int w = 0; w < num_workers_; ++w) { - file << " W" << w << ": "; - - std::string row(60, '-'); - - for (const auto& te : timeline_events_) { - if (te.worker_id != w) continue; - - int start_col = static_cast((te.wut_start - wut_min) / wut_range * 60); - int end_col = static_cast((te.wut_end - wut_min) / wut_range * 60); - start_col = std::max(0, std::min(59, start_col)); - end_col = std::max(0, std::min(59, end_col)); - - char fill_char = '#'; - if (te.result == "PAUSED") - fill_char = '%'; - else if (te.result == "INTEGER") - fill_char = '*'; - else if (te.result == "FATHOMED" || te.result == "INFEASIBLE") - fill_char = 'x'; - - for (int c = start_col; c <= end_col; ++c) { - row[c] = fill_char; - } - } - - file << row << "\n"; - - // Labels row - file << " "; - std::string labels(60, ' '); - for (const auto& te : timeline_events_) { - if (te.worker_id != w) continue; - int mid_col = - static_cast(((te.wut_start + te.wut_end) / 2 - wut_min) / wut_range * 60); - mid_col = std::max(0, std::min(55, mid_col)); - - std::string label = "N" + std::to_string(te.final_id); - for (size_t i = 0; i < label.size() && mid_col + i < 60; ++i) { - labels[mid_col + i] = label[i]; - } - } - file << labels << "\n"; - } - - file << std::string(width, '-') << "\n"; - file << "Legend: ### solving %%% paused *** integer xxx fathomed/infeasible\n"; - file << std::string(width, '=') << "\n"; - } - - void emit_tree_node_recursive(std::ofstream& file, - const mip_node_t* node, - f_t upper_bound) - { - if (node == nullptr) return; - - // Determine fill color based on status - std::string color = "white"; - std::string status_str; - switch (node->status) { - case node_status_t::PENDING: - color = "lightgray"; - status_str = "PENDING"; - break; - case node_status_t::INTEGER_FEASIBLE: - color = "lightgreen"; - status_str = "INTEGER"; - break; - case node_status_t::INFEASIBLE: - color = "lightcoral"; - status_str = "INFEASIBLE"; - break; - case node_status_t::FATHOMED: - color = "lightsalmon"; - status_str = "FATHOMED"; - break; - case node_status_t::HAS_CHILDREN: - color = "lightblue"; - status_str = "BRANCHED"; - break; - case node_status_t::NUMERICAL: - color = "orange"; - status_str = "NUMERICAL"; - break; - } - - file << " N" << node->node_id << " [label=\"N" << node->node_id; - file << " (w" << node->origin_worker_id << ":" << node->creation_seq << ")"; - file << "\\nlb=" << std::fixed << std::setprecision(1) << node->lower_bound; - file << "\\n" << status_str; - file << "\" style=filled fillcolor=" << color << "];\n"; - - // Emit edges to children - if (node->get_down_child() != nullptr) { - file << " N" << node->node_id << " -> N" << node->get_down_child()->node_id << " [label=\"x" - << node->branch_var << "<=" << std::floor(node->fractional_val) << "\"];\n"; - emit_tree_node_recursive(file, node->get_down_child(), upper_bound); - } - - if (node->get_up_child() != nullptr) { - file << " N" << node->node_id << " -> N" << node->get_up_child()->node_id << " [label=\"x" - << node->branch_var << ">=" << std::ceil(node->fractional_val) << "\"];\n"; - emit_tree_node_recursive(file, node->get_up_child(), upper_bound); - } - } -}; - -// ============================================================================ -// Convenience Macros -// ============================================================================ -// -// These macros provide a clean interface for BSP debug logging. -// - When BSP_DEBUG_ENABLED is defined: macros are active (controlled by settings at runtime) -// - When BSP_DEBUG_ENABLED is not defined: macros are no-ops (zero overhead) -// -// The 'settings' parameter is a bsp_debug_settings_t& that controls which features are enabled. - -#ifdef BSP_DEBUG_ENABLED - -#define BSP_DEBUG_LOG_HORIZON_START(settings, logger, h, ws, we) \ - do { \ - if ((settings).any_enabled()) (logger).log_horizon_start(h, ws, we); \ - } while (0) -#define BSP_DEBUG_LOG_HORIZON_END(settings, logger, h, wut) \ - do { \ - if ((settings).any_enabled()) (logger).log_horizon_end(h, wut); \ - } while (0) -#define BSP_DEBUG_LOG_HORIZON_HASH(settings, logger, h, wut, hash) \ - do { \ - if ((settings).any_enabled()) (logger).log_horizon_hash(h, wut, hash); \ - } while (0) -#define BSP_DEBUG_LOG_NODE_ASSIGNED(settings, logger, wut, w, nid, fid, lb) \ - do { \ - if ((settings).any_enabled()) (logger).log_node_assigned(wut, w, nid, fid, lb); \ - } while (0) -#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) \ - do { \ - if ((settings).any_enabled() && (settings).flush_every_horizon) (logger).flush_assign_trace(); \ - } while (0) -#define BSP_DEBUG_LOG_SOLVE_START(settings, logger, wut, w, nid, fid, wl, resumed) \ - do { \ - if ((settings).any_enabled()) (logger).log_solve_start(wut, w, nid, fid, wl, resumed); \ - } while (0) -#define BSP_DEBUG_LOG_SOLVE_END(settings, logger, wut, w, nid, fid, result, lb) \ - do { \ - if ((settings).any_enabled()) (logger).log_solve_end(wut, w, nid, fid, result, lb); \ - } while (0) -#define BSP_DEBUG_LOG_BRANCHED(settings, logger, wut, w, pid, pfid, did, uid) \ - do { \ - if ((settings).any_enabled()) (logger).log_branched(wut, w, pid, pfid, did, uid); \ - } while (0) -#define BSP_DEBUG_LOG_PAUSED(settings, logger, wut, w, nid, fid, acc) \ - do { \ - if ((settings).any_enabled()) (logger).log_paused(wut, w, nid, fid, acc); \ - } while (0) -#define BSP_DEBUG_LOG_INTEGER(settings, logger, wut, w, nid, obj) \ - do { \ - if ((settings).any_enabled()) (logger).log_integer(wut, w, nid, obj); \ - } while (0) -#define BSP_DEBUG_LOG_FATHOMED(settings, logger, wut, w, nid, lb) \ - do { \ - if ((settings).any_enabled()) (logger).log_fathomed(wut, w, nid, lb); \ - } while (0) -#define BSP_DEBUG_LOG_INFEASIBLE(settings, logger, wut, w, nid) \ - do { \ - if ((settings).any_enabled()) (logger).log_infeasible(wut, w, nid); \ - } while (0) -#define BSP_DEBUG_LOG_PRUNED(settings, logger, wut, nid, fid, lb, ub) \ - do { \ - if ((settings).any_enabled()) (logger).log_pruned(wut, nid, fid, lb, ub); \ - } while (0) -#define BSP_DEBUG_LOG_SYNC_PHASE_START(settings, logger, wut, ne) \ - do { \ - if ((settings).any_enabled()) (logger).log_sync_phase_start(wut, ne); \ - } while (0) -#define BSP_DEBUG_LOG_SYNC_PHASE_END(settings, logger, wut) \ - do { \ - if ((settings).any_enabled()) (logger).log_sync_phase_end(wut); \ - } while (0) -#define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(settings, logger, pid, fid) \ - do { \ - if ((settings).any_enabled()) (logger).log_final_id_assigned(pid, fid); \ - } while (0) -#define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(settings, logger) \ - do { \ - if ((settings).any_enabled() && (settings).flush_every_horizon) \ - (logger).flush_final_ids_trace(); \ - } while (0) -#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(settings, logger, wut, obj) \ - do { \ - if ((settings).any_enabled()) (logger).log_heuristic_received(wut, obj); \ - } while (0) -#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(settings, logger, wut, obj, src) \ - do { \ - if ((settings).any_enabled()) (logger).log_incumbent_update(wut, obj, src); \ - } while (0) -#define BSP_DEBUG_LOG_HEAP_ORDER(settings, logger, fids) \ - do { \ - if ((settings).any_enabled()) (logger).log_heap_order(fids); \ - } while (0) -#define BSP_DEBUG_LOG_LP_INPUT(settings, logger, w, nid, ph, d, vsh, bh) \ - do { \ - if ((settings).any_enabled()) (logger).log_lp_input(w, nid, ph, d, vsh, bh); \ - } while (0) -#define BSP_DEBUG_LOG_LP_OUTPUT(settings, logger, w, nid, ph, st, it, ob, sh) \ - do { \ - if ((settings).any_enabled()) (logger).log_lp_output(w, nid, ph, st, it, ob, sh); \ - } while (0) -#define BSP_DEBUG_LOG_BRANCH_DECISION(settings, logger, bv, sh, nt) \ - do { \ - if ((settings).any_enabled()) (logger).log_branch_decision(bv, sh, nt); \ - } while (0) -#define BSP_DEBUG_EMIT_TREE_STATE(settings, logger, h, root, ub) \ - do { \ - if ((settings).any_enabled() && (settings).flush_every_horizon) \ - (logger).emit_tree_state(h, root, ub); \ - } while (0) -#define BSP_DEBUG_EMIT_STATE_JSON( \ - settings, logger, h, ws, we, nfid, ub, lb, ne, nu, workers, heap, events) \ - do { \ - if ((settings).any_enabled() && (settings).flush_every_horizon) \ - (logger).emit_state_json(h, ws, we, nfid, ub, lb, ne, nu, workers, heap, events); \ - } while (0) -#define BSP_DEBUG_FINALIZE(settings, logger) \ - do { \ - if ((settings).any_enabled()) (logger).finalize(); \ - } while (0) - -#else - -#define BSP_DEBUG_LOG_HORIZON_START(settings, logger, h, ws, we) ((void)0) -#define BSP_DEBUG_LOG_HORIZON_END(settings, logger, h, wut) ((void)0) -#define BSP_DEBUG_LOG_HORIZON_HASH(settings, logger, h, wut, hash) ((void)0) -#define BSP_DEBUG_LOG_NODE_ASSIGNED(settings, logger, wut, w, nid, fid, lb) ((void)0) -#define BSP_DEBUG_FLUSH_ASSIGN_TRACE(settings, logger) ((void)0) -#define BSP_DEBUG_LOG_SOLVE_START(settings, logger, wut, w, nid, fid, wl, resumed) ((void)0) -#define BSP_DEBUG_LOG_SOLVE_END(settings, logger, wut, w, nid, fid, result, lb) ((void)0) -#define BSP_DEBUG_LOG_BRANCHED(settings, logger, wut, w, pid, pfid, did, uid) ((void)0) -#define BSP_DEBUG_LOG_PAUSED(settings, logger, wut, w, nid, fid, acc) ((void)0) -#define BSP_DEBUG_LOG_INTEGER(settings, logger, wut, w, nid, obj) ((void)0) -#define BSP_DEBUG_LOG_FATHOMED(settings, logger, wut, w, nid, lb) ((void)0) -#define BSP_DEBUG_LOG_INFEASIBLE(settings, logger, wut, w, nid) ((void)0) -#define BSP_DEBUG_LOG_PRUNED(settings, logger, wut, nid, fid, lb, ub) ((void)0) -#define BSP_DEBUG_LOG_SYNC_PHASE_START(settings, logger, wut, ne) ((void)0) -#define BSP_DEBUG_LOG_SYNC_PHASE_END(settings, logger, wut) ((void)0) -#define BSP_DEBUG_LOG_FINAL_ID_ASSIGNED(settings, logger, pid, fid) ((void)0) -#define BSP_DEBUG_FLUSH_FINAL_IDS_TRACE(settings, logger) ((void)0) -#define BSP_DEBUG_LOG_HEURISTIC_RECEIVED(settings, logger, wut, obj) ((void)0) -#define BSP_DEBUG_LOG_INCUMBENT_UPDATE(settings, logger, wut, obj, src) ((void)0) -#define BSP_DEBUG_LOG_HEAP_ORDER(settings, logger, fids) ((void)0) -#define BSP_DEBUG_LOG_LP_INPUT(settings, logger, w, nid, ph, d, vsh, bh) ((void)0) -#define BSP_DEBUG_LOG_LP_OUTPUT(settings, logger, w, nid, ph, st, it, ob, sh) ((void)0) -#define BSP_DEBUG_LOG_BRANCH_DECISION(settings, logger, bv, sh, nt) ((void)0) -#define BSP_DEBUG_EMIT_TREE_STATE(settings, logger, h, root, ub) ((void)0) -#define BSP_DEBUG_EMIT_STATE_JSON( \ - settings, logger, h, ws, we, nfid, ub, lb, ne, nu, workers, heap, events) \ - ((void)0) -#define BSP_DEBUG_FINALIZE(settings, logger) ((void)0) - -#endif - -} // namespace cuopt::linear_programming::dual_simplex From c91127b5764efdbbc1f346e73f8bb616f6665b4a Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 30 Jan 2026 13:35:29 +0000 Subject: [PATCH 325/366] refactoring and simplification work --- cpp/src/dual_simplex/basis_solves.cpp | 4 - cpp/src/dual_simplex/basis_updates.cpp | 5 - cpp/src/dual_simplex/bb_worker_state.hpp | 118 ++++++------- cpp/src/dual_simplex/branch_and_bound.cpp | 197 ++++++++++------------ cpp/src/dual_simplex/branch_and_bound.hpp | 20 +-- cpp/src/mip/local_search/local_search.cu | 3 +- cpp/src/mip/solver.cu | 2 +- 7 files changed, 150 insertions(+), 199 deletions(-) diff --git a/cpp/src/dual_simplex/basis_solves.cpp b/cpp/src/dual_simplex/basis_solves.cpp index 409656064..49ac5d102 100644 --- a/cpp/src/dual_simplex/basis_solves.cpp +++ b/cpp/src/dual_simplex/basis_solves.cpp @@ -12,15 +12,11 @@ #include #include #include -#include #include namespace cuopt::linear_programming::dual_simplex { -// Import instrumented vector type -using cuopt::ins_vector; - template i_t reorder_basic_list(const std::vector& q, std::vector& basic_list) { diff --git a/cpp/src/dual_simplex/basis_updates.cpp b/cpp/src/dual_simplex/basis_updates.cpp index 4a7b216e0..bedf11103 100644 --- a/cpp/src/dual_simplex/basis_updates.cpp +++ b/cpp/src/dual_simplex/basis_updates.cpp @@ -9,8 +9,6 @@ #include #include #include -#include - #include #include @@ -18,9 +16,6 @@ namespace cuopt::linear_programming::dual_simplex { -// Import instrumented vector type -using cuopt::ins_vector; - template i_t basis_update_t::b_solve(const std::vector& rhs, std::vector& solution) const { diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 5f82fa6e6..7d2950ca3 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -117,9 +117,9 @@ struct bb_worker_state_t { bb_event_batch_t events; int event_sequence{0}; - std::unique_ptr> leaf_problem; - std::unique_ptr> basis_factors; - std::unique_ptr> node_presolver; + lp_problem_t leaf_problem; + basis_update_mpf_t basis_factors; + bounds_strengthening_t node_presolver; std::vector basic_list; std::vector nonbasic_list; work_limit_context_t work_context; @@ -148,23 +148,19 @@ struct bb_worker_state_t { std::vector pc_num_up_snapshot; std::vector pc_num_down_snapshot; - explicit bb_worker_state_t(int id) - : worker_id(id), work_context("BB_Worker_" + std::to_string(id)) - { - } - - void initialize(const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - i_t refactor_frequency, - bool deterministic) - { - leaf_problem = std::make_unique>(original_lp); - const i_t m = leaf_problem->num_rows; - basis_factors = std::make_unique>(m, refactor_frequency); - std::vector row_sense; - node_presolver = - std::make_unique>(*leaf_problem, Arow, row_sense, var_types); + explicit bb_worker_state_t(int id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + i_t refactor_frequency, + bool deterministic) + : worker_id(id), + work_context("BB_Worker_" + std::to_string(id)), + leaf_problem(original_lp), + basis_factors(original_lp.num_rows, refactor_frequency), + node_presolver(original_lp, Arow, std::vector{}, var_types) + { + const i_t m = leaf_problem.num_rows; basic_list.resize(m); nonbasic_list.clear(); work_context.deterministic = deterministic; @@ -350,9 +346,9 @@ struct bsp_diving_worker_state_t { double horizon_end{0.0}; work_limit_context_t work_context; - std::unique_ptr> leaf_problem; - std::unique_ptr> basis_factors; - std::unique_ptr> node_presolver; + lp_problem_t leaf_problem; + basis_update_mpf_t basis_factors; + bounds_strengthening_t node_presolver; std::vector basic_list; std::vector nonbasic_list; bool recompute_bounds_and_basis{true}; @@ -382,23 +378,21 @@ struct bsp_diving_worker_state_t { double total_runtime{0.0}; double total_nowork_time{0.0}; - explicit bsp_diving_worker_state_t(int id, bnb_worker_type_t type) - : worker_id(id), diving_type(type), work_context("Diving_Worker_" + std::to_string(id)) - { - } - - void initialize(const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - i_t refactor_frequency, - bool deterministic) - { - leaf_problem = std::make_unique>(original_lp); - const i_t m = leaf_problem->num_rows; - basis_factors = std::make_unique>(m, refactor_frequency); - std::vector row_sense; - node_presolver = - std::make_unique>(*leaf_problem, Arow, row_sense, var_types); + explicit bsp_diving_worker_state_t(int id, + bnb_worker_type_t type, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + i_t refactor_frequency, + bool deterministic) + : worker_id(id), + diving_type(type), + work_context("Diving_Worker_" + std::to_string(id)), + leaf_problem(original_lp), + basis_factors(original_lp.num_rows, refactor_frequency), + node_presolver(original_lp, Arow, std::vector{}, var_types) + { + const i_t m = leaf_problem.num_rows; basic_list.resize(m); nonbasic_list.clear(); dive_lower = original_lp.lower; @@ -406,6 +400,11 @@ struct bsp_diving_worker_state_t { work_context.deterministic = deterministic; } + bsp_diving_worker_state_t(const bsp_diving_worker_state_t&) = delete; + bsp_diving_worker_state_t& operator=(const bsp_diving_worker_state_t&) = delete; + bsp_diving_worker_state_t(bsp_diving_worker_state_t&&) = default; + bsp_diving_worker_state_t& operator=(bsp_diving_worker_state_t&&) = default; + void reset_for_horizon(double start, double end, f_t upper_bound) { clock = start; @@ -508,22 +507,19 @@ struct bsp_diving_worker_state_t { template class bsp_diving_worker_pool_t { public: - bsp_diving_worker_pool_t() = default; - - void initialize(int num_workers, - const std::vector& diving_types, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - i_t refactor_frequency, - bool deterministic) + bsp_diving_worker_pool_t(int num_workers, + const std::vector& diving_types, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + i_t refactor_frequency, + bool deterministic) { - workers_.clear(); workers_.reserve(num_workers); for (int i = 0; i < num_workers; ++i) { bnb_worker_type_t type = diving_types[i % diving_types.size()]; - workers_.emplace_back(i, type); - workers_.back().initialize(original_lp, Arow, var_types, refactor_frequency, deterministic); + workers_.emplace_back( + i, type, original_lp, Arow, var_types, refactor_frequency, deterministic); } } @@ -562,20 +558,16 @@ class bsp_diving_worker_pool_t { template class bb_worker_pool_t { public: - bb_worker_pool_t() = default; - - void initialize(int num_workers, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - i_t refactor_frequency, - bool deterministic) + bb_worker_pool_t(int num_workers, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + i_t refactor_frequency, + bool deterministic) { - workers_.clear(); workers_.reserve(num_workers); for (int i = 0; i < num_workers; ++i) { - workers_.emplace_back(i); - workers_.back().initialize(original_lp, Arow, var_types, refactor_frequency, deterministic); + workers_.emplace_back(i, original_lp, Arow, var_types, refactor_frequency, deterministic); } } diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index bed0ffee6..d57e51d22 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -358,8 +358,8 @@ void branch_and_bound_t::set_new_solution(const std::vector& solu } template -void branch_and_bound_t::set_new_solution_deterministic(const std::vector& solution, - double work_unit_ts) +void branch_and_bound_t::queue_external_solution_deterministic( + const std::vector& solution, double work_unit_ts) { // In BSP mode, queue the solution to be processed at the correct work unit timestamp // This ensures deterministic ordering of solution events @@ -501,8 +501,8 @@ void branch_and_bound_t::repair_heuristic_solutions() } template -mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t& solution, - f_t lower_bound) +void branch_and_bound_t::set_final_solution(mip_solution_t& solution, + f_t lower_bound) { if (solver_status_ == mip_status_t::NUMERICAL) { settings_.log.printf("Numerical issue encountered. Stopping the solver...\n"); @@ -572,7 +572,6 @@ mip_status_t branch_and_bound_t::set_final_solution(mip_solution_t @@ -581,10 +580,7 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, i_t leaf_depth, bnb_worker_type_t thread_type) { - bool send_solution = false; - bool improved_incumbent = false; - i_t nodes_explored = exploration_stats_.nodes_explored; - i_t nodes_unexplored = exploration_stats_.nodes_unexplored; + bool send_solution = false; settings_.log.debug("%c found a feasible solution with obj=%.10e.\n", feasible_solution_symbol(thread_type), @@ -595,8 +591,7 @@ void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, incumbent_.set_incumbent_solution(leaf_objective, leaf_solution); upper_bound_ = leaf_objective; report(feasible_solution_symbol(thread_type), leaf_objective, get_lower_bound(), leaf_depth); - send_solution = true; - improved_incumbent = true; + send_solution = true; } if (send_solution && settings_.solution_callback != nullptr) { @@ -747,21 +742,8 @@ dual::status_t branch_and_bound_t::solve_node_lp( leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); } - bool feasible; - { - raft::common::nvtx::range scope_bs("BB::bound_strengthening"); - f_t bs_start = tic(); - feasible = - node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); - f_t bs_runtime = toc(bs_start); - - bs_features_.m = leaf_problem.num_rows; - bs_features_.n = leaf_problem.num_cols; - bs_features_.nnz = leaf_problem.A.col_start[leaf_problem.num_cols]; - bs_features_.nnz_processed = node_presolver.last_nnz_processed; - bs_features_.runtime = bs_runtime; - bs_features_.log_single(bs_features_.m, bs_features_.n, bs_features_.nnz); - } + bool feasible = + node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; @@ -770,23 +752,19 @@ dual::status_t branch_and_bound_t::solve_node_lp( f_t lp_start_time = tic(); std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; - { - raft::common::nvtx::range scope_lp("BB::node_lp_solve"); - lp_status = dual_phase2_with_advanced_basis(2, - 0, - recompute_bounds_and_basis, - lp_start_time, - leaf_problem, - lp_settings, - leaf_vstatus, - basis_factors, - basic_list, - nonbasic_list, - leaf_solution, - node_iter, - leaf_edge_norms, - nullptr); - } + lp_status = dual_phase2_with_advanced_basis(2, + 0, + recompute_bounds_and_basis, + lp_start_time, + leaf_problem, + lp_settings, + leaf_vstatus, + basis_factors, + basic_list, + nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms); if (lp_status == dual::status_t::NUMERICAL) { log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); @@ -1584,7 +1562,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut if (root_status == lp_status_t::WORK_LIMIT) { solver_status_ = mip_status_t::WORK_LIMIT; - return set_final_solution(solution, -inf); + set_final_solution(solution, -inf); + return solver_status_; } assert(root_vstatus_.size() == original_lp_.num_cols); @@ -1776,7 +1755,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut lower_bound = upper_bound_.load(); } } - return set_final_solution(solution, lower_bound); + set_final_solution(solution, lower_bound); + return solver_status_; } // ============================================================================ @@ -1856,13 +1836,12 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t>(); - bsp_workers_->initialize(num_bfs_workers, - original_lp_, - Arow, - var_types_, - settings_.refactor_frequency, - settings_.deterministic); + bsp_workers_ = std::make_unique>(num_bfs_workers, + original_lp_, + Arow, + var_types_, + settings_.refactor_frequency, + settings_.deterministic); if (num_diving_workers > 0) { std::vector diving_types; @@ -1883,14 +1862,14 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t>(); - bsp_diving_workers_->initialize(num_diving_workers, - diving_types, - original_lp_, - Arow, - var_types_, - settings_.refactor_frequency, - settings_.deterministic); + bsp_diving_workers_ = + std::make_unique>(num_diving_workers, + diving_types, + original_lp_, + Arow, + var_types_, + settings_.refactor_frequency, + settings_.deterministic); } } @@ -2114,13 +2093,13 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) bb_event_batch_t all_events = bsp_workers_->collect_and_sort_events(); - process_history_and_sync(all_events); + sort_replay_events(all_events); prune_worker_nodes_vs_incumbent(); - merge_diving_solutions(); + collect_diving_solutions(); - populate_diving_heap_at_sync(); + populate_diving_heap(); assign_diving_nodes(); @@ -2266,20 +2245,18 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t double work_units_at_start = worker.work_context.global_work_units_elapsed; double clock_at_start = worker.clock; - std::fill(worker.node_presolver->bounds_changed.begin(), - worker.node_presolver->bounds_changed.end(), + std::fill(worker.node_presolver.bounds_changed.begin(), + worker.node_presolver.bounds_changed.end(), false); if (worker.recompute_bounds_and_basis) { - worker.leaf_problem->lower = original_lp_.lower; - worker.leaf_problem->upper = original_lp_.upper; - node_ptr->get_variable_bounds(worker.leaf_problem->lower, - worker.leaf_problem->upper, - worker.node_presolver->bounds_changed); + worker.leaf_problem.lower = original_lp_.lower; + worker.leaf_problem.upper = original_lp_.upper; + node_ptr->get_variable_bounds( + worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); } else { - node_ptr->update_branched_variable_bounds(worker.leaf_problem->lower, - worker.leaf_problem->upper, - worker.node_presolver->bounds_changed); + node_ptr->update_branched_variable_bounds( + worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); } double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); @@ -2298,13 +2275,13 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t #ifndef BSP_DISABLE_BOUNDS_STRENGTHENING raft::common::nvtx::range scope_bs("BB::bound_strengthening"); f_t bs_start_time = tic(); - feasible = worker.node_presolver->bounds_strengthening( - worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + feasible = worker.node_presolver.bounds_strengthening( + worker.leaf_problem.lower, worker.leaf_problem.upper, lp_settings); f_t bs_actual_time = toc(bs_start_time); if (settings_.deterministic) { // TEMP; - worker.work_context.record_work(worker.node_presolver->last_nnz_processed / 1e8); + worker.work_context.record_work(worker.node_presolver.last_nnz_processed / 1e8); } #endif @@ -2321,8 +2298,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } // Solve LP relaxation - lp_solution_t leaf_solution(worker.leaf_problem->num_rows, - worker.leaf_problem->num_cols); + lp_solution_t leaf_solution(worker.leaf_problem.num_rows, worker.leaf_problem.num_cols); std::vector& leaf_vstatus = node_ptr->vstatus; i_t node_iter = 0; f_t lp_start_time = tic(); @@ -2332,10 +2308,10 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t 0, worker.recompute_bounds_and_basis, lp_start_time, - *worker.leaf_problem, + worker.leaf_problem, lp_settings, leaf_vstatus, - *worker.basis_factors, + worker.basis_factors, worker.basic_list, worker.nonbasic_list, leaf_solution, @@ -2345,11 +2321,11 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t if (lp_status == dual::status_t::NUMERICAL) { settings_.log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); - lp_status_t second_status = solve_linear_program_with_advanced_basis(*worker.leaf_problem, + lp_status_t second_status = solve_linear_program_with_advanced_basis(worker.leaf_problem, lp_start_time, lp_settings, leaf_solution, - *worker.basis_factors, + worker.basis_factors, worker.basic_list, worker.nonbasic_list, leaf_vstatus, @@ -2413,7 +2389,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t i_t leaf_num_fractional = fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); + f_t leaf_objective = compute_objective(worker.leaf_problem, leaf_solution.x); // TODO if (settings_.node_processed_callback != nullptr) { @@ -2468,7 +2444,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t pc_.obj_estimate(leaf_fractional, leaf_solution.x, node_ptr->lower_bound, log); search_tree.branch( - node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, *worker.leaf_problem, log); + node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, worker.leaf_problem, log); search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); i_t down_child_id = node_ptr->get_down_child()->node_id; @@ -2521,8 +2497,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t } template -void branch_and_bound_t::process_history_and_sync( - const bb_event_batch_t& events) +void branch_and_bound_t::sort_replay_events(const bb_event_batch_t& events) { // Infeasible solutions from GPU heuristics are queued for repair; process them now { @@ -2874,7 +2849,7 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() // ============================================================================ template -void branch_and_bound_t::populate_diving_heap_at_sync() +void branch_and_bound_t::populate_diving_heap() { // Clear diving heap from previous horizon diving_heap_.clear(); @@ -2961,7 +2936,7 @@ void branch_and_bound_t::assign_diving_nodes() } template -void branch_and_bound_t::merge_diving_solutions() +void branch_and_bound_t::collect_diving_solutions() { if (!bsp_diving_workers_) return; @@ -3096,11 +3071,11 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tbounds_changed.begin(), - worker.node_presolver->bounds_changed.end(), + std::fill(worker.node_presolver.bounds_changed.begin(), + worker.node_presolver.bounds_changed.end(), false); dive_tree.root.get_variable_bounds( - worker.dive_lower, worker.dive_upper, worker.node_presolver->bounds_changed); + worker.dive_lower, worker.dive_upper, worker.node_presolver.bounds_changed); const i_t max_nodes_per_dive = settings_.diving_settings.node_limit; const i_t max_backtrack_depth = settings_.diving_settings.backtrack_limit; @@ -3136,20 +3111,18 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tbounds_changed.begin(), - worker.node_presolver->bounds_changed.end(), + std::fill(worker.node_presolver.bounds_changed.begin(), + worker.node_presolver.bounds_changed.end(), false); if (worker.recompute_bounds_and_basis) { - worker.leaf_problem->lower = worker.dive_lower; - worker.leaf_problem->upper = worker.dive_upper; - node_ptr->get_variable_bounds(worker.leaf_problem->lower, - worker.leaf_problem->upper, - worker.node_presolver->bounds_changed); + worker.leaf_problem.lower = worker.dive_lower; + worker.leaf_problem.upper = worker.dive_upper; + node_ptr->get_variable_bounds( + worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); } else { - node_ptr->update_branched_variable_bounds(worker.leaf_problem->lower, - worker.leaf_problem->upper, - worker.node_presolver->bounds_changed); + node_ptr->update_branched_variable_bounds( + worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); } double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); @@ -3164,8 +3137,8 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_tbounds_strengthening( - worker.leaf_problem->lower, worker.leaf_problem->upper, lp_settings); + bool feasible = worker.node_presolver.bounds_strengthening( + worker.leaf_problem.lower, worker.leaf_problem.upper, lp_settings); if (!feasible) { worker.recompute_bounds_and_basis = true; @@ -3181,8 +3154,8 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t leaf_solution(worker.leaf_problem->num_rows, - worker.leaf_problem->num_cols); + lp_solution_t leaf_solution(worker.leaf_problem.num_rows, + worker.leaf_problem.num_cols); std::vector& leaf_vstatus = node_ptr->vstatus; i_t node_iter = 0; f_t lp_start_time = tic(); @@ -3192,10 +3165,10 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t::dive_from_bsp(bsp_diving_worker_state_t::dive_from_bsp(bsp_diving_worker_state_t leaf_fractional; fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - f_t leaf_objective = compute_objective(*worker.leaf_problem, leaf_solution.x); + f_t leaf_objective = compute_objective(worker.leaf_problem, leaf_solution.x); if (node_ptr->branch_var >= 0) { const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; @@ -3289,7 +3262,7 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t(*worker.leaf_problem, + branch_result = coefficient_diving(worker.leaf_problem, leaf_fractional, leaf_solution.x, var_up_locks_, @@ -3318,7 +3291,7 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t #include #include -#include #include #include #include @@ -21,14 +20,12 @@ #include #include #include - #include #include #include #include #include -#include #include namespace cuopt::linear_programming::dual_simplex { @@ -117,7 +114,7 @@ class branch_and_bound_t { // BSP-aware solution injection for deterministic mode // This queues the solution to be processed at the correct work unit timestamp - void set_new_solution_deterministic(const std::vector& solution, double work_unit_ts); + void queue_external_solution_deterministic(const std::vector& solution, double work_unit_ts); void set_concurrent_lp_root_solve(bool enable) { enable_concurrent_lp_root_solve_ = enable; } @@ -177,9 +174,6 @@ class branch_and_bound_t { // Structure with the general info of the solver. bnb_stats_t exploration_stats_; - // Bounds strengthening feature tracking (non-deterministic path) - bounds_strengthening_features_t bs_features_; - // Mutex for repair omp_mutex_t mutex_repair_; std::vector> repair_queue_; @@ -222,7 +216,7 @@ class branch_and_bound_t { void report(char symbol, f_t obj, f_t lower_bound, i_t node_depth); // Set the final solution. - mip_status_t set_final_solution(mip_solution_t& solution, f_t lower_bound); + void set_final_solution(mip_solution_t& solution, f_t lower_bound); // Update the incumbent solution with the new feasible solution // found during branch and bound. @@ -308,8 +302,8 @@ class branch_and_bound_t { // Main BSP coordinator loop - runs in deterministic mode void run_bsp_coordinator(const csr_matrix_t& Arow); - // Process history and synchronize - the "brain" of BSP - void process_history_and_sync(const bb_event_batch_t& events); + // Gather all events generated, sort by WU timestamp, apply + void sort_replay_events(const bb_event_batch_t& events); // Prune nodes held by workers based on new incumbent void prune_worker_nodes_vs_incumbent(); @@ -344,16 +338,17 @@ class branch_and_bound_t { mip_node_t starting_node); // Populate diving heap from BFS worker backlogs at sync - void populate_diving_heap_at_sync(); + void populate_diving_heap(); // Assign starting nodes to diving workers from diving heap void assign_diving_nodes(); // Collect and merge diving solutions at sync - void merge_diving_solutions(); + void collect_diving_solutions(); private: // BSP state + // unique_ptr as we only want to initialize these if we're in the determinism codepath std::unique_ptr> bsp_workers_; std::unique_ptr bsp_scheduler_; std::atomic bsp_terminated_{false}; @@ -386,6 +381,7 @@ class branch_and_bound_t { // ============================================================================ // Diving worker pool + // unique_ptr as we only want to initialize these if we're in the determinism codepath std::unique_ptr> bsp_diving_workers_; // Diving heap - nodes available for diving, sorted by objective estimate diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 3f66a3c37..fe0d1b59d 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -180,10 +180,9 @@ void local_search_t::start_cpufj_deterministic( // Set up callback to send solutions to B&B with work unit timestamps deterministic_cpu_fj.fj_cpu->improvement_callback = [&bb](f_t obj, const std::vector& h_vec, double work_units) { - bb.set_new_solution_deterministic(h_vec, work_units); + bb.queue_external_solution_deterministic(h_vec, work_units); }; - // Start the CPUFJ thread deterministic_cpu_fj.start_cpu_solver(); // Signal that registration is complete - B&B can now wait on producers diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index c2d0a783c..188cd2d09 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -240,7 +240,7 @@ solution_t mip_solver_t::run_solver() // TODO once deterministic GPU heuristics are integrated // context.problem_ptr->branch_and_bound_callback = // [bb = branch_and_bound.get()](const std::vector& solution) { - // bb->set_new_solution_deterministic(solution, 0.0); + // bb->queue_external_solution_deterministic(solution, 0.0); // }; } From 12e095c080d329062a0df5f2302a78b45a004acb Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 30 Jan 2026 14:22:23 +0000 Subject: [PATCH 326/366] switch to openmp for syncs, add infeas test --- cpp/src/dual_simplex/branch_and_bound.cpp | 82 ++++++----------- cpp/src/dual_simplex/branch_and_bound.hpp | 3 +- cpp/src/utilities/work_unit_scheduler.cpp | 103 +++++----------------- cpp/src/utilities/work_unit_scheduler.hpp | 41 +++------ cpp/tests/mip/determinism_test.cu | 31 +++++++ 5 files changed, 96 insertions(+), 164 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d57e51d22..6d32ee743 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1831,10 +1831,10 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t>(num_bfs_workers, original_lp_, @@ -1904,10 +1904,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tset_sync_callback([this](double sync_target) -> bool { - bsp_sync_callback(0); - return bsp_terminated_.load(); - }); + bsp_scheduler_->set_sync_callback([this](double) { bsp_sync_callback(0); }); for (auto& worker : *bsp_workers_) { worker.set_snapshots(upper_bound_.load(), @@ -2026,15 +2023,7 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& { raft::common::nvtx::range scope("BB::worker_loop"); - while (!bsp_terminated_.load() && !bsp_scheduler_->is_stopped() && - solver_status_ == mip_status_t::UNSET) { - if (toc(exploration_stats_.start_time) > settings_.time_limit) { - solver_status_ = mip_status_t::TIME_LIMIT; - bsp_terminated_.store(true); - bsp_scheduler_->stop(); - break; - } - + while (bsp_global_termination_status_ == mip_status_t::UNSET) { if (worker.has_work()) { mip_node_t* node = worker.dequeue_node(); if (node == nullptr) { continue; } @@ -2066,10 +2055,9 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& } // No work - advance to sync point to participate in barrier - f_t nowork_start = tic(); - cuopt::sync_result_t result = bsp_scheduler_->wait_for_next_sync(worker.work_context); + f_t nowork_start = tic(); + bsp_scheduler_->wait_for_next_sync(worker.work_context); worker.total_nowork_time += toc(nowork_start); - if (result == cuopt::sync_result_t::STOPPED) { break; } } } @@ -2165,28 +2153,29 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) f_t abs_gap = upper_bound - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); - bool should_terminate = false; - if (abs_gap <= settings_.absolute_mip_gap_tol || rel_gap <= settings_.relative_mip_gap_tol) { - should_terminate = true; + bsp_global_termination_status_ = mip_status_t::OPTIMAL; } - bool diving_has_work = bsp_diving_workers_ && bsp_diving_workers_->any_has_work(); - if (!bsp_workers_->any_has_work() && !diving_has_work) { should_terminate = true; } + if (!bsp_workers_->any_has_work()) { + // Tree exhausted - check if we found a solution + if (upper_bound == std::numeric_limits::infinity()) { + printf("OI!\n"); + bsp_global_termination_status_ = mip_status_t::INFEASIBLE; + } else { + bsp_global_termination_status_ = mip_status_t::OPTIMAL; + } + } if (toc(exploration_stats_.start_time) > settings_.time_limit) { - solver_status_ = mip_status_t::TIME_LIMIT; - should_terminate = true; + bsp_global_termination_status_ = mip_status_t::TIME_LIMIT; } // Stop early if next horizon exceeds work limit if (bsp_current_horizon_ > settings_.work_limit) { - solver_status_ = mip_status_t::WORK_LIMIT; - should_terminate = true; + bsp_global_termination_status_ = mip_status_t::WORK_LIMIT; } - if (should_terminate) { bsp_terminated_.store(true); } - f_t obj = compute_user_objective(original_lp_, upper_bound); f_t user_lower = compute_user_objective(original_lp_, lower_bound); std::string gap_user = user_mip_gap(obj, user_lower); @@ -3032,16 +3021,7 @@ void branch_and_bound_t::run_diving_worker_loop( { raft::common::nvtx::range scope("BB::diving_worker_loop"); - while (!bsp_terminated_.load() && !bsp_scheduler_->is_stopped() && - solver_status_ == mip_status_t::UNSET) { - // Check time limit directly - don't wait for sync if time is up - if (toc(exploration_stats_.start_time) > settings_.time_limit) { - solver_status_ = mip_status_t::TIME_LIMIT; - bsp_terminated_.store(true); - bsp_scheduler_->stop(); // Wake up workers waiting at barrier - break; - } - + while (bsp_global_termination_status_ == mip_status_t::UNSET) { // Process dives from queue until empty or horizon exhausted auto node_opt = worker.dequeue_dive_node(); if (node_opt.has_value()) { @@ -3050,10 +3030,10 @@ void branch_and_bound_t::run_diving_worker_loop( } // Queue empty - wait for next sync point where we'll be assigned new nodes - f_t nowork_start = tic(); - cuopt::sync_result_t result = bsp_scheduler_->wait_for_next_sync(worker.work_context); + f_t nowork_start = tic(); + bsp_scheduler_->wait_for_next_sync(worker.work_context); worker.total_nowork_time += toc(nowork_start); - if (result == cuopt::sync_result_t::STOPPED) { break; } + // Termination status is checked in loop condition } } @@ -3083,20 +3063,12 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t settings_.time_limit) { - solver_status_ = mip_status_t::TIME_LIMIT; - bsp_terminated_.store(true); - bsp_scheduler_->stop(); // Wake up workers waiting at barrier - break; - } - - // Check horizon budget + // Check horizon budget - sync if exhausted if (worker.work_context.global_work_units_elapsed >= worker.horizon_end) { bsp_scheduler_->wait_for_next_sync(worker.work_context); - if (bsp_terminated_.load()) break; + if (bsp_global_termination_status_ != mip_status_t::UNSET) break; } mip_node_t* node_ptr = stack.front(); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 3d23c0537..6ed080328 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -324,6 +324,7 @@ class branch_and_bound_t { void run_worker_loop(bb_worker_state_t& worker, search_tree_t& search_tree); // BSP sync callback - executed when all workers reach barrier + // Sets bsp_global_termination_status_ if termination is needed void bsp_sync_callback(int worker_id); // ============================================================================ @@ -351,7 +352,7 @@ class branch_and_bound_t { // unique_ptr as we only want to initialize these if we're in the determinism codepath std::unique_ptr> bsp_workers_; std::unique_ptr bsp_scheduler_; - std::atomic bsp_terminated_{false}; + mip_status_t bsp_global_termination_status_{mip_status_t::UNSET}; double bsp_horizon_step_{5.0}; // Work unit step per horizon (tunable) double bsp_current_horizon_{0.0}; // Current horizon target bool bsp_mode_enabled_{false}; // Whether BSP mode is active diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp index 11a3c1f62..0cf5a0e3b 100644 --- a/cpp/src/utilities/work_unit_scheduler.cpp +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -21,9 +21,10 @@ #include #include -#include #include +#include + #include namespace cuopt { @@ -34,15 +35,12 @@ work_unit_scheduler_t::work_unit_scheduler_t(double sync_interval) : sync_interv void work_unit_scheduler_t::register_context(work_limit_context_t& ctx) { - std::lock_guard lock(mutex_); contexts_.push_back(ctx); - last_sync_target_[&ctx] = 0.0; - ctx.scheduler = this; + ctx.scheduler = this; } void work_unit_scheduler_t::deregister_context(work_limit_context_t& ctx) { - std::lock_guard lock(mutex_); ctx.scheduler = nullptr; contexts_.erase(std::remove_if(contexts_.begin(), contexts_.end(), @@ -50,60 +48,41 @@ void work_unit_scheduler_t::deregister_context(work_limit_context_t& ctx) return &ref.get() == &ctx; }), contexts_.end()); - last_sync_target_.erase(&ctx); - cv_.notify_all(); } +void work_unit_scheduler_t::set_sync_interval(double interval) { sync_interval_ = interval; } + void work_unit_scheduler_t::on_work_recorded(work_limit_context_t& ctx, double total_work) { - // Early exit if scheduler is stopped - if (stopped_.load()) { return; } - if (verbose) { - double sync_target = current_sync_target(); CUOPT_LOG_DEBUG("[%s] Work recorded: %f, sync_target: %f (gen %zu)", ctx.name.c_str(), total_work, - sync_target, + current_sync_target(), barrier_generation_); } // Loop to handle large work increments that cross multiple sync points - while (!stopped_.load() && total_work >= current_sync_target()) { + while (total_work >= current_sync_target()) { wait_at_sync_point(ctx, current_sync_target()); } } void work_unit_scheduler_t::set_sync_callback(sync_callback_t callback) { - std::lock_guard lock(mutex_); sync_callback_ = std::move(callback); } -bool work_unit_scheduler_t::is_stopped() const { return stopped_.load(); } - -void work_unit_scheduler_t::stop() +void work_unit_scheduler_t::wait_for_next_sync(work_limit_context_t& ctx) { - stopped_.store(true); - cv_.notify_all(); -} - -sync_result_t work_unit_scheduler_t::wait_for_next_sync(work_limit_context_t& ctx) -{ - if (stopped_.load()) { return sync_result_t::STOPPED; } - - // Advance work to next sync point double next_sync = current_sync_target(); ctx.global_work_units_elapsed = next_sync; wait_at_sync_point(ctx, next_sync); - - return stopped_.load() ? sync_result_t::STOPPED : sync_result_t::CONTINUE; } double work_unit_scheduler_t::current_sync_target() const { if (sync_interval_ <= 0) return std::numeric_limits::infinity(); - std::unique_lock lock(mutex_); return (barrier_generation_ + 1) * sync_interval_; } @@ -111,71 +90,33 @@ void work_unit_scheduler_t::wait_at_sync_point(work_limit_context_t& ctx, double { auto wait_start = std::chrono::high_resolution_clock::now(); - std::unique_lock lock(mutex_); - - last_sync_target_[&ctx] = sync_target; - size_t my_generation = barrier_generation_; - contexts_at_barrier_++; - if (verbose) { - CUOPT_LOG_DEBUG("[%s] Waiting at sync point %.2f (gen %zu, %zu/%zu contexts)", + CUOPT_LOG_DEBUG("[%s] Waiting at sync point %.2f (gen %zu)", ctx.name.c_str(), sync_target, - my_generation, - contexts_at_barrier_, - contexts_.size()); + barrier_generation_); } - if (contexts_at_barrier_ == contexts_.size()) { + // All threads wait at this barrier +#pragma omp barrier + + // One thread executes the sync callback +#pragma omp single + { current_sync_target_ = sync_target; barrier_generation_++; - if (verbose) { - CUOPT_LOG_DEBUG("[%s] All contexts arrived, new generation %zu, notifying", - ctx.name.c_str(), - barrier_generation_); - } - // Execute sync callback if registered (last arrival executes it) - if (sync_callback_) { - lock.unlock(); - bool should_stop = sync_callback_(sync_target); - lock.lock(); - if (should_stop) { stopped_.store(true); } - } - cv_.notify_all(); - } else { - cv_.wait(lock, [&] { return barrier_generation_ != my_generation || stopped_.load(); }); - if (verbose) { CUOPT_LOG_DEBUG("[%s] Woke up from first wait", ctx.name.c_str()); } - if (stopped_.load()) { - // Decrement barrier count before returning to avoid leaving others stuck - contexts_at_barrier_--; - cv_.notify_all(); - return; - } - } - - size_t my_exit_generation = exit_generation_; - contexts_at_barrier_--; - if (contexts_at_barrier_ == 0) { - exit_generation_++; if (verbose) { - CUOPT_LOG_DEBUG("[%s] All contexts finished callbacks at sync point %.2f (exit gen %zu)", - ctx.name.c_str(), + CUOPT_LOG_DEBUG("All contexts arrived at sync point %.2f, new generation %zu", sync_target, - exit_generation_); - } - cv_.notify_all(); - } else { - if (verbose) { - CUOPT_LOG_DEBUG("[%s] Waiting for other contexts to finish callbacks (%zu remaining)", - ctx.name.c_str(), - contexts_at_barrier_); + barrier_generation_); } - cv_.wait(lock, [&] { return exit_generation_ != my_exit_generation || stopped_.load(); }); - if (verbose) { CUOPT_LOG_DEBUG("[%s] Woke up from second wait", ctx.name.c_str()); } + + if (sync_callback_) { sync_callback_(sync_target); } } + // Implicit barrier at end of single block ensures callback is complete + // before any thread proceeds - // Track sync time auto wait_end = std::chrono::high_resolution_clock::now(); double wait_secs = std::chrono::duration(wait_end - wait_start).count(); ctx.total_sync_time += wait_secs; diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index 773117b51..083fda09f 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -16,66 +16,53 @@ */ #pragma once -#include -#include #include -#include -#include #include namespace cuopt { struct work_limit_context_t; -enum class sync_result_t { - CONTINUE, // Continue processing - STOPPED // Scheduler has been stopped -}; - +// Simplified scheduler using OpenMP barriers for synchronization. +// Termination is managed externally (e.g., by branch_and_bound_t::bsp_global_termination_status_). +// Workers should check termination status after each sync point. class work_unit_scheduler_t { public: explicit work_unit_scheduler_t(double sync_interval = 5.0); void set_sync_interval(double interval); - double get_sync_interval() const; + double get_sync_interval() const { return sync_interval_; } void register_context(work_limit_context_t& ctx); void deregister_context(work_limit_context_t& ctx); void on_work_recorded(work_limit_context_t& ctx, double total_work); - // Sync callback support - callback is executed when all contexts reach sync point - // If callback returns true, scheduler stops and all workers exit cleanly - using sync_callback_t = std::function; + // Sync callback - executed by one thread when all contexts reach sync point + // Callback should set external termination status if stopping is desired + using sync_callback_t = std::function; void set_sync_callback(sync_callback_t callback); - bool is_stopped() const; - - // Stop the scheduler immediately and wake up all waiting workers - void stop(); // Wait for next sync point (for idle workers with no work) - sync_result_t wait_for_next_sync(work_limit_context_t& ctx); + // After returning, caller should check external termination status + void wait_for_next_sync(work_limit_context_t& ctx); + + // Get the current sync target (for work tracking) + double current_sync_target() const; public: bool verbose{false}; - double sync_interval_; private: - double current_sync_target() const; void wait_at_sync_point(work_limit_context_t& ctx, double sync_target); + double sync_interval_; std::vector> contexts_; - std::unordered_map last_sync_target_; - mutable std::mutex mutex_; - std::condition_variable cv_; - size_t contexts_at_barrier_{0}; - double current_sync_target_{0}; size_t barrier_generation_{0}; - size_t exit_generation_{0}; + double current_sync_target_{0}; // Sync callback - executed when all contexts reach sync point sync_callback_t sync_callback_; - std::atomic stopped_{false}; }; // RAII helper for registering multiple contexts with automatic cleanup diff --git a/cpp/tests/mip/determinism_test.cu b/cpp/tests/mip/determinism_test.cu index ff2922f8f..05751e554 100644 --- a/cpp/tests/mip/determinism_test.cu +++ b/cpp/tests/mip/determinism_test.cu @@ -85,6 +85,37 @@ TEST_F(DeterministicBBTest, reproducible_objective) } } +TEST_F(DeterministicBBTest, reproducible_infeasibility) +{ + auto path = make_path_absolute("/mip/stein9inf.mps"); + auto problem = mps_parser::parse_mps(path, false); + handle_.sync_stream(); + + mip_solver_settings_t settings; + settings.time_limit = 60.0; + settings.determinism_mode = CUOPT_MODE_DETERMINISTIC; + settings.num_cpu_threads = 8; + settings.work_limit = 100; // High enough to fully explore + + auto seed = std::random_device{}() & 0x7fffffff; + std::cout << "Tested with seed " << seed << "\n"; + settings.seed = seed; + + auto solution1 = solve_mip(&handle_, problem, settings); + auto status1 = solution1.get_termination_status(); + EXPECT_EQ(status1, mip_termination_status_t::Infeasible) + << "First run should detect infeasibility"; + + for (int i = 2; i <= 5; ++i) { + auto solution = solve_mip(&handle_, problem, settings); + auto status = solution.get_termination_status(); + + EXPECT_EQ(status1, status) << "Termination status differs on run " << i; + EXPECT_EQ(status, mip_termination_status_t::Infeasible) + << "Run " << i << " should detect infeasibility"; + } +} + // Test determinism under high thread contention TEST_F(DeterministicBBTest, reproducible_high_contention) { From e68941eb6055fb1b6009809d82a69b0b0daca8ca Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 30 Jan 2026 15:04:43 +0000 Subject: [PATCH 327/366] minor touchups --- cpp/src/dual_simplex/branch_and_bound.cpp | 93 ++--------------------- 1 file changed, 7 insertions(+), 86 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 6d32ee743..418374964 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1567,17 +1567,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } assert(root_vstatus_.size() == original_lp_.num_cols); - - { - const i_t expected_basic_count = original_lp_.num_rows; - i_t actual_basic_count = 0; - for (const auto& status : root_vstatus_) { - if (status == variable_status_t::BASIC) { actual_basic_count++; } - } - assert(actual_basic_count == expected_basic_count && - "root_vstatus_ BASIC count mismatch - LP solver returned invalid basis"); - } - set_uninitialized_steepest_edge_norms(edge_norms_); root_objective_ = compute_objective(original_lp_, root_relax_soln_.x); @@ -1661,27 +1650,10 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut original_lp_, log); - { - uint32_t lp_hash = detail::compute_hash(original_lp_.objective); - lp_hash ^= detail::compute_hash(original_lp_.A.x.underlying()); - settings_.log.debug("lp A.x hash: %08x\n", detail::compute_hash(original_lp_.A.x.underlying())); - lp_hash ^= detail::compute_hash(original_lp_.A.i.underlying()); - settings_.log.debug("lp A.j hash: %08x\n", detail::compute_hash(original_lp_.A.i.underlying())); - lp_hash ^= detail::compute_hash(original_lp_.A.col_start.underlying()); - settings_.log.debug("lp A.col_start hash: %08x\n", - detail::compute_hash(original_lp_.A.col_start.underlying())); - lp_hash ^= detail::compute_hash(original_lp_.rhs); - settings_.log.debug("lp rhs hash: %08x\n", detail::compute_hash(original_lp_.rhs)); - lp_hash ^= detail::compute_hash(original_lp_.lower); - settings_.log.debug("lp lower hash: %08x\n", detail::compute_hash(original_lp_.lower)); - lp_hash ^= detail::compute_hash(original_lp_.upper); - settings_.log.printf( - "Exploring the B&B tree using %d threads (best-first = %d, diving = %d) [LP hash: %08x]\n\n", - settings_.num_threads, - settings_.num_bfs_workers, - settings_.num_threads - settings_.num_bfs_workers, - lp_hash); - } + settings_.log.printf("Exploring the B&B tree using %d threads (best-first = %d, diving = %d)\n\n", + settings_.num_threads, + settings_.num_bfs_workers, + settings_.num_threads - settings_.num_bfs_workers); exploration_stats_.nodes_explored = 0; exploration_stats_.nodes_unexplored = 2; @@ -1749,11 +1721,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut // Non-BSP mode: use node_queue or fall back to root lower_bound = node_queue_.best_first_queue_size() > 0 ? node_queue_.get_lower_bound() : search_tree_.root.lower_bound; - // If queue is empty and we have an incumbent, the tree is fully explored - if (node_queue_.best_first_queue_size() == 0 && exploration_stats_.nodes_unexplored == 0 && - incumbent_.has_incumbent) { - lower_bound = upper_bound_.load(); - } } set_final_solution(solution, lower_bound); return solver_status_; @@ -1845,8 +1812,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t 0) { std::vector diving_types; - diving_types.reserve(4); - if (settings_.diving_settings.pseudocost_diving != 0) { diving_types.push_back(bnb_worker_type_t::PSEUDOCOST_DIVING); } @@ -1874,7 +1839,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t(bsp_horizon_step_); - // bsp_scheduler_->verbose = true; scoped_context_registrations_t context_registrations(*bsp_scheduler_); for (auto& worker : *bsp_workers_) { @@ -2160,7 +2124,6 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) if (!bsp_workers_->any_has_work()) { // Tree exhausted - check if we found a solution if (upper_bound == std::numeric_limits::infinity()) { - printf("OI!\n"); bsp_global_termination_status_ = mip_status_t::INFEASIBLE; } else { bsp_global_termination_status_ = mip_status_t::OPTIMAL; @@ -2188,8 +2151,8 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) } } - settings_.log.printf("S%-4d %8d %8lu %+13.6e %+10.6e %s %8.2f [%08x]%s%s\n", - bsp_horizon_number_, + settings_.log.printf("W%-4g %8d %8lu %+13.6e %+10.6e %s %8.2f [%08x]%s%s\n", + bsp_horizon_number_ * bsp_horizon_step_, exploration_stats_.nodes_explored, exploration_stats_.nodes_unexplored, obj, @@ -2209,28 +2172,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t { raft::common::nvtx::range scope("BB::solve_node_bsp"); - { - const i_t expected_basic_count = original_lp_.num_rows; - i_t actual_basic_count = 0; - for (const auto& status : node_ptr->vstatus) { - if (status == variable_status_t::BASIC) { actual_basic_count++; } - } - if (actual_basic_count != expected_basic_count) { - settings_.log.printf( - "ERROR: Node %d (worker %d, seq %d) vstatus has %d BASIC entries, expected %d (num_rows)\n", - node_ptr->node_id, - node_ptr->origin_worker_id, - node_ptr->creation_seq, - actual_basic_count, - expected_basic_count); - settings_.log.printf(" vstatus.size() = %zu, num_cols = %d\n", - node_ptr->vstatus.size(), - original_lp_.num_cols); - assert(actual_basic_count == expected_basic_count && - "vstatus BASIC count mismatch - this indicates vstatus corruption"); - } - } - double work_units_at_start = worker.work_context.global_work_units_elapsed; double clock_at_start = worker.clock; @@ -2269,7 +2210,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t f_t bs_actual_time = toc(bs_start_time); if (settings_.deterministic) { - // TEMP; + // TEMP APPROXIMATION; worker.work_context.record_work(worker.node_presolver.last_nnz_processed / 1e8); } #endif @@ -2322,26 +2263,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t lp_status = convert_lp_status_to_dual_status(second_status); } - // Validate vstatus after LP solve - check for corruption during simplex - { - const i_t expected_basic_count = original_lp_.num_rows; - i_t actual_basic_count = 0; - for (const auto& status : leaf_vstatus) { - if (status == variable_status_t::BASIC) { actual_basic_count++; } - } - if (actual_basic_count != expected_basic_count) { - settings_.log.printf( - "ERROR: After LP solve, node %d vstatus has %d BASIC entries, expected %d\n", - node_ptr->node_id, - actual_basic_count, - expected_basic_count); - settings_.log.printf(" lp_status = %d, recompute_basis = %d\n", - static_cast(lp_status), - worker.recompute_bounds_and_basis ? 1 : 0); - assert(actual_basic_count == expected_basic_count && "vstatus corrupted during LP solve"); - } - } - double work_performed = worker.work_context.global_work_units_elapsed - work_units_at_start; worker.clock += work_performed; From 00158e0b1d48cc16e0499d6b2a6642022e4263e8 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 30 Jan 2026 16:58:07 +0000 Subject: [PATCH 328/366] unify update_tree, fix timing issue --- cpp/src/dual_simplex/bb_worker_state.hpp | 28 +- cpp/src/dual_simplex/branch_and_bound.cpp | 483 +++++++++++---------- cpp/src/dual_simplex/branch_and_bound.hpp | 8 + cpp/src/mip/diversity/diversity_manager.cu | 7 +- 4 files changed, 278 insertions(+), 248 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 7d2950ca3..e1cc80fdf 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -223,7 +223,11 @@ struct bb_worker_state_t { solution); } - void enqueue_node(mip_node_t* node) { plunge_stack.push_front(node); } + void enqueue_node(mip_node_t* node) + { + plunge_stack.push_front(node); + ++total_nodes_assigned; + } // Enqueue children with plunging: move any existing sibling to backlog, push both children mip_node_t* enqueue_children_for_plunge(mip_node_t* down_child, @@ -298,41 +302,43 @@ struct bb_worker_state_t { node->lower_bound, branch_var, branch_val)); + ++nodes_processed_this_horizon; + ++total_nodes_processed; + ++total_nodes_branched; } void record_integer_solution(mip_node_t* node, f_t objective) { record_event( bb_event_t::make_integer_solution(clock, worker_id, node->node_id, 0, objective)); + ++nodes_processed_this_horizon; + ++total_nodes_processed; + ++total_integer_solutions; } void record_fathomed(mip_node_t* node, f_t lower_bound) { record_event( bb_event_t::make_fathomed(clock, worker_id, node->node_id, 0, lower_bound)); + ++nodes_processed_this_horizon; + ++total_nodes_processed; + ++total_nodes_pruned; } void record_infeasible(mip_node_t* node) { record_event(bb_event_t::make_infeasible(clock, worker_id, node->node_id, 0)); + ++nodes_processed_this_horizon; + ++total_nodes_processed; + ++total_nodes_infeasible; } void record_numerical(mip_node_t* node) { record_event(bb_event_t::make_numerical(clock, worker_id, node->node_id, 0)); - } - - void track_node_processed() - { ++nodes_processed_this_horizon; ++total_nodes_processed; } - - void track_node_branched() { ++total_nodes_branched; } - void track_node_pruned() { ++total_nodes_pruned; } - void track_node_infeasible() { ++total_nodes_infeasible; } - void track_integer_solution() { ++total_integer_solutions; } - void track_node_assigned() { ++total_nodes_assigned; } }; // Per-worker state for BSP diving (operates on detached node copies) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 418374964..3573673b7 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -669,6 +669,222 @@ branch_variable_t branch_and_bound_t::variable_selection( } } +// ============================================================================ +// Policies for update_tree +// These allow sharing the tree update logic between the default and deterministic codepaths +// ============================================================================ + +template +struct opportunistic_tree_update_policy_t { + branch_and_bound_t& bnb; + bnb_worker_type_t thread_type; + logger_t& log; + + f_t upper_bound() const { return bnb.upper_bound_.load(); } + + void update_pseudo_costs(mip_node_t* node, f_t leaf_obj) + { + bnb.pc_.update_pseudo_costs(node, leaf_obj); + } + + void handle_integer_solution(mip_node_t* node, f_t obj, const std::vector& x) + { + bnb.add_feasible_solution(obj, x, node->depth, thread_type); + } + + branch_variable_t select_branch_variable(mip_node_t* node, + const std::vector& fractional, + const std::vector& x) + { + return bnb.variable_selection(node, fractional, x, thread_type); + } + + void on_numerical_issue(mip_node_t* node) + { + if (thread_type == bnb_worker_type_t::BEST_FIRST) { + fetch_min(bnb.lower_bound_ceiling_, node->lower_bound); + log.printf("LP returned numerical issue on node %d. Best bound set to %+10.6e.\n", + node->node_id, + compute_user_objective(bnb.original_lp_, bnb.lower_bound_ceiling_.load())); + } + } + + void graphviz(search_tree_t& tree, + mip_node_t* node, + const char* label, + f_t value) + { + tree.graphviz_node(log, node, label, value); + } + + void on_optimal_callback(const std::vector& x, f_t objective) + { + if (thread_type == bnb_worker_type_t::BEST_FIRST && + bnb.settings_.node_processed_callback != nullptr) { + std::vector original_x; + uncrush_primal_solution(bnb.original_problem_, bnb.original_lp_, x, original_x); + bnb.settings_.node_processed_callback(original_x, objective); + } + } + + void on_node_completed(mip_node_t*, node_status_t, rounding_direction_t) {} +}; + +template +struct bsp_tree_update_policy_t { + branch_and_bound_t& bnb; + bb_worker_state_t& worker; + + f_t upper_bound() const { return worker.local_upper_bound; } + + void update_pseudo_costs(mip_node_t* node, f_t leaf_obj) + { + if (node->branch_var < 0) return; + f_t change = leaf_obj - node->lower_bound; + f_t frac = node->branch_dir == rounding_direction_t::DOWN + ? node->fractional_val - std::floor(node->fractional_val) + : std::ceil(node->fractional_val) - node->fractional_val; + if (frac > 1e-10) { + worker.queue_pseudo_cost_update(node->branch_var, node->branch_dir, change / frac); + } + } + + void handle_integer_solution(mip_node_t* node, f_t obj, const std::vector& x) + { + if (obj < worker.local_upper_bound) { + worker.local_upper_bound = obj; + worker.integer_solutions.push_back( + {obj, x, node->depth, worker.worker_id, worker.next_solution_seq++}); + } + } + + branch_variable_t select_branch_variable(mip_node_t*, + const std::vector& fractional, + const std::vector& x) + { + i_t var = worker.variable_selection_from_snapshot(fractional, x); + auto dir = martin_criteria(x[var], bnb.root_relax_soln_.x[var]); + return {var, dir}; + } + + void on_node_completed(mip_node_t* node, node_status_t status, rounding_direction_t dir) + { + switch (status) { + case node_status_t::INFEASIBLE: worker.record_infeasible(node); break; + case node_status_t::FATHOMED: worker.record_fathomed(node, node->lower_bound); break; + case node_status_t::INTEGER_FEASIBLE: + worker.record_integer_solution(node, node->lower_bound); + break; + case node_status_t::HAS_CHILDREN: + worker.record_branched(node, + node->get_down_child()->node_id, + node->get_up_child()->node_id, + node->branch_var, + node->fractional_val); + bnb.exploration_stats_.nodes_unexplored += 2; + worker.enqueue_children_for_plunge(node->get_down_child(), node->get_up_child(), dir); + break; + case node_status_t::NUMERICAL: worker.record_numerical(node); break; + default: break; + } + if (status != node_status_t::HAS_CHILDREN) { worker.recompute_bounds_and_basis = true; } + } + + void on_numerical_issue(mip_node_t* node) + { + worker.local_lower_bound_ceiling = + std::min(node->lower_bound, worker.local_lower_bound_ceiling); + } + + void graphviz(search_tree_t&, mip_node_t*, const char*, f_t) {} + + void on_optimal_callback(const std::vector&, f_t) {} +}; + +template +std::pair update_tree_impl( + mip_node_t* node_ptr, + search_tree_t& search_tree, + lp_problem_t& leaf_problem, + lp_solution_t& leaf_solution, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + pseudo_costs_t& pc, + dual::status_t lp_status, + Policy& policy) +{ + constexpr f_t inf = std::numeric_limits::infinity(); + const f_t abs_fathom_tol = settings.absolute_mip_gap_tol / 10; + const f_t upper_bound = policy.upper_bound(); + node_status_t status = node_status_t::PENDING; + rounding_direction_t round_dir = rounding_direction_t::NONE; + + if (lp_status == dual::status_t::DUAL_UNBOUNDED) { + node_ptr->lower_bound = inf; + policy.graphviz(search_tree, node_ptr, "infeasible", 0.0); + search_tree.update(node_ptr, node_status_t::INFEASIBLE); + status = node_status_t::INFEASIBLE; + + } else if (lp_status == dual::status_t::CUTOFF) { + f_t leaf_obj = compute_objective(leaf_problem, leaf_solution.x); + node_ptr->lower_bound = upper_bound; + policy.graphviz(search_tree, node_ptr, "cut off", leaf_obj); + search_tree.update(node_ptr, node_status_t::FATHOMED); + status = node_status_t::FATHOMED; + + } else if (lp_status == dual::status_t::OPTIMAL) { + std::vector leaf_fractional; + i_t num_frac = fractional_variables(settings, leaf_solution.x, var_types, leaf_fractional); + f_t leaf_obj = compute_objective(leaf_problem, leaf_solution.x); + + policy.graphviz(search_tree, node_ptr, "lower bound", leaf_obj); + policy.update_pseudo_costs(node_ptr, leaf_obj); + node_ptr->lower_bound = leaf_obj; + policy.on_optimal_callback(leaf_solution.x, leaf_obj); + + if (num_frac == 0) { + policy.handle_integer_solution(node_ptr, leaf_obj, leaf_solution.x); + policy.graphviz(search_tree, node_ptr, "integer feasible", leaf_obj); + search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); + status = node_status_t::INTEGER_FEASIBLE; + + } else if (leaf_obj <= upper_bound + abs_fathom_tol) { + auto [branch_var, dir] = + policy.select_branch_variable(node_ptr, leaf_fractional, leaf_solution.x); + round_dir = dir; + + logger_t log; + log.log = false; + node_ptr->objective_estimate = + pc.obj_estimate(leaf_fractional, leaf_solution.x, node_ptr->lower_bound, log); + + search_tree.branch( + node_ptr, branch_var, leaf_solution.x[branch_var], node_ptr->vstatus, leaf_problem, log); + search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); + status = node_status_t::HAS_CHILDREN; + + } else { + policy.graphviz(search_tree, node_ptr, "fathomed", leaf_obj); + search_tree.update(node_ptr, node_status_t::FATHOMED); + status = node_status_t::FATHOMED; + } + } else if (lp_status == dual::status_t::TIME_LIMIT) { + policy.graphviz(search_tree, node_ptr, "timeout", 0.0); + status = node_status_t::PENDING; + } else if (lp_status == dual::status_t::WORK_LIMIT) { + policy.graphviz(search_tree, node_ptr, "work limit", 0.0); + status = node_status_t::PENDING; + } else { + policy.on_numerical_issue(node_ptr); + policy.graphviz(search_tree, node_ptr, "numerical", 0.0); + search_tree.update(node_ptr, node_status_t::NUMERICAL); + status = node_status_t::NUMERICAL; + } + + policy.on_node_completed(node_ptr, status, round_dir); + return {status, round_dir}; +} + template dual::status_t branch_and_bound_t::solve_node_lp( mip_node_t* node_ptr, @@ -802,101 +1018,16 @@ std::pair branch_and_bound_t::upd dual::status_t lp_status, logger_t& log) { - const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; - std::vector& leaf_vstatus = node_ptr->vstatus; - - if (lp_status == dual::status_t::DUAL_UNBOUNDED) { - // Node was infeasible. Do not branch - node_ptr->lower_bound = inf; - search_tree.graphviz_node(log, node_ptr, "infeasible", 0.0); - search_tree.update(node_ptr, node_status_t::INFEASIBLE); - return {node_status_t::INFEASIBLE, rounding_direction_t::NONE}; - - } else if (lp_status == dual::status_t::CUTOFF) { - // Node was cut off. Do not branch - node_ptr->lower_bound = upper_bound_; - f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); - search_tree.graphviz_node(log, node_ptr, "cut off", leaf_objective); - search_tree.update(node_ptr, node_status_t::FATHOMED); - return {node_status_t::FATHOMED, rounding_direction_t::NONE}; - - } else if (lp_status == dual::status_t::OPTIMAL) { - // LP was feasible - std::vector leaf_fractional; - i_t leaf_num_fractional = - fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - - f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); - node_ptr->lower_bound = leaf_objective; - search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); - pc_.update_pseudo_costs(node_ptr, leaf_objective); - - if (thread_type == bnb_worker_type_t::BEST_FIRST) { - if (settings_.node_processed_callback != nullptr) { - std::vector original_x; - uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); - settings_.node_processed_callback(original_x, leaf_objective); - } - } - - if (leaf_num_fractional == 0) { - // Found a integer feasible solution - add_feasible_solution(leaf_objective, leaf_solution.x, node_ptr->depth, thread_type); - search_tree.graphviz_node(log, node_ptr, "integer feasible", leaf_objective); - search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); - return {node_status_t::INTEGER_FEASIBLE, rounding_direction_t::NONE}; - - } else if (leaf_objective <= upper_bound_ + abs_fathom_tol) { - // Choose fractional variable to branch on - auto [branch_var, round_dir] = - variable_selection(node_ptr, leaf_fractional, leaf_solution.x, thread_type); - - assert(leaf_vstatus.size() == leaf_problem.num_cols); - assert(branch_var >= 0); - assert(round_dir != rounding_direction_t::NONE); - - // Note that the exploration thread is the only one that can insert new nodes into the heap, - // and thus, we only need to calculate the objective estimate here (it is used for - // sorting the nodes for diving). - if (thread_type == bnb_worker_type_t::BEST_FIRST) { - logger_t pc_log; - pc_log.log = false; - node_ptr->objective_estimate = - pc_.obj_estimate(leaf_fractional, leaf_solution.x, node_ptr->lower_bound, pc_log); - } - - search_tree.branch( - node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, leaf_problem, log); - search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); - return {node_status_t::HAS_CHILDREN, round_dir}; - - } else { - search_tree.graphviz_node(log, node_ptr, "fathomed", leaf_objective); - search_tree.update(node_ptr, node_status_t::FATHOMED); - return {node_status_t::FATHOMED, rounding_direction_t::NONE}; - } - } else if (lp_status == dual::status_t::TIME_LIMIT) { - search_tree.graphviz_node(log, node_ptr, "timeout", 0.0); - return {node_status_t::PENDING, rounding_direction_t::NONE}; - } else if (lp_status == dual::status_t::WORK_LIMIT) { - search_tree.graphviz_node(log, node_ptr, "work limit", 0.0); - return {node_status_t::PENDING, rounding_direction_t::NONE}; - } else { - if (thread_type == bnb_worker_type_t::BEST_FIRST) { - fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); - log.printf( - "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " - "to " - "%+10.6e.\n", - lp_status, - node_ptr->node_id, - compute_user_objective(original_lp_, lower_bound_ceiling_.load())); - } - - search_tree.graphviz_node(log, node_ptr, "numerical", 0.0); - search_tree.update(node_ptr, node_status_t::NUMERICAL); - return {node_status_t::NUMERICAL, rounding_direction_t::NONE}; - } + opportunistic_tree_update_policy_t policy{*this, thread_type, log}; + return update_tree_impl(node_ptr, + search_tree, + leaf_problem, + leaf_solution, + var_types_, + settings_, + pc_, + lp_status, + policy); } template @@ -1864,9 +1995,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tcreation_seq = 1; (*bsp_workers_)[0].enqueue_node(search_tree_.root.get_down_child()); - (*bsp_workers_)[0].track_node_assigned(); (*bsp_workers_)[1 % num_bfs_workers].enqueue_node(search_tree_.root.get_up_child()); - (*bsp_workers_)[1 % num_bfs_workers].track_node_assigned(); bsp_scheduler_->set_sync_callback([this](double) { bsp_sync_callback(0); }); @@ -1945,7 +2074,8 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tsize() > 0) { - settings_.log.printf("\nBSP Diving Worker Statistics:\n"); + settings_.log.printf("\n"); + settings_.log.printf("BSP Diving Worker Statistics:\n"); settings_.log.printf(" Worker | Type | Dives | Nodes | IntSol | Clock | NoWork\n"); settings_.log.printf(" -------+--------+---------+--------+--------+----------+-------\n"); for (const auto& worker : *bsp_diving_workers_) { @@ -1999,7 +2129,6 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& if (node->lower_bound >= upper_bound || rel_gap < settings_.relative_mip_gap_tol) { worker.current_node = nullptr; worker.record_fathomed(node, node->lower_bound); - worker.track_node_pruned(); search_tree.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; continue; @@ -2151,7 +2280,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) } } - settings_.log.printf("W%-4g %8d %8lu %+13.6e %+10.6e %s %8.2f [%08x]%s%s\n", + settings_.log.printf("W%-5g %8d %8lu %+13.6e %+10.6e %s %8.2f [%08x]%s%s\n", bsp_horizon_number_ * bsp_horizon_step_, exploration_stats_.nodes_explored, exploration_stats_.nodes_unexplored, @@ -2219,8 +2348,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t node_ptr->lower_bound = std::numeric_limits::infinity(); search_tree.update(node_ptr, node_status_t::INFEASIBLE); worker.record_infeasible(node_ptr); - worker.track_node_infeasible(); - worker.track_node_processed(); --exploration_stats_.nodes_unexplored; ++exploration_stats_.nodes_explored; worker.recompute_bounds_and_basis = true; @@ -2271,139 +2398,28 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t ++exploration_stats_.nodes_explored; --exploration_stats_.nodes_unexplored; - // Process LP result - if (lp_status == dual::status_t::DUAL_UNBOUNDED) { - node_ptr->lower_bound = std::numeric_limits::infinity(); - - worker.record_infeasible(node_ptr); - worker.track_node_infeasible(); - worker.track_node_processed(); - worker.recompute_bounds_and_basis = true; - - search_tree.update(node_ptr, node_status_t::INFEASIBLE); - return node_solve_info_t::NO_CHILDREN; - - } else if (lp_status == dual::status_t::CUTOFF) { - node_ptr->lower_bound = worker.local_upper_bound; - - worker.record_fathomed(node_ptr, node_ptr->lower_bound); - worker.track_node_pruned(); - worker.track_node_processed(); - worker.recompute_bounds_and_basis = true; - - search_tree.update(node_ptr, node_status_t::FATHOMED); - return node_solve_info_t::NO_CHILDREN; - - } else if (lp_status == dual::status_t::OPTIMAL) { - std::vector leaf_fractional; - i_t leaf_num_fractional = - fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - - f_t leaf_objective = compute_objective(worker.leaf_problem, leaf_solution.x); - - // TODO - if (settings_.node_processed_callback != nullptr) { - // std::vector original_x; - // uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); - // settings_.node_processed_callback(original_x, leaf_objective); - } - - // Queue pseudo-cost update for deterministic application at sync - if (node_ptr->branch_var >= 0) { - const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; - const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN - ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) - : std::ceil(node_ptr->fractional_val) - node_ptr->fractional_val; - if (frac > 1e-10) { - worker.queue_pseudo_cost_update( - node_ptr->branch_var, node_ptr->branch_dir, change_in_obj / frac); - } - } - - node_ptr->lower_bound = leaf_objective; - - if (leaf_num_fractional == 0) { - // Integer feasible - queue for deterministic processing at sync - if (leaf_objective < worker.local_upper_bound) { - worker.local_upper_bound = leaf_objective; - worker.integer_solutions.push_back({leaf_objective, - leaf_solution.x, - node_ptr->depth, - worker.worker_id, - worker.next_solution_seq++}); - } - - worker.record_integer_solution(node_ptr, leaf_objective); - worker.track_integer_solution(); - worker.track_node_processed(); - worker.recompute_bounds_and_basis = true; - - search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); - return node_solve_info_t::NO_CHILDREN; - - } else if (leaf_objective <= worker.local_upper_bound + settings_.absolute_mip_gap_tol / 10) { - // Branch - use worker-local upper bound for deterministic pruning decision - // Use pseudo-cost snapshot for variable selection - const i_t branch_var = - worker.variable_selection_from_snapshot(leaf_fractional, leaf_solution.x); - - logger_t log; - log.log = false; - - node_ptr->objective_estimate = - pc_.obj_estimate(leaf_fractional, leaf_solution.x, node_ptr->lower_bound, log); - - search_tree.branch( - node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, worker.leaf_problem, log); - search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); - - i_t down_child_id = node_ptr->get_down_child()->node_id; - i_t up_child_id = node_ptr->get_up_child()->node_id; - worker.record_branched( - node_ptr, down_child_id, up_child_id, branch_var, leaf_solution.x[branch_var]); - worker.track_node_branched(); - worker.track_node_processed(); - - exploration_stats_.nodes_unexplored += 2; - - rounding_direction_t preferred = - martin_criteria(leaf_solution.x[branch_var], root_relax_soln_.x[branch_var]); - worker.enqueue_children_for_plunge( - node_ptr->get_down_child(), node_ptr->get_up_child(), preferred); - - return preferred == rounding_direction_t::DOWN ? node_solve_info_t::DOWN_CHILD_FIRST - : node_solve_info_t::UP_CHILD_FIRST; - - } else { - worker.record_fathomed(node_ptr, leaf_objective); - worker.track_node_pruned(); - worker.track_node_processed(); - worker.recompute_bounds_and_basis = true; - - search_tree.update(node_ptr, node_status_t::FATHOMED); - return node_solve_info_t::NO_CHILDREN; - } - - } else if (lp_status == dual::status_t::TIME_LIMIT) { - return node_solve_info_t::TIME_LIMIT; - } else if (lp_status == dual::status_t::WORK_LIMIT) { - return node_solve_info_t::WORK_LIMIT; - - } else { - // Update local lower bound ceiling for numerical issues (merged to global at sync) - if (node_ptr->lower_bound < worker.local_lower_bound_ceiling) { - worker.local_lower_bound_ceiling = node_ptr->lower_bound; - } - settings_.log.printf( - "LP returned numerical issue on node %d. Local best bound set to %+10.6e.\n", - node_ptr->node_id, - compute_user_objective(original_lp_, worker.local_lower_bound_ceiling)); - - worker.record_numerical(node_ptr); - worker.recompute_bounds_and_basis = true; - search_tree.update(node_ptr, node_status_t::NUMERICAL); + bsp_tree_update_policy_t policy{*this, worker}; + auto [status, round_dir] = update_tree_impl(node_ptr, + search_tree, + worker.leaf_problem, + leaf_solution, + var_types_, + settings_, + pc_, + lp_status, + policy); + + // Convert node_status_t to node_solve_info_t + if (status == node_status_t::HAS_CHILDREN) { + return round_dir == rounding_direction_t::DOWN ? node_solve_info_t::DOWN_CHILD_FIRST + : node_solve_info_t::UP_CHILD_FIRST; + } else if (status == node_status_t::PENDING) { + return lp_status == dual::status_t::TIME_LIMIT ? node_solve_info_t::TIME_LIMIT + : node_solve_info_t::WORK_LIMIT; + } else if (status == node_status_t::NUMERICAL) { return node_solve_info_t::NUMERICAL; } + return node_solve_info_t::NO_CHILDREN; } template @@ -2721,7 +2737,6 @@ void branch_and_bound_t::balance_worker_loads() for (size_t i = 0; i < all_nodes.size(); ++i) { size_t worker_idx = worker_order[i % num_workers]; (*bsp_workers_)[worker_idx].enqueue_node(all_nodes[i]); - (*bsp_workers_)[worker_idx].track_node_assigned(); } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 6ed080328..ea76cacc8 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -80,6 +80,11 @@ struct bnb_stats_t { omp_atomic_t nodes_since_last_log = 0; }; +template +struct opportunistic_tree_update_policy_t; +template +struct bsp_tree_update_policy_t; + template class branch_and_bound_t { public: @@ -347,6 +352,9 @@ class branch_and_bound_t { // Collect and merge diving solutions at sync void collect_diving_solutions(); + friend struct opportunistic_tree_update_policy_t; + friend struct bsp_tree_update_policy_t; + private: // BSP state // unique_ptr as we only want to initialize these if we're in the determinism codepath diff --git a/cpp/src/mip/diversity/diversity_manager.cu b/cpp/src/mip/diversity/diversity_manager.cu index e08fae6df..72b558657 100644 --- a/cpp/src/mip/diversity/diversity_manager.cu +++ b/cpp/src/mip/diversity/diversity_manager.cu @@ -311,6 +311,10 @@ solution_t diversity_manager_t::run_solver() context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC ? "deterministic" : "opportunistic"); + // to automatically compute the solving time on scope exit + auto timer_raii_guard = + cuopt::scope_guard([&]() { stats.total_solve_time = timer.elapsed_time(); }); + // Debug: Allow disabling GPU heuristics to test B&B tree determinism in isolation const char* disable_heuristics_env = std::getenv("CUOPT_DISABLE_GPU_HEURISTICS"); if (context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC) { @@ -349,9 +353,6 @@ solution_t diversity_manager_t::run_solver() const f_t time_limit = timer.remaining_time(); const f_t lp_time_limit = std::min(diversity_config.max_time_on_lp, time_limit * diversity_config.time_ratio_on_init_lp); - // to automatically compute the solving time on scope exit - auto timer_raii_guard = - cuopt::scope_guard([&]() { stats.total_solve_time = timer.elapsed_time(); }); // after every change to the problem, we should resize all the relevant vars // we need to encapsulate that to prevent repetitions recombine_stats.reset(); From 5f929f4ce0790c3a2d8279987cf9da8bede9e600 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 30 Jan 2026 18:13:31 +0000 Subject: [PATCH 329/366] unify b&b worker struct --- cpp/src/dual_simplex/bb_worker_state.hpp | 209 ++++---- cpp/src/dual_simplex/branch_and_bound.cpp | 578 ++++++++-------------- cpp/src/dual_simplex/branch_and_bound.hpp | 46 +- 3 files changed, 348 insertions(+), 485 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index e1cc80fdf..2e0bf501c 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -15,6 +15,7 @@ #include #include #include +#include #include #include @@ -93,11 +94,9 @@ struct queued_integer_solution_t { } }; -// Per-worker state for BSP branch-and-bound +// BSP-specific state nested within bb_worker_state_t template -struct bb_worker_state_t { - int worker_id{0}; - +struct bsp_state_t { // Plunge stack: LIFO queue for depth-first exploration std::deque*> plunge_stack; @@ -117,14 +116,6 @@ struct bb_worker_state_t { bb_event_batch_t events; int event_sequence{0}; - lp_problem_t leaf_problem; - basis_update_mpf_t basis_factors; - bounds_strengthening_t node_presolver; - std::vector basic_list; - std::vector nonbasic_list; - work_limit_context_t work_context; - bool recompute_bounds_and_basis{true}; - i_t nodes_processed_this_horizon{0}; i_t total_nodes_processed{0}; i_t total_nodes_pruned{0}; @@ -148,6 +139,36 @@ struct bb_worker_state_t { std::vector pc_num_up_snapshot; std::vector pc_num_down_snapshot; + // LP iteration snapshot for diving iteration limits + i_t total_lp_iters_snapshot{0}; +}; + +// Per-worker state for branch-and-bound (both BSP and non-BSP modes) +template +struct bb_worker_state_t { + int worker_id{0}; + + // Common LP solving state + lp_problem_t leaf_problem; + basis_update_mpf_t basis_factors; + bounds_strengthening_t node_presolver; + std::vector basic_list; + std::vector nonbasic_list; + work_limit_context_t work_context; + bool recompute_bounds_and_basis{true}; + + // Upper bound access (non-BSP uses global pointer, BSP uses bsp.local_upper_bound) + omp_atomic_t* global_upper_bound_ptr{nullptr}; + + f_t get_upper_bound() const + { + return global_upper_bound_ptr ? global_upper_bound_ptr->load() : bsp.local_upper_bound; + } + + // BSP-specific state (only used in deterministic mode) + bsp_state_t bsp; + + // BSP constructor (deterministic mode with local upper bound) explicit bb_worker_state_t(int id, const lp_problem_t& original_lp, const csr_matrix_t& Arow, @@ -166,19 +187,39 @@ struct bb_worker_state_t { work_context.deterministic = deterministic; } + // Non-BSP constructor (opportunistic mode with global upper bound) + explicit bb_worker_state_t(int id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + i_t refactor_frequency, + omp_atomic_t* global_ub) + : worker_id(id), + work_context("BB_Worker_" + std::to_string(id)), + leaf_problem(original_lp), + basis_factors(original_lp.num_rows, refactor_frequency), + node_presolver(original_lp, Arow, std::vector{}, var_types), + global_upper_bound_ptr(global_ub) + { + const i_t m = leaf_problem.num_rows; + basic_list.resize(m); + nonbasic_list.clear(); + work_context.deterministic = false; + } + void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) { - clock = horizon_start; + bsp.clock = horizon_start; work_context.global_work_units_elapsed = horizon_start; - events.clear(); - events.horizon_start = horizon_start; - events.horizon_end = horizon_end; - event_sequence = 0; - nodes_processed_this_horizon = 0; - local_upper_bound = global_upper_bound; - local_lower_bound_ceiling = std::numeric_limits::infinity(); - integer_solutions.clear(); - pseudo_cost_updates.clear(); + bsp.events.clear(); + bsp.events.horizon_start = horizon_start; + bsp.events.horizon_end = horizon_end; + bsp.event_sequence = 0; + bsp.nodes_processed_this_horizon = 0; + bsp.local_upper_bound = global_upper_bound; + bsp.local_lower_bound_ceiling = std::numeric_limits::infinity(); + bsp.integer_solutions.clear(); + bsp.pseudo_cost_updates.clear(); } void set_snapshots(f_t global_upper_bound, @@ -186,47 +227,49 @@ struct bb_worker_state_t { const std::vector& pc_sum_down, const std::vector& pc_num_up, const std::vector& pc_num_down, + i_t total_lp_iters, double new_horizon_start, double new_horizon_end) { - local_upper_bound = global_upper_bound; - pc_sum_up_snapshot = pc_sum_up; - pc_sum_down_snapshot = pc_sum_down; - pc_num_up_snapshot = pc_num_up; - pc_num_down_snapshot = pc_num_down; - horizon_start = new_horizon_start; - horizon_end = new_horizon_end; + bsp.local_upper_bound = global_upper_bound; + bsp.pc_sum_up_snapshot = pc_sum_up; + bsp.pc_sum_down_snapshot = pc_sum_down; + bsp.pc_num_up_snapshot = pc_num_up; + bsp.pc_num_down_snapshot = pc_num_down; + bsp.total_lp_iters_snapshot = total_lp_iters; + bsp.horizon_start = new_horizon_start; + bsp.horizon_end = new_horizon_end; } // Queue pseudo-cost update for global sync and apply to local snapshot void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) { - pseudo_cost_updates.push_back({variable, direction, delta, clock, worker_id}); + bsp.pseudo_cost_updates.push_back({variable, direction, delta, bsp.clock, worker_id}); if (direction == rounding_direction_t::DOWN) { - pc_sum_down_snapshot[variable] += delta; - pc_num_down_snapshot[variable]++; + bsp.pc_sum_down_snapshot[variable] += delta; + bsp.pc_num_down_snapshot[variable]++; } else { - pc_sum_up_snapshot[variable] += delta; - pc_num_up_snapshot[variable]++; + bsp.pc_sum_up_snapshot[variable] += delta; + bsp.pc_num_up_snapshot[variable]++; } } i_t variable_selection_from_snapshot(const std::vector& fractional, const std::vector& solution) const { - return variable_selection_from_pseudo_costs(pc_sum_down_snapshot.data(), - pc_sum_up_snapshot.data(), - pc_num_down_snapshot.data(), - pc_num_up_snapshot.data(), - (i_t)pc_sum_down_snapshot.size(), + return variable_selection_from_pseudo_costs(bsp.pc_sum_down_snapshot.data(), + bsp.pc_sum_up_snapshot.data(), + bsp.pc_num_down_snapshot.data(), + bsp.pc_num_up_snapshot.data(), + (i_t)bsp.pc_sum_down_snapshot.size(), fractional, solution); } void enqueue_node(mip_node_t* node) { - plunge_stack.push_front(node); - ++total_nodes_assigned; + bsp.plunge_stack.push_front(node); + ++bsp.total_nodes_assigned; } // Enqueue children with plunging: move any existing sibling to backlog, push both children @@ -234,24 +277,24 @@ struct bb_worker_state_t { mip_node_t* up_child, rounding_direction_t preferred_direction) { - if (!plunge_stack.empty()) { - backlog.push(plunge_stack.back()); - plunge_stack.pop_back(); + if (!bsp.plunge_stack.empty()) { + bsp.backlog.push(bsp.plunge_stack.back()); + bsp.plunge_stack.pop_back(); } down_child->origin_worker_id = worker_id; - down_child->creation_seq = next_creation_seq++; + down_child->creation_seq = bsp.next_creation_seq++; up_child->origin_worker_id = worker_id; - up_child->creation_seq = next_creation_seq++; + up_child->creation_seq = bsp.next_creation_seq++; mip_node_t* first_child; if (preferred_direction == rounding_direction_t::UP) { - plunge_stack.push_front(down_child); - plunge_stack.push_front(up_child); + bsp.plunge_stack.push_front(down_child); + bsp.plunge_stack.push_front(up_child); first_child = up_child; } else { - plunge_stack.push_front(up_child); - plunge_stack.push_front(down_child); + bsp.plunge_stack.push_front(up_child); + bsp.plunge_stack.push_front(down_child); first_child = down_child; } return first_child; @@ -260,40 +303,40 @@ struct bb_worker_state_t { // Dequeue: current_node first, then plunge_stack, then backlog heap mip_node_t* dequeue_node() { - if (current_node != nullptr) { - mip_node_t* node = current_node; - current_node = nullptr; + if (bsp.current_node != nullptr) { + mip_node_t* node = bsp.current_node; + bsp.current_node = nullptr; return node; } - if (!plunge_stack.empty()) { - mip_node_t* node = plunge_stack.front(); - plunge_stack.pop_front(); + if (!bsp.plunge_stack.empty()) { + mip_node_t* node = bsp.plunge_stack.front(); + bsp.plunge_stack.pop_front(); return node; } - auto node_opt = backlog.pop(); + auto node_opt = bsp.backlog.pop(); return node_opt.has_value() ? node_opt.value() : nullptr; } bool has_work() const { - return current_node != nullptr || !plunge_stack.empty() || !backlog.empty(); + return bsp.current_node != nullptr || !bsp.plunge_stack.empty() || !bsp.backlog.empty(); } size_t queue_size() const { - return plunge_stack.size() + backlog.size() + (current_node != nullptr ? 1 : 0); + return bsp.plunge_stack.size() + bsp.backlog.size() + (bsp.current_node != nullptr ? 1 : 0); } void record_event(bb_event_t event) { - event.event_sequence = event_sequence++; - events.add(std::move(event)); + event.event_sequence = bsp.event_sequence++; + bsp.events.add(std::move(event)); } void record_branched( mip_node_t* node, i_t down_child_id, i_t up_child_id, i_t branch_var, f_t branch_val) { - record_event(bb_event_t::make_branched(clock, + record_event(bb_event_t::make_branched(bsp.clock, worker_id, node->node_id, 0, @@ -302,42 +345,42 @@ struct bb_worker_state_t { node->lower_bound, branch_var, branch_val)); - ++nodes_processed_this_horizon; - ++total_nodes_processed; - ++total_nodes_branched; + ++bsp.nodes_processed_this_horizon; + ++bsp.total_nodes_processed; + ++bsp.total_nodes_branched; } void record_integer_solution(mip_node_t* node, f_t objective) { - record_event( - bb_event_t::make_integer_solution(clock, worker_id, node->node_id, 0, objective)); - ++nodes_processed_this_horizon; - ++total_nodes_processed; - ++total_integer_solutions; + record_event(bb_event_t::make_integer_solution( + bsp.clock, worker_id, node->node_id, 0, objective)); + ++bsp.nodes_processed_this_horizon; + ++bsp.total_nodes_processed; + ++bsp.total_integer_solutions; } void record_fathomed(mip_node_t* node, f_t lower_bound) { record_event( - bb_event_t::make_fathomed(clock, worker_id, node->node_id, 0, lower_bound)); - ++nodes_processed_this_horizon; - ++total_nodes_processed; - ++total_nodes_pruned; + bb_event_t::make_fathomed(bsp.clock, worker_id, node->node_id, 0, lower_bound)); + ++bsp.nodes_processed_this_horizon; + ++bsp.total_nodes_processed; + ++bsp.total_nodes_pruned; } void record_infeasible(mip_node_t* node) { - record_event(bb_event_t::make_infeasible(clock, worker_id, node->node_id, 0)); - ++nodes_processed_this_horizon; - ++total_nodes_processed; - ++total_nodes_infeasible; + record_event(bb_event_t::make_infeasible(bsp.clock, worker_id, node->node_id, 0)); + ++bsp.nodes_processed_this_horizon; + ++bsp.total_nodes_processed; + ++bsp.total_nodes_infeasible; } void record_numerical(mip_node_t* node) { - record_event(bb_event_t::make_numerical(clock, worker_id, node->node_id, 0)); - ++nodes_processed_this_horizon; - ++total_nodes_processed; + record_event(bb_event_t::make_numerical(bsp.clock, worker_id, node->node_id, 0)); + ++bsp.nodes_processed_this_horizon; + ++bsp.total_nodes_processed; } }; @@ -592,10 +635,10 @@ class bb_worker_pool_t { { bb_event_batch_t all_events; for (auto& worker : workers_) { - for (auto& event : worker.events.events) { + for (auto& event : worker.bsp.events.events) { all_events.add(std::move(event)); } - worker.events.clear(); + worker.bsp.events.clear(); } all_events.sort_for_replay(); return all_events; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 3573673b7..3e1d5e8ab 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -735,7 +735,7 @@ struct bsp_tree_update_policy_t { branch_and_bound_t& bnb; bb_worker_state_t& worker; - f_t upper_bound() const { return worker.local_upper_bound; } + f_t upper_bound() const { return worker.bsp.local_upper_bound; } void update_pseudo_costs(mip_node_t* node, f_t leaf_obj) { @@ -751,10 +751,10 @@ struct bsp_tree_update_policy_t { void handle_integer_solution(mip_node_t* node, f_t obj, const std::vector& x) { - if (obj < worker.local_upper_bound) { - worker.local_upper_bound = obj; - worker.integer_solutions.push_back( - {obj, x, node->depth, worker.worker_id, worker.next_solution_seq++}); + if (obj < worker.bsp.local_upper_bound) { + worker.bsp.local_upper_bound = obj; + worker.bsp.integer_solutions.push_back( + {obj, x, node->depth, worker.worker_id, worker.bsp.next_solution_seq++}); } } @@ -792,8 +792,8 @@ struct bsp_tree_update_policy_t { void on_numerical_issue(mip_node_t* node) { - worker.local_lower_bound_ceiling = - std::min(node->lower_bound, worker.local_lower_bound_ceiling); + worker.bsp.local_lower_bound_ceiling = + std::min(node->lower_bound, worker.bsp.local_lower_bound_ceiling); } void graphviz(search_tree_t&, mip_node_t*, const char*, f_t) {} @@ -886,37 +886,53 @@ std::pair update_tree_impl( } template -dual::status_t branch_and_bound_t::solve_node_lp( - mip_node_t* node_ptr, - lp_problem_t& leaf_problem, - lp_solution_t& leaf_solution, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list, - bounds_strengthening_t& node_presolver, - bnb_worker_type_t thread_type, - bool recompute_bounds_and_basis, - const std::vector& root_lower, - const std::vector& root_upper, - bnb_stats_t& stats, - logger_t& log) +dual::status_t branch_and_bound_t::solve_node_lp(bb_worker_state_t& worker, + mip_node_t* node_ptr, + lp_solution_t& leaf_solution, + bnb_worker_type_t thread_type, + const std::vector& root_lower, + const std::vector& root_upper, + bnb_stats_t& stats) { - raft::common::nvtx::range scope("BB::solve_node"); + raft::common::nvtx::range scope("BB::solve_node_lp"); + + double work_units_at_start = worker.work_context.global_work_units_elapsed; std::vector& leaf_vstatus = node_ptr->vstatus; - assert(leaf_vstatus.size() == leaf_problem.num_cols); + assert(leaf_vstatus.size() == worker.leaf_problem.num_cols); - simplex_solver_settings_t lp_settings = settings_; + // Reset the bound_changed markers + std::fill(worker.node_presolver.bounds_changed.begin(), + worker.node_presolver.bounds_changed.end(), + false); + + // Set the correct bounds for the leaf problem + if (worker.recompute_bounds_and_basis) { + worker.leaf_problem.lower = root_lower; + worker.leaf_problem.upper = root_upper; + node_ptr->get_variable_bounds( + worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); + } else { + node_ptr->update_branched_variable_bounds( + worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); + } + + double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); + if (remaining_time <= 0) { return dual::status_t::TIME_LIMIT; } + + simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); - lp_settings.cut_off = upper_bound_ + settings_.dual_tol; + lp_settings.cut_off = worker.get_upper_bound() + settings_.dual_tol; lp_settings.inside_mip = 2; - lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); + lp_settings.time_limit = remaining_time; lp_settings.scale_columns = false; + // Iteration limit for diving threads if (thread_type != bnb_worker_type_t::BEST_FIRST) { - i_t bnb_lp_iters = exploration_stats_.total_lp_iters; - f_t factor = settings_.diving_settings.iteration_limit_factor; - i_t max_iter = factor * bnb_lp_iters; + i_t bnb_lp_iters = worker.work_context.deterministic ? worker.bsp.total_lp_iters_snapshot + : exploration_stats_.total_lp_iters.load(); + f_t factor = settings_.diving_settings.iteration_limit_factor; + i_t max_iter = factor * bnb_lp_iters; lp_settings.iteration_limit = max_iter - stats.total_lp_iters; if (lp_settings.iteration_limit <= 0) { return dual::status_t::ITERATION_LIMIT; } } @@ -943,64 +959,61 @@ dual::status_t branch_and_bound_t::solve_node_lp( node_ptr->vstatus[node_ptr->branch_var]); #endif - // Reset the bound_changed markers - std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); - - // Set the correct bounds for the leaf problem - if (recompute_bounds_and_basis) { - leaf_problem.lower = root_lower; - leaf_problem.upper = root_upper; - node_ptr->get_variable_bounds( - leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); + bool feasible = worker.node_presolver.bounds_strengthening( + worker.leaf_problem.lower, worker.leaf_problem.upper, lp_settings); - } else { - node_ptr->update_branched_variable_bounds( - leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); + if (worker.work_context.deterministic) { + worker.work_context.record_work(worker.node_presolver.last_nnz_processed / 1e8); } - bool feasible = - node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); - - dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; + if (!feasible) { return dual::status_t::DUAL_UNBOUNDED; } - if (feasible) { - i_t node_iter = 0; - f_t lp_start_time = tic(); - std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; + i_t node_iter = 0; + f_t lp_start_time = tic(); + std::vector leaf_edge_norms = edge_norms_; - lp_status = dual_phase2_with_advanced_basis(2, - 0, - recompute_bounds_and_basis, - lp_start_time, - leaf_problem, - lp_settings, - leaf_vstatus, - basis_factors, - basic_list, - nonbasic_list, - leaf_solution, - node_iter, - leaf_edge_norms); + // Pass work_context only for deterministic mode + work_limit_context_t* work_ctx = + worker.work_context.deterministic ? &worker.work_context : nullptr; - if (lp_status == dual::status_t::NUMERICAL) { - log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); - lp_status_t second_status = solve_linear_program_with_advanced_basis(leaf_problem, - lp_start_time, - lp_settings, - leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - leaf_vstatus, - leaf_edge_norms); + dual::status_t lp_status = dual_phase2_with_advanced_basis(2, + 0, + worker.recompute_bounds_and_basis, + lp_start_time, + worker.leaf_problem, + lp_settings, + leaf_vstatus, + worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms, + work_ctx); - lp_status = convert_lp_status_to_dual_status(second_status); - } + if (lp_status == dual::status_t::NUMERICAL) { + settings_.log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); + lp_status_t second_status = solve_linear_program_with_advanced_basis(worker.leaf_problem, + lp_start_time, + lp_settings, + leaf_solution, + worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + leaf_vstatus, + leaf_edge_norms); + lp_status = convert_lp_status_to_dual_status(second_status); + } - stats.total_lp_solve_time += toc(lp_start_time); - stats.total_lp_iters += node_iter; + // Update work clock for deterministic mode + if (worker.work_context.deterministic) { + double work_performed = worker.work_context.global_work_units_elapsed - work_units_at_start; + worker.bsp.clock += work_performed; } + stats.total_lp_solve_time += toc(lp_start_time); + stats.total_lp_iters += node_iter; + #ifdef LOG_NODE_SIMPLEX lp_settings.log.printf("\nLP status: %d\n\n", lp_status); #endif @@ -1076,30 +1089,19 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod return; } - // Make a copy of the original LP. We will modify its bounds at each leaf - lp_problem_t leaf_problem = original_lp_; - std::vector row_sense; - bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); - - const i_t m = leaf_problem.num_rows; - basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); - std::vector basic_list(m); - std::vector nonbasic_list; + // Create a temporary worker for the ramp-up phase + bb_worker_state_t worker( + 0, original_lp_, Arow_, var_types_, settings_.refactor_frequency, &upper_bound_); + worker.recompute_bounds_and_basis = true; - lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); - dual::status_t lp_status = solve_node_lp(node, - leaf_problem, + lp_solution_t leaf_solution(worker.leaf_problem.num_rows, worker.leaf_problem.num_cols); + dual::status_t lp_status = solve_node_lp(worker, + node, leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, bnb_worker_type_t::BEST_FIRST, - true, original_lp_.lower, original_lp_.upper, - exploration_stats_, - settings_.log); + exploration_stats_); if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; return; @@ -1111,7 +1113,7 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod auto [node_status, round_dir] = update_tree(node, search_tree_, - leaf_problem, + worker.leaf_problem, leaf_solution, bnb_worker_type_t::BEST_FIRST, lp_status, @@ -1137,27 +1139,22 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod } template -void branch_and_bound_t::plunge_from(i_t task_id, - mip_node_t* start_node, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list) +void branch_and_bound_t::plunge_from(bb_worker_state_t& worker, + mip_node_t* start_node) { raft::common::nvtx::range scope("BB::explore_subtree"); - bool recompute_bounds_and_basis = true; + worker.recompute_bounds_and_basis = true; std::deque*> stack; stack.push_front(start_node); while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET && is_running) { - if (task_id == 0) { repair_heuristic_solutions(); } + if (worker.worker_id == 0) { repair_heuristic_solutions(); } mip_node_t* node_ptr = stack.front(); stack.pop_front(); f_t lower_bound = node_ptr->lower_bound; - f_t upper_bound = upper_bound_; + f_t upper_bound = worker.get_upper_bound(); f_t abs_gap = upper_bound - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); @@ -1167,20 +1164,20 @@ void branch_and_bound_t::plunge_from(i_t task_id, // - The current node and its siblings uses the lower bound of the parent before solving the LP // relaxation // - The lower bound of the parent is lower or equal to its children - assert(task_id < local_lower_bounds_.size()); - local_lower_bounds_[task_id] = lower_bound; + assert(worker.worker_id < local_lower_bounds_.size()); + local_lower_bounds_[worker.worker_id] = lower_bound; if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { search_tree_.graphviz_node(settings_.log, node_ptr, "cutoff", node_ptr->lower_bound); search_tree_.update(node_ptr, node_status_t::FATHOMED); - recompute_bounds_and_basis = true; + worker.recompute_bounds_and_basis = true; --exploration_stats_.nodes_unexplored; continue; } f_t now = toc(exploration_stats_.start_time); - if (task_id == 0) { + if (worker.worker_id == 0) { f_t time_since_last_log = exploration_stats_.last_log == 0 ? 1.0 : toc(exploration_stats_.last_log); @@ -1203,20 +1200,15 @@ void branch_and_bound_t::plunge_from(i_t task_id, break; } - lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); - dual::status_t lp_status = solve_node_lp(node_ptr, - leaf_problem, + lp_solution_t leaf_solution(worker.leaf_problem.num_rows, + worker.leaf_problem.num_cols); + dual::status_t lp_status = solve_node_lp(worker, + node_ptr, leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, bnb_worker_type_t::BEST_FIRST, - recompute_bounds_and_basis, original_lp_.lower, original_lp_.upper, - exploration_stats_, - settings_.log); + exploration_stats_); if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; @@ -1231,13 +1223,13 @@ void branch_and_bound_t::plunge_from(i_t task_id, auto [node_status, round_dir] = update_tree(node_ptr, search_tree_, - leaf_problem, + worker.leaf_problem, leaf_solution, bnb_worker_type_t::BEST_FIRST, lp_status, settings_.log); - recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; + worker.recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; if (node_status == node_status_t::HAS_CHILDREN) { // The stack should only contain the children of the current parent. @@ -1271,15 +1263,9 @@ void branch_and_bound_t::best_first_thread(i_t task_id) f_t abs_gap = inf; f_t rel_gap = inf; - // Make a copy of the original LP. We will modify its bounds at each leaf - lp_problem_t leaf_problem = original_lp_; - std::vector row_sense; - bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); - - const i_t m = leaf_problem.num_rows; - basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); - std::vector basic_list(m); - std::vector nonbasic_list; + // Create worker state with global upper bound pointer + bb_worker_state_t worker( + task_id, original_lp_, Arow_, var_types_, settings_.refactor_frequency, &upper_bound_); while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && @@ -1296,7 +1282,7 @@ void branch_and_bound_t::best_first_thread(i_t task_id) node_queue_.unlock(); if (start_node.has_value()) { - if (upper_bound_ < start_node.value()->lower_bound) { + if (worker.get_upper_bound() < start_node.value()->lower_bound) { // This node was put on the heap earlier but its lower bound is now greater than the // current upper bound search_tree_.graphviz_node( @@ -1307,20 +1293,14 @@ void branch_and_bound_t::best_first_thread(i_t task_id) } // Best-first search with plunging - plunge_from(task_id, - start_node.value(), - leaf_problem, - node_presolver, - basis_factors, - basic_list, - nonbasic_list); + plunge_from(worker, start_node.value()); active_subtrees_--; } lower_bound = get_lower_bound(); - abs_gap = upper_bound_ - lower_bound; - rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); + abs_gap = worker.get_upper_bound() - lower_bound; + rel_gap = user_relative_gap(original_lp_, worker.get_upper_bound(), lower_bound); } is_running = false; @@ -1333,23 +1313,17 @@ void branch_and_bound_t::best_first_thread(i_t task_id) } template -void branch_and_bound_t::dive_from(mip_node_t& start_node, +void branch_and_bound_t::dive_from(bb_worker_state_t& worker, + mip_node_t& start_node, const std::vector& start_lower, const std::vector& start_upper, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list, bnb_worker_type_t diving_type) { raft::common::nvtx::range scope("BB::diving_thread"); - logger_t log; - log.log = false; - const i_t diving_node_limit = settings_.diving_settings.node_limit; - const i_t diving_backtrack_limit = settings_.diving_settings.backtrack_limit; - bool recompute_bounds_and_basis = true; + const i_t diving_node_limit = settings_.diving_settings.node_limit; + const i_t diving_backtrack_limit = settings_.diving_settings.backtrack_limit; + worker.recompute_bounds_and_basis = true; search_tree_t dive_tree(std::move(start_node)); std::deque*> stack; stack.push_front(&dive_tree.root); @@ -1365,31 +1339,21 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, stack.pop_front(); f_t lower_bound = node_ptr->lower_bound; - f_t upper_bound = upper_bound_; + f_t upper_bound = worker.get_upper_bound(); f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); if (node_ptr->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - recompute_bounds_and_basis = true; + worker.recompute_bounds_and_basis = true; continue; } if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } if (dive_stats.nodes_explored > diving_node_limit) { break; } - lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); - dual::status_t lp_status = solve_node_lp(node_ptr, - leaf_problem, - leaf_solution, - basis_factors, - basic_list, - nonbasic_list, - node_presolver, - diving_type, - recompute_bounds_and_basis, - start_lower, - start_upper, - dive_stats, - log); + lp_solution_t leaf_solution(worker.leaf_problem.num_rows, + worker.leaf_problem.num_cols); + dual::status_t lp_status = solve_node_lp( + worker, node_ptr, leaf_solution, diving_type, start_lower, start_upper, dive_stats); if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; @@ -1400,9 +1364,11 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, ++dive_stats.nodes_explored; - auto [node_status, round_dir] = - update_tree(node_ptr, dive_tree, leaf_problem, leaf_solution, diving_type, lp_status, log); - recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; + logger_t log; + log.log = false; + auto [node_status, round_dir] = update_tree( + node_ptr, dive_tree, worker.leaf_problem, leaf_solution, diving_type, lp_status, log); + worker.recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; if (node_status == node_status_t::HAS_CHILDREN) { if (round_dir == rounding_direction_t::UP) { @@ -1425,15 +1391,9 @@ void branch_and_bound_t::dive_from(mip_node_t& start_node, template void branch_and_bound_t::diving_thread(bnb_worker_type_t diving_type) { - // Make a copy of the original LP. We will modify its bounds at each leaf - lp_problem_t leaf_problem = original_lp_; - std::vector row_sense; - bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); - - const i_t m = leaf_problem.num_rows; - basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); - std::vector basic_list(m); - std::vector nonbasic_list; + // Create worker state with global upper bound pointer (worker_id = -1 for diving threads) + bb_worker_state_t worker( + -1, original_lp_, Arow_, var_types_, settings_.refactor_frequency, &upper_bound_); std::vector start_lower; std::vector start_upper; @@ -1444,7 +1404,9 @@ void branch_and_bound_t::diving_thread(bnb_worker_type_t diving_type) if (reset_starting_bounds) { start_lower = original_lp_.lower; start_upper = original_lp_.upper; - std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); + std::fill(worker.node_presolver.bounds_changed.begin(), + worker.node_presolver.bounds_changed.end(), + false); reset_starting_bounds = false; } @@ -1459,7 +1421,7 @@ void branch_and_bound_t::diving_thread(bnb_worker_type_t diving_type) if (node_ptr.has_value()) { node_ptr.value()->get_variable_bounds( - start_lower, start_upper, node_presolver.bounds_changed); + start_lower, start_upper, worker.node_presolver.bounds_changed); start_node = node_ptr.value()->detach_copy(); } node_queue_.unlock(); @@ -1467,19 +1429,12 @@ void branch_and_bound_t::diving_thread(bnb_worker_type_t diving_type) if (start_node.has_value()) { reset_starting_bounds = true; - if (upper_bound_ < start_node->lower_bound) { continue; } - bool is_feasible = node_presolver.bounds_strengthening(start_lower, start_upper, settings_); + if (worker.get_upper_bound() < start_node->lower_bound) { continue; } + bool is_feasible = + worker.node_presolver.bounds_strengthening(start_lower, start_upper, settings_); if (!is_feasible) { continue; } - dive_from(start_node.value(), - start_lower, - start_upper, - leaf_problem, - node_presolver, - basis_factors, - basic_list, - nonbasic_list, - diving_type); + dive_from(worker, start_node.value(), start_lower, start_upper, diving_type); } } } @@ -2005,6 +1960,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t 0) ? (100.0 * sync_time / total_time) : 0.0; settings_.log.printf(" %6d | %7d | %8d | %6d | %7d | %6d | %8d | %7.3fs | %4.1f%% | %5.2fs\n", worker.worker_id, - worker.total_nodes_processed, - worker.total_nodes_branched, - worker.total_nodes_pruned, - worker.total_nodes_infeasible, - worker.total_integer_solutions, - worker.total_nodes_assigned, + worker.bsp.total_nodes_processed, + worker.bsp.total_nodes_branched, + worker.bsp.total_nodes_pruned, + worker.bsp.total_nodes_infeasible, + worker.bsp.total_integer_solutions, + worker.bsp.total_nodes_assigned, total_time, std::min(99.9, sync_percent), - worker.total_nowork_time); + worker.bsp.total_nowork_time); } // Print diving worker statistics @@ -2122,35 +2078,59 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& mip_node_t* node = worker.dequeue_node(); if (node == nullptr) { continue; } - worker.current_node = node; + worker.bsp.current_node = node; - f_t upper_bound = worker.local_upper_bound; + f_t upper_bound = worker.bsp.local_upper_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node->lower_bound); if (node->lower_bound >= upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - worker.current_node = nullptr; + worker.bsp.current_node = nullptr; worker.record_fathomed(node, node->lower_bound); search_tree.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; continue; } - bool is_child = (node->parent == worker.last_solved_node); + bool is_child = (node->parent == worker.bsp.last_solved_node); worker.recompute_bounds_and_basis = !is_child; - node_solve_info_t status = solve_node_bsp(worker, node, search_tree, worker.horizon_end); - worker.last_solved_node = node; - - if (status == node_solve_info_t::TIME_LIMIT || status == node_solve_info_t::WORK_LIMIT) { + lp_solution_t leaf_solution(worker.leaf_problem.num_rows, + worker.leaf_problem.num_cols); + dual::status_t lp_status = solve_node_lp(worker, + node, + leaf_solution, + bnb_worker_type_t::BEST_FIRST, + original_lp_.lower, + original_lp_.upper, + exploration_stats_); + worker.bsp.last_solved_node = node; + + if (lp_status == dual::status_t::TIME_LIMIT || lp_status == dual::status_t::WORK_LIMIT) { continue; } - worker.current_node = nullptr; + + ++exploration_stats_.nodes_explored; + --exploration_stats_.nodes_unexplored; + + bsp_tree_update_policy_t policy{*this, worker}; + auto [node_status, round_dir] = update_tree_impl(node, + search_tree, + worker.leaf_problem, + leaf_solution, + var_types_, + settings_, + pc_, + lp_status, + policy); + + worker.recompute_bounds_and_basis = (node_status != node_status_t::HAS_CHILDREN); + worker.bsp.current_node = nullptr; continue; } // No work - advance to sync point to participate in barrier f_t nowork_start = tic(); bsp_scheduler_->wait_for_next_sync(worker.work_context); - worker.total_nowork_time += toc(nowork_start); + worker.bsp.total_nowork_time += toc(nowork_start); } } @@ -2197,13 +2177,13 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) state_data.push_back(std::bit_cast(lb)); for (auto& worker : *bsp_workers_) { - if (worker.current_node != nullptr) { - state_data.push_back(worker.current_node->get_id_packed()); + if (worker.bsp.current_node != nullptr) { + state_data.push_back(worker.bsp.current_node->get_id_packed()); } - for (auto* node : worker.plunge_stack) { + for (auto* node : worker.bsp.plunge_stack) { state_data.push_back(node->get_id_packed()); } - for (auto* node : worker.backlog.data()) { + for (auto* node : worker.bsp.backlog.data()) { state_data.push_back(node->get_id_packed()); } } @@ -2220,6 +2200,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) pc_.pseudo_cost_sum_down, pc_.pseudo_cost_num_up, pc_.pseudo_cost_num_down, + exploration_stats_.total_lp_iters.load(), horizon_end, bsp_current_horizon_); } @@ -2274,7 +2255,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) std::string idle_workers; for (const auto& w : *bsp_workers_) { - if (!w.has_work() && w.current_node == nullptr) { + if (!w.has_work() && w.bsp.current_node == nullptr) { if (!idle_workers.empty()) idle_workers += ","; idle_workers += "W" + std::to_string(w.worker_id); } @@ -2293,135 +2274,6 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) idle_workers.c_str()); } -template -node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t& worker, - mip_node_t* node_ptr, - search_tree_t& search_tree, - double current_horizon) -{ - raft::common::nvtx::range scope("BB::solve_node_bsp"); - - double work_units_at_start = worker.work_context.global_work_units_elapsed; - double clock_at_start = worker.clock; - - std::fill(worker.node_presolver.bounds_changed.begin(), - worker.node_presolver.bounds_changed.end(), - false); - - if (worker.recompute_bounds_and_basis) { - worker.leaf_problem.lower = original_lp_.lower; - worker.leaf_problem.upper = original_lp_.upper; - node_ptr->get_variable_bounds( - worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); - } else { - node_ptr->update_branched_variable_bounds( - worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); - } - - double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); - if (remaining_time <= 0) { return node_solve_info_t::TIME_LIMIT; } - - // Bounds strengthening - simplex_solver_settings_t lp_settings = settings_; - lp_settings.set_log(false); - - lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; - lp_settings.inside_mip = 2; - lp_settings.time_limit = remaining_time; - lp_settings.scale_columns = false; - - bool feasible = true; -#ifndef BSP_DISABLE_BOUNDS_STRENGTHENING - raft::common::nvtx::range scope_bs("BB::bound_strengthening"); - f_t bs_start_time = tic(); - feasible = worker.node_presolver.bounds_strengthening( - worker.leaf_problem.lower, worker.leaf_problem.upper, lp_settings); - f_t bs_actual_time = toc(bs_start_time); - - if (settings_.deterministic) { - // TEMP APPROXIMATION; - worker.work_context.record_work(worker.node_presolver.last_nnz_processed / 1e8); - } -#endif - - if (!feasible) { - node_ptr->lower_bound = std::numeric_limits::infinity(); - search_tree.update(node_ptr, node_status_t::INFEASIBLE); - worker.record_infeasible(node_ptr); - --exploration_stats_.nodes_unexplored; - ++exploration_stats_.nodes_explored; - worker.recompute_bounds_and_basis = true; - return node_solve_info_t::NO_CHILDREN; - } - - // Solve LP relaxation - lp_solution_t leaf_solution(worker.leaf_problem.num_rows, worker.leaf_problem.num_cols); - std::vector& leaf_vstatus = node_ptr->vstatus; - i_t node_iter = 0; - f_t lp_start_time = tic(); - std::vector leaf_edge_norms = edge_norms_; - - dual::status_t lp_status = dual_phase2_with_advanced_basis(2, - 0, - worker.recompute_bounds_and_basis, - lp_start_time, - worker.leaf_problem, - lp_settings, - leaf_vstatus, - worker.basis_factors, - worker.basic_list, - worker.nonbasic_list, - leaf_solution, - node_iter, - leaf_edge_norms, - &worker.work_context); - - if (lp_status == dual::status_t::NUMERICAL) { - settings_.log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); - lp_status_t second_status = solve_linear_program_with_advanced_basis(worker.leaf_problem, - lp_start_time, - lp_settings, - leaf_solution, - worker.basis_factors, - worker.basic_list, - worker.nonbasic_list, - leaf_vstatus, - leaf_edge_norms); - lp_status = convert_lp_status_to_dual_status(second_status); - } - - double work_performed = worker.work_context.global_work_units_elapsed - work_units_at_start; - worker.clock += work_performed; - - exploration_stats_.total_lp_solve_time += toc(lp_start_time); - exploration_stats_.total_lp_iters += node_iter; - ++exploration_stats_.nodes_explored; - --exploration_stats_.nodes_unexplored; - - bsp_tree_update_policy_t policy{*this, worker}; - auto [status, round_dir] = update_tree_impl(node_ptr, - search_tree, - worker.leaf_problem, - leaf_solution, - var_types_, - settings_, - pc_, - lp_status, - policy); - - // Convert node_status_t to node_solve_info_t - if (status == node_status_t::HAS_CHILDREN) { - return round_dir == rounding_direction_t::DOWN ? node_solve_info_t::DOWN_CHILD_FIRST - : node_solve_info_t::UP_CHILD_FIRST; - } else if (status == node_status_t::PENDING) { - return lp_status == dual::status_t::TIME_LIMIT ? node_solve_info_t::TIME_LIMIT - : node_solve_info_t::WORK_LIMIT; - } else if (status == node_status_t::NUMERICAL) { - return node_solve_info_t::NUMERICAL; - } - return node_solve_info_t::NO_CHILDREN; -} - template void branch_and_bound_t::sort_replay_events(const bb_event_batch_t& events) { @@ -2559,7 +2411,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t*> all_integer_solutions; for (auto& worker : *bsp_workers_) { - for (auto& sol : worker.integer_solutions) { + for (auto& sol : worker.bsp.integer_solutions) { all_integer_solutions.push_back(&sol); } } @@ -2613,7 +2465,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t> all_pc_updates; for (auto& worker : *bsp_workers_) { - for (auto& upd : worker.pseudo_cost_updates) { + for (auto& upd : worker.bsp.pseudo_cost_updates) { all_pc_updates.push_back(upd); } } @@ -2631,7 +2483,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t::prune_worker_nodes_vs_incumbent() // Check nodes in plunge stack - filter in place { std::deque*> surviving; - for (auto* node : worker.plunge_stack) { + for (auto* node : worker.bsp.plunge_stack) { if (node->lower_bound >= upper_bound) { search_tree_.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; @@ -2652,13 +2504,13 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() surviving.push_back(node); } } - worker.plunge_stack = std::move(surviving); + worker.bsp.plunge_stack = std::move(surviving); } // Check nodes in backlog heap - filter and rebuild { std::vector*> surviving; - for (auto* node : worker.backlog.data()) { + for (auto* node : worker.bsp.backlog.data()) { if (node->lower_bound >= upper_bound) { search_tree_.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; @@ -2666,9 +2518,9 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() surviving.push_back(node); } } - worker.backlog.clear(); + worker.bsp.backlog.clear(); for (auto* node : surviving) { - worker.backlog.push(node); + worker.bsp.backlog.push(node); } } } @@ -2709,10 +2561,10 @@ void branch_and_bound_t::balance_worker_loads() std::vector*> all_nodes; for (auto& worker : *bsp_workers_) { - for (auto* node : worker.backlog.data()) { + for (auto* node : worker.bsp.backlog.data()) { all_nodes.push_back(node); } - worker.backlog.clear(); + worker.bsp.backlog.clear(); } if (all_nodes.empty()) return; @@ -2751,17 +2603,17 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() // Check all BFS worker queues for (const auto& worker : *bsp_workers_) { // Check paused node (current_node) - if (worker.current_node != nullptr) { - lower_bound = std::min(worker.current_node->lower_bound, lower_bound); + if (worker.bsp.current_node != nullptr) { + lower_bound = std::min(worker.bsp.current_node->lower_bound, lower_bound); } // Check plunge stack nodes - for (auto* node : worker.plunge_stack) { + for (auto* node : worker.bsp.plunge_stack) { lower_bound = std::min(node->lower_bound, lower_bound); } // Check backlog heap nodes - for (auto* node : worker.backlog.data()) { + for (auto* node : worker.bsp.backlog.data()) { lower_bound = std::min(node->lower_bound, lower_bound); } } @@ -2790,11 +2642,11 @@ void branch_and_bound_t::populate_diving_heap() std::vector*, f_t>> candidates; for (auto& worker : *bsp_workers_) { - for (auto* node : worker.backlog.data()) { + for (auto* node : worker.bsp.backlog.data()) { if (node->lower_bound < upper_bound) { f_t score = node->objective_estimate; if (!std::isfinite(score)) { score = node->lower_bound; } - candidates.push_back({node, score}); + candidates.push_back(std::make_pair(node, score)); } } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index ea76cacc8..e079d8725 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -51,16 +51,6 @@ enum class mip_exploration_status_t { WORK_LIMIT = 6, // The solver reached a deterministic work limit }; -enum class node_solve_info_t { - NO_CHILDREN = 0, // The node does not produced children - UP_CHILD_FIRST = 1, // The up child should be explored first - DOWN_CHILD_FIRST = 2, // The down child should be explored first - TIME_LIMIT = 3, // The solver reached a time limit - ITERATION_LIMIT = 4, // The solver reached a iteration limit - NUMERICAL = 5, // The solver encounter a numerical error when solving the node - WORK_LIMIT = 6, // The solver reached a deterministic work limit -}; - template class bounds_strengthening_t; @@ -240,13 +230,7 @@ class branch_and_bound_t { // We use best-first to pick the `start_node` and then perform a depth-first search // from this node (i.e., a plunge). It can only backtrack to a sibling node. // Unexplored nodes in the subtree are inserted back into the global heap. - void plunge_from(i_t task_id, - mip_node_t* start_node, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_update, - std::vector& basic_list, - std::vector& nonbasic_list); + void plunge_from(bb_worker_state_t& worker, mip_node_t* start_node); // Each "main" thread pops a node from the global heap and then performs a plunge // (i.e., a shallow dive) into the subtree determined by the node. @@ -254,34 +238,24 @@ class branch_and_bound_t { // Perform a deep dive in the subtree determined by the `start_node` in order // to find integer feasible solutions. - void dive_from(mip_node_t& start_node, + void dive_from(bb_worker_state_t& worker, + mip_node_t& start_node, const std::vector& start_lower, const std::vector& start_upper, - lp_problem_t& leaf_problem, - bounds_strengthening_t& node_presolver, - basis_update_mpf_t& basis_update, - std::vector& basic_list, - std::vector& nonbasic_list, bnb_worker_type_t diving_type); // Each diving thread pops the first node from the dive queue and then performs // a deep dive into the subtree determined by the node. void diving_thread(bnb_worker_type_t diving_type); - // Solve the LP relaxation of a leaf node - dual::status_t solve_node_lp(mip_node_t* node_ptr, - lp_problem_t& leaf_problem, + // Solve the LP relaxation of a leaf node (unified for BSP and non-BSP) + dual::status_t solve_node_lp(bb_worker_state_t& worker, + mip_node_t* node_ptr, lp_solution_t& leaf_solution, - basis_update_mpf_t& basis_factors, - std::vector& basic_list, - std::vector& nonbasic_list, - bounds_strengthening_t& node_presolver, bnb_worker_type_t thread_type, - bool recompute_bounds_and_basis, const std::vector& root_lower, const std::vector& root_upper, - bnb_stats_t& stats, - logger_t& log); + bnb_stats_t& stats); // Update the tree based on the LP relaxation. Returns the status // of the node and, if appropriated, the preferred rounding direction @@ -316,12 +290,6 @@ class branch_and_bound_t { // Balance worker loads - redistribute nodes only if significant imbalance detected void balance_worker_loads(); - // BSP-specific node solving that records events - node_solve_info_t solve_node_bsp(bb_worker_state_t& worker, - mip_node_t* node_ptr, - search_tree_t& search_tree, - double current_horizon); - // Compute accurate lower bound from all BSP sources (called during sync phase) f_t compute_bsp_lower_bound(); From 8c4f417a51f2f4f7b5140a6170734908242ad0a1 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 30 Jan 2026 18:14:51 +0000 Subject: [PATCH 330/366] Revert "unify b&b worker struct" This reverts commit 5f929f4ce0790c3a2d8279987cf9da8bede9e600. --- cpp/src/dual_simplex/bb_worker_state.hpp | 209 ++++---- cpp/src/dual_simplex/branch_and_bound.cpp | 578 ++++++++++++++-------- cpp/src/dual_simplex/branch_and_bound.hpp | 46 +- 3 files changed, 485 insertions(+), 348 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 2e0bf501c..e1cc80fdf 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -15,7 +15,6 @@ #include #include #include -#include #include #include @@ -94,9 +93,11 @@ struct queued_integer_solution_t { } }; -// BSP-specific state nested within bb_worker_state_t +// Per-worker state for BSP branch-and-bound template -struct bsp_state_t { +struct bb_worker_state_t { + int worker_id{0}; + // Plunge stack: LIFO queue for depth-first exploration std::deque*> plunge_stack; @@ -116,6 +117,14 @@ struct bsp_state_t { bb_event_batch_t events; int event_sequence{0}; + lp_problem_t leaf_problem; + basis_update_mpf_t basis_factors; + bounds_strengthening_t node_presolver; + std::vector basic_list; + std::vector nonbasic_list; + work_limit_context_t work_context; + bool recompute_bounds_and_basis{true}; + i_t nodes_processed_this_horizon{0}; i_t total_nodes_processed{0}; i_t total_nodes_pruned{0}; @@ -139,36 +148,6 @@ struct bsp_state_t { std::vector pc_num_up_snapshot; std::vector pc_num_down_snapshot; - // LP iteration snapshot for diving iteration limits - i_t total_lp_iters_snapshot{0}; -}; - -// Per-worker state for branch-and-bound (both BSP and non-BSP modes) -template -struct bb_worker_state_t { - int worker_id{0}; - - // Common LP solving state - lp_problem_t leaf_problem; - basis_update_mpf_t basis_factors; - bounds_strengthening_t node_presolver; - std::vector basic_list; - std::vector nonbasic_list; - work_limit_context_t work_context; - bool recompute_bounds_and_basis{true}; - - // Upper bound access (non-BSP uses global pointer, BSP uses bsp.local_upper_bound) - omp_atomic_t* global_upper_bound_ptr{nullptr}; - - f_t get_upper_bound() const - { - return global_upper_bound_ptr ? global_upper_bound_ptr->load() : bsp.local_upper_bound; - } - - // BSP-specific state (only used in deterministic mode) - bsp_state_t bsp; - - // BSP constructor (deterministic mode with local upper bound) explicit bb_worker_state_t(int id, const lp_problem_t& original_lp, const csr_matrix_t& Arow, @@ -187,39 +166,19 @@ struct bb_worker_state_t { work_context.deterministic = deterministic; } - // Non-BSP constructor (opportunistic mode with global upper bound) - explicit bb_worker_state_t(int id, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - i_t refactor_frequency, - omp_atomic_t* global_ub) - : worker_id(id), - work_context("BB_Worker_" + std::to_string(id)), - leaf_problem(original_lp), - basis_factors(original_lp.num_rows, refactor_frequency), - node_presolver(original_lp, Arow, std::vector{}, var_types), - global_upper_bound_ptr(global_ub) - { - const i_t m = leaf_problem.num_rows; - basic_list.resize(m); - nonbasic_list.clear(); - work_context.deterministic = false; - } - void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) { - bsp.clock = horizon_start; + clock = horizon_start; work_context.global_work_units_elapsed = horizon_start; - bsp.events.clear(); - bsp.events.horizon_start = horizon_start; - bsp.events.horizon_end = horizon_end; - bsp.event_sequence = 0; - bsp.nodes_processed_this_horizon = 0; - bsp.local_upper_bound = global_upper_bound; - bsp.local_lower_bound_ceiling = std::numeric_limits::infinity(); - bsp.integer_solutions.clear(); - bsp.pseudo_cost_updates.clear(); + events.clear(); + events.horizon_start = horizon_start; + events.horizon_end = horizon_end; + event_sequence = 0; + nodes_processed_this_horizon = 0; + local_upper_bound = global_upper_bound; + local_lower_bound_ceiling = std::numeric_limits::infinity(); + integer_solutions.clear(); + pseudo_cost_updates.clear(); } void set_snapshots(f_t global_upper_bound, @@ -227,49 +186,47 @@ struct bb_worker_state_t { const std::vector& pc_sum_down, const std::vector& pc_num_up, const std::vector& pc_num_down, - i_t total_lp_iters, double new_horizon_start, double new_horizon_end) { - bsp.local_upper_bound = global_upper_bound; - bsp.pc_sum_up_snapshot = pc_sum_up; - bsp.pc_sum_down_snapshot = pc_sum_down; - bsp.pc_num_up_snapshot = pc_num_up; - bsp.pc_num_down_snapshot = pc_num_down; - bsp.total_lp_iters_snapshot = total_lp_iters; - bsp.horizon_start = new_horizon_start; - bsp.horizon_end = new_horizon_end; + local_upper_bound = global_upper_bound; + pc_sum_up_snapshot = pc_sum_up; + pc_sum_down_snapshot = pc_sum_down; + pc_num_up_snapshot = pc_num_up; + pc_num_down_snapshot = pc_num_down; + horizon_start = new_horizon_start; + horizon_end = new_horizon_end; } // Queue pseudo-cost update for global sync and apply to local snapshot void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) { - bsp.pseudo_cost_updates.push_back({variable, direction, delta, bsp.clock, worker_id}); + pseudo_cost_updates.push_back({variable, direction, delta, clock, worker_id}); if (direction == rounding_direction_t::DOWN) { - bsp.pc_sum_down_snapshot[variable] += delta; - bsp.pc_num_down_snapshot[variable]++; + pc_sum_down_snapshot[variable] += delta; + pc_num_down_snapshot[variable]++; } else { - bsp.pc_sum_up_snapshot[variable] += delta; - bsp.pc_num_up_snapshot[variable]++; + pc_sum_up_snapshot[variable] += delta; + pc_num_up_snapshot[variable]++; } } i_t variable_selection_from_snapshot(const std::vector& fractional, const std::vector& solution) const { - return variable_selection_from_pseudo_costs(bsp.pc_sum_down_snapshot.data(), - bsp.pc_sum_up_snapshot.data(), - bsp.pc_num_down_snapshot.data(), - bsp.pc_num_up_snapshot.data(), - (i_t)bsp.pc_sum_down_snapshot.size(), + return variable_selection_from_pseudo_costs(pc_sum_down_snapshot.data(), + pc_sum_up_snapshot.data(), + pc_num_down_snapshot.data(), + pc_num_up_snapshot.data(), + (i_t)pc_sum_down_snapshot.size(), fractional, solution); } void enqueue_node(mip_node_t* node) { - bsp.plunge_stack.push_front(node); - ++bsp.total_nodes_assigned; + plunge_stack.push_front(node); + ++total_nodes_assigned; } // Enqueue children with plunging: move any existing sibling to backlog, push both children @@ -277,24 +234,24 @@ struct bb_worker_state_t { mip_node_t* up_child, rounding_direction_t preferred_direction) { - if (!bsp.plunge_stack.empty()) { - bsp.backlog.push(bsp.plunge_stack.back()); - bsp.plunge_stack.pop_back(); + if (!plunge_stack.empty()) { + backlog.push(plunge_stack.back()); + plunge_stack.pop_back(); } down_child->origin_worker_id = worker_id; - down_child->creation_seq = bsp.next_creation_seq++; + down_child->creation_seq = next_creation_seq++; up_child->origin_worker_id = worker_id; - up_child->creation_seq = bsp.next_creation_seq++; + up_child->creation_seq = next_creation_seq++; mip_node_t* first_child; if (preferred_direction == rounding_direction_t::UP) { - bsp.plunge_stack.push_front(down_child); - bsp.plunge_stack.push_front(up_child); + plunge_stack.push_front(down_child); + plunge_stack.push_front(up_child); first_child = up_child; } else { - bsp.plunge_stack.push_front(up_child); - bsp.plunge_stack.push_front(down_child); + plunge_stack.push_front(up_child); + plunge_stack.push_front(down_child); first_child = down_child; } return first_child; @@ -303,40 +260,40 @@ struct bb_worker_state_t { // Dequeue: current_node first, then plunge_stack, then backlog heap mip_node_t* dequeue_node() { - if (bsp.current_node != nullptr) { - mip_node_t* node = bsp.current_node; - bsp.current_node = nullptr; + if (current_node != nullptr) { + mip_node_t* node = current_node; + current_node = nullptr; return node; } - if (!bsp.plunge_stack.empty()) { - mip_node_t* node = bsp.plunge_stack.front(); - bsp.plunge_stack.pop_front(); + if (!plunge_stack.empty()) { + mip_node_t* node = plunge_stack.front(); + plunge_stack.pop_front(); return node; } - auto node_opt = bsp.backlog.pop(); + auto node_opt = backlog.pop(); return node_opt.has_value() ? node_opt.value() : nullptr; } bool has_work() const { - return bsp.current_node != nullptr || !bsp.plunge_stack.empty() || !bsp.backlog.empty(); + return current_node != nullptr || !plunge_stack.empty() || !backlog.empty(); } size_t queue_size() const { - return bsp.plunge_stack.size() + bsp.backlog.size() + (bsp.current_node != nullptr ? 1 : 0); + return plunge_stack.size() + backlog.size() + (current_node != nullptr ? 1 : 0); } void record_event(bb_event_t event) { - event.event_sequence = bsp.event_sequence++; - bsp.events.add(std::move(event)); + event.event_sequence = event_sequence++; + events.add(std::move(event)); } void record_branched( mip_node_t* node, i_t down_child_id, i_t up_child_id, i_t branch_var, f_t branch_val) { - record_event(bb_event_t::make_branched(bsp.clock, + record_event(bb_event_t::make_branched(clock, worker_id, node->node_id, 0, @@ -345,42 +302,42 @@ struct bb_worker_state_t { node->lower_bound, branch_var, branch_val)); - ++bsp.nodes_processed_this_horizon; - ++bsp.total_nodes_processed; - ++bsp.total_nodes_branched; + ++nodes_processed_this_horizon; + ++total_nodes_processed; + ++total_nodes_branched; } void record_integer_solution(mip_node_t* node, f_t objective) { - record_event(bb_event_t::make_integer_solution( - bsp.clock, worker_id, node->node_id, 0, objective)); - ++bsp.nodes_processed_this_horizon; - ++bsp.total_nodes_processed; - ++bsp.total_integer_solutions; + record_event( + bb_event_t::make_integer_solution(clock, worker_id, node->node_id, 0, objective)); + ++nodes_processed_this_horizon; + ++total_nodes_processed; + ++total_integer_solutions; } void record_fathomed(mip_node_t* node, f_t lower_bound) { record_event( - bb_event_t::make_fathomed(bsp.clock, worker_id, node->node_id, 0, lower_bound)); - ++bsp.nodes_processed_this_horizon; - ++bsp.total_nodes_processed; - ++bsp.total_nodes_pruned; + bb_event_t::make_fathomed(clock, worker_id, node->node_id, 0, lower_bound)); + ++nodes_processed_this_horizon; + ++total_nodes_processed; + ++total_nodes_pruned; } void record_infeasible(mip_node_t* node) { - record_event(bb_event_t::make_infeasible(bsp.clock, worker_id, node->node_id, 0)); - ++bsp.nodes_processed_this_horizon; - ++bsp.total_nodes_processed; - ++bsp.total_nodes_infeasible; + record_event(bb_event_t::make_infeasible(clock, worker_id, node->node_id, 0)); + ++nodes_processed_this_horizon; + ++total_nodes_processed; + ++total_nodes_infeasible; } void record_numerical(mip_node_t* node) { - record_event(bb_event_t::make_numerical(bsp.clock, worker_id, node->node_id, 0)); - ++bsp.nodes_processed_this_horizon; - ++bsp.total_nodes_processed; + record_event(bb_event_t::make_numerical(clock, worker_id, node->node_id, 0)); + ++nodes_processed_this_horizon; + ++total_nodes_processed; } }; @@ -635,10 +592,10 @@ class bb_worker_pool_t { { bb_event_batch_t all_events; for (auto& worker : workers_) { - for (auto& event : worker.bsp.events.events) { + for (auto& event : worker.events.events) { all_events.add(std::move(event)); } - worker.bsp.events.clear(); + worker.events.clear(); } all_events.sort_for_replay(); return all_events; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 3e1d5e8ab..3573673b7 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -735,7 +735,7 @@ struct bsp_tree_update_policy_t { branch_and_bound_t& bnb; bb_worker_state_t& worker; - f_t upper_bound() const { return worker.bsp.local_upper_bound; } + f_t upper_bound() const { return worker.local_upper_bound; } void update_pseudo_costs(mip_node_t* node, f_t leaf_obj) { @@ -751,10 +751,10 @@ struct bsp_tree_update_policy_t { void handle_integer_solution(mip_node_t* node, f_t obj, const std::vector& x) { - if (obj < worker.bsp.local_upper_bound) { - worker.bsp.local_upper_bound = obj; - worker.bsp.integer_solutions.push_back( - {obj, x, node->depth, worker.worker_id, worker.bsp.next_solution_seq++}); + if (obj < worker.local_upper_bound) { + worker.local_upper_bound = obj; + worker.integer_solutions.push_back( + {obj, x, node->depth, worker.worker_id, worker.next_solution_seq++}); } } @@ -792,8 +792,8 @@ struct bsp_tree_update_policy_t { void on_numerical_issue(mip_node_t* node) { - worker.bsp.local_lower_bound_ceiling = - std::min(node->lower_bound, worker.bsp.local_lower_bound_ceiling); + worker.local_lower_bound_ceiling = + std::min(node->lower_bound, worker.local_lower_bound_ceiling); } void graphviz(search_tree_t&, mip_node_t*, const char*, f_t) {} @@ -886,53 +886,37 @@ std::pair update_tree_impl( } template -dual::status_t branch_and_bound_t::solve_node_lp(bb_worker_state_t& worker, - mip_node_t* node_ptr, - lp_solution_t& leaf_solution, - bnb_worker_type_t thread_type, - const std::vector& root_lower, - const std::vector& root_upper, - bnb_stats_t& stats) +dual::status_t branch_and_bound_t::solve_node_lp( + mip_node_t* node_ptr, + lp_problem_t& leaf_problem, + lp_solution_t& leaf_solution, + basis_update_mpf_t& basis_factors, + std::vector& basic_list, + std::vector& nonbasic_list, + bounds_strengthening_t& node_presolver, + bnb_worker_type_t thread_type, + bool recompute_bounds_and_basis, + const std::vector& root_lower, + const std::vector& root_upper, + bnb_stats_t& stats, + logger_t& log) { - raft::common::nvtx::range scope("BB::solve_node_lp"); - - double work_units_at_start = worker.work_context.global_work_units_elapsed; + raft::common::nvtx::range scope("BB::solve_node"); std::vector& leaf_vstatus = node_ptr->vstatus; - assert(leaf_vstatus.size() == worker.leaf_problem.num_cols); - - // Reset the bound_changed markers - std::fill(worker.node_presolver.bounds_changed.begin(), - worker.node_presolver.bounds_changed.end(), - false); - - // Set the correct bounds for the leaf problem - if (worker.recompute_bounds_and_basis) { - worker.leaf_problem.lower = root_lower; - worker.leaf_problem.upper = root_upper; - node_ptr->get_variable_bounds( - worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); - } else { - node_ptr->update_branched_variable_bounds( - worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); - } - - double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); - if (remaining_time <= 0) { return dual::status_t::TIME_LIMIT; } + assert(leaf_vstatus.size() == leaf_problem.num_cols); - simplex_solver_settings_t lp_settings = settings_; + simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); - lp_settings.cut_off = worker.get_upper_bound() + settings_.dual_tol; + lp_settings.cut_off = upper_bound_ + settings_.dual_tol; lp_settings.inside_mip = 2; - lp_settings.time_limit = remaining_time; + lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); lp_settings.scale_columns = false; - // Iteration limit for diving threads if (thread_type != bnb_worker_type_t::BEST_FIRST) { - i_t bnb_lp_iters = worker.work_context.deterministic ? worker.bsp.total_lp_iters_snapshot - : exploration_stats_.total_lp_iters.load(); - f_t factor = settings_.diving_settings.iteration_limit_factor; - i_t max_iter = factor * bnb_lp_iters; + i_t bnb_lp_iters = exploration_stats_.total_lp_iters; + f_t factor = settings_.diving_settings.iteration_limit_factor; + i_t max_iter = factor * bnb_lp_iters; lp_settings.iteration_limit = max_iter - stats.total_lp_iters; if (lp_settings.iteration_limit <= 0) { return dual::status_t::ITERATION_LIMIT; } } @@ -959,60 +943,63 @@ dual::status_t branch_and_bound_t::solve_node_lp(bb_worker_state_tvstatus[node_ptr->branch_var]); #endif - bool feasible = worker.node_presolver.bounds_strengthening( - worker.leaf_problem.lower, worker.leaf_problem.upper, lp_settings); + // Reset the bound_changed markers + std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); - if (worker.work_context.deterministic) { - worker.work_context.record_work(worker.node_presolver.last_nnz_processed / 1e8); + // Set the correct bounds for the leaf problem + if (recompute_bounds_and_basis) { + leaf_problem.lower = root_lower; + leaf_problem.upper = root_upper; + node_ptr->get_variable_bounds( + leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); + + } else { + node_ptr->update_branched_variable_bounds( + leaf_problem.lower, leaf_problem.upper, node_presolver.bounds_changed); } - if (!feasible) { return dual::status_t::DUAL_UNBOUNDED; } + bool feasible = + node_presolver.bounds_strengthening(leaf_problem.lower, leaf_problem.upper, lp_settings); - i_t node_iter = 0; - f_t lp_start_time = tic(); - std::vector leaf_edge_norms = edge_norms_; + dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; - // Pass work_context only for deterministic mode - work_limit_context_t* work_ctx = - worker.work_context.deterministic ? &worker.work_context : nullptr; + if (feasible) { + i_t node_iter = 0; + f_t lp_start_time = tic(); + std::vector leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; - dual::status_t lp_status = dual_phase2_with_advanced_basis(2, - 0, - worker.recompute_bounds_and_basis, - lp_start_time, - worker.leaf_problem, - lp_settings, - leaf_vstatus, - worker.basis_factors, - worker.basic_list, - worker.nonbasic_list, - leaf_solution, - node_iter, - leaf_edge_norms, - work_ctx); + lp_status = dual_phase2_with_advanced_basis(2, + 0, + recompute_bounds_and_basis, + lp_start_time, + leaf_problem, + lp_settings, + leaf_vstatus, + basis_factors, + basic_list, + nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms); - if (lp_status == dual::status_t::NUMERICAL) { - settings_.log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); - lp_status_t second_status = solve_linear_program_with_advanced_basis(worker.leaf_problem, - lp_start_time, - lp_settings, - leaf_solution, - worker.basis_factors, - worker.basic_list, - worker.nonbasic_list, - leaf_vstatus, - leaf_edge_norms); - lp_status = convert_lp_status_to_dual_status(second_status); - } + if (lp_status == dual::status_t::NUMERICAL) { + log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); + lp_status_t second_status = solve_linear_program_with_advanced_basis(leaf_problem, + lp_start_time, + lp_settings, + leaf_solution, + basis_factors, + basic_list, + nonbasic_list, + leaf_vstatus, + leaf_edge_norms); - // Update work clock for deterministic mode - if (worker.work_context.deterministic) { - double work_performed = worker.work_context.global_work_units_elapsed - work_units_at_start; - worker.bsp.clock += work_performed; - } + lp_status = convert_lp_status_to_dual_status(second_status); + } - stats.total_lp_solve_time += toc(lp_start_time); - stats.total_lp_iters += node_iter; + stats.total_lp_solve_time += toc(lp_start_time); + stats.total_lp_iters += node_iter; + } #ifdef LOG_NODE_SIMPLEX lp_settings.log.printf("\nLP status: %d\n\n", lp_status); @@ -1089,19 +1076,30 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod return; } - // Create a temporary worker for the ramp-up phase - bb_worker_state_t worker( - 0, original_lp_, Arow_, var_types_, settings_.refactor_frequency, &upper_bound_); - worker.recompute_bounds_and_basis = true; + // Make a copy of the original LP. We will modify its bounds at each leaf + lp_problem_t leaf_problem = original_lp_; + std::vector row_sense; + bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); - lp_solution_t leaf_solution(worker.leaf_problem.num_rows, worker.leaf_problem.num_cols); - dual::status_t lp_status = solve_node_lp(worker, - node, + const i_t m = leaf_problem.num_rows; + basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); + std::vector basic_list(m); + std::vector nonbasic_list; + + lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); + dual::status_t lp_status = solve_node_lp(node, + leaf_problem, leaf_solution, + basis_factors, + basic_list, + nonbasic_list, + node_presolver, bnb_worker_type_t::BEST_FIRST, + true, original_lp_.lower, original_lp_.upper, - exploration_stats_); + exploration_stats_, + settings_.log); if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; return; @@ -1113,7 +1111,7 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod auto [node_status, round_dir] = update_tree(node, search_tree_, - worker.leaf_problem, + leaf_problem, leaf_solution, bnb_worker_type_t::BEST_FIRST, lp_status, @@ -1139,22 +1137,27 @@ void branch_and_bound_t::exploration_ramp_up(mip_node_t* nod } template -void branch_and_bound_t::plunge_from(bb_worker_state_t& worker, - mip_node_t* start_node) +void branch_and_bound_t::plunge_from(i_t task_id, + mip_node_t* start_node, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_factors, + std::vector& basic_list, + std::vector& nonbasic_list) { raft::common::nvtx::range scope("BB::explore_subtree"); - worker.recompute_bounds_and_basis = true; + bool recompute_bounds_and_basis = true; std::deque*> stack; stack.push_front(start_node); while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET && is_running) { - if (worker.worker_id == 0) { repair_heuristic_solutions(); } + if (task_id == 0) { repair_heuristic_solutions(); } mip_node_t* node_ptr = stack.front(); stack.pop_front(); f_t lower_bound = node_ptr->lower_bound; - f_t upper_bound = worker.get_upper_bound(); + f_t upper_bound = upper_bound_; f_t abs_gap = upper_bound - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); @@ -1164,20 +1167,20 @@ void branch_and_bound_t::plunge_from(bb_worker_state_t& work // - The current node and its siblings uses the lower bound of the parent before solving the LP // relaxation // - The lower bound of the parent is lower or equal to its children - assert(worker.worker_id < local_lower_bounds_.size()); - local_lower_bounds_[worker.worker_id] = lower_bound; + assert(task_id < local_lower_bounds_.size()); + local_lower_bounds_[task_id] = lower_bound; if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { search_tree_.graphviz_node(settings_.log, node_ptr, "cutoff", node_ptr->lower_bound); search_tree_.update(node_ptr, node_status_t::FATHOMED); - worker.recompute_bounds_and_basis = true; + recompute_bounds_and_basis = true; --exploration_stats_.nodes_unexplored; continue; } f_t now = toc(exploration_stats_.start_time); - if (worker.worker_id == 0) { + if (task_id == 0) { f_t time_since_last_log = exploration_stats_.last_log == 0 ? 1.0 : toc(exploration_stats_.last_log); @@ -1200,15 +1203,20 @@ void branch_and_bound_t::plunge_from(bb_worker_state_t& work break; } - lp_solution_t leaf_solution(worker.leaf_problem.num_rows, - worker.leaf_problem.num_cols); - dual::status_t lp_status = solve_node_lp(worker, - node_ptr, + lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); + dual::status_t lp_status = solve_node_lp(node_ptr, + leaf_problem, leaf_solution, + basis_factors, + basic_list, + nonbasic_list, + node_presolver, bnb_worker_type_t::BEST_FIRST, + recompute_bounds_and_basis, original_lp_.lower, original_lp_.upper, - exploration_stats_); + exploration_stats_, + settings_.log); if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; @@ -1223,13 +1231,13 @@ void branch_and_bound_t::plunge_from(bb_worker_state_t& work auto [node_status, round_dir] = update_tree(node_ptr, search_tree_, - worker.leaf_problem, + leaf_problem, leaf_solution, bnb_worker_type_t::BEST_FIRST, lp_status, settings_.log); - worker.recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; + recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; if (node_status == node_status_t::HAS_CHILDREN) { // The stack should only contain the children of the current parent. @@ -1263,9 +1271,15 @@ void branch_and_bound_t::best_first_thread(i_t task_id) f_t abs_gap = inf; f_t rel_gap = inf; - // Create worker state with global upper bound pointer - bb_worker_state_t worker( - task_id, original_lp_, Arow_, var_types_, settings_.refactor_frequency, &upper_bound_); + // Make a copy of the original LP. We will modify its bounds at each leaf + lp_problem_t leaf_problem = original_lp_; + std::vector row_sense; + bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); + + const i_t m = leaf_problem.num_rows; + basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); + std::vector basic_list(m); + std::vector nonbasic_list; while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && @@ -1282,7 +1296,7 @@ void branch_and_bound_t::best_first_thread(i_t task_id) node_queue_.unlock(); if (start_node.has_value()) { - if (worker.get_upper_bound() < start_node.value()->lower_bound) { + if (upper_bound_ < start_node.value()->lower_bound) { // This node was put on the heap earlier but its lower bound is now greater than the // current upper bound search_tree_.graphviz_node( @@ -1293,14 +1307,20 @@ void branch_and_bound_t::best_first_thread(i_t task_id) } // Best-first search with plunging - plunge_from(worker, start_node.value()); + plunge_from(task_id, + start_node.value(), + leaf_problem, + node_presolver, + basis_factors, + basic_list, + nonbasic_list); active_subtrees_--; } lower_bound = get_lower_bound(); - abs_gap = worker.get_upper_bound() - lower_bound; - rel_gap = user_relative_gap(original_lp_, worker.get_upper_bound(), lower_bound); + abs_gap = upper_bound_ - lower_bound; + rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); } is_running = false; @@ -1313,17 +1333,23 @@ void branch_and_bound_t::best_first_thread(i_t task_id) } template -void branch_and_bound_t::dive_from(bb_worker_state_t& worker, - mip_node_t& start_node, +void branch_and_bound_t::dive_from(mip_node_t& start_node, const std::vector& start_lower, const std::vector& start_upper, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_factors, + std::vector& basic_list, + std::vector& nonbasic_list, bnb_worker_type_t diving_type) { raft::common::nvtx::range scope("BB::diving_thread"); + logger_t log; + log.log = false; - const i_t diving_node_limit = settings_.diving_settings.node_limit; - const i_t diving_backtrack_limit = settings_.diving_settings.backtrack_limit; - worker.recompute_bounds_and_basis = true; + const i_t diving_node_limit = settings_.diving_settings.node_limit; + const i_t diving_backtrack_limit = settings_.diving_settings.backtrack_limit; + bool recompute_bounds_and_basis = true; search_tree_t dive_tree(std::move(start_node)); std::deque*> stack; stack.push_front(&dive_tree.root); @@ -1339,21 +1365,31 @@ void branch_and_bound_t::dive_from(bb_worker_state_t& worker stack.pop_front(); f_t lower_bound = node_ptr->lower_bound; - f_t upper_bound = worker.get_upper_bound(); + f_t upper_bound = upper_bound_; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); if (node_ptr->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - worker.recompute_bounds_and_basis = true; + recompute_bounds_and_basis = true; continue; } if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } if (dive_stats.nodes_explored > diving_node_limit) { break; } - lp_solution_t leaf_solution(worker.leaf_problem.num_rows, - worker.leaf_problem.num_cols); - dual::status_t lp_status = solve_node_lp( - worker, node_ptr, leaf_solution, diving_type, start_lower, start_upper, dive_stats); + lp_solution_t leaf_solution(leaf_problem.num_rows, leaf_problem.num_cols); + dual::status_t lp_status = solve_node_lp(node_ptr, + leaf_problem, + leaf_solution, + basis_factors, + basic_list, + nonbasic_list, + node_presolver, + diving_type, + recompute_bounds_and_basis, + start_lower, + start_upper, + dive_stats, + log); if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; @@ -1364,11 +1400,9 @@ void branch_and_bound_t::dive_from(bb_worker_state_t& worker ++dive_stats.nodes_explored; - logger_t log; - log.log = false; - auto [node_status, round_dir] = update_tree( - node_ptr, dive_tree, worker.leaf_problem, leaf_solution, diving_type, lp_status, log); - worker.recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; + auto [node_status, round_dir] = + update_tree(node_ptr, dive_tree, leaf_problem, leaf_solution, diving_type, lp_status, log); + recompute_bounds_and_basis = node_status != node_status_t::HAS_CHILDREN; if (node_status == node_status_t::HAS_CHILDREN) { if (round_dir == rounding_direction_t::UP) { @@ -1391,9 +1425,15 @@ void branch_and_bound_t::dive_from(bb_worker_state_t& worker template void branch_and_bound_t::diving_thread(bnb_worker_type_t diving_type) { - // Create worker state with global upper bound pointer (worker_id = -1 for diving threads) - bb_worker_state_t worker( - -1, original_lp_, Arow_, var_types_, settings_.refactor_frequency, &upper_bound_); + // Make a copy of the original LP. We will modify its bounds at each leaf + lp_problem_t leaf_problem = original_lp_; + std::vector row_sense; + bounds_strengthening_t node_presolver(leaf_problem, Arow_, row_sense, var_types_); + + const i_t m = leaf_problem.num_rows; + basis_update_mpf_t basis_factors(m, settings_.refactor_frequency); + std::vector basic_list(m); + std::vector nonbasic_list; std::vector start_lower; std::vector start_upper; @@ -1404,9 +1444,7 @@ void branch_and_bound_t::diving_thread(bnb_worker_type_t diving_type) if (reset_starting_bounds) { start_lower = original_lp_.lower; start_upper = original_lp_.upper; - std::fill(worker.node_presolver.bounds_changed.begin(), - worker.node_presolver.bounds_changed.end(), - false); + std::fill(node_presolver.bounds_changed.begin(), node_presolver.bounds_changed.end(), false); reset_starting_bounds = false; } @@ -1421,7 +1459,7 @@ void branch_and_bound_t::diving_thread(bnb_worker_type_t diving_type) if (node_ptr.has_value()) { node_ptr.value()->get_variable_bounds( - start_lower, start_upper, worker.node_presolver.bounds_changed); + start_lower, start_upper, node_presolver.bounds_changed); start_node = node_ptr.value()->detach_copy(); } node_queue_.unlock(); @@ -1429,12 +1467,19 @@ void branch_and_bound_t::diving_thread(bnb_worker_type_t diving_type) if (start_node.has_value()) { reset_starting_bounds = true; - if (worker.get_upper_bound() < start_node->lower_bound) { continue; } - bool is_feasible = - worker.node_presolver.bounds_strengthening(start_lower, start_upper, settings_); + if (upper_bound_ < start_node->lower_bound) { continue; } + bool is_feasible = node_presolver.bounds_strengthening(start_lower, start_upper, settings_); if (!is_feasible) { continue; } - dive_from(worker, start_node.value(), start_lower, start_upper, diving_type); + dive_from(start_node.value(), + start_lower, + start_upper, + leaf_problem, + node_presolver, + basis_factors, + basic_list, + nonbasic_list, + diving_type); } } } @@ -1960,7 +2005,6 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t 0) ? (100.0 * sync_time / total_time) : 0.0; settings_.log.printf(" %6d | %7d | %8d | %6d | %7d | %6d | %8d | %7.3fs | %4.1f%% | %5.2fs\n", worker.worker_id, - worker.bsp.total_nodes_processed, - worker.bsp.total_nodes_branched, - worker.bsp.total_nodes_pruned, - worker.bsp.total_nodes_infeasible, - worker.bsp.total_integer_solutions, - worker.bsp.total_nodes_assigned, + worker.total_nodes_processed, + worker.total_nodes_branched, + worker.total_nodes_pruned, + worker.total_nodes_infeasible, + worker.total_integer_solutions, + worker.total_nodes_assigned, total_time, std::min(99.9, sync_percent), - worker.bsp.total_nowork_time); + worker.total_nowork_time); } // Print diving worker statistics @@ -2078,59 +2122,35 @@ void branch_and_bound_t::run_worker_loop(bb_worker_state_t& mip_node_t* node = worker.dequeue_node(); if (node == nullptr) { continue; } - worker.bsp.current_node = node; + worker.current_node = node; - f_t upper_bound = worker.bsp.local_upper_bound; + f_t upper_bound = worker.local_upper_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node->lower_bound); if (node->lower_bound >= upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - worker.bsp.current_node = nullptr; + worker.current_node = nullptr; worker.record_fathomed(node, node->lower_bound); search_tree.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; continue; } - bool is_child = (node->parent == worker.bsp.last_solved_node); + bool is_child = (node->parent == worker.last_solved_node); worker.recompute_bounds_and_basis = !is_child; - lp_solution_t leaf_solution(worker.leaf_problem.num_rows, - worker.leaf_problem.num_cols); - dual::status_t lp_status = solve_node_lp(worker, - node, - leaf_solution, - bnb_worker_type_t::BEST_FIRST, - original_lp_.lower, - original_lp_.upper, - exploration_stats_); - worker.bsp.last_solved_node = node; - - if (lp_status == dual::status_t::TIME_LIMIT || lp_status == dual::status_t::WORK_LIMIT) { + node_solve_info_t status = solve_node_bsp(worker, node, search_tree, worker.horizon_end); + worker.last_solved_node = node; + + if (status == node_solve_info_t::TIME_LIMIT || status == node_solve_info_t::WORK_LIMIT) { continue; } - - ++exploration_stats_.nodes_explored; - --exploration_stats_.nodes_unexplored; - - bsp_tree_update_policy_t policy{*this, worker}; - auto [node_status, round_dir] = update_tree_impl(node, - search_tree, - worker.leaf_problem, - leaf_solution, - var_types_, - settings_, - pc_, - lp_status, - policy); - - worker.recompute_bounds_and_basis = (node_status != node_status_t::HAS_CHILDREN); - worker.bsp.current_node = nullptr; + worker.current_node = nullptr; continue; } // No work - advance to sync point to participate in barrier f_t nowork_start = tic(); bsp_scheduler_->wait_for_next_sync(worker.work_context); - worker.bsp.total_nowork_time += toc(nowork_start); + worker.total_nowork_time += toc(nowork_start); } } @@ -2177,13 +2197,13 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) state_data.push_back(std::bit_cast(lb)); for (auto& worker : *bsp_workers_) { - if (worker.bsp.current_node != nullptr) { - state_data.push_back(worker.bsp.current_node->get_id_packed()); + if (worker.current_node != nullptr) { + state_data.push_back(worker.current_node->get_id_packed()); } - for (auto* node : worker.bsp.plunge_stack) { + for (auto* node : worker.plunge_stack) { state_data.push_back(node->get_id_packed()); } - for (auto* node : worker.bsp.backlog.data()) { + for (auto* node : worker.backlog.data()) { state_data.push_back(node->get_id_packed()); } } @@ -2200,7 +2220,6 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) pc_.pseudo_cost_sum_down, pc_.pseudo_cost_num_up, pc_.pseudo_cost_num_down, - exploration_stats_.total_lp_iters.load(), horizon_end, bsp_current_horizon_); } @@ -2255,7 +2274,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) std::string idle_workers; for (const auto& w : *bsp_workers_) { - if (!w.has_work() && w.bsp.current_node == nullptr) { + if (!w.has_work() && w.current_node == nullptr) { if (!idle_workers.empty()) idle_workers += ","; idle_workers += "W" + std::to_string(w.worker_id); } @@ -2274,6 +2293,135 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) idle_workers.c_str()); } +template +node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t& worker, + mip_node_t* node_ptr, + search_tree_t& search_tree, + double current_horizon) +{ + raft::common::nvtx::range scope("BB::solve_node_bsp"); + + double work_units_at_start = worker.work_context.global_work_units_elapsed; + double clock_at_start = worker.clock; + + std::fill(worker.node_presolver.bounds_changed.begin(), + worker.node_presolver.bounds_changed.end(), + false); + + if (worker.recompute_bounds_and_basis) { + worker.leaf_problem.lower = original_lp_.lower; + worker.leaf_problem.upper = original_lp_.upper; + node_ptr->get_variable_bounds( + worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); + } else { + node_ptr->update_branched_variable_bounds( + worker.leaf_problem.lower, worker.leaf_problem.upper, worker.node_presolver.bounds_changed); + } + + double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); + if (remaining_time <= 0) { return node_solve_info_t::TIME_LIMIT; } + + // Bounds strengthening + simplex_solver_settings_t lp_settings = settings_; + lp_settings.set_log(false); + + lp_settings.cut_off = worker.local_upper_bound + settings_.dual_tol; + lp_settings.inside_mip = 2; + lp_settings.time_limit = remaining_time; + lp_settings.scale_columns = false; + + bool feasible = true; +#ifndef BSP_DISABLE_BOUNDS_STRENGTHENING + raft::common::nvtx::range scope_bs("BB::bound_strengthening"); + f_t bs_start_time = tic(); + feasible = worker.node_presolver.bounds_strengthening( + worker.leaf_problem.lower, worker.leaf_problem.upper, lp_settings); + f_t bs_actual_time = toc(bs_start_time); + + if (settings_.deterministic) { + // TEMP APPROXIMATION; + worker.work_context.record_work(worker.node_presolver.last_nnz_processed / 1e8); + } +#endif + + if (!feasible) { + node_ptr->lower_bound = std::numeric_limits::infinity(); + search_tree.update(node_ptr, node_status_t::INFEASIBLE); + worker.record_infeasible(node_ptr); + --exploration_stats_.nodes_unexplored; + ++exploration_stats_.nodes_explored; + worker.recompute_bounds_and_basis = true; + return node_solve_info_t::NO_CHILDREN; + } + + // Solve LP relaxation + lp_solution_t leaf_solution(worker.leaf_problem.num_rows, worker.leaf_problem.num_cols); + std::vector& leaf_vstatus = node_ptr->vstatus; + i_t node_iter = 0; + f_t lp_start_time = tic(); + std::vector leaf_edge_norms = edge_norms_; + + dual::status_t lp_status = dual_phase2_with_advanced_basis(2, + 0, + worker.recompute_bounds_and_basis, + lp_start_time, + worker.leaf_problem, + lp_settings, + leaf_vstatus, + worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + leaf_solution, + node_iter, + leaf_edge_norms, + &worker.work_context); + + if (lp_status == dual::status_t::NUMERICAL) { + settings_.log.printf("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); + lp_status_t second_status = solve_linear_program_with_advanced_basis(worker.leaf_problem, + lp_start_time, + lp_settings, + leaf_solution, + worker.basis_factors, + worker.basic_list, + worker.nonbasic_list, + leaf_vstatus, + leaf_edge_norms); + lp_status = convert_lp_status_to_dual_status(second_status); + } + + double work_performed = worker.work_context.global_work_units_elapsed - work_units_at_start; + worker.clock += work_performed; + + exploration_stats_.total_lp_solve_time += toc(lp_start_time); + exploration_stats_.total_lp_iters += node_iter; + ++exploration_stats_.nodes_explored; + --exploration_stats_.nodes_unexplored; + + bsp_tree_update_policy_t policy{*this, worker}; + auto [status, round_dir] = update_tree_impl(node_ptr, + search_tree, + worker.leaf_problem, + leaf_solution, + var_types_, + settings_, + pc_, + lp_status, + policy); + + // Convert node_status_t to node_solve_info_t + if (status == node_status_t::HAS_CHILDREN) { + return round_dir == rounding_direction_t::DOWN ? node_solve_info_t::DOWN_CHILD_FIRST + : node_solve_info_t::UP_CHILD_FIRST; + } else if (status == node_status_t::PENDING) { + return lp_status == dual::status_t::TIME_LIMIT ? node_solve_info_t::TIME_LIMIT + : node_solve_info_t::WORK_LIMIT; + } else if (status == node_status_t::NUMERICAL) { + return node_solve_info_t::NUMERICAL; + } + return node_solve_info_t::NO_CHILDREN; +} + template void branch_and_bound_t::sort_replay_events(const bb_event_batch_t& events) { @@ -2411,7 +2559,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t*> all_integer_solutions; for (auto& worker : *bsp_workers_) { - for (auto& sol : worker.bsp.integer_solutions) { + for (auto& sol : worker.integer_solutions) { all_integer_solutions.push_back(&sol); } } @@ -2465,7 +2613,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t> all_pc_updates; for (auto& worker : *bsp_workers_) { - for (auto& upd : worker.bsp.pseudo_cost_updates) { + for (auto& upd : worker.pseudo_cost_updates) { all_pc_updates.push_back(upd); } } @@ -2483,7 +2631,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t::prune_worker_nodes_vs_incumbent() // Check nodes in plunge stack - filter in place { std::deque*> surviving; - for (auto* node : worker.bsp.plunge_stack) { + for (auto* node : worker.plunge_stack) { if (node->lower_bound >= upper_bound) { search_tree_.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; @@ -2504,13 +2652,13 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() surviving.push_back(node); } } - worker.bsp.plunge_stack = std::move(surviving); + worker.plunge_stack = std::move(surviving); } // Check nodes in backlog heap - filter and rebuild { std::vector*> surviving; - for (auto* node : worker.bsp.backlog.data()) { + for (auto* node : worker.backlog.data()) { if (node->lower_bound >= upper_bound) { search_tree_.update(node, node_status_t::FATHOMED); --exploration_stats_.nodes_unexplored; @@ -2518,9 +2666,9 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() surviving.push_back(node); } } - worker.bsp.backlog.clear(); + worker.backlog.clear(); for (auto* node : surviving) { - worker.bsp.backlog.push(node); + worker.backlog.push(node); } } } @@ -2561,10 +2709,10 @@ void branch_and_bound_t::balance_worker_loads() std::vector*> all_nodes; for (auto& worker : *bsp_workers_) { - for (auto* node : worker.bsp.backlog.data()) { + for (auto* node : worker.backlog.data()) { all_nodes.push_back(node); } - worker.bsp.backlog.clear(); + worker.backlog.clear(); } if (all_nodes.empty()) return; @@ -2603,17 +2751,17 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() // Check all BFS worker queues for (const auto& worker : *bsp_workers_) { // Check paused node (current_node) - if (worker.bsp.current_node != nullptr) { - lower_bound = std::min(worker.bsp.current_node->lower_bound, lower_bound); + if (worker.current_node != nullptr) { + lower_bound = std::min(worker.current_node->lower_bound, lower_bound); } // Check plunge stack nodes - for (auto* node : worker.bsp.plunge_stack) { + for (auto* node : worker.plunge_stack) { lower_bound = std::min(node->lower_bound, lower_bound); } // Check backlog heap nodes - for (auto* node : worker.bsp.backlog.data()) { + for (auto* node : worker.backlog.data()) { lower_bound = std::min(node->lower_bound, lower_bound); } } @@ -2642,11 +2790,11 @@ void branch_and_bound_t::populate_diving_heap() std::vector*, f_t>> candidates; for (auto& worker : *bsp_workers_) { - for (auto* node : worker.bsp.backlog.data()) { + for (auto* node : worker.backlog.data()) { if (node->lower_bound < upper_bound) { f_t score = node->objective_estimate; if (!std::isfinite(score)) { score = node->lower_bound; } - candidates.push_back(std::make_pair(node, score)); + candidates.push_back({node, score}); } } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index e079d8725..ea76cacc8 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -51,6 +51,16 @@ enum class mip_exploration_status_t { WORK_LIMIT = 6, // The solver reached a deterministic work limit }; +enum class node_solve_info_t { + NO_CHILDREN = 0, // The node does not produced children + UP_CHILD_FIRST = 1, // The up child should be explored first + DOWN_CHILD_FIRST = 2, // The down child should be explored first + TIME_LIMIT = 3, // The solver reached a time limit + ITERATION_LIMIT = 4, // The solver reached a iteration limit + NUMERICAL = 5, // The solver encounter a numerical error when solving the node + WORK_LIMIT = 6, // The solver reached a deterministic work limit +}; + template class bounds_strengthening_t; @@ -230,7 +240,13 @@ class branch_and_bound_t { // We use best-first to pick the `start_node` and then perform a depth-first search // from this node (i.e., a plunge). It can only backtrack to a sibling node. // Unexplored nodes in the subtree are inserted back into the global heap. - void plunge_from(bb_worker_state_t& worker, mip_node_t* start_node); + void plunge_from(i_t task_id, + mip_node_t* start_node, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_update, + std::vector& basic_list, + std::vector& nonbasic_list); // Each "main" thread pops a node from the global heap and then performs a plunge // (i.e., a shallow dive) into the subtree determined by the node. @@ -238,24 +254,34 @@ class branch_and_bound_t { // Perform a deep dive in the subtree determined by the `start_node` in order // to find integer feasible solutions. - void dive_from(bb_worker_state_t& worker, - mip_node_t& start_node, + void dive_from(mip_node_t& start_node, const std::vector& start_lower, const std::vector& start_upper, + lp_problem_t& leaf_problem, + bounds_strengthening_t& node_presolver, + basis_update_mpf_t& basis_update, + std::vector& basic_list, + std::vector& nonbasic_list, bnb_worker_type_t diving_type); // Each diving thread pops the first node from the dive queue and then performs // a deep dive into the subtree determined by the node. void diving_thread(bnb_worker_type_t diving_type); - // Solve the LP relaxation of a leaf node (unified for BSP and non-BSP) - dual::status_t solve_node_lp(bb_worker_state_t& worker, - mip_node_t* node_ptr, + // Solve the LP relaxation of a leaf node + dual::status_t solve_node_lp(mip_node_t* node_ptr, + lp_problem_t& leaf_problem, lp_solution_t& leaf_solution, + basis_update_mpf_t& basis_factors, + std::vector& basic_list, + std::vector& nonbasic_list, + bounds_strengthening_t& node_presolver, bnb_worker_type_t thread_type, + bool recompute_bounds_and_basis, const std::vector& root_lower, const std::vector& root_upper, - bnb_stats_t& stats); + bnb_stats_t& stats, + logger_t& log); // Update the tree based on the LP relaxation. Returns the status // of the node and, if appropriated, the preferred rounding direction @@ -290,6 +316,12 @@ class branch_and_bound_t { // Balance worker loads - redistribute nodes only if significant imbalance detected void balance_worker_loads(); + // BSP-specific node solving that records events + node_solve_info_t solve_node_bsp(bb_worker_state_t& worker, + mip_node_t* node_ptr, + search_tree_t& search_tree, + double current_horizon); + // Compute accurate lower bound from all BSP sources (called during sync phase) f_t compute_bsp_lower_bound(); From 71d127268e1f6542893aead15ab6c5d4893d39d7 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 3 Feb 2026 13:39:27 +0100 Subject: [PATCH 331/366] replaced locks with atomics --- cpp/src/dual_simplex/bnb_worker.hpp | 19 ++++--------------- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- cpp/src/dual_simplex/diving_heuristics.cpp | 4 ---- cpp/src/dual_simplex/pseudo_costs.cpp | 9 --------- cpp/src/dual_simplex/pseudo_costs.hpp | 8 ++++---- 5 files changed, 9 insertions(+), 33 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index 83e3a0640..e770edd84 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -179,10 +179,11 @@ class bnb_worker_pool_t { } } + // Here, we are assuming that the master is the only + // thread that can retrieve/pop an idle worker. bnb_worker_data_t* get_idle_worker() { std::lock_guard lock(mutex_); - if (idle_workers_.empty()) { return nullptr; } else { @@ -191,6 +192,8 @@ class bnb_worker_pool_t { } } + // Here, we are assuming that the master is the only + // thread that can retrieve/pop an idle worker. void pop_idle_worker() { std::lock_guard lock(mutex_); @@ -200,20 +203,6 @@ class bnb_worker_pool_t { } } - bnb_worker_data_t* get_and_pop_idle_worker() - { - std::lock_guard lock(mutex_); - - if (idle_workers_.empty()) { - return nullptr; - } else { - i_t idx = idle_workers_.front(); - idle_workers_.pop_front(); - num_idle_workers_--; - return workers_[idx].get(); - } - } - void return_worker_to_pool(bnb_worker_data_t* worker) { worker->is_active = false; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 16bd75022..c47700a03 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -282,7 +282,7 @@ void branch_and_bound_t::report(char symbol, f_t obj, f_t lower_bound, i_t nodes_unexplored = exploration_stats_.nodes_unexplored; f_t user_obj = compute_user_objective(original_lp_, obj); f_t user_lower = compute_user_objective(original_lp_, lower_bound); - f_t iter_node = (f_t)exploration_stats_.total_lp_iters / nodes_explored; + f_t iter_node = nodes_explored > 0 ? (f_t)exploration_stats_.total_lp_iters / nodes_explored : 0; std::string user_gap = user_mip_gap(user_obj, user_lower); settings_.log.printf("%c %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", symbol, diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 654bfc902..44101c0c6 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -88,14 +88,12 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, f_t f_down = solution[j] - std::floor(solution[j]); f_t f_up = std::ceil(solution[j]) - solution[j]; - pc.pseudo_cost_mutex[j].lock(); f_t pc_down = pc.pseudo_cost_num_down[j] != 0 ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] : pseudo_cost_down_avg; f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] : pseudo_cost_up_avg; - pc.pseudo_cost_mutex[j].unlock(); f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); @@ -167,14 +165,12 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, rounding_direction_t dir = down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; - pc.pseudo_cost_mutex[j].lock(); f_t pc_down = pc.pseudo_cost_num_down[j] != 0 ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] : pseudo_cost_down_avg; f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] : pseudo_cost_up_avg; - pc.pseudo_cost_mutex[j].unlock(); f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 84feb33be..de07ac2c4 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -270,7 +270,6 @@ template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) { - std::lock_guard lock(pseudo_cost_mutex[node_ptr->branch_var]); const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) @@ -296,8 +295,6 @@ void pseudo_costs_t::initialized(i_t& num_initialized_down, pseudo_cost_up_avg = 0; const i_t n = pseudo_cost_sum_down.size(); for (i_t j = 0; j < n; j++) { - std::lock_guard lock(pseudo_cost_mutex[j]); - if (pseudo_cost_num_down[j] > 0) { num_initialized_down++; if (std::isfinite(pseudo_cost_sum_down[j])) { @@ -351,7 +348,6 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio for (i_t k = 0; k < num_fractional; k++) { const i_t j = fractional[k]; - pseudo_cost_mutex[j].lock(); if (pseudo_cost_num_down[j] != 0) { pseudo_cost_down[k] = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; } else { @@ -363,7 +359,6 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio } else { pseudo_cost_up[k] = pseudo_cost_up_avg; } - pseudo_cost_mutex[j].unlock(); constexpr f_t eps = 1e-6; const f_t f_down = solution[j] - std::floor(solution[j]); @@ -444,8 +439,6 @@ i_t pseudo_costs_t::reliable_variable_selection( omp_mutex_t score_mutex; for (auto j : fractional) { - std::lock_guard lock(pseudo_cost_mutex[j]); - if (pseudo_cost_num_down[j] < reliable_threshold || pseudo_cost_num_up[j] < reliable_threshold) { unreliable_list.push_back(j); @@ -599,7 +592,6 @@ f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, f_t pseudo_cost_down = 0; f_t pseudo_cost_up = 0; - pseudo_cost_mutex[j].lock(); if (pseudo_cost_num_down[j] != 0) { pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; } else { @@ -611,7 +603,6 @@ f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, } else { pseudo_cost_up = pseudo_cost_up_avg; } - pseudo_cost_mutex[j].unlock(); constexpr f_t eps = 1e-6; const f_t f_down = solution[j] - std::floor(solution[j]); diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 24f0ed0c4..8d566483a 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -69,10 +69,10 @@ class pseudo_costs_t { void update_pseudo_costs_from_strong_branching(const std::vector& fractional, const std::vector& root_soln); - std::vector pseudo_cost_sum_up; - std::vector pseudo_cost_sum_down; - std::vector pseudo_cost_num_up; - std::vector pseudo_cost_num_down; + std::vector> pseudo_cost_sum_up; + std::vector> pseudo_cost_sum_down; + std::vector> pseudo_cost_num_up; + std::vector> pseudo_cost_num_down; std::vector strong_branch_down; std::vector strong_branch_up; std::vector pseudo_cost_mutex; From cc14feec6908568f04b6eb45fe839f3f3aa7effb Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 3 Feb 2026 13:41:40 +0000 Subject: [PATCH 332/366] cleanup --- cpp/src/dual_simplex/branch_and_bound.cpp | 44 +- cpp/src/dual_simplex/branch_and_bound.hpp | 11 +- ...er_state.hpp => deterministic_workers.hpp} | 538 +++++++----------- 3 files changed, 246 insertions(+), 347 deletions(-) rename cpp/src/dual_simplex/{bb_worker_state.hpp => deterministic_workers.hpp} (51%) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 4e882b149..6f095013d 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -742,7 +742,7 @@ struct opportunistic_tree_update_policy_t { template struct bsp_tree_update_policy_t { branch_and_bound_t& bnb; - bb_worker_state_t& worker; + bsp_bfs_worker_t& worker; f_t upper_bound() const { return worker.local_upper_bound; } @@ -1886,12 +1886,8 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t>(num_bfs_workers, - original_lp_, - Arow, - var_types_, - settings_.refactor_frequency, - settings_.deterministic); + bsp_workers_ = std::make_unique>( + num_bfs_workers, original_lp_, Arow, var_types_, settings_); if (num_diving_workers > 0) { // Extract diving types from worker_types (skip BEST_FIRST at index 0) @@ -1908,8 +1904,8 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_tset_sync_callback([this](double) { bsp_sync_callback(0); }); + std::vector incumbent_snapshot; + if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } + for (auto& worker : *bsp_workers_) { worker.set_snapshots(upper_bound_.load(), pc_.pseudo_cost_sum_up, pc_.pseudo_cost_sum_down, pc_.pseudo_cost_num_up, pc_.pseudo_cost_num_down, + incumbent_snapshot, + exploration_stats_.total_lp_iters.load(), 0.0, bsp_horizon_step_); } if (bsp_diving_workers_) { - std::vector incumbent_snapshot; - if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } - for (auto& worker : *bsp_diving_workers_) { worker.set_snapshots(upper_bound_.load(), - exploration_stats_.total_lp_iters.load(), pc_.pseudo_cost_sum_up, pc_.pseudo_cost_sum_down, pc_.pseudo_cost_num_up, pc_.pseudo_cost_num_down, incumbent_snapshot, - &root_relax_soln_.x, + exploration_stats_.total_lp_iters.load(), 0.0, bsp_horizon_step_); } @@ -2056,7 +2053,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t -void branch_and_bound_t::run_worker_loop(bb_worker_state_t& worker, +void branch_and_bound_t::run_worker_loop(bsp_bfs_worker_t& worker, search_tree_t& search_tree) { raft::common::nvtx::range scope("BB::worker_loop"); @@ -2158,28 +2155,30 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) bsp_current_horizon_ += bsp_horizon_step_; + std::vector incumbent_snapshot; + if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } + for (auto& worker : *bsp_workers_) { worker.set_snapshots(upper_bound_.load(), pc_.pseudo_cost_sum_up, pc_.pseudo_cost_sum_down, pc_.pseudo_cost_num_up, pc_.pseudo_cost_num_down, + incumbent_snapshot, + exploration_stats_.total_lp_iters.load(), horizon_end, bsp_current_horizon_); } if (bsp_diving_workers_) { - std::vector incumbent_snapshot; - if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } for (auto& worker : *bsp_diving_workers_) { worker.set_snapshots(upper_bound_.load(), - exploration_stats_.total_lp_iters.load(), pc_.pseudo_cost_sum_up, pc_.pseudo_cost_sum_down, pc_.pseudo_cost_num_up, pc_.pseudo_cost_num_down, incumbent_snapshot, - &root_relax_soln_.x, + exploration_stats_.total_lp_iters.load(), horizon_end, bsp_current_horizon_); } @@ -2241,7 +2240,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) } template -node_solve_info_t branch_and_bound_t::solve_node_bsp(bb_worker_state_t& worker, +node_solve_info_t branch_and_bound_t::solve_node_bsp(bsp_bfs_worker_t& worker, mip_node_t* node_ptr, search_tree_t& search_tree, double current_horizon) @@ -2897,8 +2896,7 @@ void branch_and_bound_t::collect_diving_solutions() } template -void branch_and_bound_t::run_diving_worker_loop( - bsp_diving_worker_state_t& worker) +void branch_and_bound_t::run_diving_worker_loop(bsp_diving_worker_t& worker) { raft::common::nvtx::range scope("BB::diving_worker_loop"); @@ -2919,7 +2917,7 @@ void branch_and_bound_t::run_diving_worker_loop( } template -void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_state_t& worker, +void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_t& worker, mip_node_t starting_node) { raft::common::nvtx::range scope("BB::dive_from_bsp"); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index f20eaf927..03fb24dca 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -8,8 +8,8 @@ #pragma once #include -#include #include +#include #include #include #include @@ -285,7 +285,7 @@ class branch_and_bound_t { void balance_worker_loads(); // BSP-specific node solving that records events - node_solve_info_t solve_node_bsp(bb_worker_state_t& worker, + node_solve_info_t solve_node_bsp(bsp_bfs_worker_t& worker, mip_node_t* node_ptr, search_tree_t& search_tree, double current_horizon); @@ -294,7 +294,7 @@ class branch_and_bound_t { f_t compute_bsp_lower_bound(); // BSP worker loop - runs continuously until scheduler signals stop - void run_worker_loop(bb_worker_state_t& worker, search_tree_t& search_tree); + void run_worker_loop(bsp_bfs_worker_t& worker, search_tree_t& search_tree); // BSP sync callback - executed when all workers reach barrier // Sets bsp_global_termination_status_ if termination is needed @@ -305,11 +305,10 @@ class branch_and_bound_t { // ============================================================================ // Run diving worker loop - void run_diving_worker_loop(bsp_diving_worker_state_t& worker); + void run_diving_worker_loop(bsp_diving_worker_t& worker); // Perform a deterministic dive from the given starting node - void dive_from_bsp(bsp_diving_worker_state_t& worker, - mip_node_t starting_node); + void dive_from_bsp(bsp_diving_worker_t& worker, mip_node_t starting_node); // Populate diving heap from BFS worker backlogs at sync void populate_diving_heap(); diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/deterministic_workers.hpp similarity index 51% rename from cpp/src/dual_simplex/bb_worker_state.hpp rename to cpp/src/dual_simplex/deterministic_workers.hpp index 7e06b00fe..8ec50e504 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/deterministic_workers.hpp @@ -7,14 +7,10 @@ #pragma once -#include #include -#include +#include #include -#include -#include #include -#include #include #include @@ -28,18 +24,11 @@ namespace cuopt::linear_programming::dual_simplex { -// Indicate the search and variable selection algorithms used by each thread -// in B&B (See [1]). - -// Comparator for backlog heap: best-first by lower_bound with deterministic BSP identity tie-break -// Returns true if 'a' has lower priority than 'b' (for max-heap behavior in std::push_heap) template struct backlog_node_compare_t { bool operator()(const mip_node_t* a, const mip_node_t* b) const { - // Primary: prefer smaller lower_bound (best-first search) if (a->lower_bound != b->lower_bound) { return a->lower_bound > b->lower_bound; } - // Deterministic tie-breaking by BSP identity tuple if (a->origin_worker_id != b->origin_worker_id) { return a->origin_worker_id > b->origin_worker_id; } @@ -47,15 +36,13 @@ struct backlog_node_compare_t { } }; -// Queued pseudo-cost update for BSP determinism -// Updates are collected during horizon, then applied in deterministic order at sync template struct pseudo_cost_update_t { i_t variable; rounding_direction_t direction; - f_t delta; // change_in_obj / frac - double wut; // work unit timestamp when update occurred (for deterministic ordering) - int worker_id; // for tie-breaking in sort + f_t delta; + double wut; + int worker_id; bool operator<(const pseudo_cost_update_t& other) const { @@ -66,7 +53,6 @@ struct pseudo_cost_update_t { } }; -// Queued integer solution found during a horizon (merged at sync) template struct queued_integer_solution_t { f_t objective; @@ -83,94 +69,45 @@ struct queued_integer_solution_t { } }; -// Per-worker state for BSP branch-and-bound -template -struct bb_worker_state_t { - int worker_id{0}; - - // Plunge stack: LIFO queue for depth-first exploration - std::deque*> plunge_stack; - - // Backlog heap: nodes plugged when branching, ordered by lower_bound for best-first selection - heap_t*, backlog_node_compare_t> backlog; - - mip_node_t* current_node{nullptr}; - mip_node_t* last_solved_node{nullptr}; // For basis warm-start detection +template +class bsp_worker_base_t : public bnb_worker_data_t { + using base_t = bnb_worker_data_t; + public: double clock{0.0}; double horizon_start{0.0}; double horizon_end{0.0}; + work_limit_context_t work_context; - // Cumulative counter for unique node identity: (worker_id, next_creation_seq++) - int32_t next_creation_seq{0}; + // Local snapshots of global state + std::vector pc_sum_up_snapshot; + std::vector pc_sum_down_snapshot; + std::vector pc_num_up_snapshot; + std::vector pc_num_down_snapshot; + f_t local_upper_bound{std::numeric_limits::infinity()}; - bb_event_batch_t events; - int event_sequence{0}; + // Diving-specific snapshots (ignored by BFS workers) + std::vector incumbent_snapshot; + i_t total_lp_iters_snapshot{0}; - lp_problem_t leaf_problem; - basis_update_mpf_t basis_factors; - bounds_strengthening_t node_presolver; - std::vector bounds_changed; - std::vector basic_list; - std::vector nonbasic_list; - work_limit_context_t work_context; - bool recompute_bounds_and_basis{true}; + std::vector> integer_solutions; + std::vector> pseudo_cost_updates; + int next_solution_seq{0}; - i_t nodes_processed_this_horizon{0}; i_t total_nodes_processed{0}; - i_t total_nodes_pruned{0}; - i_t total_nodes_branched{0}; - i_t total_nodes_infeasible{0}; i_t total_integer_solutions{0}; - i_t total_nodes_assigned{0}; double total_runtime{0.0}; double total_nowork_time{0.0}; - f_t local_upper_bound{std::numeric_limits::infinity()}; - f_t local_lower_bound_ceiling{std::numeric_limits::infinity()}; - - std::vector> integer_solutions; - int next_solution_seq{0}; - std::vector> pseudo_cost_updates; - - // Pseudo-cost snapshots: local copies updated within horizon, merged at sync - std::vector pc_sum_up_snapshot; - std::vector pc_sum_down_snapshot; - std::vector pc_num_up_snapshot; - std::vector pc_num_down_snapshot; - - explicit bb_worker_state_t(int id, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - i_t refactor_frequency, - bool deterministic) - : worker_id(id), - work_context("BB_Worker_" + std::to_string(id)), - leaf_problem(original_lp), - basis_factors(original_lp.num_rows, refactor_frequency), - node_presolver(original_lp, Arow, std::vector{}, var_types), - bounds_changed(original_lp.num_cols, false) + bsp_worker_base_t(int id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::string& context_name) + : base_t(id, original_lp, Arow, var_types, settings), work_context(context_name) { - const i_t m = leaf_problem.num_rows; - basic_list.resize(m); - nonbasic_list.clear(); - work_context.deterministic = deterministic; - } - - void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) - { - clock = horizon_start; - work_context.global_work_units_elapsed = horizon_start; - events.clear(); - events.horizon_start = horizon_start; - events.horizon_end = horizon_end; - event_sequence = 0; - nodes_processed_this_horizon = 0; - local_upper_bound = global_upper_bound; - local_lower_bound_ceiling = std::numeric_limits::infinity(); - integer_solutions.clear(); - pseudo_cost_updates.clear(); + work_context.deterministic = true; } void set_snapshots(f_t global_upper_bound, @@ -178,22 +115,26 @@ struct bb_worker_state_t { const std::vector& pc_sum_down, const std::vector& pc_num_up, const std::vector& pc_num_down, + const std::vector& incumbent, + i_t total_lp_iters, double new_horizon_start, double new_horizon_end) { - local_upper_bound = global_upper_bound; - pc_sum_up_snapshot = pc_sum_up; - pc_sum_down_snapshot = pc_sum_down; - pc_num_up_snapshot = pc_num_up; - pc_num_down_snapshot = pc_num_down; - horizon_start = new_horizon_start; - horizon_end = new_horizon_end; + local_upper_bound = global_upper_bound; + pc_sum_up_snapshot = pc_sum_up; + pc_sum_down_snapshot = pc_sum_down; + pc_num_up_snapshot = pc_num_up; + pc_num_down_snapshot = pc_num_down; + incumbent_snapshot = incumbent; + total_lp_iters_snapshot = total_lp_iters; + horizon_start = new_horizon_start; + horizon_end = new_horizon_end; } - // Queue pseudo-cost update for global sync and apply to local snapshot + // Queue pseudo-cost update and apply to local snapshot void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) { - pseudo_cost_updates.push_back({variable, direction, delta, clock, worker_id}); + pseudo_cost_updates.push_back({variable, direction, delta, clock, this->worker_id}); if (direction == rounding_direction_t::DOWN) { pc_sum_down_snapshot[variable] += delta; pc_num_down_snapshot[variable]++; @@ -203,6 +144,7 @@ struct bb_worker_state_t { } } + // Basic variable selection from snapshots i_t variable_selection_from_snapshot(const std::vector& fractional, const std::vector& solution) const { @@ -215,13 +157,56 @@ struct bb_worker_state_t { solution); } + bool has_work() const { return static_cast(this)->has_work_impl(); } +}; + +template +class bsp_bfs_worker_t : public bsp_worker_base_t> { + using base_t = bsp_worker_base_t>; + + public: + // Node management + std::deque*> plunge_stack; + heap_t*, backlog_node_compare_t> backlog; + mip_node_t* current_node{nullptr}; + mip_node_t* last_solved_node{nullptr}; + + // Event logging for deterministic replay + bb_event_batch_t events; + int event_sequence{0}; + int32_t next_creation_seq{0}; + + // BFS-specific state + f_t local_lower_bound_ceiling{std::numeric_limits::infinity()}; + bool recompute_bounds_and_basis{true}; + i_t nodes_processed_this_horizon{0}; + + // BFS statistics + i_t total_nodes_pruned{0}; + i_t total_nodes_branched{0}; + i_t total_nodes_infeasible{0}; + i_t total_nodes_assigned{0}; + + explicit bsp_bfs_worker_t(int id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings) + : base_t(id, original_lp, Arow, var_types, settings, "BB_Worker_" + std::to_string(id)) + { + } + + bool has_work_impl() const + { + return current_node != nullptr || !plunge_stack.empty() || !backlog.empty(); + } + void enqueue_node(mip_node_t* node) { plunge_stack.push_front(node); ++total_nodes_assigned; } - // Enqueue children with plunging: move any existing sibling to backlog, push both children mip_node_t* enqueue_children_for_plunge(mip_node_t* down_child, mip_node_t* up_child, rounding_direction_t preferred_direction) @@ -231,9 +216,9 @@ struct bb_worker_state_t { plunge_stack.pop_back(); } - down_child->origin_worker_id = worker_id; + down_child->origin_worker_id = this->worker_id; down_child->creation_seq = next_creation_seq++; - up_child->origin_worker_id = worker_id; + up_child->origin_worker_id = this->worker_id; up_child->creation_seq = next_creation_seq++; mip_node_t* first_child; @@ -249,7 +234,6 @@ struct bb_worker_state_t { return first_child; } - // Dequeue: current_node first, then plunge_stack, then backlog heap mip_node_t* dequeue_node() { if (current_node != nullptr) { @@ -266,11 +250,6 @@ struct bb_worker_state_t { return node_opt.has_value() ? node_opt.value() : nullptr; } - bool has_work() const - { - return current_node != nullptr || !plunge_stack.empty() || !backlog.empty(); - } - size_t queue_size() const { return plunge_stack.size() + backlog.size() + (current_node != nullptr ? 1 : 0); @@ -285,8 +264,8 @@ struct bb_worker_state_t { void record_branched( mip_node_t* node, i_t down_child_id, i_t up_child_id, i_t branch_var, f_t branch_val) { - record_event(bb_event_t::make_branched(clock, - worker_id, + record_event(bb_event_t::make_branched(this->clock, + this->worker_id, node->node_id, 0, down_child_id, @@ -295,151 +274,90 @@ struct bb_worker_state_t { branch_var, branch_val)); ++nodes_processed_this_horizon; - ++total_nodes_processed; + ++this->total_nodes_processed; ++total_nodes_branched; } void record_integer_solution(mip_node_t* node, f_t objective) { - record_event( - bb_event_t::make_integer_solution(clock, worker_id, node->node_id, 0, objective)); + record_event(bb_event_t::make_integer_solution( + this->clock, this->worker_id, node->node_id, 0, objective)); ++nodes_processed_this_horizon; - ++total_nodes_processed; - ++total_integer_solutions; + ++this->total_nodes_processed; + ++this->total_integer_solutions; } void record_fathomed(mip_node_t* node, f_t lower_bound) { - record_event( - bb_event_t::make_fathomed(clock, worker_id, node->node_id, 0, lower_bound)); + record_event(bb_event_t::make_fathomed( + this->clock, this->worker_id, node->node_id, 0, lower_bound)); ++nodes_processed_this_horizon; - ++total_nodes_processed; + ++this->total_nodes_processed; ++total_nodes_pruned; } void record_infeasible(mip_node_t* node) { - record_event(bb_event_t::make_infeasible(clock, worker_id, node->node_id, 0)); + record_event( + bb_event_t::make_infeasible(this->clock, this->worker_id, node->node_id, 0)); ++nodes_processed_this_horizon; - ++total_nodes_processed; + ++this->total_nodes_processed; ++total_nodes_infeasible; } void record_numerical(mip_node_t* node) { - record_event(bb_event_t::make_numerical(clock, worker_id, node->node_id, 0)); + record_event( + bb_event_t::make_numerical(this->clock, this->worker_id, node->node_id, 0)); ++nodes_processed_this_horizon; - ++total_nodes_processed; + ++this->total_nodes_processed; } }; -// Per-worker state for BSP diving (operates on detached node copies) template -struct bsp_diving_worker_state_t { - int worker_id{0}; - bnb_worker_type_t diving_type{bnb_worker_type_t::PSEUDOCOST_DIVING}; +class bsp_diving_worker_t : public bsp_worker_base_t> { + using base_t = bsp_worker_base_t>; - double clock{0.0}; - double horizon_start{0.0}; - double horizon_end{0.0}; - work_limit_context_t work_context; - - lp_problem_t leaf_problem; - basis_update_mpf_t basis_factors; - bounds_strengthening_t node_presolver; - std::vector bounds_changed; - std::vector basic_list; - std::vector nonbasic_list; - bool recompute_bounds_and_basis{true}; - - // Snapshots taken at horizon start - f_t local_upper_bound{std::numeric_limits::infinity()}; - i_t total_lp_iters_snapshot{0}; - std::vector incumbent_snapshot; - std::vector pc_sum_up_snapshot; - std::vector pc_sum_down_snapshot; - std::vector pc_num_up_snapshot; - std::vector pc_num_down_snapshot; - const std::vector* root_solution{nullptr}; + public: + bnb_worker_type_t diving_type{bnb_worker_type_t::PSEUDOCOST_DIVING}; + // Diving-specific node management std::deque> dive_queue; std::vector dive_lower; std::vector dive_upper; - std::vector> integer_solutions; - int next_solution_seq{0}; - std::vector> pseudo_cost_updates; + // Root LP relaxation solution (constant, set once at construction) + const std::vector* root_solution{nullptr}; + // Diving state + bool recompute_bounds_and_basis{true}; + + // Diving statistics i_t total_nodes_explored{0}; - i_t total_integer_solutions{0}; i_t total_dives{0}; i_t lp_iters_this_dive{0}; - double total_runtime{0.0}; - double total_nowork_time{0.0}; - explicit bsp_diving_worker_state_t(int id, - bnb_worker_type_t type, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - i_t refactor_frequency, - bool deterministic) - : worker_id(id), + explicit bsp_diving_worker_t(int id, + bnb_worker_type_t type, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::vector* root_sol) + : base_t(id, original_lp, Arow, var_types, settings, "Diving_Worker_" + std::to_string(id)), diving_type(type), - work_context("Diving_Worker_" + std::to_string(id)), - leaf_problem(original_lp), - basis_factors(original_lp.num_rows, refactor_frequency), - node_presolver(original_lp, Arow, std::vector{}, var_types), - bounds_changed(original_lp.num_cols, false) + root_solution(root_sol) { - const i_t m = leaf_problem.num_rows; - basic_list.resize(m); - nonbasic_list.clear(); - dive_lower = original_lp.lower; - dive_upper = original_lp.upper; - work_context.deterministic = deterministic; + dive_lower = original_lp.lower; + dive_upper = original_lp.upper; } - bsp_diving_worker_state_t(const bsp_diving_worker_state_t&) = delete; - bsp_diving_worker_state_t& operator=(const bsp_diving_worker_state_t&) = delete; - bsp_diving_worker_state_t(bsp_diving_worker_state_t&&) = default; - bsp_diving_worker_state_t& operator=(bsp_diving_worker_state_t&&) = default; + bsp_diving_worker_t(const bsp_diving_worker_t&) = delete; + bsp_diving_worker_t& operator=(const bsp_diving_worker_t&) = delete; + bsp_diving_worker_t(bsp_diving_worker_t&&) = default; + bsp_diving_worker_t& operator=(bsp_diving_worker_t&&) = default; - void reset_for_horizon(double start, double end, f_t upper_bound) - { - clock = start; - horizon_start = start; - horizon_end = end; - work_context.global_work_units_elapsed = start; - - local_upper_bound = upper_bound; - integer_solutions.clear(); - pseudo_cost_updates.clear(); - recompute_bounds_and_basis = true; - } - - void set_snapshots(f_t global_upper_bound, - i_t total_lp_iters, - const std::vector& pc_sum_up, - const std::vector& pc_sum_down, - const std::vector& pc_num_up, - const std::vector& pc_num_down, - const std::vector& incumbent, - const std::vector* root_sol, - double new_horizon_start, - double new_horizon_end) - { - local_upper_bound = global_upper_bound; - total_lp_iters_snapshot = total_lp_iters; - pc_sum_up_snapshot = pc_sum_up; - pc_sum_down_snapshot = pc_sum_down; - pc_num_up_snapshot = pc_num_up; - pc_num_down_snapshot = pc_num_down; - incumbent_snapshot = incumbent; - root_solution = root_sol; - horizon_start = new_horizon_start; - horizon_end = new_horizon_end; - } + bool has_work_impl() const { return !dive_queue.empty(); } void enqueue_dive_node(mip_node_t* node) { dive_queue.push_back(node->detach_copy()); } @@ -452,37 +370,25 @@ struct bsp_diving_worker_state_t { return node; } - bool has_work() const { return !dive_queue.empty(); } - size_t dive_queue_size() const { return dive_queue.size(); } + size_t queue_size() const { return dive_queue_size(); } // Unified interface for pool void queue_integer_solution(f_t objective, const std::vector& solution, i_t depth) { - integer_solutions.push_back({objective, solution, depth, worker_id, next_solution_seq++}); - ++total_integer_solutions; - } - - void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) - { - pseudo_cost_updates.push_back({variable, direction, delta, clock, worker_id}); - if (direction == rounding_direction_t::DOWN) { - pc_sum_down_snapshot[variable] += delta; - pc_num_down_snapshot[variable]++; - } else { - pc_sum_up_snapshot[variable] += delta; - pc_num_up_snapshot[variable]++; - } + this->integer_solutions.push_back( + {objective, solution, depth, this->worker_id, this->next_solution_seq++}); + ++this->total_integer_solutions; } branch_variable_t variable_selection_from_snapshot(const std::vector& fractional, const std::vector& solution) const { const std::vector& root_sol = (root_solution != nullptr) ? *root_solution : solution; - return pseudocost_diving_from_arrays(pc_sum_down_snapshot.data(), - pc_sum_up_snapshot.data(), - pc_num_down_snapshot.data(), - pc_num_up_snapshot.data(), - (i_t)pc_sum_down_snapshot.size(), + return pseudocost_diving_from_arrays(this->pc_sum_down_snapshot.data(), + this->pc_sum_up_snapshot.data(), + this->pc_num_down_snapshot.data(), + this->pc_num_up_snapshot.data(), + (i_t)this->pc_sum_down_snapshot.size(), fractional, solution, root_sol); @@ -491,134 +397,130 @@ struct bsp_diving_worker_state_t { branch_variable_t guided_variable_selection(const std::vector& fractional, const std::vector& solution) const { - if (incumbent_snapshot.empty()) { + if (this->incumbent_snapshot.empty()) { return variable_selection_from_snapshot(fractional, solution); } - return guided_diving_from_arrays(pc_sum_down_snapshot.data(), - pc_sum_up_snapshot.data(), - pc_num_down_snapshot.data(), - pc_num_up_snapshot.data(), - (i_t)pc_sum_down_snapshot.size(), + return guided_diving_from_arrays(this->pc_sum_down_snapshot.data(), + this->pc_sum_up_snapshot.data(), + this->pc_num_down_snapshot.data(), + this->pc_num_up_snapshot.data(), + (i_t)this->pc_sum_down_snapshot.size(), fractional, solution, - incumbent_snapshot); + this->incumbent_snapshot); } }; -template -class bsp_diving_worker_pool_t { - public: - bsp_diving_worker_pool_t(int num_workers, - const std::vector& diving_types, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - i_t refactor_frequency, - bool deterministic) - { - workers_.reserve(num_workers); - for (int i = 0; i < num_workers; ++i) { - bnb_worker_type_t type = diving_types[i % diving_types.size()]; - workers_.emplace_back( - i, type, original_lp, Arow, var_types, refactor_frequency, deterministic); - } - } - bsp_diving_worker_state_t& operator[](int worker_id) { return workers_[worker_id]; } - const bsp_diving_worker_state_t& operator[](int worker_id) const - { - return workers_[worker_id]; - } +// ============================================================================ +// CRTP Base class for BSP worker pools +// ============================================================================ +template +class bsp_worker_pool_base_t { + protected: + std::vector workers_; + public: + WorkerT& operator[](int worker_id) { return workers_[worker_id]; } + const WorkerT& operator[](int worker_id) const { return workers_[worker_id]; } int size() const { return static_cast(workers_.size()); } - void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) + bool any_has_work() const { - for (auto& worker : workers_) { - worker.reset_for_horizon(horizon_start, horizon_end, global_upper_bound); + for (const auto& worker : workers_) { + if (worker.has_work()) return true; } + return false; } - bool any_has_work() const + size_t total_queue_size() const { + size_t total = 0; for (const auto& worker : workers_) { - if (worker.has_work()) return true; + total += worker.queue_size(); } - return false; + return total; + } + + bb_event_batch_t collect_and_sort_events() + { + bb_event_batch_t all_events; + for (auto& worker : workers_) { + static_cast(this)->collect_worker_events(worker, all_events); + } + all_events.sort_for_replay(); + return all_events; } auto begin() { return workers_.begin(); } auto end() { return workers_.end(); } auto begin() const { return workers_.begin(); } auto end() const { return workers_.end(); } - - private: - std::vector> workers_; }; +// ============================================================================ +// BSP BFS Worker Pool +// ============================================================================ template -class bb_worker_pool_t { +class bb_worker_pool_t : public bsp_worker_pool_base_t, + bb_worker_pool_t> { + using base_t = + bsp_worker_pool_base_t, bb_worker_pool_t>; + public: bb_worker_pool_t(int num_workers, const lp_problem_t& original_lp, const csr_matrix_t& Arow, const std::vector& var_types, - i_t refactor_frequency, - bool deterministic) + const simplex_solver_settings_t& settings) { - workers_.reserve(num_workers); + this->workers_.reserve(num_workers); for (int i = 0; i < num_workers; ++i) { - workers_.emplace_back(i, original_lp, Arow, var_types, refactor_frequency, deterministic); - } - } - - bb_worker_state_t& operator[](int worker_id) { return workers_[worker_id]; } - const bb_worker_state_t& operator[](int worker_id) const { return workers_[worker_id]; } - int size() const { return static_cast(workers_.size()); } - - void reset_for_horizon(double horizon_start, double horizon_end, f_t global_upper_bound) - { - for (auto& worker : workers_) { - worker.reset_for_horizon(horizon_start, horizon_end, global_upper_bound); + this->workers_.emplace_back(i, original_lp, Arow, var_types, settings); } } - bb_event_batch_t collect_and_sort_events() + void collect_worker_events(bsp_bfs_worker_t& worker, + bb_event_batch_t& all_events) { - bb_event_batch_t all_events; - for (auto& worker : workers_) { - for (auto& event : worker.events.events) { - all_events.add(std::move(event)); - } - worker.events.clear(); + for (auto& event : worker.events.events) { + all_events.add(std::move(event)); } - all_events.sort_for_replay(); - return all_events; + worker.events.clear(); } +}; - bool any_has_work() const - { - for (const auto& worker : workers_) { - if (worker.has_work()) return true; - } - return false; - } +// ============================================================================ +// BSP Diving Worker Pool +// ============================================================================ +template +class bsp_diving_worker_pool_t : public bsp_worker_pool_base_t, + bsp_diving_worker_pool_t> { + using base_t = bsp_worker_pool_base_t, + bsp_diving_worker_pool_t>; - size_t total_queue_size() const + public: + bsp_diving_worker_pool_t(int num_workers, + const std::vector& diving_types, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::vector* root_solution) { - size_t total = 0; - for (const auto& worker : workers_) { - total += worker.queue_size(); + this->workers_.reserve(num_workers); + for (int i = 0; i < num_workers; ++i) { + bnb_worker_type_t type = diving_types[i % diving_types.size()]; + this->workers_.emplace_back(i, type, original_lp, Arow, var_types, settings, root_solution); } - return total; } - auto begin() { return workers_.begin(); } - auto end() { return workers_.end(); } - auto begin() const { return workers_.begin(); } - auto end() const { return workers_.end(); } - - private: - std::vector> workers_; + void collect_worker_events(bsp_diving_worker_t&, bb_event_batch_t&) {} }; } // namespace cuopt::linear_programming::dual_simplex From 70e6c73f9d75601b31e715fc147a74d27918b9ce Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 3 Feb 2026 14:24:30 +0000 Subject: [PATCH 333/366] cleanup --- cpp/src/dual_simplex/branch_and_bound.cpp | 32 ++--- .../dual_simplex/deterministic_workers.hpp | 19 +-- cpp/src/dual_simplex/diving_heuristics.cpp | 129 +++--------------- cpp/src/dual_simplex/pseudo_costs.cpp | 18 +-- cpp/src/dual_simplex/pseudo_costs.hpp | 2 - 5 files changed, 47 insertions(+), 153 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index f1e4bdd55..291f4aeeb 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1944,10 +1944,10 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::bsp_sync_callback(int worker_id) for (auto& worker : *bsp_workers_) { worker.set_snapshots(upper_bound_.load(), - pc_.pseudo_cost_sum_up, - pc_.pseudo_cost_sum_down, - pc_.pseudo_cost_num_up, - pc_.pseudo_cost_num_down, + (const f_t*)pc_.pseudo_cost_sum_up.data(), + (const f_t*)pc_.pseudo_cost_sum_down.data(), + (const i_t*)pc_.pseudo_cost_num_up.data(), + (const i_t*)pc_.pseudo_cost_num_down.data(), incumbent_snapshot, exploration_stats_.total_lp_iters.load(), horizon_end, @@ -2173,10 +2173,10 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) if (bsp_diving_workers_) { for (auto& worker : *bsp_diving_workers_) { worker.set_snapshots(upper_bound_.load(), - pc_.pseudo_cost_sum_up, - pc_.pseudo_cost_sum_down, - pc_.pseudo_cost_num_up, - pc_.pseudo_cost_num_down, + (const f_t*)pc_.pseudo_cost_sum_up.data(), + (const f_t*)pc_.pseudo_cost_sum_down.data(), + (const i_t*)pc_.pseudo_cost_num_up.data(), + (const i_t*)pc_.pseudo_cost_num_down.data(), incumbent_snapshot, exploration_stats_.total_lp_iters.load(), horizon_end, diff --git a/cpp/src/dual_simplex/deterministic_workers.hpp b/cpp/src/dual_simplex/deterministic_workers.hpp index 8ec50e504..a54ff24ba 100644 --- a/cpp/src/dual_simplex/deterministic_workers.hpp +++ b/cpp/src/dual_simplex/deterministic_workers.hpp @@ -111,20 +111,21 @@ class bsp_worker_base_t : public bnb_worker_data_t { } void set_snapshots(f_t global_upper_bound, - const std::vector& pc_sum_up, - const std::vector& pc_sum_down, - const std::vector& pc_num_up, - const std::vector& pc_num_down, + const f_t* pc_sum_up, + const f_t* pc_sum_down, + const i_t* pc_num_up, + const i_t* pc_num_down, const std::vector& incumbent, i_t total_lp_iters, double new_horizon_start, double new_horizon_end) { - local_upper_bound = global_upper_bound; - pc_sum_up_snapshot = pc_sum_up; - pc_sum_down_snapshot = pc_sum_down; - pc_num_up_snapshot = pc_num_up; - pc_num_down_snapshot = pc_num_down; + const i_t n = this->leaf_problem.num_cols; + local_upper_bound = global_upper_bound; + pc_sum_up_snapshot.assign(pc_sum_up, pc_sum_up + n); + pc_sum_down_snapshot.assign(pc_sum_down, pc_sum_down + n); + pc_num_up_snapshot.assign(pc_num_up, pc_num_up + n); + pc_num_down_snapshot.assign(pc_num_down, pc_num_down + n); incumbent_snapshot = incumbent; total_lp_iters_snapshot = total_lp_iters; horizon_start = new_horizon_start; diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 44101c0c6..af9dc590b 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -71,71 +71,14 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, const std::vector& root_solution, logger_t& log) { - i_t branch_var = -1; - f_t max_score = std::numeric_limits::lowest(); - rounding_direction_t round_dir = rounding_direction_t::NONE; - constexpr f_t eps = 1e-6; - - i_t num_initialized_down; - i_t num_initialized_up; - f_t pseudo_cost_down_avg; - f_t pseudo_cost_up_avg; - pc.initialized( - num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - - for (i_t j : fractional) { - rounding_direction_t dir = rounding_direction_t::NONE; - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - - f_t pc_down = pc.pseudo_cost_num_down[j] != 0 - ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - - f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - - f_t score_down = std::sqrt(f_up) * (1 + pc_up) / (1 + pc_down); - f_t score_up = std::sqrt(f_down) * (1 + pc_down) / (1 + pc_up); - f_t score = 0; - - if (solution[j] < root_solution[j] - 0.4) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else if (solution[j] > root_solution[j] + 0.4) { - score = score_up; - dir = rounding_direction_t::UP; - } else if (f_down < 0.3) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else if (f_down > 0.7) { - score = score_up; - dir = rounding_direction_t::UP; - } else if (pc_down < pc_up + eps) { - score = score_down; - dir = rounding_direction_t::DOWN; - } else { - score = score_up; - dir = rounding_direction_t::UP; - } - - if (score > max_score) { - max_score = score; - branch_var = j; - round_dir = dir; - } - } - - assert(round_dir != rounding_direction_t::NONE); - assert(branch_var >= 0); - - log.debug("Pseudocost diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - max_score); - - return {branch_var, round_dir}; + return pseudocost_diving_from_arrays((const f_t*)pc.pseudo_cost_sum_down.data(), + (const f_t*)pc.pseudo_cost_sum_up.data(), + (const i_t*)pc.pseudo_cost_num_down.data(), + (const i_t*)pc.pseudo_cost_num_up.data(), + (i_t)pc.pseudo_cost_sum_down.size(), + fractional, + solution, + root_solution); } template @@ -145,54 +88,14 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, const std::vector& incumbent, logger_t& log) { - i_t branch_var = -1; - f_t max_score = std::numeric_limits::lowest(); - rounding_direction_t round_dir = rounding_direction_t::NONE; - constexpr f_t eps = 1e-6; - - i_t num_initialized_down; - i_t num_initialized_up; - f_t pseudo_cost_down_avg; - f_t pseudo_cost_up_avg; - pc.initialized( - num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - - for (i_t j : fractional) { - f_t f_down = solution[j] - std::floor(solution[j]); - f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t down_dist = std::abs(incumbent[j] - std::floor(solution[j])); - f_t up_dist = std::abs(std::ceil(solution[j]) - incumbent[j]); - rounding_direction_t dir = - down_dist < up_dist + eps ? rounding_direction_t::DOWN : rounding_direction_t::UP; - - f_t pc_down = pc.pseudo_cost_num_down[j] != 0 - ? pc.pseudo_cost_sum_down[j] / pc.pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - - f_t pc_up = pc.pseudo_cost_num_up[j] != 0 ? pc.pseudo_cost_sum_up[j] / pc.pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - - f_t score1 = dir == rounding_direction_t::DOWN ? 5 * pc_down * f_down : 5 * pc_up * f_up; - f_t score2 = dir == rounding_direction_t::DOWN ? pc_up * f_up : pc_down * f_down; - f_t score = (score1 + score2) / 6; - - if (score > max_score) { - max_score = score; - branch_var = j; - round_dir = dir; - } - } - - assert(round_dir != rounding_direction_t::NONE); - assert(branch_var >= 0); - - log.debug("Guided diving: selected var %d with val = %e, round dir = %d and score = %e\n", - branch_var, - solution[branch_var], - round_dir, - max_score); - - return {branch_var, round_dir}; + return guided_diving_from_arrays((const f_t*)pc.pseudo_cost_sum_down.data(), + (const f_t*)pc.pseudo_cost_sum_up.data(), + (const i_t*)pc.pseudo_cost_num_down.data(), + (const i_t*)pc.pseudo_cost_num_up.data(), + (i_t)pc.pseudo_cost_sum_down.size(), + fractional, + solution, + incumbent); } template diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 26effef3a..26975d9fe 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -455,19 +455,11 @@ void pseudo_costs_t::initialized(i_t& num_initialized_down, f_t& pseudo_cost_up_avg) { // Count initialized variables while computing averages - num_initialized_down = 0; - num_initialized_up = 0; - const i_t n = pseudo_cost_sum_down.size(); - for (i_t j = 0; j < n; j++) { - if (pseudo_cost_num_down[j] > 0) { num_initialized_down++; } - if (pseudo_cost_num_up[j] > 0) { num_initialized_up++; } - } - - auto avgs = compute_pseudo_cost_averages(pseudo_cost_sum_down.data(), - pseudo_cost_sum_up.data(), - pseudo_cost_num_down.data(), - pseudo_cost_num_up.data(), - n); + auto avgs = compute_pseudo_cost_averages((const f_t*)pseudo_cost_sum_down.data(), + (const f_t*)pseudo_cost_sum_up.data(), + (const i_t*)pseudo_cost_num_down.data(), + (const i_t*)pseudo_cost_num_up.data(), + (i_t)pseudo_cost_sum_down.size()); pseudo_cost_down_avg = avgs.down_avg; pseudo_cost_up_avg = avgs.up_avg; } diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index a31aea660..2f1f1fa7f 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -33,8 +33,6 @@ struct pseudo_cost_averages_t { f_t up_avg; }; -// Compute average pseudo-costs from arrays -// Works with either pseudo_costs_t members or snapshot arrays template pseudo_cost_averages_t compute_pseudo_cost_averages( const f_t* pc_sum_down, const f_t* pc_sum_up, const i_t* pc_num_down, const i_t* pc_num_up, i_t n) From faa4952dc9c199e768edbceb012e404a03acae89 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 3 Feb 2026 14:42:27 +0000 Subject: [PATCH 334/366] fix bsp deadlock on timelimit --- cpp/src/dual_simplex/branch_and_bound.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 291f4aeeb..637ff7d4a 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2263,7 +2263,6 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bsp_bfs_worker_t< } double remaining_time = settings_.time_limit - toc(exploration_stats_.start_time); - if (remaining_time <= 0) { return node_solve_info_t::TIME_LIMIT; } // Bounds strengthening simplex_solver_settings_t lp_settings = settings_; From e23c16c481eb65a32c3ed84887d57c0d667cd40d Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 3 Feb 2026 15:53:06 +0000 Subject: [PATCH 335/366] unify update_tree --- cpp/src/dual_simplex/branch_and_bound.cpp | 164 +++++++--------------- cpp/src/dual_simplex/branch_and_bound.hpp | 10 -- 2 files changed, 52 insertions(+), 122 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 637ff7d4a..1777e3d2b 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -686,7 +686,7 @@ branch_variable_t branch_and_bound_t::variable_selection( template struct opportunistic_tree_update_policy_t { branch_and_bound_t& bnb; - bnb_worker_type_t thread_type; + bnb_worker_data_t* worker_data; logger_t& log; f_t upper_bound() const { return bnb.upper_bound_.load(); } @@ -698,19 +698,30 @@ struct opportunistic_tree_update_policy_t { void handle_integer_solution(mip_node_t* node, f_t obj, const std::vector& x) { - bnb.add_feasible_solution(obj, x, node->depth, thread_type); + bnb.add_feasible_solution(obj, x, node->depth, worker_data->worker_type); } branch_variable_t select_branch_variable(mip_node_t* node, const std::vector& fractional, - const std::vector& x) + const std::vector&) + { + return bnb.variable_selection(node, fractional, worker_data); + } + + void update_objective_estimate(mip_node_t* node, + const std::vector& fractional, + const std::vector& x) { - return bnb.variable_selection(node, fractional, x, thread_type); + if (worker_data->worker_type == bnb_worker_type_t::BEST_FIRST) { + logger_t pc_log; + pc_log.log = false; + node->objective_estimate = bnb.pc_.obj_estimate(fractional, x, node->lower_bound, pc_log); + } } void on_numerical_issue(mip_node_t* node) { - if (thread_type == bnb_worker_type_t::BEST_FIRST) { + if (worker_data->worker_type == bnb_worker_type_t::BEST_FIRST) { fetch_min(bnb.lower_bound_ceiling_, node->lower_bound); log.printf("LP returned numerical issue on node %d. Best bound set to %+10.6e.\n", node->node_id, @@ -728,7 +739,7 @@ struct opportunistic_tree_update_policy_t { void on_optimal_callback(const std::vector& x, f_t objective) { - if (thread_type == bnb_worker_type_t::BEST_FIRST && + if (worker_data->worker_type == bnb_worker_type_t::BEST_FIRST && bnb.settings_.node_processed_callback != nullptr) { std::vector original_x; uncrush_primal_solution(bnb.original_problem_, bnb.original_lp_, x, original_x); @@ -776,6 +787,15 @@ struct bsp_tree_update_policy_t { return {var, dir}; } + void update_objective_estimate(mip_node_t* node, + const std::vector& fractional, + const std::vector& x) + { + logger_t log; + log.log = false; + node->objective_estimate = bnb.pc_.obj_estimate(fractional, x, node->lower_bound, log); + } + void on_node_completed(mip_node_t* node, node_status_t status, rounding_direction_t dir) { switch (status) { @@ -862,11 +882,14 @@ std::pair update_tree_impl( policy.select_branch_variable(node_ptr, leaf_fractional, leaf_solution.x); round_dir = dir; + assert(node_ptr->vstatus.size() == leaf_problem.num_cols); + assert(branch_var >= 0); + assert(dir != rounding_direction_t::NONE); + + policy.update_objective_estimate(node_ptr, leaf_fractional, leaf_solution.x); + logger_t log; log.log = false; - node_ptr->objective_estimate = - pc.obj_estimate(leaf_fractional, leaf_solution.x, node_ptr->lower_bound, log); - search_tree.branch( node_ptr, branch_var, leaf_solution.x[branch_var], node_ptr->vstatus, leaf_problem, log); search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); @@ -990,106 +1013,6 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* return lp_status; } -template -std::pair branch_and_bound_t::update_tree( - mip_node_t* node_ptr, - search_tree_t& search_tree, - bnb_worker_data_t* worker_data, - dual::status_t lp_status, - logger_t& log) -{ - const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; - std::vector& leaf_vstatus = node_ptr->vstatus; - lp_problem_t& leaf_problem = worker_data->leaf_problem; - lp_solution_t& leaf_solution = worker_data->leaf_solution; - - if (lp_status == dual::status_t::DUAL_UNBOUNDED) { - // Node was infeasible. Do not branch - node_ptr->lower_bound = inf; - search_tree.graphviz_node(log, node_ptr, "infeasible", 0.0); - search_tree.update(node_ptr, node_status_t::INFEASIBLE); - return {node_status_t::INFEASIBLE, rounding_direction_t::NONE}; - - } else if (lp_status == dual::status_t::CUTOFF) { - // Node was cut off. Do not branch - node_ptr->lower_bound = upper_bound_; - f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); - search_tree.graphviz_node(log, node_ptr, "cut off", leaf_objective); - search_tree.update(node_ptr, node_status_t::FATHOMED); - return {node_status_t::FATHOMED, rounding_direction_t::NONE}; - - } else if (lp_status == dual::status_t::OPTIMAL) { - // LP was feasible - std::vector leaf_fractional; - i_t leaf_num_fractional = - fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - - f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); - node_ptr->lower_bound = leaf_objective; - search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); - pc_.update_pseudo_costs(node_ptr, leaf_objective); - - if (worker_data->worker_type == bnb_worker_type_t::BEST_FIRST) { - if (settings_.node_processed_callback != nullptr) { - std::vector original_x; - uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); - settings_.node_processed_callback(original_x, leaf_objective); - } - } - - if (leaf_num_fractional == 0) { - // Found a integer feasible solution - add_feasible_solution( - leaf_objective, leaf_solution.x, node_ptr->depth, worker_data->worker_type); - search_tree.graphviz_node(log, node_ptr, "integer feasible", leaf_objective); - search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); - return {node_status_t::INTEGER_FEASIBLE, rounding_direction_t::NONE}; - - } else if (leaf_objective <= upper_bound_ + abs_fathom_tol) { - // Choose fractional variable to branch on - auto [branch_var, round_dir] = variable_selection(node_ptr, leaf_fractional, worker_data); - - assert(leaf_vstatus.size() == leaf_problem.num_cols); - assert(branch_var >= 0); - assert(round_dir != rounding_direction_t::NONE); - - // Note that the exploration thread is the only one that can insert new nodes into the heap, - // and thus, we only need to calculate the objective estimate here (it is used for - // sorting the nodes for diving). - if (worker_data->worker_type == bnb_worker_type_t::BEST_FIRST) { - logger_t pc_log; - pc_log.log = false; - node_ptr->objective_estimate = - pc_.obj_estimate(leaf_fractional, leaf_solution.x, node_ptr->lower_bound, pc_log); - } - - search_tree.branch( - node_ptr, branch_var, leaf_solution.x[branch_var], leaf_vstatus, leaf_problem, log); - search_tree.update(node_ptr, node_status_t::HAS_CHILDREN); - return {node_status_t::HAS_CHILDREN, round_dir}; - - } else { - search_tree.graphviz_node(log, node_ptr, "fathomed", leaf_objective); - search_tree.update(node_ptr, node_status_t::FATHOMED); - return {node_status_t::FATHOMED, rounding_direction_t::NONE}; - } - } else { - if (worker_data->worker_type == bnb_worker_type_t::BEST_FIRST) { - fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); - log.printf( - "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " - "to " - "%+10.6e.\n", - lp_status, - node_ptr->node_id, - compute_user_objective(original_lp_, lower_bound_ceiling_.load())); - } - - search_tree.graphviz_node(log, node_ptr, "numerical", 0.0); - search_tree.update(node_ptr, node_status_t::NUMERICAL); - return {node_status_t::NUMERICAL, rounding_direction_t::NONE}; - } -} template void branch_and_bound_t::plunge_with(bnb_worker_data_t* worker_data, @@ -1149,8 +1072,16 @@ void branch_and_bound_t::plunge_with(bnb_worker_data_t* work ++exploration_stats_.nodes_explored; --exploration_stats_.nodes_unexplored; - auto [node_status, round_dir] = - update_tree(node_ptr, search_tree_, worker_data, lp_status, settings_.log); + opportunistic_tree_update_policy_t policy{*this, worker_data, settings_.log}; + auto [node_status, round_dir] = update_tree_impl(node_ptr, + search_tree_, + worker_data->leaf_problem, + worker_data->leaf_solution, + var_types_, + settings_, + pc_, + lp_status, + policy); worker_data->recompute_basis = node_status != node_status_t::HAS_CHILDREN; worker_data->recompute_bounds = node_status != node_status_t::HAS_CHILDREN; @@ -1247,7 +1178,16 @@ void branch_and_bound_t::dive_with(bnb_worker_data_t* worker ++dive_stats.nodes_explored; - auto [node_status, round_dir] = update_tree(node_ptr, dive_tree, worker_data, lp_status, log); + opportunistic_tree_update_policy_t policy{*this, worker_data, log}; + auto [node_status, round_dir] = update_tree_impl(node_ptr, + dive_tree, + worker_data->leaf_problem, + worker_data->leaf_solution, + var_types_, + settings_, + pc_, + lp_status, + policy); worker_data->recompute_basis = node_status != node_status_t::HAS_CHILDREN; worker_data->recompute_bounds = node_status != node_status_t::HAS_CHILDREN; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 03fb24dca..d0973240c 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -253,16 +253,6 @@ class branch_and_bound_t { bnb_stats_t& stats, logger_t& log); - // Update the tree based on the LP relaxation. Returns the status - // of the node and, if appropriated, the preferred rounding direction - // when visiting the children. - std::pair update_tree( - mip_node_t* node_ptr, - search_tree_t& search_tree, - bnb_worker_data_t* worker_data, - dual::status_t lp_status, - logger_t& log); - // Selects the variable to branch on. branch_variable_t variable_selection(mip_node_t* node_ptr, const std::vector& fractional, From 222cda0a1b0832fc41469fbbc0bfa55218d0a2a0 Mon Sep 17 00:00:00 2001 From: nicolas Date: Tue, 3 Feb 2026 17:35:05 +0100 Subject: [PATCH 336/366] fix pseudocost update --- cpp/src/dual_simplex/branch_and_bound.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index c47700a03..946d6b779 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -748,10 +748,10 @@ std::pair branch_and_bound_t::upd i_t leaf_num_fractional = fractional_variables(settings_, leaf_solution.x, var_types_, leaf_fractional); - f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); - node_ptr->lower_bound = leaf_objective; + f_t leaf_objective = compute_objective(leaf_problem, leaf_solution.x); search_tree.graphviz_node(log, node_ptr, "lower bound", leaf_objective); pc_.update_pseudo_costs(node_ptr, leaf_objective); + node_ptr->lower_bound = leaf_objective; if (worker_data->worker_type == bnb_worker_type_t::BEST_FIRST) { if (settings_.node_processed_callback != nullptr) { From cf2f5776563f44b3ae2131d81bee252be8341376 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Tue, 3 Feb 2026 17:21:55 +0000 Subject: [PATCH 337/366] more cleanup --- cpp/src/dual_simplex/bb_event.hpp | 9 +- cpp/src/dual_simplex/bb_worker_state.hpp | 526 ++++++++++++++++++ cpp/src/dual_simplex/branch_and_bound.cpp | 272 ++++----- cpp/src/dual_simplex/branch_and_bound.hpp | 8 +- .../dual_simplex/deterministic_workers.hpp | 20 +- 5 files changed, 660 insertions(+), 175 deletions(-) create mode 100644 cpp/src/dual_simplex/bb_worker_state.hpp diff --git a/cpp/src/dual_simplex/bb_event.hpp b/cpp/src/dual_simplex/bb_event.hpp index 284371be3..327fde76b 100644 --- a/cpp/src/dual_simplex/bb_event.hpp +++ b/cpp/src/dual_simplex/bb_event.hpp @@ -142,15 +142,8 @@ struct bb_event_t { template struct bb_event_batch_t { std::vector> events; - double horizon_start; - double horizon_end; - void clear() - { - events.clear(); - horizon_start = 0.0; - horizon_end = 0.0; - } + void clear() { events.clear(); } void add(bb_event_t event) { events.push_back(std::move(event)); } diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp new file mode 100644 index 000000000..3fdefb765 --- /dev/null +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -0,0 +1,526 @@ +/* clang-format off */ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + */ +/* clang-format on */ + +#pragma once + +#include +#include +#include +#include +#include + +#include + +#include +#include +#include +#include +#include +#include + +namespace cuopt::linear_programming::dual_simplex { + +template +struct backlog_node_compare_t { + bool operator()(const mip_node_t* a, const mip_node_t* b) const + { + if (a->lower_bound != b->lower_bound) { return a->lower_bound > b->lower_bound; } + if (a->origin_worker_id != b->origin_worker_id) { + return a->origin_worker_id > b->origin_worker_id; + } + return a->creation_seq > b->creation_seq; + } +}; + +template +struct pseudo_cost_update_t { + i_t variable; + rounding_direction_t direction; + f_t delta; + double wut; + int worker_id; + + bool operator<(const pseudo_cost_update_t& other) const + { + if (wut != other.wut) return wut < other.wut; + if (variable != other.variable) return variable < other.variable; + if (delta != other.delta) return delta < other.delta; + return worker_id < other.worker_id; + } +}; + +template +struct queued_integer_solution_t { + f_t objective; + std::vector solution; + i_t depth; + int worker_id; + int sequence_id; + + bool operator<(const queued_integer_solution_t& other) const + { + if (objective != other.objective) return objective < other.objective; + if (worker_id != other.worker_id) return worker_id < other.worker_id; + return sequence_id < other.sequence_id; + } +}; + +template +class bsp_worker_base_t : public bnb_worker_data_t { + using base_t = bnb_worker_data_t; + + public: + double clock{0.0}; + double horizon_start{0.0}; + double horizon_end{0.0}; + work_limit_context_t work_context; + + // Local snapshots of global state + std::vector pc_sum_up_snapshot; + std::vector pc_sum_down_snapshot; + std::vector pc_num_up_snapshot; + std::vector pc_num_down_snapshot; + f_t local_upper_bound{std::numeric_limits::infinity()}; + + // Diving-specific snapshots (ignored by BFS workers) + std::vector incumbent_snapshot; + i_t total_lp_iters_snapshot{0}; + + std::vector> integer_solutions; + std::vector> pseudo_cost_updates; + int next_solution_seq{0}; + + i_t total_nodes_processed{0}; + i_t total_integer_solutions{0}; + double total_runtime{0.0}; + double total_nowork_time{0.0}; + + bsp_worker_base_t(int id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::string& context_name) + : base_t(id, original_lp, Arow, var_types, settings), work_context(context_name) + { + work_context.deterministic = true; + } + + void set_snapshots(f_t global_upper_bound, + const std::vector& pc_sum_up, + const std::vector& pc_sum_down, + const std::vector& pc_num_up, + const std::vector& pc_num_down, + const std::vector& incumbent, + i_t total_lp_iters, + double new_horizon_start, + double new_horizon_end) + { + local_upper_bound = global_upper_bound; + pc_sum_up_snapshot = pc_sum_up; + pc_sum_down_snapshot = pc_sum_down; + pc_num_up_snapshot = pc_num_up; + pc_num_down_snapshot = pc_num_down; + incumbent_snapshot = incumbent; + total_lp_iters_snapshot = total_lp_iters; + horizon_start = new_horizon_start; + horizon_end = new_horizon_end; + } + + // Queue pseudo-cost update and apply to local snapshot + void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) + { + pseudo_cost_updates.push_back({variable, direction, delta, clock, this->worker_id}); + if (direction == rounding_direction_t::DOWN) { + pc_sum_down_snapshot[variable] += delta; + pc_num_down_snapshot[variable]++; + } else { + pc_sum_up_snapshot[variable] += delta; + pc_num_up_snapshot[variable]++; + } + } + + // Basic variable selection from snapshots + i_t variable_selection_from_snapshot(const std::vector& fractional, + const std::vector& solution) const + { + return variable_selection_from_pseudo_costs(pc_sum_down_snapshot.data(), + pc_sum_up_snapshot.data(), + pc_num_down_snapshot.data(), + pc_num_up_snapshot.data(), + (i_t)pc_sum_down_snapshot.size(), + fractional, + solution); + } + + bool has_work() const { return static_cast(this)->has_work_impl(); } +}; + +template +class bsp_bfs_worker_t : public bsp_worker_base_t> { + using base_t = bsp_worker_base_t>; + + public: + // Node management + std::deque*> plunge_stack; + heap_t*, backlog_node_compare_t> backlog; + mip_node_t* current_node{nullptr}; + mip_node_t* last_solved_node{nullptr}; + + // Event logging for deterministic replay + bb_event_batch_t events; + int event_sequence{0}; + int32_t next_creation_seq{0}; + + // BFS-specific state + f_t local_lower_bound_ceiling{std::numeric_limits::infinity()}; + bool recompute_bounds_and_basis{true}; + i_t nodes_processed_this_horizon{0}; + + // BFS statistics + i_t total_nodes_pruned{0}; + i_t total_nodes_branched{0}; + i_t total_nodes_infeasible{0}; + i_t total_nodes_assigned{0}; + + explicit bsp_bfs_worker_t(int id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings) + : base_t(id, original_lp, Arow, var_types, settings, "BB_Worker_" + std::to_string(id)) + { + } + + bool has_work_impl() const + { + return current_node != nullptr || !plunge_stack.empty() || !backlog.empty(); + } + + void enqueue_node(mip_node_t* node) + { + plunge_stack.push_front(node); + ++total_nodes_assigned; + } + + mip_node_t* enqueue_children_for_plunge(mip_node_t* down_child, + mip_node_t* up_child, + rounding_direction_t preferred_direction) + { + if (!plunge_stack.empty()) { + backlog.push(plunge_stack.back()); + plunge_stack.pop_back(); + } + + down_child->origin_worker_id = this->worker_id; + down_child->creation_seq = next_creation_seq++; + up_child->origin_worker_id = this->worker_id; + up_child->creation_seq = next_creation_seq++; + + mip_node_t* first_child; + if (preferred_direction == rounding_direction_t::UP) { + plunge_stack.push_front(down_child); + plunge_stack.push_front(up_child); + first_child = up_child; + } else { + plunge_stack.push_front(up_child); + plunge_stack.push_front(down_child); + first_child = down_child; + } + return first_child; + } + + mip_node_t* dequeue_node() + { + if (current_node != nullptr) { + mip_node_t* node = current_node; + current_node = nullptr; + return node; + } + if (!plunge_stack.empty()) { + mip_node_t* node = plunge_stack.front(); + plunge_stack.pop_front(); + return node; + } + auto node_opt = backlog.pop(); + return node_opt.has_value() ? node_opt.value() : nullptr; + } + + size_t queue_size() const + { + return plunge_stack.size() + backlog.size() + (current_node != nullptr ? 1 : 0); + } + + void record_event(bb_event_t event) + { + event.event_sequence = event_sequence++; + events.add(std::move(event)); + } + + void record_branched( + mip_node_t* node, i_t down_child_id, i_t up_child_id, i_t branch_var, f_t branch_val) + { + record_event(bb_event_t::make_branched(this->clock, + this->worker_id, + node->node_id, + 0, + down_child_id, + up_child_id, + node->lower_bound, + branch_var, + branch_val)); + ++nodes_processed_this_horizon; + ++this->total_nodes_processed; + ++total_nodes_branched; + } + + void record_integer_solution(mip_node_t* node, f_t objective) + { + record_event(bb_event_t::make_integer_solution( + this->clock, this->worker_id, node->node_id, 0, objective)); + ++nodes_processed_this_horizon; + ++this->total_nodes_processed; + ++this->total_integer_solutions; + } + + void record_fathomed(mip_node_t* node, f_t lower_bound) + { + record_event(bb_event_t::make_fathomed( + this->clock, this->worker_id, node->node_id, 0, lower_bound)); + ++nodes_processed_this_horizon; + ++this->total_nodes_processed; + ++total_nodes_pruned; + } + + void record_infeasible(mip_node_t* node) + { + record_event( + bb_event_t::make_infeasible(this->clock, this->worker_id, node->node_id, 0)); + ++nodes_processed_this_horizon; + ++this->total_nodes_processed; + ++total_nodes_infeasible; + } + + void record_numerical(mip_node_t* node) + { + record_event( + bb_event_t::make_numerical(this->clock, this->worker_id, node->node_id, 0)); + ++nodes_processed_this_horizon; + ++this->total_nodes_processed; + } +}; + +template +class bsp_diving_worker_t : public bsp_worker_base_t> { + using base_t = bsp_worker_base_t>; + + public: + bnb_worker_type_t diving_type{bnb_worker_type_t::PSEUDOCOST_DIVING}; + + // Diving-specific node management + std::deque> dive_queue; + std::vector dive_lower; + std::vector dive_upper; + + // Root LP relaxation solution (constant, set once at construction) + const std::vector* root_solution{nullptr}; + + // Diving state + bool recompute_bounds_and_basis{true}; + + // Diving statistics + i_t total_nodes_explored{0}; + i_t total_dives{0}; + i_t lp_iters_this_dive{0}; + + explicit bsp_diving_worker_t(int id, + bnb_worker_type_t type, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::vector* root_sol) + : base_t(id, original_lp, Arow, var_types, settings, "Diving_Worker_" + std::to_string(id)), + diving_type(type), + root_solution(root_sol) + { + dive_lower = original_lp.lower; + dive_upper = original_lp.upper; + } + + bsp_diving_worker_t(const bsp_diving_worker_t&) = delete; + bsp_diving_worker_t& operator=(const bsp_diving_worker_t&) = delete; + bsp_diving_worker_t(bsp_diving_worker_t&&) = default; + bsp_diving_worker_t& operator=(bsp_diving_worker_t&&) = default; + + bool has_work_impl() const { return !dive_queue.empty(); } + + void enqueue_dive_node(mip_node_t* node) { dive_queue.push_back(node->detach_copy()); } + + std::optional> dequeue_dive_node() + { + if (dive_queue.empty()) return std::nullopt; + auto node = std::move(dive_queue.front()); + dive_queue.pop_front(); + ++total_dives; + return node; + } + + size_t dive_queue_size() const { return dive_queue.size(); } + size_t queue_size() const { return dive_queue_size(); } // Unified interface for pool + + void queue_integer_solution(f_t objective, const std::vector& solution, i_t depth) + { + this->integer_solutions.push_back( + {objective, solution, depth, this->worker_id, this->next_solution_seq++}); + ++this->total_integer_solutions; + } + + branch_variable_t variable_selection_from_snapshot(const std::vector& fractional, + const std::vector& solution) const + { + const std::vector& root_sol = (root_solution != nullptr) ? *root_solution : solution; + return pseudocost_diving_from_arrays(this->pc_sum_down_snapshot.data(), + this->pc_sum_up_snapshot.data(), + this->pc_num_down_snapshot.data(), + this->pc_num_up_snapshot.data(), + (i_t)this->pc_sum_down_snapshot.size(), + fractional, + solution, + root_sol); + } + + branch_variable_t guided_variable_selection(const std::vector& fractional, + const std::vector& solution) const + { + if (this->incumbent_snapshot.empty()) { + return variable_selection_from_snapshot(fractional, solution); + } + return guided_diving_from_arrays(this->pc_sum_down_snapshot.data(), + this->pc_sum_up_snapshot.data(), + this->pc_num_down_snapshot.data(), + this->pc_num_up_snapshot.data(), + (i_t)this->pc_sum_down_snapshot.size(), + fractional, + solution, + this->incumbent_snapshot); + } +}; + +// ============================================================================ +// CRTP Base class for BSP worker pools +// ============================================================================ +template +class bsp_worker_pool_base_t { + protected: + std::vector workers_; + + public: + WorkerT& operator[](int worker_id) { return workers_[worker_id]; } + const WorkerT& operator[](int worker_id) const { return workers_[worker_id]; } + int size() const { return static_cast(workers_.size()); } + + bool any_has_work() const + { + for (const auto& worker : workers_) { + if (worker.has_work()) return true; + } + return false; + } + + size_t total_queue_size() const + { + size_t total = 0; + for (const auto& worker : workers_) { + total += worker.queue_size(); + } + return total; + } + + bb_event_batch_t collect_and_sort_events() + { + bb_event_batch_t all_events; + for (auto& worker : workers_) { + static_cast(this)->collect_worker_events(worker, all_events); + } + all_events.sort_for_replay(); + return all_events; + } + + auto begin() { return workers_.begin(); } + auto end() { return workers_.end(); } + auto begin() const { return workers_.begin(); } + auto end() const { return workers_.end(); } +}; + +// ============================================================================ +// BSP BFS Worker Pool +// ============================================================================ +template +class bsp_bfs_worker_pool_t : public bsp_worker_pool_base_t, + bsp_bfs_worker_pool_t> { + using base_t = + bsp_worker_pool_base_t, bsp_bfs_worker_pool_t>; + + public: + bsp_bfs_worker_pool_t(int num_workers, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings) + { + this->workers_.reserve(num_workers); + for (int i = 0; i < num_workers; ++i) { + this->workers_.emplace_back(i, original_lp, Arow, var_types, settings); + } + } + + void collect_worker_events(bsp_bfs_worker_t& worker, + bb_event_batch_t& all_events) + { + for (auto& event : worker.events.events) { + all_events.add(std::move(event)); + } + worker.events.clear(); + } +}; + +// ============================================================================ +// BSP Diving Worker Pool +// ============================================================================ +template +class bsp_diving_worker_pool_t : public bsp_worker_pool_base_t, + bsp_diving_worker_pool_t> { + using base_t = bsp_worker_pool_base_t, + bsp_diving_worker_pool_t>; + + public: + bsp_diving_worker_pool_t(int num_workers, + const std::vector& diving_types, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::vector* root_solution) + { + this->workers_.reserve(num_workers); + for (int i = 0; i < num_workers; ++i) { + bnb_worker_type_t type = diving_types[i % diving_types.size()]; + this->workers_.emplace_back(i, type, original_lp, Arow, var_types, settings, root_solution); + } + } + + void collect_worker_events(bsp_diving_worker_t&, bb_event_batch_t&) {} +}; + +} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 1777e3d2b..834a19aa0 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1826,7 +1826,7 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t>( + bsp_workers_ = std::make_unique>( num_bfs_workers, original_lp_, Arow, var_types_, settings_); if (num_diving_workers > 0) { @@ -2061,6 +2061,17 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) collect_diving_solutions(); + for (auto& worker : *bsp_workers_) { + worker.integer_solutions.clear(); + worker.pseudo_cost_updates.clear(); + } + if (bsp_diving_workers_) { + for (auto& worker : *bsp_diving_workers_) { + worker.integer_solutions.clear(); + worker.pseudo_cost_updates.clear(); + } + } + populate_diving_heap(); assign_diving_nodes(); @@ -2305,6 +2316,97 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bsp_bfs_worker_t< return node_solve_info_t::NO_CHILDREN; } +template +template +void branch_and_bound_t::process_worker_solutions(PoolT& pool, + WorkerTypeGetter get_worker_type) +{ + std::vector*> all_solutions; + for (auto& worker : pool) { + for (auto& sol : worker.integer_solutions) { + all_solutions.push_back(&sol); + } + } + + std::sort(all_solutions.begin(), + all_solutions.end(), + [](const queued_integer_solution_t* a, + const queued_integer_solution_t* b) { return *a < *b; }); + + f_t bsp_lower = compute_bsp_lower_bound(); + f_t current_upper = upper_bound_.load(); + + for (const auto* sol : all_solutions) { + if (sol->objective < current_upper) { + f_t user_obj = compute_user_objective(original_lp_, sol->objective); + f_t user_lower = compute_user_objective(original_lp_, bsp_lower); + i_t nodes_explored = exploration_stats_.nodes_explored.load(); + i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); + + bnb_worker_type_t worker_type = get_worker_type(pool, sol->worker_id); + + settings_.log.printf("%c %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", + feasible_solution_symbol(worker_type), + nodes_explored, + nodes_unexplored, + user_obj, + user_lower, + sol->depth, + nodes_explored > 0 + ? (double)exploration_stats_.total_lp_iters.load() / nodes_explored + : 0.0, + user_mip_gap(user_obj, user_lower).c_str(), + toc(exploration_stats_.start_time)); + + bool improved = false; + if (sol->objective < upper_bound_) { + upper_bound_ = sol->objective; + incumbent_.set_incumbent_solution(sol->objective, sol->solution); + current_upper = sol->objective; + improved = true; + } + + if (improved && settings_.solution_callback != nullptr) { + std::vector original_x; + uncrush_primal_solution(original_problem_, original_lp_, sol->solution, original_x); + settings_.solution_callback(original_x, sol->objective); + } + } + } + + for (auto& worker : pool) { + worker.integer_solutions.clear(); + } +} + +template +template +void branch_and_bound_t::merge_pseudo_cost_updates(PoolT& pool) +{ + std::vector> all_pc_updates; + for (auto& worker : pool) { + for (auto& upd : worker.pseudo_cost_updates) { + all_pc_updates.push_back(upd); + } + } + + std::sort(all_pc_updates.begin(), all_pc_updates.end()); + + for (const auto& upd : all_pc_updates) { + if (upd.direction == rounding_direction_t::DOWN) { + pc_.pseudo_cost_sum_down[upd.variable] += upd.delta; + pc_.pseudo_cost_num_down[upd.variable]++; + } else { + pc_.pseudo_cost_sum_up[upd.variable] += upd.delta; + pc_.pseudo_cost_num_up[upd.variable]++; + } + } + + for (auto& worker : pool) { + worker.pseudo_cost_updates.clear(); + } +} + template void branch_and_bound_t::sort_replay_events(const bb_event_batch_t& events) { @@ -2439,79 +2541,13 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t*> all_integer_solutions; - for (auto& worker : *bsp_workers_) { - for (auto& sol : worker.integer_solutions) { - all_integer_solutions.push_back(&sol); - } - } - - // Sort solutions for deterministic processing order (uses built-in operator<) - std::sort(all_integer_solutions.begin(), - all_integer_solutions.end(), - [](const queued_integer_solution_t* a, - const queued_integer_solution_t* b) { return *a < *b; }); - - f_t bsp_lower = compute_bsp_lower_bound(); - f_t current_upper = upper_bound_.load(); - - for (const auto* sol : all_integer_solutions) { - // improving solution found, log it - if (sol->objective < current_upper) { - f_t user_obj = compute_user_objective(original_lp_, sol->objective); - f_t user_lower = compute_user_objective(original_lp_, bsp_lower); - i_t nodes_explored = exploration_stats_.nodes_explored.load(); - i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); - settings_.log.printf( - "%c %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - feasible_solution_symbol(bnb_worker_type_t::BEST_FIRST), - nodes_explored, - nodes_unexplored, - user_obj, - user_lower, - sol->depth, - nodes_explored > 0 ? exploration_stats_.total_lp_iters.load() / nodes_explored : 0.0, - user_mip_gap(user_obj, user_lower).c_str(), - toc(exploration_stats_.start_time)); - - // Update incumbent - bool improved = false; - if (sol->objective < upper_bound_) { - upper_bound_ = sol->objective; - incumbent_.set_incumbent_solution(sol->objective, sol->solution); - current_upper = sol->objective; - improved = true; - } - - // Notify diversity manager of new incumbent - if (improved && settings_.solution_callback != nullptr) { - std::vector original_x; - uncrush_primal_solution(original_problem_, original_lp_, sol->solution, original_x); - settings_.solution_callback(original_x, sol->objective); - } - } - } - - // Merge and apply pseudo-cost updates from all workers in deterministic order - std::vector> all_pc_updates; - for (auto& worker : *bsp_workers_) { - for (auto& upd : worker.pseudo_cost_updates) { - all_pc_updates.push_back(upd); - } - } - - std::sort(all_pc_updates.begin(), all_pc_updates.end()); + // Merge integer solutions from BFS workers and update global incumbent + process_worker_solutions(*bsp_workers_, [](const bsp_bfs_worker_pool_t&, int) { + return bnb_worker_type_t::BEST_FIRST; + }); - for (const auto& upd : all_pc_updates) { - if (upd.direction == rounding_direction_t::DOWN) { - pc_.pseudo_cost_sum_down[upd.variable] += upd.delta; - pc_.pseudo_cost_num_down[upd.variable]++; - } else { - pc_.pseudo_cost_sum_up[upd.variable] += upd.delta; - pc_.pseudo_cost_num_up[upd.variable]++; - } - } + // Merge and apply pseudo-cost updates from BFS workers + merge_pseudo_cost_updates(*bsp_workers_); for (const auto& worker : *bsp_workers_) { fetch_min(lower_bound_ceiling_, worker.local_lower_bound_ceiling); @@ -2748,90 +2784,14 @@ void branch_and_bound_t::collect_diving_solutions() { if (!bsp_diving_workers_) return; - // Collect all integer solutions from diving workers - std::vector*> all_solutions; - for (auto& worker : *bsp_diving_workers_) { - for (auto& sol : worker.integer_solutions) { - all_solutions.push_back(&sol); - } - } - - // Sort solutions for deterministic processing order (uses built-in operator<) - std::sort(all_solutions.begin(), - all_solutions.end(), - [](const queued_integer_solution_t* a, - const queued_integer_solution_t* b) { return *a < *b; }); - - // Apply improving solutions to incumbent - f_t current_upper = upper_bound_.load(); - for (const auto* sol : all_solutions) { - if (sol->objective < current_upper) { - f_t user_obj = compute_user_objective(original_lp_, sol->objective); - f_t bsp_lower = compute_bsp_lower_bound(); - f_t user_lower = compute_user_objective(original_lp_, bsp_lower); - i_t nodes_explored = exploration_stats_.nodes_explored.load(); - i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); - - // Get diving type from worker for proper symbol - bnb_worker_type_t diving_type = (*bsp_diving_workers_)[sol->worker_id].diving_type; - - settings_.log.printf("%c %10d %10d %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - feasible_solution_symbol(diving_type), - nodes_explored, - nodes_unexplored, - user_obj, - user_lower, - sol->depth, - nodes_explored > 0 - ? (double)exploration_stats_.total_lp_iters.load() / nodes_explored - : 0.0, - user_mip_gap(user_obj, user_lower).c_str(), - toc(exploration_stats_.start_time)); - - bool improved = false; - mutex_upper_.lock(); - if (sol->objective < upper_bound_) { - upper_bound_ = sol->objective; - incumbent_.set_incumbent_solution(sol->objective, sol->solution); - current_upper = sol->objective; - improved = true; - } - mutex_upper_.unlock(); + // Collect integer solutions from diving workers and update global incumbent + process_worker_solutions(*bsp_diving_workers_, + [](const bsp_diving_worker_pool_t& pool, int worker_id) { + return pool[worker_id].diving_type; + }); - // Notify diversity manager of new incumbent - if (improved && settings_.solution_callback != nullptr) { - std::vector original_x; - uncrush_primal_solution(original_problem_, original_lp_, sol->solution, original_x); - settings_.solution_callback(original_x, sol->objective); - } - } - } - - // Merge pseudo-cost updates from diving workers in deterministic order - std::vector> all_diving_pc_updates; - for (auto& worker : *bsp_diving_workers_) { - for (auto& upd : worker.pseudo_cost_updates) { - all_diving_pc_updates.push_back(upd); - } - } - - std::sort(all_diving_pc_updates.begin(), all_diving_pc_updates.end()); - - for (const auto& upd : all_diving_pc_updates) { - if (upd.direction == rounding_direction_t::DOWN) { - pc_.pseudo_cost_sum_down[upd.variable] += upd.delta; - pc_.pseudo_cost_num_down[upd.variable]++; - } else { - pc_.pseudo_cost_sum_up[upd.variable] += upd.delta; - pc_.pseudo_cost_num_up[upd.variable]++; - } - } - - // Clear solution and pseudo-cost update queues - for (auto& worker : *bsp_diving_workers_) { - worker.integer_solutions.clear(); - worker.pseudo_cost_updates.clear(); - } + // Merge pseudo-cost updates from diving workers + merge_pseudo_cost_updates(*bsp_diving_workers_); } template diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index d0973240c..84e66f176 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -309,13 +309,19 @@ class branch_and_bound_t { // Collect and merge diving solutions at sync void collect_diving_solutions(); + template + void process_worker_solutions(PoolT& pool, WorkerTypeGetter get_worker_type); + + template + void merge_pseudo_cost_updates(PoolT& pool); + friend struct opportunistic_tree_update_policy_t; friend struct bsp_tree_update_policy_t; private: // BSP state // unique_ptr as we only want to initialize these if we're in the determinism codepath - std::unique_ptr> bsp_workers_; + std::unique_ptr> bsp_workers_; std::unique_ptr bsp_scheduler_; mip_status_t bsp_global_termination_status_{mip_status_t::UNSET}; double bsp_horizon_step_{5.0}; // Work unit step per horizon (tunable) diff --git a/cpp/src/dual_simplex/deterministic_workers.hpp b/cpp/src/dual_simplex/deterministic_workers.hpp index a54ff24ba..0767e0bbb 100644 --- a/cpp/src/dual_simplex/deterministic_workers.hpp +++ b/cpp/src/dual_simplex/deterministic_workers.hpp @@ -462,19 +462,19 @@ class bsp_worker_pool_base_t { // BSP BFS Worker Pool // ============================================================================ template -class bb_worker_pool_t : public bsp_worker_pool_base_t, - bb_worker_pool_t> { +class bsp_bfs_worker_pool_t : public bsp_worker_pool_base_t, + bsp_bfs_worker_pool_t> { using base_t = - bsp_worker_pool_base_t, bb_worker_pool_t>; + bsp_worker_pool_base_t, bsp_bfs_worker_pool_t>; public: - bb_worker_pool_t(int num_workers, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings) + bsp_bfs_worker_pool_t(int num_workers, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings) { this->workers_.reserve(num_workers); for (int i = 0; i < num_workers; ++i) { From ff1aaf46dc506be94e8ba4dca0dbb97e046c2142 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 4 Feb 2026 09:42:36 +0000 Subject: [PATCH 338/366] more cleanup --- cpp/src/dual_simplex/bb_event.hpp | 34 ++++++--------- cpp/src/dual_simplex/bb_worker_state.hpp | 41 ++++++------------- .../dual_simplex/deterministic_workers.hpp | 10 ++--- 3 files changed, 29 insertions(+), 56 deletions(-) diff --git a/cpp/src/dual_simplex/bb_event.hpp b/cpp/src/dual_simplex/bb_event.hpp index 327fde76b..2a62185b5 100644 --- a/cpp/src/dual_simplex/bb_event.hpp +++ b/cpp/src/dual_simplex/bb_event.hpp @@ -71,7 +71,6 @@ struct bb_event_t { static bb_event_t make_branched(double work_unit_ts, int worker, i_t node, - int seq, i_t down_id, i_t up_id, f_t lower_bound, @@ -83,58 +82,49 @@ struct bb_event_t { e.wut = work_unit_ts; e.worker_id = worker; e.node_id = node; - e.event_sequence = seq; e.payload.branched = {down_id, up_id, lower_bound, branch_var, branch_val}; return e; } - static bb_event_t make_integer_solution( - double work_unit_ts, int worker, i_t node, int seq, f_t objective) + static bb_event_t make_integer_solution(double work_unit_ts, int worker, i_t node, f_t objective) { bb_event_t e; e.type = bb_event_type_t::NODE_INTEGER; e.wut = work_unit_ts; e.worker_id = worker; e.node_id = node; - e.event_sequence = seq; e.payload.integer_solution = {objective}; return e; } - static bb_event_t make_fathomed( - double work_unit_ts, int worker, i_t node, int seq, f_t lower_bound) + static bb_event_t make_fathomed(double work_unit_ts, int worker, i_t node, f_t lower_bound) { bb_event_t e; e.type = bb_event_type_t::NODE_FATHOMED; e.wut = work_unit_ts; e.worker_id = worker; e.node_id = node; - e.event_sequence = seq; e.payload.fathomed = {lower_bound}; return e; } - static bb_event_t make_infeasible(double work_unit_ts, int worker, i_t node, int seq) + static bb_event_t make_infeasible(double work_unit_ts, int worker, i_t node) { bb_event_t e; - e.type = bb_event_type_t::NODE_INFEASIBLE; - e.wut = work_unit_ts; - e.worker_id = worker; - e.node_id = node; - e.event_sequence = seq; - e.payload.fathomed = {std::numeric_limits::infinity()}; + e.type = bb_event_type_t::NODE_INFEASIBLE; + e.wut = work_unit_ts; + e.worker_id = worker; + e.node_id = node; return e; } - static bb_event_t make_numerical(double work_unit_ts, int worker, i_t node, int seq) + static bb_event_t make_numerical(double work_unit_ts, int worker, i_t node) { bb_event_t e; - e.type = bb_event_type_t::NODE_NUMERICAL; - e.wut = work_unit_ts; - e.worker_id = worker; - e.node_id = node; - e.event_sequence = seq; - e.payload.fathomed = {std::numeric_limits::infinity()}; + e.type = bb_event_type_t::NODE_NUMERICAL; + e.wut = work_unit_ts; + e.worker_id = worker; + e.node_id = node; return e; } }; diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 3fdefb765..29422f08d 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -267,7 +267,6 @@ class bsp_bfs_worker_t : public bsp_worker_base_t::make_branched(this->clock, this->worker_id, node->node_id, - 0, down_child_id, up_child_id, node->lower_bound, @@ -281,7 +280,7 @@ class bsp_bfs_worker_t : public bsp_worker_base_t* node, f_t objective) { record_event(bb_event_t::make_integer_solution( - this->clock, this->worker_id, node->node_id, 0, objective)); + this->clock, this->worker_id, node->node_id, objective)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; ++this->total_integer_solutions; @@ -290,7 +289,7 @@ class bsp_bfs_worker_t : public bsp_worker_base_t* node, f_t lower_bound) { record_event(bb_event_t::make_fathomed( - this->clock, this->worker_id, node->node_id, 0, lower_bound)); + this->clock, this->worker_id, node->node_id, lower_bound)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; ++total_nodes_pruned; @@ -299,7 +298,7 @@ class bsp_bfs_worker_t : public bsp_worker_base_t* node) { record_event( - bb_event_t::make_infeasible(this->clock, this->worker_id, node->node_id, 0)); + bb_event_t::make_infeasible(this->clock, this->worker_id, node->node_id)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; ++total_nodes_infeasible; @@ -307,8 +306,7 @@ class bsp_bfs_worker_t : public bsp_worker_base_t* node) { - record_event( - bb_event_t::make_numerical(this->clock, this->worker_id, node->node_id, 0)); + record_event(bb_event_t::make_numerical(this->clock, this->worker_id, node->node_id)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; } @@ -411,9 +409,6 @@ class bsp_diving_worker_t : public bsp_worker_base_t class bsp_worker_pool_base_t { protected: @@ -422,31 +417,28 @@ class bsp_worker_pool_base_t { public: WorkerT& operator[](int worker_id) { return workers_[worker_id]; } const WorkerT& operator[](int worker_id) const { return workers_[worker_id]; } - int size() const { return static_cast(workers_.size()); } + size_t size() const { return workers_.size(); } bool any_has_work() const { - for (const auto& worker : workers_) { - if (worker.has_work()) return true; - } - return false; + return std::any_of( + workers_.begin(), workers_.end(), [](const auto& worker) { return worker.has_work(); }); } size_t total_queue_size() const { - size_t total = 0; - for (const auto& worker : workers_) { - total += worker.queue_size(); - } - return total; + return std::accumulate( + workers_.begin(), workers_.end(), 0, [](size_t total, const auto& worker) { + return total + worker.queue_size(); + }); } bb_event_batch_t collect_and_sort_events() { bb_event_batch_t all_events; - for (auto& worker : workers_) { + std::for_each(workers_.begin(), workers_.end(), [&](auto& worker) { static_cast(this)->collect_worker_events(worker, all_events); - } + }); all_events.sort_for_replay(); return all_events; } @@ -457,9 +449,6 @@ class bsp_worker_pool_base_t { auto end() const { return workers_.end(); } }; -// ============================================================================ -// BSP BFS Worker Pool -// ============================================================================ template class bsp_bfs_worker_pool_t : public bsp_worker_pool_base_t& var_types, const simplex_solver_settings_t& settings) { - this->workers_.reserve(num_workers); for (int i = 0; i < num_workers; ++i) { this->workers_.emplace_back(i, original_lp, Arow, var_types, settings); } @@ -491,9 +479,6 @@ class bsp_bfs_worker_pool_t : public bsp_worker_pool_base_t class bsp_diving_worker_pool_t : public bsp_worker_pool_base_t::make_branched(this->clock, this->worker_id, node->node_id, - 0, down_child_id, up_child_id, node->lower_bound, @@ -282,7 +281,7 @@ class bsp_bfs_worker_t : public bsp_worker_base_t* node, f_t objective) { record_event(bb_event_t::make_integer_solution( - this->clock, this->worker_id, node->node_id, 0, objective)); + this->clock, this->worker_id, node->node_id, objective)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; ++this->total_integer_solutions; @@ -291,7 +290,7 @@ class bsp_bfs_worker_t : public bsp_worker_base_t* node, f_t lower_bound) { record_event(bb_event_t::make_fathomed( - this->clock, this->worker_id, node->node_id, 0, lower_bound)); + this->clock, this->worker_id, node->node_id, lower_bound)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; ++total_nodes_pruned; @@ -300,7 +299,7 @@ class bsp_bfs_worker_t : public bsp_worker_base_t* node) { record_event( - bb_event_t::make_infeasible(this->clock, this->worker_id, node->node_id, 0)); + bb_event_t::make_infeasible(this->clock, this->worker_id, node->node_id)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; ++total_nodes_infeasible; @@ -308,8 +307,7 @@ class bsp_bfs_worker_t : public bsp_worker_base_t* node) { - record_event( - bb_event_t::make_numerical(this->clock, this->worker_id, node->node_id, 0)); + record_event(bb_event_t::make_numerical(this->clock, this->worker_id, node->node_id)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; } From a53b38a45131d57fd10709730f0692d7dec0be18 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 13:11:14 +0100 Subject: [PATCH 339/366] deleted assignment in omp_mutex_t to avoid double destruction. --- cpp/src/dual_simplex/pseudo_costs.cpp | 3 ++- cpp/src/utilities/omp_helpers.hpp | 37 ++++++++++++++++++++++----- 2 files changed, 32 insertions(+), 8 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index de07ac2c4..0da9cc3b3 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -488,7 +488,8 @@ i_t pseudo_costs_t::reliable_variable_selection( // Shuffle the unreliable list so every variable has the same chance to be selected. if (unreliable_list.size() > max_num_candidates) { worker_data->rng.shuffle(unreliable_list); } -#pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) +#pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) \ + shared(score_mutex) for (i_t i = 0; i < num_candidates; ++i) { const i_t j = unreliable_list[i]; std::lock_guard lock(pseudo_cost_mutex[j]); diff --git a/cpp/src/utilities/omp_helpers.hpp b/cpp/src/utilities/omp_helpers.hpp index b79026762..804c2a4ea 100644 --- a/cpp/src/utilities/omp_helpers.hpp +++ b/cpp/src/utilities/omp_helpers.hpp @@ -10,7 +10,8 @@ #ifdef _OPENMP #include -#include +#include +#include namespace cuopt { @@ -18,14 +19,36 @@ namespace cuopt { // https://www.openmp.org/spec-html/5.1/openmpse39.html#x224-2570003.9 class omp_mutex_t { public: - omp_mutex_t() { omp_init_lock(&mutex); } - virtual ~omp_mutex_t() { omp_destroy_lock(&mutex); } - void lock() { omp_set_lock(&mutex); } - void unlock() { omp_unset_lock(&mutex); } - bool try_lock() { return omp_test_lock(&mutex); } + omp_mutex_t() : mutex(new omp_lock_t) { omp_init_lock(mutex.get()); } + + omp_mutex_t(const omp_mutex_t&) = delete; + + omp_mutex_t(omp_mutex_t&& other) { *this = std::move(other); } + + omp_mutex_t& operator=(const omp_mutex_t&) = delete; + + omp_mutex_t& operator=(omp_mutex_t&& other) + { + if (&other != this) { mutex = std::move(other.mutex); } + return *this; + } + + virtual ~omp_mutex_t() + { + if (mutex) { + omp_destroy_lock(mutex.get()); + mutex.reset(); + } + } + + void lock() { omp_set_lock(mutex.get()); } + + void unlock() { omp_unset_lock(mutex.get()); } + + bool try_lock() { return omp_test_lock(mutex.get()); } private: - omp_lock_t mutex; + std::unique_ptr mutex; }; // Wrapper for omp atomic operations. See From 0826aba443bf50ea158301043cc3530f3162b96e Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 14:08:43 +0100 Subject: [PATCH 340/366] addressing reviewer comments --- .../linear_programming/cuopt/run_mip.cpp | 22 +-- .../cuopt/linear_programming/constants.h | 3 +- .../mip/solver_settings.hpp | 13 +- cpp/src/dual_simplex/bnb_worker.hpp | 23 ++- cpp/src/dual_simplex/branch_and_bound.cpp | 62 +++---- cpp/src/dual_simplex/branch_and_bound.hpp | 11 +- cpp/src/dual_simplex/pseudo_costs.cpp | 168 +++++++----------- cpp/src/dual_simplex/pseudo_costs.hpp | 46 ++++- .../dual_simplex/simplex_solver_settings.hpp | 53 +----- cpp/src/dual_simplex/solve.cpp | 6 +- cpp/src/math_optimization/solver_settings.cu | 5 +- cpp/src/mip/diversity/lns/rins.cu | 9 +- cpp/src/mip/diversity/recombiners/sub_mip.cuh | 7 +- cpp/src/mip/solver.cu | 11 +- 14 files changed, 193 insertions(+), 246 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 71034ff12..34f10530c 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -147,7 +147,7 @@ int run_single_file(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, - bool reliability_branching, + int reliability_branching, double time_limit) { const raft::handle_t handle_{}; @@ -260,7 +260,7 @@ void run_single_file_mp(std::string file_path, int num_cpu_threads, bool write_log_file, bool log_to_console, - bool reliability_branching, + int reliability_branching, double time_limit) { std::cout << "running file " << file_path << " on gpu : " << device << std::endl; @@ -361,8 +361,8 @@ int main(int argc, char* argv[]) .default_value(std::string("f")); program.add_argument("--reliability-branching") - .help("enable reliability branching (t/f)") - .default_value(std::string("t")); + .help("reliability branching: -1 (automatic), 0 (disable) or k > 0 (use k)") + .default_value(std::string("-1")); // Parse arguments try { @@ -386,13 +386,13 @@ int main(int argc, char* argv[]) std::string result_file; int batch_num = -1; - bool heuristics_only = program.get("--heuristics-only")[0] == 't'; - int num_cpu_threads = program.get("--num-cpu-threads"); - bool write_log_file = program.get("--write-log-file")[0] == 't'; - bool log_to_console = program.get("--log-to-console")[0] == 't'; - double memory_limit = program.get("--memory-limit"); - bool track_allocations = program.get("--track-allocations")[0] == 't'; - bool reliability_branching = program.get("--reliability-branching")[0] == 't'; + bool heuristics_only = program.get("--heuristics-only")[0] == 't'; + int num_cpu_threads = program.get("--num-cpu-threads"); + bool write_log_file = program.get("--write-log-file")[0] == 't'; + bool log_to_console = program.get("--log-to-console")[0] == 't'; + double memory_limit = program.get("--memory-limit"); + bool track_allocations = program.get("--track-allocations")[0] == 't'; + int reliability_branching = program.get("--reliability-branching"); if (num_cpu_threads < 0) { num_cpu_threads = omp_get_max_threads() / n_gpus; } diff --git a/cpp/include/cuopt/linear_programming/constants.h b/cpp/include/cuopt/linear_programming/constants.h index b512944a6..edfa2a376 100644 --- a/cpp/include/cuopt/linear_programming/constants.h +++ b/cpp/include/cuopt/linear_programming/constants.h @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -57,6 +57,7 @@ #define CUOPT_MIP_HEURISTICS_ONLY "mip_heuristics_only" #define CUOPT_MIP_SCALING "mip_scaling" #define CUOPT_MIP_PRESOLVE "mip_presolve" +#define CUOPT_MIP_RELIABILITY_BRANCHING "mip_reliability_branching" #define CUOPT_SOLUTION_FILE "solution_file" #define CUOPT_NUM_CPU_THREADS "num_cpu_threads" #define CUOPT_NUM_GPUS "num_gpus" diff --git a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp index f5cb1c80e..257e6b2bb 100644 --- a/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp +++ b/cpp/include/cuopt/linear_programming/mip/solver_settings.hpp @@ -78,13 +78,12 @@ class mip_solver_settings_t { friend class problem_checking_t; tolerances_t tolerances; - f_t time_limit = std::numeric_limits::infinity(); - bool heuristics_only = false; - i_t num_cpu_threads = -1; // -1 means use default number of threads in branch and bound - i_t num_gpus = 1; - bool log_to_console = true; - - bool reliability_branching = true; + f_t time_limit = std::numeric_limits::infinity(); + bool heuristics_only = false; + i_t reliability_branching = -1; + i_t num_cpu_threads = -1; // -1 means use default number of threads in branch and bound + i_t num_gpus = 1; + bool log_to_console = true; std::string log_file; std::string sol_file; diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index e770edd84..8e20ce214 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -130,8 +130,8 @@ class bnb_worker_data_t { } // Set the variables bounds for the LP relaxation of the current node. - bool set_lp_variable_bounds_for(mip_node_t* node_ptr, - const simplex_solver_settings_t& settings) + bool set_lp_variable_bounds(mip_node_t* node_ptr, + const simplex_solver_settings_t& settings) { // Reset the bound_changed markers std::fill(bounds_changed.begin(), bounds_changed.end(), false); @@ -153,7 +153,7 @@ class bnb_worker_data_t { private: // For diving, we need to store the full node instead of - // of just a pointer, since it is not store in the tree anymore. + // of just a pointer, since it is not stored in the tree anymore. // To keep the same interface across all worker types, // this will be used as a temporary storage and // will be pointed by `start_node`. @@ -177,9 +177,11 @@ class bnb_worker_pool_t { std::make_unique>(i, original_lp, Arow, var_type, settings); idle_workers_.push_front(i); } + + is_initialized = true; } - // Here, we are assuming that the master is the only + // Here, we are assuming that the scheduler is the only // thread that can retrieve/pop an idle worker. bnb_worker_data_t* get_idle_worker() { @@ -192,7 +194,7 @@ class bnb_worker_pool_t { } } - // Here, we are assuming that the master is the only + // Here, we are assuming that the scheduler is the only // thread that can retrieve/pop an idle worker. void pop_idle_worker() { @@ -211,13 +213,15 @@ class bnb_worker_pool_t { num_idle_workers_++; } - f_t get_lower_bounds() + f_t get_lower_bound() { f_t lower_bound = std::numeric_limits::infinity(); - for (i_t i = 0; i < workers_.size(); ++i) { - if (workers_[i]->worker_type == BEST_FIRST && workers_[i]->is_active) { - lower_bound = std::min(workers_[i]->lower_bound.load(), lower_bound); + if (is_initialized) { + for (i_t i = 0; i < workers_.size(); ++i) { + if (workers_[i]->worker_type == BEST_FIRST && workers_[i]->is_active) { + lower_bound = std::min(workers_[i]->lower_bound.load(), lower_bound); + } } } @@ -229,6 +233,7 @@ class bnb_worker_pool_t { private: // Worker pool std::vector>> workers_; + bool is_initialized = false; omp_mutex_t mutex_; std::deque idle_workers_; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 946d6b779..8cbea68cc 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -250,7 +250,7 @@ f_t branch_and_bound_t::get_lower_bound() f_t lower_bound = lower_bound_ceiling_.load(); f_t heap_lower_bound = node_queue_.get_lower_bound(); lower_bound = std::min(heap_lower_bound, lower_bound); - lower_bound = std::min(worker_pool_.get_lower_bounds(), lower_bound); + lower_bound = std::min(worker_pool_.get_lower_bound(), lower_bound); return std::isfinite(lower_bound) ? lower_bound : -inf; } @@ -282,7 +282,8 @@ void branch_and_bound_t::report(char symbol, f_t obj, f_t lower_bound, i_t nodes_unexplored = exploration_stats_.nodes_unexplored; f_t user_obj = compute_user_objective(original_lp_, obj); f_t user_lower = compute_user_objective(original_lp_, lower_bound); - f_t iter_node = nodes_explored > 0 ? (f_t)exploration_stats_.total_lp_iters / nodes_explored : 0; + f_t iter_node = nodes_explored > 0 ? (f_t)exploration_stats_.total_lp_iters / nodes_explored + : exploration_stats_.total_lp_iters; std::string user_gap = user_mip_gap(user_obj, user_lower); settings_.log.printf("%c %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", symbol, @@ -577,18 +578,16 @@ branch_variable_t branch_and_bound_t::variable_selection( switch (worker_data->worker_type) { case bnb_worker_type_t::BEST_FIRST: - if (settings_.reliability_branching_settings.enable) { - simplex_solver_settings_t rb_settings = settings_; - rb_settings.reliability_branching_settings.num_tasks = worker_pool_.num_idle_workers(); - + if (settings_.reliability_branching != 0) { branch_var = pc_.reliable_variable_selection(node_ptr, fractional, solution, - rb_settings, + settings_, var_types_, worker_data, exploration_stats_, upper_bound_, + worker_pool_.num_idle_workers(), log); } else { branch_var = pc_.variable_selection(fractional, solution, log); @@ -666,9 +665,9 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* node_ptr->vstatus[node_ptr->branch_var]); #endif - bool feasible = worker_data->set_lp_variable_bounds_for(node_ptr, settings_); + bool feasible = worker_data->set_lp_variable_bounds(node_ptr, settings_); dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; - worker_data->leaf_edge_norms = edge_norms_; // = node.steepest_edge_norms; + worker_data->leaf_edge_norms = edge_norms_; if (feasible) { i_t node_iter = 0; @@ -816,8 +815,7 @@ std::pair branch_and_bound_t::upd } template -void branch_and_bound_t::plunge_with(bnb_worker_data_t* worker_data, - mip_solve_mode_t mode) +void branch_and_bound_t::plunge_with(bnb_worker_data_t* worker_data) { std::deque*> stack; stack.push_front(worker_data->start_node); @@ -912,7 +910,7 @@ void branch_and_bound_t::plunge_with(bnb_worker_data_t* work } } - if (mode == mip_solve_mode_t::BNB_PARALLEL) { + if (settings_.num_threads > 1) { worker_pool_.return_worker_to_pool(worker_data); active_workers_per_type_[BEST_FIRST]--; } @@ -1016,20 +1014,11 @@ void branch_and_bound_t::run_scheduler() } #endif - worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); - active_workers_per_type_.fill(0); - f_t lower_bound = get_lower_bound(); f_t abs_gap = upper_bound_ - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); i_t last_node_depth = 0; - is_running_ = true; - - settings_.log.printf( - " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " - "| Time |\n"); - while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && (active_workers_per_type_[0] > 0 || node_queue_.best_first_queue_size() > 0)) { @@ -1108,7 +1097,7 @@ void branch_and_bound_t::run_scheduler() launched_any_task = true; #pragma omp task affinity(worker) - plunge_with(worker, mip_solve_mode_t::BNB_PARALLEL); + plunge_with(worker); } else { std::optional*> start_node = node_queue_.pop_diving(); @@ -1133,30 +1122,22 @@ void branch_and_bound_t::run_scheduler() } // If no new task was launched in this iteration, suspend temporarily the - // execution of the master. As of 8/Jan/2026, GCC does not + // execution of the scheduler. As of 8/Jan/2026, GCC does not // implement taskyield, but LLVM does. if (!launched_any_task) { std::this_thread::sleep_for(std::chrono::milliseconds(1)); } } - - is_running_ = false; } template void branch_and_bound_t::single_threaded_solve() { - bnb_worker_data_t worker(0, original_lp_, Arow_, var_types_, settings_); + bnb_worker_data_t* worker = worker_pool_.get_idle_worker(); f_t lower_bound = get_lower_bound(); f_t abs_gap = upper_bound_ - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound_.load(), lower_bound); i_t last_node_depth = 0; - is_running_ = true; - - settings_.log.printf( - " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " - "| Time |\n"); - while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && node_queue_.best_first_queue_size() > 0) { bool launched_any_task = false; @@ -1200,10 +1181,8 @@ void branch_and_bound_t::single_threaded_solve() } worker.init_best_first(start_node.value(), original_lp_); - plunge_with(&worker, mip_solve_mode_t::BNB_SINGLE_THREADED); + plunge_with(worker); } - - is_running_ = false; } template @@ -1482,7 +1461,16 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut calculate_variable_locks(original_lp_, var_up_locks_, var_down_locks_); } - if (solve_mode == mip_solve_mode_t::BNB_PARALLEL) { + worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); + active_workers_per_type_.fill(0); + + is_running_ = true; + + settings_.log.printf( + " | Explored | Unexplored | Objective | Bound | Depth | Iter/Node | Gap " + "| Time |\n"); + + if (settings_.num_threads > 1) { #pragma omp parallel num_threads(settings_.num_threads) { #pragma omp master @@ -1493,6 +1481,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut single_threaded_solve(); } + is_running_ = false; + f_t lower_bound = node_queue_.best_first_queue_size() > 0 ? node_queue_.get_lower_bound() : search_tree_.root.lower_bound; set_final_solution(solution, lower_bound); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 3efaaa6d6..df76f2d71 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -36,11 +36,6 @@ enum class mip_status_t { UNSET = 6, // The status is not set }; -enum class mip_solve_mode_t { - BNB_PARALLEL = 0, // Parallel B&B (default) - BNB_SINGLE_THREADED = 1, // Single threaded B&B for SubMIP and RINS -}; - template void upper_bound_callback(f_t upper_bound); @@ -91,7 +86,7 @@ class branch_and_bound_t { lp_status_t solve_root_relaxation(simplex_solver_settings_t const& lp_settings); // The main entry routine. Returns the solver status and populates solution with the incumbent. - mip_status_t solve(mip_solution_t& solution, mip_solve_mode_t solve_mode); + mip_status_t solve(mip_solution_t& solution); private: const user_problem_t& original_problem_; @@ -188,13 +183,13 @@ class branch_and_bound_t { // We use best-first to pick the `start_node` and then perform a depth-first search // from this node (i.e., a plunge). It can only backtrack to a sibling node. // Unexplored nodes in the subtree are inserted back into the global heap. - void plunge_with(bnb_worker_data_t* worker_data, mip_solve_mode_t mode); + void plunge_with(bnb_worker_data_t* worker_data); // Perform a deep dive in the subtree determined by the `start_node` in order // to find integer feasible solutions. void dive_with(bnb_worker_data_t* worker_data); - // Run the scheduler (aka the master) whose will schedule and manage + // Run the scheduler whose will schedule and manage // all the other workers. void run_scheduler(); diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 0da9cc3b3..9d527a684 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -154,6 +154,7 @@ f_t trial_branching(const lp_problem_t& original_lp, child_problem.lower[branch_var] = branch_var_lower; child_problem.upper[branch_var] = branch_var_upper; + const bool initialize_basis = false; simplex_solver_settings_t child_settings = settings; child_settings.set_log(false); i_t lp_iter_upper = settings.reliability_branching_settings.upper_max_lp_iter; @@ -173,7 +174,7 @@ f_t trial_branching(const lp_problem_t& original_lp, dual::status_t status = dual_phase2_with_advanced_basis(2, 0, - false, + initialize_basis, start_time, child_problem, child_settings, @@ -266,6 +267,22 @@ void strong_branching(const lp_problem_t& original_lp, pc.update_pseudo_costs_from_strong_branching(fractional, root_soln); } +template +f_t pseudo_costs_t::calculate_pseudocost_score(i_t j, + const std::vector& solution, + f_t pseudo_cost_up_avg, + f_t pseudo_cost_down_avg) const +{ + constexpr f_t eps = 1e-6; + i_t num_up = pseudo_cost_num_up[j]; + i_t num_down = pseudo_cost_num_down[j]; + f_t pc_up = num_up > 0 ? pseudo_cost_sum_up[j] / num_up : pseudo_cost_up_avg; + f_t pc_down = num_down > 0 ? pseudo_cost_sum_down[j] / num_down : pseudo_cost_down_avg; + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + return std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); +} + template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) @@ -287,7 +304,7 @@ template void pseudo_costs_t::initialized(i_t& num_initialized_down, i_t& num_initialized_up, f_t& pseudo_cost_down_avg, - f_t& pseudo_cost_up_avg) + f_t& pseudo_cost_up_avg) const { num_initialized_down = 0; num_initialized_up = 0; @@ -327,11 +344,8 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio const std::vector& solution, logger_t& log) { - const i_t num_fractional = fractional.size(); - std::vector pseudo_cost_up(num_fractional); - std::vector pseudo_cost_down(num_fractional); - std::vector score(num_fractional); - + i_t branch_var = fractional[0]; + f_t max_score = -1; i_t num_initialized_down; i_t num_initialized_up; f_t pseudo_cost_down_avg; @@ -345,44 +359,19 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio pseudo_cost_down_avg, pseudo_cost_up_avg); - for (i_t k = 0; k < num_fractional; k++) { - const i_t j = fractional[k]; - - if (pseudo_cost_num_down[j] != 0) { - pseudo_cost_down[k] = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; - } else { - pseudo_cost_down[k] = pseudo_cost_down_avg; - } - - if (pseudo_cost_num_up[j] != 0) { - pseudo_cost_up[k] = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; - } else { - pseudo_cost_up[k] = pseudo_cost_up_avg; - } - - constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - score[k] = - std::max(f_down * pseudo_cost_down[k], eps) * std::max(f_up * pseudo_cost_up[k], eps); - } - - i_t branch_var = fractional[0]; - f_t max_score = -1; - i_t select = -1; + for (auto j : fractional) { + f_t score = calculate_pseudocost_score(j, solution, pseudo_cost_up_avg, pseudo_cost_down_avg); - for (i_t k = 0; k < num_fractional; k++) { - if (score[k] > max_score) { - max_score = score[k]; - branch_var = fractional[k]; - select = k; + if (score > max_score) { + max_score = score; + branch_var = j; } } log.debug("Pseudocost branching on %d. Value %e. Score %e.\n", branch_var, solution[branch_var], - score[select]); + max_score); return branch_var; } @@ -397,6 +386,7 @@ i_t pseudo_costs_t::reliable_variable_selection( bnb_worker_data_t* worker_data, const bnb_stats_t& bnb_stats, f_t upper_bound, + int max_num_tasks, logger_t& log) { f_t start_time = bnb_stats.start_time; @@ -415,24 +405,25 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_down_avg, pseudo_cost_up_avg); - const int64_t bnb_total_lp_iter = bnb_stats.total_lp_iters; - const int64_t bnb_nodes_explored = bnb_stats.nodes_explored; - const i_t bnb_lp_iter_per_node = bnb_total_lp_iter / bnb_stats.nodes_explored; + const int64_t branch_and_bound_lp_iters = bnb_stats.total_lp_iters; + const int64_t branch_and_bound_explored = bnb_stats.nodes_explored; + const i_t branch_and_bound_lp_iter_per_node = + branch_and_bound_lp_iters / bnb_stats.nodes_explored; - i_t reliable_threshold = settings.reliability_branching_settings.reliable_threshold; + i_t reliable_threshold = settings.reliability_branching; if (reliable_threshold < 0) { - const i_t max_threshold = settings.reliability_branching_settings.max_reliable_threshold; - const i_t min_threshold = settings.reliability_branching_settings.min_reliable_threshold; - const f_t iter_factor = settings.reliability_branching_settings.bnb_lp_factor; - const i_t iter_offset = settings.reliability_branching_settings.bnb_lp_offset; - const int64_t alpha = iter_factor * bnb_total_lp_iter; - const int64_t max_iter = alpha + settings.reliability_branching_settings.bnb_lp_offset; - - f_t gamma = (max_iter - sb_total_lp_iter) / (sb_total_lp_iter + 1.0); - gamma = std::min(1.0, gamma); - gamma = std::max((alpha - sb_total_lp_iter) / (sb_total_lp_iter + 1.0), gamma); + const i_t max_threshold = reliability_branching_settings.max_reliable_threshold; + const i_t min_threshold = reliability_branching_settings.min_reliable_threshold; + const f_t iter_factor = reliability_branching_settings.bnb_lp_factor; + const i_t iter_offset = reliability_branching_settings.bnb_lp_offset; + const int64_t alpha = iter_factor * branch_and_bound_lp_iters; + const int64_t max_iter = alpha + reliability_branching_settings.bnb_lp_offset; + + f_t gamma = (max_iter - strong_branching_lp_iter) / (strong_branching_lp_iter + 1.0); + gamma = std::min(1.0, gamma); + gamma = std::max((alpha - strong_branching_lp_iter) / (strong_branching_lp_iter + 1.0), gamma); reliable_threshold = (1 - gamma) * min_threshold + gamma * max_threshold; - reliable_threshold = sb_total_lp_iter < max_iter ? reliable_threshold : 0; + reliable_threshold = strong_branching_lp_iter < max_iter ? reliable_threshold : 0; } std::vector unreliable_list; @@ -445,15 +436,7 @@ i_t pseudo_costs_t::reliable_variable_selection( continue; } - f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - - constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); + f_t score = calculate_pseudocost_score(j, solution, pseudo_cost_up_avg, pseudo_cost_down_avg); if (score > max_score) { max_score = score; @@ -468,19 +451,20 @@ i_t pseudo_costs_t::reliable_variable_selection( return branch_var; } - const int num_tasks = std::max(settings.reliability_branching_settings.num_tasks, 1); - const int task_priority = settings.reliability_branching_settings.task_priority; - const i_t max_num_candidates = settings.reliability_branching_settings.max_num_candidates; + const int num_tasks = std::max(max_num_tasks, 1); + const int task_priority = reliability_branching_settings.task_priority; + const i_t max_num_candidates = reliability_branching_settings.max_num_candidates; const i_t num_candidates = std::min(unreliable_list.size(), max_num_candidates); assert(task_priority > 0); assert(max_num_candidates > 0); assert(num_candidates > 0); + assert(num_tasks > 0); log.printf( "RB iters = %d, B&B iters = %d, unreliable = %d, num_tasks = %d, reliable_threshold = %d\n", - sb_total_lp_iter.load(), - bnb_total_lp_iter, + strong_branching_lp_iter.load(), + branch_and_bound_lp_iters, unreliable_list.size(), num_tasks, reliable_threshold); @@ -509,9 +493,9 @@ i_t pseudo_costs_t::reliable_variable_selection( worker_data->leaf_problem.lower[j], std::floor(solution[j]), upper_bound, - bnb_lp_iter_per_node, + branch_and_bound_lp_iter_per_node, start_time, - sb_total_lp_iter); + strong_branching_lp_iter); if (!std::isnan(obj)) { f_t change_in_obj = obj - node_ptr->lower_bound; @@ -535,9 +519,9 @@ i_t pseudo_costs_t::reliable_variable_selection( std::ceil(solution[j]), worker_data->leaf_problem.upper[j], upper_bound, - bnb_lp_iter_per_node, + branch_and_bound_lp_iter_per_node, start_time, - sb_total_lp_iter); + strong_branching_lp_iter); if (!std::isnan(obj)) { f_t change_in_obj = obj - node_ptr->lower_bound; @@ -548,18 +532,10 @@ i_t pseudo_costs_t::reliable_variable_selection( } if (toc(start_time) > settings.time_limit) { continue; } - f_t pc_up = pseudo_cost_num_up[j] > 0 ? pseudo_cost_sum_up[j] / pseudo_cost_num_up[j] - : pseudo_cost_up_avg; - f_t pc_down = pseudo_cost_sum_down[j] > 0 ? pseudo_cost_sum_down[j] / pseudo_cost_num_down[j] - : pseudo_cost_down_avg; - constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - f_t score = std::max(f_down * pc_down, eps) * std::max(f_up * pc_up, eps); + f_t score = calculate_pseudocost_score(j, solution, pseudo_cost_up_avg, pseudo_cost_down_avg); - std::lock_guard score_lock(score_mutex); - if (score > max_score) { + if (std::lock_guard score_lock(score_mutex); score > max_score) { max_score = score; branch_var = j; } @@ -587,29 +563,15 @@ f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - for (i_t k = 0; k < num_fractional; k++) { - const i_t j = fractional[k]; - - f_t pseudo_cost_down = 0; - f_t pseudo_cost_up = 0; - - if (pseudo_cost_num_down[j] != 0) { - pseudo_cost_down = pseudo_cost_sum_down[j] / pseudo_cost_num_down[j]; - } else { - pseudo_cost_down = pseudo_cost_down_avg; - } - - if (pseudo_cost_num_up[j] != 0) { - pseudo_cost_up = pseudo_cost_sum_up[j] / pseudo_cost_num_up[j]; - } else { - pseudo_cost_up = pseudo_cost_up_avg; - } - + for (auto j : fractional) { constexpr f_t eps = 1e-6; - const f_t f_down = solution[j] - std::floor(solution[j]); - const f_t f_up = std::ceil(solution[j]) - solution[j]; - estimate += - std::min(std::max(pseudo_cost_down * f_down, eps), std::max(pseudo_cost_up * f_up, eps)); + i_t num_up = pseudo_cost_num_up[j]; + i_t num_down = pseudo_cost_num_down[j]; + f_t pc_up = num_up > 0 ? pseudo_cost_sum_up[j] / num_up : pseudo_cost_up_avg; + f_t pc_down = num_down > 0 ? pseudo_cost_sum_down[j] / num_down : pseudo_cost_down_avg; + f_t f_down = solution[j] - std::floor(solution[j]); + f_t f_up = std::ceil(solution[j]) - solution[j]; + estimate += std::min(pc_down * f_down, pc_up * f_up); } log.printf("pseudocost estimate = %e\n", estimate); diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 8d566483a..94b2504e3 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -20,6 +20,39 @@ namespace cuopt::linear_programming::dual_simplex { +template +struct reliability_branching_settings_t { + // Lower bound for the maximum number of LP iterations for a single trial branching + i_t lower_max_lp_iter = 10; + + // Upper bound for the maximum number of LP iterations for a single trial branching + i_t upper_max_lp_iter = 500; + + // Priority of the tasks created when running the trial branching in parallel. + // Set to 1 to have the same priority as the other tasks. + i_t task_priority = 5; + + // The maximum number of candidates initialized by strong branching in a single + // node + i_t max_num_candidates = 100; + + // Define the maximum number of iteration spent in strong branching. + // Let `bnb_lp_iter` = total number of iterations in B&B, then + // `max iter in strong branching = bnb_lp_factor * bnb_lp_iter + bnb_lp_offset`. + // This is used for determining the `reliable_threshold`. + f_t bnb_lp_factor = 0.5; + i_t bnb_lp_offset = 100000; + + // Maximum and minimum points in curve to determine the value + // of the `reliable_threshold` based on the current number of LP + // iterations in strong branching and B&B. Since it is a + // a curve, the actual value of `reliable_threshold` may be + // higher than `max_reliable_threshold`. + // Only used when `reliable_threshold` is negative + i_t max_reliable_threshold = 5; + i_t min_reliable_threshold = 1; +}; + template class pseudo_costs_t { public: @@ -46,7 +79,7 @@ class pseudo_costs_t { void initialized(i_t& num_initialized_down, i_t& num_initialized_up, f_t& pseudo_cost_down_avg, - f_t& pseudo_cost_up_avg); + f_t& pseudo_cost_up_avg) const; f_t obj_estimate(const std::vector& fractional, const std::vector& solution, @@ -65,10 +98,19 @@ class pseudo_costs_t { bnb_worker_data_t* worker_data, const bnb_stats_t& bnb_stats, f_t upper_bound, + int max_num_tasks, logger_t& log); void update_pseudo_costs_from_strong_branching(const std::vector& fractional, const std::vector& root_soln); + + f_t calculate_pseudocost_score(i_t j, + const std::vector& solution, + f_t pseudo_cost_up_avg, + f_t pseudo_cost_down_avg) const; + + reliability_branching_settings_t reliability_branching_settings; + std::vector> pseudo_cost_sum_up; std::vector> pseudo_cost_sum_down; std::vector> pseudo_cost_num_up; @@ -77,7 +119,7 @@ class pseudo_costs_t { std::vector strong_branch_up; std::vector pseudo_cost_mutex; omp_atomic_t num_strong_branches_completed = 0; - omp_atomic_t sb_total_lp_iter = 0; + omp_atomic_t strong_branching_lp_iter = 0; }; template diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 2fdaf0356..a2837868b 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -43,50 +43,6 @@ struct diving_heuristics_settings_t { i_t backtrack_limit = 5; }; -template -struct reliability_branching_settings_t { - // Enable or disable reliability branching - bool enable = false; - - // Lower bound for the maximum number of LP iterations for a single trial branching - i_t lower_max_lp_iter = 10; - - // Upper bound for the maximum number of LP iterations for a single trial branching - i_t upper_max_lp_iter = 500; - - // Priority of the tasks created when running the trial branching in parallel. - // Set to 1 to have the same priority as the other tasks. - i_t task_priority = 5; - - // The number of tasks spawned for performing strong branching. - i_t num_tasks = -1; - - // The maximum number of candidates initialized by strong branching in a single - // node - i_t max_num_candidates = 100; - - // Define the maximum number of iteration spent in strong branching. - // Let `bnb_lp_iter` = total number of iterations in B&B, then - // `max iter in strong branching = bnb_lp_factor * bnb_lp_iter + bnb_lp_offset`. - // This is used for determining the `reliable_threshold`. - f_t bnb_lp_factor = 0.5; - i_t bnb_lp_offset = 100000; - - // Threshold for determining for the number of pseudocost updates. Used for - // determining if the pseudocost is reliable or not. - // - <0: automatic - // - 0: disable (use pseudocost branching instead) - // - >0: will use the value for the threshold. - i_t reliable_threshold = -1; - - // Maximum and minimum points of the curve to determine the value - // of the `reliable_threshold` based on the current number of LP - // iterations in strong branching and B&B. - // Only used when `reliable_threshold` is negative - i_t max_reliable_threshold = 5; - i_t min_reliable_threshold = 1; -}; - template struct simplex_solver_settings_t { public: @@ -139,6 +95,7 @@ struct simplex_solver_settings_t { first_iteration_log(2), num_threads(omp_get_max_threads() - 1), random_seed(0), + reliability_branching(-1), inside_mip(0), solution_callback(nullptr), heuristic_preemption_callback(nullptr), @@ -205,8 +162,12 @@ struct simplex_solver_settings_t { i_t random_seed; // random seed diving_heuristics_settings_t diving_settings; // Settings for the diving heuristics - reliability_branching_settings_t - reliability_branching_settings; // Settings for reliability branching + + // Settings for the reliability branching. + // - -1: automatic + // - 0: disable (use pseudocost branching instead) + // - k > 0, a variable is considered reliable if it has been branched on k times. + i_t reliability_branching; i_t inside_mip; // 0 if outside MIP, 1 if inside MIP at root node, 2 if inside MIP at leaf node std::function&, f_t)> solution_callback; diff --git a/cpp/src/dual_simplex/solve.cpp b/cpp/src/dual_simplex/solve.cpp index cec1eb92d..1f31a757d 100644 --- a/cpp/src/dual_simplex/solve.cpp +++ b/cpp/src/dual_simplex/solve.cpp @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -599,7 +599,7 @@ i_t solve(const user_problem_t& problem, if (is_mip(problem) && !settings.relaxation) { branch_and_bound_t branch_and_bound(problem, settings); mip_solution_t mip_solution(problem.num_cols); - mip_status_t mip_status = branch_and_bound.solve(mip_solution, mip_solve_mode_t::BNB_PARALLEL); + mip_status_t mip_status = branch_and_bound.solve(mip_solution); if (mip_status == mip_status_t::OPTIMAL) { status = 0; } else { @@ -638,7 +638,7 @@ i_t solve_mip_with_guess(const user_problem_t& problem, if (is_mip(problem)) { branch_and_bound_t branch_and_bound(problem, settings); branch_and_bound.set_initial_guess(guess); - mip_status_t mip_status = branch_and_bound.solve(solution, mip_solve_mode_t::BNB_PARALLEL); + mip_status_t mip_status = branch_and_bound.solve(solution); if (mip_status == mip_status_t::OPTIMAL) { status = 0; } else { diff --git a/cpp/src/math_optimization/solver_settings.cu b/cpp/src/math_optimization/solver_settings.cu index 4e3dc6465..a22aa6b71 100644 --- a/cpp/src/math_optimization/solver_settings.cu +++ b/cpp/src/math_optimization/solver_settings.cu @@ -1,6 +1,6 @@ /* clang-format off */ /* - * SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 */ /* clang-format on */ @@ -88,7 +88,8 @@ solver_settings_t::solver_settings_t() : pdlp_settings(), mip_settings {CUOPT_ORDERING, &pdlp_settings.ordering, -1, 1, -1}, {CUOPT_BARRIER_DUAL_INITIAL_POINT, &pdlp_settings.barrier_dual_initial_point, -1, 1, -1}, {CUOPT_NUM_GPUS, &pdlp_settings.num_gpus, 1, 2, 1}, - {CUOPT_NUM_GPUS, &mip_settings.num_gpus, 1, 2, 1} + {CUOPT_NUM_GPUS, &mip_settings.num_gpus, 1, 2, 1}, + {CUOPT_MIP_RELIABILITY_BRANCHING, &mip_settings.reliability_branching, -1, std::numeric_limits::max(), -1} }; // Bool parameters diff --git a/cpp/src/mip/diversity/lns/rins.cu b/cpp/src/mip/diversity/lns/rins.cu index c4927c9c2..e656a545e 100644 --- a/cpp/src/mip/diversity/lns/rins.cu +++ b/cpp/src/mip/diversity/lns/rins.cu @@ -258,9 +258,9 @@ void rins_t::run_rins() std::min(current_mip_gap, (f_t)settings.target_mip_gap); branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; branch_and_bound_settings.num_threads = 1; - branch_and_bound_settings.reliability_branching_settings.enable = false; - branch_and_bound_settings.log.log = false; - branch_and_bound_settings.log.log_prefix = "[RINS] "; + branch_and_bound_settings.reliability_branching = 0; + branch_and_bound_settings.log.log = false; + branch_and_bound_settings.log.log_prefix = "[RINS] "; branch_and_bound_settings.solution_callback = [&rins_solution_queue](std::vector& solution, f_t objective) { rins_solution_queue.push_back(solution); @@ -268,8 +268,7 @@ void rins_t::run_rins() dual_simplex::branch_and_bound_t branch_and_bound(branch_and_bound_problem, branch_and_bound_settings); branch_and_bound.set_initial_guess(cuopt::host_copy(fixed_assignment, rins_handle.get_stream())); - branch_and_bound_status = branch_and_bound.solve( - branch_and_bound_solution, dual_simplex::mip_solve_mode_t::BNB_SINGLE_THREADED); + branch_and_bound_status = branch_and_bound.solve(branch_and_bound_solution); if (!std::isnan(branch_and_bound_solution.objective)) { CUOPT_LOG_DEBUG("RINS submip solution found. Objective %.16e. Status %d", diff --git a/cpp/src/mip/diversity/recombiners/sub_mip.cuh b/cpp/src/mip/diversity/recombiners/sub_mip.cuh index 340e0b9ff..d03f6dd2c 100644 --- a/cpp/src/mip/diversity/recombiners/sub_mip.cuh +++ b/cpp/src/mip/diversity/recombiners/sub_mip.cuh @@ -103,8 +103,8 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; branch_and_bound_settings.num_threads = 1; - branch_and_bound_settings.reliability_branching_settings.enable = false; - branch_and_bound_settings.solution_callback = [this](std::vector& solution, + branch_and_bound_settings.reliability_branching = 0; + branch_and_bound_settings.solution_callback = [this](std::vector& solution, f_t objective) { this->solution_callback(solution, objective); }; @@ -113,8 +113,7 @@ class sub_mip_recombiner_t : public recombiner_t { branch_and_bound_settings.log.log = false; dual_simplex::branch_and_bound_t branch_and_bound(branch_and_bound_problem, branch_and_bound_settings); - branch_and_bound_status = branch_and_bound.solve( - branch_and_bound_solution, dual_simplex::mip_solve_mode_t::BNB_SINGLE_THREADED); + branch_and_bound_status = branch_and_bound.solve(branch_and_bound_solution); if (solution_vector.size() > 0) { cuopt_assert(fixed_assignment.size() == branch_and_bound_solution.x.size(), "Assignment size mismatch"); diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index f18e25732..a4ab2eb31 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -167,8 +167,7 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.absolute_mip_gap_tol = context.settings.tolerances.absolute_mip_gap; branch_and_bound_settings.relative_mip_gap_tol = context.settings.tolerances.relative_mip_gap; branch_and_bound_settings.integer_tol = context.settings.tolerances.integrality_tolerance; - branch_and_bound_settings.reliability_branching_settings.enable = - solver_settings_.reliability_branching; + branch_and_bound_settings.reliability_branching = solver_settings_.reliability_branching; if (context.settings.num_cpu_threads < 0) { branch_and_bound_settings.num_threads = std::max(1, omp_get_max_threads() - 1); @@ -176,11 +175,6 @@ solution_t mip_solver_t::run_solver() branch_and_bound_settings.num_threads = std::max(1, context.settings.num_cpu_threads); } - dual_simplex::mip_solve_mode_t solve_mode = - branch_and_bound_settings.num_threads > 1 - ? dual_simplex::mip_solve_mode_t::BNB_PARALLEL - : dual_simplex::mip_solve_mode_t::BNB_SINGLE_THREADED; - // Set the branch and bound -> primal heuristics callback branch_and_bound_settings.solution_callback = std::bind(&branch_and_bound_solution_helper_t::solution_callback, @@ -231,8 +225,7 @@ solution_t mip_solver_t::run_solver() branch_and_bound_status_future = std::async(std::launch::async, &dual_simplex::branch_and_bound_t::solve, branch_and_bound.get(), - std::ref(branch_and_bound_solution), - solve_mode); + std::ref(branch_and_bound_solution)); } // Start the primal heuristics From 929e0b5e6d9241d8516da4ebb66f6bbc35ef0083 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 14:40:30 +0100 Subject: [PATCH 341/366] fix compilation --- .../linear_programming/cuopt/run_mip.cpp | 8 +++----- cpp/src/dual_simplex/branch_and_bound.cpp | 18 +++++++++--------- cpp/src/dual_simplex/pseudo_costs.cpp | 10 ++++++++-- 3 files changed, 20 insertions(+), 16 deletions(-) diff --git a/benchmarks/linear_programming/cuopt/run_mip.cpp b/benchmarks/linear_programming/cuopt/run_mip.cpp index 34f10530c..308c7087b 100644 --- a/benchmarks/linear_programming/cuopt/run_mip.cpp +++ b/benchmarks/linear_programming/cuopt/run_mip.cpp @@ -197,9 +197,6 @@ int run_single_file(std::string file_path, } } } - - CUOPT_LOG_INFO("Reliability branching: %d\n", reliability_branching); - settings.time_limit = time_limit; settings.heuristics_only = heuristics_only; settings.num_cpu_threads = num_cpu_threads; @@ -362,7 +359,8 @@ int main(int argc, char* argv[]) program.add_argument("--reliability-branching") .help("reliability branching: -1 (automatic), 0 (disable) or k > 0 (use k)") - .default_value(std::string("-1")); + .scan<'i', int>() + .default_value(-1); // Parse arguments try { @@ -392,7 +390,7 @@ int main(int argc, char* argv[]) bool log_to_console = program.get("--log-to-console")[0] == 't'; double memory_limit = program.get("--memory-limit"); bool track_allocations = program.get("--track-allocations")[0] == 't'; - int reliability_branching = program.get("--reliability-branching"); + int reliability_branching = program.get("--reliability-branching"); if (num_cpu_threads < 0) { num_cpu_threads = omp_get_max_threads() / n_gpus; } diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 8cbea68cc..e30fb203a 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -282,8 +282,9 @@ void branch_and_bound_t::report(char symbol, f_t obj, f_t lower_bound, i_t nodes_unexplored = exploration_stats_.nodes_unexplored; f_t user_obj = compute_user_objective(original_lp_, obj); f_t user_lower = compute_user_objective(original_lp_, lower_bound); - f_t iter_node = nodes_explored > 0 ? (f_t)exploration_stats_.total_lp_iters / nodes_explored - : exploration_stats_.total_lp_iters; + f_t iter_node = nodes_explored > 0 + ? static_cast(exploration_stats_.total_lp_iters) / nodes_explored + : static_cast(exploration_stats_.total_lp_iters.load()); std::string user_gap = user_mip_gap(user_obj, user_lower); settings_.log.printf("%c %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", symbol, @@ -1005,6 +1006,9 @@ void branch_and_bound_t::run_scheduler() std::array max_num_workers_per_type = bnb_get_max_workers(num_workers, worker_types); + worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); + active_workers_per_type_.fill(0); + #ifdef CUOPT_LOG_DEBUG for (auto type : worker_types) { settings_.log.debug("%c%d: max num of workers = %d", @@ -1131,7 +1135,7 @@ void branch_and_bound_t::run_scheduler() template void branch_and_bound_t::single_threaded_solve() { - bnb_worker_data_t* worker = worker_pool_.get_idle_worker(); + bnb_worker_data_t worker(0, original_lp_, Arow_, var_types_, settings_); f_t lower_bound = get_lower_bound(); f_t abs_gap = upper_bound_ - lower_bound; @@ -1181,7 +1185,7 @@ void branch_and_bound_t::single_threaded_solve() } worker.init_best_first(start_node.value(), original_lp_); - plunge_with(worker); + plunge_with(&worker); } } @@ -1289,8 +1293,7 @@ lp_status_t branch_and_bound_t::solve_root_relaxation( } template -mip_status_t branch_and_bound_t::solve(mip_solution_t& solution, - mip_solve_mode_t solve_mode) +mip_status_t branch_and_bound_t::solve(mip_solution_t& solution) { logger_t log; log.log = false; @@ -1461,9 +1464,6 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut calculate_variable_locks(original_lp_, var_up_locks_, var_down_locks_); } - worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); - active_workers_per_type_.fill(0); - is_running_ = true; settings_.log.printf( diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 9d527a684..f10759865 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -148,6 +148,8 @@ f_t trial_branching(const lp_problem_t& original_lp, f_t upper_bound, i_t bnb_lp_iter_per_node, f_t start_time, + i_t upper_max_lp_iter, + i_t lower_max_lp_iter, omp_atomic_t& total_lp_iter) { lp_problem_t child_problem = original_lp; @@ -157,8 +159,8 @@ f_t trial_branching(const lp_problem_t& original_lp, const bool initialize_basis = false; simplex_solver_settings_t child_settings = settings; child_settings.set_log(false); - i_t lp_iter_upper = settings.reliability_branching_settings.upper_max_lp_iter; - i_t lp_iter_lower = settings.reliability_branching_settings.lower_max_lp_iter; + i_t lp_iter_upper = upper_max_lp_iter; + i_t lp_iter_lower = lower_max_lp_iter; child_settings.iteration_limit = std::clamp(bnb_lp_iter_per_node, lp_iter_lower, lp_iter_upper); child_settings.cut_off = upper_bound + settings.dual_tol; child_settings.inside_mip = 2; @@ -495,6 +497,8 @@ i_t pseudo_costs_t::reliable_variable_selection( upper_bound, branch_and_bound_lp_iter_per_node, start_time, + reliability_branching_settings.upper_max_lp_iter, + reliability_branching_settings.lower_max_lp_iter, strong_branching_lp_iter); if (!std::isnan(obj)) { @@ -521,6 +525,8 @@ i_t pseudo_costs_t::reliable_variable_selection( upper_bound, branch_and_bound_lp_iter_per_node, start_time, + reliability_branching_settings.upper_max_lp_iter, + reliability_branching_settings.lower_max_lp_iter, strong_branching_lp_iter); if (!std::isnan(obj)) { From af718df518ea266a1ef4202dfa767099b5c833ca Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 4 Feb 2026 13:55:28 +0000 Subject: [PATCH 342/366] update naming --- cpp/src/dual_simplex/bb_worker_state.hpp | 118 +++---- cpp/src/dual_simplex/branch_and_bound.cpp | 287 +++++++++--------- cpp/src/dual_simplex/branch_and_bound.hpp | 65 ++-- .../dual_simplex/deterministic_workers.hpp | 127 ++++---- cpp/src/dual_simplex/mip_node.hpp | 17 +- cpp/src/utilities/work_unit_scheduler.hpp | 5 +- 6 files changed, 305 insertions(+), 314 deletions(-) diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp index 29422f08d..3bc603fc7 100644 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ b/cpp/src/dual_simplex/bb_worker_state.hpp @@ -70,7 +70,7 @@ struct queued_integer_solution_t { }; template -class bsp_worker_base_t : public bnb_worker_data_t { +class determinism_worker_base_t : public bnb_worker_data_t { using base_t = bnb_worker_data_t; public: @@ -99,12 +99,12 @@ class bsp_worker_base_t : public bnb_worker_data_t { double total_runtime{0.0}; double total_nowork_time{0.0}; - bsp_worker_base_t(int id, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings, - const std::string& context_name) + determinism_worker_base_t(int id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::string& context_name) : base_t(id, original_lp, Arow, var_types, settings), work_context(context_name) { work_context.deterministic = true; @@ -161,8 +161,9 @@ class bsp_worker_base_t : public bnb_worker_data_t { }; template -class bsp_bfs_worker_t : public bsp_worker_base_t> { - using base_t = bsp_worker_base_t>; +class determinism_bfs_worker_t + : public determinism_worker_base_t> { + using base_t = determinism_worker_base_t>; public: // Node management @@ -187,11 +188,11 @@ class bsp_bfs_worker_t : public bsp_worker_base_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings) + explicit determinism_bfs_worker_t(int id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings) : base_t(id, original_lp, Arow, var_types, settings, "BB_Worker_" + std::to_string(id)) { } @@ -313,8 +314,9 @@ class bsp_bfs_worker_t : public bsp_worker_base_t -class bsp_diving_worker_t : public bsp_worker_base_t> { - using base_t = bsp_worker_base_t>; +class determinism_diving_worker_t + : public determinism_worker_base_t> { + using base_t = determinism_worker_base_t>; public: bnb_worker_type_t diving_type{bnb_worker_type_t::PSEUDOCOST_DIVING}; @@ -335,13 +337,13 @@ class bsp_diving_worker_t : public bsp_worker_base_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings, - const std::vector* root_sol) + explicit determinism_diving_worker_t(int id, + bnb_worker_type_t type, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::vector* root_sol) : base_t(id, original_lp, Arow, var_types, settings, "Diving_Worker_" + std::to_string(id)), diving_type(type), root_solution(root_sol) @@ -350,10 +352,10 @@ class bsp_diving_worker_t : public bsp_worker_base_t -class bsp_worker_pool_base_t { +class determinism_worker_pool_base_t { protected: std::vector workers_; @@ -450,26 +452,29 @@ class bsp_worker_pool_base_t { }; template -class bsp_bfs_worker_pool_t : public bsp_worker_pool_base_t, - bsp_bfs_worker_pool_t> { - using base_t = - bsp_worker_pool_base_t, bsp_bfs_worker_pool_t>; +class determinism_bfs_worker_pool_t + : public determinism_worker_pool_base_t, + determinism_bfs_worker_pool_t> { + using base_t = determinism_worker_pool_base_t, + determinism_bfs_worker_pool_t>; public: - bsp_bfs_worker_pool_t(int num_workers, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings) + determinism_bfs_worker_pool_t(int num_workers, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings) { for (int i = 0; i < num_workers; ++i) { this->workers_.emplace_back(i, original_lp, Arow, var_types, settings); } } - void collect_worker_events(bsp_bfs_worker_t& worker, + void collect_worker_events(determinism_bfs_worker_t& worker, bb_event_batch_t& all_events) { for (auto& event : worker.events.events) { @@ -480,23 +485,24 @@ class bsp_bfs_worker_pool_t : public bsp_worker_pool_base_t -class bsp_diving_worker_pool_t : public bsp_worker_pool_base_t, - bsp_diving_worker_pool_t> { - using base_t = bsp_worker_pool_base_t, - bsp_diving_worker_pool_t>; +class determinism_diving_worker_pool_t + : public determinism_worker_pool_base_t, + determinism_diving_worker_pool_t> { + using base_t = determinism_worker_pool_base_t, + determinism_diving_worker_pool_t>; public: - bsp_diving_worker_pool_t(int num_workers, - const std::vector& diving_types, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings, - const std::vector* root_solution) + determinism_diving_worker_pool_t(int num_workers, + const std::vector& diving_types, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::vector* root_solution) { this->workers_.reserve(num_workers); for (int i = 0; i < num_workers; ++i) { @@ -505,7 +511,7 @@ class bsp_diving_worker_pool_t : public bsp_worker_pool_base_t&, bb_event_batch_t&) {} + void collect_worker_events(determinism_diving_worker_t&, bb_event_batch_t&) {} }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 834a19aa0..cbc2cd5ee 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -37,7 +37,7 @@ #include #include -// #define BSP_DISABLE_BOUNDS_STRENGTHENING +// #define DETERMINISM_DISABLE_BOUNDS_STRENGTHENING namespace cuopt::linear_programming::dual_simplex { @@ -357,7 +357,7 @@ template void branch_and_bound_t::queue_external_solution_deterministic( const std::vector& solution, double work_unit_ts) { - // In BSP mode, queue the solution to be processed at the correct work unit timestamp + // In deterministic mode, queue the solution to be processed at the correct work unit timestamp // This ensures deterministic ordering of solution events if (solution.size() != original_problem_.num_cols) { @@ -751,9 +751,9 @@ struct opportunistic_tree_update_policy_t { }; template -struct bsp_tree_update_policy_t { +struct determinism_tree_update_policy_t { branch_and_bound_t& bnb; - bsp_bfs_worker_t& worker; + determinism_bfs_worker_t& worker; f_t upper_bound() const { return worker.local_upper_bound; } @@ -1712,9 +1712,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut calculate_variable_locks(original_lp_, var_up_locks_, var_down_locks_); } - // Choose between BSP coordinator (deterministic) and scheduler-based exploration if (settings_.deterministic) { - run_bsp_coordinator(Arow_); + run_determinism_coordinator(Arow_); } else if (solve_mode == mip_solve_mode_t::BNB_PARALLEL) { #pragma omp parallel num_threads(settings_.num_threads) { @@ -1727,8 +1726,8 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut // Compute final lower bound f_t lower_bound; - if (bsp_mode_enabled_) { - lower_bound = compute_bsp_lower_bound(); + if (determinism_mode_enabled_) { + lower_bound = compute_lower_bound_deterministic(); if (lower_bound == std::numeric_limits::infinity() && incumbent_.has_incumbent) { lower_bound = upper_bound_.load(); } @@ -1741,11 +1740,11 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut } // ============================================================================ -// BSP (Bulk Synchronous Parallel) Deterministic implementation +// Deterministic implementation // ============================================================================ -// The BSP model is based on letting independent workers execute during virtual time intervals, -// and exchange data during serialized interval sync points. +// The deterministic BSP model is based on letting independent workers execute during virtual time +// intervals, and exchange data during serialized interval sync points. /* Work Units: 0 0.5 1.0 @@ -1803,11 +1802,11 @@ Legend: ▓▓▓ = actively working ░░░ = waiting at barrier [hash */ template -void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t& Arow) +void branch_and_bound_t::run_determinism_coordinator(const csr_matrix_t& Arow) { - raft::common::nvtx::range scope("BB::bsp_coordinator"); + raft::common::nvtx::range scope("BB::determinism_coordinator"); - bsp_horizon_step_ = 0.50; + determinism_horizon_step_ = 0.50; // Compute worker counts using the same formula as reliability-branching scheduler const i_t num_workers = 2 * settings_.num_threads; @@ -1821,12 +1820,12 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t>( + determinism_workers_ = std::make_unique>( num_bfs_workers, original_lp_, Arow, var_types_, settings_); if (num_diving_workers > 0) { @@ -1838,51 +1837,52 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t>(num_diving_workers, - diving_types, - original_lp_, - Arow, - var_types_, - settings_, - &root_relax_soln_.x); + determinism_diving_workers_ = + std::make_unique>(num_diving_workers, + diving_types, + original_lp_, + Arow, + var_types_, + settings_, + &root_relax_soln_.x); } } - bsp_scheduler_ = std::make_unique(bsp_horizon_step_); + determinism_scheduler_ = std::make_unique(determinism_horizon_step_); - scoped_context_registrations_t context_registrations(*bsp_scheduler_); - for (auto& worker : *bsp_workers_) { + scoped_context_registrations_t context_registrations(*determinism_scheduler_); + for (auto& worker : *determinism_workers_) { context_registrations.add(worker.work_context); } - if (bsp_diving_workers_) { - for (auto& worker : *bsp_diving_workers_) { + if (determinism_diving_workers_) { + for (auto& worker : *determinism_diving_workers_) { context_registrations.add(worker.work_context); } } - int actual_diving_workers = bsp_diving_workers_ ? (int)bsp_diving_workers_->size() : 0; + int actual_diving_workers = + determinism_diving_workers_ ? (int)determinism_diving_workers_->size() : 0; settings_.log.printf( - "BSP Mode: %d BFS workers + %d diving workers, horizon step = %.2f work " + "Deterministic Mode: %d BFS workers + %d diving workers, horizon step = %.2f work " "units\n", num_bfs_workers, actual_diving_workers, - bsp_horizon_step_); + determinism_horizon_step_); search_tree_.root.get_down_child()->origin_worker_id = -1; search_tree_.root.get_down_child()->creation_seq = 0; search_tree_.root.get_up_child()->origin_worker_id = -1; search_tree_.root.get_up_child()->creation_seq = 1; - (*bsp_workers_)[0].enqueue_node(search_tree_.root.get_down_child()); - (*bsp_workers_)[1 % num_bfs_workers].enqueue_node(search_tree_.root.get_up_child()); + (*determinism_workers_)[0].enqueue_node(search_tree_.root.get_down_child()); + (*determinism_workers_)[1 % num_bfs_workers].enqueue_node(search_tree_.root.get_up_child()); - bsp_scheduler_->set_sync_callback([this](double) { bsp_sync_callback(0); }); + determinism_scheduler_->set_sync_callback([this](double) { determinism_sync_callback(0); }); std::vector incumbent_snapshot; if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } - for (auto& worker : *bsp_workers_) { + for (auto& worker : *determinism_workers_) { worker.set_snapshots(upper_bound_.load(), (const f_t*)pc_.pseudo_cost_sum_up.data(), (const f_t*)pc_.pseudo_cost_sum_down.data(), @@ -1891,11 +1891,11 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t::run_bsp_coordinator(const csr_matrix_t 0) ? (100.0 * sync_time / total_time) : 0.0; @@ -1954,12 +1954,12 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_tsize() > 0) { + if (determinism_diving_workers_ && determinism_diving_workers_->size() > 0) { settings_.log.printf("\n"); - settings_.log.printf("BSP Diving Worker Statistics:\n"); + settings_.log.printf("Diving Worker Statistics:\n"); settings_.log.printf(" Worker | Type | Dives | Nodes | IntSol | Clock | NoWork\n"); settings_.log.printf(" -------+--------+---------+--------+--------+----------+-------\n"); - for (const auto& worker : *bsp_diving_workers_) { + for (const auto& worker : *determinism_diving_workers_) { const char* type_str = "???"; switch (worker.diving_type) { case bnb_worker_type_t::PSEUDOCOST_DIVING: type_str = "PC"; break; @@ -1993,12 +1993,12 @@ void branch_and_bound_t::run_bsp_coordinator(const csr_matrix_t -void branch_and_bound_t::run_worker_loop(bsp_bfs_worker_t& worker, - search_tree_t& search_tree) +void branch_and_bound_t::run_deterministic_bfs_loop( + determinism_bfs_worker_t& worker, search_tree_t& search_tree) { raft::common::nvtx::range scope("BB::worker_loop"); - while (bsp_global_termination_status_ == mip_status_t::UNSET) { + while (determinism_global_termination_status_ == mip_status_t::UNSET) { if (worker.has_work()) { mip_node_t* node = worker.dequeue_node(); if (node == nullptr) { continue; } @@ -2018,8 +2018,9 @@ void branch_and_bound_t::run_worker_loop(bsp_bfs_worker_t& w bool is_child = (node->parent == worker.last_solved_node); worker.recompute_bounds_and_basis = !is_child; - node_solve_info_t status = solve_node_bsp(worker, node, search_tree, worker.horizon_end); - worker.last_solved_node = node; + node_solve_info_t status = + solve_node_deterministic(worker, node, search_tree, worker.horizon_end); + worker.last_solved_node = node; if (status == node_solve_info_t::TIME_LIMIT || status == node_solve_info_t::WORK_LIMIT) { continue; @@ -2030,19 +2031,19 @@ void branch_and_bound_t::run_worker_loop(bsp_bfs_worker_t& w // No work - advance to sync point to participate in barrier f_t nowork_start = tic(); - bsp_scheduler_->wait_for_next_sync(worker.work_context); + determinism_scheduler_->wait_for_next_sync(worker.work_context); worker.total_nowork_time += toc(nowork_start); } } template -void branch_and_bound_t::bsp_sync_callback(int worker_id) +void branch_and_bound_t::determinism_sync_callback(int worker_id) { - raft::common::nvtx::range scope("BB::bsp_sync_callback"); + raft::common::nvtx::range scope("BB::determinism_sync_callback"); - ++bsp_horizon_number_; - double horizon_start = bsp_current_horizon_ - bsp_horizon_step_; - double horizon_end = bsp_current_horizon_; + ++determinism_horizon_number_; + double horizon_start = determinism_current_horizon_ - determinism_horizon_step_; + double horizon_end = determinism_current_horizon_; double wait_start = tic(); producer_sync_.wait_for_producers(horizon_end); @@ -2053,7 +2054,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) work_unit_context_.global_work_units_elapsed = horizon_end; - bb_event_batch_t all_events = bsp_workers_->collect_and_sort_events(); + bb_event_batch_t all_events = determinism_workers_->collect_and_sort_events(); sort_replay_events(all_events); @@ -2061,12 +2062,12 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) collect_diving_solutions(); - for (auto& worker : *bsp_workers_) { + for (auto& worker : *determinism_workers_) { worker.integer_solutions.clear(); worker.pseudo_cost_updates.clear(); } - if (bsp_diving_workers_) { - for (auto& worker : *bsp_diving_workers_) { + if (determinism_diving_workers_) { + for (auto& worker : *determinism_diving_workers_) { worker.integer_solutions.clear(); worker.pseudo_cost_updates.clear(); } @@ -2084,11 +2085,11 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) state_data.push_back(static_cast(exploration_stats_.nodes_explored)); state_data.push_back(static_cast(exploration_stats_.nodes_unexplored)); f_t ub = upper_bound_.load(); - f_t lb = compute_bsp_lower_bound(); + f_t lb = compute_lower_bound_deterministic(); state_data.push_back(std::bit_cast(ub)); state_data.push_back(std::bit_cast(lb)); - for (auto& worker : *bsp_workers_) { + for (auto& worker : *determinism_workers_) { if (worker.current_node != nullptr) { state_data.push_back(worker.current_node->get_id_packed()); } @@ -2104,12 +2105,12 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) state_hash ^= pc_.compute_state_hash(); } - bsp_current_horizon_ += bsp_horizon_step_; + determinism_current_horizon_ += determinism_horizon_step_; std::vector incumbent_snapshot; if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } - for (auto& worker : *bsp_workers_) { + for (auto& worker : *determinism_workers_) { worker.set_snapshots(upper_bound_.load(), (const f_t*)pc_.pseudo_cost_sum_up.data(), (const f_t*)pc_.pseudo_cost_sum_down.data(), @@ -2118,11 +2119,11 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) incumbent_snapshot, exploration_stats_.total_lp_iters.load(), horizon_end, - bsp_current_horizon_); + determinism_current_horizon_); } - if (bsp_diving_workers_) { - for (auto& worker : *bsp_diving_workers_) { + if (determinism_diving_workers_) { + for (auto& worker : *determinism_diving_workers_) { worker.set_snapshots(upper_bound_.load(), (const f_t*)pc_.pseudo_cost_sum_up.data(), (const f_t*)pc_.pseudo_cost_sum_down.data(), @@ -2131,35 +2132,35 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) incumbent_snapshot, exploration_stats_.total_lp_iters.load(), horizon_end, - bsp_current_horizon_); + determinism_current_horizon_); } } - f_t lower_bound = compute_bsp_lower_bound(); + f_t lower_bound = compute_lower_bound_deterministic(); f_t upper_bound = upper_bound_.load(); f_t abs_gap = upper_bound - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); if (abs_gap <= settings_.absolute_mip_gap_tol || rel_gap <= settings_.relative_mip_gap_tol) { - bsp_global_termination_status_ = mip_status_t::OPTIMAL; + determinism_global_termination_status_ = mip_status_t::OPTIMAL; } - if (!bsp_workers_->any_has_work()) { + if (!determinism_workers_->any_has_work()) { // Tree exhausted - check if we found a solution if (upper_bound == std::numeric_limits::infinity()) { - bsp_global_termination_status_ = mip_status_t::INFEASIBLE; + determinism_global_termination_status_ = mip_status_t::INFEASIBLE; } else { - bsp_global_termination_status_ = mip_status_t::OPTIMAL; + determinism_global_termination_status_ = mip_status_t::OPTIMAL; } } if (toc(exploration_stats_.start_time) > settings_.time_limit) { - bsp_global_termination_status_ = mip_status_t::TIME_LIMIT; + determinism_global_termination_status_ = mip_status_t::TIME_LIMIT; } // Stop early if next horizon exceeds work limit - if (bsp_current_horizon_ > settings_.work_limit) { - bsp_global_termination_status_ = mip_status_t::WORK_LIMIT; + if (determinism_current_horizon_ > settings_.work_limit) { + determinism_global_termination_status_ = mip_status_t::WORK_LIMIT; } f_t obj = compute_user_objective(original_lp_, upper_bound); @@ -2168,7 +2169,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) std::string idle_workers; i_t idle_count = 0; - for (const auto& w : *bsp_workers_) { + for (const auto& w : *determinism_workers_) { if (!w.has_work() && w.current_node == nullptr) { ++idle_count; // if (!idle_workers.empty()) idle_workers += ","; @@ -2178,7 +2179,7 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) idle_workers = idle_count > 0 ? std::to_string(idle_count) + " idle" : ""; settings_.log.printf("W%-5g %8d %8lu %+13.6e %+10.6e %s %8.2f [%08x]%s%s\n", - bsp_horizon_number_ * bsp_horizon_step_, + determinism_current_horizon_, exploration_stats_.nodes_explored, exploration_stats_.nodes_unexplored, obj, @@ -2191,12 +2192,13 @@ void branch_and_bound_t::bsp_sync_callback(int worker_id) } template -node_solve_info_t branch_and_bound_t::solve_node_bsp(bsp_bfs_worker_t& worker, - mip_node_t* node_ptr, - search_tree_t& search_tree, - double current_horizon) +node_solve_info_t branch_and_bound_t::solve_node_deterministic( + determinism_bfs_worker_t& worker, + mip_node_t* node_ptr, + search_tree_t& search_tree, + double current_horizon) { - raft::common::nvtx::range scope("BB::solve_node_bsp"); + raft::common::nvtx::range scope("BB::solve_node_deterministic"); double work_units_at_start = worker.work_context.global_work_units_elapsed; double clock_at_start = worker.clock; @@ -2225,7 +2227,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bsp_bfs_worker_t< lp_settings.scale_columns = false; bool feasible = true; -#ifndef BSP_DISABLE_BOUNDS_STRENGTHENING +#ifndef DETERMINISM_DISABLE_BOUNDS_STRENGTHENING raft::common::nvtx::range scope_bs("BB::bound_strengthening"); f_t bs_start_time = tic(); feasible = worker.node_presolver.bounds_strengthening( @@ -2292,7 +2294,7 @@ node_solve_info_t branch_and_bound_t::solve_node_bsp(bsp_bfs_worker_t< ++exploration_stats_.nodes_explored; --exploration_stats_.nodes_unexplored; - bsp_tree_update_policy_t policy{*this, worker}; + determinism_tree_update_policy_t policy{*this, worker}; auto [status, round_dir] = update_tree_impl(node_ptr, search_tree, worker.leaf_problem, @@ -2333,13 +2335,13 @@ void branch_and_bound_t::process_worker_solutions(PoolT& pool, [](const queued_integer_solution_t* a, const queued_integer_solution_t* b) { return *a < *b; }); - f_t bsp_lower = compute_bsp_lower_bound(); - f_t current_upper = upper_bound_.load(); + f_t determinism_lower = compute_lower_bound_deterministic(); + f_t current_upper = upper_bound_.load(); for (const auto* sol : all_solutions) { if (sol->objective < current_upper) { f_t user_obj = compute_user_objective(original_lp_, sol->objective); - f_t user_lower = compute_user_objective(original_lp_, bsp_lower); + f_t user_lower = compute_user_objective(original_lp_, determinism_lower); i_t nodes_explored = exploration_stats_.nodes_explored.load(); i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); @@ -2413,7 +2415,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t> to_repair; - // TODO: support repair queue in BSP mode + // TODO: support repair queue in deterministic mode // mutex_repair_.lock(); // if (repair_queue_.size() > 0) { // to_repair = repair_queue_; @@ -2426,7 +2428,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t& a, const std::vector& b) { return a < b; }); if (to_repair.size() > 0) { - settings_.log.debug("BSP sync: Attempting to repair %ld injected solutions\n", + settings_.log.debug("Deterministic sync: Attempting to repair %ld injected solutions\n", to_repair.size()); for (const std::vector& potential_solution : to_repair) { std::vector repaired_solution; @@ -2437,7 +2439,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t::sort_replay_events(const bb_event_batch_t heuristic_solutions; mutex_heuristic_queue_.lock(); { std::vector future_solutions; for (auto& sol : heuristic_solution_queue_) { - if (sol.wut < bsp_current_horizon_) { + if (sol.wut < determinism_current_horizon_) { heuristic_solutions.push_back(std::move(sol)); } else { future_solutions.push_back(std::move(sol)); @@ -2506,10 +2508,11 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t::infinity(); @@ -2542,14 +2545,15 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t&, int) { - return bnb_worker_type_t::BEST_FIRST; - }); + process_worker_solutions(*determinism_workers_, + [](const determinism_bfs_worker_pool_t&, int) { + return bnb_worker_type_t::BEST_FIRST; + }); // Merge and apply pseudo-cost updates from BFS workers - merge_pseudo_cost_updates(*bsp_workers_); + merge_pseudo_cost_updates(*determinism_workers_); - for (const auto& worker : *bsp_workers_) { + for (const auto& worker : *determinism_workers_) { fetch_min(lower_bound_ceiling_, worker.local_lower_bound_ceiling); } } @@ -2559,7 +2563,7 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() { f_t upper_bound = upper_bound_.load(); - for (auto& worker : *bsp_workers_) { + for (auto& worker : *determinism_workers_) { // Check nodes in plunge stack - filter in place { std::deque*> surviving; @@ -2596,7 +2600,7 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() template void branch_and_bound_t::balance_worker_loads() { - const size_t num_workers = bsp_workers_->size(); + const size_t num_workers = determinism_workers_->size(); if (num_workers <= 1) return; constexpr bool force_rebalance_every_sync = false; @@ -2608,7 +2612,7 @@ void branch_and_bound_t::balance_worker_loads() size_t min_work = std::numeric_limits::max(); for (size_t w = 0; w < num_workers; ++w) { - auto& worker = (*bsp_workers_)[w]; + auto& worker = (*determinism_workers_)[w]; work_counts[w] = worker.queue_size(); total_work += work_counts[w]; max_work = std::max(max_work, work_counts[w]); @@ -2627,7 +2631,7 @@ void branch_and_bound_t::balance_worker_loads() if (!needs_balance) return; std::vector*> all_nodes; - for (auto& worker : *bsp_workers_) { + for (auto& worker : *determinism_workers_) { for (auto* node : worker.backlog.data()) { all_nodes.push_back(node); } @@ -2636,8 +2640,6 @@ void branch_and_bound_t::balance_worker_loads() if (all_nodes.empty()) return; - // Sort by BSP identity for deterministic distribution - // Uses lexicographic order of (origin_worker_id, creation_seq) auto deterministic_less = [](const mip_node_t* a, const mip_node_t* b) { if (a->origin_worker_id != b->origin_worker_id) { return a->origin_worker_id < b->origin_worker_id; @@ -2655,12 +2657,12 @@ void branch_and_bound_t::balance_worker_loads() // Distribute nodes for (size_t i = 0; i < all_nodes.size(); ++i) { size_t worker_idx = worker_order[i % num_workers]; - (*bsp_workers_)[worker_idx].enqueue_node(all_nodes[i]); + (*determinism_workers_)[worker_idx].enqueue_node(all_nodes[i]); } } template -f_t branch_and_bound_t::compute_bsp_lower_bound() +f_t branch_and_bound_t::compute_lower_bound_deterministic() { // Compute lower bound from BFS worker local structures only const f_t inf = std::numeric_limits::infinity(); @@ -2668,7 +2670,7 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() if (!std::isfinite(lower_bound)) lower_bound = inf; // Check all BFS worker queues - for (const auto& worker : *bsp_workers_) { + for (const auto& worker : *determinism_workers_) { // Check paused node (current_node) if (worker.current_node != nullptr) { lower_bound = std::min(worker.current_node->lower_bound, lower_bound); @@ -2688,19 +2690,15 @@ f_t branch_and_bound_t::compute_bsp_lower_bound() return lower_bound; } -// ============================================================================ -// BSP Diving -// ============================================================================ - template void branch_and_bound_t::populate_diving_heap() { // Clear diving heap from previous horizon diving_heap_.clear(); - if (!bsp_diving_workers_ || bsp_diving_workers_->size() == 0) return; + if (!determinism_diving_workers_ || determinism_diving_workers_->size() == 0) return; - const int num_diving = bsp_diving_workers_->size(); + const int num_diving = determinism_diving_workers_->size(); constexpr int target_nodes_per_worker = 10; const int target_total = num_diving * target_nodes_per_worker; f_t upper_bound = upper_bound_.load(); @@ -2708,7 +2706,7 @@ void branch_and_bound_t::populate_diving_heap() // Collect candidate nodes from BFS worker backlog heaps std::vector*, f_t>> candidates; - for (auto& worker : *bsp_workers_) { + for (auto& worker : *determinism_workers_) { for (auto* node : worker.backlog.data()) { if (node->lower_bound < upper_bound) { f_t score = node->objective_estimate; @@ -2720,7 +2718,6 @@ void branch_and_bound_t::populate_diving_heap() if (candidates.empty()) return; - // Sort candidates by score with deterministic tie-breaking by BSP identity // Technically not necessary as it stands since the worker assignments and ordering are // deterministic std::sort(candidates.begin(), candidates.end(), [](const auto& a, const auto& b) { @@ -2741,7 +2738,7 @@ void branch_and_bound_t::populate_diving_heap() template void branch_and_bound_t::assign_diving_nodes() { - if (!bsp_diving_workers_ || bsp_diving_workers_->size() == 0) { + if (!determinism_diving_workers_ || determinism_diving_workers_->size() == 0) { diving_heap_.clear(); return; } @@ -2750,17 +2747,17 @@ void branch_and_bound_t::assign_diving_nodes() // Round-robin assignment to balance load across workers int worker_idx = 0; - const int num_workers = bsp_diving_workers_->size(); + const int num_workers = determinism_diving_workers_->size(); while (!diving_heap_.empty()) { - auto& worker = (*bsp_diving_workers_)[worker_idx]; + auto& worker = (*determinism_diving_workers_)[worker_idx]; // Skip workers that already have enough nodes if ((int)worker.dive_queue_size() >= target_nodes_per_worker) { worker_idx = (worker_idx + 1) % num_workers; // Check if all workers are full bool all_full = true; - for (auto& w : *bsp_diving_workers_) { + for (auto& w : *determinism_diving_workers_) { if ((int)w.dive_queue_size() < target_nodes_per_worker) { all_full = false; break; @@ -2782,44 +2779,44 @@ void branch_and_bound_t::assign_diving_nodes() template void branch_and_bound_t::collect_diving_solutions() { - if (!bsp_diving_workers_) return; + if (!determinism_diving_workers_) return; // Collect integer solutions from diving workers and update global incumbent - process_worker_solutions(*bsp_diving_workers_, - [](const bsp_diving_worker_pool_t& pool, int worker_id) { - return pool[worker_id].diving_type; - }); + process_worker_solutions(*determinism_diving_workers_, + [](const determinism_diving_worker_pool_t& pool, + int worker_id) { return pool[worker_id].diving_type; }); // Merge pseudo-cost updates from diving workers - merge_pseudo_cost_updates(*bsp_diving_workers_); + merge_pseudo_cost_updates(*determinism_diving_workers_); } template -void branch_and_bound_t::run_diving_worker_loop(bsp_diving_worker_t& worker) +void branch_and_bound_t::run_deterministic_diving_loop( + determinism_diving_worker_t& worker) { raft::common::nvtx::range scope("BB::diving_worker_loop"); - while (bsp_global_termination_status_ == mip_status_t::UNSET) { + while (determinism_global_termination_status_ == mip_status_t::UNSET) { // Process dives from queue until empty or horizon exhausted auto node_opt = worker.dequeue_dive_node(); if (node_opt.has_value()) { - dive_from_bsp(worker, std::move(node_opt.value())); + deterministic_dive(worker, std::move(node_opt.value())); continue; } // Queue empty - wait for next sync point where we'll be assigned new nodes f_t nowork_start = tic(); - bsp_scheduler_->wait_for_next_sync(worker.work_context); + determinism_scheduler_->wait_for_next_sync(worker.work_context); worker.total_nowork_time += toc(nowork_start); // Termination status is checked in loop condition } } template -void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_t& worker, - mip_node_t starting_node) +void branch_and_bound_t::deterministic_dive(determinism_diving_worker_t& worker, + mip_node_t starting_node) { - raft::common::nvtx::range scope("BB::dive_from_bsp"); + raft::common::nvtx::range scope("BB::deterministic_dive"); // Create local search tree for the dive search_tree_t dive_tree(std::move(starting_node)); @@ -2838,12 +2835,12 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_t& worker.lp_iters_this_dive = 0; worker.recompute_bounds_and_basis = true; - while (!stack.empty() && bsp_global_termination_status_ == mip_status_t::UNSET && + while (!stack.empty() && determinism_global_termination_status_ == mip_status_t::UNSET && nodes_this_dive < max_nodes_per_dive) { // Check horizon budget - sync if exhausted if (worker.work_context.global_work_units_elapsed >= worker.horizon_end) { - bsp_scheduler_->wait_for_next_sync(worker.work_context); - if (bsp_global_termination_status_ != mip_status_t::UNSET) break; + determinism_scheduler_->wait_for_next_sync(worker.work_context); + if (determinism_global_termination_status_ != mip_status_t::UNSET) break; } mip_node_t* node_ptr = stack.front(); @@ -2881,7 +2878,7 @@ void branch_and_bound_t::dive_from_bsp(bsp_diving_worker_t& lp_settings.time_limit = remaining_time; lp_settings.scale_columns = false; -#ifndef BSP_DISABLE_BOUNDS_STRENGTHENING +#ifndef DETERMINISM_DISABLE_BOUNDS_STRENGTHENING bool feasible = worker.node_presolver.bounds_strengthening( worker.leaf_problem.lower, worker.leaf_problem.upper, worker.bounds_changed, lp_settings); diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 84e66f176..fbc18387f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -76,7 +76,7 @@ void upper_bound_callback(f_t upper_bound); template struct opportunistic_tree_update_policy_t; template -struct bsp_tree_update_policy_t; +struct determinism_tree_update_policy_t; template class branch_and_bound_t { @@ -110,7 +110,6 @@ class branch_and_bound_t { // Set a solution based on the user problem during the course of the solve void set_new_solution(const std::vector& solution); - // BSP-aware solution injection for deterministic mode // This queues the solution to be processed at the correct work unit timestamp void queue_external_solution_deterministic(const std::vector& solution, double work_unit_ts); @@ -259,11 +258,11 @@ class branch_and_bound_t { bnb_worker_data_t* worker_data); // ============================================================================ - // BSP (Bulk Synchronous Parallel) methods for deterministic parallel B&B + // Deterministic BSP (Bulk Synchronous Parallel) methods for deterministic parallel B&B // ============================================================================ - // Main BSP coordinator loop - runs in deterministic mode - void run_bsp_coordinator(const csr_matrix_t& Arow); + // Main determinism coordinator loop + void run_determinism_coordinator(const csr_matrix_t& Arow); // Gather all events generated, sort by WU timestamp, apply void sort_replay_events(const bb_event_batch_t& events); @@ -274,31 +273,24 @@ class branch_and_bound_t { // Balance worker loads - redistribute nodes only if significant imbalance detected void balance_worker_loads(); - // BSP-specific node solving that records events - node_solve_info_t solve_node_bsp(bsp_bfs_worker_t& worker, - mip_node_t* node_ptr, - search_tree_t& search_tree, - double current_horizon); + node_solve_info_t solve_node_deterministic(determinism_bfs_worker_t& worker, + mip_node_t* node_ptr, + search_tree_t& search_tree, + double current_horizon); - // Compute accurate lower bound from all BSP sources (called during sync phase) - f_t compute_bsp_lower_bound(); + f_t compute_lower_bound_deterministic(); - // BSP worker loop - runs continuously until scheduler signals stop - void run_worker_loop(bsp_bfs_worker_t& worker, search_tree_t& search_tree); + void run_deterministic_bfs_loop(determinism_bfs_worker_t& worker, + search_tree_t& search_tree); - // BSP sync callback - executed when all workers reach barrier - // Sets bsp_global_termination_status_ if termination is needed - void bsp_sync_callback(int worker_id); + // Executed when all workers reach barrier + // Handles termination logic serially in deterministic mode + void determinism_sync_callback(int worker_id); - // ============================================================================ - // BSP Diving methods - // ============================================================================ - - // Run diving worker loop - void run_diving_worker_loop(bsp_diving_worker_t& worker); + void run_deterministic_diving_loop(determinism_diving_worker_t& worker); - // Perform a deterministic dive from the given starting node - void dive_from_bsp(bsp_diving_worker_t& worker, mip_node_t starting_node); + void deterministic_dive(determinism_diving_worker_t& worker, + mip_node_t starting_node); // Populate diving heap from BFS worker backlogs at sync void populate_diving_heap(); @@ -316,18 +308,17 @@ class branch_and_bound_t { void merge_pseudo_cost_updates(PoolT& pool); friend struct opportunistic_tree_update_policy_t; - friend struct bsp_tree_update_policy_t; + friend struct determinism_tree_update_policy_t; private: - // BSP state // unique_ptr as we only want to initialize these if we're in the determinism codepath - std::unique_ptr> bsp_workers_; - std::unique_ptr bsp_scheduler_; - mip_status_t bsp_global_termination_status_{mip_status_t::UNSET}; - double bsp_horizon_step_{5.0}; // Work unit step per horizon (tunable) - double bsp_current_horizon_{0.0}; // Current horizon target - bool bsp_mode_enabled_{false}; // Whether BSP mode is active - int bsp_horizon_number_{0}; // Current horizon number (for debugging) + std::unique_ptr> determinism_workers_; + std::unique_ptr determinism_scheduler_; + mip_status_t determinism_global_termination_status_{mip_status_t::UNSET}; + double determinism_horizon_step_{5.0}; // Work unit step per horizon (tunable) + double determinism_current_horizon_{0.0}; // Current horizon target + bool determinism_mode_enabled_{false}; + int determinism_horizon_number_{0}; // Current horizon number (for debugging) // Producer synchronization for external heuristics (CPUFJ) // B&B waits for registered producers at each horizon sync @@ -338,7 +329,7 @@ class branch_and_bound_t { double max_producer_wait_time_{0.0}; i_t producer_wait_count_{0}; - // BSP heuristic solution queue - solutions received from GPU heuristics + // Determinism heuristic solution queue - solutions received from GPU heuristics // Stored with work unit timestamp for deterministic ordering struct queued_heuristic_solution_t { std::vector solution; @@ -349,12 +340,12 @@ class branch_and_bound_t { std::vector heuristic_solution_queue_; // ============================================================================ - // BSP Diving state + // Determinism Diving state // ============================================================================ // Diving worker pool // unique_ptr as we only want to initialize these if we're in the determinism codepath - std::unique_ptr> bsp_diving_workers_; + std::unique_ptr> determinism_diving_workers_; // Diving heap - nodes available for diving, sorted by objective estimate struct diving_entry_t { diff --git a/cpp/src/dual_simplex/deterministic_workers.hpp b/cpp/src/dual_simplex/deterministic_workers.hpp index 3086b65fd..8b7c0a3c9 100644 --- a/cpp/src/dual_simplex/deterministic_workers.hpp +++ b/cpp/src/dual_simplex/deterministic_workers.hpp @@ -70,7 +70,7 @@ struct queued_integer_solution_t { }; template -class bsp_worker_base_t : public bnb_worker_data_t { +class determinism_worker_base_t : public bnb_worker_data_t { using base_t = bnb_worker_data_t; public: @@ -99,12 +99,12 @@ class bsp_worker_base_t : public bnb_worker_data_t { double total_runtime{0.0}; double total_nowork_time{0.0}; - bsp_worker_base_t(int id, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings, - const std::string& context_name) + determinism_worker_base_t(int id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::string& context_name) : base_t(id, original_lp, Arow, var_types, settings), work_context(context_name) { work_context.deterministic = true; @@ -162,8 +162,9 @@ class bsp_worker_base_t : public bnb_worker_data_t { }; template -class bsp_bfs_worker_t : public bsp_worker_base_t> { - using base_t = bsp_worker_base_t>; +class determinism_bfs_worker_t + : public determinism_worker_base_t> { + using base_t = determinism_worker_base_t>; public: // Node management @@ -188,11 +189,11 @@ class bsp_bfs_worker_t : public bsp_worker_base_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings) + explicit determinism_bfs_worker_t(int id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings) : base_t(id, original_lp, Arow, var_types, settings, "BB_Worker_" + std::to_string(id)) { } @@ -314,8 +315,9 @@ class bsp_bfs_worker_t : public bsp_worker_base_t -class bsp_diving_worker_t : public bsp_worker_base_t> { - using base_t = bsp_worker_base_t>; +class determinism_diving_worker_t + : public determinism_worker_base_t> { + using base_t = determinism_worker_base_t>; public: bnb_worker_type_t diving_type{bnb_worker_type_t::PSEUDOCOST_DIVING}; @@ -336,13 +338,13 @@ class bsp_diving_worker_t : public bsp_worker_base_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings, - const std::vector* root_sol) + explicit determinism_diving_worker_t(int id, + bnb_worker_type_t type, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::vector* root_sol) : base_t(id, original_lp, Arow, var_types, settings, "Diving_Worker_" + std::to_string(id)), diving_type(type), root_solution(root_sol) @@ -351,10 +353,10 @@ class bsp_diving_worker_t : public bsp_worker_base_t -class bsp_worker_pool_base_t { +class determinism_worker_pool_base_t { protected: std::vector workers_; @@ -456,23 +455,23 @@ class bsp_worker_pool_base_t { auto end() const { return workers_.end(); } }; -// ============================================================================ -// BSP BFS Worker Pool -// ============================================================================ template -class bsp_bfs_worker_pool_t : public bsp_worker_pool_base_t, - bsp_bfs_worker_pool_t> { - using base_t = - bsp_worker_pool_base_t, bsp_bfs_worker_pool_t>; +class determinism_bfs_worker_pool_t + : public determinism_worker_pool_base_t, + determinism_bfs_worker_pool_t> { + using base_t = determinism_worker_pool_base_t, + determinism_bfs_worker_pool_t>; public: - bsp_bfs_worker_pool_t(int num_workers, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings) + determinism_bfs_worker_pool_t(int num_workers, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings) { this->workers_.reserve(num_workers); for (int i = 0; i < num_workers; ++i) { @@ -480,7 +479,7 @@ class bsp_bfs_worker_pool_t : public bsp_worker_pool_base_t& worker, + void collect_worker_events(determinism_bfs_worker_t& worker, bb_event_batch_t& all_events) { for (auto& event : worker.events.events) { @@ -490,27 +489,25 @@ class bsp_bfs_worker_pool_t : public bsp_worker_pool_base_t -class bsp_diving_worker_pool_t : public bsp_worker_pool_base_t, - bsp_diving_worker_pool_t> { - using base_t = bsp_worker_pool_base_t, - bsp_diving_worker_pool_t>; +class determinism_diving_worker_pool_t + : public determinism_worker_pool_base_t, + determinism_diving_worker_pool_t> { + using base_t = determinism_worker_pool_base_t, + determinism_diving_worker_pool_t>; public: - bsp_diving_worker_pool_t(int num_workers, - const std::vector& diving_types, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings, - const std::vector* root_solution) + determinism_diving_worker_pool_t(int num_workers, + const std::vector& diving_types, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_types, + const simplex_solver_settings_t& settings, + const std::vector* root_solution) { this->workers_.reserve(num_workers); for (int i = 0; i < num_workers; ++i) { @@ -519,7 +516,7 @@ class bsp_diving_worker_pool_t : public bsp_worker_pool_base_t&, bb_event_batch_t&) {} + void collect_worker_events(determinism_diving_worker_t&, bb_event_batch_t&) {} }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index 033c01a43..982adf3d2 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -238,7 +238,7 @@ class mip_node_t { copy.fractional_val = fractional_val; copy.objective_estimate = objective_estimate; copy.node_id = node_id; - // Copy BSP fields + copy.accumulated_wut = accumulated_wut; copy.origin_worker_id = origin_worker_id; copy.creation_seq = creation_seq; @@ -261,12 +261,11 @@ class mip_node_t { std::vector vstatus; - // BSP fields for deterministic parallel B&B - f_t accumulated_wut{0.0}; // Work units accumulated on this node so far + f_t accumulated_wut{0.0}; // Work units spent on this node so far - // Worker-local identification for deterministic BSP ordering: + // Worker-local identification for deterministic ordering: // - origin_worker_id: which worker created this node - // - creation_seq: sequence number within that worker (cumulative across horizons) + // - creation_seq: sequence number within that worker (cumulative across horizons, serial) // The tuple (origin_worker_id, creation_seq) is unique and stable int32_t origin_worker_id{-1}; int32_t creation_seq{-1}; @@ -301,7 +300,7 @@ void remove_fathomed_nodes(std::vector*>& stack) } } -// Comparator for global heap (used in non-BSP mode) +// Comparator for global heap template class node_compare_t { public: @@ -321,10 +320,10 @@ class node_compare_t { } }; -// BSP-specific comparator for worker-local priority queues +// Determinism-specific comparator for worker-local priority queues // Uses (origin_worker_id, creation_seq) tuple for deterministic ordering within a worker template -class bsp_node_compare_t { +class determinism_node_compare_t { public: // Returns true if 'a' has lower priority than 'b' (for max-heap behavior) bool operator()(const mip_node_t* a, const mip_node_t* b) const @@ -332,7 +331,7 @@ class bsp_node_compare_t { // Primary: lower_bound (best-first search - prefer smaller bound) if (a->lower_bound != b->lower_bound) { return a->lower_bound > b->lower_bound; } - // Tie-breaker: lexicographic comparison of BSP identity tuple + // Tie-breaker: lexicographic comparison of identity tuple // This is deterministic regardless of node creation order if (a->origin_worker_id != b->origin_worker_id) { return a->origin_worker_id > b->origin_worker_id; diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index 083fda09f..8ed40ad93 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -24,8 +24,9 @@ namespace cuopt { struct work_limit_context_t; // Simplified scheduler using OpenMP barriers for synchronization. -// Termination is managed externally (e.g., by branch_and_bound_t::bsp_global_termination_status_). -// Workers should check termination status after each sync point. +// Termination is managed externally (e.g., by +// branch_and_bound_t::determinism_global_termination_status_). Workers should check termination +// status after each sync point. class work_unit_scheduler_t { public: explicit work_unit_scheduler_t(double sync_interval = 5.0); From 36b211b9188d84efedfa51f97a234ed0271881e8 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 15:01:53 +0100 Subject: [PATCH 343/366] additional refactoring --- cpp/src/dual_simplex/bnb_worker.hpp | 45 +++++------ cpp/src/dual_simplex/branch_and_bound.cpp | 77 ++++++++++--------- cpp/src/dual_simplex/branch_and_bound.hpp | 2 +- cpp/src/dual_simplex/pseudo_costs.hpp | 2 +- .../utilities/{pcg.hpp => pcgenerator.hpp} | 8 +- 5 files changed, 68 insertions(+), 66 deletions(-) rename cpp/src/utilities/{pcg.hpp => pcgenerator.hpp} (95%) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index 8e20ce214..1b2f41afc 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -12,7 +12,7 @@ #include #include -#include +#include #include #include @@ -28,7 +28,7 @@ constexpr int bnb_num_worker_types = 5; // // [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, // Berlin, 2007. doi: 10.14279/depositonce-1634. -enum bnb_worker_type_t : int { +enum bnb_search_strategy_t : int { BEST_FIRST = 0, // Best-First + Plunging. PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) @@ -51,7 +51,7 @@ template class bnb_worker_data_t { public: const i_t worker_id; - omp_atomic_t worker_type; + omp_atomic_t search_strategy; omp_atomic_t is_active; omp_atomic_t lower_bound; @@ -70,7 +70,7 @@ class bnb_worker_data_t { std::vector start_upper; mip_node_t* start_node; - PCG rng; + pcgenerator_t rng; bool recompute_basis = true; bool recompute_bounds = true; @@ -81,7 +81,7 @@ class bnb_worker_data_t { const std::vector& var_type, const simplex_solver_settings_t& settings) : worker_id(worker_id), - worker_type(BEST_FIRST), + search_strategy(BEST_FIRST), is_active(false), lower_bound(-std::numeric_limits::infinity()), leaf_problem(original_lp), @@ -91,37 +91,37 @@ class bnb_worker_data_t { nonbasic_list(), node_presolver(leaf_problem, Arow, {}, var_type), bounds_changed(original_lp.num_cols, false), - rng(PCG::default_seed ^ worker_id, PCG::default_stream + worker_id) + rng(pcgenerator_t::default_seed ^ worker_id, pcgenerator_t::default_stream + worker_id) { } // Set the `start_node` for best-first search. void init_best_first(mip_node_t* node, const lp_problem_t& original_lp) { - start_node = node; - start_lower = original_lp.lower; - start_upper = original_lp.upper; - worker_type = BEST_FIRST; - lower_bound = node->lower_bound; - is_active = true; + start_node = node; + start_lower = original_lp.lower; + start_upper = original_lp.upper; + search_strategy = BEST_FIRST; + lower_bound = node->lower_bound; + is_active = true; } // Initialize the worker for diving, setting the `start_node`, `start_lower` and // `start_upper`. Returns `true` if the starting node is feasible via // bounds propagation. bool init_diving(mip_node_t* node, - bnb_worker_type_t type, + bnb_search_strategy_t type, const lp_problem_t& original_lp, const simplex_solver_settings_t& settings) { internal_node = node->detach_copy(); start_node = &internal_node; - start_lower = original_lp.lower; - start_upper = original_lp.upper; - worker_type = type; - lower_bound = node->lower_bound; - is_active = true; + start_lower = original_lp.lower; + start_upper = original_lp.upper; + search_strategy = type; + lower_bound = node->lower_bound; + is_active = true; std::fill(bounds_changed.begin(), bounds_changed.end(), false); node->get_variable_bounds(start_lower, start_upper, bounds_changed); @@ -219,7 +219,7 @@ class bnb_worker_pool_t { if (is_initialized) { for (i_t i = 0; i < workers_.size(); ++i) { - if (workers_[i]->worker_type == BEST_FIRST && workers_[i]->is_active) { + if (workers_[i]->search_strategy == BEST_FIRST && workers_[i]->is_active) { lower_bound = std::min(workers_[i]->lower_bound.load(), lower_bound); } } @@ -241,9 +241,10 @@ class bnb_worker_pool_t { }; template -std::vector bnb_get_worker_types(diving_heuristics_settings_t settings) +std::vector bnb_get_search_strategies( + diving_heuristics_settings_t settings) { - std::vector types; + std::vector types; types.reserve(bnb_num_worker_types); types.push_back(BEST_FIRST); if (settings.pseudocost_diving != 0) { types.push_back(PSEUDOCOST_DIVING); } @@ -255,7 +256,7 @@ std::vector bnb_get_worker_types(diving_heuristics_settings_t template std::array bnb_get_max_workers( - i_t num_workers, std::vector worker_types) + i_t num_workers, std::vector worker_types) { std::array max_num_workers; max_num_workers.fill(0); diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index e30fb203a..321f86c97 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -207,14 +207,14 @@ inline char feasible_solution_symbol(bnb_worker_type_t type) } } #else -inline char feasible_solution_symbol(bnb_worker_type_t type) +inline char feasible_solution_symbol(bnb_search_strategy_t type) { switch (type) { - case bnb_worker_type_t::BEST_FIRST: return 'B'; - case bnb_worker_type_t::COEFFICIENT_DIVING: return 'D'; - case bnb_worker_type_t::LINE_SEARCH_DIVING: return 'D'; - case bnb_worker_type_t::PSEUDOCOST_DIVING: return 'D'; - case bnb_worker_type_t::GUIDED_DIVING: return 'D'; + case bnb_search_strategy_t::BEST_FIRST: return 'B'; + case bnb_search_strategy_t::COEFFICIENT_DIVING: return 'D'; + case bnb_search_strategy_t::LINE_SEARCH_DIVING: return 'D'; + case bnb_search_strategy_t::PSEUDOCOST_DIVING: return 'D'; + case bnb_search_strategy_t::GUIDED_DIVING: return 'D'; default: return 'U'; } } @@ -518,7 +518,7 @@ template void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - bnb_worker_type_t thread_type) + bnb_search_strategy_t thread_type) { bool send_solution = false; @@ -576,8 +576,8 @@ branch_variable_t branch_and_bound_t::variable_selection( std::vector current_incumbent; std::vector& solution = worker_data->leaf_solution.x; - switch (worker_data->worker_type) { - case bnb_worker_type_t::BEST_FIRST: + switch (worker_data->search_strategy) { + case bnb_search_strategy_t::BEST_FIRST: if (settings_.reliability_branching != 0) { branch_var = pc_.reliable_variable_selection(node_ptr, @@ -598,24 +598,24 @@ branch_variable_t branch_and_bound_t::variable_selection( return {branch_var, round_dir}; - case bnb_worker_type_t::COEFFICIENT_DIVING: + case bnb_search_strategy_t::COEFFICIENT_DIVING: return coefficient_diving( original_lp_, fractional, solution, var_up_locks_, var_down_locks_, log); - case bnb_worker_type_t::LINE_SEARCH_DIVING: + case bnb_search_strategy_t::LINE_SEARCH_DIVING: return line_search_diving(fractional, solution, root_relax_soln_.x, log); - case bnb_worker_type_t::PSEUDOCOST_DIVING: + case bnb_search_strategy_t::PSEUDOCOST_DIVING: return pseudocost_diving(pc_, fractional, solution, root_relax_soln_.x, log); - case bnb_worker_type_t::GUIDED_DIVING: + case bnb_search_strategy_t::GUIDED_DIVING: mutex_upper_.lock(); current_incumbent = incumbent_.x; mutex_upper_.unlock(); return guided_diving(pc_, fractional, solution, current_incumbent, log); default: - log.debug("Unknown variable selection method: %d\n", worker_data->worker_type); + log.debug("Unknown variable selection method: %d\n", worker_data->search_strategy); return {-1, rounding_direction_t::NONE}; } } @@ -636,7 +636,7 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); lp_settings.scale_columns = false; - if (worker_data->worker_type != bnb_worker_type_t::BEST_FIRST) { + if (worker_data->search_strategy != bnb_search_strategy_t::BEST_FIRST) { int64_t bnb_lp_iters = exploration_stats_.total_lp_iters; f_t factor = settings_.diving_settings.iteration_limit_factor; int64_t max_iter = factor * bnb_lp_iters; @@ -753,7 +753,7 @@ std::pair branch_and_bound_t::upd pc_.update_pseudo_costs(node_ptr, leaf_objective); node_ptr->lower_bound = leaf_objective; - if (worker_data->worker_type == bnb_worker_type_t::BEST_FIRST) { + if (worker_data->search_strategy == bnb_search_strategy_t::BEST_FIRST) { if (settings_.node_processed_callback != nullptr) { std::vector original_x; uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); @@ -764,7 +764,7 @@ std::pair branch_and_bound_t::upd if (leaf_num_fractional == 0) { // Found a integer feasible solution add_feasible_solution( - leaf_objective, leaf_solution.x, node_ptr->depth, worker_data->worker_type); + leaf_objective, leaf_solution.x, node_ptr->depth, worker_data->search_strategy); search_tree.graphviz_node(log, node_ptr, "integer feasible", leaf_objective); search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); return {node_status_t::INTEGER_FEASIBLE, rounding_direction_t::NONE}; @@ -780,7 +780,7 @@ std::pair branch_and_bound_t::upd // Note that the exploration thread is the only one that can insert new nodes into the heap, // and thus, we only need to calculate the objective estimate here (it is used for // sorting the nodes for diving). - if (worker_data->worker_type == bnb_worker_type_t::BEST_FIRST) { + if (worker_data->search_strategy == bnb_search_strategy_t::BEST_FIRST) { logger_t pc_log; pc_log.log = false; node_ptr->objective_estimate = @@ -798,7 +798,7 @@ std::pair branch_and_bound_t::upd return {node_status_t::FATHOMED, rounding_direction_t::NONE}; } } else { - if (worker_data->worker_type == bnb_worker_type_t::BEST_FIRST) { + if (worker_data->search_strategy == bnb_search_strategy_t::BEST_FIRST) { fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); log.printf( "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " @@ -923,9 +923,9 @@ void branch_and_bound_t::dive_with(bnb_worker_data_t* worker logger_t log; log.log = false; - bnb_worker_type_t diving_type = worker_data->worker_type; - const i_t diving_node_limit = settings_.diving_settings.node_limit; - const i_t diving_backtrack_limit = settings_.diving_settings.backtrack_limit; + bnb_search_strategy_t search_strategy = worker_data->search_strategy; + const i_t diving_node_limit = settings_.diving_settings.node_limit; + const i_t diving_backtrack_limit = settings_.diving_settings.backtrack_limit; worker_data->recompute_basis = true; worker_data->recompute_bounds = true; @@ -992,7 +992,7 @@ void branch_and_bound_t::dive_with(bnb_worker_data_t* worker } worker_pool_.return_worker_to_pool(worker_data); - active_workers_per_type_[diving_type]--; + active_workers_per_type_[search_strategy]--; } template @@ -1002,19 +1002,19 @@ void branch_and_bound_t::run_scheduler() const i_t num_workers = 2 * settings_.num_threads; if (!std::isfinite(upper_bound_)) { diving_settings.guided_diving = false; } - std::vector worker_types = bnb_get_worker_types(diving_settings); + std::vector strategies = bnb_get_search_strategies(diving_settings); std::array max_num_workers_per_type = - bnb_get_max_workers(num_workers, worker_types); + bnb_get_max_workers(num_workers, strategies); worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); active_workers_per_type_.fill(0); #ifdef CUOPT_LOG_DEBUG - for (auto type : worker_types) { + for (auto strategy : strategies) { settings_.log.debug("%c%d: max num of workers = %d", - feasible_solution_symbol(type), - type, - max_num_workers_per_type[type]); + feasible_solution_symbol(strategy), + strategy, + max_num_workers_per_type[strategy]); } #endif @@ -1038,11 +1038,11 @@ void branch_and_bound_t::run_scheduler() if (settings_.diving_settings.guided_diving != diving_settings.guided_diving) { if (std::isfinite(upper_bound_)) { diving_settings.guided_diving = settings_.diving_settings.guided_diving; - worker_types = bnb_get_worker_types(diving_settings); - max_num_workers_per_type = bnb_get_max_workers(num_workers, worker_types); + strategies = bnb_get_search_strategies(diving_settings); + max_num_workers_per_type = bnb_get_max_workers(num_workers, strategies); #ifdef CUOPT_LOG_DEBUG - for (auto type : worker_types) { + for (auto type : strategies) { settings_.log.debug("%c%d: max num of workers = %d", feasible_solution_symbol(type), type, @@ -1072,14 +1072,14 @@ void branch_and_bound_t::run_scheduler() break; } - for (auto type : worker_types) { - if (active_workers_per_type_[type] >= max_num_workers_per_type[type]) { continue; } + for (auto strategy : strategies) { + if (active_workers_per_type_[strategy] >= max_num_workers_per_type[strategy]) { continue; } // Get an idle worker. bnb_worker_data_t* worker = worker_pool_.get_idle_worker(); if (worker == nullptr) { break; } - if (type == BEST_FIRST) { + if (strategy == BEST_FIRST) { // If there any node left in the heap, we pop the top node and explore it. std::optional*> start_node = node_queue_.pop_best_first(); @@ -1097,7 +1097,7 @@ void branch_and_bound_t::run_scheduler() worker_pool_.pop_idle_worker(); worker->init_best_first(start_node.value(), original_lp_); last_node_depth = start_node.value()->depth; - active_workers_per_type_[type]++; + active_workers_per_type_[strategy]++; launched_any_task = true; #pragma omp task affinity(worker) @@ -1112,12 +1112,13 @@ void branch_and_bound_t::run_scheduler() continue; } - bool is_feasible = worker->init_diving(start_node.value(), type, original_lp_, settings_); + bool is_feasible = + worker->init_diving(start_node.value(), strategy, original_lp_, settings_); if (!is_feasible) { continue; } // Remove the worker from the idle list. worker_pool_.pop_idle_worker(); - active_workers_per_type_[type]++; + active_workers_per_type_[strategy]++; launched_any_task = true; #pragma omp task affinity(worker) diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index df76f2d71..aa06e8f35 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -175,7 +175,7 @@ class branch_and_bound_t { void add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - bnb_worker_type_t thread_type); + bnb_search_strategy_t thread_type); // Repairs low-quality solutions from the heuristics, if it is applicable. void repair_heuristic_solutions(); diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 94b2504e3..73e0a645c 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -14,7 +14,7 @@ #include #include #include -#include +#include #include diff --git a/cpp/src/utilities/pcg.hpp b/cpp/src/utilities/pcgenerator.hpp similarity index 95% rename from cpp/src/utilities/pcg.hpp rename to cpp/src/utilities/pcgenerator.hpp index 5422fd8df..f6b0ef71c 100644 --- a/cpp/src/utilities/pcg.hpp +++ b/cpp/src/utilities/pcgenerator.hpp @@ -14,7 +14,7 @@ // on the CPU (.cpp file) since the raft header includes CUDA code. // The original code is from https://www.pcg-random.org/. namespace cuopt { -class PCG { +class pcgenerator_t { public: static constexpr uint64_t default_seed = 0x853c49e6748fea9bULL; static constexpr uint64_t default_stream = 0xda3e39cb94b95bdbULL; @@ -27,9 +27,9 @@ class PCG { * unique set of random numbers. This can be achieved by initializing the generator with same * rng_state for all the threads and diststreamt values for subsequence. */ - PCG(const uint64_t seed = default_seed, - const uint64_t subsequence = default_stream, - uint64_t offset = 0) + pcgenerator_t(const uint64_t seed = default_seed, + const uint64_t subsequence = default_stream, + uint64_t offset = 0) { set_seed(seed, subsequence, offset); } From 5edbfbf8db80e74896706bad44c0b138cc402c8e Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 15:20:56 +0100 Subject: [PATCH 344/366] variable renaming --- cpp/src/dual_simplex/bnb_worker.hpp | 8 ++++---- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/bnb_worker.hpp index 1b2f41afc..29dc5ed0f 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/bnb_worker.hpp @@ -21,7 +21,7 @@ namespace cuopt::linear_programming::dual_simplex { -constexpr int bnb_num_worker_types = 5; +constexpr int bnb_num_search_strategies = 5; // Indicate the search and variable selection algorithms used by each thread // in B&B (See [1]). @@ -245,7 +245,7 @@ std::vector bnb_get_search_strategies( diving_heuristics_settings_t settings) { std::vector types; - types.reserve(bnb_num_worker_types); + types.reserve(bnb_num_search_strategies); types.push_back(BEST_FIRST); if (settings.pseudocost_diving != 0) { types.push_back(PSEUDOCOST_DIVING); } if (settings.line_search_diving != 0) { types.push_back(LINE_SEARCH_DIVING); } @@ -255,10 +255,10 @@ std::vector bnb_get_search_strategies( } template -std::array bnb_get_max_workers( +std::array bnb_get_max_workers( i_t num_workers, std::vector worker_types) { - std::array max_num_workers; + std::array max_num_workers; max_num_workers.fill(0); i_t bfs_workers = std::max(worker_types.size() == 1 ? num_workers : num_workers / 4, 1); diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 321f86c97..83a0bc349 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -1003,7 +1003,7 @@ void branch_and_bound_t::run_scheduler() if (!std::isfinite(upper_bound_)) { diving_settings.guided_diving = false; } std::vector strategies = bnb_get_search_strategies(diving_settings); - std::array max_num_workers_per_type = + std::array max_num_workers_per_type = bnb_get_max_workers(num_workers, strategies); worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); From 2b9da53ef6125608b49be1ed804e1b3ee76b4993 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 16:14:55 +0100 Subject: [PATCH 345/366] fix compilation --- cpp/src/dual_simplex/branch_and_bound.cpp | 16 +++++++++------- cpp/src/dual_simplex/branch_and_bound.hpp | 2 +- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 83a0bc349..c61b330ee 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -913,7 +913,7 @@ void branch_and_bound_t::plunge_with(bnb_worker_data_t* work if (settings_.num_threads > 1) { worker_pool_.return_worker_to_pool(worker_data); - active_workers_per_type_[BEST_FIRST]--; + active_workers_per_strategy_[BEST_FIRST]--; } } @@ -992,7 +992,7 @@ void branch_and_bound_t::dive_with(bnb_worker_data_t* worker } worker_pool_.return_worker_to_pool(worker_data); - active_workers_per_type_[search_strategy]--; + active_workers_per_strategy_[search_strategy]--; } template @@ -1007,7 +1007,7 @@ void branch_and_bound_t::run_scheduler() bnb_get_max_workers(num_workers, strategies); worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); - active_workers_per_type_.fill(0); + active_workers_per_strategy_.fill(0); #ifdef CUOPT_LOG_DEBUG for (auto strategy : strategies) { @@ -1025,7 +1025,7 @@ void branch_and_bound_t::run_scheduler() while (solver_status_ == mip_status_t::UNSET && abs_gap > settings_.absolute_mip_gap_tol && rel_gap > settings_.relative_mip_gap_tol && - (active_workers_per_type_[0] > 0 || node_queue_.best_first_queue_size() > 0)) { + (active_workers_per_strategy_[0] > 0 || node_queue_.best_first_queue_size() > 0)) { bool launched_any_task = false; lower_bound = get_lower_bound(); abs_gap = upper_bound_ - lower_bound; @@ -1073,7 +1073,9 @@ void branch_and_bound_t::run_scheduler() } for (auto strategy : strategies) { - if (active_workers_per_type_[strategy] >= max_num_workers_per_type[strategy]) { continue; } + if (active_workers_per_strategy_[strategy] >= max_num_workers_per_type[strategy]) { + continue; + } // Get an idle worker. bnb_worker_data_t* worker = worker_pool_.get_idle_worker(); @@ -1097,7 +1099,7 @@ void branch_and_bound_t::run_scheduler() worker_pool_.pop_idle_worker(); worker->init_best_first(start_node.value(), original_lp_); last_node_depth = start_node.value()->depth; - active_workers_per_type_[strategy]++; + active_workers_per_strategy_[strategy]++; launched_any_task = true; #pragma omp task affinity(worker) @@ -1118,7 +1120,7 @@ void branch_and_bound_t::run_scheduler() // Remove the worker from the idle list. worker_pool_.pop_idle_worker(); - active_workers_per_type_[strategy]++; + active_workers_per_strategy_[strategy]++; launched_any_task = true; #pragma omp task affinity(worker) diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index aa06e8f35..d3d28e593 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -146,7 +146,7 @@ class branch_and_bound_t { // Count the number of workers per type that either are being executed or // are waiting to be executed. - std::array, bnb_num_worker_types> active_workers_per_type_; + std::array, bnb_num_search_strategies> active_workers_per_strategy_; // Worker pool bnb_worker_pool_t worker_pool_; From 62a058bb380431f039a722c93ecab91d1c95fd5b Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 16:16:55 +0100 Subject: [PATCH 346/366] coderabbit suggestions --- cpp/src/utilities/omp_helpers.hpp | 5 ++++- cpp/src/utilities/pcgenerator.hpp | 1 + 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/cpp/src/utilities/omp_helpers.hpp b/cpp/src/utilities/omp_helpers.hpp index 804c2a4ea..f6e66472d 100644 --- a/cpp/src/utilities/omp_helpers.hpp +++ b/cpp/src/utilities/omp_helpers.hpp @@ -29,7 +29,10 @@ class omp_mutex_t { omp_mutex_t& operator=(omp_mutex_t&& other) { - if (&other != this) { mutex = std::move(other.mutex); } + if (&other != this) { + if (mutex) { omp_destroy_lock(mutex.get()); } + mutex = std::move(other.mutex); + } return *this; } diff --git a/cpp/src/utilities/pcgenerator.hpp b/cpp/src/utilities/pcgenerator.hpp index f6b0ef71c..29a865f02 100644 --- a/cpp/src/utilities/pcgenerator.hpp +++ b/cpp/src/utilities/pcgenerator.hpp @@ -8,6 +8,7 @@ #pragma once #include +#include #include // Copied from raft/PCGenerator (rng_device.cuh). We cannot use raft implementation From 5a54f85cc69e5dadb2085f8ab682aad3f2ed3e49 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Wed, 4 Feb 2026 15:59:31 +0000 Subject: [PATCH 347/366] more cleanup --- cpp/src/dual_simplex/bb_worker_state.hpp | 517 ------------------ cpp/src/dual_simplex/branch_and_bound.cpp | 12 +- .../dual_simplex/deterministic_workers.hpp | 14 + cpp/src/dual_simplex/pseudo_costs.cpp | 10 +- cpp/src/dual_simplex/pseudo_costs.hpp | 63 ++- 5 files changed, 78 insertions(+), 538 deletions(-) delete mode 100644 cpp/src/dual_simplex/bb_worker_state.hpp diff --git a/cpp/src/dual_simplex/bb_worker_state.hpp b/cpp/src/dual_simplex/bb_worker_state.hpp deleted file mode 100644 index 3bc603fc7..000000000 --- a/cpp/src/dual_simplex/bb_worker_state.hpp +++ /dev/null @@ -1,517 +0,0 @@ -/* clang-format off */ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. - * SPDX-License-Identifier: Apache-2.0 - */ -/* clang-format on */ - -#pragma once - -#include -#include -#include -#include -#include - -#include - -#include -#include -#include -#include -#include -#include - -namespace cuopt::linear_programming::dual_simplex { - -template -struct backlog_node_compare_t { - bool operator()(const mip_node_t* a, const mip_node_t* b) const - { - if (a->lower_bound != b->lower_bound) { return a->lower_bound > b->lower_bound; } - if (a->origin_worker_id != b->origin_worker_id) { - return a->origin_worker_id > b->origin_worker_id; - } - return a->creation_seq > b->creation_seq; - } -}; - -template -struct pseudo_cost_update_t { - i_t variable; - rounding_direction_t direction; - f_t delta; - double wut; - int worker_id; - - bool operator<(const pseudo_cost_update_t& other) const - { - if (wut != other.wut) return wut < other.wut; - if (variable != other.variable) return variable < other.variable; - if (delta != other.delta) return delta < other.delta; - return worker_id < other.worker_id; - } -}; - -template -struct queued_integer_solution_t { - f_t objective; - std::vector solution; - i_t depth; - int worker_id; - int sequence_id; - - bool operator<(const queued_integer_solution_t& other) const - { - if (objective != other.objective) return objective < other.objective; - if (worker_id != other.worker_id) return worker_id < other.worker_id; - return sequence_id < other.sequence_id; - } -}; - -template -class determinism_worker_base_t : public bnb_worker_data_t { - using base_t = bnb_worker_data_t; - - public: - double clock{0.0}; - double horizon_start{0.0}; - double horizon_end{0.0}; - work_limit_context_t work_context; - - // Local snapshots of global state - std::vector pc_sum_up_snapshot; - std::vector pc_sum_down_snapshot; - std::vector pc_num_up_snapshot; - std::vector pc_num_down_snapshot; - f_t local_upper_bound{std::numeric_limits::infinity()}; - - // Diving-specific snapshots (ignored by BFS workers) - std::vector incumbent_snapshot; - i_t total_lp_iters_snapshot{0}; - - std::vector> integer_solutions; - std::vector> pseudo_cost_updates; - int next_solution_seq{0}; - - i_t total_nodes_processed{0}; - i_t total_integer_solutions{0}; - double total_runtime{0.0}; - double total_nowork_time{0.0}; - - determinism_worker_base_t(int id, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings, - const std::string& context_name) - : base_t(id, original_lp, Arow, var_types, settings), work_context(context_name) - { - work_context.deterministic = true; - } - - void set_snapshots(f_t global_upper_bound, - const std::vector& pc_sum_up, - const std::vector& pc_sum_down, - const std::vector& pc_num_up, - const std::vector& pc_num_down, - const std::vector& incumbent, - i_t total_lp_iters, - double new_horizon_start, - double new_horizon_end) - { - local_upper_bound = global_upper_bound; - pc_sum_up_snapshot = pc_sum_up; - pc_sum_down_snapshot = pc_sum_down; - pc_num_up_snapshot = pc_num_up; - pc_num_down_snapshot = pc_num_down; - incumbent_snapshot = incumbent; - total_lp_iters_snapshot = total_lp_iters; - horizon_start = new_horizon_start; - horizon_end = new_horizon_end; - } - - // Queue pseudo-cost update and apply to local snapshot - void queue_pseudo_cost_update(i_t variable, rounding_direction_t direction, f_t delta) - { - pseudo_cost_updates.push_back({variable, direction, delta, clock, this->worker_id}); - if (direction == rounding_direction_t::DOWN) { - pc_sum_down_snapshot[variable] += delta; - pc_num_down_snapshot[variable]++; - } else { - pc_sum_up_snapshot[variable] += delta; - pc_num_up_snapshot[variable]++; - } - } - - // Basic variable selection from snapshots - i_t variable_selection_from_snapshot(const std::vector& fractional, - const std::vector& solution) const - { - return variable_selection_from_pseudo_costs(pc_sum_down_snapshot.data(), - pc_sum_up_snapshot.data(), - pc_num_down_snapshot.data(), - pc_num_up_snapshot.data(), - (i_t)pc_sum_down_snapshot.size(), - fractional, - solution); - } - - bool has_work() const { return static_cast(this)->has_work_impl(); } -}; - -template -class determinism_bfs_worker_t - : public determinism_worker_base_t> { - using base_t = determinism_worker_base_t>; - - public: - // Node management - std::deque*> plunge_stack; - heap_t*, backlog_node_compare_t> backlog; - mip_node_t* current_node{nullptr}; - mip_node_t* last_solved_node{nullptr}; - - // Event logging for deterministic replay - bb_event_batch_t events; - int event_sequence{0}; - int32_t next_creation_seq{0}; - - // BFS-specific state - f_t local_lower_bound_ceiling{std::numeric_limits::infinity()}; - bool recompute_bounds_and_basis{true}; - i_t nodes_processed_this_horizon{0}; - - // BFS statistics - i_t total_nodes_pruned{0}; - i_t total_nodes_branched{0}; - i_t total_nodes_infeasible{0}; - i_t total_nodes_assigned{0}; - - explicit determinism_bfs_worker_t(int id, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings) - : base_t(id, original_lp, Arow, var_types, settings, "BB_Worker_" + std::to_string(id)) - { - } - - bool has_work_impl() const - { - return current_node != nullptr || !plunge_stack.empty() || !backlog.empty(); - } - - void enqueue_node(mip_node_t* node) - { - plunge_stack.push_front(node); - ++total_nodes_assigned; - } - - mip_node_t* enqueue_children_for_plunge(mip_node_t* down_child, - mip_node_t* up_child, - rounding_direction_t preferred_direction) - { - if (!plunge_stack.empty()) { - backlog.push(plunge_stack.back()); - plunge_stack.pop_back(); - } - - down_child->origin_worker_id = this->worker_id; - down_child->creation_seq = next_creation_seq++; - up_child->origin_worker_id = this->worker_id; - up_child->creation_seq = next_creation_seq++; - - mip_node_t* first_child; - if (preferred_direction == rounding_direction_t::UP) { - plunge_stack.push_front(down_child); - plunge_stack.push_front(up_child); - first_child = up_child; - } else { - plunge_stack.push_front(up_child); - plunge_stack.push_front(down_child); - first_child = down_child; - } - return first_child; - } - - mip_node_t* dequeue_node() - { - if (current_node != nullptr) { - mip_node_t* node = current_node; - current_node = nullptr; - return node; - } - if (!plunge_stack.empty()) { - mip_node_t* node = plunge_stack.front(); - plunge_stack.pop_front(); - return node; - } - auto node_opt = backlog.pop(); - return node_opt.has_value() ? node_opt.value() : nullptr; - } - - size_t queue_size() const - { - return plunge_stack.size() + backlog.size() + (current_node != nullptr ? 1 : 0); - } - - void record_event(bb_event_t event) - { - event.event_sequence = event_sequence++; - events.add(std::move(event)); - } - - void record_branched( - mip_node_t* node, i_t down_child_id, i_t up_child_id, i_t branch_var, f_t branch_val) - { - record_event(bb_event_t::make_branched(this->clock, - this->worker_id, - node->node_id, - down_child_id, - up_child_id, - node->lower_bound, - branch_var, - branch_val)); - ++nodes_processed_this_horizon; - ++this->total_nodes_processed; - ++total_nodes_branched; - } - - void record_integer_solution(mip_node_t* node, f_t objective) - { - record_event(bb_event_t::make_integer_solution( - this->clock, this->worker_id, node->node_id, objective)); - ++nodes_processed_this_horizon; - ++this->total_nodes_processed; - ++this->total_integer_solutions; - } - - void record_fathomed(mip_node_t* node, f_t lower_bound) - { - record_event(bb_event_t::make_fathomed( - this->clock, this->worker_id, node->node_id, lower_bound)); - ++nodes_processed_this_horizon; - ++this->total_nodes_processed; - ++total_nodes_pruned; - } - - void record_infeasible(mip_node_t* node) - { - record_event( - bb_event_t::make_infeasible(this->clock, this->worker_id, node->node_id)); - ++nodes_processed_this_horizon; - ++this->total_nodes_processed; - ++total_nodes_infeasible; - } - - void record_numerical(mip_node_t* node) - { - record_event(bb_event_t::make_numerical(this->clock, this->worker_id, node->node_id)); - ++nodes_processed_this_horizon; - ++this->total_nodes_processed; - } -}; - -template -class determinism_diving_worker_t - : public determinism_worker_base_t> { - using base_t = determinism_worker_base_t>; - - public: - bnb_worker_type_t diving_type{bnb_worker_type_t::PSEUDOCOST_DIVING}; - - // Diving-specific node management - std::deque> dive_queue; - std::vector dive_lower; - std::vector dive_upper; - - // Root LP relaxation solution (constant, set once at construction) - const std::vector* root_solution{nullptr}; - - // Diving state - bool recompute_bounds_and_basis{true}; - - // Diving statistics - i_t total_nodes_explored{0}; - i_t total_dives{0}; - i_t lp_iters_this_dive{0}; - - explicit determinism_diving_worker_t(int id, - bnb_worker_type_t type, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings, - const std::vector* root_sol) - : base_t(id, original_lp, Arow, var_types, settings, "Diving_Worker_" + std::to_string(id)), - diving_type(type), - root_solution(root_sol) - { - dive_lower = original_lp.lower; - dive_upper = original_lp.upper; - } - - determinism_diving_worker_t(const determinism_diving_worker_t&) = delete; - determinism_diving_worker_t& operator=(const determinism_diving_worker_t&) = delete; - determinism_diving_worker_t(determinism_diving_worker_t&&) = default; - determinism_diving_worker_t& operator=(determinism_diving_worker_t&&) = default; - - bool has_work_impl() const { return !dive_queue.empty(); } - - void enqueue_dive_node(mip_node_t* node) { dive_queue.push_back(node->detach_copy()); } - - std::optional> dequeue_dive_node() - { - if (dive_queue.empty()) return std::nullopt; - auto node = std::move(dive_queue.front()); - dive_queue.pop_front(); - ++total_dives; - return node; - } - - size_t dive_queue_size() const { return dive_queue.size(); } - size_t queue_size() const { return dive_queue_size(); } // Unified interface for pool - - void queue_integer_solution(f_t objective, const std::vector& solution, i_t depth) - { - this->integer_solutions.push_back( - {objective, solution, depth, this->worker_id, this->next_solution_seq++}); - ++this->total_integer_solutions; - } - - branch_variable_t variable_selection_from_snapshot(const std::vector& fractional, - const std::vector& solution) const - { - const std::vector& root_sol = (root_solution != nullptr) ? *root_solution : solution; - return pseudocost_diving_from_arrays(this->pc_sum_down_snapshot.data(), - this->pc_sum_up_snapshot.data(), - this->pc_num_down_snapshot.data(), - this->pc_num_up_snapshot.data(), - (i_t)this->pc_sum_down_snapshot.size(), - fractional, - solution, - root_sol); - } - - branch_variable_t guided_variable_selection(const std::vector& fractional, - const std::vector& solution) const - { - if (this->incumbent_snapshot.empty()) { - return variable_selection_from_snapshot(fractional, solution); - } - return guided_diving_from_arrays(this->pc_sum_down_snapshot.data(), - this->pc_sum_up_snapshot.data(), - this->pc_num_down_snapshot.data(), - this->pc_num_up_snapshot.data(), - (i_t)this->pc_sum_down_snapshot.size(), - fractional, - solution, - this->incumbent_snapshot); - } -}; - -template -class determinism_worker_pool_base_t { - protected: - std::vector workers_; - - public: - WorkerT& operator[](int worker_id) { return workers_[worker_id]; } - const WorkerT& operator[](int worker_id) const { return workers_[worker_id]; } - size_t size() const { return workers_.size(); } - - bool any_has_work() const - { - return std::any_of( - workers_.begin(), workers_.end(), [](const auto& worker) { return worker.has_work(); }); - } - - size_t total_queue_size() const - { - return std::accumulate( - workers_.begin(), workers_.end(), 0, [](size_t total, const auto& worker) { - return total + worker.queue_size(); - }); - } - - bb_event_batch_t collect_and_sort_events() - { - bb_event_batch_t all_events; - std::for_each(workers_.begin(), workers_.end(), [&](auto& worker) { - static_cast(this)->collect_worker_events(worker, all_events); - }); - all_events.sort_for_replay(); - return all_events; - } - - auto begin() { return workers_.begin(); } - auto end() { return workers_.end(); } - auto begin() const { return workers_.begin(); } - auto end() const { return workers_.end(); } -}; - -template -class determinism_bfs_worker_pool_t - : public determinism_worker_pool_base_t, - determinism_bfs_worker_pool_t> { - using base_t = determinism_worker_pool_base_t, - determinism_bfs_worker_pool_t>; - - public: - determinism_bfs_worker_pool_t(int num_workers, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings) - { - for (int i = 0; i < num_workers; ++i) { - this->workers_.emplace_back(i, original_lp, Arow, var_types, settings); - } - } - - void collect_worker_events(determinism_bfs_worker_t& worker, - bb_event_batch_t& all_events) - { - for (auto& event : worker.events.events) { - all_events.add(std::move(event)); - } - worker.events.clear(); - } -}; - -template -class determinism_diving_worker_pool_t - : public determinism_worker_pool_base_t, - determinism_diving_worker_pool_t> { - using base_t = determinism_worker_pool_base_t, - determinism_diving_worker_pool_t>; - - public: - determinism_diving_worker_pool_t(int num_workers, - const std::vector& diving_types, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_types, - const simplex_solver_settings_t& settings, - const std::vector* root_solution) - { - this->workers_.reserve(num_workers); - for (int i = 0; i < num_workers; ++i) { - bnb_worker_type_t type = diving_types[i % diving_types.size()]; - this->workers_.emplace_back(i, type, original_lp, Arow, var_types, settings, root_solution); - } - } - - void collect_worker_events(determinism_diving_worker_t&, bb_event_batch_t&) {} -}; - -} // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index cbc2cd5ee..be0861a88 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2889,10 +2889,10 @@ void branch_and_bound_t::deterministic_dive(determinism_diving_worker_ #endif { - // f_t factor = settings_.diving_settings.iteration_limit_factor; - // i_t max_iter = (i_t)(factor * worker.total_lp_iters_snapshot); - // lp_settings.iteration_limit = max_iter - worker.lp_iters_this_dive; - // if (lp_settings.iteration_limit <= 0) { break; } + f_t factor = settings_.diving_settings.iteration_limit_factor; + i_t max_iter = (i_t)(factor * worker.total_lp_iters_snapshot); + lp_settings.iteration_limit = max_iter - worker.lp_iters_this_dive; + if (lp_settings.iteration_limit <= 0) { break; } } // Solve LP relaxation @@ -3026,6 +3026,10 @@ void branch_and_bound_t::deterministic_dive(determinism_diving_worker_ continue; } + // Update objective estimate for node selection heuristics + node_ptr->objective_estimate = + worker.obj_estimate_from_snapshot(leaf_fractional, leaf_solution.x, leaf_objective); + // Create children logger_t log; log.log = false; diff --git a/cpp/src/dual_simplex/deterministic_workers.hpp b/cpp/src/dual_simplex/deterministic_workers.hpp index 8b7c0a3c9..f4e79fe8a 100644 --- a/cpp/src/dual_simplex/deterministic_workers.hpp +++ b/cpp/src/dual_simplex/deterministic_workers.hpp @@ -410,6 +410,20 @@ class determinism_diving_worker_t solution, this->incumbent_snapshot); } + + f_t obj_estimate_from_snapshot(const std::vector& fractional, + const std::vector& solution, + f_t lower_bound) const + { + return obj_estimate_from_arrays(this->pc_sum_down_snapshot.data(), + this->pc_sum_up_snapshot.data(), + this->pc_num_down_snapshot.data(), + this->pc_num_up_snapshot.data(), + (i_t)this->pc_sum_down_snapshot.size(), + fractional, + solution, + lower_bound); + } }; template diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 26975d9fe..757b56f75 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -455,11 +455,11 @@ void pseudo_costs_t::initialized(i_t& num_initialized_down, f_t& pseudo_cost_up_avg) { // Count initialized variables while computing averages - auto avgs = compute_pseudo_cost_averages((const f_t*)pseudo_cost_sum_down.data(), - (const f_t*)pseudo_cost_sum_up.data(), - (const i_t*)pseudo_cost_num_down.data(), - (const i_t*)pseudo_cost_num_up.data(), - (i_t)pseudo_cost_sum_down.size()); + auto avgs = compute_pseudo_cost_averages(pseudo_cost_sum_down.data(), + pseudo_cost_sum_up.data(), + pseudo_cost_num_down.data(), + pseudo_cost_num_up.data(), + pseudo_cost_sum_down.size()); pseudo_cost_down_avg = avgs.down_avg; pseudo_cost_up_avg = avgs.up_avg; } diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 2f1f1fa7f..110480552 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -33,16 +33,27 @@ struct pseudo_cost_averages_t { f_t up_avg; }; -template -pseudo_cost_averages_t compute_pseudo_cost_averages( - const f_t* pc_sum_down, const f_t* pc_sum_up, const i_t* pc_num_down, const i_t* pc_num_up, i_t n) +// used to get T from omp_atomic_t based on the fact that omp_atomic_t::operator++ returns T +template +using underlying_type = decltype(std::declval()++); + +// Necessary because omp_atomic_t may be passed instead of f_t +template +auto compute_pseudo_cost_averages(const MaybeWrappedF* pc_sum_down, + const MaybeWrappedF* pc_sum_up, + const MaybeWrappedI* pc_num_down, + const MaybeWrappedI* pc_num_up, + size_t n) { - i_t num_initialized_down = 0; - i_t num_initialized_up = 0; - f_t pseudo_cost_down_avg = 0; - f_t pseudo_cost_up_avg = 0; + using underlying_f_t = underlying_type; + using underlying_i_t = underlying_type; + + underlying_i_t num_initialized_down = 0; + underlying_i_t num_initialized_up = 0; + underlying_f_t pseudo_cost_down_avg = 0.0; + underlying_f_t pseudo_cost_up_avg = 0.0; - for (i_t j = 0; j < n; ++j) { + for (size_t j = 0; j < n; ++j) { if (pc_num_down[j] > 0) { ++num_initialized_down; if (std::isfinite(pc_sum_down[j])) { @@ -56,11 +67,10 @@ pseudo_cost_averages_t compute_pseudo_cost_averages( } pseudo_cost_down_avg = - (num_initialized_down > 0) ? pseudo_cost_down_avg / num_initialized_down : f_t(1.0); - pseudo_cost_up_avg = - (num_initialized_up > 0) ? pseudo_cost_up_avg / num_initialized_up : f_t(1.0); + (num_initialized_down > 0) ? pseudo_cost_down_avg / num_initialized_down : 1.0; + pseudo_cost_up_avg = (num_initialized_up > 0) ? pseudo_cost_up_avg / num_initialized_up : 1.0; - return {pseudo_cost_down_avg, pseudo_cost_up_avg}; + return pseudo_cost_averages_t{pseudo_cost_down_avg, pseudo_cost_up_avg}; } // Variable selection using pseudo-cost product scoring @@ -99,6 +109,35 @@ i_t variable_selection_from_pseudo_costs(const f_t* pc_sum_down, return branch_var; } +// Objective estimate using pseudo-costs (lock-free implementation) +// Returns lower_bound + estimated cost to reach integer feasibility +template +f_t obj_estimate_from_arrays(const f_t* pc_sum_down, + const f_t* pc_sum_up, + const i_t* pc_num_down, + const i_t* pc_num_up, + i_t n_vars, + const std::vector& fractional, + const std::vector& solution, + f_t lower_bound) +{ + auto [pc_down_avg, pc_up_avg] = + compute_pseudo_cost_averages(pc_sum_down, pc_sum_up, pc_num_down, pc_num_up, n_vars); + + f_t estimate = lower_bound; + constexpr f_t eps = f_t(1e-6); + + for (i_t j : fractional) { + f_t pc_down = pc_num_down[j] != 0 ? pc_sum_down[j] / pc_num_down[j] : pc_down_avg; + f_t pc_up = pc_num_up[j] != 0 ? pc_sum_up[j] / pc_num_up[j] : pc_up_avg; + const f_t f_down = solution[j] - std::floor(solution[j]); + const f_t f_up = std::ceil(solution[j]) - solution[j]; + estimate += std::min(std::max(pc_down * f_down, eps), std::max(pc_up * f_up, eps)); + } + + return estimate; +} + template class pseudo_costs_t { public: From 0f6df39b5bb3425b81e272c0a7756d8bafefe7c3 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 18:51:25 +0100 Subject: [PATCH 348/366] fix negative pseudocost --- cpp/src/dual_simplex/pseudo_costs.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index f10759865..a5f8e187f 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -289,10 +289,12 @@ template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) { - const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; + constexpr f_t eps = 1e-6; + const f_t change_in_obj = std::max(leaf_objective - node_ptr->lower_bound, eps); const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) : std::ceil(node_ptr->fractional_val) - node_ptr->fractional_val; + if (node_ptr->branch_dir == rounding_direction_t::DOWN) { pseudo_cost_sum_down[node_ptr->branch_var] += change_in_obj / frac; pseudo_cost_num_down[node_ptr->branch_var]++; From 56d5ead225a6d796fdcaf8651cfee1ac1a8f6c45 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 18:57:43 +0100 Subject: [PATCH 349/366] changed initial score --- cpp/src/dual_simplex/diving_heuristics.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 44101c0c6..f3ac61608 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -72,7 +72,7 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, logger_t& log) { i_t branch_var = -1; - f_t max_score = std::numeric_limits::lowest(); + f_t max_score = 0; rounding_direction_t round_dir = rounding_direction_t::NONE; constexpr f_t eps = 1e-6; @@ -146,7 +146,7 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, logger_t& log) { i_t branch_var = -1; - f_t max_score = std::numeric_limits::lowest(); + f_t max_score = 0; rounding_direction_t round_dir = rounding_direction_t::NONE; constexpr f_t eps = 1e-6; From a72c85c879d2ed79fb4a7916550dde19a6b8f321 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 18:59:56 +0100 Subject: [PATCH 350/366] adding more safeguards --- cpp/src/dual_simplex/pseudo_costs.cpp | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index a5f8e187f..caacbe5b1 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -31,6 +31,7 @@ void strong_branch_helper(i_t start, const std::vector& edge_norms, pseudo_costs_t& pc) { + constexpr f_t eps = 1e-6; lp_problem_t child_problem = original_lp; constexpr bool verbose = false; @@ -86,7 +87,7 @@ void strong_branch_helper(i_t start, } if (branch == 0) { - pc.strong_branch_down[k] = obj - root_obj; + pc.strong_branch_down[k] = std::max(obj - root_obj, eps); if (verbose) { settings.log.printf("Thread id %2d remaining %d variable %d branch %d obj %e time %.2f\n", thread_id, @@ -97,7 +98,7 @@ void strong_branch_helper(i_t start, toc(start_time)); } } else { - pc.strong_branch_up[k] = obj - root_obj; + pc.strong_branch_up[k] = std::max(obj - root_obj, eps); if (verbose) { settings.log.printf( "Thread id %2d remaining %d variable %d branch %d obj %e change down %e change up %e " @@ -393,9 +394,10 @@ i_t pseudo_costs_t::reliable_variable_selection( int max_num_tasks, logger_t& log) { - f_t start_time = bnb_stats.start_time; - i_t branch_var = fractional[0]; - f_t max_score = -1; + constexpr f_t eps = 1e-6; + f_t start_time = bnb_stats.start_time; + i_t branch_var = fractional[0]; + f_t max_score = -1; i_t num_initialized_down; i_t num_initialized_up; f_t pseudo_cost_down_avg; @@ -504,7 +506,7 @@ i_t pseudo_costs_t::reliable_variable_selection( strong_branching_lp_iter); if (!std::isnan(obj)) { - f_t change_in_obj = obj - node_ptr->lower_bound; + f_t change_in_obj = std::max(obj - node_ptr->lower_bound, eps); f_t change_in_x = solution[j] - std::floor(solution[j]); pseudo_cost_sum_down[j] += change_in_obj / change_in_x; pseudo_cost_num_down[j]++; @@ -532,7 +534,7 @@ i_t pseudo_costs_t::reliable_variable_selection( strong_branching_lp_iter); if (!std::isnan(obj)) { - f_t change_in_obj = obj - node_ptr->lower_bound; + f_t change_in_obj = std::max(obj - node_ptr->lower_bound, eps); f_t change_in_x = std::ceil(solution[j]) - solution[j]; pseudo_cost_sum_up[j] += change_in_obj / change_in_x; pseudo_cost_num_up[j]++; From 0b14897dab845acb891ae19782ac9ae0ec6b2195 Mon Sep 17 00:00:00 2001 From: nicolas Date: Wed, 4 Feb 2026 20:46:20 +0100 Subject: [PATCH 351/366] fix initial value --- cpp/src/dual_simplex/diving_heuristics.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index f3ac61608..7b6683a30 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -72,7 +72,7 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, logger_t& log) { i_t branch_var = -1; - f_t max_score = 0; + f_t max_score = -1; rounding_direction_t round_dir = rounding_direction_t::NONE; constexpr f_t eps = 1e-6; @@ -146,7 +146,7 @@ branch_variable_t guided_diving(pseudo_costs_t& pc, logger_t& log) { i_t branch_var = -1; - f_t max_score = 0; + f_t max_score = -1; rounding_direction_t round_dir = rounding_direction_t::NONE; constexpr f_t eps = 1e-6; From d65f2582dc7d97101fa5f670c4db2cc1387abd5e Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 5 Feb 2026 15:36:55 +0100 Subject: [PATCH 352/366] renaming variables --- cpp/src/dual_simplex/branch_and_bound.cpp | 195 +++++++++--------- cpp/src/dual_simplex/branch_and_bound.hpp | 22 +- ...worker.hpp => branch_and_bound_worker.hpp} | 58 +++--- cpp/src/dual_simplex/pseudo_costs.cpp | 56 ++--- cpp/src/dual_simplex/pseudo_costs.hpp | 6 +- 5 files changed, 171 insertions(+), 166 deletions(-) rename cpp/src/dual_simplex/{bnb_worker.hpp => branch_and_bound_worker.hpp} (82%) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index d3f746edc..ab48b5c13 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -201,26 +201,26 @@ std::string user_mip_gap(f_t obj_value, f_t lower_bound) } #ifdef SHOW_DIVING_TYPE -inline char feasible_solution_symbol(bnb_worker_type_t type) +inline char feasible_solution_symbol(bnb_worker_type_t strategy) { - switch (type) { - case bnb_worker_type_t::BEST_FIRST: return 'B'; - case bnb_worker_type_t::COEFFICIENT_DIVING: return 'C'; - case bnb_worker_type_t::LINE_SEARCH_DIVING: return 'L'; - case bnb_worker_type_t::PSEUDOCOST_DIVING: return 'P'; - case bnb_worker_type_t::GUIDED_DIVING: return 'G'; + switch (strategy) { + case search_strategy_t::BEST_FIRST: return 'B'; + case search_strategy_t::COEFFICIENT_DIVING: return 'C'; + case search_strategy_t::LINE_SEARCH_DIVING: return 'L'; + case search_strategy_t::PSEUDOCOST_DIVING: return 'P'; + case search_strategy_t::GUIDED_DIVING: return 'G'; default: return 'U'; } } #else -inline char feasible_solution_symbol(bnb_search_strategy_t type) +inline char feasible_solution_symbol(search_strategy_t strategy) { - switch (type) { - case bnb_search_strategy_t::BEST_FIRST: return 'B'; - case bnb_search_strategy_t::COEFFICIENT_DIVING: return 'D'; - case bnb_search_strategy_t::LINE_SEARCH_DIVING: return 'D'; - case bnb_search_strategy_t::PSEUDOCOST_DIVING: return 'D'; - case bnb_search_strategy_t::GUIDED_DIVING: return 'D'; + switch (strategy) { + case search_strategy_t::BEST_FIRST: return 'B'; + case search_strategy_t::COEFFICIENT_DIVING: return 'D'; + case search_strategy_t::LINE_SEARCH_DIVING: return 'D'; + case search_strategy_t::PSEUDOCOST_DIVING: return 'D'; + case search_strategy_t::GUIDED_DIVING: return 'D'; default: return 'U'; } } @@ -667,7 +667,7 @@ template void branch_and_bound_t::add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - bnb_search_strategy_t thread_type) + search_strategy_t thread_type) { bool send_solution = false; @@ -716,17 +716,17 @@ template branch_variable_t branch_and_bound_t::variable_selection( mip_node_t* node_ptr, const std::vector& fractional, - bnb_worker_data_t* worker_data) + branch_and_bound_worker_t* worker) { logger_t log; log.log = false; i_t branch_var = -1; rounding_direction_t round_dir = rounding_direction_t::NONE; std::vector current_incumbent; - std::vector& solution = worker_data->leaf_solution.x; + std::vector& solution = worker->leaf_solution.x; - switch (worker_data->search_strategy) { - case bnb_search_strategy_t::BEST_FIRST: + switch (worker->search_strategy) { + case search_strategy_t::BEST_FIRST: if (settings_.reliability_branching != 0) { branch_var = pc_.reliable_variable_selection(node_ptr, @@ -734,7 +734,7 @@ branch_variable_t branch_and_bound_t::variable_selection( solution, settings_, var_types_, - worker_data, + worker, exploration_stats_, upper_bound_, worker_pool_.num_idle_workers(), @@ -747,33 +747,34 @@ branch_variable_t branch_and_bound_t::variable_selection( return {branch_var, round_dir}; - case bnb_search_strategy_t::COEFFICIENT_DIVING: + case search_strategy_t::COEFFICIENT_DIVING: return coefficient_diving( original_lp_, fractional, solution, var_up_locks_, var_down_locks_, log); - case bnb_search_strategy_t::LINE_SEARCH_DIVING: + case search_strategy_t::LINE_SEARCH_DIVING: return line_search_diving(fractional, solution, root_relax_soln_.x, log); - case bnb_search_strategy_t::PSEUDOCOST_DIVING: + case search_strategy_t::PSEUDOCOST_DIVING: return pseudocost_diving(pc_, fractional, solution, root_relax_soln_.x, log); - case bnb_search_strategy_t::GUIDED_DIVING: + case search_strategy_t::GUIDED_DIVING: mutex_upper_.lock(); current_incumbent = incumbent_.x; mutex_upper_.unlock(); return guided_diving(pc_, fractional, solution, current_incumbent, log); default: - log.debug("Unknown variable selection method: %d\n", worker_data->search_strategy); + log.debug("Unknown variable selection method: %d\n", worker->search_strategy); return {-1, rounding_direction_t::NONE}; } } template -dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* node_ptr, - bnb_worker_data_t* worker_data, - bnb_stats_t& stats, - logger_t& log) +dual::status_t branch_and_bound_t::solve_node_lp( + mip_node_t* node_ptr, + branch_and_bound_worker_t* worker, + branch_and_bound_stats_t& stats, + logger_t& log) { #ifdef DEBUG_BRANCHING i_t num_integer_variables = 0; @@ -811,7 +812,7 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* #endif std::vector& leaf_vstatus = node_ptr->vstatus; - assert(leaf_vstatus.size() == worker_data->leaf_problem.num_cols); + assert(leaf_vstatus.size() == worker->leaf_problem.num_cols); simplex_solver_settings_t lp_settings = settings_; lp_settings.set_log(false); @@ -820,7 +821,7 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* lp_settings.time_limit = settings_.time_limit - toc(exploration_stats_.start_time); lp_settings.scale_columns = false; - if (worker_data->search_strategy != bnb_search_strategy_t::BEST_FIRST) { + if (worker->search_strategy != search_strategy_t::BEST_FIRST) { int64_t bnb_lp_iters = exploration_stats_.total_lp_iters; f_t factor = settings_.diving_settings.iteration_limit_factor; int64_t max_iter = factor * bnb_lp_iters; @@ -850,9 +851,9 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* node_ptr->vstatus[node_ptr->branch_var]); #endif - bool feasible = worker_data->set_lp_variable_bounds(node_ptr, settings_); - dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; - worker_data->leaf_edge_norms = edge_norms_; + bool feasible = worker->set_lp_variable_bounds(node_ptr, settings_); + dual::status_t lp_status = dual::status_t::DUAL_UNBOUNDED; + worker->leaf_edge_norms = edge_norms_; if (feasible) { i_t node_iter = 0; @@ -860,30 +861,29 @@ dual::status_t branch_and_bound_t::solve_node_lp(mip_node_t* lp_status = dual_phase2_with_advanced_basis(2, 0, - worker_data->recompute_basis, + worker->recompute_basis, lp_start_time, - worker_data->leaf_problem, + worker->leaf_problem, lp_settings, leaf_vstatus, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, - worker_data->leaf_solution, + worker->basis_factors, + worker->basic_list, + worker->nonbasic_list, + worker->leaf_solution, node_iter, - worker_data->leaf_edge_norms); + worker->leaf_edge_norms); if (lp_status == dual::status_t::NUMERICAL) { log.debug("Numerical issue node %d. Resolving from scratch.\n", node_ptr->node_id); - lp_status_t second_status = - solve_linear_program_with_advanced_basis(worker_data->leaf_problem, - lp_start_time, - lp_settings, - worker_data->leaf_solution, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, - leaf_vstatus, - worker_data->leaf_edge_norms); + lp_status_t second_status = solve_linear_program_with_advanced_basis(worker->leaf_problem, + lp_start_time, + lp_settings, + worker->leaf_solution, + worker->basis_factors, + worker->basic_list, + worker->nonbasic_list, + leaf_vstatus, + worker->leaf_edge_norms); lp_status = convert_lp_status_to_dual_status(second_status); } @@ -902,14 +902,14 @@ template std::pair branch_and_bound_t::update_tree( mip_node_t* node_ptr, search_tree_t& search_tree, - bnb_worker_data_t* worker_data, + branch_and_bound_worker_t* worker, dual::status_t lp_status, logger_t& log) { const f_t abs_fathom_tol = settings_.absolute_mip_gap_tol / 10; std::vector& leaf_vstatus = node_ptr->vstatus; - lp_problem_t& leaf_problem = worker_data->leaf_problem; - lp_solution_t& leaf_solution = worker_data->leaf_solution; + lp_problem_t& leaf_problem = worker->leaf_problem; + lp_solution_t& leaf_solution = worker->leaf_solution; if (lp_status == dual::status_t::DUAL_UNBOUNDED) { // Node was infeasible. Do not branch @@ -954,7 +954,7 @@ std::pair branch_and_bound_t::upd pc_.update_pseudo_costs(node_ptr, leaf_objective); node_ptr->lower_bound = leaf_objective; - if (worker_data->search_strategy == bnb_search_strategy_t::BEST_FIRST) { + if (worker->search_strategy == search_strategy_t::BEST_FIRST) { if (settings_.node_processed_callback != nullptr) { std::vector original_x; uncrush_primal_solution(original_problem_, original_lp_, leaf_solution.x, original_x); @@ -965,14 +965,14 @@ std::pair branch_and_bound_t::upd if (leaf_num_fractional == 0) { // Found a integer feasible solution add_feasible_solution( - leaf_objective, leaf_solution.x, node_ptr->depth, worker_data->search_strategy); + leaf_objective, leaf_solution.x, node_ptr->depth, worker->search_strategy); search_tree.graphviz_node(log, node_ptr, "integer feasible", leaf_objective); search_tree.update(node_ptr, node_status_t::INTEGER_FEASIBLE); return {node_status_t::INTEGER_FEASIBLE, rounding_direction_t::NONE}; } else if (leaf_objective <= upper_bound_ + abs_fathom_tol) { // Choose fractional variable to branch on - auto [branch_var, round_dir] = variable_selection(node_ptr, leaf_fractional, worker_data); + auto [branch_var, round_dir] = variable_selection(node_ptr, leaf_fractional, worker); assert(leaf_vstatus.size() == leaf_problem.num_cols); assert(branch_var >= 0); @@ -981,7 +981,7 @@ std::pair branch_and_bound_t::upd // Note that the exploration thread is the only one that can insert new nodes into the heap, // and thus, we only need to calculate the objective estimate here (it is used for // sorting the nodes for diving). - if (worker_data->search_strategy == bnb_search_strategy_t::BEST_FIRST) { + if (worker->search_strategy == search_strategy_t::BEST_FIRST) { logger_t pc_log; pc_log.log = false; node_ptr->objective_estimate = @@ -1004,7 +1004,7 @@ std::pair branch_and_bound_t::upd return {node_status_t::FATHOMED, rounding_direction_t::NONE}; } } else { - if (worker_data->search_strategy == bnb_search_strategy_t::BEST_FIRST) { + if (worker->search_strategy == search_strategy_t::BEST_FIRST) { fetch_min(lower_bound_ceiling_, node_ptr->lower_bound); log.printf( "LP returned status %d on node %d. This indicates a numerical issue. The best bound is set " @@ -1022,12 +1022,12 @@ std::pair branch_and_bound_t::upd } template -void branch_and_bound_t::plunge_with(bnb_worker_data_t* worker_data) +void branch_and_bound_t::plunge_with(branch_and_bound_worker_t* worker) { std::deque*> stack; - stack.push_front(worker_data->start_node); - worker_data->recompute_basis = true; - worker_data->recompute_bounds = true; + stack.push_front(worker->start_node); + worker->recompute_basis = true; + worker->recompute_bounds = true; while (stack.size() > 0 && solver_status_ == mip_status_t::UNSET) { mip_node_t* node_ptr = stack.front(); @@ -1043,13 +1043,13 @@ void branch_and_bound_t::plunge_with(bnb_worker_data_t* work // - The current node and its siblings uses the lower bound of the parent before solving the LP // relaxation // - The lower bound of the parent is lower or equal to its children - worker_data->lower_bound = lower_bound; + worker->lower_bound = lower_bound; if (lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { search_tree_.graphviz_node(settings_.log, node_ptr, "cutoff", node_ptr->lower_bound); search_tree_.update(node_ptr, node_status_t::FATHOMED); - worker_data->recompute_basis = true; - worker_data->recompute_bounds = true; + worker->recompute_basis = true; + worker->recompute_bounds = true; --exploration_stats_.nodes_unexplored; continue; } @@ -1064,8 +1064,7 @@ void branch_and_bound_t::plunge_with(bnb_worker_data_t* work break; } - dual::status_t lp_status = - solve_node_lp(node_ptr, worker_data, exploration_stats_, settings_.log); + dual::status_t lp_status = solve_node_lp(node_ptr, worker, exploration_stats_, settings_.log); if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; @@ -1079,10 +1078,10 @@ void branch_and_bound_t::plunge_with(bnb_worker_data_t* work --exploration_stats_.nodes_unexplored; auto [node_status, round_dir] = - update_tree(node_ptr, search_tree_, worker_data, lp_status, settings_.log); + update_tree(node_ptr, search_tree_, worker, lp_status, settings_.log); - worker_data->recompute_basis = node_status != node_status_t::HAS_CHILDREN; - worker_data->recompute_bounds = node_status != node_status_t::HAS_CHILDREN; + worker->recompute_basis = node_status != node_status_t::HAS_CHILDREN; + worker->recompute_bounds = node_status != node_status_t::HAS_CHILDREN; if (node_status == node_status_t::HAS_CHILDREN) { // The stack should only contain the children of the current parent. @@ -1118,29 +1117,29 @@ void branch_and_bound_t::plunge_with(bnb_worker_data_t* work } if (settings_.num_threads > 1) { - worker_pool_.return_worker_to_pool(worker_data); + worker_pool_.return_worker_to_pool(worker); active_workers_per_strategy_[BEST_FIRST]--; } } template -void branch_and_bound_t::dive_with(bnb_worker_data_t* worker_data) +void branch_and_bound_t::dive_with(branch_and_bound_worker_t* worker) { logger_t log; log.log = false; - bnb_search_strategy_t search_strategy = worker_data->search_strategy; - const i_t diving_node_limit = settings_.diving_settings.node_limit; - const i_t diving_backtrack_limit = settings_.diving_settings.backtrack_limit; + search_strategy_t search_strategy = worker->search_strategy; + const i_t diving_node_limit = settings_.diving_settings.node_limit; + const i_t diving_backtrack_limit = settings_.diving_settings.backtrack_limit; - worker_data->recompute_basis = true; - worker_data->recompute_bounds = true; + worker->recompute_basis = true; + worker->recompute_bounds = true; - search_tree_t dive_tree(std::move(*worker_data->start_node)); + search_tree_t dive_tree(std::move(*worker->start_node)); std::deque*> stack; stack.push_front(&dive_tree.root); - bnb_stats_t dive_stats; + branch_and_bound_stats_t dive_stats; dive_stats.total_lp_iters = 0; dive_stats.total_lp_solve_time = 0; dive_stats.nodes_explored = 0; @@ -1150,21 +1149,21 @@ void branch_and_bound_t::dive_with(bnb_worker_data_t* worker mip_node_t* node_ptr = stack.front(); stack.pop_front(); - f_t lower_bound = node_ptr->lower_bound; - f_t upper_bound = upper_bound_; - f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); - worker_data->lower_bound = lower_bound; + f_t lower_bound = node_ptr->lower_bound; + f_t upper_bound = upper_bound_; + f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); + worker->lower_bound = lower_bound; if (node_ptr->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { - worker_data->recompute_basis = true; - worker_data->recompute_bounds = true; + worker->recompute_basis = true; + worker->recompute_bounds = true; continue; } if (toc(exploration_stats_.start_time) > settings_.time_limit) { break; } if (dive_stats.nodes_explored > diving_node_limit) { break; } - dual::status_t lp_status = solve_node_lp(node_ptr, worker_data, dive_stats, log); + dual::status_t lp_status = solve_node_lp(node_ptr, worker, dive_stats, log); if (lp_status == dual::status_t::TIME_LIMIT) { solver_status_ = mip_status_t::TIME_LIMIT; @@ -1175,10 +1174,10 @@ void branch_and_bound_t::dive_with(bnb_worker_data_t* worker ++dive_stats.nodes_explored; - auto [node_status, round_dir] = update_tree(node_ptr, dive_tree, worker_data, lp_status, log); + auto [node_status, round_dir] = update_tree(node_ptr, dive_tree, worker, lp_status, log); - worker_data->recompute_basis = node_status != node_status_t::HAS_CHILDREN; - worker_data->recompute_bounds = node_status != node_status_t::HAS_CHILDREN; + worker->recompute_basis = node_status != node_status_t::HAS_CHILDREN; + worker->recompute_bounds = node_status != node_status_t::HAS_CHILDREN; if (node_status == node_status_t::HAS_CHILDREN) { if (round_dir == rounding_direction_t::UP) { @@ -1197,7 +1196,7 @@ void branch_and_bound_t::dive_with(bnb_worker_data_t* worker } } - worker_pool_.return_worker_to_pool(worker_data); + worker_pool_.return_worker_to_pool(worker); active_workers_per_strategy_[search_strategy]--; } @@ -1208,9 +1207,9 @@ void branch_and_bound_t::run_scheduler() const i_t num_workers = 2 * settings_.num_threads; if (!std::isfinite(upper_bound_)) { diving_settings.guided_diving = false; } - std::vector strategies = bnb_get_search_strategies(diving_settings); - std::array max_num_workers_per_type = - bnb_get_max_workers(num_workers, strategies); + std::vector strategies = get_search_strategies(diving_settings); + std::array max_num_workers_per_type = + get_max_workers(num_workers, strategies); worker_pool_.init(num_workers, original_lp_, Arow_, var_types_, settings_); active_workers_per_strategy_.fill(0); @@ -1245,8 +1244,8 @@ void branch_and_bound_t::run_scheduler() if (settings_.diving_settings.guided_diving != diving_settings.guided_diving) { if (std::isfinite(upper_bound_)) { diving_settings.guided_diving = settings_.diving_settings.guided_diving; - strategies = bnb_get_search_strategies(diving_settings); - max_num_workers_per_type = bnb_get_max_workers(num_workers, strategies); + strategies = get_search_strategies(diving_settings); + max_num_workers_per_type = get_max_workers(num_workers, strategies); #ifdef CUOPT_LOG_DEBUG for (auto type : strategies) { @@ -1286,7 +1285,7 @@ void branch_and_bound_t::run_scheduler() } // Get an idle worker. - bnb_worker_data_t* worker = worker_pool_.get_idle_worker(); + branch_and_bound_worker_t* worker = worker_pool_.get_idle_worker(); if (worker == nullptr) { break; } if (strategy == BEST_FIRST) { @@ -1347,7 +1346,7 @@ void branch_and_bound_t::run_scheduler() template void branch_and_bound_t::single_threaded_solve() { - bnb_worker_data_t worker(0, original_lp_, Arow_, var_types_, settings_); + branch_and_bound_worker_t worker(0, original_lp_, Arow_, var_types_, settings_); f_t lower_bound = get_lower_bound(); f_t abs_gap = upper_bound_ - lower_bound; diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 7e5671d88..1d6947681 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -7,7 +7,7 @@ #pragma once -#include +#include #include #include #include @@ -142,7 +142,7 @@ class branch_and_bound_t { mip_solution_t incumbent_; // Structure with the general info of the solver. - bnb_stats_t exploration_stats_; + branch_and_bound_stats_t exploration_stats_; // Mutex for repair omp_mutex_t mutex_repair_; @@ -171,10 +171,10 @@ class branch_and_bound_t { // Count the number of workers per type that either are being executed or // are waiting to be executed. - std::array, bnb_num_search_strategies> active_workers_per_strategy_; + std::array, num_search_strategies> active_workers_per_strategy_; // Worker pool - bnb_worker_pool_t worker_pool_; + branch_and_bound_worker_pool_t worker_pool_; // Global status of the solver. omp_atomic_t solver_status_; @@ -206,7 +206,7 @@ class branch_and_bound_t { void add_feasible_solution(f_t leaf_objective, const std::vector& leaf_solution, i_t leaf_depth, - bnb_search_strategy_t thread_type); + search_strategy_t thread_type); // Repairs low-quality solutions from the heuristics, if it is applicable. void repair_heuristic_solutions(); @@ -214,11 +214,11 @@ class branch_and_bound_t { // We use best-first to pick the `start_node` and then perform a depth-first search // from this node (i.e., a plunge). It can only backtrack to a sibling node. // Unexplored nodes in the subtree are inserted back into the global heap. - void plunge_with(bnb_worker_data_t* worker_data); + void plunge_with(branch_and_bound_worker_t* worker); // Perform a deep dive in the subtree determined by the `start_node` in order // to find integer feasible solutions. - void dive_with(bnb_worker_data_t* worker_data); + void dive_with(branch_and_bound_worker_t* worker); // Run the scheduler whose will schedule and manage // all the other workers. @@ -230,8 +230,8 @@ class branch_and_bound_t { // Solve the LP relaxation of a leaf node dual::status_t solve_node_lp(mip_node_t* node_ptr, - bnb_worker_data_t* worker_data, - bnb_stats_t& stats, + branch_and_bound_worker_t* worker, + branch_and_bound_stats_t& stats, logger_t& log); // Update the tree based on the LP relaxation. Returns the status @@ -240,14 +240,14 @@ class branch_and_bound_t { std::pair update_tree( mip_node_t* node_ptr, search_tree_t& search_tree, - bnb_worker_data_t* worker_data, + branch_and_bound_worker_t* worker, dual::status_t lp_status, logger_t& log); // Selects the variable to branch on. branch_variable_t variable_selection(mip_node_t* node_ptr, const std::vector& fractional, - bnb_worker_data_t* worker_data); + branch_and_bound_worker_t* worker); }; } // namespace cuopt::linear_programming::dual_simplex diff --git a/cpp/src/dual_simplex/bnb_worker.hpp b/cpp/src/dual_simplex/branch_and_bound_worker.hpp similarity index 82% rename from cpp/src/dual_simplex/bnb_worker.hpp rename to cpp/src/dual_simplex/branch_and_bound_worker.hpp index 00a5f1cf2..b119b0853 100644 --- a/cpp/src/dual_simplex/bnb_worker.hpp +++ b/cpp/src/dual_simplex/branch_and_bound_worker.hpp @@ -21,14 +21,14 @@ namespace cuopt::linear_programming::dual_simplex { -constexpr int bnb_num_search_strategies = 5; +constexpr int num_search_strategies = 5; // Indicate the search and variable selection algorithms used by each thread // in B&B (See [1]). // // [1] T. Achterberg, “Constraint Integer Programming,” PhD, Technischen Universität Berlin, // Berlin, 2007. doi: 10.14279/depositonce-1634. -enum bnb_search_strategy_t : int { +enum search_strategy_t : int { BEST_FIRST = 0, // Best-First + Plunging. PSEUDOCOST_DIVING = 1, // Pseudocost diving (9.2.5) LINE_SEARCH_DIVING = 2, // Line search diving (9.2.4) @@ -37,7 +37,7 @@ enum bnb_search_strategy_t : int { }; template -struct bnb_stats_t { +struct branch_and_bound_stats_t { f_t start_time = 0.0; omp_atomic_t total_lp_solve_time = 0.0; omp_atomic_t nodes_explored = 0; @@ -48,10 +48,10 @@ struct bnb_stats_t { }; template -class bnb_worker_data_t { +class branch_and_bound_worker_t { public: const i_t worker_id; - omp_atomic_t search_strategy; + omp_atomic_t search_strategy; omp_atomic_t is_active; omp_atomic_t lower_bound; @@ -75,11 +75,11 @@ class bnb_worker_data_t { bool recompute_basis = true; bool recompute_bounds = true; - bnb_worker_data_t(i_t worker_id, - const lp_problem_t& original_lp, - const csr_matrix_t& Arow, - const std::vector& var_type, - const simplex_solver_settings_t& settings) + branch_and_bound_worker_t(i_t worker_id, + const lp_problem_t& original_lp, + const csr_matrix_t& Arow, + const std::vector& var_type, + const simplex_solver_settings_t& settings) : worker_id(worker_id), search_strategy(BEST_FIRST), is_active(false), @@ -110,7 +110,7 @@ class bnb_worker_data_t { // `start_upper`. Returns `true` if the starting node is feasible via // bounds propagation. bool init_diving(mip_node_t* node, - bnb_search_strategy_t type, + search_strategy_t type, const lp_problem_t& original_lp, const simplex_solver_settings_t& settings) { @@ -161,7 +161,7 @@ class bnb_worker_data_t { }; template -class bnb_worker_pool_t { +class branch_and_bound_worker_pool_t { public: void init(i_t num_workers, const lp_problem_t& original_lp, @@ -172,8 +172,8 @@ class bnb_worker_pool_t { workers_.resize(num_workers); num_idle_workers_ = num_workers; for (i_t i = 0; i < num_workers; ++i) { - workers_[i] = - std::make_unique>(i, original_lp, Arow, var_type, settings); + workers_[i] = std::make_unique>( + i, original_lp, Arow, var_type, settings); idle_workers_.push_front(i); } @@ -182,7 +182,7 @@ class bnb_worker_pool_t { // Here, we are assuming that the scheduler is the only // thread that can retrieve/pop an idle worker. - bnb_worker_data_t* get_idle_worker() + branch_and_bound_worker_t* get_idle_worker() { std::lock_guard lock(mutex_); if (idle_workers_.empty()) { @@ -204,7 +204,7 @@ class bnb_worker_pool_t { } } - void return_worker_to_pool(bnb_worker_data_t* worker) + void return_worker_to_pool(branch_and_bound_worker_t* worker) { worker->is_active = false; std::lock_guard lock(mutex_); @@ -231,7 +231,7 @@ class bnb_worker_pool_t { private: // Worker pool - std::vector>> workers_; + std::vector>> workers_; bool is_initialized = false; omp_mutex_t mutex_; @@ -240,11 +240,11 @@ class bnb_worker_pool_t { }; template -std::vector bnb_get_search_strategies( +std::vector get_search_strategies( diving_heuristics_settings_t settings) { - std::vector types; - types.reserve(bnb_num_search_strategies); + std::vector types; + types.reserve(num_search_strategies); types.push_back(BEST_FIRST); if (settings.pseudocost_diving != 0) { types.push_back(PSEUDOCOST_DIVING); } if (settings.line_search_diving != 0) { types.push_back(LINE_SEARCH_DIVING); } @@ -254,22 +254,22 @@ std::vector bnb_get_search_strategies( } template -std::array bnb_get_max_workers( - i_t num_workers, std::vector worker_types) +std::array get_max_workers( + i_t num_workers, const std::vector& strategies) { - std::array max_num_workers; + std::array max_num_workers; max_num_workers.fill(0); - i_t bfs_workers = std::max(worker_types.size() == 1 ? num_workers : num_workers / 4, 1); + i_t bfs_workers = std::max(strategies.size() == 1 ? num_workers : num_workers / 4, 1); max_num_workers[BEST_FIRST] = bfs_workers; i_t diving_workers = (num_workers - bfs_workers); - i_t m = worker_types.size() - 1; + i_t m = strategies.size() - 1; - for (size_t i = 1, k = 0; i < worker_types.size(); ++i) { - i_t start = (double)k * diving_workers / m; - i_t end = (double)(k + 1) * diving_workers / m; - max_num_workers[worker_types[i]] = end - start; + for (size_t i = 1, k = 0; i < strategies.size(); ++i) { + i_t start = (double)k * diving_workers / m; + i_t end = (double)(k + 1) * diving_workers / m; + max_num_workers[strategies[i]] = end - start; ++k; } diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index d46f2c040..d9c5b5bd3 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -543,8 +543,8 @@ i_t pseudo_costs_t::reliable_variable_selection( const std::vector& solution, const simplex_solver_settings_t& settings, const std::vector& var_types, - bnb_worker_data_t* worker_data, - const bnb_stats_t& bnb_stats, + branch_and_bound_worker_t* worker, + const branch_and_bound_stats_t& bnb_stats, f_t upper_bound, int max_num_tasks, logger_t& log) @@ -573,18 +573,19 @@ i_t pseudo_costs_t::reliable_variable_selection( i_t reliable_threshold = settings.reliability_branching; if (reliable_threshold < 0) { - const i_t max_threshold = reliability_branching_settings.max_reliable_threshold; - const i_t min_threshold = reliability_branching_settings.min_reliable_threshold; - const f_t iter_factor = reliability_branching_settings.bnb_lp_factor; - const i_t iter_offset = reliability_branching_settings.bnb_lp_offset; - const int64_t alpha = iter_factor * branch_and_bound_lp_iters; - const int64_t max_iter = alpha + reliability_branching_settings.bnb_lp_offset; - - f_t gamma = (max_iter - strong_branching_lp_iter) / (strong_branching_lp_iter + 1.0); - gamma = std::min(1.0, gamma); + const i_t max_threshold = reliability_branching_settings.max_reliable_threshold; + const i_t min_threshold = reliability_branching_settings.min_reliable_threshold; + const f_t iter_factor = reliability_branching_settings.bnb_lp_factor; + const i_t iter_offset = reliability_branching_settings.bnb_lp_offset; + const int64_t alpha = iter_factor * branch_and_bound_lp_iters; + const int64_t max_reliability_iter = alpha + reliability_branching_settings.bnb_lp_offset; + + f_t gamma = + (max_reliability_iter - strong_branching_lp_iter) / (strong_branching_lp_iter + 1.0); + gamma = std::min(1.0, gamma); gamma = std::max((alpha - strong_branching_lp_iter) / (strong_branching_lp_iter + 1.0), gamma); reliable_threshold = (1 - gamma) * min_threshold + gamma * max_threshold; - reliable_threshold = strong_branching_lp_iter < max_iter ? reliable_threshold : 0; + reliable_threshold = strong_branching_lp_iter < max_reliability_iter ? reliable_threshold : 0; } std::vector unreliable_list; @@ -631,7 +632,12 @@ i_t pseudo_costs_t::reliable_variable_selection( reliable_threshold); // Shuffle the unreliable list so every variable has the same chance to be selected. - if (unreliable_list.size() > max_num_candidates) { worker_data->rng.shuffle(unreliable_list); } + if (unreliable_list.size() > max_num_candidates) { worker->rng.shuffle(unreliable_list); } + + if (toc(start_time) > settings.time_limit) { + log.printf("Time limit reached"); + return branch_var; + } #pragma omp taskloop if (num_tasks > 1) priority(task_priority) num_tasks(num_tasks) \ shared(score_mutex) @@ -642,16 +648,16 @@ i_t pseudo_costs_t::reliable_variable_selection( if (toc(start_time) > settings.time_limit) { continue; } if (pseudo_cost_num_down[j] < reliable_threshold) { // Do trial branching on the down branch - f_t obj = trial_branching(worker_data->leaf_problem, + f_t obj = trial_branching(worker->leaf_problem, settings, var_types, node_ptr->vstatus, - worker_data->leaf_edge_norms, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, + worker->leaf_edge_norms, + worker->basis_factors, + worker->basic_list, + worker->nonbasic_list, j, - worker_data->leaf_problem.lower[j], + worker->leaf_problem.lower[j], std::floor(solution[j]), upper_bound, branch_and_bound_lp_iter_per_node, @@ -670,17 +676,17 @@ i_t pseudo_costs_t::reliable_variable_selection( if (toc(start_time) > settings.time_limit) { continue; } if (pseudo_cost_num_up[j] < reliable_threshold) { - f_t obj = trial_branching(worker_data->leaf_problem, + f_t obj = trial_branching(worker->leaf_problem, settings, var_types, node_ptr->vstatus, - worker_data->leaf_edge_norms, - worker_data->basis_factors, - worker_data->basic_list, - worker_data->nonbasic_list, + worker->leaf_edge_norms, + worker->basis_factors, + worker->basic_list, + worker->nonbasic_list, j, std::ceil(solution[j]), - worker_data->leaf_problem.upper[j], + worker->leaf_problem.upper[j], upper_bound, branch_and_bound_lp_iter_per_node, start_time, diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index b551234a2..82cee38a9 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -8,7 +8,7 @@ #pragma once #include -#include +#include #include #include #include @@ -95,8 +95,8 @@ class pseudo_costs_t { const std::vector& solution, const simplex_solver_settings_t& settings, const std::vector& var_types, - bnb_worker_data_t* worker_data, - const bnb_stats_t& bnb_stats, + branch_and_bound_worker_t* worker, + const branch_and_bound_stats_t& bnb_stats, f_t upper_bound, int max_num_tasks, logger_t& log); From 725363d0add9f2cc3c48f2d48b72410b8c74878a Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 5 Feb 2026 15:52:25 +0100 Subject: [PATCH 353/366] split locks in pseudocost --- cpp/src/dual_simplex/pseudo_costs.cpp | 7 ++++++- cpp/src/dual_simplex/pseudo_costs.hpp | 9 ++++++--- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index d9c5b5bd3..173cb6387 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -643,9 +643,10 @@ i_t pseudo_costs_t::reliable_variable_selection( shared(score_mutex) for (i_t i = 0; i < num_candidates; ++i) { const i_t j = unreliable_list[i]; - std::lock_guard lock(pseudo_cost_mutex[j]); if (toc(start_time) > settings.time_limit) { continue; } + + pseudo_cost_mutex_down[j].lock(); if (pseudo_cost_num_down[j] < reliable_threshold) { // Do trial branching on the down branch f_t obj = trial_branching(worker->leaf_problem, @@ -673,8 +674,11 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_num_down[j]++; } } + pseudo_cost_mutex_down[j].unlock(); if (toc(start_time) > settings.time_limit) { continue; } + + pseudo_cost_mutex_up[j].lock(); if (pseudo_cost_num_up[j] < reliable_threshold) { f_t obj = trial_branching(worker->leaf_problem, settings, @@ -701,6 +705,7 @@ i_t pseudo_costs_t::reliable_variable_selection( pseudo_cost_num_up[j]++; } } + pseudo_cost_mutex_up[j].unlock(); if (toc(start_time) > settings.time_limit) { continue; } diff --git a/cpp/src/dual_simplex/pseudo_costs.hpp b/cpp/src/dual_simplex/pseudo_costs.hpp index 82cee38a9..1d3b0deef 100644 --- a/cpp/src/dual_simplex/pseudo_costs.hpp +++ b/cpp/src/dual_simplex/pseudo_costs.hpp @@ -61,7 +61,8 @@ class pseudo_costs_t { pseudo_cost_sum_up(num_variables), pseudo_cost_num_down(num_variables), pseudo_cost_num_up(num_variables), - pseudo_cost_mutex(num_variables) + pseudo_cost_mutex_up(num_variables), + pseudo_cost_mutex_down(num_variables) { } @@ -73,7 +74,8 @@ class pseudo_costs_t { pseudo_cost_sum_up.assign(num_variables, 0); pseudo_cost_num_down.assign(num_variables, 0); pseudo_cost_num_up.assign(num_variables, 0); - pseudo_cost_mutex.resize(num_variables); + pseudo_cost_mutex_up.resize(num_variables); + pseudo_cost_mutex_down.resize(num_variables); } void initialized(i_t& num_initialized_down, @@ -117,7 +119,8 @@ class pseudo_costs_t { std::vector> pseudo_cost_num_down; std::vector strong_branch_down; std::vector strong_branch_up; - std::vector pseudo_cost_mutex; + std::vector pseudo_cost_mutex_up; + std::vector pseudo_cost_mutex_down; omp_atomic_t num_strong_branches_completed = 0; omp_atomic_t strong_branching_lp_iter = 0; }; From 46d59b3634c9fb38b03a929a198d731690f0d76f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Thu, 5 Feb 2026 19:21:41 +0000 Subject: [PATCH 354/366] fix build --- cpp/src/dual_simplex/branch_and_bound.cpp | 5 +++++ cpp/src/dual_simplex/folding.cpp | 16 ++++++++-------- cpp/src/utilities/work_unit_scheduler.cpp | 6 +++++- cpp/src/utilities/work_unit_scheduler.hpp | 8 ++++++++ 4 files changed, 26 insertions(+), 9 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 52022b8ff..5e311b2d7 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2711,6 +2711,11 @@ void branch_and_bound_t::determinism_sync_callback(int worker_id) determinism_global_termination_status_ = mip_status_t::WORK_LIMIT; } + // Signal shutdown to prevent threads from entering barriers after termination + if (determinism_global_termination_status_ != mip_status_t::UNSET) { + determinism_scheduler_->signal_shutdown(); + } + f_t obj = compute_user_objective(original_lp_, upper_bound); f_t user_lower = compute_user_objective(original_lp_, lower_bound); std::string gap_user = user_mip_gap(obj, user_lower); diff --git a/cpp/src/dual_simplex/folding.cpp b/cpp/src/dual_simplex/folding.cpp index 960a72453..5ff2eda71 100644 --- a/cpp/src/dual_simplex/folding.cpp +++ b/cpp/src/dual_simplex/folding.cpp @@ -143,9 +143,9 @@ void compute_sums(const csc_matrix_t& A, compute_sums_of_refined_vertices(refining_color.color, refining_color.vertices, vertices_to_refine, - Arow.row_start, - Arow.j, - Arow.x, + Arow.row_start.underlying(), + Arow.j.underlying(), + Arow.x.underlying(), col_color_map, vertex_to_sum, max_sum_by_color); @@ -154,8 +154,8 @@ void compute_sums(const csc_matrix_t& A, // Find all vertices (rows) that have a neighbor in the refining color colors_to_update.reserve(num_row_colors); find_vertices_to_refine(refining_color.vertices, - A.col_start, - A.i, + A.col_start.underlying(), + A.i.underlying(), row_color_map, marked_vertices, vertices_to_refine, @@ -171,9 +171,9 @@ void compute_sums(const csc_matrix_t& A, compute_sums_of_refined_vertices(refining_color.color, refining_color.vertices, vertices_to_refine, - A.col_start, - A.i, - A.x, + A.col_start.underlying(), + A.i.underlying(), + A.x.underlying(), row_color_map, vertex_to_sum, max_sum_by_color); diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp index 0cf5a0e3b..c810d5511 100644 --- a/cpp/src/utilities/work_unit_scheduler.cpp +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -54,6 +54,8 @@ void work_unit_scheduler_t::set_sync_interval(double interval) { sync_interval_ void work_unit_scheduler_t::on_work_recorded(work_limit_context_t& ctx, double total_work) { + if (is_shutdown()) return; + if (verbose) { CUOPT_LOG_DEBUG("[%s] Work recorded: %f, sync_target: %f (gen %zu)", ctx.name.c_str(), @@ -63,7 +65,7 @@ void work_unit_scheduler_t::on_work_recorded(work_limit_context_t& ctx, double t } // Loop to handle large work increments that cross multiple sync points - while (total_work >= current_sync_target()) { + while (total_work >= current_sync_target() && !is_shutdown()) { wait_at_sync_point(ctx, current_sync_target()); } } @@ -75,6 +77,8 @@ void work_unit_scheduler_t::set_sync_callback(sync_callback_t callback) void work_unit_scheduler_t::wait_for_next_sync(work_limit_context_t& ctx) { + if (is_shutdown()) return; + double next_sync = current_sync_target(); ctx.global_work_units_elapsed = next_sync; wait_at_sync_point(ctx, next_sync); diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index 8ed40ad93..8275604ec 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -16,6 +16,7 @@ */ #pragma once +#include #include #include @@ -50,6 +51,10 @@ class work_unit_scheduler_t { // Get the current sync target (for work tracking) double current_sync_target() const; + // Signal shutdown - prevents threads from entering barriers after termination + void signal_shutdown() { shutdown_.store(true, std::memory_order_release); } + bool is_shutdown() const { return shutdown_.load(std::memory_order_acquire); } + public: bool verbose{false}; @@ -64,6 +69,9 @@ class work_unit_scheduler_t { // Sync callback - executed when all contexts reach sync point sync_callback_t sync_callback_; + + // Shutdown flag - prevents threads from entering barriers after termination is signaled + std::atomic shutdown_{false}; }; // RAII helper for registering multiple contexts with automatic cleanup From d1989cca1da6f14e0e51a21d870d4d38dec17f90 Mon Sep 17 00:00:00 2001 From: nicolas Date: Thu, 5 Feb 2026 21:55:31 +0100 Subject: [PATCH 355/366] fix crash in timtab1 due to double infinite pseudocost --- cpp/src/dual_simplex/diving_heuristics.cpp | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/cpp/src/dual_simplex/diving_heuristics.cpp b/cpp/src/dual_simplex/diving_heuristics.cpp index 7b6683a30..b3e91bc09 100644 --- a/cpp/src/dual_simplex/diving_heuristics.cpp +++ b/cpp/src/dual_simplex/diving_heuristics.cpp @@ -126,6 +126,15 @@ branch_variable_t pseudocost_diving(pseudo_costs_t& pc, } } + // If we cannot choose the variable, then arbitrarily pick the first + // fractional variable and round it down. This only happens when + // there is only one fractional variable and its the pseudocost is + // infinite for both direction. + if (round_dir == rounding_direction_t::NONE) { + branch_var = fractional[0]; + round_dir = rounding_direction_t::DOWN; + } + assert(round_dir != rounding_direction_t::NONE); assert(branch_var >= 0); From f6d2a94cb9326da9507e84341161f984ab00c0c5 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 6 Feb 2026 00:33:43 -0800 Subject: [PATCH 356/366] fix race condition --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 5e311b2d7..783a5881b 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -483,10 +483,12 @@ void branch_and_bound_t::queue_external_solution_deterministic( return; } + mutex_original_lp_.lock(); std::vector crushed_solution; crush_primal_solution( original_problem_, original_lp_, solution, new_slacks_, crushed_solution); f_t obj = compute_objective(original_lp_, crushed_solution); + mutex_original_lp_.unlock(); // Validate solution before queueing f_t primal_err; From 641707437dc312807d21f6fc6f21900d89478d95 Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 6 Feb 2026 10:37:08 +0100 Subject: [PATCH 357/366] small tweaks --- cpp/src/dual_simplex/basis_updates.hpp | 2 ++ cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- .../dual_simplex/branch_and_bound_worker.hpp | 3 ++- cpp/src/dual_simplex/pseudo_costs.cpp | 21 +++++++++++-------- 4 files changed, 17 insertions(+), 11 deletions(-) diff --git a/cpp/src/dual_simplex/basis_updates.hpp b/cpp/src/dual_simplex/basis_updates.hpp index b75c14242..d9783053f 100644 --- a/cpp/src/dual_simplex/basis_updates.hpp +++ b/cpp/src/dual_simplex/basis_updates.hpp @@ -384,6 +384,8 @@ class basis_update_mpf_t { std::vector& nonbasic_list, std::vector& vstatus); + void set_refactor_frequency(i_t new_frequency) { refactor_frequency_ = new_frequency; } + private: void clear() { diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index ab48b5c13..cbf04cb23 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -201,7 +201,7 @@ std::string user_mip_gap(f_t obj_value, f_t lower_bound) } #ifdef SHOW_DIVING_TYPE -inline char feasible_solution_symbol(bnb_worker_type_t strategy) +inline char feasible_solution_symbol(search_strategy_t strategy) { switch (strategy) { case search_strategy_t::BEST_FIRST: return 'B'; diff --git a/cpp/src/dual_simplex/branch_and_bound_worker.hpp b/cpp/src/dual_simplex/branch_and_bound_worker.hpp index b119b0853..99a59a871 100644 --- a/cpp/src/dual_simplex/branch_and_bound_worker.hpp +++ b/cpp/src/dual_simplex/branch_and_bound_worker.hpp @@ -91,7 +91,8 @@ class branch_and_bound_worker_t { nonbasic_list(), node_presolver(leaf_problem, Arow, {}, var_type), bounds_changed(original_lp.num_cols, false), - rng(pcgenerator_t::default_seed ^ worker_id, pcgenerator_t::default_stream + worker_id) + rng(settings.random_seed + pcgenerator_t::default_seed + worker_id, + pcgenerator_t::default_stream ^ worker_id) { } diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 173cb6387..3e15f6c44 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -33,7 +33,6 @@ void strong_branch_helper(i_t start, const std::vector& edge_norms, pseudo_costs_t& pc) { - constexpr f_t eps = 1e-6; lp_problem_t child_problem = original_lp; constexpr bool verbose = false; @@ -89,7 +88,7 @@ void strong_branch_helper(i_t start, } if (branch == 0) { - pc.strong_branch_down[k] = std::max(obj - root_obj, eps); + pc.strong_branch_down[k] = std::max(obj - root_obj, 0.0); if (verbose) { settings.log.printf("Thread id %2d remaining %d variable %d branch %d obj %e time %.2f\n", thread_id, @@ -100,7 +99,7 @@ void strong_branch_helper(i_t start, toc(start_time)); } } else { - pc.strong_branch_up[k] = std::max(obj - root_obj, eps); + pc.strong_branch_up[k] = std::max(obj - root_obj, 0.0); if (verbose) { settings.log.printf( "Thread id %2d remaining %d variable %d branch %d obj %e change down %e change up %e " @@ -177,6 +176,9 @@ f_t trial_branching(const lp_problem_t& original_lp, std::vector child_nonbasic_list = nonbasic_list; basis_update_mpf_t child_basis_factors = basis_factors; + // Only refactor the basis if we encounter numerical issues. + child_basis_factors.set_refactor_frequency(upper_max_lp_iter); + dual::status_t status = dual_phase2_with_advanced_basis(2, 0, initialize_basis, @@ -445,8 +447,7 @@ template void pseudo_costs_t::update_pseudo_costs(mip_node_t* node_ptr, f_t leaf_objective) { - constexpr f_t eps = 1e-6; - const f_t change_in_obj = std::max(leaf_objective - node_ptr->lower_bound, eps); + const f_t change_in_obj = std::max(leaf_objective - node_ptr->lower_bound, 0.0); const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) : std::ceil(node_ptr->fractional_val) - node_ptr->fractional_val; @@ -519,7 +520,7 @@ i_t pseudo_costs_t::variable_selection(const std::vector& fractio pseudo_cost_down_avg, pseudo_cost_up_avg); - for (auto j : fractional) { + for (i_t j : fractional) { f_t score = calculate_pseudocost_score(j, solution, pseudo_cost_up_avg, pseudo_cost_down_avg); if (score > max_score) { @@ -591,7 +592,7 @@ i_t pseudo_costs_t::reliable_variable_selection( std::vector unreliable_list; omp_mutex_t score_mutex; - for (auto j : fractional) { + for (i_t j : fractional) { if (pseudo_cost_num_down[j] < reliable_threshold || pseudo_cost_num_up[j] < reliable_threshold) { unreliable_list.push_back(j); @@ -711,10 +712,12 @@ i_t pseudo_costs_t::reliable_variable_selection( f_t score = calculate_pseudocost_score(j, solution, pseudo_cost_up_avg, pseudo_cost_down_avg); - if (std::lock_guard score_lock(score_mutex); score > max_score) { + score_mutex.lock(); + if (score > max_score) { max_score = score; branch_var = j; } + score_mutex.unlock(); } log.printf( @@ -739,7 +742,7 @@ f_t pseudo_costs_t::obj_estimate(const std::vector& fractional, initialized(num_initialized_down, num_initialized_up, pseudo_cost_down_avg, pseudo_cost_up_avg); - for (auto j : fractional) { + for (i_t j : fractional) { constexpr f_t eps = 1e-6; i_t num_up = pseudo_cost_num_up[j]; i_t num_down = pseudo_cost_num_down[j]; From 91e016889001752109c0b371e06fdda836c2f9eb Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 6 Feb 2026 12:26:30 +0100 Subject: [PATCH 358/366] renamed variable --- cpp/src/dual_simplex/pseudo_costs.cpp | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index 3e15f6c44..682bdaa6f 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -581,11 +581,12 @@ i_t pseudo_costs_t::reliable_variable_selection( const int64_t alpha = iter_factor * branch_and_bound_lp_iters; const int64_t max_reliability_iter = alpha + reliability_branching_settings.bnb_lp_offset; - f_t gamma = + f_t iter_fraction = (max_reliability_iter - strong_branching_lp_iter) / (strong_branching_lp_iter + 1.0); - gamma = std::min(1.0, gamma); - gamma = std::max((alpha - strong_branching_lp_iter) / (strong_branching_lp_iter + 1.0), gamma); - reliable_threshold = (1 - gamma) * min_threshold + gamma * max_threshold; + iter_fraction = std::min(1.0, iter_fraction); + iter_fraction = std::max((alpha - strong_branching_lp_iter) / (strong_branching_lp_iter + 1.0), + iter_fraction); + reliable_threshold = (1 - iter_fraction) * min_threshold + iter_fraction * max_threshold; reliable_threshold = strong_branching_lp_iter < max_reliability_iter ? reliable_threshold : 0; } From 515d17cf3a6141fea7e5ddffac6577c3f7fef1ae Mon Sep 17 00:00:00 2001 From: nicolas Date: Fri, 6 Feb 2026 14:02:26 +0100 Subject: [PATCH 359/366] fix incorrect lower bounds during the cut passes --- cpp/src/dual_simplex/branch_and_bound.cpp | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 9f8905fea..c6e8b0d17 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -273,7 +273,8 @@ branch_and_bound_t::branch_and_bound_t( } #endif - upper_bound_ = inf; + upper_bound_ = inf; + root_objective_ = std::numeric_limits::quiet_NaN(); } template @@ -283,7 +284,14 @@ f_t branch_and_bound_t::get_lower_bound() f_t heap_lower_bound = node_queue_.get_lower_bound(); lower_bound = std::min(heap_lower_bound, lower_bound); lower_bound = std::min(worker_pool_.get_lower_bound(), lower_bound); - return std::isfinite(lower_bound) ? lower_bound : -inf; + + if (std::isfinite(lower_bound)) { + return lower_bound; + } else if (std::isfinite(root_objective_)) { + return root_objective_; + } else { + return -inf; + } } template From d8cde66862182e4e3f9420942ef05241800c010e Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 6 Feb 2026 13:11:06 +0000 Subject: [PATCH 360/366] cleanup, fix race --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- cpp/src/dual_simplex/sparse_matrix.hpp | 4 ++-- cpp/src/utilities/work_limit_timer.hpp | 2 +- cpp/src/utilities/work_unit_scheduler.hpp | 8 -------- 4 files changed, 4 insertions(+), 12 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 12b7852d3..e9670110f 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -488,7 +488,6 @@ void branch_and_bound_t::queue_external_solution_deterministic( crush_primal_solution( original_problem_, original_lp_, solution, new_slacks_, crushed_solution); f_t obj = compute_objective(original_lp_, crushed_solution); - mutex_original_lp_.unlock(); // Validate solution before queueing f_t primal_err; @@ -496,6 +495,7 @@ void branch_and_bound_t::queue_external_solution_deterministic( i_t num_fractional; bool is_feasible = check_guess( original_lp_, settings_, var_types_, crushed_solution, primal_err, bound_err, num_fractional); + mutex_original_lp_.unlock(); if (!is_feasible) { // Queue for repair diff --git a/cpp/src/dual_simplex/sparse_matrix.hpp b/cpp/src/dual_simplex/sparse_matrix.hpp index fc265608f..8f8f62251 100644 --- a/cpp/src/dual_simplex/sparse_matrix.hpp +++ b/cpp/src/dual_simplex/sparse_matrix.hpp @@ -181,8 +181,8 @@ class csr_matrix_t { i_t m; // number of rows i_t n; // number of cols ins_vector row_start; // row pointers (size m + 1) - ins_vector j; // column inidices, size nz_max - ins_vector x; // numerical valuse, size nz_max + ins_vector j; // column indices, size nz_max + ins_vector x; // numerical values, size nz_max static_assert(std::is_signed_v); }; diff --git a/cpp/src/utilities/work_limit_timer.hpp b/cpp/src/utilities/work_limit_timer.hpp index 08499f55b..2b5545269 100644 --- a/cpp/src/utilities/work_limit_timer.hpp +++ b/cpp/src/utilities/work_limit_timer.hpp @@ -80,7 +80,7 @@ class work_limit_timer_t { finished = true; double actual_elapsed_time = timer.elapsed_time(); // 10% timing error - if (work_limit > 0 && abs(actual_elapsed_time - work_limit) / work_limit > 0.10) { + if (work_limit > 0 && std::abs(actual_elapsed_time - work_limit) / work_limit > 0.10) { CUOPT_LOG_ERROR( "%s:%d: %s(): Work limit timer finished with a large discrepancy: %fs for %fwu " "(global: %f, start: %f)", diff --git a/cpp/src/utilities/work_unit_scheduler.hpp b/cpp/src/utilities/work_unit_scheduler.hpp index 8275604ec..84e7b95fa 100644 --- a/cpp/src/utilities/work_unit_scheduler.hpp +++ b/cpp/src/utilities/work_unit_scheduler.hpp @@ -24,10 +24,6 @@ namespace cuopt { struct work_limit_context_t; -// Simplified scheduler using OpenMP barriers for synchronization. -// Termination is managed externally (e.g., by -// branch_and_bound_t::determinism_global_termination_status_). Workers should check termination -// status after each sync point. class work_unit_scheduler_t { public: explicit work_unit_scheduler_t(double sync_interval = 5.0); @@ -40,18 +36,14 @@ class work_unit_scheduler_t { void on_work_recorded(work_limit_context_t& ctx, double total_work); // Sync callback - executed by one thread when all contexts reach sync point - // Callback should set external termination status if stopping is desired using sync_callback_t = std::function; void set_sync_callback(sync_callback_t callback); // Wait for next sync point (for idle workers with no work) - // After returning, caller should check external termination status void wait_for_next_sync(work_limit_context_t& ctx); - // Get the current sync target (for work tracking) double current_sync_target() const; - // Signal shutdown - prevents threads from entering barriers after termination void signal_shutdown() { shutdown_.store(true, std::memory_order_release); } bool is_shutdown() const { return shutdown_.load(std::memory_order_acquire); } From 1df294be4278e264d0945d74a732c336f8da8225 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 6 Feb 2026 14:58:49 +0000 Subject: [PATCH 361/366] more cleanup --- cpp/src/dual_simplex/branch_and_bound.cpp | 252 ++++++++++------------ cpp/src/dual_simplex/branch_and_bound.hpp | 23 +- 2 files changed, 116 insertions(+), 159 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 582116013..91046e57b 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -948,9 +948,14 @@ struct determinism_tree_update_policy_t { const std::vector& fractional, const std::vector& x) { - logger_t log; - log.log = false; - node->objective_estimate = bnb.pc_.obj_estimate(fractional, x, node->lower_bound, log); + node->objective_estimate = obj_estimate_from_arrays(worker.pc_sum_down_snapshot.data(), + worker.pc_sum_up_snapshot.data(), + worker.pc_num_down_snapshot.data(), + worker.pc_num_up_snapshot.data(), + (i_t)worker.pc_sum_down_snapshot.size(), + fractional, + x, + node->lower_bound); } void on_node_completed(mip_node_t* node, node_status_t status, rounding_direction_t dir) @@ -987,6 +992,97 @@ struct determinism_tree_update_policy_t { void on_optimal_callback(const std::vector&, f_t) {} }; +template +struct determinism_diving_tree_update_policy_t { + branch_and_bound_t& bnb; + determinism_diving_worker_t& worker; + std::deque*>& stack; + i_t max_backtrack_depth; + + f_t upper_bound() const { return worker.local_upper_bound; } + + void update_pseudo_costs(mip_node_t* node, f_t leaf_obj) + { + if (node->branch_var < 0) return; + f_t change = leaf_obj - node->lower_bound; + f_t frac = node->branch_dir == rounding_direction_t::DOWN + ? node->fractional_val - std::floor(node->fractional_val) + : std::ceil(node->fractional_val) - node->fractional_val; + if (frac > 1e-10) { + worker.queue_pseudo_cost_update(node->branch_var, node->branch_dir, change / frac); + } + } + + void handle_integer_solution(mip_node_t* node, f_t obj, const std::vector& x) + { + if (obj < worker.local_upper_bound) { + worker.local_upper_bound = obj; + worker.queue_integer_solution(obj, x, node->depth); + } + } + + branch_variable_t select_branch_variable(mip_node_t*, + const std::vector& fractional, + const std::vector& x) + { + switch (worker.diving_type) { + case search_strategy_t::PSEUDOCOST_DIVING: + return worker.variable_selection_from_snapshot(fractional, x); + + case search_strategy_t::LINE_SEARCH_DIVING: + if (worker.root_solution) { + logger_t log; + log.log = false; + return line_search_diving(fractional, x, *worker.root_solution, log); + } + return worker.variable_selection_from_snapshot(fractional, x); + + case search_strategy_t::GUIDED_DIVING: return worker.guided_variable_selection(fractional, x); + + case search_strategy_t::COEFFICIENT_DIVING: { + logger_t log; + log.log = false; + return coefficient_diving( + worker.leaf_problem, fractional, x, bnb.var_up_locks_, bnb.var_down_locks_, log); + } + + default: return worker.variable_selection_from_snapshot(fractional, x); + } + } + + void update_objective_estimate(mip_node_t* node, + const std::vector& fractional, + const std::vector& x) + { + node->objective_estimate = worker.obj_estimate_from_snapshot(fractional, x, node->lower_bound); + } + + void on_node_completed(mip_node_t* node, node_status_t status, rounding_direction_t dir) + { + if (status == node_status_t::HAS_CHILDREN) { + if (dir == rounding_direction_t::UP) { + stack.push_front(node->get_down_child()); + stack.push_front(node->get_up_child()); + } else { + stack.push_front(node->get_up_child()); + stack.push_front(node->get_down_child()); + } + if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > max_backtrack_depth) { + stack.pop_back(); + } + worker.recompute_bounds_and_basis = false; + } else { + worker.recompute_bounds_and_basis = true; + } + } + + void on_numerical_issue(mip_node_t*) {} + + void graphviz(search_tree_t&, mip_node_t*, const char*, f_t) {} + + void on_optimal_callback(const std::vector&, f_t) {} +}; + template template std::pair branch_and_bound_t::update_tree_impl( @@ -2445,7 +2541,7 @@ void branch_and_bound_t::run_determinism_coordinator(const csr_matrix_ (*determinism_workers_)[0].enqueue_node(search_tree_.root.get_down_child()); (*determinism_workers_)[1 % num_bfs_workers].enqueue_node(search_tree_.root.get_up_child()); - determinism_scheduler_->set_sync_callback([this](double) { determinism_sync_callback(0); }); + determinism_scheduler_->set_sync_callback([this](double) { determinism_sync_callback(); }); std::vector incumbent_snapshot; if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } @@ -2575,7 +2671,7 @@ void branch_and_bound_t::run_deterministic_bfs_loop( f_t upper_bound = worker.local_upper_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, node->lower_bound); - if (node->lower_bound >= upper_bound || rel_gap < settings_.relative_mip_gap_tol) { + if (node->lower_bound > upper_bound || rel_gap < settings_.relative_mip_gap_tol) { worker.current_node = nullptr; worker.record_fathomed(node, node->lower_bound); search_tree.update(node, node_status_t::FATHOMED); @@ -2586,9 +2682,8 @@ void branch_and_bound_t::run_deterministic_bfs_loop( bool is_child = (node->parent == worker.last_solved_node); worker.recompute_bounds_and_basis = !is_child; - node_solve_info_t status = - solve_node_deterministic(worker, node, search_tree, worker.horizon_end); - worker.last_solved_node = node; + node_solve_info_t status = solve_node_deterministic(worker, node, search_tree); + worker.last_solved_node = node; if (status == node_solve_info_t::TIME_LIMIT || status == node_solve_info_t::WORK_LIMIT) { continue; @@ -2605,13 +2700,12 @@ void branch_and_bound_t::run_deterministic_bfs_loop( } template -void branch_and_bound_t::determinism_sync_callback(int worker_id) +void branch_and_bound_t::determinism_sync_callback() { raft::common::nvtx::range scope("BB::determinism_sync_callback"); ++determinism_horizon_number_; - double horizon_start = determinism_current_horizon_ - determinism_horizon_step_; - double horizon_end = determinism_current_horizon_; + double horizon_end = determinism_current_horizon_; double wait_start = tic(); producer_sync_.wait_for_producers(horizon_end); @@ -2768,13 +2862,11 @@ template node_solve_info_t branch_and_bound_t::solve_node_deterministic( determinism_bfs_worker_t& worker, mip_node_t* node_ptr, - search_tree_t& search_tree, - double current_horizon) + search_tree_t& search_tree) { raft::common::nvtx::range scope("BB::solve_node_deterministic"); double work_units_at_start = worker.work_context.global_work_units_elapsed; - double clock_at_start = worker.clock; std::fill(worker.bounds_changed.begin(), worker.bounds_changed.end(), false); @@ -2802,10 +2894,8 @@ node_solve_info_t branch_and_bound_t::solve_node_deterministic( bool feasible = true; #ifndef DETERMINISM_DISABLE_BOUNDS_STRENGTHENING raft::common::nvtx::range scope_bs("BB::bound_strengthening"); - f_t bs_start_time = tic(); - feasible = worker.node_presolver.bounds_strengthening( + feasible = worker.node_presolver.bounds_strengthening( lp_settings, worker.bounds_changed, worker.leaf_problem.lower, worker.leaf_problem.upper); - f_t bs_actual_time = toc(bs_start_time); if (settings_.deterministic) { // TEMP APPROXIMATION; @@ -3413,7 +3503,7 @@ void branch_and_bound_t::deterministic_dive(determinism_diving_worker_ // Prune check using snapshot upper bound f_t rel_gap = user_relative_gap(original_lp_, worker.local_upper_bound, node_ptr->lower_bound); - if (node_ptr->lower_bound >= worker.local_upper_bound || + if (node_ptr->lower_bound > worker.local_upper_bound || rel_gap < settings_.relative_mip_gap_tol) { worker.recompute_bounds_and_basis = true; continue; @@ -3506,129 +3596,9 @@ void branch_and_bound_t::deterministic_dive(determinism_diving_worker_ break; } - if (lp_status == dual::status_t::DUAL_UNBOUNDED || lp_status == dual::status_t::CUTOFF) { - worker.recompute_bounds_and_basis = true; - continue; - } - - if (lp_status == dual::status_t::OPTIMAL) { - std::vector leaf_fractional; - fractional_variables(settings_, worker.leaf_solution.x, var_types_, leaf_fractional); - - f_t leaf_objective = compute_objective(worker.leaf_problem, worker.leaf_solution.x); - - if (node_ptr->branch_var >= 0) { - const f_t change_in_obj = leaf_objective - node_ptr->lower_bound; - const f_t frac = node_ptr->branch_dir == rounding_direction_t::DOWN - ? node_ptr->fractional_val - std::floor(node_ptr->fractional_val) - : std::ceil(node_ptr->fractional_val) - node_ptr->fractional_val; - if (frac > 1e-10) { - worker.queue_pseudo_cost_update( - node_ptr->branch_var, node_ptr->branch_dir, change_in_obj / frac); - } - } - - node_ptr->lower_bound = leaf_objective; - - if (leaf_fractional.empty()) { - // Integer feasible solution found! - if (leaf_objective < worker.local_upper_bound) { - worker.queue_integer_solution(leaf_objective, worker.leaf_solution.x, node_ptr->depth); - } - worker.recompute_bounds_and_basis = true; - continue; - } - - if (leaf_objective <= worker.local_upper_bound + settings_.absolute_mip_gap_tol / 10) { - // Branch - select variable using diving-type-specific strategy - branch_variable_t branch_result; - - switch (worker.diving_type) { - case search_strategy_t::PSEUDOCOST_DIVING: - branch_result = - worker.variable_selection_from_snapshot(leaf_fractional, worker.leaf_solution.x); - break; - - case search_strategy_t::LINE_SEARCH_DIVING: - if (worker.root_solution) { - logger_t log; - log.log = false; - branch_result = line_search_diving( - leaf_fractional, worker.leaf_solution.x, *worker.root_solution, log); - } else { - branch_result = - worker.variable_selection_from_snapshot(leaf_fractional, worker.leaf_solution.x); - } - break; - - case search_strategy_t::GUIDED_DIVING: - branch_result = - worker.guided_variable_selection(leaf_fractional, worker.leaf_solution.x); - break; - - case search_strategy_t::COEFFICIENT_DIVING: { - logger_t log; - log.log = false; - branch_result = coefficient_diving(worker.leaf_problem, - leaf_fractional, - worker.leaf_solution.x, - var_up_locks_, - var_down_locks_, - log); - } break; - - default: - branch_result = - worker.variable_selection_from_snapshot(leaf_fractional, worker.leaf_solution.x); - break; - } - - i_t branch_var = branch_result.variable; - rounding_direction_t round_dir = branch_result.direction; - - if (branch_var < 0) { - worker.recompute_bounds_and_basis = true; - continue; - } - - // Update objective estimate for node selection heuristics - node_ptr->objective_estimate = worker.obj_estimate_from_snapshot( - leaf_fractional, worker.leaf_solution.x, leaf_objective); - - // Create children - logger_t log; - log.log = false; - dive_tree.branch(node_ptr, - branch_var, - worker.leaf_solution.x[branch_var], - (i_t)leaf_fractional.size(), - leaf_vstatus, - worker.leaf_problem, - log); - - // Add children to stack (preferred direction first) - if (round_dir == rounding_direction_t::UP) { - stack.push_front(node_ptr->get_down_child()); - stack.push_front(node_ptr->get_up_child()); - } else { - stack.push_front(node_ptr->get_up_child()); - stack.push_front(node_ptr->get_down_child()); - } - - // Limit backtracking depth - if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > max_backtrack_depth) { - stack.pop_back(); - } - - worker.recompute_bounds_and_basis = false; - } else { - // Fathomed by bound - worker.recompute_bounds_and_basis = true; - } - } else { - // Numerical or other error - worker.recompute_bounds_and_basis = true; - } + determinism_diving_tree_update_policy_t policy{ + *this, worker, stack, max_backtrack_depth}; + update_tree_impl(node_ptr, dive_tree, &worker, lp_status, policy); } } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index f7e5edd35..92db0634b 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -44,21 +44,6 @@ enum class mip_status_t { WORK_LIMIT = 7, // The solver reached a deterministic work limit }; -enum class mip_solve_mode_t { - BNB_PARALLEL = 0, // Parallel B&B (default) - BNB_SINGLE_THREADED = 1, // Single threaded B&B for SubMIP and RINS -}; - -enum class mip_exploration_status_t { - UNSET = 0, // The status is not set - TIME_LIMIT = 1, // The solver reached a time limit - NODE_LIMIT = 2, // The maximum number of nodes was reached (not implemented) - NUMERICAL = 3, // The solver encountered a numerical error - RUNNING = 4, // The solver is currently exploring the tree - COMPLETED = 5, // The solver finished exploring the tree - WORK_LIMIT = 6, // The solver reached a deterministic work limit -}; - enum class node_solve_info_t { NO_CHILDREN = 0, // The node does not produced children UP_CHILD_FIRST = 1, // The up child should be explored first @@ -79,6 +64,8 @@ template struct opportunistic_tree_update_policy_t; template struct determinism_tree_update_policy_t; +template +struct determinism_diving_tree_update_policy_t; template class branch_and_bound_t { @@ -323,8 +310,7 @@ class branch_and_bound_t { node_solve_info_t solve_node_deterministic(determinism_bfs_worker_t& worker, mip_node_t* node_ptr, - search_tree_t& search_tree, - double current_horizon); + search_tree_t& search_tree); f_t compute_lower_bound_deterministic(); @@ -333,7 +319,7 @@ class branch_and_bound_t { // Executed when all workers reach barrier // Handles termination logic serially in deterministic mode - void determinism_sync_callback(int worker_id); + void determinism_sync_callback(); void run_deterministic_diving_loop(determinism_diving_worker_t& worker); @@ -357,6 +343,7 @@ class branch_and_bound_t { friend struct opportunistic_tree_update_policy_t; friend struct determinism_tree_update_policy_t; + friend struct determinism_diving_tree_update_policy_t; private: // unique_ptr as we only want to initialize these if we're in the determinism codepath From a05d4c2649f2726afbb46787a36d3c2bc351777f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 6 Feb 2026 17:08:35 +0000 Subject: [PATCH 362/366] more cleanup --- cpp/src/dual_simplex/branch_and_bound.cpp | 362 +++++++----------- cpp/src/dual_simplex/branch_and_bound.hpp | 60 ++- .../dual_simplex/deterministic_workers.hpp | 32 +- cpp/src/dual_simplex/mip_node.hpp | 2 +- cpp/src/dual_simplex/phase2.cpp | 118 +++--- cpp/src/dual_simplex/pseudo_costs.cpp | 1 - .../dual_simplex/simplex_solver_settings.hpp | 2 +- cpp/src/dual_simplex/solve.cpp | 2 +- cpp/src/mip/feasibility_jump/fj_cpu.cu | 6 +- cpp/src/mip/feasibility_jump/fj_cpu.cuh | 58 +-- cpp/src/mip/problem/problem.cu | 1 + cpp/src/mip/solve.cu | 1 - cpp/src/mip/solver.cu | 3 +- cpp/src/mip/solver_context.cuh | 4 +- cpp/src/utilities/memory_instrumentation.hpp | 17 +- cpp/src/utilities/work_limit_context.hpp | 46 +++ cpp/src/utilities/work_limit_timer.hpp | 196 ---------- cpp/src/utilities/work_unit_scheduler.cpp | 2 +- cpp/tests/mip/determinism_test.cu | 4 +- 19 files changed, 357 insertions(+), 560 deletions(-) create mode 100644 cpp/src/utilities/work_limit_context.hpp delete mode 100644 cpp/src/utilities/work_limit_timer.hpp diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 91046e57b..1017323b8 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -841,7 +841,7 @@ branch_variable_t branch_and_bound_t::variable_selection( // ============================================================================ template -struct opportunistic_tree_update_policy_t { +struct opportunistic_tree_update_callbacks_t { branch_and_bound_t& bnb; branch_and_bound_worker_t* worker; logger_t& log; @@ -907,17 +907,17 @@ struct opportunistic_tree_update_policy_t { void on_node_completed(mip_node_t*, node_status_t, rounding_direction_t) {} }; -template -struct determinism_tree_update_policy_t { +template +struct determinism_tree_update_policy_base_t { branch_and_bound_t& bnb; - determinism_bfs_worker_t& worker; + WorkerT& worker; f_t upper_bound() const { return worker.local_upper_bound; } void update_pseudo_costs(mip_node_t* node, f_t leaf_obj) { if (node->branch_var < 0) return; - f_t change = leaf_obj - node->lower_bound; + f_t change = std::max(leaf_obj - node->lower_bound, f_t(0)); f_t frac = node->branch_dir == rounding_direction_t::DOWN ? node->fractional_val - std::floor(node->fractional_val) : std::ceil(node->fractional_val) - node->fractional_val; @@ -926,12 +926,20 @@ struct determinism_tree_update_policy_t { } } + void on_numerical_issue(mip_node_t*) {} + void graphviz(search_tree_t&, mip_node_t*, const char*, f_t) {} + void on_optimal_callback(const std::vector&, f_t) {} +}; + +template +struct determinism_bfs_tree_update_callbacks_t + : determinism_tree_update_policy_base_t> { void handle_integer_solution(mip_node_t* node, f_t obj, const std::vector& x) { - if (obj < worker.local_upper_bound) { - worker.local_upper_bound = obj; - worker.integer_solutions.push_back( - {obj, x, node->depth, worker.worker_id, worker.next_solution_seq++}); + if (obj < this->worker.local_upper_bound) { + this->worker.local_upper_bound = obj; + this->worker.integer_solutions.push_back( + {obj, x, node->depth, this->worker.worker_id, this->worker.next_solution_seq++}); } } @@ -939,8 +947,8 @@ struct determinism_tree_update_policy_t { const std::vector& fractional, const std::vector& x) { - i_t var = worker.variable_selection_from_snapshot(fractional, x); - auto dir = martin_criteria(x[var], bnb.root_relax_soln_.x[var]); + i_t var = this->worker.variable_selection_from_snapshot(fractional, x); + auto dir = martin_criteria(x[var], this->bnb.root_relax_soln_.x[var]); return {var, dir}; } @@ -948,76 +956,58 @@ struct determinism_tree_update_policy_t { const std::vector& fractional, const std::vector& x) { - node->objective_estimate = obj_estimate_from_arrays(worker.pc_sum_down_snapshot.data(), - worker.pc_sum_up_snapshot.data(), - worker.pc_num_down_snapshot.data(), - worker.pc_num_up_snapshot.data(), - (i_t)worker.pc_sum_down_snapshot.size(), - fractional, - x, - node->lower_bound); + node->objective_estimate = + obj_estimate_from_arrays(this->worker.pc_sum_down_snapshot.data(), + this->worker.pc_sum_up_snapshot.data(), + this->worker.pc_num_down_snapshot.data(), + this->worker.pc_num_up_snapshot.data(), + (i_t)this->worker.pc_sum_down_snapshot.size(), + fractional, + x, + node->lower_bound); } void on_node_completed(mip_node_t* node, node_status_t status, rounding_direction_t dir) { switch (status) { - case node_status_t::INFEASIBLE: worker.record_infeasible(node); break; - case node_status_t::FATHOMED: worker.record_fathomed(node, node->lower_bound); break; + case node_status_t::INFEASIBLE: this->worker.record_infeasible(node); break; + case node_status_t::FATHOMED: this->worker.record_fathomed(node, node->lower_bound); break; case node_status_t::INTEGER_FEASIBLE: - worker.record_integer_solution(node, node->lower_bound); + this->worker.record_integer_solution(node, node->lower_bound); break; case node_status_t::HAS_CHILDREN: - worker.record_branched(node, - node->get_down_child()->node_id, - node->get_up_child()->node_id, - node->branch_var, - node->fractional_val); - bnb.exploration_stats_.nodes_unexplored += 2; - worker.enqueue_children_for_plunge(node->get_down_child(), node->get_up_child(), dir); + this->worker.record_branched(node, + node->get_down_child()->node_id, + node->get_up_child()->node_id, + node->branch_var, + node->fractional_val); + this->bnb.exploration_stats_.nodes_unexplored += 2; + this->worker.enqueue_children_for_plunge(node->get_down_child(), node->get_up_child(), dir); break; - case node_status_t::NUMERICAL: worker.record_numerical(node); break; + case node_status_t::NUMERICAL: this->worker.record_numerical(node); break; default: break; } - if (status != node_status_t::HAS_CHILDREN) { worker.recompute_bounds_and_basis = true; } + if (status != node_status_t::HAS_CHILDREN) { this->worker.recompute_bounds_and_basis = true; } } void on_numerical_issue(mip_node_t* node) { - worker.local_lower_bound_ceiling = - std::min(node->lower_bound, worker.local_lower_bound_ceiling); + this->worker.local_lower_bound_ceiling = + std::min(node->lower_bound, this->worker.local_lower_bound_ceiling); } - - void graphviz(search_tree_t&, mip_node_t*, const char*, f_t) {} - - void on_optimal_callback(const std::vector&, f_t) {} }; template -struct determinism_diving_tree_update_policy_t { - branch_and_bound_t& bnb; - determinism_diving_worker_t& worker; +struct determinism_diving_tree_update_callbacks_t + : determinism_tree_update_policy_base_t> { std::deque*>& stack; i_t max_backtrack_depth; - f_t upper_bound() const { return worker.local_upper_bound; } - - void update_pseudo_costs(mip_node_t* node, f_t leaf_obj) - { - if (node->branch_var < 0) return; - f_t change = leaf_obj - node->lower_bound; - f_t frac = node->branch_dir == rounding_direction_t::DOWN - ? node->fractional_val - std::floor(node->fractional_val) - : std::ceil(node->fractional_val) - node->fractional_val; - if (frac > 1e-10) { - worker.queue_pseudo_cost_update(node->branch_var, node->branch_dir, change / frac); - } - } - void handle_integer_solution(mip_node_t* node, f_t obj, const std::vector& x) { - if (obj < worker.local_upper_bound) { - worker.local_upper_bound = obj; - worker.queue_integer_solution(obj, x, node->depth); + if (obj < this->worker.local_upper_bound) { + this->worker.local_upper_bound = obj; + this->worker.queue_integer_solution(obj, x, node->depth); } } @@ -1025,28 +1015,33 @@ struct determinism_diving_tree_update_policy_t { const std::vector& fractional, const std::vector& x) { - switch (worker.diving_type) { + switch (this->worker.diving_type) { case search_strategy_t::PSEUDOCOST_DIVING: - return worker.variable_selection_from_snapshot(fractional, x); + return this->worker.variable_selection_from_snapshot(fractional, x); case search_strategy_t::LINE_SEARCH_DIVING: - if (worker.root_solution) { + if (this->worker.root_solution) { logger_t log; log.log = false; - return line_search_diving(fractional, x, *worker.root_solution, log); + return line_search_diving(fractional, x, *this->worker.root_solution, log); } - return worker.variable_selection_from_snapshot(fractional, x); + return this->worker.variable_selection_from_snapshot(fractional, x); - case search_strategy_t::GUIDED_DIVING: return worker.guided_variable_selection(fractional, x); + case search_strategy_t::GUIDED_DIVING: + return this->worker.guided_variable_selection(fractional, x); case search_strategy_t::COEFFICIENT_DIVING: { logger_t log; log.log = false; - return coefficient_diving( - worker.leaf_problem, fractional, x, bnb.var_up_locks_, bnb.var_down_locks_, log); + return coefficient_diving(this->bnb.original_lp_, + fractional, + x, + this->bnb.var_up_locks_, + this->bnb.var_down_locks_, + log); } - default: return worker.variable_selection_from_snapshot(fractional, x); + default: return this->worker.variable_selection_from_snapshot(fractional, x); } } @@ -1054,7 +1049,6 @@ struct determinism_diving_tree_update_policy_t { const std::vector& fractional, const std::vector& x) { - node->objective_estimate = worker.obj_estimate_from_snapshot(fractional, x, node->lower_bound); } void on_node_completed(mip_node_t* node, node_status_t status, rounding_direction_t dir) @@ -1070,17 +1064,11 @@ struct determinism_diving_tree_update_policy_t { if (stack.size() > 1 && stack.front()->depth - stack.back()->depth > max_backtrack_depth) { stack.pop_back(); } - worker.recompute_bounds_and_basis = false; + this->worker.recompute_bounds_and_basis = false; } else { - worker.recompute_bounds_and_basis = true; + this->worker.recompute_bounds_and_basis = true; } } - - void on_numerical_issue(mip_node_t*) {} - - void graphviz(search_tree_t&, mip_node_t*, const char*, f_t) {} - - void on_optimal_callback(const std::vector&, f_t) {} }; template @@ -1199,7 +1187,7 @@ std::pair branch_and_bound_t::upd dual::status_t lp_status, logger_t& log) { - opportunistic_tree_update_policy_t policy{*this, worker, log}; + opportunistic_tree_update_callbacks_t policy{*this, worker, log}; return update_tree_impl(node_ptr, search_tree, worker, lp_status, policy); } @@ -2389,7 +2377,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut // Compute final lower bound f_t lower_bound; if (determinism_mode_enabled_) { - lower_bound = compute_lower_bound_deterministic(); + lower_bound = determinism_compute_lower_bound(); if (lower_bound == std::numeric_limits::infinity() && incumbent_.has_incumbent) { lower_bound = upper_bound_.load(); } @@ -2546,30 +2534,11 @@ void branch_and_bound_t::run_determinism_coordinator(const csr_matrix_ std::vector incumbent_snapshot; if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } - for (auto& worker : *determinism_workers_) { - worker.set_snapshots(upper_bound_.load(), - (const f_t*)pc_.pseudo_cost_sum_up.data(), - (const f_t*)pc_.pseudo_cost_sum_down.data(), - (const i_t*)pc_.pseudo_cost_num_up.data(), - (const i_t*)pc_.pseudo_cost_num_down.data(), - incumbent_snapshot, - exploration_stats_.total_lp_iters.load(), - 0.0, - determinism_horizon_step_); - } - + determinism_broadcast_snapshots( + *determinism_workers_, incumbent_snapshot, 0.0, determinism_horizon_step_); if (determinism_diving_workers_) { - for (auto& worker : *determinism_diving_workers_) { - worker.set_snapshots(upper_bound_.load(), - (const f_t*)pc_.pseudo_cost_sum_up.data(), - (const f_t*)pc_.pseudo_cost_sum_down.data(), - (const i_t*)pc_.pseudo_cost_num_up.data(), - (const i_t*)pc_.pseudo_cost_num_down.data(), - incumbent_snapshot, - exploration_stats_.total_lp_iters.load(), - 0.0, - determinism_horizon_step_); - } + determinism_broadcast_snapshots( + *determinism_diving_workers_, incumbent_snapshot, 0.0, determinism_horizon_step_); } const int total_thread_count = num_bfs_workers + num_diving_workers; @@ -2682,12 +2651,9 @@ void branch_and_bound_t::run_deterministic_bfs_loop( bool is_child = (node->parent == worker.last_solved_node); worker.recompute_bounds_and_basis = !is_child; - node_solve_info_t status = solve_node_deterministic(worker, node, search_tree); - worker.last_solved_node = node; + node_status_t status = solve_node_deterministic(worker, node, search_tree); + worker.last_solved_node = node; - if (status == node_solve_info_t::TIME_LIMIT || status == node_solve_info_t::WORK_LIMIT) { - continue; - } worker.current_node = nullptr; continue; } @@ -2718,11 +2684,11 @@ void branch_and_bound_t::determinism_sync_callback() bb_event_batch_t all_events = determinism_workers_->collect_and_sort_events(); - sort_replay_events(all_events); + determinism_sort_replay_events(all_events); - prune_worker_nodes_vs_incumbent(); + determinism_prune_worker_nodes_vs_incumbent(); - collect_diving_solutions(); + determinism_collect_diving_solutions(); for (auto& worker : *determinism_workers_) { worker.integer_solutions.clear(); @@ -2735,11 +2701,11 @@ void branch_and_bound_t::determinism_sync_callback() } } - populate_diving_heap(); + determinism_populate_diving_heap(); - assign_diving_nodes(); + determinism_assign_diving_nodes(); - balance_worker_loads(); + determinism_balance_worker_loads(); uint32_t state_hash = 0; { @@ -2747,7 +2713,7 @@ void branch_and_bound_t::determinism_sync_callback() state_data.push_back(static_cast(exploration_stats_.nodes_explored)); state_data.push_back(static_cast(exploration_stats_.nodes_unexplored)); f_t ub = upper_bound_.load(); - f_t lb = compute_lower_bound_deterministic(); + f_t lb = determinism_compute_lower_bound(); state_data.push_back(std::bit_cast(ub)); state_data.push_back(std::bit_cast(lb)); @@ -2763,6 +2729,14 @@ void branch_and_bound_t::determinism_sync_callback() } } + if (determinism_diving_workers_) { + for (auto& diving_worker : *determinism_diving_workers_) { + for (const auto& dive_entry : diving_worker.dive_queue) { + state_data.push_back(dive_entry.node.get_id_packed()); + } + } + } + state_hash = detail::compute_hash(state_data); state_hash ^= pc_.compute_state_hash(); } @@ -2772,33 +2746,14 @@ void branch_and_bound_t::determinism_sync_callback() std::vector incumbent_snapshot; if (incumbent_.has_incumbent) { incumbent_snapshot = incumbent_.x; } - for (auto& worker : *determinism_workers_) { - worker.set_snapshots(upper_bound_.load(), - (const f_t*)pc_.pseudo_cost_sum_up.data(), - (const f_t*)pc_.pseudo_cost_sum_down.data(), - (const i_t*)pc_.pseudo_cost_num_up.data(), - (const i_t*)pc_.pseudo_cost_num_down.data(), - incumbent_snapshot, - exploration_stats_.total_lp_iters.load(), - horizon_end, - determinism_current_horizon_); - } - + determinism_broadcast_snapshots( + *determinism_workers_, incumbent_snapshot, horizon_end, determinism_current_horizon_); if (determinism_diving_workers_) { - for (auto& worker : *determinism_diving_workers_) { - worker.set_snapshots(upper_bound_.load(), - (const f_t*)pc_.pseudo_cost_sum_up.data(), - (const f_t*)pc_.pseudo_cost_sum_down.data(), - (const i_t*)pc_.pseudo_cost_num_up.data(), - (const i_t*)pc_.pseudo_cost_num_down.data(), - incumbent_snapshot, - exploration_stats_.total_lp_iters.load(), - horizon_end, - determinism_current_horizon_); - } + determinism_broadcast_snapshots( + *determinism_diving_workers_, incumbent_snapshot, horizon_end, determinism_current_horizon_); } - f_t lower_bound = compute_lower_bound_deterministic(); + f_t lower_bound = determinism_compute_lower_bound(); f_t upper_bound = upper_bound_.load(); f_t abs_gap = upper_bound - lower_bound; f_t rel_gap = user_relative_gap(original_lp_, upper_bound, lower_bound); @@ -2859,7 +2814,7 @@ void branch_and_bound_t::determinism_sync_callback() } template -node_solve_info_t branch_and_bound_t::solve_node_deterministic( +node_status_t branch_and_bound_t::solve_node_deterministic( determinism_bfs_worker_t& worker, mip_node_t* node_ptr, search_tree_t& search_tree) @@ -2910,7 +2865,7 @@ node_solve_info_t branch_and_bound_t::solve_node_deterministic( --exploration_stats_.nodes_unexplored; ++exploration_stats_.nodes_explored; worker.recompute_bounds_and_basis = true; - return node_solve_info_t::NO_CHILDREN; + return node_status_t::INFEASIBLE; } // Solve LP relaxation @@ -2957,26 +2912,16 @@ node_solve_info_t branch_and_bound_t::solve_node_deterministic( ++exploration_stats_.nodes_explored; --exploration_stats_.nodes_unexplored; - determinism_tree_update_policy_t policy{*this, worker}; + determinism_bfs_tree_update_callbacks_t policy{*this, worker}; auto [status, round_dir] = update_tree_impl(node_ptr, search_tree, &worker, lp_status, policy); - // Convert node_status_t to node_solve_info_t - if (status == node_status_t::HAS_CHILDREN) { - return round_dir == rounding_direction_t::DOWN ? node_solve_info_t::DOWN_CHILD_FIRST - : node_solve_info_t::UP_CHILD_FIRST; - } else if (status == node_status_t::PENDING) { - return lp_status == dual::status_t::TIME_LIMIT ? node_solve_info_t::TIME_LIMIT - : node_solve_info_t::WORK_LIMIT; - } else if (status == node_status_t::NUMERICAL) { - return node_solve_info_t::NUMERICAL; - } - return node_solve_info_t::NO_CHILDREN; + return status; } template template -void branch_and_bound_t::process_worker_solutions(PoolT& pool, - WorkerTypeGetter get_worker_type) +void branch_and_bound_t::determinism_process_worker_solutions( + PoolT& pool, WorkerTypeGetter get_worker_type) { std::vector*> all_solutions; for (auto& worker : pool) { @@ -2990,7 +2935,7 @@ void branch_and_bound_t::process_worker_solutions(PoolT& pool, [](const queued_integer_solution_t* a, const queued_integer_solution_t* b) { return *a < *b; }); - f_t determinism_lower = compute_lower_bound_deterministic(); + f_t determinism_lower = determinism_compute_lower_bound(); f_t current_upper = upper_bound_.load(); for (const auto* sol : all_solutions) { @@ -3001,19 +2946,8 @@ void branch_and_bound_t::process_worker_solutions(PoolT& pool, i_t nodes_unexplored = exploration_stats_.nodes_unexplored.load(); search_strategy_t worker_type = get_worker_type(pool, sol->worker_id); - - settings_.log.printf("%c %10d %10lu %+13.6e %+10.6e %6d %7.1e %s %9.2f\n", - feasible_solution_symbol(worker_type), - nodes_explored, - nodes_unexplored, - user_obj, - user_lower, - sol->depth, - nodes_explored > 0 - ? (double)exploration_stats_.total_lp_iters.load() / nodes_explored - : 0.0, - user_mip_gap(user_obj, user_lower).c_str(), - toc(exploration_stats_.start_time)); + report( + feasible_solution_symbol(worker_type), sol->objective, determinism_lower, sol->depth, 0); bool improved = false; if (sol->objective < upper_bound_) { @@ -3038,7 +2972,7 @@ void branch_and_bound_t::process_worker_solutions(PoolT& pool, template template -void branch_and_bound_t::merge_pseudo_cost_updates(PoolT& pool) +void branch_and_bound_t::determinism_merge_pseudo_cost_updates(PoolT& pool) { std::vector> all_pc_updates; for (auto& worker : pool) { @@ -3065,7 +2999,26 @@ void branch_and_bound_t::merge_pseudo_cost_updates(PoolT& pool) } template -void branch_and_bound_t::sort_replay_events(const bb_event_batch_t& events) +template +void branch_and_bound_t::determinism_broadcast_snapshots( + PoolT& pool, const std::vector& incumbent_snapshot, double horizon_start, double horizon_end) +{ + for (auto& worker : pool) { + worker.set_snapshots(upper_bound_.load(), + (const f_t*)pc_.pseudo_cost_sum_up.data(), + (const f_t*)pc_.pseudo_cost_sum_down.data(), + (const i_t*)pc_.pseudo_cost_num_up.data(), + (const i_t*)pc_.pseudo_cost_num_down.data(), + incumbent_snapshot, + exploration_stats_.total_lp_iters.load(), + horizon_start, + horizon_end); + } +} + +template +void branch_and_bound_t::determinism_sort_replay_events( + const bb_event_batch_t& events) { // Infeasible solutions from GPU heuristics are queued for repair; process them now { @@ -3179,16 +3132,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t::infinity()) { - f_t user_obj = compute_user_objective(original_lp_, new_upper); - f_t user_lower = compute_user_objective(original_lp_, get_lower_bound()); - std::string gap = user_mip_gap(user_obj, user_lower); - - settings_.log.printf( - "H %+13.6e %+10.6e %s %9.2f\n", - user_obj, - user_lower, - gap.c_str(), - toc(exploration_stats_.start_time)); + report_heuristic(new_upper); if (settings_.solution_callback != nullptr) { std::vector original_x; @@ -3200,13 +3144,13 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t&, int) { - return search_strategy_t::BEST_FIRST; - }); + determinism_process_worker_solutions(*determinism_workers_, + [](const determinism_bfs_worker_pool_t&, int) { + return search_strategy_t::BEST_FIRST; + }); // Merge and apply pseudo-cost updates from BFS workers - merge_pseudo_cost_updates(*determinism_workers_); + determinism_merge_pseudo_cost_updates(*determinism_workers_); for (const auto& worker : *determinism_workers_) { fetch_min(lower_bound_ceiling_, worker.local_lower_bound_ceiling); @@ -3214,7 +3158,7 @@ void branch_and_bound_t::sort_replay_events(const bb_event_batch_t -void branch_and_bound_t::prune_worker_nodes_vs_incumbent() +void branch_and_bound_t::determinism_prune_worker_nodes_vs_incumbent() { f_t upper_bound = upper_bound_.load(); @@ -3253,7 +3197,7 @@ void branch_and_bound_t::prune_worker_nodes_vs_incumbent() } template -void branch_and_bound_t::balance_worker_loads() +void branch_and_bound_t::determinism_balance_worker_loads() { const size_t num_workers = determinism_workers_->size(); if (num_workers <= 1) return; @@ -3277,7 +3221,6 @@ void branch_and_bound_t::balance_worker_loads() bool needs_balance; if (force_rebalance_every_sync) { - // Always rebalance if there's more than one node to distribute needs_balance = (total_work > 1); } else { needs_balance = (min_work == 0 && max_work >= 2) || (min_work > 0 && max_work > 4 * min_work); @@ -3317,7 +3260,7 @@ void branch_and_bound_t::balance_worker_loads() } template -f_t branch_and_bound_t::compute_lower_bound_deterministic() +f_t branch_and_bound_t::determinism_compute_lower_bound() { // Compute lower bound from BFS worker local structures only const f_t inf = std::numeric_limits::infinity(); @@ -3346,7 +3289,7 @@ f_t branch_and_bound_t::compute_lower_bound_deterministic() } template -void branch_and_bound_t::populate_diving_heap() +void branch_and_bound_t::determinism_populate_diving_heap() { // Clear diving heap from previous horizon diving_heap_.clear(); @@ -3391,7 +3334,7 @@ void branch_and_bound_t::populate_diving_heap() } template -void branch_and_bound_t::assign_diving_nodes() +void branch_and_bound_t::determinism_assign_diving_nodes() { if (!determinism_diving_workers_ || determinism_diving_workers_->size() == 0) { diving_heap_.clear(); @@ -3400,7 +3343,7 @@ void branch_and_bound_t::assign_diving_nodes() constexpr int target_nodes_per_worker = 10; - // Round-robin assignment to balance load across workers + // Round-robin assignment int worker_idx = 0; const int num_workers = determinism_diving_workers_->size(); @@ -3423,7 +3366,9 @@ void branch_and_bound_t::assign_diving_nodes() } auto entry = diving_heap_.pop(); - if (entry.has_value()) { worker.enqueue_dive_node(entry.value().node); } + if (entry.has_value()) { + worker.enqueue_dive_node(entry.value().node, original_lp_, settings_); + } worker_idx = (worker_idx + 1) % num_workers; } @@ -3432,17 +3377,17 @@ void branch_and_bound_t::assign_diving_nodes() } template -void branch_and_bound_t::collect_diving_solutions() +void branch_and_bound_t::determinism_collect_diving_solutions() { if (!determinism_diving_workers_) return; // Collect integer solutions from diving workers and update global incumbent - process_worker_solutions(*determinism_diving_workers_, - [](const determinism_diving_worker_pool_t& pool, - int worker_id) { return pool[worker_id].diving_type; }); + determinism_process_worker_solutions(*determinism_diving_workers_, + [](const determinism_diving_worker_pool_t& pool, + int worker_id) { return pool[worker_id].diving_type; }); // Merge pseudo-cost updates from diving workers - merge_pseudo_cost_updates(*determinism_diving_workers_); + determinism_merge_pseudo_cost_updates(*determinism_diving_workers_); } template @@ -3453,9 +3398,9 @@ void branch_and_bound_t::run_deterministic_diving_loop( while (determinism_global_termination_status_ == mip_status_t::UNSET) { // Process dives from queue until empty or horizon exhausted - auto node_opt = worker.dequeue_dive_node(); - if (node_opt.has_value()) { - deterministic_dive(worker, std::move(node_opt.value())); + auto entry_opt = worker.dequeue_dive_node(); + if (entry_opt.has_value()) { + deterministic_dive(worker, std::move(entry_opt.value())); continue; } @@ -3469,20 +3414,17 @@ void branch_and_bound_t::run_deterministic_diving_loop( template void branch_and_bound_t::deterministic_dive(determinism_diving_worker_t& worker, - mip_node_t starting_node) + dive_queue_entry_t entry) { raft::common::nvtx::range scope("BB::deterministic_dive"); // Create local search tree for the dive - search_tree_t dive_tree(std::move(starting_node)); + search_tree_t dive_tree(std::move(entry.node)); std::deque*> stack; stack.push_front(&dive_tree.root); - // Initialize bounds from root node - worker.dive_lower = original_lp_.lower; - worker.dive_upper = original_lp_.upper; - std::fill(worker.bounds_changed.begin(), worker.bounds_changed.end(), false); - dive_tree.root.get_variable_bounds(worker.dive_lower, worker.dive_upper, worker.bounds_changed); + worker.dive_lower = std::move(entry.resolved_lower); + worker.dive_upper = std::move(entry.resolved_upper); const i_t max_nodes_per_dive = settings_.diving_settings.node_limit; const i_t max_backtrack_depth = settings_.diving_settings.backtrack_limit; @@ -3492,12 +3434,6 @@ void branch_and_bound_t::deterministic_dive(determinism_diving_worker_ while (!stack.empty() && determinism_global_termination_status_ == mip_status_t::UNSET && nodes_this_dive < max_nodes_per_dive) { - // Check horizon budget - sync if exhausted - if (worker.work_context.global_work_units_elapsed >= worker.horizon_end) { - determinism_scheduler_->wait_for_next_sync(worker.work_context); - if (determinism_global_termination_status_ != mip_status_t::UNSET) break; - } - mip_node_t* node_ptr = stack.front(); stack.pop_front(); @@ -3596,7 +3532,7 @@ void branch_and_bound_t::deterministic_dive(determinism_diving_worker_ break; } - determinism_diving_tree_update_policy_t policy{ + determinism_diving_tree_update_callbacks_t policy{ *this, worker, stack, max_backtrack_depth}; update_tree_impl(node_ptr, dive_tree, &worker, lp_status, policy); } diff --git a/cpp/src/dual_simplex/branch_and_bound.hpp b/cpp/src/dual_simplex/branch_and_bound.hpp index 92db0634b..579754d94 100644 --- a/cpp/src/dual_simplex/branch_and_bound.hpp +++ b/cpp/src/dual_simplex/branch_and_bound.hpp @@ -24,7 +24,7 @@ #include #include #include -#include +#include #include #include @@ -44,16 +44,6 @@ enum class mip_status_t { WORK_LIMIT = 7, // The solver reached a deterministic work limit }; -enum class node_solve_info_t { - NO_CHILDREN = 0, // The node does not produced children - UP_CHILD_FIRST = 1, // The up child should be explored first - DOWN_CHILD_FIRST = 2, // The down child should be explored first - TIME_LIMIT = 3, // The solver reached a time limit - ITERATION_LIMIT = 4, // The solver reached a iteration limit - NUMERICAL = 5, // The solver encounter a numerical error when solving the node - WORK_LIMIT = 6, // The solver reached a deterministic work limit -}; - template class bounds_strengthening_t; @@ -61,11 +51,13 @@ template void upper_bound_callback(f_t upper_bound); template -struct opportunistic_tree_update_policy_t; +struct opportunistic_tree_update_callbacks_t; +template +struct determinism_tree_update_policy_base_t; template -struct determinism_tree_update_policy_t; +struct determinism_bfs_tree_update_callbacks_t; template -struct determinism_diving_tree_update_policy_t; +struct determinism_diving_tree_update_callbacks_t; template class branch_and_bound_t { @@ -256,7 +248,7 @@ class branch_and_bound_t { // to find integer feasible solutions. void dive_with(branch_and_bound_worker_t* worker); - // Run the scheduler (aka the master) whose will schedule and manage + // Run the scheduler whose will schedule and manage // all the other workers. void run_scheduler(); @@ -300,19 +292,19 @@ class branch_and_bound_t { void run_determinism_coordinator(const csr_matrix_t& Arow); // Gather all events generated, sort by WU timestamp, apply - void sort_replay_events(const bb_event_batch_t& events); + void determinism_sort_replay_events(const bb_event_batch_t& events); // Prune nodes held by workers based on new incumbent - void prune_worker_nodes_vs_incumbent(); + void determinism_prune_worker_nodes_vs_incumbent(); // Balance worker loads - redistribute nodes only if significant imbalance detected - void balance_worker_loads(); + void determinism_balance_worker_loads(); - node_solve_info_t solve_node_deterministic(determinism_bfs_worker_t& worker, - mip_node_t* node_ptr, - search_tree_t& search_tree); + node_status_t solve_node_deterministic(determinism_bfs_worker_t& worker, + mip_node_t* node_ptr, + search_tree_t& search_tree); - f_t compute_lower_bound_deterministic(); + f_t determinism_compute_lower_bound(); void run_deterministic_bfs_loop(determinism_bfs_worker_t& worker, search_tree_t& search_tree); @@ -324,26 +316,32 @@ class branch_and_bound_t { void run_deterministic_diving_loop(determinism_diving_worker_t& worker); void deterministic_dive(determinism_diving_worker_t& worker, - mip_node_t starting_node); + dive_queue_entry_t entry); // Populate diving heap from BFS worker backlogs at sync - void populate_diving_heap(); + void determinism_populate_diving_heap(); // Assign starting nodes to diving workers from diving heap - void assign_diving_nodes(); + void determinism_assign_diving_nodes(); // Collect and merge diving solutions at sync - void collect_diving_solutions(); + void determinism_collect_diving_solutions(); template - void process_worker_solutions(PoolT& pool, WorkerTypeGetter get_worker_type); + void determinism_process_worker_solutions(PoolT& pool, WorkerTypeGetter get_worker_type); template - void merge_pseudo_cost_updates(PoolT& pool); + void determinism_merge_pseudo_cost_updates(PoolT& pool); - friend struct opportunistic_tree_update_policy_t; - friend struct determinism_tree_update_policy_t; - friend struct determinism_diving_tree_update_policy_t; + template + void determinism_broadcast_snapshots(PoolT& pool, + const std::vector& incumbent_snapshot, + double horizon_start, + double horizon_end); + + friend struct opportunistic_tree_update_callbacks_t; + friend struct determinism_bfs_tree_update_callbacks_t; + friend struct determinism_diving_tree_update_callbacks_t; private: // unique_ptr as we only want to initialize these if we're in the determinism codepath diff --git a/cpp/src/dual_simplex/deterministic_workers.hpp b/cpp/src/dual_simplex/deterministic_workers.hpp index 1f5613e5b..ec15ca880 100644 --- a/cpp/src/dual_simplex/deterministic_workers.hpp +++ b/cpp/src/dual_simplex/deterministic_workers.hpp @@ -11,7 +11,7 @@ #include #include #include -#include +#include #include @@ -314,6 +314,13 @@ class determinism_bfs_worker_t } }; +template +struct dive_queue_entry_t { + mip_node_t node; + std::vector resolved_lower; + std::vector resolved_upper; +}; + template class determinism_diving_worker_t : public determinism_worker_base_t> { @@ -323,7 +330,7 @@ class determinism_diving_worker_t search_strategy_t diving_type{search_strategy_t::PSEUDOCOST_DIVING}; // Diving-specific node management - std::deque> dive_queue; + std::deque> dive_queue; std::vector dive_lower; std::vector dive_upper; @@ -360,15 +367,28 @@ class determinism_diving_worker_t bool has_work_impl() const { return !dive_queue.empty(); } - void enqueue_dive_node(mip_node_t* node) { dive_queue.push_back(node->detach_copy()); } + void enqueue_dive_node(mip_node_t* node, + const lp_problem_t& original_lp, + const simplex_solver_settings_t& settings) + { + dive_queue_entry_t entry; + entry.resolved_lower = original_lp.lower; + entry.resolved_upper = original_lp.upper; + std::vector bounds_changed(original_lp.num_cols, false); + node->get_variable_bounds(entry.resolved_lower, entry.resolved_upper, bounds_changed); + this->node_presolver.bounds_strengthening( + settings, bounds_changed, entry.resolved_lower, entry.resolved_upper); + entry.node = node->detach_copy(); + dive_queue.push_back(std::move(entry)); + } - std::optional> dequeue_dive_node() + std::optional> dequeue_dive_node() { if (dive_queue.empty()) return std::nullopt; - auto node = std::move(dive_queue.front()); + auto entry = std::move(dive_queue.front()); dive_queue.pop_front(); ++total_dives; - return node; + return entry; } size_t dive_queue_size() const { return dive_queue.size(); } diff --git a/cpp/src/dual_simplex/mip_node.hpp b/cpp/src/dual_simplex/mip_node.hpp index 27bb53bd3..347d75aa2 100644 --- a/cpp/src/dual_simplex/mip_node.hpp +++ b/cpp/src/dual_simplex/mip_node.hpp @@ -284,7 +284,7 @@ class mip_node_t { static_cast(static_cast(creation_seq)); } - uint64_t compute_path_hash() const + uint32_t compute_path_hash() const { std::vector path_steps; const mip_node_t* node = this; diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 5bddfd13a..798a49c3d 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -18,9 +18,8 @@ #include #include -#include - #include +#include #include @@ -1121,8 +1120,8 @@ i_t flip_bounds(const lp_problem_t& lp, const std::vector& nonbasic_list, i_t entering_index, std::vector& vstatus, - ins_vector& delta_x_flip, - ins_vector& atilde_mark, + ins_vector& delta_x, + ins_vector& mark, ins_vector& atilde, ins_vector& atilde_index) { @@ -1137,8 +1136,8 @@ i_t flip_bounds(const lp_problem_t& lp, settings.dual_tol; // lower to 1e-7 or less will cause 25fv47 and d2q06c to cycle if (vstatus[j] == variable_status_t::NONBASIC_LOWER && z[j] < -dual_tol) { const f_t delta = lp.upper[j] - lp.lower[j]; - scatter_dense(lp.A, j, -delta, atilde, atilde_mark, atilde_index); - delta_x_flip[j] += delta; + scatter_dense(lp.A, j, -delta, atilde, mark, atilde_index); + delta_x[j] += delta; vstatus[j] = variable_status_t::NONBASIC_UPPER; #ifdef BOUND_FLIP_DEBUG settings.log.printf( @@ -1147,8 +1146,8 @@ i_t flip_bounds(const lp_problem_t& lp, num_flipped++; } else if (vstatus[j] == variable_status_t::NONBASIC_UPPER && z[j] > dual_tol) { const f_t delta = lp.lower[j] - lp.upper[j]; - scatter_dense(lp.A, j, -delta, atilde, atilde_mark, atilde_index); - delta_x_flip[j] += delta; + scatter_dense(lp.A, j, -delta, atilde, mark, atilde_index); + delta_x[j] += delta; vstatus[j] = variable_status_t::NONBASIC_LOWER; #ifdef BOUND_FLIP_DEBUG settings.log.printf( @@ -2404,20 +2403,18 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, std::vector& y = sol.y; std::vector& z = sol.z; - // Declare instrumented vectors used during initialization (before manifold setup) + // Declare instrumented vectors used during initialization (before aggregator setup) // Perturbed objective ins_vector objective(lp.objective); ins_vector c_basic(m); ins_vector xB_workspace(m); - // Create instrumentation manifold early to capture init section memory operations - instrumentation_manifold_t manifold; - // manifold.add("x", x); - // manifold.add("y", y); - // manifold.add("z", z); - manifold.add("objective", objective); - manifold.add("c_basic", c_basic); - manifold.add("xB_workspace", xB_workspace); + // Create instrumentation aggregator early to capture init section memory operations + instrumentation_aggregator_t aggregator; + + aggregator.add("objective", objective); + aggregator.add("c_basic", c_basic); + aggregator.add("xB_workspace", xB_workspace); dual::status_t status = dual::status_t::UNSET; @@ -2579,15 +2576,14 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, #endif csc_matrix_t A_transpose(1, 1, 0); - manifold.add("A_transpose.col_start", A_transpose.col_start); - manifold.add("A_transpose.i", A_transpose.i); - manifold.add("A_transpose.x", A_transpose.x); + aggregator.add("A_transpose.col_start", A_transpose.col_start); + aggregator.add("A_transpose.i", A_transpose.i); + aggregator.add("A_transpose.x", A_transpose.x); { PHASE2_NVTX_RANGE("DualSimplex::transpose_A"); lp.A.transpose(A_transpose); } f_t obj = compute_objective(lp, x); - init_scope.pop(); // End phase2_advanced_init range const i_t start_iter = iter; @@ -2614,46 +2610,46 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, sparse_vector_t v_sparse(m, 0); // For steepest edge norms sparse_vector_t atilde_sparse(m, 0); // For flip adjustments - // Add remaining instrumented vectors to manifold (x, y, z, objective, c_basic, xB_workspace added - // earlier) Delta vectors - manifold.add("delta_y", delta_y); - manifold.add("delta_z", delta_z); - manifold.add("delta_x", delta_x); - manifold.add("delta_x_flip", delta_x_flip); - manifold.add("atilde", atilde); - manifold.add("atilde_mark", atilde_mark); - manifold.add("atilde_index", atilde_index); - manifold.add("nonbasic_mark", nonbasic_mark); - manifold.add("basic_mark", basic_mark); - manifold.add("delta_z_mark", delta_z_mark); - manifold.add("delta_z_indices", delta_z_indices); - manifold.add("v", v); - manifold.add("squared_infeasibilities", squared_infeasibilities); - manifold.add("infeasibility_indices", infeasibility_indices); - manifold.add("bounded_variables", bounded_variables); - - // Add sparse vector internal arrays to manifold - manifold.add("delta_y_sparse.i", delta_y_sparse.i); - manifold.add("delta_y_sparse.x", delta_y_sparse.x); - manifold.add("UTsol_sparse.i", UTsol_sparse.i); - manifold.add("UTsol_sparse.x", UTsol_sparse.x); - manifold.add("delta_xB_0_sparse.i", delta_xB_0_sparse.i); - manifold.add("delta_xB_0_sparse.x", delta_xB_0_sparse.x); - manifold.add("utilde_sparse.i", utilde_sparse.i); - manifold.add("utilde_sparse.x", utilde_sparse.x); - manifold.add("scaled_delta_xB_sparse.i", scaled_delta_xB_sparse.i); - manifold.add("scaled_delta_xB_sparse.x", scaled_delta_xB_sparse.x); - manifold.add("rhs_sparse.i", rhs_sparse.i); - manifold.add("rhs_sparse.x", rhs_sparse.x); - manifold.add("v_sparse.i", v_sparse.i); - manifold.add("v_sparse.x", v_sparse.x); - manifold.add("atilde_sparse.i", atilde_sparse.i); - manifold.add("atilde_sparse.x", atilde_sparse.x); + // Add remaining instrumented vectors to aggregator (x, y, z, objective, c_basic, xB_workspace + // added earlier) Delta vectors + aggregator.add("delta_y", delta_y); + aggregator.add("delta_z", delta_z); + aggregator.add("delta_x", delta_x); + aggregator.add("delta_x_flip", delta_x_flip); + aggregator.add("atilde", atilde); + aggregator.add("atilde_mark", atilde_mark); + aggregator.add("atilde_index", atilde_index); + aggregator.add("nonbasic_mark", nonbasic_mark); + aggregator.add("basic_mark", basic_mark); + aggregator.add("delta_z_mark", delta_z_mark); + aggregator.add("delta_z_indices", delta_z_indices); + aggregator.add("v", v); + aggregator.add("squared_infeasibilities", squared_infeasibilities); + aggregator.add("infeasibility_indices", infeasibility_indices); + aggregator.add("bounded_variables", bounded_variables); + + // Add sparse vector internal arrays to aggregator + aggregator.add("delta_y_sparse.i", delta_y_sparse.i); + aggregator.add("delta_y_sparse.x", delta_y_sparse.x); + aggregator.add("UTsol_sparse.i", UTsol_sparse.i); + aggregator.add("UTsol_sparse.x", UTsol_sparse.x); + aggregator.add("delta_xB_0_sparse.i", delta_xB_0_sparse.i); + aggregator.add("delta_xB_0_sparse.x", delta_xB_0_sparse.x); + aggregator.add("utilde_sparse.i", utilde_sparse.i); + aggregator.add("utilde_sparse.x", utilde_sparse.x); + aggregator.add("scaled_delta_xB_sparse.i", scaled_delta_xB_sparse.i); + aggregator.add("scaled_delta_xB_sparse.x", scaled_delta_xB_sparse.x); + aggregator.add("rhs_sparse.i", rhs_sparse.i); + aggregator.add("rhs_sparse.x", rhs_sparse.x); + aggregator.add("v_sparse.i", v_sparse.i); + aggregator.add("v_sparse.x", v_sparse.x); + aggregator.add("atilde_sparse.i", atilde_sparse.i); + aggregator.add("atilde_sparse.x", atilde_sparse.x); // Add A matrix for entering column access during basis update - manifold.add("A.col_start", lp.A.col_start); - manifold.add("A.i", lp.A.i); - manifold.add("A.x", lp.A.x); + aggregator.add("A.col_start", lp.A.col_start); + aggregator.add("A.i", lp.A.i); + aggregator.add("A.x", lp.A.x); // Track iteration interval start time for runtime measurement f_t interval_start_time = toc(start_time); @@ -2663,7 +2659,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, i_t remaining_iters = iter - last_feature_log_iter; if (remaining_iters <= 0) return; - auto [total_loads, total_stores] = manifold.collect_and_flush(); + auto [total_loads, total_stores] = aggregator.collect_and_flush(); // features.byte_loads = total_loads; // features.byte_stores = total_stores; @@ -3377,7 +3373,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, if ((iter % FEATURE_LOG_INTERVAL) == 0 && work_unit_context) { i_t iters_elapsed = iter - last_feature_log_iter; - auto [total_loads, total_stores] = manifold.collect_and_flush(); + auto [total_loads, total_stores] = aggregator.collect_and_flush(); // features.byte_loads = total_loads; // features.byte_stores = total_stores; diff --git a/cpp/src/dual_simplex/pseudo_costs.cpp b/cpp/src/dual_simplex/pseudo_costs.cpp index cf090ace4..de8994e80 100644 --- a/cpp/src/dual_simplex/pseudo_costs.cpp +++ b/cpp/src/dual_simplex/pseudo_costs.cpp @@ -470,7 +470,6 @@ void pseudo_costs_t::initialized(i_t& num_initialized_down, f_t& pseudo_cost_down_avg, f_t& pseudo_cost_up_avg) const { - // Count initialized variables while computing averages auto avgs = compute_pseudo_cost_averages(pseudo_cost_sum_down.data(), pseudo_cost_sum_up.data(), pseudo_cost_num_down.data(), diff --git a/cpp/src/dual_simplex/simplex_solver_settings.hpp b/cpp/src/dual_simplex/simplex_solver_settings.hpp index 2e33f823e..815e22923 100644 --- a/cpp/src/dual_simplex/simplex_solver_settings.hpp +++ b/cpp/src/dual_simplex/simplex_solver_settings.hpp @@ -157,7 +157,7 @@ struct simplex_solver_settings_t { bool barrier_presolve; // true to use barrier presolve bool cudss_deterministic; // true to use cuDSS deterministic mode, false for non-deterministic bool barrier; // true to use barrier method, false to use dual simplex method - bool deterministic; // true to use B&B deterministic mode, false to use non-deterministic mode + bool deterministic; // true to use B&B deterministic mode, false to use non-deterministic mode bool eliminate_dense_columns; // true to eliminate dense columns from A*D*A^T int num_gpus; // Number of GPUs to use (maximum of 2 gpus are supported at the moment) i_t folding; // -1 automatic, 0 don't fold, 1 fold diff --git a/cpp/src/dual_simplex/solve.cpp b/cpp/src/dual_simplex/solve.cpp index af09f5350..133ed453d 100644 --- a/cpp/src/dual_simplex/solve.cpp +++ b/cpp/src/dual_simplex/solve.cpp @@ -194,7 +194,7 @@ lp_status_t solve_linear_program_with_advanced_basis( edge_norms.clear(); dual::status_t phase1_status; { - raft::common::nvtx::range scope_phase1("DualSimplex::phase2"); + raft::common::nvtx::range scope_phase1("DualSimplex::phase1"); phase1_status = dual_phase2(1, 1, start_time, diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cu b/cpp/src/mip/feasibility_jump/fj_cpu.cu index 2fa510959..d1e62a10c 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cu +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cu @@ -401,12 +401,12 @@ static void log_regression_features(fj_cpu_climber_t& fj_cpu, // Build per-wrapper memory statistics string std::stringstream wrapper_stats; - auto per_wrapper_stats = fj_cpu.memory_manifold.collect_per_wrapper(); + auto per_wrapper_stats = fj_cpu.memory_aggregator.collect_per_wrapper(); for (const auto& [name, loads, stores] : per_wrapper_stats) { wrapper_stats << " " << name << "_loads=" << loads << " " << name << "_stores=" << stores; } - fj_cpu.memory_manifold.flush(); + fj_cpu.memory_aggregator.flush(); // Print everything on a single line using precomputed features CUOPT_LOG_DEBUG( @@ -1538,7 +1538,7 @@ bool fj_t::cpu_solve(fj_cpu_climber_t& fj_cpu, f_t in_time_l if (fj_cpu.iterations % 100 == 0 && fj_cpu.iterations > 0) { // Collect memory statistics - auto [loads, stores] = fj_cpu.memory_manifold.collect(); + auto [loads, stores] = fj_cpu.memory_aggregator.collect(); double biased_work = (loads + stores) * fj_cpu.work_unit_bias / 1e10; fj_cpu.work_units_elapsed += biased_work; diff --git a/cpp/src/mip/feasibility_jump/fj_cpu.cuh b/cpp/src/mip/feasibility_jump/fj_cpu.cuh index a69ce5930..187cd300d 100644 --- a/cpp/src/mip/feasibility_jump/fj_cpu.cuh +++ b/cpp/src/mip/feasibility_jump/fj_cpu.cuh @@ -32,33 +32,33 @@ struct fj_cpu_climber_t { #define ADD_INSTRUMENTED(var) \ std::make_pair(#var, std::ref(static_cast(var))) - // Initialize memory manifold with all ins_vector members - memory_manifold = instrumentation_manifold_t{ADD_INSTRUMENTED(h_reverse_coefficients), - ADD_INSTRUMENTED(h_reverse_constraints), - ADD_INSTRUMENTED(h_reverse_offsets), - ADD_INSTRUMENTED(h_coefficients), - ADD_INSTRUMENTED(h_offsets), - ADD_INSTRUMENTED(h_variables), - ADD_INSTRUMENTED(h_obj_coeffs), - ADD_INSTRUMENTED(h_var_bounds), - ADD_INSTRUMENTED(h_cstr_lb), - ADD_INSTRUMENTED(h_cstr_ub), - ADD_INSTRUMENTED(h_var_types), - ADD_INSTRUMENTED(h_is_binary_variable), - ADD_INSTRUMENTED(h_objective_vars), - ADD_INSTRUMENTED(h_binary_indices), - ADD_INSTRUMENTED(h_tabu_nodec_until), - ADD_INSTRUMENTED(h_tabu_noinc_until), - ADD_INSTRUMENTED(h_tabu_lastdec), - ADD_INSTRUMENTED(h_tabu_lastinc), - ADD_INSTRUMENTED(h_lhs), - ADD_INSTRUMENTED(h_lhs_sumcomp), - ADD_INSTRUMENTED(h_cstr_left_weights), - ADD_INSTRUMENTED(h_cstr_right_weights), - ADD_INSTRUMENTED(h_assignment), - ADD_INSTRUMENTED(h_best_assignment), - ADD_INSTRUMENTED(cached_cstr_bounds), - ADD_INSTRUMENTED(iter_mtm_vars)}; + // Initialize memory aggregator with all ins_vector members + memory_aggregator = instrumentation_aggregator_t{ADD_INSTRUMENTED(h_reverse_coefficients), + ADD_INSTRUMENTED(h_reverse_constraints), + ADD_INSTRUMENTED(h_reverse_offsets), + ADD_INSTRUMENTED(h_coefficients), + ADD_INSTRUMENTED(h_offsets), + ADD_INSTRUMENTED(h_variables), + ADD_INSTRUMENTED(h_obj_coeffs), + ADD_INSTRUMENTED(h_var_bounds), + ADD_INSTRUMENTED(h_cstr_lb), + ADD_INSTRUMENTED(h_cstr_ub), + ADD_INSTRUMENTED(h_var_types), + ADD_INSTRUMENTED(h_is_binary_variable), + ADD_INSTRUMENTED(h_objective_vars), + ADD_INSTRUMENTED(h_binary_indices), + ADD_INSTRUMENTED(h_tabu_nodec_until), + ADD_INSTRUMENTED(h_tabu_noinc_until), + ADD_INSTRUMENTED(h_tabu_lastdec), + ADD_INSTRUMENTED(h_tabu_lastinc), + ADD_INSTRUMENTED(h_lhs), + ADD_INSTRUMENTED(h_lhs_sumcomp), + ADD_INSTRUMENTED(h_cstr_left_weights), + ADD_INSTRUMENTED(h_cstr_right_weights), + ADD_INSTRUMENTED(h_assignment), + ADD_INSTRUMENTED(h_best_assignment), + ADD_INSTRUMENTED(cached_cstr_bounds), + ADD_INSTRUMENTED(iter_mtm_vars)}; #undef ADD_INSTRUMENTED } @@ -187,8 +187,8 @@ struct fj_cpu_climber_t { double cstr_degree_cv{0.0}; double problem_density{0.0}; - // Memory instrumentation manifold - instrumentation_manifold_t memory_manifold; + // Memory instrumentation aggregator + instrumentation_aggregator_t memory_aggregator; // TODO atomic ref? c++20 std::atomic& preemption_flag; }; diff --git a/cpp/src/mip/problem/problem.cu b/cpp/src/mip/problem/problem.cu index 4694ee097..4fa6983df 100644 --- a/cpp/src/mip/problem/problem.cu +++ b/cpp/src/mip/problem/problem.cu @@ -212,6 +212,7 @@ problem_t::problem_t(const problem_t& problem_, const raft::handle_t* handle_ptr_) : original_problem_ptr(problem_.original_problem_ptr), tolerances(problem_.tolerances), + deterministic(problem_.deterministic), handle_ptr(handle_ptr_), integer_fixed_problem(problem_.integer_fixed_problem), integer_fixed_variable_map(problem_.integer_fixed_variable_map, handle_ptr->get_stream()), diff --git a/cpp/src/mip/solve.cu b/cpp/src/mip/solve.cu index ce5986edb..8822082fc 100644 --- a/cpp/src/mip/solve.cu +++ b/cpp/src/mip/solve.cu @@ -23,7 +23,6 @@ #include #include #include -#include #include #include diff --git a/cpp/src/mip/solver.cu b/cpp/src/mip/solver.cu index 06f0b9edf..ee1d9c2e5 100644 --- a/cpp/src/mip/solver.cu +++ b/cpp/src/mip/solver.cu @@ -107,11 +107,10 @@ solution_t mip_solver_t::run_solver() context.problem_ptr->post_process_solution(sol); return sol; } - dm.timer = timer_; const bool run_presolve = context.settings.presolve; f_t time_limit = context.settings.determinism_mode == CUOPT_MODE_DETERMINISTIC - ? timer_.get_time_limit() + ? std::numeric_limits::infinity() : timer_.remaining_time(); bool presolve_success = run_presolve ? dm.run_presolve(time_limit) : true; if (!presolve_success) { diff --git a/cpp/src/mip/solver_context.cuh b/cpp/src/mip/solver_context.cuh index bddb3aaea..c6b6a6ec0 100644 --- a/cpp/src/mip/solver_context.cuh +++ b/cpp/src/mip/solver_context.cuh @@ -10,8 +10,8 @@ #include #include #include - -#include +#include +#include #include diff --git a/cpp/src/utilities/memory_instrumentation.hpp b/cpp/src/utilities/memory_instrumentation.hpp index 87d25f5c8..d33e7de0b 100644 --- a/cpp/src/utilities/memory_instrumentation.hpp +++ b/cpp/src/utilities/memory_instrumentation.hpp @@ -23,7 +23,6 @@ * // When enabled: tracking occurs, counters accumulate * // When disabled: direct passthrough, compiler optimizes away all overhead */ -// Thank you Cursor! #pragma once @@ -94,13 +93,13 @@ struct memory_instrumentation_base_t { #if CUOPT_ENABLE_MEMORY_INSTRUMENTATION -// Manifold class to collect statistics from multiple instrumented objects -class instrumentation_manifold_t { +// aggregator class to collect statistics from multiple instrumented objects +class instrumentation_aggregator_t { public: - instrumentation_manifold_t() = default; + instrumentation_aggregator_t() = default; // Construct with initializer list of (description, instrumented object) pairs - instrumentation_manifold_t( + instrumentation_aggregator_t( std::initializer_list< std::pair>> instrumented) @@ -165,11 +164,11 @@ class instrumentation_manifold_t { #else -// No-op manifold when instrumentation is disabled -class instrumentation_manifold_t { +// No-op aggregator when instrumentation is disabled +class instrumentation_aggregator_t { public: - instrumentation_manifold_t() = default; - instrumentation_manifold_t( + instrumentation_aggregator_t() = default; + instrumentation_aggregator_t( std::initializer_list< std::pair>>) { diff --git a/cpp/src/utilities/work_limit_context.hpp b/cpp/src/utilities/work_limit_context.hpp new file mode 100644 index 000000000..fa06dec5a --- /dev/null +++ b/cpp/src/utilities/work_limit_context.hpp @@ -0,0 +1,46 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights + * reserved. SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include +#include + +#include + +#include "timer.hpp" +#include "work_unit_scheduler.hpp" + +namespace cuopt { + +struct work_limit_context_t { + double global_work_units_elapsed{0.0}; + double total_sync_time{0.0}; // Total time spent waiting at sync barriers (seconds) + bool deterministic{false}; + work_unit_scheduler_t* scheduler{nullptr}; + std::string name; + + work_limit_context_t(const std::string& name) : name(name) {} + + void record_work(double work) + { + if (!deterministic) return; + global_work_units_elapsed += work; + if (scheduler) { scheduler->on_work_recorded(*this, global_work_units_elapsed); } + } +}; + +} // namespace cuopt diff --git a/cpp/src/utilities/work_limit_timer.hpp b/cpp/src/utilities/work_limit_timer.hpp deleted file mode 100644 index 2b5545269..000000000 --- a/cpp/src/utilities/work_limit_timer.hpp +++ /dev/null @@ -1,196 +0,0 @@ -/* - * SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights - * reserved. SPDX-License-Identifier: Apache-2.0 - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#pragma once - -#include -#include - -#include - -#include "timer.hpp" -#include "work_unit_scheduler.hpp" - -namespace cuopt { - -struct work_limit_context_t { - double global_work_units_elapsed{0.0}; - double total_sync_time{0.0}; // Total time spent waiting at sync barriers (seconds) - bool deterministic{false}; - work_unit_scheduler_t* scheduler{nullptr}; - std::string name; - - work_limit_context_t(const std::string& name) : name(name) {} - - void record_work(double work) - { - if (!deterministic) return; - global_work_units_elapsed += work; - if (scheduler) { scheduler->on_work_recorded(*this, global_work_units_elapsed); } - } -}; - -// In determinism mode, relies on a work limit accumulator; otherwise rely on a timer -// in non-determinism mode: 1s = 1wu -// In deterministic mode, all timers share a global work units counter (via work_limit_context_t), -// and each timer records the snapshot at construction time to determine when its own limit is -// reached. -class work_limit_timer_t { - public: - work_limit_timer_t() - : deterministic(false), work_limit(0), timer(0), work_context(nullptr), work_units_at_start(0) - { - } - - // Constructor taking work limit context reference (for deterministic mode) - work_limit_timer_t(work_limit_context_t& context, double work_limit_) - : deterministic(context.deterministic), - work_limit(work_limit_), - timer(work_limit_), - work_context(&context), - work_units_at_start(context.deterministic ? context.global_work_units_elapsed : 0) - { - } - - bool check_limit(const char* caller = __builtin_FUNCTION(), - const char* file = __builtin_FILE(), - int line = __builtin_LINE()) const noexcept - { - // TODO check against global timer - - if (deterministic) { - if (!work_context) { return false; } - // Check if global work has exceeded our budget (snapshot + limit) - double elapsed_since_start = work_context->global_work_units_elapsed - work_units_at_start; - bool finished_now = elapsed_since_start >= work_limit; - if (finished_now && !finished) { - finished = true; - double actual_elapsed_time = timer.elapsed_time(); - // 10% timing error - if (work_limit > 0 && std::abs(actual_elapsed_time - work_limit) / work_limit > 0.10) { - CUOPT_LOG_ERROR( - "%s:%d: %s(): Work limit timer finished with a large discrepancy: %fs for %fwu " - "(global: %f, start: %f)", - file, - line, - caller, - actual_elapsed_time, - work_limit, - work_context->global_work_units_elapsed, - work_units_at_start); - } - } - return finished; - } else { - return timer.check_time_limit(); - } - } - - // in determinism mode, add the work units to the global work limit accumulator - void record_work(double work_units, - const char* caller = __builtin_FUNCTION(), - const char* file = __builtin_FILE(), - int line = __builtin_LINE()) - { - if (deterministic && work_context) { - CUOPT_LOG_DEBUG("%s:%d: %s(): Recorded %f work units in %fs, total %f", - file, - line, - caller, - work_units, - timer.elapsed_time(), - work_context->global_work_units_elapsed); - work_context->record_work(work_units); - } - } - - double remaining_units() const noexcept - { - if (deterministic) { - if (!work_context) { return work_limit; } - double elapsed_since_start = work_context->global_work_units_elapsed - work_units_at_start; - return work_limit - elapsed_since_start; - } else { - return timer.remaining_time(); - } - } - - double remaining_time() const noexcept { return remaining_units(); } - - double elapsed_time() const noexcept - { - if (deterministic) { - return work_context->global_work_units_elapsed - work_units_at_start; - } else { - return timer.elapsed_time(); - } - } - - bool check_time_limit(const char* caller = __builtin_FUNCTION(), - const char* file = __builtin_FILE(), - int line = __builtin_LINE()) const noexcept - { - return check_limit(caller, file, line); - } - - bool check_half_time() const noexcept - { - if (deterministic) { - if (!work_context) { return false; } - double elapsed_since_start = work_context->global_work_units_elapsed - work_units_at_start; - return elapsed_since_start >= work_limit / 2; - } else { - return timer.check_half_time(); - } - } - - double clamp_remaining_time(double desired_time) const noexcept - { - return std::min(desired_time, remaining_time()); - } - - double get_time_limit() const noexcept - { - if (deterministic) { - return work_limit; - } else { - return timer.get_time_limit(); - } - } - - void print_debug(std::string msg) const - { - if (deterministic) { - printf("%s work_limit: %f remaining_work: %f elapsed_work: %f \n", - msg.c_str(), - work_limit, - remaining_time(), - elapsed_time()); - } else { - timer.print_debug(msg); - } - } - - timer_t timer; - double work_limit{}; - mutable bool finished{false}; - bool deterministic{false}; - // Pointer to work limit context (shared across all timers in deterministic mode) - work_limit_context_t* work_context{nullptr}; - // Snapshot of global work units when this timer was created - double work_units_at_start{0}; -}; -} // namespace cuopt diff --git a/cpp/src/utilities/work_unit_scheduler.cpp b/cpp/src/utilities/work_unit_scheduler.cpp index c810d5511..314219b8b 100644 --- a/cpp/src/utilities/work_unit_scheduler.cpp +++ b/cpp/src/utilities/work_unit_scheduler.cpp @@ -17,7 +17,7 @@ #include "work_unit_scheduler.hpp" -#include "work_limit_timer.hpp" +#include "work_limit_context.hpp" #include #include diff --git a/cpp/tests/mip/determinism_test.cu b/cpp/tests/mip/determinism_test.cu index 05751e554..4650796ce 100644 --- a/cpp/tests/mip/determinism_test.cu +++ b/cpp/tests/mip/determinism_test.cu @@ -130,7 +130,7 @@ TEST_F(DeterministicBBTest, reproducible_high_contention) settings.work_limit = 1; auto seed = std::random_device{}() & 0x7fffffff; - ; + std::cout << "Tested with seed " << seed << "\n"; settings.seed = seed; @@ -188,7 +188,7 @@ TEST_P(DeterministicBBInstanceTest, deterministic_across_runs) // Get a random seed for each run auto seed = std::random_device{}() & 0x7fffffff; - ; + std::cout << "Tested with seed " << seed << "\n"; mip_solver_settings_t settings; From 6914c8aed2e312c50c76dc88fd4d04edc820071f Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 6 Feb 2026 17:10:50 +0000 Subject: [PATCH 363/366] bump1 --- cpp/src/mip/local_search/local_search.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 228d8985a..6464bdba6 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -34,7 +34,7 @@ local_search_t::local_search_t(mip_solver_context_t& context fj_sol_on_lp_opt(context.problem_ptr->n_variables, context.problem_ptr->handle_ptr->get_stream()), fj(context), - // fj_tree(fj), + // fj_tree(fj),1 constraint_prop(context), line_segment_search(fj, constraint_prop), fp(context, From ff47875a5971eeddeb0748d637ad6ec62a4918fe Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 6 Feb 2026 17:11:08 +0000 Subject: [PATCH 364/366] bump2 --- cpp/src/mip/local_search/local_search.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cpp/src/mip/local_search/local_search.cu b/cpp/src/mip/local_search/local_search.cu index 6464bdba6..228d8985a 100644 --- a/cpp/src/mip/local_search/local_search.cu +++ b/cpp/src/mip/local_search/local_search.cu @@ -34,7 +34,7 @@ local_search_t::local_search_t(mip_solver_context_t& context fj_sol_on_lp_opt(context.problem_ptr->n_variables, context.problem_ptr->handle_ptr->get_stream()), fj(context), - // fj_tree(fj),1 + // fj_tree(fj), constraint_prop(context), line_segment_search(fj, constraint_prop), fp(context, From 57129b7da65b0f7cf15ccd2c7b892adbe911aa01 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 6 Feb 2026 17:46:39 +0000 Subject: [PATCH 365/366] address review comments --- cpp/src/dual_simplex/bb_event.hpp | 4 ++-- cpp/src/dual_simplex/bounds_strengthening.cpp | 4 ++-- cpp/src/dual_simplex/branch_and_bound.cpp | 1 + cpp/src/dual_simplex/deterministic_workers.hpp | 11 ++++++----- cpp/src/dual_simplex/phase2.cpp | 2 +- cpp/tests/mip/determinism_test.cu | 5 +++++ 6 files changed, 17 insertions(+), 10 deletions(-) diff --git a/cpp/src/dual_simplex/bb_event.hpp b/cpp/src/dual_simplex/bb_event.hpp index 5970f1cbf..f2e16466f 100644 --- a/cpp/src/dual_simplex/bb_event.hpp +++ b/cpp/src/dual_simplex/bb_event.hpp @@ -64,8 +64,8 @@ struct bb_event_t { bool operator<(const bb_event_t& other) const { - return std::tie(wut, worker_id, event_sequence) < - std::tie(other.wut, other.worker_id, other.event_sequence); + return std::tie(wut, worker_id, node_id, event_sequence) < + std::tie(other.wut, other.worker_id, other.node_id, other.event_sequence); } static bb_event_t make_branched(double work_unit_ts, diff --git a/cpp/src/dual_simplex/bounds_strengthening.cpp b/cpp/src/dual_simplex/bounds_strengthening.cpp index 8fd9f1bda..37ab114a7 100644 --- a/cpp/src/dual_simplex/bounds_strengthening.cpp +++ b/cpp/src/dual_simplex/bounds_strengthening.cpp @@ -116,7 +116,7 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t col_start = A.col_start[j]; const i_t col_end = A.col_start[j + 1]; for (i_t p = col_start; p < col_end; ++p) { - const i_t i = A.i[p]; + const i_t i = A_i[p]; constraint_changed[i] = true; } } @@ -192,7 +192,7 @@ bool bounds_strengthening_t::bounds_strengthening( const i_t col_end = A.col_start[k + 1]; nnz_processed += (col_end - col_start); for (i_t p = col_start; p < col_end; ++p) { - const i_t i = A.i[p]; + const i_t i = A_i[p]; if (!constraint_changed[i]) { continue; } const f_t a_ik = A_x[p]; diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 1017323b8..71d78cb54 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2385,6 +2385,7 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut lower_bound = node_queue_.best_first_queue_size() > 0 ? node_queue_.get_lower_bound() : search_tree_.root.lower_bound; } + solver_status_ = determinism_global_termination_status_; set_final_solution(solution, lower_bound); return solver_status_; } diff --git a/cpp/src/dual_simplex/deterministic_workers.hpp b/cpp/src/dual_simplex/deterministic_workers.hpp index ec15ca880..2117dae7c 100644 --- a/cpp/src/dual_simplex/deterministic_workers.hpp +++ b/cpp/src/dual_simplex/deterministic_workers.hpp @@ -268,7 +268,7 @@ class determinism_bfs_worker_t { record_event(bb_event_t::make_branched(this->clock, this->worker_id, - node->node_id, + node->creation_seq, down_child_id, up_child_id, node->lower_bound, @@ -282,7 +282,7 @@ class determinism_bfs_worker_t void record_integer_solution(mip_node_t* node, f_t objective) { record_event(bb_event_t::make_integer_solution( - this->clock, this->worker_id, node->node_id, objective)); + this->clock, this->worker_id, node->creation_seq, objective)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; ++this->total_integer_solutions; @@ -291,7 +291,7 @@ class determinism_bfs_worker_t void record_fathomed(mip_node_t* node, f_t lower_bound) { record_event(bb_event_t::make_fathomed( - this->clock, this->worker_id, node->node_id, lower_bound)); + this->clock, this->worker_id, node->creation_seq, lower_bound)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; ++total_nodes_pruned; @@ -300,7 +300,7 @@ class determinism_bfs_worker_t void record_infeasible(mip_node_t* node) { record_event( - bb_event_t::make_infeasible(this->clock, this->worker_id, node->node_id)); + bb_event_t::make_infeasible(this->clock, this->worker_id, node->creation_seq)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; ++total_nodes_infeasible; @@ -308,7 +308,8 @@ class determinism_bfs_worker_t void record_numerical(mip_node_t* node) { - record_event(bb_event_t::make_numerical(this->clock, this->worker_id, node->node_id)); + record_event( + bb_event_t::make_numerical(this->clock, this->worker_id, node->creation_seq)); ++nodes_processed_this_horizon; ++this->total_nodes_processed; } diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 798a49c3d..646de8fbf 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -3371,7 +3371,7 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, // Feature logging for regression training (every FEATURE_LOG_INTERVAL iterations) if ((iter % FEATURE_LOG_INTERVAL) == 0 && work_unit_context) { - i_t iters_elapsed = iter - last_feature_log_iter; + [[maybe_unused]] i_t iters_elapsed = iter - last_feature_log_iter; auto [total_loads, total_stores] = aggregator.collect_and_flush(); // features.byte_loads = total_loads; diff --git a/cpp/tests/mip/determinism_test.cu b/cpp/tests/mip/determinism_test.cu index 4650796ce..1e59fba64 100644 --- a/cpp/tests/mip/determinism_test.cu +++ b/cpp/tests/mip/determinism_test.cu @@ -164,6 +164,11 @@ TEST_F(DeterministicBBTest, reproducible_solution_vector) settings.num_cpu_threads = 8; settings.work_limit = 2; + auto seed = std::random_device{}() & 0x7fffffff; + + std::cout << "Tested with seed " << seed << "\n"; + settings.seed = seed; + auto solution1 = solve_mip(&handle_, problem, settings); auto solution2 = solve_mip(&handle_, problem, settings); From 1332fbdbf2c08938a90073dc437e7ed83c15c6b7 Mon Sep 17 00:00:00 2001 From: Alice Boucher Date: Fri, 6 Feb 2026 17:58:45 +0000 Subject: [PATCH 366/366] review comments --- cpp/src/dual_simplex/branch_and_bound.cpp | 2 +- cpp/src/dual_simplex/phase2.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/cpp/src/dual_simplex/branch_and_bound.cpp b/cpp/src/dual_simplex/branch_and_bound.cpp index 71d78cb54..de8a01953 100644 --- a/cpp/src/dual_simplex/branch_and_bound.cpp +++ b/cpp/src/dual_simplex/branch_and_bound.cpp @@ -2381,11 +2381,11 @@ mip_status_t branch_and_bound_t::solve(mip_solution_t& solut if (lower_bound == std::numeric_limits::infinity() && incumbent_.has_incumbent) { lower_bound = upper_bound_.load(); } + solver_status_ = determinism_global_termination_status_; } else { lower_bound = node_queue_.best_first_queue_size() > 0 ? node_queue_.get_lower_bound() : search_tree_.root.lower_bound; } - solver_status_ = determinism_global_termination_status_; set_final_solution(solution, lower_bound); return solver_status_; } diff --git a/cpp/src/dual_simplex/phase2.cpp b/cpp/src/dual_simplex/phase2.cpp index 646de8fbf..3d46d0f7c 100644 --- a/cpp/src/dual_simplex/phase2.cpp +++ b/cpp/src/dual_simplex/phase2.cpp @@ -2652,8 +2652,8 @@ dual::status_t dual_phase2_with_advanced_basis(i_t phase, aggregator.add("A.x", lp.A.x); // Track iteration interval start time for runtime measurement - f_t interval_start_time = toc(start_time); - i_t last_feature_log_iter = iter; + [[maybe_unused]] f_t interval_start_time = toc(start_time); + i_t last_feature_log_iter = iter; cuopt::scope_guard work_unit_guard([&]() { i_t remaining_iters = iter - last_feature_log_iter;